Inclusive Ranking of Indian States via Bayesian Bradley-Terry Model

2026-02-20

AI Summary

This study proposes a comprehensive framework for evaluating the performance of Indian states that jointly accounts for social and economic dimensions to support more equitable policymaking. Leveraging data from the National Family Health Survey (NFHS-5) and state-level per capita income, the authors introduce per capita income as a prior in a Bayesian Bradley–Terry pairwise comparison model for the first time. By integrating Markov Chain Monte Carlo (MCMC) inference and diagnostic techniques, the approach coherently combines human development indicators with economic context. The resulting methodology yields robust and interpretable state rankings, enhancing both the inclusivity and accuracy of regional assessments while providing policymakers with a quantitative foundation for resource allocation and regional development strategies.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Evaluating the performance of different administrative regions within a country is crucial for its development and policy formulation. The performance evaluators are mostly based on health, education, per capita income, awareness, family planning and so on. Not only evaluating regions, but also ranking them is a crucial step, and various methods have been proposed to date. We aim to provide a ranking system for Indian states that uses a Bayesian approach via the famous Bradley-Terry model for paired comparisons. The ranking method uses indicators from the NFHS-5 dataset with the prior information of per-capita incomes of the states/UTs, thus leading to a holistic ranking, which not only includes human development factors but also take account the economic background of the states. We also carried out various Markov chain Monte Carlo diagnostics required for the reliability of the estimates of merits for these states. These merits thus provide a ranking for the states/UTs and can further be utilised to make informed policy decisions.

Problem

• inclusive ranking
• Indian states
• human development
• economic background
• performance evaluation

Innovation

• Bayesian Bradley-Terry model
• inclusive ranking
• MCMC diagnostics
• NFHS-5 dataset
• state-level policy evaluation