Sugam Agarwal (p)

Carlos Mendez

This paper explores whether the industrial agglomeration measures suited to areal data consider neighbourhood effects for service industries at an Indian district level. Using the 2013 Economic Census data, we quantify the spatial concentration for 120 service industries by applying the spatially weighted Ellison-Glaeser (EG) vs unweighted EG index. Empirical results reveal that the spatially weighted EG index does not adequately control the weighted attributes while considering India's macro-aggregate analysis for highly-concentrated service industries. It suggests that the spatial attributes of the neighbourhood impact the concentration of employment of the workers overshadowing while considering all industries and covering all states in India. This leads us to consider another spatial structure where we measure geographical concentration within India's top three highly employable Indian states. The results reveal that the neighbourhood effect plays an essential role in the geographic concentration of industries. It implies that when measuring geographical concentration within Indian states, out of 120 service industries, there are specific industries for which the neighbourhood effect is prevalent. The results suggest that Indian policymakers consider each industry differently within Indian states, as they show spillover effects across various districts. At last, results show spatially weighted Ellison Glaeser index better accounts for neighbourhood effects when quantifying geographical concentration within Indian states.

Carlos Mendez (p), Ayush Patnaik

Sugam Agarwal

Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.