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Female Labour Force Participation Across SAARC Countries Using Dynamic Panel Model

Author

Listed:
  • Deepika Sharma

    (Patliputra University
    National Institute of Technology (NIT) Patna)

  • Abhishek Kumar

    (Amity University
    IIT Kanpur)

  • Deepak Kumar Behera

    (National Institute of Technology (NIT) Patna
    University of Hyderabad)

Abstract

The paper examines the trends of female labour force participation and gender disparity across groups of countries and the SAARC Nations. The objective of the paper is to examine the relationship between female labour force participation rate of the age group of 15–64 years and GDP per capita, fertility rate, survival rate and their lag effect, life expectancy and average years of schooling which includes primary, secondary and tertiary schooling over the period from 2000 to 2018. The panel data have been created by taking the time-series countrywise data from International Labour Organisation statistics (ILO modelled estimates). The study tries to analyse the effect of the log of per capita GDP, human capital stock for the female population and other variables using GMM estimates on Dynamic Panel Model setup for SAARC nations (excluding Bhutan and Maldives). This study uses the lag value of the log of GDP per capita as an instrumental variable to get an efficient estimator in a Dynamic Panel Model setting.

Suggested Citation

  • Deepika Sharma & Abhishek Kumar & Deepak Kumar Behera, 2020. "Female Labour Force Participation Across SAARC Countries Using Dynamic Panel Model," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 63(3), pages 677-687, September.
    • Handle: RePEc:spr:ijlaec:v:63:y:2020:i:3:d:10.1007_s41027-020-00244-3
    DOI: 10.1007/s41027-020-00244-3
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    References listed on IDEAS

    as
    1. Florence Jaumotte, 2003. "Female Labour Force Participation: Past Trends and Main Determinants in OECD Countries," OECD Economics Department Working Papers 376, OECD Publishing.
    2. Vinod Mishra & Ingrid Nielsen & Russell Smyth, 2010. "On the relationship between female labour force participation and fertility in G7 countries: evidence from panel cointegration and Granger causality," Empirical Economics, Springer, vol. 38(2), pages 361-372, April.
    3. David Bloom & David Canning & Günther Fink & Jocelyn Finlay, 2009. "Fertility, female labor force participation, and the demographic dividend," Journal of Economic Growth, Springer, vol. 14(2), pages 79-101, June.
    4. Schultz, T Paul, 1990. "Women's Changing Participation in the Labor Force: A World Perspective," Economic Development and Cultural Change, University of Chicago Press, vol. 38(3), pages 457-488, April.
    5. A. Chevalier & T. K. Viitanen, 2002. "The causality between female labour force participation and the availability of childcare," Applied Economics Letters, Taylor & Francis Journals, vol. 9(14), pages 915-918.
    6. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    7. Psacharopoulos, George & Tzannatos, Zafiris, 1989. "Female Labor Force Participation: An International Perspective," The World Bank Research Observer, World Bank, vol. 4(2), pages 187-201, July.
    8. Sonalde Desai & Omkar Joshi, 2019. "The Paradox of Declining Female Work Participation in an Era of Economic Growth," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 62(1), pages 55-71, March.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    FLPR; Human capital stock; GMM estimate; Fertility rate; Enrolment ratio;
    All these keywords.

    JEL classification:

    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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