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Telecommunications and Growth

Given the digital divide between the developed and developing world, and recent findings that mobile phones can bridge this divide, we develop a causal model that analyses the effect of telecom penetration on economic development in developing economies. The paper addresses the following questions to understand the dynamics of this causal connection, i e, is it telecommunication services that accelerates economic growth or overall economic growth that creates the demand for more telecommunication services for their growth to occur? In the context of developing economies, what are the factors that determine demand for and supply of telecom services? Finally, given the importance of telecom infrastructure in growth, what determines changes in telecom penetration in these economies? We present select quantitative and qualitative evidence from a few developing countries to understand the nature of the impact telecommunications has on their economy and society.

Telecommunications and Growth

Causal Model, Quantitative and Qualitative Evidence

Given the digital divide between the developed and developing world, and recent findings that mobile phones can bridge this divide, we develop a causal model that analyses the effect of telecom penetration on economic development in developing economies. The paper addresses the following questions to understand the dynamics of this causal connection, i e, is it telecommunication services that accelerates economic growth or overall economic growth that creates the demand for more telecommunication services for their growth to occur? In the context of developing economies, what are the factors that determine demand for and supply of telecom services? Finally, given the importance of telecom infrastructure in growth, what determines changes in telecom penetration in these economies? We present select quantitative and qualitative evidence from a few developing countries to understand the nature of the impact telecommunications has on their economy and society.


onvergence between information and communications technologies (ICT), in particular the internet, and its related applications, has enabled low-cost diffusion of information technology products and many telecommunication services in developing economies. While the telecommunications sector continues to be deregulated worldwide, the coexistence of stark poverty and islands of technology innovation in many developing countries has received little attention in the literature. This paradox provides the motivation for our research regarding the more specific relationship between telecommunications and the state of economic growth in developing countries.

The literature on general ICT infrastructure and its impact on growth is steadily growing. A number of researchers [Norton 1992] have hypothesised that ICT (including telecommunication) infrastructure lowers both the fixed costs of acquiring information and the variable costs of participating in markets. They point out that as the ICT infrastructure improves, transaction costs reduce, and output increases for firms in various sectors of the economy [Röller and Waverman 2001]. Thus investment in ICT including telecommunications infrastructure and derived services provide significant benefits to the economy. At the First World Summit on Information Society, Klaus Schwab, executive chairman of the World Economic Forum pointed out that ICT continues to be the best hope for developing countries to accelerate their development process.

However, in terms of the Network Readiness Index (NRI) published by the World Economic Forum (2003), developing countries1 continue to be far behind (Table 1).2 Wong (2002) finds that the disparity in the intensity of ICT adoption among Asian countries is wider than disparities in their GDP per capita, and that Asia’s share of global consumption of ICT goods, while gradually increasing over time, was consistently lower than its share in global production. While this implies that the competence of the developing economies to benefit from ICT developments is limited, recent discussions [Economist 2005], quoting Len Waverman of the London Business School, focus on how mobile phones, not expensive PCs, are closing the digital divide. A report from Parker (2005) highlights how the expansion of wireless telecommunications in sub-Saharan Africa is bridging the technological divide between them and the industrialised world. This report finds that in 2004, in sub-Saharan Africa, there were more new mobile phone subscribers than in the whole of north America.

According to the World Bank, the private sector invested $ 230 billion in telecommunications infrastructure in the developing world between 1993 and 2003, and that countries with well-regulated competitive markets have seen the greatest extent of investment. Given these findings, it is important to study the relationship between telecommunications and economic growth, if developing countries have to benefit from recent developments in this emerging area to further their economic growth.

Motivation and ObjectivesMotivation and ObjectivesMotivation and ObjectivesMotivation and ObjectivesMotivation and Objectives

While the literature spanning ICT and its effects on growth is now considerably large, this paper specifically focuses on one aspect of ICT – telecommunications and its relationship to growth. Worldwide, the telecom sector has been deregulated, and market structures in this sector have become highly competitive. Because of this, prices of telecom services have decreased everywhere throughout the developing world, creating something close to a revolution in growth of telecommunication services. Simultaneously, telecom technology has also leapfrogged into second and higher generation cellular mobile systems. But developing countries still continue to be quite poor, and the digital divide is much larger than the income divide between the developed and developing world, as pointed out by Wong (2002). The intriguing question that has cropped up is how technology can be used to decrease the cost of access of providing telecom services in rural and remote parts of the developing world. If yes, how can the provision of telecom services affect growth more generally.

There are empirical investigations [Röller and Waverman 2001] that study how telecommunications infrastructure affects economic growth in developed OECD economies, taking into account the two-way causation between them. These relationships have not been studied in the context of developing economies.

Our objective is to develop a causal model that analyses the effect of telecom penetration on economic growth in developing economies, taking into account the two-way causation that exists between them. Based on the research, the contribution of telecommunication services towards growth can be used as benchmark to gain insights into developing policies for diffusion of these services in developing countries.

The paper addresses the following questions to understand the dynamics of this causal connection, i e, is it telecommunication services that accelerates economic growth or overall economic growth that creates the demand for more telecommunication services for their growth to occur? In the context of developing economies, what are the factors that determine demand for and supply of telecom services? Finally, given the importance of telecom infrastructure in growth, what determines the growth in telecom penetration in these economies?

The following section summarises the literature on the subject. The section following the literature survey describes the causal model. We present select quantitative and qualitative evidence from a few projects in developing countries to understand the nature of the impact telecommunications has on their economy and society. The final section discusses data limitations in empirical modelling, summarises the policy implications, and concludes.

Review of LiteratureReview of LiteratureReview of LiteratureReview of LiteratureReview of Literature

The literature investigates the feasibility of telecommunication as one of the determinants of growth, and attempts to entangle the reverse causality between growth and the demand for telecommunication services.

The past literature clearly highlights how most infrastructure investments, including telecommunications, can favourably influence the economy in several ways. First, they reduce the cost of production and increase revenues, for reinvestment by firms. Productivity increases, made possible with the use of telephones (e g, physically dispersed activity possible), increase the productivity of all industries. There is some recent literature that shows that the internet has changed the markets by allowing more efficient search. Similar to other infrastructure investments, investing in telecommunication will increase the demand for the goods and services used in their production and increase total national output. Such investments can increase employment through both direct and indirect effects [Alleman et al 2002].

When compared with other traditional infrastructure, however, there are grounds to expect that the effect of telecommunication services on growth will be somewhat more pronounced. Telecommunications is a little different because of the existence of network externalities, a phenomenon that increases the value of a service with increase in the number of users. Kim et al (1997) demonstrates this phenomenon in their analysis of online service competition.

The impact of telecommunications on growth was first found by Hardy (1980) based on data from 45 countries, with the largest effect of telecommunication investment on GDP found in the least developed economies and the smallest effect, in the mostdeveloped economies.

Garbade and Silber (1978) found that the telegraph and (Trans-Atlantic) cable led to efficient markets by narrowing inter market price differentials. Interesting research by Bayes et al (1999) found that half of all telephone calls involved economic purposes such as discussing employment opportunities, prices of the commodities, land transactions, remittances and other business items. Bayes et al also noted that, the average prices of agricultural commodities were higher in villages with phones than in villages without phones. Leff (1984) argues that firms can have more physically dispersed activity with increased telecom services (in today’s context, this could even mean employee telecommuting) and enjoy economy of scale and scope.3 Using the Peterson Index, Cronin et al (1993b) find a statistically significant causal relationship between productivity growth and the portion attributable to telecommunications.

Eggleston et al (2002) show how basic telecommunication infrastructure can create a “digital provide” by making markets efficient through information dissemination to isolated local residents and improve the living standards of the world’s poor, which in turn accelerates growth. As Eggleston et al (2002) point out, their analysis is based on references and examples, more careful analysis is needed in the context of developing countries.

Common sense suggests that increases in purchasing power (contributed by increased telecom services) also increase demand for such services. Chatterjee et al (1998) point out that income patterns decide the disposable income levels, i e, purchasing power for telecommunication services, and in turn the growth of services.

The reverse causality between telecom services and growth has also been investigated by Cronin et al (1991). Roller and Waverman (2001), in their econometric model, used a simultaneous approach to validate the hypothesis of reverse causality in the context of data for OECD countries. Sridhar and Sridhar (2004) study the impact of mobile phones on growth in developing countries. Waverman, Meschi and Fuss (2005) study

Table 1: Network Readiness Index Ranking of DevelopingTable 1: Network Readiness Index Ranking of DevelopingTable 1: Network Readiness Index Ranking of DevelopingTable 1: Network Readiness Index Ranking of DevelopingTable 1: Network Readiness Index Ranking of Developing

(1=high; 102=low)

Country Network Readiness Index

Angola 99 Bangladesh 93 Cameroon 83 Ethiopia 101 Ghana 74 Gambia 82 Haiti 100 Indonesia 73 India 45 Kenya 84 Madagascar 92 Mali 96 Mozambique 97 Malawi 88 Nigeria 79 Nicaragua 94 Pakistan 76 Senegal 81 Chad 102 Tanzania 71 Uganda 80 Ukraine 78 Zambia 85 Zimbabwe 95

Source: World Economic Forum, 2003.

the impact of mobile telephony in developed and developing economies.

In this paper, we examine the relationship between telecom infrastructure and economic growth in developing economies, as these countries can use diffusion of telephones for spreading growth more rapidly. In developing countries rural teledensity is very low. One of the reasons is the high cost of providing telecommunication services in rural areas and low purchasing power of rural population. While in developed countries, 90 per cent of the households can afford monthly expenditure of US$30 on telecommunication services, only 5-6 per cent of the households can afford such expenditure in developing countries such as India [Jhunjhunwala 2000].

One way to improve rural teledensity is to reduce the cost of access loop for providing telecom services using wireless technologies [Jain and Sridhar 2003].4 Jha and Majumdar (1999) also note that for developing countries, where penetration rates of telephones are extremely low, catching up with developed countries in terms of telecom infrastructure has meant investment in wireless and cellular mobile systems, bypassing investment in fixed landlines. Reduced per line cost, quick deployment and better available technology are reasons for such growth of cellular services in developing countries [Jain and Sridhar 2003; Jha and Majumdar 1999]. Further, it is easy to see why mobile phones could have a great impact in developing countries, they do not rely on a permanent source of electricity supply.

Thus, there are a number of issues that are relevant to be considered exclusively in the context of developing countries. This provides the motivation for us to more comprehensively model the growth of telecommunication services and investigate the strength of its causal relationship with the level of economic growth, and examine how telecommunications can be used as a tool to enable growth in developing countries.

Calculations from the data from developing countries show that in these economies as a whole, the compounded annual growth rate (CAGR) of cellphones over the period 1996-2001 was 78 per cent compared to a growth of mere 7 per cent for main telephone lines over 1990-2001 (Table 2). Further, we find simple Pearson’s correlation coefficients between GDP per capita and total, main and cellphone penetration to be respectively 0.59, 0.58 and 0.24 (all statistically significant). Based on this, one can expect to find substantial effects of telecom penetration on GDP.

Below we discuss our approach and the model that can be potentially used to empirically model such relationships.

Approach and ModelApproach and ModelApproach and ModelApproach and ModelApproach and Model

We develop a structural model that traverses from the microlevel demand for and supply of telecom services to aggregate changes in telecom penetration and the macro production function in which GDP is determined by traditional inputs including capital (net of telecom), and labour stock, along with telephones. We do not attempt to provide a general explanation of the determinants of national output. This means we have not included measures of government deficits or of trade openness that, the literature shows, affect national output. Rather, we use a macro production function approach that relates inputs to output. We endogenise telecom investment. The causal model we develop demonstrates various relationships between telecom penetration and growth, factors that determine the demand for and supply of telecom services, and those that influence the change in telecom penetration.

While this model has been developed by Roller and Waverman (2001), we view the contribution of the model in this paper as being two-fold. First, in modelling these relationships, we

Table 2: Annual Growth of GDP Per Capita, Main TelephoneTable 2: Annual Growth of GDP Per Capita, Main TelephoneTable 2: Annual Growth of GDP Per Capita, Main TelephoneTable 2: Annual Growth of GDP Per Capita, Main TelephoneTable 2: Annual Growth of GDP Per Capita, Main Telephone
Lines and Cellphone Penetration in Developing EconomiesLines and Cellphone Penetration in Developing EconomiesLines and Cellphone Penetration in Developing EconomiesLines and Cellphone Penetration in Developing EconomiesLines and Cellphone Penetration in Developing Economies

(in per cent)

Country CAGR, 1990-CAGR, 1990-2001, CAGR, 1996-2001, 2001, GDP Mainlines per Cellphones per Per Capita 100 Inhabitants 100 Inhabitants

Afghanistan NA -4.29 NA Angola -2.29 -2.09 66.54 Armenia -2.95 -0.96 101.53 Azerbaijan -2.33 2.10 81.94 Burundi -1.19 0.88 NA Benin 1.54 9.49 83.99 Burkina Faso 2.06 8.70 100.00 Bangladesh 2.75 5.74 NA Bhutan 3.03 17.41 NA Central African

Republic -0.65 2.92 45.95 Cote d’Ivoire -0.53 9.29 88.32 Cameroon -0.85 5.68 101.53 Republic of Congo -1.20 0.00 NA Comoros -45.66 4.14 NA Eritrea 3.67 7.71 NA Ethiopia 0.95 4.28 NA Georgia -6.78 4.84 130.89 Ghana 1.08 12.25 53.90 Guinea 1.17 4.97 62.26 Gambia 0.21 12.18 57.49 Guinea-Bissau -0.77 4.03 NA Haiti -45.26 2.88 NA Indonesia 2.48 15.85 49.45 India 3.34 16.66 66.10 Kenya -1.08 2.65 140.19 Kyrgyz Republic -3.93 0.72 NA Cambodia 1.30 19.33 40.05 Lao PDR 3.52 16.30 37.89 Liberia 0.66 -4.02 NA Lesotho 1.53 2.95 87.89 Moldova -7.93 2.65 152.07 Madagascar 0.23 3.90 90.30 Mali 1.76 11.50 87.89 Myanmar NA 11.80 6.99 Mongolia -1.11 4.15 142.43 Mauritania 1.48 10.49 NA Malawi 1.11 4.40 54.31 Niger -1.06 4.66 NA Nigeria 0.80 3.63 87.17 Nicaragua -0.39 7.32 70.62 Nepal 2.62 12.76 NA Pakistan 1.33 9.91 49.58 Rwanda -1.54 3.93 NA Sudan 3.05 15.57 NA Senegal 0.96 12.44 132.02 Solomon Islands -2.29 1.93 16.06 Sierra Leone -4.78 3.07 NA Somalia NA 6.20 NA Sao Tome Principe -44.01 5.50 NA Chad 0.04 5.95 NA Togo -1.14 11.23 NA Tajikistan -8.22 -1.92 NA Tanzania 0.61 3.84 86.69 Uganda 2.75 3.44 96.75 Ukraine -5.80 3.80 104.74 Uzbekistan -1.49 -0.25 35.72 Vietnam 5.07 31.55 60.53 Yemen 0.32 6.11 54.31 Democratic

Republic of Congo 17.06 18.78 NA Zambia -2.03 -0.69 83.62 Zimbabwe -0.68 4.93 NA Average, all

developing countries -2.03 6.61 77.99

Figure: Causal Model of Telecommunications and GrowthFigure: Causal Model of Telecommunications and GrowthFigure: Causal Model of Telecommunications and GrowthFigure: Causal Model of Telecommunications and GrowthFigure: Causal Model of Telecommunications and Growth

Economic Growth (GDP)

+++-++-++++ + + -+ + -+ + + Demand SideDemand SideDemand SideDemand SideDemand Side Supply SideSupply SideSupply SideSupply SideSupply Side Capital (Net of Telecom Investment) Labour Force Demand for Telecom Competition in the Market Place Price of Telecom Services Government Deficits/Debt Telecom Investment Land Area Telecom Penetration

identify variables that are of relevance, and operationalise them in the context of developing economies. Next, we highlight the use of different variables than what Roller and Waverman (2001) use in their paper, based on certain considerations.

In the figure, we summarise factors that affect telecom penetration and GDP of the country through a causal model, highlighting the existence of all major cause-and-effect links, indicating the direction (cause → effect) of each relationship. The relationship is positive (or negative) if a change in the causal factor produces a change in the same (or opposite) direction in growth. A closed sequence of causal links represents a causal loop. The causal loop is positive if it has all positive links or even number of negative links. Otherwise, it is a negative loop.

The primary relationship in the figure is the explanation of growth, which depends on traditional inputs, apart from telephones. We expect all the inputs highlighted in the figure – capital (net of telecom), labour, and telecom penetration – to have a positive effect on total national output (GDP). In the figure, there is at least one positive loop indicating increased growth of both economic growth and telecom penetration.

Note that, based on the importance of mobile phones, and data available, it is possible to empirically estimate at least three specifications of the model shown in the figure, as it concerns telecom penetration – main landlines only, mobile phones only, and all telephone lines. Further, when one empirically estimates the impact of telecom penetration on growth, unobserved country-specific characteristics are to be taken into account, if one intends to do this cross-regionally.

Our next step in the causal model, demonstrated in the figure, is to examine demand for telecom service in the network of relationships that determine growth. Increased economic growth increases the disposable income of population and hence affects demand for telecom services positively. In most of the developing countries, initially, the government was providing telecommunications service and there was a huge waiting list for main telephones. For example, in India, even after private operators were allowed to provide competitive service, the waiting line for main telephones was around 1.649 million in 2001. Even after the introduction of cellular services in 1995, the waiting list continued to grow from 2.28 million to 2.89 million in 1996 and 2.71 million in 1997. As this example demonstrates, the waiting list for mainline telephones tends to be quite long in developing countries. Hence, to operationalise demand for telephones in the context of developing countries, one should define effective demand for telecommunications infrastructure as the sum of existing teledensity and waiting list for mainline telephones, as Roller and Waverman (2001) rightly point out.

We model the demand for telecom as for a normal good or service, as being dependent on income and price. As the figure shows, this demand is a function of the real price of telecommunication services and real per capita GDP. One may use telephone service revenue per user as a measure of telecom price, similar to Roller and Waverman (2001). Alternatively, the monthly subscription charge may be used to operationalise this. These charges are normally referred to as rentals. While the user pays for usage, rental as a measure of telephone price is valid because monthly rentals are normally used to recover the capital cost of providing telecom services. Although we are interested only in penetration, one should ideally use measures of access to telecom infrastructure (rental), as well as actual usage of the infrastructure (revenue per user) as prices.5 As in traditional microeconomics, we expect the price (both measures) elasticity of demand to be negative, and the income elasticity, to be positive.

To model the supply side of telecommunications, as the figure shows, annual telecom investment (TTI) is determined by traditional economic variables such as demand and price. These factors may be operationalised using telecommunication service price (both price measures highlighted above), and the market potential as represented by the waiting lines for mainlines, especially important in the context of developing economies.6 In general, price has a positive effect on supply and on telecom investment, as shown in the figure.

Next, the supply of telecom investment depends on potential demand measured by the waiting list for main telephone lines. As with the price, the expected effect is positive.

Further, competition and liberalisation in the market in developing countries has encouraged private operators in addition to government operators, to invest in telecommunication services. The more competitive the market structure is, the greater would be the supply of telecom investment that would be forthcoming, other factors remaining the same. However, as the standard framework shows, as the competition increases, prices also decrease which could have a negative effect on telecom investment, all else remaining the same. Empirical work should throw light on the relative strengths of these effects.

Government deficit or central government debt, determine governments’ ability to invest in telecom infrastructure. While higher telecom investment by the government, could, in principle, cause higher deficits or government debt, the first-order effect is that lower government deficits or debts cause telecom investment to be higher. This is, of course, assuming that the government is a major player in telecom investment, as they are in developing countries.

The empirically oriented reader might note that the causeeffect relationships that are indicated in dotted lines in the figure are those that may or may not be necessarily captured in the empirical model, depending on the availability of appropriate data. See Sridhar and Sridhar (2004), for examples of the typical data limitations one would encounter in empirically validating the model in the context of developing countries.

Finally, consistent with Roller and Waverman (2001), we characterise the growth of telecom penetration as a function of telecommunications investment and the country’s geographic area. We expect that total telecom investment will have a positive impact on the penetration rate, whether it is landlines, cellphones, or total penetration. The land area is relevant for determining the extent of change in telecom penetration. Other things (such as investment) remaining the same, a country with a large land area would have a slower rate of change in its teledensity, when compared to one with a smaller land area.

In sum, the figure shows that telecom penetration, which positively impacts GDP, is determined by GDP per capita and the price of telecom services. Competition or market structure determines the price of telecom services and the magnitude of total investment in the sector. To close the loop, telecom investment affects penetration positively if other factors were to remain the same. Sure enough then, the model shows that competition is the only exogenous policy variable that affects both demand for and supply of telecom services through their effect on prices. This summary of the model also clearly demonstrates that single equation or OLS methods would be unsuitable for empirically estimating it, systems and simultaneous methods would be more appropriate.

Further, in modelling telecom services and their effects on growth, it would be difficult to empirically examine threshold effects, which network externalities in telecom services suggest could be important. Both diffusion of innovation theory [Rogers 1983] and economics of network externalities [Rai et al 1998] suggest that the cumulative number of subscribers of communication services over time is expected to follow a non-decreasing S-shaped distribution.7 After a “critical mass” is attained, the services take-off following an exponential growth pattern. Finally, given a finite potential population, the growth levels off representing the saturation level.

There are two reasons why one cannot empirically examine saturation levels or threshold effects in modelling telecom services in the context of developing countries:

  • (1) Most of the developing countries are currently witnessing growth of telecom services (Table 2) and are in the growth phase. The saturation limit depends on a number of factors including growth in disposable income of potential subscribers, price of services, competition in the market place, price and availability of alternative communication channels, such as the internet and regulatory policies regarding spectrum allocation and interconnection. Most countries have witnessed a steep drop in prices due to competition, which is one of the main factors for the tremendous growth in the subscriber base that is observed in most of the developing countries. Hence explicit upper bounds on subscriber base are not very relevant in the context of these countries yet.
  • (2) Further, attempts to empirically model saturation effects have not been successful so far. Rai et al (1998) point out that previous attempts to determine saturation levels (using Logistic and Gompertz models) for internet growth have not been successful. This is because the saturation level that was predicted, was found to be much less than the observed size of the network in 1998. Further, the saturation limit, as discussed above, depends on a number of factors including government policies regarding spectrum allocation and the rate and nature of technology change. It is extremely difficult to accurately model all these factors to determine saturation limits. This is a reason why empirical modelling of saturation effects in the past has not been very successful.
  • Note that the relationships shown in the figure can be empirically estimated separately for mainline and mobile phones. In Table 3, we provide a list of the variables that can be used in these models of telecom services, if they were to be empirically estimated, as in Sridhar and Sridhar (2004).

    Quantitative EvidenceQuantitative EvidenceQuantitative EvidenceQuantitative EvidenceQuantitative Evidence

    Roller and Waverman (2001) find a 1 per cent increase in the number of main telephone lines per capita, increases economic growth by 0.15 per cent in the OECD countries, not controlling for country-specific fixed effects, and a much lower, 0.045 per cent when controlled for fixed effects.

    Table 3: Variables that Can Be Used in Empirical WorkTable 3: Variables that Can Be Used in Empirical WorkTable 3: Variables that Can Be Used in Empirical WorkTable 3: Variables that Can Be Used in Empirical WorkTable 3: Variables that Can Be Used in Empirical Work


    Real gross domestic product (GDP)

    Real GDP per capita

    Number of main telephones per 100 inhabitants

    Number of cellular subscribers per 100 inhabitants

    Total telecom penetration computed as the sum of mainline (MTEL) and cellular teledensity

    Waiting list for mainlines per 100 population

    Sum of mainline teledensity and waiting list for mainlines per 100 inhabitants

    Sum of total telecom penetration and waiting list for mainlines per 100 inhabitants

    Growth of total telecom, mainline and cellular penetration

    Annual real gross fixed capital formation net of telecom investment

    Real residential telephone monthly subscription

    Real cellular monthly subscription

    Average of monthly subscription charges for mainline and cellular service

    Total telephone revenue per user

    Main telephone revenue per user

    Cell phone revenue per user

    Real annual telecommunications investment

    Total labour force

    Time period

    Sridhar and Sridhar (2004) empirically estimate the model in the figure, using country-specific fixed effects and 3SLS for developing countries defined as low-income by the World Bank. They find that that a 1 per cent increase in tele-density (total telephones (including landline and mobile phones) per 100 population) increases national output by 0.15 per cent without fixed effects and by 0.10 per cent with fixed effects for developing countries. They find the income elasticity of demand for telecom services is positive and greater than 1 (being 1.15 and 1.14 respectively without and with fixed effects), indicating elastic demand. This implies that the reverse causation we suspect exists between telecom and economic growth, indeed, is true. Sridhar and Sridhar (2004) also estimate a separate system of equations for cellphones. Even with country-specific fixed effects, they find that cellphones have a positive role in contributing to national output. Specifically, that for every 1 per cent increase in cellphone penetration, national output increases by 0.01 per cent, small, but positive and significant. Further, they find that price has a positive effect on telecom investment, as expected.

    Waverman, Meschi and Fuss (2005) in their study of low and middle-income countries, find that mobile telephony has a positive and significant impact on economic growth, which is twice as large in developing countries when compared to that in developed countries.

    So, broadly the empirical evidence so far, seems to concur with the model developed in the figure.

    Qualitative EvidenceQualitative EvidenceQualitative EvidenceQualitative EvidenceQualitative Evidence

    The previous section highlighted the effect of telecom penetration on growth, in the network of several exogenous and endogenous relationships. In this section of the paper, we focus distinctively on the applications of telecom, and how they have contributed to growth and social development in many developing countries.

    A number of projects initiated by voluntary non-profit organisations and entrepreneurs in developing countries have demonstrated the positive impacts of telecom penetration on economic and social development in these countries. This section summarises representative projects as qualitative evidence.

    Village Phone Programme in Rural Bangladesh

    Richardson et al [2000] summarises rural Bangladesh’s Grameen Telecom’s Village Phone Programme. Bangladesh’s Grameen Bank, with expertise in microcredit, collaborated with Grameen Telecom, a wireless telecommunications service provider, enabling women members of the Grameen Bank’s revolving credit system, to retail cellular phone services in rural areas. This pilot project involved the setting up of 950 village phones providing telephone access to more than 65,000 people. When fully completed, the access was envisaged to increase to about 40,000 village phone operators, having begun from a modest 950. When completed, the village phone operators would be employed for a combined net income of $24 million per annum.

    This study estimates that the consumer surplus for a single phone call from a village to Dhaka, a call that replaces a physical trip to the city, ranged from 2.64 per cent to 9.8 per cent of mean monthly household income. This study also found that 42 per cent of calls made using the village phones, were regarding secure remittance of savings into Bangladesh from other countries. The income village phone operators derived, was about 24 per cent of the household income on average, and in some cases it was as high as 40 per cent of the household income.

    This project represents a case in which village phone operators become socially and economically empowered, and one in which telephones can be used for the overall economic growth of the local economies.

    Manobi’s Mobile Phones

    This project from Manobi, Senegal, from Infodev 2003, demonstrates the use of wireless e-services to strengthen the livelihood of Sénégalese fishermen in early 2003. This project used mobile phones to provide Manobi’s fishermen with up to date weather reports and market price information. In addition to this information, the fishermen were able to use interactive technology to input fish stock information for marketing purposes, and to log departures and estimated times of return so that local fishing unions could be alerted if fishing boats failed to return on time. In 2003, some 57 individual users (41 buyers and 16 fishermen) had registered for the service. Recently Manobi extended the short message service (SMS) on mobile phones and launched ‘Xam Marsé’, a free-access SMS market information service to cater to Senegalese farmers, traders, hoteliers or housewives. Senegalese now receive everyday, on their telephone, a free SMS containing information on the product of their choice on any selected market.

    This project thus enabled fishermen in this community to improve their sales and profit margins and created safer working environments.

    Chennai’s Foundation of Occupational Development(FOOD)

    Based in Chennai, FOOD began the inter-city marketing network project in April 2001 to help poor women in urban areas increase their incomes [Infodev 2003]. Initially, FOOD worked with some 100 existing women’s self-help groups representing between 1,000 and 2,000 women and their families. An initial survey of these groups indicated that while many women derived a small income from producing goods at home (food products, soap, repackaged food items), they were generally weak at marketing their products and finding customers. Typically, they sold their products to visiting middlemen and made little profit. FOOD provided them training in marketing and the use of “social capital,” encouraging the groups to focus on production, or marketing, or both. FOOD provided each group with a cellphone to facilitate contact between production and marketing groups, and between groups and customers. The initial provision of 100 mobile phones catered to the needs of about 1,000-2,000 women. The use of mobile phones enabled these women to earn monthly profits of Rs 500-2,000 for part-time work, and definitely increased their profit margins, although marginally.

    This project highlights how mobile phones have the potential to increase sales volumes of women’s marketing and production groups in general.

    Nigeria’s Use of Satellite Phones

    The Fantsuam Foundation in Kafanchan, Nigeria, works more generally to give local communities access to health and educational resources through the Internet. In the first phase of the project, Fantsuam worked with local communities to establish three Community Learning Centres (tele-centres). The goal of this project was to increase access, particularly of women to ICT facilities in southern Nigeria. These facilities were primarily used by healthcare workers, nurses (most of whom were women), students and staff of healthcare training institutions. A mobile community tele-centre with satellite connectivity was formed, and a satellite phone was deployed to be used by local community members for making emergency calls.

    These case studies, while being primarily pilot projects in nature, demonstrate qualitatively, but clearly the many applications of telephones, especially of mobile phones, and how they can be used to further the social and economic growth of remote areas in developing countries.

    Description of Telecom Services and GrowthDescription of Telecom Services and GrowthDescription of Telecom Services and GrowthDescription of Telecom Services and GrowthDescription of Telecom Services and Growth
    Parameters in Developing CountriesParameters in Developing CountriesParameters in Developing CountriesParameters in Developing CountriesParameters in Developing Countries

    Tables 4 and 5 give descriptive details of various parameters of telecom services and growth, for all developing countries defined as low income by the World Bank. As Table 5 shows, the observations for cellphones are lower than those for main lines, as most of the developing countries started experiencing rapid cellphone penetration only after 1995. The average change in penetration for landlines and cellphones are greater than 1 and 2 respectively (Tables 4 and 5) suggesting continual increase in landline penetration, and more so for cellphones. Interestingly, the average and maximum waiting lists for landlines (per 100 population) in countries with cellphones (the smaller sample, Table 5), are much smaller than those in the full sample. This shows that countries with rapid cellphone penetration did not have waiting lists to the same extent as those without. The average telecom investment for the smaller sample of countries that experienced rapid cellphone penetration is also higher than that for the full sample.

    On average, the total telephone penetration in the developing countries we examined, is much lower (being 2.6 per 100 inhabitants) than that observed in the OECD countries by Roller and Waverman (2001) (30 per 100 inhabitants). Among developing countries, the maximum total tele-density is itself 20, observed for Ukraine in 1999. The landline penetration is even less. On average the GDP per capita for these countries is much lower than that observed for the OECD group of countries, reasonable to believe.

    In developing countries, the average mainline price (monthly subscription) is quite low when compared to that for cellphone subscription. Here it may be relevant to note that in developing economies, for basic mainlines, the tariffs are always kept low by the regulator to make the service more affordable. In contrast, the revenue per user is higher for the total telephone lines and landlines than they are for cellphones. This is due to the larger subscriber base and usage of landlines.


    Note that developing countries with low penetration rates for main telephone lines, find the infrastructure for cellphones to be relatively inexpensive and less time-consuming to install. Cellphone penetration in developing countries started increasing rapidly during the second half of the 1990s due to changes in telecom regulation, and move to competitive market structures.

    Table 4: Description of Relevant Data for Full (of Landlines and Cellphones) Sample (N=256)Table 4: Description of Relevant Data for Full (of Landlines and Cellphones) Sample (N=256)Table 4: Description of Relevant Data for Full (of Landlines and Cellphones) Sample (N=256)Table 4: Description of Relevant Data for Full (of Landlines and Cellphones) Sample (N=256)Table 4: Description of Relevant Data for Full (of Landlines and Cellphones) Sample (N=256)

    Variable Mean Maximum Minimum Std Dev
    GDP (constant 1995 $) 34,649,884,169 496,018,000,000 354,976,100 85,397,493,376
    Time trend 6.55 12.00 2.00 3.00
    Total telephone lines per 100 population 2.61 20.32 0.07 4.46
    Total landlines per 100 population 2.37 19.89 0.07 4.31
    Change in total telephone penetration (over previous year) 1.13 2.05 0.65 1.16
    Change in main landline penetration (over previous year) 1.08 1.58 0.65 1.10
    Waiting line for main landlines per 100 population 0.72 7.07 0.00 1.31
    Capital stock (net of telecommunications capital), constant 1995 $ 1,110,798,788 110,587,821,261 41,882,003 5.89
    Labour force 7,765,659 460,533,000 491,188 4.74
    GDP per capita, in constant US $ (1995=100) 447.79 1620.91 92.21 254.50
    Total telephone revenue per user (constant 1995 US $) 10498.13 671057.99 0.98 7.48
    Landline phone revenue per user (constant 1995 US $) 11116.85 671057.99 0.99 7.40
    Landlines, monthly subscription charges (in US constant $) 0.05 0.41 0.00 0.06
    Average (land and cell) monthly subscription charges (in US constant $) 0.10 1.97 0.00 0.15
    Telecom investment (constant 1995 US $) 34,597,511 3,617,878,739 13,051 6.54

    Table 5: Description of Relevant Data for Cellphones (N=65)Table 5: Description of Relevant Data for Cellphones (N=65)Table 5: Description of Relevant Data for Cellphones (N=65)Table 5: Description of Relevant Data for Cellphones (N=65)Table 5: Description of Relevant Data for Cellphones (N=65)

    Variable GDP Time trend Cellphone penetration, per 100 population Change in cellphone penetration (over previous period) Waiting line for main landlines per 100 population Capital stock (net of telecommunications capital) Labour force GDP per capita, in constant US $ (1995=100) Total telephone revenue per user Cellphones, monthly subscription charges (in US constant $) Average (land and cell) monthly subscription charges (in US constant $) Cellphone revenue per user Telecom investment Mean 60,358,480,231 10.08 0.73 2.05 0.53 2,420,660,220 15,642,012 418.88 9,431.04 0.11 0.06 7,488.53 94,726,760 Maximum 496,018,000,000 12.00 5.13 7.58 5.37 110,587,821,261 460,533,000 1,131.17 276,606.54 0.37 0.22 321,370.40 3,617,878,739 Minimum 1,454,436,000 6.00 0.01 1.00 0.00 235,725,168 1,761,167 115.99 98.17 0.02 0.00 140.16 2,322,581 Std Dev 119,589,193,478 1.50 1.09 1.56 0.91 6 4 1.80 6.49 0.08 0.04 5.93 5
    Economic and Political Weekly June 24, 2006 2617

    Most of the developing countries leap-frogged into secondgeneration mobile cellular systems, deploying them at a much greater rate compared to landline installations.

    Waverman, Meschi and Fuss (2005) argue that for economies without fixed lines, or where mobile phones supplement the low roll-out of fixed lines, there should be no inherent difference in the growth dividend of a phone, whether it is mobile or fixed. However, there are some reasons why we may expect cellphones to contribute positively to national output, which are not always available with service providers of landlines. Cellphone penetration reduces transaction costs, including, but not limited to, decisions relating to production of goods and services. For instance, value added services such as stock quotes and commodity prices provided by cellular service providers at affordable prices using the latest digital cellular technologies, may be expected to produce tangible economic outcomes. The Manobi case also demonstrates this further and shows the wide-ranging social applications of mobile phones in rural communities, which may not be available with landline phones.

    Based on the data, we find that a number of countries that experienced rapid increases in cellphone penetration experienced political turmoil during the 1990s. These countries – Cote d’Ivoire, Moldova, Armenia, Ukraine, Kenya, Kyrgyz Republic, Togo, Zimbabwe – experienced rapid decreases in their national output during the 1990s (Table 2), specifically Armenia, Moldova, Kyrgyz Republic and Ukraine, due to the splitting of the USSR. While cellphones may be expected to contribute positively to national output, Sridhar and Sridhar (2004) find that the effect of cellphones may not be strong enough to offset the negative impacts of the country-specific factors that decrease national output significantly. This is not to say that cellphones decrease output, but a disclaimer to remember that their positive effect may not be strong enough to offset the effect of overarching exogenous factors such as political turmoil that caused to decrease national output in these countries quite significantly.

    Further, we believe that traditional economic factors – price and income – that determine the demand for other telephone services (including main landlines) should explain demand for cellphones as well. Moreover, the income elasticity of demand must be higher for cellphones than for landlines, since cellphones are still viewed as a luxury and mobile handsets continue to be expensive in developing countries. Thus, we expect micro, household decisions relating to cellphone services to be dependent on economic factors, as with other phone services.

    Finally, when one empirically examines the effect of investment on the rate of change in tele-density, we note that in most of the developing countries, cellular services are greenfield projects, requiring huge capital investment to commence. Initial investment in interconnection facilities to connect to other networks may not immediately translate in to increase in subscriber base. It is possible that there could be some time lag before greenfield projects translate to increased penetration in the case of cellular phone services.

    Limitations of Empirical WorkLimitations of Empirical WorkLimitations of Empirical WorkLimitations of Empirical WorkLimitations of Empirical Work

    There are data limitations that could limit the value of the model presented in the figure, if it were to be empirically estimated for main landlines and mobile phones. The cellphone sample could be quite small since data on cellphone related information are reliable and available for all developing countries only post 1996, as is clear from Table 5.

    Government deficit, as demonstrated in the causal model, determines governments’ ability to invest in telecom services or infrastructure, but reliable estimates of government deficit are not available either from the International Telecommunications Union (ITU) or from World Development Indicators (WDI). Data on central government debt, as a proportion of GDP, available from the WDI database, are highly erratic across countries, time periods and sparse. Land area data are relevant for determining the extent of change in telecom penetration, but are time-invariant. Hence one would be unable to use this variable in panel data models. Finally, one would be unable to use an adequate indicator for regulatory market structure, relevant for explaining the supply of telecom investment, since time-series data on them are not available, although cross-sectionally they can be distinguished. It is possible that when these data limitations are overcome, the model proposed in the figure can be empirically estimated in a robust manner for landlines and mobile phones, enabling to determine their growth impacts in developing countries.

    Policy ImplicationsPolicy ImplicationsPolicy ImplicationsPolicy ImplicationsPolicy Implications

    Our model shows, how, in the context of developing economies, we can expect telecom penetration to affect GDP and how telecom investment can impact penetration. When empirically estimated, it has implications for how developing economies can increase their penetration with increases in telecom investment, and if they do, how much they can expect their national output to grow. The case studies demonstrate the many applications specifically of mobile phones to improve the incomes of communities and to socially empower them.

    While the extent of cellphone contribution to growth might, in theory, be small, policy-makers need to create a conducive competitive climate for the growth of this industry segment. As we may recall from the figure, competition in the market, including entry barriers (such as licence fees), are the most important exogenous policy variable that could determine the extent of telecom’s effects on growth, through their impacts on prices and investment. Countries such as India, set an upper limit on foreign direct investment and cite security concerns for restricting the flow of foreign investment in the telecom sector. Foreign investors also are reluctant to invest when telecom policies are not transparent and stable [Sridhar 2000]. Policy-makers and regulators should promote a conducive and competitive climate for foreign investment so that the capital investment required for building telecom infrastructure can be met.

    Concluding RemarksConcluding RemarksConcluding RemarksConcluding RemarksConcluding Remarks

    There is no doubt regarding the fact that most developing economies have leap-frogged in cellular telephony as a quick and inexpensive way of increasing telecom penetration. Most of these economies have significantly deregulated their telecom sector, and investment to increase telecom penetration (especially using the wireless services) does not seem to be the big issue any more. The big question that continues to haunt many of these economies is how improved ICT infrastructure can be used to accelerate their economic growth and alleviate poverty. The cases presented here should be some kind of a guide to answer these questions. As demonstrated in the cases, reducing the digital divide is largely possible only by empowering residents of developing countries through information regarding prices, job opportunities, and markets. This is not a substitute for actual economic growth, and also may not offset negative economic effects caused by overarching exogenous shocks, but a good enabler for economic growth to trickle down, once it occurs.




    [Thanks are due to M Govinda Rao for facilitating preliminary review of the paper. We are thankful to Nirvikar Singh for helpful comments regarding an earlier version. The authors thank the faculty at Management Development Institute for their useful comments during a seminar where this paper was presented. We thank the Indian Institute of Management, Lucknow, India, for facilitating access to the WDI online database. Finally, we thank the National Institute of Public Finance and Policy, and Management Development Institute, for facilitating the research. Thanks are due to the participants at the UNU-WIDER jubilee conference where a revised version of this paper was presented.]

    1 Low-income economies as defined by the World Bank (2002). 2 NRI is defined as a nation’s degree of preparation to participate and benefit from ICT developments [WEF 2003].

    3 Sridhar and Sridhar (2003) look at the impact of telecommunication infrastructure and the telecommuting it enables, on spatial dispersion of population, using data from the US. They find that telecommuting, in fact, contributes to centralisation of American cities.

    4 For an understanding of relevant issues in rural telecom in India, see Sridhar et al (2000).

    5 These measures have the flexibility that in the case of mainlines and mobile phones, the data would be available separately. One, for instance, could use the rental charges for mainlines, cellular services in models for mainline and cellphones separately, and use the average of the two in the model for total telecom penetration and to explain supply of telecom investment.

    6 Note one consideration with the use of price on the supply side of telecom investment (mainlines or cellphones). In modelling the demand for mainline and cellphone services, this is straightforward – one can easily use the price (revenue per user or the rental) of getting a main landline and cellular service respectively, as the telecom price. The supply of telecom infrastructure (investment), is, however, more complex. Telecom infrastructure is composed of access networks (landlines and cellular access) and backbone networks that interconnect access networks. Completing a landline or a cellular call depends on the existence of interconnection across these networks. This makes it wrong or inadequate to make supply of telecom depend only on mainline price or cellphone price. For these very reasons, the International Telecommunications Union (ITU) does not report annual telecom investment by landline/cellular services. Hence, one should use either average telephone price or total revenue per user and average of total subscription price, as the appropriate price variable to explain the supply of telecom investment.

    7 Researchers have used a logistic formulation of S-curve pattern in estimating demand for Bell systems residence main telephones [Gurbaxani 1990]. Both logistic and Gompertz growth models have been used for predicting growth of the internet [Gurbaxani 1990].


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