Precomitting Increases Donations

Hyperbolic Discounting and Charitable Giving
Intervention Charitable Giving

The results show that donors were more likely to increase their donation amount when they were committing to increasing their donation at a future time.

Background

The commitment bias describes how people and groups are willing to support their past ideas and decisions – even when new evidence or events makes doing so irrational. This bias is also known as escalation of commitment or the sunk cost fallacy. It was initially described by Barry M. Staw in his 1976 paper, “Knee deep in the big muddy: A study of escalating commitment to a chosen course of action”.

The commitment bias explains that we tend towards being consistent with our prior commitments, actions, thoughts and dispositions, even when it is against our own interests.

Description

The commitment bias describes how people and groups are willing to support their past ideas and decisions – even when new evidence or events makes doing so irrational. This bias is also known as escalation of commitment or the sunk cost fallacy. It was initially described by Barry M. Staw in his 1976 paper, “Knee deep in the big muddy: A study of escalating commitment to a chosen course of action”.

The commitment bias explains that we tend towards being consistent with our prior commitments, actions, thoughts and dispositions, even when it is against our own interests.

Beliefs

We tend to invest heavily in our beliefs about the world and about ourselves. The more time and energy we invest in our beliefs —think money, effort, or pain – the more sunk costs we accumulate. The greater the sunk costs, the harder it becomes to change our mind. Whether or not we are actually right, we have strong convictions to believe that we are. Think about the last time you assembled a shelf. Even if the shelf has deficiencies, or doesn’t fit well into a room, you will likely find it much harder to give it away.

How it's used

  • “Don’t make any promises that you can’t keep.” – The commitment bias can be helpful in relationships as it binds people to uphold their promises.
  • Commitment biases can be advantageous – Pre-commitment devices can be used to stave off procrastination and temptation in order to achieve goals.
  • Marketing – Companies often use the commitment bias to their advantageous by trying to get customers to make small commitments early on such as signing up for a trail membership). Once a commitment has been made, it is less likely people will alter their behavior.

Examples

Health

Pre-commitment devices can be used to stave off procrastination and temptation in order to achieve goals. For example, if someone publicly commit to their intentions (like going to the gym three times a week) so they become more likely to follow through on their plans. The website Stickk allows people to publicly commit to a positive behavior change (e.g. give up junk food). The goal-setting platform created by behavioral economists at Yale University and draws on the principle of loss aversion. For example, if a user wants to lose weight, the decision to not go to the gym may be coupled with the fear of loss (see loss aversion) —a cash penalty in the case of non-compliance.

Education – Commitment to Beliefs

In his book Outliers, Malcolm Gladwell talks about how teachers who tend to identify students as smart not only affected how the teachers saw the student’s work but, more importantly, affected the opportunities teachers gave the students. Smarter students received better opportunities which shaped them to be better students. The teacher’s early commitment to an individual student’s intelligence can perpetuate a self-fulfilling prophecy.

Sources

[1] The Behavioural Insights Team. (2017). The Behavioural Insights Team Update Report 2016-17. Retrieved from: http://38r8om2xjhhl25mw24492dir.wpengine.netdna-cdn.com/wp-content/uploads/2017/10/BIT_Update-16-17_E_.pdf

[2] R. B. Cialdini, A. Levy, C. P. Herman, L. T. Kozlowski & R. E. Petty. (1976). Elastic shifts of opinion: Determinants of direction and durability. Journal of Personality and Social Psychology, 34(4), 663-672. Retrieved from: http://dx.doi.org/10.1037/0022-3514.34.4.663

[3] J. Guszcza. (2015). The last-mile problem: How data science and behavioral science can work together. Deloitte Review, 16. Retrieved from: https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-16/behavioral-economics-predictive-analytics.html

[4] M. Kosinski, D. Stillwel & T. Graepel. (2013). Private traits and attributes are predictable from digital records of human behavior. PNAS, 110(1), 5802-5805. Retrieved from: http://www.pnas.org/content/110/15/5802.full.pdf

[5] Y. Wang & M. Kosinski. (2017). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Retrieved from: osf.io/zn79k

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