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Economics of court performance: an empirical analysis

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Abstract

This paper proposes an empirical analysis of Spanish court performance using the economic approach. An econometric model will be estimated in order to answer two basic questions: (1) why some courts’ output it is greater than others? (2) Could courts produce a higher output using their actual resources? In addition it will be determine, by means of an analysis of variance (ANOVA), whether courts showing higher than average output have dictated resolutions with a higher reversal rate.

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Notes

  1. Since the seminal work of North (1990), it is well known that markets functioning and economic development depend in a considerable extent, on the quality, effectiveness and efficiency of the legal and judicial institutions, their design and performance.

  2. See, among others, Buscaglia and Dakolias (1996), Dakolias (1996, 1999), Malik (1996), Dakolias and Said (1999), Messick (1999a, b, 2002), Rigo (1999), Baldwin (2000), Dietrich (2000), Harris (2000), Zuckerman (2000), World Bank (2001, 2002a, b, c, 2003a, b, 2004).

  3. See “Ley Orgánica 19/2003, de 23 de diciembre, de modificación de la Ley Orgánica 6/1985, de 1 de julio del Poder Judicial.” It was published in the BOE No 309. December 26, 2003.

  4. For more details about the judicial data in Spain, see “Memoria del Consejo General del Poder Judicial (2004), Bendala (2003), Pastor and Vargas (2001).

  5. See Pastor (2003c), Cabrillo and Pastor (2001).

  6. See Toharia (2003). The rate of congestion in 2006 was 1.6 (in the civil jurisdiction, the one in we will focus in this paper).

  7. This problem is well explained by the Law and Economics approach. In section two a brief review of the relevant literature will be presented.

  8. In Spain, Pastor (1993, 2003b, c) and Pedraja and Salinas (1995), have raised similar questions, as well in other countries, Kittelsen and Forsund (1992), Buscaglia and Ulen (1997), Buscaglia and Dakolias (1999), Hammergren (1999), Djankov (2001), Vargas (2003), Fix-Fierro (2003), among others.

  9. This situation does not occur only in Spain, see among others, Lawson and Gletne (1980) and Derr (2001).

  10. “The courts are in many ways typical of public service production units. The courts are mainly organized in small units with 5–15 employees, although some city courts are substantially larger. The products are numerous and heterogeneous, and each unit has been assigned the task of satisfying demand for judicial services from a defined geographical area without the use of market prices.” Kittelsen and Forsund (1992).

  11. See Pastor (1993, 2003b, c), Hammergren (1999, 2000, 2002), Cabrillo and Pastor (2001), Vargas (2003).

  12. See Pastor (2003a).

  13. In the absence of data produced by their own judicial systems of each country, researchers in many have had to develop surveys to key players (judges, lawyers, consumers of justice) to build indicators for assessing the performance in different court countries. See among others, Buscaglia and Ulen (1997), Buscaglia and Dakolias (1999), Dietrich (2000) and Djankov et al. (2001). For more detail on the importance of data in the evaluation of judicial performance, see. For a guide to build performance indicators, see Pastor and Maspons (2003, 2004).

  14. See Lawson and Gletne (1980), Derr (2001). It is also often argued that the assessing of judicial performance may run counter to the principle of judicial independence, see U.S Agency for International Development (2002).

  15. See Pastor (1993), Buscaglia and Ulen (1997), Zuckerman (2000), Cabrillo and Pastor (2001), Fix Fierro (2003). The literature on incentives to litigate is very broad. For a review of this literature see Kessler and Rubinfeld (2004) and Shavell (2004).

  16. Some exceptions are Posner (1972, 1993, 2000), Landes and Posner (1976, 1980), Cohen (1991), Pastor (1993), Macey (1994), Buscaglia and Ullen (1997), Allen et al. (1998), Buscaglia and Dakolias (1999), Figueiredo and Figueiredo (2002), Fix-Fierro (2003) and Levy (2005).

  17. See Levitt and Joyce (1988).

  18. For goods produced by the public sector, there is what Levitt and Joyce (1988) have called “final outputs” that are nothing more than the “social consequences”. According to the authors, in practice, the final output is difficult to measure; therefore it is necessary to take the “intermediate outputs”. In this case, justice is the final output of the trial; however, it can be measured from intermediate outputs, such as number of sentences + number of warrants.

  19. Much of the literature on the measurement of efficiency in the public sector has its origins in the work of Farrell (1957) “The measurement of productive efficiency.” To Färe et al. (1994), the first to develop a rigorous analytical approach for measuring the efficiency were Koopmans (1951) and Debreu (1951). For more details on the concept of productive efficiency see Schmidt (1985–1986).

  20. However, Buscaglia and Ulen (1997) found that the effect of a higher spending on Justice on judicial efficiency is not necessarily positive. This is because more resources could increase the demand of justice and thus the number of cases filed into the system. The same result was obtained by Rosales-López (2007) when assessing the effect of the New Code of Civil Procedure in Spain on the judicial output.

  21. As we mention above, the seminal works in this field are Landes (1971), Gould (1973), Posner (1973) y Shavell (1982a). Also, see among others, Cooter and Rubinfeld (1989), Posner (1992), Cooter and Ullen (1997), Shavell (2004), Polinsky and Shavell (2005).

  22. See Png (1987) and Cooter and Ulen (1997).

  23. American and British rules have been analyzed more frequently by the literature. For a formal demonstration on the effects of other litigation rules on the parties’ behaviour, see Baye et al. 2005). See also Reinganum and Wilde (1986), Hause (1989) and Hughes and Snyder (1995, 1998).

  24. See Shavell (1989:183), Cooter and Rubinfeld (1994), Mackenna and Wiggins (1997–1998) Shepherd (1999).

  25. See Shavell (1982b, 2004).

  26. See Shavell (2004) and Buscaglia and Stephan (2005).

  27. See Buscaglia and Ulen (1997), Buscaglia and Dakolias (1999).

  28. Unfortunately the system does not produce data such as the duration of the court proceedings, the cost of each procedure, the judge's years of experience and education, the time he or she spends in administrative and jurisdictional tasks and the technology available by court -to mention just some variables that might have explanatory power-. To obtain these data was out of the reach of this research.

  29. We will not get into the controversy between the use of econometric techniques and no parametric techniques (such Data Enveloped Analysis—DEA) to evaluate the efficiency of productive units. Following Levitt and Joyce (1988) we decided to evaluate the relative performance of courts (that implies we will not evaluate the court’s productive efficiency.) We will evaluate the court’s performance based on the expected performance and not on the best observed performance. In this case there is no controversy for the use of econometric models. For more details, see among others, Schmidt (1985–1986), Lovell and Schmidt (1988), Smith (1990). Another methodology recently applied to compare the costs of justice organizations is the panel data; see for example Stephen (2005).

  30. There are a total of 65 civil first instance court without family cases “juzgados de primera instancia de lo civil” in the Region of Andalusia. We dropped four of them because they are functioning at the same time as a Civil Register Office.

  31. Where “court’s output” = sentences + warrants. And this paper refers to level of product, output or resolutions indifferently.

  32. Where workload = filled cases + pending cases.

  33. For details about this variable see Table 2 in the next section.

  34. We find consistent results for a sample of 202 first instance and instruction courts from the Region of Andalusia.

  35. For details see Appendix 1.

  36. Where reversal rate it is a variable that measures the percentage of resolutions (only sentence + warrants dictated in the first instance) totally reversed by the second instance. For more details on the reversal rate use in this analysis see Appendix 2.

  37. Table 5 shows the basic statistics for both groups.

  38. We decided to eliminate of the sample court # 54 because it was an outlier.

  39. For details see Appendix 3.

  40. Reversal rate = [warrants + sentences totally reversed by the second instance) ÷ total of sentence + warrants dictated in the first instance] by court.

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Acknowledgements

The Ministry of Education of Spain (through FPU program) provided financial support for this research. I appreciate the comments made by Santos Pastor, Rosa Bendala, Ricardo García, Sabela Oubiña, Pedro Francés, Jesús Robledo and two anonymous reviewers. Different versions of this paper were presented in the VIII Applied Economics Meeting in Spain, in the 23rd European Association of Law and Economics Conference and in the 11th International Society for New Institutional Economics Annual Conference, where I received valuable comments.

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  1. Facultad de Ciencias Económicas y Empresariales, Department of Applied Economics, Universidad de Granada, Campus de la Cartuja, s/n., 18071, Granada, Spain

    Virginia Rosales-López

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Appendices

Originally the model was fitted with the variables named in spanish, for this paper we traduced then as follow: resol = resol; perjud = judstaff; carga = workload; rfzo = reinf; servcom = cps; rotjuez = judturn.

Where desempenho = performance and tasarevoc = reversal rate.

Appendix 1

Originally the model was fitted with the variables named in spanish, for this paper we traduced then as follow: resol = resol; perjud = judstaff; carga = workload; rfzo = reinf; servcom = cps; rotjuez = judturn.

Multiple regression analysis

Dependent variable: log(resol)

Parameter

Estimate

Standard error

t-Statistic

p-Value

 

Constant

2.2771

0.717461

3.17382

0.0025

 

log(perjud)

0.625681

0.321179

1.94807

0.0565

 

log(carga)

0.308864

0.123584

2.49922

0.0155

 

refzo

0.132708

0.0615228

2.15706

0.0354

 

servcom

0.542555

0.129795

4.18009

0.0001

 

rotjuez

−0.171155

0.0721363

−2.37266

0.0212

 

Analysis of variance

Source

Sum of squares

df

Mean square

F-ratio

p-Value

Model

2.20507

5

0.441014

15.15

0.0000

Residual

1.60113

55

0.0291115

  

Total (Corr.)

3.8062

60

   
  1. R-squared = 57.9337%
  2. R-squared (adjusted for df) = 54.1095%
  3. Standard Error of Est. = 0.170621
  4. Mean absolute error = 0.129588

Appendix 2: Reversal rate

It is a variable that measures the percentage of resolutions (only sentence + warrants dictated in the first instance) totally reversed by the second instance.Footnote 41 The data used in this section comes from the General Council of the Judiciary of Spain. Table 6 shows the basic statistics for the reversal rate by court.

Table 6 Reversal rate (%)
Full size table

During the 2002, an average of 3.44% of court’s resolutions was reversed. 3.44 stands low in the sample distribution. Table 7 shows the rate of reversal by court.

Table 7 Reversal rate of Andalusia’s civil first instance courts, 2002
Full size table

Appendix 3: Analysis of variance

Where desempenho = performance and tasarevoc = reversal rate.

Before testing the Hypothesis in section 5, we must know if the variable “reversal rate” is normal and has a constant variance. Table 8 shows the summary statistics, and as while the “standard skewness” than “standard kurtosis” was between −2 and +2, the reversal rate’s distribution is normal. Table 9 shows a test based on the F distribution Fischer-Snedecor (where the Ho: “the standard deviation it is equal in both groups”) we obtain a p value > 0.05 and there is not significant difference between the standard deviation of the two groups. Table 10 shows the Analysis of Variance (ANOVA).

Table 8 Basic statistics. Summary statistics for tasarevoc * 100
Full size table
Table 9 Comparison of standard deviations for tasarevoc * 100
Full size table
Table 10 Analysis of variance
Full size table

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Rosales-López, V. Economics of court performance: an empirical analysis. Eur J Law Econ 25, 231–251 (2008). https://doi.org/10.1007/s10657-008-9047-9

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