Driving Electric Rides: Experimental Evidence on Vehicle Access Contracts, Labor Productivity, and Driver-Firm Incidence

Last registered on March 07, 2026

Pre-Trial

Trial Information

General Information

Title
Driving Electric Rides: Experimental Evidence on Vehicle Access Contracts, Labor Productivity, and Driver-Firm Incidence
RCT ID
AEARCTR-0014462
Initial registration date
October 18, 2024

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
October 19, 2024, 9:48 PM EDT

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

Last updated
March 07, 2026, 12:13 PM EST

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

Locations

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Primary Investigator

Affiliation
World Bank Group

Other Primary Investigator(s)

PI Affiliation
World Bank
PI Affiliation
KDI School of Public Policy and Management
PI Affiliation
World Bank
PI Affiliation
KDI School of Public Policy and Management
PI Affiliation
KDI School of Public Policy and Management

Additional Trial Information

Status
On going
Start date
2024-09-19
End date
2026-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We examine how asset-access contracts shape labor productivity and the distribution of returns between workers and firms, drawing on randomized and natural experiments with motorcycle drivers and electric-vehicle (EV) fleet and battery firms in Nairobi. Offering applicants a fixed-wage opportunity to drive EVs increases daily labor hours by 1.37 hours (17 percent) and earnings by USD 1.8 (27 percent), while reducing gasoline expenditures by USD 1.4 (57 percent). Transitioning drivers from the fixed-wage contract to a lease-to-own (LTO) arrangement raises labor hours by 3.52 hours (38 percent) and earnings by USD 4.2 (33 percent), and lowers gasoline consumption by USD 0.45 (20 percent). Treating energy as an intermediate input, we augment drivers’ value-added production function with the labor-effort first-order condition, estimate post-LTO parameters, and backcast them to pre-LTO data to evaluate productivity dynamics and the incidence of returns. Within two months, LTO increases labor productivity by 37 percent through greater income and time flexibility, raises battery-vendor revenues net of grid-electricity costs by USD 1.48 (44 percent), and shifts the fleet supplier toward a mo financially sustainable
model. Despite longer work hours, drivers’ average short-run welfare remains unchanged under LTO, although job-satisfaction and mental-health measures remain stable. A one year follow-up survey is completed and long-term consequences and counterfactual analyses are underway as of March, 2026.

Registration Citation

Citation
Lee, Narae et al. 2026. "Driving Electric Rides: Experimental Evidence on Vehicle Access Contracts, Labor Productivity, and Driver-Firm Incidence." AEA RCT Registry. March 07. https://doi.org/10.1257/rct.14462-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
1) Guaranteed salary hiring scheme for drivers on fixed labor hours and wage contracts, 2) Lease-To-Own asset access program for Electric Vehicles (EVs)
Intervention Start Date
2024-09-19
Intervention End Date
2025-03-21

Primary Outcomes

Primary Outcomes (end points)
labor hours, earnings, fuel costs and consumption, other labor conditions
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
firm profits, market welfare, carbon implications, road behavior
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In the first phase of the experiment, we recruit taxi drivers and randomly assign them to either a treatment group (using an EV-based platform taxi hiring) or a control group. We track labor and other outcomes for those hired on a fixed salary, compared to those who remain self-employed. In the second phase, we examine the labor dynamics, driving behavior, market welfare, and other impacts as drivers transition from the EV-based platform model back to self-employment under an EV Lease-To-Own program.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted in the office using computer coding, with the driver list from the baseline survey data.
Randomization Unit
Individual driver
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
49 individuals for first experiment, 427 individuals for second experiment, no clustering
Sample size: planned number of observations
Survey: 49*90 = 4,410. 427*120 = 51,240. GPS: Millions per each individual (each vehicle).
Sample size (or number of clusters) by treatment arms
25 treated, 24 control and then 427 phase-in treatment changes
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
0.06, 0.02
IRB

Institutional Review Boards (IRBs)

IRB Name
KDI School Institutional Review Board for Human Subject Research
IRB Approval Date
2024-07-26
IRB Approval Number
2024-23