Table 4: Baseline estimation results for specification (5).

|Variables|**(Model 1)**|**(Model 2)**|Dependent<br>**(Model 3)**|variable is the<br>**(Model 4)**|cdfat50km<br>**(Model 5)**|<br>**(Model 6)**|**(Model 7)**|
|---|---|---|---|---|---|---|---|
|Ad valorem trucking costs|-0.265<sup>_a_</sup>|-0.337<sup>_b_</sup>|-0.263<sup>_b_</sup>|-0.250<sup>_b_</sup>|-0.183<sup>_b_</sup>|-0.208<sup>_b_</sup>||
||(0.037)|(0.155)|(0.122)|(0.098)|(0.078)|(0.088)||
|Ad valorem trucking costs (residual)|||||||-0.261<sup>_a_</sup><br>(0.078)|
|Asian share of imports|||-1.639<sup>_a_</sup>||-1.309<sup>_a_</sup>|-1.132<sup>_a_</sup>|-1.118<sup>_a_</sup>|
||||(0.413)||(0.379)|(0.380)|(0.383)|
|oecdshare of imports|||-1.103<sup>_a_</sup>||-0.638<sup>_c_</sup>|-0.491|-0.475|
||||(0.400)||(0.341)<br>|(0.344)|(0.345)|
|naftashare of imports|||-1.188<sup>_a_</sup>||-0.715<sup>_b_</sup>|-0.562<sup>_c_</sup>|-0.549<sup>_c_</sup>|
||||(0.363)||(0.327)|(0.327)|(0.327)|
|Asian share of exports|||0.370||0.371|0.482|0.482|
||||(0.542)||(0.422)|(0.405)|(0.412)|
|oecdshare of exports|||0.366||0.423<sup>_b_</sup>|0.440<sup>_b_</sup>|0.443<sup>_b_</sup>|
||||(0.274)||(0.210)|(0.189)|(0.193)|
|naftashare of exports|||0.298||0.279|0.319|0.318|
||||(0.294)||(0.206)|(0.196)|(0.201)|
|Input distance||||-0.359<sup>_a_</sup>|-0.343<sup>_a_</sup>|-0.361<sup>_a_</sup>|-0.358<sup>_a_</sup>|
|||||(0.064)|(0.059)|(0.055)|(0.055)|
|Output distance||||-0.262<sup>_a_</sup>|-0.290<sup>_a_</sup>|-0.313<sup>_a_</sup>|-0.318<sup>_a_</sup>|
|||||(0.046)|(0.044)|(0.042)|(0.043)|
|Average minimum distance||||-0.323<sup>_a_</sup>|-0.300<sup>_a_</sup>|-0.296<sup>_a_</sup>|-0.294<sup>_a_</sup>|
|||||(0.046)|(0.047)|(0.039)|(0.039)|
|Industry controls included|No|No|No|No|No|Yes|Yes|
|Number ofnaicsindustries|257|257|257|257|257|257|257|
|Number of years|17|17|17|17|17|17|17|
|Year dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations (naics_×_ years)|4,369|4,369|4,369|4,369|4,369|4,369|4,369|
|_R_<sup>2</sup>|0.165|0.054|0.096|0.442|0.473|0.516|0.518|

_Notes:_ The dependent variable is the unweighted (count based) Duranton-Overman _K_ -density cdf at 50 kilometers distance.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> denote coefficients significant at the 1%, 5% and 10% levels, respectively. We use simple ols. Standard errors are clustered at the industry level and given in parentheses. Our measures of input and output distances, as well as average minimum distance, are computed using _N_ = 5. ‘Ad valorem trucking costs (residual)’ denotes the residual of the regression of ‘Ad valorem trucking costs’ on industry multi-factor productivity. A constant term is included in all regressions but not reported. **Model 6** includes the following industry controls (Total industry employment; Firm Herfindahl index (employment based); Mean plant size; Share of plants affiliated with multiplant firms; Share of plants controlled by foreign firms; Natural resource share of inputs; Energy share of inputs; Share of hours worked by all workers with post-secondary education; In-house R&D share of sales). Last, our preferred specification, **Model 7** , uses the residual trucking rate as the main explanatory variable.

competition from low-wage countries leads to significant exit of large plants that produce standardized ‘main segment’ goods that are very exposed to competition.<sup>16</sup> Since those plants are the ones that are predominantly clustered at short distances, their exit will significantly reduce the extent of measured geographic concentration.<sup>17</sup> As can also be seen from **Model 3**

> 16We cannot disentangle the impact of exit vs relocation on the spatial structure. However, we control for the size of the industry, which at least partly picks up entry and exit dynamics. Note that relocations are quite rare and should have little impact on our results. The bulk of the variation is driven by entry and exit.

> 17This is a somewhat surprising result, because we would expect the productivity enhancing effects of geographic concentration to shelter firms from low-wage competition. Yet, one should keep in mind that clustering

17