Using Environmental Features to Maximize Prediction of Regional Intergroup Bias

Abstract

The present research adopts a data-driven approach to identify how characteristics of the environment are related to different types of regional in-group biases. After consolidating a large data set of environmental attributes ( N = 813), we used modern model selection techniques (i.e., elastic net regularization) to develop parsimonious models for regional implicit and explicit measures of race-, religious-, sexuality-, age-, and health-based in-group biases. Developed models generally predicted large amounts of variance in regional biases, up to 62%, and predicted significantly and substantially more variance in regional biases than basic regional demographics. Human features of the environment and events in the environment strongly and consistently predicted biases, but nonhuman features of the environment and population characteristics inconsistently predicted biases. Results implicate shared psychological causes of different regional intergroup biases, reveal distinctions between biases, and contribute to developing theoretical models of regional bias.

City Population, Majority Group Size, and Residential Segregation Drive Implicit Racial Biases in U.S. Cities

“…Along these lines, other research has identified environmental variables related to area deprivation associated with inter-city variance in implicit racial bias (46). However, with our model, we find that measures of area deprivation independently explain only a small portion of the variance in inter-city differences above and beyond the three structural factors we identify here (Supplementary Tables 14-17).…”

Regional intergroup bias

“…With this limitation in mind, we sought to identify characteristics of the environment that are related to regional intergroup bias but are logically unlikely to be direct consequences of bias (Hehman et al, 2020). We began by compiling a very large dataset of environmental attributes (N > 800) based on administrative data reflecting population demographics, health and healthcare metrics, topographical features, weather, temperature, and crime.…”

The project implicit international dataset: Measuring implicit and explicit social group attitudes and stereotypes across 34 countries (2009–2019)

“…For instance, with PI:International data researchers could test whether some clusters of countries reveal systematically higher (or lower) mean levels in attitudes (Bergh & Akrami, 2016 ; Meeusen & Kern, 2016 ); whether those spatial clusters of “generalized bias” are similar for both implicit and explicit attitudes; and even whether the countries in those clusters have changed over time. Additionally, researchers could investigate how the variability within countries (such as the variability across states or counties in a country; e.g., Green et al, 2005 ; Hehman et al, 2021 ; Hester et al, 2021 ) compares to the variability across countries and, again, whether such within- versus across-country variability differs depending on the type of measurement. Finally, researchers may also be interested in explaining the patterns of change across time for implicit versus explicit attitudes (Charlesworth & Banaji, 2019 ) by investigating how change differs across countries and whether such country-level differences in change can be predicted by ecological and social factors (Jackson et al, 2019 ).…”