Predicting Economic Development using Geolocated Wikipedia Articles
This addresses data scarcity for poverty and development tracking, particularly in Sub-Saharan Africa, with potential applications in social science research and policy-making.
The paper tackles the lack of socioeconomic data in developing countries by proposing a method to estimate indicators like asset wealth and education using geolocated Wikipedia articles, achieving state-of-the-art performance when combined with satellite imagery.
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty --- the first of seventeen SDGs --- is both spatially sparse and infrequently collected in Sub-Saharan Africa due to the high cost of surveys. Here we propose a novel method for estimating socioeconomic indicators using open-source, geolocated textual information from Wikipedia articles. We demonstrate that modern NLP techniques can be used to predict community-level asset wealth and education outcomes using nearby geolocated Wikipedia articles. When paired with nightlights satellite imagery, our method outperforms all previously published benchmarks for this prediction task, indicating the potential of Wikipedia to inform both research in the social sciences and future policy decisions.