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.

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
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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
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47. Statistical discrimination at young age:
Summary
Availability of individual level database
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48. Statistical discrimination at young age:
Summary
Adjusted vs raw gender wage gap
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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
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50. Statistical discrimination at young age:
Summary
Evolution of the adjusted gender wage gap
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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 -
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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 -
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