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Determinants and Welfare Impacts of Export Crop Cultivation - Empirical Evidence from Ghana

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  • Kuhlgatz, Christian
  • Abdulai, Awudu

Abstract

This paper investigates the determinants of farm households‟ participation in export cropping and the impact of export cropping on household welfare, using cross-sectional data obtained from the Ghanaian living standards survey 2005-6. Given the problem of selectivity bias that arise when households self-select into export cropping, we employ the full information maximum likelihood approach to analyze the participation decision, and generalized propensity matching approach to examine the welfare impacts of participation. The empirical results indicate that farmers facing lower transport costs and having better access to credit facilities are more likely to participate in export cropping. Estimates of the welfare impacts of export cropping generally reveal a positive relationship between engagement in export cropping and farm household welfare. However, a consideration of the impact of extent of export cropping shows a non-linear relationship with household welfare indicators, with per capita expenditures rising and poverty declining only at higher levels of export specialization.

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Bibliographic Info

Paper provided by European Association of Agricultural Economists in its series 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland with number 114692.

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Date of creation: 02 Sep 2011
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Handle: RePEc:ags:eaae11:114692

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Related research

Keywords: Export crops; Farm households; Household welfare; Poverty; Generalized propensity score; Crop Production/Industries; International Relations/Trade;

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  1. Balat, Jorge & Brambilla, Irene & Porto, Guido, 2009. "Realizing the gains from trade: Export crops, marketing costs, and poverty," Journal of International Economics, Elsevier, vol. 78(1), pages 21-31, June.
  2. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," IZA Discussion Papers 2846, Institute for the Study of Labor (IZA).
  3. Michela Bia & Alessandra Mattei, 2008. "A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score," Stata Journal, StataCorp LP, vol. 8(3), pages 354-373, September.
  4. Shepherd, Ben, 2008. "Geographical Diversification of Developing Country Exports," MPRA Paper 11267, University Library of Munich, Germany.
  5. Govereh, Jones & Jayne, T. S., 2003. "Cash cropping and food crop productivity: synergies or trade-offs?," Agricultural Economics, Blackwell, vol. 28(1), pages 39-50, January.
  6. Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 12 Feb 2014.
  7. Puhani, Patrick A, 2000. " The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
  8. 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. Ibrahim, Mohammed & Florkowski, Wojciech J. & Kolavalli, Shashidhara, 2012. "Determinants of Farmer Adoption of Improved Peanut Varieties and their Impact on Farm Income: Evidence from Northern Ghana," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125000, Agricultural and Applied Economics Association.

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