across time to assess changes therein. In this paper, we employ – for the first time to our knowledge – a long panel of continuous measures of geographic concentration, computed from micro-geographic plant-level data using the approach of Duranton and Overman (2005).<sup>1</sup> Using panel data allows us to look at the time-series variation over a nearly 20 year period and to studies that have looked at the cross-sectional variation in the go beyond existing mainly geographic concentration of industries.

Second, we need detailed measures of industry-specific transport costs. We devote substantial effort to the construction of domestic ad valorem trucking rates for 257 industries and 20 years. Most of the literature has looked at infrastructure as a source of variation in transport costs (see Redding and Turner, 2015). By contrast, we build our trucking rates time series from the microdata files on truck shipments within Canada. These measures capture time-changing domestic transport costs and are invariant to the spatial structure of industry, thereby sidestepping the often endogenous nature of standard transportation measures (e.g, transportation margins from input-output accounts).

Third, as suggested by a simple general equilibrium model that we construct, to identify the causal effect of transport costs on the geographic concentration of industries we need to control for both time-varying changes in international trade exposure and general equilibrium effects of access to intermediate input suppliers and customers in vertical production chains. We tackle this rich measures of trade and challenge by developing exposure microgeographic measures of plants’ access to potential suppliers and customers.

Last, we need to deal with the possible endogeneity of our main covariates. For example, it is well documented that productivity rises as an industry concentrates geographically (see, e.g., Rosenthal and Strange, 2004; Combes and Gobillon, 2015). If these productivity gains are passed on to consumers in the form of lower prices that affect also ad valorem trucking rates, the causality may actually run from agglomeration to transport costs and not the other way round. We deal with this issue directly by purging our measure of ad valorem trucking costs of the effect multi-factor productivity. We also use us industry price indices to construct external instruments for our trucking rate series.

The remainder of the is structured as follows. Section 2 the theoretical paper presents background that informs our empirical strategy. Section 3 briefly documents the evolutions of geographic concentration and of transport costs in Canada, including a detailed discussion of the construction of the transport cost variable. Section 4 describes our empirical strategy and discusses the identification issues. Section 5 presents our results and provides a large number of robustness checks and iv estimates. Finally, Section 6 concludes. Technical details are relegated to an extensive set of appendices and a Supplementary Online appendix.

> 1See Holmes and Stevens (2004) for an exhaustive survey of location patterns in North America. They do not report results using continuous measures. Ellison, Glaeser, and Kerr (2010) use a ‘lumpy (county-level) approximation’ of the Duranton and Overman (2005) measure and apply it to us manufacturing data.

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