Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit It

Last registered on March 07, 2016
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Pre-Trial

Trial Information

General Information

Title
Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit It
RCT ID
AEARCTR-0001109
Initial registration date
March 07, 2016

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 07, 2016, 7:54 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
March 07, 2016, 7:56 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
India
Region
Maharashtra

Primary Investigator

Name
Natalia Rigol
Affiliation
Harvard Business School
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Reshmaan Hussam
PI Affiliation
Yale
Contact Investigator
PI Name
Benjamin Roth
PI Affiliation
MIT
Contact Investigator

Additional Trial Information

Status
On going
Start date
2016-01-01
End date
2017-01-01
Keywords
Finance & Microfinance
Additional Keywords
JEL code(s)
Secondary IDs
Abstract
This project tests a novel method of assessing microenterprise potential by harnessing community information. We ask: can community information—knowledge that neighbors, customers, community leaders, family members, and friends hold about one another—help identify which would-be microentrepreneurs have the most growth potential? Previous studies have demonstrated that community members have information about one another’s assets. Here, we study whether community members can also predict who high-potential business owners are.
External Link(s)

Registration Citation

Citation
Hussam, Reshmaan, Natalia Rigol and Benjamin Roth. 2016. "Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit It." AEA RCT Registry. March 07. https://doi.org/10.1257/rct.1109-2.0
Former Citation
Hussam, Reshmaan, Natalia Rigol and Benjamin Roth. 2016. "Knowing Thy Neighbor: What Information Neighbors Have and How Best to Elicit It." AEA RCT Registry. March 07. https://www.socialscienceregistry.org/trials/1109/history/7163
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2016-03-01
Intervention End Date
2016-04-30

Primary Outcomes

Primary Outcomes (end points)
Our outcomes of interest are whether people can predict

1. Education

(a) We only ask for quintile rankings

2. Marginal returns to a Rs.6000 grant

(a) We ask for relative and quintile rankings

3. Average monthly income over the past year

(a) We ask for relative and quintile rankings

4. Projected monthly profits with an Rs.6000 grant

(a) We ask for relative and quintile rankings

5. Total value of household assets

(a) We ask for relative and quintile rankings

6. Number of hours worked by business owner

7. Medical expenses

8. Loan repayment trouble

9. Digitspan

We also ask who deserves the grant (according to any criteria the respondent herself chooses).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We cross-randomize 3 treatments arms:

1. Rankings Used for Allocation Decisions (R0 vs R1) – A random subset of households will be informed that their reports will influence the probability that their peers receive cash grants.

2. Public Rankings (P0 vs P1): Respondents will be randomized to make reports in public (visible to the entire group) or in private (group members do not see reports).

3. Incentives (I0 vs I1): We will randomize whether or not respondents receive monetary incentives for the accuracy of their reports.
Experimental Design Details
After all baseline data is collected in a neighborhood, groups will be invited to conduct the ranking exercises in a large community hall. One group will be invited to conduct the exercise at a time. Once in the hall, group members will be paired with a surveyor at a surveying station. The station is completely private with a section divider so that respondents cannot observe each others’ rankings while sitting with the surveyor. To minimize variation across surveyors in implementation of the treatments, we have created animated videos to show to respondents. In the videos we explain: what is a quintile and how to do a quintile ranking, what are marginal returns, profits, income, and assets.

The videos also explain the treatments. For the incentive treatments, the videos explain how incentives are paid. The payment rule is difficult to explain in a seminar, let alone to person with low levels of literacy. Following Prelec (2004) and Rigol Roth (2016), respondents are told that their payments will depend on their rankings and the rankings of their peers. The message that is emphasized is that truthful answers are rewarded more highly than untruthful answers. We also explain what second order beliefs are and collect this data along with the first order rankings. Incentives are paid at the end of each question (7 times in total). In the public treatment, after each ranking, respondents are asked to gather in the center of the room while the surveyors process the data. They take their ranking sheets along with them and these sheets are visible to all group members. In the private treatment, respondents are assured that their individual
rankings will never be observed by anyone other than the researchers. The respondents remain behind their privacy screens.

Lastly, in the revealed treatments, the videos explain how the rankings can affect who receives the grant. Respondents are told when they arrive in the hall that at the end of the exercise, a lottery will be conducted to randomly select the lottery winner. They are each given 20 tickets. In the revealed treatment, the video explains that there are extra lottery tickets that will be awarded after each IAP round (Q4-Q7).
The lottery tickets will be awarded to the person who was most highly ranked in each round. Once all rankings are completed, a lottery is conducted. Group members will put their lottery tickets in a bucket and one or two winners will be selected, depending on the randomization status of the group.
Randomization Method
Public lottery
Randomization Unit
The randomization unit is the ranking group. There are 8 treatments so we created clusters of groups and randomized the 8 treatments within those clusters at the neighborhood leve.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
36
Sample size: planned number of observations
1500
Sample size (or number of clusters) by treatment arms
187 for 8 treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We are powered to detect treatment effects on marginal returns to a grant on par with De Mel, McKenzie, Woodruff (2008).
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT IRB
IRB Approval Date
2014-03-31
IRB Approval Number
(IRB#: 1403006218).
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials