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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
Source: Autors calculations
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)
Mob Revenue (Mob_Revit) 0.098*** [0.034] 0.098*** [0.034]
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
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 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
Source: Autors calculations
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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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References
Aaron, H. J. (1990). Discussion of Why is Infrastructure Important? in Alicia H.
Munnell, ed., Is There a Shortfall in Public Capital Investment? Federal Reserve Bank of
Boston Conference Series, (34), 51-63.
Aker, J. (2008), Does Digital Divide or Provide. The Impact of Cell Phones on Grain
Markets in Niger. BREAD Working Paper No. 177, February 2008.
Arthur, Brian, (1989) Competing Technologies, Increasing Returns, and Lock-In by
Historical Events, The Economic Journal, Volume 99, Number 394, pp 116-131.
Barro, R. (1991), "Economic Growth in a Cross Section of Countries", The Quarterly
Journal of Economics, 106: 407-443.
Bresnahan, Timothy F. and Trajtenberg, Manuel, (1995). General Purpose Technologies
"Engines of Growth?" NBER Working Paper Series, Vol. w4148.
Demirguc-Kunt, Asli, Beck, Thorsten and Honohan Patrick. (2007). Finance for
All? Policies and Pitfalls in Expanding Access, World Bank, November 13
Duranton, Gilles and Turner, Mathew A. (2007) Urban growth and transportation.
University of Toronto, Department of Economics, Working Paper 305.
Economides Nikolas & Himmelberg Charles (1995) Critical Mass and Network Size with
Application to the US Fax Market, Economics Working Papers, Stern School of
Business, NYU, EC-95-11
8/13/2019 Gruber Koutroumpis
46/59
45
Gruber, H. (2005). The economics of mobile communications, Cambridge, Cambridge
University Press.
Gruber, H. 2007 3G mobile telecommunications licenses in Europe: a critical review.
Info, vol. 9, n.6, 2007, 35-44.
Gruber, H. and Koutroumpis, P., (2010). Mobile Communications: Diffusion Facts and
Prospects. Communications and Strategies, No 77(1). pp.133-145.
Gruber, H. and Verboven, F., (2001). The evolution of markets under entry and standards
regulation the case of global mobile telecommunications. International Journal of
Industrial Organisation 19, pp.1189-1212.
Hardy, A. (1980). The role of the telephone in economic development.
Telecommunications Policy. 4(4), 278-286.
Helpman, E. (1998) General Purpose Technologies and Economic Growth. MIT Press,
Cambridge (MA).
Hulten, C. & Schwab, R. (1984). Regional productivity growth in U.S. manufacturing:
1951-78.American Economic Review, 74, 152-162
Hughes N. and Lonie S. (2007) M-Pesa: Mobile Money for the Unbanked. Turning
Cellphones into 24-Hour Tellers in Kenya. Innovations, winter&spring, 63-81.
Ioannides Y.M., Overman H.G., Rossi-Hansberg E., and Schmidheiny K. (2008) The
effect of information and communication technologies on urban structure. Economic
Policy, April, 201-242.
8/13/2019 Gruber Koutroumpis
47/59
8/13/2019 Gruber Koutroumpis
48/59
47
Mbogo, M (2010) The Impact of Mobile Payments on the Success and Growth of Micro
Business: The Case of M-Pesa in Kenya. The Journal of Language, Technology &
Entrepreneurship in Africa, 2,1, 182-203.
McConnaughey, J. (2001). Taking the measure of the digital divide: Net effects of
research and policy (summarizing results of the US Department of Commerce, 2000).
Presented to Web Workshop, Department of Sociology, University of Maryland.
Munnell, A. H. (1992). Policy Watch: Infrastructure Investment and Economic Growth.
The Journal of Economic Perspectives, 6(4), 189-198.
Muto, Megumi. (2008) Impacts of mobile phone coverage expansion on market
participation: panel data evidence from Uganda. World Development, 37(12) 1887-
1896.
OECD (2005) Financing ICTs for development efforts of DAC Members. Paris.
Qiang, C. and Rossotto, C.M. (2009), Economic impacts of broadband. Information and
Communications for Development. Extending the reach and increasing impact. The
World Bank. pp. 25-50.
Rice, Ronald and Katz, James (2003). Comparing internet and mobile phone usage:
digital divides of usage, adoption, and dropouts, Telecommunications Policy, Volume
27, Issues 8-9, pp. 597-623
8/13/2019 Gruber Koutroumpis
49/59
48
Roeller, Lars-Hendrik and Waverman, Leonard. (2001). Telecommunications
Infrastructure and Economic Development: A Simultaneous Approach. American
Economic Review, 91(4), pp.909-23.
Sala-i-Martin, X. (2003).Economic Growth, Cambridge, Massachusetts: The MIT Press.
Sridhar, Kala Seetharam and Sridhar, Varadharajan (2007). Telecommunications
Infrastructure and Economic Growth: Evidence from Developing Countries. Applied
Econometrics and International Development, 7(2).
Valente Thomas (1996). Network Models of the Diffusion of Innovations,
Computational and Mathematical Organizational Theory, Volume 2, Issue 2, pp 163-164.
Vogelsang, Ingo (2009). The relationship between mobile and fixed line
communications: A survey, The Georgetown Center for Business and Public Policy
Conference on Wireless Technologies: Enabling Innovation and Economic Growth,
Washington, D.C., April 17, 2009
Waverman, Leonard, Meschi, Meloria and Fuss Melvin. (2005) The Impact of Telecoms
on Economic Growth in Developing Countries, working paperThe Vodafone Policy
Paper Series, 3, March 10-23,.
Wellenius Bjorn (1993) Telecommunications : World Bank experience and strategy
World Bank discussion papers, 192. Washington, D.C.,
Wooldridge Jeffrey M. (2003) Econometric Analysis of Cross Section and Panel Data,
MIT Press
8/13/2019 Gruber Koutroumpis
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49
World Bank, 2006. Economic Implications of Remittances and Migration," Global
Economic Prospect, Washington.
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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
Source: Autors calculations
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)
Mob Revenue (Mob_Revit) 0.745*** [0.018] 0.758*** [0.018]
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
Penetration, Mobile Investment and Penetration
***, **, * 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)
Mob Revenue (Mob_Revit) 0.637*** [0.035] 0.637*** [0.035]
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
Penetration, Mobile Investment and Penetration
***, **, * 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