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Exploring the dynamics of the efficiency in the Italian hospitality sector. A regional case study

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Abstract

This paper introduces a methodology to describe and compare the economic relative performance of the hospitality sector of the Italian regions during the period 2000-2004. Dynamics of the hospitality sector of each region is represented by the evolution of its economic efficiency. The investigation involves the following steps - a static Data Envelopment Analysis (DEA) to estimate the pure economic efficiency; two different notions of distances between time series and hierarchical clustering techniques are used to classify the economies in the sample. By using a correlation-based distance, three main clusters are detected, while two clusters are identified when the average distance is used. The trend patterns, identified by employing the correlation distance, can be interpreted in terms of exogenous factors that influence the economic efficiency of the group of regions, causing shocks picked up by the high volatility as well as structural breaks. By employing the average distance, one infers information on the cluster that have had similar efficiency values over the period under analysis. This efficiency can be also interpreted in terms of a particular type of hospitality management as well as the firm structure. Following the analysis, some policy and management implications are presented.

Suggested Citation

  • M. Deidda & N. Garrido & M. Pulina, 2011. "Exploring the dynamics of the efficiency in the Italian hospitality sector. A regional case study," Working Paper CRENoS 201117, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:201117
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    4. Brida, Juan Gabriel & Risso, Wiston Adrián, 2008. "Multidimensional minimal spanning tree: The Dow Jones case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5205-5210.
    5. Cullinane Kevin & Song Dong-Wook & Ji Ping & Wang Teng-Fei, 2004. "An Application of DEA Windows Analysis to Container Port Production Efficiency," Review of Network Economics, De Gruyter, vol. 3(2), pages 1-23, June.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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    Cited by:

    1. Y. Shi & A. N. Gorban & T. Y. Yang, 2013. "Is it possible to predict long-term success with k-NN? Case Study of four market indices (FTSE100, DAX, HANGSENG, NASDAQ)," Papers 1307.8308, arXiv.org.

    More about this item

    Keywords

    regional hospitality sector; window dea; hierarchical clustering;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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