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Firms and Innovation

Model definitions to identify appropriate benchmarks in judiciary

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Pages 339-360 | Received 02 Sep 2021, Accepted 16 Dec 2021, Published online: 23 Mar 2022

References

  • Agrell, P. J., Mattsson, P., & Månsson, J. (2019). Impacts on efficiency of merging the Swedish district courts. Annals of Operations Research, 1–27.
  • Badunenko, O., & Kumbhakar, S. C. (2017). Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter? European Journal of Operational Research, 260(2), 789–803.
  • 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.
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
  • Christensen, R. K., & Szmer, J. (2012). Examining the efficiency of the US courts of appeals: Pathologies and prescriptions. International Review of Law and Economics, 32(1), 30–37.
  • Coelli, T. (1996). A guide to DEAP Version 2.1: A data envelopment analysis (computer) program. CEPA working paper, 96/08.
  • Coelli, T., Rao Prasada, D. S., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Norwell, MA, US: Kluwer Academic Publishers.
  • Cooper, W. W., Seiford, L. M., & Tone, K. (2007). A comprehensive text with models, applications, references and DEA-solver software. Springer Science+ Business Media.
  • Dakolias, M. (1999). Court performance around the world: A comparative perspective. The World Bank.
  • Daraio, C., & Simar, L. (2005). Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis, 24(1), 93–121.
  • Daraio, C., & Simar, L. (2007a). Advanced robust and nonparametric methods in efficiency analysis: Methodology and application. Berlin, Germany: Springer.
  • Daraio, C., & Simar, L. (2007b). Conditional nonparametric frontier models for convex and nonconvex technologies: A unifying approach. Journal of Productivity Analysis, 28(1–2), 13–32.
  • Daraio, C., & Simar, L. (2016). Efficiency and benchmarking with directional distances: A data-driven approach. Journal of the Operational Research Society, 67(7), 928–944.
  • Daraio, C., Simar, L., & Wilson, P. W. (2015). Testing the” separability” condition in two-stage nonparametric models of production (No. 2015/21). LEM working paper series.
  • Daraio, C., Simar, L., & Wilson, P. W. (2018). Central limit theorems for conditional efficiency measures and tests of the “separability” condition in nonparametric, two-stage models of production. Econometrics Journal, 21(2), 170–191.
  • Deyneli, F. (2012). Analysis of relationship between efficiency of justice services and salaries of judges with two-stage DEA method. European Journal of Law and Economics, 34(3), 477–493.
  • Falavigna, G., & Ippoliti, R. (2021). Reform policy to increase the judicial efficiency in Italy: The opportunity offered by EU post-Covid funds. Journal of Policy Modeling, 43(5), 923–943.
  • Falavigna, G., Ippoliti, R., & Manello, A. (2019). Judicial efficiency and immigrant entrepreneurs. Journal of Small Business Management, 57(2), 421–449.
  • Falavigna, G., Ippoliti, R., & Ramello, G. B. (2018). DEA-based Malmquist productivity indexes for understanding courts reform. Socio-Economic Planning Sciences, 62, 31–43.
  • Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Boston Kluwer Academic Publishers.
  • Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharmacies 1980–1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1–2), 85–101.
  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253–290.
  • Ferrandino, J. (2012). The impact of revision 7 on the technical efficiency of Florida’s circuit courts. Justice System Journal, 33(1), 22–46.
  • Ferro, G., Oubiña, V., & Romero, C. (2020). Benchmarking labor courts: An efficiency frontier analysis. International Journal for Court Administration, 11(2), 7.
  • Ferro, G., Romero, C. A., & Romero-Gómez, E. (2018). Efficient courts? A frontier performance assessment. Benchmarking: An International Journal, 25(9), 3443–3458.
  • Finocchiaro Castro, M., & Guccio, C. (2015). Bottlenecks or Inefficiency? An assessment of first instance Italian courts’ performance. Review of Law & Economics, 11(2), 317–354.
  • Finocchiaro Castro, M., & Guccio, C. (2018). Measuring potential efficiency gains from mergers of Italian first instance courts through nonparametric model. Public Finance Review, 46(1), 83–116.
  • Giacalone, M., Nissi, E., & Cusatelli, C. (2020). Dynamic efficiency evaluation of Italian judicial system using DEA based malmquist productivity indexes. Socio-Economic Planning Sciences, 100952.
  • Giacomelli, S., & Menon, C. (2017). Does weak contract enforcement affect firm size? Evidence from the neighbour’s court. Journal of Economic Geography, 17(6), 1251–1282.
  • Gitto, S. (2017). Efficiency change, technological change and capital accumulation in Italian regions: A sectoral study. International Review of Applied Economics, 31(2), 191–207.
  • Ippoliti, R., & Tria, G. (2020). Efficiency of judicial systems: Model definition and output estimation. Journal of Applied Economics, 23(1), 385–408.
  • Isik, I., & Hassan, M. K. (2003). Financial deregulation and total factor productivity change: An empirical study of Turkish commercial banks. Journal of Banking & Finance, 27(8), 1455–1485.
  • Kittelsen, S. A., & Førsund, F. R. (1992). Efficiency analysis of Norwegian district courts. Journal of Productivity Analysis, 3(3), 277–306.
  • Kneip, A., Simar, L., & Wilson, P. W. (2016). Testing hypotheses in nonparametric models of production. Journal of Business and Economic Statistics, 34(3), 435–456.
  • Lovell, C. K. (2003). The decomposition of Malmquist productivity indexes. Journal of Productivity Analysis, 20(3), 437–458.
  • Mattsson, P., & Tidanå, C. (2018). Potential efficiency effects of merging the Swedish district courts. Socio-Economic Planning Sciences. Forthcoming.
  • Mussard, S., & Peypoch, N. (2006). On multi-decomposition of the aggregate Malmquist productivity index. Economics Letters, 91(3), 436–443.
  • Örkényi, L. (2021). Duration of court events required for litigation–an empirical study to provide a theoretical basis for an objective measurement system for judicial workload. International Journal for Court Administration, 12(1). doi:10.36745/ijca.370
  • Peyrache, A., & Zago, A. (2016). Large courts, small justice!: The inefficiency and the optimal structure of the Italian justice sector. Omega, 64, 42–56.
  • Ray, S. C., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87, 1033–1039.
  • Schneider, M. R. (2005). Judicial career incentives and court performance: An empirical study of the German labour courts of appeal. European Journal of Law and Economics, 20(2), 127–144.
  • Silva, M. C. A. (2018). Output-specific inputs in DEA: An application to courts of justice in Portugal. Omega, 79, 43–53.
  • Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115(3), 459–471.
  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.
  • Simar, L., & Wilson, P. W. (2020). Hypothesis testing in nonparametric models of production using multple sample splits. Journal of Productivity Analysis, 53(3), 287–303.
  • Tone, K. (2004). Malmquist productivity index. In Handbook on data envelopment analysis (pp. 203–227). Boston, MA: Springer.
  • Wheelock, D. C., & Wilson, P. W. (1999). Technical progress, inefficiency, and productivity change in U.S. banking, 1984–1993. Journal of Money, Credit, and Banking, 31(2), 212–234.
  • Wilson, P. W. (2008). FEAR: A software package for frontier efficiency analysis with R. Socio-economic Planning Sciences, 42(4), 247–254.