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.
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ATECO is the Italian classification of economic activities (ATtività ECOnomiche). The 2002 version is based on NACE Rev. 1.1.
The correspondence table (tavola di raccordo tra ATECO 2007 e ATECO 2002) is available through the ISTAT website.
The database allows mitigation of the possible trade-off between industrial and spatial disaggregation that may emerge in studies like this one. In fact, because of privacy issues, it is usual for firms not to disclose at the same time details on industrial and spatial details. In that case, it would be possible to identify firms that produce a very specific type of good.
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To avoid misconducts, the access to the Historical Orbis platform was allowed only to a dedicated PC at the Technology Transfer Office of the Politecnico di Milano. I thank Massimo Barbieri for his support during this process.
In most of the cases, each record found in Orbis refers to a specific establishment, even if it belongs to a larger company. For instance, this is the case of national-level companies belonging to a larger group (e.g. Adidas has national branches, each of them representing a single entity). Dropping C2 records from the sample has exactly the aim of preventing the data from containing both the holding company, embedding all employees from all branches, and branches themselves. Therefore, through this methodology the database contains all the different establishments of a group if the information is separated and available; otherwise, if only the data for the headquarter is available, this is considered alone.
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See Table 5.12.
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.Footnote 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).Footnote 23
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.
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Dellisanti, R. (2023). Where Is Creativity? Data and Methodology to Measure CCIs Across EU Regions. In: Cultural and Creative Industries and Regional Development. Contributions to Regional Science. Springer, Cham. https://doi.org/10.1007/978-3-031-29624-6_5
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