Data and Code for: Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design In The Field

Principal Investigator(s): View help for Principal Investigator(s) Reshmaan Hussam, Harvard University. Harvard Business School. Division of Research; Natalia Rigol, Harvard University. Harvard Business School. Division of Research; Benjamin Roth, Harvard University. Harvard Business School. Division of Research

Version: View help for Version V1

Name	File Type	Size	Last Modified
PRS_data_replication			11/14/2021 10:38:AM

Project Citation:

Hussam, Reshmaan , Rigol, Natalia, and Roth, Benjamin. Data and Code for: Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design In The Field. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-02-24. https://doi.org/10.3886/E151841V1

Project Description

Summary: 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% per month, microentrepreneurs reported in the top third of the community are estimated to have marginal return to capital between 24% and 30% 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.

Scope of Project

JEL Classification:
L26 Entrepreneurship
O16 Economic Development: Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

Geographic Coverage: Amravati, Maharashtra, India

Time Period(s): 12/1/2015 – 3/30/2017

Collection Date(s): 12/1/2015 – 3/30/2017

Universe: Male and female poor microentrepreneurs.

Data Type(s): experimental data; survey data

Methodology

Response Rate: 7 of the 1345 households dropped out of baseline. All other households are surveyed in all survey waves.

Sampling: We conducted a census of 9 neighborhoods of Amravati, Maharashtra, India. We identified 1576 eligible microentrepreneurs. We were able to enroll 1345 into the study.

Data Source: Hussam, Reshmaan, Natalia Rigol, and Benjamin N. Roth. 2016. “Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit It.” , (AEA RCT Registry.): March 07.

Collection Mode(s): face-to-face interview

Unit(s) of Observation: 1345

Geographic Unit: Neighborhood of Amravati

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Published Versions

V1 [2022-02-24]

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