How do people understand inequality in Chile? A study through attitude network analysis

This article constitutes the first application of the attitude network approach to peoples’ views on inequality. We adopt a network model in which nodes represent survey variables and edges their conditional associations. This allows us to conceptualize perceptions, beliefs, and judgments about inequality as a network of connected evaluative reactions. We analyze data from the 2019 ISSP Social Inequality Module for Chile, one of the most unequal countries in the world. Relying on a network approach, we systematically analyze the wide-ranging indicators measuring subjective inequality. Results show that conceptions regarding inequality, redistribution, taxation, and wages form a moderately connected unified belief system with a small-world structure. In addition, we stratify the sample by education, income, and social class, obtaining six attitude networks. We com- pare the structures of these networks, investigating differences in community membership, node centrality, and network connectivity, evidencing that people in lower social positions have a more multidimensional understanding of inequality. Our work contributes to social justice research by proposing an innovative conceptualization of these attitudes and providing evidence of their struc- tural variation across different socioeconomic groups.