The Electoral Effects of Green Industrial Policies

Last registered on January 22, 2026

Pre-Trial

Trial Information

General Information

Title
The Electoral Effects of Green Industrial Policies
RCT ID
AEARCTR-0017636
Initial registration date
January 12, 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
January 22, 2026, 6:09 AM EST

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

Locations

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Primary Investigator

Affiliation
ETH Zurich

Other Primary Investigator(s)

PI Affiliation
ETH Zurich

Additional Trial Information

Status
In development
Start date
2026-01-14
End date
2026-02-14
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This pre-registration describes a survey experiment designed to complement a qua-si-experimental difference-in-difference analysis examining the electoral consequences of green industrial investments under the U.S. Inflation Reduction Act (IRA), distinguishing between investments into renewable energy (RE) deployment (e.g., wind farms, solar farms) and investments into low-carbon technology manufacturing (e.g., EV plants, battery plants). The main electoral channel is expected to operate through symbolic (identity-based) and material (economic) channels. The survey experiment is designed to test these mechanistic pathways.
External Link(s)

Registration Citation

Citation
Fesenfeld, Lukas and Patricia Maissen. 2026. "The Electoral Effects of Green Industrial Policies." AEA RCT Registry. January 22. https://doi.org/10.1257/rct.17636-1.0
Experimental Details

Interventions

Intervention(s)
Respondents will be randomized into different groups. Randomization occurs in three stages:
• Stage 1 (domain level): Each respondent is randomly assigned to either the RE Deployment arm or the Low-carbon Technology Manufacturing arm (50:50 split).
• Stage 2 (framing level): Within each domain, respondents are randomly as-signed to control group, neutral framing group or partisan framing group (33:33:33 split).
• Stage 3 (technology level): Within each treated arm, respondents are ran-domly assigned to one of two possible technologies (50:50 split):
o RE deployment arms: solar power park vs. wind power park
o Low-carbon Technology Manufacturing arms: EV manufacturing plant vs. battery manufacturing plant

All respondents within a domain are presented with a general information (“Across the United States, new clean [energy generation/technology manufacturing] pro-jects have been built or announced in many areas."). In addition, treated individuals are informed about a recent, salient project of their respective technology in their state. The treated respondents are informed with a short text, which includes the specific project, as well as a mock newspaper article. The mock newspaper article differs slightly depending on (a) the respondent’s framing arm (neutral vs. partisan), (b) the technology they were sampled into and (c) the state they live in.
Here is an example for the treatment information about the technology, here a wind power park (Ohio), shown before the neutral or partisan-framed newspaper article:
“Across the United States, new clean energy generation projects have been built or announced in many areas, such as wind power parks.
In Ohio, such parks have recently been opened. For example, the Martin Marietta Wind Project is located in Sandusky County. Wind power parks have also been built in other counties in Ohio.
Here is an illustrative example of media reporting on such projects:”
After this technology and project information, dependent on the randomized treatment arm and respondent’s state, mock newspaper articles are shown.

Randomization is conducted by the survey platform using simple random assign-ment without stratification. Randomization is independent of all measured covari-ates. We will verify balance on key pre-treatment covariates (e.g. age, gender, edu-cation, party identification) in the final data and report any imbalances in the analysis.
Intervention Start Date
2026-01-14
Intervention End Date
2026-02-14

Primary Outcomes

Primary Outcomes (end points)
• Vote Intention (Presidential and House): Democratic candidate, Republican candidate, other candidate or abstention
• Overall Investment Support: 7-point scale from "Strongly oppose" to "Strongly support" for the relevant project type (RE deployment or low-carbon technol-ogy manufacturing) as well as for relevant specific technologies of the project type
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
• Material Benefit/Cost Evaluations. Composite index from 7-point bipolar scales measuring perceived impacts on: incomes, jobs, economic growth, electricity prices, cost of living, and U.S. global economic competitiveness
• Symbolic Benefit/Cost Evaluations. Composite index from 7-point bipolar scales measuring perceived impacts on: community life, U.S. global image, identity and values, climate protection, landscapes, and middle class stand-ing
• Attribution of Responsibility. Multi-select question identifying which actors (Biden/Harris Administration, Trump/Vance Administration, Congress, state officials, private companies, etc.) are seen as responsible for projects, plus a forced-choice question identifying which political party is primarily responsi-ble
• IRA Knowledge. Categorical question assessing with four items respondents’ knowledge about the IRA.
• IRA Effects on Investments. 7-point scale measuring perceived responsibility of the IRA for investments into RE deployment and low-carbon technology manufacturing
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The full sample is randomly divided into two parallel survey arms, each focusing on one investment domain:
• Domain 1: RE Deployment Projects. Respondents answer questions about RE deployment projects (e.g., wind power parks, solar power parks).
• Domain 2: Low-carbon Technology Manufacturing Projects. Respondents answer questions about low-carbon technology manufacturing projects (e.g., EV manufacturing plants, battery manufacturing plants).
Within the domain, respondents in the treatment group get an information treat-ment—with a neutral or a partisan framing—on different domain technologies (cf. “Interventions”), but the questions before and after the treatment are the same for everyone within the respective domain (e.g., “Overall, how much do you support or oppose clean energy generation projects?”, and not “Overall, how much do you support or oppose solar energy generation projects?”). Between the two domains, the questions are identical except for the domain mentioned as well as the specific technologies mentioned in individual questions (e.g., differing project types for the question “How much do you support or oppose the following specific types of clean [energy generation/technology manufacturing] projects?”).
Before treatment occurs, respondents are asked about their demographic character-istics; political preferences and past voting behaviors; salience of, opinion on and perceived exposure to the projects from the respective domain.
In the primary analysis, the two respective technologies will be analyzed at the do-main level (RE deployment/low-carbon technology manufacturing), and not at the technology level (solar, wind, EV, battery). Additional explorative analyses allow us to investigate specific technology-level differences.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted by the survey platform using simple random assignment without stratification. Randomization is independent of all measured covariates. We will verify balance on key pre-treatment covariates (e.g. age, gender, edu-cation, party identification) in the final data and report any imbalances in the analy-sis.
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not clustered
Sample size: planned number of observations
6,000 respondents
Sample size (or number of clusters) by treatment arms
2,000 Michigan; 2,000 Ohio; 1,000 Texas; 1,000 California

Across all 6,000:
16.67% RE Group - Control
8.33% RE Group - Solar - Neutral Framing
8.33% RE Group - Solar - Partisan Framing
8.33% RE Group - Wind - Neutral Framing
8.33% RE Group - Wind - Partisan Framing
16.67% Manufacturing Group - Control
8.33% Manufacturing Group - EV - Neutral Framing
8.33% Manufacturing Group - EV - Partisan Framing
8.33% Manufacturing Group - Batteries - Neutral Framing
8.33% Manufacturing Group - Batteries - Partisan Framing
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
ETH Zurich Ethics Commission
IRB Approval Date
2025-12-22
IRB Approval Number
Project 25 ETHICS-399
Analysis Plan

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