the results are robust. Our analysis shows that assessor characteristics and decision protocols do not significantly affect the classification of places. And the prediction approaches used to extend the classification beyond the original sample do not make a major difference either. Therefore, the proposed methodology may contribute to a growing literature in urban economics that applies innovative approaches to the identification of urban areas (de Bellefon et al. 2018, Diegel et al. 2019, Rozenfeld et al. 2011).

The paper is organized as follows. In Section 2 we present the stratified sampling strategy. In Section 3 we describe the images used for assessment and the collection of human judgments from different assessors. We also compare classifications based on different human judgment exercises. In Section 4 we introduce the additional sources of data and apply three approaches—Logit model, and LASSO and random forests methods—to predict the urban status of places in the sample. In Section 5 we show the results on the estimated urbanization rate for India in 2011, and also for fine administrative types and for states. Finally, we compare our results with urbanization rates from other studies.

# **2. A sample of places**

Important information about cities, towns and villages is captured by the administrative boundaries of localities. These boundaries are generally built on historical data and tend to reflect the best knowledge available on the spatial distribution of economic activities and people. They also define the constituency to which each local government provides services and is accountable. In this study, we used the administrative units adopted by the Census of India as our unit of analysis.

## _The universe of places_

To compile the universe of places we took advantage of newly digitized boundaries of administrative units in India, available down to the town or village level, to define the place of analysis. These boundaries are based on India’s _Administrative Atlas_  _2011_ (ORGI 2011b). They were generated as part of a broader research project, the Spatial Database for South Asia (Li et al. 2015). The construction of the boundaries required scanning and georeferencing 6,598 physical maps: 35 maps of states and union territories, 640 district maps, and 5,923 subdistrict maps that present the boundaries of towns and villages. The location, size, and shape of these towns and villages were digitized in the form of 649,818 boundary polygons. Attributions such as place codes, names, and administrative types were added to the polygons.

Administrative units need to be integrated further in the case of metropolitan areas, which represent broader integrated labor markets. The preferred approach in this respect is to base the integration process on commuter pattern data (Duranton 2015; US Office of Management and Budget 2010). This type of data is unfortunately not available for most developing countries, including India. In its absence, we simply merged the polygons of cities spreading over multiple administrative units, based on their names and unique geo-codes.

We made two other adjustments to the digitized administrative boundaries. In the original Spatial Database some villages were presented as points on the source maps as their boundaries were unavailable they are mostly in the mountainous or forest areas of the states of Arunachal Pradesh, Chhattisgarh, Himachal Pradesh, and Meghalaya. And population information was not available in the 2011 Census of India for some villages, which raises concerns about the quality of the corresponding official maps. We therefore excluded villages without boundaries or population data.

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