This item is only available as the following downloads:
1 MARKET COMPETITION, STATION OWNERSHIP AND PROGRAMMING ON LOCAL BROADCAST TELEVISION: AN EMPIRICAL ANALYSIS By CANDACE A. HOLLAND A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMEN T OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN MASS COMMUNICATION UNIVERSITY OF FLORIDA 2012
2 2012 Candace A. Holland
3 To my Parents
4 ACKNOWLEDGMENTS I wish to thank Amy Coffey, who served as head of my thesis committee and pr ovided invaluable advice, guidance and assistance in bringing this project to fruition. Additionally, I wish to thank the other members of my committee Sylvia Chan Olmsted and Johanna Cleary for their time, input and support throughout the conceptualizatio n, analytical and summation process. Finally, I wish to thank my parents for their continual support and encouragement.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 I NTRODUCTION ................................ ................................ ................................ .... 11 2 LITERATURE REVIEW ................................ ................................ .......................... 15 Why Does Diversity Matter? Diversity, Media Policy and the Marketplace of Ideas ................................ ................................ ................................ .................... 15 Regulating Diversity ................................ ................................ ................................ 17 Defining Diversity ................................ ................................ ................................ .... 19 Criticisms of Program Type Div ersity ................................ ............................... 23 Categories of Program Type Diversity ................................ .............................. 24 Concentration and Diversity ................................ ................... 25 Global and National Ownership Trends ................................ ............................ 25 The Local T elevision (TV) Landscape ................................ .............................. 26 Drivers of a cquisi tion ................................ ................................ .................. 26 Ownership t rends ................................ ................................ ....................... 27 Reasons for Concern ................................ ................................ ....................... 29 Market Structure and Program Type Diversity ................................ ........................ 31 Theoretical Perspectives ................................ ................................ .................. 31 Empirical Research ................................ ................................ .......................... 34 Ownership and Program Type Diversity ................................ ................................ 37 Informational Programming ................................ ................................ ..................... 38 Market Factors: Level of Competition ................................ ............................... 38 Market Factors: Market Size ................................ ................................ ............. 40 Station Factors: Local Ownership ................................ ................................ ..... 41 Station Factors: Revenue ................................ ................................ ................. 43 Measuring Diversity ................................ ................................ ................................ 44 Research Questions ................................ ................................ ............................... 48 3 METHODOLOGY ................................ ................................ ................................ ... 65 Independent Variables ................................ ................................ ............................ 66 Operational Definitions ................................ ................................ ............................ 67 Sampling of Stations for Ownership Analysis ................................ ......................... 68 Selection Criteria ................................ ................................ .............................. 68
6 Station Selection ................................ ................................ .............................. 69 Sampling of Markets for Market Analysis ................................ ................................ 71 Program Sampling ................................ ................................ ................................ .. 72 Program T ype Categories ................................ ................................ ................ 73 Assessing Vertical Diversity ................................ ................................ .................... 76 Assessing Horizontal Diversity ................................ ................................ ................ 77 Assessing Public Service ................................ ................................ ........................ 78 Assessing Localism (Program Origination) ................................ ............................. 79 4 RESULTS ................................ ................................ ................................ ............... 91 Regression Results Station Characteristics ................................ ....................... 91 Vertical Program Type Diversity ................................ ................................ ....... 91 Diversity of Programming Sources ................................ ................................ ... 92 Total Informational Programming Provided ................................ ...................... 93 Local Informational Programming Provided ................................ ...................... 93 Descriptive Statistics: Station Variables ................................ ................................ .. 94 Total Informational Programming ................................ ................................ ..... 94 Local Informational Programming ................................ ................................ ..... 96 Regression Results: Market Characteristics ................................ ........................... 97 Program Type Diversity (HHI) ................................ ................................ .......... 98 Horizontal Program Type Diversity (COI) ................................ ......................... 98 Diversity of p rogramming s ources ................................ .............................. 99 Total informational p rogramming p rovided ................................ ................. 99 Local Informational Programming Provided ................................ .................... 100 5 DISCUSSION AND CONCLUSION ................................ ................................ ...... 114 REFERENCES ................................ ................................ ................................ ............ 125 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 137
7 LIST OF TABLES Table page 2 1 Theore tical perspectives on market structure and diversity ................................ ... 50 2 2 Summary of previous research on market structure and diversity ......................... 51 2 3 Summary of previous research on market structure and informational content (broadcast and print.) ................................ ................................ ......................... 57 2 4 Summary of previous research on ownership characteristics and informational content ( broadcast and print.) ................................ ................................ ............. 61 3 1 Station/ownership variables. ................................ ................................ .................. 81 3 2 Station/ownership variables with definition and values. ................................ ......... 82 3 3 Market variables. ................................ ................................ ................................ .... 84 3 4 Market variables with definitions and values. ................................ ......................... 85 3 5 Collapsed program categories and distribution of all programming by type ........... 87 4 1 Model summary: Station characteristics and vertical diversity (HHI) .................... 101 4 2 Coefficients for final model: Station characteristics and vertical diversity (HHI) ... 101 4 3 Model summary: Station characteristics and program origin d iversity .................. 102 4 4 Coefficients for final model: Station characteristics and program origin diversity 102 4 5 Model summary: Stati on characteristics and provision of total informational programming ................................ ................................ ................................ .... 103 4 6 Coefficients for final model: Station characteristics and provision of total informational programming ................................ ................................ ............... 103 4 7 Model summary: Station characteristics and provision of local informational programming ................................ ................................ ................................ .... 104 4 8 Coefficients for final model: Station characteristics and provision of local informational programming ................................ ................................ ............... 104 4 9 Regression results: Station ownership summary ................................ ................. 105 4 10 Descriptive statistics provision of any news and/or public affairs programming (station/ownership characteristics) ................................ ................................ ... 106
8 4 11 Descriptive statistics provision of local news and/or public affair s programming station/ownership characteristics) ................................ ................................ ..... 107 4 12 Model summary: Market characteristics and vertical diversity (HHI) .................. 108 4 13 Coefficients for final model: Market characteristics and vertical diversity (HHI) 108 4 14 Model summary: Market characteristics and horizontal diversity (COI) .............. 109 4 15 Coefficients for final model: Market characteristics and horizontal diversity (COI) ................................ ................................ ................................ ................. 109 4 16 Model summary: Market characteristics and program or igin diversity ................ 110 4 17 Coefficients for final model: Market characteristics and program origin diversity ................................ ................................ ................................ ............ 110 4 18 Model summary : Market characteristics and provision of total informational programming ................................ ................................ ................................ .... 111 4 19 Coefficients for final model: Market characteristics and provision of total informational programming ................................ ................................ ............... 111 4 20 Model summary: Market characteristics and provision of local informational programming ................................ ................................ ................................ .... 112 4 21 Coefficients for final model: Station characteristics and provision of local informational programming ................................ ................................ ............... 112 4 22 Regression results: Market structure summary ................................ .................. 113
9 LIST OF ABBREVIATION S COI C hoice Option Index. Average number of simultaneous program types available during a given time period. HHI Herfindahl Hirschman Index. A measure of market concentration. It is calculated by summing the squared market share of each firm in a market. O&O Rem ember to use a tab between the ab breviations and the definitions
10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Ma ster of Arts in Mass Communication MARKET COMPE TITION, STATION OWNERSHIP AND PROGRMMING ON LOCAL BROADCST TELEVISON: AN EMPIRICAL ANALYSIS By Candace A. Holland August 2012 Chair: Amy Jo Coffey Major: Mass Communication This study examines the relationship between ownership structure, competitive co nditions in local television markets and the programming provided by commercial broadcast stations. Previous research on the nature of these relationships has yielded mixed results; this study seeks to provide some degree of clarification. Additionally, th rough the inclusion of non English language stations, it expands the sampling f rame used in previous studies. In particular this study examines program type and source diversity as well and the quantity of total and local informational programming broadca st. Results from an analysis of 212 stations and 42 markets show that station financial strength and the intensity of market competition (as measured by the number of commercial stations) are the most important predictors of the programming provided. Both were positively related to program type diversity and the quantity of informational programming (total and local.) Interestingly, local ownership was not related to any of the programming variables. These results suggest that consolidation may not be as h armful as critics fear. They also suggest greater attention should be devoted to the number of stations and the resources available to them than on the characteristics of individual owners.
11 CHAPTER 1 I NTRODUCTION Because they use a scarce public resource, it is widely held that broadcasters have an obligation to serve the public and support the democratic process (Napoli, 2001b; Siebert, Peterson & Schramm, 1963). Indeed, broadcasting generally falls under the social responsibility theory of the press outl ined by Siebert, Peterson and Schramm (1963) which holds that the power and near monopoly position of the media impose on them an obligation to be socially responsible, to see that all sides are fairly presented and that the public has enough information to decide; and that if the media do not take on themselves such responsibility it may be necessary for some other agency of the public to enforce it (p.5). In the case of broadcast media, the agency alluded to by Siebert et al., has traditionally been the federal government (via the FCC), which has strived to ensure intenance of diverse viewpoints, some degree of local control and local program orientation, a general balance of programming (including controversial topics), Diversity, along with competition and localism, is widely recognized as one of the chief public policy goals underlying evaluation of broadcast operator performance (Barrett, 2005; Compaine, 1995). Diverse programming is expected to cater to majority and minority interests, nat ional and local tastes and provide a balance of entertainment and information content (with greater emphasis on the latter) (Levin, 1980). Given its inherent value, policymakers have long been concerned with ensuring audiences are exposed to diverse conten t from a variety of sources, and have enacted a number of
12 structural regulations (e.g. ownership rules) aimed at influencing the content provided to audiences. The provision of diverse content is contingent on both market and station factors. ation behaviors are a function of the competitive dynamics and revenue characteristics of station owners may influence programming output (Napoli, 2004). In recent years, despi the public forum, the government has loosened ownership restrictions as proponents of deregulation have argued that the potential efficiencies that could be captured by large owners with access to sizeable resource pools would yield greater diversity. The Telecommunications Act of 1996 represented the largest overhaul of broadcast law since the Communications Act of 1934 was enacted more than sixty years earlier. In 1999, the FCC lifted its ban on duopoly ownership (Federal Communications Commission, 1999). Later, in 2003, the FCC relaxed cross ownership rules (Federal Communications Commission, 2003), though they were subsequently vacated in 2011 by the Third U.S. Circuit Court of Appeals (Pearson & Shields, 2011). Whether deregulation has accomplished its intended goals is uncertain. While there is a substantial body of literature on the relationship between competition and program type diversity, previous research has failed to uncover any defini tive relationships. Rather, the findings have been mixed, with some studies suggesting a positive link and others asserting a negative association. The relationship between ownership and diversity is equally unclear.
13 The present study assesses horizontal ( market wide) and vertical (station level) diversity in local television ( TV ) markets and stations. Particular attention is devoted to program origination and the quantity of news and public affairs programming provided. It is assumed that locally produced programming better reflects community concerns, interests, and needs, and enhances citizen knowledge and participation in the democratic process. Literature regarding the provision of news and local affairs programming (in relation to competition and stati on ownership) and program source diversity is limited and findings have been largely qualified, holding under certain circumstances. Consequently, this study seeks to fill gaps in the existing literature. This study is also unique in that it includes a lar ger variety of stations, specifically non English language stations, which were previously excluded from the sampling frame. For the ownership analysis, six data fields were obtained for the 212 stations comprising the sample: owner type, national reach, duopoly status, owner location, network affiliation and station revenue. Programming data was analyzed for all stations for 14 randomly selected days with this information being used to measure vertical diversity (measured using the HHI), source diversity (also measured with the HHI) and the quantity of total and local informational programming provided (in total minutes.) Multiple regression was used to analyze the relationship between station ownership and programming. A similar approach was used for the market analysis. Data for 42 markets was collected, including the total number of stations in the market, the number of commercial stations, the number of non commercial stations and the number of TV households. As with the station analysis, programming da ta was analyzed for a constructed 14 day
14 period; this data was used to calculate horizontal diversity (measured using both the HHI and COI), source diversity (HHI) and the quantity of total and local informational programming provided (total minutes). Mult iple regression was used to examine the relationship between variables. Overall, the findings of this study suggest policymakers should not be as concerned with who owns the media, as they are with the number of stations in a market and the resources avail able to them. Financial strength was the strongest ownership related predictor of programming, while the number of commercial stations was positively related to program type diversity and the quantity of informational programming provided. Together, these findings suggest that, contrary to the fears of media consolidation critics, ownership regulations may have limited impact on programming. Moreover, market forces may drive innovation. It is important to note that while it has implications for policymaking this study is of an empirical nature and is not a policy paper.
15 CHAPTER 2 LITERATURE REVIEW Why Does Diversity Matter? Diversity, Media Policy and the Marketplace of Ideas It is widely held that democracy is directly related to a media system of diverse sources and owners (Horwitz, 2005). The marketplace of ideas concept suggests media play a crucial social role, aiding the democratic process by supporting cultural pluralism, citizen welfare, participation in well informed decision making (Croteau & Hoyn es, 2005; (Ishikawa & Muramatsu, 1996, p 201) Indeed, the Supreme Court ble dissemination of information from diverse and antagonistic sources is essential to the Associated Press v. United States 1945). Diversity is central to .38) and media contribute to diversity by reflecting societal differences, providing audience members a number of choices and exposing audiences to viewpoints and attitudes different from their own ( Ishikawa & Muramatsu, 1996; McQuail, 1992; Napoli, 1999) That is, media bolster the marketplace of ideas as delivered from a wide range of sources (source diversity). The citizens then partake of this diversity (exposure diversity) to increase their knowledge, counter opposing viewpoints and become well informed decision makers who are better capable of fulfilling their democratic responsibilities in a self governing society (Napoli, 1999, p.9). Thus, it can be concluded that society i While its value is agreed upon, a deep divide exists regarding how to best achieve diversity, with proponents of the m arketplace model on one side, and supporters of the
16 public sphere model on the other (Croteau & Hoynes, 2005; Einstein, 2004; Iosifides, the breadth of diverse voices i hold that these efficien cies can only be realized when the market is unencumbered by government regulation. Critics of this school, however, believe it promotes cultural standardization, homogeneity and excludes minority views (Iosifides, 1999). Free market detractors hold that m edia companies put profit before the public interest; driven by the bottom line, media firms produce programs that will attract the largest audience and, accordingly, produce the greatest profits under a system of advertiser supported media. The result is to majority tastes. Overall, society is less well off and minority viewers (in terms of tastes, not ethnicity) are less satisfied as their preferences are undersupplied (Owen & Wildman, 1992, pp 99 100). Proponents of the public sphere model are also deeply (discussed in more detail later) (Einstein, 2002). Reflecting the concerns of the public sphere school, gover nment regulation or legislation has been adopted to protect the integrity of the marketplace. Examples of diversity initiatives enacted by the FCC are the Fairness Doctrine, Prime Time Access Rules, Financial Interest and Syndication Rule and national, mul tiple and cross ownership rules (Napoli, 1999; Wakshlag & Adams, 1985; Yan & Park, 2009). The past 20 years, however, have seen a notable policy shift as the FCC has adopted a market
17 philosophy, leading to massive deregulation culminating in the Telecommun ications Act of 1996 (Einstein, 2004). In all cases it is important to note that a robust marketplace of ideas is not the end, but merely a means of achieving broader social goals. Policy alone cannot guarantee a well functioning democracy or society. None theless, promoting diversity and ensuring a healthy marketplace of ideas remains a critical concern. Policies enacted to achieve these goals are discussed in greater detail below. Regulating Diversity To promote a robust marketplace of ideas, the governmen t established limits on the number of stations that could be owned by a single entity (Horwitz, 2005). These rules attempt to promote the public interest against intense commercial pressures; that wpoints as well as to prevent Proposed Rulemaking, cited in Einstein, 2004, p. 16). A s Smith (2004) explains, two concepts underlie media regulation. First, airwaves serve as a modern day town hall, a place where citizens can go to make better decisions when they are exposed to a variety of opinions and viewpoints. The c 367). In policymaking, particular attention is devoted to localism, one of the guidin g principles of broadcast regulation. Its importance is related to U.S. political structure (Smith, 2004): because government control is primarily concentrated in local
18 communities, attention to local affairs is critical. The FCC has consistently held that closer to local needs and concerns, and thus the station would more accurately reflect the FCC prohibited common ownership of two stations in the same market. However, the FCC now permits such duopolies if eight independent voices remain post merger and only one station is among the top four in the market (FCC, 1999). National ownership limits aim to strike a balance between the national networks and local stations, allowing large owners to capitalize on economic efficiencies, yet ensuring stations remain responsive to their local communities (Barrett, 2005; Horwitz, 2005). They are premised on particular area, the less chance there is that a single person or group can have an (Amendment of Sections 73.75 Multiple Ownership of AM, FM, Television Broadcast Stations as cited in Horwitz, 2005). The FCC began regulating television ownership in 1940 when it established a limit of three stations per owner (H oward, 2006). In 1944, the cap was increased to five stations (Howard, 2006). The limit was once again raised in 1954 this time to seven stations per owner (Barrett, 2005). With changes in technology and the competitive landscape, philosophy began to shi ft from the public sphere perspective toward the ability to produce program type diversity and serve minority interests (Napoli, 2004). Consequently, in 1984, the FCC revise d the limits to reflect the dramatic changes that
19 was raised from seven to twelve, and, subsequently, group owners were prohibited from reaching more than 25% of the nationa l audience (Blevins, 2002). The era of deregulation continued with the passage of the Telecommunications Act of 1996, which eliminated numerical caps on the number of stations owned by a single source and raised the limit on national reach to 35% (Blevins, 2002). This legislation was driven by the belief that competition would be best promoted by removing regulatory barriers to efficiency. In 2003, the FCC raised the national ownership limit to 45% (FCC, 2003). Following heavy criticism from consumer groups Congress intervened and established a 39% cap (Barrett, 2005; Free Expression Policy Project n.d ); this limit has remained unchanged since its enactment. Defining Diversity While the importance of diversity is universally recognized, it is difficult t o define both conceptually and operationally (Entman & Wildman, 1992; Le Duc, 1982; Napoli, 1997; Owen, 1978). Einstein (2002) suggests this difficulty stems, in part, from the conflict between the First Amendment and the regulation of speech. In cases lik e United (1968) the Supreme Court has consistently ruled that regulation must Therefore, attempts to regulate specific programming are likely to be ru led unconstitutional. 1 As a consequence, policymakers have attempted to secure content diversity through structural regulation (Einstein, 2002; Gomery, 2000; Horwitz, 2005; Kleiman, 1991), which is seen as a proxy for behavioral regulation. Such regulation s are 1 because of its message, its subject m Police v. Mosley, 1972, p. 95).
20 many people make television programming, you will affect the different types of Beyond these constitutional issues, diversity is simply difficult to define because it p.154). Indeed, the te rm may refer to a multiplicity of outlets, of owners, of formats. Diversity may entail offering informational programming. It may be the provision of racially or ethnically based programming (Barron, 2000). At the most basic level, reception) and unrestricted access to the means to impart information (freedom of Many scholars have developed their own definitions of diversity, but t here is no consensus within the academic community on the best meaning. van der Wurff and Van Cuilenburg (2001) distinguish between reflective and open diversity. Reflective diversity roportion as Van Cuilenburg 2001); that is, whether program supply corresponds with consumer demand. Open diversity suggests media express all ideas and topics equally, regardless of publi c preferences (van der Wur ff & van Cuilenburg 2001; McQuail, 1992); this is typically the dimension that receives greater emphasis in policy circles. Napoli (1999) identified three aspects of diversity: source (which encompasses programming, outlet ownership and workforce composit ion), content (including format or program type, demographic and
21 idea/viewpoint diversity) and exposure diversity (the content actually viewed). Wildman and Owen (1985) also identify content diversity as one of three dimensions of diversity, alongside dive rsity of ideas and access diversity. The former means media should provide a broad array of views/ideas and not display favoritism toward one side, while the latter refers to the idea that media should provide fair access to all. Similarly, Entman and Wild man (1992) distinguish between product, idea and access diversity, while still others identify diversity of ideas, products, issues, content, person and geography (Iosifides, 1999). Academics and policymakers have traditionally devoted greater attention t o diversity of supply than diversity of consumption (Entman & Wildman, 1992; McQuail, 1992; Napoli, 1997, 1999; Webster & Phalen, 1994). Policy makers are particularly concerned about the impact of source and outlet diversity on content, arguably the most Schurz Communications v. Federal Communications Commission, 1992, p.1054). Given the attention and concern devoted to content diversity, this dimension is a focus of the current study. In particular, this study analyzes progra m type diversity, the distribution of programs by category/genre (Noll, Peck & McGowan, 1973). This is the cation of the more general economic concept of product variety the range of differentiated products a market makes available to consumers mix increases viewer choice and the number of potential substitutes (Lev in, 1980).
22 of maximizing the satisfaction of more categories of viewers and hence serves the It is important to note that the ul timate goal of regulation is the maximization of viewpoint diversity which refers to the availability of a variety of social, political, and cultural perspectives in media content (Ho & Quinn, 2009; Napoli, 1999). A component of content diversity, viewpoi nt diversity is most closely tied to a well functioning democracy and robust marketplace of ideas. It is believed that society benefits when a variety of opinions and criticisms are available to its citizens (Entman & Wildman, 1992). However, as Horwitz (2 005) explains, there are inherent difficulties in measuring such an elusive concept, how can an empirically oriented social scientist measure viewpoint diversity? A perceptive reader can reasonably discern general differences of tone and coverage among th e Wall Street Journal, USA Today, and The Nation for instance. But even in this comparison, does one analyze the coverage according to some general reckoning or by particular item? By news article or op/ed column? Over a comprehensive week or by random sa mpling? And according to what kind of scale? If quantitative, by the frequency of with which an issue or event is covered or by column inches? If qualitative, what exactly does one look for when one conducts a content analysis? And this is news and opinion presumably conducive to viewpoint analysis? How does one evaluate the viewpoints embedded in entertainment programming? (p. 199). The majority of studies that have attempted to quantify viewpoint diversity have utilized indices that are methodologically flawed. These measures fail to address measurement uncertainty, are highly subjective, not easily replicable and based on small samples of media content, thus limiting their generalizability (Ho & Quinn, 2009). Consequently, policymakers have predominantly conceptualized content diversity solely in terms of program type. Program type diversity allows researchers to establish connections
23 between economics (station and market factors) and the provision of content (Einstein, 2004) and is easier to assess objec tively and quantitatively than viewpoint diversity. Criticisms of Program Type Diversity It is important to point out the weaknesses of measuring diversity via program types. First, program type measures do not give a complete picture of content diversity (Wildman, 2007). This approach considers only broad program types, and as Owen (1978) suggests, there are differences not just among program types, but within them as well. Moreover, genres are fluid and may evolve or disappear over time. However, as Hillv rather homogenous in their general tenor, so that the variation is greater between than within th (Hellman, 2001, p.196), but rather to aid empirical analysis of a complex, multidimensional concept. Critics of program type diversity also contend simple classifications do not reveal whether programs provide a single viewpoint or a multiplicity (Einstei n, 2004). Furthermore, the provision of diverse programs does not necessarily mean audiences will consume them (Napoli, 1997). Likewise, knowing what proportion of all shows one program type represents does not tell us about the sources providing the cont ent. To gain a complete picture of programming diversity on local television, all three components must be considered. That is, researchers must look at who supplies content, the actual content provided, and how it is consumed. Measuring consumption
24 is dep endent on confidential third party data that is prohibitively expensive; nevertheless, the present study includes a variable for source diversity in an effort to provide a more complete picture. While entertainment programs necessarily contain some viewpoi nt, informational programming is inherently better at articulating political perspectives (Horwitz, 2005), which lay at the core of the marketplace of ideas and good governance. That is, news and public affairs programming is perceived as better at deliver entertainment. Recognizing this difference, informational programming is included in the current study as a proxy for viewpoint diversity. Catego ries of Program Type Diversity Within the broad category of program type diversity, researchers distinguish between vertical and horizontal diversity. The former, also referred to as channel diversity (Hillve, et al., 1997), is the overall distribution of programs by type provided by a station over a period of time (Einstein, 2002; Grant, 1994; Li & Chiang, 2001; Litman, 1979). However, this only reflects the options available through a single source. A more complete picture is provided by horizontal, or sy stem diversity (Hillve et al., 1997), which evaluates the variety of programs types available across all outlets during a given time period. That is, it reflects the full range of options available to viewers (Einstein, 2002; Grant, 1994; Li & Chaing, 2001 be low, diversity across the entire system may be enhanced as these stations may offer program types not availab le through other outlets (Hellman, 2001; Hillve et al., 1997).
25 distinctly differentiated schedules (low channel diversity), may result in great overall variety (high system type order to und erstand what is happening in the market. Concentration and Diversity Fears about the effects of concentration on mass media industries have persisted since the early days of American media and proliferate across industries. For example, newspaper magnate William Randolph Hearst used his publications to spark the Spanish American War (Noam, 2009). Similarly, when the big three broadcast networks minds, pock the media industry have further magnified concerns and spurred intense debate between free Global and National Ownership Trends Over the past three decades, the media system has undergone sweeping changes. Driven largely by technological change, markets have become more competitive and international, as potential economies of scale and scope have become more pronounced. This has encouraged corporate expansion through vertical, horizontal and diagonal integration (Doyle, 2002). Paradoxically, as media giants have grown larger, ownership rules have been relaxed, fueling further consolidation. While the number of outlets has increased, the number of distinct owners has dwindled (Horwitz, 2005). Bagdikian (2004) asserts that in 2003, five firms dominated
26 the global media industry, compared to 50 in 1983. McChesney (2004b) is slightly more generous in his assessment of industry conditions, recognizing there are thousands of media firms, but of differing significance. In his view, American media is essentially a three tiered system. In the fir conglomerates own the major film studios, the major broadcast and cable networks, nearly all of the cable systems and major publishing houses, among others. The second tier includes 15 to 20 firms that specialize in one or two areas and are among the top 700 U.S. companies (e.g. Gannett and Clear Channel). The third tier comprises thousands of small firms that fill unmet niches (2004b). as Bagdikian suggests, the fact remains that 20 to 25 large firms control the overwhelming majority of media content produced worldwide. Moreover, 20 companies account for three quarters of global advertising revenue (McChesney, 2004a). Consequently, it c ould be argued competitive in any meaningful The Local Television ( TV ) Landscape The consolidation phenomenon is not unique to global or even nationa l media, however. The trend also applies to local television markets, the focus of the present stu dy. Included below are an overview of the factors driving consolidation, current ut the changing landscape. Drivers of acquisition Station acquisition is driven primarily by economic efficiencies. Group owners are able to provide services to multiple stations at a lower cost than if the same stations operated independently (Besen & Johnson, 1985). These owners primarily benefit from
27 economies of scale, including centralized management (Besen & Johnson, 1985) and addition, large station groups maintain advantages over their smaller and/or independent counterparts in retransmission consent, syndication, and talent negotiations (Albiniak, 2010). Groups (particularly those with stations in top markets) have greater bargaining power in retransmission consent and cable carriage agreements (Albiniak, 2010). In syndication negotiations, large groups may secure better deals because they are able to provide access to multiple markets (Einstein, 2002; Besen & Johnson, 1985). Large groups also have greater leverage in negotiations with national spot advertisers and service providers like The Nielsen Company or The Associated Press for similar reasons (Levin, 1970). Ownership trends concentrated and most are highly co roughly nine in ten stations (85.9%) were group owned (Howard, 2006). Further, the number of group owners decreased by nearly a third (32.4%) from 1995 to 2003 (from 210 to 142 groups) as the largest groups took over stations operated by their smaller counterparts (Howard, 2006). The number of stations per owner rose from 3.7 in 1985 to 8.1 in 2003 (Howard, 2006). 0 ), the Big Four networ ks continue to dominate local television ownership. In 2009, four of the top five group owners were the parent companies of the major broadcast networks (see below). Further, in 2008, owned and operated stations accounted for $6.1 billion of $20.1 billion (30%) of total industry revenue.
28 As discussed previously, federal law prohibits a single owner from reaching more than 39% of the national audience ( Free Expression Policy Project n.d.). According to Broadcasting & Cable, in 2010, the three largest group s (CBS, News Corporation and General Electric) each reached more than 30% of the national audience. CBS, the largest owner, had 29 stations with a combined reach of 35.48%; these stations brought in $2.36 billion revenue in 2009. In terms of national reach owner, General Electric (32 stations, 30.33%). Rounding out the top five were the Tribune lt Disney Company (10 stations; 23.14%) (Albiniak, 2010). Broadcasting & Cable also tracks media ownership using a different methodology. market reach toward the national c ap (the methodology used for the statistics above). Following the digital transition, the majority of stations moved from VHF to UHF channels, rendering this discount virtually moot. When the discount is removed, all of the top seven station groups (Ion Me dia Networks, Univision Television Group, CBS Corp., Fox Television Stations, NBC General Electric, Trinity Broadcasting, and Tribune Co.) reach at least 35% of the national audience. Further, the top two station groups (Ion and Univision) actually exceed the national ownership limit (63.9% and 42% coverage, respectively) (Albiniak, 2010). It is possible, therefore, that the same programs, talents and viewpoints are seen by one third or more of the national audience as owners share programming across statio ns in order to exploit economic efficiencies.
29 Reasons for Concern These trends are troubling because of the apparent power they grant a few media (Bagdikian, 2004, p. 4). Indeed, following the announcement of the pending merger of AOL and Time Warner, the largest deal in history at the time, McChesney asserted "The system has become the plaything of a handful of billionaire investor s who use their power to commercially carpet bomb every possible moment of our lives" (Hazen, 2000). Certainly concentration is not a media specific phenomenon, but media are unique in that they do not sell consumer goods, but rather values and viewpoints (Bagdikian, 2004). Put another way, media are machines of cultural production whose goods are consumed by nearly the entire population. As such, concentration in the communications industry poses a substantial threat to democracy. arguments against consolidation. Behind all of these 2005, p.181). In other words, the fear is that a few voices will dominate communication channels, effectively muting the marketplace of ideas and undermining the public interest (Doyle, 2002). Media owners privilege some viewpoints and silence others (Bagdikian, 2004; Horwitz, 2005).
30 V iewpoint diversity suffers as corporations force out smaller, local competitors (Smith, 2004). on which so many people depend to make decisions about everything from whom to Driven solely by commercial interests, media companies produce duplicative, bland programming in order to generate mass appeal (Bagdikian, 2004; Blevins, 2002; Golding & Murdock, 1996; Gomery, 1993; Meier & Trappel, 1998; Smith, Concentration discourages product innovation as a single concept can be efficiently repackaged for multiple outlets (Blevins, 2002) and favors marketable products over those with less commercial appeal (Horwitz, 2005). The interests of stockholders and advertisers prevail over the public interest (Smith, 2004). Programs with positive externalities (e.g., news and public affairs) are under produced relative to their importance (Baker, 2002). Media advance consumerism and inequality, and marginalize civic values and political action (McChesney, 2004 Journalistic values suffer in the face of pressure to maximize profits and the 005), particularly as outlets are unlikely to publish criticisms of their own company or those policies they support (Shah, 2009). News and public affairs programs have become more about entertainment and n creating an informed citizenry (Shah, 2009). Indeed, Williams (2002) found that networks devoted more that were less integrated and diversified showed less synergy bias t han highly integrated and diversified companies. These results were confirmed by Cleary and Adams Bloom (2009) who found networks were more likely to cover entertainment other sourc es; network products were also treated more positively than those produced by other sources. Concentration creates nearly insurmountable barriers to entry for new firms (Bagdikian, 2004; Gomery, 1993; Shah, 2009).
31 While compelling, these arguments do not capture the full complexity of the relationship between concentration and the content produced. It cannot be taken as a given that concentration negatively impacts programming. All commercial stations face the same commercial pressures and are driven by th e same basic goal: profit sized media firms can be irresponsible and profit That is, conglomerates may be capable of providing more diversity because large organizations are able to capture economies of scale and spread production costs over a wide audience (Iosifides, 1999); large firms may have the resources to innovate and increase the range of output (Doyle, 2002). For example, station groups may convert the efficiencies achieved into greater amounts of local informational programming (Yan & Napoli, 2006). Theoretical perspectives (based in e conomics) and empirical research on the relationship between concentration and television content are discussed further below. Market Structure and Program Type Diversity Theoretical Perspectives Program choice models strive to explain programming behavio r in terms of market structure (Beebe, 1977; Spence & Owen, 1977; Steiner, 1952). Schumpeter (1950) held that monopolistic and oligopolistic market structures encourage innovation because firms have the incentive to develop and test new products. This lead s to product diversity. Similarly, Steiner (1952) asserted that under certain conditions, monopoly ownership promotes greater format diversity than competition. When all stations are
32 owned by a single entity, the owner will offer distinct programming on ea ch station to avoid cannibalizing its own audience, resulting in greater program type diversity. These relationships, however, are rarely straightforward and are mediated by additional variables. For example, diversity is contingent on the distribution of audience preferences. When audiences have a strong preference for a particular format, multiple stations will provide it. In this case, it is preferable for the firm/programmer to capture a portion of the majority than offering minority preferences with a relatively small potential audience. Diversity decreases as competitors duplicate the popular format. In this scenario, a monopoly produces greater format diversity. When preferences are weak and distributed across a number of formats, competition and mon opoly produce similar that audiences will consume only their preferred format; they will choose not to view if this format is not available. That is, non viewing represe nts the second best option. Audience behavior indicators suggest that this assumption rarely, if ever, holds. Indeed, Klein (1971) asserted that audiences consume the medium (television) rather than particular programs. That is, television is an end in its elf rather than the means to access a specific show. Viewers first decide to watch television and then choose from the available programs, settling for the program that offends them the least. This is demonstrated by relatively consistent viewing levels on a given evening for a particular timeslot year after year, regardless of the content broadcast. secondary audience preferences. When this variable is added, competition contributes to equal or greater diversity than under a monopoly structure. The monopoly will offer
33 the minimum number of formats it takes to attract a majority of viewers. Broadcasters select programs that please as many viewers as possible and offend the fewest in o rder to attract the largest share of the TV audience. If most audience members will accept their second or third choice, then the monopolist is better served offering only one format with mass appeal. In a competitive market, stations may find it more lucr ative to appealing to specialized, heterogeneous tastes. The result is greater program diversity under competition than monopoly. Spence and Owen (1977) consider producti on costs and audience valuation in their model. If a program type is to be offered, the ad revenue generated by the program must cover or exceed the costs of broadcasting the format. Ad revenue is contingent on if the audience for a program is not highly valued, the revenue generated is unlikely to cover costs and the format will not be offered. Because stations seek to maximize profits, they are also unlikely to offer expensive programs that cut into the bottom line. Thus, high program costs and low audience valuations for minority taste programs contribute to the loss of program type diversity, leaving some audience preferences unsatisfied. These findings hold regardless of market structure. Audience value is i nfluenced by market dynamics (market factors), the characteristics of the outlet (media factors) and the traits of the audience (demographic factors) (Napoli, 2003). Intense competition in the market is associated with low audience value because there are more suppliers of audiences and these suppliers are often substitutes for one another (Napoli, 2003). Given that the provision of a program
34 type is contingent on sufficient revenue, and that competition tends to drive down audience value, it would seem tha competition negatively impacts program type diversity. Also important in determining audience value are ownership and affiliation characteristics. Network affiliates and group owned stations can charge higher rates than in dependent stations (Wirth & Wollert, 1984). Accordingly, given their substantial resource advantage, it could be expected that these stations would produce greater program diversity than independent owners, though this may be offset by their obligation to carry network programming. More important is the fact that content tends to skew toward the preferences of highly valued audience members, reducing the amount of minority targeted programming (minority does not necessarily refer to race or ethnicity, but r ather viewing preferences that fall outside the majority) (Napoli, 2003). Table 2 1 outlines the major theories regarding market structure and the range of program choices provided. Empirical Research Empirical research has failed to support any definitiv e relationship between structural factors and program type diversity. While many studies have found that intensifying competition leads to greater product diversity, others have found an inverse relationship. It is clear that market structure affects dive rsity, but the nature of this relationship is more complex than theory would suggest. Regardless of the specific findings, diversity research can be divided into two broad categories. First are descriptive studies, which provide an overview of how diversi ty has shifted over time and help identify the structures associated with the highest diversity levels (Napoli, 1997). For example, Dominick and Pearce (1976) found
35 an overall decrease in diversity from 1953 to 1974. While peaks occurred, from 1961 to 1963 and 1969 to 1970, there was a negative trend over the broader period. A number of descriptive studies focusing on other mediums beyond broadcast television have been conducted as well and have predominantly focused on cable (Bae, 1999; Barrett, 1995; Chan Olmsted, 1996; DeJong & Bates 1991). The second category of diversity research includes studies that attempt to explain how diversity arises (Napoli, 1997) researchers may use a longitudinal approach to track diversity, but the goal is explanation. Ex amples of explanatory research are included below. Long (1979) found the death of the DuMont network in the 1950s led to a decline in horizontal program type diversity and the number of specialized appeal programs (e.g., news, educational/instructional). T he negative trend noted by Dominick and Pearce continued into mid from third to first in 1976 as a result of increased program expenditures and product innovation. These market shifts encouraged CBS and NBC to offer new programming to stay competitive and gain audience share A short period of increasing program diversity (larger number of program categories) occurred until competitive norms were restored in the 1980s. This restoration resulted in de creasing program type diversity (Wakshlag & Adams, 1985). Barnett, 1971), the change in diversity resulting from a change in the number of outlets in a given market. Levin ( 1971) found that diversity increases with new station entry, but as more stations are added, the rate of increase slows, resulting in diminishing marginal
36 returns. Indeed, as more stations are added, the additional content provided is likely to be of an al ready popular program type rather than an entirely new category because successful shows are imitated in the hopes of attaining similar success while less popular shows (with lower revenue potential) are not emulated. van der Wurff and van Cuilenburg (2001 ) also found a nonlinear relationship between competition and diversity. While moderate competition encourages diversity, ruinous competition between competition and dive rsity cannot be blindly accepted. Instead, the level of diversity may be dependent on the type and degree of competition (Park, 2005; van der Wurff & van Cuilenburg 2001). A number of explanatory studies have been conducted beyond the television industry as well. In studies of the U.S. music industry, Peterson and Berger (1975) along with Rothenbuhler and Dimmick (1982) found intense market competition was associated with more hit songs and a greater diversity of producers and lyrical themes. In radio, fo rmat diversity was positively correlated with the number of competitors serving the market (Berry & Waldfogel, 2001; Chambers, 2003; Polinksy, 2007; Rogers & Woodbury, 1996). Studies of the radio industry have tended to focus on local stations more than co mparable studies in television that have generally tended to focus on the behaviors of national networks, which are largely absent in radio. Consequently, they are of great relevance to this study of local television markets. Though the majority of studie s (like those above) have supported a positive (even if mediated) link between competition and program type diversity, a number have found the opposite across a range of industries (Burnett, 1992; Hellman & Soramaki, 1985;
37 Lin, 1995b). For example, Lin (19 95a) found stable levels of diversity during the 1980s despite the proliferation of alternate delivery systems that were expected to increase diversity. In addition, Drushel (1998) found increasing concentration in the radio industry following the passage of the 1996 Telecommunication Act did not yield increased listener choice. Overall, while the literature suggests a link may exist between market structure and program diversity, the direction of this relationship is uncertain. As Napoli (1999) summarizes whether relationships exist and the relative strength of these associations. Table 2 2 outli nes the research that has been conducted on market structure and diversity across a number of industries, as well as the major variables used in these studies. Ownership and Program Type Diversity e local single station ownership, reducing program diversity in the process, and pose potential anti Therefore, the FCC has enacted group ownership rules to ersity These regulations are expected to produce economic efficiency and program balance as well as diversity (Levin, 1970). The majority of the research on diversity and own ership characteristics comes from studies of radio and television news programming (discussed further below.)
38 Informational Programming As noted previously, of greater concern than the sheer number of program types available is the quantity of news and pub lic affairs programs provided. These programs are perceived as particularly important because of their positive impact on society. As Napoli (2004) explains: The value of such programming extends beyond the revenue it generates and the satisfaction consume rs gain by consuming it. These positive externalities include enhanced citizen knowledge and decision making, better informed political participation, and a citizenry better capable of influencing government to pursue its best interests (p. 112). In addit ion, the quantity of news and public affairs programming provided may serve as a proxy for viewpoint diversity (Wildman, 2007). Therefore, the relationship between station and market factors and the provision of news and public affairs programming merits s pecial attention. A number of market factors may affect the quantity of news and public affairs programming provided. Market Factors: Level of Competition First, the intensity of market competition may be an important driver of the provision of informat ional programming. Napoli and Yan (2007) found that while competition (the number of commercial stations in a market) had no impact on the likelihood a station would provide local news programming, it was positively related to the amount of news aired by s tations already offering this program type. In a similar study, Yan and Napoli (2006) found stations in markets with a large number of commercial stations were more likely to provide local public affairs programming, though this variable was not related to the quantity provided. Moreover, Napoli (2001b) found a statistically significant (albeit weak) positive relationship between the number of
39 commercial stations in a market and the amount of local affairs programming provided. Powers (2001) found that as c ompetition intensified, the number of newscasts increased as stations provided more news programming in order to compete. Outside broadcast television, Barrett (1995) found that competition among cable operators within the same city yielded more local pro gramming. Further, the majority of studies from the newspaper industry have found a positive relationship between competition and content diversity (Everett & Everett, 1989; George, 2007; Lacy, 1987, 1988; Litman & Bridges, 1986). While the literature sug gests competitive conditions lead to greater provision of informational programming, it is possible competition actually harms diversity because market pressures and the public interest are inherently at odds. As Hamilton (2006, p. re of news outlets to earn revenues from the value of better voting decisions means that news programs or products that focus on hard news will be programming (which often draws larger audiences) over informational fare (Park, 2005). Competition may increase overall diversity, but the source of these gains must be scrutinized closely; they are of limited value if they come at the cost of decreased informational programming. While not directly related to this study, there is a notable body of literature exploring the impact of competition on news content Atwater (1984) found a positive relationship between the number of stations in a market and the number of unique news sto ries. Similarly, in Denmark, competition led to more diverse news content (Powers, Kristjansdottir & Sutton, 1994). Bae (1999) found greater content differentiation in cable
4 0 news programming as a result of competition. In a study of the Swedish television industry, however, news was less informative and more commercialized as a result of decreased competition (Hvitfelt as cited in Li and Chiang, 2001). Similarly, studies of the newspaper industry have found little support for the relationship between conten t quality and market competition (Lacy, 1988; McCombs, 1987, 1988). As Iosifides (1999) beyond the scope of the present study, however. Rather, the current analysis focuses solely on the provision of informational content. Market Factors: Market Size Provision of informational programming is also related to market size. Therefore, in large markets, la rger potential audiences equate to greater revenue potential for a given program (Napoli, 2004) and, consequently, a heightened ability to supply specialized content (Doyle, 2002). Traditional program choice models assume programmers consider potential aud ience size in deciding what programs to air (Beebe, 1977; Owen, Beebe, & Manning, 1974; Spence & Owen, 1977; Steiner, 1952). With regards to empirical research, the FCC (1984) found a positive relationship between market size and the provision of local new s and public affairs programming when these formats are considered in combination. Napoli (2004) also found a positive relationship between market size and the quantity of local news and public affairs programming provided; in fact, market size was the mos t important explanatory factor. Table 2 3 outlines the research that has been conducted on market structure and informational programming for both the broadcast and print industries. It also includes the variables used in these studies.
41 Station Factors: L ocal Ownership Ownership of is among the key station characteristics that impact the quantity of informational programming provided, and represents a particularly contentious policy area. For more than 80% of Americans, local TV stations and newspapers are the principle source of information about their local communities ( Kimmelman n.d.b). Yet, 2009). As stations increasingly fall into the hands of fewer, often distant, corp orate owners, some fear that non locally owned stations may ignore the needs of those they are mandated to serve. That is, they may be unresponsive to community interests and less likely to provide coverage of local issues. The oft cited example of the de trimental impact of consolidation is the coverage (or, more appropriately, lack thereof) of a chemical spill in Minot, North Dakota (Bagdikian, 2004; Consumers Union, n.d ). Following the spill, local police reached out to the local radio stations after th e emergency warning systems failed. They were unable to reach anyone at the stations for an hour and a half because all six were operated from afar by remote control; the content was prerecorded and used on stations hannel) was located in San Antonio, thousands of miles away from the community of license. Hundreds of local residents were hospitalized from exposure to the spill (Bagdikian, 2004; Consumers Union, n.d. ) As the Minot example demonstrates, as a consequenc is critical to all community members is not covered as extensively, making it more n.d.).
42 Certainly as critics of the Minot case wo uld point out, local owners may be more in touch with community interests than distant chain or network owners and, thus, provide more programming focusing on the local community. Indeed, FCC license allocation policies favoring local owners are premised o n the assumption these owners have greater ties to the market and will, thus, be more sensitive to the needs and interests of the community of license than owners geographically removed from the communities rogramming arrangements that favor local autonomy over national level decision making (e.g., independent stations vs. It may also be, however, that stations owned by a strong national network or station group are able to provide greater amounts of news and public affairs programming because they have the resources to fund such productions and may be able to capitalize on economies of scale. This is the argument often ma de by those in favor of greater deregulation. Empirical research has thus far failed to generate conclusive evidence for either argument. Wirth and Wollert (1979) found no relationship between group ownership and the quantity of news or public affairs pro gramming provided. A number of studies, however, have supported the arguments in favor of local ownership put forth by critics of consolidation. Alexander and Brown (2004) found that local ownership is associated with an increase in the total time devoted to news, with particular increases in local and on location news. Similarly, Napoli (2002) found evidence local ownership was positively related to the quantity of public affairs programming provided.
43 Supporting the view of pro deregulation advocates, Spa vins, Denison, Roberts & Frenette (2002) found that, compared to non network owned affiliates, network O&Os provided a greater quantity of news and public affairs programming. These results were supported by Napoli (2004), who found that when news and publ ic affairs programming are analyzed in combination, network owned and operated stations provided more news and public affairs programming than other affiliates. This relationship holds only for news programming; when the two are examined independently, the re is no difference between owner types in terms of the quantity of public affairs programming provided. Nonetheless, these findings suggest that, contrary to the fears of lity Station Factors: Revenue Another station characteristic impacting content output is station revenue. Financially strong stations with the means to support the high costs of news pr oduction may be more likely to provide this type of programming. Empirical support for this assumption is inconclusive. It was supported by Wirth and Wollert (1979) who found a significant relationship between revenues and local news programming. More rece ntly, however, Napoli (2004) found no relationship between revenues and local news provision. Table 2 4 includes an overview of the research that has been conducted on ownership characteristics and informational content for both the broadcast and print i ndustries.
44 Measuring Diversity Diversity and concentration fall on opposite ends of the same spectrum (McDonald the reverse of common measures of diversity and therefore al so refer to the same Albarran (1996) suggests television concentration can be assessed in two ways: calculating the top ratings data for a large, national sample, the current study used the latter approach A number of indices have been dev eloped to assess diversity across research the four firm concentration ratio (CR4) is the most commonly used index (Shepherd, 1970, 1979). This ratio is calculate d by summing the market shares of the four largest firms in an industry (Shughart, 2008). A market is considered concentrated if the top four firms claim at least 50% of industry revenues (Albarran, 1996). However, this index s relative share; that is, it cannot distinguish between a market dominated by a single firm and those with four firms of similar size (Noam, 2009). This index has been used very rarely, if ever, within media research to assess program type diversity. Rath er, four dominant approaches have emerged. These apply to vertical diversity only, with horizontal diversity indices being discussed later. accounted for by the most common pro gram categories (somewhat like the CR4 index).
45 subtracting from 100...A low score would indicate a restricted range of choices for the pends on the number of identified categories, with a minimum of zero in all cases if the top three categories represent the only program types available. The maximum is calculated by dividing 100 by the number of categories (assuming content is equally div ided among categories). This number, indicating the maximum for any one type if all categories are present, is then multiplied by three and subtracted from 100. For example, if content is equally divided in 14 program types, then a single type can equal, a t most, 7% of the programming. The top three would represent 21% of the programming, leaving a maximum index value of 79 (100 21). The nstein, 2002, p. 6), but is of limited use in evaluating the overall schedule beyond the top three program types (Einstein, 2002; Kambara, 1992; Napoli, 1999). dimensional; it simply measures the largest categories wi thout consideration of the total number of program types. Litman the number of categories and to the size distribution of programming within these categories (p. 206). The (Junge, 1994, p.16 cited in McDonald & Dimmick, 2003). The first dimension involves the classification or categorization of elements, while the second consists of assigning or allocating elements to these categories. Dual concept measures better capture the
46 true meaning of divers ity by revealing how evenly elements are distributed across categories (McDonald & Dimmick, 2003). These same authors discuss 13 measures of D and those based on the work of Goo d (1953, cited in McDonald & Dimmick, 2003) that use logarithmic transformation. D is the second primary diversity index. It is calculated by summing the squared probabilities from all the categories and then subtracting this total from 1.0 (McD onald & Dimmick, 2003). D is the probability that two randomly selected elements come from the same category. A probability of 0 indicates all elements fall within one category (no diversity), while 1.0 indicates all the items are from different categories (perfect diversity) (McDonald & Lin, 2004). Though easy to calculate and interpret, D has been primarily used outside the field of communications (McDonald & Dimmick, 2003). A third diversity measure is the relative entropy index (H), conceived by Shannon considers both the number of program types offered and the concentration within those categories (the time devoted to this type.) Entropy is calculated using the formul a H= i log 2 p i, where H is variety and p is the probability of seeing program type i (Napoli, 2001a; Shannon & Weaver, 1963; Wakshlag & Adams, 1985). A value of zero represents absolute concentration with all programs falling within one type. The maximu m value depends on the number of categories identified, but represents maximum unpredictability with all types present in equal proportion (Wakshlag & Adams, 1985). This index has been used in a number of previous studies, including
47 Aslama et al. (2004), H illve et al. (1997), Ishikawa & Muramatsu (1996) and Wakshlag & Adams (1985). The fourth index, the Hirschman Herfindahl Index, is the mostly commonly used in communications research (Bates, 1993; Einstein, 2002; Napoli, 1999). A portion of the HHI is math D, but values are inversely related to diversity. That is, high values on the HHI actually correspond to low diversity, while the D. The precise calculation of the Herfindahl Index is discussed further in the Methodology section. McDonald and Dimmick (2003) tested all thirteen measures of dual concept diversity using a common data set and found that in many cases it does not matter which index is used. Some indices may be more appropriate in certain situations (e.g., when the index must be particularly sensitive to the number of categories), but that discussion is outside the scope of the current study. In the present study, the HHI is used to measure vertical diver sity given its widespread use in previous research. Two indices were used to assess horizontal diversity: the HHI and Choice Option Index (COI). The first measures the breadth of offerings; the second, the difference between the programs provided by all st ations in the market (Hellman, 2001). HHI is calculated the same way for both vertical and horizontal diversity, but the latter captures the concentration of programming provided by all stations in the market. That is, the index summarizes the overall dist ribution of programming by type and reflects how varied and balanced programming is in the market. For both vertical and horizontal diversity, the larger the range of genres
48 available and the more evenly programs are distributed across these categories, th e greater the diversity provided. Examining how much time is devoted to each program type does not provide a complete picture of horizontal diversity, however. It is also important to evaluate how given point (referred to as dissimilarity by Aslama et al., 2004). This is achieved via the Choice Option Index (COI) ., 15 minutes or half hour). The COI is the more commonly used approach to measuring horizontal diversity. It has been used in previous research by Einstein (2002, 2004), Levin (1971, 1980), Li and Chiang (2001) and Litman (1979). Research Questions As di scussed above, previous research has found both positive and negative relationships between station ownership, program type and origin diversity as well as the provision of informational programming. The following research questions were explored to examin e the relationship between these variables. RQ1a: How does station ownership influence the diversity of content broadcast by local stations? RQ1b: How does station ownership influence the diversity of program origination sources provided by local televisi on stations? RQ1c: How does station ownership influence the quantity of local news and public affairs programming provided by local television stations? Uncertainty also exists regarding the relationship between market competition and programming. The fo llowing research questions were asked to further investigate this relationship.
49 RQ2a: How does the level of competition in local markets affect the diversity of programming provided by stations in these markets? RQ2b: How does the level of competition in local markets influence the diversity of program origination sources in these markets? RQ2c: How does the level of competition in local markets influence the quantity of local news and public affairs programming provided in these markets?
50 Table 2 1. Th eoretical perspectives on market structure and diversity Author (year) Theory Schumpeter (1950) Innovation is more likely to occur under monopolistic or oligopolistic market structure because firms possess the means and have the incentive to finance innov ation Steiner (1952) Monopoly may promote greater format diversity as an owner may provide a number of different offerings to avoid cannibalizing its own audience. Under competition, broadcasters fight for a slice of the audience of the most lucrative pro gram types and, thus, Beebe (1977) program costs, differing viewer preferences and unlimited channel capacity. Concluded that ideal market structure depends on viewer preferences and channel capacity. Overall, expansion of channels is preferred choices, which favors a competitive structure over monopoly. Spence and Owen (1977) When the costs of developing a program are greater than expected adverti sing revenue, the program will not be produced, reducing the variety of programs available (loss of diversity.)
51 Table 2 2. Summary of previous research on market structure and diversity Author (year) Findings Dependent variable(s) Independent variable(s ) Aslama, Hellman and Sauri (2004) Intensifying competition leads to decreasing diversity, but increasing dissimilarity (the differences between channels.) Diversity (probability of seeing different program types on a channel), dissimilarity (probabilit y of seeing different program types when switching between channels) Intensity of competition (HHI) Dominick and Pearce (1976) A substantial drop in diversity in network primetime schedules between 1953 and 1974 corresponded with indicators of oligopolist ic activity, including increased network profitability and a pricing cartel. Program type diversity (top three index percentage of the total amount of programming accounted for by top three program types) Market conditions (over time) Einstein (2002) Fina ncial Interest and Syndication (fin syn) rules, intended to promote content diversity, coincided with decreasing diversity. Their repeal coincided with increasing diversity (and verticalization in broadcasting.) Horizontal diversity (COI), vertical diversi ty (HHI) Time period (before, during and after enactment of fin syn) Grant (1994) Diversity of program types within a channel is negatively related to the number of channels with a channel type; however, horizontal diversity of each channel type is positi vely related to the number of channels within that type. Program type diversity (adapted HHI) Number of channels within a channel type
52 Table 2 2. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Levin (1971) Diversity in creases as the number of stations rises; however, as more stations enter, the rate of increase of options declines. Diversity (number of program options) Number of commercial stations Li and Chiang (2001) Although market competition increased over a 10 ye ar span, programming diversity declined. Vertical diversity (HHI), horizontal diversity (COI) Network entry (before and after) Litman (1979) Vertical and horizontal diversity increased as market competition intensified. Vertical program type diversity (H HI), horizontal diversity (COI) Competition (over time) Long (1979) Program diversity declined following a reduction in the number of networks from four to three. Program type diversity (Choice Option Index, though not explicitly referred to by that name) Number of national networks (measured before and after demise of DuMont) McDonald and Lin (2004) The emergence and growth of new networks was associated with increased overall system diversity. However, traditional networks provided a constant level of d iversity; instead, the increase in system diversity was driven by new networks. System diversity, network diversity, traditional network diversity, new network diversity Year (which reflects the intensity of competition) Park (2005) Diversity declines as competition increases. Program type diversity index (adapted HHI) Number of commercial broadcasters that entered the market)
53 Table 2 2. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Tsourvakas (2004) The introduc tion of competition resulted in reduced diversity on public television stations as broadcasters imitated the programming of their commercial counterparts. Program type diversity (total time each type covered) Introduction of commercial competition van der Wurff and van Cuilenburg (2001) There is not a linear relationship between competition and diversity. Moderate competition encourages diversity. If competition becomes too intense, however, excessive sameness results. Excessive sameness, Open diversity In tensity of competition (adapted HHI) van der Wurff (2004) When a large number of channels compete in a market, broadcasters engage in ruinous competition as they provide similar program types and diversity declines. Under moderate competition, diversity i ncreases as channels add special interest programs to mainstream content in order to serve the entire audience as best as possible. Diversity Intensity of competition Wakshlag and Adams (1985) The introduction of the Prime Time Access Rule, intended to pr omote competition in program production, actually coincided with declining program diversity. Program type diversity (relative entropy) Regulation (before and after) Barrett (1995) Direct competition among cable system operators produced increased program ming choices for subscribers, particularly local content. Number of channels and quantity of local programming Number of cable operators (before and after second operator began providing service)
54 Table 2 2. Continued. Author (year) Findings Dependent v ariable(s) Independent variable(s) De Jong and Bates (1991) Increasing diversity occurred alongside deregulation and the growth of the cable industry. Average of absolute diversity (number of different channel types carried by a cable system divided by to tal number of channel types for the cable industry) and relative diversity (number of different channel types divided by the channel capacity of the system) Year (diversity assessed before and after deregulation) Lin (1995a) Network program type diversity remained relatively constant during the 1980s despite increased competition from alternative delivery systems. Program type diversity (HHI) Intensity of competition (from new media) Lin (1995b) Facing competition from new video media, TV networks adopte d conservative programming strategies, duplicating successful formats and narrowing program choices. Type of programming strategies used Intensity of competition (from new media) Alexander (1997) In the music industry, a nonlinear relationship exists bet ween concentration and variety. Higher and lower levels of concentration yield lessened variety. Moderate concentration is optimal. Product variety (entropy) Market concentration (HHI and four firm ratio)
55 Table 2 2. Continued. Author (year) Findings D ependent variable(s) Independent variable(s) Berry and Waldfogel (2001) Consolidation increased the number of radio formats available relative to the number of stations. Number of different programming formats broadcast in a market Market concentration B urnett (1992) Between 1981 and 1989, diversity increased despite high concentration (low competition) in the music industry. Diversity (number of top selling records) Concentration (proportion of best selling records produced by the leading firms) Chambe rs (2003) In the radio industry, an increase in format diversity was associated with an increasing number of competitors. Format diversity (number of formats broadcasting in a market) Number of stations Drushel (1998) No link between concentration and for mat diversity in radio markets. Program format diversity index (number of distinct stations divided by number of stations) Market concentration (HHI) Lopes (1992) Significant levels of innovation and diversity in the contemporary popular music industry d espite high market concentration and oligopolistic control. Innovation (number of new and established artists appearing on charts), diversity (number of artists appearing on singles and albums charts and number of Top 10 and Number One singles each year) Market concentration
56 Table 2 2. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Peterson and Berger (1975) There is an inverse relationship between concentration and diversity in the music industry; periods of increased concentration corresponded with low diversity. Diversity of musical forms Market concentration (four and eight firm concentration ratio) Polinsky (2007) Format diversity positively related to number of stations serving the market. Format diversity (num ber of formats broadcasting in a market) Number of stations Rogers and Woodbury (1996) Moderate positive relationship between number of radio stations in the market and the number of formats available. Format diversity (number of formats broadcasting in a market) Number of stations Rothenbuhler and Dimmick (1982) As concentration in the music industry increased between 1974 and 1980, diversity fell. Diversity (rate of turnover in the top slots on the popularity charts) Market concentration Hellman and S oramaki (1985) Concentration in the video rental industry was associated with increased diversity. Diversity (each video total number of cassettes appearing on sales and rental charts) Concentration in video market Hellman and Soramak i (1994) Increased competition was not associated with greater range of product types (content) or consumer choice. Range of program choice Concentration in video market (HHI, four and eight firm ratios)
57 Table 2 3. Summary of previous research on mark et structure and informational content (broadcast and print.) Author (year) Findings Dependent variable(s) Independent variable(s) Atwater (1984) Additional stations contribute to the diversity of news content in terms of the number of unique stories. Lar ger markets covered a greater number of unique stories and devoted more local news time to these stories. Number of unique news stories, news content diversity (ratio of number of unique news stories to total number of stories broadcast in a market) Number of stations in a market Bae (1999) The entrance of new news channels resulted in increased diversity of topics covered. Each additional network contributed to the diversity of news content by providing a number of unique stories. Topics covered, number o f unique stories Number of national cable news networks Bishop and Hakanen (2002) The number of locally produced public affairs programs declined steeply as a result of deregulation in the 1980s. Large market stations better serve community needs than tho se in smaller markets. Quantity of public affairs programming (number of programs, total hours) Deregulation (before and after), market size (market rank) Busterna (1980) Multiple ownership did not have an adverse effect on quality of local news. Also, m arket size was associated with greater news expenditures News quality (news expenditures) Multiple ownership, market size (number of stations in market)
58 Table 2 3. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Davie and Lee (1993) Stations in larger markets tend to air a greater number of unique news stories. Consonance (percentage of stories duplicated by two or more stations within a market on the same news day) Market size (market rank) FCC (1984) Market size pos itively related to provision of news and public affairs programming when these types are considered together. Quantity of news and public affairs programming Market size (TV households) Hvitfelt (1994) After competition, news became more commercialized an d less informational. News content (commercialization, information) Network entry (before and after) Napoli (2001a) Broadcasters devote a minimum of total broadcast time to public affairs programming. Small markets are particularly likely to receive no pu blic affairs programs on a regular basis. Quantity of public affairs programming provided Market size (TV households) Napoli (2001b) Significant, but weak, positive relationship between number of commercial stations in the market and provision of public a ffairs programming (though only when local and nonlocal programs are considered in combination.) Quantity of public affairs programming provided Market size (TV households, number of public stations, number of commercial stations)
59 Table 2 3. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Napoli (2004) The number of non commercial stations in the market and the amount of news programming offered by competing affiliates are positively related to the provision of local new s. Also, stations in larger markets provide more local news programming. Quantity of local news and public affairs programming provided (in combination and separately) Total number of commercial stations, total number of non commercial stations in a statio competing Big average hours of programming Napoli and Yan (2007) Market size, the number of commercial stations and the number of noncommercial stations in a market had no impact on the likelihood a station decided to offer news However, number of commercial stations was positively related to the quantity of news provided. Amount of local news programming broadcast TV households, number of commercial stations, number of public stations Powers (2001) In small markets only finan cially strong, highly rated stations will increase news programming. In large markets, trailing stations compete by offering more news programming throughout the day. That is, stations may increase news programming output in response to the offerings of co mpetitors. Amount of news programming Number of competitors in each market
60 Table 2 3. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Yan and Napoli (2006) Stations in markets with large number of commercial stations a re more likely to offer local public affairs programming, but there was no relationship with the quantity provided. Likelihood of offering public affairs programming, Quantity of public affairs programming Market size (TV households), Number of commercial stations, Number of non commercial stations in a George (2007) Concentration in the newspaper industry leads to greater product differentiation and variety of coverage. Number of different topical beats covered in an area Market concentra tion Lacy (1988) In the newspaper industry, as the intensity of intercity competition increases, more space is given to news, particularly local coverage. Space given to news Intensity of intercity competition (percentage of households in a co unty that read another daily newspaper) Litman and Bridges (1986) Newspapers in competitive markets devote more space to news. Overall space for news Daily newspaper competition McCombs (1987 and 1988) The presence or absence of competition had little ef fect on newspaper content. Content (range of topics, geographical focus and number of locally produced stories) Market competition
61 Table 2 4. Summary of previous research on ownership characteristics and informational content (broadcast and print.) Aut hor (year) Findings Dependent variable(s) Independent variable(s) Alexander and Brown (2004) Locally owned stations air more total, local and local on location news than network owned and operated and non locally owned stations Quantity of news provided O wner location Napoli (2004) Provision of local public affairs programming is a function of station revenues; no relationship with provision of news programming. Quantity of news and public affairs programming Station revenue, owner type (broadcast network ) Napoli and Yan (2007) Station revenues, Big Four affiliation, local ownership and group size increased probability a station provided local news. First two variables also positively related to amount of local news programming. Ownership by a Big Four n etworks reduced the probability of offering local news. Duopoly ownership had no relation to the likelihood of offering local news, but was negatively related to quantity of news programming provided. Quantity of news programming Big Four affiliation, st ation revenue, owner location, owner type (network, chain, independent), duopoly, group size household reach)
62 Table 2 4. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Spavins, Denison, Roberts and F renette (2002) In markets in which network affiliates and O&Os compete directly, O&Os produce a greater quantity of local news and public affairs programming than affiliates. Quantity of news and public affairs programming provided Owner type (broadcast ne twork) Wirth and Wollert (1979) Significant relationship between station revenues and the provision of news programming. No relationship between group/chain ownership and provision of news or public affairs programming. Quantity of news and public affair s programming Station revenue, owner type (chain ownership), network affiliation Yan and Napoli (2006) Stations owned by larger groups are more likely to offer local public affairs programming. Ownership by one of the Big Four networks is negatively rel ated to probability of offering local public affairs programming. These stations also aired less public affairs programming. Duopoly unrelated to provision of local public affairs programming. Quantity of public affairs programming Owner size (reach), Own er type (chain, network, independent), Duopoly Yan and Park (2009) No significant difference in the amount of news or public affairs programming broadcast by duopoly and non duopoly stations. Also, duopoly stations did not devote more time than their coun terparts to informational programming. Quantity of news and public affairs programming Duopoly
63 Table 2 4. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Project for Excellence in Journalism (2003) Quality of news progr amming is higher for small station groups than large ones. Locally owned stations produce below average news. Quality (number of topics covered, number of sources and points of view presented, authoritativeness of localism, significa nce and informativeness) Owner type Scott, Gobetz and Chanslor (2008) Small chain TV news departments aired more local news, more locally produced video, more stories featuring on air reporters and fewer news promotions than larger chain based broadcast g roups. Amount of local news, quantity of locally produced video, number of stories with on air reporters, number of newscast promotions Owner size Williams (2002) Networks devoted more news coverage to products than s coverage was generally favorable. Topics covered, time and placement of products and services, valence (tone of coverage) Network owner Coulson and Hanson (1995) The purchase of a local newspaper by a group owner harmed newspaper quali ty, particularly localism. Story length, hard news coverage, use of wire services Group ownership (before and after)
64 Table 2 4. Continued. Author (year) Findings Dependent variable(s) Independent variable(s) Gilens and Hertzman (2000) Corporate owners hip influenced newspaper coverage of TV ownership deregulation. Papers owned by companies that stood to gain from deregulation provided positive coverage of the proposed changes while those with no vested interest offered unfavorable coverage. Amount, ton e of coverage Ownership interest in deregulation (newspapers with no TV holdings, those with five or more stations and those with nine or more) Hicks and Featherstone (1978) Papers under common ownership produce less duplicated content. Duplication of con tent Ownership structure (common or separate ownership of morning and afternoon papers) Lacy (1991) Chain owned papers devote less space to news and editorials than independently owned papers, but devoted more editorial and op ed space to the city in whic h they were located. Space devoted to news and editorials, geographic subject of editorials Group ownership
65 CHAPTER 3 METHODOLOGY This study utilized a secondary data analysis using information provided by BIA/Kelsey and Tribune Media Services (TMS). Ow nership and market data were obtained from Broadcast Industry Analysts (BIA), which publishes the Investing in Television Market Report and Investing in Television Ownership Report each quarter. Stations were selected using stratified sampling; three strat a were identified (large, medium and small markets.) The same method was used for the selection of the markets for analysis. Program data was obtained from TMS. Programming was analyzed for a constructed two week period. The days comprising this period we re randomly selected from two periods that were determined to be representative of a typical broadcast schedule. Nineteen data fields were obtained for every program, including the title, description, duration, airdate and airtime and program type. Program duration and type were used to calculate the program type diversity indices (HHI and COI) and the total amount of informational programming provided. These same variables, as well as program source, were used to calculate the diversity of sources and the quantity of local informational programming provided. Descriptive analysis was performed for the station characteristics. In addition, multiple regression was used to further investigate the relationship between variables. Regression was also used for the analysis of market conditions.
66 Independent Variables Variables used to examine the relationship between ownership pattern and programming output included owner type (independent owner, group owner, or network is part of a station group or network owned), whether a station is part of a duopoly, whether a station is locally owned, whether the station is a network affiliate, and station revenue. All of these variables have been used in previous research. Table 3 1 contains all station/ownership variables used in the study. Table 3 2 includes a description as well as the mean and standard deviation of all variables used in the regression analysis; for categorical variables, the number of stations in each category i s included. Five variables were included to assess how market forces are related to the programming offered by local stations, including the total number of stations in the market, the number of commercial stations, the number of non commercial stations, t he number of television households and combined market revenue (for all commercial stations). These variables reflect the intensity of competition in the market; all have been used in previous research. A more precise measure of competition, like the HHI, could not be calculated because not all stations report their revenues to BIA; therefore it was impossible to is only concerned with the diversity provided by commercia l stations, the number of non commercial stations is included because they likely have a strong commitment to informational programming and their presence may encourage commercial stations to offer alternative formats (to appeal to a different audience) ra ther than competing head to head (Napoli, 2004). Table 3 3 contains a list of market variables used in the study.
67 Table 4 4 includes a description, as well as the mean and standard deviation of all market variables used in the present study. Operational De finitions including weather and stock market reports, and commentary, analysis or sports news 172). Public affairs programs are typically more focused on political and social issues than news programs, and are defined as those dealing with local, state, regional, national or int ernational issues or problems, documentaries, mini documentaries, panels, roundtables and vignettes, and extended coverage (whether live or recorded) of public events or proceedings, such as local council meetings, congressional hearings and the like. (FCC 1984, p. 172) While news and public affairs programming are distinct program types, for the purposes of this study, they are combined under the larger category of informational programming. These programs are among the primary means through which station s fulfill their public service obligations; therefore, the amount provided can be used to processes. Following the definition put forth by Alexander and Brown (2004), local o wners are Broadcasting, headquartered in Baltimore, is considered a local owner for its Baltimore station, WBFF, but not for its Buffalo station, WUTV. Non commercial statio ns are defined as those that do not generate advertising revenues. Instead, they are funded through viewer donations, government subsidies
68 and corporate underwriting. Commercial stations are those that generate revenue through advertising. Sampling of Stat ions for Ownership Analysis Selection Criteria commercial (see above definition), religious an d low power stations were removed from the list and not considered in the diversity analysis; digital subchannels were also excluded. To qualify for inclusion, stations had to be full power, commercial, non religious, primary/main signal U.S. stations. Two hundred forty stations were randomly selected from the final list. Non commercial stations were excluded because their primary mission is public service. Therefore, it is assumed these stations have a strong commitment to community interests and provide commercial stations. Additionally, while these stations offer a meaningful contribution to diversity, their audiences are generally much smaller than their commercial counterparts and, thus, their inclu sion may suggest more diversity exists than is actually consumed by viewers. Most importantly, this study examines the influence of competitive pressures on programming. Public stations operate under a vastly different business model than commercial statio ns, receiving government funding and soliciting viewer donations rather than competing for advertising dollars. As a result, non commercial stations, while not entirely isolated, face less pressure to attract the largest audience in the market (in order to maximize revenue) than their commercial counterparts. Therefore, comparisons between these very different station types are tenuous at best.
69 Many religious stations (though they hold a commercial license) operate under a similar model (collecting donation s) and, consequently, are also excluded from this study. Only primary/main signal stations were included because many digital subchannels do not yet generate meaningful revenues given the relative novelty of multicasting. In addition, subchannels are no t readily available to all viewers (e.g., ADS subscribers whose providers are not legally required to carry all broadcast feeds). Moreover, many broadcasters are still experimenting with their subchannels. Many simply carry the feed from one of the smaller networks like RTV or This TV or provide 24 hour weather, etc. Therefore, it was assumed that using primary signals, which are actively programmed, would better reflect the impact of ownership and market factors on the level of diversity provided. Station Selection Stations were selected using stratified random sampling. Stratified sampling was used to ensure adequate representation of relevant variables and that sample selection is made from a group with similar characteristics (a homogeneous population) ( Wimmer & Dominick, 2006). In the current study, the strata were based on the Designated Market Areas (DMAs) for the 2009 2010 season defined by The Nielsen Company (2009). It is important to note that while the programming information was obtained in duri ng the initial quarter of 2011, the station and market data was for 2009, the most recent year available at the time of acquisition (Fall 2010). As a result, there is a gap between these variables and their relationship with the diversity of offerings prov ided. While this represents a limitation of the study, the findings should not be discredited completely but rather viewed with the understanding that any relationships between market or ownership characteristics and programming tend to remain stable over time
70 and are not likely to vary significantly over a short period of time. For example, while a undoubtedly change from year to year, they should remain relatively consistent; maj or shifts would not be expected to occur over a period of a year or two. The 210 DMAs were divided into 3 similarly sized strata based on market size: large market stations were defined as those in markets containing 500,000 or more TV households (DMAs 1 64), mid market stations were those in markets with 175,000 to 499,999 households (DMAs 65 138) and small market stations were those in markets with fewer than 175,000 TV households (DMAs 139 210). Within each stratum, stations were ordered first by marke t rank, then alphabetically within each market. For example, New York (DMA 1) stations were placed ahead of Houston (DMA 10) stations; within New York, WABC was listed ahead of WCBS and WNBC. All stations were assigned a unique number. Using a random numbe r generator, eighty stations from each stratum were selected to ensure all market sizes were equally represented. The final analysis included 212 stations. Eight of the original 240 stations selected were excluded because they did not attract a large enoug h audience to generate meaningful revenue during 2010 or had gone dark since their selection and were no longer broadcasting during the dates selected for analysis. Additionally, 20 more were excluded because they were foreign language stations. These were included in the sampling frame because they represent true choices, particularly for bilingual viewers able to switch between English and non English options. In addition, in some markets, they account for a substantial proportion of revenue and market sh are. Therefore,
71 obtaining the data, it was clear that these stations would likely distort the true relationships between diversity and station/ownership characteristics becaus e, for reasons discussed below, all programming on these stations was considered to be of a single type. Sampling of Markets for Market Analysis Forty station/ownership analysis, the markets we re selected using stratified random sampling. The strata were once again, large (DMAs 1 64), mid sized (DMAs 65 138) and small (DMAs 139 210) markets. To ensure all market sizes were equally represented, fifteen markets from each stratum were selected usin g a random number generator. Programming data was collected for all qualifying stations within each DMA. Criteria for inclusion were the same as for the individual stations (full power, commercial, non religious, primary/main signal U.S. stations.) Two U. S. border markets (Laredo, TX and Harlingen Weslaco Brownsville McAllen, TX) were excluded from analysis because Mexican stations accounted for a portion of market revenues. A third market, Great Falls, MT, was excluded because it included multiple dark st ations that were not broadcasting during the days selected for analysis. Non English language stations were included in the market level analysis because, as discussed above, these stations represent true choices for viewers and may be major players in th e market. Previous research has focused on English language stations only. Therefore, the present study fills a gap in the literature.
72 Spanish and Asian language programs were considered separate types, but all programs on these stations were classified as either Spanish or Asian language regardless of genre. For example, Spanish language news programs and telenovelas were both considered Spanish programming. The reasons for this approach were twofold: 1) for simplicity and 2) because the researcher was not as familiar with foreign language program titles and genres, which made program type verification (discussed below) problematic. All Asian language programs fell under this broad umbrella regardless of the specific language spoken; that is, programs in Ch inese were treated the same as those in Korean, Japanese, etc. Program Sampling Following the model of Yan and Napoli (2006), Napoli and Yan (2007) and Yan and Park (2009), station programming schedules were examined for a constructed 14 day (two week) pe riod for both the ownership and market analyses. Days comprising the constructed weeks were selected from two periods: January 21 to February 2, 2011 and March 4 to March 27, 2011. These periods were selected because they were considered reasonably represe ntative of a typical broadcast schedule. First, they fell in the heart of the broadcast season, which runs from September to May. In addition, they fell outside February sweeps (February 3 to March 2, 2011) when stations are likely to air special programmi ng and/or promotions to maximize audience size. January 25 was of the Union address; given the widespread coverage devoted to this event, its inclusion likely would have artificially inflated the amount of public affairs programming presented. Each day during the defined period was assigned a unique number, and 14 days were selected using a random number generator. Dates selected for analysis were
73 January 21 (Friday), Janu ary 24 (Monday), January 26 (Wednesday), January 29 (Saturday), March 4 (Friday), March 7 (Monday), March 9 (Wednesday), March 10 (Thursday), March 13 (Sunday), March 16 (Wednesday), March 18 (Friday), March 22 (Tuesday), March 24 (Thursday) and March 27 ( Sunday). Program schedules were obtained from Tribune Media Services (TMS, 2011). Nineteen data fields were obtained for every program, including the title, description, duration, airdate and airtime. Also included was a program type field, which placed pr ograms in one of over 100 categories (e.g., talk show, daytime soap, sports event, public affairs etc.). While direct program coding has advantages over using pre established classifications developed by the data provider, the latter approach was more fea sible than obtaining copies of programming from stations nationwide and Yan, 2007, p.46). Program type Categories The data provider identified 106 program types. Several types were aired only infrequently (if at all) across all stations and markets. Moreover, though they were classified as distinct types, several were closely related and differed only slightly from one another (e.g., motorcycle racing vs. motorsports). Con sequently, it could be predicted that 106 genres would artificially inflate the level of diversity provided, particularly the COI, which measures the number of unique programs available at any given time. For example, using all of the types identified, it is possible that in a market with four stations the COI would also be four even if all the programs aired were different sporting events or sports related programs that were classified as unique types nto 39 broad categories
74 with similar characteristics. Each category is mutually exclusive, and every program falls into only one of the categories. The literature provided only limited explanation regarding how previous studies using program types had dev ised these classifications. Consequently, with the exception of the informational and public affairs programming categories, which were based on FCC definitions discussed above, the collapsed categories used within this study were entirely original. The br oad categories were developed by grouping programs with similar content or themes. For instance, the comedy category includes all programs that seek to get a laugh from viewers, regardless of whether they are filmed in a studio (e.g., sitcoms) or in front of a live audience outside a studio (e.g., stand up programs.) Similarly, the sports and recreation category includes sporting events and talk or other programs focusing on athletics, regardless of the particular sport discussed or shown. A few program typ es merit further discussion and are defined in greater detail below. The legal/criminal justice category includes non fiction programs related to legal affairs or crime; examples include or call in programs where viewers can discuss legal matters with attorneys. These are distinguished from crime dramas, which are scripted in nature. This category is also distinct from reality programs, which are ostensibly nonfiction, but include characterization and story development resembling scri pted programs. Dramedy includes programs that contain the humorous elements of a comedy and serious content of a drama. They are a hybrid of two types that cannot be truly classified as either a drama or a comedy. They may be more serialized than traditio nal comedies,
75 with greater emphasis devoted to character and storyline development; in addition, greater emphasis may be placed on character backstories. may include movies and dramas targeted at children as well as cartoons. This also includes educational programs for children. The educational category includes programs intended to inform or instruct. It is distinguished from the instructional category in that it tends to b e of a formal academic nature, while the latter tends to focus on soft skills. The community category includes entertainment programs produced by community members (e.g., local variety shows.) The entertainment focus is what distinguishes these programs f rom public affairs programming, which are intended to inform. Certainly, these classifications are somewhat subjective and the same types could have been collapsed a number of other ways. However, the approach used in this study attempted to balance the di fferences between programs while recognizing the similarities within them in order to practically quantify diversity. Table 3 3 includes a full list of the collapsed categories and the original types rtion of total programming minutes for both the station and market analyses. The collapsed classifications were used in combination with program duration to calculate two of the dependent variables in the study: level of diversity and amount of information al programming provided by each station/market over the two week period. Tribune also provided a program origination field, which was used to determine the third
76 and fourth dependent variables: the diversity of program sources (network, syndicated, local, block) offered by local stations, and the quantity of locally produced informational programming provided. A number of programs in the data set were not assigned program type, lacked origination information or were misclassified. In these instances, statio n websites were consulted and/or a general online search was performed to determine the nature and/or origin of the program. Programs that could not be classified (by either type and/or origin source) were excluded and did not count toward total program mi nutes for each station and/or market. Assessing Vertical Diversity This study uses the HHI to assess vertical diversity because it is the most widely cited index in the communications literature. The HHI is defined as where s i is the market share o f program type i and N is the total number of program minutes. Time was the basic unit of analysis in order to account for variety in program counting the number of each p rogram type provided. Share, therefore, was calculated by summing the total minutes of a given program type across the entire two weeks and dividing by the total minutes of programming provided by a station over the same period (Bates, 1993; Litman, 1979; Napoli, 1999). The share for each program type was squared and all squared shares were summed to yield the HHI.
77 small, to 10,000, where a single type accounts for 100% of all pro gramming (Noam, 2009). The following guide to interpreting scores is typically applied to media markets, but has been adapted for program types. Scores below 100 indicate highly diverse programming; between 100 1000, diverse programming; and above 1,800, h ighly concentrated programming (low diversity). If the index falls between 1000 and 1800, programming is moderately diverse (based on Baseman & Owen, 1982). Bates (1993) suggests that because media markets tend to fall within the latter level, greater disc rimination among categories is needed. This also holds true for program types. Therefore, scores between 1800 and 2750 indicate high concentration (low diversity), while indices greater than 2750 reflect very high concentration (very low diversity) (based on Bates, 1993). The index score and program type diversity are inversely related; HHI rises as program types become more concentrated and declines as they become more diverse (Litman, 1979). The index for each station was regressed against ownership char acteristics to determine the strength of the relationship between variables. Assessing Horizontal Diversity Following the model of Hellman (2001), both the HHI and Choice Option Index were used to measure horizontal diversity. For both measures, program data were collected for all qualifying stations in the 42 selected DMAs for the 14 days comprising the constructed 2 week sampling period. To calculate the Choice Option Index, the number of distinct program types provided across all stations for each 15 m inute period for the entire day (12 am to 11:59:59 pm) was counted. For example, if there were three stations in a market and all
78 aired a comedy during a given 15 minute interval, the number of distinct program types was 1; if the stations offered a comedy drama, and reality program, respectively, the number was 3. The count for every interval for 14 days was summed and divided by the total number of intervals to obtain an average measure of diversity. Perfect imitation (all stations present the exact same program type) yields a score of 1, while the score for perfect diversity is equal to the number of stations in the market (in a market with 3 stations, a 3 represents perfect diversity; in a market with 15 stations, 15 would indicate perfect diversity) (E instein, 2002, 2004; Hellman, 2001; Levin, 1980; Oba, 2004). HHI was used to measure the breadth of programming provided by the market. Share was calculated by summing the total minutes of each program type provided by all stations in the market over the entire two weeks and dividing by the total minutes of programming provided. The horizontal indices for each market were regressed against market characteristics to determine the strength of the relationship between variables. Assessing Public Service Whil e the availability of diverse programming options is valuable to consumers and arguably furthers the public interest, it does not ensure provision of the most important programs like news and public affairs that directly contribute to an active marketplace of not necessarily imply greater access to so this study devoted particular attention to the relationship between sta tion ownership, market structure and the provision of informational programming by local TV stations. It further distinguishes between news and public affairs from any source and those that are locally produced. Network provided programming may be a key (p erhaps, the only) source of informational programming for many stations; while valuable, of greater
79 importance to the local marketplace of ideas is the provision of news and public affairs that is locally produced and focuses on the needs and interests of the community. In addition, different factors may affect the provision of any news / public affairs programming and the provision of locally produced news / public affairs programming. Consequently, both are included in the current study. Quantity of info rmational programming provided was calculated by summing the total minutes of news and/or public affairs programming aired across all 14 days. For market analysis, the total minutes provided by all stations in the market were summed. The quantity of local informational programming provided was derived using the program type and source classifications provided by TMS. Programs that were identified as news or public affairs and were locally produced were counted in the analysis. Multiple regression was used to estimate the effects of market competition and station ownership characteristics on the quantity of informational programming provided. In addition, t tests and a one way ANOVA were used to examine how ownership impacts the amount of informational prog ramming provided. Assessing Localism (Program Origination) As touched on above, locally produced programs (of any genre) are assumed to further the goal of localism better than those from a distant source. Consequently, program origination was examined to assess how market and ownership structure impact localism. Program origination was analyzed at both the station and market level using the HHI. Programs were classified as being of network, syndicated, block or local origination. A Block is used in conj unction with regional sports coverage and occurs when a source (FOX, CBS, etc.) has the option to carry multiple team vs. team events
80 that would occupy the same timeslot; the matchup aired depends on the region. These programs could be either network or sy ndicated programs and were thus distinguished as a separate program source. Share of schedule devoted to each of the four sources was calculated by summing the total minutes of each type provided across the entire two weeks and dividing by the total minut es of programming provided on that station. For the system level analysis, share was calculated by summing total minutes of each type provided by all stations in the market over the 14 days and dividing by the total minutes of programming provided by all s tations. Regression analysis was used to estimate the effects of market competition and station ownership characteristics on the program origin diversity index.
81 Table 3 1. Station/ownership variables. Dependent variables Independent variables Vertical diversity Owner type Source diversity network owned station only) Total informational programming Duopoly Local informational programming Local ownership Network affiliation Station revenue
82 Table 3 2. Station/o wnership variables with definition and values. Variable Definition M SD Yes No Dependent variables Vertical diversity The distribution of programs by type. Measured using HHI. 1927.03 873.61 Source diversity The distribution of programs by origin source (network, syndicate, local or block). Measured using HHI. 5197.03 2001.31 Total informational programming Amount of news and/or public affairs programming broadcast by a station during the 14 day sample period (in total minutes.) 3231.61 2479.02 Local informational programming Amount of locally produced news and/or public affairs programming broadcast by a station during the 14 day sample period (in total minutes.) 1850.9 1589.88
83 Table 3 2. Continued. Variable Definition M SD Yes No Inde pendent variables Owner type Whether the station is owned by a chain, network or independent entity (categorical) Chain: 156 Network: 23 Independent: 33 reach (if part of station group or network O&O) Percentage of national televisio n households parent company (continuous) 5.55 8.47 Duopoly Whether the owner has multiple stations in the same market (categorical) 33 179 Local owner Whether the station is owned by local or distant party (categorical) 36 176 Affiliation Whether the station is a network affiliate (categorical) 202 10 Station revenue Station revenues in 2009 (in thousands continuous) 11113.21 21360.96
84 Table 3 3. Market variables. Dependent variables Independent variables Horizontal dive rsity (HHI and COI) Total number of stations Source diversity Number of commercial stations Total informational programming Number of non commercial stations Local informational programming TV households Market revenue
85 Table 3 4. Market variables with definitions and values. Variable Definition M SD Dependent variables Horizontal diversity The distribution of programs by type across all stations in a given market. Measured using HHI and COI. HHI: 1325.28 COI: 3.49 HHI: 287.21 COI: 1.47 Source diversity The distribution of programs by origin source (network, syndicate, local or block). Measured using HHI. 4015.81 342.07 Total informational programming Amount of news and public affairs programming broadcast by all stations in the market during the 14 day sample period (in total minutes.) 17759.48 7103.21 Local informational programming Amount of locally produced news and/or public affairs programming broadcast by all stations in the market during the 14 day sample period (in total minutes.) 11706.60 6559.54
86 Table 3 4. Continued. Variable Definition M SD Independent variables Total stations Number of commercial and non commercial stations in the market 34.62 21.68 Commercial stations Number of commercial stations in the market 7.57 5.06 Non commercial stations Number of non commercial stations in the market. 1.83 1.43 TV households Number of television households in the market (in thousands) 685.98 1038.92 Market revenue Combined revenue for all stations in the market (in thousan ds) 107214.29 214541.61
87 Table 3 5. Collapsed program categories and distribution of all programming by type Collapsed program types and original types comprising them Mean proportion of all programming for selected stations Mean proportion of all prog ramming for selected markets Talk and Interview 18.90% 20.31% Talk Interview Informational 15.41% 16.07% News Newsmagazine Weather Paid Programming 11.27% 9.11% Shopping Consumer Comedy 10.64% 9.28% Sitcom Comedy Romance co medy Musical comedy Reality 7.69% 7.20% Drama 7.43% 6.84% Crime drama Drama Historical drama Docudrama Sports and Recreation 5.07% 5.29% Sports talk Sports event Sports non event Outdoors Wrestling Auto Boat Golf Hunting Skiing Snowmobile Roller derby Motorsports Motorcycle racing Mixed martial arts Fishing Boxing Action sports Football Skating
88 Table 3 5. Continued. Collapsed program types and original types comprising them Mean proportion of all programming for selected stations Mean proportion of all programming for selected markets Game show 3.99% 4.48% Soap opera 3.51% 3.29% Soap Soap talk Religious 2.86% 2.42% Movie 2.33% 1.86% Spanish 2.11% 6.47% Arts and E ntertainment 2.10% 2.10% Art Dance Entertainment Fashion Performing arts Variety Science Fiction and Horror 1.36% 1.00% Science Fiction Fantasy Paranormal Horror 1.34% 1.14% Children Children special Childr en talk Music 0.71% 0.18% Music Music talk Holiday music special Music special Musical Public affairs 0.71% 0.63% Documentary Public affairs Politics Home and Garden 0.53% 0.53% House/garden Agriculture Home improvement Asian language/interest 0.32% 0.25% Japanese Other Asian language Asian interest
89 Table 3 5. Continued. Collapsed program types and original types comprising them Mean proportion of all programming for selected stations Mean proportion of all programming for selected markets Legal/Criminal justice 0.30% 0.30% Law Crime Animals 0.28% 0.28% Animal Dog show Business and financial 0.19% 0.12% Dramedy (Drama/Comedy) 0.16% 0.10% Action/adventure 0.15% 0.12% Action Adventure I nstructional 0.12% 0.08% Cooking How to Self improvement Parenting Travel 0.10% 0.12% History and Biography 0.09% 0.09% History Biography Western 0.07% 0.06% Community 0.06% 0.06% Health and Wellness 0.06% 0.08% Medical Exercise Health Educational 0.02% 0.04% Miniseries and Anthology 0.02% 0.01% Miniseries Anthology Mystery and Suspense 0.02% 0.02% Mystery Suspense Science, Nature and Technology 0.02% 0.02% Science Environmental Nature Technology
90 Table 3 5. Continued. Collapsed program types and original types comprising them Mean proportion of all programming for selected stations Mean proportion of all programming for selected markets Special 0.02% 0.02% Fundraiser Special Holiday spe cial Holiday Collectibles 0.01% 0.01% Romance 0.01% 0.00% Animated 0.00% 0.00% War 0.00% 0.00%
91 CHAPTER 4 RESULTS Regression Results Station Characteristics This section provides results of regression analyses that estimate how ownership chara cteristics are related to programming diversity and the amount of informational programming provided by a station. The enter method was used for all analyses. For the evaluation of station characteristics, owner type was a categorical variable with three levels (chain, network or independent); consequently, a dummy variable was used. Tolerance statistics and correlation coefficients indicated no significant multicollinearity problems among the independent variables. Analysis did reveal violations of the no rmality assumption. Consequently, transformation was imposed on selected variables in the data set; when transformation alone did not produce normality, outliers were also omitted. Data were defined as normally distributed when skewness and kurtosis were i n the +/ 1 range. When transformation was performed (noted in the text), the natural log was used. Vertical Program Type Diversity Thirteen cases were removed in order to normalize the data. A significant regression equation was found ( F (7,191) = 49.102, p < .001), with an R 2 of .643. relationship with program type diversity 1 while network affiliation and duopoly status were positively related to diversity. Programming on stations t hat were part of a group that reached a large national audience (owner reach) was concentrated into a handful of 1 Note that the HHI is a measure of program type concentration. Therefore, positive values indicate increasing concentration and decreasing diversity. Alternatively, negative values indicate increasin g diversity.
92 types, while network affiliates and duopoly stations offered a greater mix of program types. An examination of beta coefficients indicates tha .147). Owner type, owner location and station revenue were not signif icant predictors in this model. A summary of the regression model is presented in Tables 4 1 and 4 2. Diversity of Programming Sources Regression results indicate an overall model of three variables (chain ownership, duopoly status and station revenue) tha t significantly predict the diversity of programming sources provided ( F (7,204) = 5.702, p < .001). This model accounted for 16.4% of variation in source diversity ( R 2 = .164, see Table 4 3) Chain ownership and station revenue are positively related to so urce diversity. Chain owned stations offered programming from a larger variety of sources than independents, and stations that generated higher revenue in 2009 also broadcast programs from a greater variety of sources (see Table 4 4). These findings unders core variety of sources. While duopoly status is positively related to the diversity of program types provided, it is negatively related to source diversity. That is, d uopoly stations offer a more diverse programming mix, but the programs aired come from fewer sources. Network ownership, owner location, owner reach and network affiliation were not significant predictors. A summary of the regression model is presented in Tables 4 3 and 4 4.
93 Total Informational Programming Provided Owner reach and station revenue were transformed. A significant regression model was found ( F (7,204) = 23.414, p < .001), with an R 2 of .445 (see Table 4 5). There was a significant negative rel reach and minutes of news and/or public affairs programming (see Table 4 6). Duopoly ownership was also negatively related to the provision of informational programming. This is consistent with the findings of Napoli and Yan (2007). Taken together, these findings challenge the logic that has driven the relaxation of national and multiple ownerships limits: that large group owners would exploit economies of scale to facilitate greater production of informational programming. It is striking to note, however, that network affiliation is positively related to the provision of informational programming. Overall, the most powerful predictor of the provision of informational programming is station revenue, which was p ositively related t Owner type and location were not significant predictors in this model. Local Informational Programming Provided A significant regression model was found ( F (7,204) = 12.220, p < .001), with an R 2 of .295. Among the inde pendent variables, only chain ownership and station revenue were significant predictors of the amount of local informational programming provided by local stations. Both were positively related to local news and/or public affairs programming minutes. Once the remaining station variables (network ownership, owner location, owner reach, duopoly status and network affiliation) were significantly related to local informational program ming minutes. Full regression results are presented in Tables 4 7 and 4 8.
94 A summary of all the findings related to ownership characteristics is presented in Table 4 9. Descriptive Statistics: Station Variables The majority of programming for the selected stations (56.22%) fell into four categories: talk and interview, informational, paid programming and comedy. Moreover, the top seven types accounted for more than three quarters (76.41%) of all programming provided by local stations. While these ratios su ggest programming is not particularly diverse, the HHI score of 1030.99 suggests programming is only moderately concentrated ( Baseman & Owen, 1982). It should be noted, however, that these proportions are for all stations taken together; there are differen ces in the level of diversity provided across individual stations. Average program type diversity for the sample stations during the sample period was 1927, indicating high concentration. Moreover, the large standard deviation (873.61) indicates a wide ran ge of diversity among the stations. One of the research questions asks how station ownership influences the amount of informational programming provided by stations. On the aggregate level, informational programming is second in terms of total minutes pro vided; however, as noted above, individual differences exist between stations. These differences are reflected in the results of the regression analysis. Total Informational Programming Of the 212 stations sampled, 185 (87.3%) broadcast at least one minute of informational programming during the sample period. On average, the stations 3231.61 minutes of this programming during the constructed period.
95 A greater proportion of chain owned stations (89.1%) aired informational programming than network (87%) or independently owned (78.8%) stations, though these differences were not tested for statistical significance. The amount of informational programming provided by the three owner types (chain, network, independent) was compared using a one way ANOVA. A sign ificant difference was found among owners ( F (2,209) = 9.43, p HSD was used to determine the nature of the differences. This analysis revealed that stations that were part of a chain aired more informational programming ( m = 3657.81, sd = 24 14.83) than network ( m = 2042.22, sd = 2584.88) or independently ( m = 2045.85, sd = 2080.94) owned stations. O&O and independent stations were not significantly different from one another. Non duopoly stations (88.3%) were more likely to air informational programming than stations that are part of a duopoly (81.8%), but these differences were not tested for statistical significance. An independent samples t test was conducted to compare the mean minutes of informational programming broadcast by duopoly and non duopoly stations. The latter broadcast more news and/or public affairs programming ( m = 3321.80, sd = 2436.35) than their counterparts ( m = 2742.42, sd = 2685.47), but the differences were not statistically significant ( t (210) = 1.24, p > .05). Amon g the 36 locally owned stations in the sample, 28 (77.8%) aired some news and/or public affairs programming. In contrast, 157 of the 176 (89.2%) non locally owned stations broadcast informational programming during the sample period. However, a t test reve aled no significant difference in the amount of news and local affairs programming broadcast by locally and non locally owned stations ( t (210) = .466,
96 p > .05); the mean minutes aired by stations owned by a local entity ( m = 3407.50, sd = 2522.23) did not significantly differ from the amount provided by stations owned by a distant party ( m = 3195.64, sd = 2475.83). A greater proportion of network affiliated stations (88.1%) aired informational programming than independent stations (70.0%). Affiliates also aired significantly more of this programming, broadcasting more than 3300 minutes ( m = 3322.24, sd = minutes ( m = 1401.00, sd = 1819.46), ( t (210) = 2.42, p < .05). Local Inf ormational Programming Of the 212 stations sampled, 174 (82.1%) broadcast at least one minute of local informational programming during the sample period. On average, the stations in the full sample aired 1850.90 minutes of this programming during the con structed period. A greater proportion of network O&Os (82.6%) aired informational programming than chain (84.6%) or independently owned (69.7%) stations. The amount of local informational programming provided by these owner types was compared using a one way ANOVA. A significant difference was found among owners ( F (2,209) = 6.11, p < .01). Stations that were part of a chain broadcast more informational programming ( m = 2049.40, sd = 1511.91) than independently ( m = 1020.48, sd = 1207.42) owned stations. O& O stations ( m = 1696.00, sd = 2163.08) were not significantly different from either of the other two groups. Among the 33 stations that are part of a duopoly, 25 (75.8%) aired some local news and/or public affairs programming. In contrast, 149 of the 179 (83.2%) non duopoly stations broadcast local informational programming during the sample period. In addition, during the two week sample period, duopoly stations ( m = 1886.91, sd =
97 1912.71) aired slightly more local informational programming than their non duopoly counterparts ( m = 1844.26, sd = 1529.17). The difference, however, was not statistically significant ( t( 210) = .141, p > .05). A greater proportion of non locally owned stations (84.1%) aired local news and/or public affairs programming than the ir locally owned counterparts (72.2%). However, on average, locally owned stations provided more minutes of local informational programming ( m = 2077.64, sd = 1747.84) than those owned by a distant entity ( m = 1804.52, sd = 1556.92). The difference was not statistically significant ( t( 210) = 0.939, p > .05). A greater proportion of network affiliated stations (82.7%) aired local informational programming than independent stations (70.0%). No significant difference was found in terms of the amount of local news and/or public affairs programming provided by network affiliates ( m = 1876.14, sd = 1582.64) and independent stations ( m = 1341.00, sd = 1737.49) ( t (210) = 1.04, p > .05). While such descriptive analyses provide insight into the relationship between ownership variables and provision of informational programming, they should be interpreted with caution as other variables may mediate these relationships. Consequently, multivariate analysis was conducted to further investigate the relationship between ow nership variables and the provision of informational programming. Regression Results: Market Characteristics Multiple regression was conducted to determine which market characteristics (number of TV households, total number of stations in the market, numbe r of commercial stations, number of non commercial stations and market revenue) were
98 predictors of five dependent variables measuring program type and source diversity as well as the provision of informational programming. The enter method was used for all analyses. Tolerance statistics and correlation coefficients indicated a mutlicollinearity problem; the number of TV households and market revenue were strongly related. Consequently, market revenue was removed from the analysis for two reasons. First, mor e recent data was available for TV households. In addition, while the analysis focused on market level trends, the true concern was the programming provided by the individual stations within the market; therefore, it was believed that the potential audienc e for any given program was more directly related to station programming strategies than overall market revenue. The normality assumption was violated in all analyses. Consequently, logarithmic transformation was imposed on all independent variables in th e data set; when transformation alone did not yield normality, outliers were also removed. Program Type Diversity (HHI) Two cases were removed in order to normalize the data. A significant regression model was found ( F (4,35) = 3.718, p < .05), with an R 2 of .298. However, none of the independent variables were significant predictors of the level of vertical program diversity provided by all stations in the market. A summary of the regression model is presented in Tables 4 12 and 4 13. Horizontal Program Ty pe Diversity (COI) Regression results indicate an overall model of two variables (TV households and number of commercial stations) that significantly predict the breadth of program type diversity provided across stations ( F (4,37) = 45.489, p < .001). This model accounted
99 for 83.1% of variation in the diversity of programming sources provided ( R 2 = .831, see Tables 4 14 and 4 15) Both the number of TV households and the number of commercial stations in the market were positively related to horizontal diver sity. In large markets with many competing stations, a greater number of unique program types were available during any given time period. The degree of competition (as measured by the number of commercials stations) was the most important explanatory fact number of stations and number of non commercial stations were not significant predictors. Diversity of programming sources Two cases were removed in order to normalize the data. The regression model was not significant ( F (4,35) = 1.860, p > .05), with an R 2 of .172. None of the variables can be used to predict the diversity of programming sources provided by all stations in the market. Full results are presented in Table 4 16 and 4 17. Total informational programming provided Regre ssion results indicate an overall model of two variables (TV households and number of commercial stations) that significantly predict the amount of total informational programming provided ( F (4,37) = 66.397, p < .001). This model accounted for 87.8% of var iation in the diversity of programming sources provided ( R 2 = .878) (see Tables 4 18 and 4 19 .) Both the number of TV households and number of commercials stations in the market were associated with an increase in the amount of news and/or public affairs programming offered by local stations. The significant positive relation between the number of commercial stations and informational programming minutes is consistent
100 with the findings of Napoli (2001a) and Napoli and Yan (2007). TV households, however, we re found to be of greater importance in the current study than in previous research. There was no significant relation between the total number of stations or the number of non commercial stations and the provision of informational programming. Local Info rmational Programming Provided A significant regression model was found ( F (4,37) = 58.315, p < .001), with an R 2 of .863 (see Table 4 20). The number of TV households and number of commercial stations in the market were positively related to the amount of local informational programming provided by all stations. Stations in large, competitive markets provided more local informational programming. The total number of stations and the number of non commercial stations were not significant predictors of local informational programming minutes (see Table 4 21.) A summary of regression results is presented in Table 4 22.
101 Table 4 1. Model summary: Station characteristics and vertical diversity (HHI) R 2 F P df 1 df 2 .643 49.102 .000 7 191 Table 4 2. Coeffic ients for final model: Station characteristics and vertical diversity (HHI) B T Chain Owner 94.833 .105 1.620 Network O&O 93.538 .075 .612 Owner Location 14.699 .013 .259 Owner Reach 19.736 .431 4.052** Duopoly ownership 160.418 .147 3.036* Network Affiliate 2506.942 .771 17.521** Station Revenue .001 .034 .676 Note: *Indicates significance at p < .01 **Indicates significance at p < .001
102 Table 4 3. Model summary: Station characteristics and program origin diversity R 2 F p df 1 df 2 .164 5.702 .000 7 204 Table 4 4. Coefficients for final model: Station characteristics and program origin diversity B T Chain Owner 909.818 .201 2.160* Network O&O 74.016 .012 .064 Owner Location 140.283 .026 .353 Owner Reach 16.199 .069 .436 Duopoly ownership 1310.720 .238 3.347** Network Affiliate 657.671 .070 1.073 Station Revenue .029 .306 4. 087*** Note: *Indicates significance at p < .05 **Indicates significance at p < .01 ***Indicates significance at P < .001
103 Table 4 5. Model summary: Station characteristics and provision of total informational programming R 2 F p df 1 df 2 .445 23.414 000 7 204 Table 4 6. Coefficients for final model: Station characteristics and provision of total informational programming B T Chain Owner 990.767 .177 1.974 Network O&O 15.600 .002 .019 Owner Location 104.006 .016 .262 Owner Reach 258.733 .193 2.126* Duopoly ownership 1283.352 .188 3.241** Network Affiliate 1298.944 .111 2.088* Station Revenue 1122.327 671 10.618*** Note: *Indicates significance at p < .05 **Indicates significance at p < .01 ***Indicates significance at p < .001
104 Table 4 7. Model summary: Station characteristics and provision of local informational programming R 2 F p df 1 df 2 .295 1 2.220 .000 7 204 Table 4 8. Coefficients for final model: Station characteristics and provision of local informational programming B T Chain Owner 868.173 .241 2.827* Network O&O 241.719 .047 .287 Owner Location 181.239 .043 .626 Owner Reach 5.122 .0271 .189 Duopoly ownership 418.875 .096 1.467 Network Affiliate 363.895 .049 .814 Station Revenue .040 .531 .000** No te: *Indicates significance at p < .01 **Indicates significance at p < .001
105 Table 4 9. Regression results: Station ownership summary Independent variable: significantly related to dependent variable? (+/ ) Dependent variable Vertical diversity Source diversity Total informational programming Local informational programming Chain ownership No Yes (+) No Yes (+) Network ownership No No No No Local ownership No No No No Owner reach Yes ( ) No Yes ( ) No Duopoly ownership Yes (+) Yes ( ) Yes ( ) No N etwork affiliation Yes (+) No Yes (+) No Station revenue No Yes (+) Yes (+) Yes (+)
106 Table 4 10. Descriptive statistics provision of any news and/or public affairs programming (station/ownership characteristics) Aired any news and/or public affairs? Yes No Total Mean minutes of news and public affairs Total sample 185 27 212 3231.61 By ownership type Chain 139 17 156 3657.81* Network 20 3 23 2042.22* Independent 26 7 33 2045.85* F (2,209) = 9.43, p < .01 Indicates significant difference b etween groups ( p < .01) By duopoly ownership Yes 27 7 33 2742.42 No 158 23 179 3321.80 t (210) = 1.24, p > .05 By local ownership Yes 28 8 36 3407.50 No 157 19 176 3195.64 t (210) = .466, p > .05 By network affiliate Yes 178 24 202 3 322.24 No 7 3 10 1401.00 t (210) = 2.42, p < .05
107 Table 4 11. Descriptive statistics provision of local news and/or public affairs programming station/ownership characteristics) Aired any local news and/or public affairs? Yes No Total Mean minutes (hours) of news and public affairs Total sample 174 38 212 1850.90 By ownership type Chain 132 24 156 2049.40* Network 19 4 23 1696.00 Independent 23 10 33 1020.48* F (2,209) = 6.11, p < .01 Indicates significant difference between groups ( p < 01) By duopoly ownership Yes 25 8 33 1886.91 No 149 30 179 1844.26 t (210) = 0.141, p > .05 By local ownership Yes 26 10 36 2077.64 No 148 28 176 1804.52 t (210) = 0.939, p > .05 By network affiliate Yes 167 35 202 1876.14 No 7 3 1 0 1341.00 t (210) = 1.04, p > .05
108 Table 4 12. Model summary: Market characteristics and vertical diversity (HHI) R 2 F p df 1 df 2 .298 3.718 .013 4 35 Table 4 13. Coefficients for final model: Market characteristics and vertical diversity (HHI) B t TV Households 12.519 .064 .264 Total Stations 39.691 .111 .431 Commercial Stations 223.481 .507 1.542 Non Commercial Stations 104.351 .205 1.080
109 Table 4 14. Model summary: Market characteristics and horizontal diversity (COI) R 2 F p df 1 df 2 .831 45.489 .000 4 37 Table 4 15. Coefficients for final model: Market characteristics and horizontal diversity (COI) B t TV Households .347 .297 2.473* Total Stations .156 .072 .584 Commercial Stations 1.854 .702 4.271** Non Commercia l Stations .044 .014 .162 Note: *Indicates significance at p < .05 **Indicates significance at p < .01
110 Table 4 16. Model summary: Market characteristics and program origin diversity R 2 F p df 1 df 2 .175 1.860 .139 4 35 Table 4 17. Coefficients f or final model: Market characteristics and program origin diversity B t TV Households 27.849 .129 .468 Total Stations 17.189 .044 .155 Commercial Stations 309.439 .641 1.713 Non Commercial Stations 125.439 .227 1.116
111 Table 4 18. Model summary: Market characteristics and provision of total informational pro gramming R 2 F p df 1 df 2 .878 66.397 .000 4 37 Table 4 19. Coefficients for final model: Market characteristics and provision of total informational programming B t TV Households 3460.596 .614 6.012** Total Stations 383.507 .037 .351 Commerci al Stations 4470.238 .351 2.512* Non Commercial Stations 925.376 .064 .840 Note: *Indicates significance at p < .05 **Indicates significance at p < .001
112 Table 4 20. Model summary: Market characteristics and provision of local informational programmi ng R 2 F p df 1 df 2 .863 58.315 .000 4 37 Table 4 21. Coefficients for final model: Station characteristics and provision of local informational programming B t TV Households 3542.461 .681 6.299** Total Stations 1789.193 .186 1.675 Commercial Stations 3588.066 .305 2.054* Non Commercial Stations 2149.088 .160 1.995 Note: *Indicates significance at p < .05 **Indicates significance at p < .001
113 Table 4 22. Regression results: Market structure summary Independent variable: significantly related to dependent variable? (+/ ) Dependent variable Horizontal diversity HHI Horizontal diversity COI Source diversity Total informational programming Local informational programming Number of TV households No Yes (+) No Yes (+) Yes (+) Number of non commercial stations No No No No No Number of commercial stations No Yes (+) No Yes (+) Yes (+) Total number of stations No No No No No
114 CHAPTER 5 DISCUSSI ON AND CONCLUSION Over the past few decades, media industries have become increasingly consolidated, and local television is no exception. Critics of consolidation contend programs. Ownership regulations have been relaxed in recent years, diverse programming serving a variety of tastes and interests. It is unclear, however, whether these a ssumptions hold true. The present study has attempted to provide clarity by examining the relationship between ownership characteristics and market conditions and the programming provided by local television stations. In particular, it was concerned with h ow these factors influence the level of program type and program origin diversity as well as the quantity of total and local informational programming provided. In terms of the impact on program type diversity, this study found evidence both in favor of a nd against consolidation. While large group owners provided less diverse programming, network affiliation and duopoly ownership were positively related to program type diversity. With regard to informational programming, descriptive statistics show that th e vast majority of stations (87.3%) aired some news and/or public affairs programming during the sample period. Chain owned stations broadcast significantly more informational programming than network or independently owned stations, and network affiliates broadcast more informational programming than independents. Significant differences were not observed among the other station variables. Turning to the multivariate
115 analysis, station revenue was the most significant predictor of the quantity of both total and local informational programming provided. These results are in line with previous research in the area (Napoli, 2004; Napoli & Yan, 2007; Wirth & Wollert, 1979) and nd public affairs programming. While all stations have a strong incentive to produce informational programming (particularly news) due to its lucrative revenue potential, it may be that only stations with the resources to cover the high costs of production will provide such content. Source diversity was also greater for financially strong stations. Once again, this points to the importance of station resources on the programming provided by local stations. Wealthier stations are better positioned to take o n the costs of local production and may be less dependent on relatively inexpensive syndicated programming than those with fewer resources. According to the resource based view (RBV ) of strategy, a competitive advantage is In this view, firms are comprised of resources and capabilities that can be arranged to create a competitive advantage. Put an influence how it behaves (the strategic choices made) in the external environment (Barney, 1991; Henry, 2011). These resources may be tangible, such as physical, financial, or human resources, or they may be intangible like intellectual or technical resources and reputation (Henry, 2011). This theory explains a number of the findings in this study. Regression analysis revealed that compared to independent stations, chain
116 owned stations offered programming fro m a greater variety of sources along with more local informational programming. These stations have a number of resource advantages over their counterparts. First, they tend to be financially strong, which enables them to afford the equipment and personnel required to produce more informational programming. In addition, they have access to a network of stations with which they can share resources and information. They are also likely to have greater clout in negotiating syndication agreements as well as som e centralized management providing leadership and support. Network affiliation was associated with greater vertical diversity and informational programming. Like chain owned stations, affiliates are typically among the financially strongest stations in a m arket, enabling them to take on the additional costs incurred by informational programming. Moreover, these stations have access to a powerful national network from which they receive programming; this is a particularly advantageous relationship in terms o f newsgathering. RBV seems to have little explanatory power in terms of the duopoly and reach variables as they have an opposite/confounding effect from what might be expected. For example, supporters of deregulation argued that relaxing duopoly ownership rules would allow stations to capture economies of scale that would encourage greater production of informational programming. The current study found mixed evidence for these benefits. While duopoly was positively related to program type diversity, duopo ly ownership was negatively related to the provision of total informational programming. There was no significant relationship between duopoly ownership and the amount of local informational programming provided; this in line with previous research in the area (Yan & Napoli, 2006; Yan & Park, 2009). Given that previous research has provided
117 limited insight and the results of this study suggest that duopoly seems to both promote and harm programming, further research should be conducted in this area. In term s of reach, this was found to be negatively associated with vertical diversity and the provision of informational programming. Potential explanations for these findings are discussed further below. Interestingly, local ownership was not significantly relat ed to any of the dependent variables. That is, the assumption that local owners are more responsive to and better serve community needs and interests appears to lack empirical support. These findings also call into question the emphasis placed on local own ership. This is not to say local ownership is unimportant, but rather that other station characteristics have a greater impact on programming and more effectively support localism. In terms of RQ2a, the results provide mixed support for the argument that greater competition encourages stations to provide more diverse content. Regression results indicate market structure bears no relationship to the breadth of programming provided (HHI). However, a positive relationship was found between the number of house holds and number of commercial stations in a market and the number of simultaneous program types available (COI). These findings support previous research that found competition leads to increasing dissimilarity in programming (Aslama, Hellman & Sauri, 200 4; Grant, 1994; Litman, 1979). Overall, it appears that, facing strong competition, stations counterprogram in order to compete. The results suggest market incentives alone are not sufficient to promote the provision of diverse content. While the options a vailable at any given point in time are enhanced, overall diversity appears to be unaffected by the degree of competition.
118 In terms of RQ2b, none of the market variables were significantly related to the mix of origin sources. It is unlikely competition wi ll compel stations to offer more programming from a greater variety of sources. As suggested by previous research in the area (Napoli, 2001b; Napoli & Yan, 2007; Powers, 2001; Yan & Napoli, 2006), competition does appear to compel greater provision of inf ormational programming (RQ2c). Overall, more than 20% of all programming shown by qualifying stations in all markets was informational in nature. In addition, the number of TV households and number of commercial stations in the market were positively relat ed to the quantity of informational programming (both total and local) provided. These results support marketplace logic that market forces provide sufficient incentive to induce the provision of informational programming and the relaxation of ownership li mits that has taken place in recent years. They likely reflect the greater revenue potential inherent in large markets; stations are willing to incur the heavy costs of news and public affairs production because the incentive to do so (potential profit) is strong and costs can be amortized through access to larger audiences. The number of non commercial stations had no relationship with any of the programming variables. In the area of informational programming, previous research found mixed support for a r elationship between programming and the presence of non commercial stations. Napoli (2004) found a negative relationship between the number of non commercial stations in the market and the quantity of both local news and total local informational programmi ng (news and public affairs together). Later studies (Yan & Napoli, 2006: Napoli & Yan, 2007), however, found no relationship with the quantity of
119 local news programming provided. Researchers have consistently found no relationship between public stations and the quantity of public affairs programming when this program type is considered on its own (Napoli, 2001a, 2004). Because this study considered news and public affairs programming together (like Napoli, 2004), it was expected that a strong public stati on presence would potentially result in the provision of less news and public affairs programming by commercial stations, as the latter would offer alternative programming in order to avoid competing with public broadcasters, who have a particularly strong commitment to the public interest. This does not appear to be the case, however; rather, it appears the behavior of commercial stations is unaffected by that of their public counterparts. It may be that commercial stations do not view these stations as co mpetitors; moreover, these stations may exist in a different product market entirely. This is particularly true if one considers the content of the informational programs offered. The news programs provided by non commercial stations are of a very differen t brand than those provided by commercial stations and, consequently, attract a very different audience. In particular, these programs (e.g., NewsHour) air content that is longer, more in depth and on topics that are not always popular with an impatient, f ickle audience that also needs to be "entertained." These programs also tend to avoid the use of sound bites, relying instead on extended portions of news conferences or interviews. Furthermore, as discussed above, the influence of non commercial stations on the provision of informational programming has historically had mixed results. In previous studies, this variable has been far from conclusive and has had a weak predictive history. Therefore, it may be that, all things considered, the number of non com mercial stations is simply a weak driver of programming behavior.
120 While this been found to be of some significance in other studies and/or under different circumstances, it is provides little (and in this study, no) explanatory power compared to other mark et variables. It is also worth noting that considering news and public affairs programming in combination may distort the overall programming picture. For instance, public stations may be less likely than their commercial counterparts to incur the costs of producing news, but more likely to provide public affairs programming. That is, commercial stations and their non commercial counterparts may demonstrate a stronger relationship with a particular type of informational programming (either news or public af fairs). It would be interesting, therefore, to examine news and public affairs programming separately (as previous studies have done) to get a better picture of how each station type impacts diversity. Overall, the findings of the present study suggest pol icy makers should not be as concerned with the owners of media as with the number of stations in a market and the resources available to them. Financial strength was the single most important station related predictor of programming, and the intensity of m arket competition (as measured by the number of commercial stations) was positively related to the number of unique programming options available at any given time as well as the quantity of informational programming provided. Moreover, while the relations hip between some variables (such detrimental effect on diversity and/or the provision of informational programming, many other station variables (e.g., network affiliation and chain ownership) were actually positively related to these variables. Taken together, these findings suggest that ownership regulations may have limited effect on the programming viewers receive and
121 that market forces may actually improve diversity. This i s not to say that regulations should be entirely lifted; however, it does suggest that the fears of consolidation critics may be overstated. It also suggests that licensing policies should be revisited in order to encourage the greatest number of commercia l stations possible per market. Obviously the guiding principles of media policy), but as technology continues to improve, the number of available frequencies sho uld also increase. The digital transition increased viewing options by default as multicasting increased the number of streams available for broadcasting; it will be interesting to see how digital subchannels impact the media landscape as multicasting eme rges. The current study is, of course, not without its limitations. First, for reasons mentioned above, this study focused only on primary signals. This approach, however, fails to reflect all of the options currently available to audiences. As mentioned above, within the broadcast realm alone, digital subchannels, have the potential to increase diversity as they provide additional viewing options in the market (assuming, of course, they are actually utilized). In addition, many viewers subscribe to cable or satellite television services, which provide a seemingly unlimited number of additional options, including informational content. Therefore, the focus on local television is somewhat shortsighted as it does not capture the alternatives available via oth er means. Consequently, future research should explore what diversity looks like when all delivery platforms are considered. In addition, this study intentionally excluded the programming provided by religious and public stations. However, these represent true choices for viewers in a market and
122 should be considered in future studies of market structure. Moreover, the current study the sake of simplicity. However, great diversity exists within these categories, and viewers most definitely tune in for content reasons (just as some may select English programming for the same reasons.) Therefore, future research should attempt to distinguish between the programs provided on foreign language stations. The conclusions presented must also be mediated by the fact that the current study was only concerned with the quantity of informational programming provided. However, this is not the only gauge of the extent to which stations f ulfill their obligations to their local communities. Beyond news and public affairs programming, stations air public service announcements and political advertisements; these also contribute the marketplace of ideas and, thus, merit inclusion. Future studi es should also look at the more intangible aspects of public service, such as newscast quality. Ownership and market conditions may impact not only the provision of informational programming, but also the content itself. Consequently, future research shoul d look beyond quantitative assessments of the extent to which broadcasters are serving local interests and needs; it should also include indicators of quality such as subject matter, time devoted to different topics, or production location, among others. Another limitation is that this study utilized a mixed measurement approach. That is, it used both continuous and categorical independent variables. Categorical variables do not provide as much precision or statistical power as continuous variables. Moreov er, dummy variables are proxy variables with little mathematical value. Therefore, while they allow prediction and a sense of comparison, they do not provide a
123 than if c ontinuous variables alone were used. This distinction as a measure may help explain why reach (continuous) and chain ownership (categorical) seem to work in opposing directions. Overall, the use of a mixed measurement approach may not have provided a true picture of the relationships between variables, particularly for those that were categorical. Future studies, therefore, should attempt to operationalize all variables in a way that allows them to be measured as continuous variables. Furthermore, the pres ent study was concerned only with the supply of diverse programming, and ignored the demand side of the relationship. While there is great concern about the supply of diverse programming, little attention has been paid to consumer demand that may limit con sumption of undesirable programming (e.g., news, public affairs or minority interest programs). While media should certainly provide informational and minority interest programming, it is difficult to objectively determine what constitutes a sufficient amo unt. The quantities currently available may satisfy, or even exceed, audience demand for such programming. Therefore, analysis of consumption data is needed to determine whether stations are effectively fulfilling their social responsibilities. Perhaps mos t importantly, while program type diversity is a suitable measure of product variety, it does not get to the heart of what policymakers truly seek to promote: viewpoint diversity. The current study uses abundance as a proxy for diversity, but numerical div ersity does not ensure qualitative diversity that is, a diversity of messages. As suggested for informational programming, future research must seek to examine not only the type of programming provided, but also the content of those
124 programs. While previ ous attempts to quantify viewpoint diversity have faced methodological challenges, a direct approach must be taken in order to truly gauge how well stations are providing access to a variety of views.
125 REFERENCES Albarran, A.B. (1996). Media economics: Und erstanding markets, industries and concepts. Ames: Iowa State University Press. Broadcasting & Cable. Retrieved from http://www.broadcastingcable.com/article/451325 B_C_s_Top_25_Station_Groups_2010.php Alexander, P. (1997). Product variety and market structure. Journal of Economic Behavior & Organization, 32, 207 214. doi: 10 .1016/S0167 2681(96)00902 X Alexander, P.J., & Brown, K. (2004, June) Do local owners deliver more localism? Some evidence from local broadcast news. Retrieved from http://hraunfoss.fcc.gov/edocs_public/attachmatch/DOC 267448A1.pdf Aslama, M., Hellman, H., & Sauri, T. (2004, April). Does market entry regulation matter?: Competition in television broad casting and programme diversity in Finland, 1993 2002. International Communication Gazette, 66, 113 132. doi:10.1177/0016549204041473 Associated Press v. United States 326 U.S. 1 (1945). Atwater, T. (1984). Product differentiation in local TV news. Jo urnalism Quarterly, 61 (4), 757 762. Retrieved from Communication & Mass Media Complete database. Bae, H. S. (1999). Product differentiation in cable programming: The case in the cable national all news networks. Journal of Media Economics, 12 (4), 265 277. Retrieved from Communication & Mass Media Complete database. Bagdikian, B.H. (1989). The lords of the global village. The Nation, 248 (23), 805 20. Bagdikian, B. H. (2004). The new media monopoly. Boston: Beacon Press. Baker, C.E. (2002). Media concentratio n: Giving up on democracy Florida Law Review, 54, 839 919. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17 (1), 99 120. Barrett, M. (1995). Direct competition in cable television delivery: A case study of Pa ragould, Arkansas. Journal of Media Economics, 8 (3) 77 93. network affiliate relation. Journal of Media and Media Economics, 18 (1), 1 19. Barron, J.A. (2000). Structural regu lation of the media and the diversity rationale. Federal Communications Law Journal, 52, p. 555 560.
126 Baseman, K.C., & Owen, B.M. (1982). A framework of economic analysis of electronic media concentration issues. Prepared for the National Cable Television A ssociation for submission in FCC docket no. 82 434. Washington, DC: Economists Inc. Bates, B. J. (1993). Concentration in local television markets. Journal of Media Economics 6 (3), 3 21. Beebe, J. (1977). Institutional structure and program choices in t elevision markets. Quarterly Journal of Economics 91 (1), 15 37. Retrieved from Business Source Premier database. Berry, S.T. & Waldfogel, J. (2001). Do mergers increase product variety? Evidence from radio broadcasting. Quarterly Journal of Economics, 116 (3), 1009 1025. Retrieved from Business Source Premier database. Besen, S.M., & Johnson, L.L. (1985). Regulation of broadcast station ownership: Evidence and theory. In E.M. Noam (Ed.), Video media competition: Regulation, economics, and technology. New Yo rk: Columbia University Press BIA/Kelsey. (2010). Television station information [Data file]. Unpublished data set. Chantilly, VA: Author. Bishop, R., & Hakanen, E. A. (2002). In the public interest? The state of local television programming fifteen years after deregulation. Journal of Co mmunication Inquiry, 26(3), 261 276. doi: 10.1177/0196859902026003002 Blevins, J.L. (2002). Source diversity after the Telecommunication Act of 1996: Media oligarchs begin to colonize cyberspace. Television & New media, 3 (1 ), p. 95 112. Burnett, R. (1992). The implications of ownership changes on concentration and diversity in the phonogram industry. Communication Research 19 (6), 749 769. Retrieved from Communication & Mass Media Complete database. Busterna, J.C. (1980). Ow nership, CATV and expenditures for local television news. Journalism Quarterly, 57 (2), 287 291. Chambers, T. (2003). Radio programming diversity in the era of consolidation. Journal of Radio Studies 10 (1), 33 45. Retrieved from Communication & Mass Media Complete database. Chan Olmsted, S. (1996). From Sesame Street to Wall Street: An analysis of market competition in commercial children's television. Journal of Broadcasting & Electronic Media 40 (1), 30 44. Retrieved from Communication & Mass Media Comple te database.
127 Cleary, J. & Adams Bloom, T. (2009). The Family Business: Entertainment Products and the Network Morning News Shows. Mass Communication and Society 12 (1), 78 96. doi:10.1080/15205430801936006 Communications Act of 1934, Pub. L. No 73 416, 48 Stat. 1064 (1934). Compaine, B.M. (1995). The impact of ownership on content: Does it matter? Cardozo Arts & Entertainment Law Journal, 13 p.755 775. Consumers Union (n.d.). Local News. Retrieved September 24, 2010, from http://www.hearusnow.org/mediaownership/14/ Cooper, M. (2003). Media ownership and democracy in the digital information age: Promoting diversity with First Amendment principles and market structure analysis Palo Alto, CA: Stanfo rd Law School, Center for Internet & Society. Coulson, D. C., & Hansen, A. (1995). The Louisville Courier Journal news content after purchase by Gannett. Journalism & Mass Communication Quarterly, 72 (1), 205 215. Croteau, D. R., & Hoynes, W. (2005). The bu siness of media: Corporate media and the public interest Thousand Oaks, California: Pine Forge Press. Davie, W.R., & Lee, J.S. (1993). Television news technology: Do more sources mean less technology? Journal of Broadcasting & Electronic Media, 37 (4) 453 464. De Jong, A. S., & Bates, B. J. (1991). Channel diversity in cable television. Journal of Broadcasting & Electronic Media, 35, 15 9 166. Retrieved from HeinOnline Law Journal Library. Dominick, J. R., & Pearce, M. C. (1976). Trends in network prime tim e programming, 1953 74. Journal of Communication 26 70 80. Doyle, G. (2002). Media ownership: The economics and politics of convergence and concentration in the UK and European media London: SAGE. Drushel, B. (1998). The Telecommunications Act of 1996 and radio market structure. Journal of Media Economics 11 (3), 3 20. Retrieved from Communication & Mass Media Complete database. Einstein, M. (2002). Program diversity and the program selection process on broadcast network television. Washington, DC: Fed eral Communications Commission Media Bureau. Einstein, M. (2004). Media diversity: Economics, ownership, and the FCC Mahwah, N.J: L. Erlbaum Associates.
128 Entman, R. M., & Wildman, S. S. (1992). Reconciling economic and non economic perspectives on media po licy: Transcending the "marketplace of ideas." Journal of Communication, 42 (1), 5 19. Everett, S. C., & Everett, S. E. (1989). How readers and advertisers benefit from local newspaper competition. Journalism Quarterly, 66, 76 79. Federal Communications Com mission (1969, Feb. 9). FCC Report & Order in Docket No. 16068. Federal Communications Commission. (1984). Revision of programming and commercialization policies, ascertainment requirements, and program log requirements for commercial television stations, 1984 FCC LEXIS 2105. Federal Communications Commission (1999, August 5). FCC revises local television ownership rules. Retrieved November 25, 2009, from http://www.fcc.go v/Bureaus/Mass_Media/News_Releases/1999/nrmm9019.html Federal Communications Commission. (2003). 2002 biennial regulatory review pursuant to section 202 of the Telecommunications Act of 1996 report and order and notice of proposed rulemaking, 03 FCC 127 (2003). Retrieved September 24, 2010 from http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC 03 127A1.doc Free Expression Policy Project. Fact Sheets on Media Democracy. Retrieved September 24, 2010, from http://www.fepproject.org/factsheets/mediademocracy.html George, L. (200 7). What's fit to print: The effects of ownership concentration on product variety in daily newspaper markets. Information Economics and Policy, 19 (3 4), 285 303. doi:10.1016/j.infoecopol.2007.04.002 Gilens, M., & Hertzman, C. (2000). Corporate ownership a nd news bias: Newspaper coverage of the 1996 Telecommunications Act. Journal of Politics, 62 (2), 369 386. Glasser, T. L. (1984). Competition and diversity among radio formats: Legal and structural issues. Journal of Broadcasting, 28 127 142. Retrieved fro m HeinOnline Law Library. Golding, P., & Murdock, G. (1996). Culture, communications, and political economy. In J. Curran and M. Gurevitch (Eds.), Mass Media & Society (pp. 11 30). London: Arnold. Gomery, D. (1993). The centrality of media economics. Jour nal of Communication, 43 (3), 190 198.
129 Gomery, D. (2000). The television industries: Broadcast, cable, and satellite. In B.M. Compaine, & D. Gomery (Eds.), Who owns the media?: Competition and concentration in the mass media industry (pp.193 283). Mahwah, N J: Lawrence Erlbaum Associates. Grant, A. (1994). The promise fulfilled? An empirical analysis of program diversity on television. Journal of Media Economics, 7 (1), 51. Retrieved from Communication & Mass Media Complete database. Greenberg, E., & Barnett H. J. (1971, May). TV program diversity New evidence and old theories. American Economic Review 89 93. Retrieved from Business Source Premier database. Hamilton, J. (2006). All the news that's fit to sell: How the market transforms information into new s Princeton, N.J: Princeton University Press. Hazen, D. (2000, April 26). Consumers Likely on the Short End as AOL Gobbles up Time Warner in World's Biggest Merger...So far. Retrieved from http://www.altern et.org/story/569 Hellman, H., & Soramaki, M. (1985). Economic concentration in the videocassette industry: A cultural comparison. Journal of Communication, 35 (3), 122 134. Hellman, H., & Soramki, M. (1994). Competition and Content in the U.S. Video Marke t. Journ al of Media Economics, 7(1), 29 49. doi: 10.1207/s15327736me0701_3 Hellman, H. (2001). Diversity An end in itself? European Journal of Communication, 16 (2), 181 208. doi: 10.1177/0267323101016002003. Henry, A. (2011). Understanding strategic manag ement. New York, NY. Oxford University Press. Hicks, R. G., & Featherstone, J. S. (1978). Duplication of Newspaper Content in Contrasting Ownership Situations Journalism Quarterly, 55 (3), 549 553. Retrieved from EBSCOhost. Hillve, P., Majanen, P., & Rosen gren, K.E. (1997). Aspects of quality in TV programming: Structural diversity compared over time and space. European Journal of Communication, 12 (3), 291 318. Ho, D.E., & Quinn, K.M. (2009). Viewpoint diversity and consolidation: An empirical study. Stanfo rd Law Review, 61 (4), 781 868. Horwitz, R. (2005). On Media Concentration and the Diversity Question. Information Society 21 (3), 181 204. doi:10.1080/01972240490951908.
130 Howard, H. (2006). Television Station Ownership in the United States: A Comprehensive Study (1940 2005). Journalism & Communication Monographs 8 (1), 1 86. Retrieved from Communication & Mass Media Complete database. Iosifides, P. (1999). Diversity versus concentration in the deregulated mass media domain. Journalism & Mass Communication Qu arterly, 76 (1), 152 162. Ishikawa, S., & Muramatsu, Y. (1996). Why measure diversity? In S. Ishikawa (Ed.), Quality assessment of television (pp. 199 202) Luton, Bedfordshire: University of Luton Press Kambara, N. (1992, March). Study of the diversi ty indices used for programming analysis. Studies of Broadcasting, 28, 195 206. Kimmelman, Gene. "Deregulation of Media: Dangerous to Democracy." Retrieved September 24, 2010, from http:/ /www.consumersunion.org/telecom/kimmel 303.htm Kleiman, H. (1991). Content diversity and the FCC's minority and gender licensing policies. Journal of Broadcasting & Electronic Media, 35 (4), 411. Retrieved from Communication & Mass Media Complete database. Klein, P. (July 24, 1971). Why you watch what you watch when you watch. TV Guide 6 10. Lacy, S. (1987). The effects of intracity competition on daily newspaper content. Journalism Quarterly,64, 281 290. Retrieved from Communication & Mass Media Complete database. Lacy, S. (1988). The impact of intercity competition on daily newspaper content. Journalism Quarterly 65 (2), 399 406. Retrieved from Communication & Mass Media Complete database. Lacy, S. (1991). Effects of group ownership on daily newspaper con tent. Journal of Media Economics, 4 (1), 35 47. doi: 10.1080/08997769109358202 Le Duc, D. R. (1982). Deregulation and the dream of diversity. Journal of Communication, 32 (4), 164 178. Levin, H.J. (1970). Competition, diversity, and the television group owne rship rule. Columbia Law Review, 70 (5), 791 835. Levin, H. (1971). Program duplication, diversity, and effective viewer choices: Some empirical findings. American Economic Review, 61 (2), 81 88. Retrieved from Business Source Premier database. Levin, H. J. (1980). Fact and fancy in television regulation: An economic study of policy alternatives New York: Russell Sage Foundation.
131 Li, S. S., & Chiang, C. (2001). Market competition and programming diversity: A study on the TV market in Taiwan. The Journal of Media Economics, 14 (2), 105 119. Retrieved from Communication & Mass Media Complete database. Lin, C.A. (1995a). Diversity of network prime time program formats during the 1980s. Journal of Media Economics, 8(4), 17 48. Retrieved from Communication & Mass Media Complete database. Lin, C.A. (1995b). Network prime time programming strategies in the 1980s. Journal of Broadcasting & Electronic Media, 39, 482 495. Retrieved from Communication & Mass Media Complete database. Litman, B. R. (1979). The television networks, competition, and program diversity. Journal of Broadcasting 23 (4), 393 409. Retrieved from HeinOnline Law Jour Lopes, P.D. (1992). Innovation and diversity in the popular music industry, 1969 to 1990. American Sociological Review, 57 (1), 56 71 Litman, B. R., & Bridges, J. (1986). An Economic Analysis of Daily Newspaper Performance. Newspaper Research Journal 7 (3), 9 26. Retrieved from EBSCO host Litman, B.., & Hasegawa, K. (1996). Measuring diversity in US television programming: New evidenc e. In S. Ishikawa (Ed.), Quality assessment of television (pp. 199 202) Luton, Bedfordshire: University of Luton Press. Long, S.L. (1979). A fourth television network and diversity: Some historical evidence. Journalism Quarterly, 56 3 41 345. Retrieved fr om Communication & Mass Media Complete database. Lopes, P.D. (1992). Innovation and diversity in the popular music industry, 1969 to 1990. American Sociological Review, 57 (1), 56 71. McCombs, M. (1987). Effect of monopoly in Cleveland on diversity of news paper content. Journalism Quarterly 64 (4), 740 792. Retrieved from Communication & Mass Media Complete database. McCombs, M.E. (1988). Concentration, monopoly, and content. In R.G. Picard, J.P. Winter, M.E. McCombs, & S. Lacy (Eds.), Press concentration and monopoly: New perspectives on newspaper ownership and operation (pp. 129 1 37). Norwood, NJ: Ablex. McChesney, R.W. (2004a). The political economy of international communications In P. Thomas & Z. Nain (Eds.), Who owns the media?: Global trends and lo cal resistances (pp.3 22) London: Zed Books. McChesney, R.W. (2004b). The problem of the media: US communication politics in the 21 st century. New York: Monthly Review Press.
132 McDonald, D.G. & Dimmick, J. (2003). The conceptualization and measurement of di versity. Communication Research, 30 (1), 60 79. doi:10.1177/0093650202239026. McDonald, D.G. & Lin, S.F. (2004). The effect of new networks on U.S. television diversity. Journal of Media Economics, 17 (2), 105 121. Retrieved from Communication & Mass Media Complete database. McQuail, D. (1992). Media performance: Mass communication and the public interest Newbury Park, CA: Sage. Meier, W.A., & Trappel, J. (1998). Media concentration and the public interest. In D. McQuail, & K. Siune (Eds.), Media policy: Co nvergence, concentration, and commerce (pp. 38 59) London: Sage Publications. Napoli, P.M. (1997). Rethinking Program Diversity Assessment: An Audience Centered Approach. Journal of Media Economics 10 (4), 59. Retrieved from Communication & Mass Media Co mplete database. Napoli, P.M. (1999). Deconstructing the diversity principle. Journal of Communication 49 (4), 7 34. doi:10.1111/j.1460 2466.1999.tb02815.x Napoli, P.M. (2001a). Markets conditions and public affairs programming: implications for digital te levision policy. Harvard International Journal of Press/Politics, 6 (2), 15 29. Napoli, P.M. (2001b). Social responsibility and commercial broadcast television: An assessment of public affairs programming. JMM: The International Journal on Media Management 3(4), 226 233. Napoli, P.M. (2002, August). Television station ownership characteristics and commitment to public service: An analysis of public affairs programming. Paper presented at the annual meeting of the Association for Education in Journalism and Mass Communication, Miami, FL. Napoli, P.M. (2003). Audience economics: Media institutions and the audience marketplace. New York: Columbia University Press. Napoli, P. M. (2004). Television station ownership characteristics and news and public affairs pr ogramming: An expanded analysis of FCC data. Info: The Journal of Policy, Regulation, and Strategy for Telecommunications, Information, and Media, 6 (2), 112 121. Napoli, P.M., & Yan, M. (2007). Media ownership regulations and local news programming on broa dcast television: An empirical analysis. Journal of Broadcasting & Electronic Media 51 (1), 39 57. doi:10.1080/08838150701308010.
133 The Nielsen Company. (2009). Local Television Market Universe Estimates. Retrieved July 23, 2010 from http://blog.nielsen.com/nielsenwire/wp content/uploads/2009/08/2009 2010 dma ranks.pdf Noam, E. M. (2009). Media ownership and concentration in America. Oxford: Oxford Universit y Press. Noll, R. G., Peck, M. J., & McGowan, J. J. (1973). Economic aspects of television regulation Washington: Brookings Institution. Oba, G. (2004). A case study of program type diversity in Japanese evening television. Keio Communication Review 26 101 121. Owen, B. M., Beebe, J. H., & Manning, W. G. (1974). Television economics Lexington, Mass: Lexington Books. Owen, B. M. (1978). The economic view of programming. Journal of Communication 28 (2), 43 50. Owen, B. M., & Wildman, S. S. (1992). Video economics Cambridge, Massachusetts: Harvard University Press. Park, S. (2005). Competition's Effects on Programming Diversity of Different Program Types. JMM: The International Journal on Media Management 7 (1/2), 39 54. doi:10.1207/s14241250ijmm0701&2_5 Pearson, S., & Shields, T. (2011, July 7). ownership are vacated by federal appeals court. Bloomberg. Retrieved from http://www.bloomberg.com/news/2011 07 07/federal appeals court vacates fcc rules on media ownership.html Peterson, R.A., & Berger, D.G. (1975). Cycles in symbol production: The case of popular music. American Sociological Review, 40 (1), 158 173. Police v. Mosley 408 U.S. 92 (1972). Polinsky, H. (2007). The factors affecting radio format diversity after the Telecommunications Act of 1996: Ownership concentration, stations and audience. Journal of Radio Studies 14 (2), 122 143. doi:10.1080/1095504 0701583205 Powers, A. (2001). Toward monopolistic competition in U.S. local television news. Journal of Media Economics, 14 (2), 77 86. Retrieved from Communication & Mass Media Complete database. Powers, A., Kristjansdottir, H., & Sutton, H. (1994). Compet ition in Danish television news. Journal of Media Economics 7 (4), 21. Retrieved from Communication & Mass Media Complete database.
134 Project for Excellence in Journalism. (2003). Does ownership matter in local television news: A five year study of ownership and quality. Retrieved from http://www.journalism.org/node/243 Project for Excellence in Journalism. (2010). Ownership. In The state of the news media. Retrieved from http://stateofthemedia.org/2010/local tv summary essay/ownership/ Rogers, R. P., & Woodbury, J. R. (1996). Market structure, program diversity, and radio audience size. Contemporary Economic Policy 14 (1), 81 91 Rothenbuhler, E.W., & Dimmick, J.W. (1982). Popular music: Concentration and diversity in the industry, 1974 1980. Journal of Communication, 32 (4), 143 149. Schumpeter, J.A. (1950). Capitalism, socialism, and democracy (3 rd ed.). New York: Harper and Row Schurz Communications, Incorporated v. Federal Communications Commission and the United States of America. 982 F.2d 1043. (7th Cir. 1992). Scott, D., Gobetz, R., & Chanslor, M. (2008). Chain versus independent television station ownership: Toward an inve stment model of commitment to local news quality. Communication Studies, 59 (1), 84 98. doi:10.1080/10510970701648624. Shah, A. (2009, January 2). Media conglomerates, mergers, concentration of ownership. Retrieved from http://www.globalissues.org/article/159/media conglomerates mergers concentration of ownership Shannon, C.E., & Weaver, W. (1963). The mathematical theory of communication Univers ity of Illinois Press. Shepherd, W. G. (1970). Market power and economic welfare. New York: Random House. Shepherd, W. G. (1979). The economics of industrial organization. Englewood Cliffs, NJ: Prentice Hall. Shughart II, W.F. (2008). Industrial concentrat ion. The Concise Encyclopedia of Economics Library of Economics and Liberty. Retrieved November 23, 2009 from http://www.econlib.org/library/Enc/IndustrialConcentration.html S iebert, F. S., Peterson, T., & Schramm, W. (1963). Four theories of the press: The authoritarian, libertarian, social responsibility, and Soviet communist concepts of what the press should be and do Urbana: University of Illinois Press.
135 Smith, L.K. (2004). Consolidation and news content: How media ownership policy impacts local television news (Doctoral dissertation). Retrieved from htt p://repositories.lib.utexas.edu/bitstream/handle/2152/1264/smithl25339.pdf?se quence=2 Spavins, T.C., Denison, L., Roberts, S., & Frenette, J. (2002). The measurement of local television news and public affairs programs. Washington, DC: Federal Communicati ons Commission. Retrieved September 24, 2010, from http://hraunfoss.fcc.gov/edocs_public/attachmatch/DOC 226838A12.pdf Spence, A.M. & Owen, B.M. (1977). Television program ming: Monopolistic competition and welfare. Quarterly Journal of Economics, 91 (1), 103 126. Retrieved from Business Source Premier database. Steiner, P. (1952). Program patterns and preferences, and the workability of competition in radio broadcasting. Qu arterly Journal of Economics 66 (2), 194 223. Retrieved from Business Source Premier database. Telecommunications Act of 1996, Pub. L. No 104 114, 110 Stat. 56 (1996). Tribune Media Services. (2011). TV schedules (Version 5.2) [Data file]. Unpublished data set. Queensbury, NY: Author. Tsourvakas, G. (2004). Public television programming strategy before and after competition: The Greek case. Journal of Media Economics, 17 (3), 193 205. doi: 10.1207/s15327736me1703_5 68). van der Wurff, R., & van Cuilenburg J. (2001). Impact of moderate and ruinous competition on diversity: The Dutch television market Journal of Media Economics, 14 (4), 213 229. Retrieved from Communication & Mass Media Complete database. van der Wur ff, R. (2004). Program choices of multichannel broadcasters and diversity of program supply in the Netherlands. Journal of Broadcasting & Electronic Media, 48, 134 150. Wakshlag, J., & Adams, W. J. (1985). Trends in program variety and the prime time acces s rule. Journal of Broadcasting and Electronic Media, 29, 23 34. Retrieved from HeinOnline Law Journal Library. Webster, J. G., & Phalen, P. F. (1994). Victim, consumer, or commodity? Audience models in communication policy. In J. S. Ettema & D. C. Whitney (Eds.), Audiencemaking: How the media create the audience Thousand Oaks, CA: Sage.
136 Wildman, S.S., & Owen, B.M. (1985). Program competition, diversity and multichannel bundle in the new video industry. In E. Noam (Ed.), Video Media Competition: Regulatio n, Economics and Technology. New York: Columbia University Press. Wildman, S.S. Indexing diversity. In P.M. Napoli (Ed.). (2007). Media diversity and localism: Meaning and metrics (pp. 151 176). Mahwah, NJ: Lawrence Erlbaum Associates. Williams, D. (2002). Synergy Bias: Conglomerates and Promotion in the News. Journal of Broadcasting & Electronic Media 46 (3), 453 472. doi:10.1207/s15506878jobem4603_8 Wimmer, R. D., & Dominick, J. R. (2006). Mass media research. Boston: Wadsworth. Wirth, M.O. and Wollert, J .A. (1979). Public interest programming: Taxation by regulation. Journal of Broadcasting, 23 (3), 319 3 30. Wirth, M.O., & Wollert, J.A. (1984). The effects of market structure on television news pricing. Journal of Broadcasting, 28 (2), 215 224. Yan, M., & Napoli, P. (2006). Market competition, station ownership, and local public affairs programming on broadcast television. Journal of Communication 56 (4), 795 812. doi:10.1111/j.1460 2466.2006.00320.x. Yan, M.Z. & Park, Y.J. (2009). Duopoly ownership and loc al informational programming on broadcast television: Before after comparisons. The Journal of Broadcasting and Electronic Media, 53 (3), 383 399 doi:10.1080/08838150903102709.
137 BIOGRAPHICAL SKETCH Candace Holland was born in Charleston, West Virginia, but grew up in Crystal River, Florida. She graduated summa cum laude with a Bachelor of Science in t elecommunication with a specialization in m anagement from the University of Florida in 2009. She also received a minor in b usiness a dministration. Upon complet ion of her graduate coursework, she worked as a Market Research Analyst for Ypartnership, an Orlando, Florida based advertising and public relations agency. She is currently t marketing and information company, where she also completed multiple internships during her undergraduate studies.