Table 1: Changes in geographic concentration with respect to _t_ , _τ_ , and _fR_ .

||||(1)|(2)|(3)||
|---|---|---|---|---|---|---|
|Para<br>_t_|meter v<br>_τ_|alues<br>_fR_|Agglomeration fnal<br>�<br>_Nr_<br>_N_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Agglomeration interm.<br>�<br>_nr_<br>_n_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Agglomeration both<br>�<br>_Nr_+_nr_<br>_N_+_n_ <sup>_−Lr_</sup><br>_L_<br>�<br>_×_100%|Relative wage<br>_ws_/_wr_|
||||<br><br>|<br><br><br>_(a) Changes in domestic_|<br><br><br>_transport costs, t_||
|1.5|1.5|0.2|0.279|-0.452|-0.037|0.980|
|**1.4**|**1.5**|0.2|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.35|1.5|0.2|1.396|-0.731|0.408|0.981|
|1.3|1.5|0.2|1.823|-0.877|0.554|0.982|
|1.25|1.5|0.2|2.364|-1.082|0.731|0.983|
|||||_(b) Changes in internati_|_onal trade costs, τ_||
|1.4|1.6|0.2|2.266|-0.912|0.814|0.975|
|**1.4**|**1.5**|0.2|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.4|1.4|0.2|0.915|-0.600|0.226|0.981|
|1.4|1.3|0.2|0.851|-0.594|0.199|0.982|
|1.4|1.2|0.2|0.790|-0.590|0.173|0.982|
|||||_(c) Changes in imported_<br>|_intermediates, fR_||
|1.4|1.5|0.1|1.036|-0.616|0.275|0.981|
|**1.4**|**1.5**|**0.2**|**1.010**|**-0.617**|**0.268**|**0.981**|
|1.4|1.5|0.3|0.986|-0.616|0.259|0.981|
|1.4|1.5|0.4|0.961|-0.616|0.250|0.981|
|1.4|1.5|0.5|0.935|-0.615|0.241|0.981|

_Notes:_ Simulations for the baseline parameter values _α_ = 0.6, _σ_ = 5, _β_ = 0.33, _Lr_ = 11, _Ls_ = 10, _F_ = 1, _LR_ = 1, _λR_ = 0.3, _fR_ = 0.2, and _cR_ = 1. The baseline case with _t_ = 1.4 and _τ_ = 1.5 is displayed in bold font. All simuations have been carried out using Mathematica 10. The notebook is available online as supporting material. See Appendix A for details on the model structure.

(column 1), slightly more geographic concentration of the intermediate sector (column 2), and less geographic concentration overall (column 3). As can further be seen from the bottom panel (c) of Table 1, changes in the mass of imported intermediates also have a dispersive effect, this time on both sectors, although that effect is clearly smaller in magnitude. Last, although we do not explicitly summarize it in Table 1, it should be clear that the equilibrium level of geographic concentration depends on the model’s structural parameters – the strength of scale economies, consumers’ preferences for the goods, and the cost structure in each sector.

Our key qualitative findings can be summarized as follows: (i) decreasing domestic transport costs lead to more geographic concentration; (ii) decreasing international trade costs lead to more geographic dispersion; (iii) changes in the trading environment of one industry induce changes in the geographic concentration of vertically linked industries; and (iv) industryspecific fixed parameters related to the degree of scale economies, costs, and consumers’ preferences influence the degree of geographic concentration.<sup>3</sup> What are the empirical implications of the foregoing results? Three conclusions can be drawn for our empirical exercise. First, given the opposite effects of domestic transport and

> 3Results (i), (ii), and (iv) are qualitatively in line with those obtained by Behrens _et al._ (2007) in a two-country four-region model with quadratic quasi-linear preferences. Contrary to our model, there are no vertically linked industries in their model.

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