The Statistical Advantages of Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy for Estimating Intersectional Inequalities

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a multilevel regression approach grounded in intersectionality theory. It examines inequalities across intersections of social identities (e.g., gender, ethnicity, class) and is argued to provide more accurate predictions of intersectional means than conventional methods that estimate group means directly or via regressions with all interactions. This study evaluates that claim using analytic expressions and an empirical illustration to compare simple and MAIHDA-predicted means against population values. Predictive accuracy is assessed via variance, correlation, bias, and mean squared error. Results show that MAIHDA estimates generally outperform simple means, particularly when decomposing intersectional means into additive and non-additive identity effects. The magnitude of the advantage depends on inequality patterns and group sample sizes. MAIHDA is especially valuable when inequalities are subtle or data for marginalized intersections are sparse—conditions common in practice. These findings highlight MAIHDA's practical relevance for quantitative intersectionality research.