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Residents’ Satisfaction with Cultural City Life: Evidence from EU Cities

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Abstract

This paper investigates the determinants of residents’ satisfaction with the cultural facilities supplied in their city. This topic is relevant from a policy perspective since the active participation of citizens to the cultural life of their town is crucial for the sustainable utilization of tangible cultural assets. By using the results of a large survey study conducted in 63 EU cities, the present paper shows that the exploitation of the urban cultural assets for the attraction of tourists and visitors is likely to produce a positive externality on residents’ satisfaction with the cultural life of their city, but just up to a certain threshold of tourist pressure. Above this threshold, a further increase in the number of tourists is associated with a reduction of residents’ contentment.

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Notes

  1. The source of data employed in the present work is the Flash Eurobarometer Study n.277 - ZA5214 (European Commission, 2010). Cities included in the analysis are the following ones: Wien, Graz (Austria), Brussels, Antwerpen, Liege (Belgium), Sofia, Burgas (Bulgaria), Praha, Ostrava (Czech Republic), Kobenhaven, Aalborg (Denmark), Tallin (Estonia), Helsinki, Oulu (Finland), Paris, Lille, Strasborurg, Rennes, Bordeaux, Marseille (France), Berlin, München, Hamburg, Essen, Dortmund, Leipzig (Germany), Athinia, Irakleio (Greece), Budapest, Miskolc (Hungary), Dublin (Ireland), Roma, Torino, Verona, Bologna, Napoli, Palermo (Italy), Riga (Latvia), Vilnius (Lithuania), Luxembourg (Luxembourg), Amsterdam, Rotterdam, Groningen (The Netherlands), Warszawa, Krakow, Bialystock, Gdansk (Poland), Lisboa, Braga (Portugal), Bucaresti, Cluj-Napoca, Piatra Neamt (Romania) Bratislava, Kosice (Slovakia), Ljubljana (Slovenia), Madrid, Barcelona, Oviedo, Malaga (Spain), Stockholm, Malmö (Sweden), Manchester, Belfast (UK). The survey covers also some other municipalities in Turkey (Istanbul, Ankara, Antalya and Diyarbakir), Croatia (Zagreb), Cyprus (Lefkosia), Malta (Valletta), and UK (London, Newcastle, Cardiff and Glasgow). These cities are excluded from the data set employed in the analysis due to the lack of evidence on some city-specific characteristics (see Table 1). The towns covered in the survey are mostly medium and big-size cities, which might pose an issue about the sample composition since, first, small urban areas are not considered and, second, different cities might be characterized by different cultural needs and facilities. As it will be better explained in “Culture in Cities: an Empirical Measurement,” this issue is addressed by including a numerous set of controls capturing city-specific characteristics.

  2. The question wording was the following one: “Generally speaking, please tell me if you are very satisfied, rather satisfied, rather unsatisfied or not at all satisfied with cultural facilities such as concert halls, theaters, museums, and libraries in your city”. The 4% of the respondents claimed to be not at all satisfied, while the 9% was rather unsatisfied. Forty-one percent of individuals answered to be rather satisfied, whilst the share of very satisfied interviewees was equal to 38%.

  3. These characteristics are measured at the NUTS3 (Nomenclature of Territorial Units for Statistics) level, which is the closest geographical level to the urban units for which statistical information is available.

  4. Data at the urban level are not available; therefore the level of trust is measured for the NUTS2 region of the city.

  5. Data are provided by Eurostat at NUTS2 level. The following NACE sectors are considered as part of the cultural industry: C14—manufacture of wearing apparel; C15—manufacture of leather and related products; C18—printing and reproduction of recorded media; C31—manufacture of furniture; G476—retail sale of cultural and recreation goods in specialized stores; J58—publishing activities; J59—motion picture, video and television program production, sound recording, and music publishing activities; J60—programming and broadcasting activities; J63—information service activities; M71—architectural and engineering activities; technical testing and analysis; M73—advertising and market research; and N81—services to buildings and landscape activities.

  6. The source of these data is the ESPON 1.3.3 project “The Role and Spatial Effects of Cultural Heritage and Identity” (ESPON, 2006). Despite of the problematic issues of collecting data from a broad variety of statistical sources at national level, this database constitutes a precious source of evidence on a topic only incidentally covered by official statistics, at least at regional level. Cultural assets are measured at the NUTS3 level.

  7. More details about the PCA technique applied are provided in Appendix A.

  8. Monuments are measured by the number of historical buildings (churches, palaces, bridges, fountains, etc.) and sites (caves, battlefields, etc.). Museums and galleries include the movable tangible heritage collections exhibited to the public. Cultural landscapes are represented by the heritage conjuncts with spatial extension (battlefields, parks and gardens, historical, and walled cities).

  9. Also this indicator is measured at the NUTS3 level.

  10. Pearson correlation coefficient of the association between the two variables is equal to 0.829, with lower than 0.01.

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Authors and Affiliations

  1. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Giovanni Perucca

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  1. Giovanni Perucca
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Correspondence to Giovanni Perucca.

Appendices

Appendix A

In order to make the empirical evidence on the cultural assets in EU cities more easy to interpret, it can be reduced to a lower-dimensional form through principal component analysis (PCA). The choice of this method is based on two motivations. The first, empirical, one relies on the positive and statistically significant association of different cultural elements within the same area (i.e., cultural amenities and tangible heritage). The second, conceptual, motivation is that (as discussed in the paper) these elements share the same characteristics in terms of rivalry, reproducibility, and scarcity.

In the case of cultural amenities, the PCA was applied to a set of three variables, measuring the density of cinemas, theaters, and libraries. The first extracted component explains most of the variance (0.81%) and all the three indicators are positively associated to it.

As far as the tangible heritage is concerned, PCA was applied on the density of monuments, museums, and cultural landscapes. In this case, the first extracted component explains accounts for most of the variance (0.52%) and all the three indicators are positively associated to it. The eigenvalue of the second component is lower than 1 (0.9) and therefore it was not extracted.

Appendix B

Table 3 reports the estimates of Eq. (1) where the dependent variable is treated as continuous instead of categorical. Therefore, an OLS multilevel model is applied to the same data set presented in the main text of the paper. Results are fully consistent with those discussed in the paper.

Table 3 Satisfaction with cultural facilities as a function of cultural assets, individual and city characteristics (OLS estimates)
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Perucca, G. Residents’ Satisfaction with Cultural City Life: Evidence from EU Cities. Applied Research Quality Life 14, 461–478 (2019). https://doi.org/10.1007/s11482-018-9623-2

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