Abstract. Mapping the distribution of poverty in developing countries is essential for humanitarian organizations and policymakers to formulate targeted programs and aid. However, traditional methods for obtaining socioeconomic data can be time-consuming, expensive, and labor-intensive. Recent studies have demonstrated the effectiveness of combining machine learning and satellite images to estimate wealth in sub-Saharan African countries (Xie et al., 2016, Jean et al., 2016). In this study, we investigate the extent to which this method can be applied in the context of the Philippine archipelago to predict four different socioeconomic indicators: wealth level, years of education, access to electricity, and access to water. We also propose an alternative, cost-effective approach that leverages a combination of volunteered geographic information from OpenStreetMap and nighttime lights satellite imagery for estimating socioeconomic indicators. The best models, which incorporate regional indicators as predictors, explain approximately 63% of the variation in asset-based wealth. Our findings also indicate that models trained on publicly available, volunteer-curated geographic data achieve the same predictive performance as that of models trained using proprietary satellite images.

The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world's most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people's lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socioeconomic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India.