Drivers of Media Bias, Welfare Effects of Multi-Mode Television Service, and Effects of Television Service Agreements on Local News Coverage

Abstract

This issue of JME has three studies that are relevant for media policies or media effects. The first study shows how newspaper coverage of unemployment is influenced by the paper’s ideology and by competition in the paper’s market. The second study estimates the welfare effects from adding new digital channels to broadcast television stations in Korea. The third study finds a relationship between service agreements that allow U.S. television stations to cooperate and the stations’ coverage of local news. Each study adds to our understanding of how and why the output from media firms influences their audiences’ knowledge and welfare. The first study, “What Drives Media Bias? New Evidence from Recent Newspaper Closures,” is by Cagdas Agirdas. This study examines how newspaper news coverage changed after rival newspapers closed. Coverage will change after a rival’s closure if a newspaper’s biases are “demand-driven, [because] then a surviving newspaper could expand its reader base by moderating its bias to reach out to former readers of the closed rival newspaper in the same market” (p. 123). The study examines changes in the amount of unemployment news reported before and after rival newspapers closed. The author argues that newspapers that support a Democratic or Republican president will report less unemployment news when a favored incumbent holds office compared with more unemployment news when a disfavored incumbent holds office. However, this bias will be reduced after a rival closes if the surviving newspapers want to attract new readers. The study measures unemployment news because changes in the unemployment rate predict political success for an incumbent president’s party. A previous study also found that unemployment news was correlated with politically partisan newspaper endorsements. The author analyzes panel data for 99 newspapers from 1990 to 2009. The data set includes 24 markets where newspapers closed. Unemployment news stories were identified using keyword searches of online newspaper archives.