Uptake of insurance-embedded credit in presence of credit rationing: evidence from a randomized controlled trial in Kenya

Abstract

PurposeDrought-related climate risk and access to credit are among the major risks to agricultural productivity for smallholder farmers in Kenya. Farmers are usually credit-constrained due to either involuntary quantity rationing or voluntary risk rationing. By exploiting randomized distribution of weather risk-contingent credit (RCC) and traditional credit, the authors estimate the causal effect of bundling weather index insurance to credit on uptake of agricultural credits among rural smallholders in Eastern Kenya. Further, the authors assess farmers' credit rationing, its determinants and effects on credit uptake.Design/methodology/approachThe study design was a randomized controlled trial (RCT) conducted in Machakos County, Kenya. 1,170 sample households were randomly assigned to one of three research groups, namely control, RCC and traditional credit. This paper is based on baseline household survey data and the first phase of loan implementation data.FindingsThe authors find that 48% of the households were price-rationed, 41% were risk-rationed and 11% were quantity-rationed. The average credit uptake rate was 33% with the uptake of bundled credit being significantly higher than that of traditional credit. Risk rationing seems to influence the credit uptake negatively, whereas premium subsidies do not have any significant association with credit uptake. Among the socio-economic variables, training attendance, crop production being the main household head occupation, expenditure on food, maize labour requirement, hired labour, livestock revenue and access to credit are found to influence the credit uptake positively, whereas the expenditure on non-food items is negatively related with credit uptake.Research limitations/implicationsThe study findings provide important insights on the factors of credit demand. Empirical results suggest that risk rationing is pervasive and discourages farmers to take up credit. The study results also imply that credit demand is inelastic although relatively small sample size for RCC premium subsidy groups may be a limiting factor to the authors’ estimation.Originality/valueBy implementing a multi-arm RCT, the authors estimate the factors affecting the uptake of insurance bundled agricultural credits along with eliciting credit rationing among rural smallholders in Eastern Kenya. This paper provides key empirical findings on the uptake of RCC and the effect of credit rationing on uptake of agricultural credits, a field which has been majorly theoretical.

Heterogeneous Demand and Supply for an Insurance‐linked Credit Product in Kenya: A Stated Choice Experiment Approach

“…These are expected in any lender‐borrower relationship. Lenders will prefer a shorter‐term loan to reduce loan default while farmers prefer longer terms to smooth liquidity; lenders require collateral to secure the loan, while farmers (particularly those that are risk rationed, see Ndegwa et al ., 2020) are less willing to put collateral at risk; and lenders would prefer a loan for specific uses that provide a return to capital, while farmers prefer greater fungibility in loan purpose. Loans offered in both long and short seasons are preferred for both demand‐ and supply‐side stakeholders.…”

Parametric insurance and technology adoption in developing countries

“…Studies by (Carter et al (2014)), McIntosh et al (2013), Cole et al (2017), and Hill et al (2019), for example, find an overall positive effect of insurance on technology adoption. Preliminary results (baseline and first wave) from an experiment in Kenya, presented in Ndegwa et al (2019), also reveal little effect of combining insurance with credit on take-up rates. Giné and Yang (2009), on the other hand, find a negative effect of insurance on technology adoption.…”

Parametric Insurance and Technology Adoption in Developing Countries

“…Studies by (Carter et al (2014)), McIntosh et al (2013), Cole et al (2017), andHill et al (2019), for example, find an overall positive effect of insurance on technology adoption. Preliminary results (baseline and first wave) from an experiment in Kenya, presented in Ndegwa et al (2019), also reveal little effect of combining insurance with credit on take-up rates. Giné and Yang (2009), on the other hand, find a negative effect of insurance on technology adoption.…”