We explore the changes in the gender wage gap as women age. For this, we build on the DiNardo, Fortin and Lemieux decomposition to separate age and cohort effects. Our results suggest that the differences in wages increase during the life-cycle, possibly in a non-monotonic fashion. In turn, our results imply that policies addressing this issue should also consider age effects.

1. Gender and Age wage patterns in Germany
Gender and Age wage patterns in Germany
An analysis form the GSOEP
I. van Staveren J. Tyrowicz L. van der Velde
Warsaw International Economic Meeting,
July 3, 2015

2. Gender and Age wage patterns in Germany
Introduction
Introduction
Motivation
Ageing process in Europe.
Gender issues in Germany.

3. Gender and Age wage patterns in Germany
Introduction
Introduction
Motivation
Ageing process in Europe.
Gender issues in Germany.
What we do
Explore the effects of the life-cycle in women’s earnings.
Use the DiNardo, Fortin and Lemieux (DFL) decomposition.
Data: German Socio-Economic Panel for 1984-2008

4. Gender and Age wage patterns in Germany
Introduction
Where GWG comes from?- The classics
Division of roles inside the household
(Becker 1985)
Intermittent labour market participation (and its anticipation)
(Ben-Porath, 1967;Mincer & Polachek, 1979)
Different career plans and earnings expectations
(Blau & Ferber, 1990)
→ Problem of reverse causality.

5. Gender and Age wage patterns in Germany
Introduction
Where the GWG comes from? - A modern approach
Wage bargaining & reference wages
(Babcock & Laschever, 2003)
Job-shopping
(Manning, 2003).
”Double penalty”: age and gender
(Duncan & Loretto, 2004)
Occupation seggregation and wage-hours non-linearities
(Goldin, 2013)

6. Gender and Age wage patterns in Germany
Introduction
Putting the pieces together
Expected pattens: age and adjusted GWG

7. Gender and Age wage patterns in Germany
Data and method
Sample:The German Socio-Economic Panel
Yearly surveys covering a broad range of topics.
Almost 500 000 observations for 24 years (1984-2008).
1 3000 individual are observed for a decade or longer.
2 300 of them are present in each wave.

8. Gender and Age wage patterns in Germany
Data and method
A quick look at the sample

9. Gender and Age wage patterns in Germany
Data and method
A quick look at the sample

10. Gender and Age wage patterns in Germany
Data and method
A quick look at the sample

11. Gender and Age wage patterns in Germany
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.

12. Gender and Age wage patterns in Germany
Data and method
What are we doing
We pursue three analyses:
1 Decompose the GWG using the DFL decomposition for
different cohorts across time.
2 Panel analysis of determinants of changes in the Adjusted
GWG over time.
3 Double decomposition.

13. Gender and Age wage patterns in Germany
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 DFL 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.

14. Gender and Age wage patterns in Germany
Results
Panel estimates: cohort 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.

15. Gender and Age wage patterns in Germany
Results
Additional controls
Mean q(.25) q(.75) Mean q(.25) q(.75)
30-34 0.126** -0.002 0.150** 0.136** 0.111** 0.055
35-39 0.135** -0.003 0.163** 0.129** 0.081** 0.076◦
40-44 0.183** 0.079* 0.185** 0.160** 0.135** 0.115**
45-49 0.189** 0.117** 0.180**
50-54 0.143** 0.055 0.133*
55-59 0.262** 0.158* 0.227*
% fem. main earner -0.479* -0.099 0.010
Place for kid<3 0.008◦ 0.015** -0.003
Fertility rate
Year -0.010** -0.005* -0.013** -0.018◦ -0.026** -0.000
Observations 175 175 175 76 76 76
R-squared 0.298 0.302 0.154 0.364 0.347 0.209
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 participation rates for men and women.

16. Gender and Age wage patterns in Germany
Results
Double decomposition: different cohorts
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

17. Gender and Age wage patterns in Germany
Conclusions
Conclusions
1 The gender wage gap increases with age, possibly in a
non-monotonic fashion.
2 The pattern is more evident at the top of the earning distribution.
3 The wage gap decreased over time, it was more important in the
raw gap.
4 We find some support for the human capital hypothesis, as aging
women tended to accummulate capital at a lower speed.

18. Gender and Age wage patterns in Germany
Conclusions
Final slide
Questions or suggestions?
Thank you for your attention

19. Gender and Age wage patterns in Germany
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).

20. Gender and Age wage patterns in Germany
Appendix
Fertility patterns

21. Gender and Age wage patterns in Germany
Appendix
Day care facilities

22. Gender and Age wage patterns in Germany
Appendix
Household earnings

23. Gender and Age wage patterns in Germany
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

24. Gender and Age wage patterns in Germany
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)

25. Gender and Age wage patterns in Germany
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)
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)

26. Gender and Age wage patterns in Germany
Appendix
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
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)

27. Gender and Age wage patterns in Germany
Appendix
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
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.

28. Gender and Age wage patterns in Germany
Appendix
Double decomposition
Presented in Simon and Welch(1985) to study convergence in
black workers’ wages

29. Gender and Age wage patterns in Germany
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
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

30. Gender and Age wage patterns in Germany
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
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

31. Gender and Age wage patterns in Germany
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
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

32. Gender and Age wage patterns in Germany
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