Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field By Reshmaan Hussam, Natalia Rigol, and Benjamin N. Roth

 Mechanisms designed to elicit truthful reporting in the laboratory sometimes are cumbersome to administer and difficult to explain.  Here's a paper that finds that simple attempts to incentivize truthful reporting (including allowing other participants to hear each report, as well as small payments for reports that conform to community consensus) can help eliminate incentives to boost family and friends, when reports concern who could make most effective use of a cash grant.

Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field By Reshmaan Hussam, Natalia Rigol, and Benjamin N. Roth   American Economic Review 2022, 112(3): 861–898   https://doi.org/10.1257/aer.20200751

Abstract: "Identifying high-growth microentrepreneurs in low-income countries remains a challenge due to a scarcity of verifiable information. With a cash grant experiment in India we demonstrate that community knowledge can help target high-growth microentrepreneurs; while the average marginal return to capital in our sample is 9.4 percent per month, microentrepreneurs reported in the top third of the community are estimated to have marginal returns to capital between 24 percent and 30 percent per month. Further we find evidence that community members distort their predictions when they can influence the distribution of resources. Finally, we demonstrate that simple mechanisms can realign incentives for truthful reporting."

"Not everyone has what it takes to be a successful entrepreneur. Numerous experimental studies of microentrepreneurs in the developing world find widely heterogeneous returns to cash and credit.1  Yet governments, lenders, and nongovernmental organizations often lack hard information with which to target resources to high-growth entrepreneurs.

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"In this paper we argue that harnessing community information directly from a microentrepreneur’s peers may provide a viable approach to identifying high-growth microentrepreneurs.

"Our argument has three parts. First, we demonstrate that entrepreneurs in peri-urban Maharashtra have high quality information about one another along a variety of dimensions including marginal returns to capital. Their information is valuable for identifying high-growth microentrepreneurs even after controlling for a wide range of demographic and business characteristics. Second we demonstrate that entrepreneurs manipulate their reports to favor themselves, their friends, and their family when the distribution of resources is at stake. Finally we identify several simple techniques motivated by mechanism design that effectively realign incentives for accuracy.

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"Our first main finding is that community members can identify high-return entrepreneurs. While the average marginal return to the grant was about 9.4 percent per month, our point estimates of the marginal returns to capital of entrepreneurs ranked in the top third range from 24 percent to 30 percent. Had we distributed our grants using community reports instead of random assignment, we would have roughly tripled the total return on our investment.

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"Our second main finding is that strategic misreporting is a first-order concern when eliciting community information. By random assignment, half of respondents were told that their reports would be used only for research purposes (the “no stakes” treatment) and the other half were told that their reports would be used to allocate US$100 grants to members of their community (the “high stakes” treatment). The correlation between community reports and true outcomes is on average 27 percent to 35 percent lower when allocation of resources is at stake, which significantly lowers the value of peer elicitation.

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"Our third main finding is that methods grounded in mechanism design theory can be used to design a peer-elicitation environment in which truth telling is incentive compatible. Monetary payments and public reporting do little to improve the accuracy of self-reports. But payments substantially increase the predictive power of reports that entrepreneurs make about other group members. We provide direct evidence that monetary payments reduce the likelihood that respondents favor their family members or their close friends. Finally, we find that public reporting increases the predictive accuracy of reports about others when there are no stakes, but has no effect in a high stakes setting. This nuanced finding may reflect a heterogeneous treatment effect, or a noisily estimated impact of observability on the quality of reports."