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Factors that influence female labor force supply in China

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  • Chen, Jianxian
  • Shao, Xiaokuai
  • Murtaza, Ghulam
  • Zhao, Zhongxiu

Abstract

Using the China Health and Nutrition Survey (CHNS) database of the employment of married women in 2006, this paper analyzed the factors that influenced the labor force participation of female workers in urban and rural areas, from the perspectives of individuals and families. The Probit Regression suggested that factors associated with families were of more importance than individual factors and there existed large differences between urban and rural areas. For women from urban areas, individual factors played a more important role than those from rural areas, while family factors were more significant for those who came from rural areas. Thus labor market policies should consider the structural difference between urban and rural women.

Suggested Citation

  • Chen, Jianxian & Shao, Xiaokuai & Murtaza, Ghulam & Zhao, Zhongxiu, 2014. "Factors that influence female labor force supply in China," Economic Modelling, Elsevier, vol. 37(C), pages 485-491.
    • Handle: RePEc:eee:ecmode:v:37:y:2014:i:c:p:485-491
    DOI: 10.1016/j.econmod.2013.11.043
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    Cited by:

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    2. Prathi Seneviratne, 2017. "Female Labour Force Participation and Economic Development in Labour Abundant Countries: Evidence from Sri Lanka," Working Papers 2017-02, Carleton College, Department of Economics.
    3. Verme, Paolo & Barry, Abdoul Gadiry & Guennouni, Jamal, 2014. "Female labor participation in the Arab world : some evidence from panel data in Morocco," Policy Research Working Paper Series 7031, The World Bank.
    4. Wenkai Sun & Xianghong Wang & Xiaoxi Zhang, 2015. "Minimum wage effects on employment and working time of Chinese workers——evidence based on CHNS," IZA Journal of Labor & Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-22, December.
    5. Mishra, Vinod & Smyth, Russell, 2015. "Estimating returns to schooling in urban China using conventional and heteroskedasticity-based instruments," Economic Modelling, Elsevier, vol. 47(C), pages 166-173.
    6. Xie, Hualin & Wang, Wei & Yang, Zihui & Choi, Yongrok, 2016. "Measuring the sustainable performance of industrial land utilization in major industrial zones of China," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 207-219.
    7. repec:zbw:bofitp:2021_007 is not listed on IDEAS
    8. Yu, Haiyue & Cao, Jin & Kang, Shulong, 2021. "Who cares : Deciphering China’s female employment paradox," BOFIT Discussion Papers 7/2021, Bank of Finland, Institute for Economies in Transition.
    9. Wang, Qingbin & Zou, Yang & Fan, Dan, 2019. "Gender imbalance in China’s marriage migration: Quantitative evidence and policy implications," Economic Modelling, Elsevier, vol. 83(C), pages 406-414.
    10. Buhari Dogan & Mürsel Akyüz, 2017. "Female Labor Force Participation Rate And Economic Growth In The Framework Of Kuznets Curve: Evidence From Turkey," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 19, pages 33-54, June.
    11. Paolo Verme & Abdoul Gadiry Barry & Jamal Guennouni, 2016. "Female Labor Participation in the Arab World: Evidence from Panel Data in Morocco," LABOUR, CEIS, vol. 30(3), pages 258-284, September.
    12. Seneviratne, Prathi, 2020. "Gender wage inequality during Sri Lanka’s post-reform growth: A distributional analysis," World Development, Elsevier, vol. 129(C).
    13. Gómez, Manuel A., 2016. "Are taxes a good predictor of time use patterns? Examining the role of some key elasticities," Economic Modelling, Elsevier, vol. 55(C), pages 394-400.

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