Abstract
This study explores the causal relationships between information and communications technology (ICT) and economic growth. It spans the post-Great Recession period of 2008–2017, which is both a period of economic tumult and slow recovery but also significant pathbreaking developments in ICT technologies. Generalized Method of Moments estimation is applied to a dynamic panel of 107 countries to examine causality between ICT measures and economic growth. The study is unique in that it distinguishes between more affluent and less affluent countries, which makes a significant difference in the results. Stronger and statistically significant causal impacts exist in less affluent countries from mobile internet penetration to GDP per capita. Conversely, causal impacts are statistically detected from GDP per capita growth to mobile internet penetration in both more affluent and less affluent countries. However, a weak and delayed causal impact occurs from GDP per capita to fixed broadband penetration in more affluent countries; none in less affluent countries. Granger-causality between mobile internet penetration and GDP per capita is, indeed, bidirectional, at least in less affluent countries.
This paper is dedicated to the memory of Professor Gary Madden (1952–2017) who inspired all of us with his academic rigor and energy. Not only was he a prolific researcher, he freely helped the rest of us in our research. Blessed with a wry sense of humor, Gary was a great colleague, mentor, and friend, not to mention a very decent human being. We feel the pain of his untimely passing.
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Notes
- 1.
There were some exceptions, namely, the United States and Canada (where the inventor of the telephone started companies) and some Scandinavian systems.
- 2.
Robert M. Solow famously stated that “You can see the computer age everywhere but in the productivity statistics;” this became known as Solow’s Paradox (Brynjolfsson 1993). The ICT literature addressed this “Paradox.”
- 3.
See International Telecommunication Union (2018).
- 4.
Farhadi et al. (2012) was the first to do so, to the best of our knowledge.
- 5.
Anderson and Hsiao (1981) were the first to propose an instrumental variable estimator in this circumstance, but their estimator proved to be asymptotically inefficient.
- 6.
- 7.
As Fig. 3.1 shows, the post-2008 period is marked by rapid acceleration of mobile telephony and broadband, a more moderate acceleration of fixed broadband, and a steady decline in fixed-line telephony. In many ways, this is a very different period for communications technologies and their penetration than the earlier periods for which most causality tests have been performed in the past.
- 8.
See International Telecommunication Union (2018).
- 9.
See Farhadi et al. (2012) for another instance of such a finding.
- 10.
The last measure includes both mobile and fixed access to broadband. The measures considered here are similar to those in the Toader et al. (2018) study of 28 European Union countries.
- 11.
Because all variables are expressed in logarithms, the coefficients have the usual interpretation as elasticities. For example, in Table 3.6, the coefficient for Ln(PMI) in less affluent countries is 0.0091. This implies that a 1% increase in mobile internet penetration boosts GDP per capita contemporaneously by 0.91%.
- 12.
- 13.
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Banerjee, A., Rappoport, P.N., Alleman, J. (2020). A Cross-Country Analysis of ICT Diffusion, Economic Growth, and Global Competitiveness. In: Alleman, J., Rappoport, P., Hamoudia, M. (eds) Applied Economics in the Digital Era. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40601-1_3
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