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Who are the key players? Listeners vs spreaders vs others

Author

Listed:
  • Sumin Kim
  • Kyu-Min Lee
  • Euncheol Shin

Abstract

The literature on social learning examines how people learn from their neighbors and reach a consensus. The DeGroot social learning model describes the social learning process as one in which people form their opinions by taking a weighted average of their neighbors’ opinions. In the model, the influence structure is represented by a single matrix. In this paper, we empirically identify the role of the hub and authority centralities based on this matrix using data on microfinance adoption in rural Indian villages. Controlling for other well-known centrality measures, authority centrality is positively associated with final adoption rates in the villages, but hub centrality is not. Furthermore, we find that authority centrality is the most informative variable predicting microfinance diffusion success from LASSO regressions.

Suggested Citation

  • Sumin Kim & Kyu-Min Lee & Euncheol Shin, 2023. "Who are the key players? Listeners vs spreaders vs others," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0286369
    DOI: 10.1371/journal.pone.0286369
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    References listed on IDEAS

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    1. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    2. Choi, S. & Goyal, S. & Moisan, F. & To, Y. Y. T., 2022. "Learning in Canonical Networks," Cambridge Working Papers in Economics 2235, Faculty of Economics, University of Cambridge.
    3. repec:cam:camjip:2212 is not listed on IDEAS
    4. Pogorelskiy. Kirill & Shum, Matthew, 2019. "News We Like to Share : How News Sharing on Social Networks Influences Voting Outcomes," The Warwick Economics Research Paper Series (TWERPS) 1199, University of Warwick, Department of Economics.
    5. repec:bla:jfinan:v:59:y:2004:i:1:p:137-163 is not listed on IDEAS
    6. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    7. Abhijit Banerjee & Esther Duflo & Rachel Glennerster & Cynthia Kinnan, 2015. "The Miracle of Microfinance? Evidence from a Randomized Evaluation," American Economic Journal: Applied Economics, American Economic Association, vol. 7(1), pages 22-53, January.
    8. Pogorelskiy, Kirill & Shum, Matthew, 2019. "News We Like to Share: How News Sharing on Social Networks Influences Voting Outcomes," CAGE Online Working Paper Series 427, Competitive Advantage in the Global Economy (CAGE).
    9. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
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