Unfortunately, the answers are limited so far. Multiple explanations have been proposed in the urban economics literature on the potential sources of agglomeration economies: pooling of labor, sharing of resources and productive amenities, knowledge spillovers and so on (Marshall 1890, Jacobs 1969, Krugman 1991). Other factors that have been listed as potentially stimulating the formation and expansion of cities are pleasant climate and abundance of natural resources and so on (Glaeser and Gottlieb 2009). But in the end, this is an empirical question and the answer is bound to vary from country to country.

Carefully exploring the role of these potential channels and factors in India’s case is a research agenda on its own. And solid answers are bound to require not just the estimation of a cross-section of location effects, as we have done in this paper. A dynamic analysis on how the magnitude of the location effects evolves over time is also required. This may be the most promising way of identifying the best predictors of strong local performance, and our research following up on this paper goes in that direction. For now, as a preliminary probe into that direction, we assess the correlations between the factors that potentially drive agglomeration economies, or contribute to faster productivity growth, and the tier that a location falls into.

The factors we consider in this correlation analysis are not meant to be exhaustive, but they do capture some of the key channels and factors discussed in the literature. We define a series of spatial indicators related to population density, employment structure, skills, or infrastructure, and we compare the value of these indicators across tiers of locations. The unit of observation is either place or district, depending on data availability. Our reference for the comparison is the value of these indicators for the 100 top locations in the country. We assess whether the values are different across tiers using two-sample t tests for means and two-sample Kolmogorove-Smirnov tests for distributional dominance (Tables 6 and 7 respectively). The detailed location characteristics of each of the clusters, top locations, catchment areas, and other major Indian cities are available on request.

Population density is the first factor of interest. The urban economics literature suggests greater population density is associated with higher productivity driven by agglomeration forces. Consistent with the literature, population density increases from bottom to average locations, from average locations to catchment areas and top locations. Statistically, top locations register significantly higher population density than the other tiers, including catchment areas though the difference is smaller.

A related factor is employment structure. Ultimately, agglomeration economies are driven by firms and workers collocating and engaging more frequently and intensely. Labor pooling is an important channel underlying agglomeration economies. Employment density thus may capture the underlying ideas of agglomeration economies more accurately than population density. In the context of advanced economies, employment density was first introduced by Ciccone and Hall (1996) and has been widely used by the urban economics literature. In the context of developing countries such as India, most people work, especially the poor because they cannot afford not to. The nature of economic activity of the working poor, however, is often different from the nature of general employment discussed in the context of the advanced economies (Banerjee and Duflo 2011, World Bank 2012). Employment density may lose its advantage over population density.

We therefore use employment structure instead, measured through the shares of main workers and regular wage workers in total employment. These two types of jobs are more likely to resemble the jobs found in advanced economies. According to the Census of India 2011, main workers are those who worked for six months or longer in the past 12 months versus marginal workers working less than six months. Based on NSS 2011-12, regular wage workers are those who worked in other people’s farm or non-farm enterprises 31