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    Bordalo, Pedro, Katherine Coffman, Nicola Gennaioli, and Andrei Shleifer. 2016. “Stereotypes.” Quarterly Journal of Economics 131 (4): 1753-1794. Publisher's Version Abstract

    We present a model of stereotypes based on Kahneman and Tversky’s representativeness heuristic. A decision maker assesses a target group by overweighting its representative types, defined as the types that occur more frequently in that group than in a baseline reference group. Stereotypes formed this way contain a ‘‘kernel of truth’’: they are rooted in true differences between groups. Because stereotypes focus on differences, they cause belief distortions, particularly when groups are similar. Stereotypes are also context dependent: beliefs about a group depend on the characteristics of the reference group. In line with our predictions, beliefs in the lab about abstract groups and beliefs in the field about political groups are context dependent and distorted in the direction of representative types. JEL Codes: D03, D83, D84, C91.

    Barberis, Nicholas, Robin Greenwood, Lawrence Jin, and Andrei Shleifer. 2018. “Extrapolation and Bubbles.” Journal of Financial Economics 129 (2): 203-227.

    Biography and CV

    Brief Biography


    Andrei Shleifer is John L. Loeb Professor of Economics at Harvard University. He holds an undergraduate degree from Harvard and a Ph.D. from MIT. Before coming to Harvard in 1991, he has taught at Princeton and the Chicago Business School. Shleifer has worked in the areas of comparative corporate governance, law and finance, behavioral finance, as well as institutional economics. He has published seven books, including The Grabbing Hand (with Robert Vishny), Inefficient Markets: An...

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    Greenwood, Robin, Andrei Shleifer, and Yang You. 2019. “Bubbles for Fama.” Journal of Financial Economics 131 (1): 20-43. Abstract

    We evaluate Eugene Fama’s claim that stock prices do not exhibit price bubbles. Based on US industry returns 1926-2014 and international sector returns 1985-2014, we present four findings: (1) Fama is correct in that a sharp price increase of an industry portfolio does not, on average, predict unusually low returns going forward; (2) such sharp price increases predict a substantially heightened probability of a crash; (3) attributes of the price run-up, including volatility, turnover, issuance, and the price path of the run-up can all help forecast an eventual crash and future returns; and (4) some of these characteristics can help investors earn superior returns by timing the bubble. Results hold similarly in US and international samples.

    Bordalo, Pedro, Katie Coffman, Nicola Gennaioli, and Andrei Shleifer. 2019. “Beliefs about Gender.” American Economic Review 109 (3): 739-773. Abstract
    We conduct laboratory experiments that explore how gender stereotypes shape beliefs about ability of oneself and others in different categories of knowledge. The data reveal two patterns. First, men’s and women’s beliefs about both oneself and others exceed observed ability on average, particularly in difficult tasks. Second, overestimation of ability by both men and women varies across categories. To understand these patterns, we develop a model that separates gender stereotypes from mis-estimation of ability related to the difficulty of the task. We find that stereotypes contribute to gender gaps in self-confidence, assessments of others, and behavior in a cooperative game.
    Glaeser, Edward L., Giacomo A. M. Ponzetto, and Andrei Shleifer. Working Paper. “Securing Property Rights”. Abstract

    A central challenge in securing property rights is the subversion of justice through legal skill, bribery, or physical force by the strong—the state or its powerful citizens—against the weak. We present evidence that undue influence on judges is a common concern in many countries, especially among the poor. We then present a model of a water polluter whose discharges contaminate adjacent land. If this polluter can subvert the assessment of damages caused by his activity, there is an efficiency case for granting the landowner the right to an injunction that stops the polluter, rather than the right to compensation for the harm. If the polluter can subvert even the determination of his responsibility for harm, there is an efficiency case for regulation that restricts pollution regardless of its effects. We then conduct an empirical analysis of water quality in the U.S. before and after the Clean Water Act, and show how regulation brought about cleaner water, particularly in states with higher corruption.

    Bordalo, Pedro, Nicola Gennaioli, Rafael LaPorta, and Andrei Shleifer. 2019. “Diagnostic Expectations and Stock Returns.” Journal of Finance 74 (6): 2839-2874. Abstract

    We revisit La Porta's finding that returns on stocks with the most optimistic analyst long‐term earnings growth forecasts are lower than those on stocks with the most pessimistic forecasts. We document the joint dynamics of fundamentals, expectations, and returns of these portfolios, and explain the facts using a model of belief formation based on the representativeness heuristic. Analysts forecast fundamentals from observed earnings growth, but overreact to news by exaggerating the probability of states that have become more likely. We find support for the model's predictions. A quantitative estimation of the model accounts for the key patterns in the data.

    Bordalo, Pedro, Katherine Coffman, Nicola Gennaioli, Frederik Schwerter, and Andrei Shleifer. Working Paper. “Memory and Representativeness”. Abstract

    We explore the idea that judgment by representativeness reflects the workings of episodic memory, especially interference. In a new laboratory experiment on cued recall, participants are shown two groups of images with different distributions of colors. We find that i) decreasing the frequency of a given color in one group significantly increases the recalled frequency of that color in the other group, ii) for a fixed set of images, different cues for the same objective distribution entail different interference patterns and different probabilistic assessments. Selective retrieval and interference may offer a foundation for the representativeness heuristic, but more generally for understanding the formation of probability judgments from experienced statistical associations.