Following Chatterjee et al. (2015), we classify the first-stage units (villages or urban frame survey blocks) of each district into four groups, based on the average population size of their substratum. The four groups are: 1) small rural areas with a population less than 5,000; 2) large rural areas with a population above 5,000; 3) small urban areas with a population less than one million; and 4) large urban areas with a population greater than one million. Breakdowns of this sort are not unusual in urban economics (e.g. Glaeser and Maré 2001). In what follows we use the word “place” to refer to a population size group within a district, and interpret the location subscript _l_ in our benchmark specification as referring to places. In most cases, a place includes more than one first-stage unit. In the case of large urban areas and some small urban areas, a place can be interpreted as corresponding to a city.

In principle, each district could include first-stage units belonging to all four population size groups. But in reality not all districts host large urban areas, or even small urban areas; some do not even include firststage units in the large rural category. Also, because of limited information, population size ranges cannot be estimated for the union territory of Andaman and Nicobar Islands, the union territory of Daman and Diu, and the state of Nagaland (see Chatterjee et al. 2015 for details). Our analysis excludes the island state of Lakshadweep, due to problems with the measurement of distance between districts, a variable needed in the analysis below. Furthermore, to reduce measurement errors caused by the mismatch between the districts defined by NSS 2011-12 and by the Census 2011, we merge all observations from the union territory of Delhi into one district.

As a result of these constraints and adjustments, our analysis covers 1,406 places from 599 districts in 31 states or union territories. Among them, 579 are small rural areas, 221 are large rural areas, 581 are small urban areas, and 25 are large urban areas. Our final sample includes the 96,227 households in the sample who live in the 1,406 places retained and who report information on all the household characteristics considered in the analysis.

In order to incorporate the geographic distribution of these places in our analysis, we digitized the administrative boundaries into a standard digital vector storage format for spatial data, or shapefile, relying on the Administrative Atlas of India 2011 (Office of the Registrar General and Census Commissioner 2011b). Based on NSS 2011-12, first-stage units can be identified down to the district level. Unfortunately, there is not sufficient information for us to go further down, to the tehsil level. Therefore, the digitization is conducted at the district level.

There are also inconsistencies in the spatial framework of the NSS and the Atlas which require further adjustments. The Atlas contains 640 district-level polygons corresponding to the 640 districts defined by the Census of India 2011 (Office of the Registrar General and Census Commissioner 2011a). However, the NSS 2011-12 only includes 621 districts, because its sample frame is based on the administrative boundaries defined by the Census of India 2001 (Office of the Registrar General and Census Commissioner 2001). We match the 621 districts in the NSS 2011-12 to the 640 districts of the Census of India 2011 using information available from the Atlas, the official websites of the districts, and other relevant sources. We restrict our analysis to the districts that exist in both NSS 2011-12 and the Census of India 2011. Because we combine observations of Delhi into one district, we further merge the district level polygons of Delhi into one. Similarly, because NSS 2011-12 subsumes the district of Mumbai into the district of Mumbai suburban, we merge the polygons of these two districts as well. Finally, we do not consider districts that emerged after the NSS 2011-12 defined its sample frame.

8