Analysis of Subscription Demand for Pay-TV†
Manabu Shishikura*
Norihiro Kasuga**
Akio Torii***
Abstract

In this paper, we will conduct an analysis from an empirical perspective concerning
broadcasting demand behavior and characteristics. More concretely, we will assume three
options (subscription patterns), namely, nonsubscription, cable TV subscription, andsubscription to broadcasting services via communications satellites (CS), and we will conduct
an analysis of broadcasting service demand behavior using a discrete choice model.
According to our result, price elasticity is low for cable TV and CS subscribers, which
is a contrast to the United States, where price elasticity for paid broadcasting is high. It showsthat factors other than price continue to have a strong influence on demand behavior(subscription choices) in the Japanese market for regular and paid broadcasting (themultichannel market) and that the market has not yet entered the phase of price competition
between cable TV and CS broadcasting.
With regard to channel number elasticity, the figures are high for basic channel
service and low for special channel services. This indicates that basic services are an
important concern for viewers. This suggests that, in line with reports that the spreadof cable TV in the United States was promoted by difficulty in receiving terrestrial
broadcasts, basic channel services may be more important than special channels as keys
to the spread of paid broadcasting in Japan.

1. Introduction
In order to evaluate the effects on the welfare of broadcasting services resulting from the shift tomultichannel broadcasting, it is necessary to clarify the structure of demand for broadcasting
services in the context of this switch. It has also been noted that “desirable” market conditions for†

Our conclusions, opinions, and other statements in this paper are those of the authors and are not necessarily of
our affiliated organizations. We are very grateful for useful comments from the conference participants of The
Japan Economic Policy Association.

*

Senior Researcher, Institute for Information and Communications Policy, E-mail: m-shishikura@soumu.go.jp**

Associate Professor, Faculty of Economics, Nagasaki University, E-mail: n-kasuga@net.nagasaki-u.ac.jp***

Professor, Faculty of Business Administration, Yokohama National University, E-mail: atorii@ynu.ac.jp

1

the maximization of social welfare, funding approaches (public, commercial, and paid broadcasting),
levels and structures of competition (including monopoly, oligopoly, and competitive markets) all
depend greatly on demand-side features, such as substitutability among programs and channels.1
Analyses of demand for broadcasting services include ones that focus on the measurementof their economic value and ones that focus on evaluation of competitive conditions in the
broadcasting market; in the United States the latter sort have been more commonly conducted as partof antitrust/competition policy. In particular, against the backdrop of the further intensification ofcompetition with the spread of satellite broadcasting and the entry of cable companies and regional
telephone companies into the market, in recent years analyses have been directed at the cable TV
market and DBS (direct broadcast satellite) broadcasting.2 Kieschnick and McCullough (1998)
made the following three points about the policy utility of demand analyses of broadcasting services:(i) They offer grounds for judgment in discussion of the position of terrestrial broadcasting
as the only medium that allows non-affluent households to watch programs and of the maintenanceof universal service and public utility.
(ii) They offer grounds for judgment concerning media concentration and businessefficiency in connection with the FCC’s (Federal Communications Commission’s) ownership rules
concerning regional broadcasters, including the question of whether cable stations should be taken
into consideration or not.
(iii) They offer grounds for judgment about policy measures (intervention) aimed atmaintaining local terrestrial broadcasters—such as the idea that imposing a “must carry” rule on
cable TV and satellite broadcasters does not ensure the financial health of all (terrestrial)broadcasters, but it does aim for the survival of a basic number of broadcasters—and about market
neutrality (competitive environment).3
Given the fact that the spread of cable TV has already created a multichannel broadcasting
market in the United States, it may not be possible to apply arguments advanced there to today’s
Japan without modification. But there is an undeniable possibility that points like the market
environment, business efficiency, media concentration, and the “national minimum” may also
become issues here. In particular, it seems quite plausible that points like business efficiency andmedia concentration (item ii above) and the promotion of a multichannel market and the1

Spence and Owen (1977).
Karikari et al. (2003) conducted an analysis of the factors behind spread of paid broadcasting, taking DBS
broadcasting into account, showing that it progressed in areas where cable TV charges were high because ofregulation and did not progress in areas where the entry of cable companies and regional phone companies hadcreated intense competition. Also, Goolsebee and Petrin (2004), using subscriber data in a discrete choice model,
estimated the own-price elasticity and cross-price elasticity of cable service (basic and premium) against DBS
broadcasting and terrestrial broadcasting. They showed that demand for premium cable service and for DBS
broadcasting was much more elastic than that for basic cable service, and that subscribers saw DBS and premium
cable as substitutable services.
3
Turner Broadcasting System Inc. v. FCC, 117 Sup. Ct. 1174 (1997).
22

maintenance of a national minimum (item iii above) will fully merit consideration in Japan.4 In thispaper, we will conduct an analysis from an empirical perspective concerning broadcasting demand
behavior and characteristics, including the move toward multichannel broadcasting and subscription
activity in paid broadcasting, taking into account the progress toward multichannel broadcasting.
Section 2 of this chapter sums up the characteristics of broadcasting service demand.
Section 3 explains the methodology of our empirical study. Section 4 presents the results of ourstudy. Section 5 discusses the implications to be drawn from these results.

2. Broadcasting Service Demand Behavior
The relationship between the shift to multichannel broadcasting and broadcasting service demand
behavior has not been made entirely clear up to this point. In this section, prior to our empiricalanalysis, we will sum up this behavior with a focus on multichannel broadcasting.
Broadcasting service demand has two aspects: viewing decisions and the purchase ofchannel subscriptions. Purchasing channel subscriptions does not in itself produce any direct utility;
utility only achieved through viewership. The level of utility rises with the increase of viewing time
(volume of use), and marginal utility tends to diminish; in these respects broadcasting service
demand is like demand for ordinary goods and services. If viewing time is taken as a given, then thelevel of utility rises with the viewing of programs that are close to the preferences of the viewers.
Particularly in the case of broadcasting services, which the suppliers offer unilaterally within shortperiods, there is an issue of probability involved in viewers’ being able to see the sort of programs
that they prefer, but the larger the number of available channels is, the better the chances are thatthey will be able to find programs matching their preferences. So it would seem that increasing thenumber of channels available (i.e., increasing diversity) should increase users’ utility per viewing
hour. And we can think of the utility function as a function of the number of channels and theamount of viewing time.
As limiting conditions, with respect to the purchase of channel subscriptions, it would
seem that the same sort of budgetary constraints would apply as in the case of the use of ordinary
goods and services, while with respect to viewing, inasmuch as this is a time-consuming leisureservice, time constraints act as important limiting conditions. So we may consider that broadcasting
services demand (viewing time and channel purchases) behavior should be formulated in the shapeof the double constraints of budget and time.5
Above we have assumed that channels and cost burdens can be freely chosen, but it is4

In some respects this sort of consideration has already begun, as in the review of the principle of nonconcentration,
but there is room for further discussion of issues like the position of paid broadcasting.
5
For the relationship between a specific broadcasting demand model and an empirical model, see Shishikura et al.
(2005).

3

probably appropriate to view the choice as involving the provision of channel services that havebeen packaged to some degree. Particularly in Japan’s case, purchasing a TV set and paying
reception fees6 allows one to use basic channel services; selection of channel services to which one
is not yet subscribed, along with the acceptance of cost burdens, is conducted through discrete
choices to subscribe to paid broadcasting services based on comparison of the available channels andcost in the case of nonsubscription against the available channels and cost in the case of subscription.
Based on this perspective, here we will assume three options (subscription patterns), namely,
nonsubscription7, cable TV subscription, and subscription to broadcasting services via
communications satellites (CS),8 and we will conduct an analysis of broadcasting service demand
behavior using a discrete choice model.9

3. Empirical Choice Model of Broadcasting Services Demand
We assumed the following linear function for deterministic term Vin (Model 1).

Vin = α1 Di =CATV + β1 Bchi + β 2 Echi + β 3 Mchi + ϕ1 pi + δ1 Di = ANT Yn(1)

We use the number of channels in three categories of service, namely, basic ( Bchi ), extended basic( Echi ), and special ( Mchi ), as the basic attribute variable for the various subscription states, andcharges (amount paid, pi ) as the variable indicating the cost burden.10 Di =CATV and Di = ANT are
dummy variables that are equal to 1 when subscribed to cable TV and when not subscribed to paid
broadcasting, respectively. We also include household income ( Yn ) as a household-specificattribute.11 The data are non-aggregated data12 from a questionnaire survey.13 See Table 1 for the
6

These may be considered a sort of fixed charge.
This is defined to include those households that use public broadcasting (including broadcasting satellite, or BS,
services and terrestrial commercial broadcasting.
8
Note that these options are not entirely mutually exclusive.
9
In the discrete choice model, a consumer is assumed to acquire utility from various features (price, quality, content)
7of a good. It is further assumed that utility U in can be expressed as the linear combination ofterm explained by the above features, and stochastic terms

Vin , a deterministic

ε in , with actor n choosing among a set of alternativesAnin such a way as to maximize his or her utility. The conditions for actor n to choose alternative i are expressed asfollows:
U in = Vin + ε in > U jn = V jn + ε jn
i ≠ j j∈ A、

nMultiple estimation methods exist, depending on what sort of distribution is assumed for the above stochastic terms.
10“Basic” service refers to public broadcasting channels providing “comprehensive programming” (including
education, news, and entertainment) and commercial channels. “Extended basic” refers to public and commercial
channels transmitted by broadcast satellite (digital and analog). “Special” refers to channels providing special
programs, such as films and music.
11
We also looked at other attributes, such as the number of household members and their average age, but we did not
find any significant results, so we have omitted them here.
12
The average value is used for the attributes of alternatives that were not chosen, but since the numbers of channels

4descriptive statistics for each of the variables. We also conducted an estimation for a case where
viewing time for each category is included (Model 2), namely, basic ( Bwatchi ), extended basic( Ewatchi ), and special ( Mwatchi ), as the variable indicating viewing preferences.14

Vin = α1 Di =CATV + β1 Bchi + β 2 Echi + β 3 Mchi + γ 1 Bwatchi + γ 2 Ewatchi + γ 3 Mwatchi + ϕ1 pi + δ 1 Di = ANT Yn(2)

To sum up the conditions for the attribute variables for each type of subscription status,
those who choose nonsubscription have access only to the basic channels,15 and their cost burden
consists of payment of the reception fees collected by public broadcaster NHK (Japan Broadcasting
Corporation). Those who choose cable TV subscription have access to additional basic channels andalso to special channels, and their cost burden consists of the cable TV charges in addition to the
NHK charges.16 Those who choose CS service have access to special channels in addition to thebasic channels available to nonsubscribers, and their cost burden consists of the NHK charges plus
the charges for the number of channels for which they have contracted.
The difference between cable TV and CS services is expressed in the numbers ofadditional basic and special channels to which they provide access and in the cost burden. The
choice to subscribe to one or the other is made in consideration of the channels available tononsubscribers and the users’ preferences and cost burdens with respect to the basic channels andspecial channels.

------------------------------Insert Table 1 about here ------------------------------

available to nonsubscribers and to cable TV subscribers vary from region to region, regional averages were used as
the opportunity number of viewable channels for nonsubscribers and cable TV subscribers. The average for the entire
sample was used for CS subscribers, because there are no regional differences in this service.
13
The survey was conducted in March 2004 by the Institute for Information and Communications Policy (IICP) andthe Foundation for MultiMedia Communications (FMMC), directed at households in Tokyo and surrounding
prefectures and in the Hokuriku region. Note that the geographical target was limited to these two regions; also, prioradjustments were made in order to secure a certain number of subscribers for sampling. Weighting was applied insuch a way as to match the nationwide spread of services in setting the sampling shares for aggregation purposes, but
we have some reservations about the estimation results. For details see Shishikura et al. (2005).
14
We also made estimates for cases that allowed for regional variables, such as dummies for each prefecture and aregional (Hokuriku) dummy, as well as for household attributes, such as the number of household members, but we
did not find significant results for any of these, so we have omitted them here.
15
The number of available basic channels varies, however, depending on whether the household has access to BS
services, on the residential environment, and on the region (number of terrestrial channels).
16
The degree of freedom of choice, however, is not high, and the charges are set under fixed-rate plans.

54. Empirical Results
Below we give an overview of the estimation results for three logit models, namely, multinomial,
nested,17 and mixed, in our two discrete choice models (See table 2). With regard to the signcondition and statistical significance of each variable, basic channel service both satisfies the signcondition and achieves an adequate level of significance, while special channel service meets thesign condition but does not have a high level of significance. The coefficient of extended basic
channel service fails to meet the sign condition and also has a low level of significance. With respect
to level of charges, the sign condition is met in all cases, and the level of significance is adequate
overall.

------------------------------Insert Table 2 about here -----------------------------It is difficult to identify which of the three methods of estimation is most appropriate.
Pseudo R2 (McFadden’s R2) is high in each model, and no major differences were found for the
goodness of fit. Also, we conducted IIA (Independence from Irrelevant Alternatives) tests todetermine the appropriateness of the assumption that the disturbances are independent andhomoscedastic among the alternatives, but neither the Hausman test nor the likelihood ratio test forthe model without the CS alternative showed any clear tendency (Table 3). Even in the mixed logit
with relaxed constraints for dispersion of the parameters, the level of significance of the correlation
parameters was not very high. Though there were some differences in estimation results among themethods, these were not great, so below we will focus our consideration mainly on the results for themixed logit model, which has the least constraints and good significance of coefficients.

------------------------------Insert Table 3 about here -----------------------------In Table 4 we present our calculations for the elasticity with respect to charges andchannels. For charges, all the values are less than 1, indicating inelasticity. For channels, basic
service shows an extremely high elasticity of more than 6, but special channel service shows
extremely low elasticity of under 0.3.

------------------------------Insert Table 4 about here ------------------------------

17

For the nested structure we use (nonsubscription) vs. (cable TV vs. CS). In other words, we assume thathouseholds first decide whether or not to subscribe to paid broadcasting and, if they choose to subscribe, they thenchoose between cable TV and CS.

6

5. Implications
Let us now sum up the implications of the above empirical results. With regard to price elasticity, it
may be natural that price elasticity is low for nonsubscribers with no room for choice, but the figures
for cable TV and CS subscribers are also on the low side, which is a contrast to the United States,
where price elasticity for paid broadcasting is high.18
Price elasticity is a useful indicator of the progress of competition in a market, but we may
take the above findings to show that factors other than price continue to have a strong influence on
demand behavior (subscription choices) in the Japanese market for regular and paid broadcasting(the multichannel market) and that the market has not yet entered the phase of price competition
between cable TV and CS broadcasting.19
With regard to channel number elasticity, the figures are high for basic channel service andlow for special channel services. Though we should probably not consider concrete values to becertain, it does seem clear that there is a major difference in elasticity between the two.20 This ishighly significant when considering cable and CS from the perspectives of their spread and thecompetition between the two. It suggests the possibility that the broadcasting market, including thepaid broadcasting market, may be heavily impacted in the period to come depending on how basic
channel services are (re)transmitted. It also suggests that in cases where there are differences in thenumbers of basic channels that can be directly received because of geographical or other conditions,basic channel transmission may have an impact on the competition between cable and CS for
subscribers.21
If we look at viewing times, we find that even among households subscribed to paid
broadcasting, the basic channels (terrestrial broadcasts) still account for the overwhelming majorityof hours; this indicates that basic services are an important concern for viewers. This suggests that,in line with reports that the spread of cable TV in the United States was promoted by difficulty inreceiving terrestrial broadcasts, basic channel services may be more important than special channels
as keys to the spread of paid broadcasting in Japan.

18

See Goolsbee and Petrin (2004). Though it is not possible to compare line by line, because the methods of
estimation and the formulations are different, the elasticity values they find are in the range of 1.5 to above 2, which
are high by comparison with the figures for Japan even after allowing for such differences.
19
Own-price elasticity for CS and cable is high by comparison with that for nonsubscription, and the figures for thetwo are close; it is thus possible to judge that cable and CS are in somewhat of a competitive relationship regarding
services and prices.
20
The difference in elasticity between the two reflects the dearth of channels and the substitutability of the content
provided. This may be taken to reflect the fact that basic channel services are provided generally on acomprehensive-programming basis, consisting largely of original content, while much of the content of special
channel services competes with other services, such as packaged video, and the same programs are broadcast
repeatedly.
21
In terms of policy implications this may offer a certain perspective concerning the proper shape of retransmission
for basic channel services (the “must carry” rule).

7

References
Goolsbee, A., and A. Petrin (2004) “The consumer gains from direct broadcast satellites and thecompetition with cable TV,” Econometrica Vol. 72 No. 2., pp. 351–381.
Karikari, J., S. Brown, and A. Abramowitz (2003) “Subscriptions for direct broadcast satellite andcable television in the US: An empirical analysis,” Information Economics and Policy Vol. 15.,
pp. 1–15.
Kieschnick, R., and B. McCullough (1998) “Why do people not subscribe to cable television? A
review of the evidence,” Paper presented at the 28th TPRC (Telecommunications Policy
Research Conference). http://www.tprc.org/agenda98.htm.

Shishikura, M., N. Kasuga and A. Torii (2005) “The Shift to Multimedia and Multichannel
Broadcasting and the Future of the Broadcasting Industry – The analysis of subscription
choice for pay-TV -,” Paper presented at The Japan Economic Policy Association (inJapanese), May 28, (http://prof.mt.tama.hosei. ac.jp/~kazukuro/jepa/newpage1.html).
Spence, M. and B. Owen (1977), “Television Programming, Monopolistic Competition, andWelfare,” Quarterly Journal of Economics, Vol. 91, No. 1, pp. 103-126.

8

Table 1. Basic Statistics for the Variables
Expression Nonsubscribed

Cable TV

139

221

153

6.918

9.529

6.819

0.667

8.000

1.961

0.000

19.71821.606pi

1600.714

7471.639

5484.806

Bwatch i

699.990650.761577.862

time
Extended(average) Special

Ewatch i

6.579

28.25728.147

Mwatch i

0.000

48.33991.935

Average annual incomeYn592.086708.145760.784

Alternative
Number of respondents
Number Basicof
Extendedchannels(average) Special
Charges (averagemonthly payments)
Viewing Basic

Bch i
Echi
Mch i

9

CS

Table 2

Estimation Results for Models 1 and 2

Model 1
Multinomial logit
Variable

Coefficient

Cable subscription constant-1.1354**

Basic channels

1.8660***Extended basic channels-0.0078

Special channels

0.0162

Charges-0.0001SD
0.4798Model 2

Nested logit
CoefficientMultinomial logit

Mixed logit
SD-1.1866**

0.5166***

CoefficientSD-1.1358***

0.43240.2233

1.8676***

0.0735-0.0078

CoefficientSD

Nested logit
Coefficient

Mixed logit
SD-0.9429*0.4984-1.1371**0.57000.1056

1.9816***0.17392.1552***0.0514-0.0101

0.07530.00520.01290.01690.1612

1.87990.0718-0.0037a0.01240.0200a0.01470.0162a0.01260.0075

*

a

**

**

0.43600.2773

1.9824***0.11660.0835-0.0100

0.05230.01630.00740.01330.0001-0.0001

0.0001-0.0001

0.0001-0.00020.0001-0.00020.0001-0.0002

-

-

-

-

-

-

0.00000.00030.00000.00030.00000.0003Extended basic channel viewing hours

-

-

-

-

-

-

-0.0025

0.0024-0.0028

0.0026-0.0025

0.0021

Income (nonsubscriber households)-0.0005

a

-

-

-

-

0.0003-0.0007a0.0004-0.0005a**SD-0.9438

Basic channel viewing hours
Special channel viewing hours**

Coefficient***

0.0001-0.0046***0.00110.0052***0.00130.0046***0.00090.0004-0.0006a0.0003-0.0009*

0.0005-0.0006a0.0004

Free--

0.9875***0.1669----0.8592***0.1534

-

-

Pay

-

-

0.9683***0.1668----0.8311***0.1518-0.2869δ (cable constant)

-

-

-

-

0.00790.2833----0.0145

δ（BCH)----

0.03730.0883----0.0272

0.0937

δ（ECH)----

0.00110.0303----0.00310.0309

δ（P)

-

-

-

-

0.00110.0058----0.00080.0057

δ（MCH)----

0.00000.0000----0.00000.0000δ（WBCH)

-

-

-

-

-

-

-

-

-

-

0.00000.0002

δ（WECH)

-

-

-

-

-

-

-

-

-

-

0.00020.0019

δ（WMCH)

-

-

-

-

-

-

-

-

-

-

0.00010.0008

δ（Income)

-

-

-

-

0.00000.0002----0.00000.0002ln Likelihood-227.8573-227.6987-227.7971-218.2827-217.4627-218.2356

ln Likelihood (zero coefficient constraint)-563.5881-614.8215-563.5881-563.5881-614.8215-563.5881

0.59330.62670.59100.60930.64250.6059

513

513

513

513

513

513

McFadden's R2
Sample***: 1% significance**: 5% significance*: 10% significancea: 20% significance10

Table 3

Goodness of Fit and IIA Test Results for Each Model

MethodMultinomial

NestedIIA test
HausmanLikelihood

Model 1

76.0%

76.2%

Mixed
86.4%

Dismissed (5% level)

Cannot be dismissed

Model 2

77.0%

76.8%

87.1%

Cannot be dismissed

Cannot be dismissed

11

Table 4. Price and Channel Elasticity

Price
Model 1ε Own

Basic channel service

ε Cross

ε Own

ε Cross

Special channel services

ε Own

ε Cross

Unsubscribed-0.138

0.049

8.059

-3.445

0.000

0.000

Cable TV

-0.504

0.379

6.423

-7.5930.189-0.135CS-0.450

0.192

7.660

-3.8590.243-0.108Model 2ε Own

ε Cross

ε Own

ε Cross

ε Own

ε Cross

Unsubscribed-0.186

0.066

8.569

-3.642

0.000

0.000

Cable TV

-0.677

0.508

6.818

-8.0600.087-0.062CS-0.603

0.259

8.115

-4.111

0.111-0.05012