On the Ranking Uncertainty of Labor Market Wage Gaps
AbstractThis paper uses multiple comparison methods to perform inference on labor market wage gap estimates from a regression model of wage determination. The regression decomposes a sample of workers' wages into a human capital component and a gender specific component; the gender component is called the gender differential or wage gap and is sometimes interpreted as a measure of sexual discrimination. Using data on fourteen industry classifications (e.g. retail sales, agriculture), a new relative estimator of the wage gap is calculated for each industry. The industries are then ranked based on the magnitude of these estimators, and inference experiments are performed using "multiple comparisons with the best" and "multiple comparisons with a control". The inference indicates that differences in gender discrimination across industry classifications is statistically insignificant at the 95% confidence level and that previous studies which have failed to perform inference on gender wage gap order statistics may be misleading.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0206003.
Length: 17 pages
Date of creation: 19 Jun 2002
Date of revision:
Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 17; figures: included. Multiple comparison inference techniques applied to labor amrket wage gap estimation
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Labor economics; discrimination; wage differentials; multiple comparisons with the best;
Other versions of this item:
- William C. Horrace, 2005. "On the ranking uncertainty of labor market wage gaps," Journal of Population Economics, Springer, vol. 18(1), pages 181-187, 09.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
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- Lin, Eric S., 2010. "Gender wage gaps by college major in Taiwan: Empirical evidence from the 1997-2003 Manpower Utilization Survey," Economics of Education Review, Elsevier, vol. 29(1), pages 156-164, February.
- William C. Horrace & Beyza P. Ural & Jin Hwa Jung, 2006.
"Inter-Industry Gender Wage Gaps by Knowledge Intensity: Discrimination and Technology in Korea,"
Center for Policy Research Working Papers
79, Center for Policy Research, Maxwell School, Syracuse University.
- Beyza Ural & William Horrace & Jin Hwa Jung, 2009. "Inter-industry gender wage gaps by knowledge intensity: discrimination and technology in Korea," Applied Economics, Taylor & Francis Journals, vol. 41(11), pages 1437-1452.
- William C. Horrace & Joseph T. Marchand & Timothy M. Smeeding, 2005.
"Ranking Inequality: Applications of Multivariate Subset Selection,"
Center for Policy Research Working Papers
70, Center for Policy Research, Maxwell School, Syracuse University.
- William Horrace & Joseph Marchand & Timothy Smeeding, 2008. "Ranking inequality: Applications of multivariate subset selection," Journal of Economic Inequality, Springer, vol. 6(1), pages 5-32, March.
- William C. Horrace & Joseph T. Marchand & Timothy M. Smeeding, 2006. "Ranking Inequality: Applications of Multivariate Subset Selection," Working Papers 21, ECINEQ, Society for the Study of Economic Inequality.
- William C. Horrace, 2002. "Selection Procedures for Order Statistics in Empirical Economic Studies," Econometrics 0206005, EconWPA.
- Yun, Myeong-Su, 2006. "Revisiting Inter-Industry Wage Differentials and the Gender Wage Gap: An Identification Problem," IZA Discussion Papers 2427, Institute for the Study of Labor (IZA).
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