Identifying and Addressing Structural Racism

Last registered on December 19, 2022

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Pre-Trial

Trial Information

General Information

Title

Identifying and Addressing Structural Racism

RCT ID

AEARCTR-0007246

Initial registration date

February 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

February 24, 2021, 10:28 AM EST

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

Last updated

December 19, 2022, 6:42 PM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Crystal Yang

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Marcella Alsan

PI Affiliation

Harvard Kennedy School

Contact Investigator

PI Name

Damon Jones

PI Affiliation

Chicago Harris

Contact Investigator

Additional Trial Information

Status

In development

Start date

2023-06-01

End date

2023-12-31

Keywords

Behavior, Crime, Violence, & Conflict, Health

Additional Keywords

Criminal Justice, Discrimination, Race

JEL code(s)

I12, I14, J15, D91

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Nearly a half century after the civil rights movement, racial inequity remains a defining feature of U.S. society. We formalize a model of structural racism and empirically test a central implication – that beliefs regarding racial discrimination in one system will tend to be correlated with discrimination in other systems – through two complementary RCTs. Focusing on the settings of policing and healthcare provision, our first experiment examines the impact of providing information about racial discrimination in policing on perceptions of discrimination in the healthcare sector among 1,000 Black and White individuals. In a second experiment, we then investigate whether counter signals made by healthcare professionals affect this correlation in beliefs among 7,500 Black respondents, and whether such signals enhance actual healthcare demand. Understanding how racial discrimination affects beliefs and behavior across systems is critical to reducing racial inequality in the United States given the history of structural racism. We focus on the consequences of discrimination in criminal justice, one of the key institutions generating inequality and an issue of pressing public policy, and on healthcare because the medical system plays an important role in combating the severe consequences of the COVID-19 pandemic and mitigating its disparate impact on marginalized groups.

External Link(s)

Registration Citation

Citation

Alsan, Marcella, Damon Jones and Crystal Yang. 2022. "Identifying and Addressing Structural Racism." AEA RCT Registry. December 19. https://doi.org/10.1257/rct.7246-2.0

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Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2023-06-01

Intervention End Date

2023-12-31

Primary Outcomes

Primary Outcomes (end points)

perceptions of racial discrimination, reported healthcare demand, healthcare take-up

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We hypothesize that in the presence of structural racism, a negative signal about one system will influence outcomes in the other. Our first experiment will elicit priors on experiences in the criminal justice system and the healthcare system, with a focus on understanding participant willingness to take up healthcare. We will then randomize respondents to either a treatment or control group. The treatment group will receive a series of statistics and a narrative on racial inequality in the criminal justice system, and the passive control will receive statistics and a narrative about lightning strikes (i.e. a placebo). The narrative vignettes related to discrimination in the criminal justice system will be informed by details of actual cases of discrimination and will be produced by the journalism production company The Invisible Institute.

After making salient racial disparities in the criminal justice system, we will again measure perceptions of racial discrimination in both the healthcare and criminal justice systems as well as other systems such as financial markets and education. In the absence of structural racism, and/or the absence of perceived structural racism, individuals in the first treatment group may only update regarding criminal justice; however, if the two systems are “correlated” due to structural racism and if individuals internalize this correlation, i.e., if a signal in one system is informative about the distribution of outcomes of another, then making discrimination in the criminal justice system more salient will have spillover effects on perceptions of the healthcare system. Individuals in this group may then be less willing to seek medical care.

In the second experiment, we will test the hypothesis that the updating process observed in Experiment 1 can be weakened by making salient the actions of medical professionals who denounce racial discrimination. In this experiment, individuals will again be randomized into one of four groups: first, they will be assigned to one of the two experimental arms from our first experiment, and then separately randomized into a second treatment: in this second treatment, the treatment group will be shown images and a narrative video of doctors protesting against police violence in the wake of the death of George Floyd. The control group will be shown unrelated images of doctors protesting climate change. We will then measure the effect of this second treatment on healthcare demand. We will measure self-reported health demand as in Experiment 1. We will also measure actual take-up of a coupon for telehealth services from a third-party provider, Plush Care. This telehealth provider is willing to create a signal of solidarity specific to their firm, linking the treatment to the same healthcare provider to which participants will be given subsidized access.

Experimental Design Details

Randomization Method

Randomization will be conducted using a computer random number generator.

Randomization Unit

We will randomize at the level of the individual.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

N/A

Sample size: planned number of observations

Our target sample size is 1,000 individuals for our first experiment and 7,500 individuals for our second experiment. Reflecting the focus of our study on communities affected by racial discrimination, 80% of our respondents in Experiment 1 will be non-Hispanic Black, while 20% of the respondents will be non-Hispanic White. All respondents in Experiment 2 will be non-Hispanic Black. As we are particularly interested in examining marginalized Americans who may be reluctant to access healthcare services due to structural racism, we will restrict our sample in both experiments to individuals who do not have a regular primary care physician. We will require all participants to be age 18 or older and currently live in the United States. To ensure our sample is representative of this population, we will institute race-specific age, gender, education, region, and income quotas during recruitment.

Sample size (or number of clusters) by treatment arms

In Experiment 1, 500 individuals will be assigned to the treatment and control groups, respectively. In Experiment 2, 3,750 individuals will first be randomized to each of the two experimental arms from our first experiment, and then separately randomized into a second group: 1,875 individuals will be randomized to the second treatment group and 1,875 individuals will be randomized to the second control group.

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

In a recent project by Alsan and Eichmeyer (in progress), the authors leverage an experiment to identify the most persuasive ways to encourage low socioeconomic males living in the U.S. to take up seasonal flu vaccination. We use the means and standard deviations for the control group post-intervention across three outcomes from this study as a basis for our power calculations, as our experiment will also focus on low socioeconomic respondents and features healthcare demand as an outcome. We find that if there are underlying effect sizes of 8.5 percentage points for Experiment 1 and 5.9 percentage points for the key interaction term in Experiment 2, we should be able to detect a significant difference in outcomes. Based on these MDEs, we propose recruiting a sample of 1,000 participants for the first experiment and 7,500 participants for the second experiment. We believe that the MDEs are realistic given the economics literature on nudges and medical care take-up. For example, Alsan et al. (2019) study the effect of a race concordant health provider on the demand for preventive care among African American men and find that participants assigned to a Black doctor are 18 percentage points more likely to take up preventive care relative to those assigned to non-Black doctors. Similarly, Altmann and Traxler (2014) randomly provide patients with reminder messages about dental health prevention and find that individuals who receive a reminder are roughly 10 percentage points more likely to schedule a check-up, on average.

Supporting Documents and Materials

IRB