In 1990, at the onset of transition, the female employment ratio in former communist countries was up to 20 percentage points above the employment ratio of women in Western European economies. Thirty years later, the positions are inverted. Women in transition countries lag behind women in advanced economies, and closing the breach appears as a long-term goal. So, what had happened over those years? Our research explores the role of two elements: removal of work orders and changes in opportunity costs.

1. How (Not) to Make Women Work?
How (Not) to Make Women Work?
Evidence from Transition Countries
Karolina Goraus Joanna Tyrowicz Lucas van der Velde
Warsaw School of Economics
FAME — GRAPE
SGH internal seminar
March 2018

2. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates

3. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates
Female employment rates puzzle:
→ ⇓ in transition countries
→ ⇑ in developed countries

4. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates
Female employment rates puzzle:
→ ⇓ in transition countries
→ ⇑ in developed countries
Contribution
Role of opportunity costs of woking

5. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates
Female employment rates puzzle:
→ ⇓ in transition countries
→ ⇑ in developed countries
Contribution
Role of opportunity costs of woking
GEG estimates from 1400+ databases

6. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates
Female employment rates puzzle:
→ ⇓ in transition countries
→ ⇑ in developed countries
Contribution
Role of opportunity costs of woking
GEG estimates from 1400+ databases
New stylized facts on female employment rate in transition
countries

7. How (Not) to Make Women Work?
Motivation
Introduction
Motivation
Ageing societies in Europe → increase participation rates
Female employment rates puzzle:
→ ⇓ in transition countries
→ ⇑ in developed countries
Contribution
Role of opportunity costs of woking
GEG estimates from 1400+ databases
New stylized facts on female employment rate in transition
countries

8. How (Not) to Make Women Work?
Motivation
Motivation
Female employment rates
fall in transition countries...
50
55
60
65
70
Employmentrate
1990 1995 2000 2005 2010 2015
Year
Transition, NMS Advanced

9. How (Not) to Make Women Work?
Motivation
Motivation
Female employment rates
fall in transition countries...
50
55
60
65
70
Employmentrate
1990 1995 2000 2005 2010 2015
Year
Transition, NMS Advanced
... but
so did male employment rate
65
70
75
80
Employmentrate
1990 1995 2000 2005 2010 2015
Year
Transition, NMS Advanced
Notes: Average employment ratio in transition (NMS) and advanced economies

10. How (Not) to Make Women Work?
Motivation
Motivation
Female employment rates
fall in transition countries...
50
55
60
65
70
Employmentrate
1990 1995 2000 2005 2010 2015
Year
Transition, NMS Advanced
... but
so did male employment rate
65
70
75
80
Employmentrate
1990 1995 2000 2005 2010 2015
Year
Transition, NMS Advanced
Notes: Average employment ratio in transition (NMS) and advanced economies
What is the net change?

11. How (Not) to Make Women Work?
Motivation
Motivation
Estimates of time effects with 95% CI
−.05
0
.05
.1
.15
.2
Coeff.and95%CI
1990 1995 2000 2005 2010 2015
Year
Transition economies Advanced economies
Notes: Regression of employment ratios on time and country F.E.
Source: Employment rates from OECD database.

12. How (Not) to Make Women Work?
Motivation
Questions
1 What stands behind the sudden drop in employment
ratio?
2 How come female employment ratio failed to recover?

13. How (Not) to Make Women Work?
Motivation
Questions
1 What stands behind the sudden drop in employment
ratio?
Role of unemployment rate
Role of work orders
2 How come female employment ratio failed to recover?

14. How (Not) to Make Women Work?
Motivation
Questions
1 What stands behind the sudden drop in employment
ratio?
Role of unemployment rate
Role of work orders
2 How come female employment ratio failed to recover?
Role of opportunity costs
Role of institutions

15. How (Not) to Make Women Work?
Data
Sources
Gender employment gap
European Community Household Panel (ECHP)
European Union Labor Force Survey (EU-LFS)
International Social Survey Program (ISSP)
Life in Transition Survey (LiTS)
Living Standard Measurement Surveys (LSMS)
National Labor Force Surveys (LFS)
National censuses (IPUMS-I)
Detailed coverage: Transition countries Developed countries

16. How (Not) to Make Women Work?
Data
Data quality
Female employment rate: Comparison to official sources

17. How (Not) to Make Women Work?
Data
Data quality
Female employment rate: Comparison to official sources
45
50
55
60
65
0 5 10 15 20 25
Year
Transition countries− OECD data Transition countries − our replication
Developed countries − OECD data Developed countries − our replication
Note: Horizontal axis depicts time (in years), vertical axis mea-
sures the fitted shape of the time pattern in employment rates of
women. Fitted values from previous table
Regressions

18. How (Not) to Make Women Work?
Question 1
Questions
1 What stands behind the sudden drop in employment
ratio?
2 How come female employment ratio failed to recover?

19. How (Not) to Make Women Work?
Question 1
What stands behind the sudden drop in employment ratio?
General explanation
Transition related explanation

20. How (Not) to Make Women Work?
Question 1
What stands behind the sudden drop in employment ratio?
General explanation
Women are more sensitive to business cycles
→ In times of crisis, men should receive employment.
Transition related explanation

21. How (Not) to Make Women Work?
Question 1
What stands behind the sudden drop in employment ratio?
General explanation
Women are more sensitive to business cycles
→ In times of crisis, men should receive employment.
Regress female employment ratio on unemployment rate
Negative relation is weaker in transition countries Results
Transition related explanation

22. How (Not) to Make Women Work?
Question 1
What stands behind the sudden drop in employment ratio?
General explanation
Transition related explanation
Work orders kept employment ratio artificially high.
Two channels: forcing labor + facilitating entry
What happens after removal?

23. How (Not) to Make Women Work?
Question 1
What stands behind the sudden drop in employment ratio?
General explanation
Transition related explanation
Work orders kept employment ratio artificially high.
Two channels: forcing labor + facilitating entry
What happens after removal?

24. How (Not) to Make Women Work?
Question 1
Changes in female empoloyment rate
Transition countries (NMS)
0
.2
.4
.6
.8
Linearpredictionwith90%CI
22 27 32 37 42 47 52 57 62
Age
1991−1994 2004−2007

25. How (Not) to Make Women Work?
Question 1
Changes in female empoloyment rate
Transition countries (NMS)
0
.2
.4
.6
.8
Linearpredictionwith90%CI
22 27 32 37 42 47 52 57 62
Age
1991−1994 2004−2007
Developed countries
0
.2
.4
.6
.8
Linearpredictionwith90%CI
22 27 32 37 42 47 52 57 62
Age
1991−1994 2004−2007
Notes: Fitted values from a regression of average employment rates on age (one regression per country set). Other
controls include country and year interactions and country source interactions. Observations weighted by the
inverse number of observations in each country year.

26. How (Not) to Make Women Work?
Question 1
Questions
1 What stands behind the sudden drop in employment
ratio?
Little evidence of excess business cycle sensitivity for women in
transition
Role of work orders?
→ Forced employment and habit formation
→ Easing entry and educational boom
2 How come female employment ratio failed to recover?

27. How (Not) to Make Women Work?
Question 2
Questions
1 What stands behind the sudden drop in employment ratio?
2 How come female employment ratio failed to recover?

28. How (Not) to Make Women Work?
Question 2
Why did Fem. Emp. Rat. improved in advanced economies
Tentative explanations
Family-friendly insitutions
Mandel and Semyonov (2005), Blau and Kahn (2007, 2013)
Fertility decisions
Changes in skill demand
Black and Spitz-Oener (2010), Deming (2015)
Higher female enrollment in university

29. How (Not) to Make Women Work?
Question 2
Why did Fem. Emp. Rat. improved in advanced economies
Tentative explanations
Family-friendly insitutions
Mandel and Semyonov (2005), Blau and Kahn (2007, 2013)
Fertility decisions
Changes in skill demand
Black and Spitz-Oener (2010), Deming (2015)
Higher female enrollment in university

30. How (Not) to Make Women Work?
Question 2
Why did Fem. Emp. Rat. improved in advanced economies
Tentative explanations
Family-friendly insitutions
Mandel and Semyonov (2005), Blau and Kahn (2007, 2013)
Fertility decisions
Changes in skill demand
Black and Spitz-Oener (2010), Deming (2015)
Higher female enrollment in university
Working hypothesis:
Opportunity costs of employment and non-employment had
different effects in transition and advanced economies

31. How (Not) to Make Women Work?
Question 2
Empirical analysis: 1st stage
˜Nopo (2008) decomposition
One-to-many exact matching
Control for common support
Four components
∆0 = ∆M + ∆F + ∆X + ∆A
∆M , ∆F - difference in and out of common support
∆X - explained component
∆A - unexplained component
Controls: age, education level, marital status, urban.

32. How (Not) to Make Women Work?
Question 2
Empirical analysis: 2nd stage
Regress estimates on proxies of opportunity costs
Specification
∆A = β0 + β1op.cost + β2transition*op.cost + γD

33. How (Not) to Make Women Work?
Question 2
Empirical analysis: 2nd stage
Regress estimates on proxies of opportunity costs
Specification
∆A = β0 + β1op.cost + β2transition*op.cost + γD
Opportunity costs
Non-employment:
Employment:

34. How (Not) to Make Women Work?
Question 2
Empirical analysis: 2nd stage
Regress estimates on proxies of opportunity costs
Specification
∆A = β0 + β1op.cost + β2transition*op.cost + γD
Opportunity costs
Non-employment:
GDP per capita (-), % women with tertiary (population and in
tertiary) (-) , female employment rate (-).
Employment:
% houses with kids (+), early childcare facilities (-), % kids in
kindergarden (-).

35. How (Not) to Make Women Work?
Results
Are women in transition better off?
Estimates of the adjusted gender employment gap
Notes: distribution of adjusted gender employment gaps. Displayed are
unweighted averages over data sources for each available country and year.

36. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
coefficient -0.26*** -0.69*** -0.88*** -1.53***
(0.04) (0.10) (0.13) (0.08)
x transition
constant 0.43*** 0.40*** 0.49*** 1.03***
(0.05) (0.12) (0.13) (0.11)
R-squared 0.78 0.72 0.72 0.80
Wald test
Notes: Wald test is the p-value of a test that net effect is zero. Robust standard
errors presented in parentheses. Estimates come from a regression with country,
year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with weights
corresponding to the inverse of the number of available data sources for a given
year and country.

37. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
coefficient -0.26*** -0.69*** -0.88*** -1.53***
(0.04) (0.10) (0.13) (0.08)
x transition 0.41*** 0.76*** 0.98*** 0.77***
(0.03) (0.15) (0.15) (0.10)
constant 0.43*** 0.40*** 0.49*** 1.03***
(0.05) (0.12) (0.13) (0.11)
R-squared 0.78 0.72 0.72 0.80
Wald test
Notes: Wald test is the p-value of a test that net effect is zero. Robust standard
errors presented in parentheses. Estimates come from a regression with country,
year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with weights
corresponding to the inverse of the number of available data sources for a given
year and country.

38. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
coefficient -0.26*** -0.69*** -0.88*** -1.53***
(0.04) (0.10) (0.13) (0.08)
x transition 0.41*** 0.76*** 0.98*** 0.77***
(0.03) (0.15) (0.15) (0.10)
constant 0.43*** 0.40*** 0.49*** 1.03***
(0.05) (0.12) (0.13) (0.11)
R-squared 0.78 0.72 0.72 0.80
Wald test 0.00 0.54 0.31 0.00
Notes: Wald test is the p-value of a test that net effect is zero. Robust standard
errors presented in parentheses. Estimates come from a regression with country,
year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with weights
corresponding to the inverse of the number of available data sources for a given
year and country.

39. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of employment
(5) (6) (7)
% households w/ Access to early % kids in
small kids Childcare Kindergardens
coefficient 0.18*
(0.11)
x transition -0.12 -0.56* -0.18**
(0.16) (0.29) (0.09)
constant 0.27*** 0.39*** 0.31***
(0.11) (0.06) (0.07)
R-squared 0.77 0.82 0.83
Wald test 0.55
Notes: Wald test is the p-value of a test that net effect is zero. Dependent
variable is the adjusted gender employment gap. Robust standard errors presented
in parentheses. Estimates come from a regression with country, year and source
fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with weights corresponding to
the inverse of the number of available data sources for a given year and country.
Data on Kindergartens and access to early childcare facilities available only for
transition countries.

40. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost: summary
1 Op. costs work as expected in advanced countries, but...
2 ... relation is much weaker or even null in transition economies

41. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost: summary
1 Op. costs work as expected in advanced countries, but...
2 ... relation is much weaker or even null in transition economies
What is particular about transition?

42. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost: summary
1 Op. costs work as expected in advanced countries, but...
2 ... relation is much weaker or even null in transition economies
What is particular about transition?
Lower adjusted GEG → Non-linear effects?

43. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost: summary
1 Op. costs work as expected in advanced countries, but...
2 ... relation is much weaker or even null in transition economies
What is particular about transition?
Lower adjusted GEG → Non-linear effects?
→ Use unconditional quantile regression (Firpo et al. 2009)

44. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
Quantile approach
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
25th
pctile 0.01
(0.04)
50th
pctile 0.24***
(0.04)
75th
pctile 0.35***
(0.08)
no. of observations 1,441
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country.

45. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
Quantile approach
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
25th
pctile 0.01 -0.09
(0.04) (0.08)
50th
pctile 0.24*** -0.19*
(0.04) (0.11)
75th
pctile 0.35*** -1.16***
(0.08) (0.19)
no. of observations 1,441 1,544
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country.

46. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
Quantile approach
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
25th
pctile 0.01 -0.09 -0.19**
(0.04) (0.08) (0.08)
50th
pctile 0.24*** -0.19* -0.52***
(0.04) (0.11) (0.11)
75th
pctile 0.35*** -1.16*** -0.83***
(0.08) (0.19) (0.22)
no. of observations 1,441 1,544 1,544
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country.

47. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of non-employment
Quantile approach
(1) (2) (3) (4)
ln GDP pc Tertiary educ. Fem. tertiary Fem. Emp.
as % of pop. as % of tertiary Rate
25th
pctile 0.01 -0.09 -0.19** -0.49***
(0.04) (0.08) (0.08) (0.08)
50th
pctile 0.24*** -0.19* -0.52*** -0.81***
(0.04) (0.11) (0.11) (0.10)
75th
pctile 0.35*** -1.16*** -0.83*** -1.57***
(0.08) (0.19) (0.22) (0.18)
no. of observations 1,441 1,544 1,544 1,544
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country.

48. How (Not) to Make Women Work?
Results
Adjusted GEG and opportunity cost of employment
(5) (6) (7)
% households w/ Access to early % kids in
small kids Childcare Kindergardens
25th
pctile 0.19*** -0.33 -0.19*
(0.04) (0.51) (0.10)
50th
pctile 0.35*** -1.22*** -0.46***
(0.07) (0.45) (0.11)
75th
pctile 0.61** 0.42 -0.04
(0.25) (0.40) (0.10)
no. of observations 931 424 441
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country. Data on Kindergartens and access to early childcare
facilities available only for transition countries.

49. How (Not) to Make Women Work?
Results
Did cohorts experienced transition equally?

50. How (Not) to Make Women Work?
Results
Did cohorts experienced transition equally?
(1) (2) (3) (4) (5)
ln GDP pc Tertiary educ. Fem. tert. % of hh. w/ Fem. Emp.
as % of pop. as % of tert. small kids Rate
(Age > 25) -0.05** -0.03* -0.03** 0.10*** 0.01
before trans. (0.01) (0.01) (0.01) (0.02) (0.01)
Op. Cost 0.18 0.01 -0.04 0.77*** -0.62***
measure (0.04) (0.11) (0.06) (0.12) (0.07)
Constant 0.30** 0.30* 0.32** 0.30* 0.56***
(0.15) (0.15) (0.16) (0.18) (0.15)
R-squared 0.47 0.47 0.48 0.50 0.50
Notes: Dependent variable is the adjusted gender employment gap. Robust
standard errors presented in parentheses. Estimates come from a regression with
country, year and source fixed effects, *** p<0.01, ** p<0.05, * p<0.1, with
weights corresponding to the inverse of the number of available data sources for
a given year and country. Only transition countries included in sample.
Non-linear effects

51. How (Not) to Make Women Work?
Results
Robustness tests
What happens when ...
a) Use alternative weighting schemes → Same results

52. How (Not) to Make Women Work?
Results
Robustness tests
What happens when ...
a) Use alternative weighting schemes → Same results
b) Look at different component of GEG → Same results

53. How (Not) to Make Women Work?
Results
Robustness tests
What happens when ...
a) Use alternative weighting schemes → Same results
b) Look at different component of GEG → Same results
c) “Horse race” → Similar relations, larger standard errors

54. How (Not) to Make Women Work?
Results
Questions
1 What stands behind the sudden drop in employment ratio?
2 How come female employment ratio failed to recover?
Non-linear effect of opportunity costs
→ Strong when adjusted GEG is large.
→ might explain the different effect of opportunity costs.
Cohort heterogeneity
→ Younger cohorts faced larger GEG

55. How (Not) to Make Women Work?
Conclusions
Conclusions
1 Opportunity costs explain the fall in adjusted GEG... only in
advanced economies
2 Non-linear effect of opportunity costs
3 Cohort heterogeneity
→ Habit formation + transmission norms?
→ Entry frictions → higher GEG in young groups

56. How (Not) to Make Women Work?
Conclusions
Thank you for your attention

57. How (Not) to Make Women Work?
Appendix
Data coverage: transition countries
Country LFS EU LFS Census LSMS ISSP LiTS
Albania 2002-2005 1989-2006
Armenia 2001 1989-2006
Azerbaijan 1995 1989-2006
Belarus 2008-2010 1999 1989-2006
Bosnia & Herz. 2001-2004 1989-2006
Bulgaria 1995-2012 2000-2012 1995-97, 2001-03 1993-1995 1989-2006
Croatia 1996-2012 1989-2006
Czech Republic 1998-2012 1993-1995 1989-2006
Estonia 1995-2012 1997-2012 1992-1995 1989-2006
FYR Macedonia 1989-2006
Georgia 1989-2006
Hungary 1997-2012 1990, 2001 1989-1995 1989-2006
Kazakhstan 1989-2006
Kyrgyzstan 1993, 1996-1998 1989-2006
Latvia 1998-2012 1995 1989-2006
Lithuania 1998-2012 1995 1989-2006
Moldova 1989-2006
Montenegro 1989-2006
Poland 1995-2012 1997-2012 1991-1995 1989-2006
Romania 1995-2012 1997-2012 1977, 1992, 2002 1989-2006
Russia 1991-1995 1989-2006
Serbia 2002-2004, 2007 1989-2006
Slovakia 1998-2012 1995 1989-2006
Slovenia 1996-2012 2002 1991-1995 1989-2006
Tajikistan 1999, 2003, 2009 1989-2006
Ukraine 1989-2006
Uzbekistan 1989-2006
Back

58. How (Not) to Make Women Work?
Appendix
Data coverage: developed countries
Country EU LFS ECHP ISSP
Austria 1995-2012 1995-2001 1989-1995
Belgium 1992-2012 1994-2001
Denmark 1992-2012 1994-2001
Finland 1995-2012 1996-2001
France 1993-2012 1994-2001
Germany 2002-2012 1994-2001 1989-1995
Greece 1992-2012 1994-2001
Ireland 1999-2012 1994-2001 1989-1995
Italy 1992-2012 1994-2001 1989-1995
Netherlands 1996-2012 1994-2001
Norway 1996-2012 1989-1995
Portugal 1992-2012 1994-2001
Spain 1992-2012 1994-2001 1993-1995
Sweden 1995-2012 1997-2001 1994-1995
Switzerland 1996-2012
UK 1992-2012 1994-2001
Back

59. How (Not) to Make Women Work?
Appendix
Data quality
Female employment rate: Comparison to official sources
A: OECD data B: aggregates from micro-data
Time trends Advanced Transition Advanced Transition
Time 0.83*** -0.83*** 1.37*** -1.50***
(0.10) (0.12) (0.17) (0.13)
Time squared -0.01*** 0.03*** -0.02*** 0.05***
(0.00) (0.00) (0.01) (0.00)
Constant 50.80*** 59.09*** 45.89*** 57.12***
(0.55) (0.86) (1.05) (3.51)
R2 0.56 0.30 0.82 0.74
Number of countries 16 13 16 16
Observations 395 234 624 597
Note: Panel regression robust estimator with country fixed effects.
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
Back

60. How (Not) to Make Women Work?
Appendix
Women are more sensitive to business cycle fluctuations
Employment rate of women (standardized) ILO OECD EUROSTAT
Unemployment rate (standardized) -0.58*** -0.57*** -0.48***
(0.05) (0.03) (0.04)
Transition country dummy 0.33** 0.07 -0.17**
(0.15) (0.07) (0.07)
Transition x unemployment rate 0.38** 0.23*** 0.21***
(0.19) (0.06) (0.07)
Constant -0.16*** -0.04 0.06
(0.05) (0.03) (0.04)
No of observations 515 1,338 632
R2 0.266 0.310 0.250
Notes Unemployment rate and employment rates standardized within sample,
panel regression robust estimator with time fixed effects. Standard errors in
parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1
Negative relation is weaker in transition economies
Back

61. How (Not) to Make Women Work?
Appendix
Adjusted gender employment gap - time patterns
Calendar years Years from transition
(1) (2) (3) (4)
Transition country -0.6922*** -0.2105***
(0.0806) (0.0599)
Time -0.0366*** -0.0244*** 0.0152*** -0.0273***
(0.0110) (0.0052) (0.0030) (0.0035)
x transition country 0.0586*** 0.0461*** 0.0009 0.0418***
(0.0122) (0.0061) (0.0049) (0.0042)
Time2 0.0006* 0.0003 -0.0002*** 0.0001***
(0.0004) (0.0002) (0.0000) (0.0000)
x transition country -0.0013*** -0.0009*** -0.0002 -0.0005***
(0.0004) (0.0002) (0.0002) (0.0001)
Constant 1.0916*** 0.5734*** 0.6680*** 0.9435***
(0.1121) (0.0406) (0.0989) (0.0544)
Country F.E. No Yes No Yes
Observations 1,184 1,184 1,184 1,184
R-squared 0.287 0.754 0.268 0.754
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62. How (Not) to Make Women Work?
Appendix
Cohort heterogeneity: quantile models
(1) (2) (3) (4) (5)
ln GDP pc Tertiary educ. Fem. tert. % of hh. w/ Fem. Emp.
as % of pop. as % of tert. small kids Rate
25th
(Age > 25) -0.00 0.01 0.01 0.01*** 0.05***
before trans. (0.01) (0.01) (0.01) (0.02) (0.01)
Coefficient 0.01 0.24** -0.25*** 0.43*** -0.28***
(0.05) (0.10) (0.07) (0.07) (0.06)
50th
(Age > 25) -0.05*** -0.04*** -0.04*** 0.07*** 0.01
before trans. (0.01) (0.01) (0.01) (0.02) (0.01)
Coefficient 0.12*** 0.33*** -0.33*** 0.51*** -0.41***
(0.04) (0.10) (0.07) (0.09) (0.06)
75th
(Age > 25) -0.07*** -0.04* -0.08*** 0.10*** -0.01
before trans. (0.02) (0.02) (0.02) (0.04) (0.02)
Coefficient 0.28*** -0.14 -0.50** 0.87*** -0.65***
(0.09) (0.18) (0.13) (0.18) (0.11)
Observations 1,570 1,761 1,761 1,390 1,761
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63. How (Not) to Make Women Work?
Appendix
Bibliography
Black, S. E. and Spitz-Oener, A.: 2010, Explaining women’s success:
Technological change and the skill content of women’s work, Review of
Economics and Statistics 92(1), 187–194.
Blau, F. D. and Kahn, L. M.: 2007, Changes in the labor supply behavior of
married women: 1980–2000, Journal of Labor Economics 25(3).
Blau, F. D. and Kahn, L. M.: 2013, Female labor supply: Why is the United
States falling behind?, American Economic Review 103(3), 251–56.
Deming, D. J.: 2015, The growing importance of social skills in the labor
market, Working paper 21473, National Bureau of Economic Research.
Firpo, S., Fortin, N. M. and Lemieux, T.: 2009, Unconditional quantile
regressions, Econometrica 77(3), 953–973.
Mandel, H. and Semyonov, M.: 2005, Family policies, wage structures, and
gender gaps: Sources of earnings inequality in 20 countries, American
sociological review 70(6), 949–967.