International Conference on Economics, Finance & Business, Prague

PERCEIVED ECONOMIC STATUS AND INEQUALITY AVERSION: EVIDENCE FROM SLOVAKIA AND BULGARIA

JOANNA MICHALAK, MAŁGORZATA SZCZEPANIAK

Abstract:

Perceptions of income inequality are often shaped not by objective indicators but by individuals’ subjective placement within the societal income hierarchy, social comparisons, and dominant public narratives, particularly in post-communist societies. These perceptions, combined with limited upward mobility and widespread economic dissatisfaction, contribute to a growing public aversion to income inequality. This study examines the relationship between perceived economic status, objective income class, and attitudes toward income inequality in two European Union countries with contrasting levels of income disparity: Slovakia and Bulgaria. Based on original survey data collected in 2025 (N = 300 per country), we investigate how individuals’ subjective placement within the income hierarchy, whether aligned with or divergent from their objective economic position, shapes their acceptance or rejection of income inequality. The analysis reveals distinct attitudinal profiles and underscores the role of status misperception in shaping inequality aversion. We found that individuals who underestimate their relative economic position are significantly more likely to express dissatisfaction with income distribution, regardless of their actual income. These dynamics are especially pronounced in Bulgaria, where inequality is objectively higher. Our findings contribute to a deeper understanding of how perceived fairness and economic self-identification influence public support for redistributive policies. By comparing two post-transition EU economies, the study sheds light on the sociopsychological foundations of economic attitudes in contexts marked by systemic transformation and persistent disparities. To identify attitudinal clusters, we employed a range of clustering techniques, including K-Means, MiniBatchKMeans, DBSCAN, Agglomerative Clustering, Gaussian Mixture Models (GMM), and Spectral Clustering. Cluster profiling was conducted using chi-square statistics to explore the associations between external variables and cluster membership. In the final stage, we applied logistic regression analysis to estimate the probability that selected sociological and psychological characteristics influence an individual’s likelihood of belonging to a given cluster.

Keywords: Perceived economic status, Income inequality, Clustering analysis