work, however, focuses on metrics that use female-owned, incumbent businesses to determine labor market resources for new entrants.

In all of our estimations, we control in some format for the size of the incumbent districtindustry employment. This is important given that entrepreneurs often leave incumbent firms to start their companies. Klepper (2010) shows in detail the importance of this spawning process in the history of Detroit and Silicon Valley, and many econometric studies find the existing business landscape the most important factor for the spatial location of new entrants (e.g., Glaeser and Kerr 2009). We model both the totalemployment in incumbent firms for the districtindustry and the count of female-owned incumbent businesses specifically.

The first agglomeration rationale is that proximity to customers and suppliers reduces transportation costs and thereby increases productivity (e.g., Fujita et al. 1999). To test the importance of this mechanism within the manufacturing sector, we measure the extent to which districts contain potential customers and suppliers for a new entrepreneur. We begin with an input-output table for India developed by India’s Central Statistical Organization. We define Inputi←k as the share of industry i's inputs that come from industry k, and Outputi→k as the share of industry i's outputs that go to industry k. These measures run from zero (no input or output purchasing relationship exists) to one (full dependency on the paired industry). These shares are calculated relative to all input-output flows and are not symmetrical by design (Inputi←k≠Inputk→i, Inputi←k≠Outputk→i).

We summarize the quality of a district d in terms of its input flows for an industry i as Inputdi = - ∑k=1,...,Iabs(Inputi←k – Edk/Ed), where I indexes industries. This measure simply aggregates absolute deviations between the proportions of industrial inputs required by industry i and district d's actual industrial composition, with E representing employment among incumbent firms. The measure is mostly orthogonal to district size, which we separately consider, and a negative value is taken so that the metric ranges between negative two (i.e., no inputs available in the local market) and zero (i.e., all inputs are available in the local market in precise proportions). The construction of Inputdi assumes that firms have limited ability to substitute across material inputs in their production processes.<sup>8</sup> 8The input metric is not perfectly orthogonal to district size to the degree to which larger districts have more independent economic zones than smaller districts. Thus, even if the very localized input conditions within a small and large district are similar for a start-up, the measured quality of input conditions will be less in the larger district as the input metric will sum over more economic zones. We thank Juan Alcacer for pointing this out.

Chinitz (1961) emphasizes that average size of local suppliers is an important factor for entrepreneurship beyond general supplier market suitability. A number of empirical studies find support in local start-up conditions: Drucker and Feser (2007), Glaeser and Kerr (2009, 2011), Rosenthal and Strange (2010), Glaeser et al. (2010), and Ghani et al. (2011c). We do not examine the Chinitz effect in the unorganized sector context because the definition of an unorganized establishment in India is based upon establishment size.

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