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The Labour Productivity Gap between agricultural and non-agricultural sectors and Poverty in Asia

Author

Listed:
  • Katsushi S. Imai

    (Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan and School of Social Sciences, University of Manchester, UK)

  • Raghav Gaiha

    (Global Development Institute, University of Manchester, UK and Harvard School of Public Health, Harvard University Boston, USA)

  • Fabrizio Bresciani

    (Asia and the Pacific Division of International Fund for Agricultural Development (IFAD), Italy)

Abstract

The objective of this paper is to examine how agricultural and non-agricultural labour productivities have grown over time and whether the growth pattern – proxied by the labour productivity gap in two sectors – affected poverty in low and middle income countries in Asia. We first examine whether the labour productivities in agricultural and non-agricultural sectors have converged or not using a five-year average panel dataset. We have found evidence that non-agricultural labour productivity and agricultural labour productivity did not converge as the former has grown faster and the gap has increased significantly over time. We then confirm that both agricultural and non-agricultural labour productivities have converged across countries and the convergence effect is stronger for the non-agricultural sector. We have also observed that, despite the relatively lower growth in agricultural labour productivity, the agricultural sector played an important role in promoting non-agricultural labour productivity and thus in non-agricultural growth. Finally, we have found some evidence that the labour productivity gap reduces rural and urban poverty as well as the national inequality.

Suggested Citation

  • Katsushi S. Imai & Raghav Gaiha & Fabrizio Bresciani, 2017. "The Labour Productivity Gap between agricultural and non-agricultural sectors and Poverty in Asia," Discussion Paper Series DP2017-04, Research Institute for Economics & Business Administration, Kobe University, revised May 2018.
    • Handle: RePEc:kob:dpaper:dp2017-04
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    File URL: https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2017-04.pdf
    File Function: Revised version, 2018
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    References listed on IDEAS

    as
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    Cited by:

    1. K. Shanmugan & Bhagirath Prakash Baria, 2019. "Agricultural Labour Productivity and Its Determinants in India," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 62(3), pages 431-449, September.

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    More about this item

    Keywords

    Cross-border acquisitions; Target selection; Cherry-picking; Geographic proximity; Value creation;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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