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Explaining the gender wage gap: Estimates from a dynamic model of job changes and hours changes

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  • Kai Liu

Abstract

I address the causes of the gender wage gap with a new dynamic model of wage, hours, and job changes that permits me to decompose the gap into a portion due to gender differences in preferences for hours of work and in constraints. The dynamic model allows the differences in constraints to reflect possible gender differences in job arrival rates, job destruction rates, the mean and variance of the wage offer distribution, and the wage cost of part‐time work. The model is estimated using the 1996 panel of the Survey of Income and Program Participation. I find that the preference for part‐time work increases with marriage and number of children among women but not among men. These demographic factors explain a sizable fraction of the gender gap in employment, but they explain no more than 6 percent of the gender wage gap. Differences in constraints, mainly in the form of the mean offered wages and rates of job arrival and destruction, explain most of the gender wage gap. Policy simulation results suggest that, relative to reducing the wage cost of part‐time work, providing additional employment protection to part‐time jobs is more effective in reducing the gender wage gap.

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  • Kai Liu, 2016. "Explaining the gender wage gap: Estimates from a dynamic model of job changes and hours changes," Quantitative Economics, Econometric Society, vol. 7(2), pages 411-447, July.
    • Handle: RePEc:wly:quante:v:7:y:2016:i:2:p:411-447
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    Cited by:

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    3. Francesconi, Marco & Parey, Matthias, 2018. "Early gender gaps among university graduates," European Economic Review, Elsevier, vol. 109(C), pages 63-82.
    4. Kim, Jinyoung & Kim, Seonghoon & Koh, Kanghyock, 2022. "Labor market institutions and the incidence of payroll taxation," Journal of Public Economics, Elsevier, vol. 209(C).
    5. Ilaria D'Angelis, 2023. "The Search for Parental Leave and the Early-Career Gender Wage Gap," Working Papers 2023-01, University of Massachusetts Boston, Economics Department.
    6. Henriques, C.O. & Luque, M. & Marcenaro-Gutierrez, O.D. & Lopez-Agudo, L.A., 2019. "A multiobjective interval programming model to explore the trade-offs among different aspects of job satisfaction under different scenarios," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 35-46.
    7. Alexander Sohn, 2015. "Beyond Conventional Wage Discrimination Analysis: Assessing Comprehensive Wage Distributions of Males and Females Using Structured Additive Distributional Regression," SOEPpapers on Multidisciplinary Panel Data Research 802, DIW Berlin, The German Socio-Economic Panel (SOEP).

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    More about this item

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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    1. Explaining the gender wage gap: Estimates from a dynamic model of job changes and hours changes (Quantitative Economics 2016) in ReplicationWiki

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