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A field guide to economics published with Danish administrative data
How each paper builds the variables it uses: the rule, the threshold, the register and, where the authors state them, the exact register variable names and code values. Definitions come from replication code where it is public, otherwise from online appendices, working-paper appendices or the paper itself, and each one links to its source.
1222 definitions from 150 papers. By source: 558 from working paper, 289 from online appendix, 266 from replication code, 109 from paper. Register variable names and codes change between years and projects, so check the current Statistics Denmark documentation before reusing a code list. AEA replication packages sit on openICPSR, which could not be read for this page, so AEA papers rely on their appendices.
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Actual income position (rank) | Hvidberg et al. (2023) | Percentile rank of the respondent's register income within each reference group, computed from tax-return income of all members of the group. | IND, BEF, UDDA, RASposition_coh_reg, position_sex_reg, position_per_mun_reg, position_per_edu_reg, position_per_sec_reg | paper · Figure 2 notes; Table 1 notes |
| Adult SES index | Baker et al. (2026) | Row mean of years of education, higher education, average earnings 30-50 and average employment 30-50, each standardised by the control-group (not born days 1-3) mean and SD, set to missing if any component is missing, then rescaled to control-group SD 1. A labour market index (earnings at 25, average earnings 30-50, average employment) and an education index are built the same way. | UDDA, IND, IDAPses_index from edulen, higher_edu, avg_inc_30_50, avg_emp | replication code · dst_replicationpackage/do/2i_cr_merge_outcomes.do, 'INDEX VARIABLES' |
| Alternative adult income measures | Rossin-Slater and Wüst (2020) | Robustness income outcomes (municipality x birth-year cells): average age 30-60 wage income in levels; log present discounted value of age 30-60 wage income (following Chetty et al. 2011); log average age 30-60 total income; log average age 49-51 wage income; indicator for any positive wage income at ages 49-51. | Income registers | online appendix · Table A2 and notes |
| Annual earnings | Kleven et al. (2021) | Annual income from wages, salaries and self-employment. | Statistics Denmark income registers | working paper · Section 2 (Data) |
| Annual earnings | Lundborg et al. (2017) | Annual labour earnings from tax and employer-employee registers, including parental leave and sickness benefits, in 2008 DKK. | Tax registers, Employer-employee registers | paper · Section I and Table 1 notes |
| Annual earnings (register) | Caplin et al. (2026) | Annual earnings are wage income LOENMV_13 from the income register (IND); self-employment income is NETOVSKUD_13 kept separately. Realized earnings growth 2020-2021 is ln(earnings_2021*100/102) - ln(earnings_2020), dropping observations with earnings below 24,000 DKK in either year and trimming at the 1st/99th percentile. | INDLOENMV_13 (earnings), NETOVSKUD_13 (self-employment income); threshold 24,000 DKK; deflator 100/102; winsor2 trim 1/99 | replication code · script/cleaning/1_b_annual_data.R; script/analysis/fig7/preprocess_fig7.do; paper Figure 6 note |
| Annual earnings eight years after application | Heinesen et al. (2026) | Annual earnings eight years after the first observed application: wage income, self-employment income and transfers replacing such income (short-term sickness pay, paid parental leave), excluding unemployment benefits; deflated by CPI (base 2011) and converted to 1,000s of USD at 2010-2016 average rates (5.9 DKK per USD). | Income registers | working paper · Section 5.2 'Data and descriptive statistics' |
| Annual earnings incl. self-employment (robustness) | Jensen and Blundell (2024) | Annual income measures from the income register IND, used in a robustness check that includes the self-employed because self-employment income is not measured monthly. | IND | paper · Section 5.1 |
| Annual gross earnings | Hasager and Jørgensen (2026) | Annual gross earnings from tax returns in the Income Register, CPI-deflated to 2000 and converted to USD at the 27 March 2020 exchange rate. | IND | working paper · Section I |
| Annual gross labour earnings | Foged et al. (2024) | Annual gross labour market income from the income register (IND), converted to US dollars at 2015 prices; short/medium/long-run outcomes are individual averages over years 1-5, 6-10 and 11-15 since asylum. Monthly version (ALMP) multiplied by 12. | IND | working paper · Section 4.1, footnote 9; Section 5.2 |
| Annual income (November job) | Traiberman (2019) | Total salary-based income reported by IDA in November of each year, covering the worker's primary and any secondary occupation; all of it is attributed to the reported (primary) occupation. Hours are not used because they are often imputed. Deflated by the 2000 Danish CPI (1000s DKK). | IDA | online appendix · Online Appendix D.1, Income; notes to Table A.7 |
| Annual labour earnings | García-Miralles and Leganza (2024) | Taxable annual labour market earnings from the annual registers, winsorized at the 1st and 99th percentiles, deflated to 2010 prices and converted at 1 USD = 5.56 DKK. | Annual income registers | working paper · Section 3.2 |
| Annual labour earnings | Bingley and Cappellari (2019) | Annual pre-tax labour earnings from income tax returns, summed over all employments in the year, 1980-2014, deflated to 2012 DKK with a year-specific index (earn/R*6768). In code: IDA person variable lonind for 1980-2009, corloen for 2010-2011 and from the IND register for 2012-2014; negative values recoded to 0. | IDA (idaperson), IND, Income Statistics Registerlonind (IDA person 1980-2009); corloen (2010-2014) | replication code · triplet-data-creation-20160706.do ('collect earnings histories'); IZA DP10761 Section III |
| Annual labour income | Bingley et al. (2020) | Annual labour income from employment and self-employment, including sickness benefits and paternity leave benefits, from employer reports to the tax authorities (sum over all employments in the calendar year); excludes occupational pension contributions and study grants/loans. Measured at ages 25-35 and used in logs (zeros drop out); sensitivity measure excludes sickness and leave benefits. | Employer income reports to tax authorities | working paper · Section III (Data), footnotes 14, 16 |
| Annual labour income (BFL) | Jakobsen et al. (2026) | 12-month sum of monthly labour income from BFL pay slips, by calendar year for age profiles and centred on the birth month for event-study moments (e.g. income from January-February before a March birth counts as year -1); excludes income registered while on parental leave; deflated by CPI, in 1000 DKK. Estimation sample 2010-2018. | BFL (eIndkomst), OF | working paper · Section 2; Supplemental Material A |
| Annual labour market income (earnings at 30) | Jensen and Manning (2025) | Aggregate labour market income including wage income and profits from self-employment in the year the child turns 30, in 2013 prices (CPI-deflated). Also used as an income rank, IHS and log. | IND | online appendix · Online Appendix OA.1; paper Section III |
| Annualized earnings | Kleven et al. (2014) | Earnings of part-year residents are annualized based on duration of stay in the year, with duration of stay measured from the migration database (daily dates of entry and exit from CPR records). | Administrative tax records, CPR migration records | working paper · Section 4.1, footnote 23 |
| Broad income (BI) | Jakobsen and Søgaard (2022) | Labour market income plus transfers, certain pension payouts and received alimony; it equals personal income before deductions. | Danish tax records | paper · Table 1; Fig. 3 notes |
| Capital income (CI), positive and negative | Kleven and Schultz (2014) | Interest income, rental income and business capital income minus interest on debt (mortgage, bank loans, credit cards, student loans), excluding stock income; analysed separately as positive and negative capital income because they are taxed very differently. | Income Tax Register | paper · Table 1; Section I; footnote 6 |
| Child support payment | Rossin-Slater and Wüst (2018) | Tax-deductible annual child support payments by non-custodial fathers as reported to the tax authorities, so only payments above the non-deductible extra amount are observed. Formal and voluntary payments cannot be distinguished. | Danish income/tax registers | working paper · Section 4 (Outcomes) |
| Cumulative normalized earnings | Utar (2018) | Sum over 2002-2010 of annual primary-job (November) earnings deflated by CPI (2010=1), divided by the worker's pre-shock average earnings (normsal9699, the 1996-1999 average in the code; the 2015 working paper says the 1999-2000 average). Cumulative hours, total wages and personal income are built the same way. | IDArsalary (joblon, primary November job earnings); rtotwag (slon, total wages from all jobs); rpersonindk (personal income); CInc=sum(rsalary*Dum0210)/normsal | replication code · DataPrep.do ('Preparation of Cumulative variables'); ReadMe File.docx |
| Deceased earner status | Fadlon et al. (2026) | Indicator for whether the deceased spouse had labour market earnings in the year before the health event; used to split households (earner vs non-earner) and as an instrument. | Income Registry (IND) | online appendix · Supplemental Appendix, Figures B.5-B.7 notes |
| Deceased's income share (instrument) | Fadlon et al. (2026) | Household income loss is instrumented with an indicator for whether the deceased spouse had earnings and a quadratic in the share of household income earned by the deceased spouse in the year before the event; 2SLS uses observations for years -1 and 0 with household FE, calendar-year FE and a quadratic in the survivor's age; SEs clustered by household. | Income Registry (IND) | online appendix · Supplemental Appendix, Table B.7 notes |
| Disposable income | Eshaghnia et al. (2025) | Total personal income (wage income, business and self-employment income, capital income, public transfer income, property income and other non-classifiable personal income) plus the rental value of own home for owner-occupiers, minus taxes and interest expenses. Spouses' or cohabitees' resources pooled where appropriate. | IND | working paper · Section 3.2, measure 2 and footnote 18 |
| Earned income | Maire and Schjerning (2013) | Income before shifting: personal income plus retained earnings plus income shifted along other margins such as pension contributions and transfers to assisting spouses; used (adjusted for income shocks) to estimate the real bunching response. | Income Tax Register | working paper · Section 4 discussion of Figures 11-12 |
| Earned income and hourly wage | Bruze et al. (2024) | Earned income ERHVERVSINDK_13 from the income register and hourly wage TIMELON from IDA, CPI-deflated and winsorized at the 1st and 99th percentiles by year. | IND, IDAERHVERVSINDK_13; TIMELON; JOB_TIME_LOEN_SMAL (IDAN, JOB_TYPE = 'H') | replication code · 04winsorize_rep.sas; 03panel_rep.sas |
| Earnings (average ages 30-50) | Baker et al. (2026) | Annual wage earnings LOENMV_13 (negative values set to 0), inflated to 2015 prices, assigned by age (year minus birth year) 18-65, winsorised at 1% in each tail by age, and averaged over available ages 30-50 (also 25-50). | INDLOENMV_13; avg_inc_30_50, avg_inc_25_50 | replication code · dst_replicationpackage/do/2d_cr_ind.do |
| Earnings and personal income | Keller and Utar (2022) | Annual labour earnings (salary) and personal income, the latter including unemployment insurance and government transfers; in the cumulative textile-worker regressions both are expressed as multiples of the worker's own 1999 value. Earnings at the 1999 firm are also analysed separately. | IDA, INDStata variables anrsalary, anrsal99, anrperinc, anrperinc99 | working paper · Table 8 notes; Analysis1.do Table 3 |
| Earnings at ages 30-35 | Joensen and Nielsen (2018) | Log annual earnings of the younger sibling at ages 30-35, in real 1989 DKK. | Statistics Denmark income registers | working paper · Section 5 (Table 7); Appendix A Figure A1 note |
| Earnings percentile rank | Brenøe and Zölitz (2020) | Average of the individual's annual labour earnings percentile over a five-year period (11-15 and 16-20 years after high school entry), with percentiles computed by year of birth and age using the entire Danish population as reference; earnings in 2015 DKK. | working paper · Table 7 notes; Table 1 notes | |
| Earnings while studying (heterogeneity) | Hansen et al. (2024) | Low earnings while studying used as a proxy for little relevant labour-market experience; Aarhus University attendance used as a further proxy. | Statistics Denmark registers | working paper · Section 6; Table 8 panels E-F |
| Earnings, disposable income and employment | Dustmann et al. (2024) | Earnings = pre-tax wage earnings + self-employment income + business profits; disposable income = pre-tax earnings + public transfers - taxes; employment = positive earnings; measured from first year after residency; 2020 PPP-adjusted USD. | Income register (tax authority records)employment: earnings > 0 | online appendix · Appendix B.2.1 |
| Excess income and predicted payback | Søgaard (2019) | Total earned income from tax records minus the individual income limit; positive amounts translate into payback of benefits. Predicted end-of-year benefits (initial payout minus predicted payback) match reported benefits within DKK 2 for 97.9% of the core sample. | Tax returns, Student Benefit Administration records | working paper · Section 4.3; Appendix C, Table C.I |
| Family disposable income in early childhood | Houmark et al. (2025) | Mother's and father's disposable income deflated by CPI (2015=100), averaged over child ages 1-7, summed across parents and logged (set to 0 if negative). | INDdisp_income_m_1..7, disp_income_f_1..7; famdinc18 = momdispinc18 + daddispinc18 | replication code · Results (for replication).do |
| Firm human capital of workers (AKM-based) | Stoyanov and Zubanov (2012) | Firm average of worker wage net of the AKM firm effect, h = (1/N) sum (w - lambda - psi_j), from a wage equation with person and firm fixed effects (Abowd, Kramarz and Margolis 1999); new workers' human capital measured in t-1, their last full year in the previous firm. | Matched employer-employee data (Statistics Denmark) | paper · Section I.B eq. (5) |
| Gross earnings and welfare benefits | Foged et al. (2024) | Gross earnings = LOENMV_13 from the income register; welfare benefits = sum of cash assistance (KONTHJ), refugee introduction allowance (KHFLYGT), other assistance (ANDBISTANDYD) and integration allowance (INTGRAYD); all deflated by the CPI (PRIS112 from Statistikbanken) and converted to 1000 USD at 6.6 DKK/USD. | IND (INDUPD01)LOENMV_13; UA_sum = KONTHJ + KHFLYGT + ANDBISTANDYD + INTGRAYD; DISPON_13 | replication code · 8. Controls and outcomes.sas, lines ~5068-5115 |
| Gross hourly wage | Bingley et al. (2013) | Calculated by Statistics Denmark as annual labour earnings (reported by employers to the tax authorities) divided by hours worked, where hours are an annual average derived by Statistics Denmark from mandatory pension (ATP) contributions adjusted for contribution rates by sector and collective agreement. Zero wages dropped, bottom and top 0.5% trimmed per year, observations below minimum wage dropped. | Statistics Denmark administrative registers, ATP | working paper · Section 4 and footnote 13 |
| Hourly wage | Humlum and Plato (2026) | 2008-2017: yearly total labour income divided by yearly hours worked; 1995-2007: average hourly wage rate in the November job, reliable only for individuals working more than 20 hours a week. | IDA | online appendix · Supplementary Appendix, Table A.1 Panel C |
| Hourly wage | Humlum et al. (2025) | 2008-2017: yearly total labor income divided by yearly hours worked. 1995-2007: average hourly wage rate in the November job; reliable only for individuals working more than 20 hours a week in that period. | Employment/earnings registers>20 hours/week for 1995-2007 | online appendix · Supplemental Appendix Table A.1, row 13 |
| Hourly wage | Jensen and Manning (2025) | Hourly wage from monthly hours and monthly earnings for employees in the monthly register (available from 2008; self-employed excluded), in 2013 prices. Jobs are excluded if the hourly wage is above 5,000 or below 30 DKK, annual hours are above 3,500 or below 7.4, or total wages are above 10,000,000 or below 1,000 DKK. | IDAN | online appendix · Online Appendix OA.1 |
| Hourly wage | Labanca and Pozzoli (2022) | Annual earnings divided by the sum of regular and overtime hours. | IDA, LON | working paper · Section 3 |
| Hourly wage | Taber and Vejlin (2020) | Hourly wage measure of Lund and Vejlin (2015), which improves on IDA's annual average hourly wage for the job held in the last week of November; log wages are detrended by gender-education groups conditional on experience. | IDA | working paper · Section 6, footnote 21; Section 6.1 |
| Hourly wage | Kreiner et al. (2020) | Monthly earnings divided by monthly hours (felt_200/timeantF12), winsorized at the 1st/99th percentiles within event month and income year (an untrimmed version is also kept). | eIndkomst (SKAT payroll)calc_hrly_wage; calc_hrly_wage_raw | replication code · data04.do |
| Hourly wage | Bagger and Lentz (2019) | IDA estimate of the average hourly wage for jobs active in the last week of November (no wage for spells not overlapping it); trimmed at 1st and 99th percentiles annually and trended to 2003 levels using implicit deflators. | IDA | working paper · Section 5 |
| Hourly wage | Foged and Peri (2016) | Log hourly wage of native worker including mandatory pension contributions, deflated by the Danish CPI; since pension payments are available only from 1995, wages net of pension contributions are used when pre-1995 data are included. Observed only for those employed at end of November. | IDA | working paper · Section 3.1, footnote 15 |
| Hourly wage | Bagger et al. (2014) | IDA average hourly wage for jobs active in the last week of November, deflated to 2003 prices with Statistics Denmark's CPI. | IDA | working paper · Section 3 and Appendix B |
| Hourly wage | Hummels et al. (2014) | Wage rate uses annual hours from IDA (following Christensen et al. 2005); annual hours do not capture overtime. In a 2006 subsample with overtime data, a wage rate including overtime correlates 0.86 with the main wage rate. | IDA | online appendix · Data Appendix, section 1 |
| Hourly wage and within-firm wage inequality | Friedrich (2022) | Hourly wage TIMELON (job earnings JOBLON / hours) for active November jobs (JOBLON and TIMELON > 0), CPI-deflated to 2000; observations below the 0.25th or above the 99.75th percentile of the yearly hourly wage distribution dropped; firm-year inequality is the s.d. of log hourly wages and percentile gaps 90-10, 75-25, 90-50, 50-10. | IDAPTIMELON, JOBLON; hours = JOBLON/TIMELON | replication code · Setup_Firm_Outcomes.do, lines ~60-218; Setup_Workerdata.do |
| Hourly wage in new job | Fluchtmann et al. (2024) | BFL monthly earnings divided by recorded monthly paid hours in the first full month of employment in the new job. Results are similar using a 4-month average or excluding observations whose hours were imputed by Statistics Denmark. | BFL | online appendix · Online Appendix A.1, fn. 40 |
| Hourly wage rate | Dahl et al. (2013) | Sum of total labour income (from tax files in the Income Register) and mandatory pension fund payments, divided by the total number of hours worked in the year (ATP-imputed); observations with unobserved wage, wage flagged by Statistics Denmark as low precision, or in the top or bottom half percentile are discarded. | IND, IDA, ATP | working paper · Section 4 (pp. 13-14) |
| Hourly wage rate (IDA) | Kreiner et al. (2015) | Annual labour income (including mandatory pension fund payments) divided by annual hours; annual hours are based on the mandatory ATP pension contributions, so overtime is not counted and part-time hours are imprecise. | IDA | working paper · Section 3.1; Section 5 (robustness) |
| Hourly wages (incl. zeros robustness) | Lundborg et al. (2017) | Main wage regressions condition on observing positive wages; robustness extends the sample to all women by setting missing wages to 0. | Labour market registers | online appendix · Online Appendix B, Table 3 notes |
| Hours-adjusted wage rate (WSR imputation) | Kreiner et al. (2015) | Robustness check: the employer-reported hourly wage from the Wage Statistics Register (Lønstatistikken) is regressed on the IDA wage rate after dropping the top and bottom 1% and IDA wages flagged as low quality by Statistics Denmark. The fitted relationship is then used to impute an overtime- and part-time-adjusted wage for all workers. | IDA, Lønstatistikken (WSR) | working paper · Section 5 (robustness), Figure 5 |
| Household disposable income | Browning et al. (2013) | Gross income including interest income, housing transfers and dividends, minus taxes on income, taxes on shares, surplus on owner-occupied housing and tax-exempted interest expenses (labour, transfer and capital income net of personal taxes). | Income tax registers | working paper · Section 4 (Expenditure imputation) |
| Household disposable income | Browning and Leth-Petersen (2003) | Sum of gross income including interest income, housing transfers and dividends from share capital, minus taxes on income, taxes on shares, surplus on owner-occupied housing and tax-exempted interest expenses. | Income tax registers | working paper · Section 4 (Expenditure imputation) - restatement of Browning and Leth-Petersen (2003) |
| Household disposable income (bank inflows) and components | Andersen et al. (2023) | Disposable income = sum of monthly incoming direct deposits, person-to-person transfers from outside the household and cash deposits into Danske Bank accounts owned by the affected person and/or spouse (excluding transfers between own accounts). Wage income: inflow coded as wage if sender's bank registration number (4-digit Danish bank code) is the one mapped to the recipient's employer in that month (mapping = registration number from which the highest share of the employer's payroll employees, per Tax Agency payroll data, received payments; second-ranked used if top is the central-government number and employer is not central government), or if transaction type code is e.g. 'salary transfers'. Joint-account wage payments go to the spouse if from the spouse's employer, otherwise to the affected person. Government transfers: sender registration number linked to a paying agency per payroll data, or the central-government registration number when person does not work for central government. Private transfers and other income = residual. | Danske Bank transactions; BFL/eIndkomst monthly payroll and transfer records | online appendix · Appendix A (Income variables) and Appendix C |
| Household income (net of transfers tied to age/pension) | Dahl et al. (2024) | Individual income = total personal income PERINDKIALT minus public pensions (QPENSNY), cash assistance (KONTHJ/KONT_GL) and other transfers (ANDOVERFORSEL), with negative business income, negative capital income (after removing surplus from owner-occupied housing OVSKEJD02_NY/OVSKEJD07) and negative wage components added back; household income is the mean of own and spouse/cohabiting partner income (rowmean of inc and partner inc). | IND (INDUPD), BEF, FAINPERINDKIALT, QPENSNY, KONTHJ/KONT_GL, ANDOVERFORSEL, NETOVSKUD_GL, FORMUEINDK_NY, OVSKEJD02_NY, OVSKEJD07, LOENMV, ERHVERVSINDK_GL, OVERFORSINDK, RESUINK_GL; partner via EFALLE (from 1986) / C_FAELLE_ID (before 1986) | replication code · Replication files/DKRepCode/CollectData.sas (income block) and CreateData_DK.do lines ~13-69 |
| Household income and net wealth | Andersen et al. (2021) | Household totals of total income and net wealth (third-party reported to SKAT) in million year-2015 DKK, measured December 31 of the year before the transaction and winsorised at the 1st and 99th percentiles. | SKAT income and wealth records | working paper · Table 1 note; Appendix A Variable definition |
| Household income from all sources | Fadlon and Nielsen (2021) | Household income including income from any source: earnings, capital income, annuity payouts and benefits from any social program. | Income and transfer registers | online appendix · Online Appendix Table D.1 notes |
| Household income loss | Fadlon et al. (2026) | Household income decomposed into seven sources for each spouse: earnings, sick pay, DI + old-age pension (OAP), other government benefits, private pensions, capital income and other income, compared between the year before (period -1) and the year after (period 1) the index event. In 2SLS the household income loss is normalized at the treatment-group mean and winsorized at the top and bottom 1 percent. | Income Registry (IND) | online appendix · Supplemental Appendix, Tables B.4-B.7 notes |
| Household labour income | Taubinsky et al. (2026) | Total taxable wage income, benefits, bonuses, severance pay and value of stock options, averaged across spouses as for total income. | INDLOENMV_13 (income_work) | replication code · code_dstserver/code_epinion/2_build_registries.do; NBER WP w32664 Section 1.2 |
| Household total income | Taubinsky et al. (2026) | Yearly total income before taxes and labour market contributions, including labour income, public transfers, property income and most other taxable income attributable to the individual (excludes imputed rental value, pension contributions to employer schemes and lottery winnings). Household value is the average of the two spouses' values if a spouse is present in CPR, otherwise the individual value. | IND, BEFPERINDKIALT_13 (income); spouse via E_FAELLE_ID | replication code · code_dstserver/code_epinion/2_build_registries.do; NBER WP w32664 Section 1.2 and footnote 6 |
| Income concept for actual and perceived rank | Hvidberg et al. (2023) | Income earned in 2017 as on the tax statement: wage income plus self-employment income plus taxable benefits and transfers (mainly UI benefits, disability benefits and social assistance); excludes capital income, deductions and taxes. Averages 96.0% of Statistics Denmark total income. | INDinc_reg compared with perindkialt_13 (total personal income) | paper · Section 2 (Income block); Replication_Code.do line 173 |
| Income concepts | Chetty et al. (2014) | Labor income = labor earnings before taxes excluding contributions to employer-administered pensions; total compensation = labor earnings plus employer and employee contributions to employer-administered accounts; taxable income = labor income plus other income (e.g., UI) affecting tax liability. Measured at the individual level, with joint-account balances split equally. | IND (income tax register) | working paper · Section III |
| Income controls | Epper et al. (2020) | Gross income = annual income excluding capital income (DKK). | Income register | online appendix · Online Appendix Table A5 notes |
| Income counted against the SU income limit | Søgaard (2019) | Labour income (net of labour market contribution), transfers other than student benefits, and capital income, except certain types of stock income. All amounts are scaled to 2018 levels using the official wage indexation of student benefits. | Tax returns | working paper · Section 3.1, footnote 4 |
| Income measures and public expenditure | Dustmann et al. (2024) | From the income register, measured from the first year post residency: labor earnings (pre-tax), transfers (pre-tax), pre-tax gross income (= labor earnings + transfers for this sample), and post-tax disposable income (= gross income minus tax payments). Public expenditure on refugees = transfer payments minus tax payments. | Income register (Statistics Denmark, third-party reported) | online appendix · Online Appendix B.2 Data Construction and Definitions |
| Income rank (two-year lag) | Dahl et al. (2024) | Percentile rank (1-100, ties broken randomly) of household income measured two years earlier (t-2), computed within gender x birth cohort x year cells; mortality sample ages 40-100, years 2001-2014, collapsed to income tertiles for trends. | IND, BEFlaginc = hhinc[t-2]; incrank by gender birthyear year | replication code · CreateData_DK.do lines ~108-130, 195-200 |
| Income-shifting indicator | Kreiner et al. (2016) | Equals one if income in a month is high (more than 50 percent above) relative to its level in the same month of 2008 and income in the preceding month is low (more than 50 percent below) relative to its 2008 level; zero otherwise. Robustness with 25%-25% and 75%-75% cutoffs; a stricter version also requires November 2009 income to be unusually low. | eIncome register (SKAT) | working paper · Section III.A, eq. (3) and footnote 6 |
| Indexed earnings | Jensen and Zhang (2026) | Earnings from employment and self-employment, inflated to 2020 levels and indexed by the average earnings of men (women) one year before the parental death. | IND | online appendix · Supplemental Appendix Figure B.5 and B.9 notes |
| Initial income bin dummies | Jakobsen and Søgaard (2022) | Fifty dummies for initial personal income measured at the start of each period (e.g. 2004 or 2008). The DKK 300,000 bin is omitted, so coefficients measure income-growth trend differentials relative to that bin. | Danish tax records | paper · Section 4, eq. (11) |
| Labor income | Humlum et al. (2025) | Total labor market income, including bonuses, amenities, wages paid under sick and parental leave, and employer contributions to pension saving schemes. | Income registers | online appendix · Supplemental Appendix Table A.1, row 12 |
| Labor income (LI) | Kleven and Schultz (2014) | Tax-code labor income: salary, wages, honoraria, fees, bonuses, fringe benefits and business earnings. Main outcome is the three-year growth rate (s to s+3) in real labor/wage earnings; regressions weighted by labor income and restricted to positive labor income. | Income Tax Register, IDA | paper · Table 1; Section II; Table notes for labor income elasticity tables |
| Labour compensation flows | Andersen et al. (2026) | Total annual labour compensation per individual including employer pension contributions, from IND and INPI, aggregated to consumer x producer cells and scaled by 1.01 to SNA D.1. Compensation from foreign producers (IND) scaled by 1.47; payments by Danish producers in BFL to persons not in BEF are treated as foreign employees and scaled by 1.14. | IND, INPI, BFL, BEFSNA D.1; scale factors 1.01, 1.47, 1.14 | working paper · Appendix I |
| Labour earnings and gross income | Daysal et al. (2025) | Monetary outcomes are expressed in thousands of 2015 DKK; labour-market outcomes are averaged over 1-5 and 6-10 years after diagnosis; pre-diagnosis characteristics (employment status, labour earnings, gross personal income) are averaged over 2-4 years before diagnosis. | Income Statistics Register | online appendix · Notes to Appendix Table A1 and Figures A4-A5 |
| Labour earnings outcome | Kleven et al. (2025) | Total labour compensation, including contributions to employer-administered retirement savings plans; outcome is the log change between 2008 and 2010 (placebo 2006-2008). | Income tax records | working paper · Section 5.1, footnote 26 |
| Labour income | Humlum and Plato (2026) | Total labour market income including bonuses, fringe benefits, wages paid during sick and parental leave, and employer pension contributions. | IDA | working paper · Appendix Table A.1, row 9 |
| Labour income (model-consistent) | Foerster (2024) | Weekly hours (from TILKNYT bins) times 49 weeks times hourly wage TIMELON; zero if hours are zero. Register labour income LONIND is also assembled (earnings), with negative values truncated to 0. | IDAN, IDAPTIMELON (hourly wage), TILKNYT; LONIND (earnings); labor_income = work_hours*49*wage | replication code · code/datprep_untying.do lines 309-316 |
| Labour income, salary and pension contributions | Druedahl and Martinello (2022) | Labour income = ERHVERVSINDK (labour-market income incl. bonuses, compensations and self-employment income); salary = LOENMV (salary only); pension contributions = private (qpripen) plus labour-market (qarbpen) pension contributions. | INDerhv (labor_income), loenmv (salary), pencontr = qarbpen + qpripen | replication code · rawbuild.do ('INCOME UPDATE'); WP Table D.2 |
| Labour market income (LI) | Jakobsen and Søgaard (2022) | Salary, wages, honoraria, fees, bonuses, fringe benefits and business earnings, as defined in the Danish income tax system. | Danish tax records | paper · Section 3.1, Table 1 |
| Lagged income controls | Sigaard (2023) | Ten-piece spline with breakpoints at the deciles of log labour income lagged twice, to control for mean reversion and heterogeneous income trends. | IND, INDK | paper · Section 3.1 |
| Lifetime earnings | Almar et al. (2026) | Sum over 30 years after graduation of deflated log annual earned income ERHVERVSINDK_13 (employment plus self-employment income), year effects (base 2000) removed, for graduates observed for all 30 years; averaged by programme. | INDERHVERVSINDK_13 | replication code · Data/Gen_life_earnings_ambition_final/Get life-time earnings.do; Online Appendix A.2 'Income measure' |
| Log annual earnings | Sander (2026) | Wage income (LONIND, replaced with corrected wage income CORLOEN in 2010-2015) converted to 2016 DKK with fixed year factors and to US dollars (6.7327), negative values set to 0, top-coded at 400,000, and logged (also inverse hyperbolic sine); measured child ages 5-40. Gross income (BRUTTO) is treated the same way for 1976 onward. | INDLONIND, CORLOEN, BRUTTO | replication code · dofiles/createfirst.do, 'LØN INDKOMST' and 'BRUTTO INDKOMST' |
| Log average wage income ages 30-60 | Rossin-Slater and Wüst (2020) | Natural log of average wage income (in 2012 prices) over all ages between 30 and 60 observable in the 1980-2012 register data. | Income register (Statistics Denmark) | working paper · Section 4.1 |
| Log earnings | Joensen and Nielsen (2016) | Log earnings (2000 DKK) measured 9-18 years after high school graduation (table labels describe it as average income 9-18 years after starting high school); at least one year of employment required to have an earnings observation. | Labour market registers (Statistics Denmark) | working paper · Sections 3.1 and 4.1; Table 3 |
| Log hourly wage (AKM outcome) | He and Maire (2025) | Log of the IDA hourly wage, deflated to constant prices; when the hourly wage is zero it is replaced by annual labour income divided by 1800. Observations with missing firm ID, labour income <=1, missing education or experience, or unemployment degree above 500 are dropped; firms whose maximum employment is 5 or fewer are excluded. | IDAtimelon; lonind; replace timelon=lonind/1800 if timelon==0; arledgr (unemployment degree) >500 dropped; maxemp<=5 dropped | replication code · AKM_merger_cvrnr.do |
| Log total gross earnings after graduation | Hansen et al. (2024) | Log total gross earnings in each of the first five calendar years after graduation; individuals with zero earnings excluded. A separate extensive-margin outcome is an indicator for positive earnings. Monetary values in 2015 prices (CPI). | Statistics Denmark registers | working paper · Section 3.2; Table 2 notes |
| Log wage rate | Lentz et al. (2023) | Annual total salary declared by the employer to the tax authorities divided by total hours worked by the worker in that year; assigned to the first week of the year or the first week of a new employment spell starting within the year. Hours are inferred by Statistics Denmark from mandatory pension contributions that depend on weekly hours. | Danish matched employer-employee data (MEE) | working paper · Section 2, footnote 7 |
| Long-run income and education outcomes | Dustmann et al. (2023) | For assigned refugee children 1987-2018, income outcomes are taken from the income register over the full year and education from the education registers (measured in October of the year prior), with employment taken from RAS for November of the previous year. | IND, UDDA, UDDANY, RASIND: PERINDKIALT_13, LOENMV_13, ERHVERVSINDK_13, STIP, OFF_OVERFORSEL_13, NETOVSKUD_13; UDDA: HFAUDD; RAS ARBSTIL 11-37 employed, 40 unemployed (pre-1994) | replication code · do/build/prepare/Refugee_income.do |
| Low disposable income indicators | Dustmann et al. (2024) | Indicators for having post-tax disposable income below $500, $750 and $1,000 per month, where monthly income = annual disposable income (year 1, year 2, or years 3-5) divided by 12. | Income registerThresholds $500, $750, $1,000 per month (2010 PPP USD) | online appendix · Online Appendix Table A.19 notes |
| Mixed income, dividends and owner-occupied housing surplus | Andersen et al. (2026) | Mixed income links sole-proprietorship establishments in IDAN (including municipality) to owners' mixed income. Dividends (SNA D.42) are allocated to consumer cells in proportion to stock dividend income in IND and to producer cells in proportion to accounting income. Owner-occupied housing surplus (B.2G) = imputed rental values in IND scaled by 0.67. | IDAN, INDSNA B.3g, D.42, B.2G; scale 0.67 | working paper · Appendix J |
| Monthly earnings | Bargain and Jonassen (2024) | Annual job earnings (LONBLB from CONESR 2000-2005, LOENBLB from RAS 2006) for each person-firm spread evenly over the months with MIA employment at that firm; weekly earnings = ordinary employment indicator x monthly earnings. | MIA, CONESR, RASlon_md = lonsum_se / ans_mia_md; wage2 = wage * q | replication code · mia_con.do |
| Monthly earnings (payroll) | Kreiner et al. (2020) | Monthly earnings from SKAT payroll records (eIndkomst) 2012-2015: records cancelled by corrections are dropped (korkod/ibertvirkidkod/referidkod logic), payments summed by person, firm and month (from the reporting period angivpernr) and then by person-month; non-positive values set to missing and values winsorized at the 1st/99th percentiles within event month (tline) and income year. | eIndkomst (SKAT payroll, a700 files), BEFfelt_200 (reported earnings); felt_200_trim | replication code · data02.do, data04.do; READ ME.docx |
| Monthly wage change relative to 2008 | Kreiner et al. (2016) | Percentage change in individual monthly gross wage income (year t, month m) relative to the individual's average monthly gross earnings in 2008; percentage changes are used instead of logs because monthly earnings may be zero or near zero. | eIncome register (SKAT) | working paper · Section II, eq. (1) |
| Net income and components | Kleven et al. (2011) | Net income = personal income + capital income + stock income + self-employment income + foreign income - deductions. Personal income: earnings, pensions, alimony minus some retirement contributions; capital income: interest income, bond returns, net rents minus interest payments; stock income: dividends and realised capital gains on stocks; self-employment income: net profits of unincorporated businesses. | SKAT Business Object Database | working paper · Table 2 notes; Table 1 |
| Other labour market outcomes | Hansen et al. (2024) | Log disposable income (after transfers and taxes), unemployment, public-sector employment, job changes, annual earnings growth, and earnings growth with the same employer, in each of the first five years after graduation. | Statistics Denmark registers | working paper · Section 5; Table 7 |
| Own wage | Clark et al. (2009) | Individual's gross annual income from IDA, matched to ECHP respondents via CPR number; entered as ln(own wage) together with a Mundlak term (individual mean wage over waves); summary statistics in 1994 DKK real terms. | IDA, ECHP | working paper · Section 3.1-3.2; Appendix Table B1 |
| Parental controls measured before birth | Breining et al. (2020) | Maternal and paternal employment and family income are measured in the year before birth; missing parental education/employment (about 9-10 percent) are mean-imputed with a missing indicator. | working paper · Footnotes 19 and 21 | |
| Parental earnings and disposable income | Foged et al. (2023) | Annual gross earnings in year-2000 prices converted to USD at 6.6 DKK/USD. Disposable income includes public transfers net of taxes, deflated to year-2000 USD. | Income registers | working paper · Section III and III.A |
| Parental income measures | Bagger et al. (2021) | Parental income measures are trimmed by recoding observations in the 1st and 99th percentiles as missing; mother's education measured in the last year she appears in the data. | IDA | working paper · Appendix C |
| Parental income percentile | Akcigit et al. (2024) | Child's father's income percentile, used as the measure of parental income (correlation with child IQ of 0.18). | IDA | paper · Section 2.3 |
| Parental real labour income | Daysal et al. (2022) | Wage income LONIND from person data deflated to 2004 prices with the CPI; negative values set to missing; measured by years since the child's birth. | IDA (persondata)LONIND | replication code · 06 Adding parental labor outcomes rev.sas, lines ~18-100 |
| Parental wage income | Eshaghnia et al. (2025) | Pre-tax taxable wage earnings plus other wage-type income such as fringe benefits and stock options; parental resources are the sum of mother's and father's amounts irrespective of marital status, measured at each age of the child. | IND | working paper · Section 3.2, measure 1 |
| Permanent income | Druedahl and Martinello (2022) | Weighted average of current and four lagged years of disposable income: 0.45, 0.25, 0.15, 0.10, 0.05 for t to t-4; disposable income = BRUTTO + SKATFRIYD + AKTIEINDK - SKATMVIALT_NY, CPI-deflated; floored at DKK 60,000 in code. Inheritance is normalized by permanent income in the year before parental death. | INDdispinc = brutto + skatfriyd + aktieindk - skatmvialt_ny; perminc = filter(dispinc), coef(45 25 15 10 5) lags(0/4) normalise | replication code · rawbuild.do ('Income'); WP Appendix D |
| Permanent income | Kreiner et al. (2019) | Average of (register) income over 1998-2008, entered in logs; income controls also include ln(2008 register income) and the standard deviation of ln(income). | Statistics Denmark income registersYears 1998-2008 | online appendix · Online Appendix Table EA.1 notes |
| Personal income | Maire and Schjerning (2013) | Sum of wage earnings, declared firm profits (net of retained earnings), remunerations, provisions, alimony etc. minus deductions; interest income is capital income and not part of personal income. | Income Tax Register | working paper · Figure 1 notes; Section 2.1 |
| Personal income (PI) | Jakobsen and Søgaard (2022) | Broad income minus the labour market contribution and certain pension contributions (employer-administered annuity pension contributions were fully deductible until 2010, then capped at DKK 100,000 for expiring annuities). PI is the main outcome and initial-income concept, and responses are also shown with and without pension contributions. | Danish tax records | paper · Table 1; Section 3.1; footnote 21 |
| Personal income (tax sample) | Jakobsen et al. (2026) | Personal income from annual tax data: labour income from wage work, profits from own firms and labour market transfers; deflated by CPI. | INDPERINDKP | working paper · Supplemental Material A |
| Personal, stock and taxable income (PI, SI, TI) | Kleven and Schultz (2014) | Personal income PI = LI + transfers, grants, awards, gifts, received alimony minus labour market contribution and certain pension contributions; stock income SI = dividends and realized capital gains on shares; deductions D = commuting, union fees, UI contributions, other work expenses, charity, paid alimony; taxable income TI = PI + CI + SI - D (differs from the tax-code label, which excludes SI). | Income Tax Register | paper · Table 1 |
| Potential income loss from spousal death | Fadlon and Nielsen (2021) | Household income from any source in t = 1 holding the surviving spouse's earnings and social benefits (labor income, Social DI and Social Security) at their t = -1 level, divided by household income in t = -1, then normalized for treated households by the corresponding control-group mean. | Income and transfer registers | working paper · Section 5.2, footnote 22 |
| Pre-birth parental labour income | Lundborg et al. (2024) | Average annual labour income (in 10,000 DKK) of mother/father (or egg donor) over the 4 years preceding the child's birth year; post-minus-pre log income change uses average labour income over the 5 years following the birth year. | IDAPALLlonind_beforem4, lonind_beforef4, dif_logincm, dif_logincf | replication code · Read_me.pdf, Table 1 |
| Pre-intervention labour market history | Altmann et al. (2026) | From BFL records with period start before entry: mean monthly wage (lon) and employment degree (grad) in each of the three years before entry (year t-1 excludes the last 30 days), set to zero if absent; last pre-intervention occupation is the 1-digit DISCO of the most recent BFL spell. | BFLwage_tminus1-3, grad_tminus1-3; last_occ = floor(VMO_DISCO_KODE/100000) | replication code · code/data_preparation.do |
| Pre-tax earnings | García-Miralles and Leganza (2024) | Income on which individuals pay the 8 percent labor market tax, winsorized by year at the 95th percentile; 2010 prices, 5.56 DKK per USD. | Income register | working paper · Section 4.1 |
| Primary vs secondary earner status of deceased spouse | Fadlon and Nielsen (2021) | Deceased spouse is classified as primary (secondary) earner if the share of his/her average pre-event labor income in the household's average pre-event labor income was higher (lower) than one half. | Income registersShare of household labor income > 0.5 = primary earner | online appendix · Online Appendix Figure C.1 and Table C.2 notes |
| Real annual earnings | Damm (2009) | Sum of wage earnings, profits from own company and sickness benefits, deflated by the consumer price index (base year 1980); analysed for refugees with positive earnings seven years after immigration. | Income register (Statistics Denmark) | working paper · Section III.D; Table I notes |
| Recent and future household income change | Taubinsky et al. (2026) | For a respondent interviewed in any month of year t, recent change = log nominal household income in t-1 minus t-2; future change = log income in t+1 minus t-1. Changes are trimmed at the 2.5 and 97.5 percentiles. | INDLog_Income_HH_L1L2, Log_Income_HH_F1L1; winsor2 cut(2.5 97.5) trim | replication code · code_dstserver/analysis/A1c_sample_restrictions.do; NBER WP w32664 Section 1.3 |
| Relative female earnings potential | Kleven et al. (2021) | Baseline measured just before the arrival of the first child (event time -1), splitting women at the median. Forward-looking (potentially endogenous) alternatives measure it 10 years after first child arrival, after 15 years of experience, or at age 45. | UDDA, IND | online appendix · Online Appendix, Figure A.IV notes |
| Residual log annual earnings | Bingley et al. (2021) | Pre-tax annual labour earnings 1984-2011 in 2005 prices; zero-earnings observations excluded, a quarter of a percentile trimmed from each tail of the annual distribution, and at least three consecutive earnings observations required. Residuals from regressing log real gross annual earnings on year dummies and a quadratic age trend by two-year birth cohort group. | Tax-based income registers (Statistics Denmark) | working paper · Section 2 (Eq. 1) and Section 3.1 |
| Residualised log hourly wage | Almar et al. (2026) | Hourly wage in the primary job held in the last week of November (IDAN), ranking job types H, 3, A, S, M and keeping the highest; variable TIMELON before 2008 and SMAL_TIMELOEN from 2008. Log hourly wages are regressed on year dummies (base 2000) and education-specific experience profiles, and the year coefficients are subtracted. Hours underlying the wage are contractual (pre-2008 in four bins). | IDAN, IND, IDAPTIMELON (<2008), SMAL_TIMELOEN (>=2008); JOB_TYPE rank H,3,A,S,M; reg log_wage ib2000.aar i.educ_eika#c.erhverv_akk_new | replication code · Data/Gen_sample1_new/Make analysis sample 1.do; IZA DP 17814 Online Appendix A.1 fn. 35 and A.2 |
| Self-employment / business income indicator (bunching) | Jakobsen et al. (2020) | Household flagged as self-employed if business profit (NETOVSKUD_GL) is nonzero; a broader 'ext' flag also uses nonzero values of EGNKAPVK, UDLFKURS, UDLIND2 or UDLIND3 (foreign/business-related components). | INDH, INDKselv = netovskud_gl != 0; ext = netovskud_gl!=0 | egnkapvk!=0 | udlfkurs!=0 | udlind2!=0 | udlind3!=0 | replication code · Empirical Analysis/bunching_base_data.do |
| Self-employment income | Ejrnæs and Hochguertel (2013) | Income from self-employment (surplus or profit of business), entered linearly with a dummy for negative values; income and wealth measured in constant 1981 million DKK. Controls also include receipt of start-up allowance (for entrants from unemployment via active labour market programmes) and regional unemployment calculated from the micro data. | CAM 10% Sample | working paper · Section 5.2 |
| SES rank / SES index | Houmark et al. (2025) | Families are ranked in percentiles (one observation per child) on the sum of parents' years of education and on log family disposable income; the two percentile ranks are summed and re-ranked into percentiles scaled to 0-0.99 (ses_rank), and the SES index is this rank standardised to mean 0, SD 1 within the sample. | IND, UDDAxtile famedu_rank, faminc_rank nq(100); famses_rank; ses_std = std(ses_rank) | replication code · Results (for replication).do; IZA DP 15418 Section 3.4 |
| Share of rich (1985 municipal control) | Dustmann et al. (2019) | Share of adults whose disposable income as a household member is above 50% of the Danish median (one of several 1985 municipal characteristics with immigrant share, mean log gross earnings, crime rate, church-tax payers, unemployment rate, tertiary education share, immigrant welfare dependency). | Income registers, Population, education, labour force and socio-economic registers | paper · Section 2.4, footnote 22 |
| Social income (means-test income X) | Nielsen et al. (2010) | Pre-reform means-testing measure X = mother's taxable income + father's taxable income - a x number of siblings below the age limit + f(parents' wealth); in 1985 a = 16,600 DKK and f(W) = 0.1(W-447,700)1[W>447,700] + 0.15(W-895,400)1[W>895,400] + 0.25(W-1,492,100)1[W>1,492,100]. Since social income no longer exists in the registers after 1988, a best post-reform analogue is computed accounting for wealth and number of siblings; analyses use the social income percentile within cohort. | Tax/income registers | paper · Section II eq. (1) and footnotes 6, 12; Section IV end |
| Taxable income (TI) | Jakobsen and Søgaard (2022) | Personal income plus capital income (interest, rental and business capital income minus interest on debt) minus itemized deductions (commuting, union fees, unemployment insurance contributions, other work expenses, charitable donations, paid alimony). Stock income is taxed separately. | Danish tax records | paper · Table 1 |
| Top natives and top foreign employees (spillovers) | Kleven et al. (2014) | Top natives: native workers with earnings above 75% of the scheme eligibility threshold (about the top 3% of native earnings). Top foreign employees: foreign employees with less than 3 years of presence in Denmark and annualized earnings above the threshold; industry scheme use = growth in top foreign employees between 1980-1990 and 1991-2000 divided by top native employees in 1980-1990. | Administrative tax and payroll records, Employer links with 2-digit industry | working paper · Section 4.3 (industry-level spillovers), footnote 35 |
| Total earnings within 6/12 months | Altmann et al. (2026) | Sum of AJO_SMALT_LOENBELOEB over BFL records with job start date in the window after entrydate, zero if none. | BFLAJO_SMALT_LOENBELOEB (earnings4_total) | replication code · code/data_preparation.do |
| Typical wage of a job | Fluchtmann et al. (2024) | Predicted log wage from a Post-LASSO OLS model estimated on new jobs in the analysis sample. Candidate regressors are 1-, 2- and 3-digit industry and occupation dummies, the within-industry standardised AKM firm effect (set to 0 when missing, plus a missing dummy) and all pairwise interactions, 10,407 variables in total. Rigorous LASSO (Belloni et al. 2016, LASSOPACK, clustered by individual) selects 233 variables, of which 117 remain after collinearity. Out-of-sample R2 is 0.201. Alternative models without firm information or without occupation information fill in values for unlinked applications. The model uses no worker characteristics. | BFL, Joblog (Jobnet) | online appendix · Online Appendix A.4, Tables A.3-A.4 |
| Unexpected income change | Crawley and Kuchler (2023) | After dropping the top and bottom 1 percent of household after-tax-and-transfer income, income is normalized by average household income over the period and regressed on dummies for age, year, highest education, marital status, homeowner status and number of children plus age interactions with education, marital and homeowner status; the year-to-year change in the residual is the unexpected income change, trimming the top and bottom 1 percent. Expenditure residuals are constructed the same way. | Tax-authority income records, Education register, Population register | working paper · Sections 4.1 and 4.2 |
| Wage bill and wage bill ex. leave | Brenøe et al. (2024) | Total wage bill = sum of all pre-tax payments to workers in the year (from CON and RAS), including payments to workers on paid leave. Wage bill ex. leave: each worker's wage = payments / ATP hours incl. paid leave, multiplied by hours excluding paid leave; the gap (paid leave covered by the firm) is subtracted from the total wage bill. | CON, RAS, ATP | working paper · Section 4.2; Appendix C.1 |
| Wage earned distribution G | Christensen et al. (2005) | Distribution of the same establishment average hourly wages weighted by the establishment's relative employment size in November 1994. | IDA | working paper · Section 3.2 (p. 10) |
| Wage earnings and net deductions | Chetty et al. (2011) | Wage earnings from the tax register; net deductions = deductions minus non-wage income (accounting for spousal deductions), equivalently wage earnings minus taxable income; in the top-tax base, net deductions = wage earnings needed to start paying top tax minus the statutory top tax cutoff. Small-deduction group: net deductions below DKr 7,500 in magnitude. | IND (tax register)wage earnings qlontmp2; self-employment profits qovskvir, virkordind | working paper · Section 4; Appendix B |
| Wage offer distribution | Kreiner et al. (2015) | Distribution of firm-specific average wages for persons entering firms in 2005 from non-employment in 2004, weighting each firm's wage by its share of all hires from non-employment. | IDA | working paper · Section 3.1 |
| Wage offer distribution F | Christensen et al. (2005) | Distribution of establishment average hourly wages, each weighted by the fraction of all non-employed workers hired by the establishment during the year (only hires from non-employment are used, to avoid selection among job-to-job movers). | IDA | working paper · Sections 3.1-3.2 (pp. 8-10) |
| Wage rate | Kleven et al. (2019) | Earnings divided by the ATP hours measure, for those who are working (conditional on participation). | ATP pension contributions, Income registers | working paper · Section 2; Section 3.2 footnote 9 |
| Wage surplus / wage deficit | Clark et al. (2009) | Following Ferrer-i-Carbonell (2005): if own wage > establishment average wage, wage surplus = ln(own) - ln(average) and deficit = 0; if own < average, wage deficit = ln(average) - ln(own) and surplus = 0; both zero if equal. | IDA | working paper · Section 3.2 |
| Wages, gross income and income rank | Daysal et al. (2026) | Wages among the employed and gross personal income including government transfers, in 2010 USD, winsorized at the 1st and 99th percentiles; income rank is the percentile of gross personal income within the full Danish population of the same birth cohort and age; ages 18-32. | IND | paper · Section 2, footnote 12 |
| Worker-level hourly wage | Utar (2014) | Log hourly wage in worker-level regressions restricted to full-time employees; reference group = employees with high school diploma in unspecified occupations; deflated by CPI to year-2000 DKK. | IDA (Persondata) | online appendix · Notes to Tables A-8 and A-9 |
| Workplace average wage | Kreiner et al. (2015) | Each worker is assigned the average hourly wage rate of employees at his/her workplace in 2005 (following Christensen et al. 2005), after trimming wages below DKK 90 and above DKK 714. | IDA | working paper · Section 3.1 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Assets | Foerster (2024) | Individual and household assets from the income register wealth variables: FORM before 1997 and FORMREST_NY05 after 1997; household assets are the sum of own and partner's post-1997 assets in DKK 1000s. | IND (indh)FORM (assets before 1997), FORMREST_NY05 (assets post 1997) | replication code · code/assembledat_untying.sas (inc_vars1); code/datprep_untying.do |
| Assets, debt, wealth, secured debt, home ownership | Bruze et al. (2024) | Assets = QAKTIVF (before 1997) / QAKTIVF_NY05 (from 1997); debt = QPASSIV / QPASSIVN; wealth = assets - debt; secured (mortgage) debt = PRIGALD before 1995 and OBLGAELD from 1995; home owner if property value KOEJD > 0. Bank debt BANKGAELD and mortgage deed debt PANTGAELD also used. | IND, INDKQAKTIVF, QAKTIVF_NY05, QPASSIV, QPASSIVN, PRIGALD, OBLGAELD, KOEJD, BANKGAELD, PANTGAELD, GALDIO | replication code · 03panel_rep.sas, 'Assets, Debt, and Wealth' block |
| Bad-payer file registration | Kreiner et al. (2020) | Recorded in the bad-payer files of credit bureaus Experian or Debitor Registret (snapshot of persons registered in 2009); people can be recorded after at least three reminders and a warning letter, for at most five years per missing payment. | Experian bad-payer file, Debitor Registret | working paper · Section 3.1 and footnote 3 |
| Bank holdings | Bingley et al. (2020) | Bank assets minus bank debt at age 26 from financial institution reports to the tax authorities; mortgage debt and stocks/bonds excluded. | Tax authority financial reports | working paper · Section V, Table 9 panel B, footnote 19 |
| Bank liquid wealth, spending and cash withdrawals | Johannesen et al. (2026) | Bank data 2014-2016 used to compare spending and changes in consumer debt in December, January and other months by bank liquid wealth (DKK 5,000 bins), for age-eligible (65+) low-income singles vs age-ineligible (60-64) low-income singles. | Danske Bank customer data | online appendix · Supplemental Appendix, Figure A.4 notes |
| Bank-account link (bank identity) | Jensen and Johannesen (2017) | Year-end loan and deposit account balances reported by financial institutions to the tax authorities; the first four digits of each account number identify the bank branch, mapped to bank identity using hand-collected Nets lists of branch id-numbers for each year 2003-2011. | Tax authority third-party account reports, Nets branch listsFirst four digits of account number = branch id | working paper · Section 3.1, footnote 5 |
| Car ownership | Jensen and Johannesen (2017) | Number of cars owned, indicators for owning at least one and at least two cars, and average car weight, from the auto register (weight and production year of each registered automobile). | Motor vehicle register | working paper · Sections 3.1 and 6 |
| Cash-out refinancing | Andersen et al. (2020) | A refinancing associated with an increase in mortgage principal of 10% or more. | Danmarks Nationalbank mortgage data | working paper · Section 2.1 |
| Changes in bank, stock and property values | García-Miralles and Leganza (2024) | Year-t minus year-(t-1) year-end values of bank account balances and stock market account balances (reported by financial institutions to tax authorities) and of property cash values as assessed by tax authorities; winsorized at the 5th and 95th percentiles each year (also 10th/90th). | Tax-reported asset registers | working paper · Section 4.1, footnote 20 |
| Credit reform exposure | Leth-Petersen (2010) | The 1992 mortgage reform allowed mortgage loans to finance non-housing purposes, up to 60% of the house value, and extended the maximum maturity from 20 to 30 years; homeowners are treated and renters serve as a consistency check. | Administrative dwelling registers | working paper · Section 2; Section 4.3 |
| Credit-constrained (low liquid assets) | Leth-Petersen (2010) | Alternative splits used in robustness: low liquid assets = liquid assets of at most two months of disposable income in 1991; at most one month of disposable income in both 1990 and 1991; at most one month of disposable income in 1991 and subsequently experiencing an unemployment shock. | Wealth tax register, Income tax register | online appendix · Section 5 (3.1-3.3), alternative sample splits |
| Credit-constrained indicator | Epper et al. (2020) | Holding liquid assets worth less than one month's disposable income (measured in 2014 and over 2001-2015). | Third-party reported wealth and income registersLiquid assets < 1/12 of annual disposable income | online appendix · Online Appendix Figure A8 notes |
| Debt service / disposable income | Kreiner et al. (2019) | Debt service, defined as total interest payments on loans, in 2008 as percent of 2008 disposable income; underlies the high-debt-service indicator. | Statistics Denmark income registers | online appendix · Online Appendix Table EA.1 notes |
| Debt-to-income and savings-to-income ratios | Humlum et al. (2025) | Debt = debt to banks, pension funds, insurance and financial companies, credit card debt and study loans in banks, divided by labor income. Savings = mainly liquid savings: bank deposits, bond values and value of mortgage deeds, divided by labor income. Property owner = public cash valuation of owned property > 0 DKK. | Wealth/tax registersProperty valuation > 0 DKK | online appendix · Supplemental Appendix Table A.1, rows 5, 19-20 |
| Default by loan size | Kreiner et al. (2020) | Alternative severity measures defining default at defaulted loan levels exceeding DKK 0, 10,000, 50,000 and 100,000 (the defaulted amount may reflect several smaller loans). | SKAT loan register | working paper · Figure notes (Panel B, defaulting at loan levels) |
| Default liquid wealth | Johannesen et al. (2026) | Third-party-reported year-end value of current accounts, savings accounts and securities accounts (bank deposits and listed securities); excludes durables, real estate and pension accounts and does not net out debt. Available for the full population; mirrors the 'default wealth' the authorities use for the old-age check asset test. | IND, Statistics Denmark administrative wealth registers | paper · Section I.A and I.C; Table 1 notes; Figure 1 notes |
| Disaggregated consumer spending | Andersen et al. (2026) | Danske Bank customers (main account at Danske Bank and at least one spending transaction per month in 2018-2019) assigned home region monthly from bank address records and industry from the employer paying the largest share of annual income. Merchant region from geocoded transaction-string addresses and industry from an MCC-to-ISIC/NACE crosswalk; cash withdrawals allocated in proportion to in-person card payments. | Danske Bank transactionsMCC to ISIC/NACE crosswalk | working paper · Section III.A; Appendix E.A-E.C |
| Estimated home value | Daysal et al. (2021) | The public valuation of the home times the owner's share, multiplied by the mean ratio of sale price to public valuation among homes sold in the same year, municipality, housing type and valuation-quartile cluster (a SPAR-type index); woman's and partner's shares are summed. The value is fixed to the first house the woman is observed buying, and is in 100,000 DKK at 2006 prices (CPI-deflated). | State's Sales and Valuation Registry | working paper · Section 3, equation (1) |
| Excess mass below asset-test threshold | Johannesen et al. (2026) | Panel version: dummy for default liquid wealth between 50% and 100% of the asset-test threshold regressed on age dummies and individual fixed effects for low-income elderly born 1941-1945, coefficients relative to age 64; bunching jumps by 2.5 points at eligibility age 65 and keeps rising with age. Liquid wealth inflated to 2020 values with the growth rate of the asset-test threshold; DKK 5,000 bins. | IND, BEF50%-100% of threshold window; reference age 64 | online appendix · Supplemental Appendix, Figure A.3 notes |
| Expected lifetime wealth | Eshaghnia et al. (2025) | Expected present value at age t of future disposable income (adjusted for unrealized housing capital gains) discounted with a subjective stochastic discount factor (beta = 0.96), where expectations condition on an estimated information set (gender, education level, employment status, cohabitation, number of children, quartiles of mean income, mean consumption, consumption growth and consumption SD, homeownership, and interactions). | IND, UDDA, BEF, Danish Household Expenditure Survey | working paper · Sections 2.1, 3.2 measure 3 and 3.3 |
| Financial capacity (liquidity) | Kreiner et al. (2016) | Financial assets (money in bank accounts and value of shares and bonds) at end of 2008 relative to 2008 annual disposable income; groups below one month, between one and two months, and above two months of disposable income. | Background registers at Statistics Denmark | working paper · Section III.B, Figure 8 |
| Financial wealth and uncollateralized debt | Druedahl and Martinello (2022) | Financial wealth = market value of directly held stocks, bonds and mutual funds (KURSANP+KURSAKT plus PANTAKT+OBLAKT in code; WP lists KURSANP+KURSAKT+OBLAKT); uncollateralized debt = bank debts not associated with a bond (BANKGAELD). Both normalized by permanent income. | IND/FORMUEfinw = max(0, KURSANP+KURSAKT + PANTAKT+OBLAKT); liqdebts = BANKGAELD | replication code · rawbuild.do; WP Table D.1 |
| Firm bank loans | Renkin and Züllig (2024) | Account balance as of December 31 summed over all of a firm's bank loan relationships reported by banks to the Danish Tax Authority, including regular and syndicated loans, credit card debt and variable-utilisation accounts (revolving loans, overdrafts). Mortgages are not included because they are provided by specialised mortgage institutions outside the banking system. | URTEVIRK | paper · Section I.A (Loan Data) |
| First-time buyer / out-of-town buyer | Andersen et al. (2021) | First-time buyer: no member of the buyer household has previously owned real estate. Out-of-town buyer: the household buys property in a municipality it did not previously live in. | BBR, CPR | online appendix · Appendix A, Variable definition |
| Hedonic expected house value | Andersen et al. (2022) | ln(P_it) regressed on time x municipality effects, apartment indicator, and property characteristics: ln(lot size), ln(interior size), number of rooms, bathrooms, showers, unoccupied-at-sale dummy, ln(building age), rural dummy, historic-building dummy, ln(distance to nearest major city), all interacted with the apartment indicator, plus a third-order polynomial of the previous-year (biennial) SKAT tax-assessed value. R2 = 0.88 on sold listings (N = 114,303). Variants add repeat-sales residuals and lagged renovation deductions. | BBR/BOL, EJVK, EJSA, EJMT | online appendix · Online Appendix C.1; Figure L.4 notes; E |
| High debt service dummy | Kreiner et al. (2019) | Indicator equal to one if debt service is 15 percent or more of disposable income; also used as a continuous debt-service-to-disposable-income ratio. | Income tax register, SKAT account-level loan records | working paper · Section 5.3.2 |
| Historical parental asset path (instrument) | Kreiner et al. (2020) | Ratio of parental financial assets to average disposable income for each year 1987-1996; parents are assigned to behavioural types by within-cohort decile of the average asset-income ratio over the ten years, used to instrument parental default in 2011. | Income and wealth registers (Statistics Denmark) | working paper · Section 4 (Historical financial behavior of parents) |
| Holding a deposit balance | Kreiner et al. (2019) | Individuals with total deposit-account balances exceeding one month's disposable income are labelled as having a balance (to net out salary accounts credited just before 31 December); used to define the group holding both assets and debt. | SKAT account-level deposit records | working paper · Section 5.3.1 |
| Home ownership | Munch et al. (2006) | Indicator for being a home owner (living in owner-occupied housing) versus renting (private rental, social or cooperative housing). | Administrative registers | working paper · Section 2-3, pp. 8-10 |
| Home size | Gruber et al. (2021) | Area of the house in square metres used for habitation, from the housing register. | Housing register | working paper · Section 3.2 |
| Home value | Gruber et al. (2021) | Appraisal of the property by the Danish Tax Authorities made for assessing imputed rental income and taxable wealth. | Tax authority property appraisals | working paper · Section 3.2 |
| Homeownership (household-based) | Gruber et al. (2021) | Baseline measure from housing records (living in an owner-occupied home) validated against the tax-record measure based on housing wealth available from 1987, for the population above age 20; housing-register measure is slightly lower in level because it misses owners not residing in their owned property. A variant uses only 'clean' cases (defined in main text). | Housing register (BBR); tax records (housing wealth, from 1987) | online appendix · Figure A.I notes |
| Household balance sheet (tax records) | Andersen et al. (2024) | End-of-year measures from tax records: total assets = financial assets plus housing; financial assets = securities plus bank deposits; risky share = risky assets / total financial assets. DKK amounts converted to USD at the 2009-16 average rate of 5.85 DKK/USD. | Tax register (IND / wealth records)5.85 DKK/USD | online appendix · Online Appendix A, Table A1 notes |
| Household marginal interest rate (2008) | Kreiner et al. (2019) | Interest on marginal liquidity, computed from register data on all loan and deposit accounts of the household obtained from the Danish Tax Agency (SKAT), measured in 2008, in percent. | Danish Tax Agency account-level loan and deposit data | online appendix · Online Appendix Table EA.1 notes |
| Household net wealth | Browning and Leth-Petersen (2003) | Assets minus liabilities measured at year end; assets are the market value of share capital, bank deposits and the market value of bonds and securities; liabilities include mortgage and bank debt. | Wealth tax registers | working paper · Section 4 (Expenditure imputation) - restatement of Browning and Leth-Petersen (2003) |
| Household net wealth (ranking variable) | Alstadsæter et al. (2019) | 100% of national-accounts household wealth at market value distributed to households: all non-financial and financial assets minus debts, including funded pensions, excluding durables and valuables. Pension wealth imputed (40% to wage earners pro rata wage income, 60% to retirees pro rata pension income); unlisted equity imputed by capitalizing unlisted dividends and non-corporate business assets by capitalizing business income. Danish distribution taken from Jakobsen et al. (2018), based on individual wealth records. | Danish individual wealth records, National accounts | working paper · Section 3.2; Online Appendix A and D |
| Household taxable wealth (outcome) | Jakobsen et al. (2020) | Household net taxable wealth = sum over household members aged 20+ of the wealth variable FORM from the income-tax register (INDH); outcome is log(FORM). Portfolio shares use INDK components: equity share = listed stocks / total assets, housing share = (mortgage deeds + real estate value) / total assets. | INDH, INDKFORM (INDH); share_tx_equities = KURSAKT/QAKTIVF; share_tx_housing = (KURSPANT+KOEJD)/QAKTIVF; other INDK fields: FORMSK, LOFTFORM, QPASSIV, PRIGALD, KONTANT, KURSOBL, INDESTPI | replication code · Empirical Analysis/DD_base_data.do, section 1 |
| Household wealth | Jakobsen et al. (2026) | Bank deposits, bonds, stocks and property net of loans and mortgages at end of year; registered property values raised by 10% (following Leth-Petersen 2010); deflated by CPI, in 1000 DKK. | INDFORM; FORMREST_NY05 (after 1996) | working paper · Supplemental Material A |
| Housing equity | Druedahl and Martinello (2022) | Value of owned real estate (KOEJD) minus collateralized debt (mortgage bonds OBLGAELD + other mortgage debt PANTGAELD, at market value of associated bonds at year end), normalized by permanent income. | IND/FORMUEhequity = KOEJD - (OBLGAELD + PANTGAELD) | replication code · rawbuild.do; WP Table D.1 |
| Imputed household consumption | Eshaghnia et al. (2025) | Household consumption predicted from register disposable income and assets and liabilities in t and t-1 by a random forest (number of trees by 5-fold cross-validation) trained on the Danish Household Expenditure Survey (1997+); adult-equivalent using the OECD equivalence scale. | IND, Danish Household Expenditure Survey | working paper · Appendix A.1, 'Danish Household Expenditure Survey' |
| Imputed household consumption | Andersen et al. (2024) | Family disposable income (excluding imputed rent of own home) plus one-off pension payouts (ATP, SP, LD, withdrawn pensions) net of their tax, minus savings in bank deposits, minus stock savings corrected for price changes (change in stock holdings less lagged holdings x stock index return), minus foreign asset savings, minus repayments (afdrag) on mortgage, bank, mortgage-deed and foreign debt, minus private pension contributions. From 2010 the mortgage repayment uses loan-level REAL repayments (version 3). | IND, FORMUE, REALfamforbrug1a / famforbrug3 = famdispindk + (famatp_engang+famsp_pension+famld_udbetal+famhaevpen) - taxes on these - op_famindestpi - (op_famkursakt - famkursakt_lag*makro_aktie/100) - op_famudlakt - af_famoblgaeld (or af_famrealgaeld) - af_fambankgaeld - af_fampantgaeld - af_famudprigael - fampripensindbetal | replication code · 0c_Familydata.sas lines ~1586-1695 |
| Imputed household consumption | Daysal et al. (2021) | Household consumption imputed from income and year-to-year wealth changes following Browning and Leth-Petersen (2003). | Danish income and wealth registers | working paper · Section 5 (credit constraints) |
| Imputed household consumption | Giorgi et al. (2019) | Annual household consumption (spending) imputed from the budget constraint as C = Y - T - dA, where Y = gross income from all sources (labour and capital) + housing support + child support - implicit tax on the consumption value of owned housing, T = total taxes paid, and dA = change in assets (cash, bank deposits, stocks and shares, property value, cars and other vehicles, minus liabilities). Values in 2000 DKr. | Tax records (income and wealth tax data) | working paper · Section 2.1, eq. (1) |
| Imputed household expenditure | Crawley and Kuchler (2023) | C = Y - P - dNW: after-tax income (all labor and capital income, excluding capital gains) minus contributions to privately administered pension schemes minus the change in non-pension, non-housing net worth between 31 December of t-1 and t. Households in housing-transaction years and with more than trivial business income are excluded; equity holders are assumed to hold a diversified stock index; household-years with negative imputed expenditure (about 3 percent) are discarded. | Tax-authority income records, Tax-reported wealth data, Private pension contribution records | working paper · Section 4.2 |
| Imputed household total expenditure | Browning et al. (2013) | Household disposable after-tax income minus the change in net financial wealth from t-1 to t, following Browning and Leth-Petersen (2003); deflated to 1990 prices. Possible only up to 1996 because wealth-tax register data end after the wealth tax was abolished in 1997. | Income and wealth tax registers (10% sample) | working paper · Section 4 (Expenditure imputation), eq. (1), footnote 1 |
| Imputed spending | Kreiner et al. (2019) | Disposable income minus net savings (change in net bank assets) minus private pension contributions, c = y - dW - p, following Browning and Leth-Petersen (2003). | Income tax register | working paper · Section 5.1 |
| Imputed spending | Jensen and Johannesen (2017) | Spending = disposable income minus change in net wealth. Disposable income = total realized income - taxes - interest payments + payments from private pension accounts (net of taxes) + imputed capital gains on stocks (start-of-period stock value times growth in stock price index). Net wealth = market value of securities + savings and checking accounts - mortgage loans - bank debt - other loans; real estate excluded (so spending includes real estate purchases). Main regressions exclude stock owners. | Income registers, Wealth registers | online appendix · Online Appendix Table A1; WP Section 3.2 |
| Imputed total household expenditure | Leth-Petersen (2010) | Total expenditure imputed from tax registers as disposable income (taxable income plus some non-taxable components and transfers, minus final taxes) minus the change in net wealth from the end of one tax year to the end of the next, following Browning and Leth-Petersen (2003); capital gains are not netted out except for housing. Households with negative imputed expenditure are dropped. | Income tax register, Wealth tax registerc_t = y_t - dW_t (equation 14), with allowance for housing capital gains | working paper · Section 4.1, equation (14); Section 4.2 |
| Imputed total household expenditure | Browning and Leth-Petersen (2003) | Total expenditure imputed at the household level as disposable after-tax income minus the change in net wealth between end of year t-1 and end of year t (c = y - dW). Validated against total expenditure reported in the 1994-96 Danish Expenditure Survey linked to the households' register records. Relies on wealth-tax register information, which ends after the wealth tax was abolished in 1997. | Income and wealth tax registers (Statistics Denmark), Danish Expenditure Survey 1994-96 | working paper · Section 4 (Expenditure imputation), eq. (1) and footnote 1 - restatement of the Browning and Leth-Petersen (2003) definition by the same authors |
| Inferred wealth transfers (residual) | Boserup et al. (2018) | For each child, wealth at a given age minus the present value of earnings up to that age (real interest rates 0%, 3%, 6%), censored at zero, as a lower bound on transfers received. | SKAT wealth records, Income registers | working paper · Section 3.2, Table 1 |
| Liquid and net financial assets | Andersen et al. (2022) | Liquid financial assets = cash, stocks and bonds; net financial assets = liquid financial assets minus short-term non-mortgage liabilities (mainly unsecured debt). Used as household controls with age and education length. | IND (SKAT income and wealth records) | online appendix · Online Appendix K.1 |
| Liquid assets | Druedahl and Martinello (2022) | Sum of cash and savings accounts held in Danish banks (BANKAKT), floored at 0, normalized by permanent income. | IND/FORMUEBANKAKT | replication code · rawbuild.do; WP Table D.1 |
| Liquid assets / disposable income | Kreiner et al. (2019) | Liquid assets (deposits, shares and bonds) in 2008 as percent of 2008 disposable income; underlies the low-liquid-assets indicator. | Statistics Denmark income and wealth registers | online appendix · Online Appendix Table EA.1 notes |
| Liquid assets and net wealth | Taubinsky et al. (2026) | Liquid assets follow Andersen et al. (2020): total value of bank deposits, stocks and bonds reported by financial institutions to SKAT. Net wealth is SKAT's tax-purpose measure of net household assets excluding non-deposited cash and foreign assets, with real estate at tax-assessed values; both averaged across spouses and used as asinh changes. | IND | working paper · Section 1.2 and footnote 7 |
| Liquid assets, debt-to-income, mortgage payment | Andersen et al. (2024) | Liquid assets = bank deposits + bonds + stocks (market value); total assets add housing value; liabilities = bank debt + mortgage outstanding; debt-to-income = total liabilities (QPASSIVN) / gross income (PERINDKIALT_13); total mortgage payment (ydelse) = installments + interest paid + administration fee (bidrag). | FORMUE, IND, REALliq_assets = fambankakt + famoblakt + famkursakt; dti = famqpassivn/famperindkialt_13; ydelse = famafdrag_for + famrente_bet_nu + fambidrag | replication code · 4_VarDefinition.do section 2 |
| Liquid wealth | Fadlon and Nielsen (2021) | Third-party reported liquid wealth balances measured annually on 31 December, 1984-2011, including bank-account balances and lump-sum transfers from insurance companies; used in robustness checks to account for life insurance payouts. | Tax-reported wealth data | working paper · Section 3.2 |
| Liquid wealth quintiles | Crawley and Kuchler (2023) | Households are assigned to quantiles by their mean level of liquid wealth (or income, etc.) over the sample period (long-term quintiles); liquid wealth is the end-of-year balance, averaged in real terms over 2009-2015 in descriptive comparisons. | Tax-reported wealth data | online appendix · Online Appendix E.1 and Table F.1 notes |
| Liquidity | Andersen et al. (2024) | Liquid assets (deposits, stocks and other securities) at the beginning of the month relative to average monthly income; household-months are ranked within month and split into terciles. | Danske Bank data, Tax register (income) | working paper · Section 4.3 (Heterogeneity) |
| Liquidity constraint | Browning et al. (2013) | Liquidity-constrained households have liquid funds (cash and bonds) below 1.5 months of disposable income; high-liquidity households have liquid funds above half a year's disposable income. Mortgage sensitivity measured by mortgage debt / house value. | Income and wealth tax registers | working paper · Section 6 (credit constraints) and robustness section |
| Liquidity constraint indicator / financial wealth | Kreiner et al. (2020) | Financial wealth = stocks + bonds + bank deposits; indicator for having financial assets at end of year below one month's worth of average disposable income over the five years preceding the shock year. | Income and wealth registers (Statistics Denmark) | working paper · Figure 8 notes |
| Listing premium | Andersen et al. (2022) | l = ln(L) - predicted ln(P), where L is the initial listing price and predicted ln(P) is from the hedonic model. | RealView, SKAT, BBR | paper · Section 4.1 |
| Loan default (financial trouble) | Kreiner et al. (2020) | An individual is in default if at least one interest-bearing personal loan is at least 60 days late with payments at the end of the year, as reported by banks and other financial intermediaries to the Danish Tax Agency (SKAT); annual data 2004-2011 covering all loan accounts. Mortgage loans are supplied in separate files; loan type is otherwise not observed. | SKAT loan register | working paper · Section 3.1 and footnote 2; Figure 1 notes |
| Low liquid assets dummy | Kreiner et al. (2019) | Indicator equal to one if the individual holds liquid assets worth less than one month's disposable income (threshold chosen for highest predictive power); a continuous liquid-assets-to-disposable-income ratio is also used. | Income tax register | working paper · Section 5.3.2 |
| Low parental liquid assets indicator | Nielsen et al. (2010) | Basic indicator D1 = 1 if parents' liquid assets at time of high school graduation fall short of one month's income; extreme indicator D2 = 1 if below two months' income. Liquid assets are all non-housing assets: cash, shares, bonds, mortgage deeds and other assets; children of self-employed parents (liquid assets not registered) are set to D1 = D2 = 0. | Wealth/tax records | paper · Section V.B, Tables 4-6 |
| Marginal interest rate (interest on marginal liquidity) | Epper et al. (2020) | From tax-authority loan-level data: interest rate per loan r = interest paid in 2014 / (0.5 x (balance end-2013 + balance end-2014)); only non-mortgage loans, minimum denominator DKK 1,000, rates censored at the 5th and 95th percentiles. For respondents with loan accounts the marginal rate is the highest loan-specific rate; for respondents with only deposit accounts it is the smallest account-specific deposit rate. Distribution: p5 0.00, p25 0.97, p50 6.25, p75 12.73 percent. | Danish Tax Agency (SKAT) account-level loan and deposit data, 2013-2014Non-mortgage loans; denominator >= DKK 1,000; censored p5/p95 | online appendix · Online Appendix C.4; Table A8 |
| Monthly household consumer spending | Andersen et al. (2024) | Sum of three types of outgoing transactions in Danske Bank accounts: card payments (including payments initiated via mobile phone apps), bill payments and cash withdrawals. Excluded: transactions unlikely to reflect consumption, such as fee payments to the bank, payments for securities purchases and uncategorized bank transfers with unobservable purpose; Merchant Category Codes are used to exclude tax payments and debt repayments. Outgoing transactions from joint accounts are split evenly between account owners before aggregating to the household. Validated against Statistics Denmark's Household Budget Survey (table FU08, total spending minus imputed owner-occupied housing) by age of oldest household member, 2009-2016. | Danske Bank transaction data, Household Budget Survey (Statistikbanken FU08)Merchant Category Codes (MCC) used to drop tax payments and debt repayments | online appendix · Online Appendix B and Figure A1 notes |
| Mortgage balance and loan-to-value | Andersen et al. (2022) | Danmarks Nationalbank loan-level mortgage data (via Finance Denmark), annual 2009-2016, covering all mortgage banks: principal, outstanding balance, LTV and interest rate, with borrower CPR and property number. Multiple mortgages on a property are summed, with balance-weighted average interest rate and LTV. | REAL (Boligfinansiering) | online appendix · Online Appendix C.3 |
| Mortgage equity extraction and loan types (monthly) | Andersen et al. (2023) | Monthly mortgage portfolio reconstructed from annual end-of-year loan-level snapshots: loans tracked by unique loan ID; loans appearing/disappearing dated by origination of new loans, and, if ambiguous, termination month inferred by comparing interest implied by rate and balance with mortgage interest reported in annual tax returns. Adjustable-rate and interest-only indicators from loan type; average monthly payment imputed from remaining balance, time to maturity, interest rate and amortization profile; mortgage repayments = sum of average monthly payments over loans held at end of month. | Mortgage bank loan-level data (end-of-year); tax returns (interest) | online appendix · Appendix A and Appendix D |
| Mortgage termination probability | Andersen et al. (2020) | Probit of a monthly dummy for full prepayment/termination (moving, death, divorce, etc.) on household demographic dummies and rank variables (with nonlinear transforms), fitted on all households with unchanging membership and a fixed-rate mortgage 2010-2017; fitted values feed the ADL threshold. | Danmarks Nationalbank loan-level mortgage data, Population register | online appendix · Table B1, Figure B1 |
| Net investment in stocks | Andersen et al. (2024) | Gross purchases net of gross sales, computed residually as the total change in portfolio value minus the change due to capital gains. | Danske Bank portfolio data | working paper · Section 2.1 |
| Net Nominal Position (NNP) | Crawley and Kuchler (2023) | Household NNP = financial assets minus liabilities, averaged over 2009-2015 and deflated by CPI. Assets: bank deposits plus market value of securities excluding shares. Liabilities: all debt to financial institutions (including credit card debt), publicly administered student debt, tax debt and other debt to government bodies, as reported to the tax authorities by financial institutions. Other sectors from national financial accounts: net assets in currency and deposits, securities other than shares, loans, and trade credits/other accounts receivable-payable. | Tax-reported wealth data, Danmarks Nationalbank financial accounts | online appendix · Online Appendix H.1-H.2 |
| Net repayments on non-mortgage loans | Andersen et al. (2023) | Change in end-of-month balances on loan accounts (amounts owed coded as negative balances) owned by the affected person or spouse. | Danske Bank account records | online appendix · Appendix A |
| Net saving in liquid assets | Andersen et al. (2023) | Change in end-of-month balances on Danske Bank deposit accounts owned by the affected person or spouse plus outflows minus inflows of transactions with type code 'securities trade'. | Danske Bank account records | online appendix · Appendix A |
| Net wealth | Epper et al. (2020) | In the respondent vs non-respondent comparison (2015 values), wealth = real estate, deposits, stocks, bonds, mortgage deeds in deposit, cars and pension accounts minus all debt except debt to private persons; tax-assessed housing values adjusted by the average ratio of market prices to tax-assessed values among traded houses of the same property class, location and price range. Robustness variants: market-adjusted housing; adding car stock (available from 2014); adding pension wealth; household-level wealth; liquid financial wealth only (stocks, bonds, deposits). | Third-party reported wealth registers (SKAT/Statistics Denmark)Car and pension wealth only available from 2014 | online appendix · Online Appendix Table A5 notes; Table A10 |
| Net wealth | Boserup et al. (2018) | Value of assets minus liabilities measured on the last day of the year from administrative tax return records (SKAT), 1983-2011; mostly third-party reported by banks and financial institutions. Assets include deposits, stocks, bonds, real estate and deposited mortgages; liabilities include bank debt, mortgage credit debt, card debt, student debt, debt to municipalities, unpaid taxes, etc. Pension funds are not included; self-reported items (cars, boats, etc.) are excluded after the 1996 abolition of the wealth tax. | SKAT wealth records (via Statistics Denmark) | working paper · Section 2 (Data) |
| Net wealth | Browning et al. (2013) | Financial assets (market value of shares, bank deposits, market value of bonds and securities) minus debt (mortgage debt, bank debt etc.). | Wealth tax registers | working paper · Section 4 (Expenditure imputation) |
| Net wealth and liquid wealth | Caplin et al. (2026) | Net wealth = (KOEJD + BANKAKT + KURSAKT + OBLAKT + PANTAKT) - (BANKGAELD + GAELD_TIL_OFF + OBLGAELD + PANTGAELD), missing public debt set to 0. Liquid wealth = BANKAKT + KURSAKT + OBLAKT + PANTAKT. Pension wealth is the sum of PF_FORMUE across contracts in PENSFORM. | IND, PENSFORMKOEJD, BANKAKT, KURSAKT, OBLAKT, PANTAKT, BANKGAELD, GAELD_TIL_OFF, OBLGAELD, PANTGAELD; PF_FORMUE | replication code · script/cleaning/1_b_annual_data.R |
| Net worth | Druedahl and Martinello (2022) | Sum of housing equity, liquid assets and financial wealth minus uncollateralized (bank) debt, at year end, CPI-deflated (2010=100) and divided by permanent income; pension funds and durables (cars, boats) excluded. Each component floored at 0 in code; individuals with net worth outside the 1st-99th percentile in any year are dropped. | IND/FORMUE (GRUND files)totassets = stocks(KURSANP+KURSAKT) + bonds(PANTAKT+OBLAKT) + KOEJD + BANKAKT; totdebts = mortgage(OBLGAELD+PANTGAELD) + BANKGAELD; networth = totassets - totdebts | replication code · data/server/rawbuild.do ('Wealth' block, 'SAMPLE SELECTION'); CEBI WP 02-17 Table D.1 |
| Nondurable consumption proxy | Crawley and Kuchler (2023) | Imputed expenditure adjusted using registry data on household car purchases to proxy nondurable consumption (details in online appendix G, not in this file). | Car purchase registry data | working paper · Section 7.2 |
| Number of housing units owned | Druedahl and Martinello (2022) | Count of distinct properties (ejendom number x municipality) owned by the individual in the ownership register (EJER), plus total ownership share. | EJERbnr = group(ejdnr komnr); n_owned; pct_owned = total(ejerpct) | replication code · rawbuild.do ('HOUSING DATASET') |
| Once-lagged one-year home price change | Daysal et al. (2021) | One-year change in estimated home value, lagged one year relative to the fertility outcome. Public valuations were made in January of the prior year until end-2003 and in October of the prior year afterwards. Homes whose valuation rose by more than 50% in a year, and the 1st and 99th percentiles of the lagged change, are excluded. | State's Sales and Valuation Registry | working paper · Section 3 and 3.1, footnote 13 |
| Outcomes scaled by lagged disposable income | Andersen et al. (2024) | Annual changes in household consumption, bank deposits, financial assets, bank debt and mortgage debt, and levels of installments and interest payments, divided by previous-year family disposable income (DISPON_13); all censored at the 1st and 99th percentile by year. | IND, FORMUE, REALrdiffconsump = (famforbrug - L.famforbrug) / L.famdispon_13; rfamafdrag_for = famafdrag_for / L.famdispon_13 | replication code · 4_VarDefinition.do section 4 |
| Passive gains (instrument) and portfolio controls | Andersen et al. (2024) | Passive gains are the gains earned in the month by the portfolio held at the beginning of the month, used to instrument actual gains. Controls are full interactions of portfolio size (stock value on the last day of the previous month), risk (SD of portfolio returns over the past 12 months) and expected return (mean monthly return over the past 12 months). | Danske Bank portfolio and price data | working paper · Sections 2.1 and 3 |
| Pension withdrawals (borrowing-constraint indicator) | Jensen and Johannesen (2017) | Withdrawals (liquidations) from tax-favoured pension savings accounts, which carry a 60% penalty before pension age, used as an indicator of borrowing constraints. | Income and wealth registers | working paper · Section 5 (Table 4, col. 3) |
| Portfolio components | Boserup et al. (2018) | From 1995 onwards complete portfolio information on bonds, stocks, cash in banks, real estate, mortgage loans and the sum of other loans; childhood holdings summarised as bank deposits, securities and real estate. | SKAT wealth records | working paper · Sections 2 and 3.1 |
| Potential gain (vs reference price) | Andersen et al. (2022) | G = predicted ln(P) - ln(R), where R is the nominal original purchase price of the property; measurable only for properties bought in 1992 or later. | SKAT property transactions, BBR | paper · Sections 3.5 and 4.1 |
| Potential home equity | Andersen et al. (2022) | H = predicted ln(P) - ln(M), where M is the outstanding mortgage balance reported by the household's mortgage bank that year (multiple mortgages on a property summed; LTV and rate weighted); sellers with H < 20% are classed as potentially facing a binding down-payment constraint. | Danmarks Nationalbank mortgage data | paper · Sections 3.3 and 4.1 |
| Potential inheritance | Druedahl and Martinello (2022) | Deceased parent's gross bequest (max(0,housing equity) + financial wealth + liquid assets - uncollateralized debt, floored at 0, in the last observed year) split equally among all children (from FTDB), net of inheritance tax: if estate exceeds the exemption bundfr, net = (0.85*(estate - bundfr) + bundfr)/n_heirs. Where both parents die, the largest inheritance is kept. Normalized by heir's permanent income; main sample has potential inheritance > 1 year of permanent income. | IND/FORMUE, FTDB2012, Register of Causes of Deathgross_bequest; n_heirs; bundfr (yearly exemption, e.g. 264,100 DKK in 2010); inhgroup==2 if inheritance>1 | replication code · rawbuild.do ('Remove taxes from inheritance'); main_analysis.do line ~96; WP Appendix D, Table D.3 |
| Primary bank (2007) | Jensen and Johannesen (2017) | If one bank relation in 2007, that bank; if multiple relations but a loan in only one, that bank; if loans in multiple banks, the bank with the largest loan balance; if no loans, the bank with the largest deposit balance. Always a bank, never a mortgage institution. | Tax authority third-party account reports | working paper · Section 3.3 and footnote 6 |
| Propensity to spend the SP payout (survey) | Kreiner et al. (2019) | Survey (Epinion) question: considering the increase in disposable funds from the SP payout, how did you allocate it: 1) increase spending, 2) increase free savings, 3) reduce debt, 4) increase pension savings; spending share in [0,1] (96% of answers 0 or 1). Main sample includes only respondents who withdrew the SP balance (N = 5,037); extended sample adds 215 non-withdrawers coded as zero spending. Probit robustness codes share >= 0.5 as 1. | Telephone survey linked to registers on CPRProbit: 0 if share in [0,0.5], 1 if in [0.5,1] | online appendix · Online Appendix sections 4, 7, 8 (survey instruments) |
| Ranked household characteristics | Andersen et al. (2020) | Ranks computed each year across all sample households, normalized to [-0.5, 0.5]: age = age of oldest household member; education = years needed to reach the highest education of any household member (12 = high school, 16 = Bachelor's, 18 = Master's, 20 = PhD); income = total taxable household income; financial wealth = cash, bonds, stocks and mutual funds less non-mortgage debt; housing assets = value of properties. Specifications add a nonlinear transform f(x) of each rank. | Tax register (income, wealth), Education register (UDDA), Population registereducation years: 12/16/18/20 | online appendix · Tables B3, B5 notes |
| Real estate valuation | Boserup et al. (2018) | Public (tax-assessed) values of real estate are adjusted upwards each year using the average relative difference between selling price and public value of real estate traded that year, following Leth-Petersen (2010). | SKAT wealth records | working paper · Section 2, footnote 6 |
| Refinancing behaviour at IO expiry | Andersen et al. (2024) | At the expiry year: loan stays (survives, no new loan in t-1, t, t+1); new IO loan taken in t-1, t or t+1; new amortising loan (no IO); or pays out the loan (none of the above). | REALout_loan_stays, out_newloan_io, out_newloan_noio, out_paysout | replication code · 4_VarDefinition.do section 3 |
| Refinancing event | Andersen et al. (2020) | A refinancing occurs when a household changes from one fixed-rate mortgage to another mortgage (fixed- or adjustable-rate) on the same property; terminations from moves, death or divorce are handled separately as mortgage termination. Annual loan data are expanded to quarters using mortgage issue dates. | Danmarks Nationalbank mortgage data, CPR | working paper · Section 2.2.B Sample selection |
| Refinancing incentive (ADL threshold) | Andersen et al. (2020) | Interest rate spread = coupon rate on old mortgage minus yield on a newly available mortgage of roughly the same maturity. Threshold from the Agarwal, Driscoll and Laibson (2013) closed form with baseline interest-rate volatility 0.0074 and discount rate 5%; incentive = spread minus threshold. Household-specific mortgage termination probability (from a probit on moves/death/divorce terminations) enters the threshold. Robustness: volatility 0.0037, discount rate 2.5%, constant 10% termination probability, Chen and Ling (1989) thresholds. | Danmarks Nationalbank loan-level mortgage datasigma=0.0074 (baseline); discount rate 0.05 (baseline) | online appendix · Table B2; Appendices F-I, K-L |
| Register net savings | Kreiner et al. (2019) | Change in net bank assets (bank deposits minus bank debt, including credit card debt, recorded on the last day of the year) between t and t-1 from the income-tax/wealth register; used to validate survey answers for 2005-2011. | Income tax register (SKAT wealth data) | working paper · Section 5.1 |
| Renovation tax deductions (BoligJob) | Andersen et al. (2022) | Proxy for maintenance/renovation: claimed BoligJob ('Boligjobordningen') tax deductions for labour costs of service work in the home, available from 2011, aggregated by household and linked to the seller; maximum 15,000 DKK per adult 2011-2015, then 12,000 DKK for maintenance/renovation and 6,000 DKK for other services (2016-2018). Used as 1-year lag (2012-2016) and 3- and 5-year cumulative lags. | INDSLUT (Servicebidragsregister) | online appendix · Online Appendix E.3.1 |
| Rent control benefit and decile of rent control degree | Svarer et al. (2005) | Rent control benefit = predicted uncontrolled rent minus actual rent; uncontrolled rent from the Danish Tax Authorities' 1999 hedonic model for owner-occupied housing (sales 1996-1999, dwelling traits, more than 50,000 geographic areas) times an estimate of user cost; missing actual rents (about 50% of units) imputed by Heckman two-step. The regressor is the decile of the rent control degree (benefit / uncontrolled rent), using 1999 decile limits applied to 1997, 1998 and 2000; time-varying across housing units via moving dates. | Housing unit characteristics (Statistics Denmark), DTA 1999 hedonic model | working paper · Section 4.2 'The rent control benefit' |
| Reported liquid wealth | Johannesen et al. (2026) | Default liquid wealth plus self-reported corrections submitted via the government website (e.g. adding cash, reducing account balances); available only for individuals above the statutory retirement age. The variable available to the authors does not capture corrections made close to year-end. | SOCP, IND | paper · Section I.A and I.C; Table 1 notes |
| Self-reported liquidity constraint | Simonsen et al. (2021) | From the 2000 Danish National Health Interview Survey (DNHIS) question 'During the last 12 months, how often have you had trouble paying your bills'. Respondents answering at least 'some months' are coded as liquidity constrained (12%). | DNHIS 2000 (survey linked by personal ID) | paper · Section 6 (mechanisms), Table 5 |
| Spending subcategories (MCC-based) | Andersen et al. (2023) | Utilities: MCC 4900, 4812, 4814, 4821, 4899 or bill-payment label utilities/elec/gas/water/heating/internet/cable TV/telephone/TV license. Restaurants and bars: MCC 5813, 5462, 5811, 5812, 5814. Groceries: MCC 5411, 5422, 5441, 5499, 5921 or bill-payment label groceries. | Danske Bank transactionsUtilities MCC 4900,4812,4814,4821,4899; Restaurants MCC 5813,5462,5811,5812,5814; Groceries MCC 5411,5422,5441,5499,5921 | online appendix · Appendix A (Spending) |
| Stock market gains | Andersen et al. (2024) | Monthly gain G = sum over days in the month and securities of the payout-adjusted daily price change times the number of units held (adjusted for splits, mergers and dividends, so it includes dividends). The portfolio covers direct equity and mutual fund shares in all liquid securities accounts, excluding bonds, deposits and retirement accounts. | Danske Bank portfolio and price data | working paper · Section 2.1, eq. (1) |
| Stock market return | Epper et al. (2020) | (Dividend income + realised capital gains/losses during the year) / market value of stocks, averaged over 2008-2014; plus an indicator for owning stocks during the period. | Tax-based income and wealth registers | working paper · Section 3 (Table 2 col. 7 discussion) |
| Tax-assessed property value | Andersen et al. (2021) | SKAT's assessment of the property's value if sold (basis for property value tax and municipal land tax), carried out every other year and regulated by local house price growth in non-assessment years; the paper uses the assessed value in the year prior to the transaction, entered as log of thousand year-2015 DKK. | SKAT | online appendix · Appendix A, Data sources and Variable definition; paper Section 2 |
| Taxable saving (gross and net) | Chetty et al. (2014) | Gross taxable saving = change in third-party reported end-of-year assets in taxable accounts; net saving = gross saving minus change in liabilities, where liabilities cover secured and unsecured debt except home mortgages. Saving measures and rates below the 1st or above the 99th percentile in the estimation sample are set to missing. | Third-party reported wealth data (tax authority) | working paper · Section III |
| Total deductible interest expenses | Gruber et al. (2021) | Interest paid on mortgage debt, consumer debt and other loans (all deductible), for homeowners; mortgage interest cannot be separated over the whole period. | Income tax records | working paper · Section 3.2 |
| Total spending (transaction based) | Andersen et al. (2023) | Sum of outgoing card payments (incl. mobile-phone payments), bill payments and cash withdrawals from the household's bank accounts, excluding outflows identified via recipient MCC as tax or debt payments; excludes bank fees, securities purchases and uncategorized transfers. Bank aggregates raw transactions to daily totals per account x transaction type x recipient category. Household = affected person + spouse; joint-account outflows split evenly between owners. | Danske Bank transactionsMCC-based exclusion of tax/debt payments | online appendix · Appendix A (Spending) and Appendix B |
| Total wealth | Johannesen et al. (2026) | Default liquid wealth plus pension wealth and housing wealth, net of liabilities (approximates total net wealth for the low-income elderly). | IND, PENSFORM | paper · Figure 1 notes (panel B) |
| Transaction price (outcome) | Andersen et al. (2021) | Log of the realised transaction price in thousand year-2015 DKK, from property transactions announced in the Danish Gazette (Statstidende) as received by SKAT. | SKAT property transactions (Statstidende) | online appendix · Appendix A, Variable definition |
| Unhedged Interest Rate Exposure (URE) | Crawley and Kuchler (2023) | Household URE = annual savings (after-tax income minus imputed expenditure) plus maturing assets minus maturing liabilities. Maturing assets: all bank deposits (assumed floating) plus 20% of securities (5-year maturity assumed). Maturing liabilities: all bank debt (assumed floating), mortgage debt contractually due to reset its interest rate in the coming 12 months (counted at half weight since the new rate applies half the year; voluntary refinancing excluded), and other liabilities with an assumed 5-year maturity. Non-household sectors' maturity calibrated to 3.65 years so economy-wide URE sums to zero. | Tax-reported wealth data, Mortgage reset data (Danmarks Nationalbank)securities 20%; mortgage resets within 12 months | online appendix · Online Appendix H.1-H.2 |
| Visible / neutral / non-visible spending (DES) | Giorgi et al. (2019) | Spending in the Danish Expenditure Survey is split using the Heffetz (2011) visibility index: visible = tobacco and alcohol, food away from home, clothing, furniture and home goods, electrics/appliances, vehicles, entertainment, books, education, personal care; neutral = food at home; everything else (insurance, rent, etc.) non-visible. | DES (Danish Expenditure Survey) | working paper · Section 2.2; Table A6 |
| Wealth at age 50 | Almar et al. (2026) | Average net wealth at age 50 by programme, excluding pension assets, deflated to 2000 prices. | IND | working paper · Section 3, footnote 16 |
| Wealth components | Leth-Petersen (2010) | Third-party reported assets in six pre-reform categories (housing assets at tax-assessed cash value, shares, deposited mortgage deeds, cash holdings, bonds, other assets incl. self-reported non-deposited bonds, ship shares and high-value objects) and total liabilities; mortgage debt is separately available only up to 1993, so only total liabilities are consistent across years. Employer pension funds are not observed. | Wealth tax register | working paper · Section 4.2; Appendix figure A2 |
| Wealth percentile rank | Epper et al. (2020) | Within cohort x year percentile rank in the sample; robustness versions use averages over 2013-2015, 2011-2015 and 2009-2015, and household-level wealth. | Wealth registers | online appendix · Online Appendix Figure A10 notes; Table A11 |
| Wealth percentile rank | Boserup et al. (2018) | Percentile rank computed within child age-year specific wealth distributions (parents ranked on total parental wealth observed at the same time); ties, notably zero wealth among young children, are randomly broken. Adult outcome is the child's rank at age 45 (27 years after age 18). | SKAT wealth records, Population register (parent links) | working paper · Section 3.3, footnote 11; Section 3.4 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Actual labour market experience | Bagger et al. (2014) | Actual experience at end of calendar year constructed from mandatory ATP pension payments back to 1 January 1964; pre-1980 experience measured in years and post-1980 in 1/1000 of a year's full-time work. Workers dropped if pre-1980 experience changes over 1985-2003, if experience falls or rises by more than two years in a year, or exceeds age minus 15. | IDA, ATP | working paper · Section 3, footnote 11, Appendix B |
| Annual employment in full-time equivalents | Arendt et al. (2025) | Employment in annual full-time equivalents (FTE = 1 for a full year of work), from a register variable recording cumulative employment in a calendar year, observed for everyone in Denmark on 1 January. For YSR = 1 only, FTE can exceed 1 because the cumulative variable includes work in the year residency was granted. | Income registers, CPR | paper · Section 5 'Data on Refugee Immigrants' and footnote 17 |
| Annual employment rate | Foged et al. (2024) | Annual first difference in cumulative labour market experience (ERHVER/1000 + ERHVER79) from IDAP, bounded to [0,1] and set to 0 if missing; first observed year uses the level. Separately, 'employed in November' = has a workplace ID (LBNR) in IDA. | IDAPERHVER, ERHVER79; emp = exper - exper_lag, truncated to 0-1; employment_nov = (LBNR ne ' ') | replication code · 8. Controls and outcomes.sas, lines ~5016-5050, 5442, 5452-5474 |
| Cumulated job finding rate, hours and earnings | Cairo and Mahlstedt (2023) | Cumulated job finding rates by month since the intervention (extensive margin), and total working hours and labour earnings accumulated over the first 12 months after the start (intensive margin), from monthly income and employment records. | E-income (eIndkomst) | working paper · Section 4.2, Figure 3, Table 3 |
| Cumulative employment | Utar (2018) | Number of years 2002-2010 with positive primary (November) salary; a worker with an end-of-November employment with positive salary is regarded as employed that year regardless of job length. | IDAnzsal = 1 if rsalary>0; CEmp=sum(nzsal*Dum0210) | replication code · DataPrep.do; working paper 'Cumulative Effects' section |
| Employed | Traiberman (2019) | A worker is counted as employed in year t if employed in November of t (IDA November position). | IDA | online appendix · Online Appendix D.1, Employment |
| Employed / unemployed / out of labour force | Daysal et al. (2025) | Indicators of labour market status as of November from the Register-Based Labour Force Statistics, for each calendar year from diagnosis up to 10 years later. Non-survivors are assigned one for out of the labour force and zero for all other labour market outcomes. | RAS | working paper · Section 3, Outcome Variables |
| Employment | Damm (2009) | Based on labour market status in November of each year: employed if main occupation is wage-employment with at least 9 hours of weekly work, or self-employment (includes part-time of at least 9 weekly hours and full-time). Outcome measured seven years after immigration. | Immigrant population registers (Statistics Denmark), RAS | working paper · Section III.D |
| Employment (any in year) | Foged et al. (2024) | Indicator equal to one if the individual was employed at some point in the year (IDA). Appendix alternative: employment as a fraction of a full working year. For the ALMP analysis, monthly any-employment. | IDA | working paper · Section 4.1, footnote 9 |
| Employment (average ages 30-50) and out of labour force | Baker et al. (2026) | Employment indicator by age from IDAP: before 2008 PSTILL below 40 is employed; from 2008 PSOC_STATUS_KODE below 200 is employed. Out of labour force (defined only from 2008) is PSOC_STATUS_KODE 300-517. avg_emp is the mean employment indicator over ages 30-50; out of labour force is measured at age 50 (and 55). | IDAPPSTILL < 40 (pre-2008); PSOC_STATUS_KODE < 200 employed, 300-517 out of labour force (2008+) | replication code · dst_replicationpackage/do/2c_cr_idap.do |
| Employment as fraction of full working year | Hasager and Jørgensen (2026) | Fraction of a full-time working year employed: 1 if full-time employee during the whole year, less than one for part-time or part-year employment. | IDA, IND | working paper · Section I (labour market outcomes) |
| Employment indicator (extensive margin) | Jensen and Blundell (2024) | Indicator for working non-zero hours in the month in BFL. | BFL | paper · Fig. 10 and Table notes (employment indicator), Section 6 |
| Employment status | Bruze et al. (2024) | Through 2012: employed if IDA occupational status PSTILL < 38 or in 71-77; unemployed if PSTILL = 40; out of labour force if PSTILL 41-57 or 90-98. From 2013: employed if JOB_P_SOCIO_KODE 110, 120, 131-136; unemployed if 200; out of labour force if 311-517. Alternative: employed if wage income LOENMV_13 > 0. | IDAP, IDAPSTILL <38 or 71-77 (employed), 40 (unemployed), 41-57/90-98 (OLF); SOCIO 110,120,131-136 / 200 / 311-517 | replication code · 03panel_rep.sas, 'Employment' block |
| Employment status (employed / self-employed / retired / unemployed) | Andersen et al. (2021) | IDA status classified at end of November each year: employed if the majority of personal income is from paid employment, self-employed if the majority is from self-employment, retired if outside the labour market with the majority of income from private or public pensions, and unemployed otherwise. Household variables are shares of adult members (0, 0.5 or 1), measured in November of the year before the transaction. | IDA | working paper · Section 2 (data source 6); Appendix A Variable definition |
| Experience and tenure | Foged and Peri (2016) | Labour market experience = cumulative employment in years since first joining the labour force; tenure = time elapsed since hiring in the current establishment; low tenure = below sample average of 4.35 years in the establishment as of 1994. | IDA | working paper · Section 3.1; Section 5 heterogeneity |
| Firm employees (November stock) | Brenøe et al. (2024) | Standard IDA main November employment relationship: worker counted at the firm if their main job was there in the last week of November; includes workers on leave. Main job = job with most hours, ties broken by highest earnings. | IDA | working paper · Section 4.2; Appendix C.1 |
| Firm employment (FTE and head-count) | Utar (2014) | Full-time equivalent employment from firm accounts (firms with at least 0.5 FTE are covered) and head-count of employees from IDA matched via the unique firm identifier. | Regnskabsdata, IDA | working paper · Section 2; Appendix B, B.2 |
| Firm switch | Chetty et al. (2014) | Individual switches firms in year t if he has earnings from two distinct firms in t and t-1 (primary job for multiple jobholders); switches where earnings fell by more than half or more than doubled are excluded; set missing in 2003, 2005 and 2007 when firm identifiers changed. Event studies require at least 4 years of data before and after. | IDA, IND | working paper · Section IV.A, footnotes 9-10 |
| Firm tenure and general work experience | Frederiksen and Kato (2018) | Constructed by matching the linked employer-employee data with pension contribution data dating back to 1964. | IDA, Pension contribution data (ATP) | working paper · Section 2 |
| Fraction of year worked | Foged and Peri (2016) | Equals one for a full-time employee throughout the year; for part-time and/or part-year employment, the share of the regular working year spent at work. | IDA | working paper · Section 3.1 |
| Full-time and employment | Humlum and Plato (2026) | Full-time = full-time work according to RAS, defined by normal working hours above 32 hours/week. Employment = full-time work while not enrolled in a post-secondary programme and not receiving DI (DI = any weeks in the year on disability insurance). Stable pre-injury employment = full-time in all three years before the accident. | RAS, IDA | working paper · Appendix Table A.1, rows 10, 15-17; Section 1.3 |
| Full-time employment vs non-employment spell | Lentz et al. (2023) | Weekly employment and non-employment spells 1985-2013; spells with less than an average of 25 work hours per week are treated as non-employment spells (to keep only full-time workers). Non-employment is coded as firm ID 0. | Danish matched employer-employee data (MEE) | working paper · Section 2 and footnote 6; Appendix E.1 |
| Hours, employer continuity and earnings | Simonsen and Skipper (2025) | Any hours worked, hours worked per year, continued employment with the same employer (turnover measure), and earnings per year. | Employment registers (Statistics Denmark; names not given) | working paper · Table 5 panels C-F |
| Husband's pre-death work status | Fadlon and Nielsen (2021) | Indicator for whether the deceased husband worked throughout the entire five-year period preceding his death (periods -5 to -1); sample restricted to husbands who either worked in all five years or did not work at all in that period. | IDA / income registersPeriods -5 to -1 | online appendix · Online Appendix Table C.4 notes |
| Job mover / new hire | Stoyanov and Zubanov (2012) | Worker observed at a different firm than in the previous year; employment record is as of end of calendar year, so only the year of the move is observed. Sample restricted to manufacturing firms and employees aged 15-65 with positive annual salary; multiple jobs treated as different. | Matched employer-employee data (Statistics Denmark) | paper · Section I.A |
| Job-to-job transition | Taber and Vejlin (2020) | Transition between two establishments with a very short intermediate non-employment spell: non-employment spells of 2 weeks or less are allocated to the first employment spell; non-employment spells of 12 weeks or less between jobs at the same establishment are overwritten as employment (recalls). Transitions to/from establishments with missing ID and from closing or mass-layoff establishments are ignored. | Weekly spell data, IDA | working paper · Section 6 and 6.1 |
| Labor force participation | Fadlon and Nielsen (2021) | Indicator for having any positive level of annual earnings. | Income registers | working paper · Section 5 |
| Labor market attachment | Anker et al. (2021) | Cumulated time in employment, education/training and unemployment over years 1-4 after the initial charge. | IDA / labour market registers | online appendix · Table A-3, panel B |
| Labor market experience | Humlum et al. (2025) | Labor market experience measured in years since 1964. | IDA / ATP-based experience | online appendix · Supplemental Appendix Table A.1, row 15 |
| Labor market states (Employed, School, Sick Leave, DI, Other) | Humlum et al. (2025) | Employed = full-time work while not in school, on sick leave or receiving DI; School = enrollment in a higher degree in the year; Sick Leave = >15% of weeks on sickness benefits; DI = any weeks in the year receiving disability insurance; Other = residual (mainly part-time work, other unemployment and non-participation). | IDA; education register; DREAMSick leave > 15% of weeks; DI any weeks | online appendix · Supplemental Appendix Table A.1, rows 41-45 |
| Labor market status | Dustmann et al. (2024) | Three mutually exclusive states (employment, unemployment, not in the labor force) as defined by the ILO, from the register-based labour force statistics (RAS). In the 2012 repeal analysis employment is instead defined as non-zero labor earnings in a calendar year. | RAS (Registerbaseret arbejdsstyrkestatistik) / labour market registers | online appendix · Online Appendix B.2 |
| Labour force participation | Daysal et al. (2026) | Indicator equal to 1 if in the labour force (employed or unemployed searching) and 0 if out of the labour force, based on tax-record labour market status as of November of the preceding year; ages 18-32. | RAS | paper · Section 2 (Labor market and income outcomes) |
| Labour force participation / NILF | Rossin-Slater and Wüst (2018) | Fraction of post-separation years in which the father has any positive labour income, and the proportion of years not in the labour force. NILF is split by disability leave (Social Disability Pension), retirement including early retirement (Post-Employment Wage program, ages 60-64), and means-tested welfare. | Danish labour market registers | working paper · Section 6.4, Table 6, footnote 29 |
| Labour force participation by child age | Sander (2026) | For child ages 0-15, participation = positive ATP contributions in the year (hours > 0); for ages 16-40, participation = positive wage earnings (non-missing log earnings), set to 0 if missing and the mother is alive. | ATP, INDwork = hours>0; xwork = learn (loglon non-missing) for ages 16-40 | replication code · dofiles/createfirst.do, final loops |
| Labour market experience | Jakobsen et al. (2026) | Statistics Denmark's experience measure based on accumulated mandatory ATP pension payments, which depend on hours worked; part-time employment counts as 2/3 of full time. | IDAPERHVER, ERHVER79 | working paper · Section 2; Supplemental Material A |
| Labour market experience | Frederiksen et al. (2026) | Accumulated labour market experience from IDAP: erhverv_akk/1000 plus erhverv_1979/1000 (pre-1979 experience, set to 0 if missing), rounded to whole years. | IDAPIDAP erhverv_akk, erhverv_1979 | replication code · 3 Produce regression data.do, 'EXPERIENCE FROM IDAP' |
| Labour market experience | Hummels et al. (2014) | Actual time in employment since 1964. Union membership and marital status also taken from IDA. | IDA | online appendix · Data Appendix, section 1 |
| Labour market experience | Dahl et al. (2013) | Actual labour market experience accumulated since 1964. | IDA | working paper · Section 4 (p. 13) |
| Labour market experience and DI receipt | Humlum and Plato (2026) | Labour market experience measured in years since 1964. DI = any weeks within the year spent receiving disability insurance. | IDA | online appendix · Supplementary Appendix, Table A.1 Panel C |
| Labour market participation, part-time and full-time work | Jakobsen et al. (2026) | Participation means at least 481 annual hours (e.g. 37 hours for 13 weeks) on BFL pay slips together with annual income of at least 50,000 DKK; part time is 481-1,664 annual hours; full time is more than 1,664 hours. Monthly hours and income are scaled down by the share of the month registered on parental leave in the OF register. | BFL (eIndkomst), OFthresholds 481 and 1,664 annual hours; 50,000 DKK | working paper · Section 2; Supplemental Material A |
| Labour market position indicators | Utar (2018) | Built from the IDA primary labour market position pstill: self-employed, unemployed in November, retired/early retired, in education, outside the labour force. | IDAselfemp: pstill_i in 12,13,14,19; unemp_e: pstill_i==40; unemp: 40,46,47,48,51,52,55,95; earlyret: 50,93; retired: 50,92,93,94; ineduc: 46,91,98; outlab: 41-43,45-52,55-57,90-98; leave: 41,42,43,71-77 | replication code · DataPrep.do ('Labor Market Positions') |
| Labour market status (employed / unemployed / not in labour force) | Dustmann et al. (2023) | Primary labour market status in the last week of November from RAS, with code schemes varying by year. | RAS1981-1993 ARBSTIL: employed 11-39, unemployed 40, NILF 50-92 (1990+ keep NOVPRIO==1); 1994-1995 NYARB: employed 11-39, unemployed 40, NILF 41-400; 1996-2007 SOCSTIL_KODE: employed 115-199, unemployed 200, NILF 201-400; 2008-2015 SOC_STATUS_KODE (PRIMAER_STATUS_KODE==1): employed 110-138, 611, 612, unemployed 200, NILF 311-517 | replication code · do/build/clean/pool_ras_for_lm_status.do |
| Main monthly employer (BFL) | Caplin et al. (2026) | From monthly eIndkomst job records (BFL), each person-month is assigned the job with the most paid hours (AJO_LOENTIMER) as the primary employer; its CVR number, wage and hours are kept. Month and year come from the job start date field AJO_JOB_START_DATO. | BFLAJO_CVR_NR_FRA_PROD_JOB, AJO_SMALT_LOENBELOEB, AJO_LOENTIMER, AJO_JOB_START_DATO; primary job = max(AJO_LOENTIMER) within pnr-year-month | replication code · script/cleaning/1_c_monthly_data.R |
| Mass-layoff displacement and offshorer | Hummels et al. (2014) | Displaced workers separate from firms where at least 30% of the initial-year workers are not employed by the firm the following year (gross flows). Offshorers are displaced from firms whose predicted offshoring (first stage) rose at least 10% between predisplacement and displacement year. Sample: manufacturing workers with at least six years tenure in some year 1997-2000, not dying, emigrating or turning 61 in 1995-2006, at firms importing at least DKK 600,000 with 50+ employees. | IDA, FIDA, FirmStat | working paper · Section VI, footnotes 32-34, Data Appendix section 3 |
| Maternal employment after birth | Breining et al. (2020) | Mother's employment in each of years 0-10 after the child's birth: any employment, and full-time employment defined as employed at least 200 days in a year. Days of maternity leave also used as an outcome. | working paper · Section 3.1; Figure 4 | |
| Monthly employment | Serena (2025) | Employed in a month if the DREAM employment degree (GRAD, scaled to hours by 160.33) exceeds 80 hours, available from 2008 (missing GRAD set to 0); employment before 2008 is predicted with a machine-learning imputation. | DREAMgrad = GRAD_yyyy_mm*160.33; grademp = (grad>80) | replication code · CollectData_LaborMarket.sas; PsykTestLongRunRD.do; ImputeEmp.do / ImputeEmpPred.R |
| Monthly employment | Geerdsen (2006) | IDA gives employment status in November and the number of months employed in the year; monthly employment is located in the residual months not covered by other monthly/weekly states (leave schemes, UI, activation, early retirement, social benefits). | IDA | working paper · PhD thesis (SFI 2003), Paper II, Section 5.1, p. 114 |
| Monthly employment indicator | Kreiner et al. (2020) | Employed in a month if trimmed monthly payroll earnings are positive (non-missing). | eIndkomst (SKAT payroll)emplyd = felt_200_trim > 0 & felt_200 != . | replication code · data04.do |
| Monthly employment rate | Altmann et al. (2026) | emp_q = 1 if cumulative BFL hours increase between month q-1 and q after entry (emp1 = any hours in month 1); employment rate over 6 or 12 months is the mean of these monthly indicators. | BFLemp`x' = (hours4_total`x' - hours4_total`x-1' > 0); emp_rate12 = rmean(emp1..emp12) | replication code · code/data_preparation.do |
| Mother's degree of employment after birth | Gupta and Simonsen (2010) | Fraction of the year employed in 1996, 1997 and 1998 (one to three years after the 1995 birth), plus labour market experience before birth (1995, in years) and 1995 hourly wage and job level (senior/higher management, medium, lower, lowest level). | Statistics Denmark registers | working paper · Appendix Table A3 'Mother's Characteristics' |
| New hires / new hires from non-employment | Kreiner et al. (2015) | New hires are workers employed at the workplace in 2005 who were not employed there in 2004; new hires from non-employment are workers employed at the workplace in 2005 who were not employed at any workplace in 2004 (non-employment or non-participation). | IDA | working paper · Section 3.1 |
| Non-employment | Dustmann et al. (2019) | Being unemployed or not in the labour force at ages 18-65. | Labour force registers | paper · Section 2, footnote 23 |
| Non-working consumer industries | Andersen et al. (2026) | Adults without labour income are assigned to 'retired' if older than 65, 'students' if they receive a government stipend, 'long-term unemployed' if they receive unemployment or cash benefits (fully benefit-dependent with no labour income all year), and otherwise 'out of workforce'. | BEF, IND | working paper · Appendix D; footnote 5 |
| Ordinary employment (weekly) | Bargain and Jonassen (2024) | Weekly indicator equal to 1 if the person has any employment in the corresponding month in MIA (monthly A-income employer reports, linked to CONESR/RAS jobs), set to 0 in weeks with a DREAM code for study grant, wage-subsidy programmes or death. | MIA, CONESR, RAS, DREAMwage = 0 if DREAM y in 651,652 (study grant); 135,136,145,146,215,216,705,706,715,716,725,726,735,736,745,746,755,756,765,766,815,816,875,876,895,896 (wage subsidy); 997 (deceased) | replication code · baseline.do, lines ~165-180; mia_con.do |
| Out of labour force / participation | Foerster (2024) | Out of the labour force if IDA labour force status PSTILL is in 40, 41, 50, 52, 90, 92, 93 or 95; labour force participation is the complement. | IDAPPSTILL in 40,41,50,52,90,92,93,95 = out of labour force | replication code · code/datprep_untying.do (Define out of the labor force) |
| Parental employment (extensive margin) | Daysal et al. (2022) | Indicator equal to 1 if the annual change in cumulative experience ERHVER is non-zero in the year, 0 if zero. | IDA (persondata)M_extsup = 1 if dif_erhverv ne 0; 0 if dif_erhverv = 0 | replication code · 06 Adding parental labor outcomes rev.sas, lines ~700-705, 855-860 |
| Potential labour market experience | Bingley et al. (2013) | Years since entering the labour market, where year of entry is observed from the first mandatory pension (ATP) contribution; ATP was introduced in 1964, so entry is censored at 1964. | ATP | working paper · Section 4 |
| Pre-reform employment performance type | Arendt et al. (2025) | HIGH if cumulative employment by YSR = 2 is above 0.75 FTE, otherwise LOW (20% HIGH, 80% LOW). The reform's requirement is 2.5 FTE of cumulative employment. | Income registers | paper · Section 5, 'Classifying workers into performance types' |
| Quarterly employment, hours and earnings | Hasager (2024) | Quarterly employment indicator (available 2001-2019) and quarterly hours of work and earnings (from 2008) from tax-authority labour market data; earnings CPI-deflated to 2015 and converted to 1,000 USD at the 27 March 2019 exchange rate. | Labour market data from Danish tax authorities | working paper · Section 3.2, footnote 11 |
| Self-employed (main occupation) | Maire and Schjerning (2013) | Persons whose primary occupation is self-employment, classified with the Statistics Denmark occupation variables pstill2 (primary) and sstill2 (secondary); narrower than Chetty et al. (2011), excluding wage earners with self-employment as secondary occupation. | IDA, Income Tax Registerpstill2, sstill2 | working paper · Section 2.1, Table 1 notes |
| Self-employment / labour market status | Ejrnæs and Hochguertel (2013) | Labour market status is recorded in calendar week 48 (late November) of each year; individuals are classified as self-employed according to their main economic activity in that week. Persons originally classified as 'on leave' (possible for UI fund members from 1994) are re-classified to their state of origin. | CAM 10% Sample | working paper · Section 4.1 and footnote 22 |
| Sickness absence | Simonsen and Skipper (2025) | Any absence and yearly absence rate (share of scheduled hours missed), conditional on hours > 0. Absence data cover about 95% of the sample. | Absence register (Statistics Denmark; name not given) | working paper · Table 5 panels A-B; Table 1 notes |
| Stayer (same employer Dec 2020 to Dec 2021) | Caplin et al. (2026) | Among workers with an employer and monthly wage above 2,000 DKK in December 2020, stay=1 if the main employer in December 2021 is the same as in December 2020, else 0. Sample restricted to ages 20-65 and 2020 annual BFL earnings of at least 24,000 DKK. | BFLwage>2000 in Dec 2020; employer_g == F12.employer_g; sum_earnings>=24000 | replication code · script/analysis/fig4/preprocess_fig4a.do; paper Figure 4 note |
| Tenure and primary job | Frederiksen et al. (2026) | Primary job from IDAN (job_type = 'H', workplace lbnr != 0); tenure = year minus ansaar (hiring year), set to missing if > 50 or < 0. Workplace size = number of IDAN primary-job employees per lbnr and year. | IDANIDAN job_type == "H", ansaar, lbnr | replication code · 3 Produce regression data.do, 'TENURE AND WORKPLACE ID FROM IDAN' |
| Union membership | Humlum and Plato (2026) | Dummy for a positive deductible union-fee amount reported by unions to the tax authorities. | IND | working paper · Appendix Table A.1, row 14 |
| Wage earner sample | Chetty et al. (2011) | Individuals aged 15-70 in the tax register 1994-2001 classified as wage earners by IDA employment status, excluding self-employed, pensioners, wage earners with self-employment income and wage earners with unemployment; much of the analysis uses wage earners within DKr 50,000 of the top tax cutoff. Self-employed analysed separately. | IND (tax register), IDABESKST=4 (robustness: BESKST in 4, 5, 7, 8) | working paper · Section 4; Appendix B |
| Weekly labour market spells | Bagger and Lentz (2019) | Weekly spells 1985-2003 for the population aged 15-70 constructed from registers on public transfers, hourly wages, employer-reported job start/end dates and mandatory employer pension contributions; five states (employment, unemployment, retirement, self-employment, non-participation), with non-participation a residual state (neither employed, self-employed nor receiving a transfer classifying the person as unemployed or retired). | Spell data (administrative registers), IDA | working paper · Section 5 (Data sources) |
| Weekly labour market state | Taber and Vejlin (2020) | Weekly spell data 1985-2003 for all individuals aged 15-74 with states employed, unemployed, self-employed, retired and non-participation. Unemployment = receiving unemployment insurance benefits or social assistance; non-participation is the residual (not observed in any register). Unemployment and non-participation are aggregated into non-employment. | Weekly spell data (built from Statistics Denmark registers), IDA | working paper · IZA DP 9975, Section 6 (Data) |
| Weekly labour market states (spell data) | Bagger et al. (2014) | Job spells defined at firm (not establishment) level; establishment-level IDA information aggregated to the firm, with firm industry and ownership taken from its largest establishment by remaining workers. Nonparticipation is a residual state: 47% of workers have at least one nonparticipation spell and 5.5% of last-week-of-November spells are nonparticipation. | IDA, Spell data | online appendix · Working paper Appendix B, footnote 14 |
| Work/study status at age 17-18 | Dustmann et al. (2024) | For children aged 7-14 at exposure: average annual earnings at 17-18 in bins $0-1,499, $1,500-2,999, $3,000-4,499, $4,500+; working (earnings > 0) crossed with studying. | Income register; education registerworking: earnings > 0 | online appendix · Table A.7 notes |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Annual hours (ATP-based) | Dahl et al. (2013) | Imputed from the worker's ATP contribution group: no payment for 0-9 weekly hours, 1/3 of full-time payment for 9-18, 2/3 for 18-27, and full payment for 27+; workers paying full-time ATP are assigned hours corresponding to the standard 37-hour week. Overtime not captured. | ATP | working paper · Section 4 (p. 13) |
| Annual hours worked | Labanca and Pozzoli (2022) | Annual hours of regular plus overtime work from Lønstatistikken (LON), reported by employers whose pension contributions depend on hours. Only firms where hours are observed for at least 95 percent of the workforce are kept; for spells in week 48 shorter than a year, hours and earnings are annualized. | LON, IDA, FIDA | working paper · Section 3 (The data) |
| Annual hours, overtime and sick hours (job-spell analysis) | Hummels et al. (2025) | Hours paid, overtime hours and absence hours from the wage statistics register 1997-2012, merged to the worker-firm link by person, firm and year; for 2011-2012 earnings components are per-hour rates multiplied by hours. | LON, FIDA1997-2010 lon: timprae (total hours), timover (overtime), timfra (absence), sftj (gross earnings), smftj (net), bfra (sick pay); 2011-2012 lonn: timprae, timover, timfra, fortj_prae*timprae etc. | replication code · Programs/1_data_job_spell/hoursdata2.sas |
| ATP hours worked | Kleven et al. (2019) | For the OECD-comparable gender gap series, full-time workers aged 16-64 are identified using the ATP hours measure; earnings and participation gaps use the whole population aged 16-64, hours and wage gaps are conditional on participation. | ATP pension contributionsages 16-64 | online appendix · Figure A.I notes |
| ATP-based hours (1980-2018) | Kleven et al. (2025) | Hours inferred from mandatory ATP pension contributions, which employers pay based on a binned measure of hours; for monthly-paid workers annual contributions depend on monthly hours in four bins, giving 37 annual hours bins. Capped at full time in all 12 months. | ATP pension contributions | working paper · Section 3, footnotes 17-18 |
| Cumulative hours (primary job) | Utar (2018) | Sum over 2002-2010 of annual hours worked in the primary job, normalized by pre-shock average hours (normHrs9699). | IDAhrs; CHrs=sum(hrs*Dum0210)/normHrs | replication code · DataPrep.do ('Preparation of Cumulative variables') |
| FTE hours from ATP contributions | Brenøe et al. (2024) | Approximate yearly hours from mandatory ATP pension contributions (step schedule for 0-9, 9-17, 18-26 and 27+ weekly hours, topping out at full time, 37 hours), scaled to full-time equivalents (Lund and Vejlin 2016); share of year on paid parental leave subtracted since ATP continues during paid leave. Misses overtime. | ATP | working paper · Section 4.2; Appendix C.1 |
| Full-time / part-time and part-time penalty | Almar et al. (2026) | Full-time from RAS: at least 32 hours per week (threshold 30 hours in 1980-1992 and 27 hours in 1993-2007). Part-time penalty = ratio of w1 (wage in years 9-11 after graduation) of full-time workers to that of part-time workers, by programme. | RAS | working paper · Section 3 (Table 1 description) and footnotes 14-15 |
| Full-time and part-time employment (ATP-based) | Fadlon and Nielsen (2021) | From IDA, constructed from employee ATP pension contributions (available for individuals under 60): full-time means working at least 30 hours per week all 12 months of the calendar year (full-time full-year); part-time means working at some point in the year but fewer than 30 hours per week or fewer than 12 months. | IDA, ATP contributions | working paper · Section 3.2 |
| Full-time vs part-time applied-for job | Fluchtmann et al. (2024) | Mandatory Joblog field: full-time is 37 hours per week; part-time is less than full-time. | Joblog | working paper · Section 4 (non-wage job characteristics) |
| Full-time vs part-time job | Cairo and Mahlstedt (2023) | Full-time jobs have average weekly working hours at or above 90% of the full-time equivalent (37 hours per week); part-time jobs are below that. | E-income (eIndkomst) | working paper · Section 4.2, Table 3 note (a) |
| Full-time vs part-time job | Utar (2018) | Job type of the primary job from the job-length category: full-time (incl. unemployment breaks), part-time and side jobs, unknown. | IDAjoblength (jobkat): D_FT if 1 or 5; D_PST if 2,3,6,7; D_PT if 2 or 6; D_CFT if 1; unknown if 0 or 9 | replication code · DataPrep.do |
| Full-time/part-time hours code | Bargain and Jonassen (2024) | Monthly hours-of-work category from RAS (HELTID_DELTID_KODE) attached to months of MIA employment at the workplace, used for heterogeneity by hours. | RAS, MIAHELTID_DELTID_KODE | replication code · hoursofwork.do, lines ~22-140 |
| Hourly wage rate | Dustmann et al. (2024) | Register hourly wage rate estimated as annual labor earnings divided by annual hours worked; distributional effects estimated using dummies 1[wage <= x] for x from $0 to $40. | Income register; register hours/wage data | online appendix · Online Appendix B.2; Figure A.4 notes |
| Hours (ATP-based) and full-time employment | Sander (2026) | Annual ATP pension contributions (sum of orer/100 per person-year) are divided by the full-time contribution amount for the year (194.4 up to 1964, 259.2 up to 1971, 302 in 1972, 432 in 1973-1981, 1166.4 from 1982) and capped at 1. Full-time = ratio of 1; for workers the ratio is converted to weekly hours as 9 + 26 x ratio. Measured for child ages 0-35; missing set to 0 if the mother is alive. | ATPATP field ORER; divisors 194.4/259.2/302/432/1166.4 by year; full = hours>=1 | replication code · dofiles/createfirst.do, '*ATP' block and final forvalues x=0/35 loop |
| Hours coordination (dispersion of hours across skill groups) | Labanca and Pozzoli (2022) | Standard deviation across skill groups in a firm-year of the average annual (regular plus overtime) hours of each skill group; firm measure is its average over 2003-2011. Lower dispersion means greater coordination. Skill groups are deciles of the sum of the fixed and time-varying individual components of an AKM hourly-wage model; alternative: education (primary/secondary/tertiary) x occupation (manager/middle manager/blue collar) cells. | LON, IDA | working paper · Section 4.3, equation (9) |
| Hours worked | Humlum et al. (2025) | 2008-2017: yearly number of paid hours. 1995-2007: yearly labor income divided by hourly wage rate. | Employment/earnings registers (Statistics Denmark)Break in 2008 | online appendix · Supplemental Appendix Table A.1, row 11 |
| Hours worked (FTE) | Humlum and Plato (2026) | 2008-2017: yearly number of paid hours; 1995-2007: yearly labour income divided by hourly wage rate (November job). Divided by 1,924 full-time-equivalent yearly hours (37 hours/week). | IDA | working paper · Appendix Table A.1, rows 11-12 |
| Hours worked and wage rate | Kleven et al. (2021) | Hours worked from administrative, third-party-reported data on contributions to the mandated ATP (Arbejdsmarkedets Tillaegspension) pension scheme, which employers pay based on employees' individual hours worked. The wage rate is earnings divided by hours worked. | ATP | working paper · Section 2 (Data) |
| Labour market experience | Stoyanov and Zubanov (2012) | Work experience recorded in thousands of hours in the individual-level data. | Matched employer-employee data (Statistics Denmark) | paper · Section I.A |
| LFS 'super full-time' and irregular hours | Almar et al. (2026) | From LFS (AKU, 2000-2018): sft = 1 if usual weekly hours >= 38, or = 37 combined with evening, night, Saturday, Sunday or home work (answer 1 or 2) or overtime as reason (hourreas = 16); evening/night/weekend defined only when shiftwk = 3. Programme averages use yfactor weights for programmes with >= 50 (or 100) LFS observations; hwactual = 99 dropped. | AKU, RAShwusual >= 38; evenwk/nightwk/satwk/sunwk/homewk in 1,2; shiftwk == 3; hourreas == 16 | replication code · Results/fig_A1/LFS_clustering_prep_BF_dropmissing.do |
| Monthly earnings and hours | Søgaard (2019) | Monthly records of earnings and hours worked for all wage earners, available from 2008 and used for average weekly hours and the within-person variability of monthly earnings. | Monthly payroll records (from 2008) | working paper · Section 3.2, Table 3, Appendix Figure A.II |
| Monthly hours and earnings (event study) | Hummels et al. (2025) | Monthly paid hours and narrow/broad wage amounts per person and firm summed from monthly employer payroll reports. | BFL (eIndkomst)ajo_indberettede_loentimer, ajo_loentimer, ajo_loentime_kode, ajo_smalt_loenbeloeb, ajo_bredt_loenbeloeb | replication code · Programs/3_data_event_study/BFLhours1.sas |
| Monthly hours worked | Jensen and Blundell (2024) | Total hours worked per month from employer reports to the tax authorities (BFL, from 2008), summed over all jobs held in the month; for employees on parental leave with fixed contractual hours not adjusted in BFL, hours are adjusted using observed days of leave. | BFL, OF | paper · Section 5.1 and footnote 4 |
| Monthly hours worked | Kreiner et al. (2020) | Reported hours summed by person-month from the payroll records; values <=0 or >1000 set to missing and winsorized at the 1st/99th percentiles within event month and income year. | eIndkomst (SKAT payroll)timeantF12; timeantF12_trim | replication code · data02.do, data04.do |
| Monthly hours worked and absence hours | Serena (2025) | Paid hours and absence hours from the wage statistics register, spread evenly over the months of each reference period. | LON, LONNworkh = TIMPRAE / months in REF_FRADATO-REF_TILDATO; absh = TIMFRA / months | replication code · CollectData_LaborMarket.sas (LON register absenteeism) |
| Monthly uncapped hours (from 2008) | Kleven et al. (2025) | Uncapped hours worked for all workers at monthly level, available from 2008; used where precise hours variation is needed (aggregated to quarters in the promotion event study). | Monthly payroll records | working paper · Section 3; Section 4 Fact 3 |
| Non-standard working time | Frederiksen et al. (2026) | Indicators from AKU for evening, night, Saturday, Sunday and shift work (value 9 = missing). d_frequent = 1 if evening, night, Saturday or Sunday work equals 1 (frequently); d_sometimes = 1 if any equals 2 (sometimes). | AKUAKU evenwk/omaften/v205, nightwk/omnat/v206, satwk/loerdage/v207, sunwk/soendage/v208, shiftwk/skifteho/v204; 9 = missing | replication code · 2 Produce AKU data.do; 3 Produce regression data.do ('work flexibility') |
| Parental days worked (intensive labour supply) | Daysal et al. (2022) | Annual change in cumulative labour market experience ERHVER (measured in 1/1000 years) converted to days: dif_erhverv/(1000/260); person-years with negative changes (e.g., stays abroad) dropped; measured 0-15 years after the child's birth. | IDA (persondata)ERHVER; M_intsup = dif_erhverv/(1000/260) | replication code · 06 Adding parental labor outcomes rev.sas, lines ~470-705 |
| Participation and hours proxy (ATP) | Jensen and Zhang (2026) | Participation = strictly positive ATP pension contributions; intensive margin = amount of ATP contributions (paid proportionally to hours worked), following Kleven, Landais and Sogaard (2019). | ATP contributions (income registers)ATP > 0 | online appendix · Supplemental Appendix Figure B.5 notes |
| Performed hours of work | Sigaard (2023) | Contract hours plus paid overtime, minus paid absence, vacation and weekday holiday hours, summed over all employments in a year per individual (including secondary jobs). Contract hours are available from 2002, so the sample covers 2002-2016. LON covers all public firms and private firms with 100+ employees, plus a sample of private firms with 10-99 employees (about 50% of main employments). | LON (Lønstatistikken) | paper · Section 4 (Data), eq. (5); Appendix A.1 |
| Share of full-time applications | Fluchtmann et al. (2024) | Share of a recipient's applied-for jobs in a month reported as full-time by the UI recipient in Joblog (full-time in Denmark is 37 hours per week). | Joblog | working paper · Section 3.3; Online Appendix B.2 footnote 36 |
| Survey hours (LFS validation) | Kleven et al. (2025) | Validation measure of hours: actual, uncapped hours worked from the Danish component of the EU Labour Force Survey, compared with the administrative ATP pension-based hours measure; the survey measure is much noisier, especially at the top. | Danish Labour Force Survey (AKU), ATP pension contributions | online appendix · Online Appendix, Figure A.1 notes |
| Three-year log change in performed hours | Sigaard (2023) | Main outcome: log change in annual performed hours between base year t and t+3. Strictly positive hours are required in both years. Decompositions condition on staying in the same job versus switching jobs between t and t+3. | LON | paper · Section 3, eq. (1); Section 5.1, Tables 3-4 |
| Total working hours within 6/12 months | Altmann et al. (2026) | Sum of paid hours (AJO_LOENTIMER) over all BFL monthly job records whose AJO_JOB_START_DATO falls after entrydate and within q x 30.42 days (q=6, 12); zero if no records. | BFLAJO_LOENTIMER summed for AJO_JOB_START_DATO in (entrydate, entrydate+q*30.4167] | replication code · code/data_preparation.do (Monthly labor market outcomes; hours4_total) |
| Weekly work hours bins | Foerster (2024) | IDA job attachment code TILKNYT recoded into weekly hours per the Statistics Denmark key: codes 0/10/20/30 -> 0, 5/15/25/35 -> 5, 4/14/24/34 -> 15, 3/13/23/33 -> 25, 2/12/22/32 -> 34, 1/11/21/31 -> 38 hours. Set to 0 for those out of the labour force with missing hours. | IDANTILKNYT: {0,10,20,30}=0; {5,15,25,35}=5; {4,14,24,34}=15; {3,13,23,33}=25; {2,12,22,32}=34; {1,11,21,31}=38 | replication code · code/datprep_untying.do lines 1-20; assembledat_untying.sas (work_vars1) |
| Weekly working hours (actual and usual) | Frederiksen et al. (2026) | Actual and usual weekly hours from the Labour Force Survey (AKU), harmonised across file versions (hwactual/timuf/b7timuf/v51; hwusual/...); values >= 97 set to missing; only the first AKU observation per person (by year and reference week) is kept, years 1996-2018, and observations with zero or missing hours are dropped. Regressions use hours/100 and interval dummies (<37, 37, 38-47, >=48 hours). | AKUh_actual, h_usual; missing if >= 97; dummies ha0_36, ha37, ha38_47, hagt48 | replication code · 2 Produce AKU data.do; 3 Produce regression data.do (interval dummies) |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| 225-hour work requirement / sanction | Cairo and Mahlstedt (2023) | After 12 months on welfare benefits within the last three years, recipients keep the full benefit only if they worked at least 225 hours in regular non-subsidised jobs in the previous 12 months. Otherwise the benefit is reduced by about DKK 500-1,000 per month depending on family status; after a sanction the hour count resets to zero. Caseworkers may grant exemptions to those unable to work at least five hours a week. | Public employment service data (sanctions, exemptions, collected hours) | working paper · Section 2 |
| Activation participation | Geerdsen (2006) | Days in activation per month after 1994 from the Register on Labour Market Measures (AMFORA), based on the Labour Market Agency's Labour Market Policy Register (AMPO) reports of activation start and end; before 1994 from the Job Offer Data Set (ATB), 1980-1994. | AMFORA, AMPO, ATB | working paper · PhD thesis (SFI 2003), Paper II, Section 5.1, pp. 113-114 |
| Activation programme treatment | Gautier et al. (2018) | All individuals starting to collect unemployment benefits November 2005-February 2006 in Storstrom and South Jutland counties took part; those born on the 1st-15th of the month were assigned to the intensive activation programme (letter after ~1.5 weeks, 2-week JSA course after 5-6 weeks, weekly/biweekly caseworker meetings, new programme after 4 months); those born 16th-31st are controls. | National Labour Market Board benefit spell data | working paper · Sections 2.1 and 3 |
| Active vs inactive job seeker | Cairo and Mahlstedt (2023) | Active job seekers have a positive number of collected working hours at the onset of the intervention, or at least one job application registered in the public employment service portal in the last four weeks; all others are inactive. | Public employment service data (collected hours, job applications) | working paper · Section 4.4 |
| Benefit history covariates | Gautier et al. (2018) | Weeks of benefit receipt in the previous year and in the past two years, plus gender, nationality and an indicator for entering unemployment in November or December. | National Labour Market Board benefit spell data | working paper · Section 4.1.1; Table 1 |
| Benefit receipt indicators | Bruze et al. (2024) | Disability insurance if TILBTOT > 0; social assistance if cash assistance (KONT_GL, KONTANTHJ_13 from 1994) > 0; unemployment benefits if ARBLHUMV > 0 or ANDAKAS > 0. | INDTILBTOT; KONT_GL / KONTANTHJ_13; ARBLHUMV, ANDAKAS | replication code · 03panel_rep.sas, 'Disability insurance', 'Social assistance', 'Unemployment benefits' blocks |
| Change in municipal welfare generosity (2007 reform) | Andersen et al. (2026) | Percentage change in labour income plus transfers in post-diagnosis years relative to the five-year pre-diagnosis average, regressed on municipality fixed effects, age fixed effects, a cubic in pre-diagnosis income rank and year effects, separately pre-reform (1987-2006) and post-reform (2007-2018). People diagnosed 2004-2006 are excluded. The generosity change is the difference between post- and pre-reform municipality effects. | Income registers (IND), Population register (municipality) | online appendix · Online Appendix G, eq. (14) |
| Counterfactual pre-reform stipend (S_pre) | Nielsen et al. (2010) | Stipend the individual would be eligible for under the 1985 rule: compute X, find its quantile within the current cohort, take the corresponding quantile of X in the pre-reform cohort, and apply the 1985 grant schedule (maximum stipend if X < DKK 156,400, none if X > DKK 256,200 in 1985). Stipend measured as four years of grant. | Tax/income registers | paper · Section II footnote 6; Section II procedure steps (1)-(4) and footnote 13 |
| Disability and other social transfers | Humlum et al. (2025) | Effects on transfers (incl. disability and unemployment benefits) are scaled with transfer rates linked to the transfer codes of the DREAM register; education costs include tuition (~$16,500 per full-time student-year, from government rate catalogs) and SU and reskilling benefits; 6% real discount rate. | DREAM register | online appendix · Supplemental Appendix Section J |
| Disability shock | Hvidberg et al. (2023) | Starting to receive disability insurance benefits, without having received them before, in one of the years 2012-2017 according to IDA. | IDAPdisab_12_17 with disab_08_11==0 | paper · Section 3.3, footnote 21 |
| Entering other transfer program | Cairo and Mahlstedt (2023) | Indicator that the individual receives any other benefit not subject to the work requirement, such as educational support, sickness or disability benefits. | DREAM | working paper · Section 4.3 |
| Exit destination | Munch et al. (2006) | Destination after an unemployment spell: new job in the local labour market, new job in another commuting area, or other states than employment and unemployment. A worker is geographically mobile if he or she gets a job and moves to another commuting area up to 8 weeks before and 52 weeks after the start of the job spell (exact moving dates known). | Administrative registers | working paper · Section 3, p. 9 |
| Exit to employment and other exit states | Graversen and Ours (2008) | A worker is assumed to find a job as soon as he or she no longer receives benefit payments (after the 4-week rule); other exit states (e.g. early retirement pay, other benefits) and right-censoring are distinguished using the DREAM event history. | DREAM | working paper · Section 2.3 footnote 11; Appendix A Table A2 |
| Government benefits to consumers | Andersen et al. (2026) | Social benefits (SNA D.62) = all government income transfers plus private pension payouts from IND, scaled by 1.03. Other current transfers (D.7) allocated by BEF population shares; pension entitlement adjustment (D.8) from IND and INPI contributions net of payouts. | IND, INPI, BEFSNA D.62, D.7, D.8; scale 1.03 | working paper · Appendix K |
| Imputed individual right to wage compensation | Jørgensen and Søgaard (2024) | Public sector: imputed from collective-agreement rules (6-6-6 model, Apr 2008-Apr 2015: 6 weeks each parent plus 6 shared weeks if both parents work in the same public sub-sector, otherwise the shared 6 accrue to the public-sector parent) using both parents' employment sector. Private sector: equals observed compensated weeks for parents who also take uncompensated leave (exhausters); otherwise the mode of compensated weeks among exhausting co-workers with weakly higher compensation, with co-worker groups defined as firm-year cells, or 6-digit then 5-digit industry-year cells when fewer than two exhausting co-workers exist. | Parental leave benefit spell data, employer-employee links | working paper · Section 3.3, eq. (11), Table 3 |
| Individual benefit replacement rate | Jørgensen and Søgaard (2024) | Computed from total parental leave benefits paid out per parent-child spell relative to weeks of leave; statutory benefits replace 100% of pre-birth earnings for a full-time (37 hours/week) employee up to a cap of EUR 580 per week (2019 level), scaled down proportionally for part-time. | Parental leave benefit spell data | working paper · Sections 2 and 3.2, footnote 11 |
| Individual income limit | Søgaard (2019) | The baseline income limit is 12 times the monthly basic amount (DKK 7,700, 2018 levels; about DKK 93,000 annually) and rises by DKK 11,600 for each month of benefits not taken up. It is computed from monthly benefit take-up records of the Student Benefit Administration. | Student Benefit Administration records | working paper · Section 3.1, Figure 1 notes; Appendix C |
| Industry Packages ALMP exposure | Foged et al. (2024) | Exposure = share of a refugee's first year in the municipality during which the Industry Packages program was available; monthly refugee-arrival cohorts January 2008-April 2019 observed one year after placement; Vejle implemented August 2013, other municipalities 2015 to early 2018. | working paper · Section 6.1 | |
| Involuntary separation (survey) | Taber and Vejlin (2020) | From the Danish Work Environment Cohort Study (1995 and 2000), whether the job spell in November 5 years earlier ended due to the employer or the employee; matched to register data to form a moment identifying the probability of an immediate offer after job destruction. | Danish Work Environment Cohort Study, IDA | working paper · Section 6 |
| Job applications (Joblog) | Fluchtmann et al. (2024) | Applications self-reported in the Joblog section of Jobnet during the UI spell. Only applications actually sent are kept, using the first saved version of each entry. Other Joblog events (vacancies saved for later, interviews, rejections) are excluded. Mandatory fields include job title, part-time or full-time status, and firm name, zip code and city. | Joblog (Jobnet) | online appendix · Online Appendix A.1, fn. 38; A.2 |
| Job loss event | Andersen et al. (2023) | Month in which wage payments from the individual's main employer cease and total gross wage income drops below DKr 1,000; the individual must receive unemployment benefits or social insurance in months -1 to 3 and no early retirement, sickness or parental leave benefits then, must have had gross wage income of at least DKr 10,000 for at least 18 consecutive months before, and must not return to the same employer within three months. | Monthly payroll and income transfer records (Danish Tax Agency, via Statistics Denmark) | paper · Section III |
| Labour market tightness | Altmann et al. (2026) | Vacancies divided by job seekers per municipality x occupation in the experiment's starting week (week 1442), averaged over the occupations in the individual's search profile. | STAR vacancies, STAR jsjstightness = no_vacancies/jobseekers, week==1442 | replication code · code/data_preparation.do |
| Language training and employment services participation | Foged et al. (2024) | Participation in Danish language training from the language training database (DKDB 1999-2003) within 12-36 months of admission; AMFORA spells with FORANST 128-129 (language) or 127 (social) classed as integration services, other FORANST codes as employment services; hours = weeks in spell x TIMERPU. | DKDB (Danskuddannelsesdatabasen), AMFORA, VEUVFORANST in ('128','129') language; '127' social; TIMERPU | replication code · 8. Controls and outcomes.sas, lines ~43-45, 5116-5135, 5660-5680 |
| Leave take-up and exposure | Friedrich and Hackmann (2021) | Leave = worker with an establishment in t-1 has none in t, or is on maternity leave at end of November (SHSS maternity benefit months: November value 30, or positive in both November and December). Individual take-up is estimated by regressing leave on age-of-youngest-child dummies interacted with 1995 vs 1993; predicted take-up is assigned to workers based on prior-year child age, and county-sector exposure = summed expected take-up / number of active workers of that type in 1993. | SHSS, IDAP, FAMILIESHSS: BEL_BAR, VARMMSF11, VARMMSF12; child-care leave VAR_ORL_BP, VARMMO1-12 | replication code · programs/stata/Prepare_microdata_healthworkers_by_industry.do; Prepare_estimates_individual_takeup.do; Prepare_aggregate_county_exposure.do |
| Local unemployment rate | Damm (2009) | Unemployment rate within a radius of DKK 60 (approx. USD 10) of transport cost around the largest post office in the municipality, constructed by Local Government Studies. | Unemployment register (Statistics Denmark), Ministry of Transport cost statistics | working paper · Table A.I Part B |
| Long-term displacement and manual jobs (mass layoffs) | Humlum et al. (2025) | Mass layoffs defined as in Davis and von Wachter (2011). A worker is long-term displaced if he enters long-term unemployment (official definition: 52 consecutive weeks) within two years of the mass layoff; manual jobs = Autor-Levy-Murnane manual ability requirement > 0.5 SD above the mean; automation exposure = Webb (2019) robot exposure > 0.5 SD above mean; offshoring exposure = non-offshorability index (average of O*NET decision-making and face-to-face) > 0.5 SD below mean. | IDA; unemployment registers; DISCO occupation codes52 consecutive weeks; +/-0.5 SD cutoffs | online appendix · Supplemental Appendix Section H.1 |
| Long-term sick leave spells | Serena (2025) | Monthly indicator for being in a sickness-absence spell from the sickness benefit register, counting spells of at least 21 days (before 2012) or 30 days (2012 onward), using the latest registration year for each spell start. | SGDPFRAVDAGE>=21 if year<2012; FRAVDAGE>=30 if year>=2012; FOERFRAV start date | replication code · CollectData_LaborMarket.sas (Sick leave) |
| Monthly UI, cash benefits and retirement transfers | Caplin et al. (2026) | From ILME monthly non-employee e-income records, amounts across A/B income fields are summed per person-month and classified by the income-type code of the largest payment: UI if VMO_INDK_ART codes 2,5,8,16,104; cash benefits if type code 6 or art codes 40,41,43,84,87; retirement if art codes 6,9,10,13,20,24,25,44,45,74,83,86,113,115. | ILMEVMO_INDKOMST_ART_KODE UI: 2,5,8,16,104; CB: VMO_INDKOMST_TYPE_KODE 6 or ART 40,41,43,84,87; Retirement ART: 6,9,10,13,20,24,25,44,45,74,83,86,113,115 | replication code · script/cleaning/1_c_monthly_data.R |
| Months out of work after separation | Caplin et al. (2026) | A separation is a month in which the worker has a main employer with monthly wage above 2,000 DKK and next month's employer differs; only the first separation per person is used. Time out of work = months until a different non-missing employer appears (capped at 24) minus one, so job-to-job moves count as 0 and returns to the same employer are not reemployment. The paper describes separations in 2020; the posted do-file's window (date<=24 from Dec 2018) covers Dec 2018-Dec 2019. | BFLjan_sep = employer_g != F1employer_g; reemp = first k with F`k'employer_g != employer_g & non-missing; gap = reemp-1; cap 24 | replication code · script/analysis/fig4/preprocess_fig4b.do; paper Section 3.2 and Figure 4 note |
| Municipal survivors-benefit instrument | Fadlon and Nielsen (2021) | For each municipality and year, the average Social Disability Insurance (Social DI) benefits received by non-working surviving spouses is computed; each widow in the treatment group is assigned her municipality-year leave-one-out mean. Benefits in annual DKK 1,000. Sample: widows under 67 (age of transition into Old-Age Pension) in years before 1994, when there is a data break in the reporting of survivors benefits received through Social DI. Controls: municipality unemployment rate and average earnings (interacted with Treat, Post, Treat x Post), widow age, year and municipality FE; post periods 2-4. | Transfer income registers; municipality of residenceWidow age < 67; years < 1994; leave-one-out municipality-year mean | online appendix · Online Appendix Table C.3 notes |
| Pre-treatment sickness benefits and positive earnings | Lundborg et al. (2017) | Pre-treatment balance covariates measured the year before IVF treatment: years of schooling, annual earnings (100,000 DKK), sickness benefit receipt, marriage, indicator for positive earnings, and partner's schooling and earnings. | Income and transfer registers | online appendix · Online Appendix A, Table 1 |
| Predicted (typical) wage of applied-for jobs | Fluchtmann et al. (2024) | Log wage predicted for each applied-for job from re-employment wages of new hires: post-LASSO OLS (Rigorous-LASSO selects 233 of 10,407 regressors) on 1-, 2- and 3-digit industry and occupation dummies, an AKM-availability dummy, the within-industry-demeaned AKM firm effect, and all pairwise interactions; averaged per recipient-month. Robustness uses the AKM firm effect directly. | BFL, JoblogDISCO-08 and NACE Rev. 2 at 1-3 digits | working paper · Section 3.3; Online Appendix B.4, footnote 42 |
| Replacement rate | Svarer et al. (2005) | Ratio of UI benefits received to the most recent wage. | Statistics Denmark registers | working paper · Section 4.3 'Explanatory variables' |
| Share of applications to publicly posted vacancies | Fluchtmann et al. (2024) | Monthly share of applied-for jobs that the job seeker reports in Joblog as found via a publicly posted vacancy (other channels: network, contacted by firm/headhunter, cold contact). | Joblog | working paper · Section 3.3 |
| Sick leave | Humlum et al. (2025) | Share of weeks within a year in which the individual (or the employer) received sickness benefit transfers; the sick-leave labour-market state is a dummy for more than 15% of weeks in the year on sickness benefits. | DREAM transfer register> 15% of weeks | online appendix · Supplemental Appendix Table A.1, rows 18 and 43 |
| Sick leave and UI/benefit receipt (weekly) | Hansen et al. (2025) | Weekly DREAM codes for each parent from the birth week up to 5 years: sick leave if code 890 or 893-899; UI/other benefits if code 111, 115, 121, 151-153, 130, 133-139, 213-219, 299, 710, 713-719, 730, 733-739, 740, 743-749 (among others); 'working/self-sufficient' if no DREAM code in the week; parental leave benefit code 881. | DREAMsick: 890, 893-899; ui: 111,115,121,151,152,153,130,133-139,213-219,299,710,713-719,730,733-739,740,743-749,...; work: y == '' | replication code · 5_cr_dream2.do lines ~50-56, 125-128, 191-195 |
| Sickness and disability benefit receipt | Daysal et al. (2025) | Transfer outcomes (any government transfer/any welfare, sickness benefits, disability pension) are indicators measured cumulatively within 5 (or 10) years after diagnosis, not as annual averages. | DREAM | online appendix · Appendix Table A3; Figure A4-A5 notes |
| Social assistance receipt (weekly) | Bargain and Jonassen (2024) | Weekly indicator equal to 1 if the person's DREAM weekly benefit code is a social assistance code. | DREAMDREAM y_01-y_53 in 130-139, 140-149, 700-709, 710-719, 720-729, 730-739 | replication code · baseline.do, 'Defining weekly ordinary employment (wage), earnings (wage2), social assistance (sa)' |
| Start Aid welfare cut RD | Foged et al. (2024) | Refugees arriving 2001-2003 (Bosnians and Afghans omitted); treatment = admitted after 1 July 2002, when Start Aid reduced welfare benefits (cut reversed in 2012). RD with triangular kernel and 548-day bandwidth around the admission-date cutoff. | OPHG | working paper · Sections 4.2 and 6.2 |
| Time remaining to activation period | Geerdsen (2006) | Months of passive UI left before the compulsory activation period, constructed by replicating the 1994 departmental order that placed each unemployed person in the new UI system (entitlement depends on prior activation, UI use and employment before 1994). Passive period cut from 4 years (1994) to 3 years (July 1996) to 2 years (January 1998); three expectation models (perfect foresight, system foresight, no foresight) for how individuals perceive the remaining months. | CRAM, SSHS, AMFORA, IDA | working paper · PhD thesis (SFI 2003), Paper II, Sections 2.2, 4.1, 5.2 and Appendix A |
| Transfer receipt (DREAM) | Serena (2025) | Monthly indicators from weekly DREAM benefit codes (codes 997-999 excluded): sickness benefits, disability pension, UI benefits, cash/social assistance, student grants, flex-job, early retirement and parental leave; grouped into any cash assistance, any disability-type, any UI, and any non-parental-leave transfer. | DREAMfyd=floor(yd/10): sick 89; parental leave 88; UI 11 (unemployed), 21 (activation); cash 13, 71, 73, 15, 70/16, 72/14; SU 65; flexjob 77; early retirement 75 (forre), 76; 74, 81, 87; disability pension yd=783; yd=781 | replication code · CollectData_LaborMarket.sas (Dream data); PsykTestLongRunRD.do |
| Transfers / public benefits | Jensen and Manning (2025) | Yearly sum of all transfers primarily financed by government institutions, including all unemployment benefits, child benefits, housing support, student benefits and all public pensions, in 2013 prices. | IND | online appendix · Online Appendix OA.1; paper fn. 6 |
| Treatment programme components (meetings, job search and training programmes) | Graversen and Ours (2008) | Meetings with caseworkers (type and date, from PES registers, including private contractors); job search programmes (2-week courses); training programmes grouping short work experience (up to 4 weeks), employment subsidy and training/education programmes. | PES registers | working paper · Sections 2.3-2.4 |
| UI and other benefits | Utar (2018) | Annual benefit amounts deflated by CPI: unemployment insurance (arblhu), municipal sickness/maternity benefits (adagp), employer-paid sickness benefits (adagpagn), education allowance for unemployed (quddydl), other UI-fund benefits (andakas); UI benefit total = UI + education allowance + other benefits. Cumulated over 2002-2010 and normalized by pre-shock personal income. | income register files (indk_unemptc.dta, indk_bentc.dta in code)arblhu, adagp, adagpagn, quddydl, andakas | replication code · DataPrep.do ('Benefits' merges and runemp/rsickmun/... definitions) |
| UI fund membership | Bingley et al. (2013) | Annual dummy for UI fund membership, observed from tax-deductible UI fund membership contributions reported annually by the funds to the tax authorities. | Tax records | working paper · Section 4 |
| UI fund membership (insured) | Ejrnæs and Hochguertel (2013) | Membership in an unemployment insurance fund, as recorded in the administrative data; 'insured' and UI fund member are used interchangeably. Treatment enters the unemployment equation lagged one year (status in t-1). | CAM 10% Sample | working paper · Section 4.1 (fn. 16) and Section 5.1 |
| UI fund membership and replacement rate | Munch et al. (2006) | Indicator for membership of an unemployment insurance fund and, for members, the UI replacement rate; other controls are age, gender, number of dependent children aged 0-17, two-adult household, immigrant from a non-OECD country, municipality size and education. | Administrative registers | working paper · Section 3, p. 10 |
| UI spell | Geerdsen (2006) | Unemployment spell = at least 15 days of unemployment within a given month; a spell is broken if the individual receives no UI for more than 2 weeks in a month or regains the right to a new UI period mid-spell. Exit is any departure from UI (employment, leave, social benefits, early retirement, disability pension, education) in the thesis version. UI days per month come from SSHS, built from CRAM (unemployment fund records). | SSHS, CRAM | working paper · PhD thesis (SFI 2003), Paper II, Sections 5.1-5.2, pp. 113-116 |
| UI spell (start and end) | Fluchtmann et al. (2024) | New UI spell: at least 4 consecutive weeks of UI benefit payments with no UI payout in the 4 weeks before spell start (DREAM). Spell ends with 4 consecutive weeks without UI paid out; followed for a year or until exit. | DREAM | working paper · Section 3.2; Online Appendix B.2, footnote 38 |
| Unemployment | Jensen and Manning (2025) | Not working for at least half of the year and not being retired (child at age 30; parents' years of unemployment counted over the child's first 21 years). | AKM | online appendix · Online Appendix OA.1; paper fn. 5 |
| Unemployment (outcome) | Ejrnæs and Hochguertel (2013) | Indicator equal to one if the individual was registered unemployed in excess of 10% of the year according to the annual 'unemployment degree' (fraction of the year UI benefits are received), zero otherwise. 'Unemployed' means registered as such with the job centre, which also covers non-UI-members on social assistance; an alternative measure is the week-48 unemployment stock. | CAM 10% Sample | working paper · Sections 4.1 and 5.1 |
| Unemployment (UI receipt share) | Bingley et al. (2020) | Proportion of the year during which unemployment insurance benefits were received, at age 26; social assistance recipients are not counted as unemployed. | working paper · Section V, Table 9 panel A, footnote 18 | |
| Unemployment and UI benefits | Keller and Utar (2022) | Unemployment is the percentage of annual time in unemployment. UI benefits are the sum of unemployment insurance, education allowance for the unemployed and other UI benefits, deflated by CPI and expressed relative to the worker's average 1996-1999 monthly earnings. | IDA, INDarblhu (unemployment insurance), quddydl (education allowance for unemployed), andakas (other UI benefits); normalised by normsal9699 | replication code · Analysis1.do lines 684-705 (Table A5); WP Table 8 notes |
| Unemployment controls/outcome | Jensen and Johannesen (2017) | Control: dummy for unemployment spells during 2006-2007; outcome: fraction of the year spent in unemployment. | Labour market registers | working paper · Section 4 controls; Table A-notes |
| Unemployment degree and cumulative unemployment | Utar (2018) | Fraction of working time spent unemployed within a year, in per mille (arledgr); cumulative unemployment sums arledgr/1000 over 2002-2010 (in months: *12/1000). The pre-1999 control is the cumulative sum since 1980 (sumgrad/1000). | IDAarledgr (per mille); sumgrad; unemp50: arledgr>=500; unemp10: arledgr>=100 | replication code · DataPrep.do; ReadMe File.docx |
| Unemployment duration / exit from benefits | Gautier et al. (2018) | Duration (in weeks) of collecting regular unemployment benefits from the week benefits start, followed for at most two years; exits treated as exits from unemployment (linked income-transfer data show almost all exits in the first two years are to employment); durations censored otherwise. | National Labour Market Board benefit spell data, Income transfer data | working paper · Section 3 (Data) |
| Unemployment duration and exit destination | Svarer et al. (2005) | Flow sample of unemployment spells starting 1997-2000, from weekly labour-market event histories; duration measured in weeks, with destination employment recorded. Temporary layoffs (return to the previous employer, about 40% of spells) are treated as right-censored, as are exits to states outside the labour market. | 10% sample of Danish adult population (Statistics Denmark registers) | working paper · Section 4.1 'Unemployment spells and geographical mobility' |
| Unemployment shock | Hvidberg et al. (2023) | Three or more months of unemployment in at least one year of the shock period 2012-2017, among respondents with no such shock in the pre-shock period 2008-2011 and in the workforce for the whole ten-year period. | IDAP, RASunemp_12_17 with restriction unemp_08_11==0 & wf08_17<3 | paper · Section 3.3, footnote 21; Replication_Code.do lines 1839-1843 |
| Unemployment shock (event study) | Kreiner et al. (2020) | Unemployment spell of at least three months in event year 2007, 2008 or 2009 for individuals employed with no unemployment spells in the five preceding years, aged 18-38 at t-5; controls are similar individuals without a shock in t. Amounts index-adjusted to 2009-DKK within cohort. | Income register, Unemployment histories (Statistics Denmark) | working paper · Section 4 (Unemployment event analysis) |
| Unemployment spell (broad) and recall recoding | Bagger and Lentz (2019) | Non-participation recoded as unemployment; unemployment spells of 13 weeks or less followed by recall to the same employer are recoded as part of the original employment spell; unemployment spells of 2 weeks or less between employment spells with different employers are treated as a job-to-job transition. | Spell data | working paper · Section 5 (Analysis panel), footnotes 10-11 |
| Unemployment spell duration | Graversen and Ours (2008) | From weekly DREAM transfer data: the spell starts in the week the person became unemployed and continues until the person has not received UI benefits (or participated in ALMPs) for four consecutive weeks; switching to sickness benefits counts as still unemployed. Only the first spell is used; data run to mid-September 2006, so spells are censored after 26-43 weeks depending on assignment week. | DREAM, PES registers | working paper · Section 2.3 'Available data' and footnote 11 |
| Weeks with full wage compensation | Jørgensen and Søgaard (2024) | Weeks of leave with employer-provided full wage compensation, computed from the amount of parental leave benefits paid to the employer (benefits are paid to the employer as reimbursement when it pays full wage) using the individual replacement rate. | Parental leave benefit spell data | working paper · Section 3.2 |
| Welfare benefit groups and maximum monthly benefit | Agersnap et al. (2020) | Maximum monthly cash benefits (2018 USD) for non-employed households at age 40 out of work for three months; with children, the first child is aged 10 and the second aged 8. Danish scheme benefits from STAR (Danish Agency for Labour Market and Recruitment) website; other benefits computed with the OECD tax-benefit calculator. Only cash welfare, excluding UI and in-kind benefits such as housing support. | STAR scheme rules, OECD tax-benefit calculator | online appendix · Online Appendix, Figure A.II notes |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Contributions to employer-sponsored and personal retirement accounts | García-Miralles and Leganza (2024) | Annual contribution flows to employer-sponsored DC accounts in levels (winsorized at the 95th percentile by year) and indicators for positive contributions to personal retirement accounts (also indicators for contributions of various sizes). | Labour-market/income registers (tax-reported pension contributions) | working paper · Section 4.1 |
| Disability insurance and old-age pension rules | Fadlon et al. (2026) | DI for those below OAP age requires a loss of work capacity of at least 50 percent (assessed by municipality) and can also be granted for social reasons; paid until OAP age. OAP for all residents from full retirement age, rising from 65 to 67 for cohorts born after 1954; both means-tested against own and partner income and independent of earnings history. | Social pension ruleswork-capacity loss >= 50% | online appendix · Supplemental Appendix A: Institutional Background |
| Early retirement (VERP) eligibility instrument | Ejrnæs and Hochguertel (2013) | Institutional-rule indicator of eligibility for the UI-fund-linked early retirement (ER) scheme (retirement from age 60). Before 1992 members qualified with 10 years of UI membership before age 60 ('10-year rule'); the 1992 reform required at least 20 years ('20-year rule', latest joining age 40), with those aged 40-50 in 1992 required to join in 1992 and stay until 60. Identification comes from changes over year and cohort in this rule. | CAM 10% Sample | working paper · Section 2 (institutions) and Sections 5.1-5.2 |
| Early retirement / pension | Sander (2026) | From IDA labour market position, early retirement pension = PSTILL 50, 55 or 56 and old-age pension = PSTILL 92, 93 or 94; first year observed used to compute pension age. | IDAPSTILL 50, 55, 56 (early pension); 92, 93, 94 (pension) | replication code · dofiles/createfirst.do, '* PENSION' |
| Employer contribution rate (default) | Fadlon et al. (2016) | Employer-sponsored pension contributions divided by taxable labor income, computed separately for capital (lump-sum) and annuity pension accounts; the workplace-level measure is the median contribution rate within the occupation-firm cell in a year (modes used as robustness). Main outcome is the change from t-1 to t. | Income tax register (third-party reported pension contributions) | working paper · Section 2.2; Section 3 eq. setup; Appendix A |
| Individual private (IRA-type) pension contributions | Fadlon et al. (2016) | Individual contributions to private capital and annuity pension accounts outside the employer plan, as contribution rates relative to taxable labor income, used as the comparison to employer responses. | Income tax register | working paper · Section 2.3; Section 4 |
| Mandatory Savings Plan (MSP) contribution | Chetty et al. (2014) | From 1998 individuals with labor income at or above DKr 34,500 were required to contribute 1% of earnings (DKr 345 at the cutoff); RD on changes in pension contributions/saving from 1997 to 1998 for individuals within DKr 25,000 of the cutoff. | IND (income tax register)cutoff Y >= DKr 34,500; bandwidth DKr 25,000 | working paper · Section IV.B, equation (6) |
| Monthly retirement age | García-Miralles and Leganza (2024) | Absorbing-state measure from the monthly eIncome register (2008-2017): age in the last month with positive earnings before earnings permanently fall to zero. | eIncome (eIndkomst) monthly earnings register | working paper · Section 4.1, footnote 17 |
| Old-age check eligibility (age and income) | Johannesen et al. (2026) | Age-eligible = aged 65 or older (statutory retirement age in the sample period). Low-income (income-eligible) = income, net of social security and before self-reported corrections, below the threshold at which the old-age check is fully phased out (disregard DKK 42,000, phase-out rate 34% for singles in 2020). Asset test: check lost entirely when liquid wealth at end of t-1 exceeds DKK 90,000 (2020 value; threshold raised 15% in 2010). For the 2001-2007 reform analysis the 2003 income threshold is applied throughout. | IND, SOCP, BEF | paper · Section I.A; Figure 1 and 2 notes; Supplemental Appendix Figure A.4 notes |
| Old-age check parameters and indexation | Johannesen et al. (2026) | Old-age check (created 2003) parameters expressed in 2020 values by inflating with the index used to adjust tax and transfer parameters (tied to average wage earnings per worker; $1 = 6.53 DKK in 2020). Maximum check raised above indexation in 2005, 2007, 2009 and 2013; asset-test threshold raised about 15% above indexation in 2010; income test (phase-out start and end thresholds) very strict initially but unchanged in real terms since 2005 except a small increase in 2019. | Tax-transfer rules$1 = 6.53 DKK (2020) | online appendix · Supplemental Appendix, Figure A.1 notes |
| Pension claiming | García-Miralles and Leganza (2024) | Receiving pension income, either voluntary early retirement pension (VERP) or old-age pension (OAP): annual indicator for any pension income in the year (age-based design), or indicator for receiving pension income before July 1 using monthly pension benefit records (reform-based design). | Annual pension benefit registers, Monthly pension benefit records (from 2008) | working paper · Section 3.2 |
| Pension contributions by account | Chetty et al. (2014) | Annual employer and individual contributions to capital pension (lump-sum) and annuity pension accounts as reported by employers and pension funds; contributions above the 99th percentile are top-coded at the 99th percentile in each account and year. | IND (income tax register) | working paper · Section III |
| Pension-driven kink | Chetty et al. (2011) | Cap on tax-deductible pension contributions (about DKr 33,000 on average) means workers contributing up to the cap enter the top bracket at wage earnings about DKr 33,000 above the statutory cutoff; bunching there is used to measure individual (job-search) bunching. | IND (tax register) | working paper · Introduction; Section 6 |
| Reform treatment (birthdate cutoff) | García-Miralles and Leganza (2024) | Those born on or after 1 January 1954 learned in 2011 that their early retirement age (ERA), incentivized retirement age and full retirement age (FRA) rose from 60, 62 and 65 to 60.5, 62.5 and 65.5; those born one day earlier were unaffected. Running variable is birth date relative to the cutoff; RD sample is those born within 56 days (8 weeks). | Population register (exact date of birth) | working paper · Sections 2.3 and 4.2 |
| Retirement (age-based design) | García-Miralles and Leganza (2024) | Retirement is ceasing to earn labour market income: the year in which the individual earned labour income for the last time, treated as an absorbing state (1 thereafter). Small positive income up to the equivalent of one month of average earnings is allowed after retirement (holiday pay, delayed wages). A flow definition allowing multiple retirements is used as robustness. | Annual labour market/income registers | working paper · Section 3.2 (Key Variables), footnote 10 |
| Retirement at eligibility-age spikes | García-Miralles and Leganza (2024) | Control group (born before cutoff): first spike = retirement age between 60y0m and 60y5m; second spike = 62y0m to 62y5m. Treatment group: first spike = 60y6m to 60y11m; second spike = 62y6m to 62y11m. 15.8% retire at the first spike and 10.6% at the second as defined. | monthly retirement-age windows 60:00-60:05, 62:00-62:05 (control); 60:06-60:11, 62:06-62:11 (treated) | online appendix · Online Appendix B |
| SP payout / SP account balance | Kreiner et al. (2019) | SP payout is the (potential) payout, i.e. the Special Pension account balance less taxes; enters as SP payout / 2008 disposable income (percent). | ATP Special Pension (SP) account register | online appendix · Online Appendix Table EA.1 notes |
| VERP benefit claiming | García-Miralles and Leganza (2024) | Annual Voluntary Early Retirement Pension (VERP) benefit payments. | VERP benefit records | working paper · Section 4.1 |
| VERP participation indicator (qualified UI-fund contributions) | García-Miralles and Leganza (2024) | Participatory VERP contribution in a year is measured as an indicator for making qualified (VERP) contributions to unemployment insurance (UI) funds in that year; VERP eligibility requires contributions in 25 of the previous 30 years. | VERP contribution records | online appendix · Online Appendix Table A.7 notes; Appendix C.2 |
| Working past July 1 (reform-based design) | García-Miralles and Leganza (2024) | Using monthly earnings records (eIncome, from 2008), a dummy equal to one if the individual works past the first half of the year (past July 1) in a given year. | eIndkomst (monthly earnings) | working paper · Section 3.2 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Bargaining segment / wage-setting system | Dahl et al. (2013) | Workers are allocated to 36 DA/LO bargaining segments using six-digit NACE industry and four-digit DISCO (ISCO-88) occupation codes, as DA and LO do (about 85% of DA/LO employment); each segment's yearly system is classified as standard-rate (normallønssystemet), minimum-wage (minimallønssystemet), minimum-pay (mindstebetalingssystemet) or firm-level bargaining (uden lønsats). | IDA, DA/LO bargaining segment classificationNACE 6-digit x DISCO 4-digit (segment list not given) | working paper · Section 2 (p. 10) and Section 4 (p. 12) |
| Broad industry groups | Olney and Pozzoli (2021) | Firm industry from FirmStat GF_BRANCHE_07 (NACE rev.2, first two digits) grouped into 8 sectors. | FirmStatbranche2 10-33 (bra=1); bra=2 if branche2=='35' (code: branche2=='35' & branche2<='39'); 41-43 (3); 45-47 (4); 49-56 (5); 58-63 (6); 64-75 (7); 77-97 (8) | replication code · offshoring_project_will_dario.do (lines ~789-821) |
| Firm industry | He and Maire (2025) | Establishment industry is the modal worker industry code if held by more than 50% of workers; firm industry (127- and 36-group) is assigned only if at least 70% of the firm's workers share it, otherwise missing. | IDApersbrc mapped to pb07, ind127, ind36; thresholds 0.5 (establishment) and 0.7 (firm) | replication code · clean_merger.do |
| Four-sector industry aggregation | Traiberman (2019) | NACE 1.1 four-digit industry codes assigned to plants (FIRE; firm-level FIRM data used when plant data not available) and matched to workers, aggregated into four sectors: Manufacturing (incl. construction, agriculture, utilities), FIRE, Public Services (labelled Health & Education), and Other Services. | FIRE, FIRM, IDANACE 1 2-digit 0-45 = Manufacturing; 64-74 = FIRE; 75, 80, 85-90 = Public Services; all remaining = Other Services | online appendix · Online Appendix D.1, Data Cleaning: Industry (full mapping in appendix Table 16) |
| Hospital and nursing home employment | Friedrich and Hackmann (2021) | Workplace industry from IDAS (BRANCHE1, else BRANCHE03, else BRANCHE07) merged by establishment (LBNR) and year. Hospitals = industry 851100; nursing homes = 853140 or 853150. Missing codes are filled backwards within establishment and, for nursing homes, backwards by person within the same firm (CVRNR); other employed = other sectors; no workplace type = unemployment/nonparticipation. New freelancers (missing LBNR) among unskilled nurses keep their previous sector. | IDAS, IDAPBRANCHE 851100 = hospital; BRANCHE 853140, 853150 = nursing home | replication code · programs/stata/Prepare_microdata_healthworkers_by_industry.do (steps 0-4) |
| Industry / firm of applied-for job | Fluchtmann et al. (2024) | The reported firm name, zip code and city are matched to the CVR firm register and then to BFL. Steps: exact 1:1 match on firm name and zip code (66.3%); substring matches closest to the reported address (+13.9%); fuzzy matching on the 50 closest Stata matchit candidates, ranked by 5 scores (bigram similscore, token, tokensound, SAS compget and speedist), keeping the best average rank (+6.2%). Overall firm match rate is 86.4%. Industry comes from BFL using NACE Rev. 2 groupings of 10, 21 or 38 industries. Industry is missing when the firm is too new or small to have an industry recorded in BFL. | CVR, BFLNACE Rev. 2 aggregations: 10, 21 and 38 industries | online appendix · Online Appendix A.2, fn. 41, 42, 44 |
| Industry fixed effects | Renkin and Züllig (2024) | Regressions include 4-digit NACE x year (or half-year) fixed effects; robustness with 2-digit NACE x year. Consumer packaged goods approximated by non-durable consumer goods industries classified by Eurostat Prodcom codes. | Firm registerNACE 4-digit, 2-digit | online appendix · Online Appendix Tables A.4-A.5 notes; Appendix C.2 |
| Industry groups | Frederiksen et al. (2026) | Workplace industry from IDAS: 6-digit DB77/93 code (DB03 after 2003) and DB07 code, mapped to about 12 groups (primary, manufacturing, utilities, construction, trade, transport, finance, real estate/business services, public administration, education, health/social, other) by code ranges, e.g. DB07 840000-849999 public administration, 850000-859999 education, 860000-899999 health and social. | IDASARB_BRANCHE_DB7793 / ARB_BRANCHE_DB03 / ARB_BRANCHE_DB07 ranges as in do-file | replication code · 3 Produce regression data.do, 'WORKPLACE FROM IDAS' |
| Industry heterogeneity measures | Bennedsen et al. (2007) | Sample split at the median of industry production growth, output volatility (value of production), labour compensation per employee and import penetration (Statistics Denmark), workers' schooling (authors' calculation from individual schooling and industry of employment), and R&D (industries reporting R&D expenses in OECD STAN). | Statistics Denmark industry statistics, OECD STAN | working paper · Section V, footnote 11 |
| Manufacturing and service sector | Utar (2018) | Industry of the primary job from 6-digit NACE: manufacturing and service defined with different ranges before and after the 2008 NACE revision. | IDAmanu: pernace 150000-<370000 (year<2008), 100000-<340000 (year>=2008); service: 500000-<980000 (year<2008), 450000-<980000 (year>=2008) | replication code · DataPrep.do ('Industry Dummies') |
| Manufacturing industry groups (2-digit NACE) | Stoyanov and Zubanov (2012) | Nine 2-digit NACE manufacturing industry groups used for industry-specific estimates and industry-year fixed effects: Food, beverages, tobacco; Textiles, clothing, footwear; Wood, paper and products thereof; Publishing, printing, recording; Fuel, chemical and other non-metallic mineral products; Metals and products thereof (excluding machinery); Machinery and equipment; Electrical and optical equipment; Manufacturing n.e.c. | Firm accounts dataNACE 2-digit (manufacturing) | online appendix · Web Appendix A2, Table A3 |
| Private sector | Frederiksen et al. (2026) | private = 1 if RAS job function code (1996-2007) is 32, 42, 52, 80 or 90, or RAS sector code (2008-2016) is 18, 28, 32, 38, 42, 48, 52, 58, 62 or 81; 0 otherwise; job_funktion_kode 99 dropped. | RASJOB_FUNKTION_KODE in 32,42,52,80,90; JOB_SEKTOR_KODE in 18,28,32,38,42,48,52,58,62,81; drop JOB_FUNKTION_KODE 99 | replication code · 3 Produce regression data.do, RAS section |
| Private sector establishments | Christensen et al. (2005) | All establishments in the Danish private sector are included (overall 'private sector sample', 113,332 establishments). | IDA | working paper · Section 3.2 and Table 1 |
| Private-sector employment | Bagger et al. (2014) | Ownership code of the employing establishment in IDA identifies private-sector establishments; histories are truncated at entry into a public sector job, self-employment, retirement or an industry without accounting data. Firm industry and ownership taken from its largest establishment. | IDA | working paper · Section 3, footnote 10, Appendix B |
| Private-sector employment and industry | Joensen and Nielsen (2016) | Indicator for being employed in the private sector 18 years after high school graduation; also nine industry indicators (e.g. transportation, mail and communication). | Labour market registers (Statistics Denmark) | working paper · Section 5; Table 6 |
| Private-sector sample | Friedrich (2022) | Worker-level data restricted to IDA sector code 2 (private sector) before building firm outcomes. | IDAPSEKTOR == "2" | replication code · Setup_Firm_Outcomes.do, line 14 |
| Public sector | Humlum et al. (2025) | Dummy for work within the public sector, measured by 2-digit industry codes. | Employment registers (industry codes)2-digit industry codes (specific codes not listed) | online appendix · Supplemental Appendix Table A.1, row 16 |
| Public vs private sector | Caplin et al. (2026) | Main job (highest LOENTIMER in IDAN per year) is public if ARB_SEKTORKODE is non-missing and not 99, and recoded private if ARB_SEKTORKODE is in 18, 28, 89, 81, 58, 48, 42, 52. Industry groups come from DB07 ranges of ARB_HOVED_BRA_DB07 (e.g. >=840000 public services, 850000 teaching, 860000 health). | IDANARB_SEKTORKODE private: 18,28,89,81,58,48,42,52; ARB_HOVED_BRA_DB07 6-digit DB07 ranges | replication code · script/cleaning/1_b_annual_data.R |
| Public-sector employment | Kleven et al. (2019) | Indicator for working in the Danish public sector (no register code given). | Matched employer-employee data | working paper · Section 3.3, Figure 5 Panel C |
| Public/private status of firm | Lentz et al. (2023) | Time-invariant firm characteristic indicating public or private status, used as observed firm covariate. | Danish matched employer-employee data (MEE) | working paper · Section 5 |
| Study-related job | Søgaard (2019) | All jobs not in wholesale (NACE 46), retail (47), postal service (53), hotels and restaurants (55, 56), cleaning (81), elderly, social and childcare (87, 88), or sports and amusement facilities (93). | Monthly earnings records (employer/firm link)NACE 46, 47, 53, 55, 56, 81, 87, 88, 93 = not study-related | working paper · Table 3, note e |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Breadth of occupational experience (TOCC) | Frederiksen and Kato (2018) | Number of distinct 2-digit DISCO occupations held prior to the current occupation over years t-8 to t-1; alternatives are dummies OCC1-OCC7 for number of prior occupations differing from current one. | IDADISCO 2-digit | working paper · Section 3, Eq. (1) and Table 3 |
| CEO / Top CEO | Joensen and Nielsen (2016) | CEO = high-level managers as defined by raw occupation codes (7% of males, 2% of females); Top CEO = top-level managers in public and private enterprises regardless of number of employees using the Danish version of ISCO (D-ISCO) (1% of males, 0.2% of females); also Manager, Managerial Employee, Vice-Director, Middle Manager categories. Measured 18 years after graduation. | Labour market registers (Statistics Denmark)D-ISCO (specific codes not stated) | working paper · Section 5, footnote 21; Table 6 |
| Complex job (parents) | Foged et al. (2023) | Indicator for working in an occupation from managers (ISCO 1) to services and sales workers (ISCO 5), jobs that require some schooling and technical knowledge, or literacy and good communication skills. | Occupation registerISCO major groups 1-5 = complex | working paper · Section III (Data) |
| Employed in complex job | Foged et al. (2024) | Employed in an occupation with 4-digit ISCO-88 (time-consistent IDAN/AKM crosswalk) major group 1-5 is a complex job; employed (ISCO observed or employed in November) in other groups is a non-complex job; outcomes are cumulative years in each divided by years since migration. | IDAN, AKMcomplex_job = 1 if int(isco4d/1000) in 1-5 | replication code · 8. Controls and outcomes.sas, lines ~5943-5950 |
| Employment in recommended / stored occupations | Altmann et al. (2026) | Hours, earnings and monthly employment in BFL jobs whose DISCO code (VMO_DISCO_KODE truncated to 4 digits) matches one of up to five recommended occupations (Alternative_DISCO mapping of the job seeker's profile occupations) or an occupation stored in the job search profile (jsjs CVFAGID), within 183 and 365 days of entry. | BFL, STAR jsjsfloor(VMO_DISCO_KODE/100) == floor(forslag/1000) or floor(discoams_key/1000) | replication code · code/data_preparation.do (occupations_rec, occupations_stored) |
| Ever manager | Almar et al. (2026) | Fraction of a programme's graduates who reach a managerial position, i.e. hold a corresponding occupational code for at least two consecutive years. | RAS, AKM | working paper · Section 3 (Table 1 description) |
| Firm bunching and occupation earnings modes | Chetty et al. (2011) | Occupation-year modes = DKr 5,000 wage-earnings bin with most workers in each 4-digit ISCO occupation-year (occupation-years with under 7,000 workers excluded). Firm bunching b_F = excess mass at the statutory top-tax cutoff for workers with more than DKr 20,000 in deductions; individual bunching b_I = excess mass at the pension kink for the same group; compared across 2-digit ISCO occupations. | IDA, INDoccupation discok (ISCO 4-digit; 2-digit for Figure 18); deductions > DKr 20,000 | working paper · Section 6 (Predictions 2 and 3); Appendix B; Figures 14, 18 |
| Hierarchy level (position) | Frederiksen et al. (2026) | Job level from the RAS socioeconomic job code: 131 = top manager, 132 = (function) manager, 133 = white-collar, 134 or 135 = blue/grey-collar. Only persons with job_socio_kode 131-135 are extracted from RAS (from 1994 on). | RAS, IDARAS job_socio_kode 131 (top manager), 132 (function manager), 133 (white collar), 134-135 (blue/grey collar) | replication code · 3 Produce regression data.do, section 'INFORMATION ABOUT JOBLEVEL FROM RAS'; 1 Produce IDA project data.sas (RAS extract) |
| Internal vs total occupational experience (INT_OCC, INT_EXT_OCC) | Frederiksen and Kato (2018) | INT_OCC = number of occupations experienced within the present firm excluding the current one; INT_EXT_OCC = total number of all occupations experienced excluding the present one, counting the same occupation in two different firms as two occupations. Internal promotion vs external recruitment distinguished by whether the appointment occurs without switching firm. | IDADISCO 2-digit | working paper · Section 4, Tables 5-6 |
| Job (firm x occupation cell) and job switch | Kleven et al. (2025) | Job is a firm x occupation cell; a switcher moves between firm x occupation cells after the reform. Occupations use DISCO-08 from 2010 (563 occupations), DISCO-88 1991-2009 (372) bridged with the Humlum (2021) crosswalk, and an older Danish classification before 1991 (299). Switches where more than 50% of an individual's coworkers move to the same job cell in the same year are dropped. | Employer-employee matched dataDISCO-08; DISCO-88; pre-1991 Danish occupation codes | working paper · Section 3, footnote 15; Section 5.1 |
| Job changes and job characteristics | Lundborg et al. (2017) | Dummies for any workplace change and any (four-digit) occupation change in the short, medium and long run; job characteristics are averages of earnings, hours and log hourly wages by workplace and four-digit occupation over the study period computed in a representative sample of Danish women, plus workplace percent female, public sector and home-work distance. | Employer-employee registersFour-digit occupation code | paper · Section IV.C and Table 4 notes |
| Manager identification | He and Maire (2025) | A worker is a manager if their ISCO-88 occupation is 1200-1399 or their job position code is top management (pstill 31); a manager status is carried forward within an establishment in years where it has no identified manager. If no manager is found, the top earner (or anyone in the top 5% of earners) is flagged when not in a non-managerial ISCO>=2000 occupation, earns at least 1.6 times the average of co-workers, or earns more than 1.5 times the second-highest earner; further fallbacks assign the top earner with a unique occupation/position earning >1.3 times the second highest. | IDAisco 1200-1399; PSTILL==31 (top manager), PSTILL 32 exception; ratio = lonind*(n-1)/(wagebill-lonind) >= 1.6; lonind/second-highest > 1.5 | replication code · clean_manager.do |
| Managers, middle managers and blue-collar workers | Olney and Pozzoli (2021) | Sample restricted to wage earners with IDA occupational position PSTILL 31-37; manager if PSTILL=31, middle level if 32-34, blue collar otherwise. | IDApstill 31-37; manager: 31; ml: 32,33,34; bc=(manager==0 & ml==0) | replication code · Program_to_create_stata_data.sas; offshoring_project_will_dario.do |
| Manual labour share and cell characteristics | Andersen et al. (2026) | Individual does manual labour if main occupation (AKM) is manual trades, operator and assembly, transportation, other manual labour or military. Cell averages also of university degree (UDDA), total debt, liquid financial assets (deposits, stocks, bonds, fund shares) and illiquid assets (FORMGELD). | AKM, UDDA, FORMGELD | working paper · Appendix M |
| Number of hierarchy layers | Friedrich (2022) | Number of distinct occupational layers present in the firm-year (layers renumbered consecutively from the lowest present), missing if more than 20% of firm hours had imputed layers; alternative 3-layer versions collapse layers 1-2 or use DISCO 3-digit <200 for layer 3. | IDAP, FIDAlayers = max(layerN); managers: first DISCO digit = 1 | replication code · Setup_Firm_Outcomes.do, lines ~72-185 |
| Number of job switches (ages 20-50) | Kleven et al. (2025) | Count of job (firm x occupation cell) switches between ages 20 and 50 in a balanced panel of workers observed at every age 20-50, split by earnings percentile at age 50 (top 10%, top 1%, bottom 50%). | Employer-employee matched data | online appendix · Online Appendix, Figure A.3 notes |
| Occupation codes and management layers | Hummels et al. (2025) | 4-digit DISCO/ISCO-88 codes harmonised across years, missing codes filled within job spells (constant code, then forward/backward fill); hierarchical layers counted per firm-year from ISCO88 class (2-5), set missing if more than 25% of the firm-year's workers lack a class. CEO/manager dummy = ISCO major group 1. | IDAceo = disco 1000-1999; disco set missing if 0, 99, 110, 999, 9999; layers = sum of class 2-5 present | replication code · Programs/1_data_job_spell/HMXdata2.do |
| Occupation group | Caplin et al. (2026) | Main job's DISCO_KODE first digit mapped to 9 groups (1 management, 2 high knowledge, 3 medium knowledge, 4 office, 5 sales/services, 6 farming, 7 craft, 8 skilled, 9 unskilled); military (DISCO below 100000) dropped. Tenure = year - ANSAAR. | IDANDISCO_KODE first digit 1-9; DISCO_KODE<100000 excluded; ANSAAR | replication code · script/cleaning/1_b_annual_data.R |
| Occupation groups (manager, machine operator) | Keller and Utar (2022) | 1999 occupation grouped from DISCO-88 major groups: 1 Managers; 2-3 Professionals and technicians; 4-5 Clerks and service workers; 7 Craft workers; 8 Operators; 9 Labourers; set missing if 2-digit code is 99. Machine operator is DISCO 2-digit group 82. | IDADISCO-88 1-digit (disc1_99) groups {1},{2,3},{4,5},{7},{8},{9}; disc2_99==82 machine operator; disc2_99==99 missing | replication code · Analysis1.do lines 7-22 |
| Occupation of applied-for job | Fluchtmann et al. (2024) | Self-reported job titles were cleaned and mapped to DISCO (the Danish version of ISCO) in three steps: (1) unique 1:1 occurrences of DISCO occupation names in the title (43.4% of applications); (2) manual mapping of acronyms and common titles, e.g. 'sosu-hjælper' (+27.2%); (3) fuzzy matching with SAS compget, speedist and soundex plus substring matches, keeping only consistently high-ranked matches (+10.9%). In total 81.5% of applications are matched. Occupations are used at the 1-, 2- and 3-digit levels (9, 55 and 153 groups). | Joblog (Jobnet)DISCO 1-digit (9 groups), 2-digit (55), 3-digit (153) | online appendix · Online Appendix A.2, fn. 41, fn. 43 |
| Occupation skill categories | Dustmann et al. (2024) | Occupations (ILO ISCO) grouped into seven categories by type and skill intensity: (i) high skills/manager, (ii) medium skills/office related, (iii) sales/services, (iv) vocational work requiring medium/basic skills, (v) construction/primary sector work requiring basic skills, (vi) unspecified self-employment, (vii) unskilled manual labor requiring few/no skills. 'Jobs requiring some skills' = (i)-(vi); 'unskilled manual labor' = (vii). | Occupation codes (ISCO) from Statistics Denmark employment registersISCO-based 7-group classification (no numeric codes listed) | online appendix · Online Appendix B.2 |
| Occupational complexity index | Foged and Peri (2016) | O*NET abilities merged to Danish registers at the four-digit ISCO-88 level via the National Crosswalk Center crosswalk. Following Ottaviano, Peri and Wright (2013), abilities are averaged into communication, cognitive (analytical) and manual intensity indices, each standardized to [0,1] as the occupation's centile in the 2000 ranking. Complexity = ln((Communication + Analytical)/Manual). | IDA, O*NETISCO-88 4-digit (Danish version) | online appendix · Appendix A.1 |
| Occupational knowledge and hazard indices | Hummels et al. (2014) | Principal-component indices from O*NET: social sciences = 2.C.1 (all subitems), 2.C.6, 2.C.7, 2.C.8, 2.C.9, 2.C.4.e, 2.C.4.f; natural sciences = 2.C.2, 2.C.3, 2.C.5, 2.C.4.b, 2.C.4.c, 2.C.4.d, 2.C.4.g, 2.C.10, 2.A.1.f; communication and language = 4.A.4.a, 2.B.1, 1.A.1.a, 4.C.1.a.4, 4.C.1.b.1, 2.A.1.a-2.A.1.d; on-the-job hazards = 4.C.2.c, 4.C.2.b.1, 4.C.2.e.1. | IDA, O*NETISCO-88; O*NET item IDs as listed | online appendix · Data Appendix, section 1 |
| Occupational layer | Friedrich (2022) | Workers' DISCO occupation code (DISCOALLE_INDK) harmonised to 3 digits and mapped to four hierarchical layers following Caliendo et al. via a DISCO3-to-PCS layer correspondence; missing layers imputed from the same worker-firm's adjacent years (up to 2 years), then from the layer whose average hourly wage is closest; firm-years with more than 20% of hours requiring imputation are set to missing. | IDAP, FIDADISCOALLE_INDK; military code 0110/011000 recoded to 900; file correspondence_DISCO3_pcslayer | replication code · Setup_Workerdata.do |
| Occupational mobility | Foged and Peri (2016) | Occupations are reported by firms using the Danish version of ISCO-88. Missing or invalid ISCO-88 values are replaced by the next value within the worker-firm match if that value is also the most frequent ISCO-88 code within the match; authors note this (and weak incentives for firms to update codes) may understate within-firm job mobility. | IDAISCO-88 | online appendix · Appendix A.1 |
| Occupational rank / top vs bottom occupations | Kleven et al. (2025) | Occupation cells (6-digit occupation codes) ranked by mean wage earnings; for top/bottom occupations the ranking is computed among workers aged 45-50 (mean, P10 and P90 of wage earnings reported per cell). Occupational rank used in balance checks orders occupation cells by their mean earnings. | Employer-employee matched data (DISCO occupation codes)6-digit DISCO occupation codes | online appendix · Online Appendix, Table A.1 and Figure A.6 notes |
| Occupational rank and top manager | Kleven et al. (2019) | Occupation ranked in five levels: unskilled labour, skilled labour, white-collar work (low level), white-collar work (high level) and top manager, an ordering consistent with average earnings or wage rates by occupation; separate indicator for being top manager. | Occupation (matched employer-employee data) | working paper · Section 3.3, Figure 5 Panels A-B |
| Occupational skill groups | Utar (2014) | Occupation categories derived from IDA variable 'pstill2' (employee status/position in firm). Professional and technical occupations = pstill2 32 (top-level employees, e.g. engineers) and 34 (intermediate-level professional/technical employees, e.g. laboratory technician, programmer); base-level occupations (basic skills, e.g. office work, operating stationary machinery) = pstill2 35; auxiliary employees (no skill requirement: cleaning, delivery, guard, transport work) = pstill2 36; unclassified = pstill2 37. Year 1995 excluded from occupation analyses because of the 1996 switch to DISCO-88. Inactive persons on the payroll (retirees, employees on leave, owners' spouses) are dropped before computing firm-level employment characteristics. | IDA (Persondata)pstill2 in {32,34} professional/technical; 35 base-level; 36 auxiliary; 37 unspecified | online appendix · Appendix C-II, Employee Characteristics (IDA) |
| Occupational sub-samples | Christensen et al. (2005) | Pooled private-sector establishment sample partitioned by worker occupation into skilled and unskilled workers ('blue collar') and managers and salaried workers ('white collar'); distributions and counts are built separately per sub-sample. | IDA | working paper · Section 3.2 (p. 9) |
| Occupational task content | Hasager and Jørgensen (2026) | Ratio of communication and cognitive tasks to manual tasks from O*NET, merged via ISCO occupation codes, for those employed at the end of November each year. | IDA, O*NET | working paper · Section I |
| Occupational task content / physical ability | Humlum et al. (2025) | Physical ability requirement = standardized average O*NET importance of Static Strength, Explosive Strength, Dynamic Strength, Trunk Strength and Stamina. Cognitive ability requirement = standardized average importance of Fluency of Ideas, Originality, Problem Sensitivity, Deductive Reasoning, Inductive Reasoning, Information Ordering, Category Flexibility, Mathematical Reasoning and Number Facility. Physical jobs = physical requirement > 0.5 SD above the mean. Occupational injury rate = accepted (not necessarily compensated) work accidents per full-time employee. | O*NET mapped to DISCO occupation codes; AES accident recordsPhysical job: > +0.5 SD | online appendix · Supplemental Appendix Table A.1, rows 21-23; Section D.6.2 figure notes |
| Occupational tenure | Traiberman (2019) | Tenure measured at the occupation-sector pair level, constructed for early cohorts using data from 1990-1996, and censored above at 6 years to keep the measure comparable across cohorts; tenure resets upon switching occupations. | IDA | online appendix · Online Appendix D.1, Occupational Tenure; Appendix model section |
| Physical-cognitive ability gap | Humlum and Plato (2026) | ln(Physical Ability Requirements / Cognitive Ability Requirements) of the occupation, using O*NET importance of strength and stamina items vs. reasoning and idea items; both normalised. | IDA, O*NET | working paper · Appendix Table A.1, row 18 |
| Previous occupation | Graversen and Ours (2008) | Occupational classification based on the name of the UI fund that pays the individual's UI benefits. | PES registers | working paper · Section 2.3, footnote 9 |
| Promotion | Kleven et al. (2025) | Within-firm switch to an occupation cell whose median earnings are at least 10% higher (robustness 5% and 20%); promoted workers matched to unpromoted co-workers in the same firm. | Employer-employee matched data, Monthly payroll records | working paper · Section 4, Fact 3 |
| Promotion | Hvidberg et al. (2023) | Switching from a job as a regular employee in the pre-shock period (2008-2011) to a management position in 2012-2017; in the code a manager is DISCO first digit 1. | RAS, IDAPmanag_12_17 with manag_08_11==0; manager = substr(disco_kode,1,1)=="1" | replication code · Replication_Code.do lines 1519-1521, 1892; paper footnote 21 |
| Routine / non-routine task intensity | Hummels et al. (2014) | IDA occupation uses the Danish version of ISCO-88, mapped to O*NET via the National Crosswalk Center crosswalk. Non-routine = principal component of O*NET 1.A.1.c.1 (mathematical reasoning), 1.A.2.b.3 (response orientation), 1.A.3.c.3 (gross body coordination), 2.A.1.e (mathematics), 4.A.2.b.2 (thinking creatively), 4.A.2.b.6 (organizing, planning, prioritizing). Routine = 1.A.2.a.2 (manual dexterity), 1.A.2.a.3 (finger dexterity), 1.A.2.b.2 (multilimb coordination), 4.A.2.a.2 (processing information), 4.A.2.a.3 (evaluating compliance with standards). | IDA, O*NETISCO-88 (Danish version); O*NET 1.A.1.c.1, 1.A.2.b.3, 1.A.3.c.3, 2.A.1.e, 4.A.2.b.2, 4.A.2.b.6 (non-routine); O*NET 1.A.2.a.2, 1.A.2.a.3, 1.A.2.b.2, 4.A.2.a.2, 4.A.2.a.3 (routine) | online appendix · Data Appendix, section 1 |
| Scope of search index | Fluchtmann et al. (2024) | Standardized combination of average commute time to applied-for jobs (Google Maps driving times from residence municipality to firm address, Harmon 2013), share in an occupation unrelated to the previous job (not among the O*NET top-10 related occupations, mapped to 3-digit DISCO), and share in an unrelated industry (not among the 10 most skill-related 3-digit NACE Rev. 2 industries, Neffke and Henning 2013); restandardized by first-month mean and SD. | Joblog, BFLDISCO 3-digit; NACE Rev. 2 3-digit | working paper · Section 3.3; Online Appendix B.5 |
| Share of applications to recommended occupations | Altmann et al. (2026) | Among applications logged in Joblog within 1 or 12 months (30.25-day months) after entry, the share whose DISCO-AMS occupation equals a recommended occupation (or one stored in the profile). | STAR Joblogshare_recommended_t1/t12, share_stored_t1/t12 | replication code · code/data_preparation.do |
| Skill level / occupation category | Stoyanov and Zubanov (2012) | Occupation grouped into four categories: low-skilled, mid-skilled, high-skilled workers and managers. | Matched employer-employee data (Statistics Denmark) | paper · Section I.A |
| Skilled nurse / unskilled nurse / doctor (by education) | Friedrich and Hackmann (2021) | Health workers are classified by vocational education code in IDAP: skilled nurses (general nurse BSc, specialist nurse training, midwife BSc, nurse MSc); unskilled nurses (health and care workers); medical doctors (MD, PhD; medical BSc 7172 excluded). Doctors' first 7 years of experience are treated as residency and not counted as doctors. | IDAP (IDAPALL)ERHAUDD (or HFAUDD in SAS extract): skilled nurse 5166-5173, 5175, 5180, 5181, 8634; unskilled nurse 5146-5150, 5152; doctor 7170, 7180 | replication code · programs/sas/dataprep.sas; programs/stata/Prepare_microdata_healthworkers_by_industry.do; Prepare_aggregate_county_exposure.do |
| STEM occupation | Brenøe and Zölitz (2020) | Indicator for working in a high-skilled STEM occupation for at least half the years observed in 11-15 and 16-20 years after high school entry. | DISCO (Danish ISCO) codes 21, 25, 31, 35 | working paper · Footnote 22; Table 7 notes |
| Top management appointment (career success) | Frederiksen and Kato (2018) | APPOINTMENT = 1 if the worker is appointed to a top management position, defined as DISCO 12 (Corporate Managers) or DISCO 13 (General Managers), in a firm with 100 or more employees between year t and t+1 (appointments in 2000 and 2001); robust to a 50-employee cut-off. CEO-only definitions were not used because 4-digit codes are inconsistently assigned. | IDADISCO (2-digit) 12, 13; firm size >= 100 employees | working paper · Section 2 and footnotes 3, 6; Eq. (1) |
| Top management appointment (promotion) | Frederiksen et al. (2026) | prom1 = 1 if the person is a function manager (pos = 2, RAS code 132) in year t and a top manager (pos = 1, RAS code 131) in year t+1 (lead of position in the person-year panel). Logit models for prom1 are estimated on managers (pos = 2) only; in the multinomial version, promotions are split into internal (same workplace lbnr in t+1) and external (new workplace). | RAS, IDANpos==2 & F.pos==1 (job_socio_kode 132 -> 131); new_workplace = lbnr != F.lbnr | replication code · 3 Produce regression data.do ('promotion categories'); 4 Tables and figures.do (Table 6 mlogit) |
| Top manager per firm-year | He and Maire (2025) | Among identified managers, one manager per establishment-year is kept by successively preferring ISCO 1210 (CEOs), then pstill 31, managers with longer tenure in the establishment, and higher within-establishment earnings rank; only managers observed in at least two firms are retained (mobility needed for identification), establishments that were acquisition targets are excluded, and at the firm (CVR) level the manager of the largest establishment is used. | IDAisco==1210; pstill==31; target_cumu>=1 dropped | replication code · clean_manager.do |
| Two-digit occupation (cleaned) | Traiberman (2019) | Two-digit ISCO (DISCO) code taken from the four-digit occupation code in IDA, using only codes whose quality flag indicates they come from administrative surveys or pension funds (not imputation or lagged information). Missing codes are imputed if the worker has the same occupation at t-1 and t+1, or from t+2 after a firm switch; managerial codes 10-13 are combined into one, legislators and military are dropped, and small occupations are merged by skill group (e.g. 52 into 51, 92 into 61 for all skill groups). | IDA, IDASDISCO/ISCO two-digit; quality flag codes 1, 2, 4, 10 kept; ISCO 10-13 combined; high-skilled merges 42->41, 52->51, 72->71/74, 81->82/83, 91->93 per Appendix table; 21/22 merged for low/medium skill | online appendix · Online Appendix D.1, Data Cleaning: Occupations (and footnote 11) |
| White-collar occupation | Fadlon et al. (2016) | White-collar = 2-digit (D)ISCO occupation code with first digit 1-5 (1 leadership; 2 highest-level knowledge work; 3 intermediate knowledge; 4 office jobs; 5 retail sales, service and care work). Blue-collar/other = first digit 6-9. Main sample excludes public-sector workers and blue-collar occupations to avoid workplaces with collectively bargained contribution rates. | IDA (occupation code)2-digit DISCO first digit 1-5 = white-collar; full list 11,12,21-24,31-34,41,42,51,52 (white-collar); 61,71-74,81-83,91-93 | online appendix · Online Appendix E: Occupation Codes |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| AKM firm fixed effect | Fluchtmann et al. (2024) | Firm fixed effects from an AKM log-wage model estimated on BFL matched employer-employee data 2008-2015 (290,108 connected firms) with year-month fixed effects; worker-weighted industry means removed after estimation. | BFL | working paper · Online Appendix B.4 |
| AKM firm wage premium | Kleven et al. (2025) | Firm fixed effects from an AKM model log z_it = alpha_i + psi_J(i,t) + X_it beta + nu_it, with controls for year, age and tenure-in-current-firm dummies, estimated on pre-reform data 2002-2005 for firms with at least 10 employees; estimated firm effects merged onto the 2009 tax-reform sample to measure changes in firm premia for firm switchers. | Employer-employee matched data, Income tax recordsfirms >= 10 employees; estimation window 2002-2005 | online appendix · Online Appendix, Section C and Figure A.9 |
| Average interest rate on bank loans | Renkin and Züllig (2024) | Because contractual rates are often missing in the loan micro data, the average interest rate is computed as interest paid divided by the average of current and lagged end-of-year loan balances (Equation 1); regression of it on the contractual rate gives a coefficient of 0.91 for rates below 0.1. | Bank account-level loan reports to the tax authority | online appendix · Online Appendix Figure A.3 notes |
| Bartik sales instrument (HS4) | Hummels et al. (2025) | For each firm, HS4 product shares of Prodcom sales in a pre-sample year (first year the firm is in both Prodcom and the estimation sample, or its designated pre-sample year), multiplied by aggregate Danish Prodcom sales of each HS4 product in later years and summed to the firm-year; used in logs after deflating by the GDP deflator. | VARS (Prodcom)bartik = sum_hs4 share_presample * aggvrd(hs4, year), years after presample year only | replication code · Programs/1_data_job_spell/Part5sales_hs4.sas |
| Business tax scheme and retained earnings | Maire and Schjerning (2013) | Self-employed choose the personal income tax scheme, the capital returns scheme (from 1993) or the firm tax scheme (from 1987); the latter two are treated as one and allow retaining profits in the firm, taxed provisionally at the firm tax rate (34% in 1994 falling to 25% in 2009), with withdrawals taxed as personal income. Retained earnings = profits retained under these schemes. | Income Tax Register | working paper · Section 2.1 'Three Tax Schemes for the Self-employed'; Table 2 |
| Capital, materials and wage bill inputs | Buus et al. (2025) | Capital is the sum of FIRE balance-sheet asset items, deflated by the gross fixed capital formation index (NABK69) by 69-industry; material expenditures are krh (2001-2003) or krhe+kvv (2004 onward), deflated by the intermediate consumption index (NABP69); the wage bill (lgag) is deflated by the CPI (PRIS112). Firm-level inputs and sales are multiplied by the share of Prodcom sales value with non-missing quantity. | FIRE, FIRM, VARScapital = iaat+grby+atam+fmaa+aadi; expenditures = krh (<=2003), krhe+kvv (>=2004); wagebill = lgag; sales = FIRM gf_oms | replication code · BMRS_restat/1_data/code/1_2_fire.do; 2_prepare/code/2_2_variables.do |
| Close coworkers | Fadlon and Nielsen (2019) | Workplaces defined by physical establishment units in matched employer-employee data. Shocked individuals who in pre-shock periods t=-4 to t=-1 worked at workplaces with employment at or below the sample's 25th percentile (about 20 employees); their coworkers in a similar occupation class with an age gap of 5 years or less; coworkers who are also family members excluded. Robustness perturbs the 5-year age gap and the workplace-size cutoff. | IDA (matched employer-employee)workplace size <= 25th percentile (~20 employees); age gap <= 5 years | online appendix · Appendix Table 1 notes; Appendix Tables 9-10 notes |
| Co-worker (establishment) wage | Clark et al. (2009) | Average wage of all other employees at the same establishment (IDA establishment identifier); alternatives are the 75th (and 90th) percentile of the establishment wage distribution and mean wage by three occupation categories (managers, middle-ranking, lower-level). Sample restricted to establishments with at least 5 colleagues (robustness 4 and 6). | IDA | working paper · Section 3.1-3.2; Section 4 (Tables 3-4); footnotes 7, 11, 12 |
| Co-worker network (weighted) | Giorgi et al. (2019) | Co-workers are individuals working in the same plant (for public employees, the physical address of the workplace) from IDA. Baseline weights favour similar workers: with five education groups and three occupation groups (blue collar, white collar, manager), degree of separation d = |E_i - E_j| + |O_i - O_j| and weight (d+1)^-2; peers' consumption is the weighted average of co-workers' household log consumption. | IDA | working paper · Section 2.1; Section 5.1, eq. (11) |
| Co-workers | Hvidberg et al. (2023) | People working in the same workplace, defined as a single address entity (each location of a multi-location firm is a separate workplace). | RAS, IND | paper · Table 2; README.docx |
| Employer of the month | Kreiner et al. (2020) | When a person has payments from several firms in a month, the firm id, ownership form and main industry code are taken from the firm with the highest payment that month; firm information merged from the firm register file ES2015. | eIndkomst, ES2015 (firm register extract)senr; es7604_dr_form_kod (ownership form); es7606_hov_br_nr (sector/industry code) | replication code · data02.do |
| Establishment (job type) | Taber and Vejlin (2020) | Job types are establishments (not firms). Statistics Denmark's establishment identifier is the same across years if one of three criteria is met: same owner and industry; same owner and workforce; or same workforce and either same address or same industry. A job is an employment spell within an establishment. | IDA | working paper · Section 6, footnote 22 |
| Establishment average hourly wage | Christensen et al. (2005) | For each private-sector establishment and occupational sub-sample, the average of the Statistics Denmark estimate of the hourly wage of each worker of that occupational type employed during November 1994 to November 1995, in 1994 DKK; only workers aged 18-65 included, and wage rates below 69 DKK per hour (taken as the effective legal minimum wage) and in the top 1 percent are excluded from the average (but such workers are kept in employment and stayer counts). | IDA | working paper · Section 3.2 and Table 1 (pp. 9-10) |
| Establishment output for IO disaggregation | Andersen et al. (2026) | Firm output is distributed across a firm's establishments using within-firm labour compensation shares, then aggregated to 173 subindustries combining the 117-industry national IO table and DEA industries; intermediate flows are split proportional to output shares. | NIO1-NIO3 (statbank)117 NA industries; 173 subindustries | working paper · Appendix N.A |
| Exporter status and sales | Frederiksen et al. (2026) | Firm sales (oms_firm) and total exports (eksport_ialt) from the IDA firm file; exporter = 1 if exports > 0. Merged by CVR number. | FIRMoms_firm, eksport_ialt > 0 | replication code · 3 Produce regression data.do, 'EXPORT AND SALES FROM FIRM' |
| Exposed bank | Jensen and Johannesen (2017) | Individuals ordered by the 2007 loan-to-deposit ratio of their primary bank and split at the median individual: above-median primary bank = exposed (approximately equal numbers in each group). | Danish Central Bank bank balance sheets | working paper · Section 3.3 |
| Firm controls | Hummels et al. (2014) | Firm identifier from 'Old Firm Statistics' (1995-1999) and 'General Firm Statistics' (1999-2006), combined in FirmStat to track firms across the 1999 break. Employees = number of full-time equivalent workers in the year (FirmStat). Capital stock = value of land, buildings, machines, equipment and inventory (Accounting Statistics register). Gross output (net of taxes) from the VAT register. Firm skill intensity = educational attainment of individual workers in IDA aggregated to the firm via FIDA. | FirmStat, Old Firm Statistics, General Firm Statistics, Accounting Statistics, VAT register, IDA, FIDA | online appendix · Data Appendix, section 1 |
| Firm default contribution rate | García-Miralles and Leganza (2024) | All individuals over 18 are assigned to workplaces in 2010; individual contribution rate = employer-sponsored plan contributions divided by earnings; the firm default rate is the median contribution rate among the firm's workers in 2010. Predicted contributions = current earnings times the 2010 firm default rate. | Linked employer-employee data, Pension contribution records | working paper · Section 6 (employer defaults), footnote 30 |
| Firm exposure to wholesale-funded banks | Renkin and Züllig (2024) | Share of wholesale-funded banks in the firm's 2007 loan portfolio. Descriptive bins: no exposure (<0.02), low (0.02-0.5), high (0.5-0.98), full (>0.98). | Bank account-level loan reports, Bank balance sheets<0.02, 0.02-0.5, 0.5-0.98, >0.98 | online appendix · Online Appendix Figure A.4 and Table A.2 notes |
| Firm family friendliness | Kleven et al. (2019) | Share of women with children below age 15 in the firm's workforce (excluding the woman's own first child when it arrives); outcome is the percentile rank in the distribution of firm family friendliness. | Matched employer-employee data | working paper · Section 3.3, Figure 5 Panel D |
| Firm growth including exit | Lentz and Mortensen (2008) | Firm growth rate in value added (dY/Y) where an exiting firm contributes an observation of -1. | Danish Business Statistics Register | working paper · Section 5, Table 2 |
| Firm inflow (poaching) rank | Bagger and Lentz (2019) | For each firm, the fraction of all hires over the 10-year window that come directly from other firms (job-to-job); measured only for firms with more than 15 hires and at least one hire from unemployment; used as a firm productivity rank. | Spell data | working paper · Section 6 (moments), footnote 17 |
| Firm instruments (distance-3 firm shocks) | Giorgi et al. (2019) | Weighted averages over distance-3 peers of their firm's size, firm-specific change in employment, firm type dummies (publicly traded, limited liability, other) and a public-sector dummy, all expressed in first differences. A separate set uses distance-3 peers' demographics (age, age squared, years of schooling, share women, occupation shares, kids 0-6 and 7-13). | IDA | working paper · Section 5.2 (text before Table 4); Table 1 |
| Firm productivity | Labanca and Pozzoli (2022) | Value added per employee and TFP from Firmstat accounting data; in some specifications value added per employee is instrumented with TFP. Construction details are in an online appendix not included in the WP. | Firmstat | working paper · Section 4.1, footnote 15; Figure notes |
| Firm productivity (hourly value added) | Bagger et al. (2014) | Statistics Denmark's accounting-data sampling scheme by firm size: theoretical sampling probability 0.10 (0-9 employees), 0.20 (10-19), 0.50 (20-49), 1.00 (>49); firms with revenue above DKK 100 million (DKK 200 million in wholesale) are also included; in practice 10% of firms with 5-9 employees and no firms with 0-4 employees are sampled (0-4 lumped with 5-9). Empirical sampling probabilities (pooled 1999-2003 last-week-of-November cross sections) of about 0.07-0.09, 0.29-0.30, 0.60-0.62 and 0.91-0.95 are used as weights. | Accounts Statistics (regnskabsstatistik), IDAsize bins 0-9, 10-19, 20-49, >49; revenue > DKK 100m (wholesale > DKK 200m) | online appendix · Working paper Appendix B, Table 7 |
| Firm purchases and employment (validity checks) | Fluchtmann et al. (2024) | Firm purchases from FIKS, built from mandatory VAT reports; firm employment computed from BFL. | FIKS, BFL | working paper · Online Appendix B.6.4, footnote 50 |
| Firm sales, value added, industry | Friedrich (2022) | Sales (GF_OMS), value added (GF_BAV) and full-time equivalent employment (GF_AARSV) from firm statistics, CPI-deflated; industry from DB93/DB03 9-, 27- and 53-group aggregations, 2007 industry carried forward to 2008 if missing; firm age = year - start year + 1. | FIRM (GF), FIREGF_OMS, GF_BAV, GF_AARSV, GF_gr009_DB93/DB03, GF_gr027_DB93/DB03, GF_gr053_DB93/DB03 | replication code · Prepare_firmdata.do |
| Firm separation rate | Keller and Utar (2022) | Percentage of the workplace's 1998 employees who are not employed in the workplace in 1999. | IDA | working paper · Footnote 40 |
| Firm size groups and top-five paid | Kreiner et al. (2016) | Firm size groups: fewer than 25, 25-99, 100-499 and 500+ employees; large firms defined as more than 100 employees (top decile by number of employees); top-five paid employees within each firm used to isolate top management. | eIncome register (SKAT), Firm registers | working paper · Section III.B, Figures 6-7 |
| Firm spinouts and exits | He and Maire (2025) | Share of a firm's workers in establishments that move to a different firm ID next year (spinout) or disappear next year before 2011 (exit). | IDAspinout = cvrnr_nextyear!=cvrnr; exit = cvrnr_nextyear missing & year<2011 | replication code · clean_merger.do |
| Firm wage premium (AKM firm effect) | Fluchtmann et al. (2024) | AKM model on log hourly wages (BFL monthly earnings divided by monthly paid hours) with worker, firm and calendar-month fixed effects. The panel uses March and September of each year from 2008 to 2017, giving 265,425 connected firms (about 95% of firms). Firm effects are standardised within industry by subtracting the industry mean and dividing by the industry SD. Decile bins are employment-weighted by firm employment in August 2015. Gender-specific AKM effects are used as a robustness check. | BFL | online appendix · Online Appendix A.3 |
| Foreign ownership | Olney and Pozzoli (2021) | Firm is foreign-owned if its Central Business Register ownership-form code takes one of the listed values (lagged one year in regressions). | Central Business Register (foreign_ownership.dta)GF_VIRKFKOD in 160,170,180,190,196,200,210,220,290,291 | replication code · offshoring_project_will_dario.do (line ~748) |
| Gross operating margin | Renkin and Züllig (2024) | (sales - labour cost - purchases)/sales; alternative version additionally subtracts interest payments to banks from the loan data. Accounting profits are after interest, taxes and depreciation; total inventories include raw/intermediate inputs and pre-payments, final goods inventories only produced goods. | Accounting statistics, Bank loan reports | online appendix · Online Appendix table notes for firm outcomes by exposure |
| Hiring and firing rates | He and Maire (2025) | Hiring rate = workers whose firm ID differs from the previous year divided by lagged firm employment; firing rate = workers whose firm ID differs next year divided by current employment. | IDA | replication code · AKM_merger_cvrnr.do |
| Industry-adjusted OROA | Bennedsen et al. (2007) | OROA minus the equally weighted average OROA of all active firms (including non-event firms) in the same industry and year; four-digit NACE industries are used if they contain at least 20 non-event firms in a year, otherwise three- or two-digit groupings. | KOBNACE 4-digit, falling back to 3- or 2-digit; minimum 20 non-event firms | working paper · Section II.B |
| Industry-and-performance-adjusted OROA | Bennedsen et al. (2007) | OROA adjusted using as control firms in the same industry and the same industry-adjusted performance decile in the year before succession (Barber and Lyon, 1996). | KOB | working paper · Section IV.A |
| Intangible assets | Utar (2014) | Assets intended for long-term ownership or use, including licences, trademarks, copyrights, exclusive distribution rights, software and goodwill, from the accounting statistics; outcomes are log intangible assets and the logit of intangibles over total assets. Physical capital: plants, machinery, technical installations, land, buildings, furniture and office equipment. | Regnskabsdata (firm accounts) | working paper · Section 5, footnote 26; Appendix B.1 |
| Inventor | Akcigit et al. (2024) | Individual with at least one European Patent Office patent; Danish inventors matched to IDA using a DST disambiguation algorithm (about 75% matched). | EPO patents, IDA | paper · Section 2.2; Section 2.3 (Determinants of Being an Inventor) |
| Job tenure | Humlum et al. (2025) | Number of consecutive years in which the main job in November is registered with the same firm identifier. | IDA employer-employee data | online appendix · Supplemental Appendix Table A.1, row 14 |
| Labour productivity | Lentz and Mortensen (2008) | Value added per worker (Y/N) and value added per quality-adjusted worker (Y/N*), with Y in DKK. | Danish Business Statistics Register | working paper · Section 2, Figure 1 |
| Labour productivity, capital intensity, size | Olney and Pozzoli (2021) | Labour productivity = ln(sales / employees); capital intensity = ln(capital / employees) with capital from FirmStat GF_EGUL (1999+) or GF_E_GUL (before 1999); size dummies 1-19, 20-49, 50+ employees (fansh). Firms with fewer than 2 employees, missing productivity/capital, or in group bra==2 (NACE 35) dropped; sample 1995-2011. | FirmStatGF_OMS (sales), GF_EGUL/GF_E_GUL, fansh | replication code · offshoring_project_will_dario.do (firm-level controls block); Program_to_create_stata_data.sas; WP fn. 15 |
| Markups and marginal costs | Buus et al. (2025) | Firm-product markups and marginal costs from the De Loecker et al. (2016) procedure: a translog production function estimated on single-product firms by 2-digit sector, with the original tariff controls replaced by the firm- and firm-product-level export support indicators; markup = output elasticity of materials over the materials expenditure share allocated to the product. | VARS, FIRE, FIRM, UHDIoutputs lmu_x (log markup), lmc_x (log marginal cost), theta_fjt_x; tau_in1=treated_p, tau_ou1=treated | replication code · BMRS_restat/3_lgkp/code/01_sample.do, 02_pf.do; 4_reg_variables/code/4_6_vars.do |
| Mass layoff / closing establishment | Taber and Vejlin (2020) | Mass layoff: establishment has more than 15 workers in year t and less than 30 percent of them left in year t+1. Closing: establishment no longer observed in the data. | IDA | working paper · Section 6.1, footnote 28 |
| Mass layoff switchers | Kleven et al. (2025) | Mass layoffs are layoffs in which firms with at least 20 employees reduce their workforce by at least 30% in a single year; switchers are workers who move firm following such a layoff. Used in an event study of log earnings vs a linear pre-layoff trend. | Employer-employee matched datafirm size >= 20 employees; workforce reduction >= 30% within one year | online appendix · Online Appendix, Figure A.8 notes |
| Mergers and acquisitions | He and Maire (2025) | M&As are identified from establishment (workplace) IDs changing firm (CVR) IDs: a firm-year is a merger if its establishments were owned by more than one distinct firm in the previous year (ignoring spurious ownership changes where an establishment's firm ID reverts); the acquirer is the establishment whose firm ID is unchanged, and targets are establishments whose previous-year firm ID differs when the firm also has an acquirer. | IDA, IDASlbnr (establishment), cvrnr (firm); merger = nvals(cvrnr_prevyear)>1 within cvrnr-year; acquirer: cvrnr==cvrnr_prevyear; target: cvrnr!=cvrnr_prevyear & nacquirer>0 | replication code · clean_merger.do |
| Normalized labour productivity | Stoyanov and Zubanov (2012) | Natural log of firm value added per worker normalized by the 4-digit NACE industry-year average; manufacturing firms 1995-2007, with firm accounts from annual surveys (all firms with 50+ workers) and interpolated values for less frequently surveyed small firms. | Firm-level accounts (Statistics Denmark)NACE 4-digit | paper · Section I.A |
| Operating return on assets (OROA) | Bennedsen et al. (2007) | Ratio of earnings before interest and taxes (EBIT) to the book value of assets, from annual reports of limited liability firms (KOB). Performance change is the three-year average after succession minus the three-year average before, omitting the succession year. | KOB | working paper · Section II.B; footnote 13; Table IV notes |
| Physical capital assets | Utar (2014) | Plant, machinery, technical installations, land, buildings and other equipment such as computers and office furniture; deflated with capital-goods deflators, thousands of year-2000 DKK. Wages deflated with CPI; hourly wages in year-2000 DKK. | Regnskabsdata (firm accounts statistics) | online appendix · Appendix C-I |
| Plant size and workforce composition | Dahl et al. (2013) | Plant size defined as the number of employees, plus variables measuring the labour force composition at plant level; regressions also include 25 industry dummies interacted with year dummies. | IDA | working paper · Section 4 (p. 13) |
| Prodcom product sales and quantities | Buus et al. (2025) | Quarterly Prodcom reports of value and quantity per 10-digit product are summed to firm-product(CN8+ x unit)-year; missing firm IDs are imputed from report ID and alternative ID within 2002-2006 and 2007+ separately; observations with missing or zero quantity are dropped; firm industry is the DB07 code with the largest sales value. | VARSjurnr, rapport_id, saflbnr, varenr, vaerdi, maengde, mgd_tekst, fag_hoved_bra_db07 | replication code · BMRS_restat/1_data/code/1_4_vars.do; 2_prepare/code/2_1_clean.do |
| Producer cell (establishment region x industry) | Andersen et al. (2026) | Firm establishments are assigned to 2,646 producer cells by region and production industry using the employment registers; 24 work industries plus 3 housing industries without employees (private landlords, owner-occupied housing, government-owned housing). | BFL, IDAN | working paper · Appendix D; Section II |
| Producer prices (PPI microdata) | Renkin and Züllig (2024) | Monthly transaction prices (DKK, incl. temporary sales and discounts) reported by firms in the PPI survey for products classified by eight-digit HS codes; baseline uses domestic prices only. Prices are quality-adjusted, monthly price changes winsorized at +/-1 log points, and the data converted to quarterly frequency by keeping the price in the first month of each quarter. | Producer Price Index survey microdata (Statistics Denmark)8-digit Harmonized System product codes | paper · Section I.B (Price Data) |
| Productivity gap of new hires | Stoyanov and Zubanov (2012) | Split into four groups by sending firm: from more productive firms in the same 2-digit industry (gap_p_same), more productive firms in a different industry (gap_p_diff), less productive same industry (gap_n_same), less productive different industry (gap_n_diff). Regressions include five lags of value added per worker (autocorrelation insignificant at the sixth lag), industry-year fixed effects and average size of the sending firm; period 1995-2007; firm-specific trend robustness restricted to firms with 50+ workers. | Matched employer-employee data, Firm accounts datafirms >=50 workers (trend robustness) | online appendix · Web Appendix A1-A3, Tables A2-A4 |
| Productivity growth decomposition | Lentz and Mortensen (2008) | Growth in aggregate value added per worker decomposed following Foster, Haltiwanger and Krizan (2001) into within, between, cross, entry and exit terms, with firm employment shares as weights; no entry term because entrants are excluded. | Danish Business Statistics RegisterP_t = sum_e s_et p_et, p_et = Y_et/N_et, s_et = N_et/N_t | working paper · Section 5, equation (19), Table 3 |
| Quality-adjusted employment | Lentz and Mortensen (2008) | N* of firm j equals its total wage bill W_j divided by the economy-wide average wage per worker (sum of W over sum of N); for firms observed in several years the implied labour quality is averaged over time. | Danish Business Statistics RegisterN*_j = W_j / w, w = sum_j W_j / sum_j N_j | working paper · Section 2, equation (1) and footnote 2 |
| R&D subsidies | Akcigit et al. (2024) | R&D project funding following the expansion of government R&D support from 2002, including funding through the Innovation Danmark programme and specific R&D subsidies (the programme also records funding to education). | Innovation Danmark R&D funding data (DST) | paper · Section 2.2 |
| Return on capital employed and net income to assets | Bennedsen et al. (2007) | ROCE is operating income divided by book equity plus book debt; net income to assets is after-tax profits over book value of assets; assets measured as log assets in 2000 DKK. | KOB | working paper · Section II.B; footnote 13 |
| Sales, gross profit proxy and survival | Brenøe et al. (2024) | Total sales and purchases from VAT data (corrected to include exports; negative values recoded to zero). Gross profit proxy = sales minus purchases minus wage bill ex. leave (purchases include capital equipment). Total variable costs = purchases + wage bill ex. leave. Exiting firms kept with zero employees/hours/sales; shutdown proxied by zero sales. | VAT | working paper · Section 4.3; Appendix C.1 |
| Small private firm sample | Brenøe et al. (2024) | Private-sector firm (CVR) with 3-30 employees in November of the baseline year and fewer than 60 employment relationships during the baseline year; active at baseline (hours at least one FTE, positive sales and wage payments in baseline, positive sales or wages the year before); sales per employee 10,000 DKK-100 million DKK and wages per worker 10,000 DKK-1 million DKK. | IDA, VAT, CON, RAS | working paper · Section 5.2; Appendix C.2 |
| Value added | Utar (2014) | Constructed by the author from Regnskabsdata as turnover + work performed for own purposes and capitalized + (end-of-year inventory - beginning-of-year inventory) - (purchases of raw materials + energy + subcontracting expenses). Deflated with Statistics Denmark output deflators (separately for textile and apparel producers); values in thousands of year-2000 DKK. | Regnskabsdata (firm accounts statistics) | online appendix · Appendix C-I, Firm-Level Values |
| Workplace | Kreiner et al. (2015) | A workplace is an IDA establishment defined by a geographical location; most firms have one workplace but some larger firms have several. | IDA | working paper · Section 3.1, footnote 9 |
| Workplace (occupation-firm cell) | Fadlon et al. (2016) | Workplace = all employees with the same 2-digit occupation code in the same firm; employer default contribution rate measured by the workplace-level median (robustness: workplace-level mode, using only workplaces with a unique mode). | IDA; income tax register2-digit occupation code x firm ID | online appendix · Online Appendix A |
| Workplace size and stayers | Kreiner et al. (2015) | Size is the number of employees at the workplace in 2005; stayers are workers employed at the workplace in 2005 who are still employed there in 2006. | IDA | working paper · Section 3.1, Table 1 |
| Workplace, socioeconomic and family controls | Sigaard (2023) | Workplace controls from IDAN, socioeconomic and municipal controls from BEF and FAIN, children from FAMILIE, and education from UDDA; industry groups follow Statistics Denmark's standard groupings. | IDAN, BEF, FAIN, FAMILIE, UDDA | paper · Section 4 (Data); Section 3.1 footnote 6 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Broad offshoring | Hummels et al. (2014) | Total value of imports by a given manufacturing firm in a given year, from the Foreign Trade Statistics Register (Extrastat and Intrastat), recorded at CN8 and aggregated to HS6. | Foreign Trade Statistics Register (UHDI) | working paper · Section II.3 |
| Cartoon-crisis exposure (treatment) | Friedrich (2022) | Treated firms exported to Muslim-majority countries (>50% Muslim population) in 2003-2005 and had more than 0.5% of 2005 exports going to those countries; control firms had no such exports in 2003-2005. | UHDItreatment = 1 if Arabpre == 1 & Arabshare > 0.005; 0 if Arabpre == 0; country list by ISO2 (AE, AF, AL, AZ, BD, ...) | replication code · Main_Sample_Cartoon.do, lines ~127-300 |
| Export support service types | Buus et al. (2025) | Trade Council support products grouped into four types: (1) partner search/matchmaking, meeting facilitation, networks; (2) fairs, PR activities, conferences/workshops; (3) intelligence/analysis, publications, market monitoring, customs/export/import information; (4) other commercial assistance, market entry assistance, trade-policy assistance; remaining products form a fifth group. | Trade Council export promotion records | replication code · BMRS_restat/4_reg_variables/code/4_5_promotion_product.do |
| Export support treatment | Buus et al. (2025) | Dummy equal to 1 if the firm purchased an export promotion service from the Danish Trade Council for a given destination (or, at firm level, any destination) in year t or year t-1; multiple purchases within a year are not distinguished (collapsed with max). Trade Council records restricted to 2002-2015 with non-missing destination. | Trade Council export promotion records (promodata.dta)treated=1 for all promodata cases, 0 for turned-down cases (tdc.dta); treated_2 = (treated_1==1 | L.treated_1==1) | replication code · BMRS_restat/4_reg_variables/code/4_4_promotion.do; 1_data/code/1_5_promotion.do; 2_prepare/code/2_2_variables.do |
| Export unit values | Renkin and Züllig (2024) | Annual firm-level unit values for 8-digit Combined Nomenclature (CN) goods = export revenue / export quantity. CN revisions handled by identifying one-to-one, many-to-one, one-to-many and many-to-many concordance sets between consecutive years; log changes computed over mapped sets (physical weight used if units differ); aggregated to firm-level 2-digit CN geometric mean indices weighted by year y-1 sales. | Customs export dataCN8 -> CN2 | online appendix · Online Appendix B, eq. B.1-B.2 |
| Exposure to MFA quota removal (treatment) | Keller and Utar (2022) | A firm is exposed (treated) if as of 1999 it had domestic production in any 8-digit CN good for which China would no longer face a quota upon WTO entry; quota categories are matched to 8-digit CN goods and firms producing quota goods are identified from the domestic production database reporting establishment sales by 10-digit product. Workers are exposed if employed by such a firm in 1999. | IDA, VARS (domestic production / industrial commodity statistics)Stata variable affwd (1=Exposed, 0=Non-exposed); clustering on cvrnr99 (1999 firm id) | working paper · Section 2.1; Analysis1.do lines 25-26 |
| Firm-level world import demand shock | Friedrich (2022) | Firm-level Bartik shock: sum over HS6 product-destination pairs of the firm's pre-sample (first year exported, from 1998) export share times log world import demand, where world import demand is the destination country's total HS6 imports from the world excluding Denmark (Comtrade 1999-2008), aggregated to HS4/HS2 when HS6 unavailable. | UHDI, UN ComtradeUHDI ie == 2 (exports); vrd (value), vgt (weight); HS1988/92 concordance | replication code · Setup_Tradedata_Bartik.do, sections 'Main Setup' and '(A) WID' |
| Firm-product-destination exports and quantity | Buus et al. (2025) | Export value (1000 DKK) by firm, CN8+ product, destination and year from the trade-in-goods statistics, exports only. Quantity is weight in kg unless the supplementary unit is not kg, is unique within the product, and the value of observations with non-missing amounts is at least that with non-missing weights, in which case the supplementary amount is used; observations with zero/missing value or quantity are dropped. A variant keeps only flows above 7.5 (1000 DKK). | UHDIie=="2" (exports); value vrd, weight vgt, amount mgd, unit enhedskode ("00" = kg); vrd_exp_exsmall if vrd_exp>7.5 | replication code · BMRS_restat/1_data/code/1_3_export_import.do; 4_reg_variables/code/4_3_export.do |
| Foreign (import) price index by industry | Traiberman (2019) | Variety-corrected CES import price index per industry built following Broda-Weinstein/Feenstra (Sato-Vartia weights on the common set of goods, adjusted for new and disappearing varieties) from customs data aggregated to the country-product level; a similar index is built from Comtrade, and the geometric mean of the customs, Comtrade and national-accounts indices is used as the path of foreign prices. | UHDI, Comtrade (CEPII BACI), National accounts | online appendix · Online Appendix, calibration of labour demand side (foreign prices, incl. footnote 14) |
| Foreign trade reporting thresholds | Hummels et al. (2014) | Foreign Trade Statistics Register = Extrastat (non-EU trade, near-complete customs coverage) + Intrastat (intra-EU trade, reported only above an annual value threshold). In 2002 the Intrastat thresholds were DKK 2.5 million for exports and DKK 1.5 million for imports. | Foreign Trade Statistics Register (Extrastat, Intrastat)Intrastat threshold 2002: exports DKK 2.5m, imports DKK 1.5m | online appendix · Data Appendix, section 1 |
| Industry import penetration from China (economy-wide) | Keller and Utar (2022) | Change in Chinese import penetration at the six-digit industry level, instrumented with Chinese imports in other advanced countries (Australia, Finland, Germany, Japan, Netherlands, New Zealand, Switzerland, USA), log average distance of Denmark's import partners in 1996, and the share of retail trading firms; two-digit industry fixed effects included. | IDA, Foreign trade statisticsStata IP_9909 instrumented by iv8del6, lntdistw | working paper · Section 6 and footnote 51; Analysis2.do Table 5 |
| MFA quota competition exposure (binary) | Utar (2018) | Worker is treated if his/her 1999 primary employer domestically produced in 1999 (at the 8-digit CN level, per VARES) any good subject to the MFA quota removal for China; SIGL quota categories are mapped to CN 8-digit products (CN 1999) and firm ids are linked to workers through FIDA. Called CompExp^D in the paper. | VARES, FIDA, IDA, EU SIGLaffwd (0-1 exposure indicator) | replication code · ReadMe File.docx (variable definitions); working paper Section 'Data' (FREIT995) for construction |
| MFA quota competition exposure (continuous) | Utar (2018) | Revenue share of domestically produced MFA-quota goods in the 1999 employer's revenue (CompExp^C / CAffW), used as an alternative continuous treatment. | VARES, FIDAaffwd_rs | replication code · ReadMe File.docx; working paper p.16 (FREIT995) |
| MFA quota goods (MFAQ2, MFAQ5) | Utar (2014) | Chinese quota categories from the EU SIGL licence database assigned to CN 8-digit codes (CN 1999) by matching category and CN descriptions, confirmed with Annex I of Council Regulation (EEC) No 3030/93; linked across CN 1995-2007 vintages with Eurostat correspondence tables. Analysis restricted to China quotas utilized at least 10% prior to removal (excludes some unused silk and ramie fabric quotas). | EU SIGL; Udenrigshandel; VarestatistikCN 8-digit; quota utilization >= 10% | online appendix · Appendix C-III, Product Characteristics |
| Narrow offshoring | Hummels et al. (2014) | Machinery imports (HS 84 and HS 85, 16.9% of imports) are included in broad offshoring for all firms; narrow offshoring excludes them except for firms that themselves produce machinery for sale. Within HS 84 the top 20 HS6 import products (59% of HS 84 imports) are all parts rather than machines. | Foreign Trade Statistics RegisterHS 84; HS 85 | online appendix · Data Appendix, section 3.2 |
| Narrow offshoring (volume and extensive margin) | Olney and Pozzoli (2021) | Sum of a firm's imports (Foreign Trade Statistics, Intrastat/Extrastat, 1995-2011) in the same product category as the firm's production (paper: same HS4 category; code: import product code equals the firm's export product code or domestic-production product code). Extensive margin = 1 if narrow offshoring > 0; intensive margin = log of narrow offshoring volume. Broad offshoring = all imports. | UHDI (Foreign Trade Statistics: Intrastat/Extrastat), VARES (vars, firm product sales), FirmStatuhdi ie=='1' imports, ie=='2' exports; product vare (8-digit CN) -> HS4_imp=int(vare/100), HS4_exp=int(vare/100); vars varenr -> HS4_dom_1=int(varenr/10000); narrow=(HS6_imp==HS6_exp | HS6_imp==HS6_dom_1); ext_narrow_off=(narrow_off>0); int_narrow_off=ln(narrow_off) | replication code · Trade_data.sas; offshoring_project_will_dario.do (narrow offshoring block); working paper Section 3 and fn. 23 |
| New and dropped products | Utar (2014) | Products defined at CN 8-digit level (first 8 digits of the 10-digit Varestatistik code equal CN). New product = product a firm starts to sell/export in the current year that is not observed produced by the firm in previous years; dropped product = product the firm stops selling in the current year and is not observed sold in subsequent years. Logs taken of period means over 1996-2001 and 2002-2004. | Udenrigshandel (customs); VarestatistikCN 8-digit | online appendix · Appendix C-III and notes to Table A-6 |
| Offshoring indicator | Friedrich (2022) | Firm imports (ie == 1) a product at HS4 or HS6 level that it also exports in the same year. | UHDIoffshoring_hs4, offshoring_hs6 | replication code · Main_Sample_IV.do, lines ~13-43 |
| Product classification (CN8+) | Buus et al. (2025) | CN8 product codes are made consistent over 2002-2015 by aggregating to CN8+ categories using the Van Beveren et al. (2012) algorithm; each CN8+ code is assigned the CN6 category with the largest Prodcom sales value. | UHDI, VARScn8_cn8plus_2002_2015 concordance; Prodcom vnr>100, cn8=floor(varenr/100) | replication code · Readme.pdf section 1; 2_prepare/code/2_1_clean.do |
| Product demand elasticity | Renkin and Züllig (2024) | Broda and Weinstein (2006) CES demand elasticities. Baseline: 4-digit SITC Rev. 3 estimates matched to HS 6-digit codes (94% of PPI products; 98.9% after filling with 4-digit averages); for unit values, the volume-weighted mean elasticity within the firm's 2-digit CN cell (98.5% matched). Alternative: 10-digit HS estimates (86.4% of PPI, 96% of unit value sample matched). | PPI microdata, Customs export dataSITC Rev.3 4-digit; HS6; HS10 | online appendix · Online Appendix C.1 |
| Strategic complementarity | Renkin and Züllig (2024) | Product-category-specific exchange-rate pass-through into domestic-currency export prices, estimated on 1995-2007 customs data at the firm x CN8 product x destination x year level, with firm-year, product-year and product-destination fixed effects and destination controls (real GDP, export, import growth, change in unemployment rate); 45 destination countries covering 89% of export value. | Customs export dataCN8 | online appendix · Online Appendix C.3, eq. C.2 |
| Trade Council initiative (instrument) | Buus et al. (2025) | Dummy equal to 1 if the firm was contacted (approached) by the Trade Council for a destination, defined over both purchased cases and turned-down cases where contact was established but no support was bought; used as of year t or t-1 like the treatment. | Trade Council export promotion records (promodata.dta, tdc.dta)tdcinitiative=1 if firm contacted by the TC; tdcinitiative_2 = (tdcinitiative_1==1 | L.tdcinitiative_1==1) | replication code · BMRS_restat/1_data/code/1_5_promotion.do; 4_reg_variables/code/4_4_promotion.do |
| Transport cost shock | Friedrich (2022) | Predicted transport costs per product-destination from US Imports of Merchandise estimates, combining HS2 group coefficients, log Brent oil price, log distance, their interaction and (for one version) new-EU-member accession after 2003, weighted by the firm's pre-sample export shares. | UHDI, US Imports of Merchandise, Brent oil pricesneweumember = CY,CZ,EE,HU,LV,MT,PL,SI,SK & year > 2003 | replication code · Setup_Tradedata_Bartik.do, section '(B) Transport Costs' |
| Transport-cost instrument | Hummels et al. (2014) | Ad-valorem shipping cost functions estimated on US data (US Imports of Merchandise for sea and air, 1995-2006; US Transborder Surface Freight data for truck and rail) as functions of weight/value, fuel prices (crude oil; jet fuel for air) and distance, then fitted for Danish firms' exporter-HS6 inputs using pre-sample trade shares and transport-mode shares (pre-sample Danish imports: 42% sea, 20% air, 37% truck, 1% rail). | Foreign Trade Statistics RegisterHS6 | online appendix · Data Appendix, section 2 |
| Treatment: produced MFA quota goods in 1999 | Utar (2014) | MFAQProd99 = 1 if the T&C manufacturing firm is found to have produced any MFA quota good in 1999 (before China's WTO accession), identified from domestic sales (Varestatistik, firms with 10+ employees) and export data; MFAQShare99 is the share of 1999 firm sales from MFA quota goods. Interacted with a post-2002 dummy (Dum02) in firm and year fixed-effects regressions. | Varestatistik, Udenrigshandel | working paper · Section 4, eqs. (3)-(4) and footnotes 14-15 |
| World export supply and world import demand instruments | Hummels et al. (2014) | WES: country c's total supply of HS6 product k to the world market minus its supply to Denmark in year t; WID: country c's total purchases of product k from the world market less purchases from Denmark; both from COMTRADE and weighted by pre-sample firm input/output shares; plus exchange rates and transport costs fitted from US imports data. | UN COMTRADE, Foreign Trade Statistics Register | working paper · Section III and Data Appendix section 2 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| 6th-grade Danish language test score | Dustmann et al. (2024) | Compulsory national Danish test in grade 6 (from 2009 implementation), composite of language comprehension, decoding and reading comprehension; standardized following Beuchert and Nandrup (2018). | National tests (Ministry of Education) | online appendix · Appendix B.3.3 |
| 9th grade exit exam GPA | Black et al. (2021) | Cohort-standardised 9th grade exit exam grades (GPA), based on assessments by the student's teacher and an external reviewer, normalised to mean zero and standard deviation one. | School records / grade register | working paper · Sections 1-2 and Eq. (1) |
| 9th grade exit exam grades | Breining et al. (2020) | Ninth grade exit exam grades in reading and math from the School Registers, based on assessments by the student's teacher and an external reviewer; standardized to mean zero and unit SD within each cohort. | School Registers | working paper · Section 3.1 |
| 9th-grade exam grades (Danish oral, math written) | Daysal et al. (2022) | Grades from the 9th-grade school-leaving exams (proeveform 'Prøvefag 2002-2006' or 'Bundne prøvefag', not teacher 'Standpunkt' marks) in Danish oral (Mundtlig) and Mathematics written/problem-solving/skills; set to missing for cohorts born before 1986 or after 1997. In indices, non-test outcomes are standardized by birth year. | UDFKUDFK2013: GRUNDSKOLEFAG in ('Dansk','Matematik'); FAGDISCIPLIN 'Mundtlig' (Danish) / 'Skriftlig','Matematisk problemløsning','Matematiske færdigheder' (math); proeveform in ('Prøvefag 2002-2006','Bundne prøvefag') | replication code · 00 NICU.sas, lines ~2125-2205; 02 Adding outcomes.sas lines ~671-676 |
| 9th-grade GPA (standardized) | Dustmann et al. (2024) | Average grade over all 9th-grade exams, standardized to mean 0, SD 1 in the total population; also rank in the total population GPA distribution and quartile indicators; exams 2002-2019. | Ministry of Education exam register | online appendix · Appendix B.3.3; Table A.4 |
| 9th-grade GPA decile | Kreiner et al. (2020) | Mean 9th-grade exam grade from standard exams in normal classes, standardized within exam year (2007-2015) and split into deciles. | UDFKkltrin==9 & proeveart==1 & klassetype==4; grundskolekarakter; skoleaar | replication code · data06.do |
| Academic / vocational track enrollment | Daysal et al. (2022) | Ongoing education (IGUDD) from the education registers mapped to DST audit groups; ac_track = enrolled in general upper secondary (gymnasium) at any age 14-19; voc_track = enrolled in vocational education at ages 14-19; ac_track_at_18 = gymnasium enrollment at age 18; enrolled_beyond = ac_track or voc_track. | UDDAIGUDD -> udd2011_l1l2_k (igfsp_kort); gym = 2015,2016,2017,2018,2539,2551; vocational = 3039,3040,3042,3044,3046,3048,3049,3050,3520,3539,3553,3554,3555,3558,3560,3575,3580,3585,3590; higher = 4025,4039,4059,4090,5010,5020,5030,5039,5059,5090,6025,6030,6035,6039,6059,6080,6090 | replication code · 06e Adding educ track rev.sas, lines ~128-217; 09 do-file line 674 |
| Access to higher education (instrument) | Humlum et al. (2025) | Direct access to higher education through a high school diploma or a vocational degree. Travel time to higher education = shortest car travel time (minutes) from residence zip code to the zip code of the nearest facility offering a relevant higher degree (as in Harmon 2015), measured separately for Craft, Care and Other educational groups; imputed for non-access workers as a residence-zip weighted average. | Education register (UDDA); residence zip code | online appendix · Supplemental Appendix Table A.1, rows 24-25 |
| Access to reskilling | Humlum and Plato (2026) | Indicator that the worker's vocational degree grants direct entry to relevant higher education programmes (e.g. carpenters, electricians, welders) versus comparable trades that require three years of high school first (e.g. blacksmiths, iron and metal workers, manufacturing technicians). Reskilling = participation in higher education by year +10. | UDDA | working paper · Section 2.2; Figure 1 notes |
| Admission-cutoff instrument (Denmark) | Heinesen et al. (2026) | z = 1[GPA > Q1] + 1[Std <= GPA <= Q1] x Offer, where Q1 and Std are the Quota 1 and standby GPA thresholds of the preferred field; at GPA exactly equal to a threshold the offer is treated as random conditional on age (and on applying for standby). Applicants rank up to 8 programmes; where several programmes in the preferred broad field are listed, the lowest cutoff is used. | Coordinated admission system (KOT) applications | working paper · Appendix E 'Specification of instrument'; Section 5.2 |
| Advanced high school math (treatment) | Joensen and Nielsen (2016) | Indicator MathA for choosing the advanced math course in the branch-based high school; because the instrument works through the option to combine advanced math with advanced chemistry, the treatment is effectively the combined advanced math and advanced chemistry package. | Education registers (Statistics Denmark) | working paper · Sections 2 and 4.1 |
| Advanced math-science choice (MathScience) | Joensen and Nielsen (2018) | Indicator that the student chose a high-school course package combining advanced math and advanced science. In traditional schools the only such package was advanced math, advanced physics and intermediate chemistry; pilot schools also offered advanced math, advanced chemistry and intermediate physics. The choice is made at the end of the first high-school year. | Statistics Denmark education registers | working paper · Section 2 |
| Application score (high-school GPA) | Heinesen et al. (2026) | High-school GPA over all subjects of the study programme, recorded to one decimal on the 13-point scale (0-13, omitting 1, 2, 4, 12); used as the running variable, controlled linearly on each side of the cutoff. | Coordinated admission system (KOT) applications | working paper · Appendix E 'Danish admission institutions'; Section 6 |
| Apprenticeship indicator | Kreiner et al. (2020) | Monthly indicator equal to 1 between the start and end month of an apprenticeship agreement from Ministry of Education data (overlapping and nested agreements merged/dropped). | STIL_aftaler (Ministry of Education apprenticeship agreements)startdato, slutdato; apprentice | replication code · data05.do |
| Child average standardized test score | Lundborg et al. (2024) | Scores from nationwide math and reading tests in primary and lower secondary school (2010-2020), each standardized to mean 0 and SD 1 within cohort; for each child the average over all available standardized reading and math scores. Math covers numbers and algebra, geometry and applied mathematics; reading covers language comprehension, decoding and reading comprehension. Grade-specific scores: reading grades 2, 4, 6, 8; math grades 3, 6. | National test data (STIL)overall_grade; lang2 lang4 lang6 lang8 math3 math6 | replication code · Read_me.pdf, Table 1 variable description |
| Cognitive ability (draft board IQ test) | Bingley et al. (2020) | Armed Forces Day IQ test score: number of correct answers out of 78 items; used to split the sample into ability quartiles. | Danish Defence military register | working paper · Section II, footnote 10 |
| College enrollment within one year | Nielsen et al. (2010) | Indicator for accumulated enrollment in any college (short-, medium- or long-cycle higher education) by one year after high school graduation, taken from educational event histories 1973-2005; one-year horizon chosen so pre-reform cohorts decided before the reform was announced. | Educational event histories (education registers) | paper · Section II; Section V.B and Table 3 |
| College graduation and field of major | Joensen and Nielsen (2016) | Graduation from 2- or 4-year college, Master's education, and PhD measured 8-18 years after leaving high school, excluding those who already graduated at a higher level; field of major (e.g. Humanities, Health Sciences, Technical Sciences) from detailed education codes. | Education registers (Statistics Denmark) | working paper · Section 5; Tables 4-5 |
| Completed education | Dustmann et al. (2024) | Years of completed schooling from the 2020 Register on Completed Education (status on October 1); levels collapsed into (i) lower secondary (max ten years of schooling), (ii) academic high school, (iii) vocational degrees (vocational high school and short vocational college degrees), (iv) all college and university degrees. | Register on Completed Education (2020) | online appendix · Appendix B.3.2 |
| Completed education at age 46 | Bruze et al. (2015) | Three groups based on completed education at age 46: high school or less, vocational education, and college (some college or more). | UDDA | working paper · Section 2.1 |
| Completed field of study (nine broad fields) | Heinesen et al. (2026) | Field of the post-secondary degree completed within eight years of application, aggregating detailed fields into nine broad fields (Medicine, Law, Technology, Other Health, Humanities, Teaching, Social Science, Business, Science) following Kirkeboen et al. (2016), except that Technology covers integrated, vocational/professional short- and long-cycle degrees (mostly computer science and engineering) and Science covers open-ended bachelor programmes in sciences plus agriculture, forestry and aquaculture. | Education register | working paper · Section 5.2; Figure 3 |
| Compliance / enrolment in assigned school | Bjerre-Nielsen and Gandil (2024) | Binary indicator equal to one if the child is enrolled at age 7 in the district (attendance-zone) school that corresponds to the child's address at age 5. Control units are artificially assigned to nearby recipient schools. An alternative version uses enrolment in the assigned school at age 7 (see main text Section 5.4). | Statistics Denmark registers (school enrolment, addresses) | online appendix · Online Appendix Tables B.2-B.4 notes, Figure B.4 notes |
| Danish language proficiency | Arendt et al. (2025) | Indicator for having passed the standard Danish language test at the intermediate or higher level (test levels: basic, intermediate, high); observed from YSR = 1; passing is treated as absorbing, so the person counts as proficient at that level in all later periods. | Danish language test results | paper · Section 4, footnote 13; Section 5 |
| Daycare/preschool attendance | Dustmann et al. (2024) | Preschool enrollment measured from annual daycare register data. | Daycare register | online appendix · Appendix B.3.3 |
| Degree and course enrolment/completion | Humlum et al. (2025) | Degree courses = formal education programmes in the Education Register (UDDA); non-degree courses = Course Participation Register (VEUV); courses classified as Basic (primary/high school), Vocational, Higher (post-secondary). Training rate = share enrolling in a higher degree within six years after the accident; participation flow = enrollment in higher degrees in the year; stock = accumulated enrollment. Reskilling (2SLS treatment) = pursuing a higher degree within ten years after the accident; completers vs drop-outs defined by completion within ten years. | UDDA (Education Register); VEUV (Course Participation Register)Within 6 years (training rate); within 10 years (reskilling) | online appendix · Supplemental Appendix Table A.1, rows 31-38; Section D.1; Figure D notes |
| Education attained (pre-immigration) | Damm (2009) | Dummies from the education code of highest degree attained: no degree = 0-9 years, high school = 10-12 years, professional = 13+ years, plus unknown education. | Survey-based register on immigrants' pre-immigration education, Integrated pupil register | working paper · Table A.I Part A |
| Education attainment and 9th grade test scores | Daysal et al. (2026) | Indicators for high school and college graduation by ages 18-32 from highest completed schooling; 9th grade Danish (reading) and mathematics scores (2001-2019) standardized within subject and test year. | UDDA, Academic Achievement Register | paper · Section 2 (Education outcomes) |
| Education group (ISCED) | Dahl et al. (2024) | Highest completed education HFAUDD lagged one year, mapped to ISCED: low = ISCED 0-2, mid = 3-4, high = 5-6. | UDDAHFAUDD -> format c_isced_audd_afsp4d_k | replication code · CollectData.sas (education); CreateData_DK.do lines ~71-82 |
| Education groups | He and Maire (2025) | Four education groups from the first two digits of the highest completed education code: basic (<=25), vocational (35), short/medium higher (40-50), long higher (>=60). | IDA, UDDAmax_educ = int(HFFSP/1e6): <=25 basic; 35 vocational; 40-50; >=60 | replication code · AKM_merger_cvrnr.do; clean_merger.do |
| Education groups | Lentz et al. (2023) | Based on normed years of education of the highest completed degree: low is below 12 years, medium is exactly 12 years, high is above 12 years. | Education register | working paper · Section 5 |
| Education groups | Dahl et al. (2013) | Unskilled (high school or equivalent or less), vocational education (final stage of secondary education preparing for direct labour market entry), short-term higher education and long-term higher education, from the Danish education code corresponding to ISCED (higher education = ISCED 5 and 6). | UDDAISCED 5-6 = higher education | working paper · Section 4 and footnote 4 |
| Education level | Caplin et al. (2026) | Highest completed education HFAUDD (latest available year) mapped through the audd format's H1 level: 10 elementary; 20, 25 high school; 30, 35 vocational; 40, 50, 60 bachelor; 65, 70 master. | UDDAHFAUDD via uddan_2014_audd H1: 10; 20,25; 30,35; 40,50,60; 65,70 | replication code · script/cleaning/1_b_annual_data.R |
| Education level and years of schooling | Humlum and Plato (2026) | Years of schooling = prescribed years of study of highest completed degree counting from grade 1. Highest completed degree groups: Primary (pre-school, primary, preparatory courses, Danish language courses at language centres); Vocational (VET, qualifying programmes, labour market educations AMU); Secondary (upper secondary); Post-secondary (short-cycle higher, vocational bachelor, bachelor, master, PhD). School = enrolled in a post-secondary programme in the year. | UDDA | online appendix · Supplementary Appendix, Table A.1 Panel B-C |
| Education level dummies | Kreiner et al. (2019) | Short, medium and long education correspond to vocational training, college BA level, and college Master and PhD level, respectively; reference group is high school or less. | Statistics Denmark education registerShort = vocational; medium = BA; long = Master/PhD; ref = high school or less | online appendix · Online Appendix Table EA.1 notes |
| Education strata | Bagger and Lentz (2019) | Robustness estimation splits workers into less than 15 and more than 15 years of education. | IDA | working paper · Section on education-stratified estimation |
| Educational ambition type | Almar et al. (2026) | Programmes are clustered by k-means on (programme starting wage, programme wage growth), with k = 3, 4 or 5 (seed 1234), both in raw and standardised versions; the four-type classification gives the (low/high w0) x (low/high g) ambition types. | IDAN, UDDAcluster kmeans wage_start_mean_ambition wage_growth_ambition, k(4) s(kr(1234)) | replication code · Data/Gen_kmeans ambition types/kmeans ambition types.do |
| Educational attainment | Stoyanov and Zubanov (2012) | Highest completed education measured in three levels: high school, college and university. | Matched employer-employee data (Statistics Denmark) | paper · Section I.A |
| Employee education groups | Utar (2014) | Built from IDA education code 'hffsp' (highest completed education combined with professional training); first two digits give the main education group. At most high school diploma = first two digits <= 25; at least some college = first two digits >= 40. Textile/clothing technical design training (college level; industrial designer, model engineering, product developer, textile and garment engineering) = hffsp 405985. Textile/clothing production training (clothing operator, fashion craft, hand stitcher, cutter, tailor, knitting/textile operator) = hffsp 355880 and 355890. Not constructed for 2007 because the detailed education code is not reported that year. | IDA (Persondata)hffsp first 2 digits <=25 (<= high school); >=40 (some college+); 405985 textile design; 355880, 355890 textile production training | online appendix · Appendix C-II |
| Final educational programme | Almar et al. (2026) | Highest completed education (HFAUDD, four-digit Danish ISCED/audd code, about 1,800 programmes) measured at age 35, or at the oldest observed age if the person is not observed at 35; graduation year from HF_VFRA. Grades 7-9 pooled as 9th grade; 9th grade (1109) and 10th grade (1110) split into 5 sub-programmes by region of graduation (regions 81-85); old pre-1980 high-school code mapped to the new one. | UDDAHFAUDD, HF_VFRA; 1109 and 1110 split by region 81-85; 1097 mapped to 1198 | replication code · Make analysis sample 1.do ('final education'); kmeans ambition types.do; Online Appendix A.2 'Education', 'Educational programs' |
| Financial literacy | Andersen et al. (2020) | 'Financially literate' = a household member has a degree in finance or professional financial-industry training. 'Family financially literate' = non-resident parents, siblings, in-laws or children of the household are financially literate. | Education register (UDDA), Population register | online appendix · Table B3 notes |
| Formal education obtained in Denmark | Foged et al. (2024) | Highest education HFAUDD from UDDA, keeping only education whose source HF_KILDE is Danish (codes 1, 4, 6, 13, 15) and excluding Danish language training (HFAUDD 1301-1305); mapped to main levels via UDD_HOVED_L1L5_K format. | UDDAHF_KILDE in (1,13,15,4,6); exclude HFAUDD 1301-1305 | replication code · 8. Controls and outcomes.sas, lines ~47, 5182-5198, 5371-5380 |
| Formal schooling (SCH) | Frederiksen and Kato (2018) | Five levels of highest educational attainment: less than high school (reference), regular high-school diploma, vocational, college degree (3-year Bachelor), graduate (Master's and PhD combined). | Education registers | working paper · Section 3 and footnote 8 |
| French class size | Heinesen (2010) | Number of students with marks for the year's work in French in 9th grade, measured per school/year cell; sample restricted to schools offering French from 7th grade where at most 20 students attend French (so only one French class) and where some students attend German. | Administrative registers on 9th-grade marks and school attended | working paper · Section 3 (Data and descriptive statistics); footnote 4 |
| French examination mark | Heinesen (2010) | Examination mark in French at the end of 9th grade on the Danish 13-scale (values 0-13 excluding 1, 2, 4 and 12; pass at 6); marks for the year's work are not analysed. Cohorts finishing 9th grade in 2001/02-2003/04. | Administrative registers on 9th-grade marks13-scale (0,3,5,6,7,8,9,10,11,13) | working paper · Section 3 |
| High school GPA | Joensen and Nielsen (2016) | Weighted average of final exam grades for each high school course. | Education registers (Statistics Denmark) | working paper · Section 3.1 |
| High school GPA | Nielsen et al. (2010) | High school grade point average on the Danish 13-point scale (grades 0, 3, 5, 6, 7, 8, 9, 10, 11, 13; 6 and above passed); only observed for the traditional academic high school track. | High school GPA records | paper · Section V.A footnote 18; footnote 21 |
| High skilled vs less skilled | Jakobsen et al. (2026) | High skilled if at least 180 months of education (at least a bachelor's degree), measured as the highest degree earned in the sample period; less skilled otherwise. | UDDAHFPRIA >= 180 months | working paper · Section 2; Supplemental Material A |
| High- vs low-skilled worker | Hummels et al. (2014) | Robustness split of low-skilled workers into medium-skilled (vocational education, defined as the final stage of secondary education preparing for labour market entry) and very-low-skilled (high school equivalent or less); results very similar. Workers whose skill level changes during the sample period are dropped. | IDA | online appendix · Data Appendix, section 1 |
| Highest completed education categories | Humlum et al. (2025) | Years of schooling = prescribed years of study of highest completed degree counting from grade 1. Primary = pre-school, primary, preparatory courses or Danish language courses at language centers; Vocational = VET, qualifying programmes or AMU labour market educations; High School = upper secondary; Post-Secondary = short-cycle higher, vocational bachelor, bachelor, master or PhD. | Education register (UDDA) | online appendix · Supplemental Appendix Table A.1, rows 6-10 |
| Highest education (ISCED) | Taubinsky et al. (2026) | HFAUDD from UDDA mapped via an ISCED crosswalk to single-digit ISCED 2011 levels 1-6; code 9 (not known) set to missing. | UDDAHFAUDD -> ISCED_AUDD_AFSP4L_KT first digit | replication code · code_dstserver/code_epinion/2_build_registries.do |
| Highly educated workplace | Fadlon et al. (2016) | Private-sector white-collar workplaces in which more than 20 percent of workers had more than 16 years of education (used as a group less likely covered by collective bargaining). | IDAshare with >16 years education > 20% | online appendix · Online Appendix D |
| Household education level | Leth-Petersen (2010) | Highest level of education in the household, grouped into no/low vs medium/high for heterogeneity analysis. | Education register | online appendix · Section 4, Figure A6 |
| Human capital index | Rossin-Slater and Wüst (2020) | Equally weighted average of years of schooling at 50, more-than-compulsory indicator and log average wage income 30-60, each standardized by the mean and SD of the control group (municipality-by-cohort cells without an approved preschool by age 3 in the 138 ever-implementing municipalities). | Education register, Income register | working paper · Section 4.1 |
| IQ (talent proxy) | Akcigit et al. (2024) | Score on the Danish military conscription test Boerge Prien's Proeve, taken mostly at age 18; data go back to 1995 and cover most males entering the workforce or college after 1995 (about 500,000 males). | Military conscription test (Boerge Prien Proeve) | paper · Section 2.2 |
| Language test score at age 11 | Eshaghnia et al. (2025) | Language development test at age 11 from the Danish Longitudinal Survey of Children (children born 15 Sept-31 Oct 1995, 2007 wave), standardised to mean 0 and SD 1. | DALSC | working paper · Section 3.1; Appendix A.1 'DALSC' |
| Long college degree at age 30 | Joensen and Nielsen (2018) | Indicator that the younger sibling's highest completed education at age 30 is more than a 4-year college degree (also interacted with STEM field). | Statistics Denmark education registers | working paper · Table 7 |
| Low-SES patient | Kristiansen and Sheng (2025) | Patient's highest completed education level (HFAUDD mapped to level) equal to 10 (primary school); separate indicator for missing education. | UDDAlow_ses = (udd_level == '10') | replication code · 1_patient_merge.do lines ~45-46 |
| Low-SES physician (parental education) | Kristiansen and Sheng (2025) | A GP is low-SES if the mother's highest attained education level is 10 (primary school), or, if not, the father's is 10; missing set to 0 with a missing indicator; clinic-level max/min/mean share of low-SES GPs computed by provider number and year. GPs identified as doctors in private practice (T_YDER) with HFAUDD 7170 (master in medicine). | UDDA, BEF, T_YDERmor_udd_level == 10 / far_udd_level == 10; HFAUDD == 7170 | replication code · 2_GP_merge.do lines ~38-61; 2a_doctor_education.do; 2b_parent_udd.do |
| Lower secondary completion and final exam | Foged et al. (2023) | Two indicators: the child completed lower secondary (compulsory) school by age 18 (before turning 19), and the child took the final exams marking the end of lower secondary school. Exemption from the exams, e.g. for low language proficiency, is possible. | Education registers | working paper · Section III, footnote 9; Section V, Table 3 |
| Manufacturing- vs service-specific vocational education | Utar (2018) | Indicators based on the 4-digit highest attained education code in 1999 (dhf99), with explicit lists of codes classified as manufacturing-specific (MVoc) or service-related (SVoc) vocational education. | IDAMVoc: dhf in e.g. 2461,354,355,358,3842,..., 4321-4331, 4335-4351, ...; SVoc: dhf in 2010,2414,..., 3747-3775, 3800-3841, ... (full lists in MSVoc.do) | replication code · MSVoc.do |
| Margins of non-compliance (opt-out) | Bjerre-Nielsen and Gandil (2024) | Three binary outcomes measured at age 7 relative to the address at age 5: enrolled in another public school, enrolled in a private school, or moved out of the district between ages 5 and 7. Aggregate non-compliance is their sum. | Statistics Denmark registers (school enrolment, addresses) | online appendix · Online Appendix Figure B.3 notes |
| Marks in other subjects (placebo and ability) | Heinesen (2010) | Examination marks in English, Danish (oral, written, spelling), math (oral, written) and science; average mark computed over each student's non-missing marks. Academic ability dummy = average marks in other subjects at least 9. | Administrative registers on 9th-grade marks | working paper · Section 5 (Table 3); Section 6 (Table 6) |
| Maternal education and characteristics | Altındağ et al. (2024) | Mother's highest education HFAUDD mapped to AUDD2008_L1L5_KT: basic (<20 or >=90), vocational/high school (20-39), short (40-49), medium (50-59), long further education (60-89); high education = short+medium+long. Couple = C_STATUS 2 or 4-7; immigrant = IE_TYPE 2 or 3. | Grundvive (BEF/UDDA)AUDD2008_L1L5_KT; C_STATUS; IE_TYPE in 2,3 | replication code · 01_maternal_ses.do lines ~72-103; 04 Tables_days.do line 27 |
| Math test score at age 16 | Eshaghnia et al. (2025) | Mathematics grade at the 9th-grade national leaving exam for all children born 1995-1997, standardised to mean 0 and SD 1. | UDFK | working paper · Section 3.1; Appendix A.1-A.2 |
| Middle-school GPA decile | Kreiner et al. (2020) | Within-cohort deciles of grade point average from the 9th grade graduation exam in Danish and Mathematics, available for those completing 9th grade in 2001/2002 or later. | Education registers (grades) | working paper · Section 3.2 and Figure on GPA deciles |
| Mother's education before first birth | Sander (2026) | Years of education (normed months/12) completed by the year of first birth, taken from the highest, vocational or general education fields with completion dates before the first birth (or inferred from age 5 + length), with self-reported sources dropped and minimum 7 years; grouped into basic (main level 10), medium (20, 25, 35) and high. | UDDAHFPRIA, EKPRIA, ALMPRIA; HFAUDD, ERHAUDD, ALMAUDD; HF_VFRA, ERH_VFRA, ALM_VFRA; drop HF_KILDE in 3, 9, 10, 17, 18; H1 10 = basic, 20/25/35 = medium | replication code · dofiles/createfirst.do, 'UDDANNELSE FØR FØRSTE BARN' |
| Mother's education level | Gupta and Simonsen (2010) | Mother's highest education from Statistics Denmark registers: high school or below; vocational degree; short tertiary (13-14 years); medium tertiary (15-16 years); long tertiary (17 years or more). | Statistics Denmark education register | working paper · Appendix Table A3 'Mother's Characteristics' |
| Municipal school resources | Damm and Dustmann (2014) | Pupils/teacher ratio = average number of pupils in a normal class in the municipality in a school year; weekly teacher wage hours per pupil. Sources: 'Folkeskolen i de enkelte kommuner' and 'Folkeskolen i tal' (Ministry of Education, 1989/90-1998/99). | Ministry of Education publications | online appendix · Table A5.B |
| National mathematics test score | Houmark et al. (2025) | Mathematics national tests in grades 3 and 6 and, from 2018, grade 8, constructed in a similar fashion to the reading score. | Danish national testsFAGID 0203, 0206, 0208 = mathematics grades 3, 6, 8 | replication code · Results (for replication).do; IZA DP 15418 Section 3.2 |
| National reading test score | Houmark et al. (2025) | Danish national (adaptive, Rasch-scored) reading tests in grades 2, 4, 6 and 8: each of the three profile areas (text comprehension, language comprehension, decoding) is standardised by school year, averaged, and the average re-standardised to mean 0, SD 1. Repeated tests of the same subject-grade are dropped (first test kept by test time). | Danish national testsFAGID 0102, 0104, 0106, 0108 = reading grades 2, 4, 6, 8; outcome variable ztheta | working paper · Section 3.2; Results (for replication).do |
| Ongoing / completed education near age 18 | Kreiner et al. (2020) | Completed (hffsp) and ongoing (igfsp) education codes from the education register for the year of the observation closest to the 18th birthday. | UDDA (uddany files)hffsp, igfsp | replication code · data06.do |
| Parental years of education | Houmark et al. (2025) | Mother's and father's highest education category converted to years of schooling. | UDDAmhighestedu/fhighestedu: 10,15 -> 10 years; 20,25,30,35 -> 13; 40 -> 15; 50,60 -> 16; 65 -> 18; 70 -> 21 | replication code · Results (for replication).do, 'Prepare covariates' |
| Parental years of schooling and college | Lundborg et al. (2024) | Years of schooling of highest completed education of mother and father (and of egg donor, imputed when missing). College indicator equals 1 if more than 14 years of completed education. | UDDA, IDAPALLyr_edu, yr_eduf, yr_edu_donor(_imputed); univ_m/univ_f = years > 14 | replication code · Read_me.pdf, Table 1 |
| PhD enrolment | Akcigit et al. (2024) | Enrolment in a PhD programme from the PHD dataset, which records subject, enrolment date, graduation date and funding source of PhD students. | PHD, IDA | paper · Section 2.2 |
| Pilot scheme instrument (PilotIntro) | Joensen and Nielsen (2016) | PilotIntro equals one if the individual enrolled in a high school which afterwards decided to introduce the experimental (math-chemistry) curriculum for the first time, i.e. unexpectedly exposed before choosing math level; zero otherwise. Pilot school status (PilotSchool) is always controlled for. | Education registers (Statistics Denmark)PilotIntro, PilotSchool (author-constructed indicators) | working paper · Section 2 (Table 1) and Section 4.1 |
| PilotIntro / PilotSchool instrument | Joensen and Nielsen (2018) | PilotIntro = 1 if the (older sibling) enrolled in a high school that unexpectedly introduced the pilot scheme after the student's enrolment but before the advanced-course choice (cohorts entering 1984-87). PilotSchool = 1 if the school had adopted the pilot before the student's enrolment. | Statistics Denmark education registers | working paper · Section 2, Table 1 |
| Plan literacy groups | Simonsen et al. (2021) | 'Economic literates' are individuals with a master-level degree in economics or business (ISCED level 7); medical professionals hold a university degree in medicine or pharmacology (ISCED levels 7-8). Cognitive decline is proxied by age 65 and above versus below 65. | Education registerISCED 7 (economics/business); ISCED 7-8 (medicine/pharmacology) | paper · Section 6, Table 5 notes |
| Pre-reform language performance type | Arendt et al. (2025) | HIGH if the person had passed the Danish language test at the intermediate level or higher by YSR = 2 (about 16%), otherwise LOW; combined with employment type into LOW/LOW, LOW/HIGH, etc. | Danish language test results | paper · Section 5, 'Classifying workers into performance types' |
| Preferred and next-best field | Heinesen et al. (2026) | Defined from the local ranking on the application list around the admission cutoff: the preferred field is the one ranked just above the next-best field, where the applicant's score is near the preferred field's cutoff; fixed effects for preferred-field x next-best-field combinations are included. | Coordinated admission system (KOT) applications | working paper · Section 5.1 and Section 6 |
| Preschool enrollment rate (first stage) | Rossin-Slater and Wüst (2020) | Share of children aged 3-7 enrolled in preschool: enrollment counts per preschool from the nine years of preschool book publications, interpolated to every year and aggregated to municipality x year, divided by number of survivors past age four; urban municipalities, 1940-1950 (1940 first year all living 7-year-olds observed, since birth data start 1933). (Table A11 title says aged 3-6; its notes say 3-7.) | Archival preschool records; municipal births and infant deaths | online appendix · Table A11 notes |
| Primary school GPA (age 16) | Humlum et al. (2025) | Overall GPA = average of all grades given in grade 9 (compulsory) and grade 10 (non-compulsory), normalized to 0-100; Math GPA = same for the subject Math. | Grade registers (Statistics Denmark)Normalized 0-100 | online appendix · Supplemental Appendix Table A.1, rows 29-30 |
| Programme starting wage (w0) and wage growth (g) | Almar et al. (2026) | For each individual graduating in 1980 or later: w0 = mean residualised log hourly wage in years 1-5 after graduation; w1 = mean in years 9-11. Programme-level values average across graduates observed in both windows, excluding those with individual wage growth exp(w1-w0)-1 at or above the 99th percentile; programme growth g = exp(mean w1 - mean w0) - 1. | IDAN, UDDA | replication code · Make analysis sample 1.do ('peer wage growth'); Online Appendix A.2 'Starting wages and growth' |
| Proportion of female peers | Brenøe and Zölitz (2020) | Share of female students in the student's high school math-track cohort at high school entry, constructed at the cohort-school-track level and excluding the student (leave-one-out). | Student registers | working paper · Section II.C |
| Reform-induced GPA (treatment) | Hansen et al. (2024) | Deviation of a student's recoded (post-1 August 2007, 7-point scale) GPA from a cubic fit of post-recoding GPA on pre-recoding (13-scale) GPA; grades were recoded per the Ministry of Education mapping. Final GPA is a weighted average of recoded grades and grades from exams after the recoding. | University student records (Aarhus University, University of Copenhagen)13-scale to 7-point scale mapping (paper Table 1) | working paper · Sections 2 and 3.4; Table 1; Figure 3 |
| School absence | Houmark et al. (2025) | Total fraction of school days absent, including all absence regardless of reason, from the absence registries. | School absence register (fravaer) | working paper · Section 3.6 |
| School quality (teacher-based) | Houmark et al. (2025) | Following Gensowski et al. (2020), teachers are linked to schools; school-by-year averages of teacher high-school GPA, age, experience, tenure, sick-leave share and share of the previous ten years unemployed are used to predict school national test scores, and the predictions are used to rank schools by teacher quality. | Population, institution and workplace registers, Danish national testsschoolqlt3 (merged by INSTNR, skoleaar) | working paper · Section 3.6 |
| School SES (average school SES) | Bjerre-Nielsen and Gandil (2024) | Mean of the household SES index among seven-year-old children enrolled in a school in a given school-year. | Statistics Denmark registers | online appendix · Online Appendix Figure B.2 notes |
| School starting age (old-for-grade) | Landersø et al. (2017) | Timing of school start is not observed for these cohorts, so age in 8th grade is used as an approximation; SSA is modelled as a binary indicator for school starting age 7.6 versus 6.6 (old-for-grade), with robustness to a continuous measure. Validated with exact ages at all grade levels observed from 2007 onwards. | Education registers | working paper · Sections 2 and 3.2; Table A2 |
| School well-being / self-esteem index | Dustmann et al. (2024) | Average of Likert-scored responses (Very often/Often/Sometimes/Rarely/Never) to: 'How often can you solve problems if you only try hard enough?', 'How often can you succeed in what you set out to do?', 'How often does your stomach hurt?'; standardized to mean 0, SD 1 in the full population. | National well-being survey, Danish public schools | online appendix · Appendix B.3.3 |
| Schooling level | Frederiksen et al. (2026) | Highest completed education from UDDA HFAUDD grouped: 1000-1999 high school or less; 2000-3999 vocational (IVU/erhvervsfaglig); 4000-4999 short cycle (KVU); 5000-5999 and 8500-8999 medium (MVU, 'Master'); 6000-8499 long (LVU); >=9000 or missing other. HFAUDD = 1 dropped; 3-digit codes multiplied by 10. | UDDAHFAUDD ranges 1000-1999, 2000-3999, 4000-4999, 5000-5999 & 8500-8999, 6000-8499, >=9000 | replication code · 3 Produce regression data.do, 'EDUCATION FROM UDDA' |
| Schooling strata | Bagger et al. (2014) | Years of education computed from type of highest completed education, grouped as 7-11 years (primary), 12-14 years (high school or vocational) and 15-20 years (bachelor or higher); workers enter two years after graduation and are dropped if ever observed in education afterwards or if years of education exceed age minus seven. | IDA | working paper · Section 3 and Appendix B |
| Schoolmates | Hvidberg et al. (2023) | People born the same year who attended the same school in the year they turned 15. | BEF, UDDA, IND | paper · Table 2; README.docx |
| Schoolmates at age 15 | Bingley et al. (2021) | Males born in the same calendar year enrolled in the same school on 31 October of the calendar year they turn 15 (normally 9th grade), from the national pupil database (covers grade 8 and above from August 1973). Pupils at schools exclusively for special educational needs are excluded. | Education register / national pupil database | working paper · Section 3.1 and footnotes 11, 13, 14 |
| Skill group | Traiberman (2019) | Three skill groups from completed education in IDA: low = high school or short-cycle education; medium = vocational or medium-cycle education; high = bachelor's degree or more. Education is never allowed to decrease (higher earlier level imputed) and missing values are filled from the previous year, else the next reported year. | IDA | online appendix · Online Appendix D.1, Data Cleaning: Skill |
| Skill groups and years of education | Olney and Pozzoli (2021) | From the first two digits of the highest completed education code (HFFSP): skill1 (medium) if 15, 20, 25, 35 or 40; skill2 (high) if 50, 60, 65 or 70; otherwise low (incl. missing). Years of education: 10->6, 15/35->10, 20/25/39->12, 40->14, 50/60->15, 65->17, 70->20, missing->6. | IDA, UDDAedu=substr(hffsp,1,2) | replication code · offshoring_project_will_dario.do (lines ~59-80) |
| STEM enrolment and completion | Brenøe and Zölitz (2020) | Enrolment: indicator for ever having been enrolled in a STEM study at college level or higher. Completion: field of the highest obtained degree is STEM. STEM = ISCED fields Natural Sciences, Mathematics and Statistics (05), ICT (06), and Engineering, Manufacturing and Construction (07); split into subfields Biology, Math and Physics, ICT and Engineering, and a fourth group. | Education registersISCED-F 2013 fields 05, 06, 07; comparison fields ISCED 091 (health), 01 (education), 02 (arts/humanities), 031 (social sciences), 04 (business/law) | working paper · Section II.D outcome description and footnote 21 |
| STEM field education | Joensen and Nielsen (2018) | Indicator that the highest completed education is in a STEM field, following the National Science Foundation definition (math, engineering, natural and technical sciences, and some social and life sciences). It is used for parents and for younger siblings' education at age 30. | Statistics Denmark education registers | working paper · Section 3.2; notes to Tables 6-7 |
| Student experience | Søgaard (2019) | Number of years enrolled in tertiary education between 2004 and 2007 (that is, over the previous four years). | Education registers | working paper · Table 3 note a; Appendix Figure A.VI |
| Years of education | Bagger et al. (2021) | Highest completed education in 2006 from the IDA person files (8-digit HFFSP code), mapped to years of education using the first two digits per Table A.1 (e.g. 10 = primary: 7 years if born 1958 or earlier, 9 years if born 1959 or later; 20/25 = high school 12; 35 = vocational 12; 40 = short further 14; 50 = medium-length further 16; 60 = Bachelor 15; 65 = Master 18; 70 = PhD 20; 90 = unknown, missing). | IDAHFFSP 2-digit: 10->7/9, 15,17->10, 20->12, 25->12, 30->10, 35->12, 40->14, 50->16, 60->15, 65->18, 70->20, 90->missing | working paper · Appendix C and Table A.1 |
| Years of education | Epper et al. (2020) | Robustness replaces years-of-schooling indicators with indicators for 59 general educational subject groups (e.g. comparative literature studies, economics, physics). | Education register59 educational groups | online appendix · Online Appendix Table A11 notes |
| Years of education and higher education | Baker et al. (2026) | Latest available highest completed education HFAUDD (UDDA 2019 back to 1981), mapped to main level and normed length (months/12 = years). Higher education = KVU, MVU, bachelor, LVU or PhD; university = bachelor, LVU or PhD; above compulsory = anything but primary school (Grundskole). | UDDAHFAUDD via AUDD_HOVED_L1L5_KT (levels 10 Grundskole, 40 KVU, 50 MVU, 60 BACH, 70 LVU, 80 PhD) and AUDD_PRIA_L1LX_KT (length) | replication code · dst_replicationpackage/do/2e_cr_udda.do |
| Years of education by 30 | Jensen and Manning (2025) | Completed years of education by age 30 from the education register; foreign education of immigrants is registered on arrival via a survey, which causes missingness. Rounded to the nearest integer in regressions. | UDDA | online appendix · Online Appendix OA.1 |
| Years of education by age 35 | Eshaghnia et al. (2025) | Minimum number of years it takes to complete the highest degree obtained by age 35. | UDDA | working paper · Section 3.1 |
| Years of schooling | Bingley et al. (2020) | Years of schooling imputed from Statistics Denmark's highest completed education, based on enrolment and qualifications reported by educational institutions. | Education registers | working paper · Section III |
| Years of schooling (own and father's) | Bingley and Cappellari (2019) | Years of completed education computed as int(HFPRIA/12) from the latest available education register record (1980-2009), recoding 0-7 to 7 and 18-21 to 18; father's schooling linked through the fertility database father identifier. | UDDA, Fertility database (ftdb2010)hfpria; educ=int(hfpria/12), recode 0/7=7, 18/21=18 | replication code · data-creation-triplet-education.do |
| Years of schooling and more than compulsory schooling at age 50 | Rossin-Slater and Wüst (2020) | Years of schooling at age 50 adding up schooling across educational programs, and an indicator for having more than nine years of compulsory schooling at age 50. | Education register (Statistics Denmark) | working paper · Section 4.1 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Birth event | Keller and Utar (2022) | Indicator for whether a worker fathers or mothers a newborn baby in a given year (for the IV specification: parent of a new child over 2000-2007). Child births come from Statistics Denmark's Fertility Database, which links parents' personal IDs to every child born in Denmark. | Fertility Database (Statistics Denmark)Stata variable DBirth | working paper · Section 2.3; Table 1 notes; Section 6 eq. (4) |
| Birth order | Bagger et al. (2021) | Order of birth of each child within the mother's family, identified using precise date of birth obtained from Statistics Denmark and merged onto IDA (69.4% of IDA individuals matched, unmatched mostly older cohorts). | IDA, Date-of-birth data (Statistics Denmark) | working paper · Appendix C and footnote 22 |
| Birth order / exposure (second vs first born) | Black et al. (2021) | M = 1 if the child is second born ('more exposed'), 0 if first born; birth order measured from exact dates of birth in the Birth Register with parent-child links. Sample restricted to families with at least three children where the first two children have the same father; Danish births 1990-2001. | Danish Birth Register (MFR) | working paper · Sections 2-3, Eq. (1) |
| Birth order sample (second-born boys) | Breining et al. (2020) | Birth order from the Birth Register (1960-2010 births) using exact birth dates and parent links; twins excluded; sample is the first two children of families with two or more children, restricted to full biological siblings (same mother and father) with a second-born boy (boy-boy and girl-boy families). Main crime sample is birth cohorts 1981-1990. | MFR, Population register | working paper · Sections 3.1 and 3.3; Table 3 |
| Births and child identifiers | Foerster (2024) | Children linked to fathers and mothers via the fertility database (PNRF, PNRM, PNRB) with birth year from FOED_DAG; first, second and third child IDs are assigned sequentially; birth event = child born in that year. Number of children and persons in the family from the family register. | Fertility database (ftdb_father, ftdb_mother), FAMILIEPNRF, PNRM, PNRB, FOED_DAG; C_ANTBOERNF, C_ANTPERSF | replication code · code/assembledat_untying.sas (child_vars, fam_vars) |
| Brother pairs | Bingley et al. (2021) | First and second sons (born 1960-1983) of sampled fathers who share both legal parents from registration at birth and are not adopted; son birth order determined irrespective of daughters. Excludes sons also observed as fathers and brothers born less than 12 months or more than 12 years apart (122,652 pairs); singletons kept. | Civil registration system (CPR) / population register | working paper · Section 3.1 and footnote 10 |
| Child linkage and age of children | Jakobsen et al. (2026) | Children and their ages are linked to parents through the parent identifiers in BEF; almost all children can be linked to a mother. | BEF | online appendix · Supplemental Material, Data Appendix (p. S2) |
| Child penalty | Kleven et al. (2019) | Event study around year of first birth (t=0), event time -5 to +10 with t=-1 omitted, separately by gender with age and year dummies, estimated in levels; penalty P_t = (alpha_men_t - alpha_women_t) / E[counterfactual women's outcome | t], the percentage by which women fall behind men due to children. | Population register (family links) | working paper · Section 3.1, eqs. (1)-(2) |
| Cohabitation (Statistics Denmark definition) | Bruze et al. (2015) | Classified as cohabiting if sharing an address with an opposite-sex person who is not family-related and is no more than 15 years older or younger. The paper uses two alternative partnership definitions: legal marriage only, and partnerships including legal marriage and cohabitation. | BEF | working paper · Section 2.2.1 |
| Couples | García-Miralles and Leganza (2024) | Married, registered-partnership or cohabiting couples residing in Denmark 1991-2014, identified when both partners are below age 60 and followed while they remain together; age difference within couple at most 8 years. | Population/family registers | working paper · Section 3.3 |
| Couples (married and cohabiting) | Almar et al. (2026) | Relationship = married (CIVST = 'G') or with a cohabitation partner id (FAELLE_NR non-missing); couple id is the man's PNR, linked through AEGTE_NR (spouse) or FAELLE_NR (cohabitant). Only couples where both partners are observed and both are aged 19-60 are kept. | BEF, PERSONERCIVST == "G"; AEGTE_NR; FAELLE_NR | replication code · kmeans ambition types.do, 'Define marital status' and 'Make couple ID' |
| Couples / partners | Jakobsen et al. (2026) | Partners follow Statistics Denmark's family definition, including married and cohabiting couples; cohabitants are two adults at the same address registered as parents to a child, or two opposite-sex adults with an age difference under 15 years at the same address. Constructed from EFALLE in BEF; couple age is the woman's age. Estimation sample keeps couples with at most five years age difference. | BEFEFALLE; FOED_DAG; KOEN | working paper · Section 2, footnote 8; Supplemental Material A |
| Custody and imputed child support/alimony | Foerster (2024) | Custody counts children under 19 living in the same family ID after a divorce. Child support imputed from 2004 rules: 10,608 DKK per child times an earnings-dependent multiplier (thresholds 320k/340k/370k/500k/900k for one child, higher for 2-3 children) for children in the ex-partner's custody; alimony = 20% of (payer earnings minus child support minus max(ex-partner earnings, 90,000)), capped, only if ex-partner not remarried; total maintenance capped at one third of earnings. Earnings base = labour income + UI benefits (ARBLHU, KONTHJ, KONT_GL) in 2004 prices. | IND (indk, indh), FAMILIE, IDANARBLHU, KONTHJ (pre-1993), KONT_GL (post-1993); FAMILIE_ID; cs_normal2004=10608 | replication code · code/maint_imp.do |
| Daycare enrolment ages 1-3 | Breining et al. (2020) | Enrolment in out-of-home daycare at ages 1, 2 and 3, from the Register of Daycare Institutions (type and place of daycare observed yearly 1995-2008). | Register of Daycare Institutions | working paper · Section 3.1; Table A2 |
| Distance-3 peers | Giorgi et al. (2019) | Starting from households where both spouses work, distance-1 peers are co-workers, distance-2 peers are the working spouses of co-workers, and distance-3 peers are the co-workers of the spouses of co-workers. Couples working in the same plant are excluded and feedback cases (where own household would serve as instrument for the peer) are discarded. | IDA, Tax records | working paper · Sections 4.1.2 and 5.1 |
| Divorce | Hasager (2024) | Indicator equal to one if the woman is divorced (quarterly), from the central population register with spouse links. | BEF | working paper · Section 3.2 |
| Donor-conception samples | Lundborg et al. (2024) | Children conceived by IVF are split into a non-donor IVF sample, an egg-donor sample and a sperm-donor sample; treatment timing is captured by the calendar month of first IVF treatment and clinic dummies. | IVF register (Danish Health Data Authority), BOERN, FTDKone_de_ds (non-donor), de (egg donor), ds (sperm donor); first_year_month_linear; clinic | replication code · Read_me.pdf, Table 1; Table_1_do.do |
| Early public care enrolment (nursery / family day care 1997-1998) | Gupta and Simonsen (2010) | Dummies for enrolment in nursery and in family day care in 1997 and 1998, from the Statistics Denmark Day Care Register; also arranged care at six months and being on a municipal waiting list at six months (DALSC). | Day Care Register, DALSC | working paper · Appendix Table A3 |
| Eligibility: young children | Friedrich and Hackmann (2021) | Indicator that the age of the youngest child in the family is 8 or younger, from the family register (C_ALDYNGST, else ALDYNGST). | FAMILIE, BEF (Personer)youngkids = ageyoungest <= 8 (C_ALDYNGST / ALDYNGST) | replication code · programs/stata/Prepare_microdata_healthworkers_by_industry.do |
| Family background controls | Heinesen (2010) | 32 variables for siblings, family structure, parents' education, age, labour-market status, income, unemployment and housing conditions, all measured in the calendar year the child turned 12; parental high education = at least one parent with long further or higher education. | Family background registers (parents' education, income, labour market status) | working paper · Section 3; Section 6 |
| Family CEO succession | Bennedsen et al. (2007) | Indicator equal to one if the incoming CEO is related by blood or marriage to the departing CEO according to family trees built from the Civil Registration System (parents, siblings, children, marital history), zero otherwise; family successions split into children and other relatives. | CPR, Erhvervs- og Selskabsstyrelsen (ES) management records | working paper · Section II.A, II.C; equation (2) |
| Family circles (spouses, children, siblings, stepchildren, in-laws) | Fadlon and Nielsen (2019) | Spouses = all married and cohabiting couples linked before the shock; adult children only for individuals born after 1960 (when parent links are available); children-in-law = spouses of the adult children of the shocked parent; 'nearby in-laws' = children-in-law living closer than the median distance to the parents-in-law; former spouses = linked by marriage or cohabitation in period -5 but not in period -1. | Population registers (family links)children born after 1960 | online appendix · Appendix Table 1 notes; Appendix Tables 2, 10 notes |
| Family size and siblings | Bagger et al. (2021) | Siblings are children born to the same mother (families defined by mother's identity, via the IDA pointer to mother's person ID); family size is the number of siblings born to the same mother, recorded before individual-level selection. Families with children born after 1991 (any child aged 0-14 residing at mother's address in 2006) are excluded so family size reflects completed fertility. | IDA | working paper · Section 4 and Appendix C (fn. 29) |
| Family stability outcomes | Anker et al. (2021) | Married; same partner; living with child and the child's mother, measured 1, 2 and 3 years after charge. | Population register (BEF) | online appendix · Table A-3, panel C |
| Father-child co-residence | Rossin-Slater and Wüst (2018) | Indicator that the father shares a residence address with the child in any year after separation, based on one address per person per year. It captures sole-father and some joint physical custody but understates joint custody. | Danish population register (addresses) | working paper · Section 4 (Outcomes) |
| Father/first-son/second-son triads | Bingley and Cappellari (2019) | Fathers born 1935-1961 (code frame 1935-1969) with two sons by the same mother, linked through the fertility database (ftdb2010, father pnrf and mother pnrm) restricted to madopt=='1'; twins/multiple births dropped; only sons of the same mother-father match as the father's first son kept; sons who are themselves fathers in the sample are dropped; families whose triplet birth order is inconsistent with population birth order are purged; fathers younger than 18 (216 months) at first son's birth dropped; brother spacing must be 12-144 months; first sons born 1959-1985, second sons 1962-1985. | BEF/fodselsdata, Fertility database (ftdb2010)koen=='1' (male); madopt=='1'; birth order border 0=father,1=son1,2=son2 | replication code · triplet-data-creation-20160706.do; inspection.do; IZA DP10761 Section III |
| Female fertility outcomes | Rossin-Slater and Wüst (2020) | For females born 1935-1957 in the 138 municipalities with an approved preschool by 1960: no children, number of children, age at first birth, and father ever missing in child's birth record ('Dad Ever Miss.'). | Population register / Fertility database (parent-child links) | online appendix · Table A5 |
| Fertile age | Keller and Utar (2022) | Fertile-age sample is women below 37 and men below 46 years as of 1999 (fertile age limit taken as 36 for women and 45 for men). The published-version code instead uses an age 18-39 indicator (age1839) for fertility heterogeneity. | IDA, BEF | working paper · Footnote 25; Table 4 notes |
| Fertility and birth spacing | Sander (2026) | Number of children (more1-more4 indicators for at least 2-5 children) and time from first to second birth (space1-space3: second birth within 1, 2, 3 years). | BEF, FAMILIEnumkid; yrnext - yrfirst | replication code · dofiles/createfirst.do; resfirst_final_EJ.do, Table 7 |
| Fertility by age 36 | Brenøe and Zölitz (2020) | Number of children by age 36, and timing of first child. | working paper · Section V; Table 8 | |
| Firm family-friendliness | Fluchtmann et al. (2024) | Firm-level average days of parental leave per birth among employees. Built from childbirth-related payouts after 2011 in the sickness/maternity benefit register (SGDP), covering both employer reimbursements and direct payments, for workers who later return to the same pre-birth employer. Births are inferred from a new leave spell or a gap of more than half a year between payments. Leave length is censored at 14 months and demeaned by gender before averaging at the firm. Firms with no births form a separate 'no birth' category. | SGDP (sickness/maternity benefit register) | online appendix · Online Appendix A.8.1, fn. 55-57, Figure A.4 |
| Foreign adoptee | Kleven et al. (2021) | Children who (1) were born in a non-western country, (2) have two known parents born in a western country, (3) whose parents both had a legal address in Denmark with no emigration record at the time of birth, and (4) have a recorded entry (immigration record) into the Central Person Register after the date of birth. Validated against Statistics Denmark official adoption records (1988-2009), with virtually no type I or type II errors; about 16,260 foreign adoptees 1980-2017, mostly from Asia (South Korea, India, China), Latin America (Colombia) and Africa. | CPR, BEF, Adoption registry (Statistics Denmark) | online appendix · Online Appendix, Figure A.I notes and Table A.I |
| Formal childcare uptake | Jensen and Zhang (2026) | Uptake of formal childcare for children under age 5 and above age 5; only 2005-2019 used due to low coverage before 2005. | DAGI/BOERNFB | online appendix · Supplemental Appendix Figure B.14 notes |
| Gender of firstborn child (instrument) | Bennedsen et al. (2007) | Indicator equal to one if the departing CEO's first child is male and zero if female; used as an instrument for family succession in a linear first stage. | CPR | working paper · Section III, equation (2) |
| Giving birth / conception timing | Daysal et al. (2021) | Indicator for giving birth in the year, from the Birth Registry. In a robustness check, the conception date is computed from gestational length and birth date, the price change is scaled by the fraction of the year before conception, and non-mothers get randomly assigned birth months. | Medical Birth Registry | working paper · Section 3; Section 4 robustness |
| Grandparental relative labour supply rank | Kleven et al. (2019) | Relative labour supply of (maternal or paternal) grandparents based on cumulated hours worked 1964-79 from ATP pension contributions, in quintiles. Robustness controls: grandparents' within-generation wealth-rank quintiles, region of residence, length and field of education of grandmother and grandfather (22 dummies), and parents' total number of children measured in 2014. | ATP pension contributions, Wealth register, Education register, Population register | online appendix · Figure A.IX-A.X notes; Table A.II notes |
| Having children (endogenous treatment) | Lundborg et al. (2017) | Indicator equal to 1 if the woman has children and 0 otherwise, measured over periods t = 0-1, 2-5 and 6-10 after the (potential) childbirth. | Population registers | paper · Section III and Table 2 notes |
| Hours in non-parental care | Gupta and Simonsen (2010) | Number of hours per week in non-parental care (DALSC), grouped into six categories: 10 hours or less, 10-20, 20-30, 30-40, 40-50 and above 50 hours, analysed within family day care and within preschool. | DALSC | working paper · Section 5 'Effects of hours in care'; Appendix Table A3 |
| Household | Andersen et al. (2022) | Households consist of one or two adults and any children below age 25 living at the same address, linked via the CPR family identification number; individual income and wealth are aggregated to the household. Household education = average years of education across all adults in the household; household age = average age in the household. The seller is the owner (CPR) at the beginning of the year of the transaction. | CPR/BEF, UDDA, IND, EJERchildren <25 | online appendix · Online Appendix C.1, C.4 (fn 5), Figure L.29 notes |
| Household | Andersen et al. (2020) | 'Single' households have only one adult living at the address (~13% of sample); 'Married' households have two legally bound adults, including registered same-sex partnerships. 'Children in family' = children in household; 'Immigrant' = an immigrant in the household; 'Getting married' = change in marital status over the sample; 'Having children' = child born within the last 12 months. | Population register (CPR/BEF) | online appendix · Table B3 notes |
| Household (tax unit) and married status | Jakobsen et al. (2020) | Household id is the family id when the person's spouse id equals the cohabitation partner id and civil status is in the married range (up to 2007, from FAIN); from 2008 from BEF using spouse id = partner id and civil status G or P. Persons not so linked form their own household. Only persons aged 20+ are kept and wealth is summed within household. | FAIN, BEFFAIN (<=2007): aegtefaelle_id == c_faelle_id, c_status 2-5, hhid = c_familie_id; BEF (>=2008): aegte_id == efalle, civst in {G, P}; alder >= 20 | replication code · Empirical Analysis/DD_make_hh_ids.do |
| Household (unit of analysis) | Alstadsæter et al. (2019) | A household is either a single person aged 20 or above or a married couple, in both cases with dependent children if any; fractiles are defined relative to the total number of households, including those not liable to wealth tax. | Administrative wealth and income records | online appendix · Online Appendix A.1.2 |
| Household aggregation and controls | Andersen et al. (2024) | Households are formed from population-register identifiers linking cohabiting couples and dependants; spending, income and stock variables are summed within household. Controls: age of the oldest member, number of children, and highest education in the household (primary, high school, college, graduate). | Population register, Education register | working paper · Section 2.2; Section 3.3 |
| Household definition and head | Crawley and Kuchler (2023) | Households have one or two adults; two cohabiting adults are one household if (i) married or in a registered partnership, (ii) have at least one common child in the Civil Registration System, or (iii) are of opposite sex, age difference of 15 years or less, not closely related and with no other adults present. Children under 25, never married and childless belong to the parents' family. The head is the highest earner over the 13-year sample period. | Civil Registration System (CPR) family links | working paper · Section 4.1, footnotes 26-27 |
| Household dissolution (divorce year) | Foerster (2024) | Divorce year is the minimum of the formal divorce year and the first year, within 6 years before formal divorce, in which a married, never-divorced person's partner ID (FAELLE_NR) changes to a new partner or no partner. | BEFFAELLE_NR (parid); divyear=min(fdivyear, firstparswitch_year), parswitch window t_ref_fdiv in [-6,0] | replication code · code/datprep_untying.do (Define year of household dissolution) |
| Household SES index | Bjerre-Nielsen and Gandil (2024) | Population rank (uniform on the unit interval) of the first principal component of four household variables: INC = highest market-income rank among adults in the household; LCE = any adult has completed a long-cycle education; NE = any adult has no education beyond primary school or no registered education; EMP = any adult is employed. With z-scored inputs, SES = 0.62 INC + 0.38 LCE - 0.44 NE + 0.53 EMP (47 percent of variance explained). | Statistics Denmark registers (income, education, employment) | online appendix · Online Appendix A (Construction of SES index), equation (1) |
| Household types | Gillingham et al. (2022) | Eight household types from couple vs single, high vs low work distance, and high vs low income, where the income split is at the median income within the demographic cell. | Demographic and income registers | working paper · Appendix F.1 (Household types); Table 2 |
| IVF treatment success (instrument) | Lundborg et al. (2017) | Equals 1 if the first IVF treatment with embryo implants (in a sequence of treatments) led to a childbirth, 0 otherwise; treatment outcome (birth, abortion, stillbirth, failure) and dates from the IVF register (mandatory reporting 1994-2005). | IVF register (Danish National Board of Health) | paper · Section III (model) and Section II.B |
| Maintenance payments (observed) | Foerster (2024) | Post-marital maintenance paid (child support and alimony) from the income register variable UNDERHOL, deflated to 2004 prices; an indicator for any positive payment is also used. | IND (indh)UNDERHOL | replication code · code/assembledat_untying.sas (inc_vars1); code/datprep_untying.do; code/maint_imp.do |
| Marital states and transitions | Bruze et al. (2015) | Yearly states: single never married, single previously married, or married (characterised by own and spouse's marital history, spouse's education and type, and marriage duration). Divorced persons are assumed single for at least one year before remarrying; direct marriage-to-marriage moves (2.3%) have the first year of the new marriage recoded as singlehood. | BEF | working paper · Sections 4.2 (Likelihood function) |
| Marriage and divorce events | Foerster (2024) | Marriage event when marital status is unmarried in year t and married in t+1; divorce event when married in t and divorced in t+1. Marital status from CIVST (G=1 married, U=0 unmarried, F=2 divorced, D=3, E=4, L=5, O=6, P=7), with CIV_VFRA giving the date since status. | BEF (personer)CIVST in {G,U,F,D,E,L,O,P}; marriage: CIVST U->G; divorce: G->F; CIV_VFRA | replication code · code/assembledat_untying.sas (marital status recode and event definitions) |
| Marriage and divorce events | Keller and Utar (2022) | Indicators for whether a worker marries or divorces in a given year, taken from the Central Population Register and linked by personal identifier. The divorce analysis uses workers married as of 1999; marriage analysis uses workers unmarried in 1999 (1999 dropped from marriage-rate plots). Cohabiting partners are distinguished from married spouses. | CPR (Central Population Register)Stata variables EMARA (marriage), EDIVA (divorce), mar99 | working paper · Section 2.3; Table 1 notes; Analysis1.do labels |
| Marriage duration (divorce cost) | Bruze et al. (2015) | Fixed cost of divorce parameterised with ten dummies for marital duration of 1, 2, ..., 9 and 10 or more years. | BEF | working paper · Section 4.1/4.2 |
| Mode of care at age 3.5 | Gupta and Simonsen (2010) | Dummies from the 1999 DALSC wave (age 3.5): home care (parents or grandparents), municipality family day care, municipality preschool, municipality-provided programme (either), private care, other care. | DALSC | working paper · Appendix Table A3 'Child Care at age three' |
| Never-parent control group with placebo births | Kleven et al. (2019) | Separately by sex, a probit for zero lifetime fertility is estimated on non-truncated cohorts 1955-1973 with dummies for own-cohort income quartile, own-cohort wealth quartile, spouse-cohort wealth quartile, education length/degree (8 categories), decade of birth of maternal grandmother and of maternal grandfather, and region. For each cohort born after 1973, among those childless by 2013 the individuals with the highest predicted probability are designated never-parents so that their share equals the 1955-1973 average zero-fertility share. Placebo age at first child is drawn from a log-normal distribution fitted within birth cohort x education cells (for post-1973 cohorts the mean is extrapolated by a linear trend, variance held fixed). | Population register (fertility links), Income and wealth registers, Education register | online appendix · Appendix B.2 |
| Out-of-home care / single-parent family | Bingley et al. (2020) | Out-of-home care: placed in out-of-home care (foster care or residential care homes) before age 18. Single-parent family: 0 if living with both legal parents on the 17th birthday according to the central person register. | CPR | working paper · Section V, footnotes 24-25 |
| Parental background | Landersø et al. (2017) | Parents' education and labour market outcomes measured one year prior to the child's birth, aligned to cut-off (e.g. children born Dec 1981 or Jan 1982 use parental characteristics from 1980); also mother's age at first birth. | Parental education and labour market registers | working paper · Section 3.3 and footnotes 16, 22 |
| Parental background controls | Jensen and Manning (2025) | For each parent: aggregate income (2013 prices) over the child's first 21 years; years of unemployment over those years; occupation as 2-digit ISCO88 of the primary job when the child is 21, with extra categories for retired, unemployed and unknown; and years of education when the child is 21 (UDDA, foreign education from the arrival survey, rounded to an integer). | IND, AKM, UDDA, BEFISCO88 2-digit | online appendix · Online Appendix OA.1; paper Section II.C |
| Parental background controls | Hansen et al. (2024) | Parental income (mean total disposable income across observed parents, calendar year before graduation/reform), parental unemployment (mean annual unemployment), indicator for at least one parent with a university degree, and parental origin (non-Western vs Western per Statistics Denmark); missing values set to zero with missing dummies. | Statistics Denmark registers | working paper · Sections 3.2 and 4; Table 2 notes |
| Parental background controls | Black et al. (2021) | Parental age, educational attainment, labour market status, earnings, leave taking and immigrant status from administrative registers linked via the unique identification number; regression covariates include year and month of birth and child gender with family fixed effects. | Administrative registers (Statistics Denmark) | working paper · Section 2 and Section 3 |
| Parental breakup | Lundborg et al. (2024) | Equals 1 if the couple divorced within the first five years of the child's life. | FAMILIEbreakup5 | replication code · Read_me.pdf, Table 1 |
| Parental earnings decile | Kreiner et al. (2020) | Average of father's and mother's wage earnings (loenmv) over the years the child is aged 12-14 (parents identified via far_id/mor_id in BEF at age 13), ranked into cohort-specific deciles. | BEF, IND (indh)far_id, mor_id; loenmv | replication code · data07.do |
| Parental education and income controls | Joensen and Nielsen (2018) | Mutually exclusive indicators for the mother's and father's highest completed education, plus their gross income as observed at the end of the year before the child started high school (when the child was 15). | Statistics Denmark administrative registers | working paper · Section 3.1-3.2 |
| Parental leave days | Lundborg et al. (2024) | Total days of leave taken by mother/father within two (leavedays) or five (leavedays05) years after the child's year of birth. | leavedaysm, leavedaysf, leavedaysm05, leavedaysf05 | replication code · Read_me.pdf, Table 1 |
| Parental leave days | Brenøe et al. (2024) | Total days of paid and unpaid leave from payouts of parental leave benefits to individuals and leave reimbursements to firms, incl. pregnancy-related extensions; prenatal and postnatal days allocated around the birth; truncated at 38 weeks any prenatal, 6 weeks paid prenatal, 52 weeks paid postnatal, 104 weeks any postnatal. | Parental leave benefit payouts, Employer leave reimbursements | working paper · Section 4.1; Appendix C.1 |
| Parental leave take | Keller and Utar (2022) | Indicator for taking childcare/parental leave in a year, from childcare leave spells in Statistics Denmark's social benefits statistics, part of the income statistics registers. | SHSS (Sammenhaengende socialstatistik), OF (Offentligt forsoergede)Stata variable DParL | working paper · Section 2.3 |
| Parental marriage and fertility | Foged et al. (2023) | Indicator for marrying a Danish citizen, and total number of children born after admission. | Population/family registers | working paper · Section III (Data) |
| Parental wealth / initial wealth deciles | Epper et al. (2020) | Parental wealth decile = parents' position in the parental wealth distribution measured within the respondent's cohort when the respondent was 18; initial wealth decile = own position in the within-cohort wealth distribution at age 18. | Wealth register, Population register (parent links) | working paper · Table 2 notes; Figure 3 notes |
| Partner and children | Frederiksen et al. (2026) | Partner = 1 if civil status CIVST is 'G' (married) or 'P' (registered partnership); number of children and age of youngest/oldest child from the IDA family file (c_antboernf, c_aldyngst, c_aldaeldst), with dummies for youngest/oldest child aged 0-2, 3-4, 5+. | IDA personer, IDA familieCIVST in G, P; c_antboernf, c_aldyngst, c_aldaeldst | replication code · 3 Produce regression data.do, PERSONER and FAMILIE sections |
| Post-separation fertility | Rossin-Slater and Wüst (2018) | Whether the mother or father has an additional child after separation, measured from the universe of birth records through 2010 and split by birth within or outside a new partnership. | Birth records | working paper · Section 4 (Outcomes); Section 6.3 |
| Risk index / low-SES indicator | Houmark et al. (2025) | Sum of four risk indicators: both parents with 10 years of education; family disposable income in the bottom birth-cohort quintile; either parent ever with a psychiatric diagnosis; family not intact. ses2 groups risk 0-1 vs 2-4. | IND, UDDA, BEFrisk1..risk4; fdinc2 = xtile(famdinc18, n(5)) by yearborn | replication code · Results (for replication).do |
| School-age children | Andersen et al. (2021) | Indicator for having children aged 5 to 15 (not necessarily living in the household), interacted with the transaction quarter following Harding, Rosenthal and Sirmans (2003). | CPR | online appendix · Appendix A, Variable definition |
| Second-generation education by age 25 | Rossin-Slater and Wüst (2020) | Main second-generation analysis uses firstborn children of mothers born 1935-1957; robustness uses all children of these mothers; cells by mother's municipality x birth year. | Population register parent links; education register | online appendix · Table A6 notes |
| Separation from the first child's father | Sander (2026) | divor = 1 if the mother's partner ID (C_FAELLE_ID) in the population register when the first child is 16 differs from the first child's father ID (FAR_ID); missing if father unknown. | BEFC_FAELLE_ID at age 16 of first child vs FAR_ID | replication code · dofiles/createfirst.do, 'DIVORSE' block and divor definition |
| Separation year | Rossin-Slater and Wüst (2018) | The year in which the father no longer resides with his oldest child's mother, or the year he has a first child while not living with the child's mother. The sample is restricted to separation years 1999-2008. | Danish administrative population/family registers | working paper · Section 4 (Analysis Sample) |
| Sibling birth-order counts | Bingley and Cappellari (2019) | Numbers of older/younger brothers and sisters with the same father and with the same father-mother pair, computed over all children of the father (twins ordered randomly), for robustness to sibling composition. | fodselsdata, Fertility database (ftdb2010)nobsf, nybsf, nobsfm, nybsfm, nossf, nyssf, nossfm, nyssfm | replication code · triplet-data-creation-20160706.do ('find all siblings') |
| Spouses (spillover sample) | Simonsen and Skipper (2025) | Individuals married to or cohabiting with individuals employed at participating work units in the month just prior to randomisation. | Population register (Statistics Denmark) | working paper · Table 6 notes (spillovers to spouses) |
| Stable household / single man / single woman | Andersen et al. (2021) | Households use the Statistics Denmark household ID: one or two adults plus children under 25 living with them; three types are couples (married, registered and cohabiting partners), single women and single men. A household must be stable: constant number of adults in the transaction year and the year after, no divorce from two years before to one year after the transaction, and no adult member lost to death within one year before or after. | CPR, BEF (Statistics Denmark household ID) | online appendix · Appendix A, Sample selection and Variable definition |
| Stable partner / partner retention | Humlum and Plato (2026) | Stable partner = cohabiting (including married) with the same partner for 3 consecutive years; partners are the initial partner in the year before the accident, followed regardless of separation; outcome = share still in the relationship. | BEF | working paper · Appendix Table A.1, row 3; Section 2.1; Figure 3 notes |
| Teenage motherhood | Dustmann et al. (2023) | Date of first birth is the earliest birth date among children linked to the woman through the mother ID (PNRM) in the population registers; teenage motherhood (mother_1518) equals 1 if the first birth occurs at age at least 14 and below 19 (commented 'had a child by age 19'). For girls aged 15-20 at assignment, motherhood within 4 years of assignment is used, dropping those whose first birth is within 280 days of immigration. | BEFPNRM (mother ID); mother_1518 = (date_first_birth - date_birth)/365.25 in [14,19) | replication code · do/build/clean/pnr_date_first_two_births.do; do/build/prepare/kids_by_age.do; sample_girls_15_20_at_assignment.do |
| Treatment group (child benefit cap) | Jensen and Blundell (2024) | Family experienced a strictly positive reduction in child benefits due to the DKK 35,000 cap in the first and/or second quarter of 2011 (benefits based on number and ages of children in the previous quarter; simulated from population registers and validated with Q4 BOBO/BOTI payments); control group = families not affected in Q1-Q2 2011. Continuous treatment = reduction in child benefits in 2011 in thousand DKK for treated families, zero otherwise. | BEF, BOBO/BOTI | paper · Sections 3.1, 5.2, 6.2 |
| Twin (multiple) birth instrument | Bagger et al. (2021) | Multiple births identified from precise dates of birth (24,186 twin sets, 217 triplets, 5 quadruplets); twin observations themselves are dropped, retaining an indicator for a twin birth in the family and its order. Family size is instrumented with twins at the second, third or fourth birth in respective samples, and with twins at last birth. | IDA, Date-of-birth data (Statistics Denmark) | working paper · Appendix C (fn. 25) and Section 5 |
| Type A / Type B refugee couples | Dustmann et al. (2024) | Type A couples: both spouses receive residency after the Start Aid reform (July 2002). Type B couples: one spouse receives residency before and one after the reform. | Danish Immigration Service residence permit records | online appendix · Online Appendix Table A.1 notes; Figure A.3 |
| Weeks of parental leave | Jørgensen and Søgaard (2024) | Total number of parental leave weeks computed from start and end dates of benefit spells in the parental leave spell data (children born 2008-2015); includes all full-time leave taken at most three years after childbirth, separately for mother and father. | Parental leave benefit spell data, BFL/eIndkomst | working paper · Section 3.2 (Data) |
| Young first-time father sample / treatment | Dustmann and Landersø (2021) | Males who father their first child at age 20 or younger, children born 1991-2004 (birth and demographic registers), with information on both parents from the birth for up to 5 subsequent years, matched uniquely to a neighbourhood; treatment is the first child being a son. Fathers in neighbourhoods with more than one young father having a first child in the same year are excluded. | Birth register, BEF | paper · Section II.B and footnote 9 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Allocated refugees | Dustmann et al. (2019) | Refugees received in 1986-1998 who initially received a permanent residence permit, from Lebanon, Iran, Iraq, Somalia, Sri Lanka, Vietnam, Afghanistan, Ethiopia, or without citizenship; municipality of initial placement identified from the population register. Refugees from former Yugoslavia (special 1993 policy) and family-reunified spouses/children arriving more than 12 months after the refugee are excluded. | BEF (population register) | paper · Sections 2.4-2.5, footnotes 19-20 |
| Assigned refugee (dispersal-policy sample) | Dustmann et al. (2023) | Immigrants arriving 1986-1998 from refugee-sending countries (Balkan origins excluded as covered by a different placement policy), with some countries restricted by arrival year; the household head at immigration is identified via father, mother (if under 18) or spouse links, and individuals arriving more than a year after the household head, or with a gap of more than one year between immigration and first appearance in the registers, are dropped. | BEF, Immigration register (date of immigration)IELAND: Iraq 5436; Iran 5438 (arrival>=1980); Vietnam 5468, 5488; Sri Lanka 5418 (>=1984); stateless Lebanon 5103, 5456, 5473; Ethiopia 5214 (<1991); Afghanistan 5404; Somalia 5289 (>=1989); keep refugee_ieland<=8 | replication code · do/build/prepare/assigned_refugees.do |
| Asylum applications | Agersnap et al. (2020) | Asylum applications to Denmark from outside the EU at annual and quarterly frequency: applications submitted (available from 2000), applications registered for administrative review, and residence permits granted to asylum seekers; asylum seekers from Bosnia dropped. An inflow measure without a control group, used to validate timing of responses. | Statistikbanken tables VAN5 and VAN77Statistikbanken VAN5, VAN77 | online appendix · Online Appendix, Section B and Figures A.IV-A.V |
| Citizenship groups and expatriates | Kleven et al. (2014) | Nordic foreigners: citizens of Finland, Iceland, Norway, Sweden; English-speaking: Australia, Canada, Ireland, New Zealand, South Africa, UK, US. Expatriates are Danish citizens returning after a spell of at least three years abroad. | CPR citizenship records | working paper · Section 4.1, Table 2 Panels A5-A7 |
| Dispersal-policy imputed refugee share (instrument) | Foged and Peri (2016) | For t > 1994, imputed working-age population from refugee country c in municipality m = s_cm x F_ct, where s_cm is the share of new immigrants from c first settling in m during 1986-1998 under the dispersal policy and F_ct is the national working-age population from c; summed over refugee countries and divided by municipality working-age population in 1988. Also used to define top and bottom quartile exposure municipalities for the difference-in-differences. | Population registers | working paper · Section 4.3 |
| Ethnic group (enclave) size | Damm (2009) | Number of first- and second-generation immigrants (immigrants and descendants) from individual i's source country living in i's municipality of residence; ethnicity proxied by country of origin. | BEF (100 per cent sample of immigrants) | working paper · Section III.B (p. 13); Table A.I Part B |
| European descent sample restriction | Houmark et al. (2025) | Keeps children of European genetic ancestry (non_european==0) or, if missing, those whose own and both parents' region of birth is Denmark or Europe; required because the EA GWAS uses European-descent samples. | BEF, iPSYCH2015non_european; region_birth, region_birth_m, region_birth_f in {Denmark, Europe} | replication code · Results (for replication).do, top |
| First- and second-generation immigrant | Agersnap et al. (2020) | Official Statistics Denmark definitions: a first-generation immigrant is born outside Denmark with neither parent being both a Danish citizen and born in Denmark; a second-generation immigrant (descendant) is born in Denmark with neither parent being both a Danish citizen and born in Denmark. | CPR, BEF | online appendix · Online Appendix, Figure A.III notes |
| First-generation locals (parental birthplace groups) | Jensen and Manning (2025) | Groups based on the parents' country of birth in BEF: two parents born in Denmark (reference), two parents born outside Denmark, one parent born in Denmark and one abroad, and at least one parent born in Greenland or the Faroe Islands. Origin regions: Middle East = MENAP+MENAT (incl. some North African countries, Israel, Turkey, Pakistan, Afghanistan); Europe split into Nordics, EU-15 ex. Nordic (incl. UK), EU-13 (incl. Cyprus) and non-EU (incl. Yugoslavia). | BEFCountry list per region in Online Appendix Table OA.1 | online appendix · Online Appendix OA.1 and Table OA.1; paper Section II.A fn. 4 |
| Immigrant / native Dane | Bingley et al. (2020) | Statistics Denmark classification: native Danes are born in Denmark with at least one parent born in Denmark; others are first- or second-generation immigrants. | BEF | working paper · Section V, footnote 23 |
| Inflow by residence permit type | Agersnap et al. (2020) | Immigration flows by type of residence permit granted (asylum, family, work, study; available from 1997). Asylum and family permit holders are treated as potentially welfare-eligible; work/study permit holders as untreated. Asylum applications are used as a supplementary measure. | Residence permit data (Statistics Denmark) | working paper · Section 3, Figure 3, footnote 12 |
| Instrument: number of placed co-nationals | Damm (2009) | Number of immigrants from the individual's source country placed by the authorities in the individual's municipality of assignment in year t, for t = -7, ..., 0 (0 = year of immigration), i.e. initial and past inflows of placed co-nationals. | BEF (100 per cent sample of immigrants) | working paper · Table A.I Part B |
| Labor migrant comparison sample | Dustmann et al. (2024) | Labor migrants not eligible for Social Assistance/Start Aid: non-EU/EEA citizens with a work visa (requiring a pre-existing job contract in Denmark) and EU/EEA citizens excluding students; first migration to Denmark only; employment = any employment in Denmark in a given year. | Residence permit records; employment registers | online appendix · Online Appendix Table A.13 notes |
| Linguistically close vs far origin | Foged et al. (2024) | Linguistically far = refugees from countries with non-Latin-based languages; close = Latin-based (country-based because mother tongue is only available around 1999). | working paper · Section 6.2, footnote 23 | |
| Low-income origin | Jensen and Manning (2025) | A first-generation local is 'low income origin' if at least one parent comes from a country whose 1980 GDP per capita (World Bank, supplemented with UN data) was below 4,000 USD at 2022 prices. Parents with only continent observed are assigned by the majority in that continent. | BEF, World Bank WDI NY.GDP.PCAP.CD1980 GDP per capita < 4,000 USD (2022 levels) | online appendix · Online Appendix OA.1 |
| Net immigration flow | Agersnap et al. (2020) | Year-to-year change in the stock of first-generation immigrants residing in Denmark according to the Central Person Register, by group, scaled by the group's pre-scheme (2001) immigrant stock and residualized on a linear group-specific trend estimated on 1980-2001. | CPR | working paper · Section 3, footnote 11 and eq. (1) |
| Non-EU immigrant share of municipality workforce | Olney and Pozzoli (2021) | Share of workers in a municipality-year (IDA, workplace municipality akom) who are not natives, where natives are ie_type=='1' (Danish origin) or born in an EU15 country; lagged one year in regressions. 97 municipalities (Copenhagen and Frederiksberg combined; pre-2007 municipalities recoded to post-reform codes). | IDA, ie_type (immigrant type/origin from BEF)ie_type=='1' native; opr_land (ieland) EU15 codes 5126,5130,5180,5134,5142,5150,5108,5140,5156,5164,5170,5182,5104,5120 treated as natives; foreigner_nonEU=(natives!=1) | replication code · offshoring_project_will_dario.do (lines ~139-150, 216-245); working paper fn. 17 |
| Parental immigrant status | Lundborg et al. (2024) | Indicator that mother/father/egg donor is a first- or second-generation immigrant. | IDAPALLdanem, danef, don_danem | replication code · Read_me.pdf, Table 1 |
| Refugee (dispersal policy sample) | Damm and Dustmann (2014) | Individual who immigrated 1986-1998 from Lebanon, Iran, Iraq, Somalia, Sri Lanka, Vietnam, Afghanistan or Ethiopia and, at the year of receiving a residence permit, was not married to someone from a non-refugee sending country or to a refugee-country immigrant who arrived at least one year earlier; former Yugoslavia excluded. | Administrative population registers | working paper · Section II.C and footnote 18 |
| Refugee (dispersal policy) sample | Hasager and Jørgensen (2026) | Immigrants arriving 1986-1998 from Afghanistan, Ethiopia, Iran, Iraq, Lebanon, Palestine (stateless), Sri Lanka and Vietnam (1986-1998) or Somalia (1989-1998), aged 18-64 at arrival; former Yugoslavs excluded; excludes those married at arrival to a non-refugee partner or to a refugee partner who arrived earlier (family reunification). | VNDS, BEF | working paper · Section I, footnotes 14-16 |
| Refugee family characteristics | Damm and Dustmann (2014) | Assignment year = first year of receipt of residence permit; source country dummies; nuclear family = living in a two-parent household; single parent = single-parent household; years of education before immigration constructed from the education code of the highest degree attained before immigration; number of persons in the household minus the number of parents living in it. | Population register, Education register (pre-immigration survey codes) | online appendix · Table A5.A |
| Refugee identification and settlement date | Foged et al. (2024) | Refugees and family members reunified with refugees identified from admission category in the Admission Register (OPHG) from 1997 onward; refugee status before 1997 imputed from year of arrival and country of origin. Date of first settlement in Denmark and later out-migration from the migration register (VNDS); refugees followed from first settlement in a municipality (within two months of being granted status), 1986-2008 arrivals. | OPHG, VNDS | working paper · Section 4.1 |
| Refugee sample and reform treatment | Dustmann et al. (2024) | Refugees and persons family-reunified with refugees granted residency 1 January 2001-31 December 2003 aged 18-55 (Danish Immigration Service permit records), excluding Afghans and former Yugoslavs and those re-migrating within 9 years (4,843 adults). Treatment: residency granted after 1 July 2002 (Start Aid); running variable is the exact residency date relative to the cutoff. | Danish Immigration Service residence permit records | working paper · Section 2.4; Appendix B.2 |
| Refugee sample identification | Damm (2009) | Individuals from 11 main refugee-sending countries extracted by an algorithm based on country of origin and first year of residence permit (built from official asylum permit counts), immigrating under the 1986-1998 dispersal policy, aged 18-59, observed annually up to seven years after immigration (balanced panel of 13,927). Excludes those married at immigration to a Dane, an immigrant from a non-refugee-sending country, or an immigrant from a refugee-sending country who immigrated at least one year earlier (family reunification). | Immigrant population registers (Statistics Denmark) | working paper · Section III.D and footnote 12 |
| Refugee share (treatment) | Dustmann et al. (2019) | Cumulative number of refugees allocated to the municipality from 1986 until the year preceding each election, divided by the municipal population at the beginning of 1986; regressor is its change between consecutive election years (RS_it). | BEF | paper · Section 2.4 and Section 3.2, eq. (3.1) |
| Refugee-country immigrant employment share | Foged and Peri (2016) | S_mt = F_mt / P_mt, the stock of employed refugee-country immigrants divided by total employment in municipality of work m in year t; computed on the full population (outcomes use a 20 percent random sample of natives). | IDA | working paper · Section 4.1; footnote 16 |
| Refugee-country immigrants | Foged and Peri (2016) | Immigrants (foreign-born) from eight refugee-sending countries that generated substantial refugee inflows 1994-2008: Yugoslavia, Somalia, Afghanistan, Iraq, Sri Lanka, Vietnam, Iran and Lebanon (Bosnia included in actual inflow but not in the instrument). | IDA, Population registers | working paper · Section 2, footnote 12; footnote 26 |
| School non-Western share | Bjerre-Nielsen and Gandil (2024) | Share of non-Western children in the school, used as an alternative school characteristic; it is interacted with a household non-Western dummy. The appendix does not state the exact origin coding. | Statistics Denmark registers | online appendix · Online Appendix Tables B.6-B.7 notes, Figure B.2 |
| Shift-share instrument (predicted non-EU immigrant share) | Olney and Pozzoli (2021) | For each origin country d, the national number of immigrants in year t is allocated to municipalities using the municipality's 1990 share of immigrants from d, summed over non-EU origins and normalized by municipality total employment in 1990. | IDA (1990 extract idapall_eer_1990)F_hat_cmt=s_cm88*F_ct; s_hat=F_hat_no_eu/P_m88 | replication code · iv_card_1990_off_aggr.do; working paper Section 4 (IV equation) |
| Synthetic Denmark (cross-country control) | Agersnap et al. (2020) | Weighted average of Finland (0.468), Norway (0.361) and Sweden (0.171), chosen to match 1991-2001 non-EU/EFTA net immigration flows normalized by each country's 2001 non-EU/EFTA immigrant stock. Immigrants from the former Yugoslavia are dropped throughout to remove the Bosnia shock. | Nordic national statistics bureaus, OECD International Migration Database | working paper · Section 3 (cross-country strategy), footnote 18; Appendix Figure A.IV notes |
| Treated (non-EU) immigrants | Agersnap et al. (2020) | First-generation immigrants from outside the EU and the four EFTA countries (Iceland, Liechtenstein, Norway, Switzerland), who are subject to the immigrant welfare scheme. 'Inside the EU' is defined by member countries in the pre-scheme year 2001, to avoid confounding from later EU enlargements. | CPR, BEFEU membership as of 2001 + EFTA | working paper · Section 2.1 and footnote 6 |
| Treatment / running variable: parent's admission date | Foged et al. (2023) | A child is treated if the first parent was granted refugee status (admission date) on or after 1 January 1999, when the Integration Act's language training applied. The running variable is days between the admission date and the cutoff; specifications using the father's admission date are also reported. | Danish administrative registers (Statistics Denmark) | working paper · Section IV (eq. 1) |
| Treatment and control groups of foreigners (migration DiD) | Kleven et al. (2014) | Treatment: foreigners with (annualized) earnings above the eligibility threshold; controls: foreigners with earnings between 80% and 90% (control 1) and between 90% and 99.5% (control 2) of the threshold, series normalized to 1990. In the elasticity regressions controls are foreigners between 80% and 99% of the threshold; an alternative uses the 99.5-100th percentile vs the 95-97th and 97-99th percentiles of the full earnings distribution. | Administrative tax records, CPR citizenship records | working paper · Section 4.1, Figures 4-5, Table 2 |
| Treatment: change from family reunification to own refugee status | Hasager (2024) | Adult women (18+) admitted 2001-2017 as family-reunified to a refugee spouse who later apply for and obtain their own asylum (UN Convention, de facto/B-status or temporary protection), identified from application and approval dates of residence permits in the admission register; event time is the quarter of asylum recognition. Controls are not-yet-treated women recognized at most three years later; women followed from 3 years before to 1.5 years after recognition. | Admission register (Danish Immigration Service, via Statistics Denmark) | working paper · Sections 3.1 and 4 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| 30-day readmission | Friedrich and Hackmann (2021) | Among inpatient (patient type 0) stays of 100 days or less, a readmission occurs if the next admission (or scheduled referral date) of the same person is within 30 days after discharge, starting at least one day after discharge; same-day readmission counts only if referred by a GP to the same hospital. Set to missing if the patient died before discharge. | LPRC_PATTYPE == 0; D_INDDTO, D_UDDTO, D_HENDTO, C_UDM == 1, C_SGH | replication code · programs/stata/Prepare_hospitalsample_PartA.do |
| Addiction treatment drugs and opioid doses | Fadlon and Nielsen (2019) | Indicator for purchasing nicotine/alcohol dependence medication; prescription opioid consumption measured in WHO defined daily doses (DDD), i.e. prescription day equivalents (spouses only, dosage data excluded for adult children). | LMDBATC N07BA, N07BB (dependence treatment) | working paper · Section 4.2, footnotes 29, 32 |
| Antidepressant expenditure | Hummels et al. (2025) | Annual sum of prescription drug expenditure on antidepressants per person, from the prescription register 1995-2016; set to 0 for person-years with no purchases. | LMDBATC3 = N06A; eksp_anti = sum of eksp where atc3='N06A' | replication code · Programs/1_data_job_spell/antidepressants2.sas; HMXdata.sas |
| Antidepressant use | Humlum and Plato (2026) | Dummy indicating a prescription for drugs in ATC class N06A (Antidepressants) in the year. | Prescription Drug Database (LMDB)ATC N06A | working paper · Appendix Table A.1, row 19 |
| Any mental health contact | Baker et al. (2026) | Any spell in the psychiatric central register (LPSYADM2016) starting 1995-2016; also records age at first observed contact. | LPSY (Psychiatric Central Research Register)LPSYADM2016, D_INDDTO year 1995-2016 | replication code · dst_replicationpackage/do/2b_cr_diags.do |
| Baseline psychiatric treatment | Fadlon et al. (2026) | Indicator for whether the surviving spouse had psychiatric treatment (visits to psychiatrists recorded in the Health Insurance Registry from 1990), used as a heterogeneity/control variable. | SSSY, SYSI | working paper · Section I (Data); Section III.B robustness |
| Chronic-condition first-line treatments and checkups | Kristiansen and Sheng (2025) | Annual indicators for redeemed prescriptions: statins (ATC C10AA), ACE inhibitors/angiotensin agents (ATC C09), diabetes drugs incl. metformin (ATC A10), COPD medication (ATC R03AC, R03CC, R03BB); diabetes checkup if GP claim with SPECIALE 800106, 800107, 800108 or 804549. | LMDB, SSSY, SYSIATC C10AA, C09, A10, R03AC/R03CC/R03BB; SPECIALE 800106, 800107, 800108, 804549 | replication code · 1e_medicine.do; data_build.sas lines ~245-255 |
| COPD avoidable hospital admissions | Kristiansen and Sheng (2025) | Hospital admissions (LPR, from 1995) with ICD-10 diagnosis J20, J21, J40, J41, J43, J47 or J44.0, J44.1, J44.9 as ambulatory-care-sensitive COPD admissions. | LPRICD-10 J20,J21,J40,J41,J43,J47,J440,J441,J449 | replication code · 1f_hospital.do |
| Coverage year and synthetic pre-reform coverage year | Simonsen et al. (2021) | Post-reform coverage years start with the first purchase after the March 2000 reform and reset 365 days after the first purchase, with the next year starting at the next purchase. Synthetic pre-reform coverage years apply the same rule from the first observed purchase, keeping only years that end by December 1999. | Laegemiddelstatistikregisteret | paper · Section 2; Section 3 |
| Coworker sick days | Brenøe et al. (2024) | Take-up of publicly paid sick days (any sick days); eligibility for public sickness funds starts after two weeks of sickness, so the measure captures longer sickness spells. | working paper · Section 6, footnote 18; Figure 7(d) | |
| Cumulative hospital contacts | Daysal et al. (2025) | Cumulative number of inpatient and outpatient hospital contacts from diagnosis until each year after diagnosis (separate outcomes). | online appendix · Appendix Figure A8 notes | |
| Expected end-of-year (shadow) price | Simonsen et al. (2021) | Average realised end-of-year price within cells of 5 age groups x 6 sextiles of predicted health spending x 100 percentiles of year-to-date spending, for each coverage month. Pre-reform counterfactual prices for 1998 are imputed by matching percentiles to pooled coverage years starting in 2000-2001. | Laegemiddelstatistikregisteret | paper · Section 5 (price definitions b-c, steps i-iii) |
| Family emergency room visit in survey month | Taubinsky et al. (2026) | Indicator equal to one if any household member (household identifier from BEF) visited the emergency room in the calendar month of the interview. An ER visit is an acute outpatient hospital contact in the NPR (recorded as patient type ER before 2014, acute outpatient from 2014). Respondents with more than 7 family ER visits over 2008-2018 are dropped. | LPR, BEFhas_now_visit_fam; ERvisit_count_surveysample_fam <= 7 | replication code · code_dstserver/analysis/A9_healthregs.do; NBER WP Online Appendix A.1 (Emergency Room Visits Data) and ER table notes |
| Family sterilisation consultation | Jensen and Blundell (2024) | Monthly indicator equal to one if the individual has ever (from 1994 onwards) consulted a doctor regarding sterilisation; a family-level version equals one if either partner in the couple has done so. | LPRADM, SYSI, SSSY | paper · Section 5.1 |
| GP and psychologist visits | Hasager and Jørgensen (2026) | Number of visits to general practitioners and psychologists (no diagnosis codes available in these data). | SYSI, SSSY | working paper · Section I, footnote 23 |
| GP contacts and fees | Hansen et al. (2025) | Health insurance claims with SPEC2 = 80 (general practice) per service week (HONUGE); fees BRUHON summed by person and week (negative set to 0, duplicates by week x SPECIALE dropped) and by year of age relative to the birth; for children, contacts in the first 182 days after birth are excluded. | SSSY, MFRSPEC2 == '80' (raw extract keeps 80, 81, 82, 83, 89); BRUHON; HONUGE | replication code · 4_cr_gpcontacts.do |
| GP reimbursements (costs) | Kristiansen and Sheng (2025) | Total GP fees (BRUHON) per patient-year for SPECIALE starting '80'; amounts before 2005 converted from øre to kroner (divided by 100). | SSSY, SYSIBRUHON; substr(SPECIALE,1,2) = '80' | replication code · 1c_costs.do |
| GP visits and services per visit | Kristiansen and Sheng (2025) | From health insurance claims (SSSY/SYSI), restricted to general practitioners (SPECIALE starting '80'), insurance group 1, own patients (PATTYP 1/01) and excluding child-vaccination/child-exam claims (BARNMAK = 1); visits = number of distinct provider x service-week (HONUGE) combinations per patient-year; services = count of service lines; services per visit = services / visits. | SSSY, SYSIsubstr(SPECIALE,1,2) = '80'; SIKGRUP = '1'; PATTYP in ('1','01'); BARNMAK ne '1' | replication code · data_build.sas lines ~178-240; 1g_GP_services.do |
| Health care utilisation (incl. antidepressant use) | Humlum et al. (2025) | Hospitalisations (inpatient and outpatient) with diagnoses from LPR, contacts with primary-care physicians and specialists from the Health Insurance Register, and prescription drug purchases (e.g. antidepressants, painkillers/opioids) from LMDB, 1995-2017. | LPR, Sygesikringsregisteret, LMDB | working paper · Section 2.2.2; Section 3.1 |
| Heart and stroke drug expenditure | Hummels et al. (2025) | Annual sum of prescription drug expenditure per person on cardiac therapy drugs (heart) and antithrombotic agents (stroke), 1995-2016; 0 if none. Sleep medication defined analogously. | LMDBheart: ATC2 = C01; stroke: ATC2 = B01; sleep: ATC3 = N05C; depression: ATC3 = N06A (eksp summed) | replication code · Programs/1_data_job_spell/stressdrugs2.sas; 3_data_event_study/drugs1.sas |
| High-risk group | Simonsen and Skipper (2025) | Individuals with an above-median number of primary care physician consultations in the six months before randomisation; the low-risk group is at or below the median. | Primary care physician consultation records (Statistics Denmark) | working paper · Notes to Tables 2-5 |
| Hospital admissions, inpatient days and ER visits | Daysal et al. (2021) | Indicators for any hospital admission and any ER visit in the first year of life and at ages 1-5, plus the number of days hospitalised and ER visits in the first five years. Inpatient days exclude the childbirth admission and the following four days. | National Patient Register | working paper · Section 3, footnote 12 |
| Hospital nights ages 45-54 and 55-64 | Rossin-Slater and Wüst (2020) | Number of nights spent in hospital over age ranges 45-54 and 55-64 (first generation), analysed at municipality x birth-year cell level. | National Patient Register | online appendix · Table A4 |
| Hospital nights ages 45-64 | Hjort et al. (2017) | Total number of nights spent in hospital between ages 45 and 64 from the Danish Inpatient Register 1980-2012; robustness winsorizes at the 99th percentile (about 190 nights). | Danish Inpatient Register (LPR) | paper · Section III.B, footnote 39 |
| Hospital nights and ever hospitalised (ages 30-59) | Baker et al. (2026) | Inpatient admissions (patient type 0) from LPR 1977-2018, with nested spells removed; nights (V_BEHDAGE) summed per year, capped at 365, and summed over age bands 20-29, 30-39, 40-49, 50-59. Ever hospitalised = any nights at ages 30-59. | LPRC_PATTYPE == '0'; V_BEHDAGE; nights30, nights40, nights50 | replication code · dst_replicationpackage/do/2b_cr_diags.do |
| Hospital visits | Jensen and Zhang (2026) | In- and outpatient visits at non-psychiatric hospital wards (data 1994-2018). Split into self-screening visits, defined by ICD-10 Z00 (general examination and investigation of persons without complaint and reported diagnosis) and Z01 (other special examinations and investigations of persons without complaint or reported diagnosis), including subcategories, versus non-screening diagnoses. Surviving parent's health status is based on their hospital visits in the 5 years before the first parental death. | LPR_ADM, LPR_DIAGICD-10 Z00*, Z01* (screening) | online appendix · Supplemental Appendix Figures B.10, B.11, B.15 notes |
| Hospital visits and days in hospital | Humlum et al. (2025) | Number of hospital visits (admissions, outpatient treatment and ER visits); days hospitalized, with admission and release dates for admitted patients and outpatient/ER visits coded as 1-day visits. | National Patient Register (LPR)Outpatient/ER = 1 day | online appendix · Supplemental Appendix Table A.1, rows 39-40 |
| Hospital visits, accidents and mortality | Humlum and Plato (2026) | Hospital visits = number of hospital contacts (admissions, outpatient treatment and ER visits). Accidents = accumulated hospitalisations with reason for contact registered as an accident. Mortality = accumulated, individual counted as deceased from the year of death. | LPR, Death register | online appendix · Supplementary Appendix, Table A.1 Panel E |
| Hospitalisations (psychiatric / somatic) | Simonsen and Skipper (2025) | Any hospital contact and number of days admitted to hospital, separately for contacts with a psychiatric diagnosis and with a somatic diagnosis; follow-up to 2018. Diagnosis code lists are not given. | Hospital contact records (Statistics Denmark) | working paper · Table 4 panels A-D |
| Hospitalization shock | Hvidberg et al. (2023) | At least one emergency room visit or hospital visit by GP referral in 2012-2017, excluding visits due to congenital diseases, pregnancy or routine checks; sample excludes those with such a shock in 2008-2011. | LPRhealt_12_17 with healt_08_11==0 | paper · Section 3.3, footnote 21 |
| Hospitalization, psychiatric diagnosis, psychiatric medication, addiction treatment | Bingley et al. (2020) | Any admission to somatic or psychiatric hospitals including same-day patients (national hospital discharge register); psychiatric diagnoses and addiction treatment from the hospital discharge register; purchase of GP-prescribed mental-health medicine from the Danish Medicines Agency. | LPR, Danish Medicines Agency prescription data | working paper · Section V, Table 9 panels D-G, footnote 22 |
| IVF medical controls | Lundborg et al. (2017) | Sensitivity specification adds medical factors from the IVF register: number of eggs retrieved, number of embryos implanted, diagnoses, causes of infertility, type of IVF treatment, and clinic indicators. A 'healthy women' subsample consists of women not diagnosed with any fertility problem. | IVF register (Danish National Board of Health) | online appendix · Online Appendix B, Table 2 notes |
| Maternal antidepressant use | Daysal et al. (2022) | Purchases of antidepressants (ATC N06A) from the prescription register (LMDB), aggregated by person and year (accumulated DDD); available from 1995. | LMDBATC4 = 'N06A' | replication code · 06c Adding parental med use.sas, lines ~10-48 |
| Mental health medication take-up | Fadlon et al. (2026) | Indicator for redeeming a prescription for psychotropic drugs classified as psycholeptics (ATC N05) or psychoanaleptics (ATC N06) by the surviving spouse, measured at monthly and annual frequency (extensive margin). | LMDBATC N05, N06 | working paper · Section I (Variable definitions) |
| Mental health prescriptions | Jensen and Zhang (2026) | Any prescription classified as mental-health related: ATC codes N05*, N06A*, N06B*, N06C*. Prescription data available from 1995; effects measured within 5 years after first parental death. | LMDBATC N05*, N06A*, N06B*, N06C* | online appendix · Supplemental Appendix Figure B.10 notes |
| Mental healthcare visits | Daysal et al. (2026) | Visits to psychiatric hospitals (inpatient, outpatient, ED from the Psychiatric Central Research Register) and to private psychiatric clinics (Health Insurance Register reimbursements, identified by physician specialty codes), at extensive and intensive margins, ages 16-26 (data 1997-2018). | Psychiatric Central Research Register, Sygesikringsregisteretphysician specialty codes 24 or 26 | online appendix · Section 2; Appendix A |
| Opioid and alcohol-dependence prescriptions | Jensen and Zhang (2026) | Opioid painkillers: ATC N02A*; prescriptions for treatment of alcohol addiction: ATC N07BB. Prescription data from 1995. | LMDBATC N02A*; ATC N07BB | online appendix · Supplemental Appendix Figures B.9-B.10 notes |
| Opioid use | Humlum and Plato (2026) | Dummy indicating a prescription for drugs in ATC class N02A (Opioids). | Prescription Drug Database (LMDB)ATC N02A | working paper · Appendix Table A.1, row 25 |
| Predicted pharmaceutical spending (health index) | Simonsen et al. (2021) | Following Handel (2013), year-t drug spending is regressed on year t-1 gender, age and age squared, number of GP office visits, fee-for-service payments to the physician, drug spending and its square, ATC level-3 drug-type indicators (88 categories), and hospital ICD-10 level-2 diagnosis indicators (215 categories). The model is then used to predict next year's spending; for coverage-reset analyses, covariates are measured in the 365 days before the coverage year starts. | Laegemiddelstatistikregisteret, National Patient Register (hospital diagnoses), Health insurance (GP) recordsATC level 3 (88 categories); ICD-10 level 2 (215 categories) | paper · Appendix D |
| Prescription drug purchases | Simonsen and Skipper (2025) | Any prescription drug purchase and months' supply per year (through 2019), covering drugs for hypertension, cholesterol, diabetes, severe pain relief and depression. | Prescription drug purchase records (Statistics Denmark)ATC C9 (hypertension, as printed), C10, A10, N2, N05A, N05B, N05C, N06A | working paper · Table 3 notes |
| Prescription drug spending and purchases | Simonsen et al. (2021) | The full history of prescription drug purchases from the Danish Prescription Drug Register (Laegemiddelstatistikregisteret), covering 1995-2014: date, shelf price, government coverage, type of coverage and type of drug. It is aggregated to daily (descriptive) or monthly (estimation) spending within coverage years. Outcomes include spending, number of pharmacy visits, and days until next purchase truncated at 365. | Laegemiddelstatistikregisteret (Danish Prescription Drug Register) | paper · Section 3; Section 4 (outcomes before reset) |
| Primary care physician consultations | Simonsen and Skipper (2025) | Number of consultations (and any consultation) with the primary care physician, from register data on fee-for-service payments to primary care physicians; follow-up to 2020. The paper does not give the register name or speciality codes. | Primary care physician fee-for-service records (Statistics Denmark) | working paper · Section 3; Section 4 results; cost-benefit section |
| Psychiatric drug purchases | Serena (2025) | Monthly prescription purchases of psychiatric drugs (ATC N05 and N06, excluding bupropion N06AX12 used for smoking cessation): antidepressants, SSRIs, anxiolytics, hypnotics/sedatives, psychostimulants and antipsychotics; cost = IBGP minus PTP (floored at 0); prescriber classified as GP or psychiatrist via the health insurance register provider number. | LMDB, SSSYATC: antidepressants N06A; SSRI N06AB; anxiety N05B; sleep N05C; psychostimulants N06B; antipsychotics N05A; excludes N06AX12 | replication code · CollectData_Psyc_AllAges.sas (Medicine data section) |
| Psychiatric hospital contacts | Hansen et al. (2025) | Hospital contacts with an action diagnosis in ICD-10 chapter F (DF00-DF99), split into inpatient (C_PATTYPE = 0) and outpatient (other patient types), by month since birth (admission date D_INDDTO). | LPR (PSYKADM)C_ADIAG DF00-DF99; C_PATTYPE == 0 inpatient | replication code · 3_cr_psyksssy.do, 'psych hospital contacts' and 'Generate outcomes' sections |
| Psychiatric hospital contacts | Serena (2025) | Monthly psychiatric hospital contacts with an F-chapter action diagnosis from the psychiatric central register: inpatient admissions (patient type 0/1) with bed days, emergency room visits (patient type 3 before 2014; same-day type-2 contacts with discharge mode 0 in 2014), and outpatient visits; anxiety/depression contacts are those whose derived diagnosis group equals 3 or 4 (the recoding of C_ADIAG into these groups is done in a do-file not read). | LPSYPOP, LPSYBES (Psychiatric Central Research Register)C_ADIAG starting 'DF'; C_PATTYPE 0/1 inpatient, 3 ER (2014: 2 with D_HENDTO=D_INDDTO and C_HENM=0); V_SENGDAGE bed days; bamb_AnxDep/erp_AnxDep/PHosp_AnxDep use diagnosis group in (3,4) | replication code · CollectData_Psyc_AllAges.sas (Psychiatric Hospital, Ambulatory visits sections); PsykTestLongRunRD.do |
| Psychological distress | Andersen et al. (2026) | Indicator for having received treatment by a psychologist or psychiatrist following the cancer diagnosis, from data on medical visits. | Health insurance / medical visits data (name not given) | online appendix · Online Appendix F; Table I.VI |
| Psychologist and psychiatrist visits | Jensen and Zhang (2026) | Psychologist visit data available 1990-2019; GP visits also used as an outcome. | SYSI/SSSY | online appendix · Supplemental Appendix Figures B.9-B.10 notes |
| Psychologist, psychiatrist and GP contacts | Serena (2025) | Monthly counts of services (YDLANT) in the health insurance register 1995-2014: GP consultations (speciale 800101), private practising psychiatrists (speciale beginning 240-245 or 260-265), and psychologists (provider type YDTYP 93 or 94); costs from fee field BRUHON. | SYSI, SSSYGP visit: SPECIALE=800101; psychiatry: substr(SPECIALE,1,3) in 240-245, 260-265; psychologist: YDTYP in 93, 94; quantity YDLANT; fee BRUHON; doctor GP if first two SPECIALE digits = 80, psychiatrist if 24 or 26 | replication code · CollectData_Psyc_AllAges.sas (GP visits section) |
| Psychologist/psychiatrist consultations and GP mental-health tests | Hansen et al. (2025) | Mother's claims with SPEC2 = 24 or 63 (psychiatrist / psychologist) by month since birth; GP contacts with a psychological test (code comment: test for depression/mental health screening) identified by SPECIALE codes 802147, 802148, 802149, 804106, 806101. | SSSYSPEC2 in ('24','63'); SPECIALE in ('802147','802148','802149','804106','806101') | replication code · 3_cr_psyksssy.do |
| Psychometric test and GP talk therapy (event) | Serena (2025) | GP-administered psychometric tests (identified by specific health insurance service codes) define the event used for treatment timing; GP talk therapy/mental health consultations are identified by a second set of service codes. Up to four first-test events per person are used in the event-study data. | SYSI, SSSYpsychometric test: SPECIALE in 802147, 802148, 802149; therapy: SPECIALE in 804106, 806101, 804003, 804063 | replication code · CollectData_Psyc_AllAges.sas; PsykTestLongRunRD.do |
| Radiotherapy eligibility under DBCG89 protocol | Daysal et al. (2025) | DBCG89 protocol (age eligibility <75 years). Low risk (as printed in the appendix protocol table): tumor <=5cm and positive nodes and grade I (if premenopausal); high risk: tumor >5cm or positive nodes or (premenopausal and ductal grade II-III). Radiotherapy indicated after lumpectomy, when the carcinoma was not removed microradically, and additionally for: age <=45 with >=4 positive nodes (until January 1995); premenopausal with positive nodes or tumor >5cm (from January 1995). Positive nodes = cancer spread to at least one lymph node. | Danish Breast Cancer Cooperative Group (DBCG) clinical databasetumor >5cm; >=4 positive nodes; age <=45; age <75 | online appendix · Appendix Figure A1 (DBCG89 protocol) |
| Radiotherapy receipt | Daysal et al. (2025) | Indicator for receiving adjuvant radiation therapy, from the DBCG clinical Breast Cancer Database, which records treatments administered and dates of diagnosis and major interventions. | DBCG clinical Breast Cancer Database | working paper · Section 3 (Data), Treatment Variable |
| Somatic hospital contacts with mental diagnosis | Serena (2025) | Indicator for a somatic hospital contact (excluding psychiatric departments) with an F-chapter action diagnosis other than dementia (F0). | LPRC_ADIAG starting 'DF' and not 'DF0'; C_SPEC not 50, 52 | replication code · CollectData_Psyc_AllAges.sas (Somatic hospital visits with mental health diag) |
| Spot price | Simonsen et al. (2021) | Coinsurance rate at the start of coverage month m given year-to-date spending, taking values 1, 0.5, 0.25, 0.15 or 0. Under the 2000 contract: a DKK 500 deductible, 50% coinsurance to DKK 1,200, 25% to DKK 2,800, 15% beyond, and a stop-loss at DKK 18,500 capping out-of-pocket costs at DKK 3,600. The spot price is 1 for the purchase that starts a coverage year. | Laegemiddelstatistikregisteret | paper · Section 2, Figure 1; Section 5 (price definition a) |
| Statin consumption and cholesterol tests | Fadlon and Nielsen (2019) | Cholesterol (blood test) indicators are available only for residents of Greater Copenhagen; drug dosage information (e.g. opioid doses) is excluded for the adult-children sample under the data use agreement, so opioid dose outcomes are estimated only for spouses. | Prescription Drug Database (LMDB) | online appendix · Appendix B; Appendix Table 2 and Table 7 notes |
| Urgent care contacts | Fadlon and Nielsen (2019) | Non-hospital contacts with on-call local doctors or nurses initiated outside regular working hours (8 am to 4 pm). | Health Insurance Registry | working paper · Section 4.2, footnote 31 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Abortion | Dustmann et al. (2023) | Indicator for an abortion diagnosis in the National Patient Register 1980-2014 (medical, other, unspecified and failed attempted abortion), attached to refugee girls. | LPRICD-10 (DIAG first 4 chars) DO04, DO05, DO06, DO07 | replication code · do/build/prepare/abortion.do |
| Acute respiratory hospitalizations (first year of life) | Daysal et al. (2026) | Number of inpatient hospitalizations in the first year of life (also split into first and second six months) with a primary diagnosis of an acute respiratory condition, from the National Patient Register (1981-2018). | LPRICD-8 starting 46, 47, 48, 490, 079, 783; ICD-10 starting J (excluding J4) or B974 | online appendix · Section 2; Appendix A |
| Adult diagnoses (cardiovascular, heart, diabetes, cancer) | Rossin-Slater and Wüst (2020) | Indicators for diagnoses of cardiovascular disease, heart disease, diabetes and cancer (first generation); code lists not given in this appendix (follows Hjort, Solvsten and Wust 2017). | National Patient Register | online appendix · Table A4 |
| Adult good-health index | Baker et al. (2026) | Row mean of sign-reversed, control-group-standardised indicators of ever diabetes, cardiovascular disease, cancer, asthma, infection, any mental health contact and ever hospitalised (30-59), plus total hospital nights ages 30-59; missing if any component missing; rescaled to control-group SD 1. | LPR, LPSY, Cause-of-death registerhealth_index | replication code · dst_replicationpackage/do/2i_cr_merge_outcomes.do |
| Age-specific survival | Hjort et al. (2017) | Indicators for survival beyond each age 46-64, from the Danish Death Register 1980-2012. | Danish Death Register | paper · Section III.B |
| Alcohol diagnoses | Humlum and Plato (2026) | Accumulated number of hospital diagnoses related to alcohol following Browning and Heinesen (2012): alcohol poisoning, addiction syndrome, delirious abstinence, alcohol psychosis, oesophageal varices, alcoholic liver disease, alcoholic pancreatic disease. | LPRICD-10 F100, F102, F104, F105, I85, K70, K860, T500, T510 | working paper · Appendix Table A.1, row 24 |
| Alcohol, digestive, accident and suicide hospitalizations | Hummels et al. (2025) | Person-year indicators for alcohol-related and stress-related digestive diagnoses following Browning and Heinesen (2006), plus accidents and suicide attempts identified from the contact-cause field. | LPRalcohol: DF100, DF102, DF104, DF105, DI85, DK70, DK860, DT500, DT510; digestive: DK20, DK222, DK223, DK25-DK31, DK902; accident: KONTAARS=2; suicide: KONTAARS=4; self-harm DX7*, DX8*; poisoning DT36-DT39, DT4*, DT50 | replication code · Programs/1_data_job_spell/stressdiagnoses4.sas |
| All-cause and breast cancer mortality | Daysal et al. (2025) | Indicators for death from any cause and from breast cancer, using the exact date and ICD-coded cause of death in the Register of Causes of Death, measured for each year from the date of diagnosis up to 10 years after. | Register of Causes of Death | working paper · Section 3, Outcome Variables |
| AMI 30-day readmission and mortality | Friedrich and Hackmann (2021) | Patients aged 65+ with primary diagnosis acute myocardial infarction; index admissions exclude those with an ischemic heart admission in the preceding 365 days. 30-day mortality = death within 30 days of admission (DAR). | LPR, DARAMI: ICD-8 4109, 41099; ICD-10 DI21, DI22. Ischemic: ICD-8 410-414; ICD-10 DI20-DI25 | replication code · programs/stata/Prepare_hospitalsample_PartA.do |
| Birth outcomes | Lundborg et al. (2024) | Low birth weight is below 2500 grams; prematurity is pregnancy length below 37 weeks; indicator for Apgar score below 10. | low_bw, prematurity, apgar_less_10 | replication code · Read_me.pdf, Table 1 |
| Birth outcomes (APGAR, birth weight, gestation) | Landersø et al. (2017) | Child birth weight, gestational length and APGAR score (0-10, based on appearance, pulse, grimace, activity, respiration) used as background controls; register not named in the paper. | Birth records (register not named) | working paper · Section 3.3 and footnote 21 |
| Cardiovascular / respiratory diagnoses; suicide attempts | Humlum and Plato (2026) | Accumulated number of hospital diagnoses in ICD-10 chapter I (circulatory) or J (respiratory); accumulated hospitalisations where the reason for contact is registered as a suicide attempt (or accident). | LPRICD-10 chapter I; ICD-10 chapter J | working paper · Appendix Table A.1, rows 21-23, 26 |
| Cause of death categories | Dahl et al. (2024) | Underlying cause of death (ICD-10) from the cause-of-death registers: cardiovascular = I00-I78; cancer = C00-C97 and D00-D09; lifestyle-related = alcohol (K70, K74, K86.0, F10), smoking (COPD J40-J44) and obesity (E66, diabetes E10-E14); other = all remaining deaths. A Case-Deaton 'behavioural' grouping (diabetes E10-E14, suicide X60-X84/Y87.0, poisoning X40-X45/Y10-Y15/Y45/Y47/Y49, chronic liver K70/K73/K74, lung cancer C34) is also built. | DODSAASG, DODSAARSC_DODTILGRUNDL_ACME (DODSAASG), C_DOD1 (DODSAARS); ICD-10 I00-I78; C00-C97, D00-D09; K70,K74,K860,F10; J40-J44; E66,E10-E14 | replication code · CreateData_DK.do lines ~132-192 |
| Cause-specific death | Hjort et al. (2017) | Causes of death from the Death Register grouped into the 23 Danish National Board of Health groups, with groups 12-13 and 20-23 merged; both first and second cause used. Groups (ICD-10; ICD-8): 1 infection A00-B99, 000-136; 2 cancer C00-C97, 140-209; 3 other cancer D00-D48, 210-239; 4 blood D50-D89, 280-289; 5 endocrine/metabolic E00-E90, 240-246, 250-279; 6 mental F03-F99, 290-315; 7 nervous system G00-G31, 320-389; 8 heart disease G35-H95, I00-I25, I27, I30-I51, 390-398, 400-404, 410-414, 420-429; 9 other cardiovascular I26, I28, I60-I99, 430-438, 440-448, 450-458; 10 respiratory J00-J99, 460-474, 480-486, 490-493, 500-519; 11 digestive K00-K92, 520-577; 12-13 skin/musculoskeletal L00-L99, M00-M99, 680-738; 14 genitourinary N00-N98, 580-629; 15 pregnancy O00-O99, 630-678; 16 perinatal P00-P96, 760-779; 17 congenital Q00-Q99, 746-759; 18 symptoms R00-R98, R99, 780-793, 795-796; 19 accidents V01-X59, Y40-Y86, Y88, E80-E94; 20 suicide/murder/legal interventions X60-X99, Y00-Y36, Y89, R99, E95-E99 (as printed). | Danish Register of Causes of Deathsee definition | online appendix · Appendix A.1, Causes of death |
| Cause-specific mortality | Kristiansen and Sheng (2025) | Underlying cause of death (C_DODTILGRUNDL_ACME, or C_DOD1 in older register), deaths from 1995: cardiovascular = any ICD-10 I; heart = I20-I25; acute stroke = I20-I25 or I60-I69; cancer = any C (lung C34 and other subgroups); diabetes = E10-E14; COPD = J44. | DODSAASG, DODSAARSICD-10 I*, I20-I25, I60-I69, C*, C34, E10-E14, J44 | replication code · 1b_mortality.do |
| Child intellectual disability (mental retardation) diagnosis | Daysal et al. (2022) | Hospital diagnosis of mental retardation in any diagnosis field (action, secondary, psychiatric) by age 10. | LPR, PCRRICD-8 310.09-315.99 (n_adiag 31009-31599); ICD-10 DF70-DF79 | replication code · 06a Adding child diagnosis.sas, lines ~164-167, 393-404 |
| Child psychiatric diagnosis | Daysal et al. (2022) | Any psychiatric diagnosis in hospital registers: ICD-8 codes 290.09-315.99 or any ICD-10 chapter F (DF) code in any diagnosis field. | LPR, PCRRICD-8 29009-31599; ICD-10 DF* | replication code · 06a Adding child diagnosis.sas, lines ~160-162, 373-385 |
| Chronic diagnoses (diabetes, cardiovascular, cancer, asthma, infection) | Baker et al. (2026) | Action or secondary diagnoses (diag type A or B) in LPR 1977-2018 plus causes of death, treated as absorbing with onset at first year observed; diabetes uses ICD-10 only. | LPR, Cause-of-death registerDiabetes ICD-10 DE11, DE13, DE14; cardiovascular DI00-DI99 / ICD-8 390-458 (heart DI20-DI25 / 410-414); cancer DC00-DC97 / ICD-8 140-209; asthma DJ45-DJ46 / ICD-8 493; infection DA00-DB99 / ICD-8 000-136 | replication code · dst_replicationpackage/do/2b_cr_diags.do |
| Chronic drug | Simonsen et al. (2021) | Following Alpert (2016), a drug at ATC level 3 (pharmacological subgroup) is chronic if its median consumer buys it in at least three calendar months of a year (more than twice). | LaegemiddelstatistikregisteretATC level 3 | paper · Section 3 footnote 5; Appendix C |
| Chronic respiratory hospitalizations | Daysal et al. (2026) | Inpatient visits with a primary diagnosis of a chronic respiratory condition (e.g. asthma, COPD), studied at ages 16-26. | LPRICD-8 491-493; ICD-10 J4 | online appendix · Appendix A |
| Clinical eligibility controls | Daysal et al. (2025) | All possible interactions among indicators for: mastectomy, age <45 at diagnosis, number of positive nodes (0, 1-3, 4+), tumor larger than 50mm, and tumor removed micro-radically; plus separate indicators for age at diagnosis in full years, tumor size in cm, number of positive nodes (0, 1-3, 4+), micro-radical removal, type of surgery (lumpectomy, mastectomy, or lumpectomy followed by mastectomy), and chemotherapy type (DBCG89 trial arm: CMF, CEF, OA, tamoxifen regimens, pamidronate arms); and year-of-diagnosis fixed effects. Standard errors clustered by hospital. | DBCG clinical databasenodes 0/1-3/4+; tumor >50mm; age <45 | online appendix · Notes to Appendix Tables A2-A4, A9 |
| Decline in 5-year survival probability due to cancer | Andersen et al. (2026) | Logit of dying within 5 years, estimated on people aged 18-62 in 1980-2013 (diagnosed plus up to ten exactly matched controls per case) with cancer-type indicators (14 types plus multiple), age polynomial interactions, post-cancer, gender and marriage controls, separately for 1980-1992, 1993-2003 and 2004-2013. The decline is predicted probability minus the counterfactual with no diagnosis. Low survival means an above-median decline within gender (also split by quintile). | Cancer register, Death register, Population registers | online appendix · Online Appendix E |
| Depressed peer (treatment) | Hansen et al. (2025) | Mother's own EPDS (Edinburgh Postnatal Depression Scale) score from municipal health-visitor records (first postnatal screening); high = EPDS > 10. Treatment = at least one other mother in the assigned mothers' group has a high EPDS score (leave-self-out group mean of high > 0); untested mothers coded as not high. | Municipal health visitor records (EPDS), MFRhigh = (score_e1 > 10); peer_treat = (leave-one-out mean of high in group > 0); questionnaires 'EPDS gravide','EPDS nybagt','Edinburgh' variants | replication code · 10_cr_merge.do lines ~228-245; 2a_cr_efterf.do |
| Depression | Lundborg et al. (2017) | Measured as antidepressant medication use (data complete only from 1994 onward). | Prescription registry | paper · Section IV and Table 2 notes |
| Disability type and severity | Black et al. (2021) | Disabilities grouped into physical, cognitive and behavioral; severity checked using hospitalisation records (inpatient, outpatient and emergency room admissions). | Danish medical patient register | working paper · Section 4, footnotes 11-12 |
| Disease diagnoses (heart, cardiovascular, diabetes, cancer) | Hjort et al. (2017) | Diagnoses from the Danish Inpatient Register 1980-2012 (ICD-8 until 1993, ICD-10 from 1994); individuals must be hospitalized (1980-1993) or have at least one outpatient visit (1994-2012) to appear. ICD-8: diabetes 249, 250; cardiovascular disease 390-458; heart disease 410-414. ICD-10: diabetes DE10-DE14; cardiovascular disease DI00-DI99; heart disease DI20-DI25. | Danish Inpatient Register (Landspatientregisteret)ICD-8 249-250, 390-458, 410-414; ICD-10 DE10-DE14, DI00-DI99, DI20-DI25 | online appendix · Appendix A.1 |
| Fatal heart attack or stroke (death event) | Fadlon et al. (2026) | Robustness version requires the spouse to die from the same cause as the index health event within 12 months. | LPR, Death Registry | online appendix · Supplemental Appendix, Figures B.2-B.3 notes |
| Fatal shock | Fadlon and Nielsen (2019) | Death of a family member between 1985 and 2011, from the Cause of Death Registry (exact death dates and causes). | Cause of Death Registry | working paper · Section 3.2 |
| First sudden parental death (treatment) | Jensen and Zhang (2026) | Sudden deaths = deaths from heart diseases, cerebrovascular diseases, and traffic or other (external) accidents; non-sudden = all remaining causes. Sample: first parental deaths 1985-2014 for children aged 25-50 in the year of the first parental death with two known parents. | DODSAARS/DODSAARG, BEFcause categories only (no ICD list given in appendix); child age 25-50; deaths 1985-2014 | online appendix · Supplemental Appendix Figure B.2 and B.5 notes |
| Health proxy (sickness benefits) | Ejrnæs and Hochguertel (2013) | Receipt of sickness benefits is used as the health or illness measure because of data limitations. | CAM 10% Sample | working paper · Section 5.2 |
| Heart attack, stroke and circulatory hospitalizations | Hummels et al. (2025) | Person-year indicators for hospital contacts with action diagnoses for heart attack, hemorrhage, cerebral infarction, hypertension (bloodpressure) and a broad circulatory group. | LPRADIAG: heart attack DI21; hemorrhage DI61; infarction DI63; bloodpressure DI10-DI13, DI15; circulatory DI20-DI25, DI44-DI50, DI512, DI513, DR001 | replication code · Programs/1_data_job_spell/stressdiagnoses4.sas |
| High-severity non-fatal shock | Fadlon and Nielsen (2021) | Binary indicator equal to 1 if the sick spouse was hospitalized for 10 days or more following the heart attack/stroke and 0 if hospitalized for fewer than 10 days; 10 days is the 75th percentile of the distribution of hospitalization days associated with the shock in the treatment sample. | National Patient Registry (LPR)Hospitalization days >= 10 (75th percentile) | online appendix · Online Appendix Table D.1 notes |
| Infant and child mortality per 10,000 births | Altındağ et al. (2024) | mor1 = 10,000 if the child died (death register date) before the first birthday, 0 otherwise; mor5 = 10,000 if died before the fifth birthday. Deaths taken from DOD and linked to birth-register children. | DOD, MFRmor1 = 10000 if dead==1 & deathdate < bday_1yr; mor5 analogous with bday_5yr | replication code · 01 Set up RD data.do, lines ~45-60; 01_mortality_no_cause.do |
| Injury severity (earning capacity loss, personal impairment) | Humlum et al. (2025) | Earnings capacity loss = loss of earning capacity in percent as assessed by The Labour Market Insurance (AES); personal impairment = degree of personal impairment in percent based on injury diagnosis. Temporary injuries = accidents AES assesses did not cause permanent loss of earning capacity or personal impairment. Non-injured controls are assigned the injury year of their matched injured worker. | AES Industrial Injury Register (Arbejdsskaderegisteret) | online appendix · Supplemental Appendix Table A.1, rows 26-28; temporary-injury figure notes |
| Lifestyle-related disease diagnosis | Hasager and Jørgensen (2026) | Indicator for any primary or secondary hospital diagnosis (inpatient or outpatient, National Patient Register; ICD-8 before 1994, ICD-10 after) within 2-15 years, or within 8-15 years, after immigration in one of: circulatory diseases (hypertension, ischaemic heart diseases, pulmonary diseases, other heart disease, cerebrovascular and arterial diseases), nutritional/endocrine/metabolic diseases (diabetes type I and II, obesity, elevated cholesterol), COPD, hip arthrosis, and alcohol-related diseases. Exact code grouping is in Online Appendix Section B (not accessed). | LPR | working paper · Section I (data), footnotes 17-19 |
| Low / very low birth weight, prematurity | Daysal et al. (2021) | From the Birth Registry for live births: low birth weight is below 2,500 grams, very low birth weight below 1,500 grams, and premature is gestational age under 37 completed weeks; birth weight is also used continuously. | Medical Birth Registry | working paper · Section 3 |
| Low birth weight and preterm birth | Altındağ et al. (2024) | Birth weight from MFR (V_VAGT before 1997, VAEGT_BARN after) set to missing if 0 or >9,000 g; LBW = <2,500 g, VLBW = <1,500 g; preterm = gestational age < 37 weeks (fullterm37 = 0). Gestational age only available from 1978 and birth weight recorded in 500 g bands before 1978. | MFRV_VAGT / VAEGT_BARN; V_SVLANGDE / GESTATIONSALDER_DAGE | replication code · 01_MFR_1973_1996.do lines ~23-97; 02_MFR_1996_2008.do |
| Mental disorder diagnosis | Hasager and Jørgensen (2026) | Indicator for a hospital diagnosis 2-15 or 8-15 years after immigration of mental and behavioural disorders due to psychoactive substance use; schizophrenia, schizotypal and delusional disorders; mood (affective) disorders; neurotic, stress-related and somatoform disorders; behavioural syndromes associated with physiological disturbances; and disorders of adult personality and behaviour. | LPR | working paper · Section I, footnote 20 |
| Mortality | Baker et al. (2026) | Death dates pooled from the cause-of-death registers (DODSAARS2001, DOD2013, DODSAASG2017, using the statistics date if death date missing) and the Statistics Denmark death registers DOD2018 and DOD2020 (the latter without cause of death). | Cause-of-death register, DODD_DODSDTO, C_DOD1; DODDATO | replication code · dst_replicationpackage/do/2a_cr_deaths.do |
| Non-fatal cardiovascular shock | Fadlon and Nielsen (2019) | First-time heart attack or stroke (identified from the National Patient Registry, ICD diagnoses) between 1985 and 2011 where the individual survived the four-year analysis horizon (three years for stepchildren, children-in-law and coworker samples). Household matches based on period t = -1. | LPR, Cause of Death Registry | working paper · Sections 3.1-3.2 |
| Non-fatal severe health shock | Fadlon and Nielsen (2021) | Households in which one spouse experienced a heart attack or a stroke for the first time between 1985 and 2011 and survived for at least three years, with both spouses under age 60. Controls are households from the same cohorts experiencing the same event five years later. | National Patient Registry (LPR)First-ever heart attack or stroke; survival >= 3 years; both spouses < 60; years 1985-2011 (no ICD list given in appendix) | online appendix · Online Appendix Table A.1 notes |
| Nursing home residents and mortality | Friedrich and Hackmann (2021) | Nursing home addresses are identified from deaths registered in a nursing home (place-of-death institution code 2) 1990-2000, using the first 14 characters of the address code (BOPIKOM) within municipality, keeping addresses where no children live; residents aged 60+ living at those addresses are nursing home residents, and their mortality is the outcome. | DAR, BEF (Personer)C_INSTITUT == "2"; address = BKOM + substr(BOPIKOM,1,14) | replication code · programs/stata/Prepare_elderly_data.do |
| Parental psychiatric diagnosis | Houmark et al. (2025) | Indicators for the mother and father ever having received a psychiatric diagnosis (based on the date of the parent's first psychiatric diagnosis in the parental psychiatry file); an additional indicator flags a diagnosis by the child's birth year. | Psychiatric patient registers (parentalpsych file)mpsych_date, fpsych_date -> mmh_ever, fmh_ever, mmh_age0, fmh_age0 | replication code · Results (for replication).do |
| Predicted cardiovascular risk | Fadlon and Nielsen (2019) | Probit for the one-year probability of a first heart attack or stroke, estimated on the whole population aged 20-90 in 1994-2014, with age fixed effects, gender, hypertension and diabetes status (from lagged indicators of condition-specific medication), year fixed effects and education; lagged fitted probability used as individual risk. | Prescription Drug Database (LMDB), National Patient Registry (LPR), Education registerages 20-90, years 1994-2014 | online appendix · Appendix B, step I |
| Psychiatric diagnoses by age 10 | Breining et al. (2020) | Indicator for having received, by age 10, a diagnosis at a Danish hospital of a disorder of psychological development, or a behavioural and emotional disorder (e.g. ADHD), from the National Patient Registry and Psychiatric Central Registry. | LPR, Psychiatric Central RegisterICD-10 F80-F89 (psychological development); ICD-10 F90-F98 (behavioural and emotional) | working paper · Section 3.1, footnote 5 |
| RSV hospitalizations | Daysal et al. (2026) | Hospitalizations for respiratory syncytial virus, measurable only for cohorts born 1994 or later (ICD-10). | LPRICD-10 J12.1, J20.5, J21.0, B97.4 | online appendix · Appendix A |
| SDQ index (non-cognitive outcome) | Gupta and Simonsen (2010) | Strengths and Difficulties Questionnaire total index (0-40, 0 = no behavioural problems) built from emotional symptoms, conduct problems, hyperactivity/inattention and peer-relationship items (25 items answered not true/somewhat true/certainly true), reported by the primary parent in the 2003 DALSC wave at child age 7.5. | DALSC | working paper · Section 3; Appendix Table A2 |
| Shock severity | Fadlon and Nielsen (2019) | Severity of the family shock measured by the number of hospitalization days (also split at the median). | National Patient Registry (LPR) | online appendix · Appendix Table 3 notes |
| SIDS and other cause-specific deaths | Altındağ et al. (2024) | Primary cause of death (C_DOD1 in DODSAARS up to 2001; C_DODTILGRUNDL_ACME in DODSAASG from 2002) classified with ICD-8 before 1994 and ICD-10 from 1994; SIDS = ICD-8 795.0-795.9 / ICD-10 R95; per 10,000 births when death occurs before age 5. Other groups: infectious, neoplasms, nervous system, respiratory, perinatal, congenital malformations, unknown. | DODSAARS, DODSAASGSIDS: ICD-8 7950-7959, ICD-10 R950-R959; inf 0000-1399/A00-B99; neo 1400-2389/C00-C99; nerv 3200-3899/G00-H95; resp 4600-5199/J00-J99; perinatal 7600-7799/P00-P96; congenital 7400-7599/Q00-Q99; unknown 7960-7969/R96-R99 | replication code · 02_mortality_with cause.do |
| Stress, depression and burnout hospitalizations | Hummels et al. (2025) | Person-year indicators for hospital contacts with an action (primary) diagnosis in the listed ICD-10 groups: severe stress, depression, mild depression, work problems, burnout; broad burnout = burnout + work problems excluding unemployment/job change/job loss; narrow burnout = burnout + stressful work + mental strain. | LPRADIAG: severe stress DF43; depression DF32; mild depression DF320, DF321, DF329; work problems DZ56 (excl. DZ560 unemployment, DZ561 job change, DZ562 job loss for workproblems2); stressful work DZ563; mental strain DZ566; burnout DZ730-DZ733 | replication code · Programs/1_data_job_spell/stressdiagnoses4.sas |
| Sudden parental death | Druedahl and Martinello (2022) | Parental death (1996-2012) is sudden if the primary cause of death has the listed ICD-10 codes. The WP states I21*-I22*, V*, X*, Y*, R96*; the replication code additionally includes I23, I24 and I46. | Register of Causes of Death (doedsaars1), FTDB (parent-child links)WP: ICD-10 I21-I22, V*, X*, Y*, R96; code: substr(doedsaars1,1,3) in I21,I22,I23,I24,I46,R96 or first letter V, X, Y | replication code · rawbuild.do ('DEATHS AND INHERITANCE'); CEBI WP 02-17 fn. 7 and Appendix D |
| Suicide attempts and suicides | Serena (2025) | Suicide attempts are somatic hospital contacts with contact reason 'suicide attempt' (duplicates on the same day removed); poisonings by selected drug groups are tracked separately; completed suicides from the cause-of-death register (49-group list code 48). Attempts are priced using average costs by patient type and diagnosis. | LPR, DODSAARS, DODSAASGC_KONTAARS=4 (attempt); poisoning C_ADIAG DT39, DT42, DT43 (DT39 painkillers); suicide C_LISTE49='48'; somatic contacts exclude C_SPEC 50, 52 | replication code · CollectData_Psyc_AllAges.sas (Suicide attempts section); PsykTestLongRunRD.do |
| Survival beyond age 65 | Rossin-Slater and Wüst (2020) | Indicator for surviving past age 65, with data left-censored so that all individuals enter the risk period at age 50 (outcome data start in 1980). Supplementary outcomes include hospital care utilization (e.g. nights in hospital ages 55-64) and diagnoses of cardiovascular disease, heart disease, diabetes and cancer; specific diagnosis codes are not given in the main text. | Population/death registers, National Patient Register | working paper · Section 4.1 |
| Third-born sibling disability (treatment) | Black et al. (2021) | Indicator D = 1 if the third-born child is observed disabled, in Denmark from disabilities recorded in the medical (patient) registries, with the main treatment being disability first observed by age five (alternatively between ages five and ten). Most common Danish by-age-five disabilities: asthma (41%), congenital malformations of the musculoskeletal system (20%), congenital malformations of the circulatory system (10%); diagnosis code lists are not given. | Danish medical patient register | working paper · Section 2 (Data) and Eq. (1) |
| Work accident causing loss of earning capacity (treatment) | Humlum et al. (2025) | Work accidents recorded by the Labour Market Insurance (AES) that caused a loss of earning capacity, restricted to physical (not psychological) injuries, non-military workers, and workers with full-time employment in each of the three years before the accident. An injury qualifies for compensation if personal impairment exceeds 5% or earning capacity loss exceeds 15%. | Industrial Injury Register (Arbejdsskaderegisteret, AES), IDA | working paper · Sections 2.1, 2.2.1 and 3.1 |
| Work accident with permanent earning-capacity loss | Humlum and Plato (2026) | Work accident reports from Labour Market Insurance (AES) with an estimated earning capacity loss (ECL) > 0, excluding psychological shocks; collapsed to person-year. Earning capacity loss is estimated upfront by AES. Milder accidents without permanent loss serve as placebo. | AES | working paper · Section 1.3; footnote 7; Appendix Table A.2 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Charges and convictions | Foged et al. (2024) | Charges from KRSI and court rulings from KRAF restricted to Criminal Code offences (GER7 code starting with 1, or with 32 or 34); rulings with decision types 0, 413, 414, 514, 519, 550, 551, 555, 556 (non-convictions) dropped; shoplifting identified as GER7 code 1332515. | KRSI, KRAFSIG_GER7 / AFG_GER7 first digit 1 or first two digits 32, 34; AFG_AFGTYP3 excluded: 0,413,414,514,519,550,551,555,556; property_sm = 1332515 | replication code · 8. Controls and outcomes.sas, lines ~39-41, 5150-5175, 5266-5290 |
| Child criminal convictions ages 15-18 | Foged et al. (2023) | Indicator for any criminal conviction between ages 15 and 18, and separate indicators for property, violent and narcotics crime. | Criminal conviction records | working paper · Section III; Table 3 |
| Conviction | Damm and Dustmann (2014) | Court ruling that the suspect is guilty of the charge, signalled by a sentence (fine, conditional withdrawal of charge, or suspended or unsuspended prison sentence); 'not guilty' if a not-guilty verdict was recorded or the indictment dropped. Convictions are linked to the start date of committing the offence; all charges and convictions are observed even after deletion from the criminal record. | Central Police Register | working paper · Section II.B and footnote 17 |
| Convictions (guilty cases) | Dustmann et al. (2023) | Judicial decisions 1980-2018 from the conviction register; for each person-case (journr) the final decision is kept (appeals), and a case counts as guilty unless the decision type is acquittal or dismissal. Offences are classified by 7-digit offence code into violent/sexual, property, drug, other and traffic, and traffic offences are dropped; the date of the offence comes from the charge register. | KRAF (conviction register), KRSI (charge register)AFG_AFGTYP3 in 514, 411, 412, 413, 414 = acquitted/dismissed (else guilty); AFG_GER7: sexual and violent 1110505-1180705 and 1210501-1292520; property 1304505-1398590; drug 1435505-1440505 and 3210505-3210510; other 1410303-1430740, 1445705-1485720, 3410505-3410509, 3610105-3870505 (residual non-missing also other); traffic 2110005-2610180 (dropped) | replication code · do/build/clean/convictions.do |
| Crime (conviction for offence committed in year t) | Andersen et al. (2026) | Indicator equal to one if the individual is convicted of a crime committed in year t, dated by offence year and scaled to 100 in event-study figures. Offence types are classified as economic/non-economic and as property/sexual/violent (the latter following Statistics Denmark's system), covering 4,732,529 crimes 1980-2018. | Statistics Denmark crime registersOffence classification in Online Appendix Table I.I | online appendix · Online Appendix H eq. (15)-(16); Table I.I; Figure I.6 notes |
| Crime (convictions and charges) | Dustmann and Landersø (2021) | Charges or convictions for offences against the criminal code from the Central Police Register, excluding traffic offences; convictions (court rulings of guilt) are the preferred measure. Charges and convictions are dated by the date the crime was committed (exact crime dates available from 1990); records are observed even after deletion from the individual's criminal record. | Central Police Register (crime register) | paper · Section II.A and footnote 7 |
| Crime at a given age | Landersø et al. (2017) | Indicator for being charged with a crime between one birthday and the next (memoryless measure), from age 15 onwards. Charges rather than convictions are used to gain three additional years of data; results are robust to using convictions. | Danish crime registers (charges) | working paper · Section 3.1 and footnote 17 |
| Crime at or before a given age | Landersø et al. (2017) | Accumulated indicator for having been charged with a crime at or before a given age (from age 15), keeping track of earlier incidences; also number of crimes for the intensive margin. Outcomes aligned by age are measured exactly at the birthday. | Danish crime registers (charges) | working paper · Sections 3.1 and 3.3 |
| Crime categories | Dustmann and Landersø (2021) | Property crime: theft, fencing, aggravated vandalism, fraud, burglary, forgery, economic crimes. Violent crime: simple violence (assault), severe or life-threatening violence, threats, violence against or obstructing a public servant, failure to help a person in danger, coercion, attempted murder or homicide. Other crime: drug possession and sale, possession of weapons/explosives, false testimony, sexual crimes. | Central Police Register | paper · Footnote 8; online Table A1 (code breakdown, not read) |
| Crime categories | Anker et al. (2021) | Mapping of charge types to analysis categories. Sexual (Penal Code): incest, rape, pedophilia, voyeurism/flashing, other sexual violations. Violence: violence against public servant, disturbance of public peace, murder/manslaughter (incl. attempted), simple violence, major violence, threats, other violent assaults. Property: fraud, arson, theft, burglary, robbery, vandalism, other property crime. Other (penal): crimes against/as a public servant, drug smuggling or sales, obstruction of justice, restraining orders, other penal code crimes. Weapon: Weapons/Arms Act violations (explosives, firearms, knives). Not categorized: Traffic Act violations (accidents and speeding, traffic accidents with alcohol, drunk driving, other traffic), Drug Act possession/sales, other special acts (smuggling, construction, health, social fraud). | Central Crime Register (charges) | online appendix · Table A-1 |
| Crime convictions by offence date | Dustmann et al. (2024) | Convictions for offences against the criminal code (Central Police register labels, e.g. 'theft from supermarket'), defined as court rulings or pre-court settlements of guilt resulting in a sentence (fine, suspended sentence or imprisonment), dated by the exact offence date; 'crime in year 1' = offences committed within the first 365 days after residency. Crime types used: all crime, property crime, theft in supermarket, violence. | Statistics Denmark crime registers (convictions, charges) from courts and policeSentence = fine, suspended sentence or imprisonment | online appendix · Online Appendix B.2; Figure A.9 |
| Crime type (property, violence, other) | Landersø et al. (2017) | Three broad categories: property crime (most frequently shoplifting, burglary, vandalism), violent crime (ordinary and aggravated assault, threats, violence towards public servants) and residual other crime (dominated by traffic-related crime such as driving without a licence, then drug or weapon crime). | Danish crime registers (charges) | working paper · Section 3.1, Table 1 and footnote 19 |
| Criminal charges | Bargain and Jonassen (2024) | Charges from KRSI 2000-2006 (one per person and case number), dated by charge date SIG_GER1DTO and assigned to age in months; outcome is any/number of charges by age month. | KRSISIG_GER1DTO, SIG_GER7, JOURNR | replication code · crime.do |
| Criminal conviction by age 16 / 21 | Breining et al. (2020) | Indicator (x100) for having any criminal conviction by age 16, and by age 21, from the Criminal Registers; convictions observable from age 15 (age of criminal responsibility). Traffic offences are excluded; crime outcomes cover convictions for violent, property and special offences. | Danish Criminal Registers | working paper · Section 3.1; footnote 20; Table 3 notes |
| Criminal conviction from age 25 | Bingley et al. (2020) | Indicator for having been convicted of any type of crime, excluding traffic offences, from age 25, from the Central Crime Register; pre-conscription values used as controls. | Central Crime Register | working paper · Section V, Table 9 panel C |
| Criminal convictions by offence date | Dustmann et al. (2024) | Convictions for offences against the criminal code (court ruling of guilt resulting in a fine, suspended sentence or imprisonment), dated by exact offence date; e.g. 'crime in year 1' = offence committed within the first 365 days after residency. | Central Police register / crime registers | online appendix · Appendix B.3.4 |
| DNA registration (treatment) | Anker et al. (2021) | Indicator that the charged offender appears in the person-specific section of the Central DNA Database, merged to the charge register via personal identification and record numbers. | Central DNA Database (person-specific section), Charge register | working paper · Section 5.1 |
| Fast vs. slow charges (deterrence vs. detection) | Anker et al. (2021) | Solved fast = time from crime date to charge < 3 weeks; solved slowly = >= 3 weeks. Clearance rates computed from all charges and all reported crimes in 2005, excluding heavily reported but rarely solved types such as bicycle theft (charged <10% of the time). Robustness uses a 2-week cutoff (share of crimes solved within 2 weeks: 0.572 overall, 0.520 property, 0.670 violent). | Central Crime Register; National Police reported crimefast < 3 weeks; slow >= 3 weeks | online appendix · Table A-13 notes; Table A-15 notes (2-week cutoff) |
| Group (gang) crime conviction | Dustmann et al. (2023) | A conviction for a gang-type offence in a case where more than one person is convicted (gang_crime_3 requires at least three). Gang-type offences are non-sexual violence, arson/robbery-type property offences, drug offences and weapons offences; a case is gang-type if anyone in the case is convicted of a gang-type offence. | KRAFgang type: 2-digit AFG_GER7 (floor(ger7/100000)) == 12; 4-digit (floor(ger7/1000)) in 1312, 1339, 1380, 1390, 1435, 1440, 3410; 7-digit in 1366505, 1445750, 1445777-1445782, 3210510; gang_crime = N convicted in journr > 1 & gang type | replication code · do/build/clean/convictions.do |
| Guilty and dropped/not-guilty charges | Jensen and Manning (2025) | Counts by end of the year turning 30 of guilty charges and of charges dropped without conditions or ending in a not-guilty verdict (following the Danish Institute for Human Rights 2022). Charges dropped with conditions (e.g. payment of a fine) count as guilty. Traffic Law (Faerdselsloven) offences are excluded. The share dropped/not guilty is computed among those with at least one charge. | KRSI, KRKO, KRAFExcludes Faerdselsloven (traffic law) charges | online appendix · Online Appendix OA.1 |
| Individual crime outcomes ages 15-21 | Damm and Dustmann (2014) | Number of convictions for offences (except traffic offences) committed in age range a1-a2 (15-21, 15-17, 18-21); type-specific counts by category j. | Central Police Register | online appendix · Table A5.A |
| Municipal youth (gang) crime conviction rate | Dustmann et al. (2023) | Per municipality and year: number of male residents aged 15-25 (calendar year minus birth year between 15 and 26) with at least one conviction (resp. gang-crime conviction) for an offence committed that year at age above 14 and below 27, divided by the number of such male residents, times 100. Offences from 2017 onward or before 1980 are dropped. | KRAF, KRSI, BEFsh_g (gang), sh_nc (overall); KOEN==1 for men; KOM municipality | replication code · do/build/prepare/kom_year_n_criminals.do |
| Neighbourhood victimisation | Dustmann and Landersø (2021) | Based on individuals who report having been the victim of a crime (register available from 2001); victim sample includes all individuals living in the father's neighbourhood at the time of the birth, counting the number of victims in each neighbourhood. | Victim register | paper · Section II.B (victim sample) and footnote 10 |
| Offence categories | Damm and Dustmann (2014) | Mapping of Central Police Register categories: (1) violence = all sexual assault convictions (rape, offence against decency, sexual offence against children under 12, other) and all violence convictions (violence against public authority, simple violence, threats, manslaughter, other); (2) property = document fraud, burglary in bank or shop, shoplifting etc., vandalism, fraud, other property offences; (3) drug crimes = sale of narcotics (penal code) and all Drugs Act offences; (4) other = perjury, privacy violation and defamation, negligent manslaughter (road accident), other penal code offences, Arms Act, tax acts and other special acts (gambling and trade laws, tax and duty law). Traffic Act offences (incl. drunk driving, speeding) excluded. | Central Police RegisterPenal code, Traffic Act (excluded), Drugs Act, Arms Act, tax and other special acts | online appendix · Table A4 |
| Participation in crime by age 35 | Eshaghnia et al. (2025) | Indicator for appearing in the crime registers (criminal convictions from the sentencing register KRAF) by age 35, regardless of offence type. Appendix D.2 splits into violent, property (e.g. forgery, arson, burglary, theft, shoplifting, embezzlement, fraud, extortion, robbery, tax fraud, vandalism) and other crimes (all except violent, property and traffic), measured by ages 20, 25 and 30. | KRAF, KRIN | working paper · Section 3.1, footnote 17; Appendix A.1; Appendix D.2 |
| Peer group and peer crime | Dustmann and Landersø (2021) | Peers are all males within +/-3 years of the focal father's age living in the father's neighbourhood on January 1 of the year the child is born (alternatively all males aged 14-25 at the birth), regardless of later moves; the father and his family members are excluded. Peer crime is their convictions/charges. | Residential register, BEF, Central Police Register | paper · Section II.B; footnote 5 |
| Policing measures | Damm and Dustmann (2014) | Police officers per 1,000 inhabitants = detectives plus uniformed officers employed in the police district (annual police reports 1986-1999); crime detection rate = annual number of charges divided by annual number of reported crimes in the municipality or police district. | Annual police reports, Statistiske Efterretninger | online appendix · Table A5.B |
| Prison sentence / incarceration by age 16 / 21 | Breining et al. (2020) | More severe crime measures: indicator for ever having been issued a suspended prison sentence, and for an implemented (unsuspended) imprisonment sentence (incarceration), by age 16 and by age 21. Traffic offences excluded. | Danish Criminal Registers | working paper · Section 3.1; footnote 20; Figure 1 notes |
| Prison sentence by 30 | Jensen and Manning (2025) | Indicator equal to one if the child has ever been sentenced to prison, suspended or unsuspended, by the end of the year in which they turn 30. | KRAF | online appendix · Online Appendix OA.1 |
| Recidivism: subsequent convictions | Anker et al. (2021) | Convictions for crimes committed after the sample charge, counted from the day after the charge, within one, two and three years; coded as a binary (at least one conviction) and as a count top-coded at 10 convictions per follow-up year. Measured for all crime and separately for violence, property, sexual, other penal (incl. serious drug offenses) and Weapons Act violations. | Conviction register, Charge register | working paper · Section 5.2, footnote 16 |
| Reported crimes per capita | Damm and Dustmann (2014) | Reported crimes in a calendar year divided by inhabitants of the municipality (or police district); reported violent crimes per 10,000 inhabitants defined analogously. Source: 'Statistiske Efterretninger om Social Sikring og Retsvaesen' (1986-1998); inhabitants from Statistikbanken BEF1A. | Statistiske Efterretninger, Statistikbanken BEF1ABEF1A | online appendix · Table A5.B |
| Time from offence to apprehension | Andersen et al. (2026) | Log of one plus the days between when a crime is committed and when the perpetrator is apprehended, using the median time if the person commits multiple crimes in a year; only observations in the offence year are used. | Statistics Denmark crime registers | online appendix · Online Appendix Table I.IX notes |
| Violent victimization | Hasager (2024) | Quarterly indicator equal to one if police records show the woman was a victim of violence or sexual assault, or if she was hospitalized due to assault based on physician classification of injury (following Aizer 2010); captures mainly severe cases and also non-partner violence. | Police registers on victimization, National Patient Register (hospital admissions) | working paper · Section 3.2, footnote 12 |
| Violent, property and special crime convictions | Breining et al. (2020) | Convictions by age 21 split by crime type: violent (e.g. manslaughter, rape, death threats), property (e.g. forgery, arson, burglary) and special offences (e.g. drug offences, illegal weapon possession, human trafficking). Denmark birth cohorts 1981-1990. | Danish Criminal Registers | working paper · Section 5.1; Table 3 |
| Youth crime conviction rate of municipality | Damm and Dustmann (2014) | Share of individuals aged 15-25 living in the municipality convicted of an offence (except traffic) committed in that calendar year; overall crime conviction rate = same share among all residents; type-specific rates for j = violent, property, drugs, other. Computed for 1986-1998. | Central Police Register, Population registerages 15-25 | online appendix · Table A5.B; Table A3 notes |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Assignment municipality | Dustmann et al. (2023) | The first municipality (KOM) in which the household head at assignment is observed in the population registers after immigration; children assigned to a different municipality than their household head are dropped, and Copenhagen assignment is KOM 101. | BEFfirst_kom = kom[1]; copenhagen = KOM 101 | replication code · do/build/prepare/assigned_refugees.do; sample_girls_15_20_at_assignment.do |
| Assignment neighbourhood | Hasager and Jørgensen (2026) | Neighbourhood of the refugee's address on 1 January in the year after immigration, to capture permanent resettlement rather than temporary accommodation. | BEFBOP, BEFADR | working paper · Section II (identification), p. ~14 |
| Commuting areas (local labour markets) | Munch et al. (2006) | Local labour markets are commuting areas built from geographically connected municipalities such that the internal migration rate is 50% higher than the external migration rate (Andersen 2000); the 275 municipalities are merged into 51 commuting areas. | Administrative registers | working paper · Section 3, p. 9 |
| Consumer cell (region x industry of main employment) | Andersen et al. (2026) | Every adult is assigned to a region by home address at the start of 2018 (BEF), one per each of 98 municipalities, and to an industry by the NACE code of the establishment responsible for the largest part of 2018 labour income (AKM), mapped from six-digit NACE to 24 work industries. Cells with fewer than 3 individuals or establishments are censored. | BEF, AKMsix-digit NACE mapped to 24 DEA work industries (Table A.I); 98 municipalities | working paper · Appendix D; Section II |
| County | Friedrich and Hackmann (2021) | County assigned from workplace municipality (AKOM) for employed workers, otherwise residence municipality, using pre-2007 municipality code ranges mapped to 15 counties. | IDAPMunicipality codes 101-147 = county 1; 151-189 = 2; 201-237 = 3; 251-271 = 4; 301-345 = 5; 351-397 = 6; 400-409 = 7; 421-499 = 8; 501-545 = 9; 551-577 = 10; 601-631 = 11; 651-685 = 12; 701-751 = 13; 761-793 = 14; 801-861 = 15 | replication code · programs/stata/Prepare_aggregate_county_exposure.do |
| Daycare availability in 1970 parish (treatment) | Sander (2026) | Mothers are placed in their parish of residence in the 1970 Census. vg = 1 if a daycare institution for ages 0-2 (nursery or integrated institution, from digitised institution data) existed in the parish in the year of first birth; bh = same for ages 3-6 (kindergarten); availability is also tracked for each year from child age 0 to 6 (all/some/no years). | FT70 (1970 Census, parish), Daycare institution yearbooks/registersinstitution number NUMMER < 120000 = nursery (vuggestue), > 250000 = integrated; parish (SOGNNR) x year; vg, bh, daycare0-daycare6 | replication code · dofiles/createfirst.do, 'FOLKETÆLLING 1970', 'DAYCARE 0-2', 'DAYCARE 3-6' |
| Geographically mobile job finding | Svarer et al. (2005) | An unemployed worker is geographically mobile if he or she gets a job and moves to another commuting area within the window from 8 weeks before to 52 weeks after the start of the job spell; otherwise a job exit is local. | Population registers, Weekly event histories | working paper · Section 4.1 |
| Guaranteed access to preschool (GAPS) instrument | Gupta and Simonsen (2010) | Binary indicator that the child's municipality guarantees all children a (center-based) preschool slot within the municipality, used as the instrument for preschool take-up; information on child care guarantees supplied by Bureau 2000. Bornholm excluded. | Municipality child care guarantee data (Bureau 2000) | working paper · Section 2.1 'Guaranteed access to preschool (GAPS)'; acknowledgements; Appendix (instrument) |
| High work distance | Gillingham et al. (2022) | Based on the Danish tax deduction for travel distance to work, which applies only to full-time workers living more than 12 km (each way) from their workplace; households with the deduction are classed as high work distance (slightly under half of Danes). | Tax records (commuting deduction) | working paper · Appendix F.1 (Household types) |
| Immigrant share | Damm and Dustmann (2014) | Number of immigrants and descendants living in the municipality divided by inhabitants. | Population register | online appendix · Table A5.B |
| Implied commute | Fluchtmann et al. (2024) | Commuting distance or time from the applicant's municipality of residence to the job's postal code, computed with the Google Maps API (following Harmon 2015). | Joblog, BEF/IDA residence | working paper · Section 4, footnote 33 |
| Initial placement: municipal labour market strength and co-nationals | Foged et al. (2024) | For the conditionally random dispersal (refugees granted status 1987-1998): employment rate of non-Western immigrants in the municipality of first placement in the placement year, and share of co-nationals in that municipality's population; both divided by their SD. | VNDS, BEF, IDA | working paper · Sections 4.2 and 6.3, equation (4) |
| Local counselling intensity | Cairo and Mahlstedt (2023) | Average number of caseworker meetings per recipient in the municipality in the six months before the intervention, split at the median. A residualised version conditions on observable characteristics. | Public employment service data | working paper · Section 4.4, footnote 20, Appendix Table A.2 |
| Local labor demand: low/unskilled job openings per unemployed | Dustmann et al. (2024) | Job openings for low- and unskilled work posted on Jobindex.dk (from 2002) counted by postal code and aggregated to municipality, divided by the municipality's stock of unemployed (population register residence x labour market register unemployment), averaged over 2002 and 2003. Because postings only exist from 2002, the ratio is predicted from 1999-2001 municipal employment conditions (average employment rate, non-Western immigrant employment rate, unskilled employment rate, immigrants' average labor earnings and their interactions). Municipalities ranked 0-1 and split at the unweighted median (low-demand ~0.17, high-demand ~0.32 openings per unemployed). | Jobindex.dk postings; population register (BEF); labour market registerMedian split of predicted ratio | online appendix · Online Appendix B.2 and Figure B.1 |
| Local labour market | Foged and Peri (2016) | 97 municipalities identifiable consistently 1988-2008, merging Frederiksberg and Copenhagen; exposure measured by municipality of work (DiD exposure by 1994 municipality of residence). | IDA | working paper · Section 3.2; Introduction |
| Local labour market (commuting area) | Svarer et al. (2005) | Local labour markets are groups of municipalities between which the internal migration rate is 50% higher than the external migration rate (Andersen 2000), giving 51 commuting areas in Denmark. | Population registers | working paper · Section 4.1 |
| Municipal house prices and house price innovations | Browning et al. (2013) | Average sales prices of traded single-family houses by municipality (275 municipalities), 1985-2001, collected by the tax authorities. Log municipal price modelled as an AR process with municipality effects, average house size (square metres, rooms) and year effects, estimated by system GMM; the predicted change is the expected price change and the residual is the house price innovation. | Tax authority house sales statistics (Statistics Denmark) | working paper · Section 4 (House prices); Section 5 (house price process); Appendix 1 |
| Municipal poverty rate | Damm and Dustmann (2014) | Relative poverty rate = share of adults in the municipality with equivalence-scaled disposable household income below 50% of the national median equivalence-scaled disposable household income. | Tax and income registers< 50% of national median | online appendix · Table A5.B |
| Municipality characteristics (right-wing votes, county jobs, social housing) | Damm (2009) | % right-wing votes: Liberal plus Conservative votes as a share of Liberal, Conservative, Social Democratic and Socialist People's Party votes at the latest municipal election; % county jobs: persons employed in the municipality as a share of county employment; % social housing: social housing dwellings for all-year residence as a share of all such dwellings. | Election statistics, RAS, Buildings and housing statistics | working paper · Table A.I Part B |
| Municipality of assignment | Damm (2009) | The first municipality of residence observed in the registers is classified as temporary housing if the person relocates to another municipality within the same county within one year after receipt of the first residence permit; otherwise the first municipality is the municipality of assignment (algorithm based on the Danish Refugee Council's statistics on temporary housing). | BEF, Danish Refugee Council administrative statistics | working paper · Section III.D |
| Municipality of birth from parish of birth | Rossin-Slater and Wüst (2020) | Births are registered at parish level; parish of birth is mapped to 1930-1960 municipalities using DigDag. Individuals with invalid or undocumented parish codes, post-1970 municipality coding, registration by religious minorities, or hospital births are dropped (about 90 percent of Danish-born have a valid parish of birth). | Population register (parish of birth), DigDag | working paper · Section 2, Sample construction and selection, footnote 15 |
| Neighbourhood | Dustmann and Landersø (2021) | Neighbourhoods follow the classification documented in Damm and Schultz-Nielsen (2008), constructed in 2004, linked to individuals through anonymised home address identifiers in the residential register; the largest and smallest 5% of neighbourhoods are excluded from most analyses. | Residential register, BEF | paper · Section II.B |
| Neighbourhood income group (parish) | Hasager and Jørgensen (2026) | Neighbourhood = parish (alternatives: municipality, apartment building). Each year, neighbourhoods are ranked by median household disposable income per adult household member (Income Register, CPI-deflated to 2000) and split into thirds: bottom (below 33rd percentile), middle (34th-66th), top (67th-100th); groups are recomputed every year for all neighbourhoods. | IND, BEF | working paper · Section I, footnote 25 |
| Neighbours | Hvidberg et al. (2023) | For apartment dwellers, people aged 25-65 living in the same stairwell; for house dwellers, people aged 25-65 living on the same road. Neighbour links incl. road length were created by Statistics Denmark's GEO team; neighbour car and house value ranks use wealth registers. | BEF, IND, FORMBIL, FORMEJER, FORMAND | paper · Table 2; README.docx (analysisI.dta construction) |
| Non-Western immigrant employment rate (municipality) | Dustmann et al. (2024) | Fraction of 20-60-year-old non-Western immigrants in a municipality who are employed, 1999-2001, using population-register data recorded on January 1 (country of origin, age, municipality) merged with individual employment from labour market registers; municipalities ranked 0-1 and split at the median (low ~0.39 vs high ~0.55). | Population register (BEF); labour market registersAge 20-60; years 1999-2001 | online appendix · Online Appendix B.2 |
| Parent-child residential distance | Jensen and Zhang (2026) | Four categories: same municipality (99 municipalities); same NUTS3 region but not municipality (11 NUTS3 regions); same NUTS2 region but not NUTS3 (5 NUTS2 regions); greater distance. | BEFmunicipality / NUTS3 / NUTS2 | online appendix · Supplemental Appendix A.E |
| Parish neighbours at age 15 | Bingley et al. (2021) | Neighbourhood defined as parish of residence (available from 1973) from the central person register address data on 31 October of the calendar year the male turns 15; neighbours are males born in the same year living in the same parish (2,123 parishes). | Central person register (CPR addresses) | working paper · Section 3.1 and footnote 12 |
| Population density categories | Bjerre-Nielsen and Gandil (2024) | Low density: less than 1,000 per sq. km; medium: 1,000-5,000 per sq. km; high: more than 5,000 per sq. km. Used to describe enrolment for all children aged 7 in 2008-2015. | online appendix · Online Appendix Figure B.1 notes | |
| Pre-1937 municipality characteristics | Hjort et al. (2017) | From the Statistical Commune Data Archive: party vote shares in three elections, population and urbanization in 1935, percent female 1930, percent workers in agriculture/industry 1930, percent on public aid 1936, percent income/property taxpayers 1936, aggregate taxable income and assessed property value 1936; plus 1936 infant mortality and enteritis deaths per 1,000 births by medical district (Medical Reports) and distance to Copenhagen; used as controls interacted with cohort and for propensity-score matching (caliper 0.05, one control without replacement). | Danish Commune Archive, Medical Reports for the Kingdom of Denmark, DigDag | paper · Section III.A; Section II matching, footnote 26 |
| Private rental housing tenure | Svarer et al. (2005) | Housing market split into owner-occupied (51%), co-operative (6%), public rental (19%) and private rental (18%); analysis concerns private rental units, where rents are regulated except in dwellings built after 1991. | Housing unit characteristics (Statistics Denmark) | working paper · Section 3 'The Danish private rental housing market' |
| Region of graduation | Almar et al. (2026) | Residence municipality (BOPAEL_KOM_KODE) mapped from pre-2007 to post-2007 municipalities using each person's modal 2005-to-2006 transition, then grouped into the five regions 81-85 by municipality code lists; region in the graduation year (or first observed year) is used. | BEFBOPAEL_KOM_KODE; region 81 (Nordjylland) ... 85 (Sjaelland) via inlist() of municipality codes | replication code · Data/Gen_regions/region codes.do; Make analysis sample 1.do |
| Regional unemployment rate | Munch et al. (2006) | Measure constructed by the Institute for Local Government Studies (AKF): unemployment rate in an area around each municipality, defined by how far a resident can commute without a daily commuting cost exceeding DKK 60 (1987 prices); available 1987-2000. | Institute for Local Government Studies regional unemployment data | working paper · Section 3.1, p. 10 |
| Respiratory disease exposure index | Daysal et al. (2026) | Monthly number of acute respiratory hospitalizations per 100 children aged 13-71 months in the child's municipality of residence (2007 municipality structure, via crosswalk), excluding the child's own older sibling if aged 13-71 months, summed over the 12 months of the child's first year of life (also separate indices for months 1-6 and 7-12). | LPR, BEFAcute respiratory ICD codes as above | paper · Section 4 and footnote 17 |
| Share of apartments and row houses (IV) | Andersen et al. (2022) | Instrument: share of apartments and row houses (as defined in the BBR) listed in a given sub-market (municipality), capturing homogeneity of the housing stock. | BBR | online appendix · Online Appendix (IV section; Figure L.14) |
| Treatment intensity | Altmann et al. (2026) | Baseline: municipality regime (10 super-control, 44 medium with 60% treated, 44 high with 90% treated). Alternatives: share treated in own plus bordering municipalities; commuting-weighted shares using the fraction of workers residing in j who commuted to m in the three prior years; and an individual measure over zip-code x 3-digit-occupation markets applied to in the two prior years. | BFL, STAR Jobloggeogroup 1/2/3; intensity_alt1 | online appendix · OnlineAppendix.pdf Section B.1; _README.pdf; code/data_preparation.do |
| Urban municipality | Dustmann et al. (2019) | Municipalities above the 95th percentile of municipal population size are labelled urban. | BEF | paper · Introduction |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| 1999 capital pension subsidy reduction | Chetty et al. (2014) | From 1999 the deduction for capital pension contributions fell from 59 to 45 cents per DKr for individuals in the top income tax bracket (cutoff DKr 251,200 in 1998); treatment group: taxable income DKr 25,000-75,000 above the top-tax cutoff; control: DKr 25,000-75,000 below. | IND (income tax register) | working paper · Section V, V.A (Estimator 1) |
| Audit flags | Kleven et al. (2011) | Presence and number of computer-generated audit flags, a deterministic function of the return characteristics that SKAT normally uses to select returns for examiner review. | SKAT audit flag system | working paper · Section 4.1; Section 4.3 |
| Audit treatment (stage 1) | Kleven et al. (2011) | Stratified random sample of 25,020 employees and 17,764 self-employed (strata: employee/self-employed, light/heavy return complexity, 30 regional tax centres) randomly split into a 100% group subjected to unannounced comprehensive audits of returns filed in 2007 (2006 income) and a 0% group never audited; effects are intent-to-treat (actual audit rates 98.7% employees, 92% self-employed). | SKAT Business Object Database | working paper · Section 4.2 |
| Bunching (excess mass) at kinks | Chetty et al. (2011) | Excess mass b = number of individuals in a window around the kink in excess of a counterfactual density, relative to the average counterfactual density in the window; the counterfactual is a seventh-degree polynomial fitted to binned counts of the earnings distribution excluding a DKr 15,000 window centred on the kink (q=7, R=7). | IND (tax register)q=7 (polynomial degree), R=7 (bins on each side), excluded window DKr 15,000 | working paper · Section 4, equations (15)-(16) |
| Ceiling DD treatment (unbound by tax ceiling) | Jakobsen et al. (2020) | X = wealth tax actually paid (FORMSK) / theoretical wealth tax ((FORM - allowance) x tax rate). Treatment: household in top 1% of the wealth distribution with X > 0.99 in every pre-reform year of the window (e.g. 1982-1988), i.e. not bound by the tax ceiling; control: top 1% with X < 0.80 (bound by ceiling). Households above the 99.99th percentile excluded. In the panel, status is re-coded post-reform using X, LOFTFORM > 0 and wealth relative to allowance. | INDK, INDH, FAINtreatmt_2e_X80: X > .99 and top1 in 1982-1988; control_2e_X80: X < .80 and top1; drop form_pctile > .9999 | replication code · Empirical Analysis/DD_balance_and_treatment_status.do section 3.4; DD_base_data.do section 2.3 |
| Child support obligation (actual average) | Rossin-Slater and Wüst (2018) | The father's annual obligation from the Danish child support guideline (normal amount plus extra amount per child, plus a supplement of 25-300% of the normal amount above income thresholds). It is a step function of current gross income and the number of biological children under 18, including new children. It is averaged over all post-separation years and expressed in thousands of real 2000 DKK. | Danish income registers | working paper · Section 2; Section 5, equation (2) |
| Couples DD treatment | Jakobsen et al. (2020) | Treatment: married households with wealth between the (single) allowance and 2 x allowance in each pre-reform year (1988 and earlier years depending on window), which became exempt when the couples' threshold doubled in 1989; controls: (c1) singles with wealth in the same band, (c4) married couples in the top 5% with wealth below the allowance, or (c5) singles with wealth between allowance/2 and allowance. | INDH, FAINtreatmt_c1*: married & allowance <= FORM <= 2*allowance; control_c1*: unmarried, same band; control_c4*: married & FORM < allowance & top5; control_c5*: unmarried & allowance/2 <= FORM <= allowance | replication code · Empirical Analysis/DD_balance_and_treatment_status.do section 3.3 |
| Detected tax evasion (audit adjustments) | Kleven et al. (2011) | Net audit adjustment = after-audit return (A) minus filed return (F), split into under-reporting (upward adjustments raising tax liability) and over-reporting (downward adjustments), for net income, total tax and income components. | SKAT Business Object DatabaseReturns: information report (I), pre-populated (P), filed (F), after-audit (A) | working paper · Section 4.3; Section 5.1; Table 2 notes |
| Effective marginal tax / phase-out rate | Søgaard (2019) | Marginal income tax of 42% above a personal allowance of DKK 49,900. Benefits are phased out at 50% for the first DKK 11,600 above the individual income limit and at 100% beyond that, so the effective marginal rate jumps from 42% to 72% at the baseline limit under optimal take-up. If the payback exceeds DKK 9,200, the whole payback rises by 7% (a notch). The 2009 reform raised the baseline limit by 24% and the phase-out rate from 52% to 62%. | working paper · Section 3.1, footnotes 5 and 11, Figure 2 | |
| Effective marginal tax rate | Kreiner et al. (2015) | Effective marginal tax rates by annual labour income from the Kleven and Kreiner (2006) empirical tax function for 2006 (income taxes, indirect taxes and benefit claw-back), deflated to 2005 by average labour income growth; hourly wages are converted to annual income using median annual hours of 1,660. The participation tax rate m0 is 0.59 at baseline (0.82 in a robustness check). | working paper · Section 3.1, Figure 1; Section 5 | |
| Excess mass at kink (bunching) | Maire and Schjerning (2013) | Excess mass at the tax cutoff estimated following Saez (2010) within a bandwidth around the kink, applied to taxable income and to adjusted earned income; the dynamic extension separates bunching from intertemporal shifting via retained earnings. | Income Tax Register | working paper · Section 3-4, equation (3) |
| Gift-tax bunching measure | Boserup et al. (2018) | First time an individual (ages 1-12) has positive wealth, wealth at that point treated as the transfer; distance to the gift-tax basic allowance (DKK 58,700 in 2011) and to twice that amount (two donors) is plotted to look for bunching. | SKAT wealth records | working paper · Section 3.2, Figure 6 |
| High- vs low-income workers | Labanca and Pozzoli (2022) | Low-income: tax-exempt or in the bottom tax bracket in 2008. High-income: in the middle or top bracket in 2008, excluding a residual group in the top bracket in 2008 predicted (from 2008 real income and the post-reform schedule) to fall into the bottom bracket in 2011. | Danish Tax Register | working paper · Section 5.1 |
| Household tax bracket / treatment status | Gruber et al. (2021) | A taxpayer is assigned to the top bracket if she or her spouse is in the top bracket, to the middle bracket if either is in the middle and neither in the top, and to the bottom bracket if both are in the bottom. Treated are those in the top bracket in every year 1984-1986; controls are in the middle or bottom bracket in every year 1984-1986. | Income tax records, Population register spouse links | working paper · Section 4 |
| HSBC undeclared account holder | Alstadsæter et al. (2019) | Households who owned at least one account at HSBC Switzerland (including via shell companies with identified beneficial owners), declared themselves taxable in Scandinavia in 2006 and could be matched to a tax return (2006/2007 records); non-residents excluded; for Denmark all matched households are treated as evaders (about 95% of matched Danish accounts were undeclared). | Leaked HSBC list (Danish tax authority version), Tax returns | working paper · Section 3.1.1, footnotes 13-16; Online Appendix E |
| Marginal tax rate on labour income | Sigaard (2023) | Simulated using tax programs from Kleven and Schultz (2014), extended by the Danish Ministry of Finance (available 1984-2016), as [T_t(z+100) - T_t(z)]/100, with income information mainly from IND, INDK and INPI. The treatment is the three-year log change in the marginal net-of-tax rate. | IND, INDK, INPI | paper · Section 4 (Data) |
| Mechanical change in net-of-tax rate (instrument) | Kleven and Schultz (2014) | log(1 - tau_{s+3}(z_s)) - log(1 - tau_s(z_s)): change in the marginal net-of-tax rate simulated by applying year s+3 tax law to base-year s incomes; analogous mechanical change in virtual income instruments for the change in log virtual income. | Income Tax Register | paper · Section III.B |
| Mechanical net-of-tax rate change (instrument) | Jakobsen et al. (2026) | Four-year mechanical change log(1 - tau_{t+4}(z_t)) - log(1 - tau_t(z_t)) in the marginal net-of-tax rate, holding income and characteristics at year-t values, computed with a Danish tax simulator; used to instrument own and partner net-of-tax rate changes and virtual income. Restricted to couples where both have personal income of 50,000-600,000 DKK in the base year. Outcome is the 4-year difference in number of children born. | IND | working paper · Section 3; Supplemental Material A.1 |
| Mechanical net-of-tax rate change (instrument) | Labanca and Pozzoli (2022) | Post-reform (2011) marginal tax rate simulated using pre-reform (2008) income adjusted for inflation; the log change in the marginal net-of-tax rate from this mechanical rate instruments the observed change. For spillovers, the firm average of mechanical changes among high-income workers instruments the change in their average hours. | Danish Tax Register | working paper · Section 5.5 |
| Net-of-tax interest rate | Gruber et al. (2021) | (1 - tau) R, where tau is the marginal tax rate on negative capital income; the 1987 reform set this rate to 51 percent in all brackets (pre-reform top about 73, middle 62, bottom 48 percent in the average municipality), raising the net-of-tax rate by 81 percent for the top group, 29 percent for the middle and reducing it 6 percent for the bottom. | working paper · Section 3.1, Figure 1 | |
| New car registration tax | Gillingham et al. (2022) | T(P) = 1.25 x [tau1 x min(P, K) + tau2 x max(P - K, 0)], with 25% VAT on the price including tax; in 2008 K = DKK 81,000, tau1 = 105% and tau2 = 180%. Fuel prices are country averages from eof.dk. | Fuel prices (eof.dk) | working paper · Appendix F.1 |
| Predicted (mechanical) change in net-of-tax rate instrument | Sigaard (2023) | ln(1 - T'_{t+s}(z_{i,t-k})) - ln(1 - T'_t(z_{i,t-k})): marginal rates under the period t+s and t tax systems evaluated at income lagged k periods, following Weber (2014). The main specification uses k = 2, with lags 2-4 used for overidentification tests. | IND, INDK, INPI | paper · Section 3.1.1, eq. (2); Section 3.2 |
| Predicted (simulated) child support obligation | Rossin-Slater and Wüst (2018) | Instrument: the obligation in each post-separation year computed from the father's separation-year gross income and his 'original' children still under 18, so children ageing out are counted but new children and post-separation income changes are ignored. It is averaged over the post-separation period. | Danish income registers | working paper · Section 5 (Simulated IV Method), equation (3) |
| Random-audit detected evasion | Alstadsæter et al. (2019) | SKAT stratified random audits for tax years 2008, 2010 and 2012 (18,985 taxpayers; self-employed and complex returns oversampled), every line item examined; main results include all errors; deliberate evaders are those rated 0-2 on SKAT's 0-6 compliance scale. Evasion rate = taxes evaded / (taxes paid + taxes evaded) by wealth group, weighted by audit weights. | SKAT random audit dataSKAT compliance rating 0-2 = deliberate evasion | online appendix · Section 4 (Random Audit Data); Online Appendix H.1-H.2 |
| Reform treatment and simulated net-of-tax rate instrument | Kleven et al. (2025) | Treatment assigned by pre-reform baseline taxable income (labour income, transfers, pensions, alimony and certain capital income) in bins relative to the 2009 reform threshold (around the 70th percentile). The instrument is the simulated log change in the marginal net-of-tax rate from tax code changes, holding income at baseline (Gruber-Saez). Baseline sample: taxpayers with baseline income 250,000-1,000,000 DKK. | Income tax records | working paper · Section 5.1, footnote 27; Table 2 notes |
| Scheme earnings eligibility threshold | Kleven et al. (2014) | Foreigners' scheme requires annual wage earnings with the requesting employer above a threshold that grows roughly with average earnings and lies between the 99.2th and 99.4th percentile of the Danish wage earnings distribution (DKK 765,600 in 2009); it must be met on an annualized basis (e.g. a 6-month contract needs half the annual threshold). For pre-1991 years the threshold is imputed by holding the threshold-to-average-earnings ratio at its 1991 value, with average earnings computed over all workers with positive earnings. | Administrative tax and payroll records | working paper · Section 2.1 and footnote 14 |
| Scheme eligibility (prior tax liability) | Kleven et al. (2014) | To be eligible the taxpayer must have been recruited abroad and not have been tax liable in Denmark in the 3 years before going on the scheme (applied to each scheme spell separately; prior scheme time not counted); citizenship plays no formal role, so Danish citizens tax-resident abroad for 3+ years qualify. Scheme lasts at most 36 months. | Foreigners' tax scheme beneficiary records | working paper · Section 2.1 and footnote 12 |
| Share of workplace above top tax threshold | Fadlon et al. (2016) | Fraction of employees in the occupation-firm cell with labor income above the top income tax threshold (about 49 percent of workers on average), interacted with year dummies with 1998 as baseline; the 1999 reform cut the deduction value for capital pension contributions from 59 to 45 cents per DKr for top-bracket taxpayers. | Income tax register | working paper · Section 2.1; Section 2.3; Section 3 |
| Simulated marginal tax rate | Labanca and Pozzoli (2022) | Marginal tax rate on labour income simulated from the Danish Tax Register with an extension of the Kleven-Schultz (2014) tax simulator to 2006-2011: increase in tax liability from raising labour income by 100 DKK, holding other income components fixed. Changes are taken over the 2008-2011 interval. | Danish Tax Register | working paper · Section 5.2 |
| Simulated marginal tax rate | Kleven and Schultz (2014) | Marginal tax rate on income component j computed with the authors' own Danish tax simulator (1984-2005) as [T(z_j + 100) - T(z)]/100, i.e. by raising that component by DKK 100; accounts for spousal jointness of exemptions and the marginal tax ceiling. | Income Tax Register | paper · Section II; footnote 9 |
| Simulated marginal tax rate / predicted change in log net-of-tax rate | Jakobsen and Søgaard (2022) | Marginal tax rates are imputed with an extension of the Kleven and Schultz (2014) Danish tax simulator, as the increase in total household taxes from an incremental increase in labour income. The predicted change compares rates under the period-t and t-1 tax systems, holding income and characteristics fixed at their t-k levels (k = 1 baseline). | Danish tax records | paper · Section 2 (eq. 5); Section 3.3 |
| Tax schedule kinks | Maire and Schjerning (2013) | Piecewise-linear personal income tax with five brackets before 1996 and four from 1996; e.g. 2001: about 8% below 33,400 DKK, about 44% up to 179,900 DKK (bottom tax), 49% above (middle tax) and 63% above 279,900 DKK (top tax). Top-tax base is mainly individual; middle-tax base depends on household income; middle cutoff raised to the top cutoff in 2009. | Income Tax Register | working paper · Section 2 |
| Tax simulator and marginal tax rates | Chetty et al. (2011) | Authors' tax simulator computing tax liabilities and marginal tax rates from own and spouse's tax variables and previous-year municipality of residence; it matches actual municipal, regional, bottom, middle and top taxes within DKr 5 for 95% of observations. | IND (tax register)pnr, henv (spouse), glskkmnr (municipality), pfrdst, berfrdst (personal exemption), perindkp, berpi (personal income), kapindkp, berkap (capital income), lignfrdp (special deductions), kappens, fosfufrd (individual pension contributions), arbpen14, arbpen15 (employer capital pension contributions), underhol (alimony) | working paper · Appendix B |
| Taxable income relative to the effective kink | Maire and Schjerning (2013) | For each individual, the difference between actual taxable income and the taxable income needed to reach the middle or top tax bracket, computed with the authors' own tax simulator accounting for special deductions and joint taxation of couples (simulated tax within +/-5 DKK of actual for 95% of the sample); distributions plotted in 1,000 DKK bins. | Income Tax Register | working paper · Section 2 and Figure 2 |
| Taxpayers close to kink points | Kleven and Schultz (2014) | Taxpayers with income within DKK 5,000 of the top kink, DKK 3,000 of the middle kink or DKK 2,000 of the bottom kink (excluded in robustness checks). | Income Tax Register | paper · Notes to labor income elasticity table (Section V) |
| Third-party vs self-reported income and evasion | Kleven et al. (2011) | Third-party income comes from information reports; self-reported income = total reported income minus third-party income. For always-positive line items, third-party under-reporting = A-F if F<A<I, = I-F if F<I<=A, zero otherwise; self-reported under-reporting is total upward adjustment net of third-party under-reporting. Third-party under-reporting is defined only for positive items. | SKAT Business Object DatabaseF<A<I: A-F; F<I<=A: I-F; else 0 | working paper · Section 5.2; Table 2 notes |
| Threat-of-audit letters (stage 2) | Kleven et al. (2011) | Employees in both audit groups randomly assigned (thirds) to receive a letter one month before the 1 May 2008 filing deadline stating that the return would certainly be audited (100% letter), that half of the group would be audited (50% letter), or no letter. Outcome is the probability of self-reported adjustments (upward/downward) relative to the latest return at the time the letter was received. | SKAT Business Object Database | working paper · Section 4.2; Section 7 and footnote 38; Table 8 |
| Wealth tax exemption threshold and marginal wealth tax | Jakobsen et al. (2020) | Annual exemption threshold (allowance) by year; for couples it equals 2 x single allowance, except 1989-1992 when couples' allowance uses a separate married allowance x 2. Household marginal wealth tax rate tau_it = statutory rate if FORM exceeds the household allowance, else 0. Bunching analysis uses distance b = FORM - allowance, deflated, in DKK 20,000 bins from -540,000 to 540,000. | INDHAllowance (DKK): 1980-81 1,000,000; 1982 1,083,000; 1983 1,158,900; 1984 1,204,900; 1985 1,229,200; 1986 1,253,500; 1987 1,278,700; 1988 1,329,900; 1989 1,383,600; 1990 1,424,500; 1991 1,452,700; 1992 1,482,100; 1993 1,534,500; 1994 1,580,500; 1995 1,592,000; 1996 2,400,000; married: 1989-90 811,500x2, 1991 1,249,600x2, 1992-96 2x single | replication code · Empirical Analysis/DD_base_data.do section 2.2; bunching_base_data.do |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| 1960 birth cohort panel | Bruze et al. (2015) | All men and women born in 1960 followed annually 1980-2006 (ages 20-46); individuals must have a Danish address in every year (complete marital history), and those already married at age 20 are excluded, leaving 61,780 individuals. | BEF | working paper · Section 2 (The sample) |
| 1987 reform DD treatment/control groups | Kleven and Schultz (2014) | Treatment group: taxpayers experiencing an increase in the marginal net-of-tax rate on labor income (or on positive capital income) due to the 1987 reform over 1986-1989; control group: those experiencing a reduction; large-treatment group has net-of-tax rate increases of at least 15 percent. | Income Tax Register | paper · Section IV, Figure 4 discussion |
| 1999 full-time private-sector worker cohort | Keller and Utar (2022) | Cohort of workers who in 1999 were full-time employed in Denmark's private sector, had positive wages and were aged 18-56; part-time workers are excluded. IDA covers persons aged 15-70, establishments with at least one employee and jobs active in the last week of November. The textile sample is the roughly 10,000 such workers employed in the textile and clothing industry in 1999; the economy-wide sample excludes public administration, education and health. | IDA | working paper · Section 2.2 and footnote 49 |
| 1999 language training reform RD | Foged et al. (2024) | Refugees granted status 1997-2000 (Somalis excluded); treatment = recognized after 1 January 1999, giving eligibility to 430 additional hours of language training in the first 3 years. RD on days between grant date and cutoff, triangular kernel, 548-day bandwidth. | OPHG | working paper · Sections 4.2 and 6.2, footnote 22 |
| 2012 repeal event-study cohorts | Dustmann et al. (2024) | Adults aged 18-55 at residency who received residency 2008-2014. For year-1 outcomes the treatment group is those granted residency 2012-2014; for year-2 outcomes it is those granted residency 2011-2013. Employment = non-zero labor earnings in the calendar year; outcomes only for two years after residency. | Danish Immigration Service records; income registerResidency 2008-2014; repeal 1 Jan 2012 | online appendix · Online Appendix B.2 |
| 3-year birth cohort groups | Bingley and Cappellari (2019) | Individuals are grouped into 3-year birth cohorts (central years 1936, 1939, ..., 1984), with the cohort's central birth year used to compute age (ageco = year - cohort). | BEF | replication code · inspection.do |
| Age restriction (24-Year Rule) | Agersnap et al. (2020) | Main within-country specification restricts to immigrants at least 30 years old, to avoid confounding from the 2002 '24-Year Rule' requiring both spouses to be at least 24 for marriage-based residency. The cross-country analysis has no age restriction. | CPR, BEF | working paper · Section 2.1 and footnote 7 |
| Analysis panel | Lentz et al. (2023) | Panel starts in 1987 (1985-1986 used to classify tenure of the 1987 stock of jobs); only employment spells starting after the individual finished the highest level of education are kept, and spells starting after age 50 are removed. Workers are drawn from the 1987 working-age population or enter in the first week of the year after their last year of schooling. About 4 million workers and 400,000 firms. | Danish matched employer-employee data (MEE) | working paper · Section 2; Appendix E.1 |
| Analysis panel | Bagger and Lentz (2019) | 1994-2003; histories truncated at age 55 and starting at graduation from highest completed education; workers ever observed in public-sector employment, self-employment, retirement or agriculture are discarded. | Spell data, IDA | working paper · Section 5 (Analysis panel) |
| Analysis period | Akcigit et al. (2024) | Background data available 1980-2013, but the intersection of the IDA, FIDA, PHD, IQ, R&D funding and patent datasets covers 2001-2013, which is the analysis window (about 32 million employer-employee observations, 10,000 inventors, 37,000 PhDs). | IDA, FIDA, PHD | paper · Section 2.2 |
| Analysis sample | Daysal et al. (2025) | Female breast cancer patients diagnosed 1990-1998 (26,900), restricted to DBCG89 trial enrollees who are pre-menopausal and high-risk, excluding always-eligible patients whose radiotherapy intensity changed, patients missing radiotherapy, tumour size or micro-radical removal information, and women aged 55+ at diagnosis (final N = 2,823). | DBCG clinical Breast Cancer Database | working paper · Section 3 (Analysis Sample); Appendix Table A1 |
| Analysis sample | Bagger et al. (2021) | Individuals aged 25-41 in 2006 linked to a mother alive, aged 15-70 and present in IDA during 1980-2006. Excludes families with mother younger than 17 or older than 49 at birth, missing education for any member, non-twin siblings born less than seven months apart, unknown or different fathers, children born after 1991, and single-child families; 1,256,031 individuals in 581,159 families. | IDA | working paper · Section 4 (fn. 15) and Appendix C |
| Analysis sample | Kreiner et al. (2016) | Individuals employed throughout 2008 (a wage record in every month of 2008) with positive income in all years, observed January 2008 - December 2011; includes people who are both wage earners and self-employed. Sensitivity: excluding employees with any exit to unemployment during 2009-2010. | eIncome register (SKAT) | working paper · Section II, footnote 5; footnote 6 |
| Analysis sample | Fadlon et al. (2016) | Employees in private-sector firms with white-collar occupations; excludes self-employed individuals and workplaces with fewer than five employees; years 1996-2001. | Income tax register; IDAworkplace size >= 5 | online appendix · Notes to Appendix Figures 1-2 |
| Analysis sample | Bagger et al. (2014) | Men born on or after 1 January 1948 (so experience from 1964 is complete), 1985-2003, dropping firms with missing ID, ownership or industry and workers with gaps in observed histories. | Spell data, IDA | working paper · Appendix B |
| Analysis sample | Browning et al. (2013) | 10% random sample of the Danish population aged 16+, 1987-1996; married or cohabiting stable couples who were either home owners or renters throughout; split before and after the 1992-93 mortgage/collateral and tax reforms. | Income and wealth tax registers (10% sample) | working paper · Section 4 (Data); Abstract |
| Analysis sample | Maire and Schjerning (2013) | Entire population panel 1994-2009, aged 25-59, main occupation self-employment; restricted to self-employed whose spouse's primary or secondary occupation is not self-employment (because transfers to assisting spouses are only observed 1994-2001). | Income Tax Register, IDA | working paper · Section 2 'Data and Institutional Background' |
| Analysis sample | Nielsen et al. (2010) | High school graduates from the ordinary (academic) high school track in 1985-1990 (assets not observed before 1985) who graduate at 'normal' ages 19-20, with observed GPA, non-missing parental income and education, excluding those with divorced parents at graduation; the 1986 and 1987 cohorts are dropped because of anticipation of the reform and an earlier change in the means-testing age limit. Final sample 108,933. | Population register, Education registers | paper · Section V.A, Table 2; Section V.B |
| Analysis sample | Geerdsen (2006) | 10% sample of the Danish population aged 17-67, panel 1980-1998, topped up each year with 10% of new 17-year-olds and immigrants; leavers at age 68, emigration or death drop out. Restricted to UI spells beginning between January 1994 and January 1998. | IDA, CPR | working paper · PhD thesis (SFI 2003), Paper II, Section 5.2, pp. 114-115 |
| Analysis sample and event window | Andersen et al. (2023) | Baseline sample of active customers: at least five outgoing spending transactions in each month of the event window, with the partner (if any) meeting the same criterion. Exclusive-customer subsample: active customers (and partners) with no deposits or loans at other retail banks. Characteristics measured in month -6 relative to job loss (annual income over the calendar year containing month -6; shares held at other banks and mortgage debt at end of previous calendar year). | Danske Bank; tax records; mortgage data>= 5 spending transactions per month | online appendix · Table A1 notes |
| Analysis sample of fathers | Rossin-Slater and Wüst (2018) | Fathers observed in the registers every year 1998-2010 who either were married to or cohabiting with their oldest child's mother at childbirth (or in 1998 for children born earlier), or had a first child 1999-2008 while not married to or cohabiting with the mother. Also required: separation in 1999-2008, one or two children under 18 at separation, and separation-year income in a 100,000 DKK window around the first three schedule thresholds (175,000-505,000 DKK). The result is 73,303 fathers, one observation each. | Danish administrative registers | working paper · Section 4 (Analysis Sample), footnotes 17-18 |
| Analysis sample of UI recipients | Fluchtmann et al. (2024) | Individuals of Danish origin (immigrants excluded) starting UI spells September 2015-September 2017 with full two-year UI eligibility; spell lasts at least 8 weeks; applications in the last 4 weeks of a spell dropped; at least one Joblog application registered; first spell only. 127,695 spells. | DREAM, Joblog, IDA | working paper · Section 3.2; Online Appendix B.2 |
| Analysis sample restrictions | Ejrnæs and Hochguertel (2013) | Denmark-born males with Danish citizenship aged 25-59; excludes students, retirees, persons out of the labour force in every year, persons receiving public pensions, and anyone who worked as wage earner or self-employed in agriculture 1981-1998; requires at least one employment spell. Estimation sample: persons self-employed in their first three observed years and not unemployed (>10% measure) in any of those years (13,434 individuals, 86,092 obs). | CAM 10% Sample | working paper · Sections 4.1 and 5.1 |
| Applicant sample | Heinesen et al. (2026) | All applications to post-secondary education 1994-2002 (Denmark; Norway 1998-2004); first observed application kept; those already holding a post-secondary degree excluded; restricted to those who completed a field within eight years and who applied to at least two broad fields with the most-preferred field having an admission cutoff. | Coordinated admission system (KOT) applications, BEF | working paper · Section 5.2 |
| Balanced panel 1980-1996 | Jakobsen et al. (2020) | Households (persons 20+ aggregated by household id) observed with strictly positive aggregate wealth FORM in all 17 years 1980-1996. | INDH, FAINdrop if form <= 0 | missing; keep if _N == 17 (balanced_1980_96) | replication code · Empirical Analysis/DD_balance_and_treatment_status.do section 2 |
| Baseline sample | Jensen and Johannesen (2017) | Residents 2003-2011 aged 20-50 in 2007, excluding self-employed and individuals whose 2007 primary bank failed in 2008-2011 (analysed separately), 25% random sample (about 440,000 individuals); DKK variables winsorized at 1st and 99th percentiles. | Tax records, Population registers | working paper · Sections 3.1 and 3.3 |
| Baseline sample (childless, single, low-educated natives) | Bargain and Jonassen (2024) | Individuals aged 20-29 (age measured in months, weekly panel 2000-2006) who are of Danish origin, have only basic education at the start and end of the year, live alone (not in a couple), have no children and are unmarried at both start and end of year; running variable is age relative to the 25th birthday. | BEF, UDDAIE_TYPE == 1; edu (AUDD2015_L1L5_K from HFAUDD) == '10'; FM_MARK == 6; d_child == 0; CIVST == 'U' | replication code · baseline.do, 'Applying selection criteria' block; bef_red.do |
| Birth sample | Jørgensen and Søgaard (2024) | Births 2008-2015; parents must be continuously employed in the same firm in the 3 months before birth and not on non-birth-related public benefits during those 3 months; births are excluded if parents have a subsequent child within 15 months; parents must appear in the parental leave data (final sample 189,974 births). | BEF, BFL/eIndkomst, Parental leave benefit spell data | working paper · Section 3.2, Table 1 and notes |
| Birth-date discontinuity instrument and bandwidth | Landersø et al. (2017) | Instrument is being born after the January 1 cut-off (January=1); main sample uses children born within +/-30 days of January 1, cohorts born mid-1981 to mid-1993, controlling for a flexible function of distance in days to the cut-off and cohort fixed effects. Covariates are centred on cut-off dates, comparing January year t with December year t-1. | Population register (exact birth dates) | working paper · Sections 2 and 3, Section 3.3 |
| Buyer and seller identification | Andersen et al. (2021) | Buyers are the owners of the property (per BBR owner records) on January 1 in the year after the transaction; sellers are the owners on January 1 in the year of the transaction. If the sales agreement is signed in year t but ownership is transferred in t+1, buyers are the owners on January 1 of t+2, conditional on no registered trades during t+1. | BBR, SKAT property transactions (Statstidende), CPR | online appendix · Appendix A, Data sources (and footnote 1) |
| CAM 10% sample | Ejrnæs and Hochguertel (2013) | 10% random sample of the Danish population aged 16 and above from the Danish National Register, followed annually from 1981; waves up to and including 1998 are used. Unbalanced: new residents turning 16 or immigrating enter, people leave through death or emigration. | CAM 10% Sample | working paper · Section 4.1 (Register Data) |
| CEO succession sample | Bennedsen et al. (2007) | CEO successions 1994-2002 in Danish limited liability firms, kept when the departing (incoming) CEO had been (stayed) in the position at least two years, CEO names were matched to CPR numbers via the ES dataset, matching KOB financial data existed around the transition and employment (where available) was non-zero; only the first transition per firm is kept (5,334 successions). | KOB, ES, CPR | working paper · Section II.A |
| Charge-level analysis sample | Anker et al. (2021) | Charges for offenses against the Penal Code or Weapons Act (excluding traffic offenses, small-scale drug possession, Health Code and Tax Law violations) against Danish-resident males aged 18-30 at the charge with at most 10 previous charges; one charge per person per day; unit of observation is the charge. | Charge register, Population register | working paper · Section 5.1, footnotes 12 and 14 |
| Child sample | Jensen and Manning (2025) | Everyone born in Denmark 1980-1987 with both parents known in BEF and known parental origin. Observed in the year they turn 30, kept only if fully liable to pay taxes in Denmark (AKM register) and resident in Denmark (BEF). Children missing any outcome or control are dropped (missing parental occupation and education allowed), as are children whose parents were not in the population at birth. | BEF, AKM | online appendix · Online Appendix OA.1 |
| Child sample | Foged et al. (2023) | Children born 1990-2001 with at least one parent granted asylum in Denmark within a four-year window around 1 January 1999. Children were born in the first three years after the reform or were of pre-school or primary-school age on arrival. They are followed to age 18, through 2019; 6,467 children with parents born 1948-1982. | Danish administrative registers (parent-child links) | working paper · Section III (Data), footnote 8 |
| Clinic closure and patient-GP assignment | Kristiansen and Sheng (2025) | Patient-to-clinic spells built from weekly GP claims following Kjaergaard's algorithm (one practice per service week, practice-time intervals, intervals < 31 days dropped); clinic closure = last service week of the provider number (YDERNR), excluding cases where the same doctors continue under a new provider number (merger) and last dates after 2017. | SSSY, SYSI, T_YDERYDERNR, HONUGE; YDTYP == 5; YDLTID not in 2-9 | replication code · 2e_closures.do; 3a_doctor_patient_match.do |
| Cohort grouping | Bingley et al. (2021) | Data grouped into two-year birth cohorts (1960-61 to 1982-83) with age imputed from the first year of birth; each cohort observed from age 24 for at least 6 years and up to 28 years. | Population register, Income registers | working paper · Section 3.1, Table 1 |
| Cohort-approach counterfactual | Johannesen et al. (2026) | For single income-eligible individuals born 1941-1945, compare the indexed liquid wealth distribution at ages 70-74 (five to nine years into eligibility) with ages 60-64 (before eligibility); only those income-eligible in a given year are included (unbalanced panel). | IND, BEF | paper · Section II (Cohort Approach); Table 1 Panel B2 notes |
| Conditioning set (double-LASSO observables) | Fluchtmann et al. (2024) | The double-LASSO of Belloni et al. 2014 (PDSLASSO) selects 332 variables from 4,196 candidates, of which 302 remain after collinearity; they enter a logit propensity score for being male, and women are reweighted by p/(1-p). Candidates: years of education, field of study, age, total work experience and experience over the last 5 years, sector/industry/occupation of the previous job, industry- and occupation-specific experience over the last 5 years at 1/2/3-digit levels, and time on UI, social assistance and other transfers over the last 5 years, with interactions and squares of age, education and experience. | IDA, DREAM | online appendix · Online Appendix A.5-A.6, Table A.5 |
| Contestant pool | Frederiksen and Kato (2018) | Private-sector employees in year t with complete occupational histories (DISCO codes) for t-8 to t-1 whose primary occupation in t-1 is one of the 2-digit DISCO occupations from which any top manager appointed in year t came in t-1. DISCO data used for 1992-2002 (before the 2003 switch from 4-digit to 6-digit DISCO). | IDADISCO 2-digit | working paper · Section 2 |
| Control group: same shock five years later | Fadlon and Nielsen (2021) | Each treatment group of households shocked in year tau is matched to households from the same cohorts that experience the same shock in year tau + 5 (Delta = 5), who are assigned a placebo shock at t = 0 with t = year - tau; effects are identified up to four years after the shock via difference-in-differences relative to a baseline period (t = -2 in summary statistics). | working paper · Section 4.1 | |
| Core student sample | Søgaard (2019) | Individuals aged 18-35 enrolled in tertiary education, fully eligible for student benefits (SU), and with positive labour earnings, defined as earned income above DKK 8,000 per year. This gives about 75,500 observations per year, 2006-2011. | Danish administrative registers (education, tax returns), Student Benefit Administration records | working paper · Section 3.2, Table 2 |
| Couples sample | Foerster (2024) | Individuals observed from the start of their first marriage (first observed marriage event with missing prior marital-status date and no prior divorce), years 1980-2013, ages 28-58; individuals or partners with children from previous relationships are dropped. | BEF, Fertility database (ftdb_father, ftdb_mother)age 28-58; year 1980-2013; other_kids==0 & other_kids_par==0 | replication code · code/assembledat_untying.sas (sel_IDS); code/datprep_untying.do |
| Covariate set for ETI regressions | Kreiner et al. (2016) | Controls: age dummies, gender dummy, marriage dummy, 5 regional dummies, 10 sector dummies (as in paper Table 2) and 4 company-size dummies (as in paper Figure 7). | Statistics Denmark administrative registers5 regions; 10 sectors; 4 firm-size groups | online appendix · Online Appendix Table A1 notes |
| Covariates | Anker et al. (2021) | Age, immigrant background, has children, single, years of education, gross income, employment status, number of prior charges, crime type dummies and month fixed effects; standard errors clustered by personal identification number. | Population, education, income and crime registers | online appendix · Table notes throughout |
| Coworker sample | Brenøe et al. (2024) | Male and female coworkers of event women in the baseline year whose job at the baseline firm is their main labour market attachment, with hours at least half an FTE and earnings above 75,000 DKK in the baseline year; followed even if they leave the firm. | IDA, ATP | working paper · Section 5.4; Appendix C.2 |
| DALSC cohort and timing | Gupta and Simonsen (2010) | About 6,000 children born 15 September-31 October 1995 (DALSC); care mode measured in 1999 (age 3.5), outcome in 2003 (age 7.5); merged to parents' register data 1994-2003. | DALSC, Statistics Denmark registers | working paper · Section 2, Figure 1 |
| Death sales | Andersen et al. (2021) | Household terminations (death of the only member of a one-person household: single, widowed or divorced) of individuals owning real estate at death, identified by linking the Cause-of-Death Register to CPR; restricted to decedents with exactly one child (the beneficiary, linked via CPR) and to sales of the property within 1.5 years of the death, applying all full-sample selection criteria (13,953 death sales). | Cause-of-Death Register, CPR, SKAT property transactions | online appendix · Appendix A, Death data; paper Section 4 |
| Demographic controls (job-switcher regressions) | Kleven et al. (2025) | Robustness controls: dummies for age, gender, marital status and number of children, alone or combined with initial job-cell fixed effects (baseline uses job-cell fixed effects only). | Population register (BEF) | online appendix · Online Appendix, Figure A.7 notes |
| Drafted (lottery instrument) | Bingley et al. (2020) | Among men judged fit for service at the Armed Forces Day who drew a lottery number (1 to 36,000), 'drafted' means drawing a number below the Army's announced cutoff, regardless of whether they served. Military register covers 155,570 fit conscripts in birth cohorts 1976-1983. | Danish Defence military register | working paper · Sections II-III |
| Dual-earner couple restrictions | García-Miralles and Leganza (2024) | Reference individual (partner reaching own eligibility age) earned labour income at least once at ages 55-59 and was never self-employed or on disability benefits at ages 50-59; spouse earned labour income at least once at ages 50-59. Age-based sample: 1991-2013, reference individual aged 57-60 (eligibility age 60). Reform-based sample: 2008-2014, reference individuals with qualifying VERP contributions at least once at ages 50-59, born within 3 months either side of 1 January 1954, balanced panel. | Annual registers, VERP contribution records | working paper · Section 3.3, footnote 11 |
| Earnings sample trimming and age window | Bingley and Cappellari (2019) | Person-year observations kept at (cohort-central) ages 25-60; missing and non-positive earnings dropped; the lower and upper 0.5% of earnings dropped by family member type (father, son 1, son 2) and year; individuals must have at least 5 consecutive years of earnings observations. | IDA, IND | replication code · inspection.do ('EARNINGS TRIMMINGS', consecutive-observation block) |
| Employer link and full-time sample | Labanca and Pozzoli (2022) | Each employee is matched to the highest-paying employer in the employment spell of week 48 (FIDA). Sample: full-time employees aged 15-65 in 2003-2011, full-time defined (official definition) as working more than an average of 26 weekly hours over a year, in private firms covered by Firmstat. | FIDA, Firmstat, IDA | working paper · Section 3 |
| Estimation sample | Andersen et al. (2024) | Robustness variants: Full sample (keeps households invested in a firm that is the main employer of a household member); Exclusive (drops households with accounts in banks other than Danske Bank); Excl. own industry (ignores investments in industries where household members are employed when building the portfolio); Excl. unlisted (drops households with unlisted shares); Future returns (adds interactions of average passive return since month 2 in 20 bins, portfolio size at start of month 1 in 100 bins, and calendar month, for positive horizons). Spending validation sample requires at least one spending transaction per month for each adult and at least five summed across adults. | Danske Bank data, Employment register, Tax register (bank account reports) | online appendix · Online Appendix A, Figure A5 and Figure A1 notes |
| Estimation sample | Jensen and Blundell (2024) | Balanced monthly panel September 2008-December 2014 of individuals aged at least 25 in September 2008 and under 60 in December 2014; excludes immigrants and children of immigrants, parents of children with irregular child benefit payments, and (main analysis) anyone self-employed at any point; in January 2011 must have at least 2 children under 18, no more than 4 children in total, and be married or cohabiting. | BEF, OF, BFL, BOBO/BOTI | paper · Sections 5.1-5.2, footnotes 5-6 |
| Estimation sample | Crawley and Kuchler (2023) | Households whose head is aged 30-55 in 2008 (to exclude young/old whose income growth is likely anticipated and whose income variance is non-constant). Out-of-sample exposures allocated to seven bins: young (<30) heads, old (>55) heads, pension funds (incl. children's savings accounts), government, non-financial corporates, financials, rest of the world. | CPR household linkshead age 30-55 in 2008 | online appendix · Online Appendix E and G |
| Estimation sample | Sigaard (2023) | Individuals aged 25-65 with all information needed to simulate marginal taxes, who are neither jobless nor outside the labour force as primary occupation at end of November in year t, and have strictly positive hours at t and t+3. Base-year labour income must exceed DKK 250,000 (2015 prices, CPI-deflated) and all controls must be non-missing. | LON, IDAN, BEF, IND | paper · Section 4 (Data), Table A.1 |
| Estimation sample | Gillingham et al. (2022) | Universe of Danish households and all privately owned cars, 1996-2008 (estimation on holdings 1997-2008, about 39 million household-year observations of car ownership transitions). | Demographic registers, Car ownership register | working paper · Appendix F.1; Section 6 |
| Estimation sample | Taber and Vejlin (2020) | Workers censored after age 55 and when entering retirement or self-employment; spells before labour market entry (time of highest completed education, not observed in education later, or age 19) dropped; workers with errors in education information or gaps in spell histories (e.g. missing IDA year) dropped. | Weekly spell data, IDA | working paper · Section 6.1 |
| Estimation sample | Rossin-Slater and Wüst (2020) | 138 municipalities that ever had an approved preschool by 1960; individuals born 1935-1957; robustness drops cohorts born 1950-1957 in 28 municipalities lacking precise NHV initiation information, and drops NHV-treated observations in the bottom 1st percentile of the human capital index. | Population register; archival records | online appendix · Figures A1-A2 notes; Tables A13-A14 |
| Estimation sample | Giorgi et al. (2019) | Households 1980-1996 whose head is aged 18-65 and where both spouses work as employees (not self-employed); households are dropped from the year one or both members stop working or become self-employed. Peers' consumption is computed using all workers, including singles and one-earner households. | Tax records, IDA | working paper · Section 2.1; Online Appendix Table A1 (selection steps) |
| Estimation sample | Hjort et al. (2017) | Cohorts 1935-1949 observed in registers from 1980, so left-censored at different ages; main analyses impose common left-censoring at age 45 (only individuals surviving to 45). Municipality of birth from parish of birth; 87.55% of 1,119,097 cohort members have a valid parish code (excluded: born abroad 6.42%, Greenland, unknown, invalid, post-1970 codes 4.88%, county codes, other religious groups, Catholic registration, missing). | Population register (CPR) parish of birth; Inpatient and Death Registersvalid parish code | online appendix · Appendix A.2 and Table B.2 |
| Estimation sample | Joensen and Nielsen (2016) | Population of high school entrants 1984-86 who graduated in three years and have at least one year of employment (25,658 females, 19,406 males from about 140 high schools). Controls: county, entry cohort, high school controls, parental highest education and parental income at end of year before high school start. | Education registers (Statistics Denmark) | working paper · Sections 3.1-3.2 |
| Estimation sample | Kreiner et al. (2015) | Workers aged 18-65 employed in private-sector workplaces in 2005, drawn from IDA for 2004-2006; employment at each workplace is observed in a specific week in each year. Workers with hourly wage rates in the top percentile (above DKK 714) or below DKK 90 are excluded. | IDA | working paper · Section 3.1 (Data) |
| Estimation sample | Bingley et al. (2013) | Men aged 21-55, full-time private-sector employees, 1980-2003, birth cohorts 1943-1973 (31 single-year cohorts; each observed at least ten years). Individuals must have valid wage observations for at least five consecutive years. | Statistics Denmark administrative registers | working paper · Section 4 |
| Estimation sample | Dahl et al. (2013) | Full-time workers aged 25-65 employed in one of the DA/LO bargaining segments, 1992-2001, excluding scholarship recipients in ordinary education, plants with fewer than five employees, and observations with unreliable/extreme wages; a 30% random sample is drawn (about 1.14 million worker-years). | IDA, IND | working paper · Section 4 (p. 14) |
| Estimation sample (firms) | Hummels et al. (2014) | Start from 2.8 million manufacturing worker-firm-year observations after merging workers, firms and trade. Drop worker-firm-years whose job spell lasts a single year (~200,000 obs); drop workers whose skill level changes in the sample (~35,000 obs); drop firms with fewer than 50 employees and less than DKK 0.6 million in imports (about two manufacturing workers' average annual wages) (~600,000 obs). | IDA, FirmStat, Foreign Trade Statistics Registerfirm size < 50 employees and imports < DKK 0.6m dropped | online appendix · Data Appendix, section 1 |
| Estimation sample vs tax sample | Jakobsen et al. (2026) | Two samples: the BFL estimation sample of all individuals aged 25-60 with partners, 2010-2018; and the tax sample of women aged 25-40 with partners, 2004-2018 (used with personal income PERINDKP). Both split by less skilled vs high skilled. | BFL (eIndkomst), IND, BEF | online appendix · Supplemental Material, Table A.1 |
| Event sample (first child arrival) | Kleven et al. (2021) | Stepwise restrictions: children with known parents born 1980-2017 (16,260 adoptive / 2,454,200 biological); born 1985-2007; first-born only; siblings all of the same type (biological or adoptive); adoptees arrived before age 5; parents balanced on event time (final 4,580 adoptive / 527,000 biological). | BEF, CPR | online appendix · Online Appendix, Table A.II |
| Event time relative to 18th birthday | Kreiner et al. (2020) | Cohorts born 1992-1999 present in BEF 2008-2015; event time tline = calendar month minus the month of the 18th birthday (month18=(yr18-2008)*12+birth month); estimation window keeps tline in [-24, 24]. | BEFfoed_dag; tline; month18 | replication code · data01.do, data03.do, data06.do |
| Event-study sample | Kleven et al. (2019) | Treatment = balanced panel of individuals with first child 1985-2003 observed in a 15-year window around the first birth; control = balanced panel of never-parents with a placebo birth in 1985-2003 observed in the same window. | Population register | online appendix · Appendix B.2 |
| Exact-matched controls for survival estimation | Andersen et al. (2026) | For each diagnosed individual, controls who in the year before diagnosis share age, marital status and gender and are in the same income ventile and years-of-education quintile. Up to ten controls with the smallest difference in total income are kept; unmatched observations are dropped. | Population, income and education registers | online appendix · Online Appendix E |
| Experiment sample | Epper et al. (2020) | 3,620 respondents (3,552 with controls) with full register variables; robustness subsamples: both parents alive (2,367); in labour force every year and never unemployed 2011-2015 (2,265); stable relationship status 2011-2015 (2,704); socioeconomic status never indicating poor health 2008-2015 (3,030). Inverse probability weights based on respondents vs invited non-respondents or vs population, using education-year dummies, income deciles, income growth, parental wealth, wealth at age 18, age, gender, single and dependent-children dummies. | Population register (BEF); income, wealth and education registers | online appendix · Online Appendix Tables A5, A10, A11 |
| Experiment, comparison and pre-experiment samples | Gautier et al. (2018) | All 41,801 individuals applying for regular benefits November 2005-February 2006 in all Danish counties, minus 1,398 with inconsistent county of residence; non-experiment counties form the comparison group; a pre-experiment sample of 49,063 individuals starting benefits November 2004-February 2005 controls for county differences (difference-in-differences in a stratified proportional hazard model). | National Labour Market Board benefit spell data | working paper · Sections 3 and 4.1.1 |
| Experimental sample | Altmann et al. (2026) | Full stock of individuals registered as unemployed and receiving UI benefits in Denmark at the start of the experiment on March 15, 2019 (92,098 individuals); welfare recipients and later entrants excluded. Outcomes measured over windows after entrydate. | STAR experiment dataentrydate == mdy(3,15,2019) | replication code · _README.pdf (Subject pool); code/data_preparation.do |
| Experimental sample and treatment arms | Cairo and Mahlstedt (2023) | All welfare benefit recipients in stock on 15 August 2018 who were subject to the work requirement and had received the official notification letter (N = 47,294), randomised into three arms. Treated: up to six monthly messages plus access to the personalised online tool on jobnet.dk. Control I: general reminders only. Control II: neither. | Experimental data (jobnet.dk) | working paper · Sections 3.1-3.2, Table 1 notes |
| Expiring interest-only loan sample / treatment year | Andersen et al. (2024) | Households with a mortgage loan taken in year t-10 that started with an interest-only (IO) period lasting at least 9 years (IO length = years between IO start and end dates in REAL) and that survives from t-1 to t; treatment year is when the IO period expires; event time = year - treatment year. Households with any self-employed member, non-tax-liable members, or a property purchase/sale/ownership change in event years -1 to 1 are dropped. | REAL, BEF, EJERy_laanoptag = t-10 & l_afdragsfrihed_aar >= 9; drop if selvst, ikkeskattepligtig, or koebt/solgt/ejerskift in t_count -1..1 | replication code · 1_Extractdata.sas macro 'sample' lines ~119-135; 4_VarDefinition.do section 1 |
| Fatal health shock (spousal death) | Fadlon and Nielsen (2021) | Households in which one spouse died between 1985 and 2011 and was aged 45 to 80 in the year of the (actual or placebo) event. Treatment households are matched to control households from the same cohorts that experienced the same event five years later (Delta = 5); summary statistics measured at t = -2. Prime-age subsamples restrict to households before the Early Retirement Age of 60. | Cause of Death Registry; population/household registersAge at death 45-80; event years 1985-2011; Delta = 5 years | online appendix · Online Appendix Table A.1 notes; Figure C.1 notes |
| Firm panel | Lentz and Mortensen (2008) | Unbalanced annual panel 1992-1997 of privately owned firms from the Danish Business Statistics Register: all firms existing in 1992 with more than 20 workers, followed over time; entrants after 1992 excluded (about 4,872 firms). | Danish Business Statistics Register | working paper · Section 2 (Danish Firm Data); Section 5 (Danish Firm Data) |
| Firm sample | Renkin and Züllig (2024) | Starting from the complete 2007 Danish firm register, cumulative restrictions: active manufacturing firms (7,281); more than 10 employees (3,322); at least one bank connection (3,295); survival 2005-2010 (2,703); bank loans >100,000 DKK in 2006 and 2007 and 2007 loan-to-sales ratio >0.01 (1,753 firms; 213 matched to PPI, 1,176 to export unit values). Positive outstanding loans in 2006 and 2007 are required to compute the 2007 average interest rate control. | Firm register, Bank loan reports to the tax authorityemployees >10; loans >100,000 DKK; loan-to-sales >0.01 | online appendix · Online Appendix Table A.1 and Figure A.1 notes |
| Firm-year estimation sample | Buus et al. (2025) | Firm-years present in the Prodcom (manufacturers' sales) data from 2002, matched to FIRM and FIRE, in manufacturing with at least 10 employees year by year (employees >= 9.5), observed in FIRM in the previous year, and with positive sales, wage bill, capital and material expenditures. | VARS, FIRM, FIREFIRM gf_gr019_db07 (nace1)=="C"; gf_aarsv (employees)>=9.5 | replication code · BMRS_restat/4_reg_variables/code/4_1_samples.do; 2_prepare/code/2_2_variables.do |
| First stage: record contains a long (treatment) table | Baker et al. (2026) | Indicator that at least one page of the child's nurse record is a long table (the trial's table with rows such as GP referral, nutrition, vitamins, illnesses, emotional problems recorded at 15, 18, 24, 30 and 36 months). | Copenhagen nurse recordshas_long_table = 1 if any page type in 4, 5, 25, 27 | replication code · dst_replicationpackage/do/1b_cr_format_ext_data.do |
| Heir sample | Druedahl and Martinello (2022) | Heirs aged 25-59 in observed years, aged under 50 at the parental death (alder_atshock<50), with non-missing inheritance; net-worth outliers (outside p1-p99 in any year) dropped. WP: parents die unmarried between 1995 and 2012. | BEF/GRUND, FTDBinrange(alder,25,59); alder_atshock<50 | replication code · rawbuild.do ('SAMPLE SELECTION'); main_analysis.do lines 14-29; WP Section I |
| Household panel sample | Leth-Petersen (2010) | Robustness subsamples: renters (separate analysis) and house owners holding no financial assets (no stocks or bonds). | Wealth tax register | online appendix · Sections 2-3 |
| Housing sample restrictions | Daysal et al. (2021) | Residential houses and apartments in clusters with at least five sales; only normal sales between individuals (no intra-family sales). Excluded: negative valuations or sale prices, multiple addresses at the same location, and sales above 300% or below 40% of the public valuation. | State's Sales and Valuation Registry | working paper · Section 3.1, Appendix Table A-1 |
| IGE sample (1981-1982 cohorts) | Eshaghnia et al. (2025) | Children born 1981-1982 linked to parents, with neither child nor parents having migrated (105,953); child resources averaged over ages 30-35; for log-log IGE, zero/negative average income and incomes more than 4 SD from the mean dropped (100,344) and children with fewer than three observations dropped (98,686). | BEF, IND | working paper · Section 3.1; Appendix A.2 |
| Inverse probability weights for access | Humlum and Plato (2026) | Logit propensity score for access to higher education estimated on injured workers at year e-1 using age, gender, stable partner, years of schooling, labour income, hours worked, hourly wage, experience, union membership, physical-cognitive ability gap, antidepressant prescription dummy and year of injury; No-Access workers weighted by p/(1-p) (Abadie 2005), and the matched control receives the same weight. | IDA, UDDA, LMDB, BEF | online appendix · Supplementary Appendix B.1 |
| IVF sample and potential childbirth year | Lundborg et al. (2017) | Treatment success is measured at the first IVF treatment with embryo implant; t=0 is the year of (potential) childbirth. All regressions control for age at first IVF treatment, year of first IVF treatment, and pre-treatment education and earnings; pre-treatment baseline means are taken over years 1-4 before the first IVF treatment. Four samples: (i) IVF-treated women without children (baseline); (ii) IVF-treated women with children; (iii) successfully treated women with at least two embryos implanted (twin strategy); (iv) representative sample of women with a first child born 1995-2005. Balanced-panel robustness sample also used. | IVF register, Population/fertility registers | online appendix · Online Appendix A, B (Table 2 notes), D, E |
| Job-spell estimation sample | Hummels et al. (2025) | Worker-firm-years in firms with positive imports and exports, workers aged 20-60, full-time employees, non-missing output, capital-labour ratio, skill share and the import-price instrument; firms kept from the first year with at least 10 employees onward; singleton job spells (person-firm) dropped. | IDA, FIDA, FIRM, UHDIheltid==1 (full-time, HDTID2 codes 1-4 per code comment); age 20-60; empl>=10 | replication code · Programs/1_data_job_spell/HMXdata2.do |
| Large reference groups | Hvidberg et al. (2023) | Cohort: born the same year. Gender: same birth year and gender. Municipality: same birth year and current municipality. Education: same birth year and education level (basic, upper secondary, vocational, short-cycle higher, bachelor, master/PhD) using DISCED. Sector: same birth year and same sector (construction, real estate, business services, finance and insurance, trade and transport, manufacturing, information and communication, culture, agriculture, public work) based on DB07. | BEF, UDDA, RASDISCED (ISCED-based) education levels; DB07 sector codes | paper · Table 2 |
| Listings sample | Andersen et al. (2022) | From 615,040 RealView listings of owner-occupied housing (excluding cooperative housing): drop unmatched in registers (-107,679), no reference price i.e. purchased before 1992 (-144,962), owner ID not uniquely determined (-71,876), non-household buyer i.e. government/company/organization (-10,382), foreclosures (-6,416), extreme prices (<100,000 DKK or >20,000,000 DKK in 2015 prices, or flagged extreme by Statistics Denmark; -5,499), owner ID not found, missing lot size or hedonic characteristics, listing date before previous purchase date, intra-family/special-circumstance sales, missing listing price; then drop summer houses (-24,098) and professional investors owning more than 3 properties (-18,312). Final: 214,103 listings 2009-2016 (172,225 with mortgage, 41,878 without), 191,507 households, 178,933 properties; 70.4% sold, 29.6% retracted. Extensive-margin stock sample: 5,538,052 observations of 807,345 properties. | RealView listings, EJSA, EJER, EJVK, BBR/BOL, REAL, BEFprice bounds 100,000-20,000,000 DKK (2015 prices); >3 properties = investor | online appendix · Online Appendix C.5 and Table L.1 |
| Long-run balanced panel 1980-2011 | Gruber et al. (2021) | Very-long-run home size analysis uses a balanced panel of individuals observed in all years 1980-2011 (vs. main semi-balanced panel 1980-1996). Pre-trend robustness: group-specific linear trends estimated on 1983-1986 and residualized out. | Housing register; population register | online appendix · Figures A.II-A.IV notes |
| Main survey sample | Taubinsky et al. (2026) | Monthly survey respondents 2012-2019 (backcast analyses 2013-2019) aged 25-60, excluding high self-employment income, missing key survey answers (Do not know, refused or forecast >100 pp) and missing registry variables, after trimming income changes and inflation answers at 2.5/97.5 percentiles. | BEF, IND, UDDAAge<25 | Age>60 dropped | replication code · code_dstserver/analysis/A1c_sample_restrictions.do; NBER WP Section 1.3 |
| Mass layoff subsample | Andersen et al. (2023) | Mass layoff notifications from the Ministry of Employment (firms with >20 employees must report planned large-scale layoffs; 'large scale' ranges from 10 workers for firms with 20-100 employees to 30 workers for firms with >300 employees), linked by firm ID to Tax Agency payroll data; individuals whose employer's report date falls in months -7 to -1 relative to the observed layoff month; 1,156 individuals. | Ministry of Employment mass layoff notifications; BFL/eIndkomstreport in months -7 to -1 | online appendix · Appendix E |
| Master's student sample | Hansen et al. (2024) | Students enrolled in a master's program on 1 August 2007 at Aarhus University (incl. Aarhus School of Business) or University of Copenhagen with at most 40 ECTS points remaining on the recoding date, who graduated before 2011 (3,813 students). | University student records | working paper · Section 3.1 |
| Matched control group | Jensen and Zhang (2026) | Alternative control group following Fadlon and Nielsen (2021): individuals treated 6 years later serve as controls. Spillover analysis on in-laws restricts to married/cohabiting individuals matched to married/cohabiting controls who lose no parent-in-law in the window, also matching on whether own parents are alive. SEs clustered at individual-by-match ID. | BEF, DODSAARS/DODSAARG | online appendix · Supplemental Appendix A.C, A.F |
| Matched native comparison group | Arendt et al. (2025) | Danish natives selected by propensity-score matching to treated (control) refugees on YSR = 1 employment (2005 for treated, 2004 for control), age, gender, being single, having any children and county of residence (initial assignment county for refugees), used in a triple-difference check for the 2009 recession. | BEF, Income registers | paper · Section 6.3 'Comparison with natives' |
| Matched non-injured control workers | Humlum et al. (2025) | Injured workers matched one-to-one to non-injured workers in cells of event year, pre-event occupation, age, gender and level of education (baseline specification 5); occupational earnings premium computed within Year-Occupation-Industry-Education-Age-Gender match cells. | IDA; AES; education register | online appendix · Supplemental Appendix Table C.2 and Figure C.1 notes; Table F notes |
| Math-track high school sample | Brenøe and Zölitz (2020) | All students entering the math track of general (academic) high school 1980-1994; excludes students missing gender/age, students not aged 14-19 at entry, schools where under 95 percent of a cohort is aged 14-19, school-cohorts with fewer than 10 students, and schools not admitting students for at least four consecutive years. | Student registers | working paper · Section II.B |
| Military service (served) | Bingley et al. (2020) | Men who start military service aged 18-24, regardless of whether drafted; those who serve without being drafted are volunteers. | Danish Defence military register | working paper · Section II |
| Mortality sample and estate correction | Dahl et al. (2024) | Population from BEF (1986+) and FAIN (before 1986), years 1998-2014, ages 37+ (mortality analyses ages 40-100, 2001-2014); person-years after the death year are dropped (unresolved estates). Age-specific mortality by income tertile and gender is linearly trended across 2001-2014 to predict 2001 and 2014 rates. | BEF, FAIN, DODSAASG, DODSAARS | replication code · CreateData_DK.do lines ~7-9, 94-98, 195-215 |
| Mother sample and timing of first birth | Sander (2026) | Native (IE_TYPE 1) women born 1920-1959 with a first child born 1964-1975, aged at least 13 at first birth, with a 1970 parish observed and surviving until the first child is 16. First-birth year is the earliest birth year among children linked through MOR_ID (supplemented by family-register child counts). | BEF, Fødselsdata (birth data), FAMILIE, FT70IE_TYPE == 1; MOR_ID; yrfirst in 1964-1975 | replication code · dofiles/createfirst.do, 'SAMLING AF BEF...' block |
| Net analysis sample | Graversen and Ours (2008) | From 5,180 assigned: drop 27 incorrectly assigned by birth date, 43 bad-weather/work-sharing cases, 37 who died or moved abroad, and 553 who did not receive UI benefits within 6 weeks of PES registration, leaving 4,520 (2,229 treatment, 2,291 control). | PES registers, DREAM | working paper · Appendix A, Table A1 |
| New UI spell and analysis sample | Fluchtmann et al. (2024) | UI recipients of Danish nationality entering new UI spells September 2015 to September 2017. A new UI spell starts when a person who received no UI benefits in the previous 4 weeks is observed with at least 4 consecutive weeks of UI payouts. Restrictions: spell lasts at least 8 weeks; at least 4 Joblog applications registered during the spell (counted after dropping the last 4 weeks); exits UI to employment within 52 weeks; applications from the last 4 weeks before the new job starts are dropped (median transition time 6 weeks minus an assumed 2 weeks from application to offer). Observations with an estimated propensity score above 0.99 or below 0.01 are trimmed. | DREAM, Joblog (Jobnet), IDA, BFL | online appendix · Online Appendix A.1 and A.6, Table A.1, fn. 39 |
| Nurse Home Visiting (NHV) exposure in birth year | Rossin-Slater and Wüst (2020) | Indicator for an approved NHV program in the municipality in the birth year, based on DNBH approval dates 1937-1949 from the Danish National Archives (approval required sufficient nurse coverage). For 28 municipalities without an NHV program by 1949, treatment year is assigned from Skjernbaek lists (year of previous publication if listed, otherwise never treated). | Danish National Board of Health (DNBH) archival records, Skjernbaek lists | working paper · Section 2 (NHV data) |
| Outcome winsorization and time windows | García-Miralles and Leganza (2024) | Anticipation-period outcomes are averaged over 2011-2013 and pre-announcement placebo outcomes over 2008-2010; robustness uses stricter winsorization at the 10th and 90th percentiles; an additional robustness subsample excludes individuals with real estate transactions. | online appendix · Online Appendix Tables A.5-A.6, placebo table notes | |
| Parent-child sample | Kreiner et al. (2020) | All individuals aged 18-45 in 2004-2011 with at least one living parent, linked via CPR to parents in the population register; unit of observation is the parent-child-year cell (separate child-mother and child-father observations; new partners of divorced parents ignored). | Population register, SKAT loan register | working paper · Section 3.2 |
| PCD and PD samples | Boserup et al. (2018) | PCD: pooled cross-section of all individuals aged 1-40 in 2000-2011 with parents alive (parent IDs registered for Danes born from 1960). PD: panel of the 1965 birth cohort with both parents alive in 2011, followed 1983-2011 (ages 18-46). Monetary values deflated by CPI (2011 prices). | Population register (parent links), SKAT wealth records | working paper · Section 2; Tables A1-A2 |
| Population survey weights | Caplin et al. (2026) | Inverse predicted propensity scores from a probability model of survey participation estimated on 2020 administrative data on the demographics of Danes active in the labour market. | paper · Footnote 10 (details in Online Appendix B) | |
| Post-reform charge instrument and donut window | Anker et al. (2021) | Sampling window +/-24 months around the 2005 reform, excluding June-September of the reform year; placebo samples shift the reform to e.g. 2002. Robustness caps on number of prior charges at max 5, 10 or 15. | Central Crime Register+/-24 months; donut June-September 2005 | online appendix · Table A-2 and placebo table notes |
| Potential first-time homeowner women | Daysal et al. (2021) | Women not homeowners at the beginning of 1992-1993 who became homeowners between 1993 and 2004 at ages 18-42; owning means the woman, her partner or both own the home. Women whose partner bought before the couple formed, who moved out of the new home within the year, or who have incomplete income or home data are dropped, leaving women aged 20-44 observed 1995-2006. | Population Registry, State's Sales and Valuation Registry | working paper · Section 1 footnote 2; Section 3.1 |
| Pre-reform personal retirement plan users | García-Miralles and Leganza (2024) | Subsample of individuals who had used personal retirement plans before the reform announcement: those who made contributions to personal plans in two or three of the years 2008-2010. | Tax-reported pension contribution recordscontributions in >=2 of 2008, 2009, 2010 | online appendix · Online Appendix table notes (personal-plan subsample) |
| Preschool exposure at age 3 | Rossin-Slater and Wüst (2020) | Indicator equal to one for birth cohorts that had at least one government-approved preschool in their municipality of birth at age 3, using the year of government approval from archival lists of all approved preschools (Skjernbaek, nine editions 1921-1960); municipality of birth is assumed to be the early-childhood municipality. | Archival preschool lists (Skjernbaek), Parish of birth (population register), DigDag | working paper · Sections 2 (Data on preschools) and 3, eq. (1) |
| Primary analysis sample | Chetty et al. (2014) | Danish population 1995-2009 from merged income tax register, population register and IDA, excluding observations below age 18 or above 60 and observations with self-employment income (about 41 million observations on 4 million individuals). | IND (income tax register), BEF, IDA | working paper · Section III (Sample and Variable Definitions) |
| Programme-outcome estimation sample | Almar et al. (2026) | All persons living in Denmark at end of year 1980-2018 aged 19-60, irrespective of marital status (about 3.0 million per year); persons with missing education (2.6%) excluded. | BEF, PERSONER, UDDAkeep if age>=19 & age<=60 | replication code · Make analysis sample 1.do; paper Section 2 (IZA DP 17814) |
| Propensity-score matching covariates | Leth-Petersen (2010) | Probit for low-liquid-asset status in 1991 uses: housing equity/house value and its square, ln(house value) and square (owners), ln(gross income) and square, age and square, single, UI membership, size of house and square, labour supply (male, female), self-employed (male, female), education short/medium/long, number of children dummies (1-4). Monetary values in DKK, 1990 prices. Matching with replacement; SEs per Abadie and Imbens (2006). | Income tax register, Wealth tax register, Population registers | online appendix · Tables A1, A4 |
| Random assignment by birth date | Graversen and Ours (2008) | Newly unemployed UI benefit recipients (clean seniority record, i.e. eligible for the full 4-year UI period) registering at the PES in Storstrom and South Jutland counties between week 43 of 2005 and week 8 of 2006 and still unemployed on the Friday of the registration week; treatment if born on the 1st-15th of a month, control if born on the 16th or later. Bad-weather and work-sharing unemployment excluded. | PES registers, CPR | working paper · Section 2.2 and Appendix A |
| Reform and pre-reform period samples | Jakobsen and Søgaard (2022) | Four-year periods: 2004-08 (pre) and 2008-12 (reform) for the 2009-10 reform; initial years 1999 and 2003 for the 2004 reform. Individuals present in tax data with positive income over the period, and initial personal income above DKK 250,000 (2009-10 reform) or DKK 200,000 (2004 reform) at 2019 levels deflated by tax-code wage indexes. | Danish tax records | paper · Section 3.3, Table 3 |
| Refugee children sample | Damm and Dustmann (2014) | Full sample: children of immigrants from Iraq, Iran, Vietnam, Sri Lanka, Lebanon, Ethiopia, Afghanistan or Somalia who immigrated before age 15 in 1986-1998, at most one year after their refugee parent(s), and turn 21 in 1986-2006 (5,615 individuals). Balanced sample (4,425) excludes those who emigrated before 21 (975) and those living in Denmark at 21 but not observed annually in registers at ages 15-21 (215). | Population register | online appendix · Table A6 notes |
| Refugee family sample and age at exposure | Dustmann et al. (2024) | Danish Immigration Service residence permits granted 1997-2006 (252,795 permits; 67,375 to refugees: 44,232 new refugees, 23,143 family reunification), merged with the Historical Migrations database (residency date, origin, emigration); base sample = refugees granted residency within 18 months of the 2002 Start Aid reform, families with children at residence; refugees from Afghanistan and the former Yugoslavia excluded due to concurrent processing changes. Child age groups at residency 0-7, 8-14 (7-14 in some tables) and 14-18. 'Refugee permit status' = 1 if granted on asylum grounds, 0 if family-reunified spouse/child of a refugee. Standard errors clustered by residency month. | Danish Immigration Service residence permits; Historical Migrations databaseresidency within +/-18 months of reform | online appendix · Appendix B.2; Table A.3 notes |
| Reweighting of biological families | Kleven et al. (2021) | Baseline reweighting of the biological sample on pre-birth variables (year of first child, mother's age at first child, mother's pre-child education and pre-child earnings) plus subsequent-children variables (years to second child, total number of children); alternatives use no reweighting or pre-birth variables only. | BEF, UDDA, IND | online appendix · Online Appendix, Figure A.III notes |
| Robustness samples (donut, bandwidth, placebo) | Dustmann et al. (2024) | Donut sample excludes two months of residency dates on each side of the reform; reduced bandwidth +/- 12 months; placebo reforms from -5 to +5 months each using +/- 18 months around the placebo date; extended sample adds Afghan and ex-Yugoslavian refugees. | Danish Immigration Service records | online appendix · Online Appendix Table A.11 notes; Figure A.5 notes |
| Robustness subsample: no annuity contributions | Fadlon et al. (2016) | Workplaces whose median annuity contribution rate in 1996-1998 was zero and whose median capital (lump-sum) account contribution rate in 1998 exceeded 1.5 percent (4,056 observations). | Income tax register (third-party reported pension contributions)median capital contribution rate 1998 > 1.5% | online appendix · Online Appendix A, item 4 |
| Running variable / post-campaign treatment | Altındağ et al. (2024) | Days between the child's birth date and 31 December 1991 (main tables), with treatment = born after the cutoff; local linear specification with separate slopes, SEs clustered by birthday. The setup file also defines a birth-month distance to January 1992. Births up to 2006 retained. | MFRdif = birthday - mdy(12,31,1991); setup: dif = ym(byear,bmonth) - ym(1992,1), treat = dif >= 0 | replication code · 04 Tables_days.do lines ~38-42; 01 Set up RD data.do |
| Running variable: days from 1 January 1999 | Foged et al. (2024) | Number of days between the date the residence permit (refugee status) was granted (tilladelsesdato) and 1 January 1999; RD with triangular kernel, local linear, cutoff 0. Sample: refugees and family-reunified to refugees admitted from 1997, aged 18-64, restricted to age < 50 so they can be followed 18 years post-reform; Somalis excluded in main sample. | OPHG, BEFday_nr = dofd(tilladelsesdato) - date('01011999'); keep if age < 50; keep if somalia == 0 | replication code · 9. Sample selection.do, lines ~37-60, 122 |
| Sample exclusions by main activity | Jakobsen et al. (2026) | Individuals aged 25-60 with an opposite-sex partner; observations dropped if mainly student, self-employed, retired or on disability pension, based on main activity at end of November. | IDAP/AKM (socio-economic status)SOCSTIL, SOCSTIL_KODE | working paper · Section 2; Supplemental Material A |
| Sample frame | Traiberman (2019) | Raw data are all individuals in IDAS aged 23-59 in 1996-2007; the final sample applies the occupation/education imputation and cleaning and drops workers non-employed for the whole sample period. | IDAS | online appendix · Online Appendix Table A.7 notes; D.1 |
| Self-employment exclusion | Taubinsky et al. (2026) | Exclude respondents if self-employment income exceeds one fourth of household income (in absolute value) in any year from t-4 to t+1 around the interview. | INDabs(Income_selfemp_HH)/abs(Income_HH) > .25 for timestamps L4..F1 | replication code · code_dstserver/analysis/A1c_sample_restrictions.do; NBER WP footnote 11 |
| Semi-balanced panel 1980-1996 | Gruber et al. (2021) | Individuals observed in every year 1980-1996 while aged 20-80 (entering at 20, leaving after 80), giving 7 pre-reform and 10 post-reform years; about 40 million person-years. | Population register | working paper · Sections 3.2 and 4 |
| Sibling analysis sample | Daysal et al. (2026) | From all 2,278,868 children born 1981-2017: drop one-child families; keep first- and second-born singletons only; require birth spacing of at least 11 months; require known municipality of birth in municipalities averaging at least 1,000 children aged 13-71 months (91 municipalities kept); drop missing parental controls and keep complete pairs. Final sample 1,230,180 children. | MFR, BEF | paper · Section 2 (Analysis sample), footnote 14 |
| Sibling sample / family fixed effect | Houmark et al. (2025) | Families defined by the mother-father ID pair (full siblings); the sibling sample includes children with at least one full sibling observed on the same test. | BEFfef = group(pnrm pnrf) | replication code · Results (for replication).do |
| Sibling-pair sample | Joensen and Nielsen (2018) | Older siblings entering academic high school 1984-87 who graduated in three years, paired with a younger sibling who entered after 1987 and graduated in three years. The main sample has younger-sibling cohorts 1988-91 and an age gap of at most 4 years; the wider sample has cohorts 1988-97 and a gap of at most 10 years. | Statistics Denmark population and education registers | working paper · Section 3.1; Appendix Table B1 |
| Single fixed-rate mortgage sample | Andersen et al. (2020) | All Danish households with an unchanging number of household members and a single fixed-rate mortgage at the beginning of each year 2010-2017; refinancing measured as a quarterly dummy. Robustness samples exclude cash-out and maturity-extension refinancings, exclude FRM-to-ARM refinancers, or restrict to mortgages over DKK 250,000 with horizon of at least 20 years. | Danmarks Nationalbank loan-level mortgage datamortgage > DKK 250,000 and horizon >= 20 (robustness) | online appendix · Appendices C-E |
| Source data coverage thresholds | Utar (2014) | Customs trade data: universe of firm shipments 1993-2007 aggregated to firm x CN8 x country x year, but only product shipments of DKK 10,000 or more included. Varestatistik (1995-2005): only firms with 10 or more employees two years prior. Regnskabsdata: firms with at least 0.5 FTE and/or earnings above a size limit (about DKK 500,000 in wholesale, DKK 150,000-200,000 in manufacturing); manufacturing, construction and retail only until 1998, wholesale from 1998, almost all private sectors from 1999 (excluding agriculture, finance, public, education and medical services). IDA covers persons aged 15-70. | Udenrigshandel; Varestatistik; Regnskabsdata; IDAshipments >= DKK 10,000; Varestatistik >= 10 employees; Regnskabsdata >= 0.5 FTE | online appendix · Appendix C, Constructing Matched Data Sets |
| Stacked three-year difference sample | Kleven and Schultz (2014) | Stacked three-year intervals 1984-1987 to 2002-2005, keeping taxpayers also observed in year s-1 (used for pre-reform income controls: splines of log s-1 income and the log deviation between s-1 and s incomes); restricted to ages 15-70, fully tax liable in Denmark, excluding those whose base-year income comes primarily from welfare benefits (about 37 million observations). | Income Tax Register, IDA | paper · Section II; Section V table notes |
| Stayers (job separations) | Christensen et al. (2005) | For each establishment, the number of employees in November 1994 and the number of 'stayers', defined as workers who were employees both initially (November 1994) and in the following November (1995); separations are modelled as stayers out of initial employment. | IDA | working paper · Section 3.1 (p. 8) |
| Survey population | Hvidberg et al. (2023) | Individuals born 1969-1973 invited via Digital Post; main analysis uses 9,415 respondents who completed the survey (Feb-Mar 2019), linked to registers on CPR. | BEF, IND, UDDA, RAS | replication code · README.docx (analysisI.dta, analysisII.dta) |
| T95 group and instrument | Daysal et al. (2025) | Instrument is the interaction between an indicator for belonging to the T95 group and an indicator for diagnosis in or after 1995. The comparison group consists of 'always eligible' and 'never eligible' women (eligible or not eligible for radiotherapy regardless of year of diagnosis); full analysis sample N = 2,823. Robustness drops women diagnosed in 1994, women residing in areas with pilot universal screening programs, or women with breast-conserving surgery. | DBCG clinical database | online appendix · Notes to Appendix Tables A4, A9 and Figures A4-A5 |
| Tenure and potential experience groups | Lentz et al. (2023) | Short tenure is less than 100 weeks of employment (26 weeks for non-employment spells). Potential experience is time since leaving school (age minus age upon leaving school), grouped as under 5, 5-10, 11-15 and over 15 years; calendar time in 3-year intervals (9 groups 1987-2013), giving 72 covariate cells. | Danish matched employer-employee data (MEE) | working paper · Section 2; Section 5 |
| Textile and clothing worker sample | Utar (2018) | All workers with primary employment in the textile and clothing (T&C) sector in 1999 (IDA November job) who were aged 20-67 throughout 2002-2010 (cumulative analysis: born 1943-1982). T&C in code (last-job NACE) is 6-digit NACE 170000-189999 before 2008 and 130000-149999 from 2008. | IDApernace (6-digit NACE): T&C 170000-<190000 (year<2008), 130000-<150000 (year>=2008) | replication code · DataPrep.do (latc definition); working paper Section 'Data' and 'Cumulative Effects' |
| Third-child IV sample | Kleven et al. (2019) | Women with first child 1985-2003 and completed fertility of two or three when last observed in 2013; unbalanced panel with the longest available window (up to t=28 after first birth); event-time dummies around the third birth instrumented with the sex mix of the first two children. | Population register | online appendix · Appendix B.3 |
| Transaction sample restrictions | Andersen et al. (2021) | All property transactions 1994-2013 restricted to single-family houses and apartments bought and sold by individuals; drops transactions flagged by Statistics Denmark as involving price clauses or extreme prices, rented-out properties, properties traded more than once within a year, transactions within a household or between parents and children, households buying or selling more than three properties in a year, and agents outside ages 18-65. Cottages, farms and cooperative housing are excluded. | SKAT property transactions, BBR, CPR | online appendix · Appendix A, Sample selection; paper Section 2 |
| Treatment and control cohorts | Arendt et al. (2025) | Refugees who applied for asylum after 28 February 2002 and were aged 18-64 when granted temporary residency; treatment = temporary residency granted January-October 2004 (exposed to the 1 May 2007 permanent-residency reform), control = January-October 2003. | Immigration registers, BEF | paper · Sections 2, 4 and 5 |
| Treatment and control events | Brenøe et al. (2024) | Potential event = a woman's main job at a firm in a baseline year; event year = baseline + 2. Treatment event: woman gives birth in the event year but not in the year before or after; control event: no birth in the event year, the year before or the year after. Woman aged 19-33 at baseline, at least one year tenure at the baseline firm and not a student. | CPR, IDA | working paper · Sections 5.1-5.2; Appendix C.2 |
| Treatment and control groups (shock timing) | Fadlon and Nielsen (2019) | Treated = individuals whose family member (or peer) had a shock in a calendar year; controls = individuals from the same cohorts whose family member (or peer) had the same shock five years later (Delta=5). Non-fatal sample = households in which a member had a first heart attack or stroke and survived the four-year horizon; death sample = families with a member who died 1985-2011. Alternative control group: exact match cells within calendar year on predicted cardiovascular risk of family members and lagged outcome, controls weighted by representation in cell (about 94% of controls for prime-age spouses never affected). | National Patient Registry (LPR), Cause of Death RegistryDelta = 5 years | online appendix · Appendix Table 1 notes; Appendix B |
| Treatment and control groups (top-tax incentive) | Kreiner et al. (2016) | Robustness variants of the control group by average monthly 2008 earnings: narrow C-group DKK 32,000-35,000, wide C-group DKK 24,000-35,000; doughnut sample excludes individuals with average monthly 2008 earnings DKK 33,000-37,000; alternative baseline year 2009 (groups and income differences relative to 2009); alternative post-reform year 2011. T-group by percentile bins of average monthly earnings: [P80,P90), [P90,P95), [P95,P99), [P99,+]. | eIncome monthly payroll register (SKAT)Narrow C: 32,000-35,000 DKK/month; wide C: 24,000-35,000 DKK/month; doughnut excl. 33,000-37,000 DKK/month (2008) | online appendix · Online Appendix Table A2 notes; Figure A1 |
| Treatment and control groups for bunching | Johannesen et al. (2026) | Treated: low-income singles aged 65+. Controls: low-income singles aged 60-64 (age-ineligible) and singles aged 65+ with incomes between 150% and 200% of the full phase-out income threshold. Sample restricted to singles; main cross-section pools 2014-2018 (2014-2016 for bank data). | BEF, IND | paper · Section I.C (Sample); Figure 1 notes (panel C) |
| Treatment assignment and study population | Simonsen and Skipper (2025) | Work units (314 units of eldercare workers across 110 employers in 11 municipalities) were randomised in September 2008, with 144 units (46%) assigned to Healthy-at-Work. The population is individuals employed at participating work units in the month just before randomisation (7,541 of 7,660 linked to registers). Treatment status is fixed at the initial randomisation month, so post-randomisation moves are ignored and estimates are intention-to-treat. | Healthy-at-Work experimental records, Statistics Denmark registers (CPR-linked) | working paper · Sections 2.1, 3 and 4 (treatment assignment) |
| Treatment/control (Fadlon-Nielsen DiD) | Andersen et al. (2026) | Alternative design comparing individuals diagnosed in a given year with those diagnosed Delta years later (Delta from 3 to 8), with person, year-by-age, in-prison and cancer-recurrence fixed effects. The baseline sample keeps only the 10 years before cancer for pre-diagnosis observations. | Cancer register, Crime registers | online appendix · Online Appendix H; Appendix A |
| Treatment/control by event timing | Fadlon et al. (2026) | Households where one spouse has a health event 1999-2018 and dies within 12 months; treatment = event in 1999-2013, control = similar event in 2004-2018 with placebo event assigned five years earlier; both spouses aged 45-80 at baseline; balanced from four years before to four years after. Specifications include household FE, calendar-year FE and a quadratic in the surviving spouse's age. For Delta = 3-7 robustness the calendar interval of treated spousal deaths is held fixed so only the control group changes. Variables are measured in the year before the actual or placebo event. | BEF, LPR, Death Registry, IND | online appendix · Supplemental Appendix, Table B.1-B.3 and Figure B.8 notes |
| Treatment: born after municipal program implementation | Hjort et al. (2017) | Indicator for being born in a municipality after it implemented the home visiting program (exact implementation dates 1937-1949 from the Danish National Archives), assigned from date and parish of birth; parishes mapped to municipalities using the 1930, 1940 and 1950 DigDag parish/municipality structures. Implementation assumed permanent once adopted. | Administrative population data (date and parish of birth), Danish National Archives, DigDag | paper · Section III.A, footnote 34; Section II footnote 17 |
| Treatment: born on days 1-3 of the month | Baker et al. (2026) | Indicator equal to 1 if the child's day of birth (from the transcribed nurse records) is 1-3 and 0 if 4-31; this identifies eligibility for the extended toddler home-visiting trial. | Copenhagen nurse records, CPRborn13ext = 1 if BirthDateDay in 1-3; 0 if 4-31 (born13) | replication code · dst_replicationpackage/do/1b_cr_format_ext_data.do; 1d_cr_merge_ext_data_no_outcomes.do |
| Unemployment spell inflow sample | Munch et al. (2006) | Flow sample of all unemployment spells in 1997-2000 for individuals in a 2.5% random sample of the Danish population, restricted to inflow into unemployment of workers aged 19-66; spell duration measured in weeks (18,992 persons, 52,738 spells). | Administrative registers (2.5% random population sample) | working paper · Section 3 (Data and the Danish labour and housing markets), pp. 9-10 |
| VERP participant analysis sample | García-Miralles and Leganza (2024) | Four cumulative restrictions: (1) Danes born within six months of the cutoff date January 1, 1954 (71,095 individuals); (2) regular participatory VERP contributors, defined as making contributions in 70% of pre-announcement years 2001-2010 (47,047); (3) balanced panel: present in the data in every year 2006-2018 (43,348); (4) drop the self-employed, defined over the pre-announcement period using Statistics Denmark's definition of an individual's main source of income (final analysis sample 40,042). | Population register, VERP contribution records, Statistics Denmark main source of income classificationbirthdate within +/-6 months of 1954-01-01; VERP contributions in >=70% of years 2001-2010; balanced 2006-2018 | online appendix · Online Appendix Table A.1 and notes |
| VLBW treatment / running variable | Daysal et al. (2022) | Focal child's birth weight (bw) centred at 1,500 grams; treatment indicator vlbw = 1 if birth weight < 1,500 g; local linear RD with separate slopes on each side and triangular weights 1-|bw-1500|/bandwidth. | MFRvlbw = (bw < 1500); f1 = (bw-1500)*vlbw; f2 = (bw-1500)*(1-vlbw) | replication code · 09 Final data preparation and analyses.do, lines ~568-574 |
| Wage-effect repeated cross-section sample | Kleven et al. (2014) | For year t, foreigners who arrived in Denmark in year t-1 and stayed the full calendar year t; earnings between 70-95% (below) and 105-400% (above) of the threshold, excluding 95-105% (bunchers) and above 400%; years 1991-1994 excluded because scheme earnings are not computerized. | Administrative tax records, CPR migration records, Scheme beneficiary records | working paper · Section 4.2, Figure 8, Table 3, footnote 30 |
| Work accident sample and matched controls | Humlum and Plato (2026) | Universe of work accidents causing earning-capacity loss 1998-2017, restricted stepwise: ECL > 0; exclude psychological shock; collapse to person-year; person exists in register data; full-time employed before injury; vocational degrees with (or comparable without) access; male craft workers. Each injured worker matched to a random non-injured worker with the same age, gender, education, occupation, industry and access to higher education in the year before the accident (plus stable partner and parent indicators); controls inherit the year of injury of their match. | AES, IDA, BEF, UDDA | online appendix · Supplementary Appendix, Table A.1 Panel F and Table A.2 |
| Working-age native samples | Foged and Peri (2016) | Event-study regressions use a strongly balanced panel of low-skilled (and separately high-skilled) natives employed in 1994; 'cohort' specifications fix controls and municipality at 1994 values (SEs clustered by 1994 municipality), 'area' specifications use actual values (SEs clustered by actual municipality). | IDA | online appendix · Table A.1, Figure A.1 notes |
| Years since residency (YSR) | Arendt et al. (2025) | For persons granted temporary residency during year y, YSR = 1 is year y+1 (first full calendar year with employment observed as of 1 January), followed for seven years (YSR 1-7). | Immigration registers | paper · Section 4 and Section 5 |
| Variable | Paper | Definition | Registers and codes | Source |
|---|---|---|---|---|
| Awareness of shifting opportunity | Kreiner et al. (2016) | Survey respondents are classified as informed about the opportunity to shift income if they answered Q1 = 'After New Year' and Q2 = 'Legal' in the Capacent Epinion telephone survey (588 T-group and 290 C-group respondents). | eIncome monthly payroll register (SKAT) linked to telephone surveyQ1 = 'After New Year' & Q2 = 'Legal' | online appendix · Online Appendix Figure A2 notes |
| Car types (green/brown x heavy/light) | Gillingham et al. (2022) | Cars are split by whether weight is above or below the median for the car's vintage cohort, then within each half by whether fuel efficiency is above (green) or below (brown) the median, giving four types; attributes are averaged within type. Cars are grouped into 25 age groups from new to 24+ years (cars over 22 years dropped from estimation). | Car ownership register (Statistics Denmark) | working paper · Appendix F.1 (Car types); Section 6.2 |
| Financial attention (logins) | Andersen et al. (2024) | Number of days in a month on which a household member logs into any Danske Bank account (not only brokerage), averaged over months, and split into within-month terciles. | Danske Bank online login data | working paper · Sections 2.1 and 4.3 |
| Granted debt relief (treatment) | Bruze et al. (2024) | Applicants identified from Statstidende announcements (decision types P = proklama, K = kendelse/ruling, T = tilbagekaldelse/revocation, I); granted = the person's sequence of announcements contains a Kendelse (court ruling granting debt relief); alternative definition requires no subsequent revocation. | Statstidende (hand-collected), CPRgranted = 1 if any decision 'K'; granted_not_revoked = granted & no 'T' after 'K' | replication code · 01classify_rep.sas, section 'Classify individuals into those who receive debt relief and those who don't' |
| Income and credit expectations (survey) | Kreiner et al. (2019) | Survey questions on whether total 2009 income evolved better/worse than expected (5-point scale plus don't know), changes in expected 2010 income, and changes in expectations about the possibility of borrowing money in 2010 (5-point scale from much more optimistic to much more pessimistic). | Telephone survey5-point Likert scales | online appendix · Online Appendix section 8 (survey instruments) |
| Kilometres driven | Gillingham et al. (2022) | Driving measured from odometer readings recorded at mandatory vehicle inspections, which occur biannually from car age 4 (ages 4, 6, 8, 10, ...). | Vehicle inspection odometer data | working paper · Appendix F.1 and footnote 32 |
| Overall job satisfaction | Clark et al. (2009) | ECHP question 'How satisfied are you with your work or other main activity?', ordered scale from 1 (not at all satisfied) to 6 (fully satisfied); Danish ECHP waves 1994-2001, employees only. | ECHP (Danish sample) | working paper · Section 3.1; Figure 1 note |
| Patience index | Epper et al. (2020) | Patience groups by tertiles of the non-parametric patience measure with cut-offs Low [0.0,0.5], Medium [0.5,0.8], High [0.8,1.0]. Survey alternative (follow-up of 2,096 respondents) uses the 1973 Danish Longitudinal Survey of Youth job-profile question: average salary from start (low), low salary first two years then high (medium), very low salary first four years then very high (high). | Online experiment linked on CPRCut-offs 0.5 and 0.8 | online appendix · Online Appendix Figures A8-A10 |
| RD control variables | García-Miralles and Leganza (2024) | Baseline RD controls: indicator for being male, indicator for being married in 2010, and indicators for region of residence in 2010 (five regions: Hovedstaden, Sjaelland, Syddanmark, Midtjylland, Nordjylland). Regressions use separate linear polynomials in birthdate on each side of the cutoff with triangular weights. | Population register5 regions as of 2010 | online appendix · Online Appendix Table A.2 notes |
| Scrappage | Gillingham et al. (2022) | Scrappage is not observed directly: a vehicle is defined as scrapped if an ownership spell ends and no other ownership spell ever begins afterwards (ownership register extract from September 2011, last sample year 2008, so at least about 3 years without an owner). Accidents are not observed. | Car ownership register | working paper · Appendix F.1 |
| Stock of open vacancies | Gautier et al. (2018) | Monthly average number of open vacancies per day by county, January 2004-November 2007, collected by the National Labour Market Board from local job centres; used in logs. | National Labour Market Board vacancy data | working paper · Section 3 |
| Survey preference measures | Kreiner et al. (2020) | January 2014 telephone survey of 1,748 individuals asking respondents to place themselves and both parents on a 1-10 scale for risk willingness, patience and impulsivity; linked to register data. | 2014 survey linked to registers | working paper · Section 4 (survey), Figure 11 |
| Trustee leniency instrument | Bruze et al. (2024) | Leave-one-out mean grant rate of the applicant's court-appointed trustee (lawyer ID unique within court), net of the leave-one-out court grant rate; sample restricted to trustees with at least 20 cases; regressions include court x year fixed effects, SEs clustered by trustee. | Statstidende (hand-collected)iv_lawyer2 = (mean_lawyer_leniency*n_lawyer - granted)/(n_lawyer-1) - (iv_court*obs_court - granted)/(obs_court-1); keep if n_lawyer >= 20 | replication code · 05IV_regressions_rep.do, lines ~19-35 |
| Union membership | Humlum et al. (2025) | Dummy for union membership, measured by a positive deductible amount (union fees) reported by unions to the tax authorities. | Tax recordsDeductible union fees > 0 | online appendix · Supplemental Appendix Table A.1, row 17 |
| Vote share changes by party group | Dustmann et al. (2019) | Percentage-point change in municipal vote shares between election years t-1 and t (1989-98: three parliamentary and three municipal elections) for anti-immigration parties (Progress Party and Danish People's Party), centre-right, centre-left and small centre parties; seat shares also computed. | Statistics Denmark election results | paper · Sections 2.1 and 3.2, Table 1 |