IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0208019.html
   My bibliography  Save this article

Improving the estimation of educational attainment: New methods for assessing average years of schooling from binned data

Author

Listed:
  • Joseph Friedman
  • Nicholas Graetz
  • Emmanuela Gakidou

Abstract

Background: The accurate measurement of educational attainment is of great importance for population research. Past studies measuring average years of schooling rely on strong assumptions to incorporate binned data. These assumptions, which we refer to as the standard duration method, have not been previously evaluated for bias or accuracy. Methods: We assembled a database of 1,680 survey and census datasets, representing both binned and single-year education data. We developed two models that split bins of education into single year values. We evaluate our models, and compare them to the standard duration method, using out-of-sample predictive validity. Results: Our results indicate that typical methods used to split bins of educational attainment introduce substantial error and bias into estimates of average years of schooling, as compared to new approaches. Globally, the standard duration method underestimates average years of schooling, with a median error of -0.47 years. This effect is especially pronounced in datasets with a smaller number of bins or higher true average attainment, leading to irregular error patterns between geographies and time periods. Both models we developed resulted in unbiased predictions of average years of schooling, with smaller average error than previous methods. We find that one approach using a metric of distance in space and time to identify training data, had the best performance, with a root mean squared error of mean attainment of 0.26 years, compared to 0.92 years for the standard duration algorithm. Conclusions: Education is a key social indicator and its accurate estimation should be a population research priority. The use of a space-time distance bin-splitting model drastically improved the estimation of average years of schooling from binned education data. We provide a detailed description of how to use the method and recommend that future studies estimating educational attainment across time or geographies use a similar approach.

Suggested Citation

  • Joseph Friedman & Nicholas Graetz & Emmanuela Gakidou, 2018. "Improving the estimation of educational attainment: New methods for assessing average years of schooling from binned data," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
  • Handle: RePEc:plo:pone00:0208019
    DOI: 10.1371/journal.pone.0208019
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208019
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208019&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0208019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thomas, Vinod & Wang, Yan & Fan, Xibo, 2001. "Measuring education inequality - Gini coefficients of education," Policy Research Working Paper Series 2525, The World Bank.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tugce, Cuhadaroglu, 2013. "My Group Beats Your Group: Evaluating Non-Income Inequalities," SIRE Discussion Papers 2013-49, Scottish Institute for Research in Economics (SIRE).
    2. Bourguignon, Francois, 2005. "The Effect of Economic Growth on Social Structures," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 27, pages 1701-1747, Elsevier.
    3. Olivera, Javier & Andreoli, Francesco & Leist, Anja K. & Chauvel, Louis, 2018. "Inequality in old age cognition across the world," Economics & Human Biology, Elsevier, vol. 29(C), pages 179-188.
    4. Francisco H. G. Ferreira & Jérémie Gignoux, 2014. "The Measurement of Educational Inequality: Achievement and Opportunity," The World Bank Economic Review, World Bank, vol. 28(2), pages 210-246.
    5. Gangfei Luo & Shouzhen Zeng & Tomas Baležentis, 2022. "Multidimensional Measurement and Comparison of China’s Educational Inequality," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(2), pages 857-874, September.
    6. David E. Sahn & Stephen D. Younger, 2009. "Measuring intra‐household health inequality: explorations using the body mass index," Health Economics, John Wiley & Sons, Ltd., vol. 18(S1), pages 13-36, April.
    7. Park, Jungsoo, 2006. "Dispersion of human capital and economic growth," Journal of Macroeconomics, Elsevier, vol. 28(3), pages 520-539, September.
    8. Petra Sauer & Martin Zagler, 2014. "(In)equality in Education and Economic Development," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 353-379, November.
    9. Thomas Ziesemer, 2016. "Gini Coefficients of Education for 146 Countries, 1950-2010," Bulletin of Applied Economics, Risk Market Journals, vol. 3(2), pages 1-8.
    10. Lilik Sugiharti, 2017. "Education Performance and the Determinants of Secondary School Enrolment in Indonesia," GATR Journals gjbssr477, Global Academy of Training and Research (GATR) Enterprise.
    11. Shay Tsur & Eyal Argov, 2019. "Conditional Convergence and Future TFP Growth in Israel," Bank of Israel Working Papers 2019.05, Bank of Israel.
    12. Evelyne Huber & John D. Stephens, 2013. "Income Inequality and Redistribution in Post-Industrial Democracies: Demographic, Economic, and Political Determinants," LIS Working papers 602, LIS Cross-National Data Center in Luxembourg.
    13. repec:hal:spmain:info:hdl:2441/1ds77lna5j86jagcp29tfni72o is not listed on IDEAS
    14. Grimm, Michael & Harttgen, Kenneth & Klasen, Stephan & Misselhorn, Mark, 2008. "A Human Development Index by Income Groups," World Development, Elsevier, vol. 36(12), pages 2527-2546, December.
    15. Christian Morrisson & Fabrice Murtin, 2013. "The Kuznets curve of human capital inequality: 1870–2010," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(3), pages 283-301, September.
    16. Ximing Wu & Andreas Savvides & Thanasis Stengos, 2008. "The Global Joint Distribution of Income and Health," Working Papers 0807, University of Guelph, Department of Economics and Finance.
    17. Dawood Mamoon & Syed Mansoob Murshed, 2013. "Education bias of trade liberalization and wage inequality in developing countries," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 22(4), pages 572-604, June.
    18. Shyamal K. De & Bhargab Chattopadhyay, 2017. "Minimum Risk Point Estimation of Gini Index," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 247-277, November.
    19. Castro Souza Junior, Jose Ronaldo & Gross, Daniel & Figueiredo, Lizia, 2023. "The determinants of economic institutions and the knock-on effects on GDP per capita," MPRA Paper 116277, University Library of Munich, Germany.
    20. Ryan D. Edwards & Shripad Tuljapurkar, 2005. "Inequality in Life Spans and a New Perspective on Mortality Convergence Across Industrialized Countries," Population and Development Review, The Population Council, Inc., vol. 31(4), pages 645-674, December.
    21. Alessandro Giovannini & Maurizio Iacopetta & Raoul Minetti, 2013. "Financial Markets, Banks, and Growth : Disentangling the links," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 105-147.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0208019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.