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 1
Mobile Subscriptions per 100 Inhabitants Proceedings of 9th International Business and Social Science Research Conference 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 subscriptions 1 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. 120.00 Figure 1 Mobile Subscriptions 100.00 80.00 60.00 40.00 Asia Africa World 20.00 0.00 2
Mobile Subscriptions per 100 Inhabitants Fixed Lines per 100 Inhabitants Proceedings of 9th International Business and Social Science Research Conference 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. 30.00 Figure 2 Fixed Lines per 100 Inhabitants (2001-2012) 25.00 20.00 15.00 10.00 World Africa Asia 5.00 0.00 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 120 100 80 60 40 20 0 Low-Income African Countries Benin Burkina Faso Burundi Chad Comoros Congo, Dem. Rep. Eritrea Ethiopia Gambia Guinea-Bissau Kenya Madagascar Malawi 3
Mobile Subscriptions per 100 Inhabitants Mobile Subscriptions per 100 Inhabitants Proceedings of 9th International Business and Social Science Research Conference 180 160 140 120 100 80 60 40 20 0 Figure 3B Lower-Middle Income African Countries Cameroon Cape Verde Congo, Rep. of Cotd d'ivorye Dijbouti Egypt Ghana Lesotho Mauritania Morocco Nigeria Sao Tome and Principe Senegal 200 Figure 3C Upper- Middle Income African Countries 180 160 140 120 100 80 60 40 Angola Algeria Botswana Equatorial Guinea Gabon Mauritius Namibia South Africa Tunisia 20 0 Note: Equatorial Guinea, Africa s only High-Income country, is included with Upper- Middle African Countries. 4
Mobile Subscriptions per 100 Inhabitants Mobile Subscriptions per 100 Inhabitants Proceedings of 9th International Business and Social Science Research Conference 140.00 Figure 4A Low-Income Asian Countries 120.00 100.00 80.00 60.00 40.00 Afghanistan Bangladesh Cambodia Indonesia Kyrgyzstan Nepal 20.00 0.00 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 Figure 4B Lower-Middle Income Asian Countries Armenia Bhutan Georgia India Loas Mongolia Pakistan Philippines Sri Lanka Syria Uzbekistan Vietnam Yemen 5
Mobile Subscriptions per 100 Inhabitants Mobile Subscriptions per 100 Inhabitants Proceedings of 9th International Business and Social Science Research Conference 200.00 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 Figure 4C Upper-Middle Income Asian Countries Azerbaijan China Iran Iraq Jordan Kazakhstan Lebanon Malaysia Maldives Thailand Turkmenistan Turkey 250.00 200.00 150.00 100.00 50.00 0.00 Figure 4D High-Income Asian Countries Bahrain Brunei Hong Kong Israel Japan Kuwait Oman Qatar Russia Saudi Arabia Singapore South Korea UAE 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. 6
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. 7
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 countries 2 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. Y it 0 1X 1it kx kit I 1 where Y it is the number of mobile phone subscriptions per 100 inhabitants in a country i at time t Fixed Effects Model: Y it 0 1X 1it kx kit 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. 8
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. Table 1: Africa Descriptive Statistics of the Data Set Variable Mean Std. Deviation Minimum Maximum # of Mobile Subscribers 33.04 34.19 0.018 187.3 Rural Rate 60.08 17.5 13.54 91.53 Total Population 19,948,646 27,007,147 81,202 169,000,000 Population Growth 2.305 0.928-2.63 4.25 Population Density 88.21 116.0 2.34 633.5 GDP PC PPP 3838 682 2786 4881 # of Fixed Lines 3.910 6.198 0.005 33.11 Table 2: Asia Descriptive Statistics of the Data Set Variable Mean Std. Deviation Minimum Maximum # of Mobile Subscribers 64.22 49.32 0 227.93 Rural Rate 42.14 25.58 0 86.23 Total Population 92,041,459 258,000,000 277,825 1,350,000,000 Population Growth 2.082 2.426-1.609 17.48 Population Density 500.2 1377.1 1.55 7405 GDP PC PPP 14003 14427 115 90524 # of Fixed Lines 17.16 14.85 0 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. 9
Figure 5: Mobile Subscriptions and Mobile Subscriptions Growth Rate Total Subscriptions A. Growth in Subscriptions Time B. Growth in Subscriptions Time 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: MS it = (MS it - MS it-1 0 1X 1it kx kit 3 The explanatory variables in Equation 3 will then include both the level of mobile phone subscribers in the previous period (MS it-1 ) and this term squared. This allows 10
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 Model Fixed Effects Model Variable Coefficient t-statistic Coefficient t-statistic Constant 35.59672 1.722041 1633.564 6.079050 Ln(Rural Rate) -21.69893-4.912652-151.8255-9.480804 Ln(Total Population) 3.991420 5.012068-49.01740-2.407851 Ln(Population Density) 1.904809 1.868944-60.89954-2.462681 GDP PC PPP 0.004077 4.470479 0.007867 5.324111 GDP PC PPP squared -7.04E-08-2.024012-1.43E-07-4.134871 Ln(# of fixed subscribers) 5.331096 4.519198-7.042131-4.970462 R-Squared 0.401 0.933 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 11
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 Model Fixed Effects Model Variable Coefficient t-statistic Coefficient t-statistic Constant 76.32089 3.907240-384.6583-1.374499 Rural Rate 0.034651 0.244806 1.089340 1.940772 Ln(Total Population) -2.237185-2.016652 40.44910 1.736419 Ln(Population Density) 1.480927 1.104988-60.84102-2.243160 Population Growth -3.270185-2.884932 2.036344 3.105140 GDP PC PPP 0.002351 4.433686 0.001967 3.566453 GDP PC PPP squared -2.02E-08-2.694471-1.84E-08-3.213339 # of fixed subscribers 0.380098 1.500097-0.000197-0.000693 R-Squared 0.231 0.901 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. 12
Table 5: Africa Pooled and Fixed Effects Models for Change in Mobile Subscriptions per 100 People Pooled Regression Model Fixed Effects Model Variable Coefficient t-statistic Coefficient t-statistic Constant 11.89482 2.463638-243.0678-4.210976 Ln(Rural Rate) -1.416892-1.788126 27.99250 3.089677 Ln(Population Density) -0.415850-2.245450 31.24872 3.425591 Ln(GDP PC PPP) -0.378140-1.135288 1.176064 0.423823 Ln(# of fixed subscribers) 0.047002 0.219289-0.214545-0.306040 # of Mobile Subscribers 0.169263 9.669039 0.254994 7.026699 # of Mobile Subscribers -0.000521-3.760024-0.000385-2.151780 Squared R-Squared 0.426 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. 13
Table 6: Asia Pooled and Fixed Effects Models for Change in Mobile Subscriptions per 100 People Pooled Regression Model Fixed Effects Model Variable Coefficient t-statistic Coefficient t-statistic Constant 18.20591 2.372311 74.68074 0.554001 Rural Rate -0.019758-0.583685 0.026512 0.090251 Ln (Total Population) -0.326670-1.432481-4.361791-0.409396 Ln(Population Density) -0.223128-0.868843 8.134126 0.695323 Population Growth 0.026560 0.132370-0.436776-1.444225 Ln(GDP PC PPP ) -1.069332-1.433895-6.971773-3.332213 # of fixed subscribers -0.015810-0.305608 0.383250 2.781955 # of Mobile Subscribers 0.156643 5.766826 0.400221 9.687575 # of Mobile Subscribers -0.000518-3.533066-0.001169-6.755529 Squared R-Squared 0.141 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 Variable Mobile Subscriptions per 100 Change in Mobile Subcriptions per 100 Rural Rate - significant *** + significant *** Total Population - significant ** Population Density - significant ** + significant *** Population Growth GDP PC PPP + significant *** - not significant GDP PC PPP squared - significant *** # of Fixed Subscribers - significant *** - not significant # of Mobile Subscribers + significant *** Mobile Subscriptions Squared - significant ** * Significant at 10% level **Significant at 5% level *** Significant at 1% level 14
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 15
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