Identifying Age Penalty in Women’s Wages:

Identifying Age Penalty in Women’s Wages:Evidence form the GSOEP

Lucas van der Velde(with J. Tyrowicz and I. van Staveren)

Irish Economic Association Conference,

May 2016

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.

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

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 (Becker1985).

Child bearing and child rearing and its expectation (Mincerand 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

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)

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 andpart time, household characteristics, occupations, industries,type of employment...

Identifying Age Penalty in Women’s Wages:

Data and method

A quick look at the sample

Identifying Age Penalty in Women’s Wages:

Data and method

A quick look at the sample

Identifying Age Penalty in Women’s Wages:

Data and method

A quick look at the sample

Identifying Age Penalty in Women’s Wages:

Data and method

A first glance at the gender wage gap

Notes: Controls: tenure, experience, small kids in the household, married, educationlevel and year.

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 AdjustedGWG over time.

3 Double decomposition.

Identifying Age Penalty in Women’s Wages:

Data and method

Introduction to the DiNardo, Fortin and Lemieuxdecomposition (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 reweightingparameter Ψ(x)

f (w cf ) =

∫ff (w |x) Ψj(x)fj(x |g = f )dx (2)

Ψ(x) can be recovered either non-parametrically or using probit models.

Identifying Age Penalty in Women’s Wages:

Data and method

Double decomposition

Adapted from Simon and Welch (1985), similar to Blau andKahn (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 unobservablecharacteristics.

Identifying Age Penalty in Women’s Wages:

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 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.

Identifying Age Penalty in Women’s Wages:

Results

Panel estimates: Gender wage gap and age

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 ofthe wage distribution. All estimates include cohort specific effects and participation

rates for men and women.

Identifying Age Penalty in Women’s Wages:

Results

Additional controls

(1) (2) (3) (4)25-29 Base level30-34 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 175R-squared 0.235 0.428 0.461 0.443

Notes: dependent variables are estimates obtained at the mean; ***,**,* denote significance atthe 1%, 5% and 10% level. All estimations include year fixed effects and controls for employment rate.

Identifying Age Penalty in Women’s Wages:

Results

Take home message

The adjusted gender wage gap seems to increasenon-monotonically with age.

This pattern seems to be robust to the inclusion of additionalcontrols.

Howevera. Standard errors are also larger for older workers.b. Estimates might confound changes in return to characteristics

and changes in characteristics.

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the raw gap

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

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: panel analysis

Changes in the adjusted GWG: Panel analysis

Notes: The estimations also include controls for the adjusted GWG in the initialperiod and year effects

Identifying Age Penalty in Women’s Wages:

Conclusions

Conclusions

1 We separate cohort and age effect to understand changes in theGWG over age-years.

2 The gender wage gap increases with age, possibly in anon-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 cohortsspecific trends.

3 Changes in the adjusted gender wage gap suggest that older womenare more penalized.

4 Policy implication: measures to tackle the GWG should take intoaccount also the pos-productive age.

Identifying Age Penalty in Women’s Wages:

Conclusions

Questions or suggestions?

Thank you for your attention

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 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).

Identifying Age Penalty in Women’s Wages:

Appendix

Fertility patterns

Identifying Age Penalty in Women’s Wages:

Appendix

Day care facilities

Identifying Age Penalty in Women’s Wages:

Appendix

Household earnings

Identifying Age Penalty in Women’s Wages:

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

Identifying Age Penalty in Women’s Wages:

References

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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 youngwomen and men: The earnings gap and labor force participation, Journal ofHuman Resources 26(4), 581–607.

Blau, F. D. and Kahn, L. M.: 2003, Understanding international differences inthe gender pay gap, Journal of Labor Economics 21(1).

Dahlby, B.: 1983, Adverse selection and statistical discrimination: An analysisof canadian automobile insurance, Journal of Public Economics20(1), 121–130.

Duncan, C. and Loretto, W.: 2004, Never the right age? gender and age-baseddiscrimination in employment, Gender, Work & Organization 11(1), 95–115.

Goldin, C.: 2014, A grand gender convergence: Its last chapter, The AmericanEconomic Review 104(4), 1091–1119.

Goldin, C. and Katz, L. F.: 2008, Transitions: Career and family life cycles ofthe 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.

Identifying Age Penalty in Women’s Wages:

Appendix

Neumark, D., Burn, I. and Button, P.: 2015, Is it harder for older workers tofind jobs? new and improved evidence from a field experiment, NationalBureau of Economic Research, Working Paper No. 21669 .