Evaluating the impact of improved maize varieties on food security in Rural Tanzania: Evidence from a continuous treatment approach
This paper investigates impact heterogeneity in the adoption of improved maize varieties using data from rural Tanzania. We used a generalized propensity-score matching methodology, complemented with a parametric econometric method to check the robustness of results. We found a consistent result across models, indicating that adoption increased food security, and that the impact of adoption varied with the level of adoption. On average, an increase of one acre in the area allocated to improved maize varieties reduced the probabilities of chronic and transitory food insecurity from between 0.7 and 1.2 % and between 1.1 and 1.7 %, respectively. Policies that increase maize productivity and ease farmers’ adoption constraints can ensure the allocation of more land to improved technologies and, in doing so, enhance the food security of households.
KeywordsAdoption Continuous treatment Impact heterogeneity Food security Africa Tanzania
This study is supported by the Australian Centre for International Agricultural Research (ACIAR) and the Australian International Food Security Research Centre (AIFSRC) through the International Maize and Wheat Improvement Center (CIMMYT)-led Sustainable Intensification of Maize-Legume Cropping Systems in Eastern and Southern Africa (SIMLESA) program, and Adoption Pathways Project. The views expressed here are those of the authors and do not necessarily reflect the views of the donor or the authors’ institution. The usual disclaimer applies. We would also like to thank the anonymous reviewer and the chief editor of this journal for their valuable comments and suggestions which improved the quality of the paper.
- Deaton, A. (2010). Price indices, inequality, and the measurement of world poverty. Presidential Address, American Economic Association, January, Atlanta.Google Scholar
- Guardabascio, B., & Ventura, M. (2013). Estimating the dose—response. Function through a GLM Approach. German Stata Users’ Group meetings 2013, Stata Users Group.Google Scholar
- Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous treatments. In A. Gelman & X. Meng (Eds.), Applied bayesian modeling and causal inference from incomplete-data perspectives. New York: Wiley.Google Scholar
- Høgh-Jensen, H., Myaka, F. A., Sakala, W. D., Kamalongo, D., Ngwira, A., Vesterager, J. M., et al. (2007). Yields and qualities of pigeonpea varieties grown under smallholder farmers’ conditions in Eastern and Southern Africa. African Journal of Agricultural Research, 2(6), 269–278.Google Scholar
- Kabubo-Mariara, J., Linderhof, V., Kruseman, G., Atieno, R., & Mwabu, G. (2006). Household welfare, investment in soil and water conservation, and tenure security: Evidence from Kenya. Poverty Reduction and Environmental Management (PREM) Working Paper 06–06.Google Scholar
- Minot, N. (2010). Staple food prices in Tanzania. Contributed Paper Prepared for the COMESA Policy Seminar Maputo, Mozambique, 25–26 January.Google Scholar
- Smale, M., Byerlee, D., & Jayne, T. (2011). Maize revolutions in Sub-Saharan Africa. World Bank Policy Research working paper. No. WPS 5659.Google Scholar
- Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press.Google Scholar
- USDA (United States Department of Agriculture) (2010). Foreign Agricultural service supply and distribution (PSD) online database. http://www.fas.usda.gov/psdonline/psdHome.aspx. Accessed on December 2010.