Are the subjective social status inequalities persistent?

Abstract

Aiming to broaden the knowledge about Subjective Social Status Inequalities (SSSI), this short article examines the determinants WHERE and WHY people self-place themselves in the Top-Bottom societal ladder, WHAT makes them choose location. Employing multi-stage modelling on self-reported data from 27 countries in the ISSP’19 we focus on the polar ‘Higher’ and ‘Lower’ status groups, derived from 41,930 individuals' perceptions. To capture the cross-cultural perspectives of the subjective status architecture, we categorize countries into four segments based on Gini coefficient and GDP PP. Findings affirm prior studies (Kelley, Goldthorpe) that neither well-being nor income would alone explain the SSSI. Advanced machine learning analyses emerges ancestry as the primary influencer in the innovative factors’ battery, followed by social class, 'making ends meet', education, ethnicity. Subjective status groups persist across generations, residing in familiar environments, perpetuating their positions. In a cross-national context North and Central European societies display the highest subjective egalitarianism.

Keywords:

• Subjective social status inequalities (SSSI)
• higher-lower status groups in the top-bottom hierarchical ladder
• ancestry
• machine learning (ML)
• ISSP’19 module

Notes

1 GINI index, a quantified representation of a nation's Lorenz curve, has values between 0 and 100, where a GINI index of 0 expresses perfect equality, while an index of 100 expresses maximal inequality. https://data.oecd.org/inequality/income-inequality.htm

2 Even though this technique is used for predictions and propensity scorings we used it in the sense of understanding the relative importance of each driver to the overall dependent variable variance. No actual prediction or scoring was used, but actual variable importance is extracted as a measurement of how good a model divides the multidirectional space.

3 The dependent variable was a dummy coded for both Lower status groups and Higher status groups. A separate model for each group was applied as a classification task in the modelling phase.

4 Individually reported personal incomes were aggregated based on their country-by-country volumes and further classified in decile groups based on distribution

5 A binary aggregation corresponding to ethnic group distribution, e.g., a majority group is defined if a certain group frequency is three times bigger than the IQR

6 We used the following item, included in the ISSP’19 questionnaire: Thinking of your household's total income, including all the sources of income of all the members who contribute to it, how difficult or easy is it currently for your household to make ends meet? The respondents were asked to choose their answer from a 5-point scale ranging from 1 = Very difficult to 5 = Very easy, plus the option Can’t choose.

Additional information

Notes on contributors

Lilia Dimova

Lilia Dimova is a social scientist, Chair of the Think Tank organization Agency for Social Analyses (ASA), located in Sofia. Dr. Dimova is the National Principal Investigator (since 1994) for 21 ISSP research modules, and the Bulgarian National Coordinator (2005 – 2019) for 5 ESS research waves. She was a visiting professor on social inequalities in the Balkans in Vienna University, Austria (2008), a visiting researcher in the UK, USA, Ireland, Japan, Greece, and a Marie Curie fellow (2003-04). She has produced over 80 academic publications, scientific analyses, conference reports.

Martin Dimov

Martin Dimov is an experienced data scientist, modeller and statistician, awarded by European and world institutions for his contribution to innovative statistical techniques. He is the Director of the Data Science Team in Gem Seek. Dr. Dimov’s professional achievements are in the field of development of predictive models, innovative approaches and techniques for better utilization of internal data, implementing top-notch ML/AI algorithms in mega and social data analyses.