manufactured products such as textile, footwear or machinery (Table A.3). We also show in Figure A.1 that the sample for which trade policy data (and especially NTM data) are available is comparable to the rest of the sample in terms of market structure.

# **3.4 Additional data**

In our robustness exercises we use additional information on the characteristics of products (manufacturing, consumer good, differentiated or homogenous), and destination countries (gravity type variables including distance, income level, market potential information). We also use aggregated trade data to replicate our analysis at the country-pair-product level. These data and their sources are detailed in the corresponding section as well as in the online appendix Section B.1.

# **3.5 Descriptive statistics**

Table 2 shows descriptive statistics for the variables used in the regressions (see online appendix Section B for additional statistics). Our final sample contains around 55,000 firms. Unsurprisingly, trade-policy variables have the largest proportion of missing values. Both tariffs and the estimated ad valorem equivalents of NTM are low around 5 percent to 7 percent on average.<sup>19</sup> The median firm in our sample exports 2 HS 8-digit products and serves 1 destination against 6 products and 3 destinations for the average firm. This skewness in the distribution of products and destinations is consistent with stylized facts documented by the literature over the last decade: most exporters export only one product to a single destination and exports are dominated by a few very large, multi-products, multi-destinations firms (see for instance Mayer and Ottaviano, 2007).<sup>20</sup> Table 2: Descriptive statistics

||Obs.|Mean|S.D.|Q1|Median|Q3|
|---|---|---|---|---|---|---|
|unit value (USD)|648,566|835.14|8.70E+04|2.01|6.28|19.30|
|ln unit value (LCU)|648,452|5.88|2.43|4.38|6.16|7.31|
|# products (frm, _t_0)|648,566|32.51|293.08|2.00|6.00|14.00|
|bilateral RER (USD-dest.)|636,424|0.72|0.57|0.13|0.87|1.09|
|bilateral RER (orig.-dest.)|636,306|140.17|427.07|7.27|11.25|77.29|
|GDP per cap. (constant 2000 USD)|636,817|2.60E+04|1.49E+04|9.63E+03|3.00E+04|4.06E+04|
|GDP (constant 2000 USD)|642,489|3.52E+12|4.91E+12|1.32E+11|1.09E+12|2.99E+12|
|Foreign applied tarif|595,368|0.055|0.136|0.00|0.01|0.09|
|non-tarif measure (NTM AVE)|135,584|0.08|0.31|0.00|0.00|0.15|
|SPS AVE|135,584|0.01|0.24|0.00|0.00|0.00|
|TBT AVE|135,584|0.04|0.19|0.00|0.00|0.00|
|other NTM AVE|135,584|0.03|0.20|0.00|0.00|0.00|

The number of products and destinations are computed for each firm in the first year it enters the dataset. GDP per capita is reported for the destination country. Foreign import tariffs are computed as the average over the period of the corresponding country-pair-product-year applied tariffs in order to smooth out missing values. For non-tariff measures we use ad-valorem equivalents (AVEs) at the destination country-product level estimated from Cadot and Gourdon (2014, 2016). Products are defined at the six-digit level of the Harmonized System.

> 19These numbers are lower than the average levels of protection displayed in Table A.1 in the appendix, which was expected as high levels of protection deter trade and are therefore less likely to be observed in our final dataset.

> 20In Table 2, the median numbers of (HS6) products and destinations appear respectively as 6 and 4. This reflects multiple counting of multi-product multi-destinations exporters at the level of the unit of observation (firm-destination-product).

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