Estimating Time to Clear Pendency of Cases in High Courts in India using Linear Regression

TL;DR

This paper analyzes data from Indian high courts to model case pendency trends using linear regression, revealing increasing backlogs, uneven judge workloads, and proposing policy measures to reduce pending cases within set timeframes.

Contribution

It provides a data-driven analysis of case pendency trends and workload disparities, and suggests policy interventions to reduce backlog durations in Indian high courts.

Findings

Pending cases increase linearly over time.

Judge workload distribution is highly uneven.

Backlog clearance may require additional judges.

Abstract

Indian Judiciary is suffering from burden of millions of cases that are lying pending in its courts at all the levels. The High Court National Judicial Data Grid (HC-NJDG) indexes all the cases pending in the high courts and publishes the data publicly. In this paper, we analyze the data that we have collected from the HC-NJDG portal on 229 randomly chosen days between August 31, 2017 to March 22, 2020, including these dates. Thus, the data analyzed in the paper spans a period of more than two and a half years. We show that: 1) the pending cases in most of the high courts is increasing linearly with time. 2) the case load on judges in various high courts is very unevenly distributed, making judges of some high courts hundred times more loaded than others. 3) for some high courts it may take even a hundred years to clear the pendency cases if proper measures are not taken. We also suggest some policy changes that may help clear the pendency within a fixed time of either five or fifteen years. Finally, we find that the rate of institution of cases in high courts can be easily handled by the current sanctioned strength. However, extra judges are needed only to clear earlier backlogs.