Subjective social status in places that don’t matter: geographical inequalities in France and Germany

ABSTRACT

In recent decades, the rise of the service economy and the growing attractiveness of large cities have created new social inequalities within countries, which have been seen as a source of resentment for people living in the “places that don’t matter”. We study spatial inequalities in terms of subjective social status using a measure of the place in the social hierarchy that individuals believe they occupy in France (1999-2017) and Germany (1992-2021) on the basis of data from the International Social Survey Program. In France we find important and persistent inequalities between urban and rural areas, as well as between the capital region and all the other regions, partially mediated by income differences. However, the time trend does not show any consistent increase in the geographical differences in subjective status apart from a possible negative trend in rural areas from 2006 to 2010 and in rural places and the outskirts of large cities after 2013 compared to large cities. In Germany, our analysis shows weak differences in subjective social status between urban and rural areas, but large inequalities between the West and East. While this gap is still relevant today, it has partially decreased over the past decades.

KEYWORDS:

• Spatial inequalities
• geography of discontent
• subjective social status
• status anxiety

Acknowledgments

I thank the four anonymous reviewers at European Societies for their helpful feedback to this article. Special thanks to Daniel Oesch for extensive feedback.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The 2020 OECD regional report refers to 2018 data.

2 Detailed maps of 2020 US presidential election can be found on The New York Times website: https://www.nytimes.com/interactive/2021/upshot/2020-election-map.html.

3 For a map of the unequal geographic distribution of the AFD votes in Germany, see Financial Times, 2021: https://www.ft.com/content/501b1f94-67e7-4418-b2e9-eee6022bb12c [accessed on 17. 9. 2022].

4 These data are from 2020 and are available on the OECD Fiscal Decentralisation Database: https://www.oecd.org/tax/federalism/fiscal-decentralisation-database/.

5 These data refer to 2018 for France and to 2020 for Germany, and they are available on the OECD statistical database: https://stats.oecd.org/Index.aspx?Datasetcode=CITIES#.

6 For a map of the unequal geographic distribution of the votes for the Front National in the 2022 French presidential election, see Public Sénat, 2022: https://www.publicsenat.fr/article/politique/presidentielle-2022-la-carte-interactive-des-resultats-du-premier-tour-201841 [accessed on 17. 9. 2022].

7 A first release of 2018 data for France was available at the moment of the review process, but the sample is small and the geographical variables do not seem reliable, leading to a sharp and sudden increase in subjective status levels across all places.

8 Our analysis attributes each ISSP round to the year when the survey was effectively fielded rather than the official year of a module. In Germany, the ISSP modules were administered in pairs every two years (ex. The 2003 and the 2004 modules were both administrated in 2004) and the 2020 module was administrated in 2021.

9 The replication package for data preparation and for reproducing all analyses in Stata 17 is available: DOI 10.17605/OSF.IO/E28ZH

10 The corresponding question in round 6 of the European Social Survey, which we use for robustness analyses, consists of 11 categories, scored from 0 to 10.

11 The departments of Rhône and Bouches du Rhône include some municipalities that cannot be considered as part of the urban area of the departmental capital, as they are quite distant and prevalently rural. Unfortunately, no information on municipalities was available to overcome this limitation.

12 The national sampling strategies do not guarantee the representativeness of each NUT sample with respects to its actual population. Nevertheless, we aggregated NUTS3 for France into larger macro-regions and, in the end, our analyses rely on sizable regional samples. As reported in tables A2 and A3 in the appendix, each round provides more than 100 observations in each category of all geographical indicators. Moreover, we estimate the trends based on many rounds for each country and not on sporadic points in time. These elements should reduce the concerns about the regional representativeness of the samples.

13 The upper-middle class includes large employers, managers and professionals; the lower-middle class is composed by semi-professionals, associate managers and technicians; the small business owners correspond to the so-called petite bourgeoisie; the working class includes both skilled and unskilled workers.

14 We compute equivalent monthly household income based on the OECD modified scale which assigns the value of 1 to the household head, 0.5 to every additional adult and 0.3 to every child. In same cases in which it is not possible to disentangle adults and children, all the members of the household are assigned the value of 0.4.

15 As in the previous plots, the estimates were smoothed locally to better illustrate real trends and get rid of trendless fluctuations.

Additional information

Notes on contributors

Nathalie Vigna

Nathalie Vigna is PhD candidate working on different aspects of social stratification, notably comparing objective and subjective measures, and looking into geographical differences in several European countries.