**Omitted variables.** We control for a wide range of time-varying industry-specific agglomeration factors. We know since Marshall (1890) that industries may concentrate for various reasons that are unrelated to the transport costs of goods, but linked to the costs of transporting people and ideas (see Duranton and Puga, 2004, for a review). Knowledge spillovers and labor market pooling are among the most important ‘Marshallian’ factors. Industries may also concentrate geographically because of localized natural advantages like natural resources or raw materials. The importance of controling for these has been pointed out, among others, by Kim (1995), Ellison and Glaeser (1999), and Ellison _et al_ . (2010). Furthermore, various other characteristics linked to the industrial organization of industries – industry size, the mean plant size, the size distribution of in the the of multi-unit or owner- plants industry, presence firms, foreign ship – are also likely to affect their spatial structure (see, e.g., Rosenthal and Strange, 2003). We account for these time-varying factors as follows. First, we control for knowledge spillovers using as a proxy an industry’s research and development (R&D) intensity, i.e., the ratio of R&D expenditure to total output of that industry (see Rosenthal and Strange, 2001). Second, we employ a proxy related to workers’ educational attainment to control for aspects of labor market pooling. More specifically, we use the share of hours worked by all workers with postsecondary education in the total number of hours worked in the industry.<sup>11</sup> Third, we control for the importance of natural advantages using the share of inputs from natural resource-based industries, and the sectoral energy inputs as a share of total sector output. Last, we control for basic industry structure and scale effects by including the following controls: total industry employment; mean plant size; the Herfindahl index of firm-level concentration; the share of plants controlled by multi-unit firms; and the share of plants controlled by foreign firms. These variables proxy for sectoral differences in the size distribution of firms, for potential differences in the location patterns of multi-unit multinationals, and differences in ‘business culture’.

As suggested by our theoretical model in Section 2 and Appendix A, controlling for inputoutput links and changes in international trade costs is important. We construct two controls that proxy for the costs of international trade and the proximity to customers and suppliers. Concerning the former, we construct measures of industry-level trade exposure (exports and imports), broken down by broad country groups – nafta, oecd excluding nafta, and low-cost countries. in international trade in international Changes exposure broadly capture changes trade costs, as well as changes in foreign productivity and the competitive environment in 2. general. We provide details and descriptive statistics in Appendix B. We also discuss possible endogeneity concerns there. Turning to the proximity to customers and suppliers, we construct 11We also constructed proxies for labor market conditions using the non-production to production workers ratio and other educational characteristics of the workforce. The latter are available at a more aggregated industry level ( _L_ -level) from Statistics Canada’s klems database (e.g., the share of hours worked by all workers with a university degree, and the labor productivity index). These measures, however, are not statistically significant in the time series because they change quite slowly over time and are thus soaked up by the industry fixed effects.

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