Statistical discrimination at young age: This document discusses statistical discrimination based on fertility plans and childbearing. It motivates studying gender wage gaps among young labor market entrants and how delayed fertility may impact these gaps. The authors aim to test the link between the timing of fertility and adjusted gender wage gaps using individual-level data from 56 countries over four decades. They employ instrumental variable strategies using reforms to compulsory education duration, changes to military conscription, and the authorization of contraceptive pills across countries.

1. Statistical discrimination at young age:
Statistical discrimination at young age:
new evidence from four decades of individual data across 56 countries
Joanna Tyrowicz [FAME|GRAPE, University of Warsaw & IZA ]
Lucas van der Velde [FAME|GRAPE & Warsaw School of Economics]
LABFAM Seminar
June 2021

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 → implict test of statistical
discrimination

6. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps

7. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)

8. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)
Comparable measures of AGWG (across c & t) for entrants
Document different trends between AGWG among youth and total

9. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)
Comparable measures of AGWG (across c & t) for entrants
Document different trends between AGWG among youth and total
Causal evidence: several instruments
Duration of compulsory education (multiple reforms)

10. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)
Comparable measures of AGWG (across c & t) for entrants
Document different trends between AGWG among youth and total
Causal evidence: several instruments
Duration of compulsory education (multiple reforms)
Military conscription (many changes)

11. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)
Comparable measures of AGWG (across c & t) for entrants
Document different trends between AGWG among youth and total
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)
Link between “pill” and fertility is causal (Bailey, 2009)
Earlier studies: directly affected cohorts ↔ this study: current cohort

12. Statistical discrimination at young age:
Motivation
Our contribution
Test the link from timing of fertility to (adjusted) gender wage gaps
Why look at entrants?
most of the “action”
entry wage as benchmark for raises → future earnings
(Blau and Ferber, 2011; Reuben et al., 2013)
Comparable measures of AGWG (across c & t) for entrants
Document different trends between AGWG among youth and total
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)
Link between “pill” and fertility is causal (Bailey, 2009)
Earlier studies: directly affected cohorts ↔ this study: current cohort
Fertility observed in the generation of the mothers

13. Statistical discrimination at young age:
Motivation
Table of contents
1 Motivation
2 Toy model
3 Measuring adjusted gender wage gaps
4 Identification
5 Results
6 Summary

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
Two types of workers: parents (π ) and non-parents (1 − π)
Same productivity h , but there are costs (c) associated with parenthood
Employers cannot know ex ante if a young worker becomes a parent during
contract
Wages reflect the expected productivity
W = E(h) = h ∗ (1 − π) + (h − c) ∗ (π)

15. 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 − (h ∗ (1 − π) + (h − c) ∗ (π) = c · π
In this stylized framework, the adjusted GWG
increases with the costs of childbearing and childrearing (c)
increases with the probability of being a parent (π)
If employers are rational: ↓ π ⇒↓ gender wage gap

16. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
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
exact matching
Reliable even when cannot correct for selection bias
AGWG within common support

17. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
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
exact matching
Reliable even when cannot correct for selection bias
AGWG within common support
We need individual level data

18. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Data: estimation of the gender wage gap
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

19. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Data: estimation of the gender wage gap
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
In total: 56 countries from early 1980s onwards, ∼ 1258 data points.

20. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Data: estimation of the gender wage gap
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
In total: 56 countries from early 1980s onwards, ∼ 1258 data points.
For each dataset: obtain AGWG for individuals aged 20-30 years old.

21. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Data: estimation of the gender wage gap
0
20
40
60
80
number
of
countries
1971
1976
1981
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

22. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Data: estimation of the gender wage gap
−.2
0
.2
.4
.6
Gap
1980 1990 2000 2010 2020
Year
Census EU LIS Other time trend
Evolution of the adjusted gender wage gap for youth

23. Statistical discrimination at young age:
Measuring adjusted gender wage gaps
Documenting 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

24. Statistical discrimination at young age:
Identification
Method
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + c,t

25. Statistical discrimination at young age:
Identification
Method
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

26. Statistical discrimination at young age:
Identification
Method
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 #1: fertility decisions endogenous to labor force participation AGWG

27. Statistical discrimination at young age:
Identification
Method
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 #1: fertility decisions endogenous to labor force participation AGWG
→ need to instrument

28. Statistical discrimination at young age:
Identification
Method
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 #1: fertility decisions endogenous to labor force participation AGWG
→ need to instrument
But #2: many other mechanisms at play (tertiary enrollment)

29. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)

30. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation

31. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation
Authorization of contraceptive pills causally affects the female
education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)

32. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation
Authorization of contraceptive pills causally affects the female
education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)
Can be utilized as a medication against multiple health conditions

33. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation
Authorization of contraceptive pills causally affects the female
education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)
Can be utilized as a medication against multiple health conditions
Authorization purely administrative

34. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation
Authorization of contraceptive pills causally affects the female
education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)
Can be utilized as a medication against multiple health conditions
Authorization purely administrative
Authorization 6= access for contraceptive reasons

35. Statistical discrimination at young age:
Identification
Our instruments
Compulsory schooling causally affects fertility
(Black et al. 2008, Cygan-Rehm and Maeder 2013)
Military conscription causally affects the timing of family formation
Authorization of contraceptive pills causally affects the female
education, family and labor supply
(US: Goldin and Katz 2002, Bailey 2006, Ananat and Hungerman 2012)
Can be utilized as a medication against multiple health conditions
Authorization purely administrative
Authorization 6= access for contraceptive reasons
Mothers’ fertility (intergenerational transmission of norms)

36. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms

37. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms
Some European countries admitted immediately
E.g. Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: ?

38. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms
Some European countries admitted immediately
E.g. Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: ? Norway
The timing on the non-European countries also widely diverse

39. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms
Some European countries admitted immediately
E.g. Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: ? Norway
The timing on the non-European countries also widely diverse
Admission 6= availability (→ timing)
E.g. former socialist countries: admitted but unavailable
Prescriptions vs otc
The UK originally admitted it only for married women
etc

40. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms
Some European countries admitted immediately
E.g. Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: ? Norway
The timing on the non-European countries also widely diverse
Admission 6= availability (→ timing)
E.g. former socialist countries: admitted but unavailable
Prescriptions vs otc
The UK originally admitted it only for married women
etc
Until today persistent differences in adoption
∼ 38% in W. Europe; ∼ 14% E. Europe

41. Statistical discrimination at young age:
Identification
Authorization of contraceptive pill – a little bit of history
The pill first invented in 1940s in the UK, the first approved patent in the US
in 1960, substantial heterogeneity of authorization timing forms
Some European countries admitted immediately
E.g. Portugal and Spain lagged behind (late 60’s and 70’s)
The latest: ? Norway
The timing on the non-European countries also widely diverse
Admission 6= availability (→ timing)
E.g. former socialist countries: admitted but unavailable
Prescriptions vs otc
The UK originally admitted it only for married women
etc
Until today persistent differences in adoption
∼ 38% in W. Europe; ∼ 14% E. Europe but 48% (!) in Czech Republic

42. Statistical discrimination at young age:
Identification
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 in first stage is different
within component x̃i,j = xi,j − θ̂ ¯
xi
between component ¯
xi
Additional instruments are redundant in White sense
→ standard errors adjusted to unbalanced panels

43. Statistical discrimination at young age:
Identification
Additional data sources
Mean age at first birth
Eurostat, UNECE, OECD, Human Fertility Database + bureaus of
statistics + papers
The pill data: Finlay, Canning and Po (2012)
Military conscription: Mulligan and Shleifer (2005) + Military Balance
Compulsory schooling: UNESCO + papers for earlier years
Mothers’ (completed) fertility: The World Bank

44. 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
Clustering Yes Yes Yes Yes Yes Yes Yes
Time trends Yes Yes Yes Yes Yes Yes Yes
IVs All CS, MS Pill MF - - -

45. Statistical discrimination at young age:
Results
The effect of delayed fertility on AGWG - compare measures
Gender wage Youth, MAB, AGWG Youth All
gap IV OLS TFR, RGWG, OLS
(1) (5) (8) (9)
Fertility -0.026*** -0.020* 0.074 -0.048
(0.007) (0.012) (0.054) (0.035)
R-squared 0.275 0.617 0.884 0.621
F − statistic 12,162 - - -
Observations 1,067 1,128 1,226 1,186
Clustering Yes Yes Yes Yes
Time trends Yes Yes Yes Yes
IVs All - - -

46. Statistical discrimination at young age:
Results
Is this big or small?
Recall E(Wm|h) − E(Ww |h)) = c · π

47. Statistical discrimination at young age:
Results
Is this big or small?
Recall E(Wm|h) − E(Ww |h)) = c · π
We tease out c and π across (available) countries → obtain c · π
Compare to estimated AGWG

48. Statistical discrimination at young age:
Results
Is this big or small?
Recall E(Wm|h) − E(Ww |h)) = c · π
We tease out c and π across (available) countries → obtain c · π
Compare to estimated AGWG
Age-specific fertility rates: π = 1 −
R a=30
a=20
p(a)da

49. Statistical discrimination at young age:
Results
Is this big or small?
Recall E(Wm|h) − E(Ww |h)) = c · π
We tease out c and π across (available) countries → obtain c · π
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

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

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

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

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

54. 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

55. 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

56. 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

57. Statistical discrimination at young age:
Summary
Questions or suggestions?
Thank you!
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t: grape org
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