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Ex-ante Evaluation of Cassava Research for Development in Malawi: A Farm Household and Random Utility Modeling Approach

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Listed:
  • Rusike, Joseph
  • Jumbo, S.
  • Ntawuruhunga, Pheneas
  • Kawonga, J.M.
  • James, Braima
  • Okechukwu, R.
  • Manyong, Victor M.

Abstract

Ex ante evaluation of agricultural research for development projects has become important in recent years for priority setting, ex post impact assessment and learning about generalizability to other populations and contexts. We apply farm household and random utility modeling to baseline survey data and evaluate the impact of a cassava research for development project in Malawi prior to its implementation. The project is being implemented to unlock the potential of cassava in response to the global food crisis. We find that a high proportion of farm households are not self-sufficient in food production and can be assisted by increasing the productivity of land and labor in production, processing and marketing of cassava to reduce deficits and increase marketed surplus. The research for development embeds research in an innovation systems network and speeds up exposure, awareness, adoption and diffusion. This increases the likelihood that incremental benefits will be generated and accrue earlier compared to the counterfactual without the project.

Suggested Citation

  • Rusike, Joseph & Jumbo, S. & Ntawuruhunga, Pheneas & Kawonga, J.M. & James, Braima & Okechukwu, R. & Manyong, Victor M., 2010. "Ex-ante Evaluation of Cassava Research for Development in Malawi: A Farm Household and Random Utility Modeling Approach," 2010 AAAE Third Conference/AEASA 48th Conference, September 19-23, 2010, Cape Town, South Africa 96182, African Association of Agricultural Economists (AAAE);Agricultural Economics Association of South Africa (AEASA).
  • Handle: RePEc:ags:aaae10:96182
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    References listed on IDEAS

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    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    3. Edward Miguel & Michael Kremer, 2004. "Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities," Econometrica, Econometric Society, vol. 72(1), pages 159-217, January.
    4. Rodrik, Dani, 2008. "The New Development Economics: We Shall Experiment, but How Shall We Learn?," Working Paper Series rwp08-055, Harvard University, John F. Kennedy School of Government.
    5. 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.
    6. Cragg, John G, 1971. "Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods," Econometrica, Econometric Society, vol. 39(5), pages 829-844, September.
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    Resource /Energy Economics and Policy;

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