Excuses and Social Image (Part 2: Interpretation of Stigmatized Expression)
Last registered on February 19, 2020

Pre-Trial

Trial Information
General Information
Title
Excuses and Social Image (Part 2: Interpretation of Stigmatized Expression)
RCT ID
AEARCTR-0005462
Initial registration date
February 18, 2020
Last updated
February 19, 2020 3:22 PM EST
Location(s)
Region
Primary Investigator
Affiliation
Harvard University
Other Primary Investigator(s)
PI Affiliation
University of Warwick
PI Affiliation
University of Chicago
PI Affiliation
University of Bergen
Additional Trial Information
Status
In development
Start date
2020-02-18
End date
2020-02-24
Secondary IDs
Abstract
We examine the role of third-order beliefs in shaping potentially stigmatized public behavior. We hypothesize that information, in addition to its persuasive effects, potentially also affects behavior by providing an "excuse" for engaging in xenophobic actions, leading to changes in equilibrium expression even among people who do not necessarily believe the information. The central implication of our theoretical framework is that people make different inferences about agents who engage in xenophobic actions when they know those agents have an "excuse" for doing so, relative to when they believe those agents do not have an "excuse". In particular, our framework predicts that when making inferences about agents with an excuse, respondents will believe these agents to be more gullible and less biased than agents without an excuse. We test this prediction with an incentivized experiment eliciting respondents' beliefs about the gullibility and bias of agents who chose to donate to an anti-immigrant organization with or without an excuse.
External Link(s)
Registration Citation
Citation
Bursztyn, Leonardo et al. 2020. "Excuses and Social Image (Part 2: Interpretation of Stigmatized Expression)." AEA RCT Registry. February 19. https://doi.org/10.1257/rct.5462-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-02-18
Intervention End Date
2020-02-24
Primary Outcomes
Primary Outcomes (end points)
Respondent's guesses as to their matched respondent's score on the Foreign Culture Tolerance Scale and the Gullibility Scale
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Open-ended text: "Why do you think your matched respondent chose to donate to Fund the Wall"?
Secondary Outcomes (explanation)
See the text analysis pre-analysis plan and Python code file attached to this submission.
Experimental Design
Experimental Design
We recruit respondents through Lucid, a survey provider.
Experimental Design Details
We randomize respondents into one of two conditions: "excuse" or "no excuse". We provide respondents with information about a recent study (Lott 2018) that finds that undocumented immigrants in Arizona commit crimes at substantially higher rates than comparable US citizens. We (truthfully) tell participants that they have been matched with another participant who chose to authorize a donation to Fund the Wall (www.fundthewall.com), an organization that supports the proposed US-Mexico border wall. Participants in the "no excuse" group are told that their matched respondent did not see the information about Lott's study before donating, whereas participants in the "excuse" group are told that their matched respondent did see the information. Participants are then cross-randomized into one of two conditions: "bias" or "gullibility". Participants in the "bias" group are asked to guess their matched respondent's score on a Foreign Culture Tolerance Scale, which measures "tolerance toward foreign values and traditions" on a scale from 0-100. Participants in the "gullibility" group are asked to guess their matched respondent's score on a Gullibility Scale, which measures "how easily people are manipulated by evidence from untrustworthy sources" on a scale from 0 to 100. Both guesses are incentivized: participants are told that if they guess correctly, they will be entered into a lottery for a $50 Amazon gift card. References: Lott, John R., Undocumented Immigrants, U.S. Citizens, and Convicted Criminals in Arizona (February 10, 2018). Available at SSRN: https://ssrn.com/abstract=3099992 or http://dx.doi.org/10.2139/ssrn.3099992
Randomization Method
Qualtrics will randomize respondents into one of the four conditions. We use the "Evenly Presents Elements" option in the Qualtrics Randomizer to secure equal group sizes.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
We target 3000 individuals (5163 individuals when pooling with pilot)
Sample size: planned number of observations
We target 3000 individuals (5163 individuals when pooling with pilot)
Sample size (or number of clusters) by treatment arms
We target 750 individuals in excuse/gullibility, 750 individuals excuse/bias, 750 individuals no excuse/gullibility, 750 individuals no excuse/bias. To gain statistical precision, we also report some specifications in which we pool with additional pilot data (N=2163, with 517 excuse/gullibility, 510 excuse/bias, 502 no excuse/gullibility, and 511 no excuse/bias).

We ask our survey provider to restrict the survey to respondents who had not taken our pilot. To further ensure that our sample does not include repeat respondents, we also include a post-outcome question asking respondents whether they have taken a previous online survey that discussed the Lott study. We expect this number to be small and plan to exclude respondents who respond in the affirmative from our main specifications (though we will also report results with the entire sample).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on pilot results, our MDE is approximately 3.6 on our cultural and gullibility scales (which range from 0 to 100).
Supporting Documents and Materials
Documents
Document Name
Text analysis Python code
Document Type
other
Document Description
Please see the text analysis pre-analysis plan for details. The online portal does not allow .py files to be uploaded; we have changed the extension to .txt.
File
Text analysis Python code

MD5: 8f4733875da2ceddab036a24a9f370e3

SHA1: d4f4ad7e5e28a5858b64846350266bd2130d5349

Uploaded At: February 18, 2020

Document Name
Text analysis synonyms
Document Type
other
Document Description
Please see the text analysis pre-analysis plan for details. The online portal does not allow .json files to be uploaded; we have changed the extension to .txt.
File
Text analysis synonyms

MD5: 4ba77c9d34964f0c30fdf092405ce1b5

SHA1: b80a95efd9fb73a784c23361eadff458f3db2b6a

Uploaded At: February 18, 2020

IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Chicago Social and Behavioral Sciences IRB
IRB Approval Date
2019-11-28
IRB Approval Number
IRB19-1320
Analysis Plan
Analysis Plan Documents
Text analysis preanalysis plan

MD5: 1d2118f83982497a10a7db019e19dfbd

SHA1: e3824ede5fee2af8509ab10f2c73fc87d8a809e6

Uploaded At: February 18, 2020

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