Determinants of Norm Compliance: Moral Similarity and Group Identification (Additional Data Collection)

Last registered on October 17, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Determinants of Norm Compliance: Moral Similarity and Group Identification (Additional Data Collection)

RCT ID

AEARCTR-0012188

Initial registration date

October 05, 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

October 17, 2023, 10:50 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Alexander Schneeberger

Affiliation

University of Cologne

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Erin Krupka

PI Affiliation

University of Michigan

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-10-09

End date

2024-01-31

Keywords

Other

Additional Keywords

JEL code(s)

C91, D03

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

What determines whether someone complies with a social norm? The social identity approach offers a mechanism for norm compliance: a person who feels similar to a group identifies more with that group and, in turn, complies with the group's norms. We used an economics experiment to test this mechanism. We manipulated the similarity between an individual and a social group by exogenously changing their similarity in moral values. Moral values were identified using a survey developed in conjunction with moral foundations theory. In one treatment, the subject and social group's moral values were similar, and in another, they were dissimilar. Subsequently, we measured group identification and behavior. To measure behavior, we used a modified rule-following task in which the social group expressed a normative expectation that subjects follow "the rule". We found that moral similarity increased group identification, and group identification increased rule compliance. We show that this behavior change was due to increased group norm sensitivity rather than changes in the group norms. We advance the study of social identity by establishing a causal pathway between group identification and behavior change.

External Link(s)

Link to the original study

Registration Citation

Citation

Krupka, Erin and Alexander Schneeberger. 2023. "Determinants of Norm Compliance: Moral Similarity and Group Identification (Additional Data Collection)." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12188-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We randomly assign participants to one of two treatment conditions. In each treatment, we introduce the participants to the moral position of a different social group. Through this treatment variation, we exogenously alter the moral similarity between our subjects and the social group (high or low moral similarity) and, consequently, the group identification towards the social group.

Intervention Start Date

2023-10-09

Intervention End Date

2023-11-30

Primary Outcomes

Primary Outcomes (end points)

Norm compliance (the number of balls in the blue bucket of the modified rule-following task in the Choice Experiment).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Group identification (the rating of the "Inclusion of Ingroup in the Self Scale" in the Choice Experiment).

Secondary Outcomes (explanation)

In our confirmatory analysis, we utilize the distance measure of this scale rather than the overlap measure.

Experimental Design

The additional data collection includes one survey and one experiment:

The Pre-Screening Survey: In this survey, participants answer the Moral Foundations Questionnaire (Graham et al., 2011). Based on their responses, we calculate the progressivism index (Clark et al., 2017).

The Choice Experiment: We re-invite participants with a positive progressivism index. In this experiment, we introduce participants to the moral position of either social group, ask for a rating in a continuous version of the "Inclusion of Ingroup in the Self Scale" (Tropp & Wright, 2001) and let them play a modified version of the rule-following task. In this modified version, the rule "The rule is to put the balls in the blue bucket" is replaced by the rule "According to the members of Group A, the rule is to put the balls in the blue bucket".

Experimental Design Details

A full description of the experimental design can be found in our pre-analysis plan of the original study. The only relevant change from the original study is that we no longer ask participants an incentivized control question about the moral position of the assigned social group.

Randomization Method

When subjects participate in the Choice experiment, the experimental server randomly assigns them to either treatment. To achieve this, we use the Python module NumPy.

Randomization Unit

Individual level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Choice Experiment: 2 treatments with 233 participants each.

Sample size: planned number of observations

Choice Experiment: 2 treatments with 233 participants each.

Sample size (or number of clusters) by treatment arms

Choice Experiment: 2 treatments with 233 participants each.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

The sample size in the Choice Experiment is based on the following power calculation: We used the data from the original data to calculate an expected effect size of 0.23. Subsequently, we used G-Power (Faul, Erdfelder, Lang, & Buchner, 2007) to calculate the minimal required sample size for a one-tailed t-test with, with an effect size of 0.23, an alpha-error probability of 0.05, a power of 0.80 and an even allocation between treatments. Based on this power calculation, we require at least 233 subjects per treatment.

Supporting Documents and Materials

IRB