Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing?

1. Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing? Joel Waldfogel The Wharton School University of Pennsylvania

2. Introduction • YouTube: • Site hosting video • User-generated • Network content • Appeared in Feb 2005, rapid growth • Top 10 sites within year • Time’s Innovation of the Year ’06 • My question: effect on television viewing (on network controlled viewing)

3. YouTube Growth We’ve been living through an “experiment”

4. Networks huffy about unauthorized content

5. Enter the Lawyers March ’07: Viacom demands $1,000,000,000

6. Networks post shows online • late ‘05 and early ‘06 • ABC, NBC sell episodes on iTunes • CBS at Google Video Store • Experiments in free distribution • May ’06 – ABC free on web • Fall ’06 – all major networks offering multiple shows online free • Today: lots of shows available free

7. ComedyCentral.com

8. …which brings us to the question: • How do unauthorized and authorized web distribution of network content affect television viewing? • Study a relevantconvenience sampleduring a period of change • Relevant: intense web users • Convenient: on campus • …during growth of web distribution

9. Cf. file sharing literature • Music • Close substitute, quick and easy to get • Divided attention • Most studies: some displacement, not 1:1 • Movies • Web offers poor substitute, DVD copying better • Undivided attention • Nearly 1:1 displacement • TV different? • Episodes complements • Demand stimulation plausible

10. Theory: life without YouTube • Watch conventional television when valuation exceeds “price” • “price” is willingness to watch commercials, adapt lifestyle to program schedule • (Similar to TiVo)

11. Life with web distribution • Holding distribution of valuations constant, effects depends on whether v > p0. • If “low valuation” viewers watch online, then DWL↓, no reduction in TV viewing. • But: free episodes online can shift out distribution of valuations. • Even more than in music or movies, ambiguous effects

12. Data: Survey of ≈300 Penn students • How often do you watch video material obtained over the web? What authorized and unauthorized sites/sources do you use (e.g. YouTube, BitTorrent, abc.com)? • Since Fall 2006, how many hours per week do you spend viewing • Authorized video on the web? • Unauthorized video on the web • Traditional television • list TV shows you watched during the 2006-2007 television season • Authorized web, unauthorized web, TV; frequently or sometimes. • Ditto for 2005-2006 season

13. Sites used • Unauthorized • YouTube dominant • tv-links.co.uk, peekvid.com, and bittorrent • Authorized • abc.com, nbc.com, fox.com, cbs.com, and cnn.com

14. Shows watched • TV: Grey’s Anatomy, Entourage, and The Daily Show • Authorized Web: Anatomy, Lost, and The Daily Show • Unauthorized web: The Daily Show, South Park, and Scrubs

15. Weekly viewing ‘06-07

16. Series Viewing Growth: ’05-’07 • Web viewing up • Unauthorized doubles • Authorized triples – big network response worked • TV flat

17. Empirical Approaches • CX: do people watching more series on the web watch fewer on TV • Longitudinal • Aggregate: do people whose web viewing rises between seasons experience bigger changes in TV series viewing?

18. Basic CX Approach • TV= number of series watched on conventional television, • WF = number of series watched frequently on the web • WS = number of series watched sometimes on the web • X = characteristics of the respondent (age, gender, etc.), and • ε = unobserved determinant’s of the respondents’ television viewing. • “do people who watch more series on the web watch more or fewer series on conventional TV?”

19. Disaggregated CX approach • Break variables into auth & unauth components: • WF = UNF + AUF, etc • Concern: unobservables correlated with WF, WS • (people who like TV like it via all media)

20. Cross section evidence -05/06 No differences between auth & unaith channels Those who watch more shows frequently on the web watch fewer shows on TV This operates through shows viewed freq’ly on TV

21. Cross section evidence -06/07 Unauth’d coefs more negative Similar, but positive coefficients in sometimes regression

22. CX interpretation • Lots of positive coefficients • Consistent with unobserved het or complementarity • What’s interesting? • Some actual negative coefficients, suggesting substitution

23. Longitudinal approach • Attempt to get around unobserved heterogeneity • difference across seasons

24. Figure 3: Change in Frequent Viewing on Web (Unauthorized) and Television Rho = -0.20

25. Figure 4: Change in Frequent Viewing on Web (Authorized) and Television rho = -0.20 here too

26. Estimating equation • Analogous to CX specification

27. Longitudinal estimates Small effects overall Positive coefs on sometimes Little sig diff auth vs unath Negative effects on freq’ly, esp freq on freq

28. Longitudinal bottom line • Smaller than CX magnitudes • Evaluating at 2006-07 web viewing: • TVF downby 0.36 • TVS up by 0.55

29. Translating series viewing into hours • How do numbers of series viewed “frequently” or “sometimes” on TV map into weekly hours?

31. Results on hours of viewing • Implied change in weekly hours • Authorized web = 1.78 • Unauthorized web = 2.26 • Overall, TV down 0.24 hours, web viewing up 4.04 hours • Effect on networks depends on value of viewers on TV vs authorized web

32. Conclusion • Substantial use of web in this sample • Half of TV viewing • From zero, large growth in web viewing… • …with small reduction in TV viewing • Less displacement than in movies and music • Movies (1:1) … music (less) …TV ( none?) • Caveats: • Convenience sample • Would be nice to study broader population