is confirmed when we add to the estimations firm _×_ product _×_ year.

In our dataset, we can classify NTMs in three categories: SPS, TBT and others, which include Pre-Shipment Inspection, Contingent Measures and Quality Controls. While NTMs and tariffs have starkly different effects on market structure, there are fewer reasons to expect different effects across NTMs categories (in terms of sign). Still, in columns (4) and (8) we examine whether the effect on firm PTM varies across types of NTMs by including separately an interaction term between our exchange rate and these three categories. We find stronger impact of SPS regulations in column (4), but the difference across types of NTMs is much less clear in column (8). In Section C.4 of the online appendix, we further explore the heterogeneity of the effect of NTMs across industries. Given the set of fixed effects used in our estimations, repeating the analysis by HS2digit categories is not feasible due to the limited degrees of freedom. Table A.11 reports the results for three HS sections with the largest number of non-missing NTM data in our sample (HS sections 2, 4 and 11). Our results suggest that fresh products are mostly affected by SPS standards, such as for example pests control or pesticide usage regulations, while processed food products are more affected by TBT standards, including for example regulations on residues limits or packaging requirements.

Table 4: Non-tariff measures and pricing-to-market: Baseline

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. var.||||ln unit|values||||
|Sample Split||NTM|AVE|||NTM|AVE||
|Subsample||Low|High|||Low|High||
|ln RER|0.052<sup>_c_</sup>|0.112<sup>_a_</sup>|0.176<sup>_a_</sup>|0.090<sup>_a_</sup>|0.067<sup>_b_</sup>|0.073<sup>_a_</sup>|0.136<sup>_a_</sup>|0.062<sup>_c_</sup>|
||(0.030)|(0.022)|(0.034)|(0.030)|(0.031)|(0.020)|(0.049)|(0.032)|
|_×_ ln(# product_t_0)|0.022<sup>_c_</sup>|||0.012|0.019|||0.020|
||(0.012)|||(0.012)|(0.016)|||(0.016)|
|_×_ NTM AVE|0.216<sup>_a_</sup>||||0.075<sup>_c_</sup>||||
||(0.041)||||(0.044)||||
|_×_ NTM AVE (SPS)||||0.353<sup>_a_</sup>||||0.066|
|||||(0.043)||||(0.052)|
|_×_ NTM AVE (TBT)||||0.024||||0.078|
|||||(0.072)||||(0.086)|
|_×_ NTM AVE (other)||||0.023||||0.096|
|||||(0.072)||||(0.069)|
|ln(dest. GDP/cap)|0.161<sup>_a_</sup>|0.211<sup>_a_</sup>|-0.026|0.154<sup>_a_</sup>|0.060<sup>_c_</sup>|0.091<sup>_b_</sup>|0.063|0.062<sup>_c_</sup>|
||(0.045)|(0.053)|(0.075)|(0.045)|(0.032)|(0.040)|(0.108)|(0.033)|
|Observations|101283|70769|25036|101283|63011|41179|8591|63011|
|Adj. R<sup>2</sup>|0.980|0.980|0.983|0.980|0.987|0.987|0.991|0.987|
|Firm_×_product_×_dest. FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Origin_×_year FE|Yes|Yes|Yes|Yes|No|No|No|No|
|Firm_×_product_×_year FE|No|No|No|No|Yes|Yes|Yes|Yes|

> _c_ significant at 10 percent; _b_ significant at 5 percent; _a_ significant at 1 percent. Standard errors clustered by product-origin-destination-year are in parentheses. High and Low means respectively above (or equal to) the third quartile or below (or equal to) the first quartile of the corresponding variable. ln RER is the real exchange rate of the destination country vis-a-vis the USD. SPS and TBT mean respectively Sanitary and Phyto-Sanitary and Technical Barriers to Trade. ln(# product _t_ 0) is the total number of products exported by the firm at the beginning of the period.

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