rapidly urbanizing country, like India, the boundaries between rural and urban areas are often blurred. According to the Census of India 2011, 3,894 villages are administrative rural but display economic characteristics closely resembling those of cities (Office of the Registrar General and Census Commissioner 2011a). A special name, census town, has even been coined to label this gray area in the rural-urban gradation. In fact, about 30 percent of India’s urbanization between 2001 and 2011 is attributable to the reclassification of rural areas as census towns (Pradhan 2013). By considering all locations, our approach avoids the pitfalls from somewhat arbitrary administrative classifications.

# 3. The empirical strategy

Implementing the approach outlined above requires information on individual households as well as a robust mechanism to match each household observation to a particular location. Characterizing the locations, say in terms of their connectivity, also requires spatial data.

The household survey data used in this paper is from the Schedule 1.0, Household Consumer Expenditure Survey of the 68<sup>th</sup> round of National Sample Survey of India conducted between July 2011 and June 2012 (NSSO, 2012). This survey, hereafter identified as NSS 2011-12, reports household consumption information on an itemized form, based on a 30-day recall period, and based on a mixed recall period. We use monthly consumption based on the mixed recall period and household size to compute monthly per capita nominal household expenditure, which is the explained variable in our benchmark specification.

The survey also reports demographic data, educational attainment, landholdings, the source of energy used for lighting and for cooking, the social group the household belongs to, and its religious affiliation. We use this information to construct the household characteristics of the benchmark specification. It must be noted that the monthly consumption reported by the NSS includes paid rent but excludes imputed rent. This biases downward the expenditure of households who own their dwelling units. To address this data limitation, we add information of household dwelling ownership to the set of household characteristics in the analysis.

As for locations, most poverty analyses in India consider the state or the region as the spatial unit of analysis, further dividing each unit into urban and rural areas. However, that level of aggregation is too high to assess local externalities. Even data at the district level may not be disaggregated enough for that purpose. Separately identifying individual cities would be difficult too, because the actual boundaries of urban agglomerations do not match well the administrative boundaries and classifications used by household surveys (Li et al., 2015). To address these limitations, in this paper we adopt the approach developed by Chatterjee et al. (2015) to generate estimates below district level. Their approach uses the design of the NSS 2011-12 to estimate the population of first-stage sampling units. The resulting characterization of the employment structure across these different population sizes challenges the conventional wisdom of a ruralurban “divide” in India’s context.

The NSS 2011-12 covers all of India except interior villages of Nagaland situated beyond five kilometers of a bus route and villages in Andaman and Nicobar Islands. The survey follows a stratified multi-stage sampling design. Each district of a state or union territory is stratified into rural and urban areas. In the rural stratum, the first stage units are the 2001 census villages; in the urban stratum, they are urban frame survey blocks. Within each stratum, first-stage units are ordered by their population and then further stratified. The ultimate stage units are households, drawn from the selected first stage units of each substrata.

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