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Spatial dependence in museum services: an analysis of the Italian case

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Abstract

In this paper we investigate whether the services offered by museums are affected by the choices of neighbors, and we discuss whether the evidence can document that competition processes are at work. Specifically, we take into account the Italian case, where governmental and private museums coexist. Resorting to spatial auto-regressive models, we show that the choices of a museum concerning services’ supply are significantly influenced by the choices of its neighbors. However, we cast several doubts that this piece of evidence can be solely due to sound competition among museums.

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Notes

  1. In Italy, the norms concerning arts bonus (Decree 22/12/1986 updated by Law 4/11/2017) provide tax incentives for donations to governmental cultural institutions rather than to private no-profit cultural institutions.

  2. Italy is divided into 20 regions; at a lower layer, it is divided into 107 provinces and, further, into about 8000 municipalities. Several public subjects are involved in governmental museum management. They include: the State through the Ministry of Cultural Goods and Activities with its peripheral offices (Sovrintendenze), Regions, Provinces, Municipalities and also other subjects of the public sector in a broad sense, like public schools, public universities and firms under governmental or municipal control.

  3. From Fig. 1, the only northern region belonging to the lowest quartile of the distribution of museum density is Lombardy. However, it is worth noting that Lombardy—the most populated Italian region with about 10 million inhabitants—is among the Italian regions with the largest number of museums, along with Emilia-Romagna and Tuscany.

  4. More specifically, the percentage of public museums goes from 63% in the universe to 66% in the sample; galleries and museums go from 83 to 84%; within the museum and galley group, arts museums move from 26 to 25%. As for the organizational features of museums, autonomous museums move from 11 to 12% and outsourced museums from 20 to 21%. As to the geographical distribution, the museum institutions located in the northwest, the northeast and the center move from 23%, 24% and 34% to 22%, 26% and 29%, respectively. Clearly, the smallest museums, in terms of visits and surface, are mainly discarded, moving from the universe to the sample; however, we cannot provide a precise measure of the attendance or surface in the universe, to compare with the corresponding datum in the sample, precisely due to the lack of information concerning attendance and surface in each museum of the universe.

  5. The effect of tourism flows on museum attendance (and/or vice versa) is the object of a very large body of theoretical and empirical research: see, e.g., Cellini and Cuccia (2013, 2018), Carey et al. (2012), Borowiecki and Castiglione (2014).

  6. As discussed below, in some empirical estimates we have also controlled for other environmental variables, such as the aggregate and per-capita income levels, and the number of UNESCO World Heritage sites, without any significant effects in our results.

  7. Instead of considering the simple number of services provided, one could imagine to build a synthetic index, possibly attaching different weights to different services. However, the configuration of weight vector would be in any case questionable or dependent on specific purposes. We leave this exercise to future research.

  8. Intuitively, each museum is a neighbor of its neighbors, so a change that impacts on museum i will impact on its neighbors and this will exert higher-order feedback effects upon museum i itself. Notice that, however, the fact that \(\left| \rho \right| < 1\) leads to a decay of influence as higher-order effects are considered.

  9. Both the presence of a curator (DIR) and a scientific curator (CUR) are, in fact, positively and significantly correlated with both the exhibition surface (SURF) and the number of employees (EMP) in the museum, which should (at least partially) proxy for the museum dimension.

  10. Also regional aggregate and per-capita income levels, as well as the number of UNESCO World Heritage sites in the region, that we employ as covariate variables in some robustness checks, emerge to have nonsignificant coefficients.

  11. As for the auto-binomial model, it is worth recalling that the first-order effect associated with the spatial parameter ρ refers to the proportion of counts (over the upper limit) upon the neighbors’ counts; this implies that the spatial coefficients in Table 8 correspond to a first-order spatial effect of about 0.3–0.8 in the number of services. Looking at the other coefficients, the results from the auto-Poisson and the auto-binomial model are fully in line with those from the SAR model. In particular, estimates of the environmental factors still confirm that their role in explaining museums’ behavior in the number of services provided is limited. Overall, the results from the auto-binomial and the auto-Poisson model are fully in line with those from the SAR models.

  12. Detailed results are not reported for the sake of brevity; they are available on request. The spatial coefficients range within the interval 0.29–0.32 if the regional spatial matrixes are considered, and in the interval 0.18–0.23 if the provincial level is considered. We have also conducted similar robustness checks (also available upon request) in which SAR model (1) is re-estimated, excluding 5 groups of regions (corresponding to the Italian macroareas: northeast, northwest, center, south, islands) one by one, with results similar to those from the robustness checks at the regional level.

  13. Table 4 reports the spatial coefficients only; the complete results of regressions are available upon request.

  14. Full regressions are available upon request.

  15. Remember that the spatial parameter ρ of the auto-binomial specification captures the dependence of the proportion of counts (over the upper limit) upon the neighbors’ counts and has to be interpreted accordingly; the spatial coefficients in the auto-binomial specifications correspond to a first-order spatial effect of about 0.4-0.5, if referred to the number of offered services.

  16. On the optimal financing schemes for museum, see (Fernandez-Blanco and Prieto-Rodriguez 2011).

  17. In Italy, since 1993, when the Ronchey Law came into force, private firms have applied for granting the supply of supporting, web and, sometimes, accessibility services in governmental museums.

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Acknowledgements

The financial support from PTR 2016–2018 (University of Catania internal fund program for scientific research) is acknowledged. We thank Enrico Bertacchini, Gabriel Brida, Alan Collins, Chiara Dalle Nogare, Martin Falk, Calogero Guccio, Roberto Patuelli, Martin Puchet, Ilde Rizzo and Antonello Scorcu for helpful insights and comments. The paper also benefitted from discussion in different workshops and conferences (10th Workshop ‘Tourism: Economics and Management,’ Siena 2019; 9th EWACE Workshop, Copenhagen, 2019; 60th SIE RSA, Palermo, 2019). The responsibility remains with the authors only.

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  1. Department of Economics and Business (DEI), University of Catania, Corso Italia, 55, 95129, Catania, CT, Italy

    Roberto Cellini, Tiziana Cuccia & Domenico Lisi

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  1. Roberto Cellini
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  2. Tiziana Cuccia
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Correspondence to Roberto Cellini.

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Appendix

Appendix

See Tables 6, 7 and 8.

Table 6 Museum services
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Table 7 Number of total services offered: auto-Poisson models
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Table 8 Number of total services offered: auto-binomial models
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Cellini, R., Cuccia, T. & Lisi, D. Spatial dependence in museum services: an analysis of the Italian case. J Cult Econ 44, 535–562 (2020). https://doi.org/10.1007/s10824-019-09373-0

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