Statistical discrimination is a possible, rational motive behind the persistent differences in earnings between men and women. Employers could women to bear a larger share of the burden associated with having children, and subsequently discount that on wages. We test the empirical validity of this claim using data from over 50 countries and 40 years. Using IV we find causal evidence consistent with this hypothesis. Postponing birth by one year leads to large falls in the adjusted gender wage gap.

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]
Workshop in Labor Economics
April 2022

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:
Motivation
Table of contents
1 Motivation
2 Toy model
3 Taking it to the data
4 Results
5 Summary

13. Statistical discrimination at young age:
Toy model
A toy model of statistical discrimination (I)
Variation of the ideas presented by Phelps (1972)
Set up
Individuals differ on two accounts: parenting type (ci ) & Procreation probability (πi )

14. Statistical discrimination at young age:
Toy model
A toy model of statistical discrimination (I)
Variation of the ideas presented by Phelps (1972)
Set up
Individuals differ on two accounts: parenting type (ci ) & Procreation probability (πi )
Men and women different costs → Ew (ci ) ≥ Em(ci )

15. Statistical discrimination at young age:
Toy model
A toy model of statistical discrimination (I)
Variation of the ideas presented by Phelps (1972)
Set up
Individuals differ on two accounts: parenting type (ci ) & Procreation probability (πi )
Men and women different costs → Ew (ci ) ≥ Em(ci )
Individuals cannot communicate ci before birth + costly after birth
Wages reflect the expected productivity
W = E(h) = E[h ∗ (1 − πi )] + E[(h − ci ) ∗ (πi )] = h − E(pii )E(ci )

16. Statistical discrimination at young age:
Toy model
A toy model of statistical discrimination (II)
The Adjusted GWG is then:
E(Wm|h) − E(Ww |h)) = h − Em(pii )Em(ci ) − h − Ew (pii )Ew (ci ) = E(π) × Ew (ci ) − Em(ci )
In this stylized framework, the adjusted GWG increases if
↑ difference in costs of childbearing and childrearing (Ew (ci ) − Em(ci ))
↑ probability of being a parent (E(π))
If employers are rational: ↓ π ⇒↓ Adjusted GWG

17. Statistical discrimination at young age:
Taking it to the data
What we would like to do
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t

18. Statistical discrimination at young age:
Taking it to the data
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

19. Statistical discrimination at young age:
Taking it to the data
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

20. Statistical discrimination at young age:
Taking it to the data
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

21. Statistical discrimination at young age:
Taking it to the data
(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

22. Statistical discrimination at young age:
Taking it to the data
(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

23. Statistical discrimination at young age:
Taking it to the data
(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

24. Statistical discrimination at young age:
Taking it to the data
(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

25. Statistical discrimination at young age:
Taking it to the data
(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

26. Statistical discrimination at young age:
Taking it to the data
(III) Instruments
Compulsory schooling ⇒ fertility (Black et al. 2008, Cygan-Rehm and Maeder 2013)
Source: Compulsory schooling: UNESCO + papers for earlier years

27. Statistical discrimination at young age:
Taking it to the data
(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

28. Statistical discrimination at young age:
Taking it to the data
(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

29. Statistical discrimination at young age:
Taking it to the data
(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)

30. Statistical discrimination at young age:
Taking it to the data
(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

31. Statistical discrimination at young age:
Taking it to the data
(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

32. Statistical discrimination at young age:
Taking it to the data
(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

33. Statistical discrimination at young age:
Taking it to the data
(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)

34. Statistical discrimination at young age:
Taking it to the data
(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

35. Statistical discrimination at young age:
Taking it to the data
(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

36. Statistical discrimination at young age:
Taking it to the data
Estimation procedure
AGWGi,s,t = α + β × time + γ [
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

37. 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

38. Statistical discrimination at young age:
Results
The effect of delayed fertility on AGWG - IVs
Gender wage Youth, MAB, AGWG Youth All
gap IV OLS TFR, AGWG, OLS
(1) (2) (3) (4) (5) (6) (7)
Fertility -0.026*** -0.042*** -0.031*** -0.023*** -0.020* -0.055* 0.020
(0.007) (0.011) (0.013) (0.009) (0.012) (0.030) (0.018)
R-squared 0.275 0.280 0.277 0.271 0.617 0.559 0.836
F − statistic 12,162 6,891 263.6 289.4 - - -
Observations 1,067 1,081 1,120 1,100 1,128 1,186 1,226
Cluster SE Yes Yes Yes Yes Yes Yes Yes
Time trends Yes Yes Yes Yes Yes Yes Yes
IVs All CS, MS Pill MF - - -
More demanding AGWG

39. 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]

40. Statistical discrimination at young age:
Results
Benchmarking our results
Recall E(Wm|h) − E(Ww |h)) = E(π) × Ew (ci ) − Em(ci )

41. Statistical discrimination at young age:
Results
Benchmarking our results
Recall E(Wm|h) − E(Ww |h)) = E(π) × Ew (ci ) − Em(ci )
We tease out c’s and π across (available) countries and compare to estimated AGWG

42. Statistical discrimination at young age:
Results
Benchmarking our results
Recall E(Wm|h) − E(Ww |h)) = E(π) × Ew (ci ) − Em(ci )
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

43. Statistical discrimination at young age:
Results
Benchmarking our results
Recall E(Wm|h) − E(Ww |h)) = E(π) × Ew (ci ) − Em(ci )
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):
c =
(T − tm,k ) − (T − tm,∼k )
−
(T − tw,k ) − (T − tw,∼k )
T

44. Statistical discrimination at young age:
Results
Benchmarking statistical gender discrimination

45. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → GWG ↓

46. 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

47. 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

48. 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

49. Statistical discrimination at young age:
Summary
Questions or suggestions?
Thank you!
w: grape.org.pl
t: grape org
f: grape.org
e: lvandervelde grape.org.pl

50. Statistical discrimination at young age:
Summary
Availability of individual level database
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51. Statistical discrimination at young age:
Summary
Adjusted vs raw gender wage gap
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52. 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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53. Statistical discrimination at young age:
Summary
Evolution of the adjusted gender wage gap
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54. Statistical discrimination at young age:
Summary
Robustness check: only estimates with ind. occ.
AGWG Youth, MAB Youth All
IV OLS TFR, OLS
(1) (2) (3) (4) (5) (6) (7)
Fertility -0.026*** -0.035*** -0.032*** -0.022*** -0.026*** -0.038 0.0052
(0.005) (0.01) (0.007) (0.005) (0.004) (0.02) (0.02)
R-squared 0.28 0.29 0.28 0.27 0.58 0.56 0.80
F-statistic 9526.5 770.4 286.6 1380.7
Observations 825 838 864 857 873 940 941
Cluster SE Yes Yes Yes Yes Yes Yes Yes
Time trends Yes Yes Yes Yes Yes Yes Yes
IVs All CS, MS Pill MF - - -
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