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Modelling museum efficiency in producing inter-reliant outputs

Abstract

The aim of this work was to evaluate the performance of a homogeneous state-run network of museums. Nonparametric models are used to measure relative efficiency in these institutions, and we employ a complex production function embracing a number of inputs and outputs adapted to the various functions which museums fulfil: preservation, research, communication, and exhibition. Our approach considers that managers drive certain outputs, but that others escape their control since they are co-produced by visitors and determined by demand conditions and external factors. Based on this, a network two-stage data envelopment analysis approach is applied to evaluate museums’ overall performance and to distinguish between efficiency in two stages: internal management and external outcomes. The low levels of performance and gaps in the scores from the first to the second stage suggest there are external factors that might determine museum performance. We therefore apply truncated regression models to analyse how and how much certain environmental variables might shape levels of museum efficiency. In this case, we consider indicators such as accessibility, tourism capacity, cultural appeal, museum age and the institutional management model. The application is performed on a sample taken from a Spanish state-run network of museums. Results show that, in general, good levels of efficiency in terms of management do not guarantee success when attracting visitors, and there seems to be a trade-off between the two goals. Variables such as tourism capacity and heritage endowments in the surrounding area, as well as the museum’s management model, may determine museums’ efficiency levels. The research findings may prove useful for running these cultural institutions and for those responsible for public resource allocation in cultural policies as well as for scholars, who may find a fresh approach for modelling museum efficiency and for discussing drivers of museum management success.

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Notes

  1. Few works address the analysis of allocative efficiency of cultural institutions. Mention may again be made of Taalas (1997) for the case of Finish theatres; Fernández-Blanco et al. (2019), who estimate marginal costs for performing arts production of a sample of theatres in Warsaw; and Fernández-Blanco and Rodríguez-Álvarez (2018), who study allocative efficiency for a non-governmental organisation devoted to promoting cultural, humanistic and scientific values.

  2. For instance, the Prado National Museum, the National Sculpture Museum, the National Museum of Altamira, the National Museum of Ceramics and Sumptuary Arts, etc.

  3. So-called House Museums, dedicated to the life and work of certain historical figures and whose collections are considered to remain virtually unaltered and to be of mainly ethnographical interest, have been removed. Likewise, also removed from the sample are the Prado National Museum and the Queen Sofia National Museum of Contemporary Art which, due to their having an autonomous management status and given their condition as star museums (Frey 1998), would be outliers in the sample.

  4. Survey and data gathered are available upon request from the authors of the research.

  5. This indicator specifically calculates the existence of library services, archives, restoration workshops, warehouses, photography workshops, audio-visual facilities, areas for educational activities, environmental control, computerised control, cloakrooms, public car parks for the disabled, areas for rent, tourist guides, audio-guides, webpages, conference rooms, cafeterias and shops.

  6. This section does not contain any variable reflecting the cultural value of the museum collection itself, since they all enjoy the same official accreditation as “goods of cultural interest”. Additionally, a museum’s cultural value cannot be confined to the number of exhibits in the collection, given the disperse nature thereof. Nor is it possible to consider qualitative external evaluations, since these tend to be applied to the collection as a whole and fail to draw any distinction between the various pieces. Indeed, quantitative measurement of a museum’s cultural value remains one of the challenges facing economic analysis, and is one which might only prove possible by estimating stated preferences through the contingent valuation method or even following tourist valuation standards (TripAdvisor and so on) that would surely tip the balance towards collections that are better known or more accessible to tourists. However, positing any such technique or approach would fall well outside the scope of the present research. Nevertheless, the impact of a museum’s cultural value is assumed to have a direct correlation on the remaining variables, such as through museum size, which tends to be linked to the museum’s importance or to the historical value of the building where it is housed.

  7. The Wilson procedure does not require OLS residuals and can thus be used with linear programming based models.

  8. For further information concerning the calculation of the RL(i) statistic, see Andrews and Pregibon (1978) and Wilson (1993). To apply this procedure, the FEAR package for R has been used. Vid. Wilson (2008).

  9. All the estimates are available from the authors upon request.

  10. This result is also related to the fact that the efficiency values in each model depend on the number of variables considered: three inputs and four outputs for the traditional model, compared to three inputs and one output for the overall efficiency of the network model. In fact, the reliability of the results from DEA models depends on the number of inputs and outputs in the analysis, for a given number of DMUs (Kneip et al. 1998; Guccio et al. 2018).

  11. As pointed out, we do not have any estimations of the value of the museum collections. Nor does the Ministry of Culture draw any kind of distinction in terms of museums’ reputation or accreditation. All of them are given the general title of “goods of cultural interest”. Due to the disperse nature of museums, neither did we consider the number of exhibits in the artistic or archaeological collection to reflect its estimated value.

  12. These latter two reasons may ultimately be indirectly linked to museums’ reputation, both because of what it means to be a museum of long-standing repute and because national museums have a specific relevant theme.

  13. The only variable that displays slightly higher weights is the type of institution. Nevertheless, as an indication of the robustness of the results as regards maintaining the conditions of separability, the Simar–Wilson regression analysis presented below was repeated, removing this variable. Results were seen to be very similar, at least in the significance and sense of the rest of the indicators.

  14. Estimations have been made with the Simar–Wilson Stata package.

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Correspondence to María José del Barrio-Tellado.

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del Barrio-Tellado, M.J., Herrero-Prieto, L.C. Modelling museum efficiency in producing inter-reliant outputs. J Cult Econ 43, 485–512 (2019). https://doi.org/10.1007/s10824-019-09347-2

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Keywords

  • Museum management assessment
  • Technical efficiency
  • Data Envelopment Analysis (DEA)
  • Two-stage performance evaluation
  • Network DEA
  • National network of museums in Spain
  • Performance evaluation

JEL Classification

  • Z11
  • Z18
  • D24