IDEAS home Printed from https://ideas.repec.org/a/sae/soueco/v20y2019i1p106-123.html
   My bibliography  Save this article

Male–Female Wage Gap and Informal Employment in Bangladesh: A Quantile Regression Approach

Author

Listed:
  • Mustafizur Rahman
  • Md. Al-Hasan

Abstract

This article undertakes an examination of Bangladesh’s latest available Quarterly Labour Force Survey 2015–2016 data to draw in-depth insights on gender wage gap and wage discrimination in Bangladesh labour market. The mean wage decomposition shows that on average a woman in Bangladesh earns 12.2 per cent lower wage than a man, and about half of the wage gap can be explained by labour market discrimination against women. Quantile counterfactual decomposition shows that women are subject to higher wage penalty at the lower deciles of the wage distribution with the wage gap varying between 8.3 per cent and 19.4 per cent at different deciles. We have found that at lower deciles, a significant part of the gender wage gap is on account of the relatively larger presence of informal employment. Conditional quantile estimates further reveal that formally employed female workers earn higher wage than their male counterparts at the first decile but suffer from wage penalty at the top deciles. JEL: C21, J31, J46, J70

Suggested Citation

  • 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.
    • Handle: RePEc:sae:soueco:v:20:y:2019:i:1:p:106-123
    DOI: 10.1177/1391561418824477
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1391561418824477
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1391561418824477?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    3. Gong, Xiaodong & van Soest, Arthur, 2002. "Wage differentials and mobility in the urban labour market: a panel data analysis for Mexico," Labour Economics, Elsevier, vol. 9(4), pages 513-529, September.
    4. Olivier Bargain & Prudence Kwenda, 2014. "The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data," Economic Development and Cultural Change, University of Chicago Press, vol. 63(1), pages 117-153.
    5. Francine D. Blau & Lawrence M. Kahn, 2013. "Female Labor Supply: Why Is the United States Falling Behind?," American Economic Review, American Economic Association, vol. 103(3), pages 251-256, May.
    6. 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.
    7. Bernd Fitzenberger & Astrid Kunze, 2005. "Vocational Training and Gender: Wages and Occupational Mobility among Young Workers," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 21(3), pages 392-415, Autumn.
    8. repec:ilo:ilowps:483489 is not listed on IDEAS
    9. 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.
    10. Kapsos, Steven., 2008. "The gender wage gap in Bangladesh," ILO Working Papers 994134173402676, International Labour Organization.
    11. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    12. Tromp, Nikolas, 2016. "Decomposing the Gender Wage Gap Across the Wage Distribution: South Korea in 2003 vs. 2013," MPRA Paper 75123, University Library of Munich, Germany, revised 08 Nov 2016.
    13. Funkhouser, Edward, 1997. "Mobility and Labor Market Segmentation: The Urban Labor Market in El Salvador," Economic Development and Cultural Change, University of Chicago Press, vol. 46(1), pages 123-153, October.
    14. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    15. Salma Ahmed & Pushkar Maitra, 2010. "Gender Wage Discrimination in Rural and Urban Labour Markets of Bangladesh," Oxford Development Studies, Taylor & Francis Journals, vol. 38(1), pages 83-112.
    16. Ben Jann, 2008. "A Stata implementation of the Blinder-Oaxaca decomposition," ETH Zurich Sociology Working Papers 5, ETH Zurich, Chair of Sociology, revised 14 May 2008.
    17. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    18. repec:ilo:ilowps:413417 is not listed on IDEAS
    19. Jacob Mincer, 1958. "Investment in Human Capital and Personal Income Distribution," Journal of Political Economy, University of Chicago Press, vol. 66, pages 281-281.
    20. Ahmed, Salma & McGillivray, Mark, 2015. "Human Capital, Discrimination, and the Gender Wage Gap in Bangladesh," World Development, Elsevier, vol. 67(C), pages 506-524.
    21. Rahman, Rushidan I. & Islam, Rizwanul., 2013. "Female labour force participation in Bangladesh : trends, drivers and barriers," ILO Working Papers 994834893402676, International Labour Organization.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Shahadat Hossain Siddiquee & Md. Saiful Islam & Md. Raied Arman, 2021. "Gender Earnings Gap among Urban Youth Adults in Bangladesh: A Comparative Static Analysis," Research in Applied Economics, Macrothink Institute, vol. 13(3), pages 45-66, September.
    2. Avinno Faruk, 2021. "Analysing the glass ceiling and sticky floor effects in Bangladesh: evidence, extent and elements," SN Business & Economics, Springer, vol. 1(9), pages 1-23, September.
    3. 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.
    4. Khondaker Golam Moazzem & Tamim Ahmed, 2021. "Implications of COVID-19 for Bangladesh’s Graduation from the LDC Status," CPD Working Paper 140, Centre for Policy Dialogue (CPD).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rahman, Mustafizur & Al-Hasan, Md., 2018. "Male-Female wage gap and informal employment in Bangladesh: A quantile regression approach," MPRA Paper 90131, University Library of Munich, Germany.
    2. Mustafizur Rahman & Marzuka Md. Al-Hasan, 2019. "Women in Bangladesh Labour Market: Determinants of Participation, Gender Wage Gap and Returns to Schooling," CPD Working Paper 124, Centre for Policy Dialogue (CPD).
    3. Avinno Faruk, 2021. "Analysing the glass ceiling and sticky floor effects in Bangladesh: evidence, extent and elements," SN Business & Economics, Springer, vol. 1(9), pages 1-23, September.
    4. Mustafizur Rahman & Debapriya Bhattacharya & Md Al-Hasan, 2019. "Dimensions of Informality in Bangladesh Labour Market and the Consequent Wage Penalty," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 20(2), pages 224-247, September.
    5. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    6. 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.
    7. Hazarika, Bhabesh, 2017. "Decomposition of Gender Income Gap in Rural Informal Micro-enterprises: An Unconditional Quantile Approach in the Handloom Industry," Working Papers 17/216, National Institute of Public Finance and Policy.
    8. Collischon Matthias, 2019. "Is There a Glass Ceiling over Germany?," German Economic Review, De Gruyter, vol. 20(4), pages 329-359, December.
    9. Domenico Depalo & Raffaela Giordano & Evangelia Papapetrou, 2015. "Public–private wage differentials in euro-area countries: evidence from quantile decomposition analysis," Empirical Economics, Springer, vol. 49(3), pages 985-1015, November.
    10. Amal Jmaii & Damien Rousselière & Christophe Daniel, 2017. "Semi†parametric Regression†based Decomposition Methods: Evidence from Regional Inequality in Tunisia," African Development Review, African Development Bank, vol. 29(4), pages 660-673, December.
    11. Qin, Yu & Zhu, Hongjia & Zhu, Rong, 2016. "Changes in the distribution of land prices in urban China during 2007–2012," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 77-90.
    12. Bhabesh Hazarika, 2020. "Gender income gap in rural informal micro-enterprises: an unconditional quantile decomposition approach in the handloom industry," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 10(3), pages 441-473, September.
    13. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    14. Vladimir Gimpelson & Anna Lukiyanova & Anna Sharunina, 2015. "Estimating the Public-Private Wage Gap in Russia: What Does Quantile Regression Tell Us?," HSE Working papers WP BRP 104/EC/2015, National Research University Higher School of Economics.
    15. VAN KERM Philippe & YU Seunghee & CHOE Chung, 2014. "Wage differentials between native, immigrant and cross-border workers: Evidence and model comparisons," LISER Working Paper Series 2014-05, Luxembourg Institute of Socio-Economic Research (LISER).
    16. Dang, Rui, 2015. "Explaining the body mass index gaps between Turkish immigrants and Germans in West Germany 2002-2012: A decomposition analysis of socio-economic causes," Ruhr Economic Papers 580, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. Thomas Grandner & Dieter Gstach, 2012. "Decomposing wage discrimination in Germany and Austria with counterfactual densities," Department of Economics Working Papers wuwp145, Vienna University of Economics and Business, Department of Economics.
    18. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.
    19. Satinder Singh & Jajati K. Parida, 2022. "Employment and Earning Differentials Among Vocationally Trained Youth: Evidence from field studies in Punjab and Haryana in India," Millennial Asia, , vol. 13(1), pages 142-172, April.
    20. Essama-Nssah, B., 2012. "Identification of sources of variation in poverty outcomes," Policy Research Working Paper Series 5954, The World Bank.

    More about this item

    Keywords

    Gender wage gap; Oaxaca–Blinder decomposition; quantile decomposition; informal employment; quantile regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J46 - Labor and Demographic Economics - - Particular Labor Markets - - - Informal Labor Market
    • J70 - Labor and Demographic Economics - - Labor Discrimination - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:soueco:v:20:y:2019:i:1:p:106-123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.ips.lk/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.