primary naics industry; its employment; its sales; and its 6-digit postal code. The latter allows us to effectively geo-locate the plants using latitude and longitude coordinates of postal code centroids, which are spatially very fine-grained in Canada. A detailed description of the data 1. and the data sources is provided in Appendix B.

We measure the geographic concentration of industries using the Duranton and Overman (2005; henceforth, do) _K_ -densities. Since this approach has become fairly standard by now (see, e.g., Murata, Nakajima, Okamoto, and Tamura, 2014; Behrens and Bougna, 2015; Kerr and Kominers, 2015), we relegate the technicalities to the Supplementary Appendix S.1. The do _K_ -densities are kernel-smoothed distributions of the bilateral distances between plants in an industry. We compute them year by year for all industries at the naics 6-digit level.<sup>4</sup> Since _K_ -densities are distribution functions, we can also compute their cumulatives (cdf) up to some distance _d_ . The cdf of the _K_ -density at distance _d_ provides a measure of the share of plants in an industry that are located at most at distance _d_ from each other. Kerr and Kominers (2015) develop a model where the clustering of firms is driven by spatial interactions between pairs of plants and is subject to distance decay and (fixed) interaction costs. They show that the _K_ -densities can be within that framework and that information on interpreted they provide the ‘overall degree’ of geographic concentration of industries and on ‘cluster shapes’.

Table 2: Mean of the Duranton-Overman cdfs across industries, 1990 and 2009.

|||(1) Un|weighted||(2)|Employm|ent weigh|ted||(3) Sales|weighted||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||cdfat a|distance of||||||
|Year|10km|50km|100km|500km|10km|50km|100km|500km|10km|50km|100km|500km|
|1990|0.020|0.076|0.139|0.420|0.021|0.083|0.151|0.449|0.022|0.086|0.156|0.453|
|2009|0.013|0.056|0.107|0.373|0.015|0.063|0.121|0.397|0.017|0.068|0.126|0.403|
|Mean (1990–2009)|0.015|0.064|0.121|0.394|0.017|0.073|0.136|0.422|0.019|0.077|0.141|0.428|
|% change|-36.0%|-27.1%|-22.6%|-11.3%|-28.7%|-23.3%|-20.3%|-11.4%|-21.5%|-21.2%|-19.3%|-11.0%|

_Notes:_ Authors’ computations based on the Annual Survey of Manufacturers Longitudinal Microdata file, 1990–2009. We report the values for the starting and the end years only since the series in between decrease rather smoothly (see our cepr discussion paper version for the full table). The means of the cdf are based on 257 industries and are not weighted (but the cdfs for each industry are weighted by either employment in the middle columns (2), or by sales in the right columns (3); see Supplementary Appendix S.1 for details). ‘Mean (1990–2009)’ refers to the mean of the _K_ -densities over the whole 1990–2009 period, on a yearly basis. ‘% change’ is the percentage change between 1990 and 2009.

The standard _K_ -densities are computed based on plant counts, i.e., distances between pairs of plants without any weighting scheme. This relates to the theoretical measures we have used in our model as given by (1). Yet, we can also compute weighted versions. In particular, we can weight pairs of plants by either plant-level employment or plant-level sales. For these weighted versions of the _K_ -density the unit of observation is the employee or a dollar of sales. A value of 0.086 at 50 kilometers using sales weights (see panel (3) of Table 2 for 1990) means, for example, that 8.6% of the industry sales are generated in plants that are at most 50 kilometers

> 4Table 9 in the Supplementary Appendix S.2 summarizes the _K_ -densities for the geographically most concentrated industries in 1990, 1999, and 2009, respectively.

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