Abstract:
Calculation of GDP constitutes an important exercise for every country. However, due
to limited state capacity, the requisite data collection infrastructure is poor in a lot of
mid and low income countries. Owing to this, a lot of international agencies have begun
to advocate the use of big data as a proxy in socio-economic studies. In this broad
context, the dissertation studies satellite collected nightlights data and its usefulness for
estimating economic activity. The focus of the dissertation is particularly on district and
sub-district level for which the official figures come with a few years of lag. The study
also looks at the potential of nightlights data in studying economically disruptive events
such as demonetisation. The study finds nightlights corresponding with income at all
levels. The analysis shows it can be used as a predictive variable to estimate income
across regions. The study recommends that the study be scaled up to train models
better.
