Food Security

, Volume 6, Issue 2, pp 217–230 | Cite as

Evaluating the impact of improved maize varieties on food security in Rural Tanzania: Evidence from a continuous treatment approach

Original Paper


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.


Adoption 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.


  1. Alene, A. D., Menkir, A., Ajala, S. O., Badu-Apraku, A. S., Olanrewaju, V., Manyong, M., et al. (2009). The economic and poverty impacts of maize research in West and Central. Agricultural Economics, 40, 535–550.CrossRefGoogle Scholar
  2. Amare, M., Asfaw, S., & Shiferaw, B. (2012). Welfare impacts of Maize-Pigeonpea intensification in Tanzania. Agricultural Economics, 43(1), 1–17.CrossRefGoogle Scholar
  3. Asfaw, S., Kassie, M., Simtowe, F., & Leslie, L. (2012a). Poverty reduction effects of agricultural technology adoption: a micro-evidence from Rural Tanzania. Journal of Development Studies, 48(9), 1288–1305.CrossRefGoogle Scholar
  4. Asfaw, S., Shiferaw, B., Simtowe, F., & Lipper, L. (2012b). Impact of modern agricultural technologies on smallholder welfare: evidence from Tanzania and Ethiopia. Food Policy, 7(3), 283–295.CrossRefGoogle Scholar
  5. Becerril, J., & Abdulai, A. (2010). The impact of improved maize varieties on poverty in Mexico: a propensity score matching approach. World Development, 38(7), 1024–1035.CrossRefGoogle Scholar
  6. de Janvry, A., & Sadoulet, E. (2001). World poverty and the role of agricultural technology: direct and indirect effects. Journal of Development Studies, 38(4), 1–26.CrossRefGoogle Scholar
  7. Deaton, A. (2010). Price indices, inequality, and the measurement of world poverty. Presidential Address, American Economic Association, January, Atlanta.Google Scholar
  8. Djebbari, H., & Smith, J. (2008). Heterogeneous impacts in PROGRESA. Journal of Econometrics, 145, 64–80.CrossRefGoogle Scholar
  9. Pender, J., & Gebremedhin, B. (2007). Determinants of agricultural and land management practices and impacts on crop production and household income in the highlands of Tigray, Ethiopia. Journal of African Economies, 17, 395–450.CrossRefGoogle Scholar
  10. 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
  11. 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
  12. 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
  13. 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
  14. Kassie, M., Simon, W., & Jesper, S. (2014). What determines gender inequality in Household Food security in Kenya? Application of exogenous switching regression. World Development, 56, 153–171.CrossRefGoogle Scholar
  15. Kassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., & Mekuria, M. (2013). Adoption of interrelated sustainable agricultural practices in smallholder system: evidence from rural Tanzania. Technological Forecast and Social Change, 80, 525–540.CrossRefGoogle Scholar
  16. Kassie, M., Shiferaw, B., & Geoffrey, M. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.CrossRefGoogle Scholar
  17. Karanja, D. D., Renkow, M., & Crawford, E. W. (2003). Welfare effects of maize technologies in marginal and high potential regions of Kenya. Agricultural Economics, 29(3), 331–341.CrossRefGoogle Scholar
  18. Kijima, Y., Otsuka, K., & Serunkuuma, D. (2008). Assessing the impact of NERICA on income and poverty in central and western Uganda. Agricultural Economics, 38(3), 327–337.CrossRefGoogle Scholar
  19. Kluve, J., Schneider, H., Uhlendorff, A., & Zhao, Z. (2012). Evaluating continuous training programmes by using the generalized propensity score. Journal of Royal Statistical Society, 175(Part 2), 587–617.CrossRefGoogle Scholar
  20. Mallick, D., & Rafi, M. (2010). Are female-headed households more food insecure? Evidence from Bangladesh. World Development, 38(4), 593–605.CrossRefGoogle Scholar
  21. Minten, B., & Barrett, C. B. (2008). Agricultural technology, productivity, and poverty in Madagascar. World Development, 36(5), 797–822.CrossRefGoogle Scholar
  22. Minot, N. (2010). Staple food prices in Tanzania. Contributed Paper Prepared for the COMESA Policy Seminar Maputo, Mozambique, 25–26 January.Google Scholar
  23. Ravallion, M., & Lokshin, M. (2002). Self-rated economic welfare in Russia. European Economic Review, 46(8), 1453–1473.CrossRefGoogle Scholar
  24. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.CrossRefGoogle Scholar
  25. Smale, M., Byerlee, D., & Jayne, T. (2011). Maize revolutions in Sub-Saharan Africa. World Bank Policy Research working paper. No. WPS 5659.Google Scholar
  26. Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press.Google Scholar
  27. USDA (United States Department of Agriculture) (2010). Foreign Agricultural service supply and distribution (PSD) online database. Accessed on December 2010.

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2014

Authors and Affiliations

  1. 1.CIMMYTNaiorbiKenya
  2. 2.CIMMYTAddis AbabaEthiopia
  3. 3.Department of Statistics, Informatics, Applications \G. ParentiUniversity of FlorenceFlorenceItaly

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