The Effects of Stability of Inequality and Ingroup Identification on the Principle-Implementation Gap among Advantaged Groups.

Based on “OSF pre-registration” form Title: The Effects of Stability of Inequality and Ingroup Identification on the PrincipleImplementation Gap among Advantaged Groups. Description: This is an anonymous online experimental study among White citizens in the UK (advantaged group), that will be conducted via Prolific as a follow-up study of the OSF pre-registered study project entitled: “The Fragility of Privilege: How Perceived (In)stability of Inequality and the Demands of the Disadvantaged Influence Support Among the Advantaged” (DOI: https://doi.org/10.17605/OSF.IO/UYV3K). In this study we examine how stability of racial health care inequality (stable vs. unstable) affects intentions to support measures to address these inequalities. Our main framework is the Principle-Implementation Gap (PI Gap) referring to the discrepancy between abstract support for equality and resistance to concrete measures aimed at achieving it among the privileged, (Bobo, 1988; Jackman & Crane, 1986; Dixon et al., 2017). We also take into account the role of Ingroup Identification as a moderator based on exploratory findings from our a first pre-registered study and results from previous research showing that reactions from advantaged groups to inequality depend on the interplay between contextual features around inequality and ingroup identification (Teixeira et al.; 2023, Teixeira et al., 2022). Specifically, we aim to explore the impact of stability of inequality (Teixeira et al., 2023;Knight & Mehta, 2017; Scheepers et al., 2015) and ingroup identification on the width of the PI Gap among advantaged group members Participants will be white UK citizens from Prolific Academic. We will manipulate stability of racial inequality in the UK between-participants. Before the manipulation we will measure ingroup identification. Our main dependent variables will be various scales operationalizing more or less abstract support for inequality reduction. These measures are aimed at improving measurement of the P-I gap, due to shortcomings (linked to invariance and ceiling effects) observed in our first study. We will also measure some other concepts in an exploratory manner (cf. section on dependent variables). Participants will be exposed to: 1) Descriptions of stable vs unstable racial health inequality (i.e., varying more or less in time and space, depending on the condition). 2) Fictious campaign advocating for an act to be approved by the UK government promoting a series of policies aimed at distributing health care resources more equitably between white and black UK citizens. Hypotheses: We expect an interaction between stability of inequality and ingroup identification on the PI Gap. Based on our previous results and previous research, we expect the effects of identification on the P-I gap to be stronger under stable inequality. In our previous research we had measurement issues (cf. above) regarding the P-I gap. Furthermore, previous research on advantaged group members sometimes shows effects to be stronger amongst high (Teixeira et al. 2020) and sometimes amongst low identifiers (Teixeira et al. 2023). With high-identifiers sometimes showing higher levels of support for inequality reduction (Shuman et., 2020) and sometimes lower (Teixeira et al., 2020; compared to low-identifiers). Therefore, the predictive direction of the results remains tentative. Design Plan Study type Experiment Blinding No blinding is involved in this study. Study design This is a between-subjects design with one factor including 2 levels: Stability of Inequality: Stable vs. Unstable Randomization We used the embedded data faction on Qualtrics, to randomize participants in one of 2 conditions (Stable vs. Unstable). Sampling Plan Registration prior to creation of data Data collection procedures We will focus on White British citizens living in the UK recruited through Prolific Academic. Participants will be paid £1,5 given they have completed the survey and all attention checks correctly. If they fail the comprehension checks, they will get partial compensation (according to Prolific guidelines). Those who fail the attention checks will be asked to return their submissions without being compensated. Participants who complete the entire survey but do not give consent after the debriefing will be fully compensated but their data will not be stored. Participants must be at least 18 years of age. Data collection will take place on October 2025. Sample size Our target sample size is 200 participants. Sample size rationale We used the R package "Superpower and online Shiny apps", developed by Ladkens & Cadwell, which enables us to perform simulation-based power analysis for ANOVA designs of up to three withinor between-subject factors. We based our calculation on the correlations found in the first study between support (implementation) and ingroup identification under stable inequality (r = - .484) and unstable inequality (r = -.054) under redistributive demands as these were the conditions of interest. The analyses suggested 194 participants to achieve a power of .90. We will opt for 200 participants to account for potential exclusions. Stopping rule Once the 200 quota for complete responses on Prolific is reached, data collection will be automatically stopped. Variables Manipulated variables We will manipulate stability of inequality using fictitious videos and the assistance of a confederate. Participants will watch a video highlighting healthcare disparities between White and Black British citizens, with inequalities presented as either stable (consistent disparities between Black and White British citizens across time and space) or unstable (fluctuation disparities between Black and White British citizens across time and space). After the manipulation, participants will watch a second video (the same across both conditions) showcasing information about a fictitious bill. This bill proposes redistributions on healthcare budgets between White and Black British citizens to achieve equality, presented by a confederate acting as the representative of a fictitious organization. Measured variables Demographic Questions (gender, ethnicity, age) Comprehension checks: Two questions regarding stability of inequality (True/False) and two questions on the bill (asking for the name of the bill and its purpose) will be used. Open Question: (serving as a quality check) asking participants to share some of their thoughts on both videos they (a summary of the information presented and their opinions about it) Manipulation Checks: 1) Legitimacy of Inequality: (“In your opinion, to what extent are the healthcare inequalities described in the videos are…justified, 1= not at all, 7 = very much) 2) Stability of Inequality: To what extent do you agree with the following statements (e.g. “The healthcare inequalities between White and Black people in the UK are stable.”, -3 = disagree, 3 = agree) 3) Redistributions: Are the following statements about the Redistributive Healthcare Act, true? (e.g. “The Redistributive Healthcare Act suggests moving NHS funds from white to black communities., True or False) Main outcome measures: 1) Principle support: Measuring support on the general goals of the bill (e.g. “Creating a Healthcare system that is fair and does not discriminate against Black individuals, 1= Not a priority, 7 = Top priority) 2) Supportive Attitudes: measuring general attitudes towards the bill (e.g. “I support the Redistributive Healthcare Act.”, -3 = disagree, 3 = Disagree) 2) Implementation support: Measuring support on specific policies addressed in the bill (e.g. Prioritize the opening of new GP positions in municipalities mainly composed of Black residents.-3= disagree, 3 = agree), 3) Support through Action: measuring willingness to take action in support of the bill (e.g. “Sign a petition supporting the proposed Redistributive Healthcare Act bill.”, -3 = not willing at all to participate; -3 = very much willing to participate). Other measures: 1) Ingroup Identification scale (Leach et al.,2008) (e.g. “I feel committed to members of my ethnic group”, 1= Not at all, 7= Very much) 2) Political orientation: Using a scale from 1 (= left) to 11(=right) 3) Perceived blame by the disadvantaged will be measured with one item (Teixeira et al., 2020) (e.g. To what extent do you think that the advocates of the RHA blame White people for the racial inequality in healthcare?”, 1= Not at all, 7 = Very much). 4) Backlash (Adapted from Teixeira et al., 2020) measuring willingness to take actions against the bill ( e.g. “Join a demonstration against the “Redistributive Healthcare Act” campaign -3 = not willing at all to participate; -3 = very much willing to participate) 5) Resource Threat (Adapted from Teixeira et al., 2020): to measure to what extent do participants believe that, if approved, the Act will have negative consequences to their ingroup’s resources (e.g. “Decrease White British citizens' chances for quality healthcare?” 1= not; 7= likely) 6) Moral Image Threat (Adapted from Teixeira et al., 2020): To measure to what extent participants believe (e.g. “The proposed bill will make White British citizens seem unfair to the rest of the world”, 1= not likely, 7= likely) that the campaign will damage their ingroup’s image. Indices We will first conduct exploratory factor analyses on the different support scales. These results will guide the creation of mean-base indices representing the P-I gap. For the other dependent variables, we will check reliability using Cronbach’s alpha and create mean-scores of the items measuring the same concept. Analysis Plan Statistical models We will use General Linear Model Analysis (GLM) to test the interaction between stability of inequality and ingroup identification on the PI-Gap. If found, interactions will be decomposed looking at both 1) effects of identification within each condition (using regression, see below for transformations) and 2) looking at effects of stability at +1SD and -1SD of identification. No files selected Transformations Transformations The stability variable will be coded as -1 and 1. Ingroup identification will be centered. If we use regression models to test exploratory hypotheses, the P-I gap will be calculated by subtracting “implementation” from “principle” support. Inference criteria We will use the standard p <. 05 criteria (two-tailed) for determining analyses suggest that the results are significantly different from those expected if the null hypothesis were correct. Data exclusion Outliers presenting studentized residuals > = to 3 in our main dependent variables will be excluded. Missing data Participants who did not complete any of the support scales, will not be included in the analysis. Exploratory analysis If political orientation is correlated with ingroup identification, we will re-run the main analyses controlling for political orientation based on the steps suggested by Yzerbyt et al., 2005. We will also explore the effects on perceived blame and threats using the same predictors as in the main analyses.