workforces, and India is no different (Amin and Mattoo, 2008). We measure the general education level of the district’s labor force from the 2001 Census as the percentage of adults with a graduate (post-secondary) degree. Our results below are robust to alternatively defining a district’s education as the percentage of adults with higher secondary education.

Our second trait is the physical infrastructure level of the district. Functioning urban areas depend critically on their underlying infrastructure. Infrastructure likewise connects urban and rural parts of a district. The government of India is providing substantial financial resources for infrastructure investment (Ministry of Urban Development, 2008). The 2001 Census provides figures on the number of villages in a district which have paved roads, electric power access, telecommunications access, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access in any village) to four (full access to all four infrastructure components in all villages).<sup>9</sup> We empirically assess the role of education and infrastructure for development without strong theoretical priors on their role, only noting their frequent emphasis by policy makers. We believe this agnostic approach is important as anecdotal accounts of India suggest multiple relationships are at play. For example, a natural baseline for education is that agglomeration economies or urbanization premiums are higher for skilled workers and industries. Observers note, however, that skilled workers may want to live outside of Indian cities to the extent that the amenities are lower in Indian cities than in the surrounding areas. Likewise, better infrastructure typically allows strong urbanization levels. However, cities in India often experience some of the largest infrastructure failures, and production of own electricity for organized sector firms is high in both settings. Thus better infrastructure capacity may allow establishments to move to rural locations.

Cost factors also play a critical role in location choice decisions for firms. We would ideally like to model several factors like rental prices, utilities prices, wage rates, and so on, but data on cost factors are very limited. We primarily focus on the wage rate of the district— examining both its average rate and the urban-rural ratio for a district. For a given district, we anticipate that both aspects can push firms to locate in rural areas. Lower average wages in a district may also attract plants searching for lower labor costs from neighboring districts.

District wage rates are calculated using firms in the organized sector in 2000. We focus on the organized sector as many firms in the unorganized sector do not report wages (and may

> 9 In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are fully robust to excluding these major cities from the analysis sample.

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