Price disparities are not a concern when assessing social inclusion within the same place. Indeed, it is safe to assume that households living within a relatively narrow geographic area face roughly the same cost of living. In that case, an inequality measure based on nominal household expenditure per capita is equivalent to an inequality measure based on real household expenditure per capita.

On the other hand, conducting comparisons across places ideally requires correcting for differences in local prices, and this is unfortunately difficult. The price deflators used for poverty analysis in India lack the necessary granularity because they are computed at the state level, only distinguishing between urban and rural areas. An alternative would be to estimate the average rent paid by households in different places. But the NSS does not report imputed rent for households that own or occupy their dwelling, and information on housing characteristics is anyway too sparse to generate comparable rent measures across space. When comparing households across places we therefore have no other choice than to assume that nominal and real household expenditure per capita are highly correlated. This is a plausible assumption, but differences in the availability of affordable housing across places make the correlation less than perfect.

With this measurement caveat in mind, location effects provide a first and useful approximation to inclusion. The notion of a rural-urban divide suggests that urbanization ought to be associated with an increase in inequality, at least until a substantive majority of the population lives in urban areas. But this presumption may not be correct in the presence of a rural-urban gradation, especially when highproductivity clusters encompass large numbers of administratively rural areas, as is the case in India. Based on our classification of places into four tiers, 31.8 percent of the population in top locations is administratively rural, and the proportion increases to 51.4 percent in their catchment areas (Table 4). In fact, more than 10 percent of the rural population live in one of India’s 17 clusters, compared to about 18 percent in its 218 bottom locations.

Moreover, the location effects of the rural places encompassed by the 17 clusters are not very different from the location effects of the urban places in them. Among top locations, the average location effect of rural places is 0.40, not far below the 0.46 average for urban places. The gap is even smaller in catchment areas (0.27 versus 0.28 respectively). From this perspective, large spread-out clusters such as the one around Delhi, Faridabad and Gurgaon, or that around Thiruvananthapuram, make a positive contribution to social inclusion. Rather than exacerbating the alleged rural-urban divide, they make the surrounding rural populations benefit from the dynamism of their cores.

A different issue is whether inequality within a given place increases as this place becomes more productive. The presumption is that it does: stronger agglomeration economies often rests on a subset of the population being highly skilled, and rapid urban growth may also be associated with temporary rents in leading sectors of activity. To assess whether this presumption is correct, we compute the mean log deviation (MLD) of household expenditure per capita in each of the 1,406 places considered in the analysis. We do the same for each of the clusters comprising a large urban area, using the NSS weights to that effect. The MLD is a standard measure of inequality. It can be interpreted as the gap in expenditure per capita between a randomly selected person in a given population and the average person in the same group. The greater the extent of local inequality, the larger the gap. In comparison with other inequality indicators, MLD attaches equal weight to each observation and thus captures the level of inequality across the full range of households.

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