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Messaging and Low-income Solar Adoption
Last registered on May 01, 2018

Pre-Trial

Trial Information
General Information
Title
Messaging and Low-income Solar Adoption
RCT ID
AEARCTR-0002218
Initial registration date
May 15, 2017
Last updated
May 01, 2018 1:08 PM EDT
Location(s)
Primary Investigator
Affiliation
Yale University
Other Primary Investigator(s)
PI Affiliation
Duke University
PI Affiliation
Duke University
Additional Trial Information
Status
In development
Start date
2017-05-15
End date
2018-07-15
Secondary IDs
Abstract
The market for residential solar panels in the United States is growing rapidly, bolstered by substantial government support at the federal, state, and local levels. Yet there are concerns that this support is primarily benefiting wealthier households, due to different electricity rates, liquidity constraints, landlord-tenant issues, length of tenancy, and perhaps even preferences (Borenstein and Davis 2016, Borenstein 2017). This study examines the effect of information provision and preferred financing on the adoption of residential solar panels by low and moderate income (LMI) households. Specifically, we randomize LMI municipalities in Connecticut into those receiving a behavioral intervention focused on "community-based pro-social" messaging and "financial-based" messaging. At the same time, we are exploiting an income eligibility discontinuity for subsidized financing to LMI participants. We will measure the effect of the different messaging approaches above and below this discontinuity on the demand for residential solar systems in LMI municipalities. We will thus quantify the effects of different potential policies for encouraging the adoption of solar systems in LMI municipalities.
External Link(s)
Registration Citation
Citation
Bollinger, Bryan, Kenneth Gillingham and Steven Sexton. 2018. "Messaging and Low-income Solar Adoption." AEA RCT Registry. May 01. https://doi.org/10.1257/rct.2218-3.0.
Former Citation
Bollinger, Bryan, Kenneth Gillingham and Steven Sexton. 2018. "Messaging and Low-income Solar Adoption." AEA RCT Registry. May 01. https://www.socialscienceregistry.org/trials/2218/history/29008.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
In each municipality we are running a behavioral intervention designed to encourage solar adoption. This intervention is called a "Solarize" campaign, and it involves a roughly 18-week campaign, a single chosen solar installation company (based on a competitive bidding process), discount pricing, a kick-off event, solar open houses, tabling at town events, designated volunteer solar ambassadors who coordinate the campaign with SmartPower. The intervention is specifically designed to foster word-of-mouth and social interaction effects.

The difference between the community-based messaging and the financial-based messaging approaches is that all of the materials given by SmartPower to the ambassadors and potential solar customers exclusively uses very different messages about solar PV. Similarly, SmartPower employees are instructed to exclusively lead with a different set of messages in verbal discussion and to instruct the ambassadors to focus on these messages.
Intervention Start Date
2017-05-15
Intervention End Date
2018-07-15
Primary Outcomes
Primary Outcomes (end points)
The key outcome variables are solar adoptions, and mediating factors that influence adoptions.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The program will launch in phases. This allows for a greater sample size. In each phase, LMI municipalities will be randomly assigned to one of two messaging treatments arms or a control. The sample size for the first round is 8 treated plus 4 control municipalities. As campaigns are completed in the first round, the second round begins similarly, followed by the third round.

The Connecticut Green Bank collects data on all residential solar installations in the state.
Experimental Design Details
Randomization Method
The randomization was done by generating a random number and then assigning to each municipality. Balance checks were carried out to ensure that the municipality characteristics were similar across treatment and control.
Randomization Unit
The unit of randomization is the municipality itself.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
30 municipalities
Sample size: planned number of observations
30 municipalities
Sample size (or number of clusters) by treatment arms
12 municipalities receive "community-based messaging"
12 municipalities receive "financial-based messaging"
12 municipalities are in the control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Yale Institutional Review Board
IRB Approval Date
2016-09-23
IRB Approval Number
1608018306
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers