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Our analysis shows that increasing the age at first birth is associated with a substantial decline in gender wage gaps: postponing first birth by a year reduces the gap by around 15%. In order to establish causality, we propose a novel instrument that exploits international variation in approval of oral contraceptives (the pill). Our estimates are consistent with a model of statistical discrimination where employers offer lower wages to women to hedge the expected costs associated with childbearing and childrearing.

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- 1. Statistical discrimination at young age: Statistical discrimination at young age: evidence from young workers across four decades and 56 countries Joanna Tyrowicz [FAME|GRAPE, University of Warsaw & IZA ] Lucas van der Velde [FAME|GRAPE & Warsaw School of Economics] European Society for Population Economics June 2023
- 2. Statistical discrimination at young age: Motivation Motivation – textbook case for statistical discrimination Fertility (-related absences) as premise for gender inequality fertility plans → hiring decisions (Becker et al., 2019) child bearing → wage loss among mothers (not fathers) (Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014)
- 3. Statistical discrimination at young age: Motivation Motivation – textbook case for statistical discrimination Fertility (-related absences) as premise for gender inequality fertility plans → hiring decisions (Becker et al., 2019) child bearing → wage loss among mothers (not fathers) (Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014) Demographic trends: ↑ age at first birth and ↓ # of births ⇒ less reasons for statistical discrimination
- 4. Statistical discrimination at young age: Motivation Motivation – textbook case for statistical discrimination Fertility (-related absences) as premise for gender inequality fertility plans → hiring decisions (Becker et al., 2019) child bearing → wage loss among mothers (not fathers) (Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014) Demographic trends: ↑ age at first birth and ↓ # of births ⇒ less reasons for statistical discrimination What we do study gender wage gaps among labor market entrants explore the role of delayed fertility
- 5. Statistical discrimination at young age: Motivation Motivation – textbook case for statistical discrimination Fertility (-related absences) as premise for gender inequality fertility plans → hiring decisions (Becker et al., 2019) child bearing → wage loss among mothers (not fathers) (Landais & Kleven, 2019; Cukrowska-Torzewska & Matysiak, 2017; Pertold-Gebicka, 2014) Demographic trends: ↑ age at first birth and ↓ # of births ⇒ less reasons for statistical discrimination What we do study gender wage gaps among labor market entrants explore the role of delayed fertility → implicit test of statistical discrimination
- 6. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps
- 7. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps Comparable measures of AGWG (across c & t) for entrants
- 8. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps Comparable measures of AGWG (across c & t) for entrants Causal evidence: several instruments Duration of compulsory education (multiple reforms)
- 9. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps Comparable measures of AGWG (across c & t) for entrants Causal evidence: several instruments Duration of compulsory education (multiple reforms) Military conscription (many changes)
- 10. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps Comparable measures of AGWG (across c & t) for entrants Causal evidence: several instruments Duration of compulsory education (multiple reforms) Military conscription (many changes) New IV: international variation in “pill” admission (in the US: Goldin & Katz, 2002; Bailey, 2006; Oltmans-Ananat & Hungerman, 2012)
- 11. Statistical discrimination at young age: Motivation Our contribution We uncover a link from timing of fertility to (adjusted) gender wage gaps Comparable measures of AGWG (across c & t) for entrants Causal evidence: several instruments Duration of compulsory education (multiple reforms) Military conscription (many changes) New IV: international variation in “pill” admission (in the US: Goldin & Katz, 2002; Bailey, 2006; Oltmans-Ananat & Hungerman, 2012) Fertility observed in the generation of the mothers
- 12. Statistical discrimination at young age: Empirical approach What we would like to do We would like to estimate the following regression AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t
- 13. Statistical discrimination at young age: Empirical approach What we would like to do We would like to estimate the following regression AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t Fertility: use mean age at first birth TFR is noisy → we want the “risk” by employers at 20 age 30
- 14. Statistical discrimination at young age: Empirical approach What we would like to do We would like to estimate the following regression AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t Fertility: use mean age at first birth TFR is noisy → we want the “risk” by employers at 20 age 30 AGWG: obtain own estimates → adjust raw GWG for 20 age 30 But: fertility decisions endogenous to labor force participation AGWG
- 15. Statistical discrimination at young age: Empirical approach What we would like to do We would like to estimate the following regression AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t Fertility: use mean age at first birth TFR is noisy → we want the “risk” by employers at 20 age 30 AGWG: obtain own estimates → adjust raw GWG for 20 age 30 But: fertility decisions endogenous to labor force participation AGWG → need to instrument
- 16. Statistical discrimination at young age: Empirical approach (I) Fertility data We use mean age at first birth (MAB) as a measure of fertility Direct link to probability of becoming a parent Less noisy than alternatives Total fertility rate, age specific fertility, childlessness Data collected from a variety of sources Eurostat, UNECE, OECD, Human Fertility Database + bureaus of statistics + papers details
- 17. Statistical discrimination at young age: Empirical approach (II) Measuring the adjusted gender wage gap Nopo decomposition A flexible non-parametric approach based on exact matching Reliable even when when small set of covariates Reliable even when cannot correct for selection bias AGWG within common support
- 18. Statistical discrimination at young age: Empirical approach (II) Measuring the adjusted gender wage gap Nopo decomposition A flexible non-parametric approach based on exact matching Reliable even when when small set of covariates Reliable even when cannot correct for selection bias AGWG within common support We need individual level data
- 19. Statistical discrimination at young age: Empirical approach (II) Measuring the adjusted gender wage gap Collecting individual level data 1 Harmonized data sources: IPUMS + LISSY + EU (SILC, SES, ECHP) 2 Longitudinal data Canada, Germany, Korea, Russia, Sweden, the UK, Ukraine and the US 3 Labor Force Surveys and Household Budget Surveys: Albania, Argentina, Armenia, Belarus, Chile, Croatia, France, Hungary, Italy, Poland, Serbia, the UK and Uruguay 4 LSMS (The World Bank): Albania, B H, Bulgaria, Kazakhstan, Kyrgistan, Serbia and Tajikistan
- 20. Statistical discrimination at young age: Empirical approach (II) Measuring the adjusted gender wage gap Collecting individual level data 1 Harmonized data sources: 2 Longitudinal data 3 Labor Force Surveys and Household Budget Surveys: 4 LSMS (The World Bank): In total: – unbalanced panel 56 countries from early 1980s onwards – ∼ 1258 measures of the Adjusted GWG details
- 21. Statistical discrimination at young age: Empirical approach (III) Instruments Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013) Source: Compulsory schooling: UNESCO + papers for earlier years
- 22. Statistical discrimination at young age: Empirical approach (III) Instruments Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013) Source: Compulsory schooling: UNESCO + papers for earlier years Military conscription ⇒ the timing of family formation Source: Mulligan and Shleifer (2005) + Military Balance
- 23. Statistical discrimination at young age: Empirical approach (III) Instruments Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013) Source: Compulsory schooling: UNESCO + papers for earlier years Military conscription ⇒ the timing of family formation Source: Mulligan and Shleifer (2005) + Military Balance Mothers’ fertility (intergenerational transmission of norms) Source: The World Bank
- 24. Statistical discrimination at young age: Empirical approach (III) Instruments Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013) Source: Compulsory schooling: UNESCO + papers for earlier years Military conscription ⇒ the timing of family formation Source: Mulligan and Shleifer (2005) + Military Balance Mothers’ fertility (intergenerational transmission of norms) Source: The World Bank Authorization of contraceptive pills ⇒ female education, family and labor supply (US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012) Source: Finlay, Canning and Po (2012)
- 25. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960
- 26. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960 Adoption timing varied a lot, even in Europe
- 27. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960 Adoption timing varied a lot, even in Europe Eastern European countries were forerunners Portugal and Spain lagged behind (late 60’s and 70’s) The latest: Norway
- 28. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960 Adoption timing varied a lot, even in Europe Admission 6= access (→ timing)
- 29. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960 Adoption timing varied a lot, even in Europe Admission 6= access (→ timing) E.g. former socialist countries: admitted but unavailable Prescriptions vs otc The UK originally admitted it only for married women
- 30. Statistical discrimination at young age: Empirical approach (III) Instruments - a small bit of history The pill first invented in 1940s in the UK, the first approved patent in the US in 1960 Adoption timing varied a lot, even in Europe Admission 6= access (→ timing) Until today persistent differences in use as contraceptive ∼ 38% in W. Europe; ∼ 14% E. Europe but 48% (!) in Czech Republic
- 31. Statistical discrimination at young age: Empirical approach Estimation procedure AGWGi,s,t = α + β × time + βIV [ MABi,t + ξs + i,s,t MABi,t = φ + θPILLi,t + %EDUi,t + µCONSCRi,t + ςM FERTi,t + εi,t Variation in pill authorizaton: one data-point for each country We use 2SLS for panel data as in Baltagi and coauthors (1981, 1992, 2000) It is a random effects model (FGLS) but... instrumentation is different Additional instruments are redundant in White sense → standard errors adjusted to unbalanced panels
- 32. Statistical discrimination at young age: Results Raw correlation between MAB and AGWG AGWGc,t = 0.88 − 0.028 MABc,t + c,t (0.046) (0.001) More descriptives
- 33. Statistical discrimination at young age: Results The effect of delayed fertility on AGWG - IVs AGWG estimates IV (βIV ) OLS (1) (2) (3) (4) Fertility timing -0.025*** -0.033*** -0.025*** -0.019** (0.0062) (0.0093) (0.0065) (0.0097) R-squared 0.28 0.28 0.28 0.76 F-statistic 25286.6 461.1 15667.1 - Observations 1106 1161 1114 1170 Cluster SE Yes Yes Yes Yes Time trends Yes Yes Yes Yes IVs All Pill CONSC, EDU, MF - Alternative GWG specifications: More controls , Fewer controls
- 34. Statistical discrimination at young age: Results The effect of delayed fertility on AGWG - Robustness checks HDFE Quantile Regression Heterogeneous fertility (1) (2) (3) (4) (5) (6) Q25 Q50 Q75 Intercepts Slopes MAB -0.012 *** -0.023 *** -0.022 *** -0.032 *** [-0.02,-0.00] [-0.03,-0.01] [-0.03,-0.01] [-0.04,-0.02] MAB Q25 0.133 *** -0.018 [0.07,0.20] [-0.05,0.02] MAB ∈ [Q25, Q75] 0.027 -0.019 [-0.03,0.08] [-0.05,0.01] MAB Q75 -0.019 [-0.05,0.01]
- 35. Statistical discrimination at young age: Results Benchmarking our results In a simple statistical discrimination model: E(Wm|h) − E(Ww |h)) | {z } AGWG = E(π) | {z } Pr. childbearing × Ew (ci ) − Em(ci ) | {z } Diff costs cond on gender
- 36. Statistical discrimination at young age: Results Benchmarking our results In a simple statistical discrimination model: E(Wm|h) − E(Ww |h)) | {z } AGWG = E(π) | {z } Pr. childbearing × Ew (ci ) − Em(ci ) | {z } Diff costs cond on gender We tease out c’s and π across (available) countries and compare to estimated AGWG
- 37. Statistical discrimination at young age: Results Benchmarking our results In a simple statistical discrimination model: E(Wm|h) − E(Ww |h)) | {z } AGWG = E(π) | {z } Pr. childbearing × Ew (ci ) − Em(ci ) | {z } Diff costs cond on gender We tease out c’s and π across (available) countries and compare to estimated AGWG Age-specific fertility rates: π = 1 − R a=30 a=20 p(a)da
- 38. Statistical discrimination at young age: Results Benchmarking our results In a simple statistical discrimination model: E(Wm|h) − E(Ww |h)) | {z } AGWG = E(π) | {z } Pr. childbearing × Ew (ci ) − Em(ci ) | {z } Diff costs cond on gender We tease out c’s and π across (available) countries and compare to estimated AGWG Age-specific fertility rates: π = 1 − R a=30 a=20 p(a)da ISSP time use (difference in differences): Ew (ci ) − Em(ci ) = (T − tm,k ) − (T − tm,∼k ) − (T − tw,k ) − (T − tw,∼k ) T
- 39. Statistical discrimination at young age: Results Benchmarking statistical gender discrimination 0 .05 .1 .15 .2 .25 Wage penalty among young women (in % of men’s average wage) AUT (2008) ESP (2009) GBR (2000) GBR (2014) Predicted from model Simulated, caring (mean) Simulated, caring (median) Simulated, caring chores (mean) Simulated, caring chores (median)
- 40. Statistical discrimination at young age: Summary Summary Do employers discriminate statistically? Tentatively yes Delayed fertility among youth → GWG ↓
- 41. Statistical discrimination at young age: Summary Summary Do employers discriminate statistically? Tentatively yes Delayed fertility among youth → GWG ↓ IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average) Estimates stable and robust across model specifications
- 42. Statistical discrimination at young age: Summary Summary Do employers discriminate statistically? Tentatively yes Delayed fertility among youth → GWG ↓ IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average) Estimates stable and robust across model specifications IV and OLS similar, but F-statistics strong
- 43. Statistical discrimination at young age: Summary Summary Do employers discriminate statistically? Tentatively yes Delayed fertility among youth → GWG ↓ IV estimates ∼ −0.03 (out of AGWG ∼ 0.12 on average) Estimates stable and robust across model specifications IV and OLS similar, but F-statistics strong Benchmarking: ∆c × π “explains away” AGWG sometimes → employers may receive signals correctly, but rarely do
- 44. Statistical discrimination at young age: Summary Questions or suggestions? Thank you! w: grape.org.pl t: grape org f: grape.org e: lvandervelde[at]grape.org.pl
- 45. Statistical discrimination at young age: Summary Mean age at first birth and childlessness 24 26 28 30 Mean age at first birth .4 .5 .6 .7 .8 .9 P(childless|age =25) Countries included: AUT, CZE, FIN, HUN, LTU, NLD, POL, PRT, SVN, USA Note: data from Eurostat (EU countries) and Census (US). Different colors correspond to different countries back
- 46. Statistical discrimination at young age: Summary Mean age at first birth and childlessness Relationship at different ages 24 26 28 30 Mean age at first birth .75 .8 .85 .9 .95 1 P(childless|age =20) Countries included: AUT, CZE, FIN, HUN, LTU, NLD, POL, PRT, SVN, USA 24 26 28 30 Mean age at first birth .4 .5 .6 .7 .8 .9 P(childless|age =25) Countries included: AUT, CZE, FIN, HUN, LTU, NLD, POL, PRT, SVN, USA 24 26 28 30 Mean age at first birth .1 .2 .3 .4 .5 .6 P(childless|age =30) Countries included: AUT, CZE, FIN, HUN, LTU, NLD, POL, PRT, SVN, USA Note: data from Eurostat (EU countries) and Census (US). Different colors correspond to different countries back
- 47. Statistical discrimination at young age: Summary Availability of individual level database back
- 48. Statistical discrimination at young age: Summary Adjusted vs raw gender wage gap back
- 49. Statistical discrimination at young age: Summary Trends in gender wage gaps All age groups Youth Raw GWG Adjusted GWG Raw GWG Adjusted GWG (1) (2) (3) (4) Year -0.160 -0.0308 -0.164** -0.158** (0.101) (0.0662) (0.0773) (0.0705) Observations 1,151 1,151 1,128 1,128 R-squared 0.204 0.117 0.105 0.108 Mean value 16.28 17.60 7.93 12.23 back
- 50. Statistical discrimination at young age: Summary Evolution of the adjusted gender wage gap back
- 51. Statistical discrimination at young age: Summary Only AGWG estimates including controls for ind. occ. AGWG estimates IV (βIV ) OLS (β) (1) (2) (3) (4) Fertility timing -0.026*** -0.032*** -0.025*** -0.020*** (0.0051) (0.0069) (0.0053) (0.0060) R-squared 0.28 0.28 0.28 0.62 F-statistic 7058.1 379.5 3894.7 Observations 825 864 834 873 Clustering SE Yes Yes Yes Yes Time trends Yes Yes Yes Yes IVs All Pill CONSC, EDU, MF - back
- 52. Statistical discrimination at young age: Summary Only AGWG estimates excluding controls for ind. occ. AGWG estimates IV (βIV ) OLS (β) (1) (2) (3) (4) Fertility timing -0.020*** -0.030*** -0.020** -0.0076 (0.0076) (0.0098) (0.0079) (0.013) R-squared 0.26 0.27 0.26 0.74 F-statistic 20910.9 494.9 16448.2 Observations 1103 1158 1111 1167 Clustering SE Yes Yes Yes Yes Time trends Yes Yes Yes Yes IVs All Pill CONSC, EDU, MF - back