The first agglomeration rationale is that proximity to customers and suppliers reduces transportation costs and thereby increases productivity. This reduction in shipping costs is the core agglomerative force of the new economic geography (e.g., Fujita et al. 1999). Where customers and suppliers are geographically separate, firms must trade off distances. While transportation costs have declined dramatically over the past two centuries, the fundamentals remain important even in advanced economies. For example, much of the clean energy production in the United States based upon biomass is located near sources for the raw inputs.

To test the importance of this mechanism, 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).

Following Glaeser and Kerr (2009), we summarize the quality of a district d in terms of its input flows for an industry i as Inputdi = - ∑k=1,...,I abs(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. 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>10</sup> To capture the relative strength of output relationships, we also define a consolidated metric Outputdi = ∑k=1,...,I Edk/Ed∙Outputi→k. This metric multiplies the national share of industry i's output sales that go to industry k with the fraction of industry k's employment in district d. By summing across industries, we take a weighted average of the strength of local industrial sales opportunities for industry i in the focal market d. This Outputdi measure takes on higher values with greater sales opportunities. Unlike our input measure, this output metric pools across industries that normally purchase goods from industry i. By measuring the aggregate strength of 10 The input metric is not perfectly orthogonal to district size to the degree to which larger districts have more independent economic zones than smaller zones. 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.

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