Mobile telecommunications and the impact on economic development
Harald Gruber1 (European Investment Bank, Luxembourg)
Pantelis Koutroumpis (Imperial College London)
May 2010
First draft for Economic Policy
Do not quote without permission of authors
1
Corresponding author: [email protected]
1
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.
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
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.
2
3
For a survey of the industry see Gruber (2005)
Source of data is the International Telecommunications Union.
2
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.
Revenues
of
the
mobile
telecommunications account nowadays for a significant percentage of GDP especially in
developing countries, where mobile telecommunications have also been 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
the mobile network. Hence, the economic cost in terms of foregone growth is highest in
less diffusion 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
3
telecommunications. The most appropriate setting appears therefore a simultaneous
equation model. Compared to a single equation model, this corrects for possible
simultaneity biases that are most likely to underestimate the impact of mobile
telecommunications on growth. Modelling mobile telecommunications diffusion as
endogenous allows for a more accurate estimate of its impact on growth. This leads also
the ranking of countries into clusters that identify threshold market penetration levels at
which critical mass effects are enlarged.
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 database used. Section 4 presents and discusses the results. Section 5 is
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. Background information and studies The 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 muchimproved 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
4
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 on economic
development and growth (e.g. Hardy, 1980; Leff, 1984; Madden and Savage, 2000).
However relatively little empirical work has been devoted to mobile telecommunications.
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 of different
services ranging from telephony and its variants (i.e video phones, tele-conferencing) to
high-speed internet access and very diverse services (SMS, mobile banking, video
streaming, online games, tele-working etc). This technology improves the capabilities of
the labour force and the communication between firms. Users collaborate over long
distances, exchange information wherever they travel, shop in global markets and carry
useful data in their phones. The use of this infrastructure spreads to other industries and
contributes to their profits thus affecting their overall growth. While the
telecommunications industry is primarily affected by the infrastructure itself the
important spillovers of mobile networks result in externalities in the other sectors of the
economy (Koutroumpis, 2009).
The growth effects of mobile telephony in macro-level analysis certainly rely on the use
of the networks by individuals and groups. First, the direct growth promoting factors in
support of the initial hypothesis include the actual investment in infrastructure and the
5
number of employees in the sector. More obviously the equipment, the licensing and
marketing of products lead to increases in the demand for goods and services. This first
level of investment is followed by the utility derived from the use of the network. The
ways they organize their work and prevent unnecessary work or travel certainly affect
both their life quality and their productivity.
Mobile telecommunications therefore has to be seen as a network infrastructure and
should therefore be properly accounted for in terms of growth impact considering a
number of methodological concerns. For instance, the results of some early studies that
measured the returns from public infrastructures (e.g. Aschauer 1989) have often been
found to suffer from simultaneity bias and spurious correlation, which other have
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 endogenises 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
6
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
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 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
7
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
utilization of the infrastructure and the participation of large parts of the population. The
levels of participation largely affect the value of the network and define certain
milestones in its evolution. These levels are not clearly defined by rigid thresholds but by
areas of importance. An implication of network externalities is that the impact of
telecommunications and mobile infrastructure on growth might not be linear – thus
resulting in larger than proportional returns for certain mobile penetration levels. These
levels, indicating critical masses, allow us to estimate the increasing returns whenever
each of them is actually reached. It is worth emphasizing that the initial model
specification does not allow for such controls. In order to test for the existence of certain
nonlinearities in our model we have to introduce some structural changes in the
equations. What we want to capture is the level that these changes happen and to get
some idea about their extent as well.
Using the total number of hours worked annually for countries such data is available, the
model can be transformed to also provide estimates for total factor productivity.
8
3. The econometric model of growth and productivity The rationale for the simultaneous equations model
The approach used in this study is a structural econometric model within a
production function framework that endogenizes telecommunications investment. The
reason for using this type of model is the following. The effect we are trying to capture is
a two-way relationship between growth and mobile infrastructure. While we do expect
demand for goods and services to increase with individual purchasing power we want to
estimate how much the country’s 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 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.
A simultaneous equations model will be estimated using two different methods. The first
incorporates single instrumental variable estimates, which control for autocorrelation and
heteroscedasticity consistency. The second uses nonlinear general method of moments
system estimation (Koutroumpis, 2009). The limited information estimator (instrumental
variables
estimations
on
each
equation)
has
the
advantage
of
recognizing
misspecifications in the equations. However if the specification is correct the system
estimators provide more precise coefficient estimates. The use of both specifications
allows us to control for the correctness of the system specification and to obtain more
9
insightful estimates from the model (Wooldridge, 2003, p. 311). These methodological
refinements were not used in Roeller and Waverman (2001)and may affect their results.
Moreover the error terms are controlled for autocorrelation and heteroscedasticity
(clustered by time and country).
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. While finding the right instruments is not always
easy or possible, it can be said that both system and single-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.
Data and Correlations
The dataset used in this study consists of annual data from 192 countries for the
eight-teen-year-period between 1990 – 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.
10
Table 1
Variables used in the model and descriptions
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
World Bank
Firmijt
Population with full or part time work aged
15-64 in thousands
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
Note: The subscripts i and t correspond to country and time values respectively.
11
Table 2
Variables and summary statistics
Variable
GDP (USD millions, constant
2000)
GDPC (USD , constant 2000)
Labour (thousands population)
Fixed stock of capital (USD
millions, constant 2000)
Mobile penetration (%)
HHI
Mobile price (USD, constant
2000)
Urbanization (%)
Mobile Revenue
(USD millions, constant 2000)
Obs
Std. Dev
Min
Max
3428
870,000
0.0353
14,300,000
3223
3248
16,502
62,400
834
28.666
64,793
786,000
2372
245,000
0.75
6,230,000
3144
3876
33.2
0.368
0
0
207.83
1
2266
0.595
0
18.657
3524
26.494
5.4
100
2365
247
0
4,500
The model
The national aggregate economic output GDPit is used in the production function and is
related to labour and capital in each country i at time t. In particular the stock of capital
(K), labour (L) and the stock of mobile infrastructure (Mob_Pen). The stock of mobile
infrastructure is needed rather than the mobile investment because consumers demand
infrastructure and not investment per se.
Aggregate production function
GDPit = f (K it , Lit , Mob _ Penit )
(1)
GDP is a function of labour force and capital stock of capital in a country.
Moreover, there is an explicit acknowledgement of telecommunications capital,
approximated by the mobile infrastructure in terms of mobile penetration.
12
In order to differentiate between the effect of mobile telecommunications
infrastructure on GDP and the inverse we specify the following demand model.
Demand for mobile infrastructure:
Mob _ Penit = h(GDPCit , MobPrit ,URBit )
(2)
The demand equation (2) states that mobile penetration is a function of GDP per
capita, the price of a standard service for the connection to the network and the
percentage of the population that lives in densely populated areas.
Supply of mobile infrastructure:
Mob_Revit=g(Mob_Prit,URBit,HHIit)
(3)
The supply equation (3) links the aggregate mobile revenue in a country to mobile
price levels for that period, concentration index of the mobile market (HHI) and
urbanization. These parameters affect potential and existing operators as well as the
dynamics of the supply side of the market.
Mobile infrastructure production function:
ΔMob _ 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. It is 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.
13
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 = a1K it + a2 Lit + a3 Mob _ Penit + ε1it
(5)
Demand equation:
Mob _ Penit = b1 MobPrit + b2URBit + ε 2it
(6)
Supply equation:
Mob _ Revit = c1 MobPrit + c2URBit + c3 HHI it + ε 3it
(7)
Mobile infrastructure production equation:
ΔMob _ Penit = d1 Mob _ Revit + ε 4it
(8)
4. Results and discussion Global Growth Results
We test the model presented in section 3. The resulting coefficients and their statistical
significance levels are presented in Table 3, column 1.4 In the second column results are
shown including controls for each country and year. We find that the most of the results
remain unchanged in terms of signs and statistical significance. We consider this second
set of results more accurate and efficient since it takes out the unobserved country and
year effects in our sample and hence we discuss them in the sequel.
4
The single equation estimates are not presented for succinctness but are generally aligned with the system
estimates. They are available upon request.
14
The growth equation coefficient estimates are positive and highly significant at the 1%
level. As expected, labour and capital critically affect economic growth. But also mobile
telecommunications has a significant and growth promoting effect.
In the mobile demand equation, per capita income and own price elasticities have
different signs. We find an income elasticity of 62%, 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 significant but close to being inelastic (4% coefficient). We
also observe that urbanization has a positive and significant effect on mobile penetration,
which may be related to the urban lifestyle and the need for more communication in large
metropolitan areas.
On the supply side, of the concentration index HHI enters the regression with a negative
and significant sign, implying that lower levels of market concentration and hence more
competition among firms increase supply. The urbanization variable coefficient is
positive, so that it appears that firms have greater incentives to invest and supply their
services in countries with higher urbanisations levels. This may be related to easier
population coverage of networks and the existence of economies of scale in densely
populated areas. Finally, mobile price is significant for the supply of the mobile services,
though with a negative sign.
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.
15
Table 3
Econometric results
3SLS estimates1
Variables
Growth (GDPit)
Labour force (LFit)
Fixed stock of capital (Kit)
Mob Penetration (PENit)
Constant
Demand (PENit)
GDPC (GDPCit)
Mob. Price (Mob_Prit)
Urbanization (URBit)
Constant
Supply (Mob_Revit)
Mob Price (Mob_Prit)
Urbanization (URBit)
Market conc. (HHIit)
Constant
Output (ΔPenit)
Mob Revenue (Mob_Revit)
Constant
Year Effects
Country Effects
R2
Growth
Demand
Supply
Output
(1)
(2)
0.169***
0.813***
0.101***
2.943***
0.215***
0.322***
0.259***
-
0.666***
-0.039***
0.009***
-3.541***
0.622***
-0.041***
0.009***
-3.166***
-0.017***
0.050***
-3.451***
18.483***
-0.019***
0.049***
-3.450***
18.564***
0.429***
-7.630***
NO
NO
0.410***
-7.278***
YES
YES
0.96
0.57
0.37
0.38
0.99
0.57
0.38
0.37
Notes: Number of observations: 1125
(1) Random effects using 3SLS GMM estimates with robust standard errors
(2) Fixed effects using 3SLS GMM with robust standard errors
1
Three Staged Least Squares estimates with endogenous variables GDP, Mobile
Penetration, Mobile Investment and ΔPenetration
***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.
16
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,
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
17
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. So 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 countryspecific adjustments. We cluster the countries according to the achievement of predefined
penetration rate levels. These levels, hereafter referred to as ‘critical masses’, will allow
us to estimate the country specific returns.
It is worth emphasizing that the primary intention of our model was to understand and
18
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 = a1K it + a2 Lit + (a3 HIGH + a4 MEDIUM + a5 LOW )Mob _ Penit + ε 5it
(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 -2008)
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 up5. For example, Denmark was in the low penetration group from
1990-1995, moved to the medium penetration group from 1996-1999 and from 2000
5
Although 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 any
serious 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 medium
level 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.
19
onwards it remained in the high penetration cluster.
Costa Rica was in the low
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. Low and medium mobile penetrations have almost equal
coefficients (0.031 and 0.032 respectively) whereas high mobile penetration has a much
higher coefficient (0.054). 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 find 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
20
mobile adoption.
Table 4
Econometric result for critical mass effects
Variables
Growth (GDPit)
Labour force (LFit)
Fixed stock of capital (Kit)
Mob Penetration (PENit)
High(40%+)
Medium (10-40%)
Low(10%-)
Constant
Demand (PENit)
GDPC (GDPCit)
Mob. Price (Mob_Prit)
Urbanization (URBit)
Constant
Supply (Mob_Revit)
Mob Price (Mob_Prit)
Urbanization (URBit)
Market conc. (HHIit)
Constant
Output (ΔPenit)
Mob Revenue (Mob_Revit)
Constant
Year Effects
Country Effect
R2
Growth
Demand
Supply
Output
3SLS estimates1
0.264***
0.385***
0.054***
0.032***
0.031***
0.617***
-0.043***
0.010**
-3.182***
-0.023***
0.049***
-3.317***
18.487***
0.032***
-7.997***
YES
YES
0.99
0.57
0.36
0.37
Notes: Number of observations: 1125
(3) Fixed effects using 3SLS GMM with robust standard errors (with
different mobile penetration levels)
1
Three Staged Least Squares estimates with endogenous variables GDP,
Mobile Penetration, Mobile Investment and ΔPenetration
***, **, * denote statistical significance at the 1%,5% and 10% level,
respectively.
21
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 cluster, for each country we estimate a different CAGR6. The
resulting coefficients are presented in the Appendix in detail. Figure 1 shows the range of
returns for selected countries in the sample over the period of 1990-2008. We observe
that Finland enjoys the highest growth returns from mobiles, equal to 0.34% annually and
Kirgizstan less than a forth of these, around 0.08% annually.
Figure 1 Mobile telecommunication’s contribution to annual growth rate
Source: Autors’ calculations
6
We explain in the Appendix the calculations of CAGR for each level of Low, Medium and High
penetration levels.
22
The growth contribution rates vary substantially across countries. To illustrate better the
relationships, we split our sample into five income groups (based on the World Bank
clustering) and look whether these 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 2 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.
Figure 2: Contribution to growth from mobile telecommuications and average years
of high penetration rates
Source: Autors’ calculations
23
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 3 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 Bank’s five income clusters. The alignment of the three different
metrics is remarkable. Introduction timing ranges from 15 years for Low-Income
countries to 28 for High-Income; an average lag of almost 13 years. The corresponding
lag for high penetration is 8 years (on average 9 years for High-Income and 1 year for
Low-Income) indicating a catch-up effect. The annual contribution on growth follows the
same pattern too.
24
Figure 3: The relationship between mobile telecommunication’s contribution to
growth (left hand scale), average years of introduction and years of presence in high
penetration cluster (both right hand scale), by country income levels.
Source: Autors’ calculations
Table 5 provides more detailed evidence on the implications of increasing returns from
the adoption of mobile telecommunications. The years of high mobile penetration drove
economic growth. The upper and lower middle income countries have a lot to gain from
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 0.15% relatively to
their high income counterparts for the period 1990-2007. Lower middle income countries
25
have had a comparable loss of 0.13%. Surprisingly this technology gap is evident in
upper middle income countries too. This cluster has a growth gap of 0.09%. 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
High Income OECD
High Income Non-OECD
Upper Middle Income
Lower Middle Income
Low Income
Source: Autors’ calculations
Average number
of years with 40%
(or higher) mobile
penetration
(high returns)
1990-2007
9.33
7.05
4.16
1.94
0.93
Average % annual
contribution on
growth
0.28%
0.25%
0.18%
0.14%
0.12%
Foregone % annual
contribution due to
lack of mobile
infrastructure
(relatively to High
Income countries)
0.03%
0.10%
0.14%
0.16%
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
26
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:
Prodit ≡
K it
Lit
GDPit
= a1
+ a2
+ a3 Mob _ Penit + a4 hoursit + ε it
hoursit
hoursit
hoursit
(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
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
27
productivity and mobile telecommunications. Moving to the demand equation we find
that income elasticity is slightly higher for this subset of countries (0.772) indicating that
income changes have a stronger effect on the demand for mobile use. To the contrary,
OECD markets are less prone to respond to price changes resulting in a lower price
elasticity estimate (0.032). Urbanization is not significant for this subset, which might
suggest that rural areas’ mobile coverage has generally followed the metropolitan
networks, a case unlikely for many low-income and developing countries. The supply
equation shows that the concentration index enters the regression with a negative and
significant sign, as before. Prices also significantly affect supply, but here the coefficient
is positive. Urbanization is not significant for the supply of mobile infrastructure. Last, in
the output equation, the difference in mobile adoption (proxied by mobile penetration) is
positively linked to the supply of the infrastructure.
28
Table 6: Estimates from the system of equations
3SLS estimates1
Variables
Productivity (GDPit/hours)
Labour force quality (LFit/hours)
Fixed stock of capital (Kit/ hours)
Mob Penetration (PENit)
High
Low
Hours
Constant
Demand (PENit)
GDPC (GDPCit)
Mob. Price (Mob_Prit)
Urbanization (URBit)
Constant
Supply (Mob_Revit)
Mob Price (Mob_Prit)
Urbanization (URBit)
Market Conc. (HHIit)
Constant
Output (ΔPenit)
Mob Revenue (Mob_Revit)
Constant
Year effects
Country effects
R2
Growth
Demand
Supply
Mobile Output
(1)
(2)
-0.060
0.547***
0.114***
-0.158
7.295***
-0.163
0.553***
0.067***
0.037***
0.004
-
0.812***
-0.035***
-0.006
-3.866***
0.823***
-0.035***
-0.007
-3.932***
-0.001*
-0.008
-4.523***
24.149***
-0.002*
-0.007
-4.472***
24.109***
0.202***
-2.978***
YES
YES
0.234***
-3.667***
YES
YES
(1)
0.99
0.55
0.47
0.14
(2)
0.97
0.54
0.47
0.15
Notes: 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)
1
Three Staged Least Squares estimates with endogenous variables GDP, Mobile
Penetration, Mobile Investment and ΔPenetration
***, **, * denote statistical significance at the 1%,5% and 10% level, respectively.
29
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 only two dummy variables, high and low are used, with 40%
penetration level as the threshold7. The results are presented in column 2 of Table 7. As
parameters 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 (0.082 compared to 0.040). This translates into
much stronger network effects from higher penetration levels, which result in higher
productivity gains.
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
7
Average 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.
30
before. The results for different countries in the sample over the period of 1990-2008 are
presented in table 7. We observe that Nordic countries, namely Finland, Norway and
Sweden enjoy the highest contribution of mobile telecommunications to productivity
growth, equal to 0.31% annually, whereas this is remarkably lower with the last in the list
Canada with 0.22%.
Table 7: Annual productivity growth contribution from mobile telecommunications
(in %)
Finland
Norway
Sweden
Italy
Denmark
Luxembourg
United Kingdom
Iceland
Ireland
Netherlands
Portugal
Belgium
Germany
Spain
Switzerland
Greece
Australia
New Zealand
Hungary
Korea (Rep. of)
France
Japan
United States
Turkey
Mexico
Canada
0.31
0.31
0.31
0.30
0.30
0.30
0.30
0.30
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.29
0.28
0.28
0.28
0.28
0.27
0.24
0.24
0.22
0.19
0.18
Source: Autors’ calculations
31
Figure 4 shows the estimate of the mobile telecommunications impact on growth and
productivity. The most interesting result is the relatively high degree of correlation
(0.91). While Finland leads the growth tables, Netherlands has the highest returns from
productivity with marginal difference. These results might be linked with different
country level private and public sector operations and perhaps a more technologyfavourable work environment. The pattern followed by the two curves is almost identical
with few exceptions like Greece and the Republic of Korea reporting a higher than
expected productivity contribution from mobile infrastructure.
Figure 4
Mobile impact on annual contribution to growth and productivity (in %)
Source: Autors’ calculations
32
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 19972001, 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 wellfunctioning 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
33
permanently enhanced by the availability of mobile phones’. Jensen points out that
information and communication technologies are often considered a low priority for
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’ behavior 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 centers 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
34
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
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 million8. 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 neighboring
countries, Vodacom’s M-PESA had already attracted more than 1 million subscribers in
November 20099. 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
8
9
Kenya Broadcasting Corporation, 2009
Thomson Reuters ‘Tanzania's Vodacom says M-Pesa users hit 1 million’
35
remittances by 50-70%). Mobile telecommunications firms, such as Smart in the
Philippines and Safaricom and Vodafone in Kenya, are providing mobile transfer services
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. Conclusions and possible policy implications The paper has presented an assessment of the economic impact on mobile
telecommunications across the world and in particular the relevance for developing
36
countries. To tackle the problem of endogeneity of mobile telecommunications diffusion
and economic growth and productivity a simultaneous equation system has been used.
The main findings are that mobile telecommunications diffusion significantly affects both
GDP growth and productivity growth. Moreover, there is evidence for increasing returns
from the adoption of mobile telecommunications: the growth impact increases with the
level of diffusion. This is in contrast with earlier findings in the empirical literature,
which reports findings in line with decreasing returns from mobile technology adoption.
Our finding supports the theoretical implications of the literature on network
externalities. Indeed, mobile telecommunications being a network technology, the utility
of the single users increases with the size of the user community. Moreover, the
technology is to a large extent exempt from bottlenecks in adoption as the technology
does in principle not know long run capacity constraints.
This increasing return 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 favouring diffusion, such as sector liberalisation and
favouring private investment were adopted on an extensive base. These policies also
endorsed by the present study, with the additional element deriving from the results that such
policies should be pursued much more forcefully, including even elements of subsidisation in
37
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.
38
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
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
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.
39
Gruber, H. and Koutroumpis, P., (2010). Mobile Communications: Diffusion Facts and
Prospects. Communications and Strategies, No 77(1). pp.133-145.
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.
Jensen, R. (2007). The Digital Provide: Information (Technology), Market Performance,
and Welfare in the South Indian Fisheries Sector. The Quarterly Journal of Economics
August 2007, Vol. 122, Issue3, 879-924
Ivatury G. & Mas I. (2008). The early experience with mobile Banking, CGAP, April
2008
Katz Michael & Shapiro Karl (1986) Technology adoption in the presence of network
externalities, The Journal of Political Economy, Volume 94, Number 4.
Koski, H., and Kretschmer, T., (2005). Entry, Standards, and Competition: Firm
Strategies and the Diffusion of Mobile Telephony. Review of Industrial Organization
40
26:1, pp. 89 – 113
Koutroumpis, Pantelis. (2009) “The economic impact of broadband on growth: a
simultaneous approach”, Telecommunications Policy, 33(9), 471-485.
Leff, N. H. (1984). ‘Externalities, Information Costs and Social Benefit-Cost Analysis for
Economic Development: An example from Telecommunications’. Economic
Development and Cultural Change, 32(2), 255-276
Madden, G. and Savage, S. (2000). ‘Telecommunications and economic Growth’.
International Journal of Social Economics, 27(7), 893-906
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) 18871896.
41
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
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
42
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
World Bank, 2006. Economic Implications of Remittances and Migration," Global
Economic Prospect, Washington.
43
APPENDIX
1. Compound annual Growth Rates Estimations
Below 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 year’s 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.
⎡ MobPenlast − 40% ⎤
High_CAGR = ⎢
⎥⎦ *a3,
40%
⎣
if MobPenlast ≥ 100% then capped by 100%
Medium_CAGR = a4 ,
Low_CAGR = a5
⎫
(A1) ⎪
⎪
(1/Total _Years)
⎪
⎡ ∑3 CAGRi * yearsi ⎤
⎪
i=1
⎥
−1,
⎬ ⇒Total _CAGR = ⎢
⎢ Total _Years
⎥
⎣
⎦
(A2) ⎪
⎪
⎪
(A3)⎪⎭
A(4)
44
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.
⎫
⎡ MobPenlast − 46% ⎤
*aH , (A5)⎪
High_Pr od _CAGR = ⎢
⎥
(1/Total _Years)
46%
⎦
⎣
⎡ ∑2 CAGRi * yearsi ⎤
⎪⎪
i=1
⎥
⎢
if MobPenlast ≥ 100% then capped by 100%
−1, A(7)
⎬ ⇒Total _Pr od _CAGR =
⎥
⎢ Total _Years
⎪
⎦
⎣
⎪
Low_Pr od _CAGR = aL
(A6) ⎪⎭
45
2. Estimates for the individual counties of the sample and the contribution of
mobile infrastructure on growth
Country Name
Finland
Hong Kong, China
Denmark
Israel
Italy
Singapore
Austria
Ireland
Luxembourg
Netherlands
Portugal
Switzerland
United Kingdom
Norway
Sweden
Australia
New Zealand
Belgium
Germany
Spain
United Arab Emirates
Iceland
Estonia
Slovenia
Czech Republic
Bahrain
Macao, China
Hungary
Greece
Croatia
Cyprus
Slovak Republic
Malaysia
Qatar
Lithuania
St. Vincent and the Grenadines
Bahamas
Russia
Bulgaria
Saudi Arabia
%
Contribution
to growth
annually
0.34
0.32
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.29
0.29
0.29
0.29
0.28
0.28
0.28
0.27
0.27
0.27
0.26
0.26
0.25
0.25
0.24
0.23
0.23
0.23
0.23
46
Argentina
Oman
Thailand
Greenland
Poland
Romania
Trinidad and Tobago
Korea (Rep. of)
Ukraine
France
Uruguay
Jamaica
T.F.Y.R. Macedonia
Malta
Venezuela
Japan
Kuwait
United States
South Africa
Chile
Turkey
Jordan
Panama
Mauritius
Tunisia
Brazil
Peru
Gabon
Morocco
Latvia
Paraguay
Mexico
Algeria
Egypt
Pakistan
Indonesia
Sri Lanka
Philippines
China
Bolivia
Bangladesh
Canada
Puerto Rico
Gambia
Kazakhstan
Costa Rica
Ghana
Kenya
Viet Nam
0.23
0.23
0.22
0.22
0.21
0.21
0.21
0.20
0.20
0.20
0.19
0.19
0.19
0.19
0.19
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.15
0.15
0.15
0.15
0.15
0.15
0.14
0.14
0.14
0.14
47
Burundi
Myanmar
Colombia
Ecuador
Albania
Nicaragua
Cameroon
Nigeria
Tanzania
Madagascar
Azerbaijan
Belarus
Benin
Senegal
Zambia
India
Uganda
Iran (Islamic Rep. of)
Central African Rep.
Malawi
Belize
Suriname
Honduras
Moldova
Namibia
Fiji
Lesotho
Mali
Burkina Faso
Mongolia
Cote d'Ivoire
Georgia
Mozambique
Togo
Niger
Papua New Guinea
Zimbabwe
Armenia
Swaziland
Cape Verde
Rwanda
Botswana
Syria
Kyrgyzstan
Nepal
Mauritania
0.14
0.14
0.14
0.14
0.14
0.14
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.13
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.12
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.10
0.10
0.10
0.10
0.10
0.09
0.08
0.08
0.08
Source: Autors’ calculations
48
3. Countries in the sample
Albania
Ecuador
Lithuania
Papua New Guinea
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahamas
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Egypt
Estonia
Fiji
Finland
France
Gabon
Gambia
Georgia
Germany
Ghana
Greece
Guatemala
Honduras
Hong Kong,
China
Hungary
Iceland
India
Indonesia
Islamic Rep. of
Iran
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea (Rep. of)
Kuwait
Kyrgyzstan
Latvia
Lesotho
Luxembourg
Macao, China
Madagascar
Malawi
Malaysia
Mali
Malta
Mauritania
Madagascar
Malawi
Malaysia
Mali
Malta
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Romania
Russia
Rwanda
Saudi Arabia
Senegal
Singapore
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
St. Vincent and the
Grenadines
Suriname
Swaziland
Sweden
Switzerland
Syria
F.Y.R.O.M.
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Uganda
Bolivia
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cameroon
Canada
Cape Verde
Central African Rep.
Chile
China
Colombia
Costa Rica
Cote d'Ivoire
Croatia
Cyprus
Czech Republic
Denmark
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Ukraine
United Arab
Emirates
United Kingdom
United States
Uruguay
Venezuela
Viet Nam
Zambia
Zimbabwe
49