gender and age wage patterns in germany

Gender and Age wage patterns in Germany

Gender and Age wage patterns in GermanyAn analysis form the GSOEP

I. van Staveren J. Tyrowicz L. van der Velde

Warsaw International Economic Meeting,

July 3, 2015


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


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.


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)


Introduction

Putting the pieces together

Expected pattens: age and adjusted GWG


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.


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 DFL decomposition fordifferent cohorts across time.

2 Panel analysis of determinants of changes in the AdjustedGWG over time.

3 Double decomposition.


Results

Decomposition at different agesResults 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.


Results

Panel estimates: cohort effects on the Adjusted GWG

Mean 1st Quartile 3rd Quartile25-29 Base level30-34 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 175R-squared 0.649 0.624 0.661

Notes: ***,**,* indicate significance at the 1 %, 5% and 10% level respectively. Thedependent variable is the adjusted gender wage gap calculated at different points of

thedistribution. All estimates include cohort specific effects and participation rates formen and women.


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** 0.05535-39 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 0.010Place for kid<3 0.008◦ 0.015** -0.003

Fertility rateYear -0.010** -0.005* -0.013** -0.018◦ -0.026** -0.000

Observations 175 175 175 76 76 76R-squared 0.298 0.302 0.154 0.364 0.347 0.209

Notes: **,*,◦ indicate significance at the 1 %, 5% and 10% level respectively. Thedependent variable is the adjusted gender wage gap calculated at different points ofthedistribution. All estimates include participation rates for men and women.


Results

Double decomposition: different cohorts

Age Characteristics Residuals Unexplained1984-1989

30-34 -0,05 0,08 0,0535-39 -0,04 0,04 0,1540-44 -0,17 0,2 0,1345-49 0,35 -0,45 0,3650-54 0 0,01 0,22

1990-199930-34 0 -0,1 0,1435-39 0 -0,43 0,5640-44 0,03 -0,02 0,1145-49 0 -0,07 0,250-54 0,01 -0,28 0,4

2000-200830-34 0,05 -0,17 0,1435-39 -0,18 0,03 0,2240-44 -0,11 -1,16 1,4345-49 -0,12 -0,47 0,7450-54 -0,18 -0,53 0,8


Conclusions

Conclusions

1 The gender wage gap increases with age, possibly in anon-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 theraw gap.

4 We find some support for the human capital hypothesis, as agingwomen tended to accummulate capital at a lower speed.


Conclusions

Final slide

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 labormarket 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 0,0435-39 -0,01 -0,12 0,1540-44 0,16 -0,19 0,1545-49 0,02 -0,41 0,250-54 -0,26 0,25 0,05


Appendix

Introduction to the DiNardo, Fortin and Lemieuxdecomposition (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 reweightingfemale observation to make them more similar to males.

fj(wcf ,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

Introduction to the DiNardo, Fortin and Lemieuxdecomposition (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(wcf ,j)] + [fj(w

cf ,j)− fj(wf ,j)] (5)

The first term represents the unexplained component; and thesecond, the explained.


Appendix

Double decomposition

Presented in Simon and Welch(1985) to study convergence inblack workers’ wages

They decompose the change between periods t−1 and t in 4components

1 The relative changes in characteristics from t−1 to t2 Differences in characteristics in t3 Differences in wage structure in t4 The relative changes in wage structures from t−1 to t

The last component is similar to the unexplained component fromthe previous decomposition, though it is ”cleaner” for thecomparison across time. A simple difference between the adjustedgaps in two periods will also reflect the changes in thecharacteristics used as a base (women from each period) while inthis case, we use the same characteristics in the two periods


Appendix




1 The relative changes in characteristics from t−1 to t

2 Differences in characteristics in t3 Differences in wage structure in t4 The relative changes in wage structures from t−1 to t



Appendix




1 The relative changes in characteristics from t−1 to t2 Differences in characteristics in t

3 Differences in wage structure in t4 The relative changes in wage structures from t−1 to t



Appendix




1 The relative changes in characteristics from t−1 to t2 Differences in characteristics in t3 Differences in wage structure in t

4 The relative changes in wage structures from t−1 to t



Appendix




1 The relative changes in characteristics from t−1 to t2 Differences in characteristics in t3 Differences in wage structure in t4 The relative changes in wage structures from t−1 to t


gender and age wage patterns in germany

Economy & Finance