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Differences in digitalization levels: a multivariate analysis studying the global digital divide

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Abstract

This papers aims to identify and explain the differences in information and communications technologies (ICT) adoption for a sample of 142 developed and developing countries. In addition, we examine the relationships between specific combinations of technologies and the factors explaining them. Although income is a key factor for all country groups, its role is more significant for middle-digitalization countries. Using several multivariate techniques, we detect different patterns of digitalization. The patterns are explained to differing degrees by the type of country, by differences in economic development, and by socio-demographic and institutional variables. Factors such as quality of regulation and infrastructure explain ICT adoption in high-income countries. The ICT combination associated with specific income groups as well as the explanatory variables detected for each of them might be useful to implement the most appropriate policy actions to reduce the digital divide.

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Notes

  1. See Kiiski and Pohjola (2002), Dewan et al. (2005), Guillén and Suárez (2006), Bagchi (2005), Chinn and Fairlie (2007), Andonova and Diaz-Serrano (2009), and Pick and Azari (2008).

  2. See Lee and Brown (2008) for a recent review of the literature.

  3. See Hargittai (1999), Dewan et al. (2005), Crenshaw and Robison (2006), Chinn and Fairlie (2007), World Bank (2006), and Pick and Azari (2008).

  4. Out of the 208 countries included in the World Development Indicators Database in December 2007, we excluded those with total population lower than 1 million inhabitants in 2004 (56 countries) and those with lack of data in 3 or more of the 12 variables considered in the analysis (10 countries). The final database is composed by 1,657 observations, 47 missing.

  5. With the transformation to the logarithmic scale, the problem of outliers in the data disappears and the transformed variables become symmetric and mesokurtic to a large extent. These transformations are consistent with those carried out in the literature and show the non-normal shape of the data.

  6. KMO measure requires values greater than 0.5 for running a factor analysis. Barlett tests the null hypothesis that the correlation matrix is an identity matrix, which implies factor analysis would not be suitable.

  7. The gain of explained variance with respect to the former digitalization index means a linear improvement of the relationships due to the logarithmic transformation.

  8. The ranking of countries according the digitalization index with the original variables and that with the log variables is practically the same, as shown by the Spearman’s coefficient value (0.99) between both indexes.

  9. The null and alternative hypotheses for assessing the statistical significance of the first kth canonical correlations are \( H_{0}^{k}:\rho_{1} \ne \, 0,\,\rho_{2} \ne 0,\,\ldots ,\,\rho_{k} \ne 0,\,\rho_{k + 1} = 0,\,\ldots ,\,\rho_{p} = 0\quad H_{1}^{k}:\rho_{i} \ne \, 0,\quad{\text{ for}}\,{\text{same}}\,i \ge k + 1 \)

    which has an approximate Chi-square distribution assuming multivariate normal data.

  10. See Kiiski and Pohjola (2002), Corrocher and Ordanini (2002), Dewan et al. (2005), Guillén and Suárez (2006), Oyelaran-Oyeyinka and Lal (2005), and Andonova (2006).

  11. As the digitalization level is a dichotomous variable (middle/low country), the coefficients and loadings would be the same if we introduce the LOW-DIG dummy, but would have opposite signs. A −0.861 canonical loading for LOW-DIG means that the countries captured by that dimension are not lowly digitalized countries.

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Authors and Affiliations

  1. Department of Economic Development, Autonomous University of Madrid, Carretera de Colmenar Viejo, Km 15, 28049, Madrid, Spain

    Margarita Billon

  2. Department of Economics, Public University of Navarre, Campus Arrosadía s/n, 31006, Pamplona, Spain

    Fernando Lera-Lopez

  3. Department of Applied Economy, Autonomous University of Madrid, Carretera de Colmenar Viejo, Km 15, 28049, Madrid, Spain

    Rocío Marco

Authors
  1. Margarita Billon
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  2. Fernando Lera-Lopez
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  3. Rocío Marco
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Corresponding author

Correspondence to Fernando Lera-Lopez.

Appendix

Appendix

See Tables 10, 11 and 12.

Table 10 Selection of studies on the digital divide
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Table 11 Digitalization index over log transformed variables: factor analysis results
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Table 12 Digitalization index by country (log transformed variables)
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Billon, M., Lera-Lopez, F. & Marco, R. Differences in digitalization levels: a multivariate analysis studying the global digital divide. Rev World Econ 146, 39–73 (2010). https://doi.org/10.1007/s10290-009-0045-y

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