Research

IRBs can disallow high incentives they deem coercive. A vignette study on MTurk concerning participation in medical trials shows that a substantial minority of subjects concurs. They think high incentives cause more regret, and that more people would be better off without the opportunity to participate. We model observers as judging the ethicality of incentives by partially using their own utility.

The model predicts that payments are repugnant only to the extent that they affect the participation decision, and more so for larger transactions. Incentivizing poorer participants is more repugnant, and in-kind incentives are less repugnant than monetary incentives.

Our recent working paper (Ambuehl, Ockenfels, and Stewart, 2017) shows theoretically and experimentally that people with higher costs of information processing respond more to an increase in the incentive for a complex transaction, and decide to participate based on a worse understanding of its consequences. Here, we address the resulting tradeoff between the principle of informed consent and the principle of free contract.

Respondents to our vignette study on oocyte donation overwhelmingly favor the former, and support policies that require donors to thoroughly understand the transaction. This finding helps design markets that are not only efficient, but also considered ethical.

How do individuals value noisy information that guides economic decisions? In our laboratory experiment, we find that individuals underreact to increasing the informativeness of a signal, thus undervalue high-quality information, and that they disproportionately prefer information that may yield certainty. Both biases are entirely due to non-standard belief updating, rather than due to non-standard risk preferences.

We find that individuals differ consistently in their responsiveness to information - the extent that their beliefs move upon observing signals. Individual parameters of responsiveness to information have out-of-sample explanatory power in two distinct choice environments and are unrelated to proxies for mathematical aptitude.

Unraveling, the excessively early matching of future workers to employers, is a pervasive phenomenon in entry-level labor markets that leads to hiring decisions based on severely incomplete information. We provide a model of unraveling in one-to-one matching markets for prestigious positions. Its distinguishing feature is that the market operates over an extended time period during which information about potential matches arrives gradually.

We find that unraveling causes potentially thick markets to spread thinly over a long time period. In equilibrium, an employers' desirability is correlated neither with the time at which they hire, nor with the expected productivity of their matched worker. Unraveling thus significantly redistributes welfare among employers compared to a pairwise stable match.

We study policies that manipulate the availability of information about students and show that they are effective only if they provide a sudden surge in information. Our main application is the market for U.S. federal appellate court clerks, a significant input into the efficiency of the justice system. Consistent with the model, hiring times in our dataset are spread over a period of six months and are uncorrelated with the desirability of a judge as an employer.

The incentivization and remuneration of clinical trial participants is a central topic in medical ethics. The possibility to shorten the Covid-pandemic through controlled human infection studies lends the topic renewed relevance. We summarize evidence from economics to demonstrate that many of the central questions surrounding human subject payment can be answered empirically.

Winner of the Exeter Prize 2022 for the best paper published in the previous calendar year in a peer-reviewed journal in the fields of Experimental Economics, Decision Theory and Behavioural Economics.

We study experimentally when, why, and how people intervene in others' choices. Choice Architects construct opportunity sets containing bundles of time-indexed payments for Choosers. Choice Architects frequently prevent impatient choices despite opportunities to provide advice, believing Choosers benefit. They violate common behavioral welfare criteria by removing impatient options even when all payoffs are delayed.

Choice Architects intervene not by removing options they wish they could resist when choosing for themselves (mistakes-projective paternalism), but rather as if they seek to align others' choices with their own aspirations (ideals-projective paternalism). Laboratory choices predict subjects' support for actual paternalistic policies.

Selected as finalist for the TIAA Paul A. Samuelson Award.

We examine methods for evaluating interventions designed to improve decision-making quality when people misunderstand the consequences of their choices. In an experiment involving financial education, conventional outcome metrics imply that two interventions are equally beneficial even though only one reduces the average severity of errors. We trace these failures to violations of the assumptions embedded in the conventional metrics.

We propose a simple, intuitive, and broadly applicable outcome metric that properly differentiates between the interventions, and is robustly interpretable as a measure of welfare loss from misunderstanding consequences even when additional biases distort choices.

Best paper award runner-up, Association for NeuroPsychoEconomics, 2021.

Participation payments are used in many transactions about which people know little, but can learn more: incentives for medical trial participation, signing bonuses for job applicants, or price rebates on consumer durables. Who opts into the transaction when given such incentives? We theoretically and experimentally identify a composition effect whereby incentives disproportionately increase participation among those for whom learning is harder.

Moreover, these individuals use less information to decide whether to participate, which makes disappointment more likely. The learning-based composition effect is stronger in settings in which information acquisition is more difficult.

We investigate the impact of peer interaction on the quality of financial decision making in a laboratory experiment. Face-to-face communication with a randomly assigned peer significantly improves the quality of subsequent private decisions even though simple mimicry would have the opposite effect.

We present evidence that the mechanism involves general conceptual learning, and that the most effective learning relationships are horizontal rather than vertical. The benefits of demonstrably effective financial education do not propagate to peers.

Around the world, laws limit the incentives that can be paid for transactions such as human research participation, egg donation, or gestational surrogacy. A key reason are concerns about undue inducement: the influential but empirically untested idea that incentives cause harm by distorting individual decision making. Two experiments, including one based on a highly visceral transaction, show that incentives cause biased information search.

Contrary to common interpretations, such behavior is no proof for harmful effects of incentives; it is consistent with Bayesian rationality. Empirically, a substantial minority of participants make bad decisions, but incentives do not magnify them in a way that would justify allowing a transaction but capping incentives.

Collective decision making requires preference aggregation even if no ideal aggregation method exists. We investigate how individuals think groups should aggregate members' ordinal preferences: that is, how they interpret the will of the people. Our experiment elicits revealed attitudes toward ordinal preference aggregation and classifies subjects according to the rules they implicitly deploy. Majoritarianism is rare while rules that promote compromise are common.

People evaluate relative sacrifice by inferring cardinal utility from ordinal ranks. Cluster analysis reveals that our classification encompasses all important aggregation rules. Aggregation methods exhibit stability across domains and across countries with divergent traditions.

Much economic analysis derives policy recommendations based on social welfare criteria intended to model policymakers' preferences. Yet, little is known about policymakers' normative views suitable for this use. Our behavioural experiment elicits German legislators' welfare criteria.

When resolving preference conflicts across individuals, they place substantially more importance on least-favoured alternatives than on most-favoured alternatives, contrasting with common aggregation mechanisms and the equal weighting inherent in utilitarianism. When resolving preference conflicts within individuals, politicians choose inconsistently with behavioural economists' most common approach to welfare analysis with time-inconsistent preferences. In distribution, politicians largely support the same welfare criteria as the public.

Why is in-kind aid a prominent feature of welfare systems? We present a lab-in-the-field experiment involving members of the general U.S. population and SNAP recipients. After documenting a widespread desire to limit recipients' choices, we quantify the relative importance of welfarist motives, utility or disutility derived from curtailing another's autonomy, and absolutist attitudes concerning the appropriate form of aid.

Choices primarily reflect the two non-welfarist motives. Because people systematically misperceive recipient preferences, their interventions are more restrictive than they intend. Interventionist preferences and non-welfarist motives are more pronounced among the political right, particularly when recipients are black.

People often make choices with imperfect knowledge of how the variables in their decision problem are related. We study such choices when individuals face menus of conflicting and possibly misspecified models that link these variables. Do they discard inaccurate models, what types of inaccuracies do they detect, and how? Or do they instead follow models that sound appealing at face value, and what determines that appeal?

First, many individuals readily intuit the models' predicted correlations and reject models that contradict the data. Performance is high because the required inference is qualitative rather than quantitative. Second, when unable to identify the correct model, most choose cautiously by focusing on worst-case outcomes.

A burgeoning literature in economics studies how people form beliefs about the causal structures linking economic variables, and what happens when those beliefs are mistaken. We survey this research and connect it to a rich literature in cognitive science.

After providing an accessible introduction to causal Directed Acyclic Graphs, the dominant modeling approach, we review theory and evidence addressing three nested questions: how individuals reason within a fully parameterized causal structure, how they estimate its parameters, and how they learn such structures to begin with.