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Spillovers of Prosocial Motivation: Evidence from an Intervention Study on Blood Donors

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  • Bruhin, Adrian

    (University of Lausanne)

  • Götte, Lorenz

    (National University of Singapore)

  • Haenni, Simon

    (University of Lausanne)

  • Jiang, Lingqing

    (University of Lausanne)

Abstract

Spillovers of prosocial motivation are crucial for the formation of social capital. They facilitate interactions among individuals and create social multipliers that amplify the effects of policy interventions. We conducted a large-scale intervention study among dyads of blood donors to investigate whether social ties lead to motivational spillovers in the decision to donate. The intervention is a randomized phone call making donors aware of a current shortage of their blood type and serving us as an instrument for identifying motivational spillovers. About 40% of a donor's motivation spills over to the other donor, creating a significant social multiplier of 1.78.

Suggested Citation

  • Bruhin, Adrian & Götte, Lorenz & Haenni, Simon & Jiang, Lingqing, 2014. "Spillovers of Prosocial Motivation: Evidence from an Intervention Study on Blood Donors," IZA Discussion Papers 8738, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp8738
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    3. Bouckaert, Nicolas & Gielen, Anne C. & Van Ourti, Tom, 2020. "It runs in the family – Influenza vaccination and spillover effects," Journal of Health Economics, Elsevier, vol. 74(C).
    4. Xiaolong Hou & Yang Jiao & Leilei Shen & Zhuo Chen, 2024. "The lasting impact of the Tuskegee Syphilis Study: COVID-19 vaccination hesitation among African Americans," Journal of Population Economics, Springer;European Society for Population Economics, vol. 37(2), pages 1-33, June.
    5. Jiang, Lingqing & Zhu, Zhen, 2022. "Information exchange and multiple peer groups: A natural experiment in an online community," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 543-562.
    6. Lingqing Jiang, 2016. "Splash with A teammate: Peer Effects in High-Stakes Tournaments," Cahiers de Recherches Economiques du Département d'économie 16.18, Université de Lausanne, Faculté des HEC, Département d’économie.
    7. Drouvelis, Michalis & Marx, Benjamin M., 2018. "Prosociality spillovers of working with others," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 205-216.
    8. Lorenz Goette & Egon Tripodi, 2021. "Social Influence in Prosocial Behavior: Evidence from a Large-Scale Experiment [“Social Distance and Social Decisions]," Journal of the European Economic Association, European Economic Association, vol. 19(4), pages 2373-2398.
    9. Carlos Madeira, 2022. "Partial identification of nonlinear peer effects models with missing data," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-18, December.
    10. G. Pino & M. Nieto Garcia & A. Peluso & G. Viglia & R. Filieri, 2023. "Understanding how virtuous lenders encourage support for peer-to-peer platforms’ prosocial initiatives," Post-Print hal-04248928, HAL.
    11. Francesco Drago & Friederike Mengel & Christian Traxler, 2020. "Compliance Behavior in Networks: Evidence from a Field Experiment," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 96-133, April.
    12. Goette, Lorenz & Tripodi, Egon, 2020. "Does positive feedback of social impact motivate prosocial behavior? A field experiment with blood donors," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 1-8.
    13. Danijela Vuletic, 2015. "How Effective are Reminders and Frames in Incentivizing Blood Donations," CERGE-EI Working Papers wp554, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

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    More about this item

    Keywords

    social interaction; social ties; prosocial motivation; blood donation; bivariate probit;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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