and got in return a salary or wage on a regular basis. Jobs covered by these two concepts are more likely to resemble the jobs considered in the context of the advanced economies and allow us to better approximate the idea of employment density. In line with the literature, we find that the employment shares of both main workers and regular wage workers increase from bottom locations to top locations. Top locations and catchment areas have statistically higher employment shares for both types of workers than bottom and average locations. The results reported on regular wage workers are based on usual employment status, which includes both usual principal and subsidiary employment status in the past year, as defined by NSS 2011-12, but they are robust to using principal employment status instead.

We also look at the sectoral composition of local economies. Manufacturing and services are generally believed to hold higher potentials for productivity growth than agriculture. We use the employment shares of agriculture and manufacturing as indicators of the structure of economic activity at the local level. Our analysis shows that top locations and catchment areas have both significantly lower employment shares in agriculture and significantly higher shares in manufacture.

Skills have also been consistently found to drive agglomeration economies, especially by facilitating knowledge spillovers. In the context of advanced economies, skills density is often measured by the share of the population with tertiary education. While educational attainment has been improving rapidly in India, it is still much lower than in advanced economies. We thus measure average skills through the share of the working-age population with secondary education, in addition to the share with tertiary education. We find top locations and catchment areas have significantly higher shares of working-age people in these two groups than bottom and average locations.

The opportunity to share productive amenities is another potentially important factor driving agglomeration economies. Following the literature, we use measures on access to infrastructure and public services to capture the idea of productive amenities. Our results show that road density increases from bottom locations to top locations and top locations register significantly higher road density than any other tier. In addition, access to electricity, access to cellphone and access to banking services are all significantly higher in top locations and catchment areas than in other locations.

Finally, we also consider the differences between tiers of locations in terms of the social and religious backgrounds of their populations. In the literature on governance, ethno-linguistic fractionalization has been found to affect the quality of institutions and long-run growth. Culture is also increasingly highlighted as a determinant of individual aspirations and social interactions by the literature on behavioral economics. Discrimination toward people of lower castes, tribal groups and religious minorities has been a longstanding concern in India as well. Our results suggest that Scheduled Tribes are much more concentrated in bottom locations than in any other tiers of locations. Scheduled Castes, on the other hand, do not exhibit a greater concentration in bottom and average locations than in top locations and catchment areas.

Overall, catchment areas resemble top locations, except in their population density and road density. Top locations and catchment areas register more main workers and more regular wage workers, lower agriculture employment and more employment in manufacturing, higher educational attainment at both secondary and tertiary levels, and better access to infrastructure and services. These results are consistent with the higher labor productivity observed in top locations and catchment areas, relative to the rest of India. Bottom locations stand out as places with higher concentration of Scheduled Tribes. However, in interpreting these results it is important to keep in mind that correlation is not causality.

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