ON COMMERCIAL MEDIA BIAS

Abstract

Within the spokes model of Chen and Riordan (2007) that allows for non-localized competition among arbitrary numbers of media outlets, we quantify the effect of concentration of ownership on quality and bias of media content. A main result shows that too few commercial outlets, or better, too few separate owners of commercial outlets can lead to substantial bias in equilibrium. Increasing the number of outlets (commercial and non-commercial) tends to bring down this bias; but the strongest effect occurs when the number of owners is increased. Allowing for free entry provides lower bounds on fixed costs above which substantial commercial bias occurs in equilibrium.

Motivated by the recent media policy debate in the United States and ongoing attempts by the Federal Communications Commission (FCC) to loosen ownership rules there (see e.g., McChesney, 2004, for a description of the events around the 2003 attempt; another such episode occurred in 2007), we develop a model of media competition that allows for a somewhat detailed study of the quality and bias of media content for a number of different ownership structures. The analysis builds on the spokes model of Chen and Riordan (2007), which is a Hotelling type model of spatial competition that allows for arbitrary numbers of media firms and outlets (commercial and non-commercial) that compete against each other in a non-localized fashion.

We show that excessively concentrated media markets, beyond a certain cut-off, can result in substantial bias of media content. Increasing the number of separately owned media firms in the market helps towards reducing the bias; increasing the number of commercial outlets, while keeping the number of owners fixed, can also help, but clearly to a lesser extent.¹

The channel through which the bias occurs in our model is through the funding of commercial media outlets by advertisers and the internalization of the effect of the media outlets' content on the advertisers' sales and advertising budgets. A motivating example for our analysis is the coverage of tobacco related health hazards in the US. For decades, despite hundreds of thousands of deaths a year, serious statistics and medical information about the health hazards of smoking were kept away from mainstream commercial media (see e.g., Baker, 1994, and Bagdikian, 2004, for chronologies as well as references documenting the statistical impact of advertising on the coverage of tobacco related health hazards; see also Ellman and Germano, 2009, for further discussion and references). Bagdikian (2004, pp. 250-252) summarizes “there were still more stories in the daily press about the causes of influenza, polio, and tuberculosis than about the cause of one in every seven deaths in the United States," so that, in the 1980's, some “64 million Americans, obviously already addicted, smoked an average of 26 cigarettes a day" with surveys indicating that half the general and two-thirds the smoking population did not think smoking made a great difference in life expectancy, Baker (1994, p. 51).2 Our model claims that alongside advertising, concentration in the media markets plays an important role in explaining such bias.

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