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Searching for the source of technical inefficiency in Italian judicial districts: an empirical investigation

Abstract

In this paper, we conducted a two-stage analysis of technical efficiency in Italian judicial districts by focusing on civil cases in 2006. Unlike most of the works that apply the Data Envelopment Analysis technique to study the justice sector, in the first stage, we employed the smoothed bootstrap procedure to generate unbiased technical efficiency estimates. In the second stage, we used a semi-parametric technique (Simar and Wilson in J Econom 136(1): 31–64, 2007) that produces a robust inference for an unknown serial correlation between efficiency scores. Our results show that technical efficiency is explained by demand factors and supports the conclusion that opportunistic behaviour from both claimants and lawyers negatively affects technical efficiency in Italian judicial districts.

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Notes

  1. Although aiming at changing the Italian civil judicial system, the adopted reforms produced poor results (Giorgiantonio 2009).

  2. This solution cannot be applied to small court where the same judge often deals with both civil and criminal cases.

  3. An exception is offered by Rosales-López (2008). The author investigates the performance of first instance courts in Spain and determines whether achieving low reversal rates and a high level of output are incompatible goals in the judiciary system applying econometric techniques.

  4. For more details, refer to Coelli et al. (1998), Fried et al. (2008).

  5. The acronyms CRS (constant returns to scale) and VRS (variable returns to scale) are often used with reference to the CCR and BCC models from the initials of the authors, Charnes et al. (1978), Banker et al. (1989).

  6. DEA technique has been applied to several fields besides justice, such as regulation of water companies (Thanassoulis 2000), local police force (García-Sánchez 2008), gas distribution industry (Erbetta and Rappuoli 2008), higher education (Johnes 2006), health (Hollingsworth et al. 1999; Tsekouras et al. 2010), heritage Authorities (Finocchiaro Castro et al. 2011) and care for the elderly sector (Borge and Haraldsvik 2009).

  7. The first result for DEA estimator consistency is from Banker (1993). The author demonstrates the consistency for the input efficiency estimator using VRS, but it does not provide information for the rate of convergence. For convergence, Kneip et al. (1998) produced a more general result for VRS by showing that DEA estimator \( \hat{\hat{\theta }}_{i} \) \( \hat{\theta }_{\text{VRS}} \) converges in probability at the rate \( \hat{\theta }_{\text{VRS}} - \theta_{\text{VRS}} = O_{P} \left( {n^{{ - \frac{2}{d + 1}}} } \right) \), where n is the number of DMUs and d is the dimensionality space. Although the convergence rate for CRS used in our empirical analysis is unknown, we can suppose that it is faster than that for the VRS (Simar and Wilson 2008).

  8. For a numerical example of the tradeoff between sample size and number of inputs and outputs used for consistency, see Simar and Wilson (2008, p. 439).

  9. A different approach to reduce the dimensionality space was suggested by Daraio and Simar (2007).

  10. More specifically, the estimation using Eq. (2) with Tobit regression violates the assumption of independence between ε i and z i .

  11. Although there are 29 JDs in Italy, we did not have data on two, Campobasso and L’Aquila.

  12. The scarcity of data is from to the difficulty in obtaining information on the judges and administrative staff in the civil sector at the JD level (Carmignani and Giacomelli 2009).

  13. In particular, we aggregated different forms of civil litigation at the JD level, but we did not include the civil proceedings before honorary judges of peace (Giudici di pace). In fact, given the available data we cannot distinguish the number of civil cases from the number of criminal cases solved by judges of peace.

  14. For instance, among the papers adopting a two-stage DEA analysis, Deyneli (2011) reports the results of a study on the efficiency of judicial systems of some European countries employing a cross-section with 22 observations.

  15. It should be noted that Marselli and Vannini (2004) did not employ administrative staff because of the high correlation with judges. Use of the variables judges and adm_staff as input prevents us from considering input related to capital, consumables and other services, though this assumption is common in the literature that views the Courts as labor-intensive units.

  16. DEA estimations were performed using the software package FEAR 1.15 (Wilson 2007).

  17. The results from the VRS assumption are available from the authors upon request.

  18. For a discussion on this issue, see Smith (1997).

  19. For more details, refer to Pedraja-Chaparro and Salinas-Jiménez (1996).

  20. We also controlled for other variables, such as per capita caseload (for each judge working on civil cases only), population density, and index of specialisation (a ratio between the number of civil cases and the total number of cases). These variables were not significant. The results from this estimation are available upon request from the authors.

  21. For a review of the theory of litigation, see Spier (2007).

  22. However, we are aware that for some of the described environmental variables there could be a problem of reverse causality. Though, given that we are not primarily interested in establishing precise causality direction, our analysis focuses on testing the influence of some of the environmental variables discussed in literature on JD technical efficiency.

  23. This debate is related to interpretation of the second-stage DEA regressions, and in the literature, two distinct rationalisations have recently been proposed that are based on different assumptions for the DEA-score data generating process and sample variation (McDonald 2009; Ramalho et al. 2010; Simar and Wilson 2011).

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Acknowledgments

The Authors are grateful to participants in the XXII Conference for the Italian Society of Public Economics (SIEP) and to the seminar series in the DEMQ, University of Catania for the interesting comments and insights. They are indebted to Emiliano Sironi for fruitful discussions and sharing certain data on Italian lawyers. The authors wish to thank Emilio Giardina, Pasquale Catanoso, Giuseppe Di Vita, and one anonymous referee for their helpful and constructive comments and suggestions.

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Correspondence to Calogero Guccio.

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Finocchiaro Castro, M., Guccio, C. Searching for the source of technical inefficiency in Italian judicial districts: an empirical investigation. Eur J Law Econ 38, 369–391 (2014). https://doi.org/10.1007/s10657-012-9329-0

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  • DOI: https://doi.org/10.1007/s10657-012-9329-0

Keywords

  • Civil courts efficiency
  • Law enforcement
  • Non-parametric methods

JEL Classification

  • D24
  • K41
  • K49