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Inequality is rising where social network segregation interacts with urban topology

Author

Listed:
  • Gergő Tóth

    (Centre for Economic-and Regional Studies
    University College Dublin)

  • Johannes Wachs

    (Vienna University of Economics and Business
    Complexity Science Hub Vienna)

  • Riccardo Clemente

    (University of Exeter
    University College London)

  • Ákos Jakobi

    (Eötvös Loránd University
    Institute of Advanced Studies)

  • Bence Ságvári

    (Centre for Economic-and Regional Studies
    Centre for Social Sciences
    International Business School Budapest)

  • János Kertész

    (Central European University)

  • Balázs Lengyel

    (Centre for Economic-and Regional Studies
    International Business School Budapest
    Budapest Corvinus University)

Abstract

Social networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

Suggested Citation

  • Gergő Tóth & Johannes Wachs & Riccardo Clemente & Ákos Jakobi & Bence Ságvári & János Kertész & Balázs Lengyel, 2021. "Inequality is rising where social network segregation interacts with urban topology," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21465-0
    DOI: 10.1038/s41467-021-21465-0
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    Cited by:

    1. Sanghamitra Mukherjee, 2021. "A Framework to Measure Regional Disparities in Battery Electric Vehicle Diffusion in Ireland," Working Papers 202119, School of Economics, University College Dublin.
    2. Tanner Regan & Andreas Diemer & Cheng Keat Tang, 2023. "The Role of Social Connections in the Racial Segregation of US Cities," Working Papers 2023-05, The George Washington University, Institute for International Economic Policy.
    3. Wang, Xi & Pei, Tao & Song, Ci & Chen, Jie & Shu, Hua & Liu, Yaxi & Guo, Sihui & Chen, Xiao, 2023. "How does socioeconomic status influence social relations? A perspective from mobile phone data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    4. Kolkowski, Lukas & Cats, Oded & Dixit, Malvika & Verma, Trivik & Jenelius, Erik & Cebecauer, Matej & Rubensson, Isak Jarlebring, 2023. "Measuring activity-based social segregation using public transport smart card data," Journal of Transport Geography, Elsevier, vol. 110(C).
    5. Schulz, Jan & Mayerhoffer, Daniel M., 2021. "A network approach to consumption," BERG Working Paper Series 173, Bamberg University, Bamberg Economic Research Group.
    6. Stark, Oded & Bielawski, Jakub & Falniowski, Fryderyk, 2023. "Measuring Income Inequality in Social Networks," IZA Discussion Papers 16666, Institute of Labor Economics (IZA).
    7. Rüttenauer, Tobias, 2023. "Spatial Data Analysis," SocArXiv mq7te, Center for Open Science.
    8. Éva Huszti & Fruzsina Albert & Adrienn Csizmady & Ilona Nagy & Beáta Dávid, 2021. "When Spatial Dimension Matters: Comparing Personal Network Characteristics in Different Segregated Areas," Social Inclusion, Cogitatio Press, vol. 9(4), pages 375-387.
    9. Tobias Ruttenauer, 2024. "Spatial Data Analysis," Papers 2402.09895, arXiv.org.

    More about this item

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • N34 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: 1913-
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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