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The impact of extension services on farming households in Western Kenya: A propensity score approach

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  • Deschamps-Laporte, Jean-Philippe

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    (Department of Business, Economics, Statistics and Informatics)

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    Abstract

    The aim of this paper is to assess the impact of the adoption of technological packages in agriculture Kenya on the farming households, as promoted by the National Agriculture and Livestock Extension Programme (NALEP), a program run by the Government of Kenya. To this end, we collected data on beneficiaries through a survey of 1000 households in the district of Lugari, in Western Kenya. We use propensity score matching to compute the average treatment effect on the treated. We find evidence that: I) program beneficiaries changed their crop rotation practices; II) treated households increased their fertilizer dosage by 23.8%; IV) productivity per acre is not affected by the treatment; V) treated households also were less likely to store their surplus maize.

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    Bibliographic Info

    Paper provided by Örebro University, School of Business in its series Working Papers with number 2013:5.

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    Length: 45 pages
    Date of creation: 04 Apr 2013
    Date of revision: 10 Jun 2013
    Handle: RePEc:hhs:oruesi:2013_005

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    Postal: Örebro University School of Business, SE - 701 82 ÖREBRO, Sweden
    Phone: 019-30 30 00
    Fax: 019-33 25 46
    Web page: http://www.oru.se/Institutioner/Handelshogskolan-vid-Orebro-universitet/
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    Keywords: Agricultural Extension; Kenya; Propensity Score Matching; Maize; Fertilizer; Water Harvesting; Productivity;

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    1. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
    2. Godtland, Erin & Sadoulet, Elisabeth & de Janvry, Alain & Murgai, Rinku & Ortiz, Oscar, 2003. "The Impact of Farmer-Field-Schools on Knowledge and Productivity: A Study of Potato Farmers in the Peruvian Andes," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt8hp835xx, Department of Agricultural & Resource Economics, UC Berkeley.
    3. Augurzky, Boris & Schmidt, Christoph M., 2001. "The Propensity Score: A Means to An End," IZA Discussion Papers 271, Institute for the Study of Labor (IZA).
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    5. Jyotsna Jalan & Martin Ravallion, 2000. "Estimating the Benefit Incidence of an Antipoverty Program by Propensity Score Matching," Econometric Society World Congress 2000 Contributed Papers 0873, Econometric Society.
    6. 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.
    7. 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.
    8. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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    10. Dercon, Stefan & Gilligan, Daniel O. & Hoddinott, John & Woldehan, Tassew, 2008. "The impact of agricultural extension and roads on poverty and consumption growth in fifteen Ethiopian villages:," IFPRI discussion papers 840, International Food Policy Research Institute (IFPRI).
    11. Dehejia, R.H. & Wahba, S., 1998. "Propensity Score Matching Methods for Non-Experimental Causal Studies," Discussion Papers 1998_02, Columbia University, Department of Economics.
    12. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
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    14. Birkhaeuser, Dean & Evenson, Robert E & Feder, Gershon, 1991. "The Economic Impact of Agricultural Extension: A Review," Economic Development and Cultural Change, University of Chicago Press, vol. 39(3), pages 607-50, April.
    15. Alberto Abadie & Guido W. Imbens, 2006. "On the Failure of the Bootstrap for Matching Estimators," NBER Technical Working Papers 0325, National Bureau of Economic Research, Inc.
    16. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    17. Bassi, Laurie J, 1984. "Estimating the Effect of Training Programs with Non-Random Selection," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 36-43, February.
    18. Bindlish, Vishva & Evenson, Robert E, 1997. "The Impact of T&V Extension in Africa: The Experience of Kenya and Burkina Faso," World Bank Research Observer, World Bank Group, vol. 12(2), pages 183-201, August.
    19. Datt, Gaurav & Ravallion, Martin, 1998. "Farm productivity and rural poverty in India," FCND discussion papers 42, International Food Policy Research Institute (IFPRI).
    20. John DiNardo & Justin L. Tobias, 2001. "Nonparametric Density and Regression Estimation," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 11-28, Fall.
    21. 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.
    22. Daniel Friedlander & David H. Greenberg & Philip K. Robins, 1997. "Evaluating Government Training Programs for the Economically Disadvantaged," Journal of Economic Literature, American Economic Association, vol. 35(4), pages 1809-1855, December.
    23. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
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