The Effect of Messaging on Residential Battery Storage Adoption

Last registered on May 17, 2023


Trial Information

General Information

The Effect of Messaging on Residential Battery Storage Adoption
Initial registration date
May 09, 2023

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
May 17, 2023, 2:13 PM EDT

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


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Primary Investigator

Yale University

Other Primary Investigator(s)

PI Affiliation
Yale University
PI Affiliation
New York University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Residential battery storage is a relatively new technology and little is known about the motivations of potential adopters. This study analyzes the effects of messaging used in community battery storage campaigns (PowerSmart campaigns) in Connecticut. Town governments in Connecticut who have agreed to participate in a PowerSmart program are randomly assigned to “self-sufficiency” or “fossil-fuel-free” messaging as a part of a PowerSmart campaign in their town or to serve as a control.
External Link(s)

Registration Citation

Bollinger, Bryan, Kenneth Gillingham and Asa Watten. 2023. "The Effect of Messaging on Residential Battery Storage Adoption." AEA RCT Registry. May 17.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We plan to analyze the number of adoptions, as well as the number and type of events conducted by the volunteers leading the campaigns and the number of participants in the events. We also plan to analyze the post-survey results to better understand the behavioral foundations for the decisions made.

We have strong hypotheses that the two treatment strategies, based on behavioral phenomena that have been shown in other settings, will both resonate with consumers. However, we do not know a priori which one will have stronger effects, if they are different at all. The effects of the two campaigns may be heterogeneous. We will also test if campaigns had different effects based on income and political affiliation at the household level.

Each of these two treatments can be motivated by the preliminary results of the survey that was held in May and June of 2022 that describes the reasons that battery storage adopters chose to install battery storage. The first “main reason for decision to install battery storage” is “backup for power outages” and the second is “ability to use solar electricity when the sun is not shining.” We also elicited survey respondents’ beliefs about battery storage. 37% strongly agreed that battery storage “reduces your reliance on the grid” and 35% state that battery storage “gives you more control over energy usage and other costs.” Thus, there is strong evidence that control and self-sufficiency are important motivators for battery storage adoption when paired with solar. We also found that 69% of adopters of battery storage and solar said that climate change “is one of the most important issues of our time.” So clearly, reducing fossil fuel use matters to battery storage adopters, and this is an obvious motivating behavioral factor for adopters of battery storage paired with solar.

The treatments are further motivated by the literature. For example, Faraji-Rad et al. (2017) present evidence that the desire for control – the need to personally control outcomes in one’s life – can act as a barrier to new product acceptance. In the paper, they state one of their findings as: “framing new products as potentially enhancing one's sense of control increases acceptance of new products by those high in desire for control (Study 3).” Battery storage is most certainly a new product on the market and we hypothesize that control is important for battery storage adoption. In an earlier paper, Busseri et al. (2006) explore this by constructing a measure of the locus of control. They state that “the more internal their consumer control beliefs, the more likely were subjects to be planful and purposive in the act of shopping.” In addition, there is work indicating that “self-efficacy” (which is defined as an individual’s belief about their capacity to execute behaviors necessary to reach an outcome) plays a substantive role in shaping individual’s attitudes (Kulviwat et al. 2014). Self-sufficiency is closely related to “self-efficacy” and thus this work also helps motivate what we are proposing. We may even see an interaction between a focus on self-sufficiency and the utilitarian value (economic value, outages, etc.) in how decisions are made – this is something that could be explored in a post-survey.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Town governments in Connecticut who have agreed to participate in a residential battery storage program are randomly assigned to “self-sufficiency” or “fossil-fuel-free” messaging as a part of a campaign in their town or to serve as a control. We compare the number of new residential battery storage installations over time––before, during, and after campaigns.
Experimental Design Details
Not available
Randomization Method
As towns are recruited to participate, they are assigned to treatment in waves of nine or more. These waves are staggered for logistical connivance. For each wave, the towns are first stratified into groups of three towns and then randomly assigned to treatment and control within each strata with equal probability.

Stratification is done by balancing on demographic and other storage relevant variables. We find the stratification that minimizes the sum of squared pairwise Mahalanobis distance within each strata for each wave. This is achieved by computing distances for a large subset of combinations after throwing out large pair-wise distances. Bai (2022) shows that minimizing the square of Mahalanobis distances for paired strata often leads to the highest statistical precision in simulations.

The variables that are used to stratify are: population, median income, percent below poverty, percent owner occupied, the number of households with rooftop solar, the number of households with battery storage, and the sum of power outage household hours.
Randomization Unit
Strata are groups of three matched towns. Within each stratum, towns are randomized so that there is one town for each of the treatment arms and one control.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
There are 169 municipalities in Connecticut, and we plan to bring at least 42 municipalities into the overall research program.
Sample size: planned number of observations
An observation is a town-month. We plan to use battery installation data from January 2021 to December 2024 or 2016 town-months.
Sample size (or number of clusters) by treatment arms
14 towns in each treatment arm (28 total) and 14 towns in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Yale University Institutional Review Board
IRB Approval Date
IRB Approval Number