1994; Hamermesh and Biddle 1994; Postma 2014). Similarly, self-reported happiness has been adopted as a wellbeing indicator 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).

In the urban economics literature, crowd-sourced assessments of street-level images have been used to determine how safe a neighborhood feels, how clean it looks, or how lively it seems. These are aspects of cities that standard measures, such as income levels, are unable to fully capture (Salesses et al. 2013). Further, this literature has applied machine learning to extend the assessment to other cities, not covered by the original crowd-sourcing (Naik et al. 2016). Machine learning has also been used to predict a neighborhood’s socioeconomic characteristics out of its appearance (Glaeser et al. 2018; Naik et al. 2015).

Human judgment is also behind imagery interpretation in the remote sensing literature. Machine learning is often used in this literature to classify billions of “cells” of satellite imageries, for example in terms of their land use. However, satellite imageries are difficult to interpret, because they are two-dimensional, taken directly from above and generally lacking recognizable details. Therefore, an important step is to generate subjective assessments for a subset of cells that is then used as either a training sample or validation data for the machine learning exercise (Campbell and Wynne 2011).

Building on these precedents, our methodology applies subjective assessments to open-source images generated by Google for a representative sample of places in India. We overlay these images with the digitized boundaries of Indian cities, towns and villages, as administratively defined, and use the portion falling within the corresponding boundaries as the subject for human judgment.

Relying on information at the level of cities, towns and villages to shed light on important topics in economic geography has precedents in the literature (Eeckhout 2004; Levy 2009; Michaels, Rauch and Redding 2012). Fairly disaggregated administrative units have also been shown to perform as well as gridded cells when dealing with critical topics in the economic geography literature (Briant, Combes and Lafourcade 2010). An admittedly preferable approach would be to let commuting pattern data delineate local labor markets (Duranton 2015; US Office of Management and Budget 2010). However, data of this sort is rarely available in developing countries, and India is not an exception in this respect.

In our methodology, four groups of assessors independently judge whether the Google images from a place correspond to an urban or a rural settlement. The four groups differ in their familiarity with India and in their expertise in urban issues. The most knowledgeable group is made of in-house research analysts. A second group comprises university students from the US who have experience in land use classification but no exposure to India. The last two groups are made of crowdsourced anonymous viewers hired through Amazon Mechanical Turk (MTurk) who are unlikely to have expertise in urban issues. In the third group all viewers are from India while in the fourth one they are all from the US.

Assessments also follow two different protocols. The more structured one requests the assessors to sequentially evaluate the density of construction, the nature of transport infrastructure, and the availability of urban amenities, before making their judgment. All four groups of assessors follow this protocol. But before doing so, the three outsider groups are also asked to have an impromptu judgment of the urban or rural nature of the place, without any guidance.

By proceeding this way, every place in our sample of cities, towns and villages is classified a total of seven classification rounds. We find that the classifications are highly consistent, with two thirds of the localities 3