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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. 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.
    4. Drouvelis, Michalis & Marx, Benjamin M., 2018. "Prosociality spillovers of working with others," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 205-216.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Jiang, Lingqing, 2020. "Splash with a teammate: Peer effects in high-stakes tournaments," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 165-188.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

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    More about this item

    Keywords

    bivariate probit; blood donation; prosocial motivation; social ties; social interaction;
    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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