A Rank-Based Information Fusion Framework for Comparing Clustered Multivariate Socioeconomic Outcomes

TL;DR

This paper introduces a robust, rank-based framework for comparing socioeconomic outcomes across groups, effectively handling high-dimensional, clustered data without relying on parametric models.

Contribution

It develops a distribution-free, rank-based aggregation method using LRST for comparing multivariate outcomes in clustered data, applicable to policy evaluation.

Findings

Revealed systematic differences in county rankings between states with and without EITC policies.

Results remained stable under subsampling and varying cluster sizes.

Demonstrated utility of rank-based methods for complex policy data analysis.

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.