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

23 Pages Posted: 27 Jan 2021

See all articles by Nitin Bakshi

Nitin Bakshi

University of Utah

Ramandeep S. Randhawa

University of Southern California

Shagun Gupta

University of Texas at Austin

Arsh Sidana

affiliation not provided to SSRN

Date Written: January 11, 2021

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.

Keywords: judicial delay, case-management queues, data-driven simulation

JEL Classification: C44, C63

Suggested Citation

Bakshi, Nitin and Randhawa, Ramandeep S. and Gupta, Shagun and Sidana, Arsh, Reducing Judicial Delay in Resource-Constrained Settings: A Data-Driven Queueing Approach (January 11, 2021). Available at SSRN: https://ssrn.com/abstract=3764036 or http://dx.doi.org/10.2139/ssrn.3764036

Nitin Bakshi (Contact Author)

University of Utah ( email )

1655 Campus Center Drive
Salt Lake City, UT Utah 84112
United States

Ramandeep S. Randhawa

University of Southern California ( email )

Marshall School of Business
BRI 401, 3670 Trousdale Parkway
Los Angeles, CA 90089
United States

Shagun Gupta

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

Arsh Sidana

affiliation not provided to SSRN

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