🤖 AI Summary
This study addresses the challenge of comparing multidimensional socioeconomic outcomes across groups when data exhibit clustered structures and within-cluster correlations. The authors propose a distribution-free, robust cross-group comparison method that treats clusters as independent units and integrates longitudinal rank-sum tests (LRST) with order statistics to construct a rank-based multivariate aggregation framework. This approach effectively synthesizes high-dimensional, correlated indicators into an interpretable composite ranking while avoiding reliance on parametric modeling assumptions. Innovatively combining rank fusion with the inherent clustering structure of the data, the method is applied to evaluate the refundable Earned Income Tax Credit (EITC) policy, revealing systematic differences in county-level multidimensional outcomes between states that implemented the policy and those that did not. These findings remain robust across varying cluster sizes and resampling schemes.
📝 Abstract
We propose a multivariate, distribution-free ranking framework for comparing clustered, correlated outcomes across groups, motivated by the evaluation of state-level policy environments using county-level socioeconomic data. Using pooled U.S. county data from 2019-2023, we study multiple dimensions of economic well-being, including poverty, income inequality, housing cost burden, medical care costs, and per capita income, observed at a finer spatial resolution than the policy itself. Rather than relying on parametric regression models, we employ a rank-based aggregation algorithm derived from the Longitudinal Rank-Sum Test (LRST), which treats clusters as independent units and aggregates information across outcomes using order statistics. This approach provides a robust, interpretable omnibus comparison that accommodates within-cluster dependence and high-dimensional outcome structure without distributional assumptions. Applied to the comparison of states with and without refundable Earned Income Tax Credit (EITC) policies, the method reveals systematic differences in the joint ranking of county-level outcomes, with results remaining stable under repeated random subsampling of counties and varying cluster sizes. While the empirical analysis is descriptive rather than causal, the study highlights the broader utility of rank-based, multi-criteria aggregation methods as computational intelligence tools for analyzing complex, clustered data in policy and social systems.