The importance of culture, history, religion, and identity in the potential of any territory to develop is now generally accepted. Place and culture are indeed persistently intertwined. Moreover, the potentially positive impact of cultural heritage on economic development has gained more and more attention throughout different disciplines. Such impact, however, is often just assumed or thought to occur exclusively through cultural tourism, which is in fact both conceptually and empirically able to explain only partially such relationship. This paper enters this debate by elaborating conceptually and empirically on the idea that creativity—possibly expressed according to different patterns—can play a mediating role, explaining the local capability to exploit cultural heritage for economic purposes. In order to empirically address this issue, the Italian provinces (NUTS3 level) are exploited as the units of analysis and the whole process (from cultural heritage to development through creativity) is econometrically tested by a structural equation model.
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The most diffused definition of creative industries is the one provided by the UK Department of Culture, Media, and Sport (UK-DCMS 1998; 2001). According to such definition, creative industries are those having their origin in individual creativity, skill, and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property.
In fact, given the presence of interaction terms and in order to get robust standard errors, a quasi-maximum likelihood (QML) SEM was estimated. QML uses ML to fit the model parameters but relaxes the normality assumptions when estimating the standard errors. QML handles non-normality by adjusting standard errors.
For a few exceptions, please refer to the next section.
Given the relatively low number of observations and the estimation requirements of the SEM, regional fixed effects had to be included at the NUTS2 level.
01. Unskilled work; 02. machine operators; 03. skilled work; 04. agricultural workers; 05. retail activities; 06. clerks; 07. average specialization technical, administrative, or sport tasks; 08. high specialization, technical, administrative, intellectual, scientific, or artistic tasks; 09. managing tasks; 10. army.
01. Agriculture, forestry, and fishing; 02. mining and quarrying; 03. manufacturing; 04. electricity, gas, steam, and air-conditioning supply; 05. water supply, sewerage, waste management, and remediation activities; 06. construction; 07. wholesale and retail trade, repair of motor vehicles and motorcycles; 08. transportation and storage; 09. accommodation and food service activities; 10. information and communication; 11. financial and insurance activities; 12. real estate activities; 13. professional, scientific, and technical activities; 14. administrative and support service activities; 15. public administration and defense; compulsory social security; 16. education; 17. human health and social work activities; 18. arts, entertainment, and recreation; 19. other service activities; 20. activities of households as employers; undifferentiated goods and service-producing activities of households for own use; 21. activities of extra territorial organizations and bodies.
In order to compute the direct, indirect, and total effects more rigorously, the calculation was carried out on the original (non-standardized) coefficients. Non-standardized outputs of the regressions are available from the author upon request. In addition, t statistics are not reported for the augmented models since their computation would require much more complicated computation, not automatically available in STATA.
Also in this case, for greater rigorousness, the computation was carried out on the original (non-standardized) coefficients. The complete computation is available from the author upon request.
Since it is in fact impossible to build new ancient monuments overnight, we also computed the marginal effects considering the difference between a situation with a low endowment of cultural heritage with a situation with a high endowment of cultural heritage (10th and 90th percentiles, corresponding, respectively, to about 0.13 and 0.63 units of cultural heritage per square km), both in the case of average endowment of the different types of creative talents. The overall results are 0.08% higher, 0.12% higher, and 0.12% higher, respectively, for the three specifications.
R&D activities are missing in Santagata’s (2009) classification and were added within this work.
For each “macro”-sector, Santagata considered the whole “Creative Industry Production System” (CIPS), which includes (a) design and production, (b) inputs and other activities related to production, (c) retailing, and (d) activities related to the retailing.
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Conflict of interest
The author declares that she has no conflict of interest.
The author is obliged to the whole research group in Urban and Regional Economics at the ABC Department, Politecnico di Milano, for support and suggestions. Special gratitude goes to Roberta Capello, Roberto Camagni and Rocco Mosconi for their advice.
Appendix 1: Santagata (2009) classification of creative industries
In order to identify creative industries, this work refers to Santagata’s (2009) classification.Footnote 11 The author singles out some main creative “macro”-sectors, namely: industrial design and craft; fashion; food and wine; computer and software; publishing; TV, radio, and cinema; cultural heritage; music and entertainment; and architecture and engineering.
Within the above categories, the ones taken into account are those activities that Santagata listed in the “Design and Production” phase of the production chainFootnote 12 (see Table 8 for details). This choice is related to the idea that the core components of creativity lay in the conception of (creative) products. According to this view, disregarding the other phases of the production chain seemed particularly reasonable.
Santagata’s classification has got many significant advantages: it includes both manufacturing and services, and it identifies sectors very precisely, according to the ATECO classification at 4–5-digit level.
See Table 9.
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Cerisola, S. A new perspective on the cultural heritage–development nexus: the role of creativity. J Cult Econ 43, 21–56 (2019). https://doi.org/10.1007/s10824-018-9328-2