Data Envelopment Analysis: A Review and Synthesis
This chapter introduces the main concepts and models underlying the evaluation of efficiency using the Data Envelopment Analysis (DEA) technique. It starts with a historical overview of the origin of DEA models, including a brief description of the theory underlying the representation of the technology of production and the efficient frontier in DEA. The main models for evaluating efficiency are reviewed before discussing recent developments in the DEA literature. The chapter also includes a discussion of well-established and emerging areas of analysis. Successful applications of DEA, both for the support of organisations’ management and the design of public policies, are examined. In the end, some considerations regarding the role of efficiency assessment techniques in modern societies and opportunities for future developments are presented.
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References
- Abadie, A., & Cattaneo, M. D. (2018). Econometric methods for program evaluation. Annual Review of Economics,10, 465–503. ArticleGoogle Scholar
- Afsharian, M., Ahn, H., & Harms, S. G. (2021). A review of DEA approaches applying a common set of weights: The perspective of centralized management. European Journal of Operational Research, 294(1), 3–15. Google Scholar
- Agasisti, T., Hippe, R., Munda, G., et al. (2017). Efficiency of investment in compulsory education: Empirical analyses in Europe. Technical Report. Joint Research Centre (Seville site). Google Scholar
- Agrell, P. J., Bogetoft, P., et al. (2017). Regulatory benchmarking: Models, analyses and applications. Data Envelopment Analysis Journal,3(1–2), 49–91. ArticleGoogle Scholar
- Ahmad, N., Naveed, A., Ahmad, S., & Butt, I. (2020). Banking sector performance, profitability, and efficiency: A citation-based systematic literature review. Journal of Economic Surveys,34(1), 185–218. ArticleGoogle Scholar
- Ahn, H., Afsharian, M., Emrouznejad, A., & Banker, R. (2018). Recent developments on the use of DEA in the public sector. Socio-Economic Planning Science,61, 1–3. ArticleGoogle Scholar
- Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics,6(1), 21–37. ArticleGoogle Scholar
- Aigner, D. J., & Chu, S. F. (1968). On estimating the industry production function. The American Economic Review,58(4), 826–839. Google Scholar
- Aparicio, J., Crespo-Cebada, E., Pedraja-Chaparro, F., & Santín, D. (2017). Comparing school ownership performance using a pseudo-panel database: A malmquist-type index approach. European Journal of Operational Research,256(2), 533–542. ArticleGoogle Scholar
- Aragon, Y., Daouia, A., & Thomas-Agnan, C. (2005). Nonparametric frontier estimation: a conditional quantile-based approach. Econometric Theory,21(2), 358–389. ArticleGoogle Scholar
- Bădin, L., Daraio, C., & Simar, L. (2019). A bootstrap approach for bandwidth selection in estimating conditional efficiency measures. European Journal of Operational Research,277(2), 784–797. ArticleGoogle Scholar
- Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research,17(1), 35–44. ArticleGoogle Scholar
- Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research,62(1), 74–84. ArticleGoogle Scholar
- Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science,30(9), 1078–1092. ArticleGoogle Scholar
- Banker, R. D., Gadh, V. M., & Gorr, W. L. (1993). A monte carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis. European Journal of Operational Research,67(3), 332–343. ArticleGoogle Scholar
- Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research,98(2), 175–212. ArticleGoogle Scholar
- Briec, W., Kerstens, K., & Eeckaut, P. V. (2004). Non-convex technologies and cost functions: Definitions, duality and nonparametric tests of convexity. Journal of Economics,81(2), 155–192. ArticleGoogle Scholar
- Camanho, A., & Dyson, R. (2006). Data envelopment analysis and Malmquist indices for measuring group performance. Journal of Productivity Analysis,26(1), 35–49. ArticleGoogle Scholar
- Camanho, A., & Dyson, R. (2008). A generalisation of the farrell cost efficiency measure applicable to non-fully competitive settings. Omega,36(1), 147–162. ArticleGoogle Scholar
- Cazals, C., Florens, J. P., & Simar, L. (2002). Nonparametric frontier estimation: A robust approach. Journal of Econometrics,106(1), 1–25. ArticleGoogle Scholar
- Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory,70(2), 407–419. ArticleGoogle Scholar
- Chambers, R. G., Fāure, R., & Grosskopf, S. (1996). Productivity growth in APEC countries. Pacific Economic Review,1(3), 181–190. ArticleGoogle Scholar
- Charles, V., Gherman, T., & Zhu, J. (2021). Data envelopment analysis and big data: A systematic literature review with bibliometric analysis. In Data-enabled analytics (pp. 1–29). Google Scholar
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research,2(6), 429–444. ArticleGoogle Scholar
- Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through. Management Science,27(6), 668–697. ArticleGoogle Scholar
- Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics,30(1–2), 91–107. ArticleGoogle Scholar
- Charnes, A., Cooper, W., Golany, B., Halek, R., Klopp, G., Schmitz, E., & Thomas, D. (1986). Two-phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: Tradeoffs between joint services and army advertising. Tex, USA: Center for Cybernetic Studies University of Texas-Austin Austin. Google Scholar
- Chen, K., & Zhu, J. (2020). Additive slacks-based measure: Computational strategy and extension to network DEA. Omega,91, 102022. ArticleGoogle Scholar
- Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2007). An introduction to ‘benefit of the doubt’ composite indicators. Social Indicators Research,82(1), 111–145. ArticleGoogle Scholar
- Chu, J., & Zhu, J. (2021). Production scale-based two-stage network data envelopment analysis. European Journal of Operational Research,294(1), 283–294. ArticleGoogle Scholar
- Cobb, C. W., & Douglas, P. H. (1928). A theory of production. The American Economic Review,18(1), 139–165. Google Scholar
- Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research,192(1), 1–17. ArticleGoogle Scholar
- Cooper, W., Seiford, L., Tone, K., & Zhu, J. (2007). Some models and measures for evaluating performances with DEA: Past accomplishments and future prospects. Journal of Productivity Analysis,28(3), 151–163. ArticleGoogle Scholar
- Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1–39). Springer Google Scholar
- Cvetkoska, V., & Savic, G. (2021) DEA in banking: Analysis and visualization of bibliometric data. Data Envelopment Analysis Journal. Google Scholar
- Dakpo, K. H., Jeanneaux, P., & Latruffe, L. (2016). Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework. European Journal of Operational Research,250(2), 347–359. ArticleGoogle Scholar
- Daraio, C., & Simar, L. (2007). Conditional nonparametric frontier models for convex and nonconvex technologies: A unifying approach. Journal of Productivity Analysis,28(1), 13–32. ArticleGoogle Scholar
- Daraio, C., Kerstens, K. H., Nepomuceno, T. C. C., & Sickles, R. (2019). Productivity and efficiency analysis software: An exploratory bibliographical survey of the options. Journal of Economic Surveys,33(1), 85–100. ArticleGoogle Scholar
- Daraio, C., Kerstens, K., Nepomuceno, T., & Sickles, R. C. (2020). Empirical surveys of frontier applications: A meta-review. International Transactions in Operational Research,27(2), 709–738. ArticleGoogle Scholar
- Daraio, C., Simar, L., & Wilson, P. W. (2020). Fast and efficient computation of directional distance estimators. Annals of Operations Research,288(2), 805–835. ArticleGoogle Scholar
- De Witte, K., & Kortelainen, M. (2013). What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics,45(17), 2401–2412. ArticleGoogle Scholar
- De Witte, K., & López-Torres, L. (2017). Efficiency in education: A review of literature and a way forward. Journal of the Operational Research Society,68(4), 339–363. ArticleGoogle Scholar
- De Witte, K., & Marques, R. C. (2010). Incorporating heterogeneity in non-parametric models: A methodological comparison. International Journal of Operational Research,9(2), 188–204. ArticleGoogle Scholar
- Debreu, G. (1951). The coefficient of resource utilization. Econometrica: Journal of the Econometric Society 273–292 Google Scholar
- Deprins, D., Simar, L., Tulkens, H. (1984). Measuring labor inefficiency in post offices. In M. Marchand, P. Pestieau, & H. Tulkens (Eds.), The performance of public enterprises: Concepts and measurements, (pp. 243–267). Amsterdam, North-Holland. Google Scholar
- Dutta, P., Jaikumar, B., Arora, M. S. (2021). Applications of data envelopment analysis in supplier selection between 2000 and 2020: A literature review. Annals of Operations Research, 1–56 Google Scholar
- Dutu, R., & Sicari, P. (2020). Public spending efficiency in the OECD: Benchmarking health care, education, and general administration. Review of Economic Perspectives,20(3), 253–280. ArticleGoogle Scholar
- Dyckhoff, H., & Souren, R. (2022). Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review. European Journal of Operational Research,297(3), 795–816. ArticleGoogle Scholar
- Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. Journal of the Operational Research Society,39(6), 563–576. ArticleGoogle Scholar
- Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research,132(2), 245–259. ArticleGoogle Scholar
- D’Inverno, G., Smet, M., & De Witte, K. (2021). Impact evaluation in a multi-input multi-output setting: Evidence on the effect of additional resources for schools. European Journal of Operational Research,290(3), 1111–1124. ArticleGoogle Scholar
- Emrouznejad, A., & Gl, Yang. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences,61, 4–8. ArticleGoogle Scholar
- Emrouznejad, A., Banker, R. D., & Neralic, L. (2019). Advances in data envelopment analysis: Celebrating the 40th anniversary of DEA and the 100th anniversary of Professor Abraham Charnes’ birthday. European Journal of Operational Research,278(2), 365–367. ArticleGoogle Scholar
- Ennis, S., & Deller, D. (2019). Water sector ownership and operation: An evolving international debate with relevance to proposals for nationalisation in Italy. CERRE report Google Scholar
- Fall, F., Am, Akim, & Wassongma, H. (2018). DEA and SFA research on the efficiency of microfinance institutions: A meta-analysis. World Development,107, 176–188. ArticleGoogle Scholar
- Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences,34(1), 35–49. ArticleGoogle Scholar
- Färe, R., & Lovell, C. K. (1978). Measuring the technical efficiency of production. Journal of Economic theory,19(1), 150–162. ArticleGoogle Scholar
- Färe, R., Grosskopf, S., & Lovell, C. K. (1985). The measurement of efficiency of production, vol 6. Springer Science & Business Media Google Scholar
- Fare, R., Färe, R., Fèare, R., Grosskopf, S., & Lovell, C. K. (1994). Production frontiers. Cambridge University Press. Google Scholar
- Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In: Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209–240). Springer Google Scholar
- Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General),120(3), 253–281. ArticleGoogle Scholar
- Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research,204(2), 189–198. ArticleGoogle Scholar
- Gattoufi, S., Oral, M., & Reisman, A. (2004). A taxonomy for data envelopment analysis. Socio-Economic Planning Sciences,38(2–3), 141–158. ArticleGoogle Scholar
- Golany, B. (1988). An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of the Operational Research Society,39(8), 725–734. ArticleGoogle Scholar
- Greene, W. H. (1980). Maximum likelihood estimation of econometric frontier functions. Journal of Econometrics,13(1), 27–56. ArticleGoogle Scholar
- Guersola, M., Lima, E. P. D., & Steiner, M. T. A. (2018). Supply chain performance measurement: A systematic literature review. International Journal of Logistics Systems and Management,31(1), 109–131. ArticleGoogle Scholar
- Heesche, E., & Bogetoft Pedersen, P. (2021). Incentives in regulatory DEA models with discretionary outputs: The case of Danish water regulation. Technical Report, IFRO Working Paper. Google Scholar
- Hollingsworth, B. (2008). The measurement of efficiency and productivity of health care delivery. Health Economics,17(10), 1107–1128. ArticleGoogle Scholar
- Horta, I. M., & Camanho, A. S. (2015). A nonparametric methodology for evaluating convergence in a multi-input multi-output setting. European Journal of Operational Research,246(2), 554–561. ArticleGoogle Scholar
- Kaffash, S., Azizi, R., Huang, Y., & Zhu, J. (2020). A survey of data envelopment analysis applications in the insurance industry 1993–2018. European Journal of Operational Research,284(3), 801–813. ArticleGoogle Scholar
- Kerstens, K., & Van de Woestyne, I. (2021). Cost functions are nonconvex in the outputs when the technology is nonconvex: Convexification is not harmless. Annals of Operations Research, 1–26. Google Scholar
- Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. In T. C. Koopmans (Ed.), Activity analysis of production and allocation, Cowles Commission for Research in Economics. Monograph No. 13, Wiley, New York Google Scholar
- Kuosmanen, T., & Johnson, A. L. (2010). Data envelopment analysis as nonparametric least-squares regression. Operations Research,58(1), 149–160. ArticleGoogle Scholar
- Kuosmanen, T., & Kortelainen, M. (2012). Stochastic non-smooth envelopment of data: Semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis,38(1), 11–28. ArticleGoogle Scholar
- Kuosmanen, T., Cherchye, L., & Sipiläinen, T. (2006). The law of one price in data envelopment analysis: Restricting weight flexibility across firms. European Journal of Operational Research,170(3), 735–757. ArticleGoogle Scholar
- Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega,41(1), 3–15. ArticleGoogle Scholar
- Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). A survey of DEA applications. Omega,41(5), 893–902. ArticleGoogle Scholar
- Liu, J. S., Lu, L. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega,58, 33–45. ArticleGoogle Scholar
- Mahmoudi, R., Emrouznejad, A., Shetab-Boushehri, S. N., & Hejazi, S. R. (2020). The origins, development and future directions of data envelopment analysis approach in transportation systems. Socio-Economic Planning Sciences,69, 100672. ArticleGoogle Scholar
- Mardani, A., Streimikiene, D., Balezentis, T., Saman, M. Z. M., Nor, K. M., & Khoshnava, S. M. (2018). Data envelopment analysis in energy and environmental economics: An overview of the state-of-the-art and recent development trends. Energies, 11(8), 2002. Google Scholar
- Mergoni, A., & De Witte, K. (2022). Policy evaluation and efficiency: A systematic literature review. International Transactions in Operational Research,29(3), 1337–1359. ArticleGoogle Scholar
- Milán-García, J., Rueda-López, N., & De Pablo-Valenciano, J. (2021). Local government efficiency: Reviewing determinants and setting new trends. International Transactions in Operational ResearchGoogle Scholar
- Mohd Chachuli, F. S., Ahmad Ludin, N., Mat, S., & Sopian, K. (2020). Renewable energy performance evaluation studies using the data envelopment analysis (DEA): A systematic review. Journal of Renewable and Sustainable Energy,12(6), 062701. ArticleGoogle Scholar
- Oliveira, R., Zanella, A., & Camanho, A. S. (2020). A temporal progressive analysis of the social performance of mining firms based on a Malmquist index estimated with a benefit-of-the-doubt directional model. Journal of Cleaner Production,267, 121807. ArticleGoogle Scholar
- Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research,115(3), 596–607. ArticleGoogle Scholar
- Pastor, J. T., Lovell, C. K., & Aparicio, J. (2020). Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index. European Journal of Operational Research,281(1), 222–230. ArticleGoogle Scholar
- Pereira, M. A., Camanho, A. S., Figueira, J. R., & Marques, R. C. (2021). Incorporating preference information in a range directional composite indicator: The case of Portuguese public hospitals. European Journal of Operational Research,294(2), 633–650. ArticleGoogle Scholar
- Pereira, M. A., Camanho, A. S., Marques, R. C., & Figueira, J. R. (2021). The convergence of the world health organization member states regarding the united nations’ sustainable development goal ‘good health and well-being’. Omega,104, 102495. ArticleGoogle Scholar
- Podinovski, V. V. (2004). Bridging the gap between the constant and variable returns-to-scale models: Selective proportionality in data envelopment analysis. Journal of the Operational Research Society,55(3), 265–276. ArticleGoogle Scholar
- Richmond, J. (1974). Estimating the efficiency of production. International Economic Review, 515–521. Google Scholar
- Rostamzadeh, R., Akbarian, O., Banaitis, A., & Soltani, Z. (2021). Application of DEA in benchmarking: A systematic literature review from 2003–2020. Technological and Economic Development of Economy,27(1), 175–222. ArticleGoogle Scholar
- Sassanelli, C., Rosa, P., Rocca, R., & Terzi, S. (2019). Circular economy performance assessment methods: A systematic literature review. Journal of Cleaner Production,229, 440–453. ArticleGoogle Scholar
- Seiford, L. M. (1996). Data envelopment analysis: The evolution of the state of the art (1978–1995). Journal of Productivity Analysis,7(2), 99–137. ArticleGoogle Scholar
- Seiford, L. M., & Zhu, J. (1999). An investigation of returns to scale in data envelopment analysis. Omega,27(1), 1–11. ArticleGoogle Scholar
- Shephard, R. W. (1970). Theory of cost and production functions. Princeton University Press. Google Scholar
- Simar, L. (2003). Detecting outliers in frontier models: A simple approach. Journal of Productivity Analysis,20(3), 391–424. ArticleGoogle Scholar
- Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science,44(1), 49–61. ArticleGoogle Scholar
- Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E. K., Nilashi, M., & Zakuan, N. (2018). Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis. Annals of Operations Research,271(2), 915–969. ArticleGoogle Scholar
- Sotiros, D., Rodrigues, V., & Silva, M. C. (2022). Analysing the export potentials of the Portuguese footwear industry by data envelopment analysis. Omega,108, 102560. ArticleGoogle Scholar
- Sowlati, T., & Paradi, J. C. (2004). Establishing the “practical frontier’’ in data envelopment analysis. Omega,32(4), 261–272. ArticleGoogle Scholar
- Štreimikis, J., & Saraji, M. K. (2021). Green productivity and undesirable outputs in agriculture: A systematic review of DEA approach and policy recommendations. Economic Research, 1–35. Google Scholar
- Taleb, M., Khalid, R., Ramli, R., Ghasemi, M. R., & Ignatius, J. (2022). An integrated bi-objective data envelopment analysis model for measuring returns to scale. European Journal of Operational Research,296(3), 967–979. ArticleGoogle Scholar
- Thanassoulis, E., & Dunstan, P. (1994). Guiding schools to improved performance using data envelopment analysis: An illustration with data from a local education authority. Journal of the Operational Research Society,45(11), 1247–1262. ArticleGoogle Scholar
- Thanassoulis, E., & Dyson, R. (1992). Estimating preferred target input-output levels using data envelopment analysis. European Journal of Operational Research,56(1), 80–97. ArticleGoogle Scholar
- Thompson, R. G., Langemeier, L. N., Lee, C. T., Lee, E., & Thrall, R. M. (1990). The role of multiplier bounds in efficiency analysis with application to Kansas farming. Journal of Econometrics,46(1–2), 93–108. ArticleGoogle Scholar
- Tobiasson, W., Llorca, M., & Jamasb, T. (2021). Performance effects of network structure and ownership: The Norwegian electricity distribution sector. Energies,14(21), 7160. ArticleGoogle Scholar
- Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research,130(3), 498–509. ArticleGoogle Scholar
- Tran, A., Nguyen, K. H., Gray, L., & Comans, T. (2019). A systematic literature review of efficiency measurement in nursing homes. International Journal of Environmental Research and Public Health,16(12), 2186. ArticleGoogle Scholar
- Vörösmarty, G., & Dobos, I. (2020). A literature review of sustainable supplier evaluation with data envelopment analysis. Journal of Cleaner Production,264, 121672. ArticleGoogle Scholar
- Winsten, C. (1957). Discussion on Mr. Farrell’s paper. Journal of the Royal Statistical Society Series A,120, 282–284. Google Scholar
- Wong, Y. H., Beasley, J. (1990). Restricting weight flexibility in data envelopment analysis. Journal of the Operational Research Society, 41(9), 829–835. Google Scholar
- Zakowska, I., & Godycki-Cwirko, M. (2020). Data envelopment analysis applications in primary health care: A systematic review. Family Practice,37(2), 147–153. Google Scholar
- Zanella, A., Camanho, A. S., & Dias, T. G. (2015). Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. European Journal of Operational Research,245(2), 517–530. ArticleGoogle Scholar
- Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research,189(1), 1–18. ArticleGoogle Scholar
- Zhu, J. (1996). Data envelopment analysis with preference structure. Journal of the Operational Research Society,47(1), 136–150. ArticleGoogle Scholar
- Zhu, J. (2020). DEA under big data: Data enabled analytics and network data envelopment analysis. Annals of Operations Research, 1–23. Google Scholar
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- Faculdade de Engenharia, Universidade do Porto, Porto, Portugal Ana S. Camanho
- Department of Economics and Management, University of Pisa, Pisa, Italy Giovanna D’Inverno
- Faculty of Economics and Business, KU Leuven, Leuven, Belgium Giovanna D’Inverno
- Ana S. Camanho
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- Department of Mathematics, University of Aveiro, Aveiro, Portugal Pedro Macedo
- Management and Economics Department, University of Beira Interior, Covilhã, Portugal Victor Moutinho
- Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, Aveiro, Portugal Mara Madaleno
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Camanho, A.S., D’Inverno, G. (2023). Data Envelopment Analysis: A Review and Synthesis. In: Macedo, P., Moutinho, V., Madaleno, M. (eds) Advanced Mathematical Methods for Economic Efficiency Analysis. Lecture Notes in Economics and Mathematical Systems, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-031-29583-6_3
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