Charger Deployment Study

Last registered on June 22, 2026
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Pre-Trial

Trial Information

General Information

Title
Charger Deployment Study
RCT ID
AEARCTR-0018819
Initial registration date
June 16, 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
June 22, 2026, 6:51 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
Teevrat Garg
Affiliation
UCSD
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Other Primary Investigator(s)

PI Name
Craig Mcintosh
PI Affiliation
University of California San Diego
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PI Name
Gordon McCord
PI Affiliation
University of California San Diego
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PI Name
Mexico Bahena
PI Affiliation
University of California San Diego
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Additional Trial Information

Status
In development
Start date
2026-05-27
End date
2034-02-27
Keywords
Environment & Energy
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Public investment in electric vehicle (EV) charging infrastructure can be pivotal in decarbonizing the transportation sector. However, policymakers lack rigorous evidence on how to design subsidy programs that maximize additional charger deployment per public dollar while addressing other policy priorities, such as equitably expanding access in underserved communities. These areas face additional barriers to developing charging infrastructure, which limit access to public charging, slow EV adoption, and reduce equitable participation in the clean transportation transition. To address this gap, we partner with a large jurisdiction to conduct a randomized controlled trial (RCT) evaluating an EV charger rebate program. Leveraging rich administrative data, charger usage records, surveys, and EV registration data, the study will provide experimental estimates of demand and welfare impacts of public EV charging subsidies, offering actionable guidance for cost-effective and equitable clean transportation policy.
External Link(s)

Registration Citation

Citation
Bahena, Mexico et al. 2026. "Charger Deployment Study." AEA RCT Registry. June 22. https://doi.org/10.1257/rct.18819-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-08-25
Intervention End Date
2028-02-25

Primary Outcomes

Primary Outcomes (end points)
Acceptance of EV charger subsidy, installation of EV charger(s), utilization of EV charger(s), EV adoption.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We partner with a large jurisdiction to experimentally test EV charger subsidy program design. Assuming oversubscription to the jurisdiction’s subsidy program, funding will be awarded using a randomization protocol. This, combined with variation in subsidy value across multiple rounds of subsidy awards, are designed into the jurisdiction’s program and form the basis for this RCT.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer using reproducible code.
Randomization Unit
Host sites for potential EV chargers.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
550
Sample size: planned number of observations
550
Sample size (or number of clusters) by treatment arms
While subject to the (as-yet) number of applications, we hope to have approximately 272 treatment sites (based on the available budget) and at least as many control sites.
(This is based on summing up all of the treatment units we hoped for in our power calculations, and optimistically assuming we’ll have slightly more in the control group.)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
UC San Diego Institutional Review Board Administration
IRB Approval Date
2026-05-27
IRB Approval Number
814203
Analysis Plan