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

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 (DMA) Unique Beats (DMA) Topics Covered (DMA) Per Capita Sales (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) Papers0.01-0.23-0.82 -0.0024 (2.30)*(0.37)(1.08) (1.38) Constant0.2016.8035.3338.87 0.04530.0483 (6.70)**(5.34)**(18.28)**(10.22)** (17.61)* * (14.27)* * N534 498 DMA's173 249 Source: Lisa M. George, Information Economics and Policy, 2007

8. Political Competition & Diversity Source: Lisa M. George, Content in Campaigns, 2011

9. Why Study TV News? TV News remains the primary news source for 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 Local News & Lag News Viewing Population Share

15. Local News Viewing... Loyalty Matters Local News and Entertainment Market Shares by Day (2010)

16. Measuring Differentiation ABC News NBC News NBC Prime ABC Prime Other

17. Estimating Differentiation Lagged Viewing (LV) coefficients measure diversity. Pairwise estimates are summed across competitors for a station-market-year measure. Alternatives & adjustments I= Local News Indicator for S = {1,0} LV = Lagged Viewing for S S={ABC, CBS, NBC, FOX, Other} m=market = 210 DMA’s, y=Year={2006, 2008, 2010} D= Day ={M, T, W, Th, F}, T= ½ Hour Timeslot {9pm-12am}

18. Station Cross-Effects Prime Time ABCCBSNBCFOXOther Local News ABC 0.054-0.030-0.0380.0580.021 CBS -0.0060.076-0.008-0.054-0.005 NBC -0.048-0.0800.151-0.1100.019 FOX 0.013-0.020 0.033-0.015 Other NA Average effect of an additional prime-time viewer on local news viewing. 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. Mean Word Share (%) (N=1523) Market St. Deviation (N=398) Crime0.9760.205 Weather0.6150.187 Government0.3910.091 Business & Economics0.2260.045 Foreign Affairs & Trade0.1610.040 Education0.1520.031 Defense0.1380.035 TV & Media0.1120.031 Social Welfare0.1080.021 Health0.0970.029 Traffic0.0960.044 Infrastructure & Environment0.0870.022 Sports0.0690.018 Ideological Issues0.0620.017 Labor & Employment0.0540.016 Taxes0.0410.016 Agriculture0.0080.004 Death Notices0.0003 Issue Diversity Metrics

21. Political Diversity Metrics Mean Word Share (%) N=1523 Market Standard Deviation N=398 Ethnicity & Race All Minority 0.00190.0020 Non-Hispanic White 0.03200.0102 Party & Office Democrats House 0.00620.0037 Republicans House 0.00390.0022 Democrats Senate 0.01110.0042 Republicans Senate 0.01000.0036 Politician Counts MeanSt. Dev. Total Covered Politicians 5626 Average Share of Stations Covering 44%5.9%

22. Local Diversity Metrics Mean Word Share (%) N=1523 Market St. Deviation N=398 Place Coverage0.5920.114 Local Government Titles0.0860.025 Place CountsMeanSt. Dev. Total Place References1152304 Average Share of Stations Covering61%5%

23. Some Results Diversity and market structure Diversity and content – Issue Diversity – Political Diversity Diversity and viewership

24. Larger markets, more differentiation. More stations (owners), less differentiation. No relationship between racial diversity and differentiation. Market-level measures do not get us far. Differentiation & Market Structure

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 & Politics Specifications with network interactions indicate this is not a FOX effect, but rather a CBS effect.

28. Differentiation & Race Covering 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