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Showing posts sorted by date for query "school choice". Sort by relevance Show all posts

Thursday, September 3, 2026

Psychology and Economics at Stanford SITE: Sept 3-4 (Program)

 Session 18: Psychology and Economics  
Thu, Sep 3 2026, 8:00am - Fri, Sep 4 2026, 7:00pm PDT

Thursday, September 3, 2026

Sep 3

8:00 am - 9:00 am PDT

Check-in and Breakfast

Sep 3

9:00 am - 9:40 am PDT

What do we really know about risk preferences for binary lotteries?

Presented by: Charles D. Sprenger (California Institute of Technology)
Camila Farres (California Institute of Technology), Ted O’Donoghue (Cornell University)

We conduct a comprehensive experiment on decision-making for binary lotteries—specifically, lotteries that yield a positive amount or zero. Such lotteries form the basis of many empirical results revealing violations of expected utility (EU) and motivating behavioral alternatives. However, the space of binary lotteries has not been comprehensively explored, and thus the predictions of various behavioral alternatives to EU have not been fully assessed even within this limited domain. We provide this exploration, and discover empirical patterns that stand in stark contrast to the predictions of existing behavioral models. In particular, the data indicate that risk attitudes are driven largely by relative probability comparisons between two options, with absolute magnitudes of probabilities playing a minor role. We show that the data is largely consistent with the model of “upside potential” proposed by McGranaghan et al. (2025).

Sep 3

9:40 am - 10:20 am PDT

The Misallocation of High-Value Work

Presented by: Jason Somerville (University of California, Santa Barbara)
Carl Meyer (Stanford University), Germán Reyes (Middlebury College)

Aggregate productivity depends on whether productive inputs are allocated to their highest-value uses. We document a novel form of misallocation within a single individual performing a cognitive task. In an online experiment, participants complete a 30-question mathematics exam with random question order and randomly timed enhanced incentives (“bonus boosts”). Performance falls by 9.5 percentage points from the first to the last question, and 82 percent of participants exhibit a decline. Yet 58.0 percent of participants report no preference over when to receive bonus boosts, and willingness to pay for these boosts is constant across timing options. This indifference is costly: assigning boosts early rather than late raises expected bonus earnings by 9 percent. The misallocation aligns with miscalibrated beliefs about productivity: pre-exam forecasts imply constant performance, and post-exam hindcasts capture only 55 percent of the actual decline. These findings identify miscalibrated beliefs about within-worker productivity trajectories as an under-recognized source of misallocation.

Sep 3

10:20 am - 11:00 am PDT

Break

Sep 3

11:00 am - 11:40 am PDT

Model-Directed Attention and the Persistence of Wrong Mental Models: Evidence from Firm Pricing

Presented by: David Huffman (Cornell University)
Yi Han (Renmin University of China), Yiming Liu (Humboldt University of Berlin)

We show a way that wrong mental models can persist even in data-rich environments, and potentially even resist encounters with peers who have the correct model: by remaining silent about data features they cannot explain, simple wrong models direct attention away from disconfirming patterns and explanations. Field evidence comes from 13,000 gas station managers, 20 percent of whom have a simple model, discounts raise sales, that is silent about intertemporal substitution (IS), evident in the data as sales dips before and after a pre-announced discount. Managers who neglect IS show measurable inattention to these dips, believe fuel demand is more elastic, set lower prices, earn lower profits, andtheir neglect persists with experience. Online experiments on Prolific provide tighter identification and causal evidence: exposure to the simple peer model reduces attention to IS patterns, while a targeted attention intervention raises recognition of those patterns and induces switching to the correct model. Encountering the correct model at a later stage helps correct beliefs, but does not fully undo the impact of having the wrong model first.

Sep 3

11:40 am - 12:20 pm PDT

Beliefs Over Contracts

Presented by: Francis Annan (University of California, Berkeley)
Collin B. Raymond (Cornell University)

We study how firms choose among incentive contracts and how accurately managers predict their e!ects. Using a survey of managerial beliefs and a large-scale field experiment, we randomly assign agents at the market level to several widely used, expenditure-equivalent incentive schemes. In the field, the best-performing contract increases agent output and firm revenue by over 20% relative to the status quo, despite being ranked lower by managers, whereas the worst-performing contract performs as predicted. Managers correctly identify underperforming contracts but systematically underestimate top- performing ones. We document the sources of performance differences—labor supply responses rather than selection or pricing and the determinants of managerial predictability: contract complexity and managerial hierarchy. Our results highlight the importance of contract design for firm performance and reveal systematic limits to managerial cognition in shaping incentives.

Sep 3

12:20 pm - 2:00 pm PDT

Lunch

Sep 3

2:00 pm - 2:20 pm PDT

Do Firms Know What Workers Want?

Presented by: Simon Cordes (University of Bonn)
Max Müller (University of Bonn)

Labor supply depends on wages and amenities, and standard models implicitly assume that firms hold accurate beliefs about workers’ amenity valuations. In a survey with firms and workers in Germany, we measure workers’ valuations of amenities and firms’ beliefs about workers’ valuations. We find that firms systematically underestimate workers’ valuations of all amenities. These misperceptions are driven by interpersonal projection: managers project their own preferences—they value amenities less—onto workers. Through the lens of a simple model of imperfect competition, we show that firm misperceptions result in (i) labor shortages and (ii) excess labor costs for biased firms, and increase the market power of unbiased firms. Empirical tests confirm these predictions: a simple calibration suggests that non-providing firms could reduce their labor costs by 5% by providing amenities.

Sep 3

2:20 pm - 2:40 pm PDT

Insuring Wisdom: Intermediaries in the Market for Medicare Advice

Presented by: Elaine Shen (University of California, Berkeley)
Margaret Kallus (University of California, Berkeley)
Sep 3

2:40 pm - 3:00 pm PDT

Knowledge Transfer and Strategic Similarity

Presented by: Joseph Feffer (Stanford University)
Filip Tokarski (Stanford University)

This paper studies when strategic understanding acquired in one mechanism can be transferred to another. We introduce a framework in which agents’ knowledge is represented as a set of payoff comparisons they can make, and use it to formalize what it means to understand that a strategy profile is an equilibrium. We first apply this framework to mechanisms that are strategically equivalent—that is, share the same game form up to relabeling of actions—and show that agents’ understanding of equilibrium transfers across such mechanisms once the relevant action correspondences are explained to them. We then define strategic analogy, a weaker notion that allows not only actions but also types to be remapped, and show that understanding of equilibrium transfers across strategically analogous mechanisms once agents recognize how actions and types correspond. Applications include single item auctions, scoring auctions, and nonlinear pricing with capacity constraints.

Sep 3

3:00 pm - 3:20 pm PDT

Geographic Price Extrapolation, Learning, and Housing Search: Evidence from Danish Movers

Presented by: Matteo Saccarola (University of Chicago)

Using population-wide Danish administrative registers on housing transactions, I document an asymmetric, hockey-stick relationship between origin market prices and overpay- ment for comparable homes. Quantitatively, the elasticity of overpayment with respect to the origin-destination price difference is 3.9 percent (p < 0.01) when movers relocate from more expensive to cheaper housing markets. In contrast, buyers moving to more expensive locations exhibit little systematic overpayment, and their purchase prices are unrelated to prices at origin. I interpret these patterns through a housing search model in which buyers enter with price beliefs anchored in their origin market and update those beliefs gradually during search. Despite homogeneous learning, endogenous stopping generates the observed asymmetry at purchase: buyers predisposed to overpay transact quickly before fully learning the local price level, while those predisposed to underpay search longer and converge toward local prices. The model yields additional predictions that I test using administrative and survey data. The evidence supports origin-based price extrapolation with subsequent learning rather than preference-based explanations such as reference dependence.

Sep 3

3:20 pm - 4:00 pm PDT

Break

Sep 3

4:00 pm - 4:40 pm PDT

Strategically Controlling Worldviews

Presented by: Danil Dmitriev (University of Georgia)
Cuimin Ba (University of Pittsburgh), Ziqi Hang (Texas Tech University), Freddie Papazyan (Texas Tech University)

This paper studies persuasive behavior when the sender can control both the information the receiver observes and the model through which it is interpreted (the narrative). Even when the receiver begins with a correctly specified model and understands the sender’s strategic incentives, the sender can manipulate him and often secure her preferred action with probability one. The key mechanism highlights a strong complementarity between strategic communication of information and narratives, allowing the sender to strictly outperform a Bayesian persuader with commitment power. We fully characterize the sender-optimal equilibrium for a broad class of information technologies. Softer information lowers the bar for full manipulation, while harder information expands the set of environments where any manipulation is possible. The results provide a formal foundation for understanding the widespread success of disinformation.

Sep 3

4:40 pm - 5:20 pm PDT

Deception Aversion

Presented by: Evan Friedman (Paris School of Economics)
Béla Elmshauser (Paris School of Economics), Yoon Joo Jo (Texas A&M University)

In communicating private information, opportunities to lie by misreporting the truth also present opportunities to deceive by inducing inaccurate beliefs. While many studies document truth-telling despite material costs—commonly attributed to lying aversion—such behavior may also reflect aversion to deceiving others. Disentangling the two preferences is challenging because deception depends on the sender’s unobserved second-order beliefs. In a novel game, we show theoretically how to identify deception aversion from choice data alone, under minimal assumptions on beliefs. In a laboratory experiment, we find strong evidence of deception aversion: many subjects lie to avoid deception; structural estimates imply that 30% are deception-averse.

Sep 3

5:20 pm - 7:00 pm PDT

Dinner

Friday, September 4, 2026

Sep 4

8:00 am - 9:00 am PDT

Check-in and Breakfast

Sep 4

9:00 am - 9:40 am PDT

Limited Propagation and Contingent Thinking

Presented by: Ran Spiegler (Tel Aviv University & University College London)
Andrew Ellis (London School of Economics)

Abstract. We model an agent who updates her beliefs over a set of variables after observing some of them without fully propagating their implications. We provide a representation of updated beliefs that exhibit limited propagation along a directed acyclic graph, and show that it is implemented by a variant on a standard propagation algorithm. Failures of contingent thinking occur when the agent’s inferences travel through fewer graph paths from hypothetical variables relative to given ones. We characterize the model’s relationship to Bayesian updating and familiar non-Bayesian benchmarks. Contingent thinking is necessary for Bayesian updating, and failures cause correlation neglect and violations of iterated expectations. Our frame- work offers a new perspective into experimental evidence on contingent thinking, reinterpreting effects such as the winner’s curse or the Monty Hall fallacy. We illustrate the framework with applications, ranging from public good contribution games to the recreational puzzle Kakuro.

Sep 4

9:40 am - 10:20 am PDT

Intergenerational Race-Based Trauma and Financial Market Participation

Presented by: Vicki Bogan (Duke University)
Lisa A. Kramer (University of Toronto), Chi Liao (University of Manitoba), Alexandra Niessen-Ruenzi (University of Mannheim)

This paper examines whether historical race-based financial trauma shapes current household financial market participation. Our analysis exploits geographic exposure to the Freedman’s Savings Bank (FSB), established in 1865 to encourage Black Americans to save. The bank collapsed in 1874 due to fraud and mismanagement. Using restricted-use Panel Study of Income Dynamics (PSID) data, we link present day stock ownership to historical FSB branch locations. Own, paternal, and grandpaternal FSB-county exposure is associated with lower stock market participation among Black individuals. These effects persist after controlling for socioeconomic and geographic differences, migration, and broader patterns of racial exclusion. Our findings reveal intergenerational transmission of race-based financial trauma and a robust mechanism perpetuating the racial wealth gap.

Sep 4

10:20 am - 11:00 am PDT

Break

Sep 4

11:00 am - 11:40 am PDT

Deadly Stigma

Presented by: Manasvini Singh (Carnegie Mellon University)

How harmful is stigma in the “real world”? Answers are elusive because stigma is difficult to measure in observational data, and isolating its effects requires exogenous variation in stigma without variation in the stigmatized trait. This study addresses these challenges by focusing on a widespread form of stigma — weight stigma — in the high-stakes setting of inpatient healthcare. BMI categories are displayed prominently to providers in electronic medical records, and obesity is heavily stigmatized socially. The “obese” cutoff may thus discretely shift stigma while keeping constant the underlying trait. Using a regression discontinuity design that exploits this institutional feature, I find a discontinuous increase in in-hospital mortality at this cutoff, though patient health does not change. Two patterns suggest stigma-based discrimination as the mechanism. First, just-obese patients receive lower diagnostic effort than almost-obese patients. Second, a physician-validated LLM identifies a rise in stigmatizing language in clinical notes at the cutoff — specifically, statements that impose moral judgment, undermine patient credibility, and stereotype patients — that closely tracks mortality effects. Overall, this paper establishes stigma as a powerful social force that can have life-or-death consequences.

Sep 4

11:40 am - 12:20 pm PDT

Opt in? Opt out?

Presented by: Alex Chan (Harvard University)
Ayush Gupta (Boston University), Yetong Xu (Harvard University)

Cadaveric organ shortages leave thousands without life-saving transplants each year. Countries differ in using opt-in (informed consent) or opt-out (presumed consent) systems for donor registration. Using newly assembled cross-country panel data and an event-study design, this paper provides evidence that presumed-consent laws increase organ donation only when strictly enforced and family veto power is limited; weak opt-out regimes show negligible or even negative effects. A theoretical signaling model provides a plausible mechanism when opt-in or opt-out yields more donations, emphasizing the roles of donation propensity, signaling costs, and the family’s ability to overturn defaults. A large laboratory experiment further tests these mechanisms, showing that opt-in generally produces equal or higher donation rates unless signaling is costly and family veto power is minimal. The results underscore that defaults alone rarely increase donations unless paired with strong institutional enforcement.

Sep 4

12:20 pm - 2:00 pm PDT

Lunch

Sep 4

2:00 pm - 2:40 pm PDT

State Dependence and Commitment: Experimental Evidence from Crop Insurance in Uganda

Presented by: Sili Zhang (Ludwig Maximilian University of Munich)
Lorenzo Casaburi (University of Zurich), Jack Willis (Sciences Po)

According to standard economic arguments, state dependence generates the value of flexibility. This paper proposes that it can instead generate demand for commitment when individuals anticipate that future states will distort their decisions. A conceptual framework models two broad channels—state-dependent valuations (e.g., projection bias) and state-dependent decision mistakes (e.g., scarcity effects)— and shows that sophistication about such future distortions can generate demand for commitment. We test this prediction in a field experiment in Uganda, where we exclude present bias as a source of commitment demand by design. Farmers are offered pay-at-harvest crop insurance for two seasons and can choose upfront whether to commit to second-season insurance or maintain flexibility. Forty percent of farmers choose commitment. An intervention increasing sophistication raises commitment by 11 percentage points. Additional evidence suggests that both channels matter with substantial heterogeneity across farmers. Our results highlight the importance of individuals’ sophistication about future state dependence for welfare analysis and policy design, particularly in environments with high state variability.

Sep 4

2:40 pm - 3:20 pm PDT

Behavioral Inequality: The Contribution of Decision-Making Frictions to Inequality

Presented by: Stefano DellaVigna (University of California, Berkeley)
Tim de Silva (Stanford University), Rohan Jha (University of California, Berkeley)

We provide the first systematic quantification of how decision-making frictions—such as failing to claim government benefits, choosing dominated insurance plans, not saving for retirement, and not quitting smoking—aggregate to affect inequality in income, consumption, and wealth. We review the existing literature and combine it with original analysis of survey data to estimate the prevalence and financial impact of 18 frictions across the income distribution. To make these frictions comparable, we develop a framework in which each friction is characterized by three parameters: the share of the population at risk, the share affected by the friction, and the average loss conditional on being affected. Aggregating across the frictions with dollar-loss estimates, the estimated impact on annual income is 7.8% for the bottom quartile of the income distribution relative to 4.2% for the top quartile; the total loss for low-income households is approximately 7.5 times larger than the impact of a major EITC expansion. We then incorporate these frictions into a life cycle model with realistic institutional features, including tax-advantaged retirement accounts, progressive taxation, portfolio choice, and a social insurance system. The model reveals that removing frictions tends to reduce inequality in lifetime consumption, with the largest effects coming from smoking and attending for-profit colleges. Our results suggest that decision-making frictions are a quantitatively important contributor to inequality in income, consumption, and wealth.

Sep 4

3:20 pm - 4:00 pm PDT

Break

Sep 4

4:00 pm - 4:40 pm PDT

A Practical Approach to Robust Policy Evaluation with Behavioral Agents

Presented by: Dmitry Taubinsky (University of California, Berkeley)
B. Douglas Bernheim (Stanford University)
Sep 4

4:40 pm - 5:20 pm PDT

What Motivates Partisan Selective Exposure? Experimental Evidence from the 2024 US Presidential Election

Presented by: Matt Gentzkow (Stanford University)
Peter Robertson (Stanford University), Michael Thaler (University College London)

Why do partisans prefer like-minded information sources? They may want to learn the truth and believe these sources are the most accurate. Or, they may prefer them for non-accuracy psychological forces such as confirmation bias. We evaluate these motives in two large-scale experiments in which 3,785 participants choose sources to help them predict swing-state winners in the 2024 US presidential election. Partisans exhibit substantial selective exposure, choosing like-minded sources both among real news outlets and among synthetic sources we construct. This behavior remains essentially unchanged under two treatments: (i) increasing incentives for accuracy and (ii) shutting down confirmation motives by having participants delegate their predictions to sources without seeing sources' content. In contrast, participants respond strongly to experimentally-varied source accuracy, even absent incentives. Our results, interpreted in reduced form and through a discrete-choice model, suggest the selective exposure in our experiment can be almost entirely explained by demand for accuracy.


 

Thursday, August 6, 2026

Experimental Economics at Stanford SITE, August 6-7 (program)

 The summer experiment fest is on:)

Session 6: Experimental Economics | Department of Economics 

Thu, Aug 6 2026, 9:00am - Fri, Aug 7 2026, 7:00pm PDT
Gunn Building, 366 Galvez Street, Stanford, 

Thursday, August 6, 2026

Aug 6

9:00 am - 9:30 am PDT

Check-in and Breakfast

Aug 6

9:30 am - 9:45 am PDT

Introduction

Aug 6

9:45 am - 10:45 am PDT

Session 1 (Chair: Muriel)

Aug 6

9:45 am - 10:15 am PDT

AI Sycophancy and Decisions

Presented by: John J. Conlon (Carnegie Mellon University)
Peter Schwardmann (Carnegie Mellon University)

We examine whether sycophantic AI advice distorts decisions. Our experiment involves 1,500 participants in 30 decision environments that span core domains in economics and the social sciences. We find that interacting with an AI that is broadly representative of consumer-facing models depolarizes choices on average, moving participants away from their initial leanings. This result appears despite the LLM being measurably sycophantic: it disproportionately supports users’ initial leanings and uses agreeable, flattering language. Depolarization occurs across moral and non-moral, objective and subjective, strategic and non-strategic, and complex and simple tasks, and despite the vast majority of researchers in an expert survey expecting polarization. Next, we show that sycophancy is behaviorally relevant—a treatment increasing AI sycophancy reduces depolarization—but that it is outweighed by the apparent informativeness of LLM advice. Finally, we ask whether market forces are likely to push toward greater polarizing effects outside our experiment or in the future. On the supply side, we show that our baseline LLM’s level of sycophancy is typical of leading models and that these models are not becoming substantially more sycophantic over time. On the demand side, we show that participants do not prefer greater sycophancy, do not select into AI advice in tasks where it is more polarizing for them, and exhibit greater depolarizing effects when they are more frequent AI users outside of the experiment.

Aug 6

10:15 am - 10:45 am PDT

Preference for Explainable AI

Presented by: Alex Chan (Harvard University)

Participants acted as loan officers deciding whether to approve real $10, 000-loans issued by a private U.S. lender using an AI’s default-risk predictions. When explanations revealed that the AI penalized non-White or female borrowers, participants were more likely to override the AI’s profit-maximizing recommendation. When their bonuses depended on repayment, however, they sought predictions but avoided explanations, consistent with willful ignorance; this effect faded when explanations were framed as purely financial or demographics were hidden. A secondary experiment reveals a novel bias: participants failed to reason contingently and undervalued explanations even when these complemented private information and improved decision accuracy.

Aug 6

10:45 am - 11:15 am PDT

Coffee Break

Aug 6

11:15 am - 12:30 pm PDT

Session 2 (Chair: Lise)

Aug 6

11:15 am - 11:45 am PDT

Narratives, Belief Movements, and Economic Fluctuations

Presented by: Tony Q. Fan (Lehigh University)
Tilman Fries (Ludwig Maximilian University of Munich)

We study how the construction and transmission of narratives—interpretations that explain observed data—systematically distort belief formation. In a dynamic environment where signals are generated by a potentially evolving state, we show that popular, well-fitting narratives tend to overfit the data and thereby push beliefs toward greater extremeness and volatility. We test this prediction in a controlled experiment that directly compares beliefs formed with and without exposure to a singular, endogenously constructed narrative. Participants exposed to such narratives form substantially more extreme beliefs, overreact to recent data, and exhibit more volatile belief movements over time. These results identify a mechanism through which narrative-based reasoning induces overreaction to news—one that likely matters broadly given the central role of narratives in economic communication.

Aug 6

11:45 am - 12:15 pm PDT

People as Intuitive Modelers: How Model Complexity Adapts to Data

Presented by: Sevgi Yuksel (New York University)
Guillaume Frechette (New York University) and Emanuel Vespa (University of California, San Diego)

We study whether and how people respond to a fundamental tension in model selection: simple models generate high bias and low variance, whereas complex models generate low bias and high variance. We report results from an experiment in which participants rely on a limited sample of observations to construct mental models describing the relationship between two variables and are rewarded based on the out-of-sample predictive accuracy of these models. In aggregate, behavior is broadly consistent with people acting as intuitive modelers who respond to the simplicity–complexity tradeoff. Model complexity varies systematically with the size and nonlinearity of the sample, but willingness to improve in-sample fit declines when doing so requires more complex models. The main deviation from optimal benchmarks is in the direction of underinference, driven by both a preference for overly simple models and mistakes in model construction conditional on the chosen complexity level.

Aug 6

12:15 pm - 12:30 pm PDT

Communicating with Data Generating Processes: An Experimental Analysis

Presented by: Agata Farina (University of Chicago)
Clément Herman (Princeton University)

In many applications, agents can more easily influence how data are generated than manipulate the data themselves. For example, students choose which classes to take but cannot modify their grades once assigned, and firms determine how performance indicators are calculated rather than directly altering results. This paper experimentally studies information transmission when data are generated through an unobservable, strategically chosen process. We focus on settings where an informed sender—such as a student or a firm—privately selects a data-generating process (DGP)—a portfolio of classes or an accounting methodology—to shape the beliefs of an uninformed receiver, such as an employer or an investor. Across treatments, we vary which DGPs are feasible and whether some come at a cost. These variations span different levels of information verifiability and capture, within a unified framework, the core insights of disclosure, cheap-talk, and signal- ing models. Our findings reveal three main patterns. First, while senders select their DGPs strategically in line with theoretical predictions, receivers often fail to account for this strategic selection. Second, introducing differential costs across feasible DGPs mitigates this DGP selection neglect. Third, while receivers’ biases are robust across environments, their consequences vary: information transmission falters most when the evidence receivers observe is highly sensitive to the sender’s DGP choices. Finally, increasing transparency about the selected DGP—a policy often advocated in practice—does not, in general, improve information transmission.

Aug 6

12:30 pm - 2:00 pm PDT

Lunch

Aug 6

2:00 pm - 3:15 pm PDT

Session 3 (Chair: Manu)

Aug 6

2:00 pm - 2:30 pm PDT

Understanding Support for Inefficient Environmental Policy Instruments

Presented by: Dmitry Taubinsky (University of California, Berkeley)
Chenxi Jiang (Massachusetts Institute of Technology), Maximiliano Lauletta (Federal Reserve Board), Ro’ee Levy (Tel Aviv University), and Joseph S. Shapiro (University of California, Berkeley)

Many governments use environmental standards rather than more cost-effective market-based instruments like pollution taxes or cap-and-trade markets. Using a nationally representative survey experiment, we study whether and why limited understanding of economic principles helps explain this practice. Holding environmental impacts constant, respondents prefer standards over market-based instruments, and prefer producer taxes and cap-and-trade over consumer taxes. These preferences reflect consumers’ beliefs about how these policies will affect electricity bills. Respondents also prefer the weakest environmental targets for consumer taxes and the strongest targets for standards, which suggests that policymakers face a tradeoff between policy stringency and cost effectiveness. A separate survey of environmental economists shows that they have strikingly different beliefs about the effects of environmental policies than the respondents in our representative survey. For example, typical respondents—in contrast to environmental economists and textbook economic theory—believe that environmental standards increase consumer energy bills less than market-based instruments do. Educational videos on pass-through and cost-effectiveness of policies affect policy support and close some of the gap between nationally representative respondents and experts, which suggests that economic literacy is a factor in voters’ preferences.

Aug 6

2:30 pm - 2:45 pm PDT

What Motivates Partisan Selective Exposure? Experimental Evidence from the 2024 US Presidential Election

Presented by: Peter Robertson (Stanford University)
Matthew Gentzkow (Stanford University), Michael Thaler (University College London)

Partisan selective exposure — the tendency of partisans to consume like-minded news — has been widely documented in the United States, but its drivers remain debated. This paper studies the relative roles of two commonly proposed motives for polarized news choice: (i) disagreement about the accuracy value of different news sources, and (ii) disagreement about sources’ non-accuracy value, such as identity affirmation or belief confirmation. To disentangle these motives, we run large-scale experiments over 3,785 participants with both real and artificial news sources in which participants choose a source to help them predict the winner of a swing state in the 2024 US presidential election. We employ two separate experimental manipulations which affect how people are predicted to trade off accuracy and non-accuracy value in canonical models of selective exposure. Despite documenting substantial selective exposure in our experimental environment at baseline, we find precise nulls across contexts on both manipulations: neither increasing the relative value for accuracy nor non-accuracy motives affects the degree to which participants choose co-partisan sources. Meanwhile, participants are highly sensitive to source accuracy: exogenously increasing the perceived accuracy of an artificial source leads to a sizeable increase in the share of participants who choose it. Our results, both interpreted in reduced form and through the lens of a discrete choice model, indicate that partisan differences in perceived accuracy are largely sufficient to explain the level of selective exposure we observe, and that non-accuracy motives play a limited role.

Aug 6

2:45 pm - 3:15 pm PDT

Incentives, Information, and Reminders for Bureaucrats: Overcoming Barriers to the Scale Up of an Effective Policy

Presented by: Gautam Rao (University of California, Berkeley)
Patrick Agte (Stockholm School of Economics), Daniel Morales (Tecnologico de Monterrey), Christopher Neilson (Yale University), and Sebastián Otero (Columbia University)

Scaling up effective policies often requires the attention of frontline bureaucrats with many competing responsibilities. Even when policymakers adopt effective programs, implementation may not follow. In a nationwide experiment in the Dominican Republic, we test interventions to increase school principals’ implementation of an educational program proven effective in a previous RCT. Only 37% of control schools verifiably implemented the intervention when ordered to by the Ministry of Education, compared with 86% in the original trials. Implementation was no higher among schools that previously participated in the RCT, suggesting that learning costs do not explain non-adoption. We find precise null effects of sharing research evidence, providing modest financial incentives, or offering implementation assistance. In contrast, additional reminder calls increased implementation by 20 percentage points. A second experiment targeting a different mandated program yields the same pattern: reminders produce large effects, while monitoring messages have smaller effects. Our findings point to limited attention among bureaucrats as a central barrier to scaling policies.

Aug 6

3:15 pm - 3:45 pm PDT

Coffee Break

Aug 6

3:45 pm - 4:45 pm PDT

Session 4 (Chair: Christine)

Aug 6

3:45 pm - 4:15 pm PDT

Cognitive Guidance in Matching Problems

Presented by: Muriel Niederle (Stanford University)
Lucas Coffman (Boston College), Yao Luo (Boston College), and Emanuel Vespa (University of California, San Diego)
Aug 6

4:15 pm - 4:45 pm PDT

Style Over Substance: The Oversized Role of Presentation Details in School-Choice Matching Systems

Presented by: Alex Rees-Jones (University of Pennsylvania)
Lucas Coffman (Boston College), Tayfun Sönmez (Boston College), Zamir Ticknor (University of Pennsylvania), and Alexander Whitefield (University of Pennsylvania)
Aug 6

5:30 pm - 5:30 pm PDT

Drinks and Dinner at Muriel's house

Friday, August 7, 2026

Aug 7

9:00 am - 9:30 am PDT

Check-in and Breakfast

Aug 7

9:30 am - 10:45 am PDT

Session 1 (Chair: Christine)

Aug 7

9:30 am - 10:00 am PDT

When Are Decisions Improvable: An Evaluation of Diagnostic Methods

Presented by: Charles Sprenger (California Institute of Technology)
B. Douglas Bernheim (Stanford University), Aldo Lucia (Ohio State University), and Kirby Nielsen (California Institute of Technology)

We evaluate three methods for identifying improvable choices: documenting specific misconceptions (the Characterization Assessment method), gauging confidence in choices (the Decision Confidence method), and showing that specific behavioral patterns in the domain of interest also emerge in a related domain where they are objectively suboptimal (the Pattern Matching method). In experiments involving risky choice, the three methods imply that different choices are improvable and have conflicting implications regarding legitimate risk preferences. We clarify the assumptions underlying each method and reevaluate the evidence on risk-taking in light of their limitations.

Aug 7

10:00 am - 10:30 am PDT

Valuations Under Tradeoff Complexity

Presented by: Jeffrey Yang (University of California, Santa Barbara)
Cassidy Shubatt (Harvard University)

Valuation tasks are a workhorse method for testing theories of individual preferences. However, a body of evidence suggests that complexity produces systematic measurement error in valuations, which raises the question of how researchers should interpret and utilize valuation data. To formally study this issue, we develop a model of complexity-driven noise in the valuation of risky prospects, in which the difficulty of comparing options to prices produces systematic noise in their valuations. We show how this model of noise can explain a number of documented valuation patterns that are difficult to rationalize under prevailing theories of risk preferences, as well as novel experimental evidence of systematic inconsistencies across valuation formats. We then characterize which valuation-based tests of preferences are robust to complexity in our model. While complexity distorts the levels of valuations in our model, differences between valuations can be informative, so long as complexity is held fixed across valuation tasks. We provide a formal criterion for robustness and apply it to valuation designs in the literature.

Aug 7

10:30 am - 10:45 am PDT

The Structure of Sequential Updating

Presented by: Kim Sarnoff (Ohio State University)

Many real-world inference problems unfold over time: employers learn about ability across tasks, consumers evaluate products through repeated use, and policymakers revise beliefs as new data arrive. Yet despite its ubiquity, research on dynamic updating has largely focused on a single implication of Bayesian reasoning: order independence. This paper experimentally tests a broader set of restrictions implied by Bayes’ rule, emphasizing both order independence and the previously unexamined property of prior sufficiency: the principle that the most recent posterior should serve as a sufficient statistic for past information. In a multi-period updating experiment with a rich set of parameters, participants repeatedly revise beliefs after receiving signals of varying strength and structure. Three main results emerge. First, only roughly a third display order dependence, overreacting to conflicting signals. Second, violations of prior sufficiency are widespread: beliefs formed sequentially tend to grow more extreme, and models assuming prior sufficiency, such as Grether (1980), fit poorly beyond the first update. Finally, the data indicate that participants process signals in aggregate, explaining prior sufficiency violations.

Aug 7

10:45 am - 11:15 am PDT

Coffee Break

Aug 7

11:15 am - 12:30 pm PDT

Session 2 (Chair: Muriel)

Aug 7

11:15 am - 11:45 am PDT

Promise Keeping and the Internal Judge

Presented by: Chloe Tergiman (Pennsylvania State University)
Sorravich Kingsuwankul (Vrije Universiteit Amsterdam) and Marie Claire Villeval (Université Lumière Lyon)

While standard models recognize intrinsic costs of lying, they typically treat reputational concerns as a calculation of external risk—trading off the benefits of lying against the probability of detection by an outside observer. We show that honesty oaths short-circuit this calculus and function by internalizing the audience, transforming the act of lying from a calculated transgression into a categorical identity violation. In a controlled experiment, we show that while the oath dramatically increases truth-telling, those who do break their promise systematically avoid brazen, detectable lies, retreating instead to ambiguity. Crucially, this refusal to be a “brazen renegade” persists even when the oath is private and compliance cannot be traced to the participant by the experimenter. This inelasticity rejects standard reputational models: instead, our data are consistent with the oath-taker answering to an internal judge rather than an external one. Furthermore, we show that this internal audience does not strictly require the cognitive amnesia or imperfect recall required by standard self-signaling models; rather, it can also be understood as a present-moment, categorical refusal to generate inescapable evidence of one’s own transgression. Finally, we show that investors intuit this mechanism, pricing in the oath’s “psychological enforceability” by granting credibility only when the speaker has no room to hide.

Aug 7

11:45 am - 12:00 pm PDT

Hierarchical Representations

Presented by: Milena Jessen (University of Bonn)

Evidence from cognitive science suggests that people organize multi-dimensional information into hierarchies, yet we lack causal evidence on how the hierarchical ordering of dimensions shapes learning and choice. I study this question experimentally. Participants learn success probabilities of firm–project pairs over multiple rounds and repeatedly choose the pair most likely to succeed. I exogenously vary whether outcomes are grouped by firms or by projects, inducing the grouped dimension as the top level of participants’ hierarchy while holding information constant. Participants process information conditionally on this top-level dimension: they form more accurate beliefs about summaries on it and locally optimize within it, while compressing information at lower levels. As a result, participants most accurately identify the best option within whichever dimension is placed at the top of their hierarchy – and these belief distortions translate into systematic choice distortions. Choice effects are stronger among low-attention participants, and procedural data confirm that participants process their top-level dimension first. Endogenous experiments demonstrate that individuals are not passive recipients of imposed structure: they adapt their representations to both cognitive costs and environmental diagnosticity.

Aug 7

12:00 pm - 12:15 pm PDT

Fragile Learning From Others

Presented by: Alisher Batmanov (University of California, San Diego)

Behavioral biases in decision-making are widespread and often persist even in the presence of feedback diagnostic of optimal behavior. This paper examines whether exposure to others’ decisions can correct initial misconceptions and facilitate learning in an environment where departures from the theoretical benchmark arise from neglecting an informative signal in a worker hiring task. Using a laboratory experiment, I show that exposure to optimal behavior substantially improves decision quality. The analysis of the underlying mechanisms suggests that this improvement is not driven by mechanical imitation alone, as subjects respond asymmetrically to the quality of observed choices, nor entirely by the richer feedback environment that social exposure generates. I then evaluate the retention and transfer of these learning gains beyond the period of exposure and find that, for most subjects, the improvements are fragile. Comparing the effects of social exposure to those of explicit guidance further underscores the limits of observational learning as a policy tool. These findings highlight the dual role of social learning: while it can enhance decision-making, it can also generate imitation behavior that fails to generalize beyond the observed context.

Aug 7

12:15 pm - 12:30 pm PDT

Designing Experiments to Distinguish Theories

Presented by: Cassidy Shubatt (Harvard University)
Baiyun Jing (Harvard University)

We develop an interpretable measure of an experimental design’s power to distinguish between a set of competing theories. A design offers a powerful test of a theory if competing theories would be unable to explain theory-consistent behavior on the design. We use the measure to analyze a set of the most highly-cited choice under risk experiments. We show that there is substantial variation in power across designs, and that power is consequential for determining which models best fit data. We then propose and implement an algorithm for constructing power-maximizing experimental designs. We show that our algorithmically-generated designs match or exceed the power of the best designs from the literature and require substantially less data.

Aug 7

12:30 pm - 2:00 pm PDT

Lunch

Aug 7

2:00 pm - 3:15 pm PDT

Session 3 (Chair: Manu)

Aug 7

2:00 pm - 2:30 pm PDT

The Hedonic Cost of Privacy: Information Avoidance under Entertaining Content

Presented by: Eleonora Freddi (Oslo Metropolitan University)
Aug 7

2:30 pm - 3:00 pm PDT

The Impact of Work Assignments on Evaluation and Advancement

Presented by: Lise Vesterlund (University of Pittsburgh)
Marissa Lepper (Texas A&M University) and Maria Recalde (The University of Melbourne)

This paper uses controlled experiments to examine how work assignments shape evaluation and career advancement. Workers are randomly assigned tasks of varying productivity, and evaluators observe their output, assess their relative performance, and make subsequent task assignment decisions. We find that workers assigned more productive tasks are paid more and evaluated more favorably. Evaluators systematically overattribute superior output to skill rather than assignment, producing inflated performance reviews and a tendency to keep initially advantaged workers on more productive tasks. In other words, early task assignments shape perceived ability and career trajectories, entrenching unwarranted disparities. Providing evaluators with more detailed performance information only modestly attenuates this bias. The documented path dependency in task assignment is particularly concerning in light of growing evidence that stereotyped perceptions influence how tasks initially are assigned within organizations.

Aug 7

3:00 pm - 3:15 pm PDT

The Female Labor Supply Constraints of Spousal Jealousy: Experimental Evidence from India

Presented by: Kailash Rajah (Stanford University)

This study presents evidence from two field experiments studying the role of spousal jealousy in constraining married women’s employment. In a first experiment (N =1, 400), I randomize married women in India to receive a two-week job in either a mixed or women-only workplace. Women randomized to the women-only workplace are 46% more likely to apply for a job (13 percentage points) and 31% more likely to turn up (6 percentage points). A cross-randomized safety treatment suggests that workplace safety is not the main mechanism. Instead, the treatment effects are significantly stronger among women who report having more jealous and controlling husbands. In a second experiment (N = 210), I directly test for a spousal jealousy mechanism by measuring whether women are more willing to interact with a male colleague if their husbands can monitor the interaction. I offer women a job that comes with a compulsory online peer support program and give them the option to forgo 20-35% of their salary to guarantee that the peer they are matched with will be a woman as opposed to a man. Fifty-three percent of women pay for the female peer when these remote interactions are one-on-one, but this drops to 34% once their husbands have the option of joining and can therefore monitor the conversations. One-third of households still pay for a female peer even if the mentoring simply involves watching prerecorded videos of the peer, suggesting even the most innocuous interactions are enough to raise jealousy concerns.

Aug 7

3:15 pm - 3:45 pm PDT

Coffee Break

Aug 7

3:45 pm - 4:45 pm PDT

Session 4 (Chair: Lise)

Aug 7

3:45 pm - 4:15 pm PDT

Psychedelics and Well-Being: An Experiment in Brazil

Presented by: Matt Lowe (University of British Columbia)
Patrick Francois (University of British Columbia), Ieda Matavelli (University of New South Wales)

We partnered with an ayahuasca center in Brazil to study the well-being effects of a one-time ayahuasca treatment within a ritualized group setting. The center enrolled 429 first-time ayahuasca users to participate in the largest randomized controlled trial of psychedelics ever run. Relative to placebo, ayahuasca increases happiness and reduces psychological distress six months later by roughly 0.4 standard deviations. The field experimental setting allows investigation of aspects not explored in the large clinical literature. Positive effects are almost entirely driven by participants that were distressed at baseline. Improvements in well-being are strongly positively correlated with the mystical nature of trips. The mystical experience can also be induced by placebo with ritual, though less frequently, and when done so induces a similar magnitude of well-being improvement. Effects are larger for older people, consistent with the idea that psychedelics reopen a window of heightened malleability. We estimate the mental health benefits of participating in an ayahuasca ceremony to be roughly 200 times the cost of 24 USD.

Aug 7

4:15 pm - 4:45 pm PDT

The Limits of Escalating Incentives: Evidence from Substance Use Treatment

Presented by: Ariel Zucker (University of California, Santa Cruz)
Rebecca Dizon-Ross (University of Chicago) and Corina Mommaerts (University of Wisconsin Madison)

Escalating incentive contracts — where rewards increase with success — are theoretically appealing and widely used in practice, including as the standard design in contingency management programs for substance use treatment. We show that the case for escalating incentives rests on three assumptions: forward-looking behavior, limited heterogeneity in compliance costs, and a principal objective that is not too concave. We test these mechanisms in a randomized experiment in which adults in treatment for opioid and stimulant use disorders are assigned to escalating, de-escalating, or constant incentives for drug abstinence. We find that while participants are forward-looking — which favors escalation — compliance costs are highly persistent both across and within individuals. This persistence generates a targeting disadvantage for escalating contracts: they concentrate the largest payments on those with the lowest compliance costs, where incentives are least needed. Escalating contracts also generate greater dispersion in compliance outcomes, which is costly when the principal places greater weight on reducing the worst outcomes. We estimate a dynamic structural model of compliance to characterize the optimal incentive schedule. We find that escalating schedules are preferred when the objective is sufficiently convex (prioritizing complete abstinence), while de-escalating schedules are preferred when the objective is linear or concave (prioritizing reductions in severe drug use). Our results suggest a rethinking of the widespread use of escalating incentives in substance use treatment.

Aug 7

5:00 pm - 7:00 pm PDT

Dinner