Using data from Germany, we explore how the gender wage gap evolves as workers get older. Our method, based on panel data, allows to disentangle effects of age-cohort-year. We discover that the penalization grows faster during the reproductive period, and it does not fall afterwards. These results call for policies aimed to correct inequalities among older workers.

1. Identifying Age Penalty in Women’s Wages:
Identifying Age Penalty in Women’s Wages:
Evidence from the GSOEP
Lucas van der Velde
(with J. Tyrowicz and I. van Staveren)
International Association for Feminist Economics,
June 2016

2. Identifying Age Penalty in Women’s Wages:
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?
Our contribution to the literature
Explore the effects of the life-cycle in women’s earnings.
Extend the method proposed by DiNardo, Fortin and Lemieux
(1996) to separate cohort-age effects.

3. Identifying Age Penalty in Women’s Wages:
Introduction
How the gender wage gap changes over age?
Hump-shaped pattern
Unequal distribution of activities within the household (Becker
1985).
Child bearing and child rearing and its expectation (Mincer
and Polachek 1974, Goldin and Katz 2008, Goldin 2014).
Gender bias in the measurement of human capital.
Statistical discrimination from the employers (Dahlby 1983).
Differences increasing over the age-years

4. Identifying Age Penalty in Women’s Wages:
Introduction
How the gender wage gap changes over age?
Hump-shaped pattern
Differences increasing over the age-years
“Hysteresis effect” (Blau and Ferber 2011, Babcock et al.
2002)
“Double standard of ageing” (Duncan and Loretto 2004,
Neumark et al. 2015)

5. Identifying Age Penalty in Women’s Wages:
Data and method
Data
We study the West German subsample of the SOEP.
Period under analysis: 1984-2008.
Sample restricted to German nationals aged 25-59.
Almost 100 000 complete observations.
It presents great retention rates
Over 7 000 individuals are observed for a decade or longer.
25% of the original sample observed on every year.
Dependent variable: Real gross hourly wages in Euros,
converted at the official rate.
Rich set of covariates: education, tenure, experience full and
part time, household characteristics, occupations, industries,
type of employment...

6. Identifying Age Penalty in Women’s Wages:
Data and method
A quick look at the sample
0.2.4.6.8
Proportion
Married Small kids Higher education Employment
1984 1990 1996 2002 2008 Men
Aged: 25−34

7. Identifying Age Penalty in Women’s Wages:
Data and method
A quick look at the sample
0.2.4.6.8
Proportion
Married Small kids Higher education Employment
1984 1990 1996 2002 2008 Men
Aged:35−44

8. Identifying Age Penalty in Women’s Wages:
Data and method
A quick look at the sample
0.2.4.6.8
Proportion
Married Small kids Higher education Employment
1984 1990 1996 2002 2008 Men
Aged:45−59

9. Identifying Age Penalty in Women’s Wages:
Data and method
A first glance at the gender wage gap
.1.2.3.4
20 30 40 50 60
Age
Raw gap
Average
Each age
Adjusted gap
Average
Each age
Oaxaca−Blinder (1973) decomposition
Notes: Controls: tenure, experience, small kids in the household, married, education
level and year.

10. Identifying Age Penalty in Women’s Wages:
Data and method
Method
Three steps of our analysis
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.

11. Identifying Age Penalty in Women’s Wages:
Data and method
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
Given a joint distribution of wages and characteristics of the form
f (wi ) = fi (w|x) f (x|g = i)dx (1)
( i represents the gender, male or female)
We can derive a counterfactual wage structure using a reweighting
parameter Ψ(x)
f (wc
f ) = ff (w|x) Ψj (x)fj (x|g = f )dx (2)
Ψ(x) can be recovered either non-parametrically or using probit models.

12. Identifying Age Penalty in Women’s Wages:
Data and method
Double decomposition
Adapted from Simon and Welch (1985), similar to Blau and
Kahn (2003)
We can divide changes in raw gap in 3 elements:
1 Changes in characteristics.
2 Changes in returns to characteristics.
3 Interaction effects (residuals).
Sample of men and women is constant → Also unobservable
characteristics.

13. Identifying Age Penalty in Women’s Wages:
Results
Decomposition at different ages
Results for the adjusted GWG
Gender wage gap in different age groups (1984-2006).
020406080
Femaleemploymentrate
0.2.4.6.8
Adjustedgenderwagegap
25−29 30−34 35−39 40−44 45−49 50−54 55−59
Age
Cohort: 1939 1949 1959 1969
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.

14. Identifying Age Penalty in Women’s Wages:
Results
Panel estimates: Gender wage gap and age
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
the wage distribution. All estimates include cohort specific effects and participation
rates for men and women.

15. Identifying Age Penalty in Women’s Wages:
Results
Additional controls
(1) (2) (3) (4)
25-29 Base level
30-34 0.104*** 0.094*** 0.159*** 0.121***
35-39 0.098*** 0.100*** 0.166*** 0.136***
40-44 0.131*** 0.141*** 0.213*** 0.183***
45-49 0.154*** 0.145*** 0.219*** 0.186***
50-54 0.117*** 0.106*** 0.207*** 0.160***
55-59 0.238*** 0.257*** 0.435*** 0.343***
Places for kids under 3 0.014***
Fertility change 0.174
% Female main earner -1.014***
%Tert. M - % Tert. F -0.746**
Observations 133 175 175 175
R-squared 0.235 0.428 0.461 0.443
Notes: dependent variables are estimates obtained at the mean; ***,**,* denote significance at
the 1%, 5% and 10% level. All estimations include year fixed effects and controls for employment rate.

16. Identifying Age Penalty in Women’s Wages:
Results
Take home message
The adjusted gender wage gap seems to increase
non-monotonically with age.
This pattern seems to be robust to the inclusion of additional
controls.
However
a. Standard errors are also larger for older workers.
b. Estimates might confound changes in return to characteristics
and changes in characteristics.

17. Identifying Age Penalty in Women’s Wages:
Results
Double decomposition: changes in the raw gap
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

18. Identifying Age Penalty in Women’s Wages:
Results
Double decomposition: panel analysis
Changes in the adjusted GWG: Panel analysis
−.3−.2−.10.1
PointestimatesandCI(90%)
30−34 35−39 40−44 45−49 50−54 55−59
Group 55−59 as a reference
Mean
Notes: The estimations also include controls for the adjusted GWG in the initial
period and year effects

19. Identifying Age Penalty in Women’s Wages:
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).
This pattern is robust to the inclusion of controls for cohorts
specific trends.
3 Changes in the adjusted gender wage gap suggest that older women
are more penalized.
4 Policy implication: measures to tackle the GWG should take into
account also the pos-productive age.

20. Identifying Age Penalty in Women’s Wages:
Conclusions
Questions or suggestions?
Thank you for your attention

21. Identifying Age Penalty in Women’s Wages:
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).

22. Identifying Age Penalty in Women’s Wages:
Appendix
Fertility patterns
050100
Fertilityrate
20−24 25−29 30−34 35−39 40−44 45−49
Year: 1984 1994 2004

23. Identifying Age Penalty in Women’s Wages:
Appendix
Day care facilities
01000200030004000
1980 1985 1990 1995 2000
Year
Daycare Kindergarden (/10) In shool age
Number of childcare institutions

24. Identifying Age Penalty in Women’s Wages:
Appendix
Household earnings
.2.4.6.81
Percentageofmarriedwomen
1984 1990 1996 2002 2008
Year
Other earner in hh. Women main earner
Composition of household budget

25. Identifying Age Penalty in Women’s Wages:
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

26. Identifying Age Penalty in Women’s Wages:
References
Babcock, L., Gelfand, M., Small, D. and Stayn, H.: 2002, Propensity to initiate
negotiations: A new look at gender variation in negotiation behavior, IACM
15th Annual Conference.
Becker, G. S.: 1985, Human capital, effort, and the sexual division of labor,
Journal of Labor Economics 3(1), pp. S33–S58.
Blau, F. D. and Ferber, M. A.: 2011, Career plans and expectations of young
women and men: The earnings gap and labor force participation, Journal of
Human Resources 26(4), 581–607.
Blau, F. D. and Kahn, L. M.: 2003, Understanding international differences in
the gender pay gap, Journal of Labor Economics 21(1).
Dahlby, B.: 1983, Adverse selection and statistical discrimination: An analysis
of canadian automobile insurance, Journal of Public Economics
20(1), 121–130.
Duncan, C. and Loretto, W.: 2004, Never the right age? gender and age-based
discrimination in employment, Gender, Work & Organization 11(1), 95–115.
Goldin, C.: 2014, A grand gender convergence: Its last chapter, The American
Economic Review 104(4), 1091–1119.
Goldin, C. and Katz, L. F.: 2008, Transitions: Career and family life cycles of
the educational elite, The American Economic Review 98(2), 363–369.
Mincer, J. and Polachek, S.: 1974, Family investments in human capital:
Earnings of women, Journal of Political Economy 82(2), pp. S76–S108.

27. Identifying Age Penalty in Women’s Wages:
Appendix
Neumark, D., Burn, I. and Button, P.: 2015, Is it harder for older workers to
find jobs? new and improved evidence from a field experiment, National
Bureau of Economic Research, Working Paper No. 21669 .