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The Evolution of Educational Inequalities in Spain: Dynamic Evidence from Repeated Cross-Sections

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Abstract

A lack of longitudinal data prevents many countries from estimating dynamic models and, thus, from obtaining valuable evidence for policymaking in the field of education. This is the case of Spain, where recent education reforms have targeted secondary schools, but their design has been based on incomplete information regarding the evolution of student performance and far from robust evidence concerning just when educational inequalities are generated. This paper addresses the absence of longitudinal data required for performing such analyses by using a dynamic model with repeated cross-sectional data. We are able to link the reading competencies of students from the same cohort that participated in two international assessments at different ages (9/10 and 15/16) and so identify when educational gaps—in terms of gender, socio-economic status and place of birth—emerge. Our results suggest that educational inequalities in Spain originate in lower educational levels. These results stress the importance of early intervention for improving performance during the compulsory education and for tackling educational inequalities.

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Notes

  1. The Great Gatsby Curve states that countries with high level of income inequality tend to have lower levels of intergenerational mobility.

  2. Choi and Jerrim (2016) identify the Spanish case as a clear example of the so-called “PISA shock”, that is, the impact of this international assessment on policy-making discourse at the national level.

  3. For a discussion of alternative, but less efficient, empirical strategies, see Contini and Grand (2015).

  4. Our study differs, in the main, from De Simone’s (2013) in the identification strategy employed. Besides, we use different independent variables: Secondary school characteristics cannot also be observed during primary school, so we have exclude these from our empirical strategy in order to obtain consistent estimates. Similarly, we do not consider variables related to student behaviour at secondary school for fear of endogeneity problems. For its part, Contini and Grand (2015) rely on the use of one additional regressor to identify the model, whereas we include two in order to increase the efficiency of our estimates.

  5. We checked, in our auxiliary database, the correlation between attendance of pre-primary education and the socio-economic proxies used (below 0.15), as a strong association between the two would have reduced its validity as an identification variable.

  6. Unfortunately, Spain did not participate in the 2007 Trends in International Mathematics and Science Study (TIMSS) and so we are unable to replicate the analysis for maths and science.

  7. Further details can be found in Mullis et al. (2007) and in OECD (2014b).

  8. Compulsory education in Spain begins at age 6 and comprises six years of primary education and four years of lower secondary education.

  9. Precise details on the imputation models used are available from the authors upon request.

  10. Following Hox (1995) and OECD (2104b), we take into account the five plausible values, set of weights and nested nature of PISA.

  11. A discussion of the different channels via which SES can affect academic performance can be found in Willms (2006).

  12. Jerrim et al. (2016) analyse the robustness of the TSTSLS methodology and provide a recent review of articles using this approach. They also review the sample sizes of the main and auxiliary databases employed in these articles.

  13. Prior student academic performance has been identified by the literature as one of the main predictors of grade retention in both developed (Ferguson et al. 2001; Bali et al. 2005; Frey 2005; Wilson and Hughes 2009) and developing countries (Gomes-Neto and Hanushek 1994; Liddell and Rae 2001; Chen et al. 2010).

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Acknowledgements

The authors gratefully acknowledge the financial support from the Areces Foundation through its XIII National Contest for Research in Social Sciences and the Spanish Ministry of Economy and Competitiveness (project EDU2016-76414-R).

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Authors and Affiliations

  1. Faculty of Economics and Business, University of Barcelona, 690, Av. Diagonal, 08034, Barcelona, Spain

    Álvaro Choi

  2. Faculty of Economics and Business, University Autónoma of Madrid, Campus de Cantoblanco, C/Francisco Tomás y Valiente, 5, 28049, Madrid, Spain

    María Gil

  3. Faculty of Economics and Business, University of Valencia, Avda Tarongers S/N, 46022, Valencia, Spain

    Mauro Mediavilla

  4. Zaragoza, Spain

    Javier Valbuena

Authors
  1. Álvaro Choi
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  2. María Gil
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Correspondence to Mauro Mediavilla.

Appendix

Appendix

See Tables 3, 4 and 5.

Table 3 Summary statistics: variables of PIRLS (2006).
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Table 4 Summary statistics: variables of PISA (2012).
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Table 5 Alternative estimation of students’ performance in reading competencies using the cross-sectional and pseudo-panel data models, at age 15
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Choi, Á., Gil, M., Mediavilla, M. et al. The Evolution of Educational Inequalities in Spain: Dynamic Evidence from Repeated Cross-Sections. Soc Indic Res 138, 853–872 (2018). https://doi.org/10.1007/s11205-017-1701-6

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