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Do differences in the scale of irrigation projects generate different impacts on poverty and production?

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  • Dillon, Andrew

Abstract

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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Bibliographic Info

Paper provided by International Food Policy Research Institute (IFPRI) in its series IFPRI discussion papers with number 1022.

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Date of creation: 2010
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Handle: RePEc:fpr:ifprid:1022

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Keywords: Irrigation; program evaluation;

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References

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  1. Dehejia, R.H. & Wahba, S., 1998. "Propensity Score Matching Methods for Non-Experimental Causal Studies," Discussion Papers 1998_02, Columbia University, Department of Economics.
  2. 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.
  3. 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.
  4. 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.
  5. Angus Deaton & Salman Zaidi, 2002. "Guidelines for Constructing Consumption Aggregates for Welfare Analysis," World Bank Publications, The World Bank, number 14101.
  6. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-55, June.
  7. James J. Heckman, 2010. "Building Bridges Between Structural and Program Evaluation Approaches to Evaluating Policy," NBER Working Papers 16110, National Bureau of Economic Research, Inc.
  8. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
  9. 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.
  10. 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.
  11. 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).
  12. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
  13. Daniel O. Gilligan & John Hoddinott, 2007. "Is There Persistence in the Impact of Emergency Food Aid? Evidence on Consumption, Food Security, and Assets in Rural Ethiopia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 225-242.
  14. Dillon, Andrew, 2008. "Access to irrigation and the escape from poverty: Evidence from Northern Mali," IFPRI discussion papers 782, International Food Policy Research Institute (IFPRI).
  15. 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.
  16. Joshua D. Angrist & Guido W. Imbens, 1999. "Comment on James J. Heckman, "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations"," Journal of Human Resources, University of Wisconsin Press, vol. 34(4), pages 823-827.
  17. 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.
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Cited by:
  1. Peralta, Maria Alexandra & Swinton, Scott M. & Maredia, Mywish K., 2011. "Accounting for selection bias in impact analysis of a rural development program: An application using propensity score matching," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126398, International Association of Agricultural Economists.
  2. Giordano, Meredith & de Fraiture, Charlotte, 2014. "Small private irrigation: Enhancing benefits and managing trade-offs," Agricultural Water Management, Elsevier, vol. 131(C), pages 175-182.
  3. Pauw, Karl & Thurlow, James, 2013. "Prioritizing rural investments in Africa: A hybrid evaluation approach applied to Uganda," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
  4. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
  5. Kijima, Yoko & Ito, Yukinori & Otsuka, Keijiro, 2012. "Assessing the Impact of Training on Lowland Rice Productivity in an African Setting: Evidence from Uganda," World Development, Elsevier, vol. 40(8), pages 1610-1618.
  6. Takeshima, Hiroyuki & Edeh, Hyacinth, 2013. "Typology of farm households and irrigation systems: Some evidence from Nigeria," IFPRI discussion papers 1267, International Food Policy Research Institute (IFPRI).

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