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The impact of water conservation and intensification technologies: empirical evidence for rice farmers in Ghana

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  • Liane Faltermeier
  • Awudu Abdulai

Abstract

This study employs the propensity score matching model to examine the impact of the adoption of water conservation and intensification technologies on farm output and income among small‐scale lowland rice farmers in the Northern Region of Ghana. The matching was conducted based on the Mahalanobis distance combined with the propensity score. Balancing tests by checking the mean standardized absolute bias in the matched sample were conducted as well as a sensitivity analysis was conducted to check for hidden bias due to unobservable selection. The empirical results show a positive and significant impact of the adoption of bunds technology on input demand as well as a positive but insignificant impact on output supply and net returns. Adopters of the dibbling technology were found to have higher output supply, whereas no statistically significant difference was found between the incomes of adopters and nonadopters of dibbling seed method. The results, however, reveal a positive and significant effect on output and net returns when the dibbling method is combined with intensified weeding. The estimates were found to be relatively insensitive to hidden bias.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:agecon:v:40:y:2009:i:3:p:365-379
    DOI: 10.1111/j.1574-0862.2009.00383.x
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    1. Hujer, Reinhard & Caliendo, Marco & Thomsen, Stephan L., 2004. "New evidence on the effects of job creation schemes in Germany--a matching approach with threefold heterogeneity," Research in Economics, Elsevier, vol. 58(4), pages 257-302, December.
    2. Shively, Gerald E., 1999. "Risks and returns from soil conservation: evidence from low-income farms in the Philippines," Agricultural Economics, Blackwell, vol. 21(1), pages 53-67, August.
    3. 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.
    4. 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.
    5. Doss, Cheryl R. & Morris, Michael L., 2001. "How does gender affect the adoption of agricultural innovations?: The case of improved maize technology in Ghana," Agricultural Economics, Blackwell, vol. 25(1), pages 27-39, June.
    6. Bryson, Alex & Dorsett, Richard & Purdon, Susan, 2002. "The use of propensity score matching in the evaluation of active labour market policies," LSE Research Online Documents on Economics 4993, London School of Economics and Political Science, LSE Library.
    7. 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.
    8. Gerald E. Shively, 1999. "Risks and returns from soil conservation: evidence from low‐income farms in the Philippines," Agricultural Economics, International Association of Agricultural Economists, vol. 21(1), pages 53-67, August.
    9. Baidu-Forson, J., 1999. "Factors influencing adoption of land-enhancing technology in the Sahel: lessons from a case study in Niger," Agricultural Economics, Blackwell, vol. 20(3), pages 231-239, May.
    10. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
    11. Awudu Abdulai & Wallace E. Huffman, 2005. "The Diffusion of New Agricultural Technologies: The Case of Crossbred-Cow Technology in Tanzania," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 645-659.
    12. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LLC, vol. 7(1), pages 71-83, February.
    13. 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.
    14. 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, January.
    15. Michael R. Rahm & Wallace E. Huffman, 1984. "The Adoption of Reduced Tillage: The Role of Human Capital and Other Variables," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(4), pages 405-413.
    16. Abdulai, Awudu & Binder, Claudia R., 2006. "Slash-and-burn cultivation practice and agricultural input demand and output supply," Environment and Development Economics, Cambridge University Press, vol. 11(2), pages 201-220, April.
    17. 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.
    18. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    19. Richard Blundell & Monica Costa Dias, 2000. "Evaluation methods for non-experimental data," Fiscal Studies, Institute for Fiscal Studies, vol. 21(4), pages 427-468, January.
    20. 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.
    21. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LLC, vol. 2(4), pages 358-377, November.
    22. 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.
    23. Guo, Shenyang & Barth, Richard P. & Gibbons, Claire, 2006. "Propensity score matching strategies for evaluating substance abuse services for child welfare clients," Children and Youth Services Review, Elsevier, vol. 28(4), pages 357-383, April.
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