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The Determinants of Regional Educational Inequality in Western Europe

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Innovation, Growth and Competitiveness

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

This chapter provides an empirical study of the determinants of educational inequality across regions of the EU. Using the European Community Household Panel dataset for 102 regions over the period 1995–2000, it analyses how microeconomic changes in income distribution as well as in educational attainment affect educational inequality. The different static and dynamic panel data analyses conducted reveal the complexity of the interaction between income and education. Educational attainment seems to curb the increase in educational inequality. While the impact of income per capita is unclear, the relationship between income inequality and educational inequality is positive and robust to the model specification. Other results indicate that women’s access to work has a negative impact on inequality and that there is an EU North–South and urban–rural divide. Educational inequality is lower in social-democratic welfare states, in mainly Orthodox areas, and in regions with North/Central family structures. All the results are robust to changes in the definition of income distribution.

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Notes

  1. 1.

    Walters (2000: 254), however, argues that educational expansion alone does not change the relative position of social groups in the “education queue”, and elites manage to maintain their status by getting more education than the masses.

  2. 2.

    Although the boundaries of the welfare states are not well defined, the classification assumes that a country belongs to only one welfare state regime. In reality, there is no single pure case (Esping-Andersen 1990).

  3. 3.

    Sources: http://www.cia.gov/cia/publications/factbook;

    http://commons.wikimidia.org/wiki/Image:Europe_religion_map_de.png;

    http://csi-int.org/world_map_europa_religion.php

  4. 4.

    \( t = 1 \) denotes 1995, …, \( t = 6 \) denotes 2000.

  5. 5.

    The REs results are not reported because of space constraints, but may be obtained upon request.

  6. 6.

    We also controlled for work access of the population – measured as the percentage of normally working respondents (source: ECHP) and as the percentage of economic activity rate of the total population (source: EUROSTAT) – and inactivity. The economic activity rate of the total population is negatively associated with educational inequality, while the remaining two variables are not statistically significant. Greater regional access to work implies higher regional earnings which, in turn, increase the possibility of entering higher education. Conversely, the presence of pools of people with low skills would contribute to social exclusion and to the perpetuation of educational inequality (Rodríguez-Pose 2002). The coefficients of educational attainment, income per capita, and income inequality are robust to the introduction of control variables.

  7. 7.

    Controlling for inactivity, its coefficient is negative and statistically significant. It is likely to show that the higher the percentage of inactive young people, the lower the educational inequality in the long run, because more widespread access to education means that young people are kept out of the labour market, as reflected in the high incidence of youth inactivity (Rodríguez-Pose 2002). Additionally, the impact of the percentage of normally working respondents is not clear, while that of the economic activity rate of total population is negative and statistically significant.

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Acknowledgements

The authors grateful to the European Commission [DYNREG Programme, contract no 028818 (CIT5)] and Eurostat for granting access to the European Community Household Panel (ECHP). Rodríguez-Pose gratefully acknowledges the financial support of a Leverhulme Trust Major Research Fellowship during the final stages of this project. The work was also part of the PROCIUDAD research programme and of the independent UK Spatial Economics Research Centre funded by the Economic and Social Research Council (ESRC), Department for Business, Enterprise and Regulatory Reform, Communities and Local Government, and the Welsh Assembly Government. The support of the funders is acknowledged. The views expressed are those of the authors and do not represent the views of the funders or of Eurostat.

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Authors and Affiliations

  1. Department of Geography and Environment and Spatial Economics Research Centre (SERC), London School of Economics, Houghton Street, London, WC2A 2AE, UK

    Andrés Rodríguez-Pose

  2. IMDEA Social Sciences, Madrid, Spain

    Andrés Rodríguez-Pose

  3. Centre for Urban and Regional Development Studies (CURDS), University of Newcastle upon Tyne, Newcastle upon Tyne, UK

    Vassilis Tselios

  4. Spatial Economics Research Centre (SERC), London School of Economics, Houghton Street, London, WC2A 2AE, UK

    Vassilis Tselios

Authors
  1. Andrés Rodríguez-Pose
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  2. Vassilis Tselios
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Correspondence to Andrés Rodríguez-Pose .

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Editors and Affiliations

  1. VU University Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands

    Peter Nijkamp

  2. (ESRI), Economic and Social Research Institute, Whitaker Square, Sir John Rogerson's Quay, Dublin, 2, Ireland

    Iulia Siedschlag

Appendix A: Standardized Coefficients

Appendix A: Standardized Coefficients

Table A1 Independent variables are income per capita and income inequality for the (a) whole of the population (b) normally working people
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Table A.2 FEs: independent variables are income per capita and income inequality for normally working people
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Table A.3 OLS: independent variables are income per capita and income inequality for normally working people
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Table A.4 Short run GMM
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Table A.5 Long run GMM: independent variables are income per capita and income inequality for normally working people
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Table A.6 Short run GMM: independent variables are income per capita and income inequality for normally working people
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Rodríguez-Pose, A., Tselios, V. (2011). The Determinants of Regional Educational Inequality in Western Europe. In: Nijkamp, P., Siedschlag, I. (eds) Innovation, Growth and Competitiveness. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14965-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-14965-8_7

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