Reducing Judicial Delay in Resource-Constrained Settings: A Data-Driven Queueing Approach

Abstract

Shortage of judicial capacity leads to costly delays, stunted economic development, and even failure to deliver justice. This problem is endemic not only in the developing world but also in the congested appeals courts of wealthier nations. Using the Supreme Court of India as an exemplar for such resource-constrained settings, we develop a framework with data-driven queueing simulations for estimating performance metrics such as the expected case-disposition time (delay) and expected number of cases awaiting adjudication (pendency). This allows us to not only calibrate the status quo for judicial performance but to also perform counterfactual analysis that evaluates the relative effectiveness of various interventions such as additional judges, process re-engineering, and workload management. We find that the Supreme Court of India operates in a nearly critically-loaded regime (nearly 100% utilization of capacity) that is characterized by substantial delays, and small perturbations to capacity or process efficiency have dramatic impact on system performance. In particular, increasing judge capacity by 7% (adding a bench) results in a 75% - 90% reduction in average delay. Alternatively, capping the number of adjournments allowed in a case to the recommended number three, also results in a comparable delay reduction. Our findings bode well for the ability to tackle persistent delay, but scrutiny of the court s workload-management practices points to the possibility of perpetually languishing in congestion unless more effective winnowing of pent-up demand (e.g., accepting fewer appeals) is ensured.