[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/kob/dpaper/dp2014-32.html
   My bibliography  Save this paper

Agricultural Employment, Wages and Poverty in Developing Countries

Author

Listed:
  • Katsushi S. Imai

    (School of Social Sciences, University of Manchester (UK) and RIEB, Kobe University (Japan))

  • Raghav Gaiha

    (Faculty of Management Studies, University of Delhi, India and Statistics and Studies for Development(SSD) Division, Strategy and Knowledge Management Department, International Fund for Agricultural Development (IFAD), Italy)

  • Constanza Di NUCCI

    (Faculty of Management Studies, University of Delhi, India and Statistics and Studies for Development(SSD) Division, Strategy and Knowledge Management Department, International Fund for Agricultural Development (IFAD), Italy)

Abstract
Drawing upon panel data estimations, we have analysed the relationships among agricultural productivity, employment, technology, openness of the economy, inequality in land distribution and poverty. First, we have identified a number of important factors affecting agricultural productivity, such as agricultural R&D expenditure, irrigation, fertilizer use, agricultural tractor/machinery use, reduction in inequality of land distributions, or reduction in gender inequality. Second, while agricultural wage rate is negatively associated with agricultural productivity and food price in levels, the growth in agricultural wage rate is positively correlated with the growth in agricultural land or labour productivity as well as with the growth in food price, particularly after 2000. Contrary to the ILO's (2012) claim that the gap has widened recently, this suggests the narrowing gap between wage and labour productivity once we focus on the conditional relationship between the two. Third, agricultural employment per hectare tends to increase agricultural productivity after taking account of the endogeneity of the former, while the growth in agricultural employment per hectare tends to increase the growth in non-agricultural employment over time with adjustment for endogeneity of the former. In this context, we have reviewed the recent literature and emphasised the importance of enhancing agricultural productivity and employment. Fourth, both agricultural growth and non-agricultural growth tend to lead to reduction in overall inequality. Finally, increase in agricultural productivity which is treated as endogenous will reduce poverty significantly through the overall economic growth. Overall, policies to increase agricultural productivity and agricultural employment are likely to increase non-agricultural growth, overall growth and reduce poverty, where guaranteeing gender inequality is likely to be one of the key factors.

Suggested Citation

  • Katsushi S. Imai & Raghav Gaiha & Constanza Di NUCCI, 2014. "Agricultural Employment, Wages and Poverty in Developing Countries," Discussion Paper Series DP2014-32, Research Institute for Economics & Business Administration, Kobe University.
    • Handle: RePEc:kob:dpaper:dp2014-32
    as

    Download full text from publisher

    File URL: https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2014-32.pdf
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Imai, Katsushi S. & Gaiha, Raghav & Garbero, Alessandra, 2017. "Poverty reduction during the rural–urban transformation: Rural development is still more important than urbanisation," Journal of Policy Modeling, Elsevier, vol. 39(6), pages 963-982.
    2. Jinyong Hahn & Jerry Hausman & Guido Kuersteiner, 2004. "Estimation with weak instruments: Accuracy of higher-order bias and MSE approximations," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 272-306, June.
    3. Thirtle, Colin & Lin, Lin & Piesse, Jenifer, 2003. "The Impact of Research-Led Agricultural Productivity Growth on Poverty Reduction in Africa, Asia and Latin America," World Development, Elsevier, vol. 31(12), pages 1959-1975, December.
    4. Katsushi S. Imai & Raghav Gaiha, 2014. "Dynamic and Long-term Linkages among Growth, Inequality and Poverty in Developing Countries," Economics Discussion Paper Series 1410, Economics, The University of Manchester.
    5. Katsushi Imai & Raghav Gaiha & Ganesh Thapa, 2010. "Is the Millennium Development Goal on Poverty Still Achievable? The Role of Institutions, Finance and Openness," Oxford Development Studies, Taylor & Francis Journals, vol. 38(3), pages 309-337.
    6. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    8. Katsushi S. Imai & Gordon Abekah-Nkrumah & Purnima Purohit, 2014. "Is Rural Contribution to Aggregate Poverty Reduction Substantial? New Evidence," Global Development Institute Working Paper Series 20814, GDI, The University of Manchester.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    10. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    Full references (including those not matched with items on IDEAS)

    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. Imai, Katsushi S. & Gaiha, Raghav & Garbero, Alessandra, 2017. "Poverty reduction during the rural–urban transformation: Rural development is still more important than urbanisation," Journal of Policy Modeling, Elsevier, vol. 39(6), pages 963-982.
    2. Katsushi S. Imai & Wenya Cheng & Raghav Gaiha, 2017. "Dynamic and long-term linkages among agricultural and non-agricultural growth, inequality and poverty in developing countries," International Review of Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 318-338, May.
    3. Katsushi S. Imai & Raghav Gaiha, 2014. "Dynamic and Long-term Linkages among Growth, Inequality and Poverty in Developing Countries," Economics Discussion Paper Series 1410, Economics, The University of Manchester.
    4. Vieira, Flávio & MacDonald, Ronald & Damasceno, Aderbal, 2012. "The role of institutions in cross-section income and panel data growth models: A deeper investigation on the weakness and proliferation of instruments," Journal of Comparative Economics, Elsevier, vol. 40(1), pages 127-140.
    5. Abonazel, Mohamed R., 2016. "Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects," MPRA Paper 70628, University Library of Munich, Germany.
    6. Barra, Cristian & Zotti, Roberto, 2016. "Investigating the impact of national income on environmental pollution. International evidence," MPRA Paper 74149, University Library of Munich, Germany.
    7. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Working Papers in Economic Theory and Policy 07/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    8. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    9. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    10. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    11. Katsushi S. Imai & Raghav GAIHA & Wenya Cheng, 2015. "Does Agricultural Growth Reduce Inequality and Poverty in Developing Countries?," Discussion Paper Series DP2015-23, Research Institute for Economics & Business Administration, Kobe University, revised Apr 2016.
    12. Seidu, Ayuba & Onel, Gulcan & Moss, Charles Britt, 2018. "Impact of International Remittance on Out-Farm Labor Migration in Developing Countries: A Dynamic Panel Data Analysis," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266531, Southern Agricultural Economics Association.
    13. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.
    14. Maudos, Joaquín & Solís, Liliana, 2009. "The determinants of net interest income in the Mexican banking system: An integrated model," Journal of Banking & Finance, Elsevier, vol. 33(10), pages 1920-1931, October.
    15. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    16. Youngho Kang & Unjung Whang, 2018. "To Whom Does Outward FDI Give Jobs?," Open Economies Review, Springer, vol. 29(3), pages 613-639, July.
    17. Rostand Arland Yebetchou Tchounkeu & Raffaella Santolini & Giulio Palomba & Elvina Merkaj, 2024. "Healthcare Efficiency And Elderly Mortality In Italy," Working Papers 485, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    18. Fajeau, Maxime, 2021. "Too much finance or too many weak instruments?," International Economics, Elsevier, vol. 165(C), pages 14-36.
    19. Jeffry A. Jacob & Thomas Osang, 2018. "Democracy And Growth: A Dynamic Panel Data Study," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 65(01), pages 41-80, August.
    20. Badri Narayanan G., 2005. "Effects of Trade liberalisation, Environmental and Labour Regulations on Employment in India's Organised Textile Sector," Labor Economics Working Papers 22363, East Asian Bureau of Economic Research.

    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I39 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Other
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:kob:dpaper:dp2014-32. 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: Office of Promoting Research Collaboration, Research Institute for Economics & Business Administration, Kobe University (email available below). General contact details of provider: https://edirc.repec.org/data/rikobjp.html .

    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.