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Gender Wage Gap in Poland - Lucas van der Velde

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  • 1. Gender wage gap: methods and differences La brecha salarial de genero en Polonia Un analisis comparativo de los metodos disponibles (trabajo in colaboracion con Karolina Goraus y Joanna Tyrowicz) Lucas Augusto van der Velde Candidato Doctoral Asistente de investigacion en GRAPE Facultad de Ciencias Economicas Universidad de Varsovia November 5, 2013
  • 2. Gender wage gap: methods and differences Table of contents 1 Introduction 2 Available Methods 3 Data 4 Results 5 Conclusions
  • 3. Gender wage gap: methods and differences Introduction Introduction Motivation Proliferation of methods and lack of comparability W&W metanalysis showed that the selection of the method has consequences for the gap
  • 4. Gender wage gap: methods and differences Introduction Introduction Motivation Proliferation of methods and lack of comparability W&W metanalysis showed that the selection of the method has consequences for the gap Our work
  • 5. Gender wage gap: methods and differences Introduction Introduction Motivation Proliferation of methods and lack of comparability W&W metanalysis showed that the selection of the method has consequences for the gap Our work Goal: Provide a guide for the practitioner
  • 6. Gender wage gap: methods and differences Introduction Introduction Motivation Proliferation of methods and lack of comparability W&W metanalysis showed that the selection of the method has 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)
  • 7. Gender wage gap: methods and differences Introduction Introduction Motivation Proliferation of methods and lack of comparability W&W metanalysis showed that the selection of the method has 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
  • 8. Gender wage gap: methods and differences Introduction What is the gap Types of gap Raw gap Adjusted gap
  • 9. Gender wage gap: methods and differences Introduction What is the gap Types of gap Raw gap Adjusted gap Assumptions Uncounfoundedness Common Support
  • 10. Gender wage gap: methods and differences Available Methods
  • 11. 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)
  • 12. 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)
  • 13. Gender wage gap: methods and differences Available Methods Presentation of the methods Oaxaca (1973) and Blinder (1973) OLS based, estimates at the mean Juhn, Murphy and Pierce (1993) DiNardo, Fortin and Lemieux(1996)
  • 14. Gender wage gap: methods and differences Available Methods Presentation of the methods Oaxaca (1973) and Blinder (1973) OLS based, estimates at the mean ¯ ¯ ˆ ¯ ˆ ¯ Y M − Y F = βM X M − βF X F Juhn, Murphy and Pierce (1993) DiNardo, Fortin and Lemieux(1996)
  • 15. Gender wage gap: methods and differences Available Methods Presentation of the methods Oaxaca (1973) and Blinder (1973) OLS based, estimates at the mean ˆ ¯ ¯ ˆ ˆ ¯ ˆ ˆ ¯ Y M − Y F = β ∗ (X M − X F ) + (β ∗ − β F )(X F ) + (β ∗ − β M )(X M ) Juhn, Murphy and Pierce (1993) DiNardo, Fortin and Lemieux(1996)
  • 16. 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)
  • 17. 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 only one logit (or probit) model
  • 18. Gender wage gap: methods and differences Available Methods Presentation of the methods Machado Mata(2005) Nopo(2008) Firpo, Fortin and Lemieux(2009)
  • 19. 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)
  • 20. Gender wage gap: methods and differences Available Methods Presentation of the methods Machado Mata(2005) Nopo(2008) Non-parametric decomposition Firpo, Fortin and Lemieux(2009)
  • 21. 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)
  • 22. Gender wage gap: methods and differences Available Methods Presentation of the methods Machado Mata(2005) Nopo(2008) Firpo, Fortin and Lemieux(2009)
  • 23. 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)
  • 24. 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
  • 25. Gender wage gap: methods and differences Available Methods Summary Selection Bias OB OK JMP OK DFL MM OK Nopo OK RIF
  • 26. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse OB OK OK JMP OK OK DFL OK MM OK OK Nopo OK RIF OK
  • 27. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse Detailed decomposition OB OK OK OK JMP OK OK OK DFL OK MM OK OK Nopo OK RIF OK OK
  • 28. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse Detailed decomposition Distributional analysis OB OK OK OK JMP OK OK OK OK DFL OK MM OK OK OK OK Nopo OK RIF OK OK OK
  • 29. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse Detailed decomposition Distributional analysis Common Support OB OK OK OK JMP OK OK OK OK DFL OK MM OK OK OK Nopo OK OK RIF OK OK OK OK
  • 30. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse Detailed decomposition Distributional analysis Common Support Functional Form OB OK OK OK JMP OK OK OK OK DFL OK MM OK OK OK OK OK Nopo OK RIF OK OK OK OK OK
  • 31. Gender wage gap: methods and differences Available Methods Summary Selection Bias Dimensionality Curse Detailed decomposition Distributional analysis Common Support Functional Form Compare across time OB OK OK OK JMP OK OK OK OK OK DFL OK MM OK OK OK OK OK OK Nopo OK RIF OK OK OK OK OK OK
  • 32. Gender wage gap: methods and differences Available Methods How does the gap relate to... The reference wages: The wage gap is larger when expressed as a percentage of the wage of the unfavoured group
  • 33. Gender wage gap: methods and differences Available Methods How does the gap relate to... The selection bias: the gap increases if the women experience more selection than male
  • 34. Gender wage gap: methods and differences Available Methods How does the gap relate to... The addition of new variables: increases the value of the gap when the differences within are larger than between
  • 35. Gender wage gap: methods and differences Available Methods How does the gap relate to... The different quantiles: varies if the differences are larger for some groups (i.e. the better educated)
  • 36. Gender wage gap: methods and differences Available Methods How does the gap relate to... The common support: increases when the non-matched women are better endowed than men
  • 37. Gender wage gap: methods and differences Data Sample: Polish LFS 2012 Hourly wage Age (years) Experience (years) Secondary education(%) Tertiary Education(%) Married (%) Kids less than 5 (%) Rural (%) Cities (%) Mazowieckie (%) Obs Male 11,91 40,64 19,15 0,75 0,16 0,69 0,21 0,44 0,29 0,1 18534 Female 11 41,29 17,89 0,62 0,33 0,68 0,17 0,36 0,35 0,11 17479 M-F 0,91 -0,65 1,26 0,13 -0,17 0,01 0,04 0,08 -0,06 -0,01 Impact Inv. U Inv. U + + + + + + C-support 0,12 0,04 0,07 0,2 0,28 0,02 0,08 0,11 0,08 0,01
  • 38. Gender wage gap: methods and differences Data Meet the sample: Beyond the mean Total wages
  • 39. Gender wage gap: methods and differences Data Meet the sample: Beyond the mean Total wages Hourly wages
  • 40. 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-1 codes) Tenure: ”Basic” + tenure Education: ”Basic” + 9 educational fields dummies All
  • 41. Gender wage gap: methods and differences Results Size of the gap For the whole sample Heckman Raw Basic 0,16 OB. 0,09 0,17 JMP* 0,13 0,18 MM* 0,13 0,16 RIF* 0,08 0,14 Nopo Obs 33928 33928
  • 42. Gender wage gap: methods and differences Results Size of the gap For the whole sample Heckman Raw Basic Indus 0,16 0,17 OB. 0,09 0,17 0,16 JMP* 0,13 0,18 0,18 MM* 0,13 0,16 0,14 RIF* 0,08 0,14 0,13 Nopo Obs 33928 33928 33574
  • 43. Gender wage gap: methods and differences Results Size of the gap For the whole sample Heckman Raw Basic Indus Educ 0,16 0,17 0,19 OB. 0,09 0,17 0,16 0,22 JMP* 0,13 0,18 0,18 0,20 MM* 0,13 0,16 0,14 0,15 RIF* 0,08 0,14 0,13 0,17 Nopo Obs 33928 33928 33574 33928
  • 44. Gender wage gap: methods and differences Results Size of the gap For the whole sample Heckman Raw Basic Indus Educ All 0,16 0,17 0,19 0,16 OB. 0,09 0,17 0,16 0,22 0,18 JMP* 0,13 0,18 0,18 0,20 0,18 MM* 0,13 0,16 0,14 0,15 * Results at the median RIF* 0,08 0,14 0,13 0,17 0,14 Nopo Obs 33928 33928 33574 33928 33567
  • 45. 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
  • 46. 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 Raw OB. 0,09 JMP* 0,13 MM * 0,13 RIF* 0,09 Nopo 0,08 No of obs 33928
  • 47. 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 Raw Basic 0,16 OB. 0,09 0,17 JMP* 0,13 0,19 MM * 0,13 0,16 RIF* 0,09 0,15 Nopo 0,08 0,17 No of obs 33928 34223
  • 48. 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 Raw Basic Indus 0,16 0,18 OB. 0,09 0,17 0,19 JMP* 0,13 0,19 0,20 MM * 0,13 0,16 0,17 RIF* 0,09 0,15 0,18 Nopo 0,08 0,17 0,17 No of obs 33928 34223 29202
  • 49. 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 Raw Basic Indus Educ 0,16 0,18 0,19 OB. 0,09 0,17 0,19 0,22 JMP* 0,13 0,19 0,20 0,19 MM * 0,13 0,16 0,17 0,16 RIF* 0,09 0,15 0,18 0,20 Nopo 0,08 0,17 0,17 0,18 No of obs 33928 34223 29202 32237
  • 50. 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 Raw Basic Indus Educ All 0,16 0,18 0,19 0,16 OB. 0,09 0,17 0,19 0,22 0,17 JMP* 0,13 0,19 0,20 0,19 0,19 MM * 0,13 0,16 0,17 0,16 0,18 RIF* 0,09 0,15 0,18 0,20 0,20 Nopo 0,08 0,17 0,17 0,18 0,16 No of obs 33928 34223 29202 32237 3056
  • 51. 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 Raw Basic Indus Educ All 0,16 0,18 0,19 0,16 OB. 0,09 0,17 0,19 0,22 0,17 JMP* 0,13 0,19 0,20 0,19 0,19 MM * 0,13 0,16 0,17 0,16 0,18 RIF* 0,09 0,15 0,18 0,20 0,20 * Results at the median Nopo 0,08 0,17 0,17 0,18 0,16 No of obs 33928 34223 29202 32237 3056
  • 52. Gender wage gap: methods and differences Results Comparison of the methods Mean Range/Mean Mean Range/Mean Heckman OB. JMP Total Sample 0,17 0,18 0,18 0,20 0,36 0,17 Common Support 0,17 0,19 0,19 0,19 0,27 0,11 MM RIF 0,15 0,26 0,13 0,69 0,17 0,12 0,17 0,66 Nopo 0,17 0,13
  • 53. Gender wage gap: methods and differences Results Comparison of the methods Mean Range/Mean Mean Range/Mean Heckman OB. JMP Total Sample 0,17 0,18 0,18 0,20 0,36 0,17 Common Support 0,17 0,19 0,19 0,19 0,27 0,11 MM RIF 0,15 0,26 0,13 0,69 0,17 0,12 0,17 0,66 Nopo 0,17 0,13 Estimations on the common support are larger and experience smaller dispersion!
  • 54. Gender wage gap: methods and differences Conclusions Conclusions 1 The results indicated that the adjusted gap is 20% of female gap - two times the size of the raw gap. 2 The results were consistent across methods and specifications
  • 55. Gender wage gap: methods and differences Conclusions Conclusions 1 The results indicated that the adjusted gap is 20% of female gap - 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 produced slighlty higher results with a smaller dispersion. The OLS produced slightly lower results Nopo estimations are the less sensitive to the changes of specification. The quantile regressions showed larger variation between them. More sensitive to the common support
  • 56. Gender wage gap: methods and differences Conclusions Questions or suggestions?
  • 57. Gender wage gap: methods and differences Conclusions Questions or suggestions? Gracias por su atencion
  • 58. Gender wage gap: methods and differences Conclusions References Blinder, A. (1973): ”Wage Discrimination: Reduced Form and Structural Estimates”, Journal of Human Resources, 8, 436-455. DiNardo, J. , N. Fortn, and T. Lemieux, 1996 Labor market institutions and the distribution 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 Economics NBER Working paper 16045 Juhn, C., K. M. Murphy, and B. Pierce (1993): ”Wage Inequality and the Rise in Returns to Skill”, Journal of Political Economy”, 101, 410-442. Machado, J. A. F., and J. Mata (2005): ”Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression”, Journal of Applied Econometrics, 20, 445-465. Nopo, H(2008) Matching as a Tool to Decompose Wage Gaps, The review of Economics and Statistics, May 2008, vol. 90, No. 2, Pages 290 299. Weichselbaumer, D. and R. Winter-Ebmer (2003) ”A Meta-Analysis of the International Gender Wage Gap,” IZA Discussion Papers 906