urban extent is a challenge across the world, and results that are internationally comparable is the goal. This requires focusing on a set of key indicators available for the vast majority of countries.

At the same time, the set of indicators chosen is broader than is generally the case when measuring the urban extent. Official estimates of urbanization rates by national governments and international organizations rely almost exclusively on data from the ground. The key indicators considered are population size, population density and administrative category. Conversely, products recently developed by the remote‐sensing literature focus almost exclusively on data from outer space. Built‐up cover and nighttime lights are the preferred indicators in this respect. But rather than choosing one set of indicators or the other, our analysis combines data from the ground with data from outer space.

Data from the ground are taken from the Spatial Database for South Asia, which georeferenced both the 2001 and 2011 Census of India to the digitized official boundaries of administrative units (Li and others 2015). A range of georeferenced indicators from the ground are available for 2001 and 2011 across all units in the country. The indicators are population size, population density, employment structure, access to services, dependency ratio, and literacy rate. The administrative type of each unit is also available, as described earlier.

Following the practice of national governments and international organizations, we use _population size_ and _population density_ at the town and village level as our explanatory variables from the ground. This choice is consistent with that of studies for more advanced economies that rely on high‐quality microdata from the ground (Michaels, Rauch, and Redding 2012; Rozenfeld and others 2011). Population density is computed as the ratio between the population of the administrative unit and the size of the area within the corresponding polygon boundary. In some specifications, we also consider the units’ _administrative type_ as defined by the 2011 Census.

Data from outer space are selected building on the remote sensing literature, as well as on recent developments in the economics literature that use satellite imagery (Burchfield and others 2006; Donaldson and Storeygard 2016; Henderson, Storeygard, and Weil 2012; Jean and others 2016). Our indicators are derived from the best open‐source satellite imagery available for 2001 and 2011, which are the years when population was counted. The indicators are computed by overlaying the boundaries of towns and villages on the processed built‐up or nighttime light imagery.

In line with the literature, we choose _built‐up share_ and _lit‐up share_ at the town or village level as the two key indicators from outer space. Built‐up share is computed as the ratio between the built‐up area in a specific polygon boundary and the total area of the polygon. Lit‐up share is the share of the surface of the polygon whose nighttime light intensity exceeds some critical threshold. In some specifications, _built‐up area_ and _lit‐up area_ without dividing by the surface of the polygonare used as alternative indicators from outer space.

The assessment of land cover has improved rapidly over the last decade thanks to more precise spatial, spectral and radiometric resolutions of satellite imageries, and to much greater computational power to process the data. Three open‐source products on built‐up cover stand out as providing the best quality at the global scale:

- _MODIS_ relies on images taken by sensors aboard satellites from the Earth Observing System program.  

- These images known as the Land Cover Type Yearly Grid are provided by the Land Processes Distributed Active Archive Center managed by the U.S. National Aeronautics and Space

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