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Global Dataset on Education Quality : A Review and Update (2000-2017)

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  • Patrinos,Harry Anthony
  • Angrist,Noam

Abstract

This paper presents the most expansive and current cross-country dataset on education quality. The database includes 164 countries/territories covering over 98 percent of the global population from 2000-2017. Of these countries, 111 are low or middleincome economies. Harmonized learning outcomes are produced using a conversion factor to compare international and regional standardized achievement tests. These tests include PISA, TIMSS, PIRLS, SACMEQ, LLECE and PASEC. In addition, this paper includes the Early Grade Reading Assessment (EGRA) for the first time. This enables extension of the database substantially. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries that are often excluded from large international assessments. The database includes mean scores and disaggregates the data by subject, level, and gender. This paper further presents a series of methodological improvements including measures of uncertainty, fixed conversion factors for greater comparability over time and year-by-year data.

Suggested Citation

  • Patrinos,Harry Anthony & Angrist,Noam, 2018. "Global Dataset on Education Quality : A Review and Update (2000-2017)," Policy Research Working Paper Series 8592, The World Bank.
    • Handle: RePEc:wbk:wbrwps:8592
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    Citations

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    Cited by:

    1. Guriev, Sergei & Treisman, Daniel, 2020. "A theory of informational autocracy," Journal of Public Economics, Elsevier, vol. 186(C).
    2. Azevedo,Joao Pedro Wagner De & Goldemberg,Diana & Montoya,Silvia & Nayar,Reema & Rogers,F. Halsey & Saavedra,Jaime & Stacy,Brian William, 2021. "Will Every Child Be Able to Read by 2030 ? Defining Learning Poverty and Mapping the Dimensions of the Challenge," Policy Research Working Paper Series 9588, The World Bank.
    3. Touhami Abdelkhalek & Dorothée Boccanfuso, 2022. "Human Capital Index (HCI) – from uncertainty to robustness of comparisons," Applied Economics, Taylor & Francis Journals, vol. 54(28), pages 3246-3260, June.
    4. Pasquini, Alessandra & Rosati, Furio C., 2020. "A Human Capital Index for the Italian Provinces," IZA Discussion Papers 13301, Institute of Labor Economics (IZA).
    5. Burke,Andrew & Cuadra,Ernesto Pancracio & Mahon,Tony & Moreno Olmedilla,Juan Manuel & Thacker,Simon, 2020. "Transforming Teacher Education in the West Bank and Gaza : Policy Implications for Developing Countries," Policy Research Working Paper Series WP9328, The World Bank.
    6. Demirgüç-Kunt, Asli & Torre, Iván, 2022. "Measuring human capital in middle income countries," Journal of Comparative Economics, Elsevier, vol. 50(4), pages 1036-1067.
    7. Crawfurd, Lee, 2021. "Accounting for repetition and dropout in contemporaneous cross-section learning profiles: Evidence from Rwanda," International Journal of Educational Development, Elsevier, vol. 85(C).
    8. Pesta, Bryan J. & Fuerst, John & Kirkegaard, Emil O.W. & Papaleo, Brent, 2019. "Does intelligence explain national score variance on graduate admissions exams?," Intelligence, Elsevier, vol. 73(C), pages 8-15.
    9. Patrinos,Harry Anthony, 2020. "The Learning Challenge in the 21st Century," Policy Research Working Paper Series 9214, The World Bank.
    10. Gang Liu & Barbara M. Fraumeni, 2020. "A Brief Introduction to Human Capital Measures," NBER Working Papers 27561, National Bureau of Economic Research, Inc.
    11. Liu, Gang & Fraumeni, Barbara M., 2020. "A Brief Introduction to Human Capital Measures," IZA Discussion Papers 13494, Institute of Labor Economics (IZA).

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