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Cognitive Biases Stop Us From Donating Effectively

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Nov 26, 2020

In 2018, Americans donated $410 billion to charity,1 roughly equal to Norway’s nominal GDP that same year.2 If American charitable giving were a company, it would be one of the top three in the world in terms of revenue.3 Yet despite the overwhelming altruism of many individuals globally, over a million people die each year from preventable disease.4

Many more struggle with health issues that can be solved at relatively low costs. Take, for example, trachoma, an infectious eye disease that over 20 million people actively suffer from, many of whom live in conditions of extreme poverty.5 If left untreated, trachoma can cause an individual’s eyelids to turn inward, eventually resulting in blindness. The cost of treating trachoma with surgery has been estimated to be as low as $7.14,6 yet many still do not have access to this potentially life-changing procedure. These facts and figures provide a foundation for some key questions: Where is all of this money going, how much is it helping, and how can better outcomes be achieved? 

One reason why the donations described above do not necessarily achieve meaningful progress on these important outcomes, such as alleviating the suffering caused by trachoma, is because cognitive biases drive many of our donation decisions. These rationality-inhibiting biases can lead to a misallocation of resources, hereafter referred to as ‘ineffective altruism’. For the purpose of this article, ineffective altruism occurs when an individual has a given preference for a cause — such as curing blindness, protecting animal welfare, or improving access to education — and does not direct their donations to a charity that is highly effective at solving it.

Tremendous social good can be created if the biases that influence ineffective altruism are identified and corrected through better choice architecture where appropriate. In particular, two cognitive biases are important drivers of ineffective altruism: distance bias and the identifiable victim effect.

References

  1. Giving USA. (2018). Giving USA: The Annual Report on Philanthropy for the Year 2017. Giving USA Foundation. https://lclsonline.org/wp-content/uploads/2018/12/Giving-USA-2018-Annual-Report.pdf
  2. The World Bank. (2019). GDP (current US$) – Norway. World Bank Open Data. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=NO
  3. Fortune Magazine. (2020, August 10). Fortune Global 500 2020. https://fortune.com/global500/
  4. Children’s Hospital of Philadelphia. (2020, February 7). Global immunization: Worldwide disease incidence. https://www.chop.edu/centers-programs/vaccine-education-center/global-immunization/diseases-and-vaccines-world-view
  5. World Health Organization. (2020). Trachoma – Epidemiological Situation. https://www.who.int/trachoma/epidemiology/en/
  6. Jamison, D. T. (2006). Disease control priorities in developing countries (2nd ed.). World Bank Publications.
  7. Jones, B., Smith, K., & Rock, D. (2018, June 20). 3 biases that hijack performance reviews, and how to address them. Harvard Business Review. https://hbr.org/2018/06/3-biases-that-hijack-performance-reviews-and-how-to-address-them/
  8. Singer, P. (1972). Famine, affluence, and morality. Philosophy and Public Affairs, 1(3), 229-243. https://doi.org/10.4324/9781315254210-1
  9. Charities Aid Foundation. (2016, January). Gross Domestic Philanthropy: An international analysis of GDP, tax and giving. https://www.cafonline.org/docs/default-source/about-us-policy-and-campaigns/gross-domestic-philanthropy-feb-2016.pdf
  10. Hernández-Murillo, R., & Roisman, D. (2005, October 1). The economics of charitable giving: What gives? Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/publications/regional-economist/october-2005/the-economics-of-charitable-giving-what-gives
  11. Nonprofit Tech for Good. (2018). Global trends in giving report. https://rocklandcce.org/resources/2018-global-trends-in-giving-report
  12. Kogut, T., Ritov, I., Rubaltelli, E., & Liberman, N. (2018). How far is the suffering? The role of psychological distance and victims’ identifiability in donation decisions. Judgment and Decision Making, 13(5), 458–466. http://journal.sjdm.org/18/18717a/jdm18717a.pdf
  13. Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
  14. Thaler, R., & Mullainathan, S. (2000). Behavioral Economics. In International encyclopedia of the social and behavioral sciences (pp. 5-6). National Bureau of Economic Research. https://www.nber.org/papers/w7948.pdf
  15. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207-232. https://doi.org/10.1037/e301722005-001
  16. Jenni, K., & Loewenstein, G. (1997). Explaining the Identifiable Victim Effect. Journal of Risk and Uncertainty, 14, 235-257. https://link.springer.com/article/10.1023/A:1007740225484
  17. Ezeh, O. K., Agho, K. E., Dibley, M. J., Hall, J., & Page, A. N. (2014). The impact of water and sanitation on childhood mortality in Nigeria: evidence from demographic and health surveys, 2003-2013. International journal of environmental research and public health, 11(9), 9256–9272. https://doi.org/10.3390/ijerph110909256
  18. Small, D. A., Loewenstein, G., & Slovic, P. (2007). Sympathy and callousness: The impact of deliberative thought on donations to identifiable and statistical victims. Organizational Behavior and Human Decision Processes, 102(2), 143-153. https://doi.org/10.1016/j.obhdp.2006.01.005
  19. Haselton, M. G., Nettle, D., & Murray, D. R. (2015). The evolution of cognitive bias. The Handbook of Evolutionary Psychology, 1-20. https://doi.org/10.1002/9781119125563.evpsych241
  20. Aczel, B., Bago, B., Szollosi, A., Foldes, A., & Lukacs, B. (2015). Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique. Frontiers in Psychology, 6, 1120. https://doi.org/10.3389/fpsyg.2015.01120
  21. Chou, E. Y., & Murnighan, J. K. (2013). Life or death decisions: Framing the call for help. PLoS ONE, 8(3), e57351. https://doi.org/10.1371/journal.pone.0057351 
  22. Jones, C., Hine, D. W., & Marks, A. D. (2016). The future is now: Reducing psychological distance to increase public engagement with climate change. Risk Analysis, 37(2), 331-341. https://doi.org/10.1111/risa.12601
  23. Red Cross. (2014). World malaria day: 3.3 billion still at risk. https://www.redcross.ca/blog/2014/4/world-malaria-day-3-3-billion-still-at-risk

About the Author

A man is smiling at the camera, wearing a gray shirt, against a plain light-colored background.

Julian Hazell

McGill University

Julian is passionate about understanding human behavior by analyzing the data behind the decisions that individuals make. He is also interested in communicating social science insights to the public, particularly at the intersection of behavioral science, microeconomics, and data science. Before joining The Decision Lab, he was an economics editor at Graphite Publications, a Montreal-based publication for creative and analytical thought. He has written about various economic topics ranging from carbon pricing to the impact of political institutions on economic performance. Julian graduated from McGill University with a Bachelor of Arts in Economics and Management.

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