This paper summarizes and tries to reconcile evidence from the microeconometric and empirical macro growth literatures on the effect of schooling on income and GDP growth. Much microeconometric evidence suggests that education is an important causal determinant of income for individuals within countries. At a national level, however, recent studies have found that increases in educational attainment are unrelated to economic growth. This discrepancy appears to be a result of the high rate of measurement error in first-differenced cross-country education data. After accounting for measurement error, the effect of changes in educational attainment on income growth in cross-country data is at least as great as microeconometric estimates of the rate of return to years of schooling. Another finding of the macro growth literature—that economic growth depends positively on the initial stock of human capital—is not robust when the assumption of a constant-coefficient model is relaxed.

This paper examines the role of secondary data-sets in empirical economic research, taking the field of income distribution as a case study. We illustrate problems faced by users of “secondary” statistics, showing how both cross-country comparisons and time-series analysis can depend sensitively on the choice of data. After describing the genealogy of secondary data-sets on income inequality, we consider the main methodological issues and discuss their implications for comparisons of income inequality across OECD countries and over time. The lessons to be drawn for the construction and use of secondary data-sets are summarized at the end of the paper.