prices, and fuel prices. The model is estimated across three types of carriers – truck-load, less-than-truck load, and specialized – with the estimated rate being the weighted average by value of the three by commodity (see Brown, 2015, for a detailed discussion of the data and model). Because prices are measured on a quarterly basis, fixed and variable costs are to time.<sup>7</sup> Prices to are permitted vary through charged shippers predicted by commodity using their average tonnage for a 500 kilometers trip.<sup>8</sup> The final predicted price, _R_<sup>�</sup> _c_ , _t_ , is the weighted average by value of the three carrier types by commodity and year. While, as will become apparent, we only use predictions from 2008, the entire period is used in order to bring as much information to bear on the cross-sectional estimates by commodity. Predictions from the model closely match observed annual prices on aggregate.

Finally, the predicted prices _R_<sup>�</sup> _c_ , _t_ are converted to ad valorem trucking rates _T_<sup>�</sup> _c_ , _t_ by measuring the value of shipments. Since the value of shipments are not reported, they have to be estimated by multiplying the average tonnage shipped for each commodity by their respective value tonne derived from an trade in 2008. The per ‘experiment export file’ produced only ad valorem estimate at the commodity level in 2008 is _Tc_ ,2008 = _R_<sup>�</sup> _c_ ,2008/ _Vc_ ,2008, where _Vc_ ,2008 is the estimated value of the commodity for the average tonnage shipped in 2008. Using an industry-commodity concordance, the ad valorem transportation rates in 2008 for commodities _Tc_ ,2008 are aggregated to an industry basis _Ti_ ,2008.

Finally, to generate a time series, yearly trucking industry price indices **P** trans, _t_ and manufacturing industry price indices **P** _i_ , _t_ from Statistics Canada’s klems database are used to project the ad valorem rates backwards and forwards in time, thereby creating an industry-specific ad valorem transportation rate time series: Observe that our measure (4) of transport costs is more direct and detailed than those usually used in the agglomeration literature.<sup>9</sup> Yet, it is by construction unlikely to be fully exogenous to industrial location patterns since it depends on price indices. We return to this important point later. Note, however, that we estimate transport costs for a ‘representative shipment’ by truck, holding distance fixed at 500 kilometers. Hence, variable shipping distances that result from optimal location choices of plants in an industry have a priori no direct Figure 1 depicts the year-on-year changes in the (unweighted) cross-industry average transport costs for a representative 500 kilometers shipment. As can be seen, transport costs are

> 7Time enters as trends through a spline with knots set to reflect changing trends in a trucking price index generated from the same file.

> 8While we do not directly control for the time costs of transportation they will be, at least partially, embedded in the transport prices (which would capture quality of service for time-dependent trips).

> 9One exception is the work by Combes and Lafourcade (2005), who provide detailed estimates of transport costs for France. Their costs are, however, not industry specific.

10