Session 4: Psychology and Economics

Date
Mon, Aug 9 2021, 9:00am - Tue, Aug 10 2021, 12:30pm PDT
Location
Zoom
Organized by
  • B. Douglas Bernheim, Stanford University
  • John Beshears, Harvard Business School
  • Vincent Crawford, University of Oxford and University of California, San Diego
  • David Laibson, Harvard University
  • Ulrike Malmendier, University of California, Berkeley

As we have done for many years, this workshop brings together researchers working on issues at the intersection of psychology and economics. The segment will focus on evidence of and explanations for non-standard choice patterns, as well as the positive and normative implications of those patterns in a wide range of economic decision-making contexts, such as lifecycle consumption and savings, workplace productivity, health, and prosocial behavior. The presentations will frequently build upon insights from other disciplines, including psychology and sociology. Theoretical, empirical, and experimental studies will be included.

In This Session

Monday, August 9, 2021

Aug 9
9:00 am - 9:30 am PDT

The Gender Gap in Self-Promotion

Presented by: Christine Exley (Harvard Business School)
Co-author(s): Judd B. Kessler (The Wharton School, University of Pennsylvania)

In applications, interviews, performance reviews, and many other environments, individuals subjectively describe their ability and performance to others. We run a series of experiments, involving over 4,000 participants from online labor markets and over 10,000 school-aged youth. We find a large gender gap in self-promotion: Women subjectively describe their ability and performance to potential employers less favorably than equally performing men. Even when all incentives to promote are removed, however, the gender gap remains. The gender gap in self-promotion is reflective of an underlying gender gap in how individuals subjectively evaluate their own performance. This underlying gender gap proves persistent and arises as early as the sixth grade.

Aug 9
9:30 am - 10:00 am PDT

Partial Equilibrium Thinking in General Equilibrium

Presented by: Francesca Bastianello (Harvard University)
Co-author(s): Paul Fontanier (Harvard University)

We develop a theory of “Partial Equilibrium Thinking” (PET), whereby agents fail to understand the general equilibrium consequences of their actions when inferring information from endogenous outcomes. PET generates a two-way feedback effect between outcomes and beliefs, which can lead to arbitrarily large deviations from fundamentals. In financial markets, PET equilibrium outcomes exhibit over-reaction, excess volatility, high trading volume, and return predictability. We extend our model to allow for rationality of higher-order beliefs, general forms of model misspecification, and heterogenous agents. We show that more sophisticated agents may contribute to greater departures from rationality. We also draw a distinction between models of misinference and models with biases in Bayesian updating, and study how these two departures from rationality interact. Misinference from mistakenly assuming the world is rational amplifies biases in Bayesian updating.

Aug 9
10:00 am - 10:15 am PDT

Break

Aug 9
10:15 am - 10:45 am PDT

Belief Updating: Inference versus Forecast Revision

Presented by: Tony Q. Fan (Stanford University),
Co-author(s): Yucheng Liang (Carnegie Mellon University) and Cameron Peng (London School of Economics and Political Science)

Individual forecasts of economic variables show widespread overreaction to news, but laboratory experiments on belief updating typically find underinference from signals. We provide new experimental evidence to connect these two seemingly inconsistent phenomena. Building on a classic experimental paradigm, we study how people make inferences and revise forecasts in the same information environment. Subjects underreact to signals when inferring about underlying states, but overreact to signals when revising forecasts about future outcomes. This gap in belief updating is largely driven by the use of different simplifying heuristics for the two tasks. Additional treatments link our results to the difficulty of recognizing the conceptual connection between making inferences and revising forecasts.
 

Aug 9
10:45 am - 11:15 am PDT

Dynamic Preference "Reversals" and Time Inconsistency

Presented by: Dmitry Taubinsky (UC Berkeley)
Co-author(s): Philipp Strack (Yale University)

We study identification of time inconsistency when an agent at time 0 makes an advance commitment, and later at time 1 can revise their choice after possibly receiving additional
information. Roughly speaking, we prove that the only data that reject time-consistent expected utility maximization is a time-0 choice that is always strictly dominated at time 1. This holds
for rich choice sets; if the complete ranking of alternatives is observed in every period and state; when it is natural to assume additional properties like concavity; and with supplementary
cardinal information. However, time inconsistency can be point identified from willingness to pay for different alternatives in both periods, if utility from money is plausibly additively-separable and independent of time-1 information.

Aug 9
11:15 am - 11:30 am PDT

Break

Aug 9
11:30 am - 12:00 pm PDT

Does Saving Cause Borrowing?

Presented by: Michaela Pagel (Columbia GSB)
Co-author(s): Paolina Medina (Mays Business School of Texas A&M University)

We study whether or not nudging individuals to save more has the unintended consequence of additional borrowing in high-interest unsecured consumer credit. We analyze the effects of a large-scale experiment in which 3.1 million bank customers were nudged to save more via (bi-)weekly SMS and ATM messages. Using Machine Learning methods for causal inference, we build a score to sort individuals according to their predicted treatment effect. We then focus on the individuals in the top quartile of the distribution of predicted treatment effects who have a credit card and were paying interest at baseline. Relative to their control, this group increased their savings by 5.7% on average or 61.84 USD per month. At the same time, we can rule out increases in credit card interest larger than 1.25 USD with 95% statistical confidence. We thus estimate that for every additional dollar of savings, individuals incur less than 2 cents in additional borrowing cost. This is a direct test test of the predictions of rational co-holding models, and is an important result to evaluate policy proposals to increase savings via nudges or more forceful measures.

Aug 9
12:00 pm - 12:30 pm PDT

Learning in the Household

Presented by: Gautam Rao (Harvard University)
Co-author(s): John J. Conlon (Harvard University), Malavika Mani (Columbia University), Matthew Ridley (MIT), and Frank Schilbach (MIT)

We study social learning between spouses using an experiment in Chennai, India. We vary whether individuals discover information themselves or must instead learn what their spouse discovered via a discussion. Women treat their ‘own’ and their husband’s information the same. In sharp contrast, men’s beliefs respond less than half as much to information that was discovered by their wife. This is not due to a lack of communication: husbands put less weight on their wife’s signals even when perfectly informed of them. In a second experiment, when paired with mixed- and same-gender strangers, both men and women heavily discount their teammate’s information relative to their own. We conclude that people have a tendency to underweight others’ information relative to their own. The marital context creates a countervailing force for women, resulting in a gender difference in learning (only) in the household.

Tuesday, August 10, 2021

Aug 10
9:00 am - 9:30 am PDT

Safe Spaces: Shelters or Tribes?

Presented by: Jean Tirole (Toulouse School of Economics)

By making our lives more transparent than ever, technology exposes our behavior to an audience that is less like-minded than that in our private sphere. In reaction, we change our behavior. Or we incur costs to join safe spaces: reduced use of public spaces and forgone diversity and opportunities when selecting our social graph. This paper provides a framework for thinking about the endogeneity of our private sphere in environments in which issues are divisive (politics, religion, sexuality, antagonistic social views…). It studies the emergence of safe spaces of like-minded individuals and their societal consequences.

 

Aug 10
9:30 am - 10:00 am PDT

A Model of Justification

Presented by: Sarah Ridout (Harvard University)

I model decision-making constrained by morality, rationality, or other virtues. In addition to a primary preference over outcomes, the decision maker (DM) is characterized by a set of preferences that he considers justifiable. In each choice setting, he maximizes his primary preference over the subset of alternatives that maximize at least one of the justifiable preferences. The justification model unites a broad class of empirical work on distributional preferences, charitable donations, prejudice/discrimination, and corruption/bribery. I provide full behavioral characterizations of several variants of the justification model as well as practical tools for identifying primary preferences and justifications from choice behavior. I show that identification is partial in general, but full identification can be achieved by including lotteries in the domain and allowing for heterogeneity in both primary preferences and justifications. Since the heterogeneous model uses between-subject data, it is robust to consistency motives that may arise in within-subject experiments. I extend the heterogeneous model to information choice and show that it accounts for observed patterns of information demand and avoidance on ethical domains.

Aug 10
10:00 am - 10:15 am PDT

Break

Aug 10
10:15 am - 10:45 am PDT

How Flexible Is That Functional Form? Measuring the Restrictiveness of Theories

Presented by: Annie Liang (Northwestern University)
Co-author(s): Drew Fudenberg (MIT) and Wayne Gao (University of Pennsylvania)

We propose a new way to quantify the restrictiveness of an economic model, based on how well the model fits simulated, hypothetical data sets. The data sets are drawn at random from a distribution that satisfies some application-dependent content restrictions (such as that people prefer more money to less). Models that can fit almost all hypothetical data well are not restrictive. To illustrate our approach, we evaluate the restrictiveness of popular behavioral models in two experimental settings—certainty equivalents and initial play— and explain how restrictiveness reveals new insights about each of the models.

Aug 10
10:45 am - 11:15 am PDT

Choice and Complexity

Presented by: Jörg L. Spenkuch (Northwestern University)
Co-author(s): Yuval Salant (Northwestern University)

We study two dimensions of complexity that may interfere with individual choice. The first one is object complexity, which corresponds to the difficulty in evaluating any given alternative in a choice set. The second dimension is composition complexity, which increases when suboptimal alternatives become more similar to optimal ones. We develop a satisficing-with-evaluation-errors theory that incorporates both dimensions and delivers sharp empirical predictions about their effect on choice behavior. We confirm these predictions in a novel data set with information on hundreds of millions of decisions in chess endgames. First, as the object complexity of an optimal (suboptimal) alternative increases, it becomes less (more) likely to be chosen. Second, even highly experienced decision-makers are more likely to make mistakes when choosing from sets with higher composition complexity. These findings help to shed some of the first light on the effect of complexity on choice behavior outside of the laboratory.

Aug 10
11:15 am - 11:30 am PDT

Break

Aug 10
11:30 am - 12:00 pm PDT

Incentive Complexity, Bounded Rationality, and Effort Provision

Presented by: David Huffman (University of Pittsburgh)
Co-author(s): Johannes Abeler (University of Oxford) and Collin Raymond (Purdue University)

This paper shows that dynamic incentives embedded in an organization’s workplace incentive scheme can be a shrouded attribute, due to contract complexity and worker bounded rationality. This is true in field experiments within the firm, and in complementary online experiments with real eort tasks. Structural estimates indicate that rational agents who fully understand the incentive scheme would behave sigificantly dierent from what we observe. A response to dynamic incentives does emerge when we reduce complexity or look at workers with higher cognitive ability. The results illustrate the potential value of complexity to organizations, they demonstrate that complex incentive contracts may allow firms to be achieve better than second-best, they identify specific features of contracts that can influence the eectiveness of incentives through the channel of complexity, and they imply heterogeneous eects of incentives depending on worker cognitive ability.

Aug 10
12:00 pm - 12:30 pm PDT

The Negative Consequences of Loss-Framed Performance Incentives

Presented by: Alex Rees-Jones (The Wharton School, University of Pennsylvania)
Co-author(s): Lamar Pierce (Olin Business School, Washington University in St Louis) and Charlotte Blank (Maritz)

Behavioral economists have proposed that incentive contracts result in higher productivity when bonuses are "loss framed" prepaid then clawed back if targets are unmet. We test this claim in a large-scale field experiment. Holding financial incentives fixed, we randomized the pre- or postpayment of sales bonuses at 294 car dealerships. Prepayment was estimated to reduce sales by 5%, generating a revenue loss of $45 million over 4 months. We document, both empirically and theoretically, that negative effects of loss framing can arise due to an increase in incentives for "gaming" behaviors. Based on these claims, we reassess the common wisdom regarding the desirability of loss framing.