Evaluating the efficiency of museums using multiple outputs

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1.- Introduction To date few evaluation studies have explored the efficiency and performance of cultural institutions. This is even more surprising when one considers that providing culture is a prime example of a public good allocation like other services such as health, education, as well as various administrative services. Yet, the latter have been the focus of more intense analytical attention 2 . This may well be due to the uniqueness of cultural goods, resulting from their symbolic and intangible significance which makes it difficult to objectivise the services they secure or the resources they require. This may also be due to the difficulty involved in collecting reliable and representative data from the institutions charged with managing and overseeing said goods, or merely because analysing culture and cultural institutions from a financial perspective is uncommon. Whatever the reason, the field undoubtedly provides ample scope for efficiency evaluation studies, both because of the enormous growth over the last few years in the number of institutions involved in managing cultural services and goods, and as a result of the obvious need for a rational approach and efficient management of such goods and services. This is particularly true in a climate such as the present when resources are becoming increasingly scarce and subject to restriction. Viewed thus, it is vital to consider the opportunity costs of the resources invested in cultural policy, to gauge the performance of the institutions involved, and to assess the efficiency of the outcomes. Broadly speaking, efficiency studies assessing institutions which supply a public good may be divided into two categories. The first group contains works that seek to measure how well the institutions function by applying a series of performance indicators. Studies that have pursued this line, with particular regard to the area of cultural institutions, include Weil (1995), Evans (2000), and Turbide and Laurin (2009). These studies select a series of components or ratios that allow comparisons to be drawn between organisations for the various activities chosen, and even enable a balanced scorecard to be created for the institution under analysis (Weinstein and Bukovinsky, 2009). However, this technique does not allow for any hierarchisation of 1 This work has been possible thanks to the financial support provided by the Regional Ministry of Education Regional Research Support Programme at the Regional Government of Castilla y León (Ref. VA006A10-1). 2 One example is the evaluation of health centres in Goñi Legaz (1998), or Cordero Ferrera (2005) for the evaluation of secondary schools, and Prado Lorenzo and García Sánchez (2007) who assess the efficiency of municipal services over a sample of cities in Spain. 2
the units analysed as it fails to provide measures of relative efficiency that compare the outcomes obtained in terms of the resources used. The second group of studies is aimed directly at providing a specific production function, which merges a range of inputs in order to obtain goods and services corresponding to the main tasks allocated to the institution. Taking this approach as a basis, the goal is to estimate an optimal frontier in this transformation process, and to gauge the level of efficiency of the various study units as a distance from said optimal efficiency frontier. The problem lies in determining where this frontier lies, and is a hurdle which may be overcome by applying parametric or non-parametric models (Fernández et al., 2012). The former are more rigid since they require a precise definition of the functional form of the production function, although they prove more accurate in estimating the productivity linked to each factor and when dealing with stochastic error 3 . By contrast, non-parametric methods are deterministic models since they assume that any distance from the optimal frontier is the result of inefficient performance and is not random. The advantage of such models lies in their greater flexibility since, because they consist of an optimisation process that uses observed data, they do not require a prior definition of the production function and may take account of multiple output situations, which on many occasions may prove useful. These methods are also more operative since they provide a large amount of specific information about the study units, which can be utilised to establish guidelines for enhancing efficiency and indications for good practice. As we shall see later, this technique has been widely applied to assess cultural institutions and particularly museums. Our case study thus falls within this particular analytical field, since it seeks to gauge the efficiency of a regional system of museums by applying one of the most widely recognised non-parametric techniques; Data Envelopment Analysis 4 . We select museums as an instance of a cultural institution to be assessed for three main reasons. Firstly, because they are one of the most illustrative examples of the concept of cultural heritage, since they reflect the idea of preserving both present as well as accumulated 3 Applications of the evaluation of cultural institutions using this analytical approach may be found in Last and Wetzel (2010) and Zieba (2011) 4 Today, Data Envelopment Analysis (DEA) is quite a standardised technique, and is based on Farrell’s (1957) basic concepts of efficiency and measurement thereof as a distance to a frontier of cases of good practices. The theoretical basis for this optimisation method and its different variations may be consulted in Gambley and Cubbin (1992) 3
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