IDEAS home Printed from https://ideas.repec.org/p/idb/brikps/7184.html
   My bibliography  Save this paper

Planting the Seeds: The Impact of Training on Mango Producers in Haiti

Author

Listed:
  • Arráiz, Irani
  • Calero, Carla
  • Jin, Songqing
  • Peralta, Alexandra

Abstract

This paper evaluates the short-term impacts of a development project that aims to increase mango yields, sales of mango products, and the income of small mango farmers in rural Haiti. Various matching methods, in combination with difference-in-difference (DID), are used to deal with the potential selection bias associated with nonrandom treatment assignment. Robustness checks are conducted to investigate whether and to what extent the results are affected by the coexistence of other similar projects in the same sites. Rosenbaum bounds analysis is carried out to check the sensitivity of the estimated impacts---based on matching methods---to deviations from the conditional independence assumptions; the relative importance of unobserved factors in the decision to participate. Our results show that in a 16-month period, the project increased the number of young Francique trees planted---a type that has greater market and export potential than traditional mango varieties---and likely encouraged the adoption of best practices. But the project has not yet led to a noticeable increase in total sales. The adoption of improved production practices is too recent to translate into significant changes in production and sales. While the robustness check suggests that the results are not caused by the presence of other similar programs on the same sites, the Rosenbaum bounds sensitivity analysis suggests that the matching results are robust against potential "hidden bias" arising from unobserved outcome variables in some but not all cases.

Suggested Citation

  • Arráiz, Irani & Calero, Carla & Jin, Songqing & Peralta, Alexandra, 2015. "Planting the Seeds: The Impact of Training on Mango Producers in Haiti," IDB Publications (Working Papers) 7184, Inter-American Development Bank.
  • Handle: RePEc:idb:brikps:7184
    as

    Download full text from publisher

    File URL: https://publications.iadb.org/publications/english/document/Planting-the-Seeds-The-Impact-of-Training-on-Mango-Producers-in-Haiti.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    2. 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.
    3. Foster, Andrew D & Rosenzweig, Mark R, 1996. "Technical Change and Human-Capital Returns and Investments: Evidence from the Green Revolution," American Economic Review, American Economic Association, vol. 86(4), pages 931-953, September.
    4. Nkonya, Ephraim & Phillip, Dayo & Mogues, Tewodaj & Pender, John & Kato, Edward, 2012. "Impacts of Community-driven Development Programs on Income and Asset Acquisition in Africa: The Case of Nigeria," World Development, Elsevier, vol. 40(9), pages 1824-1838.
    5. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    6. Barrett, Christopher B., 2008. "Smallholder market participation: Concepts and evidence from eastern and southern Africa," Food Policy, Elsevier, vol. 33(4), pages 299-317, August.
    7. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    8. Shahidur R. Khandker & Gayatri B. Koolwal & Hussain A. Samad, . "Handbook on Impact Evaluation : Quantitative Methods and Practices," World Bank Publications, The World Bank, number 2693, September.
    9. Wanjala, Bernadette M. & Muradian, Roldan, 2013. "Can Big Push Interventions Take Small-Scale Farmers out of Poverty? Insights from the Sauri Millennium Village in Kenya," World Development, Elsevier, vol. 45(C), pages 147-160.
    10. Alberto Abadie & Guido W. Imbens, 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 1-11, January.
    11. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    12. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    13. Sascha O. Becker & Marco Caliendo, 2007. "Sensitivity analysis for average treatment effects," Stata Journal, StataCorp LP, vol. 7(1), pages 71-83, February.
    14. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    15. 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.
    16. Ogutu, Sylvester Ochieng & Okello, Julius Juma & Otieno, David Jakinda, 2014. "Impact of Information and Communication Technology-Based Market Information Services on Smallholder Farm Input Use and Productivity: The Case of Kenya," World Development, Elsevier, vol. 64(C), pages 311-321.
    17. Markelova, Helen & Meinzen-Dick, Ruth & Hellin, Jon & Dohrn, Stephan, 2009. "Collective action for smallholder market access," Food Policy, Elsevier, vol. 34(1), pages 1-7, February.
    18. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    19. Farina, Elizabeth M.M.Q. & Gutman, Graciela E. & Lavarello, Pablo J. & Nunes, Rubens & Reardon, T., 2005. "Private and public milk standards in Argentina and Brazil," Food Policy, Elsevier, vol. 30(3), pages 302-315, June.
    20. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    21. Christine M. Moser & Christopher B. Barrett, 2006. "The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar," Agricultural Economics, International Association of Agricultural Economists, vol. 35(3), pages 373-388, 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. 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.
    24. 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.
    25. Keijiro Otsuka & Yoko Kijima, 2010. "Technology Policies for a Green Revolution and Agricultural Transformation in Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 19(suppl_2), pages 60-76.
    26. Martin Ravallion, 2009. "Evaluation in the Practice of Development," The World Bank Research Observer, World Bank, vol. 24(1), pages 29-53, March.
    27. J. Ndjeunga & M.S.C. Bantilan, 2005. "Uptake of Improved Technologies in the Semi-Arid Tropics of West Africa: Why Is Agricultural Transformation Lagging Behind?," The Electronic Journal of Agricultural and Development Economics, Food and Agriculture Organization of the United Nations, vol. 2(1), pages 85-102.
    28. Maren Duvendack & Richard Palmer-Jones, 2012. "High Noon for Microfinance Impact Evaluations: Re-investigating the Evidence from Bangladesh," Journal of Development Studies, Taylor & Francis Journals, vol. 48(12), pages 1864-1880, December.
    29. Derek Byerlee & Edith Hesse de Polanco, 1986. "Farmers' Stepwise Adoption of Technological Packages: Evidence from the Mexican Altiplano," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(3), pages 519-527.
    30. 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.
    31. Pamuk, Haki & Bulte, Erwin & Adekunle, Adewale A., 2014. "Do decentralized innovation systems promote agricultural technology adoption? Experimental evidence from Africa," Food Policy, Elsevier, vol. 44(C), pages 227-236.
    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. Arraiz, Irani & Calero, Carla & Jon, Songqing & Peralta, Alexandra, 2015. "Planting the seeds: The impact of training on mando producers in Haiti," 2015 Conference, August 9-14, 2015, Milan, Italy 212622, International Association of Agricultural Economists.
    2. Alexandra Peralta & Scott M. Swinton & Songqing Jin, 2018. "The Secret to Getting Ahead Is Getting Started: Early Impacts of a Rural Development Project," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    3. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.
    4. 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.
    5. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
    6. 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.
    7. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    8. Dehejia Rajeev, 2015. "Experimental and Non-Experimental Methods in Development Economics: A Porous Dialectic," Journal of Globalization and Development, De Gruyter, vol. 6(1), pages 47-69, June.
    9. Jianxin Guo & Songqing Jin & Lei Chen & Jichun Zhao, 2018. "Impacts of Distance Education on Agricultural Performance and Household Income: Micro-Evidence from Peri-Urban Districts in Beijing," Sustainability, MDPI, vol. 10(11), pages 1-19, October.
    10. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    11. Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
    12. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    13. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    14. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    15. Sudhanshu Handa & John A. Maluccio, 2010. "Matching the Gold Standard: Comparing Experimental and Nonexperimental Evaluation Techniques for a Geographically Targeted Program," Economic Development and Cultural Change, University of Chicago Press, vol. 58(3), pages 415-447, April.
    16. Ainembabazi, John Herbert & Abdoulaye, Tahirou & Feleke, Shiferaw & Alene, Arega & Dontsop-Nguezet, Paul M. & Ndayisaba, Pierre Celestin & Hicintuka, Cyrille & Mapatano, Sylvain & Manyong, Victor, 2018. "Who benefits from which agricultural research-for-development technologies? Evidence from farm household poverty analysis in Central Africa," World Development, Elsevier, vol. 108(C), pages 28-46.
    17. Cadot, Olivier & Fernandes, Ana M. & Gourdon, Julien & Mattoo, Aaditya, 2015. "Are the benefits of export support durable? Evidence from Tunisia," Journal of International Economics, Elsevier, vol. 97(2), pages 310-324.
    18. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    19. Ostapchuk, Igor & Gagalyuk, Taras & Curtiss, Jarmila, 2021. "Post-acquisition integration and growth of farms: the case of Ukrainian agroholdings," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(4), April.
    20. Flores, Carlos A. & Mitnik, Oscar A., 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," IZA Discussion Papers 4451, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    extension services; Agriculture; impact evaluation;
    All these keywords.

    JEL classification:

    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

    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:idb:brikps:7184. 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: Felipe Herrera Library (email available below). General contact details of provider: https://edirc.repec.org/data/iadbbus.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.