Peer Information and Incentives in the Gig Economy

Last registered on September 13, 2025
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Pre-Trial

Trial Information

General Information

Title
Peer Information and Incentives in the Gig Economy
RCT ID
AEARCTR-0016393
Initial registration date
August 11, 2025

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
August 11, 2025, 10:16 AM EDT

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

Last updated
September 13, 2025, 10:00 AM EDT

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

Locations

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Primary Investigator

Name
Zhi Hao Lim
Affiliation
Columbia University
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-08-13
End date
2026-12-31
Keywords
Behavior, Firms & Productivity, Labor
Additional Keywords
Peer Information, Information Preferences, Incentives, Labor Supply, Income Targeting, Heterogeneity
JEL code(s)
C93, D91, J24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Peer information is pervasive in the workplace, but recent work shows that workers differ in how they value and respond to it (Lim, 2025). We conduct a field experiment with rideshare drivers to study demand for peer earnings information and identify the mechanisms behind it. Drivers are randomly assigned to one of three information treatments: no peer information, peer information, or an endogenous choice condition. Over the intervention period, they are also assigned all three incentive conditions: no bonus, a target-based bonus, and a proportional bonus, with the order randomized. Using administrative records and survey responses, we examine heterogeneity in information demand and labor supply responses across treatment arms. We benchmark the effects of peer information against financial incentives, examine potential interaction effects, and additionally provide experimental estimates of labor supply elasticities leveraging on exogenous variation in wages.
External Link(s)

Registration Citation

Citation
Lim, Zhi Hao. 2025. "Peer Information and Incentives in the Gig Economy." AEA RCT Registry. September 13. https://doi.org/10.1257/rct.16393-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Refer to experimental design.
Intervention Start Date
2025-09-22
Intervention End Date
2025-11-03

Primary Outcomes

Primary Outcomes (end points)
i. Labor supply decisions (i.e. total earnings, hourly wage, total trips completed, total distance travelled, total utilization hours, number of days worked per week, average hours worked per day, working hour patterns, hazard rate of stopping work, proportion of trips from each rideshare platform, indicator for whether driver works on a given day, indicator for whether driver has attrited from the platform)

ii. Survey measures of driver well-being (i.e. stress, work meaning, satisfaction, and motivation)
Primary Outcomes (explanation)
As a measure of productivity, we will construct hourly wage by dividing total earnings by utilization hours. For quits, we will construct the indicator for whether driver has attrited from the platform using the total earnings and total trips completed variables.

Secondary Outcomes

Secondary Outcomes (end points)
To complement the primary outcomes, we will request data on driver online status (i.e. timestamps for when drivers turn the app on or off each day, net active hours on app each day) to study drivers' search behavior at work and their willingness to accept rides. We also request data on driver locations while they are online to further examine their search behavior. Our ability to study these outcomes are subject to data availability by the company.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Drivers are randomly assigned to one of three information treatment groups:

1. No Info group: Drivers receive only their own earnings updates each week.
2. Info group: Drivers receive both their own earnings and peer group earnings updates each week.
3. Endogenous Info group: Drivers receive their own earnings updates each week; whether they also receive peer earnings updates depends on their stated preferences.

In addition to the information treatments (whose assignments are fixed throughout), each driver faces three different incentive conditions over the intervention period. These are:

A. No Incentive: No additional bonuses are provided.
B. Target Incentive: Drivers receive a bonus of $X for completing Y trips on a specified day.
C. Multiplier Incentive: Drivers earn an additional Z% bonus on their total earnings on a specified day.
(X, Y, and Z are placeholders that are calibrated based on the company's current incentive structure.)

Each incentive condition lasts for one week, and the order of incentives is randomized for each driver.
Experimental Design Details
Not available
Randomization Method
Randomization is done by a computer using Python.
Randomization Unit
Randomization is done at the individual level, stratified by driver baseline activity level (measured by average weekly hours worked) and tenure with the company (above- or below-median).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to recruit approximately 2,000 rideshare drivers for the study. All eligible drivers who sign up for the program will be enrolled. As a result, the final sample size may change depending on actual enrollment at the time of the baseline survey launch.
Sample size: planned number of observations
Over 644,000 driver-day level observations (2,000 workers * 7 days/week * (6 weeks of intervention + 28 weeks pre-intervention + 12 weeks of post-intervention))
Sample size (or number of clusters) by treatment arms
Our sample is evenly split between the three information treatment groups, with approximately 670 drivers in each group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We set power of 0.8, a significance level of α = 0.05, and a minimum detectable effect size of 0.2 std.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Columbia University Institutional Review Board
IRB Approval Date
2025-07-02
IRB Approval Number
IRB-AAAV7564
Analysis Plan

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