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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 trip 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. 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.
Last Published August 11, 2025 10:16 AM September 13, 2025 10:00 AM
Intervention Start Date September 15, 2025 September 22, 2025
Intervention End Date October 27, 2025 November 03, 2025
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) 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)
Experimental Design (Public) 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 two weeks, and the order of incentives is randomized for each driver. 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.
Randomization Unit Randomization is done at the individual level, stratified by driver baseline activity level (earnings / hours worked). 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).
Planned Number of Observations Over 84,000 driver-day level observations (2,000 workers * 7 days/week * (6 weeks of intervention + 28 weeks pre-intervention + 8 weeks of post-intervention)) 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))
Additional Keyword(s) Peer Information, Information Preferences, Incentives, Labor Supply Peer Information, Information Preferences, Incentives, Labor Supply, Income Targeting, Heterogeneity
Secondary Outcomes (End Points) To complement the primary outcomes, we 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. 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.
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