Intersectional social identities and loneliness: Evidence from a municipality in Switzerland

Abstract

We examined the extent to which intersectional social identities combine to shape risks of loneliness and identified the specific social clusters that are most at risk of loneliness for more precise and targeted interventions to reduce loneliness in a Swiss municipality. Based on data collected using participatory action research, we used the novel multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the predictive power of intersectional social attributes on risk of loneliness. We found that 56% of the between-strata variance was captured by intersectional interaction but was not explained by the additive effect of social identities. We also found that nationality and education had the strongest predictive power for loneliness. Interventions to reduce loneliness may benefit from understanding the resident population's intersectional identities given that individuals with the same combinations of social identities face a common set of social exposures relating to loneliness.

Keywords: community intervention; intersectionality; multilevel modeling; participatory action research; social and emotional isolation; social identity; stratification.

Figure 1

Mean stratum‐level interaction effect by nationality, education, and gender. Note: The markers indicate the average stratum‐level interaction effect calculated as the difference between the total effect (additive and interactive) and the main effect (additive only). A negative interaction effect suggests that the interactive approach captured additional privilege against loneliness, whereas a positive interaction effect indicates that the interactive approach captured additional risk of loneliness based on the intersectional social identities considered, relative to the additive fixed approach. Strata are grouped by nationality, education, and gender (regardless of age)