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Expected Work Experience: A New Human Capital Measure

Author

Listed:
  • Joseph E. Zveglich, Jr
  • Yana vander Meulen Rodger
  • Editha A. Lavina

Abstract

This paper constructs a better proxy: expected work experience—the sum of the annual probabilities that an individual worked in the past. This measure can be generated using commonly available data on labor force participation rates by age and gender to gauge the probability of past work. Applying the measure to labor force survey data from the Philippines shows that conventional proxies underestimate the contribution of gender differences in work experience in explaining the gender wage gap.

Suggested Citation

  • Joseph E. Zveglich, Jr & Yana vander Meulen Rodger & Editha A. Lavina, 2019. "Expected Work Experience: A New Human Capital Measure," Working Papers id:12982, eSocialSciences.
    • Handle: RePEc:ess:wpaper:id:12982
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    References listed on IDEAS

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    1. Jacob Mincer & Solomon Polachek, 1974. "Family Investments in Human Capital: Earnings of Women," NBER Chapters, in: Marriage, Family, Human Capital, and Fertility, pages 76-110, National Bureau of Economic Research, Inc.
    2. Tracy Regan & Ronald Oaxaca, 2009. "Work experience as a source of specification error in earnings models: implications for gender wage decompositions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 22(2), pages 463-499, April.
    3. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
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    5. S. Rummery, 1989. "The Contribution of Intermittent Labour Force Participation to the Gender Wage Differential," Economics Discussion / Working Papers 89-02, The University of Western Australia, Department of Economics.
    6. Wright, Robert E & Ermisch, John F, 1991. "Gender Discrimination in the British Labour Market: A Reassessment," Economic Journal, Royal Economic Society, vol. 101(406), pages 508-522, May.
    7. Waldfogel, Jane, 1995. "The Price of Motherhood: Family Status and Women's Pay in a Young British Cohort," Oxford Economic Papers, Oxford University Press, vol. 47(4), pages 584-610, October.
    8. Colm Harmon & Ian Walker & Niels Westergaard-Nielsen (ed.), 2001. "Education and Earnings in Europe," Books, Edward Elgar Publishing, number 2237.
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    Citations

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    Cited by:

    1. Kenneth A. Couch & Robert W. Fairlie & Huanan Xu, 2022. "The evolving impacts of the COVID‐19 pandemic on gender inequality in the US labor market: The COVID motherhood penalty," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 485-507, April.
    2. Maria Esther Oswald-Egg & Ursula Renold, 2019. "No Experience, No Employment: The Effect of Vocational Education and Training Work Experience on Labour Market Outcomes after Higher Education," KOF Working papers 19-469, KOF Swiss Economic Institute, ETH Zurich.
    3. Bonaccolto-Töpfer, Marina & Castagnetti, Carolina & Prümer, Stephanie, 2022. "Understanding the public-private sector wage gap in Germany: New evidence from a Fixed Effects quantile Approach∗," Economic Modelling, Elsevier, vol. 116(C).
    4. Oswald-Egg, Maria Esther & Renold, Ursula, 2021. "No experience, no employment: The effect of vocational education and training work experience on labour market outcomes after higher education," Economics of Education Review, Elsevier, vol. 80(C).
    5. Eunice S. Han, 2023. "The effect of changes in public sector bargaining laws on teacher union membership," British Journal of Industrial Relations, London School of Economics, vol. 61(1), pages 133-158, March.

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    More about this item

    Keywords

    eSS; gender wage gap; labor force participation; Philippines; potential experience; wage regressions; work experience; expected work experience; labour force participation; past work; labour force survey data; gender differences.;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East

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