Designing Peer Information and Testing Income Targeting in the Gig Economy

Refer to experimental design.

2026-06-01

2026-07-13

i. Labor supply outcomes (i.e., indicator for working on a given day, total daily earnings, total utilization hours, total trips completed, hourly earnings, total distance travelled, hazard rate of stopping work, indicator for whether driver has attrited from the platform)

ii. Search behavior outcomes (i.e., spatial footprint, share of trips picked up in central region, share of auto-accepted trips, mean deadhead time, and hourly earnings ex. incentives at the driver-shift level)

iii. Endline survey measures of driver well-being (i.e., work stress, meaning, satisfaction, motivation), self-reported labor supply outcomes (i.e., number of days worked per week, hours per week, multihoming patterns), information preferences and income targets

As a measure of productivity, we will construct hourly earnings by dividing total earnings by total utilization hours. For quits, we will construct the indicator for whether driver has attrited from the platform using total earnings and total trips completed variables.

Drivers are randomly assigned to one of three information groups:

- Control group (T1): Drivers receive only their own earnings updates each week.

- Standard Peer Info group (T2): Drivers receive their own earnings updates as in T1, and additionally receive peer earnings updates of a reference group comprising 10 non-participating drivers randomly drawn from the same baseline-hours bin.

- Targeted Peer Info group (T3): Drivers receive peer earnings updates of the same form as in T2, but whose reference group comprises 10 non-participating drivers drawn from lower earnings ranks within the same baseline-hours bin.

In addition to the information treatment assignments, which are fixed throughout the six-week intervention period, each driver is randomly assigned to two single-day monetary incentives:

- Low Bonus: On the assigned day, drivers earn X% more on total trip earnings during a fixed time window.
- High Bonus: On the assigned day, drivers earn Y% more on total trip earnings during a fixed time window.
(X and Y are placeholders that will be calibrated based on the firm's existing incentive structure, where X is less than Y)

Each driver is also randomly assigned to one of two announcement conditions for the monetary incentives:

- Same-day Notice: Drivers are only informed of the incentive on the incentive day.
- Advance Notice: Drivers are informed of the incentive in advance, and reminded again on the incentive day.

The assignment of Low and High Bonus days is randomized within driver, so that each driver experiences both bonus levels (X% and Y%). The specific weeks in which the bonuses apply are randomly assigned with at least one washout week between a driver's two incentive days. The announcement condition is randomized between drivers and held fixed across both of a driver's incentive days.

Not available

Randomization is done by a computer using Python.

Randomization is done at the individual driver level, stratified by baseline utilization hours, tenure with the firm, and the driver's treatment assignment in the original experiment (Lim, 2025).

No

Approximately 2,900 drivers. The sample comprises all drivers who signed up for the original study (Lim, 2025), restricted to those who remain active and in good standing on the platform at the start of the intervention period.

Over 426,300 driver-day level observations (2,900 workers * 7 days/week * (9 weeks pre-intervention + 6 weeks of intervention + 6 weeks of post-intervention))

The sample is evenly split across the three information treatment groups, with approximately 970 drivers in each group. The announcement conditions are also evenly split, with approximately 1,450 drivers in each condition.

For total daily earnings conditional on working, the MDE is approximately S$3.56 (roughly 3% of the baseline mean) under α = 0.05 and 1 – β = 0.80, using the standard error reported in Lim (2026).