IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2307.09251.html
   My bibliography  Save this paper

Socio-spatial Inequalities in a Context of "Great Economic Wealth". Case study of neighbourhoods of Luxembourg City

Author

Listed:
  • Natalia Zdanowska

Abstract

In spite of being one of the smallest and wealthiest countries in the European Union in terms of GDP per capita, Luxembourg is facing socio-economic challenges due to recent rapid urban transformations. This article contributes by approaching this phenomenon at the most granular and rarely analysed geographical level - the neighbourhoods of the capital, Luxembourg City. Based on collected empirical data covering various socio-demographic dimensions for 2020-2021, an ascending hierarchical classification on principal components is set out to establish neighbourhoods' socio-spatial patterns. In addition, Chi2 tests are carried out to examine residents' socio-demographic characteristics and determine income inequalities in neighbourhoods. The results reveal a clear socio-spatial divide along a north-west south-east axis. Moreover, classical factors such as gender or citizenship differences are revealed to be poorly determinant of income inequalities compared with the proportion of social benefits recipients and single residents.

Suggested Citation

  • Natalia Zdanowska, 2023. "Socio-spatial Inequalities in a Context of "Great Economic Wealth". Case study of neighbourhoods of Luxembourg City," Papers 2307.09251, arXiv.org.
  • Handle: RePEc:arx:papers:2307.09251
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2307.09251
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lelo, Keti & Monni, Salvatore & Tomassi, Federico, 2019. "Socio-spatial inequalities and urban transformation. The case of Rome districts," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    2. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.
    3. Dong Lin & Andrew Allan & Jianqiang Cui, 2015. "The impacts of urban spatial structure and socio-economic factors on patterns of commuting: a review," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 19(2), pages 238-255, July.
    4. Pfeffer, Fabian T. & Waitkus, Nora, 2021. "The Wealth Inequality of Nations," SocArXiv 6msuf, Center for Open Science.
    5. Mezaroş, Mădălina & Paccoud, Antoine, 2022. "Accelerating housing inequality: property investors and the changing structure of property ownership in Luxembourg," LSE Research Online Documents on Economics 116432, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seungwoo Han, 2022. "Spatial stratification and socio-spatial inequalities: the case of Seoul and Busan in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    2. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    3. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
    4. Carranza, Rafael & De Rosa, Mauricio & Flores, Ignacio, 2023. "Wealth Inequality in Latin America," IDB Publications (Working Papers) 12906, Inter-American Development Bank.
    5. Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
    6. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    7. Layal Christine Lettry, 2023. "Clustering the Swiss Pension Register," FSES Working Papers 529, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    8. Goodrich, Brittney K. & Goodhue, Rachael E., 2020. "Are All Colonies Created Equal? The Role of Honey Bee Colony Strength in Almond Pollination Contracts," Ecological Economics, Elsevier, vol. 177(C).
    9. Marcel Raab & Anette Fasang & Aleksi Karhula & Jani Erola, 2014. "Sibling Similarity in Family Formation," Demography, Springer;Population Association of America (PAA), vol. 51(6), pages 2127-2154, December.
    10. Nakamura, Hiroki, 2020. "Evaluating the value of an entrepreneurial city with a spatial hedonic approach: A case study of London," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    11. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    12. Jaewon Lim & Jae Hong Kim, 2019. "Joint Determination of Residential Relocation and Commuting: A Forecasting Experiment for Sustainable Land Use and Transportation Planning," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    13. Chan Xu & Qi An & Zichuan Guo & Xuemei Yu & Jie Zhang & Kui Tang, 2023. "Comparative Study on Socio-Spatial Structures of the Typical Plain Cities of Chengdu and Beijing in Transitional China," Sustainability, MDPI, vol. 15(5), pages 1-32, February.
    14. Ke Meng & Shouhao Li, 2023. "Welfare Regimes and Intergenerational Social Mobility: An Institutional Explanation of the Great Gatsby Curve," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(1), pages 355-375, January.
    15. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    16. David Jung-Hwi Lee & Jean-Michel Guldmann, 2023. "Optimal Regional Allocation of Future Population and Employment under Urban Boundary and Density Constraints: A Spatial Interaction Modeling Approach," Land, MDPI, vol. 12(2), pages 1-33, February.
    17. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    18. Jean-Patrick Baudry & Margarida Cardoso & Gilles Celeux & Maria Amorim & Ana Ferreira, 2015. "Enhancing the selection of a model-based clustering with external categorical variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(2), pages 177-196, June.
    19. Zilja, Flladina & Benito, Gabriel R.G. & Boustanifar, Hamid & Zhang, Dan, 2023. "CEO wealth and cross-border acquisitions by SMEs," International Business Review, Elsevier, vol. 32(6).
    20. Mostafa Ghadami & Andreas Dittmann & Taher Safarrad, 2020. "Lack of Spatial Approach in Urban Density Policies: The Case of the Master Plan of Tehran," Sustainability, MDPI, vol. 12(18), pages 1-22, September.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2307.09251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.