IDEAS home Printed from https://ideas.repec.org/a/eee/wdevel/v39y2011i10p1784-1795.html
   My bibliography  Save this article

Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda

Author

Listed:
  • Kassie, Menale
  • Shiferaw, Bekele
  • Muricho, Geoffrey

Abstract

This paper evaluates the ex post impact of adopting improved groundnut varieties on crop income and poverty in rural Uganda. The study utilizes cross-sectional data of 927 households, collected in 2006, from seven districts in Uganda. Using propensity score matching methods, we find that adopting improved groundnut varieties (technology) significantly increases crop income and reduces poverty. The positive and significant impact on crop income is consistent with the perceived role of new agricultural technologies in reducing rural poverty through increased farm household income. This study supports broader investment in agriculture research to address vital development challenges. Reaching the poor with better technologies however requires policy support for improving extension efforts, access to seeds and market outlets that simulate adoption.

Suggested Citation

  • Kassie, Menale & Shiferaw, Bekele & Muricho, Geoffrey, 2011. "Agricultural Technology, Crop Income, and Poverty Alleviation in Uganda," World Development, Elsevier, vol. 39(10), pages 1784-1795.
  • Handle: RePEc:eee:wdevel:v:39:y:2011:i:10:p:1784-1795 DOI: 10.1016/j.worlddev.2011.04.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305750X11000933
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Karanja, Daniel David & Renkow, M. & Crawford, E.W., 2003. "Welfare effects of maize technologies in marginal and high potential regions of Kenya," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 29(3), December.
    3. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71 Elsevier.
    4. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    5. Liane Faltermeier & Awudu Abdulai, 2009. "The impact of water conservation and intensification technologies: empirical evidence for rice farmers in Ghana," Agricultural Economics, International Association of Agricultural Economists, vol. 40(3), pages 365-379, May.
    6. Zdenko Stefanides & Loren W. Tauer, 1999. "The Empirical Impact of Bovine Somatotropin on a Group of New York Dairy Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(1), pages 95-102.
    7. Jalan, Jyotsna & Ravallion, Martin, 2003. "Does piped water reduce diarrhea for children in rural India?," Journal of Econometrics, Elsevier, vol. 112(1), pages 153-173, January.
    8. Mahabub HOSSAIN & Manik L. BOSE & Bazlul A. A. MUSTAFI, 2006. "Adoption And Productivity Impact Of Modern Rice Varieties In Bangladesh," The Developing Economies, Institute of Developing Economies, vol. 44(2), pages 149-166.
    9. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    10. Akhter Ali & Awudu Abdulai, 2010. "The Adoption of Genetically Modified Cotton and Poverty Reduction in Pakistan," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(1), pages 175-192.
    11. Becerril, Javier & Abdulai, Awudu, 2010. "The Impact of Improved Maize Varieties on Poverty in Mexico: A Propensity Score-Matching Approach," World Development, Elsevier, vol. 38(7), pages 1024-1035, July.
    12. Yifu Lin, Justin, 1999. "Technological change and agricultural household income distribution: theory and evidence from China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 43(2).
    13. Coelli, Tim & Fleming, Euan, 2004. "Diversification economies and specialisation efficiencies in a mixed food and coffee smallholder farming system in Papua New Guinea," Agricultural Economics, Blackwell, vol. 31(2-3), pages 229-239, December.
    14. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    15. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    16. Yoko Kijima & Keijiro Otsuka & Dick Sserunkuuma, 2008. "Assessing the impact of NERICA on income and poverty in central and western Uganda," Agricultural Economics, International Association of Agricultural Economists, vol. 38(3), pages 327-337, May.
    17. Mendola, Mariapia, 2007. "Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh," Food Policy, Elsevier, vol. 32(3), pages 372-393, June.
    18. Aliou Diagne & Matty Demont, 2007. "Taking a new look at empirical models of adoption: average treatment effect estimation of adoption rates and their determinants," Agricultural Economics, International Association of Agricultural Economists, vol. 37(2-3), pages 201-210, September.
    19. Haitao Wu & Shijun Ding & Sushil Pandey & Dayun Tao, 2010. "Assessing the Impact of Agricultural Technology Adoption on Farmers' Well-being Using Propensity-Score Matching Analysis in Rural China," Asian Economic Journal, East Asian Economic Association, vol. 24(2), pages 141-160, June.
    20. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    21. Kasenge, Valentine & Taylor, Daniel B. & Bonabana-Wabbi, Jackline, 2006. "A Limited Dependent Variable Analysis of Integrated Pest Management Adoption in Uganda," 2006 Annual meeting, July 23-26, Long Beach, CA 21040, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    22. Minten, Bart & Barrett, Christopher B., 2008. "Agricultural Technology, Productivity, and Poverty in Madagascar," World Development, Elsevier, vol. 36(5), pages 797-822, May.
    23. Paul Mosley & Sanzidur Rahman, 1999. "Impact of technological change on income distribution and poverty in Bangladesh agriculture: an empirical analysis," Journal of International Development, John Wiley & Sons, Ltd., vol. 11(7), pages 935-955.
    24. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    25. Aldas JANAIAH & Mahabub HOSSAIN & Keijiro OTSUKA, 2006. "Productivity Impact Of The Modern Varieties Of Rice In India," The Developing Economies, Institute of Developing Economies, vol. 44(2), pages 190-207.
    26. Ingrid Rhinehart & C. Michael Deom, 2007. "Peanut Research and Poverty Reduction: Impacts of Variety Improvement to Control Peanut Viruses in Uganda," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 448-460.
    Full references (including those not matched with items on IDEAS)

    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:eee:wdevel:v:39:y:2011:i:10:p:1784-1795. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/worlddev .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.