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The Reverse Gender Wage Gap in Bangladesh: Demystifying the Counterintuitive

Author

Listed:
  • Mustafizur Rahman

    (Centre for Policy Dialogue (CPD))

  • Md. Al-Hasan

    (International Food Policy Research Institute)

Abstract

There is a gender wage gap (men earning more than women) in labour market of most countries, to varying degrees, is widely recognised in relevant literature. In this backdrop, the findings of the ILO study (ILO 2018), based on an analysis of cross-country data, showing Bangladesh as an outlier in this regard, calls for an explanation and indepth investigation. Based on Bangladesh labour force data, the ILO report found factor-weighted hourly wage in Bangladesh to be 5.0% more for women compared to that of men. This counter-intuitive presence of reverse gender wage gap is a departure from results derived for all other countries in the sample of 60 countries. The study examines why the ILO results are what they are, what are the reasons that have informed the ILO findings and whether a different (improved) methodology would give other results. The study undertakes a re-estimation of gender wage gap in Bangladesh by changing the specifications in the ILO study, by deploying human capital, wage discrimination and segmented labour market theories and quantile regression. The study finds that under full specification men are found to earn more than women in the Bangladesh labour market, albeit not to a significant extent—the wage gap was found to be in the range of between 2.0 and 4.0%. While the study does not find strong embedded discrimination against women, it does find sectoral segmentation, and presence of glass ceiling in the Bangladesh labour market.

Suggested Citation

  • Mustafizur Rahman & Md. Al-Hasan, 2022. "The Reverse Gender Wage Gap in Bangladesh: Demystifying the Counterintuitive," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 65(4), pages 929-950, December.
    • Handle: RePEc:spr:ijlaec:v:65:y:2022:i:4:d:10.1007_s41027-022-00402-9
    DOI: 10.1007/s41027-022-00402-9
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    1. Tushar Agrawal, 2013. "Are There Glass-Ceiling and Sticky-Floor Effects in India? An Empirical Examination," Oxford Development Studies, Taylor & Francis Journals, vol. 41(3), pages 322-342, September.
    2. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    4. Mehrotra, Santosh & Parida, Jajati K., 2017. "Why is the Labour Force Participation of Women Declining in India?," World Development, Elsevier, vol. 98(C), pages 360-380.
    5. 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.
    6. Heckman, James J & Honore, Bo E, 1990. "The Empirical Content of the Roy Model," Econometrica, Econometric Society, vol. 58(5), pages 1121-1149, September.
    7. Sanghamitra Kanjilal-Bhaduri & Francesco Pastore, 2018. "Returns to Education and Female Participation Nexus: Evidence from India," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 61(3), pages 515-536, September.
    8. Salma Ahmed & Pushkar Maitra, 2015. "A Distributional Analysis of the Gender Wage Gap in Bangladesh," Journal of Development Studies, Taylor & Francis Journals, vol. 51(11), pages 1444-1458, November.
    9. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    10. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    11. repec:ilo:ilowps:483489 is not listed on IDEAS
    12. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    13. Ronald L. Oaxaca & Michael R. Ransom, 1999. "Identification in Detailed Wage Decompositions," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 154-157, February.
    14. Kapsos, Steven., 2008. "The gender wage gap in Bangladesh," ILO Working Papers 994134173402676, International Labour Organization.
    15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    16. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    17. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    18. repec:ilo:ilowps:413417 is not listed on IDEAS
    19. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-442, June.
    20. Malathy Duraisamy & P. Duraisamy, 2016. "Gender wage gap across the wage distribution in different segments of the Indian labour market, 1983–2012: exploring the glass ceiling or sticky floor phenomenon," Applied Economics, Taylor & Francis Journals, vol. 48(43), pages 4098-4111, September.
    21. Vimolwan Yukongdi & John Benson, 2005. "Women in Asian Management: Cracking the Glass Ceiling?," Asia Pacific Business Review, Taylor & Francis Journals, vol. 11(2), pages 139-148, June.
    22. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    23. Ahmed, Salma & McGillivray, Mark, 2015. "Human Capital, Discrimination, and the Gender Wage Gap in Bangladesh," World Development, Elsevier, vol. 67(C), pages 506-524.
    24. Rahman, Rushidan I. & Islam, Rizwanul., 2013. "Female labour force participation in Bangladesh : trends, drivers and barriers," ILO Working Papers 994834893402676, International Labour Organization.
    25. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    26. Oaxaca, Ronald L. & Ransom, Michael R., 1994. "On discrimination and the decomposition of wage differentials," Journal of Econometrics, Elsevier, vol. 61(1), pages 5-21, March.
    27. Mustafizur Rahman & Md. Al-Hasan, 2019. "Male–Female Wage Gap and Informal Employment in Bangladesh: A Quantile Regression Approach," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 20(1), pages 106-123, March.
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    More about this item

    Keywords

    Labour market; Labour force participation; Wage distribution; Quantile regression; Gender wage gap;
    All these keywords.

    JEL classification:

    • J4 - Labor and Demographic Economics - - Particular Labor Markets
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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