Data Envelopment Analysis (eBook)

Professor Joe Zhu is one of the prominent researchers in the field of Data Envelopment Analysis (DEA). His research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published over 100 articles in peer-reviewed journals including Operations Research, Sloan Management Review, European Journal of Operational Research, Journal of the Operational Research Society, Naval Research Logistics, IIE Transactions, Journal of Banking and Finance, OMEGA, and others. He is an Area Editor of OMEGA, and Associate Editor of INFOR, and the Associate Series Editor of Springer's International Series in Operations Research and Management Science.He is a Japan Society for Promotion of Science (JSPS) fellow and a William Evans Visiting Fellow of University of Otago, New Zealand. His research has been supported by KPMG Foundation, National Institute of Health, and Department of Veterans Affairs.

Preface 6
Contents 10
Contributors 12
Chapter 1: Efficiency Persistence of Bank and Thrift CEOs Using Data Envelopment Analysis 15
1.1 Introduction 16
1.2 Literature Review 17
1.3 Data and Methodology 18
1.3.1 CEO Compensation, Efficiency, and Persistence 21
1.4 Empirical Results 23
1.5 Discussion 28
1.6 Conclusion 29
References 29
Chapter 2: Assessment of Transportation Performance: A Network Structure 31
2.1 Introduction 32
2.2 Transportation Performance 33
2.3 Network Data Envelopment Analysis in Transportation 35
2.4 Other Issues for Transportation Applications 38
2.4.1 Route-Based Performance Evaluation 39
2.4.2 Environmental Factors 39
2.4.3 Undesirable Outputs 40
2.4.4 Multi-activity Framework 40
2.5 Examples 43
2.5.1 Route-Based Network DEA Model 44
2.5.1.1 The Data 44
2.5.1.2 Empirical Results 44
2.5.2 Multi-activity DEA Model with Environmental Factors and Undesirable Output 46
2.5.2.1 The Data 47
2.5.2.2 Empirical Results 48
2.5.3 Multi-activity Network DEA Model with Environmental Factors 50
2.5.3.1 The Data 50
2.5.3.2 Empirical Results 51
2.6 Conclusions 54
References 55
Chapter 3: Total-Factor Energy Efficiency and Its Extensions: Introduction, Computation and Application 58
3.1 Introduction 58
3.2 The Concept of Total-Factor Input and Output Efficiency 60
3.3 DEA Models for Energy Efficiency Study 61
3.3.1 Proportional Adjustment Models Without Undesirable Outputs 62
3.3.1.1 All Inputs Can Be Radially Adjusted 62
3.3.1.2 All Inputs Can Be Proportionally Adjusted with Different Ratios 64
3.3.1.3 Only Energy Can Be Radially Adjusted 65
3.3.2 Slack-Based Models Without Undesirable Outputs 66
3.3.3 DEA Models with Energy Use and Pollutant Emissions 68
3.4 An Illustration of Regional Energy Efficiency in China 71
3.5 Further Extended Topics 74
3.5.1 Selection of Input and Output Variables 74
3.5.2 Total-Factor Energy Productivity Growth 74
3.6 Concluding Remarks 78
References 79
Chapter 4: Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation-Evidence from the Cas... 83
4.1 Introduction 84
4.2 WTP Based Service Quality Incentives and the Optimal Quality Level 87
4.3 The Norwegian Example 89
4.3.1 Overview 89
4.3.2 Quality Regulation in Norway: Development and Status Quo 89
4.3.3 Data and Methodology 94
4.3.4 Estimation and Results 97
4.4 Conclusions 100
Appendix 101
References 102
Chapter 5: DEA Applications to Major League Baseball: Evaluating Manager and Team Efficiencies 104
5.1 Introduction 104
5.2 Application 1: Managerial Efficiency in Major League Baseball 106
5.3 Application 2: Efficient Resource Allocation in Major League Baseball 117
References 123
Chapter 6: Efficiency and Productivity in the US Property-Liability Insurance Industry: Ownership Structure, Product and Distr... 124
6.1 Introduction 124
6.2 Frontier Efficiency Methodology and Its Application in the P-L Insurance Industry 126
6.2.1 Estimation Methodology of DEA Frontier Efficiency and Malmquist Index 127
6.2.1.1 Production Frontiers and Technical Efficiency 128
6.2.1.2 Cost Frontiers and Cost Efficiency 129
6.2.1.3 Production Frontiers and Revenue Efficiency 130
6.2.1.4 Profit Efficiency 131
6.2.1.5 Total Factor Productivity and Its Decomposition 132
6.2.2 Summary of Literature on Application of DEA Efficiency in Insurance 135
6.2.2.1 Economies of Scale and Scope 135
6.2.2.2 Firm Organizational Form, Corporate Governance, Distribution Systems 135
6.2.2.3 DEA Efficiency and Regulatory Change of Insurance Market 137
6.2.2.4 DEA Efficiency and Mergers and Acquisitions 137
6.2.2.5 DEA Efficiency and Cross-Country Study of Firm Performance 137
6.2.2.6 DEA Efficiency and Risk Management 137
6.2.3 Inputs and Outputs Used in the Current Study 138
6.2.3.1 Outputs and Output Prices 138
6.2.3.2 Inputs and Input Prices 140
6.3 Productivity, US P-L insurance industryEfficiency and Productivity in the US P-L Industry 142
6.3.1 Data and Sample Selection 142
6.3.2 Average Efficiency and Variation Across Firms 142
6.3.3 Productivity of the US P-L Industry and Variation Across Firms 148
6.3.4 Economies of Scale of US P-L Insurance Firms 151
6.3.5 Efficiency by Ownership Structure 153
6.3.5.1 Efficiency by Organizational Form 153
6.3.5.2 Efficiency by Ownership Status of Firm 155
6.3.6 Efficiency by Product Line 158
6.3.7 Efficiency by Distribution System 162
6.3.8 Regression analyses, insurance efficiencyRegression Analyses on Determinants of Efficiency 165
6.4 Conclusion and Discussion 169
References 171
Chapter 7: Mutual Fund Industry Performance: A Network Data Envelopment Analysis Approach 175
7.1 Introduction 176
7.2 Background to US Mutual Fund Industry 177
7.3 Prior Research on Performance Appraisal of Mutual Funds and Mutual Fund Families 178
7.4 Development of the Two-Stage DEA Model 192
7.4.1 DEA Model for Two-Stage Network and Efficiency Decomposition 197
7.4.1.1 Efficiency Decomposition 199
7.4.1.2 Frontier Projection 203
7.5 Data and Sampling 204
7.6 Analysis of the Results 208
7.6.1 Overall Efficiency Estimated in the Two-Stage DEA Model 211
7.6.2 Operational Management Efficiency 214
7.6.3 Portfolio Management Efficiency 219
7.6.4 Variation in Efficiency Across Time and Fund Families 230
7.6.5 Frontier Projection of DMUs 232
7.7 Concluding Remarks 234
References 235
Chapter 8: DEA Performance Assessment of Mutual Funds 239
8.1 Introduction 239
8.2 DEA Literature on Mutual Funds 240
8.3 The Problem of Negative Returns in DEA Modeling 242
8.4 A DEA Model for the Performance Assessment in Periods of Financial Crisis 276
8.5 More Traditional Indicators of Mutual Fund Performance 281
8.6 An Empirical Investigation on Different Holding Periods 284
References 291
Chapter 9: Formulating Management Strategy for International Tourist Hotel Using DEA 298
9.1 Introduction 299
9.2 Research Problem 300
9.2.1 Issues 300
9.2.2 Related Literature 302
9.3 Measuring Efficiencies of the International Tourist Hotels 303
9.3.1 Object of Study 303
9.3.2 Defining Input-Output Factors 303
9.3.3 Relative Efficiency Evaluation of Models 306
9.3.4 Measurement of Efficiency Change 307
9.4 Managerial Efficiency of International Tourist Hotels 309
9.4.1 Relative Managerial Efficiency 309
9.4.2 Change of Managerial Efficiency 312
9.5 The Relationship Between Managerial Performance and Management Strategy 314
9.5.1 The Variance Analysis of Hotel Characteristics and Managerial Performance 314
9.5.2 Strategic Factor of the Hotel Industry 314
9.5.2.1 International Cooperation Management Style 315
9.5.2.2 Domestic Franchise Management Style 317
9.5.2.3 Independent Management Style 317
9.5.3 The Relationship Between Strategy Group and Managerial Performance of International Hotels 318
9.5.3.1 Hotels with High Competitiveness and a Fast Pace of Progress (Category A in Fig. 9.3) 319
9.5.3.2 Hotels with High Competitiveness but a Slower Pace of Progress(Category B in Fig. 9.3) 320
9.5.3.3 Hotels with Medium Competitiveness but a Fast Pace of Progress (Category C in Fig. 9.3) 321
9.5.3.4 Hotels with Medium Competitiveness but a Slow Pace of Progress (Category D in Fig. 9.3) 321
9.5.3.5 Hotels with Low Competitiveness but a Slow Pace of Progress (Category E in Fig. 9.3) 322
9.6 Conclusion 323
References 324
Chapter 10: Sustainable Product Design Performance Evaluation with Two-Stage Network Data Envelopment Analysis 326
10.1 Introduction 327
10.2 Literature Review 330
10.3 Research Framework 332
10.3.1 Stage 1: Industrial Design Performance 333
10.3.2 Stage 2: Bio design Performance 334
10.3.3 Design Performance EvaluationDesign performance evaluation 335
10.3.4 Strategic Implications 337
10.4 Data Collection and Research Procedure 338
10.4.1 Data 339
10.4.2 Testing Procedure 339
10.5 Research Results 341
10.5.1 Overall Performance Comparison 341
10.5.2 Individual Carline Performance Comparison 342
10.6 Conclusion 351
References 351
Chapter 11: Measuring Environmental Efficiency: An Application to U.S. Electric Utilities 354
11.1 Introduction 355
11.2 Production Models with Undesirable Outputs for Environmental Efficiency 356
11.2.1 Production Technology Assumptions 356
11.2.2 Directional Distance Function 359
11.2.3 Radial Efficiency Models 359
11.2.4 Problems Illustration by a Numerical Example 361
11.3 A Median adjusted measure (MAM) modelMedian Adjusted Measure (MAM) Model for Environmental Efficiency 362
11.4 An Application to Measuring Environmental Efficiency of U.S. Electric Utilities 364
11.5 Conclusions 373
References 373
Chapter 12: Applications of Data Envelopment Analysis in Education 376
12.1 Introduction 377
12.2 Applications of DEA in Secondary Education 378
12.2.1 Introduction 378
12.2.2 Applications Using Aggregate Pupil Data 380
12.2.2.1 An Overview of DEA Applied to Aggregate Pupil Data 380
12.2.2.2 Using Aggregate Pupil Data to Identify Differential School Effectiveness 385
12.2.2.3 On-line Platforms for Assessment of Schools 388
12.2.3 DEA Applications Using Pupil-Level Data 392
12.2.3.1 An Overview of DEA Applied to Pupil-Level Data 393
12.2.3.2 Applying DEA to Pupil-Level Data 396
Input-Output Variables 396
Dealing with Outliers 397
Obtaining Efficiency Estimates 398
Aggregation of Pupil Level Results 398
Extracting Additional Information on Performance from Longitudinal Pupil-Level Data 399
12.2.3.3 Putting to Use the Findings from DEA Assessments of Pupil-Level Data 401
Gaining Insights into Components of Performance Attributable to Pupils, Schools or Other Hierarchical Levels 401
Identifying Role Model Pupils and Setting Achievement Targets 403
Identification of Differential School Effectiveness 404
The Influence of Environment on School Performance 405
Choosing Between DEA and Parametric Multilevel Modelling 405
12.3 Applications of DEA in Higher Education: Institution Level 407
12.3.1 Assessments of Cost Efficiency in Higher Education Institutions Using DEA 408
12.3.2 Assessment of Technical Efficiency in Higher Education Institutions Using DEA 413
12.3.3 Assessment of Research Performance of Higher Education Institutions Using DEA 415
12.3.3.1 Identifying and Measuring Inputs and Outputs 415
12.3.3.2 Alternative DEA Models for Assessing Research Productivity 419
12.3.3.3 Measuring Efficiency and Effectiveness of Research Activities 420
12.3.3.4 An Illustrative Application 424
12.3.4 Using DEA to Assess Administrative Services in Universities 426
12.3.5 Using DEA to Rank Universities 431
12.3.6 Network DEA 432
12.4 Applications of DEA in Higher Education: Person Level 433
12.4.1 Assessing Academics on Research Output 433
12.4.2 Assessing Academics on Teaching 438
12.5 Conclusion 440
References 441
Chapter 13: Performance Benchmarking of School Districts in New York State 448
13.1 Introduction 448
13.2 Choosing an Appropriate Benchmarking Methodology 449
13.3 Data Envelopment Analysis 450
13.4 A DEA Model for School District Performance in New York State 453
13.5 Data and Results 455
13.6 Results for Three Example Districts 455
13.7 Statewide Results 456
13.8 Districts with One or More High Schools 456
13.9 Districts Without a High School 461
13.10 Implementation 464
13.11 Conclusions 465
Appendix: The Mathematics of the DEA Model 465
The Resource Reduction DEA Model 466
The Performance Enhancement DEA Model 468
The Mixed DEA Model 469
References 470
Chapter 14: Assessing Efficiency and Effectiveness in Marketing: Applications of Data Envelopment Analysis-Prelude to Chapters... 472
References 476
Chapter 15: Planning Merchandising Decisions to Account for Regional and Product Assortment Differences 478
15.1 Assortment and Regional Planning, and Store Performance 480
15.2 Retail Productivity and Efficiency 481
15.2.1 Model Development 483
15.3 Empirical Illustration 485
15.3.1 Determination of Inputs and Outputs 485
15.4 Analyses 486
15.5 Results 487
15.5.1 Role of Dissagreggating Overall Sales to Account for Product Assortment 487
15.5.2 Role of Regional Differences 487
15.5.3 Comparison of Regression-Based Approach to DEA-Based Approach 491
15.5.4 Slack Analysis 491
15.6 Implications and Avenues for Future Research 492
15.6.1 Goal Setting and Assortment Planning 492
15.6.2 Directions for Enhancing the Overall Performance of the Retail Chain 497
15.6.3 Avenues for Additional Research 498
References 499
Chapter 16: Evaluation of Subsidiary Marketing Performance: Combining Process and Outcome Performance Metrics 500
16.1 Marketing Performance Assessment in the Global Context 502
16.1.1 Subsidiary Marketing Performance Assessment: A Model 503
16.1.2 Market Asset Creation Process 505
16.1.3 Market Yield Processes 505
16.1.4 Strategic Classification 506
16.1.5 Combining Overall Marketing Performance and Marketing Process Performance 508
16.2 Measuring Overall and Process-Level Performance 509
16.3 Empirical Test 511
16.4 Results 512
16.5 Conclusion and Future Research Directions 518
16.5.1 Managerial Implications 518
16.5.2 Limitations and Avenues for Further Research 519
References 520
Chapter 17: Nonparametric Estimates of the Components of Productivity and Profitability Change in U.S. Agriculture 523
17.1 The Components of Profitability Change 526
17.2 Lowe TFP Indexes 529
17.3 The Components of TFP Change 532
17.4 Estimating Technical, Scale and Mix Efficiency Using DEA 537
17.5 Estimating Maximum TFP and the Rate of Technical Change 538
17.6 Data 541
17.7 The Components of Profitability and TFP Change in US Agriculture 542
17.8 Conclusion 547
References 548
Chapter 18: Research Fronts and Prevailing Applications in Data Envelopment Analysis 550
18.1 Introduction 551
18.2 Methodologies 552
18.2.1 Edge-Betweenness Based Clustering 553
18.2.2 Key-Route Main Path Analysis 554
18.3 Data 555
18.4 Overall DEA Development 556
18.4.1 Researcher Statistics 556
18.4.2 Main Paths 557
18.5 DEA Research Fronts 560
18.5.1 Bootstrapping and Two-Stage Analysis 563
18.5.2 Undesirable Factors 564
18.5.3 Cross-Efficiency and Ranking 565
18.5.4 Network DEA, Dynamic DEA, and SBM 566
18.5.5 Other Coherent Subareas 568
18.6 DEA Applications 570
18.6.1 Catching Up of DEA Applications 570
18.6.2 Prevailing DEA Application 571
18.6.3 Association Between Methodologies and Applications 572
18.7 Conclusions 574
References 575
Index 582