age-productivity patterns in talent occupations for men and women - defap/laser summer school

Age-productivity patterns in talent occupations for men and women

Age-productivity patterns in talent occupations for men andwomen

Deaton decomposition

(with Barbara Liberda and Joanna Tyrowicz)

Magdalena SmykPhD Candidate

Research Assistant in GRAPE

Faculty of EconomicsUniversity of Warsaw

June 12, 2014

Age-productivity patterns in talent occupations for men and women

Motivation

Age-productivity pattern

Age-productivity pattern

inverted U shape /humped shape

but...

it is common impact of age, yearand cohort

Age-productivity patterns in talent occupations for men and women

Motivation

Age-productivity pattern

Age-productivity pattern

inverted U shape /humped shape

but...

it is common impact of age, yearand cohort

Age-productivity patterns in talent occupations for men and women

Motivation

What is this ”talent”?

Two cumulative conditions:

education level: at least tertiary

occupation: one of the three top ISCO levels

legislators, senior officials and managers;professionals;technicians and associate professionals

Age-productivity patterns in talent occupations for men and women

Motivation

And why this group is important?

Doctors and lawyers in the USA:

in the 60’s: 94% were white men;

now: it is just 62%.

Hsieh, Hurst, Jones and Klenow (2013):

Barriers for women and blacks in accessing ”talent” occupation loweredpotential US economy output by 12%.

Age-productivity patterns in talent occupations for men and women

Motivation

And why this group is important?

Doctors and lawyers in the USA:

in the 60’s: 94% were white men;

now: it is just 62%.

Hsieh, Hurst, Jones and Klenow (2013):

Barriers for women and blacks in accessing ”talent” occupation loweredpotential US economy output by 12%.

Age-productivity patterns in talent occupations for men and women

Motivation

Research

Question: Are there any diffrences between age-productivity patterns formen and women in ”talent” occupations?

Method: Deaton decomposition

Data: Polish LFS 1995-2012

Age-productivity patterns in talent occupations for men and women

Motivation

Research

Question: Are there any diffrences between age-productivity patterns formen and women in ”talent” occupations?

Method: Deaton decomposition

Data: Polish LFS 1995-2012

Age-productivity patterns in talent occupations for men and women

Motivation

Research

Question: Are there any diffrences between age-productivity patterns formen and women in ”talent” occupations?

Method: Deaton decomposition

Data: Polish LFS 1995-2012

Age-productivity patterns in talent occupations for men and women

Insights from the literature

Gender wage gap

Glass ceilings

size of a gap - different along the distribution

talent occupation = highest earnings

Family role

consequences of child bearing and family responsibilities

Age-productivity patterns in talent occupations for men and women

Insights from the literature

Age, cohort and time effects

Interpretation (Thornton et al. 1997)

age - individual productivity

time - inflation rate and average prodcuctivity

cohort - transition

Age-productivity patterns in talent occupations for men and women

Insights from the literature

Age, cohort and time effects

Interpretation (Thornton et al. 1997)

age - individual productivity

time - inflation rate and average prodcuctivity

cohort - transition

Methods

synthetic cohort technique (Browning, Deaton and Irish, 1985)

decomposition (Deaton, 1997)

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Specification

assumption: year effects are orthogonal to a time trend and their sum isnormalized to zero

dt = yeart − [(t − 1) · year1996 − (t − 2) · year1995]

removal of first dummy of each variable and second-year dummy

OLS regression

wj,t =∑T

t=1 αtdt +∑60

j=25 βjaj +∑43

i=8 γi,t=1995ci,t=1995 + εj,t

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Specification

assumption: year effects are orthogonal to a time trend and their sum isnormalized to zero

dt = yeart − [(t − 1) · year1996 − (t − 2) · year1995]

removal of first dummy of each variable and second-year dummy

OLS regression

wj,t =∑T

t=1 αtdt +∑60

j=25 βjaj +∑43

i=8 γi,t=1995ci,t=1995 + εj,t

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Specification

assumption: year effects are orthogonal to a time trend and their sum isnormalized to zero

dt = yeart − [(t − 1) · year1996 − (t − 2) · year1995]

removal of first dummy of each variable and second-year dummy

OLS regression

wj,t =∑T

t=1 αtdt +∑60

j=25 βjaj +∑43

i=8 γi,t=1995ci,t=1995 + εj,t

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Specification

assumption: year effects are orthogonal to a time trend and their sum isnormalized to zero

dt = yeart − [(t − 1) · year1996 − (t − 2) · year1995]

removal of first dummy of each variable and second-year dummy

OLS regression

wj,t =∑T

t=1 αtdt +∑60

j=25 βjaj +∑43

i=8 γi,t=1995ci,t=1995 + εj,t

Age-productivity patterns in talent occupations for men and women

Method

Deaton decomposition

Identification problem

collinearity: cohorti = yeari − agei

Specification

assumption: year effects are orthogonal to a time trend and their sum isnormalized to zero

dt = yeart − [(t − 1) · year1996 − (t − 2) · year1995]

removal of first dummy of each variable and second-year dummy

OLS regression

wj,t =∑T

t=1 αtdt +∑60

j=25 βjaj +∑43

i=8 γi,t=1995ci,t=1995 + εj,t

Age-productivity patterns in talent occupations for men and women

Data

Polish LFS 1995-2012

Restriction:

wage-employees only

aged above 25

Descriptive statisticsVariable Mean total Mean females Mean talented Mean talented

femalesAge 40.6 40.61 39.8 39.3

Females 47.3% 100% 59.6% 100%Primary 8% 7.5% - -

Secondary 69% 63.6% - -Tertiary 23% 28.9% 100% 100%

Hourly wage 12.06 PLN 11.47 PLN 20.18 PLN 19.55 PLNTalent 15.9% 20.4% 100% 100%

No. of obs. 677 229 316 647 107 414 64 439

Age-productivity patterns in talent occupations for men and women

Results

Year and cohort - total sample

Year effects Cohort effects

Age-productivity patterns in talent occupations for men and women

Results

Age effects - all vs talented

Age effects Talent

Age-productivity patterns in talent occupations for men and women

Results

Age effects - gender differences

Total Talent

Age-productivity patterns in talent occupations for men and women

Results

Oaxaca - Blinder decomposition

Results

Totalsample

Talentoccupations

Raw 0.102*** 0.094***Endowments -0.0012** -0.0012**Coefficients 0.105*** 0.106***Interactions -0.003*** -0.0002No. ofobservations

677 229 107 414

Age effects contribution to gender wagegap

Age-productivity patterns in talent occupations for men and women

Conclusions

Conclusions

1 ”Talent” occupations in general have a steeper age productivity pattern.

2 However, talented females earnings grow slower.

3 Divergence starts in the age of 30 - which might be associated with childbearing and family responsibilities.

Age-productivity patterns in talent occupations for men and women

Conclusions

Thank you for your attention!Magdalena Smyk

msmyk@wne.uw.edu.pl