Enhancing non-compensatory composite indicators: A directional proposal

European Journal of Operational Research

Volumn 242, Issue 2, 2015, Pages 620-630

  Fusco, Elisa ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Composite indicators; Directional efficiency; Non compensatory

Indexed keywords

OPERATIONS RESEARCH;

AVERAGE RATE; COMPOSITE INDICATORS; EUROPEAN REGIONS; NON-COMPENSATORY; PREFERENCE STRUCTURES;

MATHEMATICAL MODELS;

EID: 84920732005     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2014.10.017     Document Type: Article

References (69)

1. Allen, R., Athanassopoulos, A., Dyson, R., & Thanassoulis, E. (1997).Weights restrictions and value judgements in data envelopment analysis: Evolution, development and future directions. Annals of Operations Research, 73, 13-34.
2. Bogetoft, P., & Otto, L. (2011). Benchmarking with data envelopment analysis, stochastic Frontier analysis and R. International series in operations research & management science, New York: Springer.
3. Bouyssou, D. (1986). Some remarks on the notion of compensation in MCDM. European Journal of Operational Research, 26(1), 150-160.
4. Bouyssou, D., & Vansnick, J. C. (1986). Noncompensatory and generalized noncompensatory preference structure. Theory and Decision, 21, 251-266.
5. Briec, W., & Lesourd, J. B. (1999). Metric distance, and profit functions: Some duality results. Journal of Optimization Theory and Applications, 101, 15-33.
6. Casadio Tarabusi, E., & Guarini, G. (2013). An unbalance adjustment method for development indicators. Social Indicators Research, 112, 19-45.
7. Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50 (6), 1393-1414.
8. Cazals, C., Florens, J., & Simar, L. (2002). Nonparametric frontier estimation: A robust approach. Journal of Econometrics, 106(1), 1-25.
9. Chakravarty, S. R. (2003). A generalized human development index. Review of Development Economics, 7, 99-114.
10. Chambers, R. G., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98, 351-364.
11. Charnes, A., Cooper, W., & Rhodes, W. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(4), 429-444.
12. Cherchye, L., Moesen, W., & Van Puyenbroeck, T. (2004). Legitimately diverse, yet comparable: On synthesizing social inclusion performance in the eu. Journal of Common Market Studies, 42(5), 919-955.
13. Cherchye, L., Lovell, K., Moesen, W., & Van Puyenbroeck, T. (2005). One market, one number? A composite indicator assessment of eu internalmarket dynamics. Technical Report. Working Paper Series ces0513, Katholieke Universiteit Leuven, Centrum voor Economische Studien.
14. Cherchye, L., Ooghe, E., & Van Puyenbroeck, T. (2008). Robust human development rankings. Journal of Economic Inequality, 6, 287-321.
15. Cook, W., & Kress, M. (1994). A multiple criteria composite index model for quantitative and qualitative data. European Journal of Operational Research, 78, 367-379.
16. Cooper, W. W., Ruiz, J. L., & Sirvent, I. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195, 563-574.
17. Daraio, C., Simar, L., & Rhodes, W. (2005). Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis, 24(1), 93-121.
18. Debreu, G. (1960). Topological methods in cardinal utility theory, in: K. J., Arrow, S., Karlin & P., Suppes (Eds.), Mathematical methods in social sciences, Stanford: Stanford University Press.
19. De Muro, P., Mazziotta, M., & Pareto, A. (2010). Composite indices of development and poverty: An application to mdgs. Social Indicators Research, 104(1), 1-18.
20. Despotis, D. K. (2005a). A reassessment of the human development index via data envelopment analysis. Journal of the Operational Research Society, 56, 969-980.
21. Despotis, D. K. (2005b). Measuring human development via data envelopment analysis: The case of Asia and the Pacific. Omega, 33(5), 385-390.
22. Diewert, W. E. (1976). Exact and superlative index numbers. Journal of Econometrics, 4(2), 115-145.
23. Estellita-Lins, M., da Silva, A. M., & Lovell, C. (2007). Avoiding infeasibility in deamodels with weight restrictions. European Journal of Operational Research, 181(2), 956-966.
24. Färe, R., Grosskopf, S., & Lovell, C. (1985). The measurements of efficiency of production. Studies in productivity analysis, Springer.
25. Färe, R., Grosskopf, S., Noh, D., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126, 469-492.
26. Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple-criteria decision analysis: State of the art surveys. New York: Springer International Series in Operations Research and Management Science.
27. Forman, E. (1983). The analytic hierarchy process as a decision support system. Proceedings of the IEEE computer society (Fall).
28. Freudenberg, M. (2003). Composite indicators of country performance: A critical assessment. Technical Report. OECD Science, Technology and Industry Working Papers 2003/16, OECD, Directorate for Science, Technology and Industry.
29. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103-123.
30. Hair, J. (1995). Multivariate data analysis with readings. Prentice Hall International Editions, Prentice Hall.
31. Hubert, M., Rousseeuw, P. J., & Branden, K. V. (2005). Robpca: a new approach to robust principal component analysis. Technometrics, 47(1), 64-79.
32. Kao, C., Wu, W. Y., Hsieh, W. J. Wang, T. Y., Lin, C., & Chen, L. H. (2008). Measuring the national competitiveness of Southeast Asian countries. European Journal of Operational Research, 187, 613-628.
33. Kaufmann, D., Kraay, A., & Zoido-lobatón, P. (1999). Aggregating governance indicators, WB Policy Research Paper, p. 2195.
34. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2003). Governance matters III: Governance indicators for 1996-2002. Development and Comp Systems 0308001. EconWPA.
35. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Wiley series in probability and mathematical statistics. Applied probability and statistics, Cambridge University Press.
36. Krantz, D. H., Luce, R. D., Suppes, P., & Tversky, A. (1971). Foundations of measurement, Vol. 1, Additive and polynomial representations, New York: Academic Press.
37. Lauer, J. A., Lovell, C. A. K., Murray, C. J. L., & Evans, D. B. (2004). World health system performance revisited: The impact of varying the relative importance of health system goals. Technical Report. BMC Health Services Research 4,19.
38. Lee, H., Park, Y., & Choi, H. (2010). Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach. European Journal of Operational Research, 196, 847-855.
39. Li, G., & Chen, Z. (1985). Projection-pursuit approach to robust dispersion matrices and principal components: Primary theory and Monte Carlo. Journal of the American Statistical Association, 80, 759-766.
40. Mahlberg, B., & Obersteiner, M. (2001). Remeasuring the hdi by data envelopment analysis. Technical Report. IIASA, Interim Report IR-01-069, Laxemburg, Austria.
41. Manly, B. (1994). Multivariate statistical methods: A primer. Chapman and Hall.
42. Mazziotta, C., & Vidoli, F. (2009). La costruzione di un indicatore sintetico ponderato. un'applicazione della procedura benefit of the doubt al caso della dotazione infrastrutturale in italia. Scienze Regionali, 8, 35-69.
43. McDaniel, C., & Gates, R. (1998). Contemporary marketing research. West Pub. Co.
44. Melyn, W., & Moesen, W. (1991). Towards a Synthetic Indicator of Macroeconomic Performance: Unequal Weighting when Limited Information is Available. Public economics research papers, Katholieke Universiteit Leuven.
45. Moldan, B., Billharz, S., & Matravers, R. (1997). Sustainability indicators: A report on the project on indicators of sustainable development. SCOPE Series, Wiley.
46. Munda, G. (2004). Social multi-criteria evaluation (SMCE): Methodological foundations and operational consequences. European Journal of Operational Research, 158(3), 662-677.
47. Munda, G., & Nardo, M. (2005). Constructing consistent composite indicators: The issue of weights. Technical Report. EUR 21834 EN, European Commission.
48. Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: A defensible setting. Applied Economics, 41(12), 1513-1523.
49. Munda, G., & Saisana, M. (2011). Methodological considerations on regional sustain-ability assessment based on multicriteria and sensitivity analysis. Regional Studies, 45(2), 261-276.
50. Munda, G. (2012). Choosing aggregation rules for composite indicators, Vol. 109(3), Netherlands: Springer, (pp. 337-354).
51. Munda, G. (2012). Intensity of preference and related uncertainty in non-compensatory aggregation rules. Theory and Decision, 73(4), 649-669.
52. Munda, G. (2014). On the Use of Shadow Prices for Sustainable Well-Being Measurement. Social Indicators Research, 118(2), 911-918.
53. Office For National Statistics (ONS), 2002. Towards a Measurement Framework for International e- Commerce Benchmarking. Technical Report. London.
54. Podinovskii, V. V. (1994). Criteria importance theory. Mathematical Social Sciences, 27, 237-252.
55. Rogge, N. (2012). Undesirable specialization in the construction of composite policy indicators: The environmental performance index. Ecological Indicators, 23, 143-154.
56. Roy, B. (1996). Multicriteria methodology for decision analysis. Dordrecht: Kluwer.
57. Ruymagaart, F. H. (1981). A robust principal component analysis. Journal of Multivariate Analysis, 11, 485-497.
58. Saaty, R. (1987). The analytic hierarchy process - What it is and how it is used. Mathematical Modelling, 9, 161-176.
59. Sahoo, B. K., & Acharya, D. (2010). An alternative approach to monetary aggregation in DEA. European Journal of Operational Research, 204, 672-682.
60. Shephard, R. (1970). Theory of cost and production functions. Princeton studies in mathematical economics, Princeton University Press.
61. Storrie, D., & Bjurek, H. (2000). Benchmarking european labour market performance with efficiency frontier techniques. Technical Report. CELMS Discussion papers, Goteborg University.
62. Takamura, Y., & Tone, K. (2003). A comparative site evaluation study for relocating Japanese government agencies out of Tokyo. Socioeconomic Planning Sciences, 37, 85-102.
63. Thanassoulis, E., Portela, M., & Allen, R. (2004). Incorporating value judgments in dea. In: W., Cooper, L., Seiford, J., Zhu (Eds.), Handbook on Data Envelopment Analysis. Vol. 71 of International Series in Operations Research & Management Science. US: Springer, 99-138.
64. Ting, H. M. (1971). Aggregation of attributes for multiattributed utility assessment. Technical Report. Technical report N° 66, Operations Research Center, M. I. T. Cambridge, Mass.
65. Tsutsui, M., Tone, K., & Yoshida, Y. (2009). Technical efficiency based on cost gradient measure. Discussion Paper 09-14, GRIPS Policy Information Center, Tokyo.
66. Vansnick, J. C., 1986. On the problem of weights in multiple criteria decision making (the non-compensatory approach). European Journal of Operational Research, 24, 288-294.
67. Verschelde, M., & Rogge, N. (2012). An environment-adjusted evaluation of citizen satisfaction with local police effectiveness: Evidence from a conditional Data Envelopment Analysis approach. European Journal of Operational Research, 223, 214-225.
68. Vidoli, F., & Mazziotta, C. (2013). Robust weighted composite indicators by means of frontier methods with an application to European infrastructure endowment. Statistica Applicata - Italian Journal of Applied Statistics, 23(2), 259-282.
69. World Markets Research Centre (WMRC), 2001. World Markets Research Centre Globalisation Index. Technical Report. London.