Poster Paper: Mapping Development Aid in Myanmar: Combining Satellite Imagery with Spatial Data

Thursday, November 7, 2019
Plaza Building: Concourse Level, Plaza Exhibits (Sheraton Denver Downtown)

*Names in bold indicate Presenter

Woojin Jung, University of California, Berkeley

Aid policy has the potential to alleviate global poverty by targeting areas of concentrated need. However, few aid-determinant studies have analyzed the characteristics of poverty at the sub-national level, and even those studies were conducted with their units of analysis at a high administrative level such as the state. This study intends to fill this knowledge gap by portraying poverty at the granular level and promoting the evaluation of aid towards the most marginalized communities. The goal of this study is to explore the extent to which CLD projects take place in poor villages, using the case of Myanmar. It also analyzes how two CLD models target needs differently: the NCDDP and SMU. To collect outcome variables, I develop web scraping algorithms to create comprehensive and up-to-date locations of CLD participating villages (n=12,282). As for exploratory variables, radiance values from nighttime satellite imagery are extracted to estimate wealth at the community level. In addition, I spatially interpolate the Demographic and Health Survey (DHS) wealth index to make inferences on poverty in aid sites. By geospatially matching aid and wealth-related data, I test factors that explain variation in the distribution of CLD and different approaches to community development. The results show mixed evidence of poverty-oriented targeting. First, as each increment of the share of a vulnerable population rises, the likelihood of aid presence in that community declines by 4%. Next, the density of community development projects is higher in areas shining brighter. A one unit increase in the nightlight intensity increases the number of projects by 86 within a two-degree radius of a DHS village cluster. Among villages of similar levels of nightlights and population, however, aid goes to areas with lower assets. Last, NCDDP, which emphasizes inclusion and collaboration, supports poorer villages farther away from conflict events. In contrast, SMU, which considers competition conductive to performance, supports more established areas including villages near conflict zones. Unlike studies finding that state-level aid allocation favors the richest, this more fine-grained analysis suggests that a need-based allocation is also being carried out. The nuances captured in analyses of nightlight luminosity can also improve predictions of aid distribution. Synthesizing new sources of data can be used to assess area-based interventions in the contexts of poverty and conflict where the traditional survey is too costly.