Working Paper: NBER ID: w24796
Authors: Kathryn Baragwanath Vogel; Ran Goldblatt; Gordon H. Hanson; Amit K. Khandelwal
Abstract: We propose a methodology for defining urban markets based on built-up land-cover classified from daytime satellite imagery. Compared to markets defined using minimum thresholds for nighttime light intensity, daytime imagery identify an order of magnitude more markets, capture more of India's urban population, are more realistically jagged in shape, and reveal more variation in the spatial distribution of economic activity. We conclude that daytime satellite data are a promising source for the study of urban forms.
Keywords: urban markets; satellite imagery; India; economic geography; land cover
JEL Codes: O1; O18; R1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Daytime imagery (Y91) | Identification of urban markets (R23) |
| Nighttime light intensity (Y10) | Identification of urban markets (R23) |
| Daytime imagery (Y91) | Measurement of urban market boundaries (R23) |
| Daytime imagery (Y91) | Understanding of urban structures (R11) |
| Larger landcover-based markets (Q15) | Higher nighttime light intensity (Q49) |
| Daytime imagery + Nighttime data (Y10) | Comprehensive study of urban markets (R20) |