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This paper examines the effect of market participants' information processing costs on firms' disclosure choice. Using the recent eXtensible Business Reporting Language (XBRL) regulation, I find that firms increase their quantitative footnote disclosures upon implementation of XBRL detailed tagging requirements designed to reduce information users' processing costs. These results hold in a difference-in-difference design using matched nonadopting firms as controls, as well as two additional identification strategies. Examination of the disclosure increase by footnote type suggests that both regulatory and nonregulatory market participants play a role in monitoring firm disclosures. Overall, these findings suggest that the processing costs of market participants can be significant enough to impact firms' disclosure decisions.

This paper examines whether third-party-generated product information on Twitter, once aggregated at the firm level, is predictive of firm-level sales, and if so, what factors determine the cross-sectional variation in the predictive power. First, the predictive power of Twitter comments increases with the extent to which they fairly represent the broad customer response to products and brands. The predictive power is greater for firms whose major customers are consumers rather than businesses. Second, the word-of-mouth effect of Twitter comments is greater when advertising is limited. Third, a detailed analysis of the identity of the tweet handles provides the additional insights that the predictive power of the volume of Twitter comments is dominated by \"the wisdom of crowds,\" whereas the predictive power of the valence of Twitter comments is largely attributable to expert comments. Furthermore, Twitter comments not only reflect upcoming sales, but also capture an unexpected component of sales growth.

A game of incomplete information can be decomposed into a basic game and an information structure. The basic game defines the set of actions, the set of payoff states the payoff functions and the common prior over the payoff states. The information structure refers to the signals that the players receive in the game. We characterize the set of outcomes that can arise in Bayes Nash equilibrium if players observe the given information structure but may also observe additional signals. The characterization corresponds to the set of (a version of) incomplete information correlated equilibria which we dub Bayes correlated equilibria. We identify a partial order on many player information structures (individual sufficiency) under which more information shrinks the set of Bayes correlated equilibria. This order captures the role of information in imposing (incentive) constraints on behavior.

An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I characterize optimal information disclosure and provide conditions under which full and no revelation are optimal. Assuming further that the sender's utility depends only on the sender's expected type, I provide conditions under which interval revelation is optimal. Finally, I show that the expected utilities are not monotonic in the precision of the receiver's private information.

Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty.

We find that people revise their beliefs about climate change upward when experiencing warmer than usual temperatures in their area. Using international data, we show that attention to climate change, as proxied by Google search volume, increases when the local temperature is abnormally high. In financial markets, stocks of carbon-intensive firms underperform firms with low carbon emissions in abnormally warm weather. Retail investors (not institutional investors) sell carbon-intensive firms in such weather, and return patterns are unlikely to be driven by changes in fundamentals. Our study sheds light on peoples’ collective beliefs and actions about global warming.

In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest neighbours, local kernel-weighted methods and support vector regression) in this regard is their lack of interpretability or transparency. Partial dependence plots, which are the most popular approach for visualizing the effects of the predictors with black box supervised learning models, can produce erroneous results if the predictors are strongly correlated, because they require extrapolation of the response at predictor values that are far outside the multivariate envelope of the training data. As an alternative to partial dependence plots, we present a new visualization approach that we term accumulated local effects plots, which do not require this unreliable extrapolation with correlated predictors. Moreover, accumulated local effects plots are far less computationally expensive than partial dependence plots.We also provide an R package ALEPlot as supplementary material to implement our proposed method.

We propose a technique for assessing robustness to demand effects of findings from experiments and surveys. The core idea is that by deliberately inducing demand in a structured way we can bound its influence. We present a model in which participants respond to their beliefs about the researcher’s objectives. Bounds are obtained by manipulating those beliefs with “demand treatments.” We apply the method to 11 classic tasks, and estimate bounds averaging 0.13 standard deviations, suggesting that typical demand effects are probably modest. We also show how to compute demand-robust treatment effects and how to structurally estimate the model.

This article uses an experiment embedded in a survey to analyze the response of consumers' long-run inflation expectations to information about the Federal Reserve's inflation target and past inflation. On average, respondents revise forecasts toward the 2% target with either information treatment. Forecast uncertainty and heterogeneity decline with the treatments, but remain substantial. Since the information in the treatments is publicly available, these findings are consistent with models of imperfect information in which agents do not fully and continually update their information sets or incorporate all available information into their expectations. Response to treatments varies with prior informedness and with demographic characteristics.

Data is nonrival: a person’s location history, medical records, and driving data can be used by many firms simultaneously. Nonrivalry leads to increasing returns. As a result, there may be social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard their data, leading to the inefficient use of nonrival data. Giving data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data broadly.