Assessing the productivity of the Italian hospitality sector: a post-WDEA pooled-truncated and spatial analysis

Abstract

This paper analyses the productivity of the hospitality sector (hotel and restaurants) in Italy at a regional level by using a mix of non-parametric and parametric approaches. A novel pooled-truncated and spatial analysis is employed, based upon a window data envelopment analysis (WDEA), where pure technical efficiency is computed. The WDEA results show that Lombardy is the best relative performer. However, overall Italian regions reveal important sources of inefficiency mostly related to their inputs. As a post-WDEA, the pooled-truncated estimation indicates that the rate of utilisation and regional intrinsic features positively affect hospitality efficiency. Nevertheless, the spatial analysis does not support evidence of spill-over effects amongst Italian regions.

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Fig. 1
Fig. 2

Notes

  1. 1.

    As argued by Diewert and Mendoza (1995), DEA is highly sensitive to data errors and outliers. Notably, since aggregation alleviates measurement errors at the individual level, using regional data allows one to reduce such biases.

  2. 2.

    The solution of Eq. (1) is given by either a maximisation or a minimisation approach when either one input or one output is used. However, in the presence of a multivariate input–output framework, the problem can be solved with either an output-oriented method (O-OM), by maximising the numerator while keeping the denominator constant, or an input-oriented (I-OM) method, by minimising the denominator while keeping the numerator constant. Within the O-OM, no DMU in the sample, with the same type of inputs, is able to derive a higher quantity of output. In general, this setting is employed for planning and strategic objectives. For example, it is used when a DMU needs to understand whether an expansion of its capacity is feasible, as long as the existing infrastructure has already been used at its maximum capacity given the level of the inputs (Cullinane et al. 2004).

  3. 3.

    Standard DEA scores range between zero and one, as mentioned in Sect. 3.1. Unfortunately, double bounds of the dependent variable impose a more complex procedure in solving Eq. (4). In order to simplify the econometric model, we follow the suggestion of Simar and Wilson (2007), who propose to use the inverse of standard DEA scores. This way, the new dependent variable has a single lower bound equals to one.

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Acknowledgments

Manuela Pulina and Claudio Detotto acknowledge the financial support provided by the Banco di Sardegna Foundation (Prot. 1713/2010.0163). Juan Gabriel Brida acknowledges the financial support provided by the Free University of Bolzano, projects: “L’efficienza delle imprese turistiche in Italia” and “The Contribution of Tourism to Economic Growth”. Manuela Pulina acknowledges the financial support provided by the Free University of Bolzano (SECS-P/01—Economia Politica). The views expressed here are those of the authors.

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Detotto, C., Pulina, M. & Brida, J.G. Assessing the productivity of the Italian hospitality sector: a post-WDEA pooled-truncated and spatial analysis. J Prod Anal 42, 103–121 (2014). https://doi.org/10.1007/s11123-013-0371-x

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Keywords

  • Regional hospitality sector
  • Dynamic window data envelopment analysis
  • Double bootstrap
  • Pooled-truncated regression
  • Spatial heterogeneity

JEL Classification

  • C14
  • C24
  • L83
  • R11