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A new dataset on educational inequality

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Abstract

This paper describes a new dataset, in which measures of educational level and inequality were collected for 48 countries over 13 5-year birth cohorts. Drawing on four representative international surveys (ess, eu-silc, ials and issp), we collected measures of individual educational attainment and aggregated them to generate synthetic indices of educational level and dispersion by countries and birth cohorts. The paper provides a detailed description of the procedures and methodologies adopted to build the new dataset, analyses the validity and consistency of the measures across surveys and discusses the relevance of these data for future research.

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Notes

  1. Respectively, European Social Survey, European Union Statistics on Income and Living Conditions, International Adult Literacy Survey and International Social Survey Programme.

  2. The dataset covers 34 European countries (all EU members except Malta, plus Norway, Iceland, Switzerland, Croatia, Ukraine, Russia, Israel and Flemish Belgium, which is considered separately in ials and issp), six Latin American countries (Mexico, Argentina, Brazil, Uruguay, Chile and the Dominican Republic), five large Commonwealth countries (the US, Canada, Australia, New Zealand and South Africa) and three Eastern economies (Japan, Taiwan and South Korea).

  3. United Nations Educational, Scientific and Cultural Organisation.

  4. See UNESCO (2006); OECD (1999).

  5. www.eurydice.org.

  6. Bulgaria, Czech Republic, Denmark, Estonia, Latvia, Hungary, Croatia, Slovenia, Slovak Republic, Finland, Sweden, Iceland and Norway.

  7. While formal education tends to remain invariant over the life cycle, individuals may continue to enhance their skills and qualifications throughout life in a range of situations, from training on the workplace to adult education courses. Measuring the amount of lifelong learning acquired is beyond the scope of our dataset. Moreover, using the longitudinal element of eu-silc, we assessed how isced levels change during the lifetime of adults. The data indicate that, for most individuals, isced levels remain stable after age 30. In particular, this level does not change over time in 93.5, 98 and 98.5 % of people with primary, lower secondary and upper secondary education, respectively.

  8. We acknowledge that data for older cohorts may be affected by differential mortality rates among individuals with different educational attainments. If education increases life expectancy [see for example Lleras-Muney (2005)], then indicators of educational attainment for older cohorts may be upwardly biased.

  9. Three cells include fewer than 10 observations each, nine cells had fewer than 20 observations and 15 cells had fewer than 30 observations.

  10. ess is a biennial survey that covers over 30 mostly European countries and provides detailed information on individual attitudes, beliefs and behaviour collected from nationally representative population samples. It consists of repeated cross sections, started in 2002/2003 (1st round) and continued in 2004/2005 (2nd round), 2006/2007 (3rd round) and 2008/2009 (4th round). The survey mainly focuses on peoples’ attitudes and values, although it also contains several variables capturing the social background and the educational career of respondents. For more information, see http://www.europeansocialsurvey.org.

  11. eu-silc is a collection of comparable multidimensional microdata covering EU countries plus Iceland and Norway. eu-silc is a project developed by eurostat, run yearly since 2004 and including both cross-sectional and longitudinal surveys. For more information, see http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/eu_silc.

  12. ials is a survey of information on adult literacy in representative samples in some oecd countries. It was implemented in different countries in different years, 1994, 1996 and 1998, using a common questionnaire. Although the central element of this survey is a direct assessment of the literacy skills of respondents, the background questionnaire also includes detailed information on individual socio-demographic characteristics. For more information, see http://www.statcan.gc.ca/dli-ild/data-donnees/ftp/ials-eiaa-eng.htm.

  13. issp is a continuing annual programme of cross-national collaborations on surveys covering a wide range of topics. Data were collected annually, from 1985 to 2010, in 26 countries, most of which are surveyed only in some, but not all, years. For more information, see http://www.issp.org/index.php.

  14. Given the relative homogeneity of education data across surveys, we could have potentially merged observations from different surveys and calculated a unique summary statistics. However, since the four surveys contain different individual weights, it would have been difficult to compute aggregate measures representative of the real population. We, therefore, decided to keep the datasets separate.

  15. This result is likely due to the low sample size, even if means are sufficiently close to each other as determined by dataset comparisons.

  16. Nordic countries: Denmark, Finland, Iceland, Norway and Sweden; Anglo-Saxon countries: Australia, Canada, the Great Britain, Ireland, New Zealand, South Africa and the United States; Central European countries: Austria, Belgium (including Flemish Belgium, if available), France, Germany, Luxembourg, the Netherlands and Switzerland; Mediterranean countries: Cyprus, Greece, Israel, Italy, Portugal and Spain; Eastern European countries: Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania, Slovak Republic and Slovenia; Former Soviet republics: Estonia, Latvia, Lithuania, Russia and Ukraine; Asian countries: Japan, South Korea and Taiwan; and Latin-American countries: Argentina, Brazil, Chile, Dominican Republic, Mexico and Uruguay.

  17. Since these data were not weighted by the relative size of the countries, Fig. 3 does not measure inequality within geopolitical areas, but the average inequality within countries, aggregated by geopolitical area.

  18. The historical data on the age of first tracking are drawn from Braga et al. (2013), involving an original database of different educational institutions over the last 70 years in 24 European countries.

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Acknowledgments

This paper is part of a larger research project on “Growing INequalities’ Impacts - GINI” financed by the European Commission under the 7th Framework Programme (contract n. 244592). We thank Gabriele Ballarino, Giuseppe Bertola, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco Leonardi and Claudio Lucifora for suggestions and useful discussions. Precious insights by participants in the gini’s Measurements and Methods Workshop, Amsterdam, October 2010 are also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Economics, Ca’ Foscari University of Venice, Cannaregio 873, 30121 , Venice, Italy

    Elena Meschi

  2. Department of Economics, Management and Quantitative Methods, Università di Milano, Via Conservatorio, 7, 20122 , Milan, Italy

    Francesco Scervini

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  1. Elena Meschi
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  2. Francesco Scervini
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Correspondence to Francesco Scervini.

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Meschi, E., Scervini, F. A new dataset on educational inequality. Empir Econ 47, 695–716 (2014). https://doi.org/10.1007/s00181-013-0758-6

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