Strengths and limitations of relative wealth indices derived from big data in Indonesia - Sartirano, Daniele; Kalimeri, Kyriaki; Cattuto, Ciro; Delamónica, Enrique; Garcia-Herranz, Manuel; Mockler, Anthony; Paolotti, Daniela; Schifanella, Rossano (Frontiers in Big Data, 2023)
Accurate relative wealth estimates in Low and Middle Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect highly granular data about income, consumption, or household material goods, to create index-based poverty estimates. However, these methods are costly, slow and may fail to capture the most vulnerable, e.g., migrant populations, or unhoused citizens. Novel approaches combining frontier data, computer vision and machine learning have been proposed to augment or obviate these existing approaches. However, the strengths and limitations of these big-data derived indices have been insufficiently studied. In this paper, we focus on the case of Indonesia, and examine one frontier-data derived Relative Wealth Index, created by the Facebook Data for Good initiative, that utilizes connectivity data from the Facebook Platform and satellite imagery data to produce a high-resolution estimate of wealth for 135 countries. We examine it in relation to asset-based relative wealth indices estimated from existing high quality national level traditional survey instruments, the USAID-developed Demographic Health Survey (DHS) and the Indonesian National Socio-economic survey (SUSENAS). By exploiting a temporal discontinuity in the release of these datasets, we aim to understand how the frontier-data derived index can be used to inform anti-poverty programs in Indonesia and the Asia Pacific region. First, we unveil key features that affect the comparison between the traditional and non-traditional sources, such as the publishing time and authority, and the granularity of the spatial aggregation of the data. Second, to provide operational input, we hypothesise a re-distribution of resources based on the RWI map would have on a current social program, the Social Protection Card (KPS) for the country of Indonesia, and assess it's impact. In this hypothetical scenario, we estimate the percentage of Indonesians incorrectly excluded from a social protection payment had the RWI been as wealth index. The exclusion error in that case would be 32.82%. Within the context of the KPS program targeting, we noted significant differences between the RWI map's predictions and the SUSENAS ground truth index estimates.