DOI: 10.5061/dryad.cz8w9gj8t
title: Highlighting health consequences of racial disparities sparks support for action

description: Racial disparities arise across many vital areas of American life,
 including employment, health, and interpersonal treatment. For example, 1
 in 3 Black children live in poverty (vs. 1 in 9 White children) and on
 average, Black Americans live 4 fewer years than White Americans. Which
 disparity is more likely to spark reduction efforts? We find that
 highlighting disparities in health-related (vs. economic) outcomes spurs
 greater social media engagement and support for disparity-mitigating
 policy. Further, reading about racial health disparities elicits greater
 support for action (e.g., protesting) than economic or belonging-based
 disparities. This occurs, in part, because people view health disparities
 as violating morally-sacred values which enhances perceived injustice.
 This work elucidates which manifestations of racial inequality are most
 likely to prompt Americans to action.
description: The data from Studies 1a, 1b, 3, 4a, and 4b were collected via
 online platfroms (i.e., Mturk.com, Prolific Academic, and NORC’s
 AmeriSpeak Panel). All analyses were run in R with the R code provided
 (title: Health_Disparities_Syntax.R).
description: # Highlighting Health Consequences of Racial Disparities Sparks Support
 for Action There are a
 total of 5 datasets available (Studies 1a, 1b, 3, 4a, 4b) each collected
 by the researchers from online survey platforms. All data files are .sav
 files. We recommed using SPSS or RStudio to work with the data. We provide
 our code using RStudio and a codebook with the name of all variables in
 each dataset. ## Description of the data and file structure Study 1a and
 Study 1b utilized a within-subjects experimental design (S1a: N=191; S1b,
 preregistered: N=337, 50% White participants, 50% Black participants)
 where samples of U.S. citizens recruited from MTurk.com and Prolific
 Academic read nine examples of racial disparities, three each from the
 domains of health, economics, and belonging. After each example,
 participants reported whether the disparity was unjust and fair
 (reverse-coded; 2-items averaged to create a perceived injustice scale).
 Participants also indicated their agreement (1=strongly disagree,
 7=strongly agree) that they “personally want to take action to reduce the
 given disparity” (general action). In S1b, participants additionally
 indicated how likely (1=extremely unlikely, 7=extremely likely) they were
 to engage in specific actions: donating, engaging in a protest, and
 sharing information on social media to reduce the disparity (concrete
 actions, 3-items averaged to create a concrete action scale). To assess
 the effects of domain (health, economics, belonging; S1a and S1b) and
 participant race (S1b only) on action support, we conducted multi-level
 models with domain condition (S1a and S1b) and participant race (S1b) as
 fixed effects and participant and scenario type as random effects
 (intercept-only). To assess the mediating factor of perceived injustice we
 used within-subject mediation (JSmediation package). Study 2
 (preregistered) used Facebook's ad platform and thus the data and
 analyses are collected and run by Facebook (the researchers do not have
 access to the data). Screenshots of the findings provided by Facebook are
 provided in OSF:
 . Study 3 (preregistered) is from a sample collected from NORC’s AmeriSpeak Panel via the Time-sharing Experiments for the Social Sciences Short Studies program of 1,550 U.S. residents. Participants viewed a short infographic highlighting either health-, economic-based, or belonging-based racial disparities (between-subjects design). Then all participants reported on their agreement (1=strongly disagree, 7=strongly agree) of how unjust (“These disparities are unjust”) and morally sacred (2-items averaged to create a scale; e.g., “These racial disparities involve issues or values which should never be violated”) the issue was as well as their support for general policies to reduce it (2-items averaged to create a scale, e.g., “Politicians need to prioritize creating policies that reduce these racial disparities”). To analyze the data, we conducted an ANOVA and planned contrasts to test if the type of disparity influenced policy support. To assess the proposed serial mediation (highlighting health (vs. economic) disparities elicited support for mitigating policies due to perceptions that the issue is morally sacred which in turn, induced perceived injustice), we used process mediation model 6 (process package). Study 4a (N=490, 76% White) and Study 4b (preregistered, N=1,088, 74% White) had U.S. citizens recruited from Prolific Academic indicate their support (1=strongly oppose, 7=strongly support) for the same fiscal federal policy (policy of interest: increasing taxes by 0.5% for all Americans). Specifically, participants indicated their preferences for a total of eight policies in S4a (exploratory) and two policies in S4b. Crucially, participants were randomly assigned to one of two framing conditions in which they either read that these policies would help to reduce health-based or economic-based racial disparities. To analyze the data, we conducted independent samples t-tests to test if disparity framing influenced policy support. ## Sharing/Access information This is a section for linking to other ways to access the data, and for linking to sources the data is derived from, if any. Links to other publicly accessible locations of the data: * Data and code are also provided on OSF () Data was derived from the following sources: * S1a was collected via MTurk.com; S2b was collected via Prolific Academic; S3 was collected via NORC’s AmeriSpeak Panel via the Time-sharing Experiments for the Social Sciences Short Studies; S4a and S4b were collected via Prolific Academic. ## Code/Software R code to analyze all studies is available and uploaded here (titled: HealthDisparities_Syntax.R) and via OSF: