Identifying Age Penalty in Women’s Wages:

Identifying Age Penalty in Women’s Wages:New method and evidence from Germany

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

IAFFE @ ASSA 2017

Identifying Age Penalty in Women’s Wages:

Introduction

Motivation

Women in Dutch academia

Identifying Age Penalty in Women’s Wages:

Introduction

Why it matters?

Definitely: women have gradually better educational attainment

Arguably: sorting matters less (for many occupations)

⇒ raw aggregate gender wage gap should decline

which it does .... but really slowly ...

Aging process in Europe?

Is there an age pattern?Implications for efficient policies to address gender wage gap?

Identifying Age Penalty in Women’s Wages:

Introduction

Why it matters?

Definitely: women have gradually better educational attainment

Arguably: sorting matters less (for many occupations)

⇒ raw aggregate gender wage gap should decline

which it does ....

but really slowly ...

Aging process in Europe?

Is there an age pattern?Implications for efficient policies to address gender wage gap?

Identifying Age Penalty in Women’s Wages:

Introduction

Why it matters?

Definitely: women have gradually better educational attainment

Arguably: sorting matters less (for many occupations)

⇒ raw aggregate gender wage gap should decline

which it does .... but really slowly ...

Aging process in Europe?

Is there an age pattern?Implications for efficient policies to address gender wage gap?

Identifying Age Penalty in Women’s Wages:

Introduction

Why it matters?

Definitely: women have gradually better educational attainment

Arguably: sorting matters less (for many occupations)

⇒ raw aggregate gender wage gap should decline

which it does .... but really slowly ...

Aging process in Europe?

Is there an age pattern?Implications for efficient policies to address gender wage gap?

Identifying Age Penalty in Women’s Wages:

Introduction

Motivation

Adjusted gender wage gap for selected cohorts as they aged

.1.1

5.2

.25

.3.3

5A

djus

ted

gap

25 30 35 40 45 50 55 60Age

1940−1944 1950−1954 1960−1964

Controls: tenure, experience, small kids in the household, married, education level and year.

Identifying Age Penalty in Women’s Wages:

Introduction

Theory on age pattern in gender wage gap

Unequal distribution of activities within the household (Becker 1985)

Child bearing and child rearing and its expectation (Mincer andPolachek 1974, Goldin and Katz 2008, Goldin 2014)Gender bias in the measurement of human capital

Statistical discrimination from the employers (Dahlby 1983)

“Hysteresis effect” (Babcock et al. 2002, Blau and Ferber 2011)

“Double standard of aging” (Duncan and Loretto 2004, Neumarket al. 2015)

Identifying Age Penalty in Women’s Wages:

Introduction

Theory on age pattern in gender wage gap

Unequal distribution of activities within the household (Becker 1985)

Child bearing and child rearing and its expectation (Mincer andPolachek 1974, Goldin and Katz 2008, Goldin 2014)Gender bias in the measurement of human capital

Statistical discrimination from the employers (Dahlby 1983)

“Hysteresis effect” (Babcock et al. 2002, Blau and Ferber 2011)

“Double standard of aging” (Duncan and Loretto 2004, Neumarket al. 2015)

Identifying Age Penalty in Women’s Wages:

Introduction

Theory on age pattern in gender wage gap

Unequal distribution of activities within the household (Becker 1985)

Child bearing and child rearing and its expectation (Mincer andPolachek 1974, Goldin and Katz 2008, Goldin 2014)Gender bias in the measurement of human capital

Statistical discrimination from the employers (Dahlby 1983)

“Hysteresis effect” (Babcock et al. 2002, Blau and Ferber 2011)

“Double standard of aging” (Duncan and Loretto 2004, Neumarket al. 2015)

Identifying Age Penalty in Women’s Wages:

Introduction

Theory on age pattern in gender wage gap

Unequal distribution of activities within the household (Becker 1985)

Child bearing and child rearing and its expectation (Mincer andPolachek 1974, Goldin and Katz 2008, Goldin 2014)Gender bias in the measurement of human capital

Statistical discrimination from the employers (Dahlby 1983)

“Hysteresis effect” (Babcock et al. 2002, Blau and Ferber 2011)

“Double standard of aging” (Duncan and Loretto 2004, Neumarket al. 2015)

Identifying Age Penalty in Women’s Wages:

Introduction

Intended contribution

Explore the effects of the life-cycle in women’s earnings penalty

Extend the method proposed by DiNardo, Fortin and Lemieux(1996) to separate cohort, time and age effects.

Identifying Age Penalty in Women’s Wages:

Introduction

Intended contribution

Explore the effects of the life-cycle in women’s earnings penalty

Extend the method proposed by DiNardo, Fortin and Lemieux(1996) to separate cohort, time and age effects.

Identifying Age Penalty in Women’s Wages:

Method

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)

(where i represents the gender: men or women)

then a counterfactual wage structure using a reweighting parameter Ψ(x)may be represented as

f (w cf ) =

∫ff (w |x) Ψj(x)fj(x |g = f )dx . (2)

Conveniently, Ψ(x) can be recovered using probit models.

Identifying Age Penalty in Women’s Wages:

Method

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)

(where i represents the gender: men or women)

then a counterfactual wage structure using a reweighting parameter Ψ(x)may be represented as

f (w cf ) =

∫ff (w |x) Ψj(x)fj(x |g = f )dx . (2)

Conveniently, Ψ(x) can be recovered using probit models.

Identifying Age Penalty in Women’s Wages:

Method

Methodology

By setting alternative Ψ(x), we define counterfactual distributions, e.g.

traditional: male ˆdistribution with female characteristics

our approach:male ˆdistribution if female characteristics were constant as we age+female ˆdistribution if female characteristics were constant over time

if sample of men and women is constant ⇒ also unobservablecharacteristics

⇒ how gender wage gaps change, as men and women age

Identifying Age Penalty in Women’s Wages:

Method

Methodology

By setting alternative Ψ(x), we define counterfactual distributions, e.g.

traditional: male ˆdistribution with female characteristics

our approach:male ˆdistribution if female characteristics were constant as we age

+female ˆdistribution if female characteristics were constant over time

if sample of men and women is constant ⇒ also unobservablecharacteristics

⇒ how gender wage gaps change, as men and women age

Identifying Age Penalty in Women’s Wages:

Method

Methodology

By setting alternative Ψ(x), we define counterfactual distributions, e.g.

traditional: male ˆdistribution with female characteristics

our approach:male ˆdistribution if female characteristics were constant as we age+female ˆdistribution if female characteristics were constant over time

if sample of men and women is constant ⇒ also unobservablecharacteristics

⇒ how gender wage gaps change, as men and women age

Identifying Age Penalty in Women’s Wages:

Method

Methodology

By setting alternative Ψ(x), we define counterfactual distributions, e.g.

traditional: male ˆdistribution with female characteristics

our approach:male ˆdistribution if female characteristics were constant as we age+female ˆdistribution if female characteristics were constant over time

if sample of men and women is constant ⇒ also unobservablecharacteristics

⇒ how gender wage gaps change, as men and women age

Identifying Age Penalty in Women’s Wages:

Method

Methodology

By setting alternative Ψ(x), we define counterfactual distributions, e.g.

traditional: male ˆdistribution with female characteristics

our approach:male ˆdistribution if female characteristics were constant as we age+female ˆdistribution if female characteristics were constant over time

if sample of men and women is constant ⇒ also unobservablecharacteristics

⇒ how gender wage gaps change, as men and women age

Identifying Age Penalty in Women’s Wages:

Method

Method

The raw gender wage gap in any age (∆j) is the sum of explained andunexplained component:

∆j = f (w |m, j)− f ′(w |f , j)︸ ︷︷ ︸Explained component

+ f ′(w |f , j)− f (w |f , j)︸ ︷︷ ︸Unexplained component

Hence, ∆j −∆i =∫fm,j(w |x) ((f (x |m, i)− f (x |m, j)

−(f (x |f , j))− f (x |f , i)))dx︸ ︷︷ ︸Change in explained component

+

∫(fm,i (w |x)− fm,j(w |x)

−(ff ,i (w |x)− ff ,j(w |x))) (f (x |f , i)︸ ︷︷ ︸Change in unexplained component

+ Change in residuals

Identifying Age Penalty in Women’s Wages:

Method

Method

The raw gender wage gap in any age (∆j) is the sum of explained andunexplained component:

∆j = f (w |m, j)− f ′(w |f , j)︸ ︷︷ ︸Explained component

+ f ′(w |f , j)− f (w |f , j)︸ ︷︷ ︸Unexplained component

Hence, ∆j −∆i =∫fm,j(w |x) ((f (x |m, i)− f (x |m, j)

−(f (x |f , j))− f (x |f , i)))dx︸ ︷︷ ︸Change in explained component

+

∫(fm,i (w |x)− fm,j(w |x)

−(ff ,i (w |x)− ff ,j(w |x))) (f (x |f , i)︸ ︷︷ ︸Change in unexplained component

+ Change in residuals

Identifying Age Penalty in Women’s Wages:

Data

Data

(West) German nationals aged 25-59 – SOEP

Period: 1984-2008.

SOEP has great retention rates

Over 7 000 individuals are observed for a decade or longer.25% of the original sample observed on every year.Almost 70 000+ complete observations (exclusion gender symmetric)

Dependent variable: real hourly wages

Rich set of covariates: education, tenure, experience full and parttime, household characteristics, occupations, industries, type ofemployment...

Identifying Age Penalty in Women’s Wages:

Data

Data

(West) German nationals aged 25-59 – SOEP

Period: 1984-2008.

SOEP has great retention rates

Over 7 000 individuals are observed for a decade or longer.25% of the original sample observed on every year.Almost 70 000+ complete observations (exclusion gender symmetric)

Dependent variable: real hourly wages

Rich set of covariates: education, tenure, experience full and parttime, household characteristics, occupations, industries, type ofemployment...

Identifying Age Penalty in Women’s Wages:

Data

Data

(West) German nationals aged 25-59 – SOEP

Period: 1984-2008.

SOEP has great retention rates

Over 7 000 individuals are observed for a decade or longer.25% of the original sample observed on every year.Almost 70 000+ complete observations (exclusion gender symmetric)

Dependent variable: real hourly wages

Rich set of covariates: education, tenure, experience full and parttime, household characteristics, occupations, industries, type ofemployment...

Identifying Age Penalty in Women’s Wages:

Data

A quick look at the sample

0

.2

.4

.6

.8

Pro

port

ion

Married Small kids Higher education Employment

1984 1990 1996 2002 2008 Men

Aged: 25−34

Identifying Age Penalty in Women’s Wages:

Data

A quick look at the sample

0

.2

.4

.6

.8

Pro

port

ion

Married Small kids Higher education Employment

1984 1990 1996 2002 2008 Men

Aged:35−44

Identifying Age Penalty in Women’s Wages:

Data

A quick look at the sample

0

.2

.4

.6

.8

Pro

port

ion

Married Small kids Higher education Employment

1984 1990 1996 2002 2008 Men

Aged:45−59

Identifying Age Penalty in Women’s Wages:

Results

Adjusted gender wage gap across age and cohorts

Bar: a period in the sample, colors preserve bar colors. Line: women’s participationrate at the right axis.

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the adjusted gap

Initial year Avg. changeInitial Age 1984 1989 1994 1999 2004 with age

25-29 0.04 0.07 0.09 0.01 0.05 0.0530-34 0.10 0.03 0.03 0.03 -0.02 0.0335-39 -0.04 0.15 0.00 -0.04 -0.02 0.0140-44 0.17 -0.02 0.00 0.01 -0.01 0.0345-49 -0.11 0.01 0.06 0.08 0.05 0.0250-54 -0.03 0.03 -0.14 -0.05 -0.01 -0.04

What to do about non-working years?

Include working for a wage in Ψ(x)

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the adjusted gap

Initial year Avg. changeInitial Age 1984 1989 1994 1999 2004 with age

25-29 0.04 0.07 0.09 0.01 0.05 0.0530-34 0.10 0.03 0.03 0.03 -0.02 0.0335-39 -0.04 0.15 0.00 -0.04 -0.02 0.0140-44 0.17 -0.02 0.00 0.01 -0.01 0.0345-49 -0.11 0.01 0.06 0.08 0.05 0.0250-54 -0.03 0.03 -0.14 -0.05 -0.01 -0.04

What to do about non-working years?

Include working for a wage in Ψ(x)

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the adjusted gap

Initial year Avg. changeInitial Age 1984 1989 1994 1999 2004 with age

25-29 0.04 0.07 0.09 0.01 0.05 0.0530-34 0.10 0.03 0.03 0.03 -0.02 0.0335-39 -0.04 0.15 0.00 -0.04 -0.02 0.0140-44 0.17 -0.02 0.00 0.01 -0.01 0.0345-49 -0.11 0.01 0.06 0.08 0.05 0.0250-54 -0.03 0.03 -0.14 -0.05 -0.01 -0.04

What to do about non-working years?

Include working for a wage in Ψ(x)

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the adjusted gap

Initial year Avg. changeInitial Age 1984 1989 1994 1999 2004 with age

25-29 0.04 0.07 0.10 0.04 0.07 0.0630-34 0.04 0.02 0.07 0.04 0.01 0.0435-39 -0.02 0.15 0.00 -0.03 0.00 0.0240-44 0.17 0.02 -0.02 0.09 0.04 0.0645-49 -0.13 0.03 0.18 0.11 0.07 0.0550-54 -0.04 0.05 -0.16 -0.06 -0.03 -0.05

Identifying Age Penalty in Women’s Wages:

Results

Double decomposition: changes in the adjusted gap

Initial year Avg. change No EInitial Age 1984 1989 1994 1999 2004 with age controls

25-29 0.04 0.07 0.10 0.04 0.07 0.06 0.0530-34 0.04 0.02 0.07 0.04 0.01 0.04 0.0335-39 -0.02 0.15 0.00 -0.03 0.00 0.02 0.0140-44 0.17 0.02 -0.02 0.09 0.04 0.06 0.0345-49 -0.13 0.03 0.18 0.11 0.07 0.05 0.0250-54 -0.04 0.05 -0.16 -0.06 -0.03 -0.05 -0.04

Identifying Age Penalty in Women’s Wages:

Conclusions

Take home message

Adjusted gender wage gap ...

grows with age

non-monotonically

also in post-reproductive age

Interpretation

Consistent with human capital ... to some extent

Question: is there a case for human capital story in thepost-reproductive age?

Identifying Age Penalty in Women’s Wages:

Conclusions

Take home message

Adjusted gender wage gap ...

grows with age

non-monotonically

also in post-reproductive age

Interpretation

Consistent with human capital ... to some extent

Question: is there a case for human capital story in thepost-reproductive age?

Identifying Age Penalty in Women’s Wages:

Conclusions

Summary

1 A new method for identifying age effects in adjusted GWG

2 New evidence for Germany, a country with relatively high inequality,stable over time

Policy implication 1: if Germany is typical, aggregate GWG willincrease as societies age (composition effects)Policy implication 2: overlapping penalties?

Where to now?

International context: UK, US, Canada, Russia, Korea

Hours flexibility story (Goldin 2014)

Identifying Age Penalty in Women’s Wages:

Conclusions

Summary

1 A new method for identifying age effects in adjusted GWG

2 New evidence for Germany, a country with relatively high inequality,stable over time

Policy implication 1: if Germany is typical, aggregate GWG willincrease as societies age (composition effects)

Policy implication 2: overlapping penalties?

Where to now?

International context: UK, US, Canada, Russia, Korea

Hours flexibility story (Goldin 2014)

Identifying Age Penalty in Women’s Wages:

Conclusions

Summary

1 A new method for identifying age effects in adjusted GWG

2 New evidence for Germany, a country with relatively high inequality,stable over time

Policy implication 1: if Germany is typical, aggregate GWG willincrease as societies age (composition effects)Policy implication 2: overlapping penalties?

Where to now?

International context: UK, US, Canada, Russia, Korea

Hours flexibility story (Goldin 2014)

Identifying Age Penalty in Women’s Wages:

Conclusions

Summary

1 A new method for identifying age effects in adjusted GWG

2 New evidence for Germany, a country with relatively high inequality,stable over time

Policy implication 1: if Germany is typical, aggregate GWG willincrease as societies age (composition effects)Policy implication 2: overlapping penalties?

Where to now?

International context: UK, US, Canada, Russia, Korea

Hours flexibility story (Goldin 2014)

Identifying Age Penalty in Women’s Wages:

Conclusions

Questions or suggestions?

Thank you for your attention

Identifying Age Penalty in Women’s Wages:

Conclusions

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