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EDUCATION AND INCOME INEQUALITY IN THE REGIONS OF THE EUROPEAN UNION*
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ABSTRACT This paper provides an empirical study of the determinants of income 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 human capital distribution affect income inequality for the population as a whole and for normally working people. The different static and dynamic panel data analyses conducted reveal that the relationship between income per capita and income inequality, as well as between a good human capital endowment and income inequality is positive. High levels of inequality in educational attainment are also associated with higher income inequality. The above results are robust to changes in the definition of income distribution and may be interpreted as a sign of the responsiveness of the EU labor market to differences in qualifications and skills. Other results indicate that population ageing, female participation in the labor force, urbanization, agriculture, and industry are negatively associated to income inequality, while unemployment and the presence of a strong financial sector positively affect inequality. Finally, income inequality is lower in social‐democratic welfare states, in Protestant areas, and in regions with Nordic family structures.
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“Secondly, income and income inequality are connected to educational attainment and inequality thereof, since a certain wealth and income is necessary to complete higher educational levels and higher educational attainment is associated with better paid jobs ([44, 50], pp. 389 -390).”
From The opportunity of shared autonomous vehicles to improve spatial equity in accessibility and socio-economic developments in European urban areas (2021) · cites this paper as [44]
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Metadata
- Authors
- Rodríguez‐Pose, A., & Tselios, V.
- Affiliations
- London School of Economics and Political Science (GB)
- Corresponding author
- Rodríguez‐Pose, Andrés
- Tselios, Vassilis
- Venue
- Journal of Regional Science
- Publisher
- Wiley
- Published
- 2009-08
- Volume
- 49
- Issue
- 3
- Pages
- 411-437
- ISSN
- 0022-4146 (ISSN-L)
- 1467-9787
- OpenAlex
- W1524038166
- Cited by
- 238
- References
- 130
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 18 · 2023: 30 · 2022: 20 · 2021: 26 · 2020: 17 · 2019: 16 · 2018: 14 · 2017: 12 · 2016: 6 · 2015: 9 · 2014: 9 · 2013: 9 · 2012: 8
- FWCI
- 24.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Regional Science (publishedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (submittedVersion)
- LSE Research Online Documents on Economics (submittedVersion)
- Econstor (Econstor) (submittedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- European Commission
- Economic and Social Research Council (ES/G005966/1)
- Topics
- Social Sciences › Sociology and Political Science › Income, Poverty, and Inequality
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
- Keywords
- income inequality, human capital, educational attainment, regional disparities, European Union regions, per capita income, labor market segmentation, population aging, female labor force participation, urbanization, unemployment, financial services, social democratic welfare state, Protestantism, family structure, determinants of inequality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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