Gender in intergenerational educational persistence across time and place

Abstract

Primarily using data from the 2010 European Social Survey, we analyze intergenerational educational persistence in 20 European countries, studying cross-country and cross-cluster differences; changes in the degree of intergenerational persistence over time; and the role of gender in determining educational persistence across generations. We find that persistence is highest in the Southern and Eastern European countries, and lowest in the Nordic countries. While persistence in the Nordic and Southern countries has declined over time, it has remained relatively steady in the rest of Europe. Our analysis highlights the importance of a detailed gender analysis in studying intergenerational persistence, finding that mothers’ education is a stronger determinant of daughters’ (instead of sons’) education and fathers’ education a stronger determinant of the education of their sons. For most clusters, declines in intergenerational persistence over time are largely driven by increasing mobility for younger women.

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Notes

  1. 1.

    There is, of course, also the question of the optimal level of intergenerational persistence, which this paper does not address. For an excellent review of the history of thought on intergenerational mobility in economics, which does deal with this question, see Piketty (2000).

  2. 2.

    See the development of the theory of social capital in Bourdieu (1986).

  3. 3.

    Another option slowly growing in the literature would be to measure the persistence of self-reported well-being across generations. A recent study shows that results across Europe are similar to the results we get in this paper, even along the gender dimension (Molina et al. 2011).

  4. 4.

    The calculation of the intergenerational correlations employed in these graphs is described in Sect. 3 below; they are the same as those presented in Table 2. The data on expenditure per student come from UNESCO (2014); on the age of the first tracking exam from the European Commission (2012); on pre-school enrollment from Eurostat (2012); and on private enrollment from the The World Bank (2014). These data are based on their values in 2010, since data for these policy issues are not systematically available for the times in which our respondents were in school.

  5. 5.

    Italy did not participate in the survey after round 2 (2004) and the round 5 data were not available for Austria at the time of this analysis, so we used data from rounds 2 and 4 for these countries, respectively.

  6. 6.

    Indeed most studies use only the education level of the father. Many earlier studies which estimated a joint effect of mothers and fathers dealt with differences in the education level of the two parents by taking the average of both the father’s and the mother’s education to build the parental education variable (e.g.Hertz et al. 2007), or by summing the years of education of both parents together (e.g. Oreopouos and Page 2006). In an Austrian study, Fessler et al. (2012, pp. 77–78) say that due to assortative mating of the parents, taking the average or taking the maximum of the parents’ education leads to very similar results.

  7. 7.

    When performing analyses by cluster, we take averages of the results for each country in the cluster, which gives each country an equal “weight” in determining cluster-level effects, regardless of differences in population sizes across countries in a cluster.

  8. 8.

    The associations are shown in Table 4 in the “Appendix”. For almost all countries and clusters, the correlation between parent’s and descendant’s educational attainment is higher than the association. This result occurs because the standard deviation of the parental educational distribution is generally larger than in the descendant population (see Eq. 2 and the relevant descriptive statistics in Table 1).

  9. 9.

    The OLS equation employed to fit the line is \(\gamma _{ac}=\alpha +\tau _{1}age_{ac}+\varepsilon _{ac}\), where \(\gamma \) are intergenerational educational correlations, \(\alpha \) is a constant term and \(\varepsilon \) is an error term with the usual properties for every age group \(a\) in each country \(c\). The cluster correlations are taken as the average of the \(\tau \) coefficients for the cluster.

  10. 10.

    Since there are some cases in which there are no men or women over born before 1930 in a country or a cluster, we use an eleven–year cohort (born between 1920–1930) for this group.

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Acknowledgment

The research leading to these results has received funding from the European Commission's Seventh Framework Programme FP7/2007–2013 under grant agreement no. 290647.

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Correspondence to Alyssa Schneebaum.

Appendix

Appendix

See Table 3 and Fig. 4.

Table 3 Intergenerational educational associations—respondents aged 25–65
Fig. 4
figure4figure4

Educational correlations over age of descendants (25–90 years), by country and age years. Outliers with negative values have not been included in these calculations. Most countries are missing correlations for some ages, especially for those aged 80 or above, due to a lack of data

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Schneebaum, A., Rumplmaier, B. & Altzinger, W. Gender in intergenerational educational persistence across time and place. Empirica 42, 413–445 (2015). https://doi.org/10.1007/s10663-015-9291-5

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Keywords

  • Intergenerational persistence
  • Educational attainment
  • Educational welfare states
  • Europe
  • Gender

JEL Classification

  • J62
  • I24
  • I38
  • D63