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Progress on the UN Sustainable Development Goals (SDGs) is hampered by apersistent lack of data regarding key social, environmental, and economicindicators, particularly in developing countries. For example, data on poverty--- the first of seventeen SDGs --- is both spatially sparse and infrequentlycollected in Sub-Saharan Africa due to the high cost of surveys. Here wepropose a novel method for estimating socioeconomic indicators usingopen-source, geolocated textual information from Wikipedia articles. Wedemonstrate that modern NLP techniques can be used to predict community-levelasset wealth and education outcomes using nearby geolocated Wikipedia articles.When paired with nightlights satellite imagery, our method outperforms allpreviously published benchmarks for this prediction task, indicating thepotential of Wikipedia to inform both research in the social sciences andfuture policy decisions.

Predicting Economic Development using Geolocated Wikipedia Articles