Cause: Reducing Judicial Delay in India 2022-08-03T23:53:49.682Z


Comment by Vastav Ratra on Open Thread: June — September 2022 · 2022-08-13T04:24:40.158Z · EA · GW

Hi all. Vastav here. I live in India, and am pursuing my undergraduate studies in Economics and Computer Science. I am primarily interested in issues around urban economics, judicial functioning , and quality of higher education in developing countries.

I found EA while looking for interesting topics for my undergraduate dissertation. The ITN framework really clicked for me, and helped me find a suitable passion and research agenda for myself. 

For now, I hope to pursue a PhD in economics, contributing to research and solutions in areas that pique my interest. At the same time, I am creating a research-to-action pipeline by conducting small-scale pilots and case studies to refine my toolkit of solutions. I look forward to interacting with the community on the ways to create and sustain such institutions, and engage with policymakers and decision makers in developing countries.

Comment by Vastav Ratra on Cause: Reducing Judicial Delay in India · 2022-08-11T04:55:06.022Z · EA · GW

Thanks a lot for the comment. I thought the nebulous nature of evidence in the field necessitated the use of the labels so that I do not over-state the state of knowledge on the issue.

Comment by Vastav Ratra on Cause: Reducing Judicial Delay in India · 2022-08-11T04:36:55.405Z · EA · GW

Thanks a lot for your comment. I believe that the issue is tractable in ways beyond hiring new judges. While I have not mentioned it in the post, even hiring of new judges comes with several constraints - there is certainly a difference between hiring a 100 new judges vs. The kinds of numbers needed to reduce delay.

Either ways, new judges would only make for a larger inefficient system, and donors have a role to play in pushing the system closer to the efficiency frontier.

Would be glad to talk more about the issue!

Comment by Vastav Ratra on Cause: Reducing Judicial Delay in India · 2022-08-11T04:26:06.583Z · EA · GW

Thanks a lot for your comment. The studies that look at Judicial delay suffer from a significant limitation - most of them are small-n studies relying on manual reading of a bunch of documents and classifying them under different possible causes. In my undergrad thesis, I am exploring ways to automate some of this through NLP, and will hopefully have more to say on this in future.

I am skeptical of sighting some large-n studies because they seem to have misleading results. The one I cite above - VIDHI's Delhi High Court study - while the best IMO, is also geographically constrained. This largely happens because we do not have the first step - datasets that reliably code the causes of judicial delay for large number of cases from a swathe of courts.