Tag Archives: propensity score matching

Reducing Poverty through Fertilizer Subsidy Programe “Evidence from Rwanda (Published)

Farm input subsidies are assumed to improve agricultural production and productivity for small  resource poor farmers in developing countries by promoting the use of improved farm inputs,  mainly inorganic fertilizers and hybrid seeds. This is expected to contribute to increased income from produce sales, improved food security at household and national levels, and consequently, contributing to poverty alleviation. However, little insights exist on the impacts of this program. The overall objective of the study was to determine the effect of the fertilizer subsidy program on reducing poverty among small holder farmers in Gatsibo district, Eastern province of Rwanda. Multi stage sampling techniques were employed to select respondents. Structured questionnaires was employed to collect data from 200 smallholders maize farmers including (86 farmers with fertilizer subsidy and 114 without subsidies in the period 2015B, 2016A and 2016B agricultural seasons in the selected sectors of  Kabarole, Rwimbogo and Rugarama). Propensity score matching using a “with” and “without” the fertilizer subsidy project evaluation approach was used to estimate the effect of fertilizer subsidy and  descriptive statistics using t-test was used compare effects of fertilizer subsidies across respondents. Based on the study objectives, results from propensity score matching indicated an effect on yield between fertilizer subsidy users and non-users.

Keywords: Agriculture, Agriculture input subsidies, Fertilizer Subsidies, Poverty, propensity score matching

The Impact of Farmer Field School Training On Farmers’ Technical Efficiency: Evidence from Smallholder Maize Farmers in Oromia, Ethiopia (Published)

This study examines the impact of Farmer Field School (FFS) training program on technical efficiency of smallholder farmers. The FFS program was sponsored by the Ethiopian government and launched in 2010 to scale-up best agricultural practices in the country. The study aims to compare changes in the technical efficiency of those FFS graduate and non-FFS graduate maize farmers in Ethiopia. For this, panel data were collected in two rounds from 446 randomly selected households from three districts consisting of 218 FFS graduate farmers and 228 non-FFS graduate farmers. The analytical procedure has involved three stages: in the first stage, descriptive analyses were used to detect existence of difference in the outcome indicators between the two farmer groups. In the second stage, a semi-parametric impact evaluation method of propensity score matching with several matching algorithms was employed to estimate the program impact. In the third stage, Difference-in-Difference was used as robustness check in detecting causality between program intervention and the technical efficiency changes. The Combined uses of these alternative estimation techniques indicate that the program has negative impact on the technical efficiency of the FFS graduates.  Numerous plausible explanations for this outcome are discussed, and recommendations for improvements are suggested accordingly.  

Keywords: Impact Evaluation, Technical Efficiency, difference in difference, propensity score matching

The Impact of Farmer Field School Training On Farmers’ Technical Efficiency: Evidence from Smallholder Maize Farmers in Oromia, Ethiopia (Published)

This study examines the impact of Farmer Field School (FFS) training program on technical efficiency of smallholder farmers. The FFS program was sponsored by the Ethiopian government and launched in 2010 to scale-up best agricultural practices in the country. The study aims to compare changes in the technical efficiency of those FFS graduate and non-FFS graduate maize farmers in Ethiopia. For this, panel data were collected in two rounds from 446 randomly selected households from three districts consisting of 218 FFS graduate farmers and 228 non-FFS graduate farmers. The analytical procedure has involved three stages: in the first stage, descriptive analyses were used to detect existence of difference in the outcome indicators between the two farmer groups. In the second stage, a semi-parametric impact evaluation method of propensity score matching with several matching algorithms was employed to estimate the program impact. In the third stage, Difference-in-Difference was used as robustness check in detecting causality between program intervention and the technical efficiency changes. The Combined uses of these alternative estimation techniques indicate that the program has negative impact on the technical efficiency of the FFS graduates. Numerous plausible explanations for this outcome are discussed, and recommendations for improvements are suggested accordingly.

Keywords: Impact Evaluation, Technical Efficiency, difference in difference, propensity score matching