In Ghana, insured loans increased farmers' likelihood of receiving credit by between 15 and 21 percentage points. There was no impact on the likelihood that farmers apply for credit but there was an increase in the likelihood of loan approvals of between 17 and 25 percentage points.
This study from Ghana found that index insurance lowers overall demand for agricultural loans while farmers appear to prefer micro-level insurance over meso‐level insurance. The study also shows that farmers are willing to pay to avoid basis risk.
Increasing agricultural efficiency via technology adoption remains a high priority among development practitioners. One potential tool for furthering this objective is using drought index insurance to increase access to credit.
Observations of smallholder farmer inefficiency often reflect failure to control for nature. An example would be Ivorien rice farmers effected on their production frontier once inconsistent control for soils, rain, and pests are involved. So perhaps a non-uptake adoption is optimal as well? This presentation is based on the AMA Innovation Lab projects for the Mind the Gap Workshop.
A randomized trial in urban Ghana for microenterprises separated participants by providing advice from an international consulting firm, cash, both, or nothing. The treatments led to immediately expected results, however, no treatment let to higher profits on average so businesses reverted to previous practices.
In this paper, we employ a dynamic, stochastic, heterogeneous agent model where farm households have access to contingent credit and make savings, technology and loan repayment choices.
This presentation took place in University of California Davis, United States describing a three-year research project promoting smallholders to purchase index insurance contracts via coupled credit in Ghana.
Social programs began on the notion that their beneficiaries will change some behavior (perhaps due to improved incentives or new knowledge gained during the intervention) pose unique challenges for impact evaluation. Nevertheless, it is difficult to determine when the treatment and control groups should be compared, i.e. when the program in question should be evaluated. This papers explores challenges revolving around these issues.
