Topic: Economics
The price is hot
PS roundtable: The return of Trump
3 reasons why a Trump administration may not slow climate progress as much as it could
Sports betting and financial market data show how people misinterpret new information in predictable ways
Let’s say it’s a home game for the Golden State Warriors and Steph Curry shows he’s still got it, sinking back-to-back three-pointers minutes into the first quarter. The fans at Chase Center take notice, and so do the betting markets, where the odds move in the Warriors’ favor.
Yet it’s a long game. The away team comes back, and with just 10 seconds to go, the Warriors are down by two and have just missed a shot. A victory is unlikely, and the betting odds should have shifted to reflect that near-certainty. But they don’t.
“If you look at the history of NBA games, the probability that a team with the ball, up by two with 10 seconds left, wins is north of 90%,” says Eben Lazarus, an assistant professor of finance at UC Berkeley’s Haas School of Business. “But what shows up in the betting markets is that people treat baskets as too similar over the course of the game. They overreact to information that’s not very important—early baskets—and underreact to strong signals at the end.”
This interesting pattern in how people interpret new information holds true across a range of settings, from sports betting to financial markets, according to a new paper published in the Quarterly Journal of Economics. Lazarus and coauthors Ned Augenblick from UC Berkeley Haas and Michael Thaler of University College London conducted three experiments and analyzed millions of betting transactions and prices on options contracts, and found that people consistently overreact to weak information and underreact to strong information.
“There are all kinds of situations where I might know whether piece of news is good or bad, but struggle to judge exactly how important it is,” Lazarus says. “We saw this pattern everywhere we looked, which was surprising to us given the stakes involved in betting and financial markets.”
Building on decades of behavioral science and economics research
Lazarus and his coauthors wanted a way to unify different theories about how people act in ways that aren’t quite rational when processing new information. The study builds on decades of behavioral psychology and economics research about how people update their beliefs given new information, dating back to a classic 1966 paper arguing people are overly cautious in updating, and a 1992 paper by Dale Griffin and Amos Tversky showing people tend to overfocus on information that seems dramatic but give less weight to how reliable it is.
More recent papers have shown that people make systematic errors as a result of mistakes in calculating probabilities, and when people are uncertain about what decision to make they tend to pick a middle-ground option. The paper also connects to studies looking at how financial markets sometimes overreact and sometimes underreact to news.
“We think that we have a simple framework for thinking systematically through a lot of situations in the financial markets and the real world,” he says.
As humans, we take in information all the time, whether it’s a new poll that favors our preferred candidate or feedback from a boss. The researchers theorized that most of the time we don’t have the information to accurately judge just how important that information is, so we tend default to a middle ground.
“In cases where it’s easy to figure out which direction to update your beliefs, but not quite how much you should update, people will tend to treat all ‘good’ information somewhat similarly,” he says. “Given this difficulty, you’re going to see people overreacting to news that’s fairly weak and underreacting to news that should move you close to certainty.”
Experimental evidence
The research team first tested their theory in lab experiments, including both a classic experiment involving determining which deck a particular card came from and a novel sports-related experiment where they recruited 500 NBA fans and presented them with sequences of events in a simulated basketball game. The simulations started with 2:40 left in each quarter, and participants then saw a sequence of four possessions. After each possession, participants had to predict the probability of each team winning (they could earn a $50 bonus based on their accuracy).
The researchers established the “correct” probabilities of wins in each scenario using data from the website inpredictable.com. But they found that while people understood that late-game baskets were more important than those scored early in the, they still overreacted to first-quarter baskets—giving them 60% more importance than they should—and underweighted fourth-quarter baskets by 33%.
“This gave us a good sense that people were over- or under-reacting to information in experiments, but we needed to come up with some ways to test this in higher-stakes settings in the real world,” Lazarus says.
Sports betting data from Betfair
To do that, the research team turned to sports prediction market Betfair, analyzing over 5 million betting transactions across 260,000 basketball, soccer, football, and ice hockey games. Since the researchers had no way to determine the “correct” probability of a win with certainty, they developed a new empirical method to measure whether prices were over- or underreacting to information. Again, they found that early in games, events like scores created bigger shifts in betting odds than they should have, given the high uncertainty about the outcome. Meanwhile, important events like fourth-quarter goals caused smaller shifts in the market than is justified.
Data from options markets
Lazarus and his coauthors also tested their theory in a sophisticated financial market, using option price quotes for S&P index options traded on the Chicago Board Options Exchange from 1996 to 2018. After applying multiple filters, they had over 4 million option prices corresponding to 955 expiration dates. To give a clear time horizon, they focused on those expiring in 100 trading days (~4.5 months).
They found the same pattern they observed in the sports betting market.
“…News today appears to hold relatively little information about the value of the S&P in multiple months, but the market acts as if it (does),” the authors write. “However, within two weeks of a contract’s resolution, the relationship reverses…as signals become stronger, the market begins to underreact.”
Real-world implications
While the research explains some puzzling patterns in how people and markets respond to news, Lazarus cautions that being aware of these patterns does not remove all risk.
“It’s still not a free lunch if you know that on average markets are underreacting or overreacting at different points in time,” he says. “You can still lose a lot of money if you bet against these moves.”
Still, the findings suggest that it’s wise to pay attention to how much weight to give different pieces of information, even in situations that are far more ambiguous.
“Let’s say I have a negative interaction with my boss and I’ve spent all week fretting about it,” he says. “How important is it really for my future at this company? I think people would do well to take that step back and think about how much to react.”
About the paper
Overinference from Weak Signals and Underinference from Strong Signals
By Ned Augenblick, Eben Lazarus, and Michael Thaler
The Quarterly Journal of Economics, October 14, 2024
The unconventional economic theory behind Trump’s sweeping tariff plans
Economist Malmendier calls for minimum quotas for state investments
Economic advisory council sees room for improvement
How are world economies reacting to Trump’s imminent return as U.S. president?
Economic goals hard to implement
Being sugar-deprived had major effects on these children’s health
Britain’s postwar sugar craze confirms harms of sweet diets in early life
Meet OpenAI’s first chief economist — 3 things you may not know about him
Inflation has cooled, but Americans are still seething over prices
How to spot societal tipping points in real time
Social scientists are using new methods to study pivotal societal turning points in real time. What they are learning could shape how policymakers address democratic backsliding, environmental crises, and other key challenges of our time.
The French Revolution, the fall of the Berlin Wall, Fidel Castro seizing power in Cuba—events like these fundamentally transform societies and how people within them live. Other moments seem equally full of potential for radical change, but the status quo prevails. For example, the 1989 Tiananmen Square protests in China and the 2011 Arab Spring uprising didn’t lead to lasting shifts in political and economic institutions.
Social scientists studying these rare historical turning points—which they call “critical junctures”—usually only look backward, evaluating how these moments remade institutions after a massive shift occurs. But researchers are now learning more by studying these crossroads as they are occurring, according to a new paper published in the Annual Review of Economics.
“If we identify critical junctures in real time, we can embed survey measurement and experiments in these contexts,” says Assistant Professor Jonathan Weigel, who co-authored the paper with Noam Yuchtman of Oxford and Michael Callen of the London School of Economics and Political Science. “That can help us understand why some countries get on an institutional path that leads to prosperity and others are stuck on one that leads to ruin. Is it really a coin flip, or are there certain things a leader can do in these moments of deep uncertainty?”
“If we identify critical junctures in real time, we can embed survey measurement and experiments in these contexts. That can help us understand why some countries get on an institutional path that leads to prosperity and others are stuck on one that leads to ruin. Is it really a coin flip, or are there certain things a leader can do in these moments of deep uncertainty?” —Jonathan Weigel
Studying critical junctures while they are happening can allow scholars to test competing theories about what causes institutional change, such as class conflict or social movements. Academics define institutions as the fundamental “rules of the game” that shape incentives and behavior—for example, a democratic versus an autocratic political regime. The empirical study of institutions was pioneered by Daron Acemoglu, Simon Johnson, and former UC Berkeley faculty James Robinson, who won the 2024 Nobel Prize in Economics for their contribution.
Identifying the potential for negative upheaval in the moment could also help policymakers intervene. For example, “if we value democracy, the ability to recognize when it’s truly under threat versus just grandiose rhetoric is important,” Weigel says. “Many Americans are asking themselves this question about our democracy right now.”
A new research approach
Traditional retrospective studies alone are limited because they leave out many critical junctures in which institutional change did not occur. To address this gap, Weigel and his co-authors argue that scholars can recognize pivotal moments in the present by regularly asking people what they predict the future of key institutions will be.
“For example, researchers might ask, ‘How will political power be allocated in five years?’” Weigel says. “If everyone agrees that power will be determined through democratic elections, then the society does not appear to be in a critical juncture.” If, however, many people seem uncertain, or if expectations vary widely across society, that could signal a critical juncture in which multiple future paths are possible.
One example of such a survey was deployed in Afghanistan, where NATO asked 11,500 citizens each quarter whether they believed the Afghan army would defeat the Taliban in the next few years. In 2008, when the survey started, just under 40% predicted the army would win, nearly a quarter thought it would lose, and one-third weren’t sure. This deep uncertainty about the country’s institutional future suggests that Afghanistan was in the throes of a critical juncture, Weigel says.
“You can observe how those beliefs responded to real changes in the world, such as the announcement of a surge in U.S. troop deployments,” he says. “That gives us a sense that beliefs can be a useful barometer for understanding the potential for institutional change and whether we’re getting close to a critical juncture.”
“You can observe how those beliefs responded to real changes in the world, such as the announcement of a surge in U.S. troop deployments. That gives us a sense that beliefs can be a useful barometer for understanding the potential for institutional change and whether we’re getting close to a critical juncture.” —Jonathan Weigel
Weigel and his colleagues are now building on their work by trying to embed a question on beliefs about institutions in a global survey and launching a study that will use artificial intelligence to analyze newspaper archives to identify historical and current critical junctures.
Meanwhile, Weigel points to growing disagreement about the integrity of U.S. elections as a warning sign that the country’s political system is under threat. “A large part of the electorate claims the 2020 election was stolen, and many people are reportedly planning for what happens if the 2024 elections aren’t respected. These are signs of disagreement over the future rules of the game that would suggest we’re in a critical juncture,” he says—though he does not know of any systematic measurement of these beliefs to date. “Someone should collect them!” he adds.
Work in the Democratic Republic of Congo
Weigel, who joined Haas in 2021, has been working in this field since starting his PhD in political economy and government at Harvard University in 2012. For over a decade, he has collaborated with the provincial government of the Democratic Republic of Congo, one of the world’s poorest countries, to conduct randomized control trials related to state capacity, primarily focusing on taxation.
In the DRC, the formal state lacks legitimate authority. Many actors vie for political control, and there is widespread disagreement about the future of institutions. Weigel’s research seeks to understand the state’s efforts to build its capacity and accumulate legitimacy. His study of the provincial capital Kananga’s first citywide property tax collection campaign found that it not only raised substantial revenue but also increased citizens’ perceptions of government performance and boosted political participation and demand for political accountability.
Research on institutions and randomized control trials used to be seen as separate areas of study with nothing in common. Weigel and his colleagues point out that a new body of research has emerged that bridges the gap.
“I remember a senior political economist saying, ‘You could never run a randomized controlled trial about something I’m interested in,’” Weigel says. “That was the mood for a long time.” But things are starting to change.
A growing field
Over the past 15 years, studies that use randomized controlled trials to analyze critical junctures in real time to better understand institutional change have increased fivefold, Weigel and his colleagues point out. Already, the growing field has yielded insights about the ability of states to strengthen the social compact, improve law enforcement, and expand political inclusion and accountability.
For example, recent experiments have found that giving voters more say over who parties pick as political candidates improved democratic representation in Sierra Leone; that widely deploying an election monitoring technology improved perceptions of election integrity and willingness to engage with formal state institutions in Afghanistan; and that letting companies comment on new labor laws in Vietnam increased compliance even when regulations were unchanged.
The researchers argue that expanding this area of study can shed light on key current issues, such as declines in democratic freedoms, the effects of climate change on institutions, and the rights of marginalized groups.
Inspired by Paul Farmer
Weigel’s interest in these topics dates to his time as an undergraduate at Harvard, where he studied with the late Paul Farmer, founder of Partners In Health. After graduating, Weigel spent two years as a researcher for the organization, which works with local governments to provide health care in the world’s poorest places.
“Paul had an incredible commitment to understanding the reasons why Haitians were so poor when just across the border, the Dominican Republic was much more successful,” he says. “My underlying interest is what lies at the root of why some countries have so much and other countries have so little.”
Weigel was inspired by the Partners In Health model of collaborating closely with local governments, rather than trying to build parallel structures to provide services, as many NGOs do. To that end, he founded a research organization in the DRC called ODEKA to work with the government and evaluate its efforts to build state capacity. He has led multiple randomized controlled trials that have led to improved tax collection and higher rates of political participation and is now working with ODEKA to evaluate the rollout of a more progressive and fully digitized property tax system in Kananga, DRC. “Collaborating closely with our Congolese colleagues has been one of the incredibly rewarding parts of my work,” Weigel says.
ODEKA employs a staff of nearly 70. “A lot of my papers are inspired by this close partnership,” he says. “I also personally feel really good about getting to provide stable employment for an outstanding set of individuals for the past 11 years.”
Read the full paper:
Experiments About Institutions
By Michael Callen, Jonathan L. Weigel, and Noam Yuchtman
Annual Review of Economics, August 2024