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A Comparison of Intercountry Agricultural Production Functions: A Frontier Function Approach

Author

Listed:
  • Viveka P. Kudaligama

    (Department of Agricultural and Consumer Economics, University of Illinois at Urbana)

  • John F. Yanagida

    (Department of Natural Resources and Environmental Management, University of Hawaii at Manoa)

Abstract
Unlike the work by Hayami and Ruttan in the 1970s, this study utilizes frontier meta- production functions to study intercountry agricultural productivity differences. Technical efficiency differences are examined through estimation of deterministic and stochastic frontiers for 43 countries over 1960, 1970 and 1980. In most cases, developed countries on average have higher technical efficiency levels. However, not all developed countries are fully technically efficient while certain developing countries perform comparably with other developed countries. The results also show that the productivity gap between developing and developed countries has increased over time. Yet there is potential to improve productivity of developing countries, especially by expanding their human capital stock, as indicated by high output elasticities for primary and secondary education and technical education.

Suggested Citation

  • Viveka P. Kudaligama & John F. Yanagida, 2000. "A Comparison of Intercountry Agricultural Production Functions: A Frontier Function Approach," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 25(1), pages 57-74, June.
    • Handle: RePEc:jed:journl:v:25:y:2000:i:1:p:57-74
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    References listed on IDEAS

    as
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    Cited by:

    1. Breustedt, Gunnar & Francksen, Tammo & Latacz-Lohmann, Uwe, 2007. "Estimating Non-Concave Metafrontiers Using Data Envelope Analysis," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7600, German Association of Agricultural Economists (GEWISOLA).
    2. Mon‐Chi Lio & Jin‐Li Hu, 2009. "Governance and Agricultural Production Efficiency: A Cross‐Country Aggregate Frontier Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 40-61, February.
    3. Shilpa Chaudhary, 2012. "Trends in Total Factor Productivity in Indian Agriculture: State-level Evidence using non-parametric Sequential Malmquist Index," Working papers 215, Centre for Development Economics, Delhi School of Economics.
    4. Nkamleu, Guy Blaise & Sylla, Kalilou & Zonon, Abdoulaye, 2008. "What accounts for growth in African agriculture," MPRA Paper 11102, University Library of Munich, Germany, revised 2008.
    5. Hayatullah Ahmadzai, 2017. "Crop Diversification and Technical Efficiency in Afghanistan: Stochastic Frontier Analysis," Discussion Papers 2017-04, University of Nottingham, CREDIT.
    6. Haryanto, T. & Talib, B. A. & Salleh, N. H. M., 2016. "Technical Efficiency and Technology Gap in Indonesian Rice Farming," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 8(3), pages 1-10, September.
    7. Nkamleu, Guy Blaise, 2007. "Religious faith and agricultural growth: exploring some correlations in Africa," MPRA Paper 11131, University Library of Munich, Germany, revised 2007.
    8. Breustedt, G. & Francksen, T. & Latacz-Lohmann, U., 2008. "Estimating non-concave metafrontiers using Data Envelopment Analysis," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 43, March.
    9. Neumann, Kathleen & Verburg, Peter H. & Stehfest, Elke & Müller, Christoph, 2010. "The yield gap of global grain production: A spatial analysis," Agricultural Systems, Elsevier, vol. 103(5), pages 316-326, June.

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