1. Fertility timing and statistical discrimination of women
evidence from young workers across 40 years and 56 countries
Joanna Tyrowicz Lucas van der Velde
FAME|GRAPE, University of Regensburg and IZA Warsaw School of Economics, and FAME|GRAPE
Main question
More and more, women opt to delay or forego
childbearing. How does it impact labor market
outcomes?
We study timing of fertility → gender wage gap
* Focus on young workers: statistical discrimination
* Provide comparable estimates of GWGs
* Provide causal evidence
Main challenges:
(1) Measuring gender discrimination
* Adjusted Gender Wage Gap is the dierence in
hourly wages that cannot be attributable to dier-
ences in characteristics
* We compute AGWG following Nopo (2008)
→ a non-parametric decomposition
→ that compares comparable people
* Controls for age, education, marital status and
urban location + occupation + industry.
(2) Endogeneity of fertility decisions
* Fertility might be higher in countries where the
adjusted gender wage gap is larger, for example due to
opportunity costs. To address this reverse causality,
we use 4 sets of instruments:
IV#1 Time since admission of oral contraceptive pill
(Purely administrative procedure)
IV#2 Years of compulsory education
IV#3 Length of military conscription
IV#4 Fertility of the mother's generation
Linking fertility timing to gender wage gaps among youth
AGWGi,s,t = α + βIV
× d
Fertility Timingi,t + γtime + ξs + ϵi,s,t
Fertility Timingi,t = ϕ + θPILLi,t + ϱEDUi,t + µCONSCRi,t + ςM_FERTi,t + εi,t,
where
* AGWGi,t,s is the adjusted gender wage gap for country i in period t computed fromsource s
* Fertility Timingi,t is measured as mean age at rst birth
* ξs are source xed eects and γtime a common time trend
Results
F-stat25 k F-stat= 461 F-stat15 k
.01
0
-.01
-.02
-.03
-.04
-.05
-.06
Estimated
β
None - OLS All instruments Only PILLi,t EDUi,t CONSCi,t
Instruments used
* Delaying fertility by a year
→↓ adjusted gender wage gap by 4 percent of women's wages
→ around 12% of the average gender wage gap
* Estimated β is similar if
→ we keep only estimates that control for industry and occupation
→ we allow variation over distribution of adjusted gender wage gap or mean age at rst birth
→ we use alternative estimation methods (high-dimensional xed eects)
* We do not observe an eect of fertility if
→ we focus on the entire population (instead of youth)
→ we use more noisy measures of fertility (GFR, Age-Specic FR)
Discussion
Our estimates are consistent with the existence of
statistical discrimination by employers.
This hypothesis predicts that employers pay lower
wages to women if
1. Employers anticipate costs related to childbearing
2. Employers believe costs to be higher for women than
for men
3. Employers cannot observe parenthood status and
4. Employees cannot communicate their type
Postponing fertility decreases the probability that
employers incur in costs related to childbearing thus
reducing the gender wage gap among youth.
Benchmarking our results
* We compare tted values from our model to the
implied gender wage gap from the theory using US
time use surveys and probability of becoming parents
derived from vital statistics.
0
.05
.1
.15
.2
.25
Wage
penalty
among
young
women
(in
%
of
men's
average
wage)
USA (1986) USA (2005) USA (2007) USA (2009) USA (2011) USA (2013) USA (2015)
Model prediction Simulated differences: caring (mean) caring (median) caring chores (mean) caring chores (median)
* Our empirical model predicts higher adjusted gender
wage gaps than those implied by fertility
* Statistical discrimination is not the only source of
gender inequality
Questions or suggestions?
Please send them to lvandervelde@grape.org.pl.