# _A subjective but structured process_

Subjective assessments have been used in the economic literature to complement more objective approaches. For example, in measuring individual well‐being a subjective assessment has been adopted on the grounds that everybody has his or her own views on what a good life looks like, and what makes a good life may touch on dimensions for which no reliable indicator is available (Frey and Stutzer 2002; Veenhoven 2004). Deciding whether a specific place is a city poses challenges similar to those posed by measuring well‐being.

For a subjective assessment to be thorough, researchers should ideally visit each city, town, and village in the sample and “see” how they look like. However, the time and cost implications of visiting 1,277 locations scattered across a large country are dissuasive. Building on the literature, we adopt a second‐ best approach, which is to rely on high‐quality, open‐source images by Google Earth.

Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. A zoom‐ in option is available, and ground views and different time views are also available whenever possible. This combination of perspectives enables a layperson to judge the characteristics of a specific location. No special training in remote sensing is needed.

We overlaid the digitized boundary of each sample unit on top of Google Earth images and conducted a subjective assessment of the images falling within the corresponding boundary. For the vast majority of units, we rely on images dated as having been taken in 2010–12 to be consistent with the population data from the 2011 Census of India. However, when the images for these three years are of poor quality, we complement them with images from other years.

The sample of cities, towns, and villages was randomly split into three subsets, which were in turn allocated to three research analysts. All research analysts relied on the same decision tree to independently classify the units allocated to them as urban or rural.

The common decision tree considers characteristics observable from Google Earth images: distribution of land cover types, relationship between buildings, characteristics of buildings, prevalence of transportation networks, and availability of amenities (schools, universities, hospitals, cultural sites, etc.). The selection of characteristics for the decision tree builds on the economic geography and urban economics literature:

- The simple monocentric model of land use predicts that the density of _construction_ declines as one moves away from a city center to more rural areas (Alonso 1964; Mills 1967; Muth 1969). Extensions of this model dealing with polycentric cities and scattered development have not fundamentally contradicted this prediction (Bueckner 1987; Duranton and Puga 2015). We thus expect both the share of built‐up area and the density of buildings to be higher in urban areas.

- Lumpy _infrastructure_ investments facilitating access to markets are critically important in giving rise to the concentration of economic activities (Duranton and Puga 2004; Fujita, Krugman and Venables 2001; Krugman 1991; Scotchmer 2002). We therefore expect both inter‐ and intracity transport networks to be more visible in urban areas.

- Services and _amenities_ are crucial in explaining where firms prefer to operate and households prefer to live. The self‐selection of households across locations accounts for 40–50 percent of productivity differences across cities (Ahlfeldt and others 2015; Combes and others 2010; Straszheim 1987). A

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