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Technology Adoption and Growth in sub-Saharan African Countries

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Abstract

This study analyzes the relationship between information and communication technologies (ICTs) and labor productivity growth in sub-Saharan Africa over the period 1975–2010. The results show that fixed-line and mobile telecommunications have a positive and significant impact on growth after penetration rates reach a certain critical mass. The thresholds are identified using nonparametric methods. Penetrations rates of between 20% and 30% for telephones and 5% for internet usage trigger increasing returns. FDI and openness are found to improve productivity and to help ICTs boost growth. Financial development serves as a possible transmission channel for the growth-enhancing effects of ICTs.

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Notes

  1. See Dedrick et al. (2013) and Papaioannou and Dimelis (2007) for an extensive review of the related literature.

  2. Network effects exist if consumers’ utility of using a given product or service increases with the number of other users. These effects, especially in the telecommunication industry, not only increase the sharing of data as the network expands (direct network effects), but also enable the use of complementary services (indirect network effects) such as mobile internet applications (eg, e-banking, e-health, and other related mobile telephony platform applications). The economic growth effects arise from these direct and indirect effects. However, in general, the network effects impact growth through their influence on technology adoption and diffusion. Nonetheless, the bandwagon effect, which results from the network expansion, is expected to drive output growth (Economides and Himmelberg, 1995; Rohlfs, 2001; Shapiro and Varian, 1999). Other studies have shown that the presence of network effects can have a chilling effect on growth for a number of reasons. For example, if the required network threshold is not achieved, it can hamper technology adoption, growth of complementary services, and new innovations (Birke, 2009; Farrell and Klemperer, 2007; Shy, 2001; Stremersch et al., 2007).

  3. Specifically:

    The average annual growth rate of output per worker between the years t−τ and t is calculated as (y it yitτ)/τ.

  4. The standard approach in growth literature is to average the data over 3–5 years. However, due to data limitations of most ICTs variables, this study restricts itself to a 3-year average to maximize the data points.

  5. The only exception is the human capital measure, which is averaged over the 3-year period.

  6. The role of human capital constraints has featured prominently in the debate on SSA countries’ inability to replicate East Asia’s growth miracle (Pack and Paxson, 1999; Wolf, 2007).

  7. While the success stories from the export-led growth of East Asian economies lend some support to the beneficial effects of trade openness (Dollar, 1992), most SSA countries are net importers. Unlike East Asian countries, their export sectors are characterized by primary commodity production and agriculture-based manufacturing, with potentially neutral or detrimental effects on productivity growth.

  8. In more general terms, inflation can be interpreted as a measure of macroeconomic stability, while government consumption stands for the importance of government spending/investment in the economy.

  9. Several studies have attempted to establish the direction of causality between growth and ICTs. A number of them (Dutta, 2001; Perkins et al., 2005; Wolde-Rufael, 2007) have arrived at the conclusion of bi-directional causality for both developed and developing countries. Nonetheless, other studies have also established a one-way causality, from growth to telecommunications investment (Beil et al., 2005 for US).

  10. GMM estimation technique has been employed in related studies such as Papaioannou and Dimelis (2007) to control for the endogeneity bias. However Papaioannou and Dimelis (2007) use difference rather than system GMM.

  11. Andrianaivo and Kpodar (2011) did not detect the presence of network effects in a sample of African countries, attributing it to their sampling period. They argued that network effects could not be detected because African countries had not yet reached the necessary threshold by 2007, which marks the final year of their sampling period.

  12. A study by Gebreab (2002) found that the number of mobile subscribers increases by approximately 57% with each additional service provider.

  13. Using data from 63 developing countries over the period 1990–2001, Sridhar and Sridhar (2007) showed that mobile telephones had a bigger impact on output than fixed-line telecommunication, which contrasts with our results. However, their estimates are likely to be biased because they failed to include a quadratic specification in their model.

  14. The neutral effects from human capital can be attributed to the proxy used, primary schooling, which captures low-skill effects on labor productivity, rather than a general impact of human capital. For example, it has been established that tertiary education rather than average years of schooling is important for internet diffusion in developing countries (Kiiski and Pohjola, 2002). Unfortunately, in this study, we did not have sufficient data on tertiary education for most countries in the sample. Thus, these results should be interpreted bearing the proxy used in mind.

  15. Results based on this sampling period are not reported but are readily available from the authors upon request.

  16. We used different proxies of infrastructure development for which meaningful data was available – water, sanitation, and roads – and the results were consistently similar. However, the reported results use sanitation (improved sanitation facilities as a percentage of the population with access) as a proxy of infrastructure development due to data availability for this variable compared to the other two variables. The water variable is defined as improved water sources as a percentage of the population with access. Roads is defined as the percentage of paved roads in the country. The data on all three variables were downloaded from the World Bank’s World Development Indicators online database.

  17. On the contrary, the mobile cellular and internet effects remain unchanged relative to the baseline regressions. Results for the mobile and internet variables are not reported but are available from the authors upon request. Generally the internet effects remained neutral while the mobile cellular coefficient maintained robust increasing return effects at 5% level of significance.

  18. In this paper we adopt a broader definition of ‘legal origin’, similar to La Porta et al. (2008), as a style of social control of economic life. This definition encompasses assimilation of legal systems, social institutions, and infrastructure introduced in the African countries through conquest and colonization.

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Acknowledgements

The authors would like to thank the Editor, Josef Brada, and two anonymous referees for their helpful comments and suggestions.

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Wamboye, E., Tochkov, K. & Sergi, B. Technology Adoption and Growth in sub-Saharan African Countries. Comp Econ Stud 57, 136–167 (2015). https://doi.org/10.1057/ces.2014.38

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