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Mechanism Design Meets Development

Paper Session

Sunday, Jan. 7, 2018 10:15 AM - 12:15 PM

Pennsylvania Convention Center, 106-B
Hosted By: Econometric Society
  • Chair: Pascaline Dupas, Stanford University

Pricing People Into the Market: Targeting Through Mechanism Design

Terence R. Johnson
,
University of Notre Dame
Molly Lipscomb
,
University of Virginia

Abstract

Subsidy programs are typically accompanied by large costs due to the difficulty of screening those who should receive the program from those who would have purchased the good anyway. We design and implement a platform intended to increase the take-up of improved sanitation services by targeting the poorest households for subsidies. The project proceeds in two stages: we first create a demand model based on market data and a demand elicitation experiment, and use the model to predict prices that will maximize take-up subject to an expected budget constraint. We then test the modeled prices on a new sample of households. A main feature of the platform is that prices are designed to exclude or raise revenue from households that would likely have otherwise purchased the improved service, while channelling subsidies to households that might otherwise be unable to pay. We provide evidence that the targeting strategy successfully identified households who would otherwise have failed to purchase improved services. Households in the treatment group were 1.7 percentage points more likely to purchase a mechanical desludging, leading to an increase of market share of mechanical desludging of 5.1 percentage points. The increased probability of purchasing a mechanical desludging among those with the largest subsidies was 3 percentage points. The health impacts among the poorest were large: high subsidy households saw a decrease in the probability that one of their children had diarrhea of 7.1 percentage points.

Targeting Experimentation Subsidies: A Mechanism Design Approach

Sylvain Chassang
,
New York University
Pascaline Dupas
,
Stanford University
Erik Snowberg
,
California Institute of Technology

Abstract

TBA

Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design In The Field

Reshmaan Hussam
,
Massachusetts Institute of Technology
Natalia Rigol
,
Harvard University
Benjamin Roth
,
Massachusetts Institute of Technology

Abstract

The impacts of cash grants and access to credit are known to vary widely, but progress on targeting these services to high-ability, reliable entrepreneurs is so far limited. This paper reports on a field experiment in Maharashtra, India that assesses (1) whether community members have information about one another that can be used to identify high-ability microentrepreneurs, (2) whether organic incentives for community members to misreport their information obscure its value, and (3) whether simple techniques from mechanism design can be used to realign incentives for truthful reporting. We asked 1,380 respondents to rank their entrepreneur peers on various metrics of business profitability
and growth and entrepreneur characteristics. We also randomly distributed cash grants of about $100 to measure their marginal return to capital. We find that the information provided by community members is predictive of many key business and household characteristics including marginal return to capital. While on average the marginal
return to capital is modest, preliminary estimates suggest that entrepreneurs given a community rank one standard deviation above the mean enjoy an 8.8% monthly marginal return to capital and those ranked two standard deviations above the mean enjoy a 13.9% monthly return. When respondents are told their reports influence the distribution of grants, we find a considerable degree of misreporting in favor of family members and close friends, which substantially diminishes the value of reports.
Discussant(s)
Pascaline Dupas
,
Stanford University
Lori Beaman
,
Northwestern University
David McKenzie
,
World Bank
JEL Classifications
  • A1 - General Economics