This is a decent note that, by virtue of its "shallow" scope of inquiry, leaves out some interesting policy solutions that might be cost-effective.  This is especially true for policies involving flood prevention. It has been shown that different cities flood at different rates for the same amount of rainfall (recommendations for better studies and links welcome!). In developing countries, flooding is worsened by encroachments, loss of urban lakes, concretization of surfaces, etc. As a consequence, the open space that permits seepage of water into the earth reduces. If more open spaces were present, (a) more water would seep into the ground, reducing volume of flood water, and (b) water would slow down, causing less damage (speed of water is associated with flood damages).

Thus, potential EA partners might purchase large tracts of land in flood prone cities, greenify them, and make the surface more amenable to water seepage. This has strict parallels with provision of ecosystem services like planting trees or preserving wetlands. The CEA is relatively easy. Even when urban land is expensive, strategic placement of these tracts can potentially lower damages from flooding. Studies that causally link flooding and concretization of surfaces remain scarce, with natural variations being hard to find. So, it remains an active area of inquiry.

Quick Edit: Potentially more relevant for urban, rain-induced flooding.

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.

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.

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!

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.