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Building a shield together: Addressing low vaccine uptake against cancer through social norms

Author

Listed:
  • Stanislao Maldonado
  • Deborah Martinez
  • Lina Diaz

Abstract

We present the results of a large-scale field experiment designed to measure the effect of social norms on parents' decisions to vaccinate their daughters against the human papillomavirus (HPV) in Bogota, Colombia. Because low rates of HPV vaccine adoption are an issue in developed and underdeveloped countries alike, the use of standard social norm marketing strategies to foster vaccination can have the undesirable effect of reinforcing the status quo. In our experiment, parents were exposed to text messages that incorporated variations of static and dynamic social norms. We demonstrate that dynamic social norms and injunctive norms increased the vaccination rate by 23%. Interestingly, we also find that a version of static social norms that uses a loss frame is also effective in fostering vaccination, implying that policy-makers can also benefit from them. Against a common view among academics and practitioners, we found no evidence that static norms reinforce the status quo. Our results highlight the importance of crafting social norms interventions using dynamic and injunctive elements to foster vaccination in settings where the majority has not yet adopted the desired behavior.

Suggested Citation

  • Stanislao Maldonado & Deborah Martinez & Lina Diaz, 2024. "Building a shield together: Addressing low vaccine uptake against cancer through social norms," Working Papers 202, Peruvian Economic Association.
  • Handle: RePEc:apc:wpaper:202
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    References listed on IDEAS

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    1. Joseph P. Newhouse, 2021. "An Ounce of Prevention," Journal of Economic Perspectives, American Economic Association, vol. 35(2), pages 101-118, Spring.
    2. McKenzie, David, 2012. "Beyond baseline and follow-up: The case for more T in experiments," Journal of Development Economics, Elsevier, vol. 99(2), pages 210-221.
    3. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    5. Akanksha Negi & Jeffrey M. Wooldridge, 2021. "Revisiting regression adjustment in experiments with heterogeneous treatment effects," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 504-534, April.
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    Cited by:

    1. Diaz, Lina & Villarreal, Deborah Martinez & Marquez, Karina & Scartascini, Carlos, 2025. "Combating vaccine hesitancy: The case of HPV vaccination," Social Science & Medicine, Elsevier, vol. 381(C).

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