Inclusive Ranking of Indian States and Union Territories via Bayesian Bradley-Terry Model
Arshi Rizvi, Rahul Singh

TL;DR
This paper introduces a Bayesian Bradley-Terry based inclusive ranking method for Indian states and UTs, incorporating multiple indicators and prior information to produce nuanced rankings.
Contribution
It develops a novel ranking methodology that uses a Bayesian model with covariate-based prior covariance, enabling comprehensive multi-indicator rankings.
Findings
The methodology converges reliably and includes a ranking-based stopping rule.
Applied to Indian states and UTs, it reveals significant deviations between economic and overall performance.
Different regimes show varied rankings, highlighting diverse development patterns.
Abstract
Ranking geographical or administrative units, such as countries or states, is a well-known approach for comparing developmental progress and informing evidence-based policymaking. Existing ranking methodologies typically rely on a single indicator, such as Gross Domestic Product (GDP), or a limited subset of indicators, e.g., the Human Development Index (HDI). However, to the best of our knowledge, a ranking methodology based on a large set of indicator variables is not available in the literature. To address this gap, we present an inclusive ranking methodology. We utilize the Bayesian Bradley-Terry (BT) model, which allows us to incorporate relevant prior information. We model the prior covariance of the BT merit parameters using an independent covariate, such that units with similar covariate values exhibit higher covariance, which decays as differences in the covariate increase. A…
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