Planetary Causal Inference: Implications for the Geography of Poverty

Poverty has a significant geographic component, which has been studied by human geographers and developmental economists, giving rise to techniques such as small area estimation. With the availability of accurate and high-resolution data, it is possible to produce poverty maps that display the spatial distribution of poverty, and this has been instrumental in deciphering its determinants (Gauci, 2005). The availability of high-resolution geographically specified socio-economic data has opened avenues for more precise analysis that target areas of poverty. Furthermore, the accumulation of data over time has allowed for the inclusion of temporal dynamics in understanding the persistent nature of some impoverished areas. While pockets of poverty can be spatially defined, understanding the social, economic, and physical processes that create self-perpetuating geographies of poverty remain a pressing challenge, aspects of this geography have received attention in various literature (Bird et al., 2010), involving spatial poverty traps (Jalan, Ravallion, et al., 1997), crime (Hipp, 2016), and economic aid (Briggs, 2018).