Impact of Agricultural Credit on Coffee Productivity in Kenya

Abstract

Historically, agricultural credit programs have been used as a policy instrument to improve agricultural productivity and livelihoods of smallholder farmers. The effectiveness of such credit programs has been widely deliberated with an opaque unanimity being reached since yield is stochastic. Therefore, this study examines the impact of agricultural credit provided by the Government of Kenya as an intervention to boost coffee productivity. Over the years, there has been little – if any – in-depth analysis that has been dedicated to establishing the impact of this agricultural credit on coffee productivity. This study surveyed 174 smallholder coffee farmers (participants and non-participants in the credit program) in Kiambu County in Kenya between 2015 and 2019. The paper espouses the DEA Malmquist index to estimate the efficiency of coffee productivity for participating and non-participating coffee farmers in the credit program. The empirical results disclose that participating farmers had the highest geomean for productivity change (152%), efficiency change (40.5%), technical change (53.2%) and scale efficiency (40.5%). Bayesian Modelling Average was used to assess determinants of coffee productivity. Bayesian Modelling Average (BMA) was used to assess determinants of coffee productivity. The findings from BMA analysis indicated that variety, education, extension visits and crop system had a positive impact on coffee productivity. Gender and age of farmer had a negative impact on coffee productivity. Thus, these insights from the empirical work would be instrumental in providing policy directions in terms of agricultural lending and crafting policies aimed at enhancing the efficiency of coffee productivity.