https://orcid.org/0000-0001-6942-6948
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ABSTRACT
This paper argues that e-skills – capabilities associated with the use and development of digital technologies – enhance regions’ ability to draw on existing know-how and create new industrial paths. The empirical analysis focuses on the relationship between e-skills and technological diversification for a panel of European regions in the period 2000–12. It constructs novel indices of regional e-skill endowment distinguishing between basic users, professional users and expert developers of information and communication technologies. The econometric results show that e-skills foster technological diversification dynamics in European regions, and that this effect is particularly strong for less-developed regions, and for low levels of relatedness.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
ACKNOWLEDGEMENTS
A previous draft of this paper was presented at the 2018 EU-SPRI Early Career Research Conference (ECC) on Science, Technology and Innovation: New Challenges and Practices in Valencia, Spain, May 2018; the 2018 ESCoS Conference on The Economy as a Spatial Complex System, Naples, Italy, June 2018; at the 2018 SMARTER Conference on Smart Specialisation and Territorial Development, Seville, Spain, September 2018; at the International PhD course on Economic Geography, Utrecht, the Netherlands, November 2018; at the Economic Geography Research Seminar, Utrecht, the Netherlands, January 2019; and at the RENIR workshop in Turin, Italy, May 2019. The authors thank all discussants at these workshops, as well as two anonymous reviewers and the journal editors, for helpful comments and suggestions.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
2. In March 2010, the European Commission launched the Europe 2020 Strategy, marking the intensification of policy efforts to foster e-skills and the incorporation of the e-skill issue in the Innovation Union programme. The policy strategy was revised and extended in 2015 under the Digital Single Market programme. Currently, several EU countries have national digital skills strategies in line with EC programmes.
3. OECD REGPAT database, version 2016. Although we use patent data regionalized by assignees’ addresses, our main results are robust when we use patent data regionalized by inventor’s addresses (despite a few differences). Patent data have well-known limitations but also advantages (e.g., Griliches, Citation1990; Organisation for Economic Co-operation and Development (OECD), Citation2009). Here we draw on the ability to trace the development of new technologies over a wide temporal scale and detailed geographical coverage.
4. The only exception is UK, where the level of analysis is NUTS-1.
5. Altogether, we consider 122 technological categories. These technological groups represent all three-digit IPC classes available in our data, except the seven categories ending in ‘99’. These technological categories are excluded because they do not represent specific technologies (i.e., they represent technologies that are not elsewhere classified within a given section). According to the most recent edition of IPC provided by the World Intellectual Property Organization (WIPO) (as of January 2018), there are in total 141 three-digit IPC classes.
6. In line with previous research (e.g., Audretsch & Fritsch, Citation2002), this procedure is aimed at eliminating the effects of interregional differences in terms of technological structure. This also mitigates the negative correlation between the emergence of new specializations at t + 1 and the number of technologies in which each region is not specialized at t (see Santoalha, Citation2019, for further details). All the necessary steps for the computation of the technology-adjusted diversification potential are described in Appendix A in the supplemental data online.
7. In most cases, the gain (or loss) of technologies in a region results from the introduction of completely new technologies (or full abandonment of older ones).
8. These detailed data help to explain how jobs differ from one another, not just in terms of job title, but also as to work content and, therefore, the type of know-how necessary in that occupation. The main limitation is that the skill scores are subjective assessments. Both these points have been widely debated in the labour economics literature (e.g., Autor et al., Citation2003).
9. The panel is unbalanced due to missing observations in the Eurostat LFS for some years and countries. For instance, before 2003, there is no regional information for German data. The same applies to Denmark before 2008 (due to the 2007 administrative reform which increased the number of NUTS-2 from one to five).
10. As our unit of analysis is the region, all regressions are weighted by population density to account for unexplained variation at country level (Solon, Haider, & Wooldridge, Citation2015).
11. We also conducted additional exercises using four-digit IPC to calculate the diversification indicator (instead of three digits as for the results presented in ). This robustness exercise (the results are available from the authors upon request) did not show any difference in the results.
12. Model specifications ix–xii, those including the interaction variables, are more reliable and better specified than the corresponding specifications i–viii, according to the results of the Ramsey regression equation specification error test (RESET) misspecification tests (see Table D5 in Appendix D in the supplemental data online).
13. The lower number of observations in this exercise is explained by the fact that, as we are using a five-year interval, we have a panel consisting of only two periods (2003–07 and 2008–12). Thus, the lower number of observations is not related to the number of new technologies entering regions. Recall that our observations represent a pair region-year/period.
14. We opted not to include this variable in our baseline model specification because it is constructed based on the EU LFS, which has missing observations for the Statistical Classification of Economic Activities in the European Community (NACE) code of the local unit where each individual is employed, which in turn may mean under- or overestimating this variable for some years and regions. Moreover, data are missing for 2009 and 2010. Thus, when using this variable as an additional control, we are unable to use the last two periods of our annual panel (2010–11 and 2011–12).