Behavior Matters: Redesigning the Clinical Trial

When a new type of drug or therapy is discovered, double-blind randomized controlled trials (DBRCTs) are the gold standard for evaluating them. These trials, which have been used for years, were designed to determine the true efficacy of a treatment free from patient or doctor bias, but they do not factor in the effects that patient behaviors, such as diet and lifestyle choices, can have on the tested treatment.

A recent meta-analysis of six such clinical trials, led by Caltech's Erik Snowberg, professor of economics and political science, and his colleagues Sylvain Chassang from Princeton University and Ben Seymour from Cambridge University, shows that behavior can have a serious impact on the effectiveness of a treatment—and that the currently used DBRCT procedures may not be able to assess the effects of behavior on the treatment. To solve this, the researchers propose a new trial design, called a two-by-two trial, that can account for behavior–treatment interactions.

The study was published online on June 10 in the journal PLOS ONE.

Patients behave in different ways during a trial. These behaviors can directly relate to the trial—for example, one patient who believes in the drug may religiously stick to his or her treatment regimen while someone more skeptical might skip a few doses. The behaviors may also simply relate to how the person acts in general, such as preferences in diet, exercise, and social engagement. And in the design of today's standard trials, these behaviors are not accounted for, Snowberg says.

For example, a DBRCT might randomly assign patients to one of two groups: an experimental group that receives the new treatment and a control group that does not. As the trial is double-blinded, neither the subjects nor their doctors know who falls into which group. This is intended to reduce bias from the behavior and beliefs of the patient and the doctor; the thinking is that because patients have not been specifically selected for treatment, any effects on health outcomes must be solely due to the treatment or lack of treatment.

Although the patients do not know whether they have received the treatment, they do know their probability of getting the treatment—in this case, 50 percent. And that 50 percent likelihood of getting the new treatment might not be enough to encourage a patient to change behaviors that could influence the efficacy of the drug under study, Snowberg says. For example, if you really want to lose weight and know you have a high probability—say 70 percent chance—of being in the experimental group for a new weight loss drug, you may be more likely to take the drug as directed and to make other healthy lifestyle choices that can contribute to weight loss. As a result, you might lose more weight, boosting the apparent effectiveness of the drug.

However, if you know you only have a 30 percent chance of being in the experimental group, you might be less motivated to both take the drug as directed and to make those other changes. As a result, you might lose less weight—even if you are in the treatment group—and the same drug would seem less effective.

"Most medical research just wants to know if a drug will work or not. We wanted to go a step further, designing new trials that would take into account the way people behave. As social scientists, we naturally turned to the mathematical tools of formal social science to do this," Snowberg says.

Snowberg and his colleagues found that with a new trial design, the two-by-two trial, they can tease out the effects of behavior and the interaction of behavior and treatment, as well as the effects of treatment alone. The new trial, which still randomizes treatment, also randomizes the probability of treatment—which can change a patient's behavior.

In a two-by-two trial, instead of patients first being assigned to either the experimental or control groups, they are randomly assigned to either a "high probability of treatment" group or a "low probability of treatment" group. The patients in the high probability group are then randomly assigned to either the treatment or the control group, giving them a 70 percent chance of receiving the treatment. Patients in the low probability group are also randomly assigned to treatment or control; their likelihood of receiving the treatment is 30 percent. The patients are then informed of their probability of treatment.

By randomizing both the treatment and the probability of treatment, medical researchers can quantify the effects of treatment, the effects of behavior, and the effects of the interaction between treatment and behavior. Determining each, Snowberg says, is essential for understanding the overall efficacy of treatment.

Credit: Sylvain Chassang, Princeton University

"It's a very small change to the design of the trial, but it's important. The effect of a treatment has these two constituent parts: pure treatment effect and the treatment–behavior interaction. Standard blind trials just randomize the likelihood of treatment, so you can't see this interaction. Although you can't just tell someone to randomize their behavior, we found a way that you can randomize the probability that a patient will get something that will change their behavior."

Because it is difficult to implement new trial design changes in active trials, Snowberg and his colleagues wanted to first test their idea with a meta-analysis of data from previous clinical trials. They developed a way to test this idea by coming up with a new mathematical formula that can be used to analyze DBRCT data. The formula, which teases out the health outcomes resulting from treatment alone as well as outcomes resulting from an interaction between treatment and behavior, was then used to statistically analyze six previous DBRCTs that had tested the efficacy of two antidepressant drugs, imipramine (a tricyclic antidepressant also known as Tofranil) and paroxetine (a selective serotonin reuptake inhibitor sold as Paxil).

First, the researchers wanted to see if there was evidence that patients behave differently when they have a high probability of treatment versus when they have a low probability of treatment. The previous trials recorded how many patients dropped out of the study, so this was the behavior that Snowberg and his colleagues analyzed. They found that in trials where patients happened to have a relatively high probability of treatment—near 70 percent—the dropout rate was significantly lower than in other trials with patients who had a lower probability of treatment, around 50 percent.

Although the team did not have any specific behaviors to analyze, other than dropping out of the study, they also wanted to determine if behavior in general could have added to the effect of the treatments. Using their statistical techniques, they determined that imipramine seemed to have a pure treatment effect, but no effect from the interaction between treatment and behavior—that is, the drug seemed to work fine, regardless of any behavioral differences that may have been present.

However, after their analysis, they determined that paroxetine seemed to have no effect from the treatment alone or behavior alone. However, an interaction between the treatment and behavior did effectively decrease depression. Because this was a previously performed study, the researchers cannot know which specific behavior was responsible for the interaction, but with the mathematical formula, they can tell that this behavior was necessary for the drug to be effective.

In their paper, Snowberg and his colleagues speculate how a situation like this might come about. "Maybe there is a drug, for instance, that makes people feel better in social situations, and if you're in the high probability group, then maybe you'd be more willing to go out to parties to see if the drug helps you talk to people," Snowberg explains. "Your behavior drives you to go to the party, and once you're at the party, the drug helps you feel comfortable talking to people. That would be an example of an interaction effect; you couldn't get that if people just took this drug alone at home."

Although this specific example is just speculation, Snowberg says that the team's actual results reveal that there is some behavior or set of behaviors that interact with paroxetine to effectively treat depression—and without this behavior, the drug appears to be ineffective.

"Normally what you get when you run a standard blind trial is some sort of mishmash of the treatment effect and the treatment-behavior interaction effect. But, knowing the full interaction effect is important. Our work indicates that clinical trials underestimate the efficacy of a drug where behavior matters," Snowberg says. "It may be the case that 50 percent probability isn't high enough for people to change any of their behaviors, especially if it's a really uncertain new treatment. Then it's just going to look like the drug doesn't work, and that isn't the case."

Because the meta-analysis supported the team's hypothesis—that the interaction between treatment and behavior can have an effect on health outcomes—the next step is incorporating these new ideas into an active clinical trial. Snowberg says that the best fit would be a drug trial for a condition, such as a mental health disorder or an addiction, that is known to be associated with behavior. At the very least, he says, he hopes that these results will lead the medical research community to a conversation about ways to improve the DBRCT and move past the current "gold standard."

These results are published in a paper titled "Accounting for Behavior in Treatment Effects: New Applications for Blind Trials." Cayley Bowles, a student in the UCLA/Caltech MD/PhD program, was also a coauthor on the paper. The work was supported by funding to Snowberg and Chassang from the National Science Foundation.

Exclude from News Hub: 
News Type: 
Research News
Tuesday, May 26, 2015 to Friday, May 29, 2015
Center for Student Services 360 (Workshop Space) – Center for Student Services

CTLO Presents Ed Talk Week 2015

Ditch Day? It’s Today, Frosh!

Today we celebrate Ditch Day, one of Caltech's oldest traditions. During this annual spring rite—the timing of which is kept secret until the last minute—seniors ditch their classes and vanish from campus. Before they go, however, they leave behind complex, carefully planned out puzzles and challenges—known as "stacks"—designed to occupy the underclassmen and prevent them from wreaking havoc on the seniors' unoccupied rooms.

Follow the action on Caltech's Facebook, Twitter, and Instagram pages as the undergraduates tackle the puzzles left for them to solve around campus. Join the conversation by sharing your favorite Ditch Day memories and using #CaltechDitchDay in your tweets and postings.

Frontpage Title: 
Ditch Day 2015
Exclude from News Hub: 
News Type: 
In Our Community
Friday, May 1, 2015

Caltech + Finance Symposium

Monday, May 18, 2015
Brown Gymnasium – Scott Brown Gymnasium

Jupiter’s Grand Attack

Monday, May 4, 2015
Dabney Hall, Lounge – Dabney Hall

Free Jazz Concert

Switching On One-Shot Learning in the Brain

Caltech researchers find the brain regions responsible for jumping to conclusions

Most of the time, we learn only gradually, incrementally building connections between actions or events and outcomes. But there are exceptions—every once in a while, something happens and we immediately learn to associate that stimulus with a result. For example, maybe you have had bad service at a store once and sworn that you will never shop there again.

This type of one-shot learning is more than handy when it comes to survival—think, of an animal quickly learning to avoid a type of poisonous berry. In that case, jumping to the conclusion that the fruit was to blame for a bout of illness might help the animal steer clear of the same danger in the future. On the other hand, quickly drawing connections despite a lack of evidence can also lead to misattributions and superstitions; for example, you might blame a new food you tried for an illness when in fact it was harmless, or you might begin to believe that if you do not eat your usual meal, you will get sick.

Scientists have long suspected that one-shot learning involves a different brain system than gradual learning, but could not explain what triggers this rapid learning or how the brain decides which mode to use at any one time.

Now Caltech scientists have discovered that uncertainty in terms of the causal relationship—whether an outcome is actually caused by a particular stimulus—is the main factor in determining whether or not rapid learning occurs. They say that the more uncertainty there is about the causal relationship, the more likely it is that one-shot learning will take place. When that uncertainty is high, they suggest, you need to be more focused in order to learn the relationship between stimulus and outcome.

The researchers have also identified a part of the prefrontal cortex—the large brain area located immediately behind the forehead that is associated with complex cognitive activities—that appears to evaluate such causal uncertainty and then activate one-shot learning when needed.

The findings, described in the April 28 issue of the journal PLOS Biology, could lead to new approaches for helping people learn more efficiently. The work also suggests that an inability to properly attribute cause and effect might lie at the heart of some psychiatric disorders that involve delusional thinking, such as schizophrenia.

"Many have assumed that the novelty of a stimulus would be the main factor driving one-shot learning, but our computational model showed that causal uncertainty was more important," says Sang Wan Lee, a postdoctoral scholar in neuroscience at Caltech and lead author of the new paper. "If you are uncertain, or lack evidence, about whether a particular outcome was caused by a preceding event, you are more likely to quickly associate them together."

The researchers used a simple behavioral task paired with brain imaging to determine where in the brain this causal processing takes place. Based on the results, it appears that the ventrolateral prefrontal cortex (VLPFC) is involved in the processing and then couples with the hippocampus to switch on one-shot learning, as needed.

Indeed, a switch is an appropriate metaphor, says Shinsuke Shimojo, Caltech's Gertrude Baltimore Professor of Experimental Psychology. Since the hippocampus is known to be involved in so-called episodic memory, in which the brain quickly links a particular context with an event, the researchers hypothesized that this brain region might play a role in one-shot learning. But they were surprised to find that the coupling between the VLPFC and the hippocampus was either all or nothing. "Like a light switch, one-shot learning is either on, or it's off," says Shimojo.

In the behavioral study, 47 participants completed a simple causal-inference task; 20 of those participants completed the study in the Caltech Brain Imaging Center, where their brains were monitored using functional Magnetic Resonance Imaging. The task consisted of multiple trials. During each trial, participants were shown a series of five images one at a time on a computer screen. Over the course of the task, some images appeared multiple times, while others appeared only once or twice. After every fifth image, either a positive or negative monetary outcome was displayed. Following a number of trials, participants were asked to rate how strongly they thought each image and outcome were linked. As the task proceeded, participants gradually learned to associate some of the images with particular outcomes. One-shot learning was apparent in cases where participants made an association between an image and an outcome after a single pairing.

The researchers hypothesize that the VLPFC acts as a controller mediating the one-shot learning process. They caution, however, that they have not yet proven that the brain region actually controls the process in that way. To prove that, they will need to conduct additional studies that will involve modifying the VLPFC's activity with brain stimulation and seeing how that directly affects behavior.

Still, the researchers are intrigued by the fact that the VLPFC is very close to another part of the ventrolateral prefrontal cortex that they previously found to be involved in helping the brain to switch between two other forms of learning—habitual and goal-directed learning, which involve routine behavior and more carefully considered actions, respectively. "Now we might cautiously speculate that a significant general function of the ventrolateral prefrontal cortex is to act as a leader, telling other parts of the brain involved in different types of behavioral functions when they should get involved and when they should not get involved in controlling our behavior," says coauthor John O'Doherty, professor of psychology and director of the Caltech Brain Imaging Center.

The work, "Neural Computations Mediating One-Shot Learning in the Human Brain," was supported by the National Institutes of Health, the Gordon and Betty Moore Foundation, the Japan Science and Technology Agency–CREST, and the Caltech-Tamagawa global Center of Excellence.

Kimm Fesenmaier
Frontpage Title: 
Switching on One-Shot Learning in the Brain
Listing Title: 
Switching on One-Shot Learning in the Brain
Exclude from News Hub: 
Short Title: 
One-Shot Learning in the Brain
News Type: 
Research News

English Professor Awarded Feynman Teaching Prize

This year the Richard P. Feynman Prize for Excellence in Teaching has been awarded to Professor of English Kevin Gilmartin, who has taught at Caltech for the past 24 years.

Gilmartin was nominated for this prize by students in several different disciplines, who praise his enthusiasm and accessibility, his artful handling of classroom discussion and debate, and his patient tutoring in the fine art of writing. In teaching evaluations, students describe Gilmartin as "an eloquent lecturer" and a "supportive professor" whose "enthusiasm is contagious."

The Feynman Prize committee—tasked with honoring a professor "who demonstrates, in the broadest sense, unusual ability, creativity, and innovation" in teaching—was unanimous in its support of Gilmartin, describing him as "an example to the Institute of the possibilities for engagement, discovery, and growth through classroom teaching."

Gilmartin's classes are no steady trudge through lectures and essays. Rather, they are taught seminar-style with student presentations, classroom discussions, and field trips to the Huntington Library. Gilmartin notes that he is particularly interested in helping students understand the historical context in which works of literature are produced, a theme that dominates his scholarly work as well. For example, this semester in a course on the works of Jane Austen (English 127), students are dabbling in what Gilmartin calls "a fascinating print record" from the period, ranging from manuals of conduct for young women to instructional pamphlets on everything from dancing to gardening. "Through the wonders of digital media," says Gilmartin, "students can see things that they would have previously found only in a rare books reading room."

Gilmartin also has pioneered workshops with visiting poets brought to campus through support from the Division of the Humanities and Social Sciences and the Provost's Office. "There's something remarkable about teaching a course where many of the authors that we read are still alive and are writing in ways that students feel are contemporary," says Gilmartin, "but then to have one of the writers actually present on campus has been a revelation for me and my students."

One of the Caltech students who nominated Professor Gilmartin for the Feynman Prize declares that he "vivifies the 'human' in 'humanities.'" She notes that teaching English can be an uphill battle at Caltech: "Despite a massive torrent of degradation inflicted upon the humanities by this sea of science-loving skeptics, Professor Kevin Gilmartin kindles a fire within each of his students to love English."

Gilmartin's impact on individual students is profound. As a Caltech alumna noted in her nomination of Gilmartin, "I came to Caltech believing that I was at best a mediocre writer and because of that, approached humanities courses with only a cursory effort. However, week after week, Professor Gilmartin would email me back with thoughtful and encouraging responses to my weekly write-ups. Gradually, I noticed myself spending more and more time on the assigned writings and speaking up more during class because, for the first time, I felt as if my opinions mattered."

Gilmartin himself, though certainly pleased by the award, is keen to share the credit with his colleagues and with Caltech administrators who have supported humanities programs in the classroom and beyond, notably with the recent development of the Hixon Writing Center. "I was closely involved in recruiting Susanne Hall as the director of the Hixon Center, and she has supported my teaching in extraordinary ways through her peer tutoring program," he says. "One of my most rewarding recent experiences as a teacher has been to see a number of students from my freshman humanities courses go on to become peer tutors in the writing program themselves."

Alongside his regular teaching, Gilmartin serves as faculty advisor for the student literary and visual arts magazine, Totem. Another Caltech alumnus credits Gilmartin for making it possible for Totem "to host a documentary and feature film director to discuss elements of cinema, and a JPL scientist who uses the art form of origami to do mathematical modeling." Gilmartin recalls that when he was first asked to be the magazine's faculty advisor in 2002, "I didn't know what a faculty advisor was expected to do." He learned on the job, and notes that he has been glad to help student editors with funding issues and to act as the magazine's "institutional memory" as senior editors and writers graduate and new editors and writers come in.

These and other Caltech students "make teaching easy," Gilmartin says. "Our students are extraordinarily bright, interested, and engaged. It's true that I've had to find ways to meet them halfway, and that's been a positive learning process for me as well. When classroom circumstances are right, their willingness to be engaged, their enthusiasm, their interest in literature and in challenging themselves is in no way restricted to the sciences."

The Feynman Prize has been endowed through the generosity of Ione and Robert E. Paradise and an anonymous local couple. Some of the most recent winners of the Feynman Prize include Steven Frautschi, professor of theoretical physics, emeritus; Paul Asimow, professor of geology and geochemistry; and Morgan Kousser, the William R. Kenan, Jr., Professor of History and Social Science.

Nominations for next year's Feynman Prize for Excellence in Teaching will be solicited in the fall. Further information about the prize can be found on the Provost's Office website.

Frontpage Title: 
English Professor Awarded Feynman Teaching Prize
Listing Title: 
English Professor Awarded Feynman Teaching Prize
Exclude from News Hub: 
Short Title: 
English Professor Awarded Feynman Prize
News Type: 
In Our Community

Understanding the Earth at Caltech

Created by: 
Teaser Image: 
Listing Title: 
Understanding the Earth at Caltech
Frontpage Title: 
Understanding the Earth at Caltech
Credit: Courtesy J. Andrade/Caltech

The ground beneath our feet may seem unexceptional, but it has a profound impact on the mechanics of landslides, earthquakes, and even Mars rovers. That is why civil and mechanical engineer Jose Andrade studies soils as well as other granular materials. Andrade creates computational models that capture the behavior of these materials—simulating a landslide or the interaction of a rover wheel and Martian soil, for instance. Though modeling a few grains of sand may be simple, predicting their action as a bulk material is very complex. "This dichotomy…leads to some really cool work," says Andrade. "The challenge is to capture the essence of the physics without the complexity of applying it to each grain in order to devise models that work at the landslide level."

Credit: Kelly Lance ©2013 MBARI

Geobiologist Victoria Orphan looks deep into the ocean to learn how microbes influence carbon, nitrogen, and sulfur cycling. For more than 20 years, her lab has been studying methane-breathing marine microorganisms that inhabit rocky mounds on the ocean floor. "Methane is a much more powerful greenhouse gas than carbon dioxide, so tracing its flow through the environment is really a priority for climate models and for understanding the carbon cycle," says Orphan. Her team recently discovered a significantly wider habitat for these microbes than was previously known. The microbes, she thinks, could be preventing large volumes of the potent greenhouse gas from entering the oceans and reaching the atmosphere.

Credit: NASA/JPL-Caltech

Researchers know that aerosols—tiny particles in the atmosphere—scatter and absorb incoming sunlight, affecting the formation and properties of clouds. But it is not well understood how these effects might influence climate change. Enter chemical engineer John Seinfeld. His team conducted a global survey of the impact of changing aerosol levels on low-level marine clouds—clouds with the largest impact on the amount of incoming sunlight Earth reflects back into space—and found that varying aerosol levels altered both the quantity of atmospheric clouds and the clouds' internal properties. These results offer climatologists "unique guidance on how warm cloud processes should be incorporated in climate models with changing aerosol levels," Seinfeld says.

Credit: Yan Hu/Aroian Lab/UC San Diego

Tiny parasitic worms infect nearly half a billion people worldwide, causing gastrointestinal issues, cognitive impairment, and other health problems. Biologist Paul Sternberg is on the case. His lab recently analyzed the entire 313-million-nucleotide genome of the hookworm Ancylostoma ceylanicum to determine which genes turn on when the worm infects its host. A new family of proteins unique to parasitic worms and related to the early infection process was identified; the discovery could lead to new treatments targeting those genes. "A parasitic infection is a balance between the parasites trying to suppress the immune system and the host trying to attack the parasite," Sternberg observes, "and by analyzing the genome, we can uncover clues that might help us alter that balance in favor of the host."

Credit: K.Batygin/Caltech

Earth is special, not least because our solar system has a unique (as far as we know) orbital architecture: its rocky planets have relatively low masses compared to those around other sun-like stars. Planetary scientist Konstantin Batygin has an explanation. Using computer simulations to describe the solar system's early evolution, he and his colleagues showed that Jupiter's primordial wandering initiated a collisional cascade that ultimately destroyed the first generation population of more massive planets once residing in Earth's current orbital neighborhood. This process wiped the inner solar system's slate clean and set the stage for the formation of the planets that exist today. "Ultimately, what this means," says Batygin, "is that planets truly like Earth are intrinsically not very common."

Credit: Nicolás Wey-Gόmez/Caltech

Human understanding of the world has evolved over centuries, anchored to scientific and technological advancements and our ability to map uncharted territories. Historian Nicolás Wey-Gόmez traces this evolution and how the age of discovery helped shape culture and politics in the modern era. Using primary sources such as letters and diaries, he examines the assumptions behind Europe's encounter with the Americas, focusing on early portrayals of native peoples by Europeans. "The science and technology that early modern Europeans recovered from antiquity by way of the Arab world enabled them to imagine lands far beyond their own," says Wey-Gómez. "This knowledge provided them with an essential framework to begin to comprehend the peoples they encountered around the globe."


At Caltech, researchers study the Earth from many angles—from investigating its origins and evolution to exploring its geology and inner workings to examining its biological systems. Taken together, their findings enable a more nuanced understanding of our planet in all its complexity, helping to ensure that it—and we—endure. This slideshow highlights just a few of the Earth-centered projects happening right now at Caltech.

Exclude from News Hub: 

More Money, Same Bankruptcy Risk

In general, our financial lives follow a pattern of spending and saving described by a time-honored model that economists call the life-cycle hypothesis. Most people begin their younger years strapped for cash, earning little money while also investing heavily in skills and education. As the years go by, career advances result in higher income, which can be used to pay off debts incurred early on and to save for retirement. Indeed everyone is well aware that later in life earnings will drop and spending will outpace savings.

But how does the life-cycle hypothesis hold up when the income pattern is reversed—such as in the case of young, multimillionaire NFL players who earn large sums at first, but then experience drastic income reductions in retirement just a few years later? Not too well, a new Caltech study suggests.

The study, led by Colin Camerer, Robert Kirby Professor of Behavioral Economics, was published as a working paper on April 13 by the National Bureau of Economic Research.

"The life-cycle hypothesis in economics assumes people have perfect willpower and are realistic about how long their careers will last. Behavioral economics predicts something different, that even NFL players earning huge salaries will struggle to save enough," Camerer says.

"We wanted to test this theory with NFL players because there is a lot of tension between their income in the present, as a player, and their expected income in the future, after retirement. NFL players put the theory to a really extreme test," says graduate student Kyle Carlson, the first author of the study. "We suspected that NFL players' behavior might differ from the theory because they may be too focused on the present or overconfident about their career prospects. We had also seen many media reports of players struggling with their finances."

A professional football player's career is not like that of the average person. Rather than finding an entry-level job that pays a pittance when just out of college, a football player can earn millions of dollars—more than the average person makes in an entire lifetime—in just one season. However, the young athlete's lucrative career is also likely to be short-lived. After just a few years, most pro football players are out of the game with injuries and are forced into retirement and, usually, a much smaller income. And that is when the financial troubles often begin to surface.

The researchers decided to see how the life-cycle model would respond in such a feast-or-famine income situation. They entered the publicly available income data from NFL players into a simulation to predict how well players should fare in retirement, based on their income and the model. The simulations suggested that the players' initial earnings should support them through their entire retirement. In other words, these players should never go bankrupt.

However, when the researchers looked at what actually happens, they found that approximately 2 percent of players have filed for bankruptcy within just two years of retirement, and more than 15 percent file within 12 years after retirement. "Two percent is not itself an enormous number. But the players look similar to regular people who are making way less money," Carlson says. "The players have the capacity to avoid bankruptcy by planning carefully, but many are not doing that."

Interestingly, Carlson and his colleagues also determined that a player's career earnings and time in the league had no effect on the risk of bankruptcy. That is, although a player who earned $20 million over a 10-year career should have substantially more money to support his retirement, he actually is just as likely to go bankrupt as someone who only earned $2 million in one year. Regardless of career length, the risk of bankruptcy was about the same. "It stands to reason that making more money should protect you from bankruptcy, but for these guys it doesn't," Carlson says.

The results of the study are clear: the life-cycle model does not seem to match up with the income spikes and dips of a career athlete. The cause of this disconnect between theory and reality, however, is less apparent, Carlson says.

"There are many reasons why the players may struggle to manage their high incomes," says Carlson. For example, the players, many of whom are drafted directly out of college, often do not have any experience in business or finance. Many come from economically disadvantaged backgrounds. In addition, players may be pressured to spend by other high-earning teammates.

This work raises questions for future research both for behavioral economists and for scholars of personal finance. Because football players, by nature, might be more willing to take risks than the average person, are they also more willing also make risky financial decisions? Are football players perhaps saving for retirement early in their careers, but later using bankruptcy as a tool to eliminate spending debt?

"Indeed it may well be that these high rates of bankruptcies are partly driven by the risk attitudes of football players and partly driven by regulatory practices that shield retirements assets from bankruptcy procedures," says Jean-Laurent Rosenthal, the Rea A. and Lela G. Axline Professor of Business Economics and chair of the Division of Humanities and Social Sciences, who also specializes in the field of behavioral economics.

"These results don't say why the players have a higher incidence of bankruptcy than the model would predict. We plan to investigate that in the future with additional modeling and data," Carlson says. "The one thing that we know right now is that there's something going on with these players that is different from what's in the model."

The study was published in a working paper titled, "Bankruptcy Rates among NFL Players with Short-Lived Income Spikes." In addition to Carlson and Camerer, additional coauthors include Joshua Kim from the University of Washington and Annamaria Lusardi of the George Washington University. Camerer's work is supported by a grant from the MacArthur Foundation.

Exclude from News Hub: 
News Type: 
Research News


Subscribe to RSS - HSS