Presetation for DEFAP/LASER Summer School in Milan

1. 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

2. Age-productivity patterns in talent occupations for men and women
Motivation
Age-productivity pattern
Age-productivity pattern
inverted U shape /
humped shape
but...

3. 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, year
and cohort

4. 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 ocials and managers;
professionals;
technicians and associate professionals

5. 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%.

6. 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 lowered
potential US economy output by 12%.

7. Age-productivity patterns in talent occupations for men and women
Motivation
Research
Question: Are there any dirences between age-productivity patterns for
men and women in talent occupations?

8. Age-productivity patterns in talent occupations for men and women
Motivation
Research
Question: Are there any dirences between age-productivity patterns for
men and women in talent occupations?
Method: Deaton decomposition

9. Age-productivity patterns in talent occupations for men and women
Motivation
Research
Question: Are there any dirences between age-productivity patterns for
men and women in talent occupations?
Method: Deaton decomposition
Data: Polish LFS 1995-2012

10. Age-productivity patterns in talent occupations for men and women
Insights from the literature
Gender wage gap
Glass ceilings
size of a gap - dierent along the distribution
talent occupation = highest earnings
Family role
consequences of child bearing and family responsibilities

11. Age-productivity patterns in talent occupations for men and women
Insights from the literature
Age, cohort and time eects
Interpretation (Thornton et al. 1997)
age - individual productivity
time - in
ation rate and average prodcuctivity
cohort - transition

12. Age-productivity patterns in talent occupations for men and women
Insights from the literature
Age, cohort and time eects
Interpretation (Thornton et al. 1997)
age - individual productivity
time - in
ation rate and average prodcuctivity
cohort - transition
Methods
synthetic cohort technique (Browning, Deaton and Irish, 1985)
decomposition (Deaton, 1997)

13. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

14. cation problem
collinearity: cohorti = yeari agei

15. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

16. cation problem
collinearity: cohorti = yeari agei
Speci

17. cation
assumption: year eects are orthogonal to a time trend and their sum is
normalized to zero

18. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

19. cation problem
collinearity: cohorti = yeari agei
Speci

20. cation
assumption: year eects are orthogonal to a time trend and their sum is
normalized to zero
dt = yeart [(t 1) year1996 (t 2) year1995]

21. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

22. cation problem
collinearity: cohorti = yeari agei
Speci

23. cation
assumption: year eects are orthogonal to a time trend and their sum is
normalized to zero
dt = yeart [(t 1) year1996 (t 2) year1995]
removal of

24. rst dummy of each variable and second-year dummy

25. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

26. cation problem
collinearity: cohorti = yeari agei
Speci

27. cation
assumption: year eects are orthogonal to a time trend and their sum is
normalized to zero
dt = yeart [(t 1) year1996 (t 2) year1995]
removal of

28. rst dummy of each variable and second-year dummy
OLS regression

29. Age-productivity patterns in talent occupations for men and women
Method
Deaton decomposition
Identi

30. cation problem
collinearity: cohorti = yeari agei
Speci

31. cation
assumption: year eects are orthogonal to a time trend and their sum is
normalized to zero
dt = yeart [(t 1) year1996 (t 2) year1995]
removal of

32. rst dummy of each variable and second-year dummy
OLS regression
Pwj;t =
T
t=1 tdt +
P60
j=25

33. jaj +
P43
i=8
i ;t=1995ci ;t=1995 + j;t

34. Age-productivity patterns in talent occupations for men and women
Data
Polish LFS 1995-2012
Restriction:
wage-employees only
aged above 25
Descriptive statistics
Variable Mean total Mean females Mean talented Mean talented
females
Age 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 PLN
Talent 15.9% 20.4% 100% 100%
No. of obs. 677 229 316 647 107 414 64 439

35. Age-productivity patterns in talent occupations for men and women
Results
Year and cohort - total sample
Year eects Cohort eects

36. Age-productivity patterns in talent occupations for men and women
Results
Age eects - all vs talented
Age eects Talent

37. Age-productivity patterns in talent occupations for men and women
Results
Age eects - gender dierences
Total Talent

38. Age-productivity patterns in talent occupations for men and women
Results
Oaxaca - Blinder decomposition
Results
Total
sample
Talent
occupations
Raw 0.102*** 0.094***
Endowments -0.0012** -0.0012**
Coecients 0.105*** 0.106***
Interactions -0.003*** -0.0002
No. of
677 229 107 414
observations
Age eects contribution to gender wage
gap

39. 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 child
bearing and family responsibilities.

40. Age-productivity patterns in talent occupations for men and women
Conclusions
Thank you for your attention!
Magdalena Smyk
msmyk@wne.uw.edu.pl