We analyze the evolution of the gender wage gap in Poland since transition. Using a Nopo decomposition, we find that differences in characteristics indicate that women should perceive higher wages.

1. Karolina Goraus
University ofWarsaw
September 28th, 2012
Gender Wage Gap in Poland – Can It Be
Explained by Differences in Observable
Characteristics?

2. Outline
Motivation
Literature review
Data and research method
Raw gender wage gap
Gender differences in characteristics
Results of decompositions
Conclusions

3. Motivation
When explaining gender differences in wages, some people
may claim that it is due to discrimination, and others that it
simply reflects gender differences in some observable
characteristics of the individuals that are determinants of
wages (Nõpo, 2008)
To the best of our knowledge there is no empirical research
using non-paremetric methods to Poland, while research
using parametric methods is scarce

4. Literature review (1)
Poland had a significant delay in having their academic,
business, and political elites concentrated on the issue of
gender differences in the labour market (Grajek, 2003)
Adamchik and Bedi (2003) doubted if the economic position
of females in Poland has improved along with the positive
economic performance of the country
Gender wage gap in Poland over transition : component
explained by differences in observed characteristics is quite
limited (Grajek, 2003;Adamchik and Bedi, 2003)

5. Literature review (2)
Measuring the difference in average wages between males
and females is the most basic way to assess gender wage
differentials
Decomposition methods of wage differentials
Oaxaca-Blinder decomposition
Two components: one attributable to differences in average
characteristics of the individuals, and the other – to differences in
rewards that these characteristics have

6. Literature review (3)
Attempts to refine the Oaxaca-Blinder decomposition
Male wage structure prevails in the absence if discrimination Other
non-discriminatory wage structures (Neumark, 1988; Oaxaca and
Ransom, 1994)
Outcome variable continuous and unbounded solution for binary
variable (Fairle, 2003), generalization to other discrete and limited
variables (Bauer and Sinning, 2008)
It is only informative about the average unexplained difference in wages
expansion of the method to the case of distributional parameters
besides the mean e.g. Juhn, Murphy, and Pierce (1991, 1993),
Machado, and Mata (2005), DiNardo, Fortin, and Lemieux (1996),
Firpo, Fortin, Lemieux (2007)

7. Literature review (4)
Attempts to refine the Oaxaca-Blinder decomposition
Problem: misspecification caused by differences in the supports of
the distribution of individual characteristics for females and males
There are combinations of characteristics for which it is possible
to find males but not females in the society, and vice versa.With
such distribution of characteristics one cannot compare wages
across genders (Rubin, 1977)
Nõpo (2008) adapted the tool of the program evaluation
literature, matching, to construct a non-parametric alternative to
Blinder-Oaxaca decomposition method and fix the problem of
differences in the supports of distribution of characteristics
between females and males

8. Research method
Oaxaca-Blinder decomposition
‫ݕ‬തெ − ‫ݕ‬തி = ߚመெ
‫̅ݔ‬ெ − ‫̅ݔ‬ி + (ߚመெ− ߚመி)‫̅ݔ‬ி
∆ = ∆ெ + ∆௑ + ∆଴ + ∆ி
Decomposition of Nõpo
∆ெ − can be explained by differences between „matched” and
„unmatched” males
∆௑ − can be explained by differences in the distribution of
characteristics of males and females over the common support
∆଴ − unexplained part of the gap
∆ி − can be explained by differences between „matched” and
„unmatched” females

9. Data
About occupational activity of population by demographic
and social features
Comes from the Labor Force Survey performed by Central
Statistical Office in Poland and contains quarterly data from
1995q1 to 2011q4
Persons that are self-employed, unemployed, or inactive, as
well as miners and armed forces have been removed from the
data set
Additionally the pooled data set was created
it contains 690414 observations
wages presented in PLN, constant prices of 1995
share of males is 52.5%

10. Absolute (PLN, constant prices of 1995) and relative gender wage gap,
1995-2011
Average hourly wages for females over the years 1995-2011 were
12.5PLN, while for males it was 13.7PLN - the difference amounts to
around 9.3 percent of females’ average wage
Raw gender wage gap

11. Differences in characteristics (1)
Demographic characteristics: Education and marital status
Variables Observations Percent Male Female
Education levels 690 414 100 53 47
Tertiary education 112 697 16 6.5 9.5
High school 82 203 12 3 9
High school vocational 185836 27 13 14
Vocational 240 666 35 24 11
Elementary 69 012 10 6 4
Marital status 690 141 100 53 47
Single 144 305 21 12 9
Married 505 167 73 40 33
Widowed 15 240 2 0 2
Divorced/separated 25 702 4 1 3

12. Differences in characteristics (2)
Demographic characteristics
Age: females half year older than males
Cities: 40% of females live in the city, while among males the
percentage amounts only to 36%
Mazowieckie: in Mazowieckie region live 10.2% of females and
9.8% of males
Relation of variables to wages:
Age: older people earn more
Cities: people in urban areas earn more
Mazowieckie: people in Mazowieckie region earn more
Education level: highly educated people earn more
Marital status: singles tend to earn less

13. Differences in characteristics (3)
Job-related characteristics
Occupation
Very high-skilled occupations (17% of society: higher management, policy
makers and specialists): 39% of males, 61% of females
High-skilled occupations (36% of society: technicians, middle management,
office workers, sales and personal services): 33% of males, 67% of females
Middle-skilled occupations (36% of society: farmers, fishermen, artisans,
industrial workers and machine operators): 83% of males, 17% of females
Low-skilled occupations (11% of society): 43% of males, 57% of females
Public: 51% of Polish female employees was working in public
sector, while for males the percentage was 33%
Informal: 0.8% of females working in grey economy, while for
males the percentage is 1.2%

14. Differences in characteristics (4)
Job-related characteristics
Branch of economy
Agriculture (1% of society): 75% of males, 25% of females
Industry (19% of society): 69% of males, 31% of females
Construction (17% of society): 70% of males, 30% of females
Market services (32% of society): 55% of males, 45% of females
Non-market services (31% of society): 31% of males, 69% of females
Tenure with current employer: 10.7 years for females, and
9.8 years for males
Overall tenure: 17.3 years for females, and 18 years for males
Size of the firm: the same share of females and males in small
comapnies, while in medium or large enterprises there is 1.2%
more males

15. Differences in characteristics (5)
Job-related characteristics, relation to wages:
Occupation: people in higher-skill occupations receive higher
wage
Public: higher wages in public sector
Informal: lower wages in informal sector
Branch of economy: highest wages in industry and services
Tenure with current employer/ overall tenure: more tenure
results in higher wage
Size of the firm: higher wages in bigger companies
Intuition:Characteristics of individuals does not seem to explaine
gender wage gap in Poland

16. Results of decompositions (1)
Decomposition of Nõpo: ∆ = ∆଴ + ∆ெ + ∆ி + ∆௑
Controls D D0 DM DF DX
Share of
matched
males
Share of
matched
females
Demographic variables 10% 20% 0% 0% -10% 99% 97%
+ Occupation category 10% 20% 0% 0% -10% 96% 93%
+ Industry category 10% 20% -1% -1% -9% 92% 92%
+ Private 10% 21% 0% -1% -10% 99% 95%
+ Informal 10% 21% 0% 0% -10% 99% 97%
+Median tenure 10% 21% 0% -1% -10% 99% 95%
All variables 10% 19% -2% -1% -6% 65% 74%

17. Results of decomposition (2)

18. Results of the decomposition (3)
Decomposition of Nõpo based on all variables

19. Results of
decomposition (4)
Oaxaca-Blinder
decomposition
based on demographic
variables : unexplained
component of 20%, the same
result as in non-parametric
based on all variables:
unexplained component of
21.6%, only slightly sdifferent
from non-parametric
approach

20. Results of decompositions (5)
Comparison of decompositions
Estimators of explained and unexplained gender wage gap in Poland over the
period 1995-2011 obtained with the use of methodology developed by Nõpo
has been confirmed with traditional Oaxaca-Blinder decoposition
Similar estimators of unexplained component of the gap in Oaxaca-Blinder
decomposition on the whole sample and over the common support

21. Sensitivity analysis
Adjusted wage gap bigger than the raw gap
for each wage quartile
for each age category
both in rural and urban areas
in Mazowieckie region and outside
in public and in private sector
Adjusted wage gap (slightly) smaller than the raw gap
for people with tertiary, vocational and elementary education
for occupations that require more skills
in industry and construction
in informal sector
Adjusted wage gap within particular groups of society is always positive
and vary between 12% and 27%

22. Conclusions
Females to a greater extent exhibit characteristics that are
well rewarded in the labor market
Despite better education, they are less frequently employed
in better paying positions
The raw gap over the period 1995-2011 amounts to app.
10%. However, accounting for the differences in
endowments the actual wage gap grows to as much as 20%
Despite covering already 17 years of data, we were not able
to identify any clear decreasing trend in gender
discrimination in Poland