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The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
The market for diversity in television news
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The market for diversity in television news

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The Market for Diversity in Television News. Lisa George, Hunter College and the Graduate Center, City University of New York. Felix Oberholzer-Gee. Harvard Business School. Media Economics Workshop. …

The Market for Diversity in Television News. Lisa George, Hunter College and the Graduate Center, City University of New York. Felix Oberholzer-Gee. Harvard Business School. Media Economics Workshop. New Economic School, Moscow. October 28-29, 2011

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  • FCC pursues policy goals of competition, diversity, and localism
  • In media, viewer prices play a small role, so not surprising that we find cannibalization effects important. Not so clear on the welfare side – might be that differentiation dimensions are not highly valued. Radio & Newspapers – mergers mean more variety. Mixed results on consumption.TV – business stealing effects affect programming, small oversupply of local news.
  • Gentzkow-Shapiro – demand-oriented slant in newspapers. Explains little of consumption variation.
  • Gentzkow-Shapiro – demand-oriented slant in newspapers. Explains little of consumption variation.
  • Message here: Local News Viewing Tracks PT Viewing (leads matter)Network shares of Average per capita viewers across 6 timeslots, all years (2006, 2008, 2010).Notes: Perfect loyalty would mean news market share invariant to entertainment share.We do not see this – the shares are highly correlated overall.CBS surge on Thursday (CSI), Fox on Thursday (Idol). The CBS surge reduces Fox’s news viewing, but not others. The Fox surge reduces NBC & CBS news viewing. Very indirect.
  • This is 2010 only, local news market share and market share in lagged period. (Shares used because viewing varies a lot day to day). We see that ABC local news viewing is more stable despite high prime-time variation. NBC viewing and fox viewing tracks prime time share more closely.
  • Prime-time program is clearly differentiated -- much variation in sharesWant to measure the bounce-back – how much of the prime-time loss ABC to NBC does ABC get back.
  • Effects of an additional prime-time viewer that goes on to watch local news Own effects are positive. FOX & Other effects are positive (News at different times) Roughly speaking, NBC has least loyalty. ABC has most, but influenced by positive FOX effect. Average viewing about .05. So add .01 which is 20% incrase. or
  • Easier to work with aggregated categories. We have more work to do on inferring categories from the data.
  • Table 1 of FCC report, compressed for screen.
  • Tables 2 & 3 from FCC study.
  • Title: Explaining Differentiation -- Market Structure? Question: Can our three general policy variables explain differentiation? LHS (1 & 2) – Differentiation in a marketLarger markets – more differentiation. Stations and Owners highly correlated, but jointly significant. RHS (3 & 4 )– most differentiated station More owners, less likely to have “standout” stationMessage – Consistent with expectations, but market structure does not tell us much.Note: Pooled cross-section, clustered standard errors. Differentiation measured at the market – station – year level (190 markets x 3 years x 3-4 stations). Explain why we use pooled cross-sections.
  • Key LOYALTY ON AMOUNT OF COVERAGE ON ALL STATIONS AND MOST DIFFERENTIATED STATIONThis is a split table – all variables are in the regression.Dependent variable – differentiationIndependent variable – share of words on topic.Col 1 – all stations, Col 2 most differentiated stationNot shown: Year Dummies, TVS, TVHH.These are OLS, again cross sectional inference is what we want at this stage.Market level: More differentiation associated with more business, crime, ideological issues, traffic. Less labor, social welfare, weather.Most differentiated station – some different results.
  • Key LOYALTY ON DEVIATONS FROM MARKET AVERAGE COVERAGE ON ALL STATIONS AND MOST DIFFERENTIATED STATIONThese measures are absolute deviations from the market average (ws-mean(ws))/(mkt total ws)Word Shares tell us what stations are talking about that make them different – differentiation within topic. So, for example, more discussion of ideological issues but we don’t know which ones.Deviations tell us whether differentiated stations talk more about a topic.
  • Working on a paper that focuses on race and minority viewership.
  • Note: The content measures are robust to different calculation of b’s. Viewership regressions are more sensitive, need more work. Take these as preliminary.
  • Note: The content measures are robust to different calculation of b’s. Viewership regressions are more sensitive, need more work. Take these as preliminary.
  • Transcript

    • 1. The Market for Diversity in Television NewsMedia Economics WorkshopNew Economic School, MoscowOctober 28-29, 2011Lisa George Felix Oberholzer-GeeHunter College and the Graduate Center Harvard Business SchoolCity University of New York
    • 2. Question• How does competition among local television stations influence diversity in local news programming?• Do viewers value this diversity?• Implications – Welfare? – Political engagement? – Policy?
    • 3. What We Know• Theory – Incentive to differentiate depends on relative importance of price competition & market cannibalization effects• Empirical Evidence: – Radio (Berry & Waldfogel, 2001; Sweeting 2010) – Newspapers (George 2007) – TV (Baker & George 2011)Business stealing matters, consumption effects less clear
    • 4. What We Don’t Know• What dimensions of differentiation matter to consumers in news markets? – Politics? – Issues? – Race? – Localism?
    • 5. Why Diversity Matters• Some illustrations – Hispanic political participation – Newspaper readership – Political competition & turnout
    • 6. Hispanic Political Participation Source: Oberholzer-Gee & Waldfogel, AER 2009
    • 7. Variety in Newspaper Markets Distance Unique Topics Topics Per Per (DMA) Beats Covered Covered Capita Capita (DMA) (DMA) (DMA) Sales Sales (ABC) (ABC) (1) (3) (5) (6) (1) (2)Owners -0.01 -1.02 -1.84 -1.50 -0.0021 -0.0007 (1.86)+ (2.36)* (4.43)** (2.88)** (1.83)+ (0.46)Papers 0.01 -0.23 -0.82 -0.0024 (2.30)* (0.37) (1.08) (1.38)Constant 0.20 16.80 35.33 38.87 0.0453 0.0483 (6.70)** (5.34)** (18.28)** (10.22)** (17.61)* (14.27)* * *N 534 534 534 534 498 498DMAs 173 173 173 173 249 249 Source: Lisa M. George, Information Economics and Policy, 2007
    • 8. Political Competition & Diversity25201510 5 0 0 10 20 30 40 Incument Years in Office Number of Issues Covered Fitted values Source: Lisa M. George, Content in Campaigns, 2011
    • 9. Why Study TV News?TV News remains theprimary news sourcefor US households. Source: Pew Research Center for the People and the Press, Ideological News Sources: Who Watches and Why. September 10, 2010. Sample size about 1500.
    • 10. Why TV is Hard to Study• Typical empirical strategy – Identify changes in diversity measures from (exogenous) changes in market structure• TV News – Static markets with very limited entry & exit – Regulated ownership• Need to measure content directly or infer variety from demand
    • 11. Empirical Approach• Demand-side diversity measures• Supply-side diversity measures – Issues, Politics, Race, Geography• How do they relate – – To each other? – To news viewing? – To ownership and other policy variables?
    • 12. Data• Newsbank Transcripts – 40 markets, 2006-2010• FCC Market Structure Data – Number of stations and owners• Nielsen Viewership – One month (all 210 markets) in 2006, 2008, 2010 – Total viewing by timeslot & program type – Black & Hispanic viewing (not used here)
    • 13. Demand-Side Diversity Measures• Lead-in effects are important in TV – Cost of changing the channel• Consumer tendency to switch channels from prime time programming to local news reveals programming differentiation.• Two illustrations . . .
    • 14. Local News Viewing . . . Lead-in Matters Local News and Entertainment Viewing -- Station Shares by Network Tuesday Thursday0.35 0.35 0.3 0.30.25 0.25 0.2 0.20.15 0.15 0.1 0.10.05 0.05 0 0 ABC CBS NBC Fox Cable ABC CBS NBC Fox Cable Entertainment Local News Entertainment Local News Local News & Lag News Viewing Population Share
    • 15. Local News Viewing . . . Loyalty Matters0.5 Local News and Entertainment Market Shares by Day (2010)0.40.3 CBS Local News CBS Prime Time0.2 ABC Local News ABC Prime Time0.1 00.5 Monday Tuesday Wednesday Thursday Friday0.40.3 NBC Local News NBC Prime Time0.2 FOX Local News FOX Prime Time0.1 0 Monday Tuesday Wednesday Thursday Friday
    • 16. Measuring DifferentiationABC NBC News OtherNewsABC NBCPrime Prime
    • 17. Estimating Differentiation• Lagged Viewing (LV) coefficients measure diversity.• Pairwise estimates are summed across competitors for a station-market-year measure.• Alternatives & adjustmentsI= Local News Indicator for S = m=market = 210 DMA’s,{1,0} y=Year={2006, 2008, 2010}LV = Lagged Viewing for S D= Day ={M, T, W, Th, F},S={ABC, CBS, NBC, FOX, Other} T= ½ Hour Timeslot {9pm-12am}
    • 18. Station Cross-EffectsAverage effect of an additional prime-time viewer on local news viewing. Prime Time ABC CBS NBC FOX Other ABC 0.054 -0.030 -0.038 0.058 0.021 CBS -0.006 0.076 -0.008 -0.054 -0.005 Local NBC -0.048 -0.080 0.151 -0.110 0.019 News FOX 0.013 -0.020 -0.020 0.033 -0.015 Other NA NA NA NA NA Pairwise effects summed over competing (off-diagonal) stations.
    • 19. Supply-Side Diversity Measures• Basic Measure – Word Counts – Word share (keyword frequency/total words) – Market Deviations in word shares• Issues: “Policy Agenda Projects” categories – Keywords matched to categories – Inductive keywords (next round)• Politics & Race – Members of Congress by party & race (Shares & Totals)• Geography & Localism – Local place names & local titles
    • 20. Issue Diversity Metrics Mean Word Share (%) Market St. Deviation (N=1523) (N=398)Crime 0.976 0.205Weather 0.615 0.187Government 0.391 0.091Business & Economics 0.226 0.045Foreign Affairs & Trade 0.161 0.040Education 0.152 0.031Defense 0.138 0.035TV & Media 0.112 0.031Social Welfare 0.108 0.021Health 0.097 0.029Traffic 0.096 0.044Infrastructure & Environment 0.087 0.022Sports 0.069 0.018Ideological Issues 0.062 0.017Labor & Employment 0.054 0.016Taxes 0.041 0.016Agriculture 0.008 0.004Death Notices 0.0003 0.0003
    • 21. Political Diversity Metrics Mean Word Share Market Standard (%) Deviation N=1523 N=398Ethnicity & Race All Minority 0.0019 0.0020 Non-Hispanic White 0.0320 0.0102Party & Office Democrats House 0.0062 0.0037 Republicans House 0.0039 0.0022 Democrats Senate 0.0111 0.0042 Republicans Senate 0.0100 0.0036Politician Counts Mean St. Dev.Total Covered Politicians 56 26Average Share of Stations 44% 5.9%Covering
    • 22. Local Diversity Metrics Mean Word Market St. Share (%) Deviation N=1523 N=398Place Coverage 0.592 0.114Local Government Titles 0.086 0.025Place Counts Mean St. Dev.Total Place References 1152 304Average Share of StationsCovering 61% 5%
    • 23. Some Results• Diversity and market structure• Diversity and content – Issue Diversity – Political Diversity• Diversity and viewership
    • 24. Differentiation & Market StructureLarger markets, moredifferentiation.More stations(owners), lessdifferentiation.No relationshipbetween racial diversity anddifferentiation.Market-levelmeasures donot get us far.
    • 25. Differentiation & Coverage (Word Shares)* ** * * * * * ** * All issues included in each specification, split table.
    • 26. Differentiation & Coverage (Word Deviations)* * ** * ** All issues included in each specification, split table.
    • 27. Differentiation & PoliticsSpecifications with network interactions indicate this is not a FOX effect, but rather a CBS effect.
    • 28. Differentiation & RaceCovering minority politicians does not contribute to loyalty.
    • 29. Differentiation & Viewing (All Stations)
    • 30. Differentiation & Viewing(Most Differentiated Station)
    • 31. Some Conclusions & Extensions• First attempt to measure diversity in local television news• Initial evidence on points of differentiation + Ideology - Government + Politicians• Differentiation increases viewing, largest effect for stations• Still to do – – Inductive differentiation measures – Adjusted loyalty measures• Extensions – Station loyalty & the market for advertising – Minority coverage & viewership

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