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Field
Trial Title
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Before
EV or not EV: Elon Musk and the Role of Identity in Eco-friendliness
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After
EV or not EV: Information Frictions and Identity as Drivers of Vehicle Demand
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Field
Abstract
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Before
Voluntary "eco-friendly" consumer actions are increasingly critical for mitigating environmental damages. Focusing on electric vehicle (EV) adoption, we examine how the identity associated with taking an eco-friendly action influences its uptake. Analyzing New York registration data, we find that Elon Musk's acquisition of Twitter decreased the prevalence of Teslas among EVs registered in liberal ZIP codes by 7.8 percentage points (19%). Instead, consumers in these ZIP codes substituted to relatively inefficient EVs that had lower environmental benefits. In a complementary online experiment pilot, we demonstrate that consumers actively avoid information about the relative cleanliness of Teslas when it conflicts with their anti-Musk identity. Taken together, these findings suggest that (i) consumers may prioritize identity alignment over environmental impact when making eco-friendly choices and (ii) consumers may engage in motivated reasoning to justify this trade-off.
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After
We examine the extent to which motivated reasoning drives demand for electric vehicles (EVs) via consumers holding inaccurate beliefs about environmental impact. Using a novel experiment, we aim to show that consumers hold inaccurate beliefs about EV impacts that are related to their political ideology. We will examine whether liberals overestimate the cleanliness of EVs and conservatives underestimate the cleanliness of EVs. We will further analyze the extent to which the opposite holds for Teslas, the EV brand which is most associated with conservative ideology. We estimate the proportion of the magnitude of misconceptions that is driven by ideologically-driven motivated reasoning. We combine this information with detailed sales data to estimate how misconceptions about impact drive demand for EVs.
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Trial Start Date
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Before
June 02, 2025
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After
May 30, 2025
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Field
Last Published
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Before
January 27, 2025 10:10 AM
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After
May 26, 2025 10:10 AM
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Field
Intervention (Public)
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Before
Participants are randomly divided into three groups. Group 1 is assigned information relaying the emissions of a 2024 Tesla Model 3 and the average EV. Group 2 is assigned information relaying the emissions of a similarly efficient 2024 Lucid Air Pure and the average EV. Group 3 is assigned information on price for the 2024 Lucid Air Pure and the average EV. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
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After
Experiment 1:
Prior beliefs about the ZIP-specific emissions associated with several popular EV brands are elicited. Participants are then randomly divided into three groups. Group 1 is assigned information relaying the emissions of a 2024 Tesla Model 3 and the average EV. Group 2 is assigned information relaying the emissions of a similarly efficient 2024 Lucid Air Pure and the average EV. Group 3 is assigned information on price for the 2024 Lucid Air Pure and the average EV. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
Experiment 2:
Prior beliefs about the ZIP-specific emissions associated with an average EV are elicited. Participants are randomly shown either a placebo quote or treatment quotes which vary the strength of desire to be a motivated reasoner. For liberals, the treatment quote emphasizes job gains from EVs. For conservatives, the treatment quote emphasizes job losses from EVs. Moderates are randomly divided between the two treatment quotes with 50% probability. The placebo quote is unrelated to EV jobs, instead focusing on the role of socioeconomic status in driving purchases. The placebo quote does not vary by political ideology. Each participant is then randomly assigned to one of two pieces of information. For half, this information is about commuting times. For the other half, it is the true value of average EV emissions in their ZIP code. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
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Field
Intervention Start Date
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Before
June 02, 2025
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After
May 30, 2025
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Field
Intervention End Date
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Before
August 31, 2025
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After
June 14, 2025
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Field
Primary Outcomes (End Points)
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Before
WTP to see assigned information and total belief updating upon seeing it (distance between measured prior and posterior).
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After
For both experiments, the outcomes of interest are the WTP to see assigned information and total belief updating upon seeing it (distance between measured prior and posterior). Prior beliefs will also be correlated with demographics and used in a demand estimation exercise using observational registration data.
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Field
Experimental Design (Public)
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Before
We propose to collect data via a survey administered on Prolific. The survey first determines political leanings and car ownership of all participants. From here, the survey elicits participants' prior beliefs on the emissions from several EV brands, including Teslas. This allows for a baseline measurement of how informed consumers of different political beliefs are on the relative cleanliness of different EVs.
Next, participants are randomly divided into three groups. Group 1 is assigned information relaying the emissions of a 2024 Tesla Model 3 and the average EV. Group 2 is assigned information relaying the emissions of a similarly efficient 2024 Lucid Air Pure and the average EV. Group 3 is assigned information on price for the 2024 Lucid Air Pure and the average EV. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
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After
Experiment 1:
We propose to collect data via a survey administered on Prolific. The survey first determines political leanings and car ownership of all participants. From here, the survey elicits participants' prior beliefs on the emissions from several EV brands, including Teslas. This allows for a baseline measurement of how informed consumers of different political beliefs are on the relative cleanliness of different EVs. These priors are correlated with demographics for use in a demand estimation exercise. Next, participants are randomly divided into three groups. Group 1 is assigned information relaying the emissions of a 2024 Tesla Model 3 and the average EV. Group 2 is assigned information relaying the emissions of a similarly efficient 2024 Lucid Air Pure and the average EV. Group 3 is assigned information on price for the 2024 Lucid Air Pure and the average EV. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
Experiment 2:
We propose to collect data via a second survey administered on Prolific. This survey is distinct from the first and disallows participants from experiment 1 from participating in experiment 2. The survey first determines political leanings and car ownership of all participants. From here, the survey elicits participants' prior beliefs on the emissions from an average EV versus an average GV. This allows for a baseline measurement of how informed consumers of different political beliefs are on the relative cleanliness of EVs relative to GVs, on average. These priors are correlated with demographics for use in a demand estimation exercise. Note, additionally, that these priors are specific to a participant's ZIP code due to electricity grids across the US having varying emissions intensity. Participants are then randomly shown either a placebo quote or treatment quotes which vary the strength of desire to be a motivated reasoner. For liberals, the treatment quote emphasizes job gains from EVs. For conservatives, the treatment quote emphasizes job losses from EVs. Moderates are randomly divided between the two treatment quotes with 50% probability. The placebo quote is unrelated to EV jobs, instead focusing on the role of socioeconomic status in driving purchases. The placebo quote does not vary by political ideology. Each participant is then randomly assigned to one of two pieces of information. For half, this information is about commuting times. For the other half, it is the true value of average EV emissions in their ZIP code. Before each group sees the information, willingness-to-pay (WTP) to see their assigned piece of information is measured using a multiple price list. From here, the participants are shown the information they were assigned and then posterior beliefs are elicited.
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Field
Planned Number of Clusters
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Before
3,300 individuals
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After
2,800 individuals (1,200 in experiment 1; 1,600 in experiment 2)
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Field
Planned Number of Observations
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Before
3,300 individuals
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After
2,800 individuals (1,200 in experiment 1; 1,600 in experiment 2)
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Field
Sample size (or number of clusters) by treatment arms
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Before
1,100 individuals per treatment arm, with roughly equal proportions of liberal, conservative, and moderate participants in each.
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After
Experiment 1: 1,200 individuals divided evenly by treatment (3 treatments) = 400 individuals per arm
Experiment 2: 1,600 individuals divided evenly by treatment (4 treatments) = 400 individuals per arm
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Field
Did you obtain IRB approval for this study?
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Before
No
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After
Yes
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Field
Building on Existing Work
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Before
Yes
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After
No
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