It appears that the Internet is soon going to fulfill its potential to become a giant on-demand repository of television shows (and movies) available asynchronously, greatly increasing the variety of shows available at a moment in time. As companies such as Netflix and Hulu increase their activities in this sphere, there are many unanswered questions about the impacts of this transition. In this paper, we attempt to foretell the impact of Internet-induced increased variety on the amount of time individuals devote to viewing television. We use cable and satellite television’s impact on viewing as a proxy for the likely impact that future Internet transmission of programs will have. Using country-based panel data going back to the mid-1990s, we find that the increased variety brought about by cable and satellite has had virtually no impact on the amount of time devoted to television viewing. We discuss the import of this finding for Internet business models of television transmission.
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The cable/satellite networks have also responded to the scheduling bind by providing several channels with similar programs playing at different times.
This presumed positive relationship between greater choice and greater consumption is evident in the discussions of “sampling” in the context of file-sharing. Sampling would occur when music listeners test out songs on file-sharing networks with the intention of purchasing music that they discover through this trial and error process. The claim is usually that sampling will increase demand for purchased music because consumers acquire more information about the choices available to them. But sampling, even if it occurred, would not necessarily increase the quantity of music consumed, since it merely increases the quality of each song purchased but does not necessarily increase the amount of time listening to music or the quantity purchased for the reasons explained in the main text.
There is even currently a question of whether viewers actually derive greater benefit from greater choice. See Benesch et al. (2010) and also Bruni and Stanca (2008) who suggest that greater choice has increased television viewing and lowered overall utility because viewers have less time to interact with other people. Our analysis suggests that greater choice might not lead to greater viewing and thus it might not reduce the “relational” interactions at the heart of their analysis.
This rotation does not hold in every possible instance. For example, in the case of a constant unit elasticity of demand for services, the quantity demanded of units (programs) would be fixed and would not rotate clockwise.
If the nature of the programs is considered ‘unique’ to each program, then watching an additional program does not bring the consumer closer to satiation with regard to other programs.
Weimann (1996) reports a larger initial impact on viewing after 3–4 months after the introduction of cable than was the case at the end of 12 months.
The list of countries can be found in the “Data Appendix”.
For example, Mediametrie–Eurodata TV Worldwide is the source of the data used in the reports from the European Audiovisual Observatory (a non-profit public service institution under the auspices of the Council of Europe with participation by the European Union). See Liebowitz and Zentner (2012) for an explanation of the various methods used by companies to measure television viewing.
In the USA, for example, the Nielsen company creates what are known as “designated market areas” based on the contours of the distance from the transmitter where the share of the population capable of receiving the signal dropped below a standard threshold.
A metric for variety is difficult to construct or defend since it would involve not just the number of choices but also the degree of “sameness” of those choices.
Although there could in theory be some double counting, we are doubtful that there are many households with both satellite and cable because the two distribution systems carry much duplicative programming.
By 1999, all the countries but Latvia (at 80 %), India (at 28 %), Moldova (81 %), and South Africa (56 %) were in the range of 85 % or above for the share of households owning a television.
There are 55 countries in the data set, but Serbia is removed from Table 1 because it alone had missing data for these variables.
The “Data Appendix” explains why the cell phone penetration rate measured by the ITU has been over 100 % in recent years for several countries.
This is for the 39 countries that had viewing and CabSat data from 1999 to 2008 (the two negative viewing change outliers are Mexico and Cyprus). Cable penetration decreases 11 % in Denmark, and the pattern of the data suggest that this change is likely to have been caused by a change in the data source in year 2000.
Computer-based videogames are a well-known form of computer entertainment but have always been dwarfed by videogame consoles (see yearly reports from the Entertainment Software Association).
We also checked for the possible impact of outliers. First, we ran Huber robust regressions (using a Stata routine that first eliminates observations with levels of Cook’s D that are above a particular threshold and then iteratively lowers the weight for observations with large absolute residuals until a convergence threshold is reached). The Huber robust regressions had values of approximately −0.16 and appear to be statistically significant, but the inability to cluster observations in those calculations may bias the standard error. We also looked at the DfBetas of the OLS CabSat variable but did not find any support for the more negative results indicated by the Huber Robust regressions. Similarly, we found that quantile regressions provided results much closer to the OLS regression than the Huber Robust results and thus feel that the OLS results are likely to be more reliable for this important variable.
A related but different question is the study of the extent to which watching television shows on the Internet (e.g., YouTube) displaces conventional television viewing. Waldfogel (2010) studies this question and finds evidence of modest substitution between the two outlets.
The R 2 of a regression using population as dependent variable and all covariates as independent variables is one (including in the list of covariates an individual trend for each country). This demonstrates that the population variable provides no additional information once the rest of the covariates are included in the regressions.
The value of portability in the case of music was examined in Liebowitz (2004) who found that the steepest increase in album sales occurred in the same years that had the greatest yearly increase in portable penetration.
Although Smith and Telang (2009) find that free television broadcasts do not decrease demand for older movies, this seems likely to be due to the publicity effect of the broadcast overwhelming the substitution effect of the broadcast.
It should also be noted that over-the-air broadcasters did not voluntarily forgo subscriptions but instead did not have a technological option for directly charging viewers.
For example, a recent news article in the Toronto Globe and Mail quotes an industry executive thusly: “But it is important, I think, to remember that video consumption is not a zero-sum game. We believe that there is virtually an insatiable appetite for consumers to consume more media on a multitude of platforms…our belief is that new competitors will prove to be additive to the system as opposed to serving to carve up the pie.”
With the exception of Australia and Japan, the data measure television viewing at the national level. The data for Australia measure television viewing from the top five largest metropolitan Areas. The data from Japan measure the viewing from the Kanto region (this region includes the city of Tokyo).
These data are readily available at http://www.itu.int/net4/itu-d/icteye/.
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We would like to thank Michael Smith for his helpful comments and the Center for the Analysis of Property Rights and Innovation for financial support.
Data Appendix: Television viewing, cable and satellite penetration, internet penetration, cell phone penetration, and demographics
Data Appendix: Television viewing, cable and satellite penetration, internet penetration, cell phone penetration, and demographics
We acquired data on television viewing by country for the years 1996–2008 from Mediametrie–Eurodata TV Worldwide, a company that collects information from national-level companies measuring TV viewing using mainly electronic devices (various types of meters). In all we have data for 52 countries although the panel is unbalanced (many countries do not have complete data for all the years). The countries are as follows: Argentina, Armenia, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Croatia, Cyprus, the Czech Republic, Denmark, Egypt, Estonia, Finland, France, Georgia, Germany, Greece, Hong Kong, Hungary, India, Ireland, Israel, Italy, Japan, Latvia, Lebanon, Lithuania, Mexico, Moldova, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Serbia, Singapore, the Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, the UK, the USA, Turkey, and Ukraine.Footnote 24 Data for Belgium are separated for French and Flemish communities, and data for Switzerland are separated for German, French, and Italian communities. There are a total of 55 regional markets after adding countries and communities.
Our data on cable and satellite penetration are from IHS Screen Digest. Screen Digest collects separate data for cable and satellite; we combined the two variables and created a variable called “CabSat” that measures the sum of the household penetration rates of cable and satellite.
Data on Internet penetration by countries are from the International Telecommunication Union (ITU), a United Nations agency for information and communication technology issues. The Internet penetration variable that we use measures the percentage of Internet users in the total population and includes Internet access from any device (e.g., mobile phones—although it should be noted that the iPhone was only introduced in 2007 in the USA and in 2008 in Europe).Footnote 25
We also obtained data on cell phone penetration by countries from the ITU. The variable that we use measures the mobile cellular subscriptions penetration rate. The cell phone penetration rate has been over 100 % in recent years for several countries. The explanation for a penetration rate over 100 % is that this variable includes prepaid cell phones subscriptions; and prepaid cell phone lines retain their status for 3 months after the expiration of their card while these lines are still able to receive calls. Prepaid cell phones are popular in many European countries and some individuals replace their cell phone lines several times in any given year.Footnote 26
The measure of income that we use is constructed using data from the International Monetary Fund World Economic Outlook Database, and following the guidelines proposed in the IMF discussion forum. The GDP per capita (normalized to the US dollar), called GDP PPP, accounts for the prices of goods and services in each country, and is an accepted measurement for comparing the level of development of different countries at any given time. However, the GDP in PPP values does not measure income in constant values and is therefore not appropriate for comparisons across time. A measurement of the level of development that makes the comparison of development levels across countries and across time feasible is constructed by combining the GDP in PPP values for a base year (we use 2000) and growth rates of GDP in local currency in constant values. A GDP measurement for each country and each year is constructed by multiplying the GDP in PPP US dollar values in the base year by the yearly growth rates of the GDP measured in local currency and constant values.
Finally, the measure of employment that we use is the percentage of the population older than 14 years that are employed. These data are from the World Bank.
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Liebowitz, S.J., Zentner, A. The internet as a celestial TiVo: What can we learn from cable television adoption?. J Cult Econ 40, 285–308 (2016). https://doi.org/10.1007/s10824-015-9245-6