Fertilizer subsidies and the role of targeting in crowding out: evidence from Kenya
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The impact of input subsidy programs depends on the extent to which they increase fertilizer use. We used panel data of smallholder farm households from Kenya to analyse the targeting criteria of two fertilizer subsidy programs in Kenya and how these targeting criteria affected farmers’ commercial demand for fertilizer and total fertilizer use. We found that every kilogram of subsidized fertilizer allocated to farmers reduced the quantity of commercial fertilizer purchased by 0.40 kg, a crowding-out effect that is double those found recently in Malawi and Zambia. The large magnitude of crowding out is driven by the fact that neither subsidy program focused on reaching households that had not previously been purchasing commercial fertilizer. There is little evidence that these programs systematically focused on relatively poor households either. The programs crowded out commercial fertilizer use most severely in medium/high potential zones (relative to low), and among households in the upper half of landholding/asset distributions (relative to the lower half). Different targeting criteria could substantially increase the contribution of these subsidy programs to total fertilizer use and hence to national food production and food security.
KeywordsAfrica Kenya Fertilizer subsidy Smallholder agriculture
The authors are grateful for financial support for this research from the Guiding Investments in Sustainable Agricultural Markets in Africa (GISAMA) project, a grant from the Bill and Melinda Gates Foundation (BMGF) to Michigan State University’s Department of Agricultural, Food, and Resource Economics. Further funding for this research was provided by the Food Security III Cooperative Agreement (GDGA-00-000021-00) between Michigan State University and the United States Agency for International Development, Bureau for Food Security, Office of Agriculture, Research, and Technology. The opinions expressed in this report are those of the authors alone and do not represent the views of BMGF or USAID. Neither sponsor had a role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. The authors are also grateful to Eric Kramon (George Washington University) for access to electoral data from Kenya and Jordan Chamberlain (CIMMYT) for generating and sharing spatial variables for village-level elevation and length of growing period. This article has also benefited from longstanding discussions on the topic with Joshua Ariga, John Olwande, Jake Ricker-Gilbert, Bill Burke, and Nicole Mason.
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Conflict of interest
The authors declare that they have no conflict of interest.
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