Every judge has that. The utility can be different for different judges and in reality it is different. Possible benefits are the feeling of being fair and of making effective use of scarce resources, gaining trust and respect in the legal profession and in the public at large, being influential, climbing the career ladder, becoming president of a chamber or of the whole court, the settlement of internal conflicts, pursuing a different, more lucrative profession following judicial activity, etc.
But also the increase of leisure time may be such a benefit. Not every judge is a heavy labourer. Of course, the utility maximization strategies of judges depend on the system in which they work. A judge who is appointed for life in the case of the US Supreme Court, real life will behave differently than a judge who is appointed only for a certain term and then has to be reconfirmed.
Re-appointment is possible. This rule jeopardises their independence. The governments have in many and often especially in the big cases an enormous self-interest.
In addition, certain states have a tendency to make people European judges who have been socialised over years in the national bureaucracy. The utility of such a judge may be that his government wins the cases it wants to win.
Moreover, European judges often wish to be re-appointed. I said that subjective factors influence the process of finding justice and the utilities of different judges may vary. The conventional view of judicial behavior would seem hold that the purpose of the insulation is to free judges and justices to make decisions based on a disinterested understanding of the law. Presumably, that was the meaning in the quotations provided at the outset of this paper. Posner's Theory. Posner argues that what I have called the conventional view is in conflict with basic economic theory.
If the conventional view is correct, the plethora of judicial decisions provides a sufficiently large exception to maximization of utility theory that we should conclude that it is not only falsifiable but also falsified. If, however, economic theory is correct, the conventional view is mistaken and, as the second half of Posner's title indicates, far from freeing judges and justices to make disinterested decisions, the insulation only changes the ways in which they maximize their utilities.
Posner argues that judges and justices are rational, and he tells us in what ways they pursue their self interest in the context of the unusual employment conditions.
Posner identifies two primary forces that act upon judges and justices in the process of making their decisions and says that, since the insulation has eliminated other forces, these have become the dominant ones. He says judges enjoy hearing cases and making judicial decisions and, thus they are a source of positive utility.
Posner analogizes this to spectators of theater productions. Presumably, they enjoy watching the drama unfold and they enjoy making decisions in the form of granting or withholding applause.
He argues by analogy for a positive utility in voting per se by pointing out that people vote in spite of the fact that their vote has a vanishingly small chance of affecting the outcome. He completes the analogies by telling us that judges and justices enjoy both watching arguments in cases unfold and their role as voters.
Thus, he suggests we should conclude the judges and justices have a positive utility in these aspects of the job to which they have been appointed. That fits well with utility theory since it means the judges are operating out of self-interest and it fits well enough with conventional view since that view does not hold that judges and justices should dislike their jobs.
Along with the judicial utility function, Posner identifies another major force in the lives of judges and justices. He says like most of us judges and justices enjoy their leisure.
In fact, justices may enjoy their leisure even more than others. For example, he says. I believe that this is true, at least of appellate judges. However, most of us are not as insulated from the pressures of employment. Judges and justices have considerable control over how they spend their time.
They can choose to emphasize either judicial or leisure activity. Posner tells us that, whereas most enjoy hearing cases and making decisions, they do not enjoy writing opinions. Not only is opinion writing time consuming, but in addition, poorly written opinions may subject them criticism. Posner says the first thing judges and justices do as case loads increase is to delegate opinion-writing to their clerks.
While that takes some of the burden off them, it does not remove it entirely because, in the end, their names will appear. Thus far, we have dealt primarily with procedural matters. They should arouse some concern for those who take judicial opinions to be centrally important for the philosophy of law.
The detailed precise examination of each turn of phrase in judicial opinions that characterizes much legal study in general and the philosophy of law in particular may be based upon false assumptions about the actual operations of the law.
To turn to an analogy in the philosophy of science, it would be as if we were to discover that scientists did not really take the precision of calculations seriously but were ready to accept whatever seemed convenient as produced by their graduate assistants. Nevertheless, this discovery need not concern the conventional view much since that view is directed toward a disinterested interpretation of the law rather than carefully developed judicial opinions.
However, Posner tells us the trade-off has an impact on the substance of the decisions as well. This is not the place to examine the many ways Posner cites that the maximization of judicial utility influences the decision.
As a case in point, I will discuss one which Posner calls "go along" voting. Let us turn to an hypothetical example, which will involve a three justice appellate court. We will concentrate on the behavior of judge C. Along with his colleagues, C has heard the case and read the briefs. For the purposes of this exercise, let us say C has made a private decision that the appeal is without merit and should be turned down.
C, then, has gained the benefits of spectator. Now, let us assume in conference that A expresses a strong view that the appeal has merit and the case should be decided in the appellant's favor. B expresses mild opposition to A's view. Posner argues that judges aspire effort avoidance and minimizing reversals.
Beenstock and Haitovsky acknowledge that it is possible that rather than trading-off leisure and productivity, judges trade-off productivity and quality of decision-making. Furthermore, Jonski and Mankowski 14 find that judicial lateness and irritation tends to be more likely around and beyond the breakpoint of judicial capacity, thereby also suggesting there exists a trade-off between increased productivity and quality. This means that it may not be a preference for leisure alone that explains why some studies find that an increase in judicial staff is not paralleled with a proportionate increase in court output.
Rather, this may, at least partly, be due to a preference for improving the quality of judicial decision-making. And, of course, the preference for effort avoidance and quality decision-making may vary between judges.
Second, there is the study by Jonski and Mankowski, 15 which does not question the existence of a positive correlation between caseload and productivity either, but which problematizes the linearity of this association. They hypothesize that there is a limit to the increase in productivity that judges can achieve in order to manage a growing caseload.
Supposedly, at a certain point, the judge gets overburdened and can no longer adjust to an increase in workload. Their metaphor is somewhat misleading though, since what they actually find is a further increase in productivity after a temporary flattening of the curve as caseload mounts. Third, there are several studies which findings contradict the hypothesis that an increase in the number of judges decreases the number of resolved cases when controlling for caseload. Similar empirical results are also reported by Grajzl and Silwal 19 for Nepalese district courts.
These inconclusive results may be linked to a fundamental flaw in the testing of the utility-maximizing function, which has been brought up by Jonski and Mankowski. This is remarkable as the rational choice theory is a microeconomic theory which aim is to explain decisions made by individuals. There is a small number of studies that have indeed tested the utility-maximizing function by explaining the resolved cases per judge by using caseload per judge.
It is important to mention that Beenstock and Haitowski is one of these studies, as Jonski and Mankowski mistakenly claim that this study is based on aggregated data. The plot on page undeniably shows a strong positive correlation between productivity, as measured by completed cases per judge for each court over time and caseload per judge , as measured by pending and lodged cases per judge.
As such, the findings of Beenstock and Haitowsky are congruent with the rational judge hypothesis that judges become more productive as their caseload increases. Three conclusions can be drawn from the literature review about the application of rational choice theory to adjudication.
Second, the studies that use per capita data seem to provide more consistent support for the hypothesis that an increase of caseload increases productivity than do studies that use aggregated data.
So far, we have only discussed the literature about utility-maximizing judges. During the past decades of rising caseloads in courts all over the world, the number of assistants and the duties assigned to them have increased.
Incorporating judicial assistants in rational choice theory involves two steps: first, formulating a utility function for judicial assistants and, second, formulating the expected impact of the involvement of judicial assistants in adjudication on the utility function of the judge.
Regarding the first step, we assume that the utility function of judges and judicial assistants contains identical costs and benefits.
According to rational choice theory, judicial assistants will behave such that the marginal utilities of administrative tasks and leisure are equalized. Furthermore, like judges, judicial assistants are expected to seek maximization of their utility function by trading-off between a preference for a good reputation and prestige by contributing to a decrease in backlogs and a preference for leisure or for improving the quality of judicial decisions.
This means that time spent on administrative work and leisure or high-quality decision-making varies with caseload: assistants spend less time on leisure or quality decision-making and more on administrative work as caseload increases and vice versa. As regards the second step, we expect that the involvement of judicial assistants affects the utility maximization of judges. Judicial assistants can play a role in enhancing the efficiency and cost-effectiveness of adjudication by supporting judges.
Judicial assistants are subordinate to judges. At the same time, judicial assistants can release judges of routine, administrative tasks so that judges can focus their attention on the more complex aspects of adjudicating.
Without receiving support, judges have to spend time on administrative duties themselves. According to rational choice theory, judges will behave such that the marginal utilities of judicial tasks, leisure and administrative tasks are equalized. Judicial tasks can be fulfilled only by a judge. Administrative tasks can be fulfilled by a judge or a judicial assistant. In the former situation, the judge has to devote her time to administrative tasks by decreasing time spent on adjudicating or on leisure or the quality of decision-making.
In the latter situation, the time the judge can devote to judicial tasks will not decrease because assistants will perform the administrative tasks, so that the judge will have more time to spend on leisure or improving the quality of decisions.
There is a limited number of studies that have investigated the impact of the number of judicial assistants on judicial output. For example, Gomes et al. Their study shows that the number of assistants negatively moderates the relationship between caseload and resolved cases per judge per year.
Gomes et al. Rational choice theory assumes that judges maximize their utility by dividing time between work and leisure.
Second, that appointing new judges reduces the total workload, thereby inducing incumbent judges to reduce the time they spend on judicial tasks and to allocate this additional time either on leisure or on improving the quality of their decisions.
As recommended by Richard Posner, applying rational choice theory to adjudication can be expanded by subsuming the role of judicial assistants in it. This expansion can be based on the assumption that the utility function of the assistant contains the same preferences as that of the judge. The difference between judges and assistants is that the judge is ultimately responsible for adjudication, while the duties of judicial assistants are limited to supporting judges.
Judges will have more time for leisure or delivering quality as the number of assistants increase because their workload will reduce when they are releaved of more administrative duties. As the literature review has shown, rational choice theory has been tested at the individual level and the court level.
The following four hypotheses can be derived from the theory:. To test our hypotheses, we use the longitudinal dataset from the European Commission for the Efficiency of Justice Cepej.
It is the most comprehensive and systematic dataset currently available for evaluating the European judicial system. Cepej was established in by European states to assess the efficiency of judicial systems and to propose empirically validated practical tools for increasing the efficiency of judicial services.
For these purposes, Cepej introduced the European Judicial Systems Report in , which contains cross-country judicial data for Council of Europe member countries and observer countries in Since then, every two years, Cepej has published the report.
This report has become an important source for judicial statistics in the European Union. The report constitutes country-specific data that are collected by a questionnaire that is filled-in by national correspondents from the Ministries of Justice.
Allegedly, the counting of judges and court staff differs between jurisdictions. We use the Cepej reports published in , , and For each country, we use data on pending cases of the previous year, incoming cases, resolved cases, number of judges and judicial assistants for first instance courts. First we explain which aggregated data at country level we use to test the hypotheses. Resolved cases y is the dependent variable, which is measured by the total number of resolved cases.
Ideally, two other dependent variables would also have been included in our study: one variable indicating the quality of judicial decisions, for instance the number of reversals 40 and one variable indicating the time judges spend on leisure. This would have enabled us to test the extent to which a potential decrease in resolved cases is accompanied by an increase in either the quality of judicial decisions or time spent on leisure, as expected by rational choice theory.
However, unfortunately, the Cepej dataset does not contain indicators for either variable. Caseload x is measured by summing the total number of pending cases from the previous year and the total number of the newly filed cases in the current year. Judges m is measured by the total number of professional judges sitting in courts. Assistants w is measured by the total number of the non-judge staff whose task is to assist judges directly. Question 53 of the Cepej questionnaire distinguishes between the following five categories of non-judge staff: 1 Rechtspfleger, 2 non judge staff whose task is to assist judges directly.
This category consists of judicial advisors and registrars, who assist judges in their judicial activities hearings in particular and may have to authenticate acts, 3 staff responsible for various administrative matters, 4 technical staff responsible for IT equipment, and 5 other types of non judge staff Cepej We use the second category to measure the number or judicial assistants.
Table 1 shows the summary statistics of the variables. Table 2 shows the summary statistics of the variables. For testing the hypotheses at the individual level, we will use the following measures:.
Per capita productivity of judges y j is calculated by diving the number of resolved cases y by the number of judges m. Per capita caseload of judges x j is calculated by dividing the caseload x by the number of judges m.
Per capita caseload of assistants x ja is calculated by dividing the caseload x by the number of judicial assistants w. In line with many researchers, we use panel data models to investigate at the aggregate level the relationships between caseload, the number of judges and the number of assistants on the one hand and the number of resolved cases on the other hand.
This means all countries are combined in a pool, which means that potential differences between countries are not taken into account. Next, we use panel data analysis with random effect estimation. This technique does take into account the impact country-specific characteristics may have on the number of resolved cases by allowing slopes to vary between countries.
We do both analyses to estitmate the following equation:. Where y it is the number of resolved cases per country per year, x it is the caseload per country per year, m it is the number of judges per country, w it is the number of assistants per country, i represents the countries, t represents the year. Next, we use structural equation modelling to test our hypotheses at the individual level. Moderation occurs when the direction or strength of the relationship between two variables is dependent on a third variable.
Where y j is per capita productivity of the judge, x j is the per capita caseload of the judge, x ja is the per capita caseload of the judicial assistant. The latter is the moderator and estimates the extent to which the effect of x j on y j is moderated by x ja. So that x j x ja shows the moderation effect.
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