John List

When John List and Steven Levitt first suggested that economic experiments should take place outside of the laboratory, people thought that they were crazy. How could field experiments offer the same kind of controlled environment necessary for inducing casual relationships? Just 50 years earlier, however, economists believed that laboratory experiments were just as far left-field when Vernon Smith, a famous economist, began conducting them.² Before Smith, economists did not run experiments - they jotted down theories and mathematical equations to try and explain behavior without any real evidence of how people really acted. Smith’s use of laboratory experiments enabled economists to better understand how decisions are made in various economic markets. As a result, these experiments became the norm until List and Levitt came around.²

While laboratory experiments proved to be more useful than mere economic models, List thought that even more unique and accurate insights could be garnered from conducting experiments in actual decision-making environments.² List believed that designing experiments that observed how people behave during their normal daily routines could lead to increasingly accurate insights into human behavior as a result of randomization.² The need for randomization is especially important, as laboratory experiments often use college students as their participant pool and may not reflect the behaviors of the entire population.³

Randomization and realism were the characteristics that List found most crucial for his field experiments. In laboratory experiments getting participants and manipulating them could sometimes mean that results do not represent the ways people actually make decisions.² For example, the Hawthorne effect demonstrates that people tend to work harder and perform better when they are part of an experiment.⁴

Alternatively, field experiments are able to collect data from naturally occurring behavior, giving it a sense of realism. Since participants don’t know they are in an experiment, they are not choosing to be a part of the study.³ Since laboratory experiments require people to agree to be a part of a study, the kind of individuals who put themselves forth for the experiments might not share the same behaviors or personality traits as the general population. This impacts both how the participant behaves and the data that is collected. In field experiences,a random group of individuals are selected to study, giving the experiments a better chance of reflecting the population.

This isn’t to say that List believes that laboratory experiments lack value. Instead, he hopes that field experiments can help provide greater evidence that the data obtained in the lab mimics real-life behavior. Sometimes, this is the case, but other times, List’s field experiments have demonstrated “the scale-up problem.”² The strength of casual relationships might be impacted by the scale up from research settings to population-wide implementation and some theories might be disproven.⁵

The biggest criticism of field experiments is the fact that informed consent cannot be obtained because they rely on people not knowing they are part of an experiment. There are therefore concerns that field experiments are unethical. In response to these criticisms, List proclaims that informed consent was a tool created only to protect people from experiments that could put the physical health of the participants in danger. Since List’s economic field experiments do not put participants in harm’s way, he believes that informed consent is not of the utmost importance.³ 

One area of great interest to List are the ways people make decisions regarding charity. Charitable giving is an important component of each country’s national economy, yet few organizations understand the best way to promote their cause. Over the years, List has conducted multiple field experiments to examine the factors that influence people’s charitable decision-making.

In 2006, List and fellow economist Dean Karlan conducted a field experiment to see how price changes impacted private charitable contributions.⁶ List and Karlan noted that, usually, experiments regarding price for charitable donations only look at the supply side, like whether changes in tax deductions would impact donations. List and Karlan, by contrast, wanted to look at the demand side. They conducted an experiment that examined how changes in rates of matching gifts (when a donor commits to matching donations at a particular rate) impacted charitable donations. Charitable organizations tended to believe that the higher the matching rate, the more appealing donating would become - however, there was no concrete evidence to support that belief.⁶

In their experiment, List and Karlan found that having a match offer increased the likelihood that people would donate and increased the amount that people donated. However, they found that contrary to popular belief, the ratio of the match had little impact on donations.⁶ Knowing this information can help charitable organizations focus on getting any match offer from a leading donor without being concerned with its magnitude.

Often, charitable organizations ask for donations without specifying a dollar amount, hoping that people will be inclined to give as much as possible. Sometimes, however, organizations provide a guideline for how much people should donate. In a later 2013 study, List worked with economist James Edward to examine whether these suggested amounts helped or hindered the accumulation of donations.⁷

List and Edward found that more people donated when they were given a suggested amount. People who might have been inclined to give less than the donation amount shifted their donation to being closer to the suggested amount, however, there were very few donations above the suggested amount.⁷ These results can help charitable organizations best communicate their need for donations because they reveal that suggestions are an effective tactic.  However, organizations also need to ensure that their suggested amounts are not less than people would typically be inclined to donate.

The Allais paradox is a hypothetical choice problem in behavioral economics which shows that humans make irrational and inconsistent choices. The Allais paradox contradicts the expected utility theory, which suggests that under uncertain circumstances, people will calculate the weighted average of possible outcomes and make a decision that is most likely to get them the most money. The Allais paradox instead provides evidence of prospect theory, which suggests that people perceive value in relationship to potential losses or gains instead of in absolute terms.

In List’s experiment, participants were given the following hypothetical situations:

Situation 1

• Option A1: You win $7 with 100% certainty
• Option B1: You have a 75% chance of winning $7, a 20% chance of winning $10, and a 5% chance of getting nothing.

Situation 2

• Option A2: You have a 25% chance of winning $7 and a 75% of winning 0.
• Option B2: You have a 20% chance of winning $10 and an 80% of winning 0.8

If people chose option A1 and B2, they would be adhering to expected utility, because they are most likely to get money in those options. However, the Allais paradox shows that most often, people pick A1 and A2. They choose the option with a greater expected value for Situation 1, but then decide to be risk averse in Situation 2 and pick the option with a lower expected value.

While studies have shown that non-professional economic agents often fall victim to the Allais paradox, some theories suggest that experienced economic agents like professional traders are less likely to exhibit the same behavior when it comes to risky decision-making.⁸ List found that both students and professional traders behaved in ways that confirmed the Allais paradox and there was only a small decrease in frequency in times professional traders made this cognitive mistake.⁸

List’s experiment helped provide evidence for the Allais Paradox, which demonstrates that we do not always make rational economic decisions. Not only do we not adhere to expected utility, but we are also not consistent with our choices: sometimes, we welcome risk, and other times, we are adverse to it. The Allais Paradox can cause us to make inconsistent decisions when gambling which can hinder us economically.

John List was born September 25, 1968 in Wisconsin.⁹ Growing up in a working class household in Madison, John’s father expected his son to continue in the family lorry-driving business. John, an ambitious young boy, instead dreamt of becoming a professional golfer.¹⁰ He even won a golf college scholarship, but soon after beginning at the University of Wisconsin, he realized he was a much better economist than golfer.⁹ He completed his Bachelor’s degree in Economics in 1992 and continued his education at the University of Wyoming, obtaining his PhD in Economics in 1996.⁹

List enjoyed posts as a Professor of Economics at various universities before joining the University of Chicago faculty in 2005.⁹ List acknowledges that the market must have been in his favor when he applied in 2005, because he claims that 10 years earlier, the University of Chicago “would not have opened the envelope because it said University of Wyoming on the cover” (38). ²

His earlier posts might also have helped get him valuable experience that made him attractive to the University of Chicago. In 2000, List actually moved from Central Florida to the University of Arizona to be able to work alongside Vernon Smith. Unfortunately, Smith and the experimental economist group were having issues with administration and moved shortly after.² List then moved to the University of Mainland, where he occasionally still works today. List also occasionally visits Tilburg University, where he works with Shelby Gerking, who was his supervisor during his PhD.¹¹ In 2014, List was appointed the Homer J. Livingston Distinguished Service Professor of Economics.⁹

Outside of his academic appointments, List has also had some high-profile corporate positions. In 2017, List ordered an Uber to take him to give a keynote speech in Chicago. Busy practising his speech, he didn’t notice that the Uber driver had gone in a circle and was now dropping him back at his home, as a result of a technical error. List was annoyed that Uber never sent him an apology. He called up the (now former) chief executive of Uber to discuss the issue. The chief executor asked List how he believed Uber should apologize, and eventually, this conversation led List to be appointed as chief economist of the company.¹⁰ By analyzing the data in his new role, List found that users who had a bad experience on an app would spend 10% less on the app in the future. List tested out the impact of different kinds of apologies and eventually found that an apology combined with a discount code was the most effective. If only List had received that coupon, he may never have gone on to be an executive at Uber and later on, Lyft.¹⁰

John List says his “passion is using field experiments to explore economic questions.” He views “field experiments as representing a unique manner in which to obtain data because they force the researcher to understand everyday phenomena, many of which we stumble upon frequently.”¹² 

He claims that “The reason why the field experiments are so valuable is because you randomize people into treatment and control, and those unobservable variables are then balanced. I’m not getting rid of the unobservables — you can never get rid of unobservables — but I can balance them across treatment and control cells.” ²

This passion has always existed within him, as his “first inclination is not to gather data in the lab, but to go to the field.” ² When asked why he got into experimental economics, List says that “it was a chance to apply what I was learning in college about economics to a real-life situation.” ¹³

But sometimes, field work comes with immersing oneself in other cultures. When asked about his craziest research moment, List said that he “drank goat’s blood with the chief of a patriarchal village in Tanzania during a … research trip to study early childhood and gender disparity in income. It was kind of gross, but I took one for the team.” ¹⁴

For List, working for Uber wasn’t just about helping the company improve their customer experience. Car apps have so much data, allowing List to analyze different types of consumer behavior.¹¹ He said that Uber has “mounds and mounds of data. We have millions of drivers. We have millions of observations, and 25 million driver-weeks across 196 cities,” making his work a dream come true for an economist interested in real-life behavior.¹⁵

When List first worked with United Airlines, they were one of the first companies to use academics to help make changes to their business. List identified two benefits of companies deciding to turn to academics more often. The first is that “academics provide pretty cheap labour,” because what academics care about more than compensation is obtaining companies’ data. The second is that “when you look at the historical growth and demise of firms … and you look at the Fortune 500 companies in 1955 and you look at where those companies are today, fewer than 70 of them are still in the Fortune 500 … You can ask yourself why this has happened. It’s because firms have not adapted, not changed with the times.” Academics, especially ones who like to conduct field experiments, like List, can help in this area.¹⁶

List claims that “the most valuable resource in the world is no longer oil, but data. Much like oil needs a refinery, data also needs a refiner, and that’s where academics come in.” ¹⁶

List is the author of dozens of publications in various fields. If you’re interested in reading more about the work he’s done intersecting economic theory with charitable donations, you can find all his papers on the subject here. You can find all his other publications here.

List is also the co-author, alongside Uri Gneezy, of The Why Axis, a book which outlines the adventures that Gneezy and List embarked on to find answers to a range of economic problems. Their journey took them from Mount Kilimanjaro in Tanzania to northern India, to hot wineries in California and chilly streets in Chicago. Their answers provide insights into intersections between human behavior and economics.

List is also a frequent guest on the Freakonomics podcast, an empire founded by Steven Levitt and Steven Dubner. You can find a list of episodes that List has been on here. He also has been a guest on the BETA podcast, where he talks about the importance of field experiments, and on the Big Brain podcast.

1. How John List Revolutionized Economics by Studying People in the Real World (Ep. 28). (2019, August 12). Big Brain Podcast [Audio podcast episode]. University of Chicago News. https://news.uchicago.edu/podcasts/big-brains/how-field-experiments-revolutionized-economics-with-john-list
2. https://www.richmondfed.org/-/media/richmondfedorg/publications/research/econ_focus/2012/q2-3/pdf/interview.pdf
3. List, J. A. (2011). Why economists should conduct Field experiments and 14 tips for pulling one off. SSRN Electronic Journal, 25(3), 3-16. https://doi.org/10.2139/ssrn.1915216
4. Cherry, K. (2020, October 13). The Hawthorne Effect and Behavioral Studies. Verywell Mind. https://www.verywellmind.com/what-is-the-hawthorne-effect-2795234
5. List, J. A., Suskind, D., & Al-Ubaydli, O. (2019, May 21). The science of using science: Towards an understanding of the threats to scaling experiments. Becker Friedman Institute. https://bfi.uchicago.edu/working-paper/the-science-of-using-science-towards-an-understanding-of-the-threats-to-scaling-experiments/
6. Karlan, D., & List, J. (2006). Does price matter in charitable giving? Evidence from a large-scale natural Field experiment. American Economic Review, 97(5), 1774-1793. https://doi.org/10.3386/w12338
7. Edwards, J., & List, J. (2013). Toward an understanding of why suggestions work in charitable fundraising: Theory and evidence from a natural Field experiment. Journal of Public Economics, 114, 1-13. https://doi.org/10.3386/w19665
8. List, J. A. & Haigh, M. S. (2005). A simple test of expected utility theory using professional traders. Proceedings of the National Academy of Sciences, 102(3), 945-948.
9. Bondarenko, P. (n.d.). John A. List. Encyclopedia Britannica. Retrieved December 23, 2020, from https://www.britannica.com/biography/John-A-List
10. Edmonds, D. (2020, October 24). The man who taught Uber how to say sorry. BBC News. https://www.bbc.com/news/stories-54613947
11. John A List. (2015, June 4). Alchetron.com. https://alchetron.com/John-A-List
12. UChicago Voices. (n.d.). Charitable giving. Retrieved December 23, 2020, from https://voices.uchicago.edu/jlist/research/charitable-giving/
13. Harms, W. (n.d.). Finding new insights with experimental economics. The University of Chicago. Retrieved December 23, 2020, from https://www.uchicago.edu/features/20120507_list/
14. Bianchi, L. (2019, March 29). John list talks to Crain's for the takeaway. University of Chicago Law School. https://www.law.uchicago.edu/news/john-list-talks-crains-takeaway
15. Rosalsky, G. (2018, February 6). What Can Uber Teach Us About the Gender Pay Gap? (Ep. 317). Freakonomics [Audio podcast episode]. https://freakonomics.com/podcast/what-can-uber-teach-us-about-the-gender-pay-gap/
16. INSEAD. (2020, January 26). John List on how data is the most valuable resource [Video]. YouTube. https://www.youtube.com/watch?v=AW0owHHH4AU