We therefore find that the greater the percentage of temporary judges, the lower the efficiency of the courts. The concern in the literature about the (in)efficiency of the administration of justice is not new (see e.g. North, 1990). Pedraja and Salinas' (1995) empirical analysis represents a step further in this research and more recently Rosales-López (2008) and García-Rubio and Rosales-López (2010) attempted to quantify the inefficiency of the system2,3.
In 2008, the General Council of Judges (Consejo General del Poder Judicial (CGPJ)) introduced a program for the modernization of the judiciary (Plan de modernización de la justicia) due to concerns about the performance of the courts in Spain. 6 See for example Di Vita (2010) on the effects of legislative complexity on court performance. Thus, the raw data seem to confirm the conclusions of the EEAG (2011) report cited in the introduction.
To estimate judicial inefficiency, we first need to estimate the best practice, that is, the stochastic production frontier of civil courts of first instance. Having explained the bound, we now turn to specifying the model of inefficiency effects. However, another concern should be to explain differences in the efficiency of trial courts over time.
For example, we tested whether annual variation in the percentage of immigrants, in the unemployment rate and in the total population was included.
Empirical results
This is again reasonable as we assume that these members of the justice system are key to the smooth running of the courts. In models 4 and 5, we test whether we can reject the inclusion in the model of inefficiency effects of these sets of variables, but then we cannot reject – due to the log-likelihood ratio test – the exclusion of variables referring to the rest . of the staff. Moreover, note that in the case of Model 6, through a robustness test, we checked the Cobb-Douglas specification as we had distinguished in the model the effects of inefficiency for different types of personnel, but we were still unable to confirm it Model 6 performed better, and so this serves as a reference for us to conclude that Model 4 is preferable.
However, the percentage of vacancies had no impact on any personnel category. This seems a reasonable finding, as for all staff categories – especially for judges and court secretaries – the percentage of vacancies was very low (see Table 1). In the case of the frontier, given the (flexible) functional form of Model 4, it is not as easy as in Model 1 to interpret the elasticities.
As we know, this is due to the interactions between the explanatory variables of the border. The main difference between courts is their specialization: in the more heavily congested courts is the elasticity of pending cases. Also note that over time there seems to be a trend towards the resolution of pending cases, ie.
Boundary characterization: Elasticity of resolved issues relative to new and pending issues. Note: In a court with an average level of congestion, the number of pending cases is equal to the average of the period (537), while in courts with high and low levels of congestion, the number of pending cases is 50% higher above (805) and below (268) average, respectively. According to the estimates obtained from Model 4, the average efficiency value increased over time (from 0.9574 to 0.9947), and more importantly, the dispersion in efficiency levels tends to disappear in the last period of our sample22.
22 Having access to a panel of data therefore not only increases the efficiency of the estimates of the production function (see e.g. Fried et al., 2008, p.39), but it also avoids the risk of obtaining potentially misleading rankings of efficiency due to the arbitrariness of the year chosen for the analysis. Overall, we can conclude that the decreasing trend in the resolution ratio shown in section 2.1 (figure 1) – especially since 2008 – was not due to decreasing efficiency, but rather to an increase in the amount of litigation (a factor captured by N) )23, which was not matched by a corresponding increase in supply. Indeed, if most of the increase in the volume of litigation is indeed temporary (which can be attributed to the current economic crisis, as Figure 1 suggests), it makes sense not to implement structural measures aimed at increasing the level of supply (more staff and / or courts).
Conclusions
190/2004 Una aproximación al análisis de los costes de la esquizofrenia en España: Modelos jerárquicos bayesianos. 241/2006 Evaluación de la depreciación del capital humano por sector, ocupación y en el tiempo. 263/2006 Análisis de la presencia de mujeres en los consejos de administración de las mil mayores empresas españolas.
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