Farmers are often faced with the problem of having to use a good or service before its quality is known. Was this seed better than the competition? Is that pesticide effective in my conditions? Farmers typically get one outcome data point for a complex combination of multiple inputs and other conditions. The inability to make the right choices about what input to use causes heavy losses in poor countries' agricultural sectors.
In developed countries, this fundamental problem is solved through experimental research and regulations controlling quality. In underdeveloped countries, the institutional capacity to adopt either of these solutions is typically lacking. Instead, farmers rely on their own experiences and what they learn through word of mouth. This data is usually both noisy and lagged.
Research in developed country markets has demonstrated that quality and other outcomes are substantially improved by the implementation of ratings systems. In underdeveloped countries, past attempts to replicate such systems have either been prohibitively costly, or simply not worked.
Our core solution to this problem was to create a Yelp-like system that solicited feedback proactively from farmers, aggregated and processed that information, and provided it back to the farming community as a ranking of the quality of service providers. Farmers could then use this information to switch suppliers or pressurize their existing supplier to improve the quality of supply.
Importantly, we ensured that the ratings information was truthful in two ways: (a) we conducted spot-checking of a sample of the reported information by reaching out directly to farmers and asking them to verify the information we had received; and (b) we did not start disseminating information until we had had the chance to build a large pool of responses, such that individual variations in ratings did not affect the overall rankings too much.
Our pilot successfully demonstrated, in the Artificial Insemination of large ruminants, that such a system improved the service provision for farmers (the rate of successful pregnancies following Artificial Insemination rose 27%) , and thus their economic outcomes (we demonstrated a $32 per month increase in household income for farmers living on approximately twice that amount).
This heavily managed system was an initial proof of concept. Although it had highly positive rates of return, it would rely in the long-term on unreliable government funding. We now plan to pursue a modification of our original project design to lower costs and decrease dependence on public financing.
We intend to create an app that invites distributed smartphone users proximate to our farmers to conduct interviews with them. If viable, we will also create monetary incentives for these recruits (members of our team have worked in the past on optimizing incentives in real-time in a similar context).
Recruits will see available calls and be paid, possibly through mobile money, to make those calls. Before the calls are made, the participant will be provided training to ensure they conduct high quality phone interviews. After the calls are made, we will implement random validation of call quality either by administrators or other calling agents.
In the long-term, we want to implement a more general ratings system where user-created ratings are systematically collectivized, and review requests are endogenously generated. Smartphone prices are falling rapidly, so it is now viable for individuals in even very remote areas to download and utilize an app that potentially pays them for their services.