# **1. Introduction**

What is a city? The most candid answer may be “I know one when I see it.” The subjectivity of the city concept is understandable because of the continuum between urban and rural spaces. The many terms used to describe this in-betweensuburb, exurb, edge city, and urban fringe, among otherstestifies to the absence of a clear divide. Even allegedly urban localities can differ substantially in their key attributes, as they range from compact cities to sprawling low-density areas. In this context, just like beauty, a city is in the eyes of the beholder.

The rural-urban continuum and the heterogeneity of urban settings pose an obvious challenge to identifying urban areas and measuring urbanization rates in a consistent way within and across countries. An objective methodology for distinguishing between urban and rural areas that is based on one or two metrics with fixed thresholds may not adequately capture the wide diversity of places. A richer combination of criteria would better describe the multifaceted nature of a city’s function and its environment, but the joint interpretation of these criteria may require an element of human judgment.

In this paper we turn this unavoidable subjectivity into an opportunity, by proposing a methodology to identify urban areas that combines human judgment with machine learning. Human judgment is used to classify a representative sample of places into urban or rural. Reliance on several, qualitatively different groups of assessors, as well as on different classification protocols, provides reassurance that the outcome is stable. This sample is in turn used as the training set for a machine learning exercise allowing to classify out-of-sample places as urban or rural. A comparison between the various classification approaches provides further reassurance that that the prediction outcome is robust.

We illustrate the potential of this methodology by applying it to India. Two reasons motivate this choice. First is scale: accounting for almost a fifth of the world population, India encompasses states with incomes per capita comparable to Mexico and others similar to Benin. Second is the “messiness” of its urbanization process, characterized by a wide variation in the strength of local urban authorities, from high in state capitals to dismal in other cities. Scale and messiness result in an enormous diversity of places, ranging from a glamorous metropolis such as Mumbai, capital of Maharashtra, to a shabby town such as Bagula in West Bengal, to any among hundreds of thousands of villages.

With such wide spatial diversity, not surprisingly there is considerable debate as to how urbanized India actually is. The official urbanization rate for 2011 is 31.2 percent. Several studies using alternative population thresholds or satellite imageries of built-up cover have argued that many areas of India are misclassified as rural by the administrative definition. Depending on the methodology, the urbanization rates estimated by these studies range from 42.0 to 78.0 percent (Denis and Marius-Gnanou 2011; Dijkstra et al. 2018; Ministry of Finance 2017).

Conversely, a study in this special issue relies on a range of parameters to delineate urban markets and suggests that India could be even less urbanized than official figures imply. In this other study the urbanization rate ranges from 14.8 to 33.5 percent when using nighttime light data, and from 27.1 to 39.4 percent when using built-up cover data (Baragwanath et al. 2019). Shedding light on this debate is the second motivation for choosing India to illustrate our proposed methodology.

Subjective assessments are increasingly being used to complement more objective approaches across various fields in economics. For example, good-looking ratings by independent parties have been shown to be correlated with occupational sorting, earnings differentials and physical performance (Beller et al.

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