20030418Staff 2nd Resp - attach I - 2 Main Lines Mobile Phones .pdf

Telecommunications Policy 27 (2003) 109–133
Are main lines and mobile phones substitutes or complements?
Evidence from Africa
Jacqueline Hamilton*
Assistant Professor, Department of Economics, University of Pittsburgh at Greensburg, Faculty office Building 214,
1150 Mt. Pleasant Road, Greensburg, PA 15601, USA
Abstract
Many developed and developing countries have approached telecommunications reform by opening the
market for mobile telephones to private participation and competition. One result of this strategy is the
astounding worldwide growth in mobile communications, with developing countries accounting for the
fastest growth. It is believed that mobile phones are complementary to fixed-line telephony in developed
countries, but they appear to be substitutes for main lines in developing regions where access is low or non-
existent. This paper examines the relationship between mobile and main-line telephones by accounting for
reverse causality between them in an environment with low levels of development. The results suggest that
mobile telephones act as a competitive force encouraging fixed-line providers to improve access. It is
possible that mobile and main lines are sometimes substitutes, and at other times complements in
consumption, even where fixed-line access is low.
r 2003 Elsevier Science Ltd. All rights reserved.
Keywords:Africa; Main lines; Mobile; Substitute; Complement
1. Introduction
Mobile telephone subscriptions have been growing rapidly since the 1980s in both developing
and developed regions. Subscriptions to fixed telephones have also grown, but at a slower rate
than cellular in many regions of the world (ITU, 1999). For instance, by 1997, mobile
subscriptions in Lebanon accounted for 76% of total telephone subscriptions (World
Development Indicators, 2000). If cellular continues to grow rapidly, it is likely that its
subscription will surpass fixed-line access in the near future. Currently, developing countries are
experiencing the highest levels of mobile growth (ITU, 1999).
*Corresponding author.
E-mail address:[e-mail] (J. Hamilton).
0308-5961/03/$-see front matter r 2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S0308-5961(02)00089-7
The increasing use of mobile telephony has implications for main-line access in developed
countries and in regions where access to traditional wire-line telephones is relatively low. The
growth of mobile subscription may reflect its role as a substitute for main lines. However, it is not
uncommon for calls to be connected between a fixed line and a mobile telephone, so the services
may in fact be complementary to each other.
Despite the growing importance of mobile telephony, very little is empirically established
regarding its position vis-"a-vis fixed-line telephony as stimulus to connectivity. Since the fastest
growth in mobile is occurring in developing countries, this study examines its role in fixed-line
development in Africa, illustrating the impact of mobile provision in many developing regions. As
one of the emerging markets, Africa shows high growth and increasing competitiveness in mobile
communications. It is also a region with very low access to fixed-line telephones. During the
1980s, cellular provision was practically non-existent in Africa. Today, virtually all countries in
the region have access cellular service, and many have at least two operators, one of which is
usually privately owned. These characteristics are evident in many developing countries.
Typically, cellular usage and main-line access are both growing rapidly. In many cases, regions
with rapid growth in one service also experience rapid growth in the other (compared to regions
with slower growth). Regardless of the pace of growth, or level of telephone access, mobile usually
grows faster than main-line access. For instance, South Africa enjoys growth in main-line access
of about 3.9 per 1000, per year, but enjoys growth of 4.5 per 1000, per year in mobile access. The
trend is similar in cases where access to main-telephone lines tend to be relatively low. In
Tanzania, main-line access grew by less than 0.1 per 1000 people, per year, while mobile
subscriptions increased by 0.3 per 1000 each year. Looking at South Africa and Tanzania
together, suggest that individuals may be using mobile, not necessarily as a substitute for fixed
lines, but for other reasons such as joint use. In other words, as individuals become more
sophisticated in their use of telephones, they tend to behave more like users in developed
countries.
While mobile growth in Tanzania may appear to be negligible, in actuality it is substantial,
given that mobile usage in that country started only in 1995. By 2001, mobile subscriptions in
Tanzania outweighed that of main-line access. This story is also true for Morocco, which
enjoys relatively high main-line access. Between 1985 and 1997 main-line access grew by an
average of 2.9 per 1000 individuals per year, while mobile access increased by only 0.27 per 1000
individuals per year. By 2001, however, access to mobile telephones surpassed that of main lines,
at least in urban areas. This occurred as a result of the introduction of privatization and
competition in the mobile sector. Mobile access, therefore, appears to be growing rapidly across
the continent of Africa, regardless of the stage of main-line development. For this reason, it is
difficult to determine what the relation is between the two services, by just comparing access
trends.
Today, virtually all countries in the region have access to cellular service, and many have at
least two operators, one of which is usually privately owned. This is happening as a first step
towards telecom sector privatization, since most countries, although allowing private participa-
tion in cellular services, still support state-owned main-line provision. In these markets, the only
potential threat to fixed line provision is competition from cellular. The question of the role of
mobile is thus interesting in the case where the mobile provider is privatized and not owned by the
incumbent. Real competition can only occur in a situation like this, because if the incumbent is
J. Hamilton / Telecommunications Policy 27 (2003) 109–133110
also the sole provider of mobile services, then the potential for competition is diminished to the
extent that the incumbent controls the mobile/main-line trade off from the supply side. In this
scenario, the relation in consumption would be difficult to determine. Even in cases where mobile
provision is privatized and competition exist, conceptually it remains unclear whether mobile is a
substitute or a complement for main lines in consumption.
A popular argument for the view that mobile usage is substituting for fixed lines is its
prevalence where access to main lines is low or unreliable.1 For instance, cellular phones may be
an attractive alternative where it is difficult to install fixed-line networks. Because mobile
networks can be installed more rapidly than fixed networks, they can alleviate waiting time for
potential subscribers (Minges, 1999) and reduce unsatisfied demand.
The use of pre-paid cards by mobile users also supports the view that mobile is a substitute for
main lines. With the use of pre-paid cards, users who otherwise would not qualify for a phone can
now access the service. This is especially important for users in developing regions where it is not
uncommon for people to lack credit histories. For individuals with poor credit histories, the
option to pre-pay is an important development, since people are not automatically disqualified
from using the service because of bad credit. The pre-paid alternative also provides the
opportunity for individuals to manage their telephone expenses, since the number of calls that can
be made is restricted. The result is that more people than ever now have access to mobile
telephones. The increased supply of cellular service allows an attractive alternative to fixed-line
telephony.
Just like users in developed countries, some people in developing areas are attracted to mobile
not because there are no alternatives, but because of the convenience of mobile phones (Frempong
& Atubra, 2001); furthermore, mobile phones are often used in conjunction with fixed-line
telephones. Fixed lines are usually used at home, while mobile phones are used to keep in touch
with home or office when individuals are on the road. In fact, calls from cellular phones are
commonly made to fixed lines as opposed to another cellular phone (Jha & Majumdar, 1999). In
such instances, mobile and fixed lines are used in a complementary fashion. A larger fixed-line
penetration increases the value of mobile service.
The high cost of cellular service relative to the nominal price of main lines can make cellular
phones unlikely to substitute for main lines.2 Even though the cost of cellular has been falling, it
remains high relative to the cost of fixed telephones. In regions where income is low, cellular
phones may be out of reach for most consumers. In cases like this, mobile subscriptions may be
confined to the wealthy, a relatively small group within a country. The preceding suggests that the
role of mobile is ambiguous, at least at a conceptual level. Thus, the question of substitutability
versus complementarity has to be solved empirically.
1According to the ITU (1999)report, mobile telephones are used in developed countries to complement existing fixed
lines but are emerging as a substitute for fixed lines in developing countries.
2The price of main-line service has not generally been a strong factor affecting its demand because the price
elasticities of demand for main lines tend to be low.Ahan and Lee (1999)found that the demand for mobile is positively
correlated with per capita GDP, although the price effects on mobile tend to be weak. Their research suggests that even
where income is low and costs high, subscribers may not respond very much to the price of mobile. If this is true, then
the high cost of mobile to the subscribers should not be a strong enough factor to seriously restrict the number of
individuals willing to become cellular subscribers. It is therefore feasible that individuals unable to access main lines
would be willing to use mobile phones whenever they are available.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 111
2. Recent empirical studies
A large body of research involving the development of telephone network has focused on the
role of institutions. These researchers found that a strong institutional framework is essential for
network development and expansion. Other studies examining the effects of new market
conditions for utilities have focused mainly on industry privatization and competition. The
general consensus is that competition and privatization tend to be associated with increased
efficiency and growth. However, privatization is not always associated with network expansion
(Table 1).
All of these studies except Wallsten (2001)3 examine the role of competition in basic services,
rather than competition between mobile and fixed-line provision.Ros (1999)included some
African countries in his data set to provide insight into the role of competition on main-line access
as well as efficiency in the telecom sectors of these countries. The present study examines
competition solely from the perspective of developing regions, using data from Africa. In
addition, it recognizes the importance of mobile as a competitive force in a context where more
competition occurs via cellular provision than via privatization of incumbent wire-line providers.
The role of competition in the telecommunications sector is seldom viewed from the perspective
of how mobile provision affects basic service. However, the astounding growth of mobile
communications makes it one of the largest forces in emerging markets. A better understanding of
whether mobile is a substitute for or a complement of basic services is important for policy makers
as they attempt to reform the telecommunications market to improve access and increase
efficiency.
3. Hypotheses
3.1. Mobile competition and main-line performance
The relation between mobile competition and access to main-line telephones is ambiguous.
Mobile and main-line correlation may be perceived as positive or negative, depending whether
they are complements or substitutes. When the two services are complements, the increased usage
of cellular phones will be associated with increased access to main lines. On the other hand, if
mobile subscriptions are substituting for fixed lines (for instance, by covering unsatisfied demand
for fixed lines), a negative relation is expected.4
3Wallsten (2001)looked at the relation between mobile and fixed-line access, but the data used have two main
drawbacks. The first is that it lumps together two dissimilar regions with different levels of growth and telephone
development in terms of main-line access. It is likely that there are larger variations across these regions, which may
affect how confidently the results are to be interpreted regarding any one region. In addition, only 15 of the 55 countries
in Africa were included in the sample, and five of those had monopoly mobile providers. These monopolies are usually
at least partially owned by the incumbent wire-line firm. A single country from Northern Africa was included, which
raises a question as to whether the sample was representative of the African region as a whole. In contrast, this study
focuses on a single region and uses a more representative sample of countries from all parts of Africa. Instead of using
the number of cellular providers to measure mobile competition, this study uses the actual level of mobile subscriptions.
4It is possible for mobile and main lines to be strategic substitutes rather than substitutes in consumption. Fixed-line
penetration in Africa may be growing because incumbent providers think that increasing access is the best response to
J. Hamilton / Telecommunications Policy 27 (2003) 109–133112
3.2. Other considerations
The relation between telephone access and other factors, such as per capita GDP, institutions
and political regime has been established in other studies.5 The general consensus is that strong
institutions and stable political systems are positively correlated with increased access to basic
telecommunications. Likewise, as individual income increases, the demand for telephones should
rise, especially in situations where individuals are poor to begin with and suffer from lack of access
because of the inability to pay. The relation between per capita GDP and telephone access may,
however, be small in situations where lack of access is attributable to insufficient supply rather
than low demand.
4. Background
4.1. Competition policies
Historically competition in basic services has been non-existent in Africa. Even though there
has been some privatization in the 1990s,6 providers of basic services remain monopolies in most
of Africa. In contrast, the mobile sector in Africa opened rapidly to competition, and Africa was
one of first regions to adopt mobile service (Leblanc-Wohrer & Lewington, 2000). As Table 2
shows, a number of countries currently sustain multiple (at least two) providers (over a wide range
per capita GDP).
Both foreign and private-sector participation are becoming commonplace. Ghana, for example,
is just one of the many countries that has recently opened its market to mobile competition.
CellTel Ghana has nationwide coverage, as does Anglo Telecom. Likewise, Vodacom is well
established in southern Africa. In South Africa, cellular service has created competition in the
sector. Most African countries now have access to cellular service, at least in the major cities. The
use of mobile may indicate the development of a competitive sector and may substitute for main
wire lines where penetration (teledensity) is low. For example, Uganda has one of the lowest levels
of penetration of telephone lines in Africa, but enjoys nationwide coverage in cellular service
provided by Anglo Telecom.
One has to be cautious, however, in using the increase in cellular networks across Africa as an
indication of the growth in private-sector participation and competition. In fact, some cellular
ventures (Angola and Algeria for example) are still state-owned or monopolies. Furthermore, the
existence of cellular operators may not be a perfect measure of competition since cellular service is
(footnote continued)
the competition it faces from mobile providers. If the services are strategic substitutes then the relation between the two
will be positive. Likewise the two services may be complements in consumption or strategic complements. In the first
instance individuals get connected because of an increase in the value of being connected as more and more people have
access to fixed lines. It is important to be able to distinguish the different types of substitutes and complements for the
purposes of proper interpretation of the relationship between mobile and main lines.
5See Henisz (1998),Gutierrez (1999),Hamilton (2001), and Singh (2000).
6By 1997, Cote d’Ivoire, Gabon, Ghana, Madagascar, South Africa and Tanzania had partially privatized their
communications, but most of these were still largely state-owned. See Hamilton (2001).
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 113
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J. Hamilton / Telecommunications Policy 27 (2003) 109–133 115
often available only to the wealthy,7 who represent only a small portion of African economies.
The price of the service may limit its ability to act as a strong competitive force where incomes are
low. Nevertheless, in villages call aggregators can play a role in providing access (if not ownership)
for low-income demanders.
4.2. Mobile versus fixed lines
As cellular networks continue to emerge, both fixed-line and wireless services have been
growing at a rapid pace in Africa.Table 3 shows the relative size of a country’s mobile and main-
line markets using the 23 countries in our sample.
Between 1987 and 1997, access to fixed lines almost doubled, growing from 11.29 per 1000
inhabitants to 22.341 per 1000. In 1987, South Africa, Tunisia and Algeria accounted for more
Table 2
The level of mobile competition in Africa by 1997
Country Legally
permissible
competition
Country Legally
permissible
competition
Country Legally
permissible
competition
Algeria M Ethiopia M Niger C
Angola M Gabon M Nigeria C
Benin M Gambia M Rwanda M
Botswana The Ghana C Sao Tome
Burkina Faso M Guinea C Senegal C
Burundi C Guinea Bissau Seychelles
Cameroon M Kenya M Sierra Leone C
Cape Verde C Lesotho M Somalia
C. African Rep. C Liberia South Africa C
Chad D Libya M Sudan
Comoros Madagascar C Swaziland M
D.R. of Congo C Malawi M Tanzania C
Congo M Mali M Togo D
Cote d’Ivoire C Mauritania M Tunisia M
Djibouti D Mauritius M Uganda D
Egypt D Morocco D Zambia C
E. Guinea M Mozambique M Zimbabwe C
Eritrea C Namibia M
M-monopoly, D-duopoly, C-more than two providers.
Source:Leblanc-Wohrer, and Lewington (2000)and Minges (1999).
7Main-line access and mobile subscriptions tend to be significantly higher in middle-income countries compared to
low-income countries in Africa. (Table 5 in the data analysis section gives a comparison of telephone subscriptions in
middle- and low-income countries in Africa). However, over time, as the price of cellular provision falls, this becomes
less true. Uganda and Cote d’Ivoire are examples of countries with per capita income below $1000 and with at least two
cellular operators. Cellular operators can be potential threats to incumbent firms, since they can increase penetration at
relatively low cost per additional subscriber. The threat of competition may be enough to give the incumbent the
incentive to improve service. The potential threat is enough to provide the impetus for telecom growth.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133116
than 50% of all fixed-line telephones in the sample. Ownership patterns had shifted little by 1997,
so that South Africa, Tunisia, Botswana and Egypt accounted for 56% of main-line access across
the sample. The trend shows that countries that were doing relatively well in early years continue
to lead the way in the late 1990s. Some of the same countries appear to be leaders in the mobile
market. Over the same period, (1987–1997), mobile subscription grew from close to 0 per 1000
people to 22.34 per 1000 people. South Africa, Tunisia, Morocco and Gabon are among the
countries that have the highest subscription rates. Gabon moved from having zero mobile
subscriptions in 1987 to having the second highest subscription level of 8.35 per 1000 individuals.
South Africa, by far, has had the most success in increasing access to mobile phones. By 1997
access to mobile phones in South Africa was 36 per 1000, which accounted for 63% of all cellular
subscription in Africa. This rapid rate of growth in cellular subscriptions may be attributed to the
influx of new mobile providers, both foreign and local.
While mobile subscriptions still lag behind fixed-line access, the gap between the two has been
closing over time. The ratio of new mobile subscriptions to new main-line access is increasing over
time to just under 40% in 1997.
5. The data
Annual data used in the analysis represent 23 African countries, 1985–1997.8 The time period
and number of countries used was determined by constraints on the data available for some of the
variables. The role of mobile competition in determining fixed-network development is assessed
by using main line per 1000 inhabitants as the dependent variable. The variables used in this
analysis are shown in Table 4.
5.1. Descriptive results
Table 5 summarizes access to telephones in based on regional differences and income. The data
indicate North Africa having lower access to both main lines and mobile than sub-Saharan
Africa. The higher level of mobile subscription in sub-Saharan Africa is primarily due to access in
Table 3
Relation between mobile and main lines
1987 1992 1997
Total main-line penetration (per 1000) 11.29 15.702 22.341
Total mobile subscription (per 1000) 0.004 0.056 2.565
Ratio of mobile to main line 0.00033 0.00359 0.111
Percent of new mobile to main line 1.100 37.800
Mobile as a percent of total subscription 0.033 0.358 10.285
8The countries included in the sample are Algeria, Botswana, Cameroon, Republic of Congo, Cote d’Ivoire, Egypt,
Gabon, Ghana, Kenya, Madagascar, Malawi, Mali, Morocco, Niger, Nigeria, Sierra Leone, South Africa, Tanzania,
Togo, Tunisia, Uganda, Zambia and Zimbabwe.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 117
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J. Hamilton / Telecommunications Policy 27 (2003) 109–133118
South Africa (36.95 in 1997), compared to the highest rate in North Africa of 2.7 in Tunisia in the
same year. South Africa is also the reason for the high fixed-line access in sub-Saharan Africa.
Once South Africa is excluded, mobile access in sub-Saharan Africa falls, but remain above that
of North Africa. Although the difference seems negligible, the pattern suggests that countries with
low access to fixed-line telephones tend to have relatively high access to cellular phones. This
result concurs with the view that mobile is satisfying unmet demand for main lines (when we
exclude South Africa and compare North Africa with sub-Saharan Africa).
Table 5 also shows that main-line access is about nine times higher in middle-income countries
compared to low income ones. When South Africa is excluded from the sample, mobile
subscription falls by almost one third, but access in middle-income countries remain higher than
that of low-income countries. This pattern suggests that the ability to pay for mobile may be an
important consideration even while cellular costs are falling. One explanation for this may be that
providers incorrectly perceive that lower incomes reduce the willingness of individuals to pay for
the service. In other words, they may view this as a factor that limits market size. Such providers
would therefore target higher income markets. Lower access in this case would be a supply side
problem.
Finally,Table 6 provides summary statistics based on regional differences and the level of
mobile competition within a country by 1997. As expected, countries that allow mobile
competition invest more in mobile communications. Interestingly, these same countries, on
average, tend to invest less in main lines. According to the data, access to cellular phones in
Table 5
Regional differences, income and telephone access 1985–1997
North Africa Sub-Saharan Africa Sub-Sahara without
South Africa
Middle income 34.63 45.794 25.420
(0.234) (2.685) (0.852)
Low income — 4.580 4.580
(0.068) (0.068)
The first number in each cell represents average main-line penetration. The numbers in parentheses are mobile
subscription per thousand individuals.
Table 6
Main-line penetration, income and mobile provision
Market structure for mobile
Monopoly Competition
Middle income 32.50 87.069
(0.528) (6.354)
Low income 3.349 6.00
(0.021) (0.062)
All income 21.569 10.736
(0.338) (0.527)
The first number in each cell represents average main-line penetration. The numbers in parentheses are mobile
subscription per thousand individuals.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 119
countries that allow mobile competition is approximately two times that of countries that have
monopolies in mobile service. At the same time mobile monopoly countries on average invest in
main lines by just under one and one half times more than countries that allow mobile
competition. One implication of this is that mobile competition allows cellular providers to be
better able to pick up the slack where main lines are under-supplied.
The difference between mobile subscriptions in low-income countries is miniscule when
compared to that of middle income ones. This result suggests that the ability of customers to pay
appears to be an important factor in determining mobile access. Competition can only matter to
the extent that a market for the service exists. That is, the expectation that competition will serve
to increase telephone access can only result if individuals are willing and able to pay for the
service. The very low incomes that exist in some countries in Africa may act a deterrent in this
case.
6. The empirical model
6.1. Estimation
In this section, we use panel data estimation techniques to analyze the impact of MOBILE
competition on MAINLINE access. The model is estimated using pooled data from 1985 to 1997.
We begin our data set in 1985 because the mid-1980s marked the take off of rapid telecom reform
in Africa as well a the introduction of cellular phones
Before estimating the panel data, some cross-sectional analyses are conducted to compare the
attributes of the explanatory variables at different points in time. The impact of MOBILE on
MAINLINE development is assessed for a cross-section of countries in 1987,9 1993 and at the end
of the sample period in 1997. The following equation was estimated for each of the 3 years:
MAINLINE ¼a þ bKX þ e;ð1Þ
where X is a vector of independent variables, containing LOGGDPC, LTRADE, URBAN,
CORRUP, LAW, BUREAU, CONTRACT, EXPROP, GOVOP, DISCTAX, DEMOC and
MOBILE.
DMAINLINE ¼a þ bK DX þ e;ð2Þ
where D indicates that the variables are expressed as their 1997 values minus those of 1987.
Eq. (3) is estimated to account for cross-country variation using the panel data approach.
MAINLINECT ¼aCT þ bKXCT þ dCT þ eCT;ð3Þ
where XCT is the same vector of independent variables as in Eq. (1) for country C in year T:
aCT are country dummies that account for unobserved differences across countries that may
affect the dependent variable.dCT captures year effects that take care of possible variations of the
omitted variables through time. Eq. (3) is estimated by expressing the variables as deviations from
their group means. This approach picks up any constant differences on a country-specific level
91987 as opposed to 1985 was used for the first cross-sectional estimation because there were not enough
observations of MOBILE in 1985 to generate meaningful results.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133120
through time. The model is estimated using fixed effects.10 A parsimonious specification in which
mobile subscription per 1000 individuals is the only right-hand side variable is first estimated in
order to establish the relation between mobile and main-line assess, and between mobile and
main-line demand in isolation of other independent variables. The standard errors of all
estimations are adjusted for cross-observation error dependence using the Huber/White variance
estimator.
6.2. Sample selectivity
Only 23 out of 55 countries in Africa are included in the sample used in this analysis. This raises
the concern that the results of the study may not be generalized to include the excluded countries.
If the excluded countries are different from the group that is included for some reason, then the
results from this analysis would be biased if we extend the results to all of Africa. In other words,
the regressions would not be capturing the effects of the excluded countries. Drawing general
conclusions that encompasses all of Africa would therefore be inaccurate. In order to check this
possibility, we compare the excluded countries in cross-section with those included in the analysis
along observable lines. Per capita GDP, main line and mobile access, trade and urban percentage
are used as the basis for comparison.Table 11 in the appendix compares the overall means of
these variables with and without South Africa as well as with and without Seychelles and
Mauritius.11 Table 11 shows that at the mean, the included countries tend to be similar to those
excluded. For instance, the biggest difference between main-line access is only about 2 per 1000
individuals. The difference in mobile access is negligible at less than 0.1 per 1000. Average per
capita GDP tends to be slightly higher in the included countries, but both groups tend to have an
average income in the lower income category.
To further check the possibility of selectivity bias reduced fixed effects regressions (using the
same observable variables) were conducted for both groups of countries.Table 12 in the appendix
shows the results. Apart from the trade variable, which is insignificant for the countries excluded,
the regression results were quantitatively similar. Finally, the reduced regression was conducted
using all the countries in Africa. Again, the results were quantitatively similar (in terms of sign and
significance), to the results using just the 23 countries in our sample. This informal sensitivity
analysis (by itself), suggest that selectivity may not be an important problem, at least qualitatively.
6.3. Endogeneity
MOBILE captures the competition effect in markets where direct telephony is a monopoly.
Competition is, however, a result of regulatory considerations, and monopoly firms often have
control over the regulatory regimes in Africa through the ministry. Often there is no separation of
regulatory functions and operation because both may be operating under the same ministry.
Mobile and direct lines are therefore jointly determined. If MOBILE is endogenously determined,
10The fixed effect model assumes that the counties in our sample, each have characteristics that are unique and do not
change over time. These differences are captured in differences in the constant term.
11South Africa, Seychelles and Mauritius have significantly higher access to main lines than the rest of Africa (107,
203, and 195, respectively, in 1997). These countries also tend to have higher income levels than the rest of Africa.
Except for Botswana, they are the only other upper-middle-income countries in Africa.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 121
it will be correlated with the error term. Under this condition, ordinary least-squares (OLS)
estimations will tend to attribute changes in the dependent variable (MAINLINE) caused by the
error term to MOBILE. As a result, the coefficient on MOBILE will be biased upward or
downward, depending on the sign of the correlation between MOBILE and the error term. In
addition, if MOBILE and MAINLINE are jointly determined, MOBILE cannot be considered
fixed in repeated samplings and there is a potential bias in all the estimated coefficients. Consider
the coefficient of URBAN (b3) for instance.Note that b3 is supposed to be the estimated effect of
URBAN on MAINLINE, holding MOBILE and all other right-hand side variables constant.
MOBILE is, however, not held constant when changes in MAINLINE takes place. Therefore,b3
may actually measure some mix of the effects of both URBAN and MOBILE. Because of the
potential problems that may result when OLS is used, it is vital to consider alternative estimation
techniques to reduce the simultaneity bias.
We use the instrumental variable (IV) technique to reduce the potential biases in the estimation
of Eq. (3). To use this technique, we replace MOBILE with a new variable (instrumental variable),
which should be highly correlated with MOBILE but uncorrelated with the error term.
Until 1997 only six of the 23 countries in the sample had allowed some private participation in
fixed-line telephone service provision. All governments, except for Cote d’Ivoire, which sold 51%
of its shares to the private sector in 1997, had maintained majority ownership. Private ownership
of wire-line phones was therefore practically non-existent in the sample. In contrast, private
individuals are usually licensed to provide cellular service. The nature of ownership made it more
likely that private sector credit directed toward investment in telecommunications would go into
mobile communications rather than fixed-line development. Thus, when main lines are publicly
owned, credit to the private sector will not determine public investment. We identified
PRIVCRED (the ratio of private sector credit to GDP) as an instrumental variable for MOBILE
and use two-stage least squares to estimate Eq. (3).12
It may be argued that an increase in PRIVCRED implies a reduced capacity for the government
to fund telephone investment. If true, PRIVCRED would be correlated with both MAINLINE
and MOBILE. Historically, however, telecom reform is financed largely by loans from
international lending agencies as part of their mandate to aid in the improvement of infrastructure
in lesser-developed countries. Local investors in mobile communications are likely to be more
dependent on domestic credit.13
Some analysts argue that some reverse causality exists between GDP per capita and
infrastructure (telecom) investment. (Colier & Gunning, 1999). Kerf and Smith (1996) suggest
12Although PRIVCRED is defined as credit to the private sector as a fraction of GDP, it may also randomly include
some credit to the public sector as well. Measurement errors introduced by this are assumed to be captured as part of
the random error term since the inclusion of public sector credit is not systematic.
13Because only one instrument is identified, a formal test of its validity could not be conducted. Instead, we
performed a sensitivity analysis by comparing the R2’s of the restricted regression (without PRIVCRED as a regressor)
with the unrestricted regression (including PRIVCRED), using MAINLINE as the dependent variable. The R2
remained unchanged once PRIVCRED was added to the regression. Likewise, a test of first-stage correlation was
conducted with MOBILE as the dependent variable. When PRIVCRED was added as an explanatory variable, the R2
improved noticeably, by 8%. The results of these tests indicate that PRIVCRED may be used as an instrument for
MOBILE. The results are of course based on the current specification of MAINLINE. It may be possible that
PRIVCRED would not survive these tests for a different specification of main-line development.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133122
that the poor quality of Africa’s infrastructure constrains private investment in other activities
and thus is an obstacle to economic growth. That is, countries with proper infrastructure are
expected to attract investors, which in turn generate a higher per capita GDP (Madden & Savage,
1998). This potential is unlikely to be present because the region studied is comprised of nations
that are plagued with instability and represent relatively high-risk projects. An increase in
telecommunications investment will not be sufficient to attract non-infrastructure investment that
would significantly affect GDP.
6.4. The dependent variable redefined
As discussed in Section 3, it is not entirely clear whether MOBILE is a substitute or a
complement for MAINLINE. The supply of mobile phones have grown rapidly, mainly because
many governments tend to be more willing to introduce privatization and competition in the
market for cellular rather than fixed lines. While the supply of mobile has been growing, so has the
supply of fixed lines and more people are using both fixed lines and wireless phones. It is, however,
not clear how much of the new cellular subscriptions are by individuals who also have access to
fixed-line telephones.
A positive sign could indicate that the two services are complements. In this case, it may be
that the same individuals have access to both fixed lines and cellular phones. A positive sign
may, however, result even if the services are substitutes in consumption. Cellular operators
can be potential threats to incumbent firms, since they can increase penetration at a relatively
low cost per additional subscriber, and in less time. The threat of competition may be enough to
give the incumbent the initiative to improve service. It is also unusual to observe a decline in the
level of teledensity in countries where access is already low. Both MOBILE and MAINLINE
access are therefore expected to grow. It is therefore not entirely clear how to interpret a positive
coefficient on MOBILE when we estimate the model using a supply side definition of
MAINLINE.14
To check our interpretation of the relation between MOBILE and MAINLINE, we redefine the
dependent variable to be actual total access demand for wire-line telephones, rather than just
satisfied demand. The new dependent variable is DEMAND, which measures the total number of
applications for fixed-line service.15 The equation estimated is
DEMANDCT ¼aCT þ bKXCT þ dCT þ eCT;ð4Þ
14MAINLINE access per 1000 inhabitants is a measure of how many telephone lines the respective governments or
incumbent wire line firm actually make available to the public. This is usually lower than its demand. In essence main-
line access measures the portion of demand for main lines that have been met. Since there is usually a shortage, this is in
effect satisfied demand.Ros (1999)argued that the low number of main lines is a supply rather than demand-side
constraint. Because of the absence of rate rebalancing, residential access prices are likely to be below their economic
costs. An increase in supply is overwhelmed by demand, mainly because of long waiting lists as well as low price
elasticities of demand.
15This is just main-line access per thousand individuals waiting list per 1000. Waiting list captures the number of
applications for connection to a main telephone line that have been unmet. Specifically, DEMAND ¼
ðapplications=populationÞ*1000;where applications is unmet demand for main lines+the supply of main lines.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 123
Eq. (4)16 measures the effect of satisfied demand for mobile on total demand for main lines.
Since the model is expressed in terms of demand relations, we can infer more clearly the relation
between mobile and fixed lines. If the coefficient on MOBILE is positive, then the result can be
interpreted to mean that fixed lines and cellular phones are complements. On the other hand, a
negative coefficient may be interpreted to suggest that the two services are substitutes.
An increase in MOBILE usage could have externality effects, resulting in increased MAINLILNE
demand. The growing MAINLINE usage could, however, have the same effects. To separate the
externality effects of MOBILE usage from that of MAINLINE usage itself, we introduce MAINLINE
access lagged one period (LMAINLINE) as an independent variable in the DEMAND equation.
Finally, is possible that as mobile subscriptions increase, its relation with demand for wire lines
may change. To account for this possibility, we introduce mobile squared (SQMOBILE) as an
independent variable in the demand equation.
6.5. Estimated results
Table 7 presents the results of the cross-sectional estimations. In 1987, MOBILE, was not
significantly associated with MAINLINE access, but had a large positive coefficient. This
insignificant relation may have occurred because although growing, the level of MOBILE
subscriptions was too small in 1987 to have any real effect on MAINLINE development. By 1997,
however, MOBILE had cemented its presence in the African telecom market enough to be
significantly and positively associated with MAINLINE. This is also shown in the last column of
Table 7, which captures how the growth in MOBILE access between 1987 and 1997 affected fixed-
line growth for a cross-section of countries. This could suggest that while in earlier years cellular
provision was inconsequential in the telecommunications market, it is becoming more of a
competitive force as its usage grows. The real impact of MOBILE on MAINLINE, however,
appears to be very small between 1987 and 1997. In this estimation, the mobile coefficient
corresponds to an elasticity that is close to zero.
Generally, the large changes in the size of the coefficients as well as variation in their
significance levels over the 3 years reported, indicate that the variables included in the model are
not constant overtime. These cross-sectional results also suffer from omitted variable bias, so the
coefficient size and significance may not be precise when country-specific elements are omitted.
The fixed-effect model that is later estimated takes care of the omitted variable problem and
accounts for any constant level differences in the explanatory variables over time.
Table 8 shows the result of the regression in which MOBILE is isolated from all other
independent variables. The results indicate a positive and significant correlation between mobile
usage, and the two dependent variables (MAINLINE and DEMAND).
16The dependent variable is now a demand-side variable and so is not determined by policy. Although MOBILE is
still a policy variable, it is not simultaneously determined with the new dependent variable (DEMAND). If MOBILE
was the total demand for cellular phones, then the two variables would be simultaneously determined, by virtue of the
fact that MOBILE and DEMAND for main lines are related services. MOBILE, however, measures just the part of its
demand that is satisfied. In other words, it measures demand constrained by actual supply of cellular phones. It is
therefore not a complete measure of demand. It is in fact the supply of cellular phones, which is captured by this
variable. If this is true, then we can assume that although MOBILE is a policy variable, it is not jointly determined with
total main-line demand.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133124
Tables 9 and 10 show the results of the pooled time series, cross-sectional estimations.Table 9
shows the fixed and instrumental variable estimation results using MAINLINE as the dependent
variable. All estimations show a positive and significant correlation between MOBILE and
MAINLINE. This result suggests that cellular subscriptions may be playing a complementary role
to fixed telephone lines. It is also reasonable to interpret the positive sign in terms of competition
within the industry.17 The use of cellular phones act as a competitive force, which encourages
increased investment in direct lines.
Table 7
Cross-sectional regression results for 1987a, 1997 and the change between 1985 and 1997 (dependent variable: mainline
per 1000 individuals)
Variable 1987 cross-section 1993 cross-section 1997 cross-section Change between
1987 and 1997 cross-
section
LOGGDPC 3.548 10.814* 10.772** 22.126
(0.875) (1.875) (2.308) (1.363)
LTRADE 0.104 0.005 0.229**0.160**
(1.107) (0.027) (2.025) (2.208)
URBAN 0.559** 0.024 0.599** 0.426
(2.261) (0.592) (2.044) (0.861)
CORRUP 2.391 11.526** 3.348 4.930**
(0.766) (2.213) (0.891) (1.985)
LAW 6.811***3.353 3.307 1.416
(3.401) (1.528) (0.989) (0.496)
BUREAU 4.551*7.969***6.312**4.379**
(1.800) (2.872) (2.510) (1.777)
CONTRACT 3.057 0.550 1.798 5.475***
(1.211) (0.212) (1.195) (2.732)
EXPROP 0.900 2.885 2.946 0.263
(0.308) (0.821) (1.242) (0.125)
GOVOP 0.150 1.015** 0.932* 1.046
(0.353) (2.022) (1.627) (1.130)
DISCTAX 0.531* 0.135 0.371 0.788*
(1.638) (0.446) (0.996) (1.636)
DEMOC 4.049**1.401 0.102 1.975**
(2.546) (1.535) (0.010) (2.183)
MOBILE [phone]* 1.308*** 0.503**
(1.531) (1.678) (4.135) (2.474)
N 23 23 23 23
R2 0.90 0.87 0.95 0.85
F Stat 10.29 10.79 107.14 49.61
Note:T-stats are shown in brackets; *=significant at 10%; **=significant at 5%; ***=significant at 1%.
aPrior to 1987 there was not sufficient data on MOBILE to run cross-sectional regressions.
17Ahan and Lee (1999)estimated the demand for mobile networks using data from 64 countries in both developed
and developing countries and found that mobile demand is positively correlated with the number of fixed lines per
person.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 125
It is possible that main lines and mobile phones may be complements sometimes and substitutes
in other cases. For instance it may be possible for mobile and main lines to be complements in
consumption in countries where income is relatively high and less so in areas with lower income
levels. To further explore this potential differential relationship, the relation between main lines
and mobile, we interact MOBILE with per capita GDP (MOBINC), and with a regional dummy
which equals 1 if countries are sub-Saharan and 0 if they are Northern African (MOBSUB). The
results show that the interaction of MOBILE with per capita GDP is insignificant. However, the
marginal effect of increased mobile subscription on main-line access is more than 2 times lower in
sub-Saharan Africa, than in Northern Africa. Although this result cannot be interpreted to mean
that some countries in sub-Saharan Africa use mobile as a substitute for, rather than a
complement of main line, we can conclude that mobile varies in its degree of complementarity. It
may be that mobile is used both as a substitute for and a complement of MAINLINE in some
regions of Africa, but its use as a complement outweighs its role as a substitute. This is feasible
especially in areas where access rates vary dramatically, not only across countries, but also within
countries themselves. For instance it is not uncommon for professionals to own both mobile and
fixed lines. At the same time, it is becoming increasingly commonplace to have access to one or the
other for various different reasons.18 Mobile can play the role of both a substitute and a
complement of main line in the same country at different points in time.
Although the various estimations show MOBILE as highly significant, large changes are
necessary to affect main-line development noticeably. Using the fixed-effects coefficients from
Table 9, if Gabon increased its cellular subscriptions by 10 per 1000 people, main-line access
Table 8
Parsimonious specification of panel regressions controlling for country-level differences. Data: 23 countries, 1985–1997
Variable Dependent variable: MAINLINE
access per 1000 people
Dependent variable: DEMAND
for main lines per 1000 people
MOBILE 0.931*** 0.853***
(4.951) (5.150)
N 299 239
R2 with fixed effects and other
independent variablesa
0.91 0.93
R2 with only fixed effects 0.89 0.89
F-Stat 24.51 26.52
Note:T-stats are shown in brackets; *=significant at 10%; **=significant at 5%; ***=significant at 1%.
aThe R2’s of 0.91 and 0.93 indicates that the model explains much of the variation in the dependent variables.
Specifically up to 4% more of the variation in MAINLINE and DEMAND is explained by MOBILE.
18When South Africa is excluded, the marginal effect of increasing MOBILE on MAINLINE is reduced when
income is higher (second column of Table 13 in the appendix). Since MAINLINE is a supply side measure, this result
may be indicating that providers of fixed service will find it strategic to focus more on cellular provision in high-income
areas, rather than on fixed line expansion, which is relatively time consuming. From the demand-side perspective,
(where the dependent variable is DEMAND in Table 13), MOBINC is not significant and the result for MOBSUB is
quantitatively the same as for the other estimations in Tables 9 and 10.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133126
Table 9
Panel regressions controlling for country-level differences. Data: 23 countries, 1985–1997
Dependent variable: MAINLINE access per 1000 people
Variable Fixed effects Fixed effects with
interactions
Instrumental variable:
PRIVCRED used as a
proxy for MOBILE
LOGGDPC 7.593*** 6.646** 8.262***
(2.740) (2.398) (3.063)
LTRADE 0.115***0.111***0.113***
(6.252) (6.300) (5.996)
URBAN 0.624*** 0.641*** 0.647***
(7.043) (7.307) (7.027)
CORRUP 0.067 0.167 0.270
(0.138) (0.342) (0.553)
LAW 1.295** 0.905** 1.265**
(2.500) (1.972) (2.073)
BUREAU 2.296***1.696***1.500**
(4.310) (3.508) (1.805)
CONTRACT 2.119*** 1.819*** 2.000***
(4.811) (4.406) (4.411)
EXPROP 0.060 0.242 0.068
(0.156) (0.687) (0.177)
GOVOP 0.422*** 0.412*** 0.493***
(2.975) (2.975) (3.245)
DISCTAX 0.184*** 0.162** 0.176***
(2.658) (2.378) (2.330)
DEMOC 0.441***0.407***0.467***
(4.302) (4.088) (4.241)
MOBILE 0.438*** 5.229*** 0.799**
(4.579) (3.930) (2.211)
MOBINC 0.0002
(1.486)
MOBSUB 3.923**
(2.520)
LMAINLINE
N 299 299 289
R2 with fixed
effects and other
variables
0.97 0.97 0.97
R2 with only
fixed effects
0.89 089 0.89
F-Stat 32.55 34.91 304.73
Note:T-stats are shown in brackets; *=significant at 10%; **=significant at 5%; ***=significant at 1%. A test of the
significance of a time trend could not be rejected. As a result, the time trend was excluded from our estimations.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 127
Table 10
Panel regressions controlling for country-level differences. Data: 23 countries, 1985–1997
Variable Dependent variable: DEMAND for main lines per 1000 people
Fixed effects
without
lagged
MAINLINE
Fixed effects
with lagged
MAINLINE
Fixed effects
with
interactions
Fixed effects
with lagged
MAINLINE
and
interactions
Fixed effects
with
SQMOBILE
Fixed effects
with lagged
SQMOBILE
and
interactions
LOGGDPC 3.957 3.661 2.717 2.827 4.020 3.230
(1.109) (1.215) (0.784) (0.960) (1.323) (1.085)
LTRADE 0.089***0.057***0.083***0.055***0.057*** –0.055***
(3.646) (2.884) (3.535) (2.797) (2.881) (2.798)
URBAN 0.951*** 0.887*** 0.961*** 0.898*** 0.886*** 0.897***
(10.149) (10.274) (10.421) (10.429) (10.336) (10.492)
CORRUP 0.046 0.600 0.130 0.443 0.695 0.550
(0.069) (1.165) (0.196) (0.835) (1.344) (1.030)
LAW 0.632 0.359 0.245 0.111 0.238 –0.013
(1.127) (0.736) (0.459) (0.237) (0.492) (–0.027)
BUREAU 1.597**1.311**1.047 0.952*1.275** –0.924
(2.148) (2.174) (1.437) (1.606) (2.117) (–1.555)
CONTRACT q 1.653*** 1.616*** 1.449*** 1.669*** 1.467***
(3.868) (4.112) (3.328) (3.692) (4.150) (3.728)
EXPROP 0.211 0.354 0.048 0.167 0.362 –0.178
(0.544) (1.162) (0.131) (0.562) (1.193) (–0.599)
GOVOP 0.629*** 0.631*** 0.600*** 0.611*** 0.623*** 0.603***
(3.170) (3.992) (3.084) (3.932) (3.954) (3.884)
DISCTAX 0.132 0.088 0.077 0.053 0.089 0.055
(1.218) (0.875) (0.711) (0.526) (0.880) (0.536)
DEMOC 0.402***0.283**0.333**0.244**0.285** –0.248**
(2.759) (2.337) (2.348) (2.043) (2.344) (–2.060)
MOBILE 0.496*** 0.399*** 3.966*** 2.775*** 0.770*** 3.226***
(5.280) (5.591) (3.397) (2.837) (3.732) (3.360)
SQMOBILE 0.011** –0.010**
(2.299) (–2.310)
MOBINC 0.00003 0.00002 –0.00004
(0.148) (0.110) (–0.228)
MOBSUB 3.541***2.418** –2.336**
(2.816) (2.434) (2.406)
LMAINLINE 0.171*** 0.160*** 0.168*** 0.158***
(3.189) (3.059) (3.166) (3.043)
N 239 238 239 238 238
R2 with fixed
effects and
other variables
0.98 0.98 0.98 0.99 0.99 0.99
R2 with only
fixed effects
0.92 0.92 0.92 0.92 0.92 0.92
F-Stat 41.11 67.14 42.25 67.41 57.98 59.87
Note:T-stats are shown in brackets; *=significant at 10%; **=significant at 5%; ***=significant at 1%. A test of the
significance of a time trend could not be rejected. As a result, the time trend was excluded from our estimations.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133128
would increase in that country by 22.83 telephones per 1000 inhabitants.19 This corresponds to an
elasticity of only 0.204 in 1997.20 A low mobile elasticity indicates that direct line and cellular
subscriptions are not close complements. Cellular might be too costly for individuals with per
capita incomes below $1000. Only the elite can afford cellular telephones. In addition, many
countries’ regulatory frameworks are not yet equipped to deal with the emerging competitive
markets.
Results from the instrumental variable estimation, with PRIVCRED used as an instrument for
MOBILE, show that the impact of the competition variable (MOBILE) remains positive and
statistically significant. The coefficient is larger, but the real impact of cellular competition
remains small, with elasticity close to zero.
Table 10 shows the results using DEMAND for main lines as the dependent variable. The
results for MOBILE competition are quantitatively the same as those in the MAINLINE
estimations. The positive and significant coefficient on MOBILE in this case lend more support
for the view that cellular and main-line telephones may in fact be complements.21
The second column of Table 10 accounts for the increase in value that may result from
MAINLINE usage. Once the externality effect of MAINLINE usage is accounted for (by
introducing LMAINLINE), the result indicate that the two services may in fact be complements
in consumption. The positive coefficient on MOBILE may be interpreted to mean that an increase
in satisfied demand for MOBILE is associated with an increase in total demand for main lines and
the two services are used together. The result may be capturing the effect where two or more
telephone users connect with each other, with at least one party using a cellular phone.
The coefficient on SQMOBILE (although small), suggests that when mobile usage is low (below
35 per 1000) it may be playing the role of a complement for main line. However, as usage becomes
more widespread, its role switches to be that of a substitute. This scenario does a good job of
describing the evolution of cellular usage in these developing economies, and concurs with earlier
suggestions that it is possible for the service to be play both the role of a complement and that of a
substitute. As a new innovation in the mid- to late-1980s, cellular service was relatively expensive.
It is quite possible that at this stage, only professionals and private individuals with higher
incomes (usually in urban areas) would utilize the service. These users tend to use cellular service
in conjunction with fixed line phones. As the relative price of cellular service falls overtime, usage
spreads to other users including rural, and lower income subscribers. When mobile becomes
available in regions where main-line access in non-existent or low, it becomes a substitute for main
19An increase of 22.83 does not represent a large increase in MAINLINE. This is because mobile subscription in
Africa was only 2.67 per 1000 individuals while MAINLINE access was 22.34. This is 8.7 times higher than mobile
access. When mobile is increased by 10, main-line access in Gabon increased by only 2.2 times more than the increase in
mobile even though main-line access was almost nine times higher in 1997.
20MAINLINE elasticity is calculated using 1997 values for each country. That is,b*[MOBILE/MAINLINE].
21We also ran a fixed-effects regression (result not reported) using the six countries in the sample where there is a
single cellular provider, who is also the incumbent. The result of the regression using MAINLINE as the dependent
variable shows a positive (but insignificant) coefficient on MOBILE. If the incumbent is also the only cellular provider,
then the competitive pressure of a substitute that would result in an increase in main-line access (when the mainline and
mobile are substitutes) does not exist. In cases where the incumbent is the clear provider of mobile a negative correlation
between main-line access and cellular subscriptions would indicate that the two services may be substitutes. If the
correlation is positive, it lends support to the view that the positive correlation found in our estimations indicate that
the services are complements in consumption.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 129
line. Currently, the net effect of mobile on main-line demand is greater than zero at the level of
mobile access for most countries in the data set. As such the complementary effect outweighs the
substitution effect. However, as mobile subscription increase, its role as a substitute will begin to
dominate.
Generally, the results show that both access to MAINLINES and the DEMAND for main lines
tend to be higher where income levels are relatively high. The institutional environment is
important for telecommunications development (Bergara, Henisz, & Spiller, 1998). The
institutional variables are also important in determining main-line demand. Rule of law
(LAW), security of contracts (CONTRACT), and role of markets (GOVOP) all affect penetration
in a positive manner. Political institutions appear to be important, but democracy has a negative
coefficient. Clearly, modeling these features warrants more attention in future research. In
particular, modifications could be made to consider the possibility that the measures of the
institutional environment used, may not readily combine in a linear model for their impact upon
the relation between main line and mobile subscription.
7. Conclusion
Some research has found evidence that competition generally tends to improve industry
performance and productivity. Studies that looked at the telecommunications industry have also
found competition in basic services is associated with increased telecom growth and development.
This study examines the role of MOBILE competition rather than competition in basic services.
This issue is of interest particularly since mobile competition tends to be more widespread than
competition in basic services. Many countries are privatizing, but most have stopped short of
allowing multiple provision. Mobile competition is expanding its role in many developing
countries, even where access to fixed-line telephones is very low.
The empirical results support the theory that credible institutions, including stable legal
structures are important driving forces behind the surge of modernization in Africa’s
telecommunications sector. The effect of mobile competition on main-line development and
demand, though relatively weak is significant. At different stages of cellular development, mobile
play the role of both a substitute for, and a complement of main-line demand. From the supply
side perspective, the pressure on incumbent firms to improve service provision increase with mobile
operators present, possibly leading to improvements in main-line quality as well. The empirical
results presented here suggest that competition is important in fostering telecom development: it
induces investment by wire-line investors. As more private participation and competition are
allowed, competition is expected to have a much larger effect on wire-line investments.
The important lesson is that, although there is some substitution between mobile and main line,
mobile’s role as a complement dominates. As a result, mobile is not just picking up the slack
where demand for main lines is unmet. A market for cellular phones exists beyond reducing the
waiting list for traditional wire-line phones. This possibility is important for private investors and
governments, since the potential market size for both types of service is immense. People in
developing countries do not demand cellular phones only as a second-best solution to becoming
connected. A significant impetus for mobile demand could simply be due to its convenience (or its
implications for social status). Thus, fixed-line providers should not expect a reduction in demand,
J. Hamilton / Telecommunications Policy 27 (2003) 109–133130
even as cellular usage continues to expand. Competition from mobile can do much to improve
main-line access.
Acknowledgements
I would like to thank Chunrong Ai, Sanford Berg, David Figlio, Grant Thrall, Jonathan
Hamilton, David Sappington, Larry Kenny and Cezley Sampson for their helpful comments on
an earlier version of this study. Remaining errors of fact or interpretation are the author’s
responsibility.
Appendix
Comparison of means and fixed effects regressions across countries included and excluded from
samples are given in Tables 11 and 12, respectively. Pooled time-series and cross-sectional
regression controlling for country-level differences is given in Table 13.
Table 11
Comparison of means across counties included and excluded from the sample
Variable Countries included Countries excluded Countries included
without South
Africa
Countries excluded
without Seychelles
and Mauritius
MAINLINE 15.206 16.350 11.94 9.338
MOBILE 0.439 0.349 0.170 0.100
Per capita GDP 1000.507 811.978 882.776 492.73
LTRADE 59.570 77.908 60.059 74.065
URBAN 35.547 32.286 34.938 31.194
Table 12
Comparison of fixed effect regressions across counties included and excluded from the sample. Dependent variable:
MAINLINE per 1000 individuals
Variable Countries
included
Countries
excluded
Countries
included
without South
Africa
Countries
excluded
without
Seychelles and
Mauritius
All of Africa Africa
without South
Africa,
Mauritius and
Seychelles
MOBILE 0.818*** 4.476 1.0671** 1.958*** 2.435*** 1.781***
(6.028) (11.104)* (2.189) (6.445) (3.356) (4.032)
LOGGDP 12.367*** 17.867 13.807*** 3.916** 22.530*** 7.607***
(4.276) (3.954)* (4.848) (2.556) (5.285) (4.778)
LTRADE –0.052*** –0.027 –0.049*** –0.080 –0.038** –0.026**
(–3.067) (–1.072) (–2.900) (–0.645) (–1.987) (–2.549)
URBAN 1.059*** 0.989* 1.009*** 0.826*** 1.017*** 0.945***
(12.318) (4.621) (12.052) (3.800) (8.707) (10.272)
Note. The T-stats are shown in brackets; ***=significant at 1%; **=significant at 5%.
J. Hamilton / Telecommunications Policy 27 (2003) 109–133 131
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Pooled time-series and cross-sectional regression controlling for country-level differences. Data: 1985–1997 excluding
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Variable Dependent variable: MAINLINE per 1000
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Dependent variable: DEMAND for main lines
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Fixed-effect
estimation
Fixed effect
estimation with
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Fixed effect
estimation with
interactions
Fixed effect
estimation with
lagged main line and
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