A quantitative approach to intersectional social inequality using multilevel models

Li, Yang (2022) A quantitative approach to intersectional social inequality using multilevel models. In European Sociological Association Research Network on Quantitative Methods (RN21).

Abstract

This paper uses an innovative extension of multilevel modeling to examine the extent to which intersectional social identities combine to shape risks of loneliness in a Swiss municipality. In Switzerland, 38% of the adult population experience loneliness, which is more prevalent among older adults and individuals with a migration background. While past interventions have helped to reduce loneliness by fostering social connections, these interventions were often based on unidimensional and broad demographic categorizations (e.g. older adults or foreigners), neglecting the intersectional and multiplicative nature of social identities, thereby limiting the precision of interventions to enhance social inclusion. Using data collected in 2019 from a longitudinal participatory research project (n=1,360), we sought to understand the extent to which intersectional social identities combined to shape risks of loneliness in a local municipality. Employing novel and innovative multilevel techniques from social epidemiology, we found that 56% of the variance between intersectional groups was explained by multiplicative identities (age x gender x nationality x education), above and beyond the additive effects of social identities (age + gender + nationality + education). In addition, we identified that individuals who were simultaneously non-Swiss and aged 65+ and male and have primary educational attainment only were most at risk of loneliness and would be logical intervention targets to reduce loneliness. Methodological and practical implications will be discussed.

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Contributors

Author: Yang Li

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