This paper presents simple new multisignal generalizations of the two classic methods used to justify the first-order approach to moral hazard principal–agent problems, and compares these two approaches with each other. The paper first discusses limitations of previous generalizations. Then a state-space formulation is used to obtain a new multisignal generalization of the Jewitt (1988) conditions. Next, using the Mirrlees formulation, new multisignal generalizations of the convexity of the distribution function condition (CDFC) approach of Rogerson (1985) and Sinclair-Desgagné (1994) are obtained. Vector calculus methods are used to derive easy-to-check local conditions for our generalization of the CDFC. Finally, we argue that the Jewitt conditions may generalize more flexibly than the CDFC to the multisignal case. This is because, with many signals, the principal can become very well informed about the agent's action and, even in the one-signal case, the CDFC must fail when the signal becomes very accurate.

An asymmetric information model of a finite horizon “nth order” rational asset price bubble is presented, where (all agents know that)n the asset is worthless. Also, the model has only two agents, so the first order version of the bubble is simpler than other first order bubbles in the literature.

We test for stereotyping—the exaggeration of representative traits—in a high-stakes economic environment. Using surveys administered among undergraduates at the Ohio State University as well as large-scale nationally representative data, we measure how U.S. first-year students perceive the relationship between college majors and occupations. We show that students greatly overestimate the likelihood that majors lead to their representative jobs (e.g., counselor for psychology, journalist for journalism). Using an implicit association test, we show that students associate majors with their representative careers and that these associations strongly predict belief biases, in line with a stereotyping mechanism. A simple equilibrium model of the labor market predicts that stereotyping reduces welfare by increasing misallocation, which we corroborate with correlational evidence on job/major mismatch. In a field experiment, we test a light-touch policy to reduce stereotyping and find significant effects on students’ intentions about what to study as well as the classes and majors they enroll in.

In many economic decisions, people estimate probabilities, such as the likelihood that a risk materializes or that a job applicant will be a productive employee, by retrieving experiences from memory. We model this process based on two established regularities of selective recall: similarity and interference. We show that the similarity structure of a hypothesis and the way it is described (not just its objective probability) shape the recall of experiences and thus probability assessments. The model accounts for and reconciles a variety of empirical findings, such as overestimation of unlikely events when these are cued versus neglect of noncued ones, the availability heuristic, the representativeness heuristic, conjunction and disjunction fallacies, and over- versus underreaction to information in different situations. The model yields several new predictions, for which we find strong experimental support.

For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.

Can access to a few hundred dollars of liquidity affect the career choice of a recent college graduate? In a three-year field experiment with Teach For America (TFA), a prestigious teacher placement program, we randomly increase the financial packages offered to nearly 7,300 potential teachers who requested support for the transition into teaching. The first two years of the experiment reveal that although most applicants do not respond to a marginal $600 of grants or loans, those in the worst financial position respond by joining TFA at higher rates. We continue the experiment into the third year and self-replicate our results. For the highest-need applicants, an extra $600 in loans, $600 in grants, and $1,200 in grants increase the likelihood of joining TFA by 12.2, 11.4, and 17.1 percentage points (or 20.0%, 18.7%, and 28.1%), respectively. Additional grant and loan dollars are equally effective, suggesting a liquidity mechanism. A follow-up survey bolsters the liquidity story and also shows that those drawn into teaching would have otherwise worked in private-sector firms.

We study social learning using experiments where two people independently learn relevant information and can share it to make accurate private decisions. Across three experiments, people are substantially less sensitive to information others discover than to equally-relevant information they discovered themselves. This holds when they must learn information from others through discussion; when the experimenter perfectly communicates the information; and even when participants observe others’ information with their own eyes. Our results therefore stem not from a failure to elicit information from others but a systematic tendency to underweight it relative to one’s own information. Our findings illustrate a powerful barrier to social learning that might underlie many documented cases of failure to learn from others.