they are not the same!

They Are Not The Same!

They Are Not The Same!The Estimates of Gender Wage Gap Across Age Groups

Magdalena SmykJoanna Tyrowicz

Lucas van der Velde

GRAPEGroup for Research in Applied Economics

September 6, 2014


Table of contents

1 Introduction

2 Literature review

3 Gender wage gap and age

4 Data and method

5 Results

6 Conclusions


Introduction

Introduction

Motivation

Relation between age and the adjusted gender wage gap -underresearchedAging speeds up in Europe

participation rates of workers aged above 55 are around 50% in theEuro Areathis increase is much faster for female workers

Less children and later marriages = lower gender wage gap?


Introduction

Our work

Goal

Understand the effects of the life-cycle in women’s earnings.


Introduction

Our work

Goal


Data

German Socio-Economic Panel for 1984-2008


Introduction

Our work

Goal


Data

German Socio-Economic Panel for 1984-2008

How?

Use the DiNardo, Fortin and Lemieux (DFL) et al decomposition toestimate the changes in the gender wage gap for different cohorts.


Literature review

Human capital approach - Becker (1993), Mincer (1974)

Division of roles inside the household and womens’ lower efforts inthe work place (Becker 1985)

Intermittent labour market participation (and its anticipation)(Ben-Porath, 1967)

Lower incentives for investment in a career(Mincer & Polachek, 1979)

Different career plans and earnings expectations(Blau & Ferber, 1990)

Reverse causality issues


Literature review

Alternative explanations

Wage bargaining & reference wages (Babcock & Laschever, 2003) :women do not negotiate as frequently as men do and do not ask forraises. It implies a small difference at the beginning of their careerswhich may grow with time.

Job-shopping (Manning, 2003): related to the previous: women areless likely to change job and are less concerned with money whilelooking for a job

”Double penalty”: age and gender (Duncan & Loretto, 2004): olderwomen belong to two disadvantaged groups, facing a penalty largerthan the sum of those penalties alone.


Literature review

Gender wage gap and age - patterns according to theories


Gender wage gap and age

A first glance at the gender wage gap

Notes: Dependent variables: experience, small kids in the household, married,education level, tenure and year.


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


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 VariablesMarried (%) 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 –


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 workerswith a university degree. Wages are measured using hourly real wages. The graphs show the percentage of each gender that earn at leastthe amount indicated in the horizontal axis. Male workers have higher wages, specially in the part of the distribution.


Data and method

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)


Data and method

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 the second,the explained.


Data and method

Double decomposition

Presented in Simon and Welch (1985) to study convergence in blackworkers’ 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


Results

Decomposition at different ages: Germany

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 ayear in the sample, bars of similar colors correspond to the same cohort. Blue lines represent women’s participation rate, measured in theright axis.


Results

Double decomposition: Age patterns

Age Characteristics Characteristics Wage structure Unexplained part(∆ in time) (differences) (differences) (∆ in time)

1984-198930-34 -0,05 0,08 0,00 0,0535-39 -0,04 0,13 -0,09 0,1540-44 -0,17 0,14 0,06 0,1345-49 0,35 0,13 -0,58 0,3650-54 0,00 0,13 -0,12 0,22

1990-199930-34 0,00 0,04 -0,14 0,1435-39 0,00 0,10 -0,53 0,5640-44 0,03 0,08 -0,10 0,1145-49 0,00 0,08 -0,15 0,2050-54 0,01 0,09 -0,37 0,40

2000-200830-34 0,05 -0,01 -0,16 0,1435-39 -0,18 0,24 -0,21 0,2240-44 -0,11 0,22 -1,38 1,4345-49 -0,12 0,24 -0,71 0,7450-54 -0,18 0,23 -0,76 0,80


Results

Double decomposition: Age patterns

Table : Changes for women aged 25-29 in 1984

Age Characteristics Characteristics Wage structure Unexplained part Total(∆ in time) (differences) (differences) (∆ 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


Results

Panel models

Dependent variable: Adjusted wage gap for different cohorts

Fixed Effects Random Effects Hausman-Taylor

Variable Coefficient t-stat Coefficient t-stat Coefficient 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 336R-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 conventionalstatistical significance, *** p¡0.01, ** p¡0.05, * p¡0.1. FE stands. Hausman test indicates that the results from the random effects modelare consistent and efficient, they are preferred. Hausman-Taylor added to control for the possible endogeneity of the participation rateVariables: P. Rate stands for the participation rate of women. Gross indicates that the gap was estimated using log of gross hourly wagesdeflated to 2006 prices. Period is a time trend.


Conclusions

The road so far

1 Most of the age transitions were positive, indicating that the raw genderwage gap tends to be negative. Older women are more penalized thanyounger ones.

2 Some period specific patterns are also visible. The wage gap decreasedover time, but this decrease was non-monotonic and more important inthe raw gap.

3 We find some support for the human capital hypothesis, as aging womentended to accumulate capital at a lower speed. This, however, explainsonly a part of the changes in the raw gap over time. The changes in therewards also play a significant role.

Next step:

Extend study for more countries: USA (almost done), France and UK


Conclusions

Thank you for your attention!

Magdalena [email protected]

GRAPE UW

they are not the same!

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