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Abstract
There is a large consensus among social researchers on the positive role that human capital plays in economic performances. The standard way to measure the human capital endowment is to consider the educational attainments of the resident population, usually the share of people with a university degree. CitationFlorida (2002) suggested a different measure of human capital—the “creative class”—based on the actual occupations of individuals in specific jobs like science, engineering, the arts, culture, and entertainment. However, the empirical analyses conducted so far have overlooked a serious measurement problem concerning the clear definition of the education and creativity components of human capital. This article aims to disentangle this issue by proposing a disaggregation of human capital into three nonoverlapping categories: creative graduates, bohemians, and noncreative graduates. Using a spatial error model to account for spatial dependence, we assess the concurrent effect of the human capital indicators on total factor productivity for 257 regions of EU27. Our results indicate that highly educated people working in creative occupations are the most relevant component in explaining production efficiency, noncreative graduates exhibit a lower impact, and bohemians do not show a significant effect on regional performance. Moreover, a significant influence is exerted by technological capital, cultural diversity, and industrial and geographic characteristics, thus providing robust evidence that a highly educated, innovative, open, and culturally diverse environment is becoming more central for productivity enhancements.
Acknowledgments
The research on which this article was based received funding from the ESPON project Knowledge, Innovation, Territory. We thank Barbara Dettori for her excellent assistance. We have benefited from valuable comments by participants of the DIME workshop in Pecs, the IEA conference in Beijing, and the ERSA conference in Barcelona.
Notes
1 The idea that different occupations, even among graduates, affect economic development in a differentiated way is not new in the literature. For instance CitationMurphy, Shleifer, and Vishny (1991) remarked that countries with a higher proportion of engineers grow faster, whereas countries with a higher proportion of lawyers grow more slowly.
2 CitationMarkusen (2006) was even more critical and saw the definition of creative class as an artificial construction that assembles a number of occupations with little in common.
3 Ideally, we would need individual data that were disaggregated by three-digit ISCO occupations, by educational attainment, and by NUTS2 regions. However, such detailed information is not available because of anonymization procedures, which is why individual data, like the ELFS or the European Community Household Panel, are often transformed into macrodata at the regional level (CitationRodríguez-Pose and Vilalta-Bufí 2005). Contributions based on micro individual data were recently proposed only with regard to some specific countries: CitationComunian et al. (2010) for the United Kingdom; CitationMellander (2008) for Sweden, and CitationKing et al. (2010) for the United States, Canada, and Sweden.
4 There may be few exceptions. For example, in the case of occupations like primary education teaching professionals or archivists, it is possible that, in the past, tertiary education was not a formal requirement in some European countries.
5 Our figures for the whole of Europe are in line with those reported by CitationBoschma and Fritsch (2009) for a subset of Nordic countries.
6 See the survey by CitationAudretsch and Feldman (2004) on the numerous contributions, based on different theoretical approaches, that have studied the effect of technology on economic performance.
7 “Immigrants have complementary skills to natives not only because they perform different tasks, but also because they bring different skills to the same task” (CitationFlorida et al. 2008, 620).
8 For the case of London firms, CitationNathan and Lee (2011) provided evidence that firms that are diverse in terms of ownership, teams, or management are more innovative in developing new products and in implementing new processes. They also provided an exhaustive description of how the links between cultural diversity and innovativeness work at the individual, firm, and urban levels.
9 For manufacturing, the top 5 regions were in the Czech Republic, Hungary, and Romania and that among the top 10, there was only one German and one Italian region; in the knowledge-intensive sectors, the top 10 regions were in the United Kingdom, Luxembourg, the Netherlands, France, and Brussels.
10 The only exception was the diversity proxy, which is consistently available for all our regions only for the period 2006–7, we elaborate more on this variable when we present the robustness analysis. Moreover, the education and creativity variables were available for all the 257 regions only for 2002, so we could not use previous lags. This lack of data also precluded a panel data analysis.
11 The same kind of results were obtained when we conducted the subsample analysis by dividing the entire sample into the 33 percent–67 percent or 25 percent–75 percent top-bottom performing regions.
12 For a comprehensive description of spatial models and related specifications, estimation, and testing issues see CitationLe Sage and Pace (2009) and the references therein.
13 Such normalization is sufficient and avoids strong undue restrictions, as is the case when the row-standardization method is applied.
14 For the preferred specification (Model 4, ), the robust LM error test was highly significant with a p value of 0.001, while the robust LM lag test was significant only at a level of 0.054. Some further checks for robustness on the spatial pattern specification are presented in the next section.
15 Note that the model estimated by OLS returned similar elasticities: 0.17 for creative graduates, 0.05 for noncreative graduates, and 0.02 (not significant) for bohemians. Note also that most of the VIFs for the variables included in Model (4) are well below 3 (only technological capital has a higher VIF value, 4.8, which being less than 6 does not represent an issue); more specifically, for the human capital variables VIF values are 2.2 for creative graduates, 1.4 for noncreative graduates, and 2.1 for bohemians.
16 As far as the legal profession is concerned, several studies have shown that the presence of a large number of lawyers “harms” economic performances, since lawyers are engaged mostly in rent-seeking activities (see, among others, CitationDatta and Nugent 1986; CitationMurphy et al. 1991).
17 CitationComunian et al. (2010), following a different perspective of analysis, showed that a significant mismatch is present in the U.K. labor market between those in creative occupations and bohemian graduates, who, despite their often-claimed role in driving economic growth are at a salary disadvantage when compared to nonbohemian graduates. This finding casts further doubts on the economic relevance of the bohemian group.
18 For Italy, using the microdata from the labor force survey, we calculated that the share of graduates in some occupations included in the bohemians group is 18 percent.
19 We also experimented with different proportions of misclassification error (in the range 10 percent–30 percent), and the results for Model (4) were extremely robust.
20 In the case of the lag model, the interpretation of the estimated coefficients as partial derivates with respect to a specific regressor no longer holds because of the presence of the spatially lagged dependent variable, which induces feedback loops (a given region is the neighbor of its neighbors, so that affecting them receive in turn feedback effects) and spillovers effects. The change in the dependent variable caused by a unit change in one given explanatory variable amounts to the total effect, which is given by the sum of the direct effect, generated by the change in a certain region’s own regressor, and the indirect effect due to spillovers (CitationLe Sage and Pace 2009).
21 See footnote 10.
22 No data on the foreign-born population are available for Malta, for the Belgian, German, and Greek regions.
23 Note also that the approach suggested in CitationOttaviano and Peri (2006) and CitationBellini et al. (2011), based on the use of shift-share instrumental variables for the diversity regressors, is not viable in our case, since it requires data from a far distant previous period disaggregated by immigrants’ countries of origin, which are not available for all the regions included in our sample.