Germany's Gender and Age Wage Gap Over Time

Gender and Age wage patterns in Germany
An analysis form the GSOEP
Lucas van der Velde
(with J. Tyrowicz and I. van Staveren)
Annual Conference of the Verein fur SocialPolitik,
September 2015

Introduction
Introduction
Motivation
Introduce age-factors in the study of the gender wage gap.
Ageing process in Europe.
How to craft eﬃcient policies to reduce Gender Wage Gap?

Introduction
Introduction
Motivation
Introduce age-factors in the study of the gender wage gap.
Ageing process in Europe.
How to craft efficient policies to reduce Gender Wage Gap?
What we do
Explore the effects of the life-cycle in women’s earnings.
Separate age-cohort effects
Use the DiNardo, Fortin and Lemieux (1996) decomposition.
Data: German Socio-Economic Panel for 1984-2008

Introduction
How the gender wage gap changes over age?
Hump-shaped pattern
Diﬀerences increasing over the age-years

Introduction
Hump-shaped pattern
Unequal distribution of activities within the household.
Child bearing and child rearing and its expectation.
Gender bias in the measurement of human capital.
Statistical discrimination from the employers.

Introduction
Hump-shaped pattern
“Hysteresis eﬀect”
“Double standard of ageing”

Data and method
Sample:The German Socio-Economic Panel
Yearly surveys covering a broad range of topics.
We restricted the sample to West Germany between
1984-2008.
Almost 500 000 observations
13 000 individual are observed for a decade or longer.
2 300 of them are present in each wave.
We only keep observations from German nationals aged 25-59.

Data and method
A quick look at the sample

Data and method
A ﬁrst glance at the gender wage gap
Notes: Dependent variables: tenure, experience, small kids in the household, married,
education level and year.

Data and method
What are we doing
We pursue three analyses:
1 Decompose the GWG using the DiNardo, Fortin and Lemieux
(1996) decomposition for diﬀerent cohorts across time.
2 Panel analysis of determinants of changes in the Adjusted
GWG over time.
3 Double decomposition.

Results
Decomposition at diﬀerent ages
Results for the adjusted GWG
Gender wage gap in diﬀerent age groups (1984-2006).
Notes: adjusted gap estimated at the mean with the DiNardo, Fortin and Lemieux (1996) decomposition;
smoothed (averaged over three years). Each bar represents a year in the sample, bars of similar colors correspond
to the same cohort. Red lines represent women’s participation rate, measured in the right axis.

Results
Panel estimates: age effects on the Adjusted GWG
Mean 1st Quartile 3rd Quartile
25-29 Base level
30-34 0.122*** 0.067 0.110*
35-39 0.134*** 0.068 0.159**
40-44 0.192*** 0.159*** 0.226***
45-49 0.213*** 0.193*** 0.307***
50-54 0.155** 0.125 0.312***
55-60 0.195* 0.180 0.365**
Year -0.010*** -0.008* -0.014**
Observations 175 175 175
R-squared 0.649 0.624 0.661
Notes: ***,**,* indicate significance at the 1 %, 5% and 10% level respectively. The
dependent variable is the adjusted gender wage gap calculated at different points of
thedistribution. All estimates include cohort specific effects and participation rates for
men and women.

Results
Additional controls: Household characteristics
Mean q(.25) q(.75) Mean q(.25) q(.75)
30-34 0.159** 0.063 0.152** 0.122** 0.063 0.068
35-39 0.166** 0.062 0.162** 0.119** 0.041 0.096
40-44 0.213** 0.143** 0.181** 0.154** 0.099** 0.134**
45-49 0.219** 0.182** 0.175** 0.155** 0.127** 0.136**
50-54 0.207** 0.175** 0.151* 0.112** 0.083** 0.113**
55-59 0.435** 0.442** 0.316** 0.260** 0.268** 0.261**
% female -1.014** -1.129** -0.080
main earner
% Tertiary -0.641 ◦ -1.410** 1.325*
educated
Observations 175 175 175 175 175 175
R-squared 0.461 0.456 0.269 0.434 0.455 0.286
Notes: **,*,◦ indicate signiﬁcance at the 1 %, 5% and 10% level respectively. The
thedistribution. All estimates include participation rates for men and women.

Results
Additional controls: Child-related concerns
Mean q(.25) q(.75) Mean q(.25) q(.75)
30-34 0.133** 0.107** 0.054 0.160** 0.146** 0.029
35-39 0.127** 0.078** 0.075◦ 0.168** 0.123** 0.080◦
40-44 0.158** 0.133** 0.115** 0.206** 0.197** 0.129**
Places for kids <3 0.006 0.013** -0.004
Fertility change 0.313* 0.229 -0.230
Observations 76 76 76 80 80 80
R-squared 0.360 0.341 0.209 0.633 0.464 0.558
Notes: **,*,◦ indicate signiﬁcance at the 1 %, 5% and 10% level respectively. The
thedistribution. All estimates include participation rates for men and women.

Results
Double decomposition
Age Characteristics Residuals Unexplained
1984-1989
30-34 -0,05 0,08 0,05
35-39 -0,04 0,04 0,15
40-44 -0,17 0,2 0,13
45-49 0,35 -0,45 0,36
50-54 0 0,01 0,22
1990-1999
30-34 0 -0,1 0,14
35-39 0 -0,43 0,56
40-44 0,03 -0,02 0,11
45-49 0 -0,07 0,2
50-54 0,01 -0,28 0,4
2000-2008
30-34 0,05 -0,17 0,14
35-39 -0,18 0,03 0,22
40-44 -0,11 -1,16 1,43
45-49 -0,12 -0,47 0,74
50-54 -0,18 -0,53 0,8

Results
Double decomposition: how diﬀerent
Changes in the adjusted GWG: Panel analysis
Notes: The estimations also include controls for the adjusted GWG in the initial period

Conclusions
Conclusions
1 We separate cohort and age effect to understand changes in the
GWG over age-years
2 The gender wage gap increases with age, possibly in a
non-monotonic fashion.
Steep increase in early career and later stabilization (mean)
Continuous increase in the later stages (q.75)
3 Variables connected to cohort specific trends do not affect our main
results
4 Policy implication: measures to tackle the GWG should take into
account also the pos-productive age.

Conclusions
Questions or suggestions?
Thank you for your attention

Appendix
Institutional context in Germany
Reasons
1 Restrictions on pregnant women employment.
2 Lenght of the maternity leaves (up to three years).
3 Maternity benefits (amount and non-relation to the labor
market history).
4 Only part-time work compatible with maternity benefits.
5 Insuficient childcare facilities.
6 Social constraints: the persistence of the KKK (children,
kitchen and church).

Appendix
Fertility patterns

Appendix
Day care facilities

Appendix
Household earnings

Appendix
Double decomposition: one cohort
Age Characteristics Residuals Unexplained
30-34 -0,08 0,11 0,04
35-39 -0,01 -0,12 0,15
40-44 0,16 -0,19 0,15
45-49 0,02 -0,41 0,2
50-54 -0,26 0,25 0,05

Appendix
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
Given a joint distribution of wages and characteristics of the form
fj (wi,j ) = fi,j (w|x) f (x|g = i, t = j)dx (1)
Where i represents the gender, male or female, and j represents the period
We can derive a counterfactual wage structure of the form by reweighting
female observation to make them more similar to males.
fj (wc
f ,j ) = ff ,j (w|x) Ψj (x)fj (x|g = f , t = j)dx (2)

Appendix
Given a joint distribution of wages and characteristics of the form
fj (wi,j ) = fi,j (w|x) f (x|g = i, t = j)dx (1)
Where i represents the gender, male or female, and j represents the period
We can derive a counterfactual wage structure of the form by reweighting
female observation to make them more similar to males.
fj (wc
f ,j ) = ff ,j (w|x) Ψj (x)fj (x|g = f , t = j)dx (2)
where Ψ(x) is the reweighting factor and equals
Ψj (xj ) =
fj (x|g = m, t = j)dx
fj (x|g = f , t = j)dx
(3)

Appendix
Thanks to Bayes rule, we can estimate Ψj (xj ) as follows
Ψj (xj ) =
Pr(g = m|x, j = t)Pr(g = f )
Pr(g = f |x, j = t)Pr(g = m)
(4)

Appendix
Thanks to Bayes rule, we can estimate Ψj (xj ) as follows
Ψj (xj ) =
Pr(g = m|x, j = t)Pr(g = f )
Pr(g = f |x, j = t)Pr(g = m)
(4)
We decompose the diﬀerences as
fj (wm,j ) − fj (wf ,j ) = [fj (wm,j ) − fj (wc
f ,j )] + [fj (wc
f ,j ) − fj (wf ,j )] (5)
The ﬁrst term represents the unexplained component; and the
second, the explained.

Appendix
Double decomposition
Presented in Simon and Welch(1985) to study convergence in
black workers’ wages
They decompose the change between periods t−1 and t in 4
components
1 The relative changes in characteristics from t−1 to t
2 Differences in characteristics in t
3 Differences in wage structure in t
4 The relative changes in wage structures from t−1 to t
The last component is similar to the unexplained component from
the previous decomposition, though it is ”cleaner” for the
comparison across time. A simple difference between the adjusted
gaps in two periods will also reflect the changes in the
characteristics used as a base (women from each period) while in
this case, we use the same characteristics in the two periods

Germany's Gender and Age Wage Gap Over Time

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