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Do Differences in the Scale of Irrigation Projects Generate Different Impacts on Poverty and Production?

  • Andrew Dillon

This paper investigates differences in household production and consumption among small- and large-scale irrigators to assess whether the scale of an irrigation project increases household welfare in Mali. Much of the evidence of the impact of irrigation does not use counterfactual analysis to estimate such impact or distinguish between the scale of the irrigation projects to be evaluated. In the dataset collected by the author, both a large-scale irrigation project and small-scale projects are used to construct counterfactual groups. Propensity score matching is used to estimate the average treatment effect on the treated for small and large irrigators relative to nonirrigators on agricultural production, agricultural income, and consumption per capita. Small-scale irrigation has a larger effect on agricultural production and agricultural income than large-scale irrigation, but large-scale irrigation has a larger effect on consumption per capita. This suggests that market integration and nonfarm externalities are important in realizing gains in agricultural surplus from irrigation.

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Article provided by Wiley Blackwell in its journal Journal of Agricultural Economics.

Volume (Year): 62 (2011)
Issue (Month): 2 (06)
Pages: 474-492

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Handle: RePEc:bla:jageco:v:62:y:2011:i:2:p:474-492
Contact details of provider: Web page: http://www.blackwellpublishing.com/journal.asp?ref=0021-857X

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  1. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
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  8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
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  10. Attwood, Donald W., 2005. "Big is ugly? How large-scale institutions prevent famines in Western India," World Development, Elsevier, vol. 33(12), pages 2067-2083, December.
  11. Jalan, Jyotsna & Ravallion, Martin, 2003. "Estimating the Benefit Incidence of an Antipoverty Program by Propensity-Score Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 19-30, January.
  12. Inocencio, Arlene & Kikuchi, Masao & Tonosaki, Manabu & Maruyama, Atsushi & Merrey, Douglas & Sally, Hilmy & de Jong, Ijsbrand, 2007. "Costs and performance of irrigation projects: A comparison of Sub-Saharan Africa and other developing regions," IWMI Research Reports H036214, International Water Management Institute.
  13. Dillon, A., 2008. "Access to irrigation and the escape from poverty: evidence from northern Mali," IWMI Working Papers H043847, International Water Management Institute.
  14. Ravallion, Martin & Chen, Shaohua, 2005. "Hidden impact? Household saving in response to a poor-area development project," Journal of Public Economics, Elsevier, vol. 89(11-12), pages 2183-2204, December.
  15. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
  16. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
  17. Juan Jose Diaz & Sudhanshu Handa, 2006. "An Assessment of Propensity Score Matching as a Nonexperimental Impact Estimator: Evidence from Mexico’s PROGRESA Program," Journal of Human Resources, University of Wisconsin Press, vol. 41(2).
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