Abstract
Data Envelopment Analysis (DEA) is a popular non-parametric method used to measure efficiency. It uses linear programming to identify points on a convex hull defined by the inputs and outputs of the most efficient Decision Making Units (DMUs). Two critical elements account for the strength of the DEA approach: (1) no a priori structure is placed on the production process of the firm, and (2) the models can yield a measure of efficiency even with a very small number of data points. The first point is particularly important because the measure of efficiency is based upon the best practice of the DMUs at any of the levels of output observed.
Data envelopment analysis measures efficiency and is very sensitive to the choice of variables for two reasons: the number of efficient DMUs is directly related to the number of variables, and the selection of the variables greatly affects the measure of efficiency when the number of DMUs is few and/or when the number of explanatory variables needed to compute the measure of efficiency is too large. Our approach advises which variables should be included in a DEA model. Hence, a variable selection method is presented for the deterministic DEA approach. First, a definition of different measures of efficiency and the various DEA models used to measure efficiency is provided, and then a variable selection method is proposed. The Azorean agricultural system is used as an example to illustrate the method.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alpert M, Peterson R (1972) On the interpretation of canonical analysis. J Mark Res 30:29–50
Banker R (1996) Hypothesis tests using data envelopment analysis. J Product Anal 7(2–3):139–159
Banker R, Charnes R, Cooper W (1984a) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092
Banker R, Charnes R, Cooper WW (1984b) Equivalence and interpretation of alternative methods for determining returns to scales in data envelopment analysis. Eur J Oper Res 89:473–481
Barcikowski R, Stevens J (1975) A Monte Carlo study of the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Multivar Behav Res 10:353–364
Bauernfeind U, Mitsche N (2008) The application of the data envelopment analysis for tourism website evaluation. Inf Technol Tour 10(13):245–257
Berger A, Humphrey D (1997) Efficiency of financial institutions: international survey and directions for future research. Eur J Oper Res 98:175–212
Boussofiane A, Dyson RG, Thanassoulis E (1991) Applied data envelopment analysis. Eur J Oper Res 52:1–15
Brockett PL, Golany B (1996) Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Manag Sci 42(3):466–472
Charnes A, Cooper WW (1985) Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J Econ 30(1/2):91–107
Charnes A, Cooper W, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2:429–444
Chiang WE, Tsai M-H, Wang LS-M (2004) A DEA evaluation of Taipei hotels. Annal Tour Resear 31(3):712–715
Chilingerian J (1995) Evaluating physician efficiency in hospitals: a multivariate analysis of best practices. Eur J Oper Res 80:548–574
Cloutier L, Rowley R (1993) Relative technical efficiency: data envelopment analysis and Quebec’s dairy farms. Can J Agric Econ 41:169–176
Cooper W, Seiford L, Tone K (2007) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 2nd edn. Springer, New York
Diaz-Martinez Z, Fernandez-Menendez J (2008) The DEA package, Version 0.1–2, Retrived from http://cran.r-project.org/web/packages/DEA/DEA.pdf
Färe R, Grosskopf S, Lovell C (1988) Scale elasticity and scale efficiency. J Inst Theor Econ 144:721–729
Fraser I, Cordina D (1999) An application of data envelopment analysis to irrigated dairy farms in northern Victoria, Australia. Agric Syst 59:267–282
Gifi A (1990) Nonlinear multivariate analysis. Wiley, Chichester
Gimenez-Garcia VM, Martínez-Parra JL, Frank P (2007) Improving resource utilization in multi-unit networked organizations: the case of a Spanish restaurant chain. Tour Manag 28:262–270
Golany B, Roll Y (1989) An application procedure for DEA. Technion-Israel Institute of Technology, Israel
Hallam D, Machado F (1996) Efficiency analysis with panel data – a study of Portuguese dairy farms. Eur Rev Agric Resou Econ 23(1):79–93
Hughes A, Yaisawarng S (2004) Sensitivity and dimensionality tests of DEA efficiency scores. Eur J Oper Res 154:419–422
Jaforullah M, Whiteman J (1999) Scale efficiency in the New Zealand dairy industry: a non-parametric approach. Aust J Agric Resour Econ 43(4):523–541
Jenkins L, Anderson M (2003) A multivariate statistical approach to reducing the number of variables in data envelopment analysis. Eur J Oper Res 147:51–61
Levine M (1977) Canonical analysis and factor comparison, vol 6, Quantitative applications in the social sciences series. Sage Publications, Thousand Oaks
Lewin A, Morey R, Cook T (1982) Evaluating the administrative efficiency of courts. Omega 10(4):401–411
Lovell C (1993) Production frontier and productive efficiency. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency-techniques and applications. Oxford University Press, Oxford, pp 3–67
Marianna S, David A, Peter J, Andrew L (2004) ICT paradox lost? A stepwise DEA methodology to evaluate technology investments in tourism settings. J Trav Resear 43(2):180–192
Marote E, Silva E (2002) Análise Dinâmica da Eficiência das Explorações Leiteiras da Ilha Terceira. XII Congresso de Zootecnia. November
Marote E, Silva E (2011) Importância dos Subsídios na Eficiência das Explorações Leiteiras da Terceira. Revista de Ciências Agrárias, pp 161–170
Noncheva V, Mendes A, Silva E (2009) An approach to variable aggregation in efficiency analysis. In: Classification, forecasting, data mining, international book series information science & computing. Suppl Int J Inf Technol Knowl 3(8):97–104
Noncheva V, Mendes A, Silva E (2012) Azorean agriculture efficiency by PAR. In: Mendes A, Silva E, Santos J (eds) Efficiency measures in the agricultural sector, with applications. Springer, Dordrecht, pp 53–72
Norman M, Stoker B (1991) Data envelopment analysis: the assessment of performance. Wiley, Chichester
Nunamaker T (1985) Using data envelopment analysis to measure the efficiency of non-profit organizations: a critical evaluation. Manag Decis Econ 6(1):50–58
OECD – Organisation for Economic Co-Operation and Development (2001) Environmental indicators for agriculture methods and results. Executive summary. Retrived from http://www.oecd.org/greengrowth/sustainableagriculture/1916629.pdf.
Pastor JT, Ruiz I-S (2002) A statistical test for nested radial DEA models. Oper Res 50(4):728–735
Rodriguez M, Gómez E, Lorente J (2004) Rural multifunctionality in Europe. The concepts and policies. 90th AEEA Seminar
Salinas-Jimenez J, Smith P (1996) Data envelopment analysis applied to quality in primary health care. Ann Oper Res 67:141–161
Sigala M, Airey D, Jones P, Lockwood A (2004) ICT paradox lost? A stepwise DEA methodology to evaluate technology investments in tourism settings. J Travel Res 43:180–192
Silva E, Santos C (2007) Eficiência nos Sistemas de Produção Pecuária nos Açores. APDEA Congress, Vila Real
Silva E, Arzubi A, Berbel J (1996) An application of data envelopment analysis (DEA) in Azores dairy farms. New Medit 3:39–43
Silva E, Arzubi A, Berbel J (2004) An application of data envelopment analysis (DEA) in Azores dairy farms. New Medit 3:39–43
Simar L (1996) Aspects of statistical analyses in DEA-type frontier models. J Product Anal 7:177–186
Simar L, Wilson P (2008) Statistical interference in nonparametric frontier models: recent developments and perspectives. In: Fried H, Lovell CAK, Schmidt S (eds) The measurement of productive efficiency and productivity change. Oxford University Press, New York
SREA (2007a) Estudo sobre os Turistas que visitam os Açores. 2005 – 2006. Região Autónoma dos Açores/ed. Serviço Regional de Estatística dos Açores
SREA (2007b) Anuário Estatístico dos Açores, 2007. Região Autónoma dos Açores/ed. Serviço Regional de Estatística dos Açores
Stevens J (1986) Applied multivariate statistics for the social sciences. Erlbaum, Hillsdale
Suhariyanto K (1999) Productivity growth efficiency and technical changes in Asian agriculture: a Malmquist index analysis. PhD thesis, University of Reading
Sungsoo P (2007) DEA application for the tourist satisfaction management. Tour Anal 12:201–211
Valdmanis V (1992) Sensitivity analysis for DEA model: an empirical example using public vs. NEP hospitals. J Public Econ 48:185–205
Venâncio F, Silva E (2004) A Eficiência de Exploração Agro-pecuárias dos Açores: uma abordagem paramétrica. XIV Jornadas Luso Espanholas de Gestão Científica
Wilson PW (2005) FEAR 1.0: a software package for frontier efficiency analysis with R, Retrieved from http://business.clemson.edu/Economic/faculty/wilson/courses/bcn/papers/fear.pdf
Acknowledgments
This work has been partially supported by Direcção Regional da Ciência e Tecnologia of Azores Government through the project M.2.1.2/l/009/2008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Mendes, A.B., Noncheva, V., Silva, E. (2013). Sustainable Tourism and Agriculture Multifunctionality by PAR: A Variable Selection Approach. In: Mendes, A., L. D. G. Soares da Silva, E., Azevedo Santos, J. (eds) Efficiency Measures in the Agricultural Sector. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5739-4_10
Download citation
DOI: https://doi.org/10.1007/978-94-007-5739-4_10
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5738-7
Online ISBN: 978-94-007-5739-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)