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.

翻译：评估一个国家内部不同行政区域的绩效对其发展和政策制定至关重要。绩效评估指标主要基于健康、教育、人均收入、公众意识、计划生育等方面。不仅需要评估区域绩效，对其进行排名也是关键步骤，迄今为止已提出多种排名方法。本文旨在为印度各邦构建一个排名系统，该方法通过著名的Bradley-Terry配对比较模型采用贝叶斯方法。该排名方法利用NFHS-5数据集中的指标，并结合各邦/联邦属地人均收入的先验信息，从而形成一个综合排名体系——不仅包含人类发展因素，同时兼顾各邦的经济背景。我们还进行了多种马尔可夫链蒙特卡洛诊断分析，以确保各邦效能参数估计的可靠性。由此得出的效能参数可为各邦/联邦属地提供排名依据，并进一步用于制定科学的政策决策。