Measuring Identity, Factual Beliefs, and Policy Views

Last registered on March 10, 2022


Trial Information

General Information

Measuring Identity, Factual Beliefs, and Policy Views
Initial registration date
December 14, 2021

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
December 14, 2021, 4:31 PM EST

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

Last updated
March 10, 2022, 2:06 PM EST

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Bocconi University

Other Primary Investigator(s)

PI Affiliation
Bocconi 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.
In Bonomi, Gennaioli, and Tabellini 2021 we develop a theory of identity politics that builds on two ideas. First, when policy conflict renders a certain social divide (economic or cultural) salient, a voter’s beliefs are tainted by the stereotype of the group he identifies with. Second, culture influences beliefs across many domains. One key prediction of the group is that voters’ beliefs about facts and their policy views are slanted toward the distinctive opinions of the group a person identifies with and in away from the opinions of the outgroup. By this mechanism, identity is correlated with specific forms of incorrect beliefs and polarization in policy views. In this study, we plan to ask respondents to answer questions on their: i) identity, ii) factual beliefs, and iii) policy preferences on a range of domains (economic domains: redistribution; cultural domains: immigration, abortion, affirmative action. We also inquire factual beliefs and policy views on trade policy and environmental policy). With such data, we want to test some hypotheses and predictions of the model.
Some of the questions we would like to answer are:
1. Does identity predict disagreement in terms of beliefs and policy preferences across economic and cultural domains, and also the extent to which disagreement is correlated across different domains?
2. Is disagreement stronger for individuals with a strong identity?
3. Does identity with political parties play a distinct role from identity with social groups (e.g. religious groups or economic classes)?
External Link(s)

Registration Citation

Gennaioli, Nicola and Guido Tabellini. 2022. "Measuring Identity, Factual Beliefs, and Policy Views." AEA RCT Registry. March 10.
Experimental Details


The study does not involve any treatment. We only vary the order in which factual beliefs and policy views are measured (some subjects first answer about facts and then about policy views, others the other way around).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Disagreement between people identified with different groups on policy views and beliefs;
Coherence of disagreement across economic and social/cultural policy;
Correlation between beliefs and policy preferences;
Correlation between demographics and identity with different groups.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We collect data from a sample of the US population. The survey contains questions that allow us to (i) gather general demographic information on the respondents, (ii) elicit the social or economic identity of the respondents, (iii) measure the beliefs of the respondents related to different economic and cultural phenomena, and (iv) measure the respondents’ policy preferences on the same issues. In order to control for potential framing effects, half of the sample is presented the questions on beliefs first, while the remaining half of the sample is presented the questions on policy preferences first.
The answers to the questions allow us to divide the sample in different groups based on identity. Each group is divided in sub-groups, based on the degree of progressiveness or conservatism. We then study how beliefs, policy preferences, and demographics vary between and within each group and sub-group. Furthermore, using an index of the strength of attachment to one’s identity, we look at the relationship between beliefs and policy preferences on the tails of the distribution.
Experimental Design Details
Randomization Method
Randomization done by a computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
1500 respondents answer questions about beliefs first
1500 respondents answer questions about policy preferences first
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Bocconi Research Ethics Committee
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials