Table 7: Controlling for the potential endogeneity of _Ti_ , _t_ in specification (5).

||De|pendent varia|ble is thecd|fat50kilome|ters|
|---|---|---|---|---|---|
|Variables|**(Model 11)**<br>Base|**(Mode**<br>First stage|**l 12)**<br>iv-2sls|**(Model 13)**<br>Lewbel1|**(Model 14)**<br>Lewbel2|
|Ad valorem trucking costs (residual)|-0.261<sup>_a_</sup>||-0.393<sup>_a_</sup>|-0.197<sup>_b_</sup>|-0.218<sup>_b_</sup>|
||(0.0781)||(0.0956)|(0.0929)|(0.0915)|
|Ad valorem trucking costsus(instrument)||0.485<sup>_a_</sup>||||
|||(0.111)||||
|Asian share of imports|-1.118<sup>_a_</sup>|-0.056|-1.095<sup>_a_</sup>|-1.592<sup>_a_</sup>|-1.618<sup>_a_</sup>|
||(0.383)|(0.107)|(0.381)|(0.533)|(0.501)|
|Input distance|-0.358<sup>_a_</sup>|0.035<sup>_c_</sup>|-0.356<sup>_a_</sup>|-0.143<sup>_c_</sup>|-0.224<sup>_a_</sup>|
||(0.0545)|(0.020)|(0.055)|(0.075)|(0.075)|
|Output distance|-0.318<sup>_a_</sup>|-0.011|-0.322<sup>_a_</sup>|-0.386<sup>_a_</sup>|-0.360<sup>_a_</sup>|
||(0.043)|(0.015)|(0.042)|(0.083)|(0.087)|
|Average minimum distance|-0.294<sup>_a_</sup>|0.005|-0.290<sup>_a_</sup>|||
||(0.039)|(0.014)|(0.039)|||
|Other trade shares included|Yes|Yes|Yes|Yes|Yes|
|Industry controls included|Yes|Yes|Yes|Yes|Yes|
|Industry dummies|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|
|_R_<sup>2</sup>|0.518||0.516|0.319|0.330|
|First-stage_R_<sup>2</sup>||0.628||||
|First-stage_F_ test of excluded instruments||19.07||||

_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. Our measures of input and output distances 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. Model 11 replicates our preferred Model 7 but the standard errors are bootstrapped because of the generated regressor. Model 12 instruments the ‘Ad valorem trucking costs’ using costs constructed from us price indices. Models 13 and 14 use the Lewbel (2012) methodology to instrument input-output distances and trade shares. In Model 13, only a subset of the import shares is instrumented, while all trade shares are instrumented in Model 14. See Appendix C for additional details. A constant term is included in all regressions but not reported. All 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 postsecondary education; In-house R&D share of sales) are included but not reported. All trade share variables are included in the regressions, but we do not report detailed results to save space.

variables, we use the Lewbel (2012) estimator with internal instruments for the input-output distances and the trade shares (see Lewbel, 2012, and Appendix C for more details on the implementation).<sup>26</sup> The excluded external instrument is the us price-based ad valorem trucking costs as before. As can be seen from the results in Table 7, the instrumented coefficient on the Asian share of imports increases, as do most of the other trade share coefficients. At the same time, both the magnitude of transport costs and of the input and output distances decreases slightly. However, these variables remain significant and their magnitude is in the same ballpark than in the case of ols. Thus, our results appear to be robust. Decreases in ad val26 Since there is an insignificant correlation between the oecd export share and the squared residuals, we did not include it. We substituted instead the nafta import share because it is consistently significant in the baseline set of models and it meets the criteria for being internally instrumented (see Appendix C).

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