Age-productivity patterns in talent occupations for men and women Age-productivity patterns in talent occupations for men and women Deaton decomposition (with Barbara Liberda and Joanna Tyrowicz) Magdalena Smyk PhD Candidate Research Assistant in GRAPE Faculty of Economics University of Warsaw June 12, 2014
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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