Abstract
Abstract 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of prohibitive costs and poor infrastructures; and mass urbanization can render manually surveyed figures rapidly out-of-date.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 household 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 number of 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.
Highlights
Deprivation statistics are used by governmental and non-governmental organizations to describe the standard of living in a small administrative areas (McLennan et al, 2019)
We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania
With data collection infrastructures remaining poor in developing countries, comparative judgement solutions can only become viable in practice if the amount of data required can be reduced. We address this key issue via development of a novel Bayesian Spatial Bradley–Terry (BSBT) model, which substantially decreases the amount of data required for reliable estimates of the parameters of interest
Summary
Deprivation statistics are used by governmental and non-governmental organizations to describe the standard of living in a small administrative areas (McLennan et al, 2019). With household income levels often being highly volatile in developing world contexts, and respondents often reticent to provide accurate responses due to the scale of the informal economy (Randall & Coast, 2015), this provides scope to reduce response bias and logistical costs To achieve this one might fit a Bradley–Terry (BT) model (Bradley & Terry, 1952) to pairwise comparative judgement data. We address this key issue via development of a novel Bayesian Spatial Bradley–Terry (BSBT) model, which substantially decreases the amount of data required for reliable estimates of the parameters of interest 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. The BSBT model with judge information allows us to locate areas where men and women hold notably different opinions about the deprivation level
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