Can Guilt Change Police Attitudes Towards Gender-Based Violence?

Last registered on November 24, 2021
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Pre-Trial

Trial Information

General Information

Title
Can Guilt Change Police Attitudes Towards Gender-Based Violence?
RCT ID
AEARCTR-0008611
Initial registration date
November 23, 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
November 24, 2021, 12:12 PM EST

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

Locations

Country
India
Region
Madhya Pradesh

Primary Investigator

Name
Victoria Endl-Geyer
Affiliation
ifo Institut and LMU Munich
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Other Primary Investigator(s)

PI Name
Sofia Amaral
PI Affiliation
ifo Institute and LMU Munich
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PI Name
Kimberly Chaney
PI Affiliation
University of Connecticut
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PI Name
Asmi Khushi
PI Affiliation
University of Michigan
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PI Name
Nishith Prakash
PI Affiliation
University of Connecticut
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PI Name
Abhilasha Sahay
PI Affiliation
World Bank
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Additional Trial Information

Status
On going
Start date
2021-11-17
End date
2021-12-24
Keywords
Crime, Violence, & Conflict, Gender
Additional Keywords
Gender-based Violence, Guilt, Policing
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Prevailing stigmas can lead to discrimination against specific groups. When people become aware of their biases and the effects of their biases (i.e., causing harm, being perceived negatively by others), research suggests they will experience guilt. As a negative affective state, people are motivated to alleviate their sense of guilt. One particularly effective way to induce guilt for biases is to confront, or call-out, someone’s prejudices. Research on prejudice confrontations has consistently demonstrated that people confronted for their prejudice report feeling guilty, which is in turn associated with self-regulation of biases in the future, including reducing the use of stereotypes and increasing compensatory behavior (Chaney & Sanchez, 2018; Czopp et al., 2006; Mallett & Wagner, 2011). Thus, guilt has the potential to create reflexivity and critical examinations of one’s own actions and biases. In the context of the police this can lead to unbiased investigation and better treatment of women reporting gender based violence cases -- offenses where officers tend to exhibit biased cultural norms towards victims.

The project aims to implement a field experiment with approximately 355 police officers in India using confrontation of mis-handled gender-based violence (GBV) cases as an intervention to evoke guilt, and in turn promote more positive future responses to GBV crime. It ties together insights and expertise in causal inference and field experiments from development economics, with the study of countering discrimination and prejudice from social psychology.
External Link(s)

Registration Citation

Citation
Amaral, Sofia et al. 2021. "Can Guilt Change Police Attitudes Towards Gender-Based Violence?." AEA RCT Registry. November 24. https://doi.org/10.1257/rct.8611-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Hypothesis: This research seeks to identify an intervention to decrease police bias against women reporting GBV in Madhya Pradesh. Borrowing insights from the social psychology and economics research on the role of self-regulation of bias, this project will examine if confronting police officer’s bias increases experiences of guilt, and in turn results in greater belief of women reporting GBV cases, decrease victim-blaming, and improve handling of future GBV cases.

This study will include approximately 360 officers from the districts of Bhopal, Harda, Hoshangabad, Raisen, Sehore and Vidisha in Madhya Pradesh, India.

The research team will collaborate with the Madhya Pradesh Police and elicit a roster of all police officers of the rank of police constables and head constables and randomly draw approximately 360 officers and assign them to treatment or the control group. Participants will receive compensation in the form of a meal that is worth $3 per person in Time 1 (T1) and worth $3 for Time 2 (1 week after T1) and will be randomly assigned to either be confronted at T1, or not.
Intervention Start Date
2021-11-17
Intervention End Date
2021-12-24

Primary Outcomes

Primary Outcomes (end points)
1. Guilt: indicator of whether treatment induces guilt.
a. We use a survey to capture this outcome.

2. Recognition of a crime: indicator for whether the respondents can identify a GBV case correctly.
a. We elicit this measure through the use of vignettes.

3. Priority that is given to women’s statements in investigation: in comparison to statements made by other people involved in the case.
a. We use vignettes to capture this outcome.

4. Police performance in relation to solving the crime.
a. We use vignettes to capture this outcome.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Our secondary outcomes are related to the mechanisms through which we expect the treatment to affect GBV. These include:

1. Self-reflection: Reflecting on how officers approach/think about GBV cases, feelings after baseline survey; whether officers understood the feedback; whether officers think about adjusting how they handle GBV cases.

2. Victim blaming: whether officers think that women are at fault when it comes to GBV.

3. Truth of complaints: whether officers think GBV reports are false.

4. Norms: whether officers believe that society holds specific views regarding GBV cases.

5. Personal and anticipated stigma of victims.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Time 1. Time 1 sessions will be conducted in person in compliance with local Covid health and safety protocol. These sessions will occur in a lab setting in the police headquarter in Bhopal. All COVID-19 health protocols will be followed when this study is launched in November 2021. All officers will review three cases, including one that involved a woman reporting GBV, and two neutral cases, and answer a computer-based survey on how they would handle the cases. Half the officers will be assigned to a treatment condition where a senior police officer will confront them about their biases in attending to victims of GBV crime and/or registering their complaints after completing the survey, while the other half will not receive this confrontation. Participants randomly assigned to the confront condition will be confronted by the experimenter through a private talk. All senior officers will be trained prior to sessions, ensuring they provide the same feedback in a neutral, matter-of-fact tone.

Time 2. One week after T1, participants will be asked again to come to the police headquarter in Bhopal. At Time 2, participants will review a series of cases similar to those in Time 1 and answer survey questions addressing their self-reflection, attitude towards GBV and belief in truthfulness of GBV complaints. Participants who have been confronted at T1 are predicted to demonstrate greater belief of women reporting GBV cases, decreased victim-blaming, and improved handling of GBV cases.

Note, all participants will be randomly assigned a code at Time 1 that will be the only way their survey responses at Time 1 and Time 2 can be connected together. This code will never be linked to participants themselves, thus ensuring data collection is anonymous.

Analysis: We will compare the effect of the treatment and control group’s guilt on our primary and secondary outcomes. To improve the precision of the estimates we will include individual-level controls for the officer and session-level fixed-effects. The main explanatory variable is Guilt, representing treatment, is an index validated in past research (Chaney et al., 2021). The individual level controls will include gender, age and rank. The intervention and survey instruments were pre-tested with a pilot conducted prior to the main experiment.
Experimental Design Details
Randomization Method
By computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Approximately 15 experimental sessions
Sample size: planned number of observations
Approximately 360 police officers
Sample size (or number of clusters) by treatment arms
Half of the participants get assigned to treatment/control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Connecticut
IRB Approval Date
2021-05-21
IRB Approval Number
X21-0091
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials