Abstract: Scholarly evaluation must reconcile two imperfect signals: the ex-ante judgment reflected in journal prestige and the ex-post impact measured by citations. We develop a unified axiomatic framework that integrates an ordinal journal list with cardinal citation counts and characterize two complementary families of ranking rules. Journal-based Bean-Counting rules treat list placement and citations as substitutes or complements through citation thresholds. Citation-based Asymmetric Constant Elasticity of Substitution (CES) aggregators reward publication breadth inside the journal list and citation depth outside it. Applied to 4,722 economists at universities that appeared in the QS Top 100 between 2023 and 2026, the two families produce sharply different rankings across fields, especially in microeconomic theory and economic history. Adjusting for field differences in citation rates leaves the overall clustering structure largely stable, though within-field robustness varies across methods.

Abstract: Conventional concentration measures like the Herfindahl-Hirschman Index (HHI) assume product homogeneity, failing to distinguish markets with varying degrees of substitutability and complementarity. We generalize the HHI by axiomatically deriving quadratic concentration and welfare indices that incorporate pairwise interactions. We also provide welfare-theoretic foundations for their applications in differentiated-product markets. A key insight from the quadratic concentration index is its relationship with surplus distribution: for substitutes, higher concentration correlates with a higher producers’ share—mirroring the classic HHI result—but the relationship reverses for complements. This implies stricter scrutiny for substitute-heavy markets and greater leniency for complementary ones. We further develop a parametric family of quadratic welfare indices that directly link to weighted total surplus, enabling tailored welfare standards. Applied to merger analysis, these indices suggest refined screening guidelines: output-increasing mergers warrant minimal review; when output may fall, screening tightens beyond current HHI-based guidelines.

Abstract: This paper addresses a central challenge in composite index construction: aggregating data partitioned by exogenous constraints such as cost, privacy, or administrative reporting. Standard additive approaches (e.g., weighted arithmetic means) are systematically biased when the true weightings are non-additive to capture synergies and redundancies across dimensions. We develop a family of non-additive aggregation methods by extending traditional capacities to partitioned data (represented by P), introducing P-restricted decomposition integrals, including the P-restricted concave integral and the P-restricted Choquet integral. We axiomatically characterize the two principal members. The P-restricted concave integral, which optimizes over all feasible within-block allocations, is characterized by a central axiom called Information Monotonicity: coarser data never reduces the index, embodying a “benefit of the doubt” principle. The P-restricted Choquet integral, which restricts attention to chain-based decompositions, is characterized by P-Stochastic Dominance. We show that these axioms are fundamentally incompatible for any non-degenerate integral, revealing a sharp axiomatic divide within the family. The framework applies to composite indices built from partitioned data, such as ESG ratings, GDP measurement with cross-sector interactions, and welfare measures across different family compositions. Applying it to MSCI ESG ratings, we find that the concave–Choquet gap grows monotonically with partition coarseness, that the non-additive integrals refine rather than overturn the additive baseline, and that the pillar-level P-restricted integrals generate consistent reversal rates across industries.