gender wage gap in poland: can it be explained by differences in observable characteristics?

MotivationResearch goals

Theoretical backgroundEmpirical results

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Gender Wage Gap in Poland – Can It Be Explainedby Differences in Observable Characteristics?

TIMTED 2013

Karolina Goraus

Faculty of Economic SciencesUniversity of Warsaw

Timisoara, June 7th 2013

Karolina Goraus Gender Wage Gap in Poland – Can It Be Explained by Differences in Observable Characteristics? TIMTED 2013



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Outline

1 Motivation

2 Research goals

3 Theoretical backgroundTheory of discriminationMethodology

4 Empirical results

5 Bibliography




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What is the reason of gender wage differentials?

Gender wage gaps

Italy 2005 15,7% Picchio, Mussida, 2010Italy 2008 24,16% Nopo, Daza, Ramos, 2011

Czech Republic 1998 30% Jurajda, 2001Czech Republic 2008 35,19% Nopo, Daza, Ramos, 2011




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Motivation

Weichselbaumer and Winter-Ebmer (2007)meta-analysis of 263 papers exploring gender wage gaps in 62countriesincomparability of estimators

Nopo, Daza and Ramos (2011)attempt to estimate gender wage gaps for 63 countries withthe same methodologyhuge differences in gender gaps between countriesunknown reasons




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Research goals

1 Estimation of gender wage gaps with consistent methodologyfor many countries over time

2 Analysis of differences in gender wage gaps between countriesand their changes over time

3 Construction of antydiscriminatory policy recommendationsdepending on the form and sources of gender discrimination

First stage of the research

Choosing the methodology and implementing it to the case ofPoland




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Theory of discriminationMethodology

Kenneth J. Arrow

”The fact that different groups of workers, be they skilled orunskilled, black or white, or male or female, receive different wage,invites the exlanation that the different groups must differaccording to some characteristics valued on the market. Instandard economic theory, we think first of all of differences inproductivity. The notion of discrimination involves the additionalconcept that personal characteristics of the worker that areunrelated to productivity are also valued on the market.”




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Assesment of wage differentials

Raw wage gap

Measuring the difference in average wages between males andfemales

Adjusted wage gap

Estimation of the component of raw gender wage gap that connotbe explained by differences in idividual characteristics




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Methodology

Decomposition methods1 Parametric2 Non-parametric

Parametric Oaxaca-Blinder decomposition

Two components: one attributable to differences in averagecharacteristics of the individuals, and the other – to differences inrewards that these characteristics have




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How to measure discrimination?




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Attempts to refine the Oaxaca-Blinder decomposition

Male wage structure prevails in the absence ifdiscrimination → Other non-discriminatory wage structures(Naumark, 1988; Oaxaca i Ransom, 1994;)

Outcome variable continuous and unbounded → Solutionfor binary variable (Fairle, 2003), generalization to otherdiscrete and limited variables (Bauer and Sinning, 2008)

It is only informative about the average unexplaineddifference in wages → Expansion of the method to the caseof distributional parameters besides the mean (Juhn, Murphy,and Pierce, 1991; Machado, and Mata, 2005; DiNardo, Fortin,and Lemieux, 1996; Firpo, Fortin, Lemieux, 2007)




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Attempts to refine the Oaxaca-Blinder decomposition

Problem: misspecification caused by differences in thesupports of the distribution of individual characteristicsfor females and males

There are combinations of characteristics for which it ispossible to find males but not females in the society, and viceversa → one cannot compare wages across genders (Rubin,1977)

Nopo (2008) adapted the tool of the program evaluationliterature, matching, to construct a non-parametric alternativeto Blinder-Oaxaca decomposition method and fix the problemof differences in the supports of distribution of characteristicsbetween females and males




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Methodology

Oaxaca-Blinder decomposition

yM − yF = βM(xM − xF ) + (βM − βF )xF

Decomposition of Nopo

δ = δM + δX + δ0 + δF

δM - can be explained by differences between „matched” and„unmatched” malesδX - can be explained by differences in the distribution ofcharacteristics of males and females over the common supportδO - unexplained part of the gapδF - can be explained by differences between „matched” and„unmatched” females




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Decomposition of gender wage gap in Poland

Analysis based on the Labor Force Survey performed byCentral Statistical Office in Poland

Quarterly data from 1995q1 to 2011q4

Persons that are self-employed, unemployed, or inactive, aswell as miners and armed forces have been removed from thedata setAdditionally the pooled data set was created

it contains 690414 observationswages presented in PLN, constant prices of 1995share of males is 52.5%




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Raw gender wage gap

Average hourly wages forfemales over the years1995-2011 were12.5PLN, while for malesit was 13.7PLN - thedifference amounts toaround 9.3% of females’average wage




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Males (vs. Females) on Polish labour market

Demographic characteristics

Half year younger

Less educated

More often single

Less often in cities and in Warsaw

Job related characteristics

Shorter tenure with current employer but longer overall tenure

Less often in public sector

More often in agriculture, industry and construction sector,less often in services sector

Mostly in middle-skilled occupations

More often in big and medium companies




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Results of Nopo decomposition

Characteristics D D0 DM DF DX % M % KDemographic 10% 20% 0% 0% -10% 99 97+ Occupation 10% 20% 0% 0% -10% 96 93+ Sector 10% 20% -1% -1% -9% 92 92+ Public 10% 21% 0% -1% -10% 99 95+ Informal 10% 21% 0% 0% -10% 99 97+ Tenure 10% 21% 0% -1% -10% 99 95All 10% 19% -2% -1% -6% 65 74




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Raw wage gap vs. ”discrimination”




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Results of Nopo decomposition based on all characteristics




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Oaxaca-Blinder decomposition

Based on demographicvariables : unexplainedcomponent of 20%, thesame result as innon-parametric

Based on all variables:unexplained component of21.6%




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Comparison of decompositions

Decomposition type Raw gap Unexplained componentNopo 10% 19%Oaxaca-Blinder 10.1% 21.6%Oaxaca-Blinder over 10.1% 21.1%common support




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Sensitivity analysis

Adjusted wage gap bigger than the raw gapfor each wage quartilefor each age categoryboth in rural and urban areasin Mazowieckie region and outsidein public and in private sector

Adjusted wage gap (slightly) smaller than the raw gapfor people with tertiary, vocational and elementary educationfor occupations that require more skillsin industry and constructionin informal sector

Adjusted wage gap within particular groups of society isalways positive and vary between 12% and 27%




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Conclusions

Females to a greater extent exhibit characteristics that arewell rewarded in the labor market.

Despite better education, they are less frequently employed inbetter paying positions.

The raw gap over the period 1995-2011 amounts to app.10%. However, accounting for the differences in endowmentsthe actual wage gap grows to as much as 20%.

Despite covering already 17 years of data, we were not able toidentify any clear decreasing trend in gender discrimination inPoland.




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Thank you for your attention




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gender wage gap in poland: can it be explained by differences in observable characteristics?

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