built-up area shares. Higher build-up, especially in the top quartile, is strongly associated with increased urbanization; we find similar effects with other specification formats like linear shares. A plausible interpretation of these coefficients is that land availability is a strong governor of the urbanization process. While this interpretation would match anecdotal accounts of the constraints that land availability and real estate prices have for the location choices of manufacturing firms in India, this measure is indirect (e.g., compared to real estate price data) and thus caution in interpretation is warranted.

One effect of adding this control is that the coefficient on the infrastructure index declines somewhat and becomes insignificant when including state fixed effects. This is not too surprising given that the built-up metric in part captures higher infrastructure levels. Adding this variable, however, does not affect the results on our wage variables. We take from this set of results that cost factors that slow manufacturing urbanization in India are definitely present in the labor market and most likely present in the real estate market, too. The evidence further suggests that absolute cost levels and localized urban-rural cost ratios play a role in India’s urbanization.

Finally, Columns 9 and 10 alternatively model land use intensity by organized sector firms to measure the tightness of local land markets. Inclusion of this control does not affect our core variables. Interestingly, the coefficient is positive, suggesting greater urbanization is associated with tighter land markets. This counter-intuitive result is one factor suggesting caution regarding land availability variables.

Table 6a considers the robustness of these results over different time periods. The first two columns repeat the specifications in Columns 5 and 6 of Table 5 for 1994-2005. Columns 3 and 4 then consider 1994-2000, while Columns 5 and 6 consider 2000-2005. This disaggregation is interesting for several reasons. First, Figures 1a-1c showed the substantial flattening of the urbanization changes for 2000-2005 compared to 1994-2000, and it is interesting to identify what correlates with this slowdown. Second, as a basic robustness check on potential reclassification issues, the 1994-2000 change builds on a perfectly consistent urban-rural coding framework. Third, one might worry that measuring district traits in 2000-2001 is endogenous to changes occurring from 1994-2000, and so looking at the 2000-2005 period helps somewhat as these traits are then pre-determined (but still leaves open questions of omitted variable biases).

Most of the coefficients are similar to the 1994-2000 period in Columns 3 and 4, compared to the 1994-2005 baseline. The education variable loses some of its economic strength but remains very important statistically. The infrastructure measure loses some of its statistical strength but remains overall quite important, too. The most important difference is on the cost urban development toward the periphery. This results in longer commuting trips and more challenging provision of urban infrastructure than would have been the case if land supply had been unconstrained.

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