Papers

Published and Accepted Papers

A Model of Non-Belief in the Law of Large Numbers (with Daniel Benjamin and Matthew Rabin)

            Journal of European Economic Association, 2015

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this “nonbelief in the Law of Large Numbers” by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.


Paper; Appendix


A Behavioral Analysis of Stochastic Reference Dependence (with Yusufcan Masatlioglu)

            American Economic Review, 2016


We examine the reference-dependent risk preferences of Koszegi and Rabin (2007), focusing on their choice-acclimating personal equilibria concept. Although their model has only a trivial intersection (expected utility) with other reference dependent models, it has very strong connections with models that rely on different psychological intuitions. We prove that the intersection of rank-dependent utility and quadratic utility, two well-known generalizations of expected utility, is exactly monotone linear gain-loss choice-acclimating personal equilibria. We use these relationships to identify parameters of the model, discuss loss and risk aversion, and demonstrate new applications.

PaperAppendix


Working Papers

Preferences for Truth-Telling (with Johannes Abeler and Daniele Nosenzo)

            Revise and Resubmit, Econometrica


Private information is at the heart of many economic activities. For decades, economists have assumed that individuals are willing to misreport private information if this maximizes their material payoff. We combine data from 72 experimental studies in economics, psychology and sociology, and show that, in fact, people lie surprisingly little. We then formalize a wide range of potential explanations for the observed behavior, identify testable predictions that can distinguish between the models and conduct new experiments to do so. None of the most popular explanations suggested in the literature can explain the data. We show that only combining a preference for being honest with a preference for being seen as honest can organize the empirical evidence.



Tell all the Truth, but Tell it Slant: Testing Models of Media Bias (with Sarah Taylor)

Media outlets often appear to bias news reports, but it is diffcult to identify bias clearly. In addition, it is often unclear what motivates media bias and whether this bias improves the welfare of news consumers. We develop a model of demand-driven media bias, and we test for the existence of bias using a novel data set. We consider how daily weather predictions by The New York Times in the late 19th century differ across days when the New York Giants (the local baseball team) played a home game and days when they did not. The historical context provides a clean natural experiment for identifying bias: on game days the Times' weather reports were relatively more accurate at predicting sunny weather and less accurate at predicting rainy weather. We provide evidence that a model of demand-driven bias is a more plausible explanation for the patterns observed in the data than an analogous model of supply-driven bias. This has important implications for welfare, as news reports which cater to a demand-side preference for biased news can be benecial to consumers

Paper


Preferences for Non-Instrumental Information and Skewness (with Yusufcan Masatlioglu and Yesim Orhun)


We present experimental results from a broad investigation of intrinsic preferences for information. We examine whether people prefer negatively skewed or positively skewed information structures when they are equally informative, whether people prefer Blackwell more informative information structures, and how individual preferences over the skewness and the degree of information relate to one another. The wide scope of our investigation not only reveals new insights regarding intrinsic preferences for information, but as we show, also allows for testing of existing models in this domain. We find that models based on the framework of Kreps and Porteus (1978) and Caplin and Leahy (2001), are the most consistent with the data we observe.

Group-Shift and the Consensus Effect (with David Dillenberger)

It is well documented that individuals make different choices in the context of group decisions, such as elections, from choices made in isolation. In particular, individuals tend to conform to the decisions of others — a property we call the consensus effect — which in turn implies phenomena such as group polarization and the bandwagon effect. We show that the consensus effect is equivalent to a well-known violation of expected utility, namely strict quasi-convexity of preferences. Our results qualify and extend those of Eliaz, Ray and Razin (2006), who focus on choice-shifts in group when one option is safe (i.e., a degenerate lottery). In contrast to the equilibrium outcome when individuals are expected utility maximizers, the consensus effect implies that group decisions may fail to properly aggregate preferences in strategic contexts and strictly Pareto-dominated equilibria may arise. Moreover, these problems become more severe as the size of the group grows.


One in a Million: A Field Experiment on Belief Formation and Pivotal Voting (with Alan Gerber, Mitchell Hoffman and John Morgan)


A common feature of many models of voter turnout is that increasing the perceived closeness of the election should increase voter turnout. However, cleanly testing this prediction is difficult and little is known about voter beliefs regarding the closeness of a given race. We conduct a field experiment during the 2010 US gubernatorial elections where we elicit voter beliefs about the closeness of the election before and after showing different polls, which, depending on treatment, indicate a close race or a not close race. We find that subjects update their beliefs in response to new information, but systematically overestimate the probability of a very close election. However, the decision to vote is unaffected by beliefs about the closeness of the election. A follow-up field experiment, conducted during the 2014 gubernatorial elections but at much larger scale, also points to little relationship between poll information about closeness and voter turnout.



Work In Progress

Search and Temptation 

Revealed Search Theory 




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