Hanushek and Kimko's analysis of the relationship between growth and schooling quality, as measured by scores in international tests, suffers from potential endogeneity as schooling quality is not always measured at a date strictly prior to the observed growth. To address this problem we treat the data as a panel, relating growth only to test scores at earlier dates. The estimates of the effect of schooling quality on growth are similar to those obtained from cross-section regressions.

PurposePenn World Tables (PWT) data on output measured at international prices are the data most frequently used in cross‐country growth regressions. These data are subject to revision, and the amendments can be substantial for a minority of countries, although negligible for most. The purpose of this paper is to investigate the effect of data revisions on research results using the data.Design/methodology/approachUsing Hanushek and Kimko's analysis of the relationship between growth and schooling quality and Sala‐i‐Martin's tests of model selection, the authors investigate how much the results of cross‐country growth regressions vary if the most recent vintage (6.2) of PWT data is used, rather than the previous vintage (6.1).FindingsThe variation is substantial enough to imply significant differences in research results using different vintages of the PWT data.Practical implicationsThe results reinforce the case for examining the sensitivity of growth regressions to outliers, which may be subject to subsequent data revision that might substantially affect the conclusions.Originality/valuePrevious research has identified significant revisions between successive vintages of PWT growth data, but has implied that this is not likely to affect the results of cross‐country growth regressions based on long‐run averages rather than on annual data. The findings suggest that this is not necessarily the case.

We use data from the Young Lives surveys, in Ethiopia, India, Peru, and Vietnam, to highlight an underappreciated phenomenon—that a substantial proportion of students in developing countries are the first in their families to go to school. We term these students first‐generation learners (FGLs). We both propose a simple “static” definition of FGL status—where a child’s parents have no education attainment—and utilize the panel dynamics of the Young Lives data set to look at a “dynamic” definition—where the child, in any given round of data collection, is enrolled at a level which his or her parents did not reach. We show descriptive statistics on the scale of this problem across the four countries. We find strong, consistent patterns of relative educational deprivation for FGLs. We tentatively explore the pathways through which FGL status may affect outcomes and find possible explanations through an inability to support with homework and lower aspirations. We look at how becoming an FGL affects the probability of being in school using child fixed‐effects estimations and find it increases the vulnerability of children to drop out.