IDEAS home Printed from https://ideas.repec.org/a/bla/agecon/v40y2009i3p365-379.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1574-0862.2009.00383.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1574-0862.2009.00383.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LLC, vol. 7(1), pages 71-83, February.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Menale Kassie & Precious Zikhali & John Pender & Gunnar Köhlin, 2010. "The Economics of Sustainable Land Management Practices in the Ethiopian Highlands," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(3), pages 605-627, September.
    3. 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, February.
    4. Miguel Angel Malo & Fernando Muñoz-Bullón, 2006. "Employment promotion measures and the quality of the job match for persons with disabilities," Hacienda Pública Española / Review of Public Economics, IEF, vol. 179(4), pages 79-111, September.
    5. 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.
    6. Wendimu, Mengistu Assefa & Henningsen, Arne & Gibbon, Peter, 2016. "Sugarcane Outgrowers in Ethiopia: “Forced” to Remain Poor?," World Development, Elsevier, vol. 83(C), pages 84-97.
    7. Mishra, Ashok K. & Kumar, Anjani & Joshi, Pramod K. & D'souza, Alwin, 2016. "Impact of contracts in high yielding varieties seed production on profits and yield: The case of Nepal," Food Policy, Elsevier, vol. 62(C), pages 110-121.
    8. Islam, Abu Hayat, 2015. "Can Integrated Rice-Fish System Increase Welfare of the Marginalized Extreme Poor in Bangladesh? A DID Matching Approach," 2015 Conference, August 9-14, 2015, Milan, Italy 211792, International Association of Agricultural Economists.
    9. Bagnoli, Lisa, 2019. "Does health insurance improve health for all? Heterogeneous effects on children in Ghana," World Development, Elsevier, vol. 124(C), pages 1-1.
    10. Stephen L. Morgan & David J. Harding, 2006. "Matching Estimators of Causal Effects," Sociological Methods & Research, , vol. 35(1), pages 3-60, August.
    11. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    12. Ashimwe, Olive, 2016. "An Economic Analysis Of Impact Of Weather Index-Based Crop Insurance On Household Income In Huye District Of Rwanda," Research Theses 265675, Collaborative Masters Program in Agricultural and Applied Economics.
    13. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LLC, vol. 7(3), pages 334-350, September.
    14. Paudel, G. & Krishna, V. & McDonald, A., 2018. "Why some inferior technologies succeed? Examining the diffusion and impacts of rotavator tillage in Nepal Terai," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277149, International Association of Agricultural Economists.
    15. Acheampong, Patricia & Owusu, Victor, 2015. "Impact of Improved cassava varieties' adoption on farmers' incomes in Rural Ghana," 2015 Conference, August 9-14, 2015, Milan, Italy 210875, International Association of Agricultural Economists.
    16. Justus, Ochieng & Knerr, Beatrice & Owuor, George & Ouma, Emily, 2015. "Agricultural commercialization and household food security: The case of smallholders in Great Lakes Region of Central Africa," 2015 Conference, August 9-14, 2015, Milan, Italy 212588, International Association of Agricultural Economists.
    17. Barbara Casu & Andrew Clare & Anna Sarkisyan & Stephen Thomas, 2013. "Securitization and Bank Performance," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(8), pages 1617-1658, December.
    18. Marco Caliendo & Michel Clement & Dominik Papies & Sabine Scheel-Kopeinig, 2012. "Research Note ---The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 1068-1080, September.
    19. Deschamps-Laporte, Jean-Philippe, 2013. "The impact of extension services on farming households in Western Kenya: A propensity score approach," Working Papers 2013:5, Örebro University, School of Business, revised 10 Jun 2013.
    20. Alberto Abadie & Guido W. Imbens, 2008. "On the Failure of the Bootstrap for Matching Estimators," Econometrica, Econometric Society, vol. 76(6), pages 1537-1557, November.

    More about this item

    Statistics

    Access and download statistics

    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:bla:agecon:v:40:y:2009:i:3:p:365-379. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.