Worldwide Inequality and Poverty in Cognitive Results: Cross-sectional Evidence and Time-based Trends
The Sustainable Development Goals (SDGs) for education represent a major departure from the Millennium Development Goals (MDGs) - at least if educational leaders act seriously in their pursuit - in at least two important respects. First, the goals now pertain to learning outcomes. Second, there is a great deal of focus on inequality in the SDGs. Taking note of this new dual emphasis of the SDGs, this paper assembles the largest database of learning outcomes inequality data that we know of, and explores key issues related to the measurement of inequality in learning outcomes, with a view to helping countries and international agencies come to grips with the key dimensions and features of this inequality. Two issues in particular are explored. First, whether, as countries improve their average cognitive performance (as measured by international learning assessments) from the lowest to middling levels, they typically reduce cognitive skill inequality or, more importantly perhaps, whether they reduce absolute lack of skills. Second, whether most of cognitive skills inequality is between or within countries. In dealing with these measurement issues, the paper also explores the degree to which measures of cognitive skills are “proper” cardinal variables lending themselves to generalizations from the field of income and wealth distribution—the field for which many measures of inequality and its decomposition were first applied. To do this, we look into whether using the item response theory (IRT) test scores of programmes such as TIMSS influence these types of findings, relative to the use of the underlying and more intuitive classical test scores. Patterns emerging from the classical scores are far less conclusive than those of the IRT scores, in part due to the greater ability of the IRT scores to discriminate between pupils at the bottom end of the performance spectrum. An important contribution of the paper is to examine the sensitivity of standard measures of inequality to different sets of test scores. The sensitivity is high, and the conclusion is that meaningful comparisons between test score inequality and, for instance, income inequality are not possible, at least not using the currently available toolbox of inequality statistics. Finally, the paper explores the practical use of school-level statistics from the test data to inform strategies for reducing inequalities.