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Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data

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  • Konstantin Buechel, Maximilian von Ehrlich

Abstract

Social interactions are considered pivotal to urban agglomeration forces. This study employs a unique dataset on mobile phone calls to examine how social interactions differ across cities and peripheral areas. We first show that geographical distance is highly detrimental to interpersonal exchange. We then reveal that individuals residing in high-density locations do not benefit from larger social networks, but from a more efficient structure in terms of higher matching quality and lower clustering. These results are derived from two complementary approaches: Based on a link formation model, we examine how geographical distance, network overlap, and sociodemographic (dis)similarities impact the likelihood that two agents interact. We further decompose the effects from individual, location, and time specific determinants on micro-level network measures by exploiting information on mobile phone users who change their place of residence.

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  • Konstantin Buechel, Maximilian von Ehrlich, 2016. "Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data," Diskussionsschriften credresearchpaper13, Universitaet Bern, Departement Volkswirtschaft - CRED.
  • Handle: RePEc:rdv:wpaper:credresearchpaper13
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    2. Yusra Ghafoor & Yi-Shin Chen & Kuan-Ta Chen, 2019. "Social Interaction Scaling for Contact Networks," Sustainability, MDPI, vol. 11(9), pages 1-14, May.
    3. Picard, Pierre M. & Zenou, Yves, 2018. "Urban spatial structure, employment and social ties," Journal of Urban Economics, Elsevier, vol. 104(C), pages 77-93.
    4. Feng Huang & Jie Gao, 2022. "Customer and Tax Avoidance: How Does Customer Geographic Proximity Affect a Supplier’s Tax Avoidance?," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    5. Wang, Li & Wu, Yiqi & Chen, Yaxin & Dai, Yunhao, 2023. "Distance produces the fear of loss: Customer geographic proximity and corporate cash holdings," International Review of Financial Analysis, Elsevier, vol. 87(C).
    6. Gilles Duranton & Diego Puga, 2020. "The Economics of Urban Density," Journal of Economic Perspectives, American Economic Association, vol. 34(3), pages 3-26, Summer.
    7. Chloé Duvivier & Emma Cazou & Stéphanie Truchet‐Aznar & Cédric Brunelle & Jean Dubé, 2021. "When, where, and for what industries does broadband foster establishment births?," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1377-1401, December.
    8. Behzad Vahedi & Morteza Karimzadeh & Hamidreza Zoraghein, 2021. "Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    9. Augeraud Veron, E. & Marhuenda, F. & Picard, P.M., 2021. "Local social interaction and urban equilibria," Mathematical Social Sciences, Elsevier, vol. 112(C), pages 72-83.
    10. Pierre Magontier, Maximilian v. Ehrlich, Markus Schl pfer, 2022. "The Fragility of Urban Social Networks - Mobility as a City Glue -," Diskussionsschriften credresearchpaper38, Universitaet Bern, Departement Volkswirtschaft - CRED.
    11. Kim, Jun Sung & Patacchini, Eleonora & Picard, Pierre M. & Zenou, Yves, 2017. "Urban Interactions," Working Paper Series 1192, Research Institute of Industrial Economics.
    12. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    13. Jun Sung Kim & Eleonora Patacchini & Pierre M. Picard & Yves Zenou, 2023. "Spatial interactions," Quantitative Economics, Econometric Society, vol. 14(4), pages 1295-1335, November.
    14. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    15. Theresa Kuchler & Johannes Stroebel, 2022. "Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science," CESifo Working Paper Series 9606, CESifo.
    16. Lai, Wangyang & Song, Hong & Wang, Chang & Wang, Huanhuan, 2021. "Air pollution and brain drain: Evidence from college graduates in China," China Economic Review, Elsevier, vol. 68(C).
    17. Binzhe Wang & Matias Williams & Fábio Duarte & Siqi Zheng, 2022. "Demand for social interactions: Evidence from the restaurant industry during the COVID‐19 pandemic," Journal of Regional Science, Wiley Blackwell, vol. 62(3), pages 830-857, June.
    18. Chloé Duvivier & Claire Bussière, 2022. "The contingent nature of broadband as an engine for business startups in rural areas," Journal of Regional Science, Wiley Blackwell, vol. 62(5), pages 1329-1357, November.

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    More about this item

    Keywords

    Social Interactions; Agglomeration Externalities; Network Analysis; Sorting;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • 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
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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