India‘s economy is undergoing dramatic structural changes (Desmet et al. 2011). From a starting point in the 1980s when the government used licensing to promote industrial location in regions that were not developing as quickly, the economic geography of India has been in flux as firms and new entrants shift spatially (e.g., Chari 2008, Fernandes and Sharma 2011). One feature for a district that is important in this transformation is its link to major cities. We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to the nearest of India‘s ten largest cities<sup>6</sup> as a measure of physical connectivity and across-district infrastructure. This is calculated based on data on India‘s road networks using GIS software.

We next model local labor regulations using state-level variation in policies. Several studies link labor regulations in Indian states to economic progress (Besley and Burgess 2004, Aghion et al. 2008), and strict labor regulations have been found to hurt entrepreneurship beyond self-employment in advanced economies (e.g., Bozkaya and Kerr 2011). This effect may occur through reduced likelihood of wanting to start a new firm, or through reduced likelihood of opening new facilities to avoid regulations. There may also be reduced ̳push‘ into entrepreneurship with more protected employment positions. Fallick et al. (2006) model how labor mobility affects industry structure, with implications for entrepreneurship rates. Our measure is taken from Ahsan and Pages (2007), who break down the labor regulations index proposed by Besley and Burgess (2004) into separate components affecting labor adjustment and labor disputes legislation. Using these separate measures, we create a composite labor regulations index by state.

Our final primary measure is the strength of household banking environments. A large body of work considers the link between financial constraints and entrepreneurship, as surveyed in Kerr and Nanda (2011). Our measure of local financing conditions is the percentage of households that have banking services as measured in the 2001 census. This measure is likely to be particularly reflective of financing environments for unorganized sector activity.<sup>7</sup> Beyond these core district traits, we will also model consumption per capita in the district as an additional control in robustness checks. Richer districts in India have more factories (e.g., Sharma 2005), and we will check whether this influences entry beyond the controls for existing industrial activity discussed next. Our control is calculated for 1999 at the district level.

6 These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna.

7 We have also tested additional district-level indicators, including metrics of caste/religious population, female-to-male sex ratio, conflict, trade levels, and general development levels (leading/lagging designations at the state and district level). These additional controls do not substantively affect the results presented, and we maintain the more parsimonious specification to mirror other work from outside of India. Iyer et al. (2011) investigate the relationship of India‘s caste system to entrepreneurship.

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