age-productivity patterns in talent occupations for men and women - defap/laser summer school
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Presetation for DEFAP/LASER Summer School in MilanTRANSCRIPT
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