Understanding the causes and consequences of international differences in human capital is a central concern of economics. But how can we accurately measure the global distribution of skills when people in different countries take different tests? We develop a new methodology to non-parametrically link scores from distinct populations. By administering an exam combining items from different assessments to 2,300 primary students in India, we estimate conversion functions among four of the world’s largest standardized tests spanning 80 countries. Armed with this learning “Rosetta Stone,” we revisit various well-known results, showing, inter alia, that learning differences between most- and least-developed countries are larger than existing estimates suggest. Applying our translations to microdata, we match pupils’ socio-economic status to moments of the global income distribution and document several novel facts: (i) students with the same household income score significantly higher if they live in richer countries; (ii) the income-test score gradient is steeper in countries with greater income inequality; (iii) girls read better than boys at all incomes but only outperform them in mathematics at the lowest deciles of the global income distribution; and (iv) the test-score gap between public and private schools increases with inequality, partially due to a rise in socio-economic sorting across school types.