gender wage gap in poland - lucas van der velde

Gender wage gap: methods and differences

La brecha salarial de genero en PoloniaUn analisis comparativo de los metodos disponibles

(trabajo in colaboracion con Karolina Goraus y Joanna Tyrowicz)

Lucas Augusto van der VeldeCandidato Doctoral

Asistente de investigacion en GRAPE

Facultad de Ciencias EconomicasUniversidad de Varsovia

November 5, 2013


Table of contents

1 Introduction

2 Available Methods

3 Data

4 Results

5 Conclusions


Introduction

Introduction

Motivation

Proliferation of methods and lack of comparabilityW&W metanalysis showed that the selection of the methodhas consequences for the gap

Our work

Goal: Provide a guide for the practitionerHow: Compare the gender wage gap in different methods (7)and specifications (14)Data: Polish LFS 2012


Introduction

Introduction

Motivation


Our work

Goal: Provide a guide for the practitioner

How: Compare the gender wage gap in different methods (7)and specifications (14)Data: Polish LFS 2012


Introduction

Introduction

Motivation


Our work

Goal: Provide a guide for the practitionerHow: Compare the gender wage gap in different methods (7)and specifications (14)

Data: Polish LFS 2012


Introduction

Introduction

Motivation


Our work

Goal: Provide a guide for the practitionerHow: Compare the gender wage gap in different methods (7)and specifications (14)Data: Polish LFS 2012


Introduction

What is the gap

Types of gap

Raw gap

Adjusted gap

Assumptions

Uncounfoundedness

Common Support


Available Methods


Available Methods

Methods under analysis

Linear Regressions

Oaxaca-Blinder decomposition

Juhn, Murphy and Pierce

DiNardo, Fortin and Lemieux

Machado Mata

Nopo

Firpo, Fortin and Lemieux (RIF)


Available Methods

Presentation of the methods

Oaxaca (1973) and Blinder (1973)

Juhn, Murphy and Pierce (1993)

DiNardo, Fortin and Lemieux(1996)


Available Methods



OLS based, estimates at the mean

YM − Y F = βM XM − βF X F

YM −Y F = β∗(XM − X F )+(β∗− βF )(X F )+(β∗− βM)(XM)




Available Methods




OLS based approach with a solution for the quantiles

Based on very strong assumtpions



Available Methods





Distributional approach

Reweights the entire distribution of wages, which requires onlyone logit (or probit) model


Available Methods


Machado Mata(2005)

Nopo(2008)

Firpo, Fortin and Lemieux(2009)


Available Methods


Machado Mata(2005)

Quantile regression approach

Computationally intensive

Nopo(2008)



Available Methods


Machado Mata(2005)

Nopo(2008)

Non-parametric decomposition

∆ = ∆0 + ∆F + ∆M + ∆F



Available Methods


Machado Mata(2005)

Nopo(2008)


Based on the Recentered Influence Functions (RIF)

Flexible approach that can be combined with other methods,such as OB and the DFL


Available Methods

Summary

OB JMP DFL MM Nopo RIFSelection Bias OK OK OK OK

Dimensionality Curse OK OK OK OK OK

Detailed decomposition OK OK OK

Distributional analysis OK OK OK OK

Common Support OK

Functional Form OK OK

Compare across time OK OK OK


Available Methods

How does the gap relate to...

The reference wages: The wage gap is larger whenexpressed as a percentage of the wage of the unfavouredgroup

The selection bias: the gap increases if the women experiencemore selection than male

The addition of new variables: increases the value of the gapwhen the differences within are larger than between

The different quantiles: varies if the differences are larger forsome groups (i.e. the better educated)

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


Available Methods


The reference wages: The wage gap is larger when expressedas a percentage of the wage of the unfavoured group

The selection bias: the gap increases if the womenexperience more selection than male





Available Methods




The addition of new variables: increases the value of thegap when the differences within are larger than between




Available Methods





The different quantiles: varies if the differences arelarger for some groups (i.e. the better educated)



Available Methods








Data

Sample: Polish LFS 2012

Male Female M-F Impact C-supportHourly wage 11,91 11 0,91 0,12Age (years) 40,64 41,29 -0,65 Inv. U 0,04Experience (years) 19,15 17,89 1,26 Inv. U 0,07Secondary education(%) 0,75 0,62 0,13 + 0,2Tertiary Education(%) 0,16 0,33 -0,17 + 0,28Married (%) 0,69 0,68 0,01 + 0,02Kids less than 5 (%) 0,21 0,17 0,04 + 0,08Rural (%) 0,44 0,36 0,08 - 0,11Cities (%) 0,29 0,35 -0,06 + 0,08Mazowieckie (%) 0,1 0,11 -0,01 + 0,01Obs 18534 17479


Data

Meet the sample: Beyond the mean

Total wages

Hourly wages


Data

Meet the sample: Beyond the mean

Total wages Hourly wages


Results

Different specifications

Basic: Age, Experience, Education, Married, kids, rural,cities, Mazowieckie

Industry: ”Basic” + industry dummies (Agriculture,Manufacture, Construction, services)

Industry plus: ”Industry” + Firm size & Ownership type

Occupations: ”Basic” + 9 occupational dummies (ISCO-1codes)

Tenure: ”Basic” + tenure

Education: ”Basic” + 9 educational fields dummies

All


Results

Size of the gap

For the whole sample

Heckman OB. JMP* MM* RIF* Nopo Obs

Raw 0,09 0,13 0,13 0,08 33928

Basic 0,16 0,17 0,18 0,16 0,14 33928

Indus 0,17 0,16 0,18 0,14 0,13 33574

Educ 0,19 0,22 0,20 0,15 0,17 33928

All 0,16 0,18 0,18 0,14 33567

* Results at the median


Results

Size of the gap

Inside the common support

(In red when larger than in the total sample)

Heckman OB. JMP* MM * RIF* Nopo No of obs

Raw 0,09 0,13 0,13 0,09 0,08 33928Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Size of the gap



Heckman OB. JMP* MM * RIF* Nopo No of obsRaw 0,09 0,13 0,13 0,09 0,08 33928

Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Size of the gap



Heckman OB. JMP* MM * RIF* Nopo No of obsRaw 0,09 0,13 0,13 0,09 0,08 33928Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223

Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Size of the gap



Heckman OB. JMP* MM * RIF* Nopo No of obsRaw 0,09 0,13 0,13 0,09 0,08 33928Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202

Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Size of the gap



Heckman OB. JMP* MM * RIF* Nopo No of obsRaw 0,09 0,13 0,13 0,09 0,08 33928Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237

All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Size of the gap



Heckman OB. JMP* MM * RIF* Nopo No of obsRaw 0,09 0,13 0,13 0,09 0,08 33928Basic 0,16 0,17 0,19 0,16 0,15 0,17 34223Indus 0,18 0,19 0,20 0,17 0,18 0,17 29202Educ 0,19 0,22 0,19 0,16 0,20 0,18 32237All 0,16 0,17 0,19 0,18 0,20 0,16 3056



Results

Comparison of the methods

Heckman OB. JMP MM RIF Nopo

Total Sample

Mean 0,17 0,18 0,18 0,15 0,13Range/Mean 0,20 0,36 0,17 0,26 0,69

Common Support

Mean 0,17 0,19 0,19 0,17 0,17 0,17Range/Mean 0,19 0,27 0,11 0,12 0,66 0,13

Estimations on the common support are larger andexperience smaller dispersion!


Conclusions

Conclusions

1 The results indicated that the adjusted gap is 20% of femalegap - two times the size of the raw gap.

2 The results were consistent across methods and specifications

The calculation of the bias in the common support producedslighlty higher results with a smaller dispersion.The OLS produced slightly lower resultsNopo estimations are the less sensitive to the changes ofspecification.The quantile regressions showed larger variation between them.More sensitive to the common support


Conclusions

Questions or suggestions?

Gracias por su atencion


Conclusions

References

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

DiNardo, J. , N. Fortn, 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., Fortn, N., and Lemieux, T. 2009 Unconditional Quantile regressions,Econometrica, Vol. 77, No. 3, 953-973

Fortn, N., T. Lemieux and S. Firpo, 2010 Decomposition methods in EconomicsNBER Working paper 16045

Juhn, C., K. M. Murphy, and B. Pierce (1993): ”Wage Inequality and the Risein Returns 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.

Weichselbaumer, D. and R. Winter-Ebmer (2003) ”A Meta-Analysis of theInternational Gender Wage Gap,” IZA Discussion Papers 906

gender wage gap in poland - lucas van der velde

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