Papers

CHARLES ANGELUCCI, Media Competition and News Diets

Abstract: Since the advent of the Internet and online news, the print news media has struggled with the resulting shock to advertising and readership markets that have threatened the basic economic model of news operations. In this paper, we study the effect of the introduction of a new media technology on existing news media. Specifically, we examine how the entry of television affected local newspapers as well as consumer media diets in the United States. We construct a unique dataset of newspapers’ economic performance and content choices covering over 1,500 local news markets from 1945 to 1964. Our empirical strategy exploits quasi-random variation in the timing of the entry of television in different markets, caused by a “freeze” in FCC licensing. The impact of television was heterogeneous: we find that while the entry of television led to a 3.4 percent drop in the circulation of evening newspapers and to a 5.6 percent decrease in their advertising revenues, it had minimal or even positive effects on morning newspapers, suggesting a possible complementarity between them and televised evening news. Further, we analyze how local newspapers adjusted their content in response to television’s entry.

ZSOLT KATONA, The Impact of Curation Algorithms on Social Network Content Quality and Structure

Abstract: Curation algorithms are selection and ranking algorithms on social media that help consumers experience better content. These algorithms have been blamed in the past few years for the creation of “filter bubbles” and other phenomena in social media. We analyze a platform with producers and consumers of content to understand the impact of curation algorithms on the amount of friends each consumer has and the quality of content created by each producer. Our model takes into account both vertical and horizontal differentiation and analyzes three different types of algorithms. We find that without algorithmic curation, the number of friends a consumer has and the quality of content on the platform are strategic complements. When algorithmic curation is introduced, the resulting process makes consumers less selective in their choice of whom to follow. In equilibrium, producers of content receive lower payoffs because they enter into a prisoner's dilemma-like contest. The quality of content on the platform may increase if the marginal cost of producing this quality is high enough but not too high. Both of these effects may result theoretically in more diverse content consumed by consumers, but in equilibrium we find that a few of the algorithms may reduce the horizontal distance of matched content, which may result in a filter bubble. We identify an algorithm that focuses on filtering low quality items that results in higher quality of content as well as higher diversity under specific conditions.

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ANNIE LIANG, Overabundant Information and Learning Traps

Abstract: We develop a model of learning from overabundant information: Agents have access to many sources of information, and observation of all sources is not necessary in order to learn the payoff-relevant unknown. Short-lived agents sequentially choose to acquire a signal realization from the best source for them. All signal realizations are public. Our main results characterize two starkly different possible long-run outcomes, and the conditions under which each obtains: (1) efficient information aggregation, where signal acquisitions eventually achieve the highest possible speed of learning; (2) “learning traps,” where the community gets stuck using an suboptimal set of sources and learns inefficiently slowly. A simple property of the correlation structure separates these two possibilities. In both regimes, we characterize which sources are observed in the long run and how often.

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JACOPO PEREGO, Media Competition and the Source of Disagreement

Abstract: We identify a novel channel through which competition among information providers decreases the efficiency of electoral outcomes. The critical insight we put forward is that the level of competition in the market determines the type of information that is provided in equilibrium. In our model, voters can disagree on which issues are important to them (agenda) and on how each issue in their agenda should be addressed (slant). We show that the level of competition in the market determines how much firms differentiate in terms of the type of information they produce. Importantly, differentiation leads to higher provision of information on issues where there is higher disagreement in the electorate. Although voters become individually better informed, voting decisions shift from focusing on valence issues to ideological issues. On aggregate, the share of votes going to the socially optimal candidate decreases. Our model also highlights how competition in the market for news can have negative welfare consequences even in the absence of behavioral agents or partisan media, therefore offering a new, and to some extent more distressing, perspective on the problem.

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MIKLOS SARVARY, Social Media and the News Industry

Abstract: This paper explores news providers' choice of quality when they compete in the presence of a monopolistic social network (platform) that strategically decides on the mix of content that its members are exposed to. Consumers are assumed to divide their time between the platform and the news publishers' sites depending on their relative preferences for consuming “User-Generated Content” (UGC) and professional news, as well as, on the platform's “content policy” (e.g., the design of its “newsfeed”). The goal is to assess publishers’ incentive to invest in the quality of news depending on the platform’s optimal newsfeed and the level of competition among publishers. First, we find that, in case of a monopolist publisher, the platform always shows news in its newsfeed. As a result, the equilibrium quality of news rarely increases and only under specific conditions. However, the news publisher is always worse off when the newsfeed integrates news. Next, in a model, where the publisher can opt-out of the newsfeed, news quality is always lower. For consumers, by distorting consumers’ optimal consumption mix and by leading to lower quality, the platform tends to decrease overall consumer surplus (there may be exceptions in the special cases when quality increases). In an extensions, we also show that a newsfeed customized to consumer preferences allows the platform to monopolize consumers’ attention. In this case, as the publisher's profit declines, again it has less incentive to invest in quality. Finally, under competition, although the quantity of news shown by the platform has a U-shaped effect on quality, in equilibrium, news quality is lower in the presence of the social network.

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MATT SHUM, News We Like to Share: How News Sharing on Social Networks Influences Voting Outcomes

Abstract: We study the relationship between news sharing on social media and information aggregation by voting. Our context-neutral laboratory experimental treatments mimic the features of social networks in the presence of media bias to address concerns that voters obtaining their political news via social media may become more polarized in their voting behavior. Our results suggest that these concerns are warranted: subjects selectively share news that is favorable to their party and do not account for biased news signals in their voting decisions. Overall, subjects behave as if news sharing and voting is expressive of their induced partisanship even though by design, their preferences have a common value component. Given these patterns of individual behavior, the welfare implications of social networks reflect the underlying quality of the shared news: with unbiased media, social networks raise collective decision making efficiency, but efficiency deteriorates markedly in the presence of media bias, as news signals become less reliable.

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JAMES SNYDER, Newspaper Bias in Historical Perspective