Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R

Description

In today's competitive environment, airlines are doing everything they can to improve efficiency and productivity. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R identifies and explains sources of airline efficiency and helps achieve these goals through the use of state-of-the-art measurement techniques. Each chapter measures airline performance through the data envelopment analysis (DEA) model and other DEA variants. This book thoroughly discusses topics such as cost and revenue efficiency performance, carbon emissions performance management, and complex airline data analysis, employing appropriate models for each. Model methodologies are also discussed. The in-depth coverage is useful for all audiences, including students with a basic understanding of models, researchers and airline operators and management. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R provides R codes to help readers generate results and quantify efficient practices. These results provide airline decision-makers with the essential information they need to create better policies and avoid underperforming practices. Front Cover Productivity and Efficiency Measurement of Airlines Productivity and Efficiency Measurement of Airlines:Data Envelopment Analysis using R Copyright Dedication Contents Preface 1 - Introduction 1.1 Introduction 1.2 Evolution and deregulation of the global airline industry—a brief comment 1.3 A brief history of developments in data envelopment analysis 1.4 Outline of chapters References 2 - Literature on data envelopment analysis in airline efficiency and productivity 2.1 Introduction 2.2 Literature on airline efficiency using standard data envelopment analysis model 2.3 Literature on airline cost efficiency, revenue efficiency and profit efficiency 2.4 Literature on airline productivity change performance 2.5 Literature on airline efficiency incorporating bad output 2.6 Literature on airline performance based on network DEA or DEA linked by phases 2.7 Literature on airline efficiency using other variations of DEA models 2.8 Literature on airline efficiency incorporating second-stage regression analysis 2.9 Conclusion References 3 - Measuring airline performance: standard DEA 3.1 Introduction 3.2 Data issues 3.2.1 Provision model 3.2.2 Delivery model 3.2.3 Cost and revenue efficiency model 3.3 DEA models 3.3.1 CCR model 3.3.2 BCC model 3.3.3 Cost minimization model 3.3.4 Revenue maximization model 3.4 R package 3.5 R script for DEA, results and interpretation of results 3.5.1 R script for DEA (Charnes et al. 1978) CCR model 3.5.2 Interpretation of DEA (CCR) results for the ‘provision’ model 3.5.2.1 Interpreting radial (proportionate) and slack movements 3.5.2.2 Scale efficiency 3.5.3 R script for DEA (‘delivery’ model) 3.5.4 Interpretation of DEA results for the ‘delivery’ model 3.5.5 Cost and revenue efficiency model 3.6 Reliability of results 3.6.1 Bootstrapping DEA 3.6.2 Bootstrap cost-efficiency 3.6.3 Hypothesis test for returns to scale 3.7 Conclusion Appendix A References 4 - Measuring airline productivity change 4.1 Introduction 4.2 Malmquist productivity index 4.2.1 R script for Malmquist productivity index 4.2.2 Interpretation of results 4.2.3 Final remark 4.3 Hicks–Moorsteen productivity index 4.3.1 R script for Hicks–Moorsteen productivity index 4.3.2 Interpretation of results 4.3.3 Final remark 4.4 Lowe productivity index 4.4.1 R script for Lowe productivity index 4.4.2 Interpretation of Lowe productivity and profitability change results 4.4.3 Final remark 4.5 Färe–Primont productivity index 4.5.1 R script for FP to measure productivity and profitability change 4.5.2 Interpretation of Färe–Primont productivity and profitability change results 4.5.2.1 Productivity results 4.5.2.2 Profitability results 4.5.3 Final remark 4.6 A comparisons of productivity indices 4.7 Conclusion Appendix B References 5 - DEA variants in measuring airline performance 5.1 Introduction 5.2 Metafrontier DEA 5.2.1 R script for metafrontier 5.2.2 Interpretation of metafrontier results for the ‘delivery’ model 5.3 Slacks-based measure 5.3.1 R script for slacks-bases measure 5.3.2 Interpretation of slacks-based measure results for the ‘delivery’ model 5.4 Superefficiency DEA 5.4.1 R script for Andersen and Petersen (1993) superefficiency DEA 5.4.2 Interpretation of Andersen and Petersen (1993) superefficiency results for the ‘delivery’ model 5.4.3 Cook et al. (2009) modified superefficiency DEA 5.4.4 R script for Cook et al. (2009) modified superefficiency DEA 5.4.5 Interpretation of Cook et al. (2009) modified superefficiency results for the ‘delivery’ model 5.4.6 Tone (2002) superefficiency SBM 5.4.7 R script for Tone (2002) superefficiency SBM 5.4.8 Interpretation of Tone (2002) super SBM results for the ‘delivery’ model 5.5 Potential gains DEA 5.5.1 R script for Bogetoft and Wang (2005) merger DEA 5.5.2 Interpretation of PGDEA results 5.6 Directional distance function—Chambers et al. (1996) 5.6.1 R script for Chambers et al. (1998) directional distance function 5.6.2 Interpretation of directional distance function results 5.7 Conclusion Appendix C References 6 - Measuring airline performance: incorporating bad outputs 6.1 Introduction 6.2 Environmental DEA technology model 6.3 Seiford and Zhu (2002) transformation approach 6.3.1 R script for Seiford and Zhu (2002) model 6.3.2 Interpretation of Seiford and Zhu (2002) results 6.4 Zhou et al. (2008) environmental DEA model 6.4.1 Pure environmental performance index (EPICRS) 6.4.2 NIRS environmental performance index (EPINIRS) 6.4.3 VRS environmental performance index (EPIVRS) 6.4.4 Mixed environmental performance index 6.4.5 R script for Zhou et al. (2008) environmental DEA model 6.4.6 Discussion of results 6.5 Tone’s SBM with bad outputs in Cooper et al. (2007) 6.5.1 R script for Tone's SBM with bad output 6.5.2 Interpretation of Tone's SBM with bad output results 6.6 Chung et al. (1997) Malmquist–Luenberger 6.6.1 R script for Malmquist–Luenberger model 6.6.2 Interpretation of Malmquist–Luenberger results 6.7 Conclusions Appendix D References 7 - Measuring airline performance: Network DEA 7.1 Introduction 7.2 A basic two-node network DEA 7.3 Kao and Hwang (2008) and Liang et al. (2008) network DEA centralized model 7.3.1 R script for Kao and Hwang (2008) and Liang et al. (2008) 7.3.2 Interpretation of results 7.4 Network DEA (Farrell efficiency model)—network technical efficiency 7.4.1 NTE input-oriented VRS model 7.4.2 NTE output-oriented VRS model 7.4.3 R script for NTE input- and output-oriented VRS 7.4.4 Results for the NTE input- and output-oriented VRS and CRS model 7.5 Network cost efficiency model (Fukuyama and Matousek, 2011) 7.5.1 R script for NCE VRS model 7.5.2 Results for the NCE VRS model 7.6 Network revenue efficiency model (Fukuyama and Matousek, 2017) 7.6.1 R script for NRE VRS model 7.6.2 Results for the NRE VRS model 7.7 Network DEA directional distance function inefficiency model (Fukuyama and Weber, 2012) 7.7.1 R script for NDEA-DDF VRS model 7.7.2 Results for the NDEA-DDF VRS model 7.8 Network slacks-based inefficiency model 7.8.1 R script for the NSBI model 7.8.2 Results for the NSBI model 7.9 A general network technology model to depict the airline provision–delivery model 7.9.1 R script for the NT model 7.9.2 Results for the NT model 7.10 Conclusion Appendix E References 8 - Sources of airline performance 8.1 Introduction 8.2 Data for second-stage regression 8.3 Multicollinearity test and separability test 8.3.1 R script for multicollinearity test 8.3.2 Interpretation of the multicollinearity test results 8.3.3 R script for separability test 8.3.4 Interpretation of the separability test results 8.4 Ordinary least squares regression model 8.4.1 R script for ordinary least squares 8.4.2 Interpretation of results 8.5 Generalized least squares regression model 8.5.1 R script for generalized least squares 8.5.2 Interpretation of results 8.6 Tobit regression model 8.6.1 R script for the Tobit regression 8.6.2 Interpretation of Tobit results for the ‘delivery model’ 8.7 Simar and Wilson (2007) regression model 8.7.1 R script for Simar and Wilson (2007) double-bootstrap truncated regression 8.7.2 Interpretation of Simar and Wilson's (2007) double-bootstrap truncated regression results 8.8 Conclusion Appendix F References 9 - Conclusion References Index A B C D E F G H I L M N O P R S T V W Back Cover