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

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Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing? Joel Waldfogel The Wharton School University of Pennsylvania Introduction YouTube ... – PowerPoint PPT presentation

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Title: 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
  • Times Innovation of the Year 06
  • My question effect on television viewing (on
    network controlled viewing)

3
YouTube Growth
Weve 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 relevant convenience sample during 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 11
  • Movies
  • Web offers poor substitute, DVD copying better
  • Undivided attention
  • Nearly 11 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 gt 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 Greys 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
  • e unobserved determinants 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 freqly on TV
21
Cross section evidence -06/07
Unauthd coefs more negative
Similar, but positive coefficients in sometimes
regression
22
CX interpretation
  • Lots of positive coefficients
  • Consistent with unobserved het or complementarity
  • Whats 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 freqly, esp freq on freq
28
Longitudinal bottom line
  • Smaller than CX magnitudes
  • Evaluating at 2006-07 web viewing
  • TVF down by 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?

30
(No Transcript)
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 (11) music (less) TV ( none?)
  • Caveats
  • Convenience sample
  • Would be nice to study broader population
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