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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
Gender wage gap: methods and differences
Table of contents
1 Introduction
2 Available Methods
3 Data
4 Results
5 Conclusions
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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 practitioner
How: Compare the gender wage gap in different methods (7)and specifications (14)Data: Polish LFS 2012
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
Introduction
What is the gap
Types of gap
Raw gap
Adjusted gap
Assumptions
Uncounfoundedness
Common Support
Gender wage gap: methods and differences
Introduction
What is the gap
Types of gap
Raw gap
Adjusted gap
Assumptions
Uncounfoundedness
Common Support
Gender wage gap: methods and differences
Available Methods
Gender wage gap: methods and differences
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)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
Juhn, Murphy and Pierce (1993)
DiNardo, Fortin and Lemieux(1996)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
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)
Juhn, Murphy and Pierce (1993)
DiNardo, Fortin and Lemieux(1996)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
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)
Juhn, Murphy and Pierce (1993)
DiNardo, Fortin and Lemieux(1996)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
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)
Juhn, Murphy and Pierce (1993)
DiNardo, Fortin and Lemieux(1996)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
Juhn, Murphy and Pierce (1993)
OLS based approach with a solution for the quantiles
Based on very strong assumtpions
DiNardo, Fortin and Lemieux(1996)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Oaxaca (1973) and Blinder (1973)
Juhn, Murphy and Pierce (1993)
DiNardo, Fortin and Lemieux(1996)
Distributional approach
Reweights the entire distribution of wages, which requires onlyone logit (or probit) model
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Firpo, Fortin and Lemieux(2009)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Quantile regression approach
Computationally intensive
Nopo(2008)
Firpo, Fortin and Lemieux(2009)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Non-parametric decomposition
∆ = ∆0 + ∆F + ∆M + ∆F
Firpo, Fortin and Lemieux(2009)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Non-parametric decomposition
∆ = ∆0 + ∆F + ∆M + ∆F
Firpo, Fortin and Lemieux(2009)
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Firpo, Fortin and Lemieux(2009)
Based on the Recentered Influence Functions (RIF)
Flexible approach that can be combined with other methods,such as OB and the DFL
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Firpo, Fortin and Lemieux(2009)
Based on the Recentered Influence Functions (RIF)
Flexible approach that can be combined with other methods,such as OB and the DFL
Gender wage gap: methods and differences
Available Methods
Presentation of the methods
Machado Mata(2005)
Nopo(2008)
Firpo, Fortin and Lemieux(2009)
Based on the Recentered Influence Functions (RIF)
Flexible approach that can be combined with other methods,such as OB and the DFL
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
Available Methods
How does the gap relate to...
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
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
Gender wage gap: methods and differences
Available Methods
How does the gap relate to...
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 women experiencemore selection than male
The addition of new variables: increases the value of thegap when 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
Gender wage gap: methods and differences
Available Methods
How does the gap relate to...
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 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 arelarger for some groups (i.e. the better educated)
The common support: increases when the non-matchedwomen are better endowed than men
Gender wage gap: methods and differences
Available Methods
How does the gap relate to...
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 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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
Data
Meet the sample: Beyond the mean
Total wages
Hourly wages
Gender wage gap: methods and differences
Data
Meet the sample: Beyond the mean
Total wages Hourly wages
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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 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 at the median
Gender wage gap: methods and differences
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!
Gender wage gap: methods and differences
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!
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
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
Gender wage gap: methods and differences
Conclusions
Questions or suggestions?
Gracias por su atencion
Gender wage gap: methods and differences
Conclusions
Questions or suggestions?
Gracias por su atencion
Gender wage gap: methods and differences
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