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An ever-increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications.

Following the 2016 US presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: 1) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; 2) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; 3) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and 4) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

We study symmetric information games where a number of senders choose what information to communicate. We show that the impact of competition on information revelation is ambiguous in general. We identify a condition on the information environment (i.e. the set of signals available to each sender) that is necessary and sufficient for equilibrium outcomes to be no less informative than the collusive outcome, regardless of preferences. The same condition also provides an easy way to characterize the equilibrium set and governs whether introducing additional senders or decreasing the alignment of senders' preferences necessarily increases the amount of information revealed.

Rothschild and Stiglitz (1970) represent random variables as convex functions (integrals of the cumulative distribution function). Combining this representation with Blackwell's Theorem (1953), we characterize distributions of posterior means that can be induced by a signal. This characterization provides a novel way to analyze a class of Bayesian persuasion problems.

When is it possible for one person to persuade another to change her action? We consider a symmetric information model where a sender chooses a signal to reveal to a receiver, who then takes a noncontractible action that affects the welfare of both players. We derive necessary and sufficient conditions for the existence of a signal that strictly benefits the sender. We characterize sender-optimal signals. We examine comparative statics with respect to the alignment of the sender's and the receiver's preferences. Finally, we apply our results to persuasion by litigators, lobbyists, and salespeople.

We study the design of informational environments in settings where generating information is costly. We assume that the cost of a signal is proportional to the expected reduction in uncertainty. We show that Kamenica & Gentzkow's (2011) concavification approach to characterizing optimal signals extends to these settings.

We show that most U.S. food, drugstore, and mass-merchandise chains charge nearly uniform prices across stores, despite wide variation in consumer demographics and competition. Demand estimates reveal substantial within-chain variation in price elasticities and suggest that the median chain sacrifices $16 million of annual profit relative to a benchmark of optimal prices. In contrast, differences in average prices between chains are broadly consistent with the optimal benchmark. We discuss a range of explanations for nearly uniform pricing, highlighting managerial inertia and brand image concerns as mechanisms frequently mentioned by industry participants. Relative to our optimal benchmark, uniform pricing may significantly increase the prices paid by poorer households relative to the rich, dampen the response of prices to local economic shocks, alter the analysis of mergers in antitrust, and shift the incidence of intranational trade costs.

I use variation across markets in the timing of television's introduction to identify its impact on voter turnout. The estimated effect is significantly negative, accounting for between a quarter and a half of the total decline in turnout since the 1950s. I argue that substitution away from other media with more political coverage provides a plausible mechanism linking television to voting. As evidence for this, I show that the entry of television in a market coincided with sharp drops in consumption of newspapers and radio, and in political knowledge as measured by election surveys. I also show that both the information and turnout effects were largest in off-year congressional elections, which receive extensive coverage in newspapers but little or no coverage on television.

We estimate a measure of segregation, experienced isolation, that captures individuals’ exposure to diverse others in the places they visit over the course of their days. Using Global Positioning System (GPS) data collected from smartphones, we measure experienced isolation by race. We find that the isolation individuals experience is substantially lower than standard residential isolation measures would suggest but that experienced isolation and residential isolation are highly correlated across cities. Experienced isolation is lower relative to residential isolation in denser, wealthier, more educated cities with high levels of public transit use and is also negatively correlated with income mobility.

We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We show that standard approaches suffer from a severe finite-sample bias, and we propose an estimator that applies recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s after remaining low and relatively constant over the preceding century.