having the same status regardless of the group of assessors and the protocol, and with almost 90 percent of them being classified in the same way in at least five of the seven rounds. We also show that the characteristics of the assessors and the protocol followed affect judgment results, but their overall influence is very small. Given this high level of consistency, we pool all seven rounds of assessments which results in 50 or more human judgments for each place in the sample, and then classify every place as urban or rural based on a majority rule. We treat this pooled classification as the outcome of our human judgment exercise.

We then to develop an approach to predict urban status, as assessed through human judgment, based on observable characteristics of the corresponding places. We emphasize four characteristics for each place. Total population and population density, both drawn from the population census, are key indicators in the urban economics literature. Built-up cover and nighttime lights, both from satellite imageries, have been at the core of recent attempts to estimate a country’s “true” urbanization rate. We also consider other indicators, but the four key characteristics just listed remain the most important predictors of subjective urban status.

Several approaches, drawn from classical data modeling and from machine learning, are applied for the prediction exercise as well. We first use a standard Logit model linking the urban status of a place with the values of its characteristics. We then apply more sophisticated machine learning methods, including LASSO and random forests. We find that all three approaches yield a similar prediction accuracy, with random forests performing slightly better. These approaches are then used to predict whether each of the other places in the country is urban or rural in practice.

The urbanization rate emerging from this exercise for India’s is 29.9 percent. Unlike the remarkably high rates reported by some studies, this figure is quite close to the corresponding official estimate. However, we also find important gaps with the official estimates at more disaggregated levels. Consistent with those other, recent studies, we show that many places administratively defined as villages have urban characteristics. But we also find that a significant share of “statutory towns” and “census towns” would be better classified as rural. And there are differences across states as well.

While these subnational gaps with official estimates could be interpreted as the outcome of prediction error, we show that the sign of the gaps is consistent with the fiscal incentives and statistical innovations characterizing the administrative classification of places in India. We also show that our estimate of the urbanization rate is not an outlier relative to estimates based on global urban layer products, as the main discrepancy with them arises from the population data used, rather from the underlying land use classification.

Beyond offering new insights into the debate on urbanization in India, the exercise in this paper illustrates several strengths of the proposed methodology. First, the methodology is holistic. Because it relies on human judgment to assess the “urbanness” of places, it allows the multiple characteristics of a place to be evaluated jointly. Second, it makes the most of existing data. A growing number of studies are using satellite imagery data to identify urban areas, contributing to urban studies (Donaldson and Storeygard 2016). However, our analysis shows that relying on built-up cover or nighttime lights alone can be insufficient. And third, our methodology is scalable. High-quality Google images are becoming available for an increasing number of places, and human judgment can be crowd-sourced efficiently nowadays.

A legitimate concern with our methodology is its reliability, in the sense that small changes to its design could result in important changes in its outcomes. However, the variants tried in this paper suggest that 4