In its report, the Centre for Economic and Social Progress, a non-profit research centre based in Delhi, expresses judicial efficiency and pendency in the Supreme Court, high courts and subordinate courts, through various regression techniques.
THE number of matters pending is currently the burning issue at the forefront of every judge’s mind, be it at the Supreme Court, High Court or lower court level. Effective working of the judiciary, after all, is vital to ensure its legitimacy in the eyes of the general public and retain people’s confidence in it. In this regard, India ranks 69th in the Rule of Law Index among 126 countries, performing especially disappointingly in the areas of civil justice, order, and security.
To study this, the Centre for Economic and Social Progress undertook an empirical study, titled “Analysing Judicial Efficiency of Indian Courts”. The study covers the Supreme Court, 24 high courts and the subordinate courts falling under their jurisdiction. The analysis is based on data from the Supreme Court’s annual reports from 2015-16 to 2018-19.
The report quantifies “efficiency” in terms of an input-output ratio, where input is expressed in terms of case institution and filing, and output is measured in terms of the number of cases disposed of and the quality of the judgement. If the number of cases being disposed of is lower than the number being filed, then it is an issue of pendency of cases. The report highlights that case pendency rate is an inverse measure of judicial efficiency, i.e. high pendency implies a low case clearance rate or lower efficiency and vice versa.
Through a regression model, the study explores specific aspects affecting judicial efficiency: the judge to population ratio, net state domestic product (NSDP, a measure of socio-economic conditions of a state) and procedural delays such as the large number of undertrial prisoners in criminal cases and the time taken to present evidence and constant adjournments in civil cases.
A regression model is used in machine learning to identify a statistically significant causal relationship between the dependent variable and each independent variable, thereby ascertaining with a mathematical certainty that the independent variable affects the dependent. The study uses this model to identify whether pendency (the dependent variable) is affected by these different aspects, which are then plotted as independent variables.
Judicial efficiency is analysed in terms of the institution of cases, their disposal, and pendency in the Supreme Court. To analyse the pendency in the Supreme Court, the study first estimates the least squares trend for the entire period from 1951 to 2018, and also for two sub- periods, 1950-93 (44 observations) and 1994-2018 (25 observations). The method of least squares is a widely used method of fitting a curve for given data, since it provides the best fit for time-series data analysis and minimises the sum of the residuals of points from the plotted curve.
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For the 1951 to 2018 period, the analysis measures changes in pendency with time and population growth. Time and population growth impact the number of freshly instituted cases and backlogs for the court. The regression equation (Equation I) for this period then shows that time is influencing pendency growth negatively and population growth is affecting pendency positively.
In the sub-period of 1950-1993, there are two equations run through, which are trying to determine the impact of time on pendency. In equation II, population growth has been taken as a control variable. To ‘control’ the variable means to remove population growth from the analysis in order to isolate the relationship between time and pendency. The equation shows that pendency reduces with time only if the population is held constant. When population growth is accounted for, then there is no clear relationship between time and pendency.
The report quantifies “efficiency” in terms of an input-output ratio, where input is expressed in terms of case institution and filing, and output is measured in terms of the number of cases disposed of and the quality of the judgement.
While in equation III, the control variable has been dropped to evaluate the impact of time in isolation on pendency. Equation II, fitted for the sub-period 1950-1993, indicates that time and population growth have an insignificant impact on the pendency rate. However, the control variable of population is dropped, we see that the pendency rate increases significantly with time (Equation III). Thus, time has an impact on pendency only if population growth is not considered.
In the sub-period 1994-2018, Equation IV indicates that time has a positive impact on the pendency rate, while population has a negative impact.
The last equation (Equation V) measures judge productivity over the span of 1990-2017, by looking at pendency at year-end and cases disposed per judge. Here, disposed cases are considered acting as a proxy for judge productivity. It becomes clear that judge productivity in terms of the number of cases disposed annually has improved significantly since pendency at the end of the year per judge has declined. But again, when population is added as a control variable, both time and population become insignificant in measuring judge productivity. Therefore, pendency rate is negatively impacted by judicial productivity, that is, pendency will go down with improvement in judicial productivity.
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In High Courts, there is a great number of new filings and appeals from lower courts. This continuous growth in case filing is likely due to rising population, literacy, per capita income and growing public awareness about constitutional rights. The state of pendency is varied across High Courts, and depends on its size, infrastructure and judge power.
The study finds that clearing the existing pendency in the subordinate courts would need the recruitment of 33,748 more judges.
The issue of mounting pendency can be attributed to higher case filing and lesser disposal. There is currently a pendency accumulation at the rate of 10% per annum in High Courts.
To analyse pendency in the High Courts, the independent variables taken were judge to population ratio, Growth of Net State Domestic Product (NSDP) Per Capita and civil case load. The analysis finds that the judge-population ratio is a significant determinant of case pendency in the high courts, with a negative sign indicating that an increase in the ratio considerably reduces pendency.
A rise in the ratio by 1 will reduce the pendency rate by 0.46. However, it is not only the number of judges that is essential for improving disposal efficiency, but making sure that they remain motivated and productive. Without suitable incentives for judges, increasing their number will not be effective.
The growth in the NSDP per capita indicates an improvement in economic conditions, where the state can afford to recruit more judges and therefore have a higher rate of disposal of cases. However, it also indicates an improvement in the economic conditions of the population, leading to an increase in the filing of cases since they are more aware and have the money.
Civil Cases also try to extend the time taken by asking for adjournments and causing delays, thereby increasing pendency.
Pendency in the subordinate courts is increasing due to economic capacity and overall consciousness. Of the variables, the judge-population ratio is statistically significant. Thus, it has an appreciable effect upon pendency reduction.
The study finds that clearing the existing pendency in the subordinate courts would require the recruitment of 33,748 more judges. This is based on the average yearly disposal calculated for the four years of 2015-16 to 2018-19. Further, to prevent recurring pendency pressure, 1,225 judges would need to be recruited regularly, in addition to the working judicial officers numbering 16,714. Here, analysing efficiency would also need to take into account the quality of judgments.
The other two variables, i.e., net state domestic product (the socio-economic factor) and legislative procedural law (criminal cases/total caseload) do not show any notable impact upon the pendency problem.
When comparing the pendency issues in the High Courts and Subordinate Courts, the model for effectiveness for the high courts is higher than that fitted for the subordinate courts, which shows that the former has better explanatory power than the latter. While comparing the variables influencing pendency, the judge-population ratio emerges as a common significant factor in both models.
For high courts, the impact of this variable is greater than for subordinate courts. Differences in the coefficients imply that a rise in the ratio by 1 will reduce the pendency rate by 0.46 in high courts and 0.1 for the subordinate courts. If the judge-population ratio is raised, pendency will decline.
The measure of social and economic conditions (NSDP per capita), being an exogenous variable, does not show any difference in its influence on case pendency in the subordinate courts, but has a negative and significant impact in the high court.
An increase in the civil caseload increases pendency in the high courts, whereas an increase or decrease in the number of criminal cases has no significant impact on pendency in the subordinate courts.
The average number of case filings per judge in the high courts is double than in the subordinate courts, indicating that the demand for justice can be considered a major cause for the huge pendency. This may be due to a number of fresh cases instituted, and the number of appeals against the subordinate courts in the high courts.
Pendency in the Supreme Court could be considerably reduced by improving judge productivity in terms of case disposal.
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According to the study’s calculations, the additional number of judges needed in high courts to clear pendency is 992 and in subordinate courts is 16,516. The study also notes that these numbers are far higher than the results from the pilot study by the Delhi High Court and Daksh, which estimated the requirement to be only of 43 additional judges in Delhi courts. This is due to the fact that the pilot study was of 11 courts under the Delhi High Court’s jurisdiction, while this study’s analysis is a macro one, covering all high courts and subordinate courts, and their total pendency.
The study concludes by stating that for the improvement of judicial efficiency and efficient case disposal, there needs to be a strengthening of the judiciary through the recruitment of more judges. An increase in judicial productivity can significantly bring down case pendency, it finds.
The analysis finds that the judge-population ratio is a significant determinant of case pendency in the high courts, with a negative sign indicating that an increase in the ratio considerably reduces pendency.
It suggests that the supply of judicial services can also be considerably improved by increasing the number of judicial working hours and days, and the number of courts, considering the large number of court vacations. It advises increased use of mediation and arbitration to avoid backlog.
Lastly, it recommends that the procedural complexities of litigation be kept to a minimum to save time and highlights the need for inclusion of better rural coverage of the judicial system, which will ultimately lighten the burden on subordinate and high courts.