  The impact of predictive algorithms on people's lives and livelihoods has
been noted in medicine, criminal justice, finance, hiring and admissions. Most
of these algorithms are developed using data and human capital from highly
developed nations. We tested how well predictive models of human behavior
trained in a developed country generalize to people in less developed countries
by modeling global variation in 200 predictors of academic achievement on
nationally representative student data for 65 countries. Here we show that
state-of-the-art machine learning models trained on data from the United States
can predict achievement with high accuracy and generalize to other developed
countries with comparable accuracy. However, accuracy drops linearly with
national development due to global variation in the importance of different
achievement predictors, providing a useful heuristic for policymakers. Training
the same model on national data yields high accuracy in every country, which
highlights the value of local data collection.
