Food Security

, Volume 10, Issue 2, pp 397–417 | Cite as

Fertilizer subsidies and the role of targeting in crowding out: evidence from Kenya

  • David L. Mather
  • Thomas S. Jayne
Original Paper


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.


Africa 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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Argwings-Kodhek, G., Jayne, T., Nyambane, G., Awuor, T. & Yamano, T. (1998). How can micro-level household survey data make a difference for agricultural policy making? Nairobi, Kenya: Egerton University/Tegemeo Institute of Agricultural Policy and Development. Available at:
  2. Ariga, J., & Jayne, T.S. (2009). Private sector responses to public investments and policy reforms: the case of fertilizer and maize market development in Kenya. IFPRI Discussion Paper 00921. International Food Policy Research Institute, Washington DC.Google Scholar
  3. Ariga, J., & Jayne, T. S. (2011). Fertilizer in Kenya: Factors driving the increase in usage by smallholder farmers. In P. Chuhan-Pole & M. Angwafo (Eds.), Yes, Africa can: Success stories from a dynamic continent. Washington, DC: World Bank.Google Scholar
  4. Banful, A. B. (2011). Old problems in the new solutions? Politically motivated allocation of program benefits and the “new” fertilizer subsidies. World Development, 39(7), 1166–1176.CrossRefGoogle Scholar
  5. Braun, H.M.H., & Staff of the Kenya Soil Survey. (1980). Exploratory soil map and agro-climatic zone map of Kenya. Kenya soil survey. Nairobi: Republic of Kenya Ministry of Agriculture.Google Scholar
  6. Chamberlain, G. (1984). Panel Data. In Z. Grilliches & M. D. Intriligator (Eds.), Handbook of econometrics (Vol. 2). Amsterdam: North Holland.Google Scholar
  7. Chang, E. (2005). Electoral incentives for political corruption under open-list proportional systems. Journal of Politics, 67(3), 716–730.CrossRefGoogle Scholar
  8. Cragg, J. (1971). Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica, 39(5), 829–844.CrossRefGoogle Scholar
  9. de Janvry, A., & Sadoulet, E. (1995). Quantitative development policy analysis. Baltimore: The Johns Hopkins University Press.Google Scholar
  10. de Janvry, A., & Sadoulet, E. (2006). Progress in modeling of rural Household’s behavior under market failures. In A. de Janvry & R. Kanbur (Eds.), Poverty, inequality and development (Vol. 2, pp. 155–181). New York: Springer.CrossRefGoogle Scholar
  11. Duflo, E., Kremer, M., & Robinson, J. (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. American Economic Review, 101(6), 2350–2390.CrossRefGoogle Scholar
  12. Economist Intelligence Unit. (2008). Lifting African and Asian farmers out of poverty: Assessing the investment needs. Research report for the Bill and Melinda Gates Foundation. New York: The Economist Intelligence Unit.Google Scholar
  13. Fan, S., Gulati, A., & Thorat, S. (2008). Investment, subsidies, and pro poor growth in rural India. Agricultural Economics, 39(2), 163–170.CrossRefGoogle Scholar
  14. Fischer, G., van Velthuizen, H. & Nachtergaele, F.O. (2000). Global agro-ecological zones assessment: methodology and results. Interim Report IR-00-064. International institute for applied systems analysis and the food and agricultural organization of the United Nations. The data are available from:
  15. Garcia, J., & Labeaga, J. M. (1996). Alternative approaches to modeling zero expenditure: An application to Spanish demand for tobacco. Oxford Bulletin of Economics and Statistics, 58(3), 489–504.CrossRefGoogle Scholar
  16. Goetz, S. J. (1992). A selectivity model of household food marketing behavior in sub-Saharan Africa. American Journal of Agricultural Economics, 74(2), 444–452.CrossRefGoogle Scholar
  17. Greene, W. (2004). Fixed effects and bias due to the incidental parameters problem in the Tobit model. Econometric Reviews, 23(2), 125–147.CrossRefGoogle Scholar
  18. Holden, S., & Lunduka, R. (2013). Who benefits from Malawi’s targeted farm input subsidy program? Forum for Development Studies, 40(1), 1–25.CrossRefGoogle Scholar
  19. Jayne, T. S., & Rashid, S. (2013). Input subsidy programs in sub-Saharan Africa: A synthesis of recent evidence. Agricultural Economics, 44(6), 547–562.CrossRefGoogle Scholar
  20. Jayne, T. S., Govereh, J., Mwanaumo, A., Nyoro, J. K., & Chapoto, A. (2002). False promise or false premise? The experience of food and input market reform in eastern and southern Africa. World Development, 30(11), 1967–1985.CrossRefGoogle Scholar
  21. Jones, A. M. (1992). A note on computation of the double-hurdle model with dependence with an application to tobacco expenditure. Bulletin of Economic Research, 44(1), 67–74.CrossRefGoogle Scholar
  22. Liverpool-Tasie, S. (2014). Fertilizer subsidies and private market participation: The case of Kano state, Nigeria. Agricultural Economics, 45, 1–16.Google Scholar
  23. Lunduka, R., Ricker-Gilbert, J., & Fisher, M. (2013). What are the farm-level impacts of Malawi’s farm input subsidy program? A critical review. Agricultural Economics, 44(6), 563–579.CrossRefGoogle Scholar
  24. Mason, N. (2011). The effects of the zambian food reserve agency and government fertilizer programs on smallholder farm household fertilizer use and crop production. Unpublished Ph.D. Dissertation essay, Michigan State University.Google Scholar
  25. Mason, N., & Jayne, T. S. (2013). Fertilizer subsidies and smallholder commercial fertilizer purchases: Crowding out, leakage, and policy implications for Zambia. Journal of Agricultural Economics, 64(3), 558–582.CrossRefGoogle Scholar
  26. Ministry of Agriculture. (2008). Program Design and Guidelines 2008/09. Nairobi: Government of Kenya.Google Scholar
  27. Ministry of Agriculture. (2010). Progress and way forward for the National Accelerated Agricultural Inputs Access Program (NAAIAP). Nairobi: Government of Kenya.Google Scholar
  28. Morris, M., Kelly, V., Kopicki, R., & Byerlee, D. (2007). Fertilizer use in African agriculture: Lessons learned and good practice guidelines. Washington, D.C.: World Bank.CrossRefGoogle Scholar
  29. Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46, 69–85.CrossRefGoogle Scholar
  30. Ricker-Gilbert, J., Jayne, T. S., & Chirwa, E. (2011). Subsidies and crowding out: A double-hurdle model of fertilizer demand in Malawi. American Journal of Agricultural Economics, 93(1), 26–42.CrossRefGoogle Scholar
  31. Rivers, D., & Vuong, Q. H. (1988). Limited information estimators and exogeneity tests for simultaneous Probit models. Journal of Econometrics, 39, 347–366.CrossRefGoogle Scholar
  32. Sheahan, M., Black, R., & Jayne, T. S. (2013). Are Kenyan farmers under-utilizing fertilizer? Implications for input intensification strategies and research. Food Policy, 41, 39–52.CrossRefGoogle Scholar
  33. Singh, I., Squire, L., & Strauss, J. (1986). Agricultural household models. Baltimore: Johns Hopkins University Press.Google Scholar
  34. SRTM. (2000). The SRTM (Shuttle Radar Topography Mission) data (v.4) are available from:
  35. Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26, 24–36.CrossRefGoogle Scholar
  36. Vella, F. (1993). A simple estimator for simultaneous models with censored endogenous regressors. International Economic Review, 34(2), 441–457.CrossRefGoogle Scholar
  37. Wanzala, M., Fuentes, P. & Mkumbwa, S. (2013). Practices and policy options for the improved design and implementation of fertilizer subsidy programs in Sub-Saharan Africa. NEPAD Policy Study. Randjespark, Midrand: New Partnership for Africa’s Development.Google Scholar
  38. Wooldridge, J. W. (2002). Econometric analysis of cross section and panel data. Cambridge: The MIT Press.Google Scholar
  39. Xu, Z., Burke, W. J., Jayne, T. S., & Govereh, J. (2009). Do input subsidy programs “crowd in” or “crowd out” commercial market development? Modeling fertilizer demand in a two-channel marketing system. Agricultural Economics, 40(1), 79–94.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature and International Society for Plant Pathology 2018

Authors and Affiliations

  1. 1.Department of Agriculture, Food, and Resource EconomicsMichigan State UniversityEast LansingUSA
  2. 2.Department of Agriculture, Food, and Resource EconomicsMichigan State UniversityEast LansingUSA

Personalised recommendations