Where Is Creativity? Data and Methodology to Measure CCIs Across EU Regions

Abstract

The quality of the analysis of CCIs locations depends on the quality of data employed. Official statistics miss information that is finely disaggregated, both at the industrial and at the geographical level. The aim of this chapter is to present a novel database built for this work, starting from Orbis data. The most relevant aspect is to measure two determinants of CCIs: creative employment, considered a proper indication of the amount of creatives in a given location, and the degree of innovativeness of these industries in space. The richness of the database created allows maps on the geography of CCIs to be produced according to their different degrees of innovation intensity.

Appendices

Appendix 5.1

See Table 5.12.

Table 5.12 Conversion of Santagata’s codes according to NACE Rev. 2 classification

Appendix 5.2

The first of the two ANOVA tests presented in this chapter is a multiple comparison of means across quintiles of Inventive and Replicative CCIs. Table 5.13 presents the results of both the F-test of equality of group means and the Bartlett’s test for equal variances among the groups. Both tests are important because they guide the following steps. In fact, accepting the null hypothesis of the F-test means that the model is not able to detect any differences among the quintiles. Moreover, the response of the Bartlett’s test is even more important. In case of unequal variances, a correction method should be applied in the post-hoc test for the differences.²² In case of unequal variances, Tamhane’s T2 post-hoc tests for pairwise comparisons of means are used. Indeed, in case of unequal variances among groups, classical adjustments (e.g. Bonferroni, Scheffé) are not suitable options and it is necessary to correct for this (Tamhane 1979).²³

Table 5.13 Tamhane’s T2 post-hoc test of pairwise comparisons among quintiles

Instead, the second analysis was performed to compare Inventive and Replicative by the size of these clusters (i.e. each quintile of the distribution). A t-test is conducted as it involves only two groups. More specifically, a Levene’s test is used to assess the equality of variances for a variable calculated for two or more groups (Levene 1960). When the null is rejected, a more generalised version of the t-test should be performed. The most used and applied in this work is Welch’s correction (Welch 1947). Results of this analysis are presented in Table 5.10.

Looking at the results, there emerge substantial differences among the localisation patterns of different CCIs. First, although both low Inventive and low Replicative regions are largely located in the Eastern part of the continent, for high quintiles this is true only for Replicative. Furthermore, it is possible to state that Replicative CCIs largely prefer Eastern regions. Second, considering population levels and GDP, the comments are specular. Indeed, for both variables, it is not possible to detect any difference among quintiles of Replicative CCIs while for Inventive differences exist and are relevant. The more Inventive employment is hosted in a region the higher is the population and the economic size of the region itself. This indicates that Inventive CCIs may have a strict preference for large urban areas, where generally new ideas emerge more easily and, reversed, only strongly creative CCIs may bear the cost of urban locations. This reasoning is also supported by the results for the dummy Metropolitan area that follows the same scheme. Third, the regional knowledge environment, proxied by the patents per capita and the education levels, goes in the direction of the previous results. Indeed, these variables go hand in hand with concentration of Inventive CCIs, while understanding the correlation with Replicative employment is much less straightforward. In other words, innovations call for innovations and Replicative CCIs are looking for something else. Summing up the ANOVA and post-hoc results, it is possible to say that Inventive CCIs follow a well-established scheme in regional sciences: as they are driven by innovations, they tend to cluster where there exist the conditions for the flourishing and the valorisation of new knowledge. These places are mostly Western cities and areas with highly developed knowledge environments. Instead, Replicative CCIs are much more difficult to pigeonhole as their clustering is more chaotic and less explained by classical variables of regional economic literature. The idea is that, if not innovative, these activities may cluster according to static local conditions that improve their efficiency. On the contrary, Inventive CCIs are expected to follow a dynamic logic of clustering, looking for factors that may stimulate their innovative capacity.