Missing Evidence: Tracking Academic Data Use around the World

Abstract Data-driven research is key to producing evidence-based public policies, yet little is known about where data-driven research is lacking and how it can be expanded. We propose a method for tracking academic data use by country of subject, applying natural language processing to open-access research papers. The model’s predictions produce country estimates of the number of articles using data that are highly correlated with a human-coded approach, with a correlation of 0.99. Analyzing more than 1 million academic articles, we find that high-income countries are the subject of nearly 50% of all papers, despite only making up around 17% of the world’s population. Finally, we classify countries by whether they could most benefit from increasing their supply of or demand for data, with the former applying to many poorer countries and the latter to many wealthier countries.