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Friendship networks and social status

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  • BALL, BRIAN
  • NEWMAN, M.E.J.

Abstract

In empirical studies of friendship networks, participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower ranked individual claiming friendship with a higher ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.

Suggested Citation

  • Ball, Brian & Newman, M.E.J., 2013. "Friendship networks and social status," Network Science, Cambridge University Press, vol. 1(1), pages 16-30, April.
  • Handle: RePEc:cup:netsci:v:1:y:2013:i:01:p:16-30_00
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    Cited by:

    1. Chih‐Sheng Hsieh & Xu Lin, 2021. "Social interactions and social preferences in social networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 165-189, March.
    2. Margherita Comola & Marcel Fafchamps, 2017. "The Missing Transfers: Estimating Misreporting in Dyadic Data," Economic Development and Cultural Change, University of Chicago Press, vol. 65(3), pages 549-582.
    3. Gianluca Manzo & Delia Baldassarri, 2015. "Heuristics, Interactions, and Status Hierarchies," Sociological Methods & Research, , vol. 44(2), pages 329-387, May.
    4. Comola, Margherita & Fafchamps, Marcel, 2014. "Estimating Mis-reporting in Dyadic Data: Are Transfers Mutually Beneficial?," IZA Discussion Papers 8664, Institute of Labor Economics (IZA).
    5. Brimkov, Boris & Fast, Caleb C. & Hicks, Illya V., 2019. "Computational approaches for zero forcing and related problems," European Journal of Operational Research, Elsevier, vol. 273(3), pages 889-903.
    6. Lafond, François & Astudillo-Estévez, Pablo & Bacilieri, Andrea & Borsos, András, 2023. "Firm-level production networks: what do we (really) know?," INET Oxford Working Papers 2023-08, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    7. Mattea Stein, 2021. "Know-how and Know-who: Effects of a Randomized Training on Network Changes Among Small Urban Entrepreneurs," CSEF Working Papers 622, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    8. Abdullah Almaatouq, 2016. "Complex Systems and a Computational Social Science Perspective on the Labor Market," Papers 1606.08562, arXiv.org.
    9. Kris Byron & Blaine Landis, 2020. "Relational Misperceptions in the Workplace: New Frontiers and Challenges," Organization Science, INFORMS, vol. 31(1), pages 223-242, January.
    10. Małgorzata J Krawczyk & Krzysztof Kułakowski & Janusz A Hołyst, 2018. "Hierarchical partitions of social networks between rivaling leaders," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-13, March.
    11. Mikaela Irene D. Fudolig & Daniel Monsivais & Kunal Bhattacharya & Hang-Hyun Jo & Kimmo Kaski, 2020. "Different patterns of social closeness observed in mobile phone communication," Journal of Computational Social Science, Springer, vol. 3(1), pages 1-17, April.
    12. Ready, Elspeth & Power, Eleanor, 2021. "Measuring reciprocity: double sampling, concordance, and network construction," LSE Research Online Documents on Economics 112513, London School of Economics and Political Science, LSE Library.
    13. De Bacco, Caterina & Contisciani, Martina & Cardoso Silva, Jon & Safdari, Hadiseh & Borges, Gabriela Lima & Baptista, Diego & Sweet, Tracy & Young, Jean-Gabriel & Jeremy, Koster & Ross, Cody T & McElr, 2023. "Latent network models to account for noisy, multiply reported social network data," LSE Research Online Documents on Economics 117271, London School of Economics and Political Science, LSE Library.

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