AEA RCT Registry

Trial Information

General Information

Title

Gender Differences In Reaction To Enforcement Mechanisms

RCT ID

AEARCTR-0006095

Initial registration date

July 20, 2020

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

July 20, 2020, 11:36 AM EDT

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

Locations

Country

China

Region

All over the nation

Primary Investigator

Name

Zhengyang Bao

Affiliation

Xiamen University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Difang Huang

PI Affiliation

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2017-01-01

End date

2017-04-01

Keywords

Finance & Microfinance, Gender

Additional Keywords

JEL code(s)

C93, D91, J16

Secondary IDs

Abstract

We follow borrowers from a peer-to-peer lending platform to study how females and males react to enforcement mechanisms differently. In the experiment, borrowers are randomized into treatments where they receive different text messages urging for timely repayment if they have loans due the “next day”.

External Link(s)

Registration Citation

Citation

Bao, Zhengyang and Difang Huang. 2020. "Gender Differences In Reaction To Enforcement Mechanisms." AEA RCT Registry. July 20. https://doi.org/10.1257/rct.6095-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Male and female borrowers with no overdue history are randomized into one of the treatments and receive text messages from the platform asking them to make the repayment on time. The content of the message varies across treatments. To encourage the repayment, we embed into the messages one of three types of enforcement mechanisms that are widely explored in the literature. The first mechanism is a simple reminder message to mitigate the delays caused by limited attention. The second mechanism invokes social pressure. In particular, we test how borrowers react to information about the social norm (i.e., most borrowers repaid on time) and a threat to inform the endorser (usually a person close to the borrower) if the borrower fails to repay on time. The third mechanism introduces financial incentives. We test rewards for compliance (i.e., the platform offered to decrease the interest rate for future borrowing if the borrower made the repayment on time) and punishments for non-compliance (i.e., the platform threatened to increase the interest rate if the borrower failed to make the repayment on time).

Intervention Start Date

2017-01-01

Intervention End Date

2017-04-01

Primary Outcomes

Primary Outcomes (end points)

Overdue rate.

Primary Outcomes (explanation)

Overdue rate= number of borrowers overdue/ total number of borrowers in the corresponding cell.

Secondary Outcomes

Secondary Outcomes (end points)

Days overdue, default ratio.

Secondary Outcomes (explanation)

Days overdue is constructed as the average days overdue of a corresponding cell. Default ratio = number of borrowers defaulted/ total number of borrowers in the corresponding cell.

Experimental Design

Male and female borrowers with no overdue history are randomized into one of the treatments and receive text messages from the platform asking them to make the repayment on time. The content of the message varies across treatments. To encourage the repayment, we embed into the messages one of three types of enforcement mechanisms that are widely explored in the literature. The first mechanism is a simple reminder message to mitigate the delays caused by limited attention. The second mechanism invokes social pressure. In particular, we test how borrowers react to information about the social norm (i.e., most borrowers repaid on time) and a threat to inform the endorser (usually a person close to the borrower) if the borrower fails to repay on time. The third mechanism introduces financial incentives. We test rewards for compliance (i.e., the platform offered to decrease the interest rate for future borrowing if the borrower made the repayment on time) and punishments for non-compliance (i.e., the platform threatened to increase the interest rate if the borrower failed to make the repayment on time).

Experimental Design Details

Male and female borrowers with no overdue history are randomized into one of the treatments and receive text messages from the platform asking them to make the repayment on time. The content of the message varies across treatments. To encourage the repayment, we embed into the messages one of three types of enforcement mechanisms that are widely explored in the literature. The first mechanism is a simple reminder message to mitigate the delays caused by limited attention. The second mechanism invokes social pressure. In particular, we test how borrowers react to information about the social norm (i.e., most borrowers repaid on time) and a threat to inform the endorser (usually a person close to the borrower) if the borrower fails to repay on time. The third mechanism introduces financial incentives. We test rewards for compliance (i.e., the platform offered to decrease the interest rate for future borrowing if the borrower made the repayment on time) and punishments for non-compliance (i.e., the platform threatened to increase the interest rate if the borrower failed to make the repayment on time).

Randomization Method

Randomization is done by a computer.

Randomization Unit

There is no cluster. We randomize each individual into a treatment.

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

We do not have clusters. In total we plan to have 18000 borrowers (individuals).

Sample size: planned number of observations

18000 borrowers (individuals).

Sample size (or number of clusters) by treatment arms

3600 in control, 2880 in each of Reminder, Norm, Shame, Punishment, and Reaward treatments. We oversample the baseline by 25% to increase the statistical power of each pairwise comparison.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We need 2976 participants per treatment to detect a 0.1 standard deviation difference at the 99% level.

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

The Monash University Human Research Ethics Committee

IRB Approval Date

2020-07-20

IRB Approval Number

25421

Analysis Plan

Gender Differences In Reaction To Enforcement Mechanisms

Pre-Trial

General Information

Locations

Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Registration Citation

Interventions

Primary Outcomes

Secondary Outcomes

Experimental Design

Experiment Characteristics

Institutional Review Boards (IRBs)

Post-Trial

Study Withdrawal

Intervention

Data Publication

Program Files

Relevant Paper(s)

Reports & Other Materials