much stronger than the similar growth for entry levels in Glaeser and Kerr (2009) with the additional factors. This pattern again highlights the greater relative importance in India of existing district conditions relative to incumbent positioning for explaining entrepreneurship compared to patterns in the United States.

Table 4B repeats these estimations for the unorganized manufacturing sector. Several distinct differences exist. First, local population takes a much greater role with unit elasticity in Column 1‘s simplest estimation. This greater connection of entry to the overall size of local markets almost certainly reflects unorganized entry being proportionate to market size and servicing local needs. Evidence for this relationship is also evident in the independence of entry to local population density or travel time to a major city, the stronger relationship of entry to the age profile of the district, and the higher R-Squared value in Columns 1 and 2 of Table 4B compared to Table 4A. Unorganized manufacturing clearly conforms much more closely to the overall contours of India‘s economic geography than organized manufacturing. The explanatory powers for high-income and low-income states are very comparable.

The other two district traits that are associated with strong entry rates are the strength of local, within-district physical infrastructure and the strength of local household banking environments. This contrasts with organized manufacturing entry, where education stood out. An intuitive explanation, which will also be reflected in the services estimations, is that these patterns and their differences reflect the factors on which each sector depends most. Organized manufacturing establishments, for example, may have broader resources that reduce dependency on local infrastructure and household finance. Likewise, it is reasonable to believe that the unorganized sector depends less on educated workers than the organized sector. While intuitive, we are unable to rigorously confirm these district-level observations in this study, and these results should be viewed as partial correlations.

We again find evidence for agglomeration economies within the unorganized manufacturing sector. The framework is similar to Table 4A except that we do not consider the Chinitz effect since by definition the unorganized sector is comprised of small firms. Partly as a consequence of this, the inputs metric is relatively stronger in these estimations. Mukim (2011) also finds an important role for input conditions in the informal sector. The final column shows that the unorganized sector results are very stable with the change in outcome variable. The initial gap in explanatory power between the organized and unorganized sectors that was evident in the first two columns is mostly gone by the complete estimations in Columns 3-5.

Table 5 presents comparable estimations for the services sector, with Columns 1-4 for the organized sector and Columns 5-8 for the unorganized sector. The patterns and their contrast to organized manufacturing are again quite intriguing. First, overall district population is as important as it was for unorganized manufacturing, with its elasticity greater than one. Similarly, 20