Do News Aggregators Favor Expert or Generalist News

Online media have reduced fixed costs and increased variety. This makes consumption costs more important for determining what viewers consume; transactions costs and institutions like news aggregators and search matter more. We investigate the effect of falling transaction costs on the types of media viewed and the implications for economic efficiency. In particular we focus on the difference between "expert" media that regularly publishes stories on a topic and "generalist" media that does not. In this study we first develop a theoretical model to highlight the issues involved and then examine empirical evidence of the effects of the news aggregator Google News on the types of stories viewed. To focus ideas, consider a consumer who seeks to view an online news story on a particular topic. A variety of such stories are available on the web. Let each story i have a value to the consumer v_i, which will be higher or lower depending in part on whether the story was produced by an organization with expertise on the topic. Stories are not always easy to find, so each story has a transaction cost c_i of finding and viewing it. In such an environment, a consumer will read stories on a particular topic, starting with the story with the highest net surplus v_i - c_i, until the surplus of the marginal story is equal to the opportunity cost of time. We use this simple framework to discuss three scenarios in which a news aggregator reduces the transaction cost of finding some but not all stories. In the simplest case, all values and costs are private and the consumer has complete information. Then the reduction in transaction costs may cause a reallocation of the consumer's time depending on the relationship between the story values and the transaction costs. We note that while the total net surplus V - C must rise for each consumer in this scenario, it is possible that the total gross surplus V could fall if the new aggregator systematically lowers the transaction costs of stories that are less expert. We then extend this framework to consider the case of positive externalities from some stories. These could come from a better-informed citizenry or from a greater diversity of voices in news media. Increased diversity, in particular, has been a goal of recent FCC orders. If the change in transaction costs from news aggregators is random relative to these externalities, then news aggregators have no effect on the overall market failure. But if there is a systematic bias of new aggregators toward either more or less expert stories then their presence could serve to better or worsen the social efficiency of the market. Another type of market failure could occur if viewers have incomplete information on the quality of a story before they read them. This case has two interesting properties. First, the total number of news stories and the market share of the more expert stories will affect individual decisions because they determine how many stories the consumer must sift through. Second, this means there is an opportunity for the news aggregator to serve as an information-provision mechanism, and its effect on transaction costs will have a greater impact on viewer surplus than under complete information. Our empirical analysis of these patterns is based on data on household web visits and on the stories featured on Google News. From the point of view of Google News users, the transactions costs of viewing a story are reduced when the story can be accessed using Google News. Our data on website visits comes from ComScore Inc.’s MediaMetrics series extracted through the Wharton Research Data Service (WRDS) interface. The data include complete browsing history for a sample of approximately 50,000 households that have opted to allow tracking of internet use. News sites are identified from several proprietary databases and public sources. Newspaper, radio, magazine and television outlets are identified primarily from the Burrelle’s Media Directory (2000 & 2005 edition), Bulldog Reporter’s MediaPro Directory (2008) and the Newspaper Association of America web site (2010). A list of major blogs, aggregators and other digital intermediaries was scraped from Technorati, a blog reference site. With a few exceptions, all sources linked from Google News are classified as news sites. Google News headlines are scraped from the archival site archive.org that operates a program called the "Wayback Machine." From the site we have obtained the text headline, URL and ranking for each Google News posting on each day sampled. Establishing which news websites are "expert" on a particular topic is difficult. For this research, expert news is identified based on consumers' observed viewing patterns. We look for users who visit news websites frequently and measure which groups of websites they visit together. This allows us to determine which clusters of websites satisfy the news demand of these heavy users who we assume are better at identifying expert sites. Then we investigate how viewers respond to Google News links. Specifically, we are interested in any difference in response between the people who often visit the cluster of websites that are related to that link versus people who seldom visit that cluster. Those who visit the cluster often are assumed to have lower transaction costs of identifying stories on the topic, while those who do not are assumed to have average transaction costs. By observing how the two groups respond to Google News links, we obtain an indirect measure of how much information Google News is providing. The results of this study help determine whether a news aggregator like Google News reduces the transaction costs of news viewing in a way that favors generalist news outlets those with more specific expertise. Further, it determines whether the news viewers who have demonstrated a preference for the more expert new sites respond differently to Google News links than more general news viewers.