Acceso usuarios
Alcalá de Henares, España
El objetivo de este trabajo es realizar un nuevo análisis comparativo de las ciudades europeas basado en un índice compuesto de eficiencia a partir de las dimensiones del Monitor de Ciudades Culturales y Creativas 2019 (CCCM). El análisis se basa en técnicas más robustas que las utilizadas anteriormente en este campo. En concreto, se estudiarán los índices de supere-ficiencia y de productividad total de los factores (PTF) de Hicks-Moortensen de las 190 ciudades en los años 2017 y 2019. Los principales resultados revelan que tres ciudades (París, Londres y Florencia) pueden considerarse supereficientes en los años analizados, incorporándose posteriormente Dublín. El análisis de convergencia corrobora la mejora de las ciudades que parten inicialmente de niveles más bajos de eficiencia en la gestión de los recursos. Los resultados de productividad reportan un aumento del 2,02% en términos medios en el conjunto de las 190 ciudades entre 2017 y 2019. Estas ganancias de productividad son consecuencia tanto del progreso tecnológico como de la eficiencia con valores del 0,02% y 5,39% respectivamente. El análisis de conglomerados nos permite segmentar la muestra de ciudades en tres grupos (de bajo, medio y alto rendimiento).
The objective of this paper is to conduct a new benchmark analysis of European cities based of a composite index of effi-ciency from the dimensions of the Cultural and Creative Cities Monitor 2019 (CCCM). We use two new methodological proposals in this field. In a first phase, the analysis of super-efficiency of the CCCM cities and their convergence process is addressed. As mentioned by Pavkovic’ etal. (2021) the report CCCM is produced by the Joint Research Center, the European Commission’s in-house research center according to the statistical recommendations of (Nardo etal., 2008). The C3 index, based on 29 variables, as mentioned by De Jorge-Moreno and De Jorge-Huertas (2020), evaluates three dimensions: Cul-tural Vibrancy (CV), Creative Economy (CE) and Enable Environment (EE). These dimensions have been obtained through qualitative and quantitative variables. In this paper we will use the BCC-DEA (Banker et al., 1984 and Data Envelopment Analysis) extension proposed by Andersen and Petersen (1993) to classify the efficient units (super-efficiency model, here-after). As mentioned by Santin (2014) the super-efficiency reasoning consists of comparing the evaluated unit with a linear combination of all other units in the sample except itself. The evaluated city or DMU (Decision Making Unit) is removed from the inputs and outputs constraints and omitted from the benchmark units. In a second phase, the Hicks-Moortensen indexes are used to estimate the creative productivity of the cities in the existing years. Authors such as O’Donnell (2008, 2011) mention issues such as the fact that Malmquist indices are not fully multiplicative, in addition to not satisfying the transitivity conditions.In relation to productivity performance, there is an increase of 2.02% in average terms across the 190 cities between 2017 and 2019. These productivity gains are a consequence of both technological progress and efficiency with values of 0.02% and 5.39% respectively. Cluster analyses allow us to segment the sample of cities into three groups, which could be considered low, medium and high performers based on their average productivity levels, whose values are -2.79%; 0.9% and 13.41% respectively.It is evident that within the objective of this work in the establishment of a hierarchical classification of cities, according to their efficient management of resources, that allow to establish references for the rest, other questions arise. Such as the fact that it is possible to observe in parallel a certain inequality within European cities with different creative competitive patterns and spatial disparity. However, both the aforementioned convergence analysis and the possibility of establishing good management practices through the best references found, provide a dose of optimism for the future.The methodologies used in this work could be considered useful tools for decision making aimed at finding points of improvement in the management practices of cities. Among the limitations of this work are the short time period analysed, which, although it is the only one available at the moment, also represents a novelty in this field of analysis, given that, in general, cross-sectional data have been used up to now. Possible extensions of this work could be aimed at the consideration of a second stage or exogenous variables that could influence the levels of efficiency or productivity of the cities.
