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Economic PolicyFifty-Second Panel Meeting

Hosted by EIEF,22-23 October 2010

Mobile Telecommunications and theImpact on Economic Development

Harald Gruber and Pantelis Koutroumpis

The views expressed in this paper are those of the author(s) and not those of the funding organization(s) or ofCEPR, which takes no institutional policy positions.


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Mobile telecommunications and the impact on economic development

Harald Gruber

1

(European Investment Bank, Luxembourg)

Pantelis Koutroumpis (Imperial College London)

October 2010

Panel draft for 52nd

Economic Policy Panel

Abstract

The paper assesses the impact of mobile telecommunications on growth taking the latter

as a determinant of the diffusion of mobile telecommunications. The contribution of

mobile telecommunications infrastructure to economic growth for low penetration

countries is found to be smaller than for high penetration countries, suggesting increasing

returns from mobile adoption and use. Growth effects are estimated for individual

countries and compared. More generally, the annual contribution of mobile

telecommunications infrastructure to growth for high income countries is double that of

low income countries. The increasing returns are also emerging when assessing the

impact of mobile telecommunications infrastructure on productivity growth.. Policyrecommendations are provided to further support the diffusion of mobile

telecommunications especially in low income countries.

1Corresponding author: [email protected]. The views expressed are of the authors and need not necessarily

reflect that of the EIB.


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1. Introduction

The mobile telecommunications industry has grown rapidly over the last three decades

representing one of the most intriguing stories of technology diffusion2. Since 2002

mobile subscribers have exceeded the number of fixed lines globally. The process to

achieve what fixed phones have struggled for more than 120 years took less than a fifth

of the time for mobile networks. This cross-over time of mobile users has been even

shorter for developing countries. At the end of 2009 the number of mobile

telecommunications subscribers reached 4.6 billion, which is equivalent to 67 per cent of

the world population. This technology is particularly relevant in developing countries,

where there are more than twice as many subscriptions (3.2 billion) as in developed

countries (1.4 billion)3. The importance of the telecommunications sector becomes also

evident by comparing the share of telecommunications revenues in GDP:

telecommunications services accounted for on average 4.8% of the total GDP of sub-

Saharan Africa compared to 3.1% in the European Union.

While the determinants for the diffusion of mobile telecommunications have been

extensively studied (e.g. Gruber and Verboven, 2001; Koski and Kretschmer, 2005;

Gruber and Koutroumpis, 2010) relatively little is known about the impact of this

technology at a macroeconomic level. The pervasiveness of the technology in terms of

transforming the way economic activity is organized suggests that mobile

telecommunications has features of what is referred to as general purpose technology

(Bresnahan and Trajtenberg, 1995; Helpman, 1998). In fact, mobile telecommunications

2For a survey of the industry see Gruber (2005)3Source of data is the International Telecommunications Union.


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deeply affect the way users interact and have significant externalities for the economic

activities that they are used. There is widespread anecdotal evidence about the surge of

new companies and business models with worldwide brands linked to the sector (e.g.

Nokia, Vodafone) and the appearance of new modes of communication such as personal

reachability. Because of the lower access cost to the user compared to wired

telecommunications, linked with the solution of the problem of creditworthiness of

customer through prepaid cards, the technology could reach completely new segments of

population particularly in developing countries. As revenues from mobile

telecommunications account nowadays for a significant percentage of GDP especially in

developing countries, mobile telecommunications have also become an important and

efficient means for tax collection. Moreover, telecommunications infrastructure has

significant network externalities. In line with the network economics literature, one of

their key characteristics is that the value of the network increases with the usage base.

This has frequently been referred as a direct network externality (Economides and

Himmelberg, 1995), with the implication that critical mass effects may occur when

certain threshold levels of diffusion occur which can then trigger off additional benefits,

such as the availability of new services. Ultimately one would expect increasing returns

from the adoption of the technology. The implication suggests that high mobile

penetration yields incentives for further investment, very much along the success breeds

success paradigm. As a result low penetration countries, which typically are developing

countries, could have a double disadvantage: they not only have a lower growth impact

due to lower mobile diffusion; they also have lower incentives for further development of


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the mobile network. Hence, the economic cost in terms of foregone growth is highest in

these countries.

This paper assesses the impact of mobile telecommunications on growth taking into

account the fact that economic growth is itself a determinant for the diffusion of mobile

telecommunications. In a similar setting, Roller and Waverman (2001) used a

simultaneous equations model to measure the returns from fixed telephony on growth. In

this paper we introduce a similar simultaneous equation model. Endogenizing mobile

telecommunications diffusion allows for a more accurate estimate of its impact on

growth. It also corrects for possible simultaneity biases in estimating the impact of

mobile telecommunications on growth. Results show that the contribution of mobile

telecommunications infrastructure to economic growth for low mobile penetration

countries (or in fact low income countries) is much smaller than for high penetration

countries: low income countries forego 0.20 per cent of annual growth due to lack of a

mobile telecommunications infrastructure compared to a high income country. This

suggests increasing returns from mobile penetration. The increasing returns result is also

obtained when the existing model is extended to assess the impact of mobile

telecommunications infrastructure on productivity growth: the contribution of mobile

telecommunications infrastructure to productivity growth for high penetration countries is

almost double that of countries with low mobile penetration.

This paper is organized as follows. Section 2 describes the various approaches to account

for economic growth and provides a survey of the relevant economic literature in the

context of telecommunications infrastructure. Section 3 presents the econometric model

and describes the dataset used. Section 4 presents and discusses the results. Section 5 is


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an excursus on microeconomic case studies concerning the impact of mobile

telecommunications in developing countries. Section 6 draws conclusions from the

results and discusses some policy implications.

2. BackgroundinformationandstudiesThe global rise of mobile telecommunication adoption during the last decade illustrated

the impact of new technologies and the magnitude of changes that they trigger. Unlike

preceding network technologies, mobile phone networks can be built quickly provided

the spectrum agreements are in place. Thanks to competition, they also offer much-

improved services both in terms of capabilities and in terms of information retrieval,

overcoming typical problems of inefficiencies generated by monopolies in fixed networks

(Wellenius, 1993). The closely related industries continuously exploit new opportunities

with more capable handsets and a range of applications facilitating everyday activities.

Essentially, substitution effects have already appeared in several countries (see

Vogelsang (2009) for a recent survey) indicating a decline in the number of fixed lines,

especially in high mobile-penetration areas. There is a large string of empirical literature

that shows the positive impact of telecommunications infrastructure in general on

economic development and growth (e.g. Hardy, 1980; Leff, 1984; Madden and Savage,

2000).

However, much less empirical work has been devoted to mobile telecommunications with

this respect. Nevertheless, the pervasive use of mobile telecommunications is providing

evidence that this innovation has affected the socio-economic structure of modern

societies and economic growth. Mobile networks provide the framework for the delivery


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addressed by utilizing first differences approaches or by moving to smaller data

aggregation (e.g. Aaron, 1990; Hulten and Schwab, 1990). Reverse causality, that

underpins the link from output to infrastructures as well, has also been key in this debate

(Munnell, 1992). Therefore we have to disentangle this relationship with the two different

effects: the increase of economic growth due to the increase in mobile infrastructure and

its externalities and the increase in the demand for mobile services due to higher

economic output. We are interested to measure the former effect while taking into

account the influence of the latter in each country. For this purpose we propose to use a

simultaneous equations model. This model endogenizes mobile investment by

incorporating mobile supply, demand and output equations. The system is then jointly

estimated with a macro production function hence accounting for the simultaneity effects.

The model used in this study is based on Roeller and Waverman (2001), which jointly

estimate a micro-model for telecommunications investment with a macro production

function for the OECD group of countries for the period 1970-1990. They find a strong

causal relationship between telecommunications infrastructure and productivity, and

additionally they indicate that this occurs only when telecommunications services reach a

certain threshold, which is near universal levels. Sridhar and Sridhar (2007) investigate

the simultaneous relationship between telecommunications and economic growth, using

data for developing countries. Using 3SLS they estimate a system of equations that

endogenizes economic growth and telecom penetration along with supply of telecom

investment and growth in telecom penetration. They find that there is a significant impact

of mobile telecommunications on national output, when controlling for the effects of

capital and labour. The impact of telecom penetration on total output is found to be


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significantly lower for developing countries than the reported figure for OECD countries,

dispelling the convergence hypothesis. The limitation is that OECD countries are

excluded in their study.

The modeling approach for the basic telephony network taken by Roeller and Waverman

(2001) is adapted in this study for mobile telecommunications infrastructure. The

resulting four equations model is quite demanding in terms of data availability. However

it provides an explicit methodology that deals with the two-way causality problem. In a

study on the effects of telecoms in developing countries, Waverman et al. (2005)

followed both a variant of the four equations model and an endogenous growth approach.

The former suffered from the lack of year and county-fixed effects in the econometric

analysis and therefore could not control for major inter-country and year variations.

These authors therefore settle for a single equation model deriving from the work of

Barro (1991) that assumes convergence between poorer and richer countries. The

methodology takes averages of the infrastructure over the time period of the study and

regressed them against initial GDP, ratio of investment to GDP, averaged measures of

education and others. A much-improved model for the endogenous growth approach is

presented in Sala-i-Martin et al (2003) using the Bayesian Averaging approach. In this

model the authors construct estimates as a weighted average of OLS coefficients for

every possible combination of included variables. The weights applied to individual

regressions are justified on Bayesian grounds in a way similar to the well-known

Schwarz model selection criterion. The issue of reverse causality is not tackled in this

case either.

The growth effects discussed earlier include the applications that derive from the


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coefficient of 0.54). This is confirmed also after taking into account the possible scale

effect due to country size. Figure 1 shows the correlation between per capita GDP and

mobile penetration with a correlation coefficient of 0.56.

Figure 1.Correlation between income per head and GDP

The two-way relationship between mobile telecommunications and economic growth is

central in our analysis. First, we describe the link that derives from capital and labor and

boosts economic output. In this direction, mobile telecommunications, like any major

infrastructure, has its role in promoting growth. We however argue that mobile

telecommunications are not handled as a public good like roads that are financed from the

public sector budget. The funding of mobile telecommunications depends on the users of

the service in the market. Therefore the users ability to pay, mostly determined by their

income, should be a major determinant for the deployment and use of mobile


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telecommunication services. A fine line in this relationship is the way we measure usage

of mobile services and infrastructure itself. The variable used in both cases is mobile

penetration, the ratio of the number of mobile subscribers and population. In the case of

demand, we assume that people with a mobile phone number are also users of the service.

While this might be intuitive, it is not necessarily true in practice. For example, so-called

pre-paid cards, which are quite common especially in developing countries, might remain

unused for long periods or some subscribers might have more than one mobile line. One

way to resolve the issue would be to use of mobile minutes for each country, but data

availability is limited and pricing structures vary greatly across countries (e.g. flat fees,

capped offerings, etc.). Therefore mobile adoption is a proxy for use rather than use itself.

We use mobile penetration also in terms of mobile infrastructure. We want to measure

the installed capital and its return on growth. Installed capital varies greatly across

countries over the sample period and reached levels of saturation in many developed

countries. Adoption provides a measure of this employed capital in a normalized fashion

that is relatively easy to compare across diverse environments. A monetary equivalent

would have been hard to estimate and compare in a global sample. Moreover the recourse

to coverage metrics, whenever available, is not based on clearly defined criteria and

might seriously overestimate the installed capacity of networks in place.

To summarize, while we do expect demand for goods and services to increase

with individual purchasing power we want to estimate how much the countrys growth

might be affected by their use of the mobile networks. In order to illustrate this causal

link between the two variables we use this model that explicitly disentangles the values in

a simultaneous equations model. Therefore a micro model of supply and demand is


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specified and jointly estimated with the macro production equation. This way while

endogenizing for the investment we can control for the causal effects of this two-way

relationship.

To tackle potential reverse causality, we define a simultaneous equations model similar to

Roeller and Waverman (2001) and in Koutroumpis (2009). Moreover the error terms are

controlled for autocorrelation and heteroscedasticity (clustered by time and country). If

the model specification is correct, the system estimators provide more precise coefficient

estimates than a single equation method.

As a way of checking consistency of the results we compare them with single equations

estimates, using instrumental variables. Unless cross-equation restrictions exist, each

equation from the system can be estimated separately. Individual equation estimates can

be efficient too, if the right instrument sets are properly specified. This can be quite

challenging, and as we will discuss later is one of the reasons to prefer the system

estimates. For example, Duranton and Turner (2007) have estimated the effect of the

build-out of transport infrastructure on urban growth. To tackle the problem of reverse

causality they have used past transport infrastructure as an instrument. Similar issues of

reverse causality between urban structure and telecommunications infrastructure have

been dealt with by Ioannides et al. (2008). In their analysis of the interaction of

telecommunications on the size distribution of cities they use market structure variables,

such a public or private monopoly versus competition. They find evidence that increasing

the number of fixed lines per capita raises the dispersion of population across the urban

structure, resulting in a more concentrated city size distribution. While finding the right

instruments is not always easy or possible, it can be said that both system and single-


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equation estimates are somehow comparable. Nevertheless system estimators provide

higher efficiency and accuracy compared to limited information estimators. The

downside of systems is that misspecifications may pollute the estimates of all equations.

In any case, to check the consistency of the results we have also run regression for single

equations. The use of both specifications allows us to control for the correctness of the

system specification and to obtain more insightful estimates from the model (Wooldridge,

2003, p. 311) while the limited information estimators (instrumental variables estimations

on each equation) has the advantage of recognizing misspecifications in the equations.

Data

The dataset used in this study consists of annual data from 192 countries for the

eighteen year period between 1990 and 2007. The countries included in the analysis used

are listed in the appendix.

The data used have been collected by various sources depending on their nature and

availability (see table 1). More information about the variables and the summary statistics

are found in Table 2.

The Hirschman-Herfindahl (HHIit) market concentration index for each country i

is calculated as the sum of the squares of market shares of all firms in the market at time

t.


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Table 1Variables used in the model and descriptions

Note: The subscripts i and t correspond to country and time values respectively.

Table 2 Variables and summary statistics

Variable Description Source of data

GDPit Gross domestic product in millions USD World Bank

GDPCit GDP per capita in USD World Bank

Kit Fixed stock of capital million USD World Bank

Lit Population with full or part time work aged

15-64 in thousands

World Bank

Firmijt Subscribers of firm j Informa

Mob_Penit Level of mobile penetration in 100 inhabitants Informa

MobPrit Mobile cellular monthly subscription USD ITU

URBit Percent of population living in urban areas World Bank

Mob_Revit Mobile revenues in millions USD ITU

Variable Obs Mean Std. Dev Min Max

GDP

(USD millions, constant 2000)3428 193,000 870,000 0.004 14,300,000

GDPC(USD , constant 2000)

3223 12,589 16,502 834 64,793

Labour

(Thousands population)3248 1,510 62,400 28.666 786,000

Fixed stock of capital

(USD millions, constant 2000) 2372 51,600 245,000 0.75 6,230,000Mobile penetration (%) 3144 25.19 33.2 0 207.83

HHI 3456 0.537 0.368 0 1

Mobile price

(USD, constant 2000)2266 0.393 0.595 0 18.657

Urbanization (%) 3456 54.33 26.494 5.4 100

Mobile Revenue

(USD millions, constant 2000)2365 1,680 7,850 0 160,000


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The model

The model is composed of an aggregate production function which links national

aggregate economic output GDPitto a set of production factors in each country iat time t.

In particular the stock of capital (K), labour (L) the stock of mobile infrastructure and

Urbanization (URB). The stock of mobile infrastructure is needed rather than the mobile

investment because consumers demand infrastructure and not investmentper se. There is

an explicit acknowledgement of telecommunications capital, approximated by the mobile

infrastructure in terms of mobile penetration (Mob_Pen).

Aggregate production function

GDPit f(Kit,Lit,Mob_Penit,Urbit) (1)

Real GDP thus is a function of labour force, capital stock and mobile

infrastructure. Urbanization enters the production function as a result of its direct effect

on growth. While the coefficients for labour and capital should be typical for production

functions, the coefficient of mobile penetration in equation (1) estimates the one-way

causal relationship flowing from the stock of mobile telecommunications infrastructure to

aggregate GDP. As mentioned earlier in the discussion of the empirical literature of the

effects of infrastructure one economic growth this may lead to misleading results because

of possible reverse causality. In order to disentangle the possible effects of mobile

telecommunications infrastructure on GDP and the inverse we specify a model consisting

of three equations for demand and supply of mobile infrastructure, as well as an

infrastructure output function.

Demand for mobile infrastructure:

Mob_Penit g(GDPCit,MobPrit,Urbit) (2)


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a positively sloped supply curve. Likewise the level of per capita income should also

have a positive effect on the supply side. The concentration index should have a negative

effect on the supply as a lower level of market concentration reflects a greater

competition for customers and thus should increase supply of mobile services.

Mobile infrastructure production function:

Mob_Penit = k(Mob_Revit) (4)

The infrastructure equation (4) states that the annual change in mobile penetration

is a function of the mobile revenues, taken as a proxy of the capital invested in a country

during one year. The sign is expected to be positive. Mobile telecommunications is

technologically different from fixed line infrastructure as with the latter there is a precise

mapping of subscribers and line infrastructure. With mobile infrastructure this is more

flexible as the same base station can serve a fairly high number of subscribers. However,

to ensure service quality also mobile service providers have to make sure that

infrastructure is in proportion with the number of users. It is therefore important to note

that the difference in penetration levels is a function of the infrastructural change that is

already used and utilized by the citizens of a country. There might be other parts of the

invested capital that have not yet been realized and used by the people.

Equations (2), (3) and (4) endogenize mobile telecommunications infrastructure

because they involve the supply and demand of broadband infrastructure. The

econometric specification of the model is as follows:

Aggregate Production equation:

GDPit a1Kit a2Lit a3Mob_Penit a4Urbit 1it (5)

Demand equation:


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Mob_Penit b1GDPCit b2MobPrit b3Urbit 2it (6)

Supply equation:

Mob_Revit = c1MobPrit c2GDPCit c3HHIit 3it (7)

Mobile infrastructure production equation:

Mob_Penit = d1Mob_Revit 4 it (8)

4.ResultsanddiscussionGlobal Growth Results

We estimated the model represented by equations (5)-(8). The coefficients and their

statistical significance levels resulting from the method of 3SLS GMM are presented in

Table 3, column 1.5 In the second column results are shown including after introducing a

time trend and controls for each country and year. This should provide better fits since it

takes out the unobserved country and year effects in our sample. In both cases, the

growth equation coefficient estimates are as expected positive and highly significant at

the 1% level. As expected, labour and capital significantly affect economic growth, as

does urbanisation. The mobile telecommunications coefficient is statistically significant

has with 0.15 a relatively high value. Similar results were also found in Roeller and

Waverman (2001) and they discuss this to some extent in the context of the literature on

infrastructure and economic growth. As in their case however, the size of the coefficients

5The single equation estimates using instrumental variables are in Appendix 4. They are generally closely

aligned with the system estimates and thus not discussed in much detail.


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comes to more plausible levels once critical mass effects are taken account for, as will

be seen shortly.

In the mobile demand equation, both per capita income and own price elasticity have

different signs. We find an income elasticity of 0.65, which would suggest that mobile

telecommunications are to be considered as a normal good. Across the other

econometric specifications we observe that this value remains at approximately the same

levels. Own price elasticity is negative and significant, but relatively low (-0.16

coefficient). We also observe that urbanization is not significant.

On the supply side, of the concentration index HHI enters the regression as expected with

a negative and significant sign, suggesting that lower levels of market concentration and

hence more competition among firms increase supply. Mobile price is insignificant for

the supply of the mobile services, which is not as expected, perhaps reflecting the

different pricing schemes across the world. Per capita GDP has a positive effect on

supply, providing evidence for a potential reverse causality between mobile markets and

GDP. Finally the output of the mobile industry measured by the difference in installed

equipment (proxied by the mobile penetration) between two consecutive years is

positively related to supply of mobile infrastructure which again is indicated by the firms

mobile revenues.

As mentioned, the impact of mobile telecommunications on growth was also estimated in

a single equation framework. We used mobile price and concentration index as

instruments of mobile penetration in the production function. Urbanisation was not

considered as a valid instrument because it affects GDP directly and indirectly. The IV

estimates turn out as fairly close to those found for the system estimates and are shown in


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the appendix. It is nevertheless striking to see that with 0.06 the size of the coefficient for

mobile telecommunications in the production function is considerably smaller with the

single equation system. The single equation may thus underestimate the true growth

impact of mobile telecommunications.

Table 3

Econometric results

Variables 3SLS estimates1

(1) (2)

Growth (GDPit)Labour (Lit) 0.272*** [0.015] 0.274*** [0.015]

Fixed stock of capital (Kit) 0.710*** [0.013] 0.708*** [0.013]Mob Penetration (PENit) 0.147*** [0.010] 0.145*** [0.010]

Urbanization (URBit) 0.260*** [0.038] 0.277*** [0.039]

Time Trend - 0.001** [0.000]Constant 2.699*** [0.185] 2.576*** [0.188]

Demand (PENit)

GDPC (GDPCit) 0.652*** [0.037] 0.651*** [0.037]

Mob. Price (Mob_Prit) -0.161*** [0.057] -0.159*** [0.057]

Urbanization (URBit) 0.016 [0.112] 0.017 [0.098]Constant -3.436*** [0.338] -3.436*** [0.338]

Supply (Mob_Revit)Mob Price (Mob_Prit) 0.139 [0.384] 0.141 [0.385]GDPC (GDPCit) 1.659*** [0.172] 1.659 *** [0.172]

Market conc. (HHIit) -6.132*** [1.020] -6.128*** [1.019]

Constant -7.411*** [1.692] -7.419*** [1.692]

Output (Penit)

Mob Revenue (Mob_Revit) 0.270*** [0.015] 0.271*** [0.015]

Constant -4.404*** [0.288] -4.403*** [0.288]

Year Effects No Yes

Country Effects No Yes

R2

Growth 0.95 0.95Demand 0.53 0.54

Supply 0.47 0.48

Output 0.37 0.37Notes: Number of observations: 1125(1)Random effects using 3SLS GMM estimates with robust standard errors(2)Fixed effects using 3SLS GMM with robust standard errors1Three Staged Least Squares estimates with endogenous variables GDP, Mobile

Penetration, Mobile Investment and Penetration

Standard errors reported in brackets***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.


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Critical mass effects

The discussion so far has focused on the effects of the deployment and use of mobile

infrastructures. We have identified a positive return on growth while accounting for the

simultaneity bias in our econometric specification. However does the mere existence of

some mobile subscribers compare to, say, half the population using mobile telephony?

Are there perhaps, certain levels of technology adoption that make these effects

particularly strong?

Katz and Shapiro (1986) analyzed technology adoption in the presence of network

externalities and found significant impact from standardization, technological superiority,

subsidy or support from sponsors and technological prospects. Mobile phones have

experienced the impact of all these elements during the last twenty years. Quoting the

seminal work of Arthur (1989), modern, complex technologies often display

increasing returns to adoption in that the more they are adopted, the more experience is

gained with them, and the more they are improved. On the adoption thresholds, Valente

(1996) draws a line between personal and system level adoption. Individual adoption

thresholds are defined as the proportion of a group needed to engage in a behavior

before the individual is willing to do so. Critical mass is the point at which enough

people have adopted to sustain diffusion to the remainder of the population (Valente,

1996). Without network externalities demand slopes downward. For products and

services with network externalities, like mobile phones, the willingness to pay for the last

unit increases as the expected number of users increases (Economides and Himmelberg,


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1995). A corollary would be that mobile telecommunications should display increasing

returns from the adoption and therefore the growth impact should increases with the level

of diffusion. This may be in opposition with earlier findings where the economic growth

impact of mobile adoption decreases with the penetration rate. For instance, Waverman et

al. (2005) running a single cross country regression found that there is a significant

impact of mobile telecommunications adoption on economic growth, but this decreases

with the penetration rate. Hence low income countries would have a higher growth

dividend from mobile adoption than high income countries. Similar results were

obtained with a very similar single equation approach by Qiang and Rossotto (2009).

Endogeneity problems however may significantly affect the results.

Our study uses the simultaneous equation approach and the hypothesis on the growth

effects is quite straightforward. We want to test whether the returns from higher use are

linear or not. In our first model specification the assumption of proportional returns is

somehow embodied in the use of a single metric (mobile penetration) for the effects of

this infrastructure on growth. However, the positive network effects might set in

extensively only once the diffusion of the mobile innovation has reached a significant

part of the population.

It may be quite difficult if not impossible, to clearly define the rigid thresholds at which

these effects might appear. To the contrary we consider them as areas of importance that

might be different for each country based on demographics or other socio-economic

characteristics. Nevertheless the breadth of our sample does not allow for country-

specific adjustments. We cluster the countries according to the achievement of predefined

penetration rate levels. These levels, hereafter referred to as critical masses, will allow


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us to estimate the country specific returns.

It is worth emphasizing that the primary intention of our model was to understand and

estimate the effect of mobile penetration on aggregate output, not the reverse relationship.

In this direction we will test for the existence of certain nonlinearities. To capture the

magnitude of the critical mass effects and the impact on growth, equation (5) becomes:

GDPit a1Kit a2Lit (a3HIGH a4MEDIUM a5LOW)Mob_Penit a6Urbit 1it (9)

The three dummy variables (LOW, MEDIUM, HIGH) correspond to a low, medium and

high mobile penetration level respectively. The methodology used for this clustering of

countries is explained below. Throughout the period of almost twenty years (1990 -2007)

most countries started from a mobile penetration at or close to zero. Some of them,

primarily in the EU27 region and North America moved quickly to high penetrations,

while others, like the Latin American ones, moved gradually to similar levels. Some

Asian and African countries have not yet reached these levels. We break our sample into

three equally populated clusters of mobile penetration observations. The lower part

includes observations from 0 to 10 percent penetration. The second part (medium)

includes observations from 10 to 40 percent. The last (high) consists of all observations

from 40 percent and up6. For example, Denmark was in the low penetration group from

1990-1995, moved to the medium penetration group from 1996-1999 and from 2000

onwards it remained in the high penetration cluster. Costa Rica was in the low

6Although the thresholds could be seen as arbitrary, they are indicative of the levels that affect the returns

from mobile penetration. Therefore we do not expect that minor alterations in these values will have anyserious effect on growth returns. The reason is that on average, each year mobile penetration increased by

roughly 3% in each country and therefore even if we change this threshold to 3% the change in the

estimated returns would be marginal. In particular for the low penetration sample, this value is on average

equal to 1%, for the medium penetration 3% and for the high penetration 8%. Evidently the mediumlevel can be in the region of 7%-13% and the high level region between 32%-48%. The drastic change in

the calculations is induced by the stock of several high, medium and low penetration years.


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penetration group from 1990 to 2003 and then moved to the medium penetration group

until 2008. Interestingly, Zambia and Mozambique remained in the low penetration group

for the whole period. It is striking that there exist a lot of countries with saturated mobile

markets and others with rudimentary infrastructures.

The estimation results for the modified system of equations are given in Table 4. In this

case we include fixed effects in our results to account for the country and year

specificities. Most of the estimates remain unchanged and we are therefore not further

commenting on them. We focus our attention on the growth equation with the mobile

penetration levels coefficients. The parameter estimates of all three levels are positive

and highly significant. While, low and medium mobile penetrations have only slightly

different coefficients (0.045 and 0.051 respectively), high mobile penetration has a much

higher coefficient (0.102). A similar ranking was obtained also with the IV estimates.

This seems to suggest that increased mobile penetration does not yield proportional

returns across the adoption curve. We find that high penetration countries have

consistently higher returns on growth, while controlling for the simultaneous effects. This

result is somewhat in contrast with previous empirical work by (Quiang and Rossotto,

2009) that finds that for developing countries (which typically are low mobile penetration

countries) the growth impact of mobile telecommunications is higher.

Apart from this we identify a region of importance or a critical mass level at the 40

percent of mobile adoption. Based on our clustering we find that once the level around 40

percent has been achieved, economies earn a lot more from the same infrastructure

compared to their previous returns. This provides evidence from increasing returns from

mobile adoption.


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Table 4

Econometric result for critical mass effects

Variables 3SLS estimates1

Growth (GDPit)Labour (Lit) 0.257*** [0.014]

Fixed stock of capital (Kit) 0.727*** [0.013]Mob Penetration (PENit)

High(40%+) 0.102*** [0.009]

Medium (10-40%) 0.051*** [0.012]Low(10%-) 0.045*** [0.012]

Urbanization (URBit) 0.329*** [0.039]

Time Trend 0.006*** [0.000]

Constant -

Demand (PENit)

GDPC (GDPCit) 0.537*** [0.032]Mob. Price (Mob_Prit) -0.156*** [0.050]

Urbanization (URBit) 0.001 [0.097]

Constant -2.434*** [0.286]

Supply (Mob_Revit)

Mob Price (Mob_Prit) 0.137 [0.385]

GDPC (GDPCit) 1.641*** [0.172]

Market conc. (HHIit) -6.349*** [1.006]

Constant -7.130*** [1.695]Output (Penit)Mob Revenue (Mob_Revit) 0.046*** [0.014]Constant 0.671*** [0.278]

Year Effects Yes

Country Effect Yes

R2

Growth 0.96

Demand 0.54

Supply 0.48

Output 0.34Notes: Number of observations: 11251Three Staged Least Squares estimates with endogenous variables GDP,

Mobile Penetration, Mobile Investment and Penetration

***, **, * denote statistical significance at the 1%,5% and 10% level,

respectively.Standard errors reported in brackets


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In order to get an idea of the magnitude of our results, we need to find the actual country

specific growth effect from the different levels of mobile penetration on growth. As

countries move across clusters, for each country we estimate a different CAGR7. The

resulting coefficients are presented in the Appendix in detail. Figure 2 shows the range of

returns for selected countries in the sample over the period of 1990-2008. The growth

contribution rates vary substantially across countries. We observe that Finland enjoys the

highest equal to 0.44% annually. This country has played a pioneering role in the early

adoption of mobile telecommunications and apart from being home to Nokia, a leading

supplier of mobile telecommunications handset equipment, has also strong indigenous

technology development in the field (for details, see Gruber [2005]). At the bottom are

poor developing countries, such as Nepal which has growth returns from mobiles of

around 0.12% annually, i.e. about one fourth of the leader.

7 We explain in the Appendix the calculations of CAGR for each level of Low, Medium and High

penetration levels.


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Figure 2: Mobile telecommunications contribution to annual growth rate

Source: Autors calculations

To illustrate better the relationship between income and returns from mobile adoption, we

split our sample into five income groups (based on the World Bank clustering) and look

whether mobile returns are related to income. We also want to see how much the number

of years in the High penetration cluster (above 40 percent) coincides with the

contribution to growth. Figure 3 shows that there is a clear trend between country income

clusters and the mobile contribution on growth. High income economies have higher

returns, and returns decrease with income. Moreover the average number of years that

each country is in the high penetration cluster follows almost the same trend. Low-

Income countries have on average - less than one year of high mobile penetration

whereas high-income more than nine. This observation may explain one dimension of

their lag; the delay of the deployment of new infrastructures hinders their potential and

contributes to their low income.


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Figure 3: Contribution to growth from mobile telecommuications and average years

of high penetration rates


Although this result seems to suggest that growth is primarily related to higher

penetration levels, we also attempt to test the introduction timing of each technology and

its impact on overall growth. In our sample not all countries had adopted mobile

telecommunications in 1990, and some of them not even ten years later. It is therefore

crucial to understand, how much introduction timing is related to growth contribution

from mobile telecommunications. Figure 4 presents three different metrics with this

respect. The average years of introduction, the years of high penetration (both right hand

scale) and the contribution to growth (left hand scale). Again the sample is distributed

according to the World Banks five income clusters. The alignment of the three different


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the proliferation of mobile telephony. In particular the growth impact increases with the

level of diffusion and this helps high income countries in particular. This may be in

contradiction with earlier findings in the literature quoted on the impact of mobile

telecommunications and the prevalent theory of decreasing returns from technology

adoption. The last column is an assessment of the foregone growth due to not having

access to mobile telecommunications at comparable levels as high income countries.

Low income countries have been deprived of an annual growth of 0.20% relatively to

their high income counterparts for the period 1990-2007. Lower middle income countries

have had a comparable loss of 0.16%. Surprisingly this technology gap is evident in

upper middle income countries too. This cluster has a growth gap of 0.10%. In a nutshell,

these results suggest that because of increasing returns from adoption, high mobile

penetration for several years has important effects on economic growth. Likewise, the

missed growth opportunities due to low penetration are more than proportional. These

examples are indicative of the losses and returns in this specific time-period and we could

expect them to be comparable also for future returns from this technology too.

Table 5. Summary results by income level of countries

Average number

of years with 40%(or higher) mobile

penetration(high returns)

1990-2007

Average % annualcontribution on

growth

Foregone % annual

contribution due tolack of mobile

infrastructure(relatively to High

Income countries)

High Income OECD 9.33 0.39% -

High Income Non-OECD 7.05 0.35% 0.04%

Upper Middle Income 4.16 0.29% 0.10%Lower Middle Income 1.94 0.23% 0.16%Low Income 0.93 0.19% 0.20%Source: Autors calculations


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Productivity Results

Apart from the effects on economic growth in general, mobile networks affect the

business processes and the speed of output production. Consider a case where the labour

force in a country produces a constant amount of output for a given period. The

introduction of mobile telephony acts in two different ways. First it offers the possibility

to produce more output due to the mobility benefits in communications; second, it allows

people to produce a given output more quickly, primarily because of information

availability and again, mobility. The latter effect is attributed to the productivity of the

workforce and not output per se. Rice and Katz (2003) discuss these effects from the use

of mobile telephony and internet technologies for a US sample. Evidence form the US

Department of Commerce statistics also showed that information technology in general

provides significant economic benefits, such as reducing inflation and increasing

productivity, and constitutes a major section of the economy (McConnaughey, 2001).

In order to measure this effect we will use a transformation of the aggregate production

function into a productivity function. Dividing the GDP with total hours worked we

obtain average hourly worker productivity in each country. We also divide fixed stock of

capital and the labor force with total hours worked. The aggregate production equation is

transformed into the following productivity equation (9).

Productivity equation:

ProditGDPit

hoursit a1

Kit

hoursit a2

Lit

hoursit a3Mob_Penit a4Urbit a5hoursit 1it (9)

Equation 9 replaces equation 5, while the other equations of the system remain

unchanged. The results are presented in column (1) of Table 6 Because of limited


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availability of statistics for total hours worked, we run this model for a subset of the

sample countries, primarily of the OECD members. We can therefore compare our results

for growth and productivity for this sub-sample only.

In the productivity equation, most coefficients retain the signs and significance levels of

the aggregate production function. In particular, mobile penetration coefficients are

positive and highly significant, reinforcing our hypothesis on the link between

productivity and mobile telecommunications. Moving to the demand equation we find

that income elasticity is slightly higher for this subset of countries (0.83) indicating that

income changes have a stronger effect on the demand for mobile use. Moreover, OECD

markets more prone to respond to price changes resulting in a higher price elasticity

estimate (-0.70). Urbanization is not significant. The supply equation shows that the

concentration index enters the regression with a negative and significant sign, as before.

Price is not significant. Income elasticity is positive and significant. Last, in the output

equation, the difference in mobile adoption (proxied by mobile penetration) is positively

linked to the supply of the infrastructure.

Also with this model we have defined country clusters and included the critical mass

dummy variables into productivity equation. As discussed before, the extensive use of

mobile phones might not just have a linear effect on productivity and there might be

perhaps a level where these returns are increasing. Because of the smaller number of

countries in the sample and hence much less observations (313 instead of 1125) we split

the sample in two equal parts rather than three. The main reason is that the sample refers

to OECD countries which typically have seen a rapid evolution from low to high

penetration. Thus we have only two dummy variables, high and low are used, with 46%


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penetration level as the threshold8. The results are presented in column 2 of Table 7. As

most estimates remain largely the same as in column (1) just discussed, we therefore

focus on the coefficients of mobile penetration in the productivity equation. First we find

that both mobile penetration has a significant and positive effect on productivity in both

low and high penetration countries, a result that was already observed previously.

Interestingly we notice that high mobile penetration countries enjoyed more than double

the returns compared to low penetration countries (0.063 compared to 0.024). This

translates into much stronger network effects from higher penetration levels, which result

in higher productivity gains. These differences are even larger with IV estimates, as

shown in the appendix.

8Average mobile penetration for the sample is 46% and this is the threshold in this case. A minor change in

this value does not have a significant effect on the results in table 7. The reason is that on average, each

year mobile penetration increased by roughly 6% in each country and therefore by changing this threshold

by say 3%, the impact changing the switching year to a different cluster, and thus on the estimated growth

contribution is relatively small.


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Table 6: Estimates from the system of equations

Variales 3SLS estimates1

(1) (2)

Productivity (GDPit/hours)

Labour (Lit/hours) -0.241 [0.178] -0.226 [0.175]Fixed stock of capital (Kit/ hours) 0.599*** [0.074] 0.570*** [0.024]

Mob Penetration (PENit) 0.089*** [0.044] -

High - 0.063*** [0.018]Low - 0.024*** [0.008]

Urbanization (URBit) 1.418*** [0.345] 1.095*** [0.359]

Hours -0.563 [0.197] -0.479** [0.198]

Time Trend 0.004** [0.002] 0.003** [0.002]Constant 5.819*** [0.695] 5.196 [3.309]

Demand (PENit)

GDPC (GDPCit) 0.831*** [0.095] 0.832*** [0.096]

Mob. Price (Mob_Prit) -0.704*** [0.076] -0.704*** [0.076]

Urbanization (URBit) -0.085 [0.440] -0.090 [0.439]Constant -3.127 [1.791] -3.122*** [1.791]

Supply (Mob_Revit)Mob Price (Mob_Prit) 0.297 [1.810] 1.246 [1.207]

GDPC (GDPCit) 5.666***[1.403] 5.607*** [1.403]

Market Conc. (HHIit) -23.189*** [3.524] -23.457***[3.525]Constant -40.012***[13.769] -39.213***[13.769]

Output (Penit)


Constant -0.895 [0.723] -0.893*** [0.723]

Year effects Yes Yes

Country effects Yes Yes

R2

(1) (2)

Growth 0.99 0.97

Demand 0.55 0.54

Supply 0.47 0.47Mobile Output 0.14 0.15Notes: Number of observations: 313(1) 3SLS GMM estimation with robust standard errors(2) 3SLS GMM estimation with robust standard errors (with different mobile

penetration levels)1Three Staged Least Squares estimates with endogenous variables GDP, Mobile


***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.

Standard errors reported in brackets


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In order to get an idea of the magnitude of our results, we calculate the compounded

annual growth rate for the mobile penetration rate variable with the same procedure as

before. The results for different countries in the sample over the period of 1990-2008 are

presented in table 7. We observe that the Netherlands enjoys the highest contribution of

mobile telecommunications to productivity growth, equal to 0.29% annually, followed by

other Western European countries Germany, Finland, Portugal and Norway with 0.28%,

whereas this is remarkably lower with the last in the list Canada with 0.15% and Mexico

with 0.14%.

Table 7: Annual productivity growth contribution from mobiles (in %)

Netherlands 0.286Germany 0.280Finland 0.279Portugal 0.279Norway 0.276Italy 0.273Switzerland 0.270Luxembourg 0.269Ireland 0.268United Kingdom 0.267Sweden 0.267Denmark 0.267Greece 0.265Spain 0.254Belgium 0.251Iceland 0.246Hungary 0.243New Zealand 0.235Korea (Rep. of) 0.230Australia 0.228France 0.220Japan 0.202United States 0.190Turkey 0.174Canada 0.153Mexico 0.141



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5. Case studies on the economic impact of mobile telecommunications in

developing countries

The assessment of the macroeconomic effects from the proliferation of new technologies

and particularly mobile telecommunications are essential for the discussion of policy and

regulatory aspects, but frequently tangible values of these technologies can be observed

best by their direct impact on everyday life. Some interesting micro level investigations

have been carried out in developing countries and provide useful comparisons of

economic conditions before and after mobile-introduction.

In a study on the market operation of fisheries in the Kerala region of India during 1997-

2001, Jensen (2007) finds that the introduction of mobile phones was associated with a

substantial decrease in price dispersion (convergence towards one price) and the

elimination of waste due to unsold perishable fish. Informed fishermen diverted their

catch to places with excess demand, creating thereby also a positive externality to

uninformed fishermen. The adoption of mobile telecommunications led to a Pareto

welfare improvement. Fishermen and wholesalers profits increased along with consumer

welfare. Despite their link to a specific technology, these results demonstrate the

importance of information for the functioning of markets, and the value of well-

functioning markets. Access to information and possibility of coordination as a result of

mobile telecommunications allowed markets work better, resulting in improved welfare.

Along with our earlier results on higher returns from increased participation, these results

represent persistent rather then one-time gains, since market functioning should be

permanently enhanced by the availability of mobile phones. Jensen points out that

information and communication technologies are often considered a low priority for


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developing countries relative to health and education. Nevertheless these technologies

reduce search costs and improve market coordination, and therefore can increase earnings

and indirectly lead to significant performance improvements in these sectors.

In the same vein, Aker (2008) studies the impact of mobile phone introduction on grain

market performance, traders behaviour and consumer and trader welfare in Niger. She

finds that the introduction of mobile telecommunications reduced price dispersion across

grain markets, with a particular strong effect in remote regions and with poor roads.

Mean grain prices in market with mobile telecommunications were 4.5% lower, but

because of more efficient market operations profits increased as well. Also here the

introduction of mobile telecommunications led to a Pareto improvement.

Another example of the role of access to information in fighting poverty is documented in

Muto (2008), who uses panel data from Uganda to test the effects of mobile phone

coverage on remote farmers that produce perishable crops. He observes that mobile

phone coverage expansion allows information to flow resulting in reduced cost of crop

marketing. In particular the study finds that banana farmers located farther away from

district centres participated more in the market and increased their income after the

coverage by the mobile phone network. To the contrary, less perishable crop production

was not affected by the increase in mobile penetration. Surprisingly, the market potential

of small remote farmers in Uganda was not affected by mobile phone possession but by

mobile phone coverage expansion itself.

These examples of studies generally show that mobile telecommunications improves

access to information, which again are an essential ingredient for well functioning

markets. But there are also additional benefits that can arise from the delivery of services


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other than simple voice or short messaging services that mobile operators offer. An

interesting and promising service innovation with potentially strong economic impact is

the access to financial services to persons that previously were unable to have them. For

instance, Safaricom the largest mobile operator in Kenya launched the M-PESA, a

mobile service for money transfer in 2007 (Hughes and Lonie, 2007; Mbogo, 2010). At

that time only 10% of the population - approximately 3 millions - had access to financial

services (Demirguc-Kunt et al, 2007). This new service allowed customers without bank

accounts to transfer money to mobile users and non-users alike, turn cash into airtime at

local dealers and make payments through their M-PESA accounts. By May 2009, the

service had 6.5 million users dealing with more than 2 million transactions a day. At the

same time the banked population in Kenya rose to 6.4 million9. A similar product was

launched in April 2008 in Tanzania by Vodacom, followed by its competitors Zantel and

Zain, so that now all three major mobile operators in the country provide such services.

Despite the geographic, cultural and agent network differences between the neighbouring

countries, Vodacoms M-PESA had already attracted more than 1 million subscribers in

November 200910

. Money transfer services become increasingly relevant also for

international remittances. As a matter of fact, many developing countries depend heavily

on remittances, sometimes exceeding 20% of GDP. Traditional transfer payment services

a relatively costly, frequently more than 10% of the remittance amount. The World Bank

(2006) estimated that reducing transfer charges by 2-5% could increase the flow of

remittances by 50-70%). Mobile telecommunications firms, such as Smart in the

Philippines and Safaricom and Vodafone in Kenya, are providing mobile transfer services

9Kenya Broadcasting Corporation, 200910Thomson Reuters Tanzania's Vodacom says M-Pesa users hit 1 million


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at a fraction of the original costs, which facilitates transfers and reduces the burden to

senders and recipients.

The evolution and success of mobile money transfer services is built on the scarcity of a

basic financial service infrastructure in the countries described and the contribution of

mobile telecommunications is essential. Branchless banking can dramatically reduce the

cost of delivering financial services to poor people relatively to traditional channels. It

also helps address the two key issues of access to finance: the roll-out costs (physical

presence) and the transaction handling costs. This sharp cost reduction creates the

opportunity to significantly increase the share of the population with access to formal

finance and, in particular, in rural areas where many poor people live (Ivatury and Mas,

2008). Likewise it is also possible to conduct micropayments by short messaging

services, whereby the accounting unit to be transferred are airtime minutes. In this case

mobile telecommunications is replacing the banking sector as a financial intermediary

and is itself creating money. Quantitative data on the extent of this phenomenon are not

available, but anecdotal evidence suggests that it is significant. Such micro-studies

however lend further support to the hypothesis of significant growth contribution

identified in the previous sections.

6.ConclusionsandpolicyimplicationsThe paper has presented an assessment of the economic impact on mobile

telecommunications across the world and in particular the relevance for developing

countries. To tackle the problem of endogeneity of mobile telecommunications diffusion


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and economic growth and productivity a simultaneous equation system has been used.

The main findings show that mobile telecommunications diffusion significantly affects

both GDP growth and productivity growth. The contribution of mobile

telecommunications infrastructure to economic growth is significantly smaller for low

mobile penetration countries (or in fact low income countries) than for high penetration

countries. While in high income countries the mobile telecommunications contribution to

annual GDP growth is 0.39%, for low income countries this falls to 0.19%. Low mobile

diffusion has thus a high economic cost in terms of unrealised economic growth which is

the higher the lower the mobile penetration rate: low income countries forego 0.20 % of

annual growth due to lack of a mobile telecommunications infrastructure compared to a

high income country. The growth contributions were also calculated for individual

countries and this shows a very large range of contributions. Finland enjoys the highest

growth contribution equal to 0.44% annually, while the last in the list Nepal has growth

returns from mobiles of around 0.12% annually. Qualitatively similar results and

rankings are also obtained by looking at impact of mobile telecommunications

infrastructure on productivity growth, at least for countries where the relevant data is

available: the contribution of mobile telecommunications infrastructure to productivity

growth for high penetration countries is close to double that of countries with low mobile

penetration.

As we are dealing with a network technology, to fully benefit from the adoption of

mobile telecommunications, the infrastructure has to form a critical mass of lines. This

suggests increasing returns from mobile penetration. The increasing returns result, which

is also documented by case studies of different applications in the context of developing


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countries (e.g. information gathering, access to financial services), is in sharp contrast

with earlier findings in the empirical literature on the impact of mobile

telecommunications on growth, which typically are decreasing returns from mobile

technology adoption. Our finding supports the theoretical implications of the literature on

network externalities. Indeed, the utility of the single users increases with the size of the

user community since mobile telecommunications belong to network technologies

Moreover, the technology is to a large extent exempt from bottlenecks in adoption as

users do not often experience long run capacity constraints.

This increasing returns result may have important implications, especially in the context

of economic development policies. Microeconomic case studies have reported strong

evidence for welfare improving effect from mobile telecommunications adoption. The

benefits accrue not only to the individuals with direct access to telecommunication

services. The externalities also benefit those not having direct access. Mobile

telecommunications has scope for profoundly affecting the relationships across the

different sectors of the economy and the overall performance of economies. The

favourable impact of mobile telecommunications has been noted widely and thus policies

supporting diffusion, such as sector liberalisation and favouring private investment were

adopted on an extensive base. This apparent success of diffusion of telecommunications

infrastructure has however led to a reduction of development support to

telecommunications infrastructure. Official development assistance for

telecommunications infrastructure has declined strongly since the 1990s (OECD, 2005).

The rationale for most donors to withdraw from the provision of telecommunications

infrastructure was linked to the increasingly strong role of the private sector. Moreover, it


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was unclear to what extent telecommunications infrastructure would help in attaining the

Millennium Development Goals by 2015, as defined by the United Nations in fighting

poverty. It has been pointed out that this is due to a lack of detailed indicators (ITU,

2005). The results of this study help to shed more light on these issues. The objectives for

promoting mobile telecommunications penetration through sector liberalisation policies

along with appropriate regulatory frameworks are endorsed by the present study as a

means for stimulating growth; however the additional element deriving from the results is

that such policies of promotion of mobile telecommunications penetration should be

pursued much more forcefully especially in cases where serious shortcomings exist.

Because the already high share of telecommunications revenues in GDP observed for low

income countries the issue of affordability of further expansion of mobile

telecommunications penetration emerges. This means that the private sector could

encounter limits in raising the additional resources for investment in the market. One

should therefore consider the option of subsidizing the building out of mobile networks in

particular in less developed countries. Indeed, in such countries the network effects are

still stifled by the low penetration rate of mobiles and thus the growth effects are still

reduced. This would call for special attention from donors and international financial

institutions, as well for exploring new approaches including the use of financial products

such as public private partnerships (PPP). Particular care should also be devoted to other

items where the government has a coordinating role such as the provision of radio

spectrum which is a scarce resource in the mobile telecommunications sector and the

construction of shared infrastructures such as backbone transmission networks.


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APPENDIX

1. Compound annual Growth Rates EstimationsBelow we present the method used to derive the country specific annualised growth

contributions from mobile telecommunications, depending on the mobile penetration

levels, for the 1990-2007 period. As described before the sample is divided into 3 (low,

medium and high penetration) clusters. For the low and medium penetration clusters

period we use a the coefficients found from Table 4 to estimate the country level mobile

CAGR (A1 and A2). For the high penetration cluster period we instead compute the

growth contribution based on the last years performance (A3, in the high cluster we use

observations higher than 40%). The only exception is for cases with higher than 100%

penetration as we do not expect the use of more than one mobile subscription per person

(what experts refer to as multiple subscriptions per person) to contribute to increased

growth. The resulting formula (A4) is shown below.

High_CAGRMobPenlast 40%

40%

*a3, (A1)

ifMobPenlast 100% thencappedby100%

Medium_CAGR a4, (A2)

Low_CAGR a5 (A3)

Total_CAGRCAGRi *yearsii1

3

Total_Years

(1/Total_Years)

1, A(4)

The productivity growth contribution calculations follow the same pattern as for the

growth contribution, with the appropriate changes on clusters. The expressions A5-A7

below show the steps followed in these calculations.


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High_Prod_CAGRMobPen

last 46%

46%

* a

H, (A5)

ifMobPenlast 100% thencappedby100%

Low_Prod_CAGR aL (A6)

Total_Prod_CAGRCAGR

i *yearsii12

Total_Years

(1/Total_Years )

1, A(7)


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2. Estimates for the individual counties of the sample and the contribution ofmobile infrastructure on growth

Country Name

%

Contribution

to growth

annuallyFinland 0.437Hong Kong, China 0.421Japan 0.408Denmark 0.408Singapore 0.406

Italy 0.406Israel 0.406United Kingdom 0.404Switzerland 0.404Portugal 0.404Netherlands 0.404Luxembourg 0.404Korea (Rep. of) 0.404Ireland 0.404Austria 0.404Sweden 0.395Norway 0.395

Australia 0.394New Zealand 0.390United Arab Emirates 0.389Spain 0.389Germany 0.389France 0.389Belgium 0.389United States 0.378Iceland 0.378Slovenia 0.376Estonia 0.376Macao, China 0.375Czech Republic 0.375Bahrain 0.375Hungary 0.373Malta 0.361Cyprus 0.359Croatia 0.359Greece 0.352Slovak Republic 0.347Canada 0.347Malaysia 0.345Jamaica 0.345


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Chile 0.345Qatar 0.343Lithuania 0.333

Turkey 0.331Kuwait 0.331South Africa 0.329Mauritius 0.329Venezuela 0.317St. Vincent and the Grenadines 0.315Mexico 0.315Bahamas 0.315Saudi Arabia 0.313Russia 0.313Philippines 0.313Jordan 0.313Oman 0.312Brazil 0.312Argentina 0.312Tunisia 0.310Bulgaria 0.304Morocco 0.299Thailand 0.298Greenland 0.293Romania 0.290Poland 0.290Latvia 0.290

Gabon 0.288Trinidad and Tobago 0.287China 0.283Indonesia 0.280Algeria 0.280Sri Lanka 0.278Peru 0.278Puerto Rico 0.276Paraguay 0.276Belize 0.276Ukraine 0.273Uruguay 0.269

Pakistan 0.264Egypt 0.264Ecuador 0.260Colombia 0.260Gambia 0.253Bolivia 0.253Albania 0.252T.F.Y.R. Macedonia 0.251Kazakhstan 0.244Belarus 0.244Costa Rica 0.242


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Kenya 0.239Ghana 0.239Viet Nam 0.237

Panama 0.236Suriname 0.233Azerbaijan 0.230Nicaragua 0.228Iran (Islamic Rep. of) 0.228Nigeria 0.226Bangladesh 0.223Moldova 0.217Senegal 0.214Myanmar 0.207Burundi 0.207Namibia 0.203Honduras 0.203Fiji 0.201Tanzania 0.198Cameroon 0.198Botswana 0.197Madagascar 0.196Georgia 0.188Cote d'Ivoire 0.188Armenia 0.188Zambia 0.185Benin 0.185

Uganda 0.183India 0.183Malawi 0.181Central African Rep. 0.181Cape Verde 0.175Mali 0.172Lesotho 0.172Burkina Faso 0.169Mongolia 0.161Togo 0.158Mozambique 0.158Zimbabwe 0.156

Papua New Guinea 0.156Niger 0.156Mauritania 0.153Swaziland 0.148Kyrgyzstan 0.148Rwanda 0.143Syria 0.135Nepal 0.117


3.


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3. Countries in the sample

Albania Ecuador Lithuania Papua New Guinea Ukraine

Algeria Egypt Luxembourg ParaguayUnited Arab

EmiratesArgentina Estonia Macao, China Peru United KingdomArmenia Fiji Madagascar Philippines United StatesAustralia Finland Malawi Poland UruguayAustria France Malaysia Portugal VenezuelaAzerbaijan Gabon Mali Puerto Rico Viet NamBahamas Gambia Malta Qatar ZambiaBahrain Georgia Mauritania Romania Zimbabwe

Bangladesh Germany Madagascar RussiaBelarus Ghana Malawi Rwanda

Belgium Greece Malaysia Saudi Arabia

Belize Guatemala Mali Senegal

Benin Honduras Malta Singapore

BoliviaHong Kong,

China Mauritania Slovak Republic

Botswana Hungary Mauritius Slovenia

Brazil Iceland Mexico South Africa

Bulgaria India Moldova Spain

Burkina Faso Indonesia Mongolia Sri Lanka

BurundiIslamic Rep. ofIran Morocco

St. Vincent and theGrenadines

Cameroon Ireland Mozambique Suriname

Canada Israel Myanmar Swaziland

Cape Verde Italy Namibia Sweden

Central African Rep. Jamaica Nepal Switzerland

Chile Japan Netherlands Syria

China Jordan New Zealand F.Y.R.O.M.

Colombia Kazakhstan Nicaragua Tanzania

Costa Rica Kenya Niger Thailand

Cote d'Ivoire Korea (Rep. of) Nigeria Togo

Croatia Kuwait Norway Trinidad and TobagoCyprus Kyrgyzstan Oman Tunisia

Czech Republic Latvia Pakistan Turkey

Denmark Lesotho Panama Uganda


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4. Additional ResultsEstimates with single instrumental variables (complement to table 3)

Variables IV estimates1

(1) (2)

Growth (GDPit)Labour force (Lit) 0.333*** [0.028] 0.333*** [0.028]Fixed stock of capital (Kit) 0.615*** [0.018] 0.611*** [0.018]

Mob Penetration (PENit) 0.061*** [0.009] 0.062*** [0.009]

Time Trend - 0.001 [0.001]Constant 5.038*** [0.424] -

Demand (PENit)

GDPC (GDPCit) 0.956*** [0.069] 1.099*** [0.080]Mob. Price (Mob_Prit) -0.628*** [0.080] -0.692*** [0.082]

Urbanization (URBit) 0.804 *** [0.261] 1.887*** [0.391]

Constant -9.268*** [0.836] -14.764*** [1.439]

Supply (Mob_Revit)

Mob Price (Mob_Prit) -0.298 [0.204] -0.299 [0.203]GDPC (GDPCit) 1.100*** [0.230] 1.095 *** [0.231]

Market conc. (HHIit) -2.083*** [0.592] -2.047 *** [0.558]

Constant 5.216** [2.105] -6.959*** [2.556]

Output (Penit)


Constant -13.225*** [0.345] -13.123*** [0.387]Year Effects No Yes

Country Effects No Yes

R2

Growth 0.92 0.93

Demand 0.49 0.48

Supply 0.43 0.42

Output 0.33 0.58Notes: Number of observations: 1125(1) Random effects using IV estimates with robust standard errors(2)

Fixed effects using IV with robust standard errors1Three Staged Least Squares estimates with endogenous variables GDP, Mobile


***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.Standard errors reported in bracketsInstruments for Mob. Penetration in the Growth Equation: Mob. Price and Market

Concentration


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Estimates with single instrumental variables for critical mass effects (complement to

table 4)

Variables IV estimates1

Growth (GDPit)

Labour force (Lit) 0.325*** [0.026]Fixed stock of capital (Kit) 0.636*** [0.017]

Mob Penetration (PENit)

High(40%+) 0.057*** [0.008]Medium (10-40%) 0.038*** [0.009]

Low(10%-) 0.022*** [0.016]

Time Trend 0.001 [0.000]Constant -

Demand (PENit)

GDPC (GDPCit) 1.099*** [0.080]

Mob. Price (Mob_Prit) -0.692*** [0.082]Urbanization (URBit) 1.887*** [0.391]

Constant -14.764*** [1.439]

Supply (Mob_Revit)Mob Price (Mob_Prit) -0.299 [0.203]

GDPC (GDPCit) 1.095 *** [0.231]Market conc. (HHIit) -2.047 *** [0.558]

Constant -6.959*** [2.556]

Output (Penit)

Mob Revenue (Mob_Revit) 0.758*** [0.018]

Constant -13.123*** [0.387]

Year Effects Yes

Country Effect Yes

R2

Growth 0.93

Demand 0.48

Supply 0.42Output 0.58Notes: Number of observations: 11251Three Staged Least Squares estimates with endogenous variables GDP,

Mobile Penetration, Mobile Investment and Penetration.

Estimation using country and year fixed effects with robust standard

errors.

***, **, * denote statistical significance at the 1%,5% and 10% level,

respectively.Standard errors reported in bracketsInstruments for Mob. Penetration in the Growth Equation: Mob. Price

and Market Concentration


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Estimates with single instrumental variables for productivity results (complement to

table 6)

Variables IV estimates1(1) (2)

Productivity (GDPit/hours)

Labour force quality (LFit/hours) 0.597*** [0.127] 0.592*** [0.125]Fixed stock of capital (Kit/ hours) 0.783*** [0.023] 0.764*** [0.023]

Mob Penetration (PENit) 0.013*** [0.005] -

High - 0.080*** [0.021]Low - 0.018*** [0.006]

Hours -0.001 [0.017] -0.012 [0.152]

Time Trend -0.001 [0.000] -0.001 [0.000]Constant 6.804*** [1.068] 6.829*** [1.059]

Demand (PENit)

GDPC (GDPCit) 2.049*** [0.224] 2.049*** [0.224]

Mob. Price (Mob_Prit) -1.004*** [0.150] -1.004*** [0.150]Urbanization (URBit) 2.822** [1.403] 2.822** [1.403]

Constant -29.847*** [5.631] -29.847*** [5.631]

Supply (Mob_Revit)Mob Price (Mob_Prit) -2.040** [0.944] -2.040** [0.944]

GDPC (GDPCit) 5.731*** [1.726] 5.731*** [1.726]Market Conc. (HHIit) -1.023 [2.476] -1.023 [2.476]

Constant -62.033** [20.048] -62.033** [20.048]

Output (Penit)


Constant -11.913*** [0.773] -11.913*** [0.773]

Year effects YES YES

Country effects YES YES

R2

(1) (2)

Growth 0.96 0.97

Demand 0.57 0.57

Supply 0.36 0.36

Mobile Output 0.37 0.37Notes: Number of observations: 313

(1)Fixed effects using 3SLS GMM with robust standard errors(2)Fixed effects using 3SLS GMM with robust standard errors (with different mobile

penetration levels)1Three Staged Least Squares estimates with endogenous variables GDP, Mobile


***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.

Standard errors reported in brackets.Instruments for Mob Penetration in the Growth Equation: Mob Price and Market

gruber koutroumpis

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