Elsevier

Information Economics and Policy

Volume 45, December 2018, Pages 16-29
Information Economics and Policy

How important are mobile broadband networks for the global economic development?

https://doi.org/10.1016/j.infoecopol.2018.10.001Get rights and content

Highlights

  • We find a statistically significant effect from mobile broadband (MBB) on GDP.

  • A 10 percent increase in MBB adoption causes a 0.8 percent increase in GDP.

  • The economic effect from MBB gradually decreases over time.

  • The effect from MBB is smaller in OECD-countries compared to the rest of the world.

Abstract

Since the beginning of the 21st century mobile broadband has diffused very rapidly in many countries around the world. This paper investigates to what extent the diffusion of mobile broadband has impacted economic development in terms of GDP. The study is based on data for 135 countries (90 countries once controlling for capital, employment and human capital) for the period 2002–2014. The results show that there is a statistically significant effect from mobile broadband on GDP both when mobile broadband is first introduced and gradually as mobile broadband diffuses throughout different economies. Based on a two stage model we are able to conclude that on average a 10 percent increase of mobile broadband adoption causes a 0.8 percent increase in GDP. Moreover, once we control for the years since mobile broadband was introduced, we find that the economic effect gradually decreases over time. For the country with median average growth of mobile broadband penetration, this implies that the economic effect has disappeared 6 years after introduction (if introduction is defined as a mobile penetration of 1 percent).

Introduction

Throughout history, new technology has always been an important driver of productivity and economic development. We are currently experiencing a technological revolution based on ICT. One of the major innovations within ICT, during the last decade, is the use of mobile broadband. According to GSMA (2018) mobile broadband connections have increased from approximately 27 thousand in 2001 to 4.8 billion in 2017 i.e. an average growth of 113 percent per year.1

The use of data being sent via mobile networks has been increasing exponentially at approximately 65 percent on a year-on-year basis during the period, 2010–2015 (Coyle and Williams, 2011, Ericsson Mobility Report 2016). The basis for this development has been the introduction and expansion of 3G and 4G mobile network systems and the development of smartphone devices. According to Cisco (2017) wired devices accounted for 51 percent of global IP traffic in 2016, compared to 41 percent of Wi-Fi and 7 percent for mobile devices. Cisco forecasts that mobile devices will account for 17 percent of global IP traffic in 2021.

Despite the enormous expansion of mobile broadband, it is still unclear to what extent, it has contributed to global economic development. Previous research has shown that ICT has had a large economic impact in many countries (Brynjolfsson and Hitt, 2003, Oliner and Sichel, 2000, Röller and Waverman, 2001). However, most of these studies have focused on established technologies such as fixed telephone lines and computers. Only a handful of studies have focused on mobile technologies (see for example Gruber and Koutroumpis, 2011). As more data becomes available it has become increasingly easy to also study the impact of newer technologies. This paper investigates the macroeconomic impacts of mobile broadband based on econometric methods applied to a cross-country panel data set. The primary questions that will be investigated are:

  • To what extent has mobile broadband affected macroeconomic development in terms of GDP globally?

  • If there is an impact from mobile broadband, is it an effect of mobile broadband introduction and/or a gradual process along mobile broadband penetration?

Chinn and Fairlie (2007) pointed out that there is a digital divide across countries in personal computers and Internet penetration. Thus, our questions are important from a policy perspective, because if mobile broadband has an important economic impact many countries could leapfrog in their economic development. We therefore also investigate the impact from mobile broadband in high- and low-income countries and OECD and non-OECD countries.

The paper shows that mobile broadband is positively associated with GDP based on 135 countries (90 once controlling for capital, employment and human capital). Introducing a dummy variable for mobile broadband introduction in a difference-in-difference specification, there is evidence of an introductory effect from mobile broadband. Moreover, there is also a contemporaneous effect from mobile broadband penetration. Furthermore, based on moving averages, we find stronger and larger effects from five-year differences compared to first differences. This is an indication that a lagged effect from mobile broadband penetration on GDP also exists.

Based on a two stage model controlling for simultaneity and reverse causality, we find strong evidence that mobile broadband introduction and penetration causes GDP growth rather than vice versa. The results suggest that a 10 percent increase in mobile broadband penetration causes a 0.8 percent increase in GDP. Moreover, once we control for the years since mobile broadband was introduced, we find that the economic effect gradually decreases over time. Finally, we find that our results are robust once we distinguish between low- and high-income countries. However, the effect from mobile broadband on GDP is considerably larger and more significant in non-OECD countries compared to OECD countries.

The paper is organized as follows. In Section 2 we summarize findings from earlier research and position our study in the current literature. In Section 3 we present the methodological framework, in Section 4 we describe the data, in Sections 5 and 6 we present our results based on both a fixed effect and an instrumental variable approach. Section 7 provides robustness checks and Section 8 concluding remarks.

Section snippets

Related literature

Throughout the 1980s it was unclear to many economists to what extent information and communication technology (ICT) impacted economic growth at the macro level (Solow, 1987). However, ever since economic and productivity growth took off in the US economy in the mid-1990s, there have been a plethora of studies showing links between ICT and economic development (see for example Jorgenson et al., 2008, O'Mahony and Vecchi, 2005, Oliner and Sichel, 2000, van Ark et al., 2008). Most of these

Methodology: production function framework and econometric specification

The model applied in this paper is based on the framework of the neoclassical production function. The production function framework relates output to labor, capital, intermediate inputs and TFP. In this paper we measure output as GDP, which is obtained by deducting intermediate inputs from gross output and adjusting for subsidies and sales taxes. Assuming an augmented Cobb-Douglas production function, we have the following equation:Yi,t=TFPi,tKi,tβKLi,tβLwhere Yi,t is real GDP, Ki,t is

Data

The data used in this paper has been collected from a number of different sources. Data on GDP, employment and human capital were retrieved from the Penn World Tables (Feenstra et al., 2015). The Penn World Table publishes different GDP series (see Feenstra et al., 2015). This paper uses a measure where the levels of GDP have been constructed based on multiple PPP benchmark years and therefore correct for changing prices between these benchmarks. Feenstra et al. (2015) argue that this measure

Results and discussion

A fixed effects (FE) model controls for or partials out the effects of the country specific components. An alternative to the fixed effects model is the random effects (RE) model which is used when variation across countries is assumed to be random and not correlated with the dependent and independent variables in the model. Based on a Hausman test we reject the hypothesis that the random effects model is most appropriate and instead conclude that the fixed effects model is most appropriate.8

Simultaneity

The methods used thus far have determined a correlation rather than a causal effect of mobile broadband introduction and penetration on GDP growth.

One way of addressing simultaneity is by using instruments that are correlated with the explanatory variable but not with the error term. Some of the instruments proposed from earlier studies are tax credit for ICT investment and specific types of housing (Abramovsky and Griffith, 2006, Dettling, 2013). However, none of these instruments are

Robustness

This section tests the robustness of our results based on different country groups and additional data. Due to space limitations we will only include one version of the specification, our robustness checks are themselves robust to other versions of the specification, results of which are available on request. Table 9 shows the results for the different specifications for the log of predicted mobile broadband penetration once we control for the years since mobile broadband introduction (based on

Conclusions

A number of different studies have shown that ICT is closely connected to macroeconomic development in terms of GDP. Most of these studies have focused on ICT as a whole or established technologies such as fixed telephone lines and computers. This paper investigates the effect of a much more novel technology, namely that of mobile broadband, on GDP.

Mobile broadband is measured as a percentage of total connections. Mobile broadband connections are defined as SIM cards registered on mobile

References (68)

  • HerbertG. Thompson et al.

    Economic impacts of mobile versus fixed broadband

    Telecommun. Policy

    (2011)
  • Francesco Venturini

    The modern drivers of productivity

    Res. Policy

    (2015)
  • Laura Abramovsky et al.

    Outsourcing and offshoring of business services: How important is ICT?

    J. Eur. Econ. Assoc.

    (2006)
  • Philippe Aghion et al.

    Competition and innovation: An inverted U relationship

    Q. J. Econ.

    (2005)
  • TheodoreW. Anderson et al.

    Formulation and estimation of dynamic models using panel data

    J. Econ.

    (1982)
  • Manuel Arellano et al.

    Some tests of specifications for panel data: Monte Carlo evidence and an application to employment equations

    Rev. Econ. Stat.

    (1991)
  • MakB. Arvin et al.

    Broadband penetration and economic growth nexus: Evidence from cross-country panel data

    Appl. Econ.

    (2014)
  • Beck, Thorsten, Demirgüç-Kunt, & Levine, Ross (2009)., “Financial institutions and markets across countries and over...
  • Irene Bertschek et al.

    The economic impacts of telecommunications networks and broadband internet: A survey

    Rev. Netw. Econo.

    (2015)
  • Nicholas Bloom et al.

    Trade induced technical change: The impact of chinese imports on innovation, diffusion and productivity

    Rev. Econ. Stud.

    (2016)
  • GiovanniS.F. Bruno

    Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals

    Stata J.

    (2005)
  • Erik Brynjolfsson et al.

    Computing Productivity: Firm level Evidence

    Rev. Econ. Stat.

    (2003)
  • MenzieD. Chinn et al.

    The determinants of the global digital divide: A cross-country analysis of computer and internet penetration

    Oxf. Econ. Pap.

    (2007)
  • Cisco Visual Networking Index: Forecast and Methodology, 2016–2021

    (2017)
  • Daniel Cohen et al.

    Growth and human capital: Good data, good results

    J. Econ. Growth

    (2007)
  • Carol Corrado

    Communication Capital, Metcalfe's law, and U.S. Productivity Growth

  • Coyle, Diane, & Williams, Howard (2011)., “Overview”, The Vodaphone Policy Paper Series, no. 12, pp....
  • Nina Czernich et al.

    Broadband infrastructure and economic growth

    Econ. J.

    (2011)
  • Anusua Datta et al.

    Telecommunications and economic growth: A panel data approach

    Appl. Econ.

    (2004)
  • LisaJ. Dettling

    Broadband in the labor market: The impact of residential high-speed internet on married women's labor force participation

  • Paul David

    The dynamo and the computer: An historical perspective on the modern productivity paradox

    Am. Econ. Rev.

    (1990)
  • Ericsson mobility report – on the pulse of the network society

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    We are grateful for useful comments and suggestions by Leonard Nakamura and the participants at our presentation at the 35th IARIW General Conference, Copenhagen 21–24 August 2018. We also gratefully acknowledge financial support from the ERSC, grant number, ESRC IAA PSB111_MSRH and Ericsson Research for funding the research project “The Economic Impact of Communication Equipment”.

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