Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanisation can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley--Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas' affluence, such models can both simplify logistics and circumvent biases inherent to house-hold surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley--Terry model, which substantially decreases the amount of data comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania.
翻译:确定任何国家或城市的最贫困地区是决策者设计成功干预措施的关键,然而,在发展中国家,由于传统家庭调查的后勤挑战,确定最需要的地区往往具有惊人的挑战。由于传统的家庭调查的后勤挑战,官方统计可能缓慢地更新;现有的估计数可能粗糙,这是令人望而生畏的费用和基础设施差的结果;大规模城市化可以使人工调查的数字迅速过时。诸如布拉德利-泰瑞模式等比较判断模型提供了一种有希望的解决办法。通过比较不同地区的富裕,利用当地知识,这些模型既可以简化物流,又可以绕过住户调查固有的偏见。然而,由于目前仍需要大量的数据,广泛采用的方法仍然有限。我们通过开发新的巴耶斯空间-布拉德-泰瑞模型解决这个问题,该模型大大减少了有效推断所需的数据比较数量。该模型将城市或国家的网络代表性与空间平稳性假设结合起来,使邻近地区能够了解贫困状况。我们通过在达累斯萨拉姆收集的新比较性数据,展示这一方法的实际有效性。