Influence operations by state actors into India; Households that borrow to repay; Measuring grievance and redress for pension products; Foundations for nighttime lights data analysis
Influence operations by state actors into India
We in India are used to influence operations mounted by businessmen and political parties, aimed at shaping the outcomes of the Indian political system and the behaviour of the Indian state. In the international discourse, we have seen state actors mount influence operations into foreign countries. In my column in the Business Standard today, Influence operations in India by state actors, I worry that state actors elsewhere may be doing this in India. Simplistic bans will not solve the problem.
Households that borrow to repay
Borrowing for the purpose of debt servicing is a sign of debt fragility. While this is much bemoaned, the measurement of this problem has been relatively weak. As with most questions about Indian households, the CMIE household survey opens up new possibilities for establishing facts.
In an article on The Leap Blog on 11 January, Aishwarya Gawali and Renuka Sane have established the basic facts about this phenomenon. Their article, Coping with stress: Household borrowing for debt repayment, shows five facts: how big is the phenomenon, how does it vary between urban and rural, what’s the source of this borrowing, and what are the income deciles where it is most prevalent.
Measuring grievance and redress for pension products
Vimal Balasubramaniam, Aishwarya Gawali, Nancy Gupta, Renuka Sane and Srishti Sharma have been working on establishing basic facts about the problems of consumer grievance and redress in Indian finance. Their first article with these results was about banking. They have now extended this into the field of pensions, with an article on 19 January on The Leap Blog, titled Examining grievances and redress for pension products.
Foundations for nighttime lights data analysis
Ayush Patnaik, Susan Thomas and I have been working on methods for nighttime lights measurement. We have a paper out, where we demonstrate the presence of a bias that is related to clouds, and design algorithms that partially solve this problem. Alongside this, we had a Julia package out on github, which is the first open source software for doing `conventional cleaning’ of the NASA/NOAA nighttime lights data, and also implements our new bias-correction method. This package is useful for anyone who wishes to do applications of nighttime lights data, and also for the researchers who wish to engage in methodological research. A new paper, Foundations for nighttime lights data analysis is about this software.