The European Socio-Economic Classification (ESeC) is quite popular in social science research, especially as an indicator of social class. The EU Statistics on Income and Living Conditions (EU-SILC) provides the opportunity to study the relation between ESeC and a long list of socio-economic variables (including income, employment, material deprivation, …) in a comparative and longitudinal perspective. However, rather few people work with ESeC in EU-SILC.
On this page, I bring together some preparatory methodological work, published papers and accompanying Stata do-files that make use of ESeC in EU-SILC:
- Research note on ESeC in EU-SILC
- Book chapter on consistency of ESeC between countries and over time, from the point of view of poverty by social class
- Between-class earnings inequality in Europe (article in Social Indicators Research)
- Other papers
In this research note, I document the data quality of EU-SILC (2004-2017) in terms of measuring ESeC, and propose a slightly modified version of computing ESeC compared to the routines one can find on the GESIS webiste.
- Read the research note
- Download an excel file with background tables, accompanying the note
- Download the STATA do-file (applicable to EU-SILC 2004-2017)
Please cite the research note and STATA do-file as:
Goedemé, 2019, A note on the replication of the European Socio-economic Classification (ESeC) in the EU Statistics on Income and Living Conditions (EU-SILC). INET Oxford Working Paper No. 2019-17, Oxford: Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
Goedemé, T., Paskov, M., and Nolan, B. (2021) ‘The measurement of social class in EU-SILC: comparability between countries and consistency over time’ in Guio, A.-C., Marlier, E., Nolan, B. (eds) (2021), Improving the understanding of poverty and social exclusion in Europe, Luxembourg: Publications Office of the European Union, pp. 313-328.
In this chapter we document some implications of the limitations of the ESeC measure in EU-SILC for the estimated size of social class and the monitoring of poverty by social class from a comparative perspective.
- Access the chapter [and here the entire book]
- Download detailed results in Excell [to be uploaded]
- Download the Stata-do files [to be uploaded]
Goedemé, T., Nolan, B., Paskov, M., & Weisstanner, D. (2021). Occupational Social Class and Earnings Inequality in Europe: A Comparative Assessment. in Social Indicators Research.
We study cross-country variations in between-class earnings inequalities in Europe, with EU-SILC 2018. We look at basic correlations between overall earnings inequality and between-class inequality, and – by devising a counterfactual analysis – study the degree to which between-class earnings inequalities are determined by differences in the composition of social classes and the association between earnings and other variables that also correlate with social class. Results are provided for the total population at active age and in work, as well as for women and men separately (in the Supplementary material). This article makes use of the same measurement of ESeC in EU-SILC as the book chapter highlighted above.
- Access the article online [doi, alternative access link]
- Download the pre-print
- Download the Supplementary material and the Excel files with detailed numbers
- Download the Stata do-files [for re-use license, see bottom of page)
- Goedemé, T., Paskov, M., Weisstanner, D. and Nolan, B., (2020) Social class and earnings: a cross-national study, INET Oxford Working Paper No . 2020-03, Oxford: Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
- Paskov, M., Madia, J. E., & Goedemé, T. (2018). Middle and below living standards: what can we learn from beyond income measures of economic well-being? In B. Nolan (Ed.), Generating prosperity for working families in affluent countries (pp. 282-311). Oxford University Press. http://dx.doi.org/10.1093/oso/9780198807056.003.0011
This project on social class and earnings inequality, was carried out in collaboration with Marii Paskov, David Weisstanner and Brian Nolan at the University of Oxford. I am very grateful to them for comments and discussions, as well as to Joan Madia with whom I collaborated on similar research several years ago (see publication above).
The STATA do-files published on this website are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It can be changed and re-shared for non-commercial use, as long as our original work is recognised and the revised work is made available under the same conditions.