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The information revolution, innovation diffusion and economic growth: an examination of causal links in European countries

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Abstract

Over the last 5 decades, the economic landscape in Europe has been transformed rapidly due to innovation, digitisation of the economy, and emergence of new sources of growth. However, the complex dynamics among diffusion of innovation, penetration of information and communication technology (ICT), and economic growth have not been adequately studied. This paper investigates the relationships among these three variables for European countries over 1961–2016. The goal is to determine whether the direction of causality among the variables runs both ways, one way, or not at all. Using a vector error-correction model, we find that in the long run, both innovation diffusion and ICT penetration stimulate economic growth. In the short run, however, the causal links are not always uniform and depend on proxies that are used for innovation diffusion and ICT penetration. The results provide valuable insights on the types of policies and strategies that would sustain economic growth in European economies.

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Notes

  1. ICT penetration in an economy refers to the accessibility, reliability and efficiency of computers, phones, televisions and radio sets, and the various networks that link them. The World Bank group defines ICT to consist of hardware, software, networks, and media for collection, storage, processing transmission, and presentation of information in the form of voice, data, text, and images. They range from the telephone, radio and television to the internet (World Bank 2003a, b).

  2. ICT mutually affects innovation and economic growth both at a micro-level (see, for instance, Wong et al. 2016; Mithas et al. 2011) as well as a macro-level (see, for instance, Pradhan et al. 2017b; Zhang and Li 2018; Sassi and Goaied 2013; Vu 2013; Spanos et al. 2002).

  3. Table 4 in Appendix 1 provides a brief summary of the past studies and their findings of the direction of Granger causality.

  4. Studies in the literature use different terms to describe the evolution of ICT. Some refer to ICT infrastructure and use variables such as the number of internet servers and fixed broadband. Throughout the present study and for the sake of consistency, we refer to ICT penetration and use several different proxies to capture this variable. In the literature review in this section, we use the term ‘penetration’, but depending on the study being reviewed here, ‘penetration’ could mean infrastructure, development, or prevalence—terms that are used by different authors.

  5. It can be noted that diffusion modelling studies have tried to explain and analyse the patterns of diffusion of innovations, usually over time and across population of potential adopters, and thus, forecast diffusion of the innovation. The particular emphasis has been on predicting the ultimate level of penetration (see, for instance, Bagchi et al. 2008).

  6. The countries comprise Albania, Andorra, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kazakhstan, Kosovo, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Moldova, Monaco, Montenegro, the Netherlands, Norway, Poland, Portugal, Romania, the Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, and the United Kingdom. As is evident, a few of these are transcontinental countries which span more than one continent. World Bank definitions are used.

  7. We have an unbalanced panel since data on the variables is not uniformly available for all the countries and for all the years over the period of our investigation.

  8. The procedural details are discussed in Pradhan et al. (2014a, b).

  9. CIC is the weighted average of five ICT penetration indicators, namely, TEL, MOB, INU, INS and FIB. CII is the weighted average of seven innovation diffusion indicators, namely, PAR, PAN, RDE, RRD, HTX, STJ, and TRM. PAT is not included in the construction of CII to avoid double counting PAR and PAN.

  10. The subset of countries and the years covered under each of these set ups and cases are on the basis of data availability for the selected variables. We have an unbalanced panel in this study.

  11. Cointegration entails a long-run equilibrium relationship that ties the three time-series variables even though short-term departures from equilibrium may exist (see Engle and Granger 1987 for an explanation).

  12. This result supports the findings of Pradhan et al. (2014a, 2016a, b).

  13. In the case of the short-run relationship between innovation diffusion and economic growth, there are 15 instances supporting the supply-leading hypothesis, 10 instances supporting the demand-following hypothesis, 22 instances supporting the feedback hypothesis, and 7 instances supporting the neutrality hypothesis. In the case of the short-run relationship between ICT penetration and economic growth, there are 5 instances supporting the supply-leading hypothesis, 5 instances supporting the demand-following hypothesis, 44 instances supporting the feedback hypothesis, and 0 instances supporting the neutrality hypothesis.

  14. FMOLS is fully modified ordinary least squares (OLS), a non-parametric estimation approach, taking into account the possible correlation between the error term and the first differences of regressor as well as the presence of a constant term to deal with corrections for serial correlation (Pedroni 2000).

  15. DOLS is dynamic OLS, a parametric estimation approach that adjusts the errors by augmenting the static regression with leads, lags, and contemporaneous values of the regressor in first differences (Kao and Chiang 2000).

  16. This is based on a worldwide survey of firms in nine major industrial sectors from several countries across Europe, the Americas, Asia Pacific, Middle East and Africa. The industries that are expected to experience the highest cost reductions are: Aerospace, Defense and Security; Automotive; Chemicals; Electronics; Engineering and Construction; Forest, Paper & Packaging; Industrial Manufacturing; Metals; and Transportation and Logistics. The countries included in the survey were Australia, Austria, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Italy, Japan, Mexico, Middle East, Netherlands, Poland, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, the United Kingdom and the United States.

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Acknowledgements

The authors thank the editor and two anonymous reviewers of this journal for their helpful comments, which have improved the overall quality of this paper.

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Authors and Affiliations

  1. Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur, WB, 721302, India

    Rudra P. Pradhan

  2. Department of Economics, Trent University, Peterborough, ON, K9L 0G2, Canada

    Mak B. Arvin

  3. School of Business and Global Asia in the 21st Century (GA21) Research Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Subang Jaya, Malaysia

    Mahendhiran Nair

  4. College of Business, University of Lynchburg, Lynchburg, VA, 24501, USA

    Sara E. Bennett

  5. Department of Financial Management, University of Pretoria, Pretoria, 0028, Republic of South Africa

    John H. Hall

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  1. Rudra P. Pradhan
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Appendices

Appendix 1: Literature review

See Table 4.

Table 4 Nexus between innovation diffusion, ICT penetration, and economic growth
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Appendix 2: Variables

See Table 5.

Table 5 Definition of variables
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Appendix 3: Formulation of composite indices of innovation diffusion and ICT penetration, using principal component analysis

The study forms two composite indices for innovation diffusion and ICT penetration, henceforth denoted by ‘CII’ and ‘CIC’, respectively. These two indices are constructed through principal component analysis (PCA) using the various indicators for innovation diffusion and ICT penetration (see Appendix 2). Three key steps are followed: (1) data are organized in the same order to create an input matrix for the principal components; (2) using PCA, eigenvalues, factor loadings, and principal components are derived; and (3) the principal components are used to construct CII and CIC for each country for every year. This method is well described in many econometric textbooks and numerous research papers. Hence, these are not discussed here. Tables 6 and 7 present the statistical values from our principal component analysis.

Table 6 Summary of PCA-related Information for our Innovation Diffusion Index (CII)
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Table 7 Summary of PCA-related Information for our ICT Penetration Index (CIC)
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Appendix 4: Statistics and correlation matrix

See Table 8.

Table 8 Descriptive Statistics, Unit Root Statistics, and Correlation Matrix
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Appendix 5: Mixed effects generalized linear model (GLM) estimation

See Table 9.

Table 9 Results of mixed effects GLM estimation
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Pradhan, R.P., Arvin, M.B., Nair, M. et al. The information revolution, innovation diffusion and economic growth: an examination of causal links in European countries. Qual Quant 53, 1529–1563 (2019). https://doi.org/10.1007/s11135-018-0826-2

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