Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Determinants of Mobile Phone Penetration Rates in Asia
and Africa: A Panel Data Analysis
Kokila P. Doshi and Andrew Narwold
Using panel data analysis for Asia and Africa for 2001-2012, this paper
studies the factors influencing the mobile phone subscription rate and its
growth rate. This is likely the first study in current research to explicitly
investigate the determinants of the rate of growth of mobile phone
subscriptions and to indirectly relate the estimation to the diffusion curve.
In Asia and Africa, the number of mobile phone subscriptions is growing
at an increasing rate over time. The empirical findings suggest that GDP
PC, Population, Rural Rate, Population Density and Fixed Lines
penetration are significant drivers of mobile phone adoption in Africa.
While Fixed Lines emerge as a substitute for mobile phones in Africa, the
variable is not significant in Asia. Rates of mobile phone growth in Asia
and Africa are increasing at a diminishing rate. While demographic
variables are significant in explaining the rate of growth of mobile
subscriptions in Africa, GDP PC and Fixed lines explain variation in
growth rates in Asia.
JEL Codes: O11, O33 and C23
1. Introduction
The stellar growth and rapid diffusion of mobile telephony has far reaching
implications for the economic development of countries. The mobile technology is
providing empowering access and connectivity to citizens and transforming their lifestyle and livelihoods, especially in developing regions. It is one of the first and the
fastest growing technologies to have high levels of adoption (76.6%) in developing
countries.
According to the International Telecommunications Union (ITU), there are 6.8 billion
mobile phone subscriptions worldwide. More than half of mobile subscriptions are in
the Asia-Pacific region. In Africa, the mobile phone penetration rate reached 63% in
2012. Increasingly, cellular technology is leap frogging the traditionally inadequate
landline infrastructure in many African countries. The current trends in mobile phone
subscriptions suggest that Asia and Africa will lead the future growth of mobile
telephony.
The objective of the paper is to empirically investigate the role of economic and
demographic factors influencing mobile phone penetration rates in Asia and Africa
for the period of 2001 – 2012. Using panel data analysis, the study analyzes both –
the number of mobile phones subscribers and the rate of growth of mobile phone
subscribers per 100 inhabitants. Some of the research questions addressed are:
How does per capita GDP affect the level and growth of mobile phone subscribers?
Dr. Kokila Doshi, Professor of Economics, School of Business, University of San Diego, 5998 Alcalá
Park, San Diego, CA 92110, kdoshi@sandiego.edu
Dr. Andrew Narwold, Professor of Economics, School of Business, University of San Diego, 5998
Alcalá Park, San Diego, CA 92110, drew@sandiego.edu
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Are fixed lines a substitute for mobile phones? What roles do demographic variables
such as, total population, % of population living in rural areas (rural rate) and
population density play in explaining mobile phone penetration rates? The study
provides a comparative insight into regional variations in mobile phone adoption
patterns in Asia and Africa.
This paper is organized as follows: In section 2, an overview of global and regional
trends in mobile phone diffusion is provided. Section 3 summarizes the existing
literature on drivers of mobile phone adoption. Section 4 describes the estimation
model, methodology and data. The empirical results are presented in section 5 and
section 6 provides conclusions.
2. Mobile Phone Adoption: An Overview
According to ITU estimates, the global mobile-cellular penetration rate reached
96.2% in 2012 with 6.8 billion mobile subscriptions1 worldwide. Mobile penetration
rates stand at 128% for developed countries and 89% for developing countries.
According to Portio Research, the Asia-Pacific region’s share is estimated to
increase from 51.3 in 2012 to 54.3 in 2016. Also, Africa and the Middle East as a
region is estimated to overtake Europe as the second largest region for mobile
subscriber base by 2016.
Mobile cellular subscriptions are outnumbering fixed telephone lines 6:1 globally
(mobiThinking, 2013). However, in developing countries, the trend is much more
pronounced. For instance, in Sub-Saharan Africa, there are 28 mobile phones for
every fixed telephone line. Figure 1 compares the penetration rate for mobile
subscriptions for Africa, Asia and the World for the period 2001 – 2012 for the
sample countries. Similarly, Figure 2 displays the growth of fixed line subscriptions.
Figure 1
Mobile Subscriptions per 100 Inhabitants
Mobile Subscriptions
120.00
100.00
80.00
Asia
60.00
Africa
World
40.00
20.00
0.00
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Asia’s mobile penetration rate increased from 18.06 in 2001 to 112.17 in 2012 for the
sample countries. Fixed lines penetration rates show a relatively slow growth in the
range of 18 – 23 per 100, with a slight decline in recent years. Africa is characterized
by low penetration rates of fixed lines and the trend hovers around a low 3-5
telephones per 100.
Figure 2
Fixed Lines per 100 Inhabitants (2001-2012)
30.00
Fixed Lines per 100 Inhabitants
25.00
20.00
World
15.00
Africa
Asia
10.00
5.00
0.00
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
This highlights significant leapfrogging and the importance of mobile phones in
Africa. Mobile phones penetration rate in Africa increased from 4.36 in 2001 to
74.74 in 2012. Figures 3A – 3C and Figures 4A-4D show the breakdown of countries
by income level in Africa and Asia respectively.
Figure 3A
Low-Income African Countries
120
Mobile Subscriptions per 100 Inhabitants
Benin
Burkina Faso
100
Burundi
Chad
80
Comoros
Congo, Dem. Rep.
60
Eritrea
Ethiopia
40
Gambia
Guinea-Bissau
20
Kenya
Madagascar
0
Malawi
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Figure 3B
Lower-Middle Income African Countries
180
Mobile Subscriptions per 100 Inhabitants
Cameroon
160
Cape Verde
140
Congo, Rep. of
Cotd d'Ivorye
120
Dijbouti
100
Egypt
Ghana
80
Lesotho
60
Mauritania
Morocco
40
Nigeria
20
Sao Tome and Principe
0
Senegal
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Figure 3C
Upper- Middle Income African Countries
200
Mobile Subscriptions per 100 Inhabitants
180
160
Angola
140
Algeria
Botswana
120
Equatorial Guinea
100
Gabon
Mauritius
80
Namibia
60
South Africa
Tunisia
40
20
0
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Note: Equatorial Guinea, Africa’s only High-Income country, is included with UpperMiddle African Countries.
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Figure 4A
Low-Income Asian Countries
Mobile Subscriptions per 100 Inhabitants
140.00
120.00
100.00
Afghanistan
Bangladesh
80.00
Cambodia
60.00
Indonesia
Kyrgyzstan
40.00
Nepal
20.00
0.00
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Figure 4B
Lower-Middle Income Asian Countries
180.00
Armenia
Mobile Subscriptions per 100 Inhabitants
160.00
Bhutan
140.00
Georgia
India
120.00
Loas
100.00
Mongolia
Pakistan
80.00
Philippines
60.00
Sri Lanka
40.00
Syria
Uzbekistan
20.00
Vietnam
Yemen
0.00
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Figure 4C
Upper-Middle Income Asian Countries
Mobile Subscriptions per 100 Inhabitants
200.00
180.00
Azerbaijan
160.00
China
Iran
140.00
Iraq
120.00
Jordan
100.00
Kazakhstan
Lebanon
80.00
Malaysia
60.00
Maldives
40.00
Thailand
Turkmenistan
20.00
Turkey
0.00
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Figure 4D
High-Income Asian Countries
250.00
Mobile Subscriptions per 100 Inhabitants
Bahrain
Brunei
200.00
Hong Kong
Israel
Japan
150.00
Kuwait
Oman
100.00
Qatar
Russia
Saudi Arabia
50.00
Singapore
South Korea
UAE
0.00
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
The World Bank Country Classification is used to group countries in various income
levels. While significant variations in mobile phone adoption patterns are observed
by income levels, overall both Africa and Asia are adding to mobile phone
subscriptions at an increasing rate over time.
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
3. Review of Literature
One strand of research on mobile phone adoption relates to the literature on the
diffusion of innovations. According to Rogers (2003), the spread of a new innovation
over time typically follows an S-curve, as the early adopters select the technology
first, followed by the majority until an innovation is common. Many researchers have
estimated the S-curve for mobile phone diffusion using functional forms such as
Bass Model (Bass, 1969), Logistic or Gompertz functions (Singh, 2008, Doshi, 2012,
Michalakelis, et al, 2007, Kumar and Shankar, 2007).
Existing research also includes the studies focusing on the determinants of mobile
phone penetration on a broader scale. Donner (2007) provides an extensive survey
of such studies. Previous cross-country studies (Grueber 2001, Grueber and
Verboven 2001, Koski and Kretschemer 2002) focused on industry factors such as
competition, standardization, regulation and fixed line penetration. Rouvinen (2006)
studied the mobile phone diffusion across developed and developing countries. Most
studies concluded that single standard for mobile platform and competition (number
of operators) has positive effects on mobile phone adoption. Income and urban
population were not found to be statistically significant factors. In a study of 29
countries over a period of 1993-2004, Chakravarty (2007) examined the mobile
phone penetration rates in Asia, using panel data analysis. His findings indicate that
GDP PC, fixed lines per capita, number of mobile providers and regulatory policy
have positive and statistically significant influence on mobile phone penetration
rates. Gebreab (2002) using the fixed effect model analyzed mobile phone diffusion
determinants in 41 African countries for 1987-2007. Competition was found to be the
main driving force behind mobile phone diffusion. Urbanization and main lines had
positive and significant effects, while income and population were not significant.
Hamilton (2003) explicitly addressed the issue of complementarity or substitution
between fixed lines and mobile phones in Africa using a sample of developing
countries of Africa. Using panel data estimation, his results suggest that mobile
phones are complementary to fixed telephone lines. However, this may be the result
of strategic competition within the industry. According to him, “At different stages of
cellular development, mobile can play the role of both a substitute for and a
complement of main line demand” (pp. 130). Acker and Mbiti ( 2010), provide a
qualitative overview of mobile phone coverage in Africa. In the studies on Africa,
population density, per capita income and poor quality of landlines seem to have
positive correlation with mobile phone coverage. Comer and Wilke studied the
worldwide diffusion of mobile phones during 1995-2005 and found that GNP per
capita explained more than 75 percent of variations in mobile phone penetration
rates globally and 90 percent in Asia. The study also finds that “mobile phones are
clearly a substitute for mainline phones in most African countries” (pp. 266). The
evidence is mixed for the rest of the world. Another study (Bagchi, Solis and
Gemoets, 2003), explores the relation between fixed telephone lines and cell phones
in Latin America for 1989-1999. The findings suggest that cell phone adoption
complements the adoption of fixed telephone lines. Kalba (2008) found a strong but
declining correlation between income and mobile phone penetration. His research
also showed that the fixed lines are substitutes for mobile phones, especially in
emerging African markets. He concluded that underlying income levels may be
responsible for such a relation between fixed lines and mobile phones.
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Thus, the review of existing literature shows that a growing body of research has
explored a variety of determinants of mobile phone penetration covering various
regions and time periods. Most consistently, the factors such as income, fixed lines,
industry competition and regulatory policy emerge as the most important predictors
of mobile phone penetration. However, the evidence is mixed whether fixed
telephone lines are substitutes or complements for mobile phones.
The present study contributes to the literature in many ways. Most existing research
studies have focused on supply-side and industry variables such as competition,
regulation and the telecommunications policy environment. The paper explicitly
studies the role of demand-side factors such as income, rural rate, population
density and other demographic variables using panel data methodology. Expanding
the existing research, it provides comparative and most recent evidence using a
large sample of Asian and African countries for 2001-2012. This is the time period
during which Asia and Africa witnessed accelerated mobile phone growth rates. Our
most important contribution is to systematically model the rate of growth of mobile
subscriptions. Such estimation corresponds to the familiar S-curve and offers an
alternative to study mobile phone diffusion over time. This is likely the first study that
examines the change in mobile phone penetration rates in a panel data framework
and highlights the importance of the stage of mobile phone adoption. The findings of
the study have significant policy implications for the government and industry players
in devising development policies.
4. Methodology and Model
This paper analyzes mobile phone penetration rates for a sample of 43 African
countries and 47 Asian countries2 for a period of 2001-2012, using panel data
estimation. Penetration rate is measured as number of mobile phone subscriptions
per 100 inhabitants. The use of panel data potentially allows for a dramatic increase
in the degrees of freedom for regression analysis. The paper uses Pooled
Regressions and the Fixed Effects Model for estimating total mobile phone
subscriptions and the rate of growth of mobile phone subscriptions.
Model I – Number of Mobile Phone Subscriptions (per 100)
Pooled Regression Model:
Equation (1) below specifies the Pooled Regression model which does not
differentiate country or time effects.
Yit
0
1X1it
kXkit
I
1
where Yit is the number of mobile phone subscriptions per 100 inhabitants in a
country i at time t
Fixed Effects Model:
Yit
0
1X1it
kXkit
i
t
it
where I represents country specific fixed effects and t represents time period
specific fixed effects. By capturing such fixed effects, the fixed effect model improves
upon the specification issues found in the Pooled Regression Model.
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
The same general set of variables is used to explain variation in the mobile phone
subscription rate in both Africa and Asia. The exact specification varies slightly and
the results reported are for the best fit. The explanatory variables used for the
estimation include: Rural Rate (% of total population living in rural areas), Total
Population, Population Density (number of people per square kilometer), GDP PC
PPP (Current International $) and Number of Fixed Lines Subscriptions per 100
inhabitants. Most studies use urbanization or rural rate as a proxy for population
density. Here, population density is explicitly included to capture network effect and
cost conditions. The hypothesized signs on these variables are discussed in section
5 on Results.
The data on mobile phone and fixed line subscriptions is collected from ITU (2013).
The World Development Indicators (World Bank, 2013) data is used for the
remaining explanatory variables. Table 1 and Table 2 provide descriptive statistics
for Africa and Asia respectively.
Variable
Table 1: Africa – Descriptive Statistics of the Data Set
Mean
Std. Deviation Minimum Maximum
# of Mobile Subscribers
Rural Rate
Total Population
Population Growth
Population Density
GDP PC PPP
# of Fixed Lines
Variable
33.04
60.08
19,948,646
2.305
88.21
3838
3.910
34.19
17.5
27,007,147
0.928
116.0
682
6.198
0.018
13.54
81,202
-2.63
2.34
2786
0.005
187.3
91.53
169,000,000
4.25
633.5
4881
33.11
Table 2: Asia – Descriptive Statistics of the Data Set
Mean
Std. Deviation Minimum
Maximum
# of Mobile Subscribers 64.22
Rural Rate
42.14
Total Population
92,041,459
49.32
25.58
258,000,000
0
0
277,825
227.93
86.23
1,350,000,000
Population Growth
2.082
2.426
-1.609
17.48
Population Density
GDP PC PPP
# of Fixed Lines
500.2
14003
17.16
1377.1
14427
14.85
1.55
115
0
7405
90524
61.95
Model II – Rate of Growth of Mobile Phone Subscriptions (per 100).
In addition to modeling the number of mobile phone subscriptions, this paper also
examines the rate of growth in mobile phone subscriptions. As noted previously,
many researchers have modeled mobile phone diffusion using the familiar “S-curve”
and estimating techniques such as Bass Diffusion Model. A graphical depiction of
the model is represented in Figure 5.
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Figure 5: Mobile Subscriptions and Mobile Subscriptions Growth Rate
Total Subscriptions
A.
Time
Growth in Subscriptions
B.
Time
Growth in Subscriptions
C.
Total Subscriptions
By examining the rate of change in mobile subscriptions, the relationship depicted in
Figure 5 (panel A) can be estimated using Equation (3).
For the following analysis, the dependent variable is defined as follows:
MSit = (MSit - MSit-1
3
0
1X1it
kXkit
The explanatory variables in Equation 3 will then include both the level of mobile
phone subscribers in the previous period (MSit-1) and this term squared. This allows
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
for the estimation of the relationship between the growth rate and the underlying
market size. Following the “S-curve” hypothesis, Figure 5 (panel 3) represents the
relationship between the change in mobile phone subscriptions and the level of
subscriptions.
5. Results and Discussion
Table 3 presents the results for Africa for both pooled and fixed-effects models using
equations 1 and 2 from Model I. The findings suggest that both cross-country and
time period specific effects are highly significant in explaining the variation in mobile
phone subscription rates, as there is a large difference in the R-square between the
pooled and the fixed effects model.
Table 3: Africa – Pooled and Fixed Effects Models for Mobile Subscriptions per
100 People
Pooled Regression
Fixed Effects
Model
Model
Variable
Coefficient
t-Statistic Coefficient t-Statistic
Constant
Ln(Rural Rate)
Ln(Total Population)
Ln(Population Density)
GDP PC PPP
GDP PC PPP squared
Ln(# of fixed subscribers)
R-Squared
35.59672
-21.69893
3.991420
1.904809
0.004077
-7.04E-08
5.331096
0.401
1.722041
-4.912652
5.012068
1.868944
4.470479
-2.024012
4.519198
1633.564
-151.8255
-49.01740
-60.89954
0.007867
-1.43E-07
-7.042131
0.933
6.079050
-9.480804
-2.407851
-2.462681
5.324111
-4.134871
-4.970462
For Africa, all of the variables are significant at the five percent level of significance,
except for Ln(Total Population) which is significant at the ten percent level. The
results generally conform to the hypothesized signs.
The GDP PC PPP variable representing income, affordability, and level of
development of country was expected to have a positive sign. The coefficients on
both the GDP PC PPP and GDP PC PPP squared are significant, indicating a
possible quadratic relationship. However, given the signs on these coefficients, over
the relevant sample range, mobile phone subscriptions increase at a decreasing
rate. Regarding the fixed line subscriptions, no a-priori sign could be assigned as
fixed lines could be a substitute or a complement for mobile phones. As discussed
previously, the evidence is mixed, although for Africa, mobile phones are likely to be
substitutes for landlines. Consistent with the findings of other studies (Kalba, 2008)
and (Comer and Wilke, 2011), our results show that fixed lines are substitutes for
mobile phones in Africa.
The model included three demographic variables – Total Population, Rural Rate, and
Population Density (Population Growth is not included in the best fit results reported
here for Africa).The coefficients on these variables tell interesting stories. A positive
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
sign on Total Population was expected as the variable represents market size.
However, the coefficient has a negative sign, indicating that the countries with larger
populations tend to have lower levels of mobile subscriptions rate. The coefficients
on both the Rural Rate and Population Density are negative. Rural rate reflects
agricultural population, socio-economic conditions, propensity to adopt a new
technology, quality of infrastructure and accessibility to metropolitan areas. Our
results show a negative sign suggesting that more agricultural, rural countries have
lower mobile subscription rates. Population Density, representing network effect and
cost conditions, was expected to have a positive sign. However, the coefficient on
Population Density has a puzzling negative sign in our results. Even if we consider
population density as a supply side measure representing the cost of deployment, a
positive sign would be expected.
Table 4: Asia – Pooled and Fixed Effects Models for Mobile Subscriptions per
100 People
Pooled Regression
Fixed Effects
Model
Model
Variable
Coefficient
t-Statistic
Coefficient t-Statistic
Constant
Rural Rate
Ln(Total Population)
Ln(Population Density)
Population Growth
GDP PC PPP
GDP PC PPP squared
# of fixed subscribers
R-Squared
76.32089
0.034651
-2.237185
1.480927
-3.270185
0.002351
-2.02E-08
0.380098
0.231
3.907240
0.244806
-2.016652
1.104988
-2.884932
4.433686
-2.694471
1.500097
-384.6583
1.089340
40.44910
-60.84102
2.036344
0.001967
-1.84E-08
-0.000197
0.901
-1.374499
1.940772
1.736419
-2.243160
3.105140
3.566453
-3.213339
-0.000693
Table 4 presents the results for the two models for Asia. As in the case of Africa,
country specific and time specific fixed effects are highly significant for Asia. The
number of fixed line subscriptions is not significant in explaining variation in mobile
subscriptions in Asia. All of the other variables are significant at the 5 percent level of
significance, with the exception of Total Population which is significant at the 10
percent level. Asia displays a similar relationship to Africa, with respect to GDP PC
PPP and its square. The coefficients show that the mobile subscriptions are
increasing at a decreasing rate with respect to income. Interestingly, in Asia, larger
population countries tend to have higher mobile subscription rates. Also, in contrast
to Africa, countries with larger populations living in rural areas have higher mobile
phone subscription rate. Mobile adoption in India and China are likely to influence
the results for Asia. The coefficient on population growth rate is negative and
significant for Asia, whereas it was not significant for Africa. Population Density has a
negative and statistically significant impact on mobile subscription rate which is
difficult to rationalize. For Model II, Table 5 presents estimations of the pooled and
fixed effects model for the rate of growth of mobile phone subscriptions in Africa.
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Proceedings of 9th International Business and Social Science Research Conference
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Table 5: Africa – Pooled and Fixed Effects Models for Change in Mobile
Subscriptions per 100 People
Pooled Regression
Fixed Effects
Model
Model
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
Constant
Ln(Rural Rate)
Ln(Population Density)
Ln(GDP PC PPP)
Ln(# of fixed subscribers)
# of Mobile Subscribers
# of Mobile Subscribers
Squared
R-Squared
11.89482
-1.416892
-0.415850
-0.378140
0.047002
0.169263
-0.000521
0.426
2.463638
-1.788126
-2.245450
-1.135288
0.219289
9.669039
-3.760024
-243.0678
27.99250
31.24872
1.176064
-0.214545
0.254994
-0.000385
-4.210976
3.089677
3.425591
0.423823
-0.306040
7.026699
-2.151780
0.554
The results indicate that in Africa, which is starting with fairly low levels of mobile
phone subscriptions, (a median of 21.4 mobile phones per 100), the rate of growth of
mobile phone subscriptions is increasing at a decreasing rate. GDP PC PPP and
fixed line penetration do not exert a statistically significant effect on the growth rate
of mobile subscriptions. The coefficients on demographic variables – rural rate and
population density – suggest that more rural countries with higher population
densities experience higher levels of growth rates.
The results for Asia are displayed in Table 6. Asia, with a median penetration rate of
59.4, represents a much more developed mobile phone market. However, as with
Africa, the growth rate is increasing at a decreasing rate. Interestingly, the coefficient
on GDP PC PPP is negative and significant for Asia, implying that higher income
countries tend to experience lower growth rates independent of the stage at which
they are in, in terms of mobile phone subscription rate. A similar result was found for
Latin America (Bagchi, Solis, and Gemoets, 2003). The negative sign on GDP PC
PPP implies that poorer nations in Asia are rapidly adopting mobile phones.
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Proceedings of 9th International Business and Social Science Research Conference
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Table 6: Asia – Pooled and Fixed Effects Models for Change in Mobile
Subscriptions per 100 People
Pooled Regression
Fixed Effects
Model
Model
Variable
Coefficient
t-Statistic
Coefficient
t-Statistic
Constant
Rural Rate
Ln (Total Population)
Ln(Population Density)
Population Growth
Ln(GDP PC PPP )
# of fixed subscribers
# of Mobile Subscribers
# of Mobile Subscribers
Squared
R-Squared
18.20591
-0.019758
-0.326670
-0.223128
0.026560
-1.069332
-0.015810
0.156643
-0.000518
0.141
2.372311
-0.583685
-1.432481
-0.868843
0.132370
-1.433895
-0.305608
5.766826
-3.533066
74.68074
0.026512
-4.361791
8.134126
-0.436776
-6.971773
0.383250
0.400221
-0.001169
0.554001
0.090251
-0.409396
0.695323
-1.444225
-3.332213
2.781955
9.687575
-6.755529
0.427
For Asia, demographic variables do not seem to be the significant drivers of the rate
of growth of mobile subscription rates. Finally, number of fixed line subscriptions
serves as a complement for mobile phone usage, increasing the rate of growth of
mobile subscriptions.
Are the factors affecting the level of mobile phone subscription rates different from
the factors that affect the rate of growth of mobile phone subscription rate? Tables 7
and 8 provide a comparative summary of the results for the level and rate of growth
of mobile subscription rate.
Table 7
Comparative Results for Africa
Mobile Subscriptions per 100 Change in Mobile Subcriptions per 100
- significant ***
+ significant ***
- significant **
- significant **
+ significant ***
Variable
Rural Rate
Total Population
Population Density
Population Growth
GDP PC PPP
+ significant ***
GDP PC PPP squared
- significant ***
# of Fixed Subscribers
- significant ***
# of Mobile Subscribers
Mobile Subscriptions Squared
* Significant at 10% level
**Significant at 5% level
- not significant
- not significant
+ significant ***
- significant **
*** Significant at 1% level
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
Table 8
Comparative Results for Asia
Variable
Mobile Subscriptions per 100
Change in Mobile Subcriptions per 100
Rural Rate
+ significant *
+ not significant
Total Population
+ significant **
- not significant
Population Density
- significant **
+ not significant
Population Growth
+ significant ***
- not significant
GDP PC PPP
+ significant ***
- significant ***
GDP PC PPP squared
- significant ***
# of Fixed Subscribers
- not significant
+ significant ***
# of Mobile Subscribers
+ significant ***
Mobile Subscriptions Squared
- significant ***
* Significant at 10% level
**Significant at 5% level
*** Significant at 1% level
For Africa, the results indicate that although the countries with a higher percentage
of rural population and higher population densities have lower levels of mobile phone
subscription rate, they will experience higher rates of growth. Fixed line penetration
rate and GDP PC PPP do not have a significant impact on the rate of growth, even
though both these factors were important drivers of the level of mobile phone
subscription rate.
Similarly, interesting observations emerge for Asia. None of the demographic
variables play a significant role in determining the rate of growth of mobile
subscriptions in Asia. GDP PC PPP negatively impacts the rate of growth, even
though it affects the level of mobile phone subscription rate positively. Fixed line
penetration is a significant driver of the rate of growth, although not of the level of
mobile subscription rate.
6. Conclusion
The paper examined the factors driving the level and the growth rates of mobile
phone penetration in Asia and Africa. Mobile phone subscriptions in these leading
regions are increasing at an increasing rate. By modeling the growth rate of mobile
phone subscriptions, the study indirectly estimated this initial stage of the S-curve.
No such study is found in the existing research. In Africa, the countries with a higher
rural rate, higher densities and lower levels of mobile penetration are likely to
experience a higher rate of growth. For African countries, mobile phones clearly
emerge as a substitute for fixed telephone lines.
For Asia, which is ahead in the stage of mobile phone adoption compared to Africa,
a somewhat different process of diffusion unfolds. Mobile phone growth rates are
predicted to be higher in low income Asian countries implying a potential to bridge
the digital divide. Also, the countries with higher levels of fixed line penetration are
likely to have higher growth rates of mobile phone subscriptions.
The research findings have significant policy implications for the government and
industry players in capacity building and expanding infrastructure for fixed lines
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Proceedings of 9th International Business and Social Science Research Conference
6 - 8 January, 2014, Novotel World Trade Centre, Dubai, UAE, ISBN: 978-1-922069-41-2
and/or mobile phones to rural areas as the frontiers of mobile telephony move from
urban clusters to rural areas.
The future research may consider a disaggregated analysis to capture the
heterogeneity and diversity in the region.
Endnotes
1
According to ITU, the statistics reflect number of subscriptions and not people.
There may be double counting as the statistics are based on individual accounts
rather than users. Due to multiple SIM cards and sharing of mobile phones, the
statistics may not accurately estimate mobile phone adoption.
2
Due to missing data, the following countries were omitted from Africa - Sierra
Leone, South Sudan and Guinea. For Asia, North Korea, Myanmar, Taiwan,
Tajikistan, and Thailand were omitted.
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