Statistical discrimination offers a compelling narrative on gender wage gaps during the early stages of the career. Expecting absences related to child-bearing and child-rearing, the employers discount productivity to adjust for the probable losses such as costs associated with finding substitutes, leaving customers, etc. If that is the case, lower and delayed fertility should imply lower discount in wages, and consequently reductions in the gender pay gap among entrants. We put this conjecture to test against the data. We provide a novel set of estimates of adjusted gender wage gaps among youth for 56 countries spanning four decades. We estimate that postponing childbirth by a year reduce the adjusted gap 2 percentage points (15%). We show that this estimate is consistent with statistical discrimination, but for some countries the estimates of AGWG imply that either statistical discrimination is not accurate or taste-based mechanisms are also at play.

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]
EACES Biennial Conference
September 2021

2. Statistical discrimination at young age:
Motivation
Motivation – textbook case for statistical discrimination
Fertility (-related absences) as premise for gender wag gaps
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
role of delayed fertility → role of statistical discrimination

3. 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

4. Statistical discrimination at young age:
Motivation
Table of contents
1 Motivation
2 Toy model
3 Identification
4 Results
5 Summary

5. 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: bearing the costs of parenthood (π ) and not (1 − π)
Conditional on human capital h, productivity is (hi − ci ) for the care-givers
Bearing the costs of parenthood during contract uknown ex ante
Wages reflect the expected productivity
E(w) = E(πi (h − ci )) + E((1 − πi )h) = h − E(πi ci ) = h − E(πi )E(ci )

6. Statistical discrimination at young age:
Toy model
A toy model of statistical discrimination (II)
The Adjusted GWG is then:
Em(w|h) − Ew (w|h) = E(πi ) × (cw − cm)
In this stylized framework, the adjusted GWG
increases with the differential costs of parenthood (cw − cm)
increases with the probability of being a parent (π)
If employers are rational: ↓ π ⇒↓ gender wage gap

7. Statistical discrimination at young age:
Identification
Method
We would like to estimate the following regression
AGWGc,t = βi + β × Fertilityc,t + γXc,t + ϵc,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: need estimates of AGWG at young age → need individual level data

8. 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 ̸= access for contraceptive reasons
Mothers’ fertility (intergenerational transmission of norms)

9. 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 ̸= 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

10. Statistical discrimination at young age:
Identification
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.
Nopo decomposition

11. Statistical discrimination at young age:
Identification
Data: estimation of the gender wage gap
−.4
−.2
0
.2
.4
.6
adjusted
gender
wage
gap
−.4 −.2 0 .2 .4 .6
raw gender wage gap
45 degree line linear fit

12. Statistical discrimination at young age:
Identification
Data: estimation of the gender wage gap
−.4
−.2
0
.2
.4
.6
24 26 28 30 32
Mean age at first birth
raw gender wage gap adjusted gender wage gap

13. Statistical discrimination at young age:
Identification
Documenting trends in gender wage gaps
All age groups Youth Mean age
Raw GWG Adjusted GWG Raw GWG Adjusted GWG at first birth
(1) (2) (3) (4) (5)
Year -0.160 -0.0308 -0.164** -0.158** 0.108***
(0.101) (0.0662) (0.0773) (0.0705) (0.0118)
Observations 1,151 1,151 1,128 1,128 1,128
R-squared 0.204 0.117 0.105 0.108 0.204
Mean value 16.28 17.60 7.93 12.23 27.03

14. Statistical discrimination at young age:
Identification
Estimation procedure
AGWGi,s,t = α + β × time + γ
MABi,t + ξs + ϵi,s,t
MABi,t = ϕ + θPILLi,t + ϱEDUi,t + µCONSCRi,t + ςM FERTILITYi,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)
Random effects model (FGLS) with
within component x̃i,j = xi,j − θ̂ ¯
xi
between component ¯
xi

15. 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

16. Statistical discrimination at young age:
Results
The effect of delayed fertility on AGWG - IVs
GWG Youth, MAB, AGWG Youth All
IV OLS TFR, AGWG, OLS
(1) (2) (3) (4) (5) (6) (7)
Fertility -0.025*** -0.041*** -0.033*** -0.024*** -0.023*** -0.050 0.025
(0.006) (0.011) (0.009) (0.007) (0.006) (0.038) (0.023)
IV’s All Edu + Conscr Pill Mothers - All All
R2
0.276 0.282 0.278 0.275 0.620 0.559 0.830
F 25,843 9,368 265.0 306.9
#N 1,106 1,121 1,161 1,142 1,170 1,243 1,303
Cluster Yes Yes Yes Yes Yes Yes Yes
Time Yes Yes Yes Yes Yes Yes Yes
How robust?
Consistent along distribution of AGWG
and along distribution of fertility
and to alternative estimators

17. 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):
cm − cw =
(T − tm,k ) − (T − tm,∼k )
−
(T − tw,k ) − (T − tw,∼k )
T

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

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

20. Statistical discrimination at young age:
Summary
Summary
Do employers discriminate statistically? Tentatively yes
Delayed fertility among youth → AGWG ↓
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: (cm − cw ) × π “explains away” AGWG sometimes
→ employers may receive signal correctly, but not full

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