Abstract
In Nigeria, farmers depend on government support for farm inputs in form of subsidies to improve farmers productivity and food security. In this article, the productivity impact of the Growth Enhancement Support Scheme (GESS) farm input subsidy support program that was implemented in 2011. The study employs a two-stage probability design with stratification was used to collect household survey data from 390 households in Kano State. As an analytical approach, the study employed a propensity score matching and a Two-Stage Least Square (2SLS) regression estimator that corrects for selectivity and endogeneity problems respectively while Hedges “g” was used to estimate the effect size of GESS. Maize yield and total factor productivity index were used as indicators to estimate the productivity impact of GESS program. The result from two-stage least square estimator showed that GESS subsidy increased the yield of participants by 32.3% and the difference was statistically significant (P<0.05) while the result of total factor productivity index, showed that the participants were more productive) and had an average of 14.1% net gain from the cost incurred in production in the 2016 farming season. The size of the estimated treatment effect suggests a moderate improvement in the productivity outcomes of participants. The study found that the results of the study are consistent with similar findings and therefore validate the hypothesis that the GESS subsidy programme improved the productivity of beneficiary households. The scheme obviously has enormous potentials and is also very promising for agricultural input procurement and distribution to resource-poor households in Nigeria. In addition, there is a need for capacity building of the farmers by local extension agents in the form of integrated crop management practices in order to sustain productivity gains. This study concludes that input use alone is not enough to increase maize production, improvement in input use efficiency through integrated crop management practices are also needed. Key words: Agricultural input subsidy, mobile phone, productivity outcomes, farming households.
Highlights
The above difference may not be the result of Growth Enhancement Support Scheme (GESS) participation but instead may be due to other factors, such as differences in household characteristics and farm characteristics as mentioned above so the outcome effect on individuals who participated in GESS might have been achieved even without participation, that is, the counterfactual effect
There is, the need to further investigate these outcome effects by applying other rigorous analysis to test the impact of GESS participation on farmers‟ productivity
The matching method made a comparison between those who participated in the program and those who did not and drew conclusions based only on those that participated in GESS farm subsidy programs
Summary
In the 1970s and 1980s, farm subsidies were the major drivers of agricultural development and growth They were implemented as large scale subsidies spanning over a period of 5 to 10 years in Zambia, Malawi, Tanzania, Kenya, Ghana, Zimbabwe, Mali, Nigeria. The subsidy programmes were later phased out during the liberalization programs of World Bank and IMF in the 90s on the premise that the private sector can provide farm input more efficiently through market-driven mechanisms (Ricker-Gilbert, 2014) and Tesfamicheal et al, 2017). This move led to a drastic fall in agricultural productivity. Ammani et al (2010) observed that the liberalization period led to a decrease in maize yield and other cereals. Hassan et al (2014) used time-series data between 1971 - 2010 to examine total factor productivity of maize production under various subsidy programs in Nigeria, with results revealing that the 40 years of subsidy programs produced a mean total factor productivity of 1.004, implying that a total factor productivity growth of 0.4% as well as a total factor productivity index of less than 1 shows that farmers are unproductive while fertilizer use stagnated at about 8 kg/ha compared to Sub-Saharan Africa average of 21 kg/ha
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