Mobile technology: two
decades driving economic
growthPage

0Working paper
November 20200

Authors:
Kalvin Bahia
Principal Economist, GSMA Intelligencekbahia@gsma.com
Pau Castells
Head of Economic Analysis, GSMA Intelligencepcastells@gsma.com
Xavier Pedros
Senior Economist, GSMA Intelligence
xpedros@gsma.com

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Contents
1. Introduction ........................................................................................................................................... 3
2. Research questions................................................................................................................................ 5
3. Data ....................................................................................................................................................... 7
4. Empirical strategy .................................................................................................................................. 8
5. Results ................................................................................................................................................. 13
6. Conclusions .......................................................................................................................................... 25
References ............................................................................................................................................... 27
Appendices .............................................................................................................................................. 30

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1. Introduction
A significant body of empirical research has studied the economic impacts of telecommunications overthe last two decades. The impact of fixed communications infrastructure has been covered
extensively, mostly looking at technology deployments until 2010.1 Meanwhile, the literature
analysing the impacts of mobile technology is more limited, particularly when it comes to the rolloutof more recent network technologies. While some studies have considered the rollout of mobile
connectivity until 2010, these miss the recent acceleration in the use of mobile broadband throughthe extension of 3G and 4G.2 Recently, some papers have specifically addressed the impact of mobile
broadband globally for the last decade, for example Edquist et al. (2018) and ITU (2018).
We contribute to this more recent wave of research in four areas. First, we evaluate the impact ofmobile technology from 2000 to 2017 across more than 160 countries – this covers the early
deployment of 2G to the 4G cycle, and represents one of the most comprehensive panels in theliterature (particularly in the context of the most recent research looking at mobile broadband).
Second, while related literature tends to look at the overall impact of mobile technology, we apply aframework to unpick specific impacts of 2G, 3G and 4G technology – addressing the question ofwhether there are additional impacts with newer infrastructure. Third, we evaluate heterogeneities in
the impact of mobile, including network effects from take-up, skills and economic structure –mechanisms that can shift mobile’s impact and that have been largely unexplored globally in recent
periods. Finally, using the same panel, this is a novel paper in applying several methods to address
endogeneity, including Structural Equations Models (SEM), Dynamic Panel Data (DPD) and
Instrumental Variable (IV) approaches – which have been used in the existing literature in different
settings.
Firstly, we find that mobile technology has significantly driven GDP in the 2000–2017 period – a 10%
increase in mobile adoption increased GDP by 0.5% to 1.2%, with these effects remaining broadly
stable over the period analysed and materialising over and above fixed infrastructure. Second, we
found the subsequent rollout of 2G, 3G and 4G networks has driven increasing impacts. Specifically,
mobile’s baseline economic impact increases by about 15% when connections are upgraded to 3G. For
connections upgrading from 2G to 4G, the economic impact of mobile increases by approximately
25%.

1
2

See Bertschek (2017) for a comprehensive literature review.
For example, see Thompson & Garbacz 2011, Gruber & Koutroumpis 2011, Aker 2010 or Forero 2013

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Third, our analysis for determinants of the impact of mobile reveal important conclusions. We havefound that the macroeconomic impacts of mobile increase with adoption, suggesting strong networkand learning effects. We also noted countries with more education experience stronger effects,
providing evidence of complementarities with human capital accumulation. We also found someevidence of higher impact in economies where services and manufacturing represent a more
important part of the economy, further suggesting complementarities with capital and labour in these
sectors.
The remainder of this paper is organised as follows. Section 2 explains the research questions we
explore, framing them in the relevant empirical literature. Following this, Section 3 describes the data
and Section 4 details the empirical strategy that we propose for our OLS fixed effects, SEM, IV and DPD
frameworks. Section 5 provides results, and Section 6 summarises the key conclusions andimplications, in addition to suggesting areas for further research.

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2. Research questions
Mobile infrastructure can drive economic growth in different ways. Most notably, its use lowers
information, search and transaction costs – improving the efficiency of markets for goods, services,labour and capital.3 Mobile technology also complements or replaces existing production inputs –enabling quicker, cheaper and more convenient processes of production – which improve theutilisation of labour and non-mobile capital, or overall productivity.4 Importantly, the subsequent
deployment of mobile infrastructure has enabled a series of applications triggering these type ofeffects (Table 1). Most notably, 2G introduced basic voice mobile services, 3G introduced mobile
broadband, and 4G introduced enhanced mobile broadband.
Table 1. Characteristics of mobile technology cycles
2G
Services
Illustrative
cycle length
Applications
enabledBand type
Speed

3G

4G

Voice
1990–2006

Voice and broadband
2006–2011

Voice and fast broadband
2009–Present

Voice calls, SMS, MMS,
browsing (limited)
Narrow band
14.4 kbps

High-speed browsing,applications
Wide band
3.1 Mbps

Video conferencing,mobile TV
Ultra-wide band
100 Mbps

The mobile and fixed broadband literature has generally found positive, significant GDP effects –which suggest that these mechanisms are relevant. Overall, a 10% increase in mobile or fixed
infrastructure results in increases in economic growth ranging from 0.5% to 2% (see selected studies
in Appendix 5). Recent analyses looking at mobile broadband specifically have found GDP increases by
0.8% to 2% (ITU (2018), Edquist et al. (2018)). In this study, we expand the existing evidence base byconsidering three research questions (RQ), which we discuss below.
Firstly, existing literature has looked at datasets of varying scope. In this paper we look at the impact
of mobile technology using a panel of 2000–2017 for more than 160 countries. This covers the early
development of 2G to the cycle of 4G technology and allows us to evaluate almost the entire historyof mobile technology development in most countries.
3

This encourages more transactions and improves existing exchanges. From a macroeconomic perspective, forexample, goods and services markets benefit from more trade and competition; workers in the labour market
have access to more job opportunities and firms to a wider talent pool; and capital markets see increased
borrowing and lending activity.
4
Some applications enabled by mobile infrastructure complement existing inputs, making them more productive– for instance, mobile technology applications allow firms to coordinate their labour more effectively or to use
their capital in the most productive manner. Other applications enabled by mobile infrastructure replace part oflabour or non-mobile capital, resulting in cost savings.

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This panel contributes to the existing literature by looking at the recent mobile broadband
developments (see Appendix 5). On this basis, our first question looks at the average impact of mobile
in this panel (RQ1). As part of this, we test whether it has significantly changed over time and whether
it holds controlling for fixed infrastructure.
Secondly, while studies on the role of mobile have generally analysed the impact of overall, aggregate
mobile technology, we note no global studies so far have attempted to look at the issue of 2G-, 3Gand 4G-specific returns. Some studies have looked at the impact of data speeds (which relate totechnology upgrades) in developed countries, with mixed results – Ahlfeldt et al. (2017) finds
increasing broadband speeds has diminishing returns, while Koutroumpis (2018) found the opposite.
In this study, we address the specific gains of 2G, 3G and 4G, evaluating the extent to which mobile
technology upgrades generate additional returns (RQ2).
Thirdly, the broader research on the impacts of ICT infrastructure have generally found its returns toincrease with take-up (due to positive network externalities); with skills (due to complementarities
with human capital accumulation); by economic sectors (depending on interactions with labour andcapital); and by development levels.5 The evidence of their relevance in the case of mobile technology
has been mostly unexplored, especially in the context of the wide scope of years and countries in ourpanel. We therefore also address possible heterogeneities regarding network externalities, skills,
economic structure and income (RQ3).
In summary:
RQ1: What was the average impact of mobile in the period 2000 to 2017?
RQ2: What are the specific impacts of 2G, 3G and 4G technology?
RQ3: What are the factors that enhance the impact of mobile? (3.1) Take-up levels; (3.2) skills; (3.3)
economic structure; (3.4) income.

5

See the Results section for references to the relevant ICT literature.

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3. Data
Our panel covers 164 countries, developed and developing, from 2000 to 2017.6 This allows us to lookat almost the entire rollout of mobile technology, including 2G, 3G and 4G, across a range of
economies. We measure mobile technology as the number of mobile connections,7 as a share of total
population, which we refer to as market penetration or adoption. This serves as a proxy for the extent
to which the stock of mobile technology capital is available in each country and year. We use GSMAIntelligence data on mobile connections, broken down by network technology.8 The data is primarilysourced from operators’ public reports.
Figure 1 shows historical trends in the stock of mobile technology, measured by number ofconnections. In developed countries (Panel A), 2G connections started to grow in the late 1990s andkept increasing until the mid-2000s, when 3G started being deployed. Adoption of 3G technology
increased the total stock of mobile connections, as well as enhanced the quality of service for preexisting mobile stock. The 4G cycle started in 2010 and primarily enhanced the quality of existing
mobile connections, with the additional increase in total stock being more limited. Developing
countries saw deployments of mobile technology starting later (Panel B), with total mobile
connections growing with 2G, 3G and 4G.
Figure 1. Stock of mobile connections by technology (billions)
Panel A - Developed countries

Panel B - Developing countries
7

1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

6
5
4
3
2
1

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

0

2G connections

3G connections

4G connections

Source: GSMA Intelligence
6

The panel for the DPD and IV models is reduced to 139 countries due to data availability of the external
spectrum instrument used.
7
Number of unique SIM cards that have been registered on the mobile network at the end of the period.
Connections differ from subscribers such that a unique subscriber can have multiple connections.
8
Number of connections that can connect to a 2G, 3G or 4G network (depending on mobile operator’s network
deployment).

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4. Empirical strategy
The primary challenge to address in studying the impact of mobile infrastructure on GDP is theendogenous relationship between GDP and a country’s mobile technology endowment. This is due tothe inherent circularity between growth and infrastructure development. The existing literature hasaddressed this primarily in three ways: Structural Equations Models (e.g., Gruber et al. 2014 and ITU,
2018),; Instrumental Variables (e.g. Czernich et al. (2011) and Edquist et al. (2018)); and Dynamic
Panel Data models (e.g. Arvin & Pradhan (2014) and Giday (2018)).
In this paper we start with an OLS fixed effects estimator, and then apply SEM, IV and DPD
approaches. To our knowledge, this is the first study that uses all four methods when considering the
economic impact of mobile technology. While the OLS fixed effects framework assumes theexogeneity of infrastructure, it is the most flexible framework, allowing us to address all aspects of thethree research questions. We use the rest of the approaches to isolate direction of the impact, bearing
in mind the endogenity problem. In the SEM analysis, we omit the role of fixed broadband (from RQ1)
and impacts of specific technologies (RQ2), as we cannot exogenise fixed broadband and different
mobile technologies due to data limitations (as well as due to estimation complexity).
Table 2. Roadmap to empirical strategy

RQ1 – Average impact of mobile
RQ2 – Specific impacts by mobile technology
RQ3 – Heterogeneity analysis

OLS
✓
✓
✓

SEM
✓*
✓

DPD
✓
✓
✓

IV
✓

✓ Indicates use of method in order to answer the relevant RQ.
* Approach does not include the sub-question about the role of fixed broadband.

4.1 OLS fixed effects
To address RQ1, we start with an aggregate production function drawing on a Cobb-Douglas growthframework i.e. featuring decreasing marginal products, constant output elasticity and returns to scale.
The output of country i at time t depends on its capital (measured by capital stock, 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 ); its
supply of labour (as population in working age, 𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 ); and its total stock of mobile connections(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 ) and of fixed broadband (𝐹𝑖𝑥𝑒𝑑𝐵𝐵𝑖𝑡 ). As the benefits of mobile and fixed infrastructurearise from their use, we approximate these through their level of adoption (i.e. connections as a shareof population). The terms 𝜇𝑖 , 𝜃𝑡 are parameters for country and time fixed effects.
(Eq. 1)log(𝐺𝐷𝑃𝑖𝑡 ) = 𝛽0 + 𝛽1 log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 ) + 𝛽2 log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 ) + 𝛽3 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) +𝛽4 log(𝐹𝑖𝑥𝑒𝑑𝐵𝐵𝑖𝑡 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡

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To test how the results varied over time, we break down the average impact of mobile by its effects inthree periods of infrastructure development: (i) early deployments; (ii) early mobile internet; and (iii)
fast networks. We operationalise this by interacting 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 with three country-specific,
mutually exclusive dummies in the aggregate production function (Equation 2). 𝐸𝑎𝑟𝑙𝑦𝐷𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑠𝑖𝑡
is a dummy taking value 1 from the time when 2G started being adopted until the time 3G firstbecame available, or 0 otherwise. The 𝐸𝑎𝑟𝑙𝑦𝑀𝑜𝑏𝑖𝑙𝑒𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡𝑖𝑡 dummy covers from the start of the3G cycle to the start of 4G being rolled out, and the 𝐹𝑎𝑠𝑡𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠𝑖𝑡 variable covers the time
thereafter.9 The coefficients will therefore recover the impact of increasing the total stock of mobile
connections in different periods of mobile innovation.
(Eq. 2)𝑙𝑜𝑔(𝐺𝐷𝑃𝑖𝑡 ) = 𝛽0 + 𝛽1 log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 ) + 𝛽2 log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 ) + 𝛽3 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗(𝐸𝑎𝑟𝑙𝑦𝐷𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑠𝑖𝑡 + 𝐸𝑎𝑟𝑙𝑦𝑀𝑜𝑏𝑖𝑙𝑒𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡𝑖𝑡 + 𝐹𝑎𝑠𝑡𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠𝑖𝑡 ) ++ 𝛽4 log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 ) 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡

Regarding RQ2, disentangling the impact of different technology generations is a challenging task
because the change on the type of mobile services is not linear between mobile technology
generations.10 In this paper, we propose using three cumulative building blocks. Equation 3 includes
the total stock of mobile connections (𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ); that of 3G and 4G networks (𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 ); andthat of 4G connections (𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 ). Estimating these three variables in the same equation means that
𝛾3 , 𝛾4 and 𝛾5 will allow us to capture technology impacts, as explained below.
(Eq. 3)log(𝐺𝐷𝑃𝑖𝑡 ) = 𝛾0 + 𝛾1 log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 ) + 𝛾2 log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )+ 𝛾3 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) + 𝛾4 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 ) + 𝛾5 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 )+ 𝛾6 log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡Firstly, 𝛾3 will recover the impact of increasing the total stock of mobile connections, holding constantthe increases in 3G and 4G. This therefore represents the impact of gaining 2G, or basic mobile
connectivity – we refer to this as the connectivity effect.

9 We define these dummies using mobile penetration variables, so that 𝐸𝑎𝑟𝑙𝑦𝐷𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑠

𝑖𝑡 covers the period until 3G
mobile penetration is positive; 𝐸𝑎𝑟𝑙𝑦𝑀𝑜𝑏𝑖𝑙𝑒𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡𝑖𝑡 spans from the latter until 4G penetration is positive; and𝐹𝑎𝑠𝑡𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠𝑖𝑡 , the rest. We have done sensitivities where these dummies take value 1 where mobile connections
penetration surpass 1% and 5% thresholds, with results being broadly consistent. This approach is based on Koutroumpis(2016).
10

After a given technology has been rolled out, the start of the deployment of a new technology means that theformer will be partly replaced i.e. connections on the former infrastructure tend to decrease. In particular, the2G and 3G variables’ impact would be estimated in the parts of the panel where their penetration decreases, as
they are substituted by subsequent generations. At the same time, the timeline of rollout of these technologies
partly overlaps (particularly for 2G and 3G), meaning they are collinear to some extent. Ultimately, in Equation 1,
this means we cannot simply replace 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 by three variables individually capturing 2G, 3G and 4G, as
their impacts would be confounded.

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Secondly, holding the total stock of mobile connections constant, 𝛾4 and 𝛾5 will retrieve upgrade
effects arising from existing connections transiting to 3G and 4G. In particular, the term 𝛾4 measures
the impact of increasing 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 ceteris paribus (including total stock of connections as well as
𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 ), so will necessarily capture additional benefits brought by existing 2G connections
upgrading to 3G services. Meanwhile, 𝛾5 will capture benefits of increasing 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 keeping the rest
constant, or the impact of existing 3G connections upgrading to 4G.
Regarding the heterogeneity analysis (RQ3), to assess the role of take-up we modify the basic
aggregate production function (Equation 1) adding interactions of mobile with adoption leveldummies for low, medium and high take-up (as per the 33th and 66th percentiles of the distribution of
the data, corresponding to 43% and 97%, respectively).11
To evaluate differences created by skills, economic structure and income, we follow two approaches.
First, we run regressions on sample splits. These divide the panel in countries above and below
median values of years of schooling (9 years), to assess the role of skills; share of services andmanufacturing sectors in employment (75%), to assess the role of economic structure; and developed
and developing countries12, to analyse the role of income. Second, to provide reassurance of statisticalsignificance of the differences between samples, 13 we also estimate models interacting mobile
adoption with a dummy taking value 1 when years of schooling are above the median, for the analysis
on impact of skills; and with a dummy taking value 1 when share of services and manufacturingsectors in employment is above the median, for the analysis of the effect of economic structure.
4.2 Structural Equations Model
To isolate the causal impact of mobile infrastructure on GDP, we use a Structural Equations Model.14
This specifies a micro-model for the mobile sector in each country, consisting of equations for thedemand and supply of mobile infrastructure, as well as an infrastructure function. This framework
allows us to endogenise mobile technology because it incorporates the structural, fundamental driversof the mobile infrastructure market (including GDP).

11

This approach is consistent with related wider research on telecommunications infrastructure that has also
looked at the role of take-up (for example, see Koutroumpis 2009).
12
We regard as developed countries those categorised as high-income countries as per the World Bank
groupings – developing otherwise. <https://datahelpdesk.worldbank.org/knowledgebase/articles/906519world-bank-country-and-lending-groups>.
13
While sample splits provide a flexible framework allowing for sample-specific estimates for independent
variables, we note this comes at the expense of making the assessment of statistical significance of differences incoefficients (from separate regressions) challenging. The latter is addressed through the interaction approach.
14
We implement this with Stata’s gmm command, which allows for robust standard errors clusters by country.

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We omit unpicking the specific role of each mobile technology generation (RQ2) and the role of fixed
broadband (part of RQ1), as this would require embedding additional SEMs – something we could not
do due to data limitations to estimate demand, supply and infrastructure equations and due toestimation complexity.15
We apply a SEM model including country and time fixed effects on all equations. This broadly follows
the approach extensively used in the literature – see Roller and Waverman (2001), Gruber andKoutroumpis (2011), Gruber et al. (2014), Koutroumpis (2009, 2016 and 2018) and ITU (2018). It
features an aggregate production function similar to that of Equation 1, providing the baseline
average impact of mobile infrastructure (RQ1). Note we also adjust this equation inserting the three
country-specific, mutually exclusive dummies, to assess how this effect varies over time (as inEquation 2).
(Eq. 4)Aggregate production

log(𝐺𝐷𝑃𝑖𝑡 ) = 𝛽0 + 𝛽1 log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 ) + 𝛽2 log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 ) +𝛽3 log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡(Eq. 5) Demand functionlog(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) = 𝜃0 + 𝜃1 log(𝐺𝐷𝑃𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ) + 𝜃2 log(𝐴𝑅𝑃𝑈𝑖𝑡 ) +𝜃3 log(𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡(Eq. 6) Supply function

log(𝐶𝑎𝑝𝑒𝑥𝑖𝑡 ) = 𝜌0 + 𝜌1 log(𝐺𝐷𝑃𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ) + 𝜃2 log(𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 ) +𝜃3 log(𝐻𝐻𝐼𝑖𝑡 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡(Eq. 7) Mobile infrastructurelog(∆𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) = 𝛿0 + 𝛿1 log(𝐶𝑎𝑝𝑒𝑥𝑖𝑡 ) + 𝜇𝑖 + 𝜃𝑡 + 𝜀𝑖𝑡

The demand equation (Equation 5) captures changes in mobile penetration that are driven by income(as per GDP per capita, 𝐿𝑜𝑔𝐺𝐷𝑃𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ); the prices of mobile services (proxied by average revenue
per user, 𝐿𝑜𝑔𝐴𝑅𝑃𝑈𝑖𝑡 ); and the propensity to adopt mobile technology as per urbanisation levels(share of urban population, 𝐿𝑜𝑔𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 ).
The supply model (Equation 6) implies that investment in mobile infrastructure (capital expenditures,
𝐿𝑜𝑔𝐶𝑎𝑝𝑒𝑥𝑖𝑡 ) depends on income per capita, as a measure of willingness to pay; the costs for rollingout, which are typically shifted by the population distribution, and we approximate throughurbanisation levels; as well as the degree of market concentration (𝐿𝑜𝑔𝐻𝐻𝐼𝑖𝑡 ), as a measure of theintensity of competition among firms.

15

Accounting for 2G-, 3G- and 4G-specific effects would require technology-specific data for the demand, supply
and infrastructure equations, something that is not available to us. For fixed broadband, although there ispricing and investment data from different sources, this only covers part of our panel. Moreover, inserting threebuilt-in equations for each mobile technology generation or for fixed broadband would mean estimating many
more equations – which would increase the complexity of the estimation and be very demanding for thevariation of the data.

Page | 11

Finally, the infrastructure equation (Equation 7) states that the change in mobile penetration depends
on investment in mobile infrastructure, as measured by capital expenditures, as this is typically themain source for funding infrastructure growth for mobile operators. Note Equations 3–6 also allow forcountry and time fixed effects 𝜇𝑖 and 𝜃𝑡 , as in the OLS framework.
4.3 Instrumental Variable
We evaluate the robustness of findings using instrumental variables, which also exogenises the stockof mobile connections. As an external instrument, we use the spectrum assigned to mobile operators
by governments. This is because, in order to launch mobile networks, operators need to be able to use
radio frequencies over the airwaves, or spectrum. The amount of spectrum holdings drives not only
the availability of mobile services but also the quality of these, while it should be exogenous relative
to GDP (particularly across countries of similar development levels).16 However, due to the availabilityof data, we can only capture spectrum assigned to 3G and 4G technology.
We run a 2SLS regression with country and time fixed effects, evaluating the impact of the
𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 variable on 𝐺𝐷𝑃𝑖𝑡 , with the first stage defining 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 as a function of the amountof spectrum available for 3G and 4G mobile services in each country and year. As we do not control forthe total stock of mobile connections in this framework, the parameter for 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 can beinterpreted as partly aggregating both connectivity and upgrade effects – as growth in 3G and 4G
connections has not only upgraded 2G connections but also increased the total stock of connections inparts of our panel. This IV 2SLS strategy allows us to check the robustness of our results fortechnology-specific impacts (RQ2), which we are unable to do in the SEM framework.
4.4 Dynamic Panel Data
As a final robustness check on the results for each research question, we apply a Dynamic Panel Datamodel with Arellano Bond estimators. As done in the related literature using DPD models, we add apartial adjustment mechanism (specifically, the first lag of GDP), common in growth models to betteraccount for macroeconomic time series.17 Adding this term effectively means that impact estimates
will be read as the elasticity of the GDP growth rate with respect to mobile, rather than the elasticityof the GDP level relative to mobile (as in OLS and SEM results). Regarding instruments, we implement
the third lag of internal instruments, as well as the external, exogenous spectrum variable discussed in
Section 4.3.

16Countries have assigned mobile spectrum for different mobile technologies at different points in time, giving
substantial variation in the instrument.
17
We apply a difference GMM estimator, using Stata’s command xtabond2.

Page | 12

5. Results
5.1 Average impact of mobile (RQ1)
Our OLS and SEM analysis for the average impact of mobile (columns 1–3 in Table 4) suggests that a
10% increase in the adoption of mobile technology drives an increase in GDP from 0.46% to 2.55%.
While this is a wide range, we note it is broadly consistent with the existing literature. Having said
that, we treat the upper bound of the SEM result with some caution, as the effect may be confoundedby the capital variable, which has a small impact compared to the results in this set of outputs (andelsewhere in this paper). Our DPD setting confirms the direction of this effect, relative to the GDP
growth rate.
Columns 4–6 test whether this average impact has significantly varied over time. Taking into accountstandard errors, with a 95% confidence interval we note that OLS, SEM and DPD results areoverlapping over the three time periods. This suggests that impacts have likely been fairly stable over
the three periods evaluated. In particular, OLS and SEM point to a 10% in mobile generating returns to
GDP from 0.5% to 1.2% – with results of both approaches here pointing to a narrower range ofimpacts. Overall, we can conclude that benefits brought by mobile technology have remained stable
at this range of 0.5% to 1.2% (RQ1).

Page | 13

Table 4. Average impact of mobile on GDP
log(𝐺𝐷𝑃𝑖𝑡 )(1)
OLS
log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 )Average impact
(2)
SEM18

0.046***(0.018)0.255***(0.007)

(3)
DPD

(4)
OLS

Impact over time
(5)
(6)
SEM
DPD

0.015***(0.005)log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗ 𝐸𝑎𝑟𝑙𝑦𝐷𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑠

0.072***(0.015)(0.003)(0.011)log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗ 𝐸𝑎𝑟𝑙𝑦𝑀𝑜𝑏𝑖𝐼𝑛𝑡

0.091***

0.123***

0.036**

0.037***(0.023)(0.008)(0.021)0.100***(0.015)0.106**(0.051)0.016***

0.053*(0.033)0.010*(0.006)(0.006)log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗ 𝐹𝑎𝑠𝑡𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 )0.098***

log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )0.214**

0.526***

0.082**

0.208**

0.262***

0.104**

log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )(0.084)0.362***(0.065)(0.027)0.077***(0.014)(0.084)0.343***(0.061)(0.025)0.311***(0.014)16.461***(1.786)2,468

24.16***(0.388)2,387(0.035)0.105***(0.036)0.758***(0.041)
N/A
2,222

16.954***(1.682)2,468

17.75***(0.370)2,387(0.042)0.175***(0.048)0.581***(0.065)
N/A

164
0.998
N/A

164
0.995
(a)

139
N/A
(b)

164
0.998
N/A

164
0.998
(a)

log(𝐺𝐷𝑃𝑖𝑡−1 )Constant
Observations
Number of clusters
R-squared
Diagnostics

2,222
139
N/A
(b)

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors with country clusters). (a) SEM full set of results is reported inAppendix 2. (b) The DPD AR(3) statistic suggests absence of third-order serial correlation in disturbances, while the Hansen pvalue rejects the null hypothesis that the instruments are exogenous.19

18

Table reports standard errors without these being clustered by countries, as this is not an option included in
Stata’s reg3 command. Statistical significance of the reported variables however holds when using an equivalentapplication with Stata’s gmm command.
19
Note the DPD model does not include constant term as this is differenced out in the Difference GMM
estimator, and does not report R-squared statistic as this is not a sensible goodness of fit measure in a GMM
context.

Page | 14

We note our OLS FE estimates for mobile variables remain significant after accounting for fixed
broadband, so we can conclude that impacts of mobile technology materialise above effects driven by
fixed infrastructure. We have run additional analyses using the internal instruments provided by the
DPD framework (allowing us to instrument both mobile and fixed broadband), with results being inthe same direction.20
Regarding diagnostics, labour and capital have the expected significant effects.21 For the rest, in theSEM, the drivers of demand, supply and mobile infrastructure functions are all significant and in the
expected directions (see Appendix 2)22; and the DPD test for autocorrelation suggests errors are notautocorrelated (however, we note a low value of the Hansen p value, suggesting that the instruments
may not be exogenous).
5.2 Specific impacts by technologies (RQ2)
The analysis of impacts by technologies is reported in columns 1–3 in Table 5. Increasing the total
stock of mobile connections by 10% drives an increase in GDP of 0.50% in our OLS setting, with the
DPD framework also confirming a significant impact. As this coefficient measures the impact of thetotal stock of connections ceteris paribus (including keeping 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 and 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 constant), weinterpret this as the impact of basic 2G connectivity, or a baseline connectivity effect.
Importantly, we also find statistical significance in 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 and 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 , suggesting growth in 3G
and 4G connections generates additional impacts on top of the baseline connectivity effect. Notethese effects are found while keeping the stock of connections constant, meaning these benefits arise
from existing connections being transitioned from 2G to 3G, and from 3G to 4G i.e. upgrade effects.
Firstly, for 3G, the OLS framework finds that increasing 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 by 10% (holding 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 fixed),
raises mobile’s impact on GDP by 0.07 percentage points – with the DPD result also being consistent.
Secondly, ceteris paribus, raising 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 by 10% percent further raises mobile’s GDP impact 0.05
percentage points in the OLS model, with a finding in similar direction in the dynamic model.
Overall, the above means that mobile’s baseline impact coefficient of 0.05 increases by about 15%
when connections are upgraded to 3G. For connections upgrading from 2G to 4G, the impact
increases by approximately 25%.

20

Results available in Appendix 4.
Apart from the already discussed magnitude capital in the SEM model in column 2, we also note that the DPD
labour and capital coefficient magnitudes tend to be distinct (in these results and elsewhere in the paper),simply due to these effects being partly picked up by the GDP lag.
22
The demand equation shows expected, significant drivers: a negative role for ARPU (with a PED in the inelastic
range of 0 to 1, as one expects with necessity goods); positive income elasticity; and a positive effect ofurbanisation. The supply results show a positive role for income and for urbanisation, with HHI being a negative
significant driver. The mobile infrastructure function, as expected, is positively affected by investment.
21

Page | 15

Table 5. Technology-specific effectslog(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 )log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 )log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡4𝐺 )log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 )(1)
OLS
0.050***(0.019)log(𝐺𝐷𝑃𝑖𝑡 )(2)
DPD
0.031***(0.009)0.007***

0.004***

0.013*(0.002)(0.001)(0.008)0.005*

0.002*(0.003)(0.001)(3)IV230.017***(0.006)log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )0.216***

0.0846*0.214***

log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )(0.081)0.355***(0.065)(0.0433)
0.179***(0.046)0.505***(0.085)(0.033)0.461***(0.025)log(𝐺𝐷𝑃𝑖𝑡−1 )Constant

16.794***(1.778)
2,468

2,222

13.580***(0.701)
2,222

Number of clusters

164

139

139

R-squared
Diagnostics

0.998
N/A

N/A
(a)

0.997
(b)

Observations*** p<0.01, ** p<0.05, * p<0.1. *** p<0.01, ** p<0.05, * p<0.1 (robust standard errors with country clusters). (a) AR(3)statistic suggests absence of third-order serial correlation in disturbances, while the Hansen p value rejects the null
hypothesis that the instruments are exogenous. (b) First stage results show positive, significant role of the spectrum
instrument, with instrument endogeneity and weakness test suggesting this is an exogenous, strong instrument (seeAppendix 3 for detailed results).

We note the connectivity gain is much stronger compared to the upgrade effects. This result is not
surprising, as one would intuitively expect the productivity and efficiency benefits brought by gaining
access to mobile communication services to be more transformative across economies, rather thanbringing in improvements to the quality and capabilities of already existing communications.
The effects discussed so far assume a constant stock of mobile connections, so that the 3G and 4Gspecific gains arise from pre-existing 2G connections upgrading technology.

23

IV model calculated without robust cluster standard errors because the estimated covariance matrix ofmoment conditions is not of full rank.

Page | 16

These gains, already discussed above, are illustrated in Figure 2 (panel A). On this basis, we can also
derive the effects of 3G and 4G when the total stock of mobile connections increases. These mean
aggregating connectivity and upgrade effects, simulating the benefits arising in countries where 3G
and 4G connections are added without these pre-existing in 2G. Figure 2 (panel B) below shows thatraising 2G take-up by 10% increases GDP by 0.50%, with gains rising to 0.57% and 0.62% for 3G and
4G, respectively.
Figure 2. Elasticity of GDP with respect to 2G, 3G and 4G – Point estimates and confidence intervals at
the 95% level

Based on results in columns 1–3 (Table 5).

Turning to the IV approach, the second stage shows that an increase in 10% in 𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 causes anincrease in GDP of 0.13% (column 3 in Table 5) – with the first stage showing the expected significant,positive role of spectrum, as well as diagnostics suggesting this is an exogenous and strong instrument(see Appendix 3). The magnitude of this impact is above the upgrade effects, and below theconnectivity effect we find in the OLS and DPD estimates. This result is not surprising, as it represents
impacts from both increased connectivity and upgrade effects in some countries, and just upgrade
effects in others (especially in the period of 4G growth).

Page | 17

Overall, we can therefore conclude that the rollout of subsequent mobile network generations
increases the economic impact of mobile on GDP (RQ2). The significant upgrade effects of 3G and 4G
especially point to the role of improvements in mobile data service quality in increasing mobile’s
impact on GDP (especially improvements in upload, download speeds and latency). Regarding thespecific impacts found, if we take confidence intervals into account, these are broadly consistent with
the impact found for the average impact of mobile (RQ1).
5.3 Heterogeneity analysis (RQ3)
The role of network effects
With positive network externalities, returns to mobile networks should increase the more users adopt
mobile services. This phenomenon has been reported in related literature. For example, Roller andWaverman (2001) found that fixed wireline impacts are only significant with penetration rates above
40%, while broadband adoption has been found to have lower thresholds, from 10% to 30% (Czernich
et al. (2009), Gruber et al. (2014), Koutroumpis (2009)). Gruber & Koutroumpis (2011) found increases
in impacts of mobile when this surpasses 30–40% of adoption.
Our results confirm the existence of nonlinearities, with mobile impacts increasing with the adoption
level dummies for low, medium and high take-up. OLS and SEM analysis suggest increasing impacts
once mobile population penetration surpasses the 43% and 97% thresholds, which correspond to themedium and high take-up levels (columns 1–2 in Table 6), while DPD results point to nonlinearities
being significant only at low to middle levels of mobile adoption (column 3). In particular, looking at
GDP level impacts (OLS and SEM): for low levels of take-up, a 10% increase in mobile adoption drives
an increase of GDP of 0.80–1.01%; for medium take-up, impacts increase to 1.09–1.16%; and reach
1.05-1.53% at high levels of adoption.

Page | 18

Table 6. The role of nonlinearities

log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗ 𝐿𝑜𝑤𝑇𝑎𝑘𝑒𝑈𝑝𝑖𝑡log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 )∗ 𝑀𝑒𝑑𝑖𝑢𝑚𝑇𝑎𝑘𝑒𝑈𝑝𝑖𝑡log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 ) ∗ 𝐻𝑖𝑔ℎ𝑇𝑎𝑘𝑒𝑈𝑝𝑖𝑡
log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 )log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )(1)
OLS
0.080***(0.016)
0.109***(0.028)0.105*(0.065)0.010**(0.006)0.201**(0.085)0.342***(0.064)log(𝐺𝐷𝑃𝑖𝑡 )(2)
SEM
0.101***(0.004)0.116***(0.011)0.153***(0.021)

0.262***(0.025)0.310***(0.014)log(𝐺𝐷𝑃𝑖𝑡−1 )Constant
Observations
Number of clusters
R-squared
Diagnostics

16.996***(1.758)
2,465
164
0.998
N/A

17.79***(0.370)
2,387
164
0.998
(a)

(3)
DPD
0.033***(0.009)0.065**(0.026)0.044(0.061)

0.069(0.043)0.152***(0.039)0.639***(0.062)
N/A
2,214
139
N/A
(b)

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors with country clusters). (a) SEM full set of results is reported inAppendix 2. (b) AR(3) statistic suggests absence of third-order serial correlation in disturbances, while the Hansen p value
rejects the null hypothesis that the instruments are exogenous.

Overall, these results are indicative of strong network effects and are consistent with the results foundelsewhere in the literature (RQ3).

Page | 19

The role of skills
Countries with skilled labour tend to enjoy greater impacts from ICT, as this allows them to better
absorb telecoms capital. For example, pre-existing human capital increases the impact of broadband
on firm creation and wage growth24; and regions that have thicker labour markets for complementary
services also experience stronger benefits out of broadband (Forman et al. (2012)). This question has
been less addressed with respect to mobile technology specifically, though some studies haveindicated this is likely the case.25
To explore whether mobile technologies have complementarities with human capital, Table 7 shows
regressions for the “High education” and “Low education” subsamples, based on the median of 9
years of average schooling (columns 1–6 in Table 7). This analysis unambiguously points tosignificantly greater impacts in countries with higher education levels on average. Specifically, themagnitude of our point estimates for high education countries are 1.6 to 3 times those that we see inthe sample for low education countries, depending on the approach – suggesting substantial
differences. The interaction approach – where we interact mobile with a dummy taking value 1 whena country has more years of schooling than the median (𝐻𝑖𝑔ℎ𝐸𝑑𝑖𝑡 ) – provides similar insights in all
OLS, SEM and DPD frameworks (columns 7–9), with the dummy variable being significant.

24
25

See McCoy et al. (2017); Forman et al. (2012); Atasoy (2013); Mack and Faggian (2013).
For example, Giday (2018) found low impacts of mobile internet in Sub-Saharan Africa with low internet skills.

Page | 20

Table 7. The role of skills(1)log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )

(2)

(3)

(4)

log(𝐺𝐷𝑃𝑖𝑡 )(5)

OLS
Sample split
High ed.
Low ed.

SEM
Sample split
High ed.
Low ed.

(6)
DPD
Sample split
High ed.
Low ed.

(7)OLS

0.09***

0.03*

0.35***

0.22***

0.06***

0.02***

0.04**

0.24***(0.02)(0.02)(0.02)(0.01)(0.01)(0.01)(0.01)
0.02***(0.01)

0.02*

0.01

(0.01)(0.01)

0.22*

0.16

0.50***

0.47***-0.02

0.24***(0.13)(0.19)(0.04)(0.05)(0.06)(0.07)

0.42***

0.31***

0.16***

0.12***

0.31***

0.09**(0.10)(0.08)(0.02)(0.02)(0.08)(0.046)(0.02)
0.04**(0.01)
0.01***(0.01)
0.23***(0.09)
0.36***(0.06)

0.46***

0.68***(0.07)(0.07)

23.13***

N/A

N/A(0.455)
1,394
85
0.99
(a)

1,063
67
N/A
(b)

1,148
72
N/A
(b)

16.62**(1.76)
2,444
163
0.99
N/A

23.64***(0.39)
2,353
163
0.99
(a)

log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )∗ 𝐻𝑖𝑔ℎ𝐸𝑑𝑖𝑡log(𝐹𝑖𝑥𝑒𝑑𝐵𝐵𝑖𝑡 )log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )log(𝐺𝐷𝑃𝑖𝑡−1 )Constant
Observations
N of clusters
R-squared
Diagnostics

14.82***(2.81)
1,176
78
0.99
N/A

17.99***(2.27)
1,268
85
0.99
N/A

21.61***(0.58)
959
78
0.99
(a)

(8)
SEM

(9)
DPD

All sample

0.51***(0.03)
0.10***(0.01)

0.02***(0.01)
0.03**(0.01)

0.11***(0.04)
0.18***(0.04)
0.62***(0.06)
N/A
2,206
139
N/A
(b)

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors with country clusters). (a) SEM full set of results is reported inAppendix 2. (b) AR(3) statistic suggests absence of third-order serial correlation in disturbances, while the Hansen p value
rejects the null hypothesis that the instruments are exogenous.

We therefore conclude these results indicate that productive uses of mobile technology capital havestrong complementarities with a country’s skills (RQ3).

Page | 21

The role of economic structure
The literature suggests ICT’s impact differs depending on a country’s economic structure, given
different complementarities between capital, labour and telecoms infrastructure. For example, fixedline and broadband studies have found service sectors benefit the most.26 More recently, thebroadband literature shows stronger impacts in technology-intensive, scientific and technical sectors(Atasoy (2013), Kolko (2012), McCoy et al. (2017)); in banking, trade, construction and health (Nadiri
and Nandi (2018)); or, in rural areas, the hospitality sector (Canzian et al. (2015)). These differences inaggregate impacts, meanwhile, have been less researched with regards to mobile technology.
To address complementarities of mobile technology’s impact with economic structure, Table 8 shows
regressions for the “High services & manufacturing” and “Low services & manufacturing” subsamples(based on the median of 75% of employment in these sectors). These cuts provide evidence thatcountries with more service and manufacturing activity enjoy stronger mobile impacts (columns 1–6 inTable 8). In fact, the OLS analysis suggests that this is a requirement for positive benefits to take place,while SEM and DPD predict approximately 1.4 to 7 times more gains in countries with a moreadvanced economic structure. The interaction approach – where mobile is interacted with a dummy
taking value 1 when a country has employment in services and manufacturing above the median(𝐻𝑖𝑔ℎ𝑆𝑀𝑖𝑡 ) – support the same finding in SEM and DPD, though the additional impact related to anadvanced economic structure is insignificant with OLS (columns 7–9).

26

For example, retail trade (Cronin et al. 1993; Cieslik & Kanjewsk 2004) finance, insurance and real estate(Greenstein & Spiller, 1995).

Page | 22

Table 8. The role of economic structure(1)log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )∗ 𝐻𝑔ℎ𝑆&𝑀𝑖𝑡
log(𝐹𝑖𝑥𝑒𝑑𝐵𝐵𝑖𝑡 )log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )

(2)

(3)

(4)

OLS
𝑆𝑒𝑟𝑣𝑀𝑎𝑛𝑢𝑓75𝑖𝑡 =
Low
High
S.&M.
S.&M.
0.03
0.05**(0.02)(0.02)

SEM
𝑆𝑒𝑟𝑣𝑀𝑎𝑛𝑢𝑓75𝑖𝑡 =
Low
High
S.M.
S.M.
0.33*** 0.47***(0.01)(0.02)

0.01(0.01)-0.18(0.22)
0.23***(0.07)

0.01(0.01)
0.12(0.08)
0.53***(0.10)

0.57***(0.03)
0.14***(0.02)

0.38***(0.07)
0.02(0.02)

17.90***(1.87)
1,010
75
0.99
N/A

11.75***(2.69)
1,415
86
0.99
N/A

22.38***(0.56)
1,179
75
0.99
(a)

24.27***(0.57)
1,164
86
0.99
(a)

log(𝐺𝐷𝑃𝑖𝑡−1 )Constant
Observations
N of clusters
R-squared
Diagnostics

log(𝐺𝐷𝑃𝑖𝑡 )
(5)
(6)
DPD
𝑆𝑒𝑟𝑣𝑀𝑎𝑛𝑓75𝑖𝑡 =
Low
High
S.M.
S.M.
0.01** 0.07***(0.00)(0.02)

0.02(0.09)
0.00(0.03)
0.94***(0.05)

0.04(0.05)
0.31***(0.10)
0.39***

(7)
OLS

(8)
SEM

(9)
DPD

All sample
0.05***(0.02)-0.02(0.02)
0.01**(0.01)
0.23***(0.08)
0.36***(0.06)

0.25***(0.01)
0.01**(0.00)

0.02***(0.01)
0.03**(0.01)

0.52***(0.03)
0.09***(0.01)

0.08*(0.04)
0.19***(0.06)
0.63***(0.14)(0.09)

N/A

N/A

16.43***

956
61
N/A
(b)

1,250
78
N/A
(b)

(1.77)
2,425
161
0.99
N/A

23.94***(0.387)
2,343
161
0.99
(a)

N/A
2,206
139
N/A
(b)

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors with country clusters). (a) SEM full set of results is reported inAppendix 2. (b) AR(3) statistic suggests absence of third-order serial correlation in disturbances, while the Hansen p value
rejects the null hypothesis that the instruments are exogenous.

Overall, we can conclude that there is some evidence suggesting that more developed economic
structures, with greater services and manufacturing, benefit more from the transformative use ofmobile capital stock (RQ3). The fact that we see less definitive evidence in this analysis (with the OLS
interaction approach not being supportive) may be due to mechanisms in opposite directions. For
example, several studies have documented significant productivity and efficiency gains in agricultural
sectors (Kaila and Tarp (2019), Goyal (2010), Ritter and Guerrero (2014), Salas-Garcia and Fan (2015)),
which may suggest that their less industrialised and formalised economic activity leaves more room
for mobile technology to generate gains.

Page | 23

The role of income
The role of income is theoretically ambiguous. On the one hand, developing countries have labour andcapital that tends to be less complementary with ICT infrastructure. On the other, developing
countries have more limited fixed infrastructure deployment, meaning there is more room for mobile
technology to drive productivity and efficiency gains.27 Some papers have found that mobile
broadband and mobile in general have larger impacts in low-income countries (e.g. ITU (2018)).
We also considered differences in returns to mobile technology depending on income levels, using
both sample splits and interaction terms. We obtained mixed results, with diagnostics for SEM, DPD
and IV approaches suggesting we could not draw conclusions from these analyses.
We note that the studies showing higher impacts in developing countries seem to point that the fact
these economies had poorer (fixed) communications infrastructure at the time of the arrival of mobile
is a strong impact channel. That is, this lack of pre-existing infrastructure may have left more room formobile to trigger stronger market efficiency and productivity improvements – with this more thanoffsetting the likely lower gains from lower levels of take-up (i.e. the role of nonlinearities), lesseducation (i.e. the role of skills) and less services and manufacturing activity (i.e. the role of acomplementary economic structure).

27

Lee et al. (2012) demonstrate that the impact of mobile telecommunications is stronger in developing
countries with less developed wireline telecommunications infrastructure.

Page | 24

6. Conclusions
The last two decades have seen much research devoted to understanding the economic impact oftelecoms infrastructure – primarily operating through reductions in information, search andtransaction costs and through being a complement or replacement of existing production inputs.
However, while fixed communications and broadband have been extensively researched, theliterature on mobile technology is more limited. Most studies have addressed the rollout of mobile
until 2010, which is before the recent explosion in the use of mobile broadband (especially indeveloping countries). Only a recent wave of research has aimed to address the role of mobile
broadband – but there still remains a number of research gaps.
This study contributes to the existing body of literature in four ways. First, we analyse one of the mostcomprehensive panels in the recent literature, covering 160 countries from the early rollouts of 2G in2000 to the 4G cycle in 2017. Second, importantly, this is a novel paper in unpicking the specificimpacts of 2G, 3G and 4G technology, addressing whether there are additional impacts with newer
infrastructure. Third, we evaluate heterogeneities in the macroeconomic impact of mobile,
considering nonlinearities, skills and economic structure. Fourth, this paper simultaneously applies
three separate methods to address endogeneity, including Structural Equations Models, Dynamic
Panel Data and Instrumental Variable models.
Firstly, we find that mobile technology was a significant driver of GDP in the 2000–2017 period.
Specifically, our analysis has found that a 10% increase in the rate of mobile adoption increases GDP
by 0.5–1.2%, with these effects remaining broadly stable over the period analysed. This is in line with
impacts found in related literature, and provides further reassurance of the efficiency and productivity
gains triggered by mobile technology. Our analysis has also revealed that these impacts materialiseover and above the effect of fixed broadband, implying that countries that have fixed infrastructure inplace can still benefit from further developing communications infrastructure through mobile.
Secondly, we find that the rollout of subsequent 2G, 3G and 4G network technology has driven
incremental impacts. Specifically, mobile’s baseline connectivity impact increases by about 15% whenconnections are upgraded to 3G. For connections upgrading form 2G to 4G, the impact increases byapproximately 25%.
Thirdly, we have noted three significant heterogeneities, pointing to mechanisms of impact and giving
policymakers an understanding of how they can maximise the impact of mobile technology. The
impact of mobile increases with adoption levels, suggesting network effects; countries with more skills
enjoy greater impacts, suggesting complementarities with human capital accumulation; and we foundsome evidence of higher impact where services and manufacturing represent a greater share of
economic activity, suggesting complementarities with capital and labour in these sectors.

Page | 25

Our findings point to at least three areas of further research. First, following our result of increased
impacts with 2G, 3G and 4G, there is value in looking at other quality metrics with more granularityand variability, such as data speeds. This has started gaining traction in the literature, though primarily
for developed countries (e.g. Ahlfeldt et al. (2016)). Second, further research is required to understandhow and why impacts differ in developed and developing countries. Our heterogeneity analysis
reveals some mechanisms, suggesting lower impacts in developing countries (i.e. take-up level, skills
or economic structure) – however, empirical evidence tends to suggest greater impacts in developing
economies. Having more evidence to understand why this is the case is required. Finally, as continued
innovation takes place, particularly as 5G and IoT start to expand, further analysis is required tounderstand their economic impacts.

Page | 26

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Appendices
Appendix 1. Data sources
VariableGDP
LabourCapitalGDP per capita
ARPU
Urbanisation
HHI
Capex
Fixed broadband
Mobile connections
Schooling years
Employment
distribution
Spectrum holdings

Units and source
Real GDP at constant 2011 national prices (Penn World Table)
Population in working age (World Bank)
Capital stock at constant 2011 national prices (World Bank)Real GDP at constant 2011 national prices (Penn World Table)
Revenue per subscriber, USD (GSMA Intelligence)
Share of population in urban areas (World Bank)
Herfindahl-Hirschman Index based on connections (GSMA Intelligence)
Capital investment, USD (GSMA Intelligence)Lines, as a proportion of population (ITU)
Connections, as a proportion of population (overall, and 2G, 3G and 4G) (GSMAIntelligence)
Average years of schooling (United Nations)Share of total employment in the agriculture, construction and manufacturingsectors (International Labour Organization)
Total amount of MHz allocated for mobile services (GSMA Intelligence)

Page | 30

Appendix 2. SEM baseline results
The table below reports the detailed outputs for the SEM model relating to column 2 in Table 5. The
demand equation shows expected, significant drivers: a negative role for ARPU (with a PED in the
inelastic range of 0 to 1, as one expects with necessity goods and in line with related research);
positive income elasticity; and a positive effect of urbanisation. The supply results show a positive rolefor income and for urbanisation, while HHI is negative. The mobile infrastructure function, as
expected, is positively affected by investment.
(1)
Aggregate Production Function log(𝐺𝐷𝑃𝑖𝑡 )0.255***
log(𝑀𝑜𝑏𝑖𝑙𝑒)(0.007)log(𝑀𝑜𝑏𝑖𝑙𝑒) ∗ 𝐸𝑎𝑟𝑙𝑦𝐷𝑒𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑠log(𝑀𝑜𝑏𝑖𝑙𝑒) ∗ 𝐸𝑎𝑟𝑙𝑦𝑀𝑜𝑏𝑖𝑙𝑒𝐼𝑛𝑡𝑒𝑟𝑛𝑒𝑡log(𝑀𝑜𝑏𝑖𝑙𝑒) ∗ 𝐹𝑎𝑠𝑡𝑁𝑒𝑡𝑤𝑜𝑟𝑘𝑠
0.526***(0.027)0.077***
log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙)(0.014)
Constant
24.16***(0.388)Observations
2,387
R-squared
0.995
Demand Function log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )-0.208***log(𝐴𝑅𝑃𝑈)(0.025)0.581***log(𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛)(0.129)3.010***
log(𝐺𝐷𝑃 𝑐𝑎𝑝𝑖𝑡𝑎)(0.071)
Constant-36.26***(1.012)
Observations
2,387
R-squared
0.849
Supply Function log(𝐶𝑎𝑝𝑒𝑥𝑖𝑡 )
0.143***
log(𝐴𝑅𝑃𝑈)(0.029)-0.0963**log(𝐻𝐻𝐼)(0.0421)2.434***
log(𝐺𝐷𝑃 𝑐𝑎𝑝𝑖𝑡𝑎)(0.081)
Constant-8.672***(0.983)Observations
2,387
R-squared
0.933
log(𝐿𝑎𝑏𝑜𝑢𝑟)(2)

0.098***(0.003)0.123***(0.008)0.100***(0.015)0.262***(0.025)0.311***(0.014)17.75***(0.370)2,387
0.998-0.490***(0.033)
1.183***(0.146)2.488***(0.085)-31.05***(1.164)2,387
0.864-0.121***(0.035)-0.135***(0.043)2.039***(0.090)-2.044*(1.129)
2,387
0.936

Page | 31

Mobile Infrastructure Function log(∆𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡 )1.600***(0.063)
Constant-28.17***(1.199)Observations
2,387
R-squared
0.439
log(𝐶𝐴𝑃𝐸𝑋)

1.452***(0.036)-25.41***(0.705)
2,387
0.473

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors).
Country and time fixed effects included in all equations.

Page | 32

Appendix 3. IV first stage regression
The first stage of the IV model shows spectrum has a significant, positive impact on the level of mobile
adoption. Specifically, a 10% increase of spectrum in frequency bands used for 3G and 4G services
drives an increase in mobile take up of 1.7%, on average. We note that the endogeneity test p value
leads to us not being able to reject the null hypothesis that the endogenous regressor can actually betreated as exogenous; and that the Wald F statistic is above the 10 threshold, generally accepted asindication of a strong instrument.

3𝐺4𝐺
log(𝑆𝑝𝑒𝑐𝑡𝑟𝑢𝑚𝑖𝑡)𝑙𝑜𝑔(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 )1st stage
0.171***

log(𝐺𝐷𝑃𝑖𝑡 )2nd stage(0.022)0.013*

log(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡3𝐺4𝐺 )(0.008)log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )Constant
Observations-1.012**0.214***(0.450)(0.033)1.359***(0.264)-46.02***(6.953)2,222

0.461***(0.025)
13.58***(0.701)

Number of clusters

139

R-squared
Endogeneity test p value
Cragg-Donald Wald F
statistic

0.997
0.18
62

*** p<0.01, ** p<0.05, * p<0.1 (robust standard errors)28
Country and time fixed effects included in all equations.

Appendix 4. DPD model controlling for fixed infrastructurelog(𝑀𝑜𝑏𝑖𝑙𝑒𝑖𝑡2𝐺3𝐺4𝐺 )log(𝐺𝐷𝑃𝑖𝑡 )0.0139***(0.004)log(𝐹𝑖𝑥𝑒𝑑𝑏𝑏𝑖𝑡 )0.007***(0.002)log(𝐿𝑎𝑏𝑜𝑢𝑟𝑖𝑡 )0.091**(0.038)28

IV model calculated without robust cluster standard errors because the estimated covariance matrix ofmoment conditions is not of full rank.

Page | 33

log(𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 )log(𝐺𝐷𝑃𝑖𝑡−1 )Observations

0.166***(0.038)0.617***(0.051)
2,239

Number of clusters

164

R-squared

N/A

The DPD AR(3) statistic suggests absence of third-order serial correlation in disturbances, while the Hansen p value rejects
the null hypothesis that the instruments are exogenous.29

Appendix 5. Selected recent impact estimates in the literature
Research area
Mobilebroadband

AuthorITU (2018)

Mobilebroadband

Edquist et al.
(2018)

MobileGruber &Koutroumpis(2011)
Deloitte(2012)

MobileMain finding
A 10% increase in mobile broadband drives a 1.82.0% increase in GDP in developing economies,
while impact is insignificant in developed
countries
A 10% increase in mobile broadband causes a
0.8% increase in GDP.
A 10% increase in mobile penetration generates a
1.4% increase in GDP.
A 10% substitution from 2G to 3G penetration
increases GDP per capita growth by 0.15
percentage points.
A 10% increase in broadband penetration drives a
0.5% increase in GDP.

Broadband

Koutroumpis(2018)

Fixed
broadband

Czernich etal. (2011)

Fixed
broadband

Koutroumpis(2009)A 10 percentage point increase in fixed broadband
penetration drives annual growth per capita
between 0.9 to 1.5 percentage points.
A 10% increase in fixed broadband adoption
generates a 0.23 percent increase in GDP growth.

Fixedbroadband

Gruber et al.
(2014)

A 10% increase in fixed broadband penetration
drives a 0.8% increase in GDP.

Scope
2004–2017(140 global
countries)
2002–2014(135 global
countries)
1990–2007(192 global
countries)

2002–2016(OECD
countries)
1996–2007(OECD
countries)
2002–2007(OECD
countries)
2005–2011 (EUcountries)

29

Note the DPD model does not include constant term as this is differenced out in the Difference GMM
estimator, and does not report R-squared statistic as this is not a sensible goodness of fit measure in a GMM
context.

Page | 34

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