New global estimates of mean years of schooling for 171 countries

Michaela Potancokova, Wittgenstein Centre (IIASA, VID/ÖAW, WU)
Samir K.C., Wittgenstein Centre (IIASA, VID/ÖAW, WU)
Anne Goujon, Wittgenstein Centre (IIASA, VID/ÖAW, WU)
Ramon Bauer, Wittgenstein Centre (IIASA, VID/ÖAW, WU)

The frequently used indicator of mean years of schooling (MYS) has the advantage of expressing the distribution of educational attainment in a single number. It is therefore often used for cross-country comparisons as well as in economic and environmental models as the unique indicator of educational attainment and human capital stock. The computation of MYS from a given educational attainment distribution is complex for two main reasons. First, the standard duration of different levels of schooling varies from country to country, and within countries each school level can have different lengths in different studies, for example, studies of general secondary as opposed to vocational secondary. Secondly, the calculation is biased by the presence of pupils/students who do not complete the full course at any level, which can amount to a substantial share in some countries. To contravene these difficulties, the methodology used and detailed in this paper computes MYS as the weighted mean of six educational levels based on ISCED 1997 - no formal education, incomplete primary, primary, lower secondary, upper secondary and post-secondary education – and the procedure takes into account country-specific educational systems as well as changes in these systems over time. We developed regional sets of regression models to improve estimates of MYS for the incomplete primary category and a set of correction factors to adjust higher levels. The models are built using detailed data on duration of schooling by grades completed level for 57 countries (using micro-data from the IPUMS and DHS). We apply the method to estimate MYS for 171 countries in the WIC dataset on educational attainment as well as to the new set of the Wittgenstein centre human capital projections. In the paper we also compare our results and method to the widely used Barro and Lee data and explain the differences.

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Presented in Session 78: Human capital and inequality