Gender Unemployment Gaps: Evidence fromthe New EU Member States

Alena Bicáková

CERGE-EI, Prague

European User Conference for EU-LFS and EU-SILC

March 5, 2009, Mannheim

Gender Differences in the Labor Market Outcomes

• gender wage gaps• gender unemployment gaps

Previous research focused predominantly on the the first one.

Only two cross-country papers on the second:

Azmat, Guell, Manning (JOLE 2006)

Stefanova-Lauerova, Terrell (Comparative Econ Studies 2007)

Why studying U-gap is (even more) important?

• affects lifetime / long-term earnings and income volatility• job security, unemployment stigma, skill deterioration• may affect female labor force participation,

discouraged worker• evidence on discrimination in hiring (audit studies)• may force women to accept worse jobs• may affect the observed wage gap

(Olivetti, Petrongolo JLE 2008)• may-be a trade-off between the two gaps depending on LM

institutions (wage flexibility)

Azmat, Guell, Manning JOLE 2006

based on cross-sectional cross-country comparisonusing ECHP data

they conclude that

gender unemployment gap tends to be higher in

• countries with higher overall unemployment rate• countries with lower female labor force participation• countries with lower observed gender wage gap

(effect of wage compressing institutions)

classic South - North divide

Gender U gap and Female LFP

AT

BE

DE

DK

ES

FI

FR

GR

IE

IT

LU

NL

NO

PT

SEUK

CZ

EE

HU LT

LV

PL

SI

SK

−.05

0.0

5.1

.6 .7 .8 .9 .6 .7 .8 .9

Old EU New EU

Une

mpl

oym

en G

ap

Female Labor Force Participation

New EU versus Old EU

High female labor force participation(similar to Nordic countries, Denmark, France ..).

Medium sized unemployment gaps.

No or negative gender unemployment gaps in Baltic countries.

Negative correlation between gender unemployment gapsand female labor force participation - observed for the Old EU

is not present among the New EU member states.

New Member States - Detailed Evidence

Gender unemployment gaps of prime age individuals in 2007

Country Male U Female U ratio difference t-stat

Czech Rep. 0.035 0.066 1.86 0.030 10.36Estonia 0.045 0.048 1.07 0.003 0.35Hungary 0.063 0.069 1.10 0.006 1.92Latvia 0.057 0.053 0.92 -0.004 -0.54Lithuania 0.038 0.041 1.09 0.003 0.70Poland 0.079 0.089 1.13 0.010 2.37Slovakia 0.087 0.124 1.42 0.037 5.90Slovenia 0.032 0.058 1.82 0.026 5.19

Source: EU LFS, own calculations, weighted, t-stat for the difference betweentwo independent variables with binomial distribution

Gender Unemployment Gaps 1996-2007

−5−2.5

02.5

5

−5−2.5

02.5

5

−5−2.5

02.5

5

1997 1999 2001 2003 2005 2007 1997 1999 2001 2003 2005 2007 1997 1999 2001 2003 2005 2007

1997 1999 2001 2003 2005 2007 1997 1999 2001 2003 2005 2007 1997 1999 2001 2003 2005 2007

1997 1999 2001 2003 2005 2007 1997 1999 2001 2003 2005 2007

Czech Republic Estonia Hungary

Latvia Lithuania Poland

Slovakia Slovenia

up95/low95 d_Urate

Year

New Member States - Specific Features

Pre-1989• zero unemployment rate, so zero gap• very high female labor force participation (not PL and HU)• working woman norm, supported by the state• informal child care, two-generational households

Post-1989• reduced child care provision• different social values• changes in anti-discrimination laws• different labor market policies

This paper

Document the gender unemployment gaps in the 8 New EUmember states.

Analyze the observed unconditional gaps via Oaxaca-Blindertype decomposition.

Analyze the observed unconditional gaps in terms of flows fromand to unemployment.

Explain the variation in gender unemployment gaps across theNew Member states.

Shed more light on the relationship between the genderunemployment gaps and female labor force participation.

Data: EU Labor Force Survey (1996-2007)

annual data from Q2, in cross-sectional analysis focus on 2007

prime age individuals (25-54 year old)

Country N in 2007Czech Republic 25,639Estonia 2,405Hungary 31,333Latvia 3,565Lithuania 7,148Poland 21,321Slovakia 11,409Slovenia 7,642

We exclude individuals in compulsory military service (very few)

Education

sample = labor force

Women are on average more educated than men

except in Czech Republic and Slovakia (at older ages).

Age 25-34 Age 35-44 Age 45-54 TotalM F M F M F M F

Czech Rep. 2.10 2.12 2.12 2.07 2.10 1.97 2.10 2.05Estonia 2.17 2.37 2.14 2.43 2.27 2.34 2.19 2.38Hungary 2.06 2.21 2.03 2.08 2.06 2.00 2.05 2.09Latvia 1.84 2.23 2.05 2.29 2.07 2.23 1.98 2.25Lithuania 2.21 2.42 2.17 2.32 2.17 2.24 2.18 2.33Poland 2.15 2.34 2.05 2.15 2.02 2.05 2.08 2.18Slovakia 2.11 2.14 2.10 2.05 2.09 2.00 2.10 2.06Slovenia 2.13 2.28 2.08 2.15 2.03 2.03 2.08 2.15

Weighted, year 2006.

Marital status and number of children less 15

sample = labor force

Marital status No. of ChildrenM F M F

Czech Rep. 0.631 0.668 0.667 0.595Estonia 0.527 0.519 0.600 0.545Hungary 0.601 0.623 1.155 1.019Latvia 0.612 0.552 0.778 0.742Lithuania 0.763 0.717 0.975 0.939Poland 0.755 0.755 2.038 2.026Slovakia 0.689 0.714 0.987 0.89Slovenia 0.563 0.646 0.887 0.934

Weighted, year 2006.

Flexible decomposition I

construct J subgroups based on discrete versions of X -s

the overall gender unemployment gap Ugap defined as thedifference between the female uF and male uM unemploymentrate can be written in terms of the J sub-groups

Ugap = uF − uM =∑

j

wFj uF

j −∑

j

wMj uM

j

where uGj is the unemployment rate in subgroup j for gender G

and wGj is the share of subgroup j among gender G

Flexible decomposition II

adding and subtracting terms for the overall gender-neutralunemployment rates weighted by the gender specific weights∑

j wFj uj and

∑j wM

j uj , we get

Ugap =∑

j

wFj (uF

j − uj)︸ ︷︷ ︸A

+∑

j

wMj (uj − uM

j )

︸ ︷︷ ︸B

+∑

j

(wFj − wM

j ) uj︸ ︷︷ ︸C

A and B is the part of the U-gap due to gender differences inthe respective subgroups

C is the part of the U-gap due to gender differences in thedistribution across the subgroups [i.e. differences in observedcharacteristics]

Flexible Oaxaca-Blinder Results 2007

18 groups based on age(6) and education(3)U gap A B C (A+B)

Czech Rep. 0.030 0.014 0.011 0.005 0.025Estonia 0.003 0.005 0.005 -0.006 0.010Hungary 0.006 0.005 0.004 -0.003 0.009Latvia 0.000 0.004 0.004 -0.009 0.008Lithuania 0.003 0.004 0.004 -0.005 0.008Poland 0.010 0.010 0.009 -0.008 0.018Slovakia 0.037 0.015 0.013 0.009 0.028Slovenia 0.026 0.015 0.014 -0.003 0.029

A =P

j wFj (uF

j − uj), B =P

j wMj (uj − uM

j ), C =P

j(wFj − wM

j ) uj

A + B is the U-gap if women and men equally distributedacross J groups

Flexible Oaxaca-Blinder Results: Summary

The within-group gender unemployment gap turns out to bepositive everywhere, although close to zero for Baltic countriesand Hungary.

Except for Czech Republic and Slovakia, women have morefavorable distribution across age and education categories thanmen, therefore partly reducing the unemployment gap.

17 % of the U-gap in CZ and 24 % of the U-gap in SK causedby unfavorable distribution of women across age and education

Gender Unemployment Gaps 1996-2007

−.05

0

.05

−.05

0

.05

−.05

0

.05

1999 2001 2003 2005 2007 1999 2001 2003 2005 2007 1999 2001 2003 2005 2007

1999 2001 2003 2005 2007 1999 2001 2003 2005 2007 1999 2001 2003 2005 2007

1999 2001 2003 2005 2007 1999 2001 2003 2005 2007

Czech Republic Estonia Hungary

Latvia Lithuania Poland

Slovakia Slovenia

Unexplained U−gap Raw U−gap

Year

Parametric Oaxaca-Blinder

Model the probability of being unemployed

use three specifications

• Pr(U = 1|LFP = 1, X ) = F (α + β ∗ FEM)

• Pr(U = 1|LFP = 1, X ) = F (α + β ∗ FEM + Xγ)

• Pr(U = 1|LFP = 1, X ) = F (α + β ∗FEM + Xγ + FEM ∗Xδ)

where X is a set of human capital or family relatedcharacteristics

F (Xβ) = Xβ (LPM) or F (Xβ) = Φ(Xβ) (probit)

estimation for 2007 data, by country separately

Conditional Unemployment Gap

Coefficient of female dummy in LPM, robust standard errors

human C = six age categories, educM, educH

family = number of children (0 to 4 and above), marital status

no Xs + human C Xs + family Xs + interactionscoeff se coeff se coeff se coeff se

cz 0.031 0.003 0.024 0.003 0.023 0.003 0.012 0.024ee -0.001 0.009 0.006 0.009 0.006 0.009 0.075 0.063hu 0.004 0.003 0.006 0.003 0.007 0.003 0.016 0.015lv -0.010 0.008 -0.001 0.008 -0.003 0.008 0.062 0.063lt -0.001 0.005 0.005 0.005 0.005 0.005 -0.007 0.049

pl 0.012 0.004 0.020 0.004 0.021 0.004 0.037 0.025sk 0.038 0.006 0.027 0.006 0.029 0.006 -0.102 0.040si 0.023 0.005 0.027 0.005 0.031 0.005 0.067 0.025

Parametric Oaxaca-Blinder: Summary

After conditioning on human capital and family variables(comparing the same individuals) gender unemployment gapsremain similar to the unconditional onesbut decreases in Czech Republic and Slovakia,and increases in Poland, Slovenia, (and Lithuania).

After adding interactions with female dummy (allowing theeffect of RHS variables differ by gender) female effect alonecaptures gender unemployment gap of the base categoryyoung, low educated, single, no children

and is no longer significant except in Slovakia (becomes hugeand negative) and Slovenia (doubles).

Cost of Children - Women of Age < 44

children1 children2 children3 children4coeff se coeff se coeff se coeff se

cz 0.088 0.011 0.074 0.010 0.136 0.018 0.211 0.047ee -0.017 0.024 -0.003 0.028 -0.031 0.027 0.050 0.077hu 0.034 0.010 0.035 0.011 0.079 0.017 0.082 0.040lv -0.002 0.020 0.019 0.022 0.002 0.029 -0.054 0.021lt -0.010 0.016 -0.027 0.016 -0.012 0.023 0.031 0.047pl 0.006 0.012 -0.003 0.013 -0.011 0.017 -0.025 0.024sk 0.052 0.020 0.049 0.021 0.081 0.028 0.154 0.046si -0.025 0.019 -0.053 0.017 -0.031 0.025 -0.038 0.049

Coefficients from the linear probability regression with human capital andfamily characteristics, estimated for women less or 44 years old, in 2007.

Labor Force Participation by Age Group in 2007

.6

.7

.8

.9

1

.6

.7

.8

.9

1

.6

.7

.8

.9

1

27 32 37 42 47 52 27 32 37 42 47 52 27 32 37 42 47 52

27 32 37 42 47 52 27 32 37 42 47 52 27 32 37 42 47 52

27 32 37 42 47 52 27 32 37 42 47 52

Czech Republic Estonia Hungary

Latvia Lithuania Poland

Slovakia Slovenia

males females

Age

Gender U gap and Female LFP

CZ

EE

HULT

LV

PL

SI

SK

CZ

EE

HULT

LV

PL

SI

SK

−.05

−.04

−.03

−.02

−.01

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8

.6 .7 .8 .9 .6 .7 .8 .9

Prime−age Female LFP 25−29 Age Female LFP

Une

mpl

oym

en G

ap

Female Labor Force Participation

Determinants of gender unemployment gap

Steady state unemployment rate

u =δ

δ + λ

where δ firing rate and λ is the rate of leaving unemployment

Condition that inflows equal outflows: (1 − u) δ = λ u

Azmat et al. (2006): gender differences in both δ and λ

Stefanova-Lauerova and Terrel (2007): gender differences in λ

δ typically assumed exogenous in job search models

Flow analysis - U to E transition

no Xs + human C Xs + family Xs + interactionscoeff se coeff se coeff se coeff se

cz -0.038 0.009 -0.033 0.008 -0.042 0.009 -0.061 0.024ee 0.010 0.019 -0.004 0.019 -0.024 0.020 -0.050 0.065hu -0.028 0.005 -0.023 0.005 -0.034 0.005 -0.033 0.014lv 0.004 0.017 0.004 0.017 -0.001 0.017 -0.039 0.050lt -0.061 0.017 -0.057 0.017 -0.061 0.018 -0.056 0.061

pl -0.083 0.005 -0.090 0.005 -0.103 0.005 -0.070 0.016sk -0.038 0.008 -0.026 0.008 -0.038 0.008 0.019 0.023si -0.021 0.010 -0.022 0.010 -0.030 0.010 -0.047 0.028

Coefficients of female dummy from different LPM specifications. Robuststandard errors. All available years, year fixed effects included.

Flow analysis - E to U transition

no Xs + human C Xs + family Xs + interactionscoeff se coeff se coeff se coeff se

cz -0.040 0.001 -0.038 0.001 -0.037 0.001 -0.084 0.006ee -0.010 0.003 -0.017 0.003 -0.017 0.003 -0.081 0.017hu -0.015 0.001 -0.015 0.001 -0.015 0.001 -0.045 0.004lv -0.015 0.005 -0.021 0.005 -0.023 0.005 -0.044 0.022lt 0.005 0.002 0.000 0.002 0.001 0.003 0.013 0.016

pl -0.007 0.001 -0.011 0.001 -0.011 0.001 -0.021 0.006sk -0.035 0.002 -0.036 0.002 -0.035 0.002 -0.061 0.013si -0.012 0.001 -0.014 0.001 -0.015 0.001 -0.022 0.005

Coefficients of female dummy from different LPM specifications. Robuststandard errors. All available years, year fixed effects included.

Cost of Family - U to E transition - Women Age <44

children1 children2 children3 children4coeff se coeff se coeff se coeff se

cz -0.124 0.025 -0.084 0.025 -0.153 0.032 -0.219 0.044ee 0.066 0.050 0.008 0.050 0.057 0.066 0.041 0.087hu 0.006 0.015 0.026 0.016 -0.011 0.021 -0.022 0.035lv -0.084 0.066 -0.059 0.070 -0.104 0.100 -0.111 0.144lt 0.017 0.055 0.007 0.056 -0.139 0.069 -0.092 0.091pl -0.008 0.017 -0.003 0.017 -0.018 0.020 -0.011 0.025sk -0.014 0.027 -0.027 0.025 -0.039 0.030 -0.053 0.033si -0.025 0.032 -0.003 0.032 -0.063 0.049 -0.124 0.077

Coefficients from the linear probability regression of the women’s transitionrate from unemployment to employment, human capital and familycharacteristics and year fixed effects included.

Conclusion I

Unexplained part (within education and age categories) ofunemployment gap is present in all countries but is close tozero for some.

Baltic countries have much lower unemployment gap, mostlyunrelated to marital status and children.

In Slovenia - U-gap only among younger women irrespective ofwhether married or with children.

Marital status and children increase U-gap in other countries.

Conclusion II

The largest and most persistent U-gap is in the Slovakia, CzechRepublic, and Poland.

Variation in LFP of women after childbirth seem to account forthe respective labor market costs of children, which explainmuch of the observed gender unemployment gaps.

The observed changes in LFP are in line with the generosity ofthe country-specific maternity and parental leave policies.