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Productivity Growth of ECOWAS Common Crops: A Tale of Two Competing Frontier Methods of Analysis

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  • Olusegun, Ajetomobi Joshua
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    Abstract

    This study examines productivity growth of 3 ECOWAS crops, namely, rice, cotton and millet, using both Stochastic Frontier Analysis (SFA) and Data Envelopment analysis (DEA). The data cover a 45 year period (1961-2005). Calculations are based on data collected from FAOSTAT database, International Rice research Institute (IRRI) world rice statistics, and international cotton advisory committee database. The results for both SFA and DEA show that (1) there are inefficiencies but productivity progress among ECOWAS member nations producing rice, cotton and millet. (2) Though, magnitudes of the inefficiencies and productivity progress vary across models applied and by segmentation of the data set, there is little or no conflict in the overall results. (3) Technical change has had the greatest impact on productivity, indicating that producers have a tendency to catch-up with the front runners.

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    Bibliographic Info

    Paper provided by African Farm Management Association (AFMA) in its series 2012 Eighth AFMA Congress, November 25-29, 2012, Nairobi, Kenya with number 159404.

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    Date of creation: Nov 2012
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    Handle: RePEc:ags:afma12:159404

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    Keywords: Stochastic Frontier Analysis; Data Envelopment Analysis; Crops; ECOWAS; Crop Production/Industries; Productivity Analysis; C13; C24; O33; O47;

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    1. Abdul Wadud & Ben White, 2000. "Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods," Applied Economics, Taylor & Francis Journals, vol. 32(13), pages 1665-1673.
    2. John Ruggiero & Donald F. Vitaliano, 1999. "Assessing The Efficiency Of Public Schools Using Data Envelopment Analysis And Frontier Regression," Contemporary Economic Policy, Western Economic Association International, vol. 17(3), pages 321-331, 07.
    3. Hjalmarsson, Lennart & Veiderpass, Ann, 1992. " Productivity in Swedish Electricity Retail Distribution," Scandinavian Journal of Economics, Wiley Blackwell, vol. 94(0), pages S193-205, Supplemen.
    4. Derek Headey & Mohammad Alauddin & D.S. Prasada Rao, 2010. "Explaining agricultural productivity growth: an international perspective," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 1-14, 01.
    5. Subhash C. Ray, 1991. "Resource-Use Efficiency in Public Schools: A Study of Connecticut Data," Management Science, INFORMS, vol. 37(12), pages 1620-1628, December.
    6. James Odeck, 2007. "Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2617-2630.
    7. Tim J. Coelli & D.S. Prasada Rao, 2003. "Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries,1980-2000," CEPA Working Papers Series WP022003, School of Economics, University of Queensland, Australia.
    8. Sipilainen, Timo & Kuosmanen, Timo & Kumbhakar, Subal C., 2008. "Measuring productivity differentials – An application to milk production in Nordic countries," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44277, European Association of Agricultural Economists.
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