would be higher in both 2001 and 2011. The speed of urbanization would also be the same in both years when focusing only on the built‐up share, but the urbanization rate would be much lower than using the benchmark specification. And if the lit‐up share were the only key indicator used, the change in the urbanization rate over a decade would be 11.3 percentage points, which is more than double the estimate derived from applying the preferred specification to the probabilistic model.

# **6. Conclusion**

The analysis in this paper yields a more accurate picture of urbanization in India than was previously available. We confirm that India’s urbanization rate in 2011 was close to that reported by the Census of India. However, this finding contrasts sharply with those of several studies reporting much higher urbanization rates (Denis and Marius‐Gnanou 2011; IDFC Institute 2015; Uchida and Nelson, 2009; World Bank 2015). We also find substantial gaps between the official classification and our results for several administrative categories of the census. Overall, the misclassification affects 62 million people, which is roughly equivalent to the population of France or the United Kingdom.

Among statutory urban categories, misclassification is highest for other towns. Although a large share of them have sufficient urban characteristics, many should be considered rural. The misclassification rate is much lower for villages. But given that there are more than half a million of them, the absolute number of misclassifications has non‐trivial implications.

Another important finding concerns the so‐called census towns of India, which are administratively rural but meet three criteria to be identified as urban areas according to the population census. In line with the population census and a growing literature, we confirm that most census towns are urban in practice. But this is not true of all of them.

Finally, our analysis shows that relying only on indicators from the ground or from outer space results in substantially different rates and paces of urbanization. For example, if the lit‐up share were the only key indicator used, the change in India’s urbanization rate over a decade would be 11.3 percentage points, which is more than double the figure obtained by combining data from the ground and from outer space.

Although this analysis centers on India, it has broader implications. Several recent studies have made a special effort to link the identification of urban areas more closely to the functionalities of cities. Examples of this new generation of studies are those by Davis, Dingel, and Miscio (2018); de Bellefon and others (2018); and Vogel and others (2018). This paper adds to this literature.

Our results show that there is value in relying on what one “sees,” especially where urbanization is messy. Subjective assessments can capture the multifaceted nature of cities—relatively large spaces with a higher density of construction, better access to transportation networks, and greater availability of residential amenities. In this paper, we develop what we believe is a credible structured approach to the subjective assessment of cities.

We also confirm the importance of combining indicators from the ground and from outer space when assessing the urban status of a location. Putting different sources of data together and comparing their performance in identifying urban areas reveals the weakness of some of the key criteria used traditionally. Population density, one of the workhorses of administrative approaches, performs quite poorly. And there are also pitfalls in relying exclusively on nighttime light, a method that is popular nowadays.

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