PRELIMINARY RESULTS: NOT FOR QUOTATION
Rutgers University Center for Research in Regulated Industries
23rd Annual Eastern Conference
Skytop, PA
May 19-21, 2004
Disclaimer: Nothing in this paper necessarily represents a position of the Mississippi Public
Utilities Staff or the Mississippi Public Service Commission or any Staff member.
UNIVERSAL TELECOMMUNICATIONS SERVICE:
A WORLD PERSPECTIVE1
Christopher Garbacz
Mississippi Public Utilities Staff
P. O. Box 1174
Jackson, MS 39215-1174
[email protected]
Herbert G. Thompson, Jr.
Ohio University
Athens, Ohio 45701
[email protected]
Abstract
Worldwide telecommunications demand is explored in models for business and residential mainline
telephone, and for mobile telephone service using up to eighty-five countries for the period 1995-2001. We
test for cross-price elasticities between mainline and mobile service and find that the results are generally
suggestive of current complementary relationships. We find residential monthly price elasticity to be no
different than zero for a sample of developing countries, but connection elasticities are slightly larger.
Mobile monthly service elasticity is much larger than those for mainline service, suggesting that universal
service in developing countries might be promoted more effectively with subsidies for mobile service.
Income elasticities for the residential model are modest while mobile service model income elasticities are
much higher. Proxies for poverty tend to have large elasticities. Expanding markets, income growth and
enhanced education may be the ultimate universal service promoters.
1
Acknowledgment: Bill Shughart, John Conlon and participants in a faculty seminar at the University of
Mississippi provided helpful comments. We are grateful to the following individuals for access to data:
Magpantay Esperanza for International Telecommunications Union data, Robert Barro and Jong-Wha Lee
for country schooling data and Marc Miles for Heritage Foundation data on economic freedom. Jing Liu,
graduate research assistant at Ohio University, provided considerable assistance in assembling data and
building a data base.
1. Introduction
Worldwide universal service subsidies, both current and potential, may amount to
tens of billions of dollars in incentives to connect to telecommunications networks.
These efforts vary widely across countries as a result of both differences in the stage of
economic development, and in the political priorities placed on modernizing
communication
infrastructures.
Competition
and
the
introduction
of
new
telecommunication services sweeping the world may influence these efforts. Garbacz and
Thompson (2003) find that both targeted and untargeted universal service programs
become more ineffective through time in the United States as we move to almost
complete mainline saturation.
Using panel data from the World Bank and the International Telecommunication
Union (ITU), we develop several telecommunications models to evaluate the differences
in the demand for these services.
A better understanding of the determinants of
telecommunications access and service choices is critical when forming public policy,
including policy on international trade and development.2
Universal service policy
implications across countries can be reevaluated on the basis of our results. Mainly the
issues addressed are empirical in nature. For example, there is the crucial issue of
whether fixed and mobile telephony are substitutes or complements.
Mobile telephone service is a unique service that has been adopted around the
world at an astonishing pace in the last decade. In some ways it is a different service than
The World Bank identifies a ‘3rd wave’ of international trade and globalization, starting about 1980 and
continuing to the present, as consisting largely of technical advances in communications technologies. This
‘3rd wave’ has shown the most promise in opening markets to developing nations. See World Bank (2002a)
for discussion on this topic.
2
2
fixed line service. Obviously, it is mobile as opposed to fixed. It goes anywhere and is
available at all times. Such a new service is valuable for that reason alone but clever
innovators have added still more services to mobile service that could not have been
contemplated a decade ago.
Hausman (2002) has estimated conservatively the consumer surplus of eighty-six
million mobile service subscribers in the United States in 1999 as being about equal to
the amount spent on the service or about $52.8 billion. Given the rapid growth in the
service around the world since 1999, the current consumer surplus worldwide is
enormously larger than Hausman’s 1999 estimate.
In 2002, worldwide mobile
subscribers exceeded one billion for the first time and that was larger than the number of
fixed lines in service.
Data on the rapid growth of mobile telephone service are presented in Table 1.
Note the very large growth in mobile service as well as the large absolute values for lower
income countries. Compound annual growth rates (CAGR) of mobile subscribers are
much larger than CAGR for mainlines over our period of study. The growth of mobile
telephony relative to that of mainlines (fixed lines) is clearly shown in Table 2.
Developing countries tend to have high and growing mobile to fixed line ratios compared
to developed areas, due, in part, to much lower levels of mainlines in 1995 in the
developing world.
This suggests a different form of mobile telephone diffusion in
developing counties than in developed countries, given the saturation and quality of
service of mainlines in developed countries relative to developing countries (Banerjee and
Ros, 2004). At any rate the stage appears to be set to determine if new possibilities for
universal service around the world should be explored.
3
2. Review of the Literature
Clarke and Wallsten (2002) find that universal service for the poor in developing
countries is generally bad. Programs to include the poor are ineffective. Connection
charges in some countries approach the yearly per capita GDP. Therefore, subsidies to
connection charges, they reason, may increase the number of poor households on the
network. Given the unavailability of systematic income and demographic data, secondary
school completion is a powerful indicator of inclusion on the network. Wallsten (2001)
finds that the FCC’s decision to slash international settlement rates for telecom traffic
between the United States and the rest of the world has the effect of cutting prices in
developing countries and increasing traffic due to much higher price elasticities in poorer
developing countries. He finds no relationship between the reduction in settlement rates
and total telephone revenues or mainlines or telecom investment.
Hausman (1999, 2000, 2002) estimates the demand for mobile subscription
service among major metropolitan areas in the United States for the period 1988-1993.
Price elasticity is estimated in the –0.5 range. Ahn and Lee (1999), using country data for
one year, find a complementary relationship between mobile and wireline. Rodini, Ward,
and Woroch (2004) employing U.S. household data show that mild substitution exists
between mobile telephones and fixed wirelines. Madden and Coble-Neal (2004) with
country data for 1994-2000 in a lagged dependent variable model, estimate a small
substitution effect between mobile and fixed-line service. Banerjee and Ros (2004) find
that technological substitution in some countries and economic substitution in others may
explain differential patterns of development in global fixed and mobile telephony.
4
The availability and use of the Internet also impact the choice of
telecommunications access based on findings in the U.S.. Garbacz and Thompson (2003)
find a positive relationship between mainline telephone demand and measures of Internet
penetration.
Duffy-Deno (2001) examines the assumption that demand for second
telephone lines is expected to grow as a result of Internet access demand. His results
indicate that, due largely to federal and state pricing policies, interest in second phone
lines has dropped in recent years. This may also partly be the result of the growth of
alternative, non-telephone access to the Internet at higher speeds. Duffy-Deno (2003),
however, in a study of business demand for high-speed access capacity, finds businesses
not very responsive to price, and significant differences in availability of broadband
access between urban and rural locations, suggesting the need for policy intervention.
3. The Models
In order to fully examine the relationships between economic development,
telecommunications technology and policy, we estimate four separate telephone demand
models for two different subsets of countries. Our models cover business and residential
mainline demand, and mobile telephone subscriber demand as well as a ratio of mobile to
fixed line demand. We estimate the models via OLS with fixed effects for sets of up to
eighty-five countries depending on data availabile for the period 1995-2001.3 All data are
entered in log form so coefficient estimates may be interpreted as elasticities. Models
also are estimated for a subset of developing countries (less than $8000 1995 constant
3
Fixed effects help control for the heterogeneity prevalent in international panel data. Our previous
research on telephone demand with U.S. state-level data incorporates a number of state-specific effects
based on our greater knowledge of differences between states.
5
dollar GDP per capita in 2000) to determine any major differences in the key price and
income elasticities associated with the level of economic development. Table 3 includes
the countries from which individual model data are drawn. Table 4 contains the variable
definitions. Table 5 reviews summary statistics.
These models are similar to the traditional telephone demand model (see Taylor
(1994, 2002) for the theory; see Garbacz and Thompson (1997, 2002, 2003), Crandall and
Waverman (2000), Eriksson, Kaserman and Mayo (1998), Hausman, Tardiff and
Belinfante (1993), Cain and McDonald (1991) and Perl (1983) for empirical estimates).
Generally this literature employs binary choice models appropriate for US household
penetration rate data. However, data outside of the US and a few other countries are
available in the form of mainlines per 100 population or mobile telephone subscriptions
per 100 population. This type of data does not reveal household penetration. ITU data
does provide the percentage of mainlines used for residential service, which allows
estimates for both residential and business demand. The assumption is that residential
usage per 100 population is correlated with household penetration.
We hypothesize that the demand for telephone mainlines or mobile telephone
service is a function of price, income and a number of control variables to adjust for
otherwise unaccounted for differences in poverty, living standards, and the value of
different telecommunications services, as well as other unmeasured differences between
countries and across time. A general specification (after natural logs are taken of all nonbinary variables originally expressed in multiplicative form) of these models is:
6
PEN i   i,0   i,1Yi 

l
i ,l Pi ,l


i , m Pj , m
m


i ,k Ci ,k
k


i ,n d n


n
i ,t Tt ,
t
where PEN is the measure of the availability of telecommunications service ‘i’
(residential, business or mobile service per 100 in the population), Y represents the
measure of income, the Pl are ‘i’ service prices, Pm are the related service prices, Ck
represents control variables or other indicator variables, and ‘d’ and ‘T’ are country and
time-effect dummies respectively. An additive error term is included in the empirical
equation. The ratio model is similarly specified with the exceptions that the dependant
variable is the ratio of mobile to fixed line penetration, and the service prices are the ratio
of mobile to fixed line prices.
Aggregate price data are available in basically two forms.
As is generally
understood, the price should be a two-part tariff with a connection charge that may be
related to the fixed cost of accessing the network and a usage charge usually applied as a
monthly fee4. Both charges should have a negative impact.
We also test for any cross effects between services. Mobile telephone service may
be a substitute or a complement for fixed residential or business service.
If fixed
residential price (Pj in equation ‘i’) has a positive impact in the mobile telephone demand
model, then fixed residential service is a substitute for mobile telephone service. If the
effect is negative then the two services are complements. The ratio model should provide
additional evidence of the relationship between mobile and fixed telecommunications
4
Data for usage charges per three minute calls for on peak and off peak are available from ITU, however,
the off peak data are very sparse. We decided that either both sets of data or no data of this type should be
included. Furthermore, we have no country data on whether a caller pays or receiver pays regime is in
force. Finally, there are no data on long distance charges (domestic or international). Fixed effects adjust
for the missing data.
7
demand. These are unsettled empirical questions, though there is some evidence on both
sides from the studies mentioned above.
Income is Gross Domestic Product per capita (GDP) and should be viewed as a
crude approximation of the appropriate definition of household (or business) income.
Specifically, GDP includes extraneous information not directly related to income and it
does not account for the distribution of income or poverty, which can vary significantly
across nations. To further account for these factors, we include the average years of
schooling achieved by the population over 25 years of age as a proxy for poverty (Barro
and Lee, 2001). Both of these variables should have a positive impact.
Density is the urban population percentage of the total population and is included
as a proxy for a network effect. The more the population is concentrated (aside from the
impact this has on the cost of service) the more valuable the network effect of joining
people together. This should be true in all equations of telecommunications demand. We
expect a positive impact across models.
Internet penetration is included to try and capture this new service’s effect in the
mainline models, since connection to the Internet is usually through a mainline. Internet
service price is not available except for a small number of countries.
The Index of Economic Freedom takes account of differences in the institutional
makeup of the different countries.
We know that private property protection, less
regulation, less government, sound money and a generally market oriented economy lead
to higher economic growth and economic well-being (Friedman, 1962; North, 1990; and
Easton and Walker, 1997). For these reasons, it could also impact the rate of diffusion of
8
new telecommunications technologies. We employ the regulation component of the
index in our models. Less regulation should have a positive effect in all models.
As indicated, we are estimating each of these telecommunication models for all
countries in our sample, and for a subset of developing nations. We expect to find a
stronger effect (higher elasticity estimates) generally for all variables for developing
nations, particular those variables reflecting income, poverty and relative prices
(Wallsten, 2001). As a result, we anticipate a stronger substitution effect (based on the
income effect of a price change) between mobile and fixed telephony services for this
subset of countries, though, again, the final result is still an empirical question.
A complementary relationship between different forms of telephone service is the
result of network externalities known to be a strong influence on customer choice. This
effect is enhanced by the ‘always available’ feature of mobile phones, as well as their
potential for new features. In developed countries mobile telephones often are owned by
several family members in a household that is highly likely to be connected to the fixed
mainline network. At some point, when the value per dollar of mobile service exceeds
that of fixed-line service, the substitution effect will dominate, as has happened already in
some countries. Finland may be the ultimate example where fixed lines have decreased
twenty-four percent in recent years as mobile phone service has grown.
In developing countries the network may be expanded by a single mobile
telephone user in a household not likely to be directly connected to the fixed mainline
network. Many poor mobile telephone users may use a beeping technique to alert more
affluent relatives and friends to call back in a caller-pays regime where usage charges are
higher for mobile calls than fixed line charges. When poor households make telephone
9
calls they may use the less expensive fixed line telephone by sharing or borrowing the
telephone of a friend or relative or using a payphone (This example is adapted from
Oestmann, 2003.) An important consideration for developing countries is that mobile
networks can be set-up more quickly, and fit the needs of some geographical areas better
than fixed line networks.
4. Results
Table 6 contains the full fixed effects elasticity estimates for all the models.
Beginning with the mainline Business and Residential demand models, which include ‘all
countries’, note that the residential monthly price elasticity and the business and
residential connection elasticities are statistically significant and relatively small (–0.05 or
less). The business monthly price elasticity is somewhat higher.
The density elasticity is positive and highly statistically significant in the Business
and Residential models with an elasticity range from 0.438 to 0.564. These results are
reasonable and support the network effect hypothesis.
The income elasticity in the Business model is 0.493 and 0.203 in the Residential
model. The schooling elasticity is powerful in the Business model, but at 1.414 in the
Residential model, it is remarkably larger. It may be picking up the poverty effect that
per capita GDP is unlikely to capture. The larger density result in the residential model
supports this reasoning.
Regulation is positive and statistically significant in the Business and Residential
models suggesting that less regulation in general is favorable to the expansion of
mainlines. Internet penetration is statistically significant in the Residential model with a
10
positive elasticity of about 0.064 but the large negative elasticity in the Business model is
curious. It could be that the Internet in the business community, relying more on higherspeed connections relative to mainlines, may be lowering standard telephone demand.
Note that in developing countries with less high-speed access, the Internet effect is just
the opposite.
The previous models generally deliver results that are not too surprising given the
existing literature. In fact, the results of the Residential model tend to generally confirm
the work of Garbacz and Thompson (2003) in regard to residential price elasticities;
especially in the earlier period in the United States that reflects lower penetration rates.
The large income elasticity in the Business model may be a measure of the reasonably
strong tie between GDP and Business development. The very large schooling and larger
density results indicate the level of poverty given the high number of developing
countries in the sample.
The ‘all-countries’ Mobile model generates results in several areas that are quite
different than the other models. The income elasticity is much higher at 0.703, while the
regulation and schooling variables are no longer statistically significant. The estimated
coefficients of the time dummies (not included in Table 5) are powerful in measuring the
rapid diffusion of a new service (about -2.0 in 1995 and declining in smooth fashion to a
2000 estimate of about –0.3). At –0.328, the mobile price elasticity is very strong and
statistically significant, as is the connection price elasticity of –0.035. Finally, density has
a much larger elasticity in this model than in the other models, indicating a stronger
network effect (both value and cost) than that of the fixed lines technology.
11
Now we review the cross elasticity effects. The mobile telephone monthly price
variable is negative and statistically significant both in the Business model and in the
Residential model. The mobile connection charge is negative and statistically significant
in both models. The residential monthly price elasticity is negative and statistically
significant and fairly large (-0.136) in the Mobile model. Business price is negative and
statistically significant when included in the model in place of residential price
(residential and business prices and connection charges are highly correlated and could
not be included in the same model). Therefore, the evidence from the ‘all countries’
models indicates that a complementary relationship exists between mobile service and
fixed main lines, contrary to findings of some other researchers (Madden and Coble-Neal,
2004; and Rodini, Ward and Woroch, 2004).
Finally, our ratio model provides some evidence that the two services are
substitutes at least in one direction. Both the coefficients of the mobile price-to-fixed
residential price and the mobile connection-to-fixed line connection variables are
negative and highly statistically significant. If mobile prices rise relative to residential
prices, the ratio of mobile subscribers per 100 mainline subscribers falls indicating that
mainlines substitute for mobile service5. However, in a separate model with absolute
instead of relative prices, the two residential price elasticities are no different than zero,
5
To more fully explore the substitution and complementary relationship, we estimate a Seemingly
Unrelated Regression model for pairs of mainline and mobile demand equations. This allows us to
calculate ‘net’ (as opposed to gross) cross-price effects by imposing a Slutsky expenditure compensation
restriction. Our findings further support our conclusion that income and price changes have a greater effect
in the mobile phone decision, whether absolute or relative price related. Basically, consumers would have
purchased more fixed-line service in response to a decline in mobile monthly prices, and less mobile service
for a given decline in residential mainline price than they actually did. The effect is slightly more
pronounced for developing nations. However, in no case is a net substitution effect found.
12
indicating that a rise in the price of mainlines has no effect on the ratio. The two absolute
mobile price elasticities in this model are negative and statistically significant.
Our same models estimated with the developing country sample give somewhat
different results in some models for some variables.
In the Business model the
developing countries elasticity estimates for monthly price is lower and the connection
charge is no different than zero. The income elasticity is smaller; however density shows
a larger impact as does less regulation. As indicated above, the Internet variable is
positive, large and statistically significant in the developing nation’s Business model,
indicating an added value of this service to fixed lines.
In the Residential model, monthly price is no longer statistically significant;
density has a much greater impact while regulation remains roughly unchanged. In the
Mobile model, the income elasticity is a bit higher and the density estimate somewhat
lower. Own-price elasticities are only slightly higher, as are the cross-price effects. In
the Ratio model, density and the mobile to fixed line price ratio are not statistically
significant. In general, it would appear that for developing nations, density effects play a
more important role than prices or income in fixed line demand models, whereas mobile
demand is more strongly related to income and price effects.
We further explore what impact the stage of economic development of a country
has on telecommunication demand by constructing a model that has price terms interacted
with a binary variable representing roughly the lower income one third of our sample
(1995 constant dollar GDP per capita of $1000 or less in 2000; a dummy variable with a
1995 constant dollar GDP per capita split at $500 or less in 2000 is not statistically
significant in the ‘all countries’ models). In essence, price effects are decomposed into an
13
‘average-country’ effect and a ‘low-income’ country effect. The results add support to
our finding that connection charges are more important in low-income countries, both for
own-service impacts and for cross-service prices, indicating a stronger complementary
effect for this group. For the average country prices, there is more of an indication of a
substitute relationship between mobile phone service and residential and business
mainline service. The ‘average country’ mobile monthly price is positive and significant
in the residential service model, whereas the residential monthly price is zero in the
mobile model. One implication of this finding is that substitution of service is more of a
choice in higher income countries, and the call-externality effect is the more dominant
force in lower income countries.
5. Policy Conclusions
Given our results for the developing countries model for residential customers,
universal service that seeks to put more of the poor on the network will not be successful
with untargeted residential monthly price subsidies, since our price elasticity cannot be
rejected as different than zero. A subsidy for connection has a chance of success with a
connection elasticity of –0.052, but this result does not suggest the kind of impact that
might have been expected in the face of very high connection charges in some countries
(Clarke and Wallsten, 2002). The fact that there is no statistical difference in results for
very low income countries (countries with 1995 constant dollar GDP of less than $500 in
2000) and the ‘all countries’ results in the Residential model is additional evidence of a
limited impact via a connection subsidy.
14
Mobile service may be a more promising universal service possibility based on
our results. With an elasticity of –0.346 for monthly mobile service, it may be possible to
subsidize mobile service and have a greater impact on expanding the network than
through a residential fixed-line subsidy. If mobile service is more cost effective to
implement than a mainline network this may be the route to consider and, obviously, any
main line subsidies should be reevaluated. However, the large density elasticity estimate
indicates that mobile telephones may not be as effective in meeting the
telecommunication needs in rural areas currently, a significant dimension in many
developing nations.
Further, our findings indicate for 1995-2001 data, that there may not yet be a
strong desire worldwide to substitute mobile for fixed-line telephone service. However,
other indicators in our results, and those of others, along with strong indications of
continued growth and value of mobile communications, suggest that public policy may
best focus on a mobile technology solution to developing nations’ telecommunications
needs.
Universal service subsidies can be very expensive in a developed country as our
estimates have shown for the United States. Untargeted subsidies are grossly expensive
because everyone receives the subsidy but only a small group requires the subsidy to
induce choosing and maintaining connection to the network. Targeted subsidies in a
developed country are more efficient but still very costly because the poor are mainly
already on the network and targeting those who would not otherwise choose to connect is
more difficult than imagined (about $2127 per year in 1999 dollars, to add a poor
household in 2000 in the United States). The Lifeline option in the United Kingdom has
15
had implementation problems as well (Wellenius, 2000).
The same exercise for
developing countries may yield more modest costs per new subscriber, especially if the
poor can be effectively and efficiently targeted. Our work on targeted programs in the
U.S. makes us cautious about the efficacy of targeted programs in poor countries where
income and demographic data are scarce or less reliable6. In any event, the U.S. case may
not be strictly applicable to developing countries.
How will the subsidy be funded?
If the tax is applied to business and/or
residential telephone subscribers, where the elasticities are smaller, the loss of network
participation will be minimized. The range of funding schemes is only limited by the
imaginations of policy-makers and the acceptance of governing bodies7.
But still there are the daunting low incomes in many countries that draw our
attention to the very large income, schooling and density elasticities. In the end, markets,
advances in education and income growth may be the best promoters of universal service.
6
Wellenius (2000) and Feldmann (2003) both discuss recent efforts to implement targeted programs via
prepaid mobile service and other approaches.
7
Kaserman, Mayo and Flynn (1990), Wolak (1996), Parsons (1998), Wellinius (2000), Onwumechili
(2001) and Research Center for Regulation and Competition (2002) discuss the possibilities for funding and
design of universal service programs.
16
Table 1
Number Of Mobile Service Subscribers in Selected Countries: 1995-2002
Country
China
1995
1996
1997
1998
1999
2000
2001
2002
3,629,000
6,853,000
13,233,000
23,863,000
43,296,000
85,260,000
144,820,000
206,620,000
33,785,660
44,042,992
55,312,292
69,209,320
86,047,000
109,478,032
128,374,512
140,766,848
Germany
3,725,000
5,512,000
8,276,000
13,913,000
23,446,000
48,202,000
56,245,000
60,043,000
United Kingdom
5,735,785
7,248,355
8,841,000
14,878,000
27,185,000
43,452,000
46,283,000
49,677,000
France
1,302,496
2,462,700
5,817,300
11,210,100
21,433,500
29,052,360
35,922,272
38,585,300
South Korea
1,641,293
3,180,989
6,878,786
14,018,612
23,442,724
26,816,398
29,045,596
32,342,000
Mexico
688,513
1,021,900
1,740,814
3,349,475
7,731,635
14,077,880
21,757,560
25,928,264
Turkey
437,130
806,339
1,609,809
3,506,127
8,121,517
16,133,405
19,572,896
23,374,364
South Africa
535,000
953,000
1,836,000
3,337,000
5,188,000
8,339,000
10,789,000
13,814,035
76,680
327,967
881,839
1,195,400
1,884,311
3,577,095
6,431,520
12,687,637
539,000
1,016,000
1,717,000
3,351,000
6,745,460
10,755,000
12,352,000
12,060,000
2,589,780
3,497,779
4,265,778
5,365,459
6,911,038
8,726,636
10,861,563
11,849,020
210,643
562,517
916,173
1,065,820
2,220,969
3,669,327
6,520,947
11,700,000
48,900
200,315
526,339
965,476
1,944,553
4,346,009
6,947,151
8,610,177
Portugal
340,845
663,651
1,506,958
3,074,633
4,671,458
6,664,951
7,977,537
8,528,900
Sweden
2,008,000
2,492,000
3,169,000
4,109,000
5,165,000
6,372,300
7,177,000
7,949,000
798,373
1,361,861
2,229,862
3,174,369
4,275,048
5,447,346
5,776,360
6,395,725
1,039,126
1,502,003
2,162,574
2,845,985
3,273,433
3,728,625
4,175,587
4,516,772
365,000
492,800
566,200
790,000
1,395,000
1,542,000
2,288,000
2,449,000
15,807
32,860
84,240
231,520
435,611
820,810
1,150,000
1,667,018
Kenya
2,279
2,826
6,767
10,756
23,757
127,404
600,000
1,325,222
Bolivia
7,229
33,400
118,433
239,272
420,344
582,620
779,917
872,676
Ghana
6,200
12,766
21,866
41,753
70,026
130,045
193,773
449,435
United States
India
Netherlands
Canada
Indonesia
Czech Republic
Hong Kong
Finland
New Zealand
Paraguay
17
Table 2
Mobile Subscribers per 100 Fixed Main Lines in Selected Countries: 1995-2002
Country
1995
1996
1997
1998
1999
2000
2001
2002
Paraguay
9.5
18.6
38.6
88.8
162.5
290.1
398.2
610.1
Kenya
0.9
1.1
2.5
3.7
7.8
39.6
183.9
403.9
13.4
22.4
39.5
65.7
94.5
168.1
219.1
285.2
Czech Republic
2.0
7.1
16.0
25.8
51.1
112.3
179.9
234.3
Portugal
9.4
17.4
37.7
74.7
110.4
154.5
182.0
195.9
Mexico
7.8
11.6
18.8
33.7
70.8
114.2
158.0
173.5
Hong Kong
24.4
39.5
61.2
85.1
110.5
138.8
148.2
166.9
Finland
37.0
52.9
75.6
100.2
114.8
130.9
148.8
165.7
Ghana
9.8
16.4
20.7
31.3
44.2
54.8
80.0
163.8
Bolivia
2.9
9.6
30.8
52.9
83.6
114.1
148.7
154.7
Indonesia
6.4
13.4
18.4
19.1
36.5
55.1
90.3
151.0
19.5
23.6
27.7
45.3
79.9
123.3
129.8
142.3
South Korea
8.8
16.2
33.7
69.8
114.3
122.3
127.8
139.1
New Zealand
21.2
28.5
31.9
43.7
76.1
84.2
125.5
138.8
Turkey
3.3
5.6
10.2
20.7
45.0
87.7
103.5
123.6
Sweden
33.4
41.3
50.7
64.3
79.2
94.7
106.8
120.8
Netherlands
6.6
12.1
19.4
35.9
70.2
108.8
123.5
120.6
France
4.0
7.5
17.3
32.9
63.2
85.5
105.6
113.7
Germany
8.9
12.5
18.3
29.9
48.6
96.0
107.6
111.8
China
8.9
12.5
18.8
27.3
39.8
58.9
80.3
96.4
United States
21.2
26.7
32.1
38.5
46.9
58.5
67.2
75.6
Canada
14.7
19.5
22.9
27.8
34.5
42.9
53.4
59.4
0.6
2.3
5.0
5.5
7.1
11.0
16.7
30.6
South Africa
United Kingdom
India
18
Table 3
Countries with Data Used in the Empirical Research
(Income is measured as GDP Per Capita in constant 1995 US Dollar as of 2000)
Country
Ghana
Zambia
Mozambique
Zimbabwe
Ecuador
Turkey
Uganda
Togo
Kenya
Mali
Benin
Pakistan
Gambia
Sudan
Indonesia
Armenia
Senegal
Honduras
Sri Lanka
Guyana
Costa Rica
Philippines
Venezuela
Swaziland
Paraguay
Colombia
Morocco
Guatemala
Egypt
Income
$77
$88
$119
$139
$163
$194
$203
$224
$224
$238
$295
$302
$303
$315
$325
$343
$427
$452
$573
$625
$633
$694
$858
$908
$922
$939
$1,058
$1,165
$1,244
Country
Namibia
Algeria
Jordan
Thailand
Dominican Rep.
Tunisia
Fiji
Jamaica
Botswana
Latvia
South Africa
Estonia
Poland
Hungary
Slovak Republic
Mexico
Brazil
Mauritius
Belize
Uruguay
Malaysia
Czech Republic
Trinidad & Tobago
Slovenia
Malta
Greece
South Korea
Barbados
Portugal
19
Income
$1,275
$1,315
$1,443
$1,640
$1,668
$1,738
$1,749
$1,781
$1,993
$2,097
$2,121
$2,211
$2,238
$2,313
$2,443
$2,447
$2,472
$2,817
$3,038
$3,191
$3,316
$3,610
$4,909
$6,206
$8,025
$8,230
$8,261
$8,552
$9,157
Country
Cyprus
Spain
Bahrain
New Zealand
Taiwan
Kuwait
Italy
Australia
Belgium
Netherlands
France
United Kingdom
Germany
Canada
Finland
Austria
Ireland
Singapore
Hong Kong
Iceland
Sweden
Denmark
United States
Switzerland
Norway
Japan
Luxembourg
Income
$11,288
$12,182
$12,478
$12,761
$12,883
$14,791
$16,575
$17,948
$20,373
$20,743
$20,820
$21,095
$21,169
$21,518
$21,617
$21,820
$22,109
$22,128
$23,177
$25,926
$26,390
$26,527
$31,046
$32,204
$32,994
$36,989
$41,246
Table 4
Variable Definitions*
MOBFIX
Mobile subscribers per 100 main lines. ITU.
BPEN
Business main lines per 100 population. ITU.
RPEN
Residential main lines per 100 population. ITU.
MPEN
Mobile telephone subscribers per 100 population. ITU.
INCOME
Gross Domestic Product per capita in constant dollars. ITU.
BPRICE
Monthly charge for business telephone service in constant dollars. ITU
RPRICE
Monthly charge for residential service in constant dollars. ITU
MPRICE
Monthly charge for mobile telephone service in constant dollars. ITU.
BCON
Connection charge for business telephone service in constant dollars. ITU
RCON
Connection charge for residential service in constant dollars. ITU
MCON
Connection charge for mobile telephone service in constant dollars. ITU
PRATIO
Ratio of the monthly mobile charge to the residential monthly charge. ITU
CRATIO
Ratio of the mobile connection charge to the residential connection charge. ITU
DENSITY
Percentage of population living in urban areas. World Bank
INTERNET
Internet users per 100 population. ITU.
SCHOOL
Years of school for the population 25 years and older. Barro & Lee, Harvard.
REG
Index of Economic Freedom – Regulation Component. Heritage Foundation.
* Income and price variables adjusted to constant 1995 dollars using the World Bank
Consumer Price Index.
20
Table 5
Summary Statistics
Variable
Mean
Std. Dev.
Minimum
Maximum
Observations
BPEN
7.02
6.87
0.0332
27.6124
648
RPEN
18.20
15.83
0.0646
56.86
648
MOBPEN
14.12
20.93
0.0010
96.55
714
MOBFIX
53.03
65.51
0.0271
667.16
714
INCOME
8,214.48
10,502.70
0.0669
44,581.20
703
6.05
5.19
0.0036
30.00
691
94.25
127.18
0.0010
1,214.82
684
9.22
8.79
0.0024
69.09
686
119.03
145.65
0.0010
1,214.82
683
MPRICE
18.64
14.66
0.0072
115.27
602
MCON
69.02
104.67
0.0010
1,094.08
576
PRATIO
6.13
9.54
0.2334
83.33
597
CRATIO
126.84
1,729.29
0.0000
26,092.60
570
DENSITY
0.59
0.23
0.1152
1.00
707
INTERNET
6.81
11.37
0.0001
60.00
701
SCHOOL
6.69
2.85
0.7000
12.50
623
REG
2.91
0.87
1.0000
5.00
695
RPRICE
RCON
BPRICE
BCON
Note: Observations vary as a result of missing data for some years for some countries. Values
reported above do not necessarily represent the values used in regressions since incomplete
observations are not used. Therefore, observations vary from model to model.
21
Table 6
Estimated Elasticities
All Countries and Developing Countries Models*
(Standard Errors in Parentheses)
Variables
Business
All
Dev
INCOME
0.493***
(0.038)
0.287***
(0.065)
BPRICE
-0.127***
(0.031)
-0.067*
(0.041)
RPRICE
MPRICE
-0.051**
(0.026)
-0.017
(0.035)
BCON
-0.037***
(0.013)
-0.014
(0.023)
RCON
Residential
All
Dev
Mobile
Mobfix
Dev
All
Dev
All
0.151***
(0.060)
0.221**
(0.095)
1.060***
(0.312)
0.695*
(0.391)
0.113***
(0.032)
0.077*
(0.041)
0.203***
(0.042)
0.268***
(0.068)
0.703***
(0.095)
0.905***
(0.143)
-0.056*
(0.032)
-0.037
(0.040)
-0.136**
(0.072)
-0.164**
(0.080)
-0.055**
(0.028)
- 0.061*
(0.038)
-0.328***
(0.057)
-0.346***
(0.069)
-0.040***
(0.012)
-0.052**
(0.027)
0.007
(0.033)
0.074
(0.055)
MCON
-0.011***
(0.003)
-0.010**
(0.005)
-0.010***
(0.004)
-0.010*
(0.006)
-0.035***
(0.009)
-0.040***
(0.012)
DENSITY
0.438***
(0.154)
0.769***
(0.236)
0.564***
(0.173)
0.934***
(0.261)
1.739***
(0.344)
1.449***
(0.400)
INTERNET
-0.629***
(0.164)
1.103*
(0.678)
0.064***
(0.015)
0.048**
(0.021)
SCHOOL
0.744***
(0. 094)
0.614***
(0. 130)
1.414***
(0.106)
1.186***
(0.144)
0. 399*
(0.234)
0.157
(0.277)
-0.720***
(0.216)
-0.669***
(0.265)
REG
0.225***
(0.073)
0.359***
(0.114)
0.325***
(0.082)
0.217*
(0.127)
0.094
(0.196)
-0.049
(0.274)
-0.417***
(0.167)
-0. 559***
(0.227)
PRATIO
-0.120***
(0.047)
-0.088
(0.059)
CRATIO
-0.020***
(0.009)
-0.024**
(0.012)
465
0.903
288
0.903
Observations
430
265
432
266
468
291
R Sq.(Adj.)
0.987
0.970
0.986
0.973
0.969
0.960
Note: *** indicates statistical significance at the 1% level; ** at the 5% level; and * at the 10% level.
22
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