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.
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This quantity of fertilizer and seed was intended to match the general fertilizer and seed application rates recommended for planting one acre of maize.
The authors thank Dr. Eric Kramon for sharing these data on electoral results with us. Eric is currently an Assistant Professor in the Department of Political Science, George Washington University, USA. While we note that the official electoral results of this election were disputed, these data are nevertheless the best available for our purposes in this paper.
As noted by Mason and Jayne (2013), in the event that there is significant illegal diversion by government officials of either vouchers or subsidized fertilizer away from intended villages or depots, then the actual magnitude of effects of household receipt of subsidized fertilizer in displacing commercial fertilizer demand may actually be higher than those estimated by our method here. We do not attempt to estimate the effect of diversion or leakage on the average crowding in or out estimate for these two programs in Kenya because unlike in the cases of Zambia (Mason and Jayne 2013) or Malawi (Holden and Lunduka 2013; Lunduka et al. 2013), Kenya does not have a population-based representative household survey with which one can estimate whether or not diversion has occurred and its magnitude, nor are we aware of anecdotal evidence of significant illegal diversion of government subsidized fertilizer into Kenya’s commercial fertilizer supply chain.
Binary soil group indicators are based on four of the six general soil-types found in the villages covered by the Tegemeo surveys, as categorized by Sheahan et al. (2013).
Expected main season rainfall computed as a 10-year moving average of cumulative rainfall during the main season period for each zone; expected main season rainfall shock is the fraction of 20-day periods during the main season during which cumulative rainfall (during those 20 days) is <40 mm.
Farm asset value includes the value of all livestock, farm equipment, radios, etc., as reported by households.
We add n = 4 cases of receipt of subsidized fertilizer from an NGO in 2009 to those of NAAIAP because, like NAAIAP, the subsidy amount covered by NGOs was 100%, and because one might expect NGOs to target poorer farmers on average (as NAAIAP was intended to do).
For each crop, we compute the naïve price expectation using prices from the same months over time.
Rainfall variables are derived from rainfall estimates based on data from satellites (such as cloud cover and cloud top temperatures) and rain stations, which are combined to interpolate estimates of decadal (10-day period) rainfall, which can be matched to sample households/villages using global positioning system (GPS) coordinates. Rainfall estimates were matched to GPS coordinates of each village.
Generated using spatial coordinates of each village and secondary data on elevation (SRTM (2000)) and the length of growing period from the GAEZ 3.0 database (Fischer et al. 2000), which is measured in terms of the number of days experiencing temperatures >5 °C when moisture conditions are adequate for plant growth.
The LR statistic comparing the two models is 3687, the p-value for which is 0.000, which indicates that the Cragg DH model of household commercial fertilizer demand clearly fits our data better than the Tobit in this case.
See Ricker-Gilbert et al. (2011) for a recent application of this adapted control function approach.
Our estimate of the APE of (unconditional) subsidized fertilizer quantity on household commercial fertilizer demand is −0.403 (p = 0.006) as compared to −0.412 (p = 0.007) if we did not use attrition weights.
The significance and magnitude of our average displacement result was virtually the same whether we used a balanced or unbalanced panel. This is likely because once we drop n = 28 households that were observed in the first four years but not in 2010 (we dropped these because 2010 was the only year in which subsidized fertilizer was observed), there were only n = 9 households (n = 27 cases) that were observed in 2010 as well as other years (but not for the 4 years we used).
We do not use the first wave of the panel because of data limitations for two variables (10-year expected seasonal rainfall (or rainfall shock); 1 = household suffered an adult death in past 3–4 years) that do not enable those to be included for that wave. If we run models (2) and (4) with a balanced or unbalanced panel of 5 years (assuming zero deaths prior to the first wave, and using a 3-year moving average of rainfall and rainfall shock), the significance and magnitude of the displacement effect remains virtually the same (unconditional APE of subsidized fertilizer on commercial fertilizer demand is −0.420 (p = .013)).
For example, in the probit, the joint significance of the year dummies is p = 0.062; agro-ecological zone dummies (p-value = 0.000); soil group dummies (p-value = 0.000); prices of non-maize crops (p-value = 0.000).
In the second stage of the DH, the joint significance of the year dummies is p = 0.144; agro-ecological zone dummies (p-value = 0.120); soil group dummies (p-value = 0.110); prices of non-maize crops (p-value = 0.401).
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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.
Conflict of interest
The authors declare that they have no conflict of interest.
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Mather, D.L., Jayne, T.S. Fertilizer subsidies and the role of targeting in crowding out: evidence from Kenya. Food Sec. 10, 397–417 (2018). https://doi.org/10.1007/s12571-018-0773-8
- Fertilizer subsidy
- Smallholder agriculture