what is the right gap?

Gender wage gap in Poland


A comparative analysis of available methods

Lucas Augusto van der VeldePhD Candidate

Research Assistant in GRApE

Faculty of economic sciences

University of Warsaw

May 31, 2014


Table of contents

1 Introduction

2 Methods & Speci�cations

3 Data

4 Results

5 Conclusions


Introduction

Introduction

Motivation

Our work

Goal: Provide a guide for the practitioner

How: Compare the gender wage gap in di�erent methods and speci�cations

Data: Polish LFS 2012


Introduction

Introduction

Motivation

Our workGoal: Provide a guide for the practitioner

How: Compare the gender wage gap in di�erent methods and speci�cations

Data: Polish LFS 2012


Methods & Speci�cations

Methods under analysis

Linear regressions

Oaxaca Blinder decompositions (5)

John, Murphy and Pierce

Di Nardo, Fortin and Lemieux

Machado Mata

Nopo

Firpo, Fortin and Lemieux



Summary

How should the perfect decomposition method look like?

Characteristics OB JMP DFL MM Nopo FFL

Selection Bias OK OK OK

Dimensionality Curse OK OK OK OK OK

Detailed decomposition OK OK OK

Quantile decomposition OK OK OK OK

Common Support OK

Functional Form OK OK

Compare across time OK OK OK



Summary

How should the perfect decomposition method look like?

Characteristics OB JMP DFL MM Nopo FFLSelection Bias OK OK OK

Dimensionality Curse OK OK OK OK OK

Detailed decomposition OK OK OK

Quantile decomposition OK OK OK OK

Common Support OK

Functional Form OK OK

Compare across time OK OK OK



What we expect with respect to...

The selection bias: the adjusted gap increases when women experience

more selection than men

The addition of new variables increases the adj. gap when variation withinis larger than between

The common support: the adj. gap increases when the non-matchedwomen are better endowed than men.




The selection bias: the adjusted gap increases when women experiencemore selection than men

The addition of new variables increases the adj. gap when variation

within is larger than between

The common support: the adj. gap increases when the non-matchedwomen are better endowed than men.




The selection bias: the adjusted gap increases when women experiencemore selection than men

The addition of new variables increases the adj. gap when variation withinis larger than between

The common support: the adj. gap increases when the non-matched

women are better endowed than men.


Data

The Sample

Polish Labour Force Survey (2012)

Male Female C-Support

Hourly wage 11.91 11.00 0.12

Age 40.64 41.29 0.04

Experience 19.15 17.89 0.07

Agriculture 0.41 0.21 0.32

Construction 0.15 0.01 0.39

Industry 0.35 0.43 0.12

Services 0.08 0.35 0.49

Secondary 0.75 0.62 0.20

Tertiary 0.16 0.33 0.28

Social sciences 0.07 0.28 0.40

Medicine 0.01 0.08 0.26

Engeneering 0.59 0.18 0.65

Teaching 0.01 0.03 0.11


Data

Di�erent speci�cations

Basic: Age, experience, education levels, married, kids, rural, cities,Mazowieckie

Industry: Industry dummies for agriculture (reference), manufacture,construction and services.

Industry plus: "Industry" + �rm size and ownership type

Occupations: 9 occupational dummies (ISCO-1 codes)

Tenure: "Basic" + tenure

Education: 9 educational �eld dummies


Results

Comparison across methods


Results

Comparison across speci�cations


Results

Comparison of the methods


Conclusions

Conclusions

With respect to the gap

The adjusted gap is 20% of female gap - two times the size of the raw gap.

There is evidence of a glass ceiling in Poland

We did not �nd evidence of segregation on industries nor on �eld of study

Comparison of methods

On average similar results, but great variations between methods.

After correcting for the selection bias and the common support thedi�erences were greater


Conclusions

Questions or suggestions?

Thank you for your attention


Conclusions

References

Blinder, A. (1973): "Wage Discrimination: Reduced Form and StructuralEstimates", Journal of Human Resources, 8, 436-455.

DiNardo, J. , N. Fortín, and T. Lemieux, 1996 �Labor market institutions and thedistribution of wages, 1973-1992: a Semi-parametric approach� Econometrica,Vol. 64, No.5, 1001 -1044.

Firpo, S., Fortín, N., and Lemieux, T. 2009 Unconditional Quantile regressions,Econometrica, Vol. 77, No. 3, 953-973

Fortín, N., T. Lemieux and S. Firpo, 2010 �Decomposition methods inEconomics� NBER Working paper 16045

Juhn, C., K. M. Murphy, and B. Pierce (1993): "Wage Inequality and the Rise inReturns to Skill", Journal of Political Economy", 101, 410-442.

Machado, J. A. F., and J. Mata (2005): "Counterfactual Decomposition ofChanges in Wage Distributions using Quantile Regression", Journal of AppliedEconometrics, 20, 445-465.

Nopo, H(2008) Matching as a Tool to Decompose Wage Gaps, The review ofEconomics and Statistics, May 2008, vol. 90, No. 2, Pages 290 � 299.

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