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EV Charging: Subscription Pricing Experiment

Last registered on March 12, 2026
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Pre-Trial

Trial Information

General Information

Title
EV Charging: Subscription Pricing Experiment
RCT ID
AEARCTR-0018076
Initial registration date
March 11, 2026

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
March 12, 2026, 4:37 AM EDT

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

Locations

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Primary Investigator

Name
Jeffrey Myers
Affiliation
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Other Primary Investigator(s)

PI Name
Teevrat Garg
PI Affiliation
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Additional Trial Information

Status
In development
Start date
2026-03-12
End date
2026-08-31
Keywords
Behavior, Environment & Energy
Additional Keywords
Electric vehicles
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As electric vehicle (EV) charging networks emerge, they must confront challenges in developing sustainable business models amidst rapidly evolving markets. Operators with excess charger capacity may set pricing policies attempting to encourage utilization of EV chargers, particularly when linked to utilization of a scarce linked resource (such as parking spaces). In this experiment, we test whether a subscription discount policy changes drivers’ charging volume (kWh) and frequency (charging days).
External Link(s)

Registration Citation

Citation
Garg, Teevrat and Jeffrey Myers. 2026. "EV Charging: Subscription Pricing Experiment." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.18076-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-04-01
Intervention End Date
2026-06-30

Primary Outcomes

Primary Outcomes (end points)
(1) total kWh charged on campus; (2) number of days with at least one charging session on campus; (3) the timing of charging (measured by the hour in which sessions are initiated).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Session duration (time spent plugged in; in hours), charging duration (time the vehicle was actively charging; in hours).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We test the effect of subscription pricing on demand for EV charging at the workplace. We randomize drivers in our sample into one of three groups. The control group, the default group that receives lower unit prices automatically, and an offer group that can purchase a subscription for a lower unit price for charging. We stratify our sample by car type (battery EV, or BEV, vs plug-in hybrid, or PHEV) and activity (a binary indicator for whether or not the driver charged at least once in the current academic year).
Experimental Design Details
Not available
Randomization Method
Randomization by computer code in office
Randomization Unit
Driver
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
~1,900 (number of drivers, no clustering)
Sample size: planned number of observations
Approximately 1,900 driver-quarters; this reflects the number of eligible drivers as of March 1, 2026 and a single quarter-long (three month) intervention. We experiment with and analyze plug-in hybrid (PHEV) and fully battery electric (BEV) drivers separately, with approximately 350 PHEV rivers and approximately 1,550 BEV drivers.
Sample size (or number of clusters) by treatment arms
We treat plug-in hybrid (PHEV) and fully battery electric (BEV) drivers separately throughout the study with no pooled analysis spanning these groups. As such, the sample sizes are specific to each type of vehicle: approximately 175 drivers in the PHEV-control group, 175 drivers in the PHEV-defaulted subscription group, 310 drivers in the BEV-control group, 310 drivers in the BEV-defaulted subscription group, and 920 drivers in the BEV-offer group. An unknown number of drivers in the offer group will opt-in to the offer, meaning the non-/complier split is unknowable until the experiment begins.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of California San Diego Office of Institutional Review Board Administration
IRB Approval Date
2026-01-27
IRB Approval Number
805222
Analysis Plan

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