Learning-adjusted years of schooling (LAYS): Defining a new macro measure of education |
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| Institution: | 1. University of Virginia, Charlottesville, VA 22904, United States;2. United States Military Academy, West Point, NY 10996, United States;3. The College of William & Mary, Williamsburg, VA 23187, United States;1. Department of Agricultural Economics, National Taiwan University, Taiwan;2. Department of Economics, Fu Jen Catholic University, Taiwan;3. Institute of Sociology, Academia Sinica, Taiwan;1. Education Global Practice, World Bank Group, 1818 H Street, NW, Washington, DC 20433, USA;2. Harvard Graduate School of Education, Harvard University, 13 Appian Way, Cambridge, MA 02138, USA;1. Migration Policy Centre (RSCAS), European University Institute, Villa Malafrasca, Via Boccaccio 151, I-50133 Florence, Italy;2. Kiel University, and Head of Research Center ‘‘Poverty Reduction, Equity and Development’’, Kiel Institute for the World Economy, D-24105 Kiel, Germany |
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| Abstract: | The standard summary metric of education-based human capital used in macro analyses is a quantity-based one: The average number of years of schooling in a population. But as recent research shows, students in different countries who have completed the same number of years of school often have vastly different learning outcomes. We therefore propose a new summary measure, the Learning-Adjusted Years of Schooling (LAYS). This measure combines quantity and quality of schooling into a single easy-to-understand metric of progress, revealing considerably larger cross-country education gaps than the standard metric. We show that the comparisons produced by this measure are robust to different ways of adjusting for learning and that LAYS is consistent with other evidence, including other approaches to quality adjustment. Like other learning measures, LAYS reflects learning, and barriers to learning, both inside and outside of school; also, cross-country comparability of LAYS rests on assumptions related to learning trajectories and the validity, reliability, and comparability of test data. Acknowledging these limitations, we argue that LAYS nonetheless improves on the standard metric in key ways. |
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