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.

Finally, we 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 Ghani et al. (2011c) finds labor regulations suppress Indian entrepreneurship generally. 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.

# _Agglomeration Theories_

The above factors are district-level phenomena, and a useful first exercise is to relate the female entrepreneurship ratio to these factors. The focus of our study, however, is on the local industrial conditions of district-industries and how they shape female entrepreneurship. We develop metrics that unite the incumbent industrial structures of cities with the extent to which industries interact through the traditional agglomeration rationales first defined by Marshall (1920). Duranton and Puga (2004) and Rosenthal and Strange (2004) review the subsequent literature in detail. Prior work emphasizes the importance of these conditions for explaining overall entry rates,<sup>7</sup> but this work does not explore the impact of favorable local conditions on the gender balance of entrepreneurs.

We also want to explore the importance of whether the gender profile of the incumbent industrial structure affects the gender profile of new entrants. The role of business networks among women in developing countries is frequently mentioned by development economists, but we are not aware of any prior work that systematically considers female businessownership across regions and industries in multiple economic sectors. For simplicity, we describe our metrics below in a generic language that discusses district-industry employment. Our empirical 6These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna.

7This conceptual approach is used to describe location choice decisions and city structures by Glaeser and Kerr (2009) for the United States, Jofre-Monseny et al. (2011) for Spain, Dauth (2011) for Germany, and Ghani et al. (2011c) and Mukim (2011) for India. Alcacer and Chung (2010) and Mukim and Nunnenkamp (2011) use this framework to consider foreign direct investment decisions.Deichmann et al. (2008) review a broader literature on location choice in developing countries. Industry linkages for agglomeration are more broadly considered by Henderson (2003), Ellison et al. (2010), Delgado et al. (2010), and Helsley and Strange (2010).Greve and Salaff (2003) review social networks and entrepreneurship. A broad and related literature considers network formation in developing economies. Examples of recent work include Khwaja et al. (2005), Bandiera and Raul (2006), Krishnan and Sciubba (2009), and Conley and Udry (2010).

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