This document summarizes research on estimating the gender wage gap across different age groups using data from the German Socio-Economic Panel from 1984-2008. The researchers find that the raw gender wage gap tends to decrease with age, indicating older women face greater penalties than younger women. When decomposing the wage gap using the DiNardo-Fortin-Lemieux method, they find some support for human capital explanations but that changes in wage structures also play a significant role. Panel models show the wage gap is higher for older age cohorts and decreases slightly over time, as women's participation rates increase. The researchers plan to extend this analysis to other countries like the US, France and UK.

1. They Are Not The Same!
They Are Not The Same!
The Estimates of Gender Wage Gap Across Age Groups
Magdalena Smyk
Joanna Tyrowicz
Lucas van der Velde
GRAPE
Group for Research in Applied Economics
September 6, 2014

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Table of contents
1 Introduction
2 Literature review
3 Gender wage gap and age
4 Data and method
5 Results
6 Conclusions

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Introduction
Introduction
Motivation
Relation between age and the adjusted gender wage gap -
underresearched
Aging speeds up in Europe
participation rates of workers aged above 55 are around 50% in the
Euro Area
this increase is much faster for female workers
Less children and later marriages = lower gender wage gap?

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Introduction
Our work
Goal
Understand the eects of the life-cycle in women's earnings.

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Introduction
Our work
Goal
Understand the eects of the life-cycle in women's earnings.
Data
German Socio-Economic Panel for 1984-2008

6. They Are Not The Same!
Introduction
Our work
Goal
Understand the eects of the life-cycle in women's earnings.
Data
German Socio-Economic Panel for 1984-2008
How?
Use the DiNardo, Fortin and Lemieux (DFL) et al decomposition to
estimate the changes in the gender wage gap for dierent cohorts.

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Literature review
Human capital approach - Becker (1993), Mincer (1974)
Division of roles inside the household and womens' lower eorts in
the work place (Becker 1985)
Intermittent labour market participation (and its anticipation)
(Ben-Porath, 1967)
Lower incentives for investment in a career
(Mincer Polachek, 1979)
Dierent career plans and earnings expectations
(Blau Ferber, 1990)
Reverse causality issues

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Literature review
Alternative explanations
Wage bargaining reference wages (Babcock Laschever, 2003) :
women do not negotiate as frequently as men do and do not ask for
raises. It implies a small dierence at the beginning of their careers
which may grow with time.
Job-shopping (Manning, 2003): related to the previous: women are
less likely to change job and are less concerned with money while
looking for a job
Double penalty: age and gender (Duncan Loretto, 2004): older
women belong to two disadvantaged groups, facing a penalty larger
than the sum of those penalties alone.

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Literature review
Gender wage gap and age - patterns according to theories

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Gender wage gap and age
A

11. rst glance at the gender wage gap
Notes: Dependent variables: experience, small kids in the household, married,
education level, tenure and year.

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Data and method
The sample
The German Socio-Economic Panel
25 years; from 1984 to 2008
about 60.000 unique individuals
panel: 2300 participants observed in each wave

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Data and method
The sample: GSOEP (1984 - 2008)
Changes in women characteristics (25-30 years old)
Variable 1984 1994 2004 trend
HC variables
Tertiary education (%) 7,07 6,89 13,1 +
Tenure (years) 2,89 2,53 2,44 {
Experience (years) 4,72 4,17 3,09 {
Employed (%) 41,4 47,3 46,6 +
Part time (%) 17,3 1,05 18,6 ?
HH Variables
Married (%) 53,2 40,2 36,0 {
Small kids in the hh.(%) 53,5 37,8 27,5 {
Hours in hh act. 6,18 2,68 2,07 {

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Data and method
First steps in the labor market
Notes: Red is for male workers, Blue for female. In the upper row, we have workers without tertiary eduation and in the lower workers
with a university degree. Wages are measured using hourly real wages. The graphs show the percentage of each gender that earn at least
the amount indicated in the horizontal axis. Male workers have higher wages, specially in the part of the distribution.

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Data and method
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
Given a joint distribution of wages and characteristics of the form
fj (wi ;j ) =
Z
fi ;j (wjx) f (xjg = 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 ) =
Z
ff ;j (wjx) j (x)fj (xjg = f ; t = j)dx (2)
where (x) is the reweighting factor and equals
j (xj ) =
fj (xjg = m; t = j)dx
fj (xjg = f ; t = j)dx
(3)

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Data and method
Introduction to the DiNardo, Fortin and Lemieux
decomposition (1996)
Thanks to Bayes rule, we can estimate j (xj ) as follows
j (xj ) =
Pr(g = mjx; j = t)Pr(g = f )
Pr(g = f jx; j = t)Pr(g = m)
(4)
We decompose the dierences as
fj (wm;j ) fj (wf ;j ) = [fj (wm;j ) fj (wc
f ;j )] + [fj (wc
f ;j ) fj (wf ;j )] (5)
The

17. rst term represents the unexplained component; and the second,
the explained.

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Data and method
Double decomposition
Presented in Simon and Welch (1985) to study convergence in black
workers' wages
They decompose the change between periods t1 and t in 4
components
1 The relative changes in characteristics from t1 to t
2 Dierences in characteristics in t
3 Dierences in wage structure in t
4 The relative changes in wage structures from t1 to t

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Results
Decomposition at dierent ages: Germany
Gender wage gap in dierent 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. Blue lines represent women's participation rate, measured in the
right axis.

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Results
Double decomposition: Age patterns
Age Characteristics Characteristics Wage structure Unexplained part
( in time) (dierences) (dierences) ( in time)
1984-1989
30-34 -0,05 0,08 0,00 0,05
35-39 -0,04 0,13 -0,09 0,15
40-44 -0,17 0,14 0,06 0,13
45-49 0,35 0,13 -0,58 0,36
50-54 0,00 0,13 -0,12 0,22
1990-1999
30-34 0,00 0,04 -0,14 0,14
35-39 0,00 0,10 -0,53 0,56
40-44 0,03 0,08 -0,10 0,11
45-49 0,00 0,08 -0,15 0,20
50-54 0,01 0,09 -0,37 0,40
2000-2008
30-34 0,05 -0,01 -0,16 0,14
35-39 -0,18 0,24 -0,21 0,22
40-44 -0,11 0,22 -1,38 1,43
45-49 -0,12 0,24 -0,71 0,74
50-54 -0,18 0,23 -0,76 0,80

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Results
Double decomposition: Age patterns
Table : Changes for women aged 25-29 in 1984
Age Characteristics Characteristics Wage structure Unexplained part Total
( in time) (dierences) (dierences) ( in time) change
30-34 -0,08 0,02 0,09 0,04 0,07
35-39 -0,01 -0,02 -0,10 0,15 0,02
40-44 0,16 -0,05 -0,14 0,15 0,12
45-49 0,02 -0,19 -0,22 0,20 -0,18
50-54 -0,26 0,16 0,09 0,05 0,04

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Results
Panel models
Dependent variable: Adjusted wage gap for dierent cohorts
Fixed Eects Random Eects Hausman-Taylor
Variable Coecient t-stat Coecient t-stat Coecient t-stat
30 to 34 0.05*** (2.62) 0.04* (1.90) 0.05*** (2.89)
35 to 39 0.05*** (2.64) 0.06*** (3.19) 0.07*** (3.79)
40 to 44 0.08*** (4.55) 0.10*** (5.37) 0.12*** (6.17)
45 to 49 0.08*** (4.24) 0.10*** (5.63) 0.13*** (6.71)
50 to 54 0.05*** (2.85) 0.09*** (4.48) 0.11*** (5.54)
55 to 59 0.13*** (5.87) 0.12*** (5.07)
Period -0.00*** (-3.21) -0.01*** (-8.00) -0.01*** (-8.00)
P. rate 0.17** (2.49) 0.18*** (2.82) 0.14** (2.37)
Gross -0.06*** (-5.92) -0.06*** (-3.84)
Part time -0.02 (-1.48) -0.02 (-0.80)
Household -0.02*** (-2.58) -0.02 (-1.60)
Constant 6.03*** (3.23) 10.86*** (8.13) 11.93*** (8.11)
Observations 336 336 336
R-squared 0.20
Number of groups 144 144 144
Notes: The dependent variable is the adjusted raw gap calculated using DFL decomposition at the mean. Asteriks represent conventional
statistical signi

23. cance, *** p0.01, ** p0.05, * p0.1. FE stands. Hausman test indicates that the results from the random eects model
are consistent and ecient, they are preferred. Hausman-Taylor added to control for the possible endogeneity of the participation rate
Variables: P. Rate stands for the participation rate of women. Gross indicates that the gap was estimated using log of gross hourly wages
de
ated to 2006 prices. Period is a time trend.

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Conclusions
The road so far
1 Most of the age transitions were positive, indicating that the raw gender
wage gap tends to be negative. Older women are more penalized than
younger ones.
2 Some period speci

25. c patterns are also visible. The wage gap decreased
over time, but this decrease was non-monotonic and more important in
the raw gap.
3 We

26. nd some support for the human capital hypothesis, as aging women
tended to accumulate capital at a lower speed. This, however, explains
only a part of the changes in the raw gap over time. The changes in the
rewards also play a signi

27. cant role.
Next step:
Extend study for more countries: USA (almost done), France and UK

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Conclusions
Thank you for your attention!
Magdalena Smyk
msmyk@wne.uw.edu.pl
GRAPE UW