8 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE but now for the extended sample of countries. In the preferred specification with origin-year and destination-year fixed effects, the IME is 0.38 among origin-destination pairs with at least 100 exporting firms and 0.52 among all origin-destination pairs.<sup>11</sup> To make sure the IME is not driven by small exporting firms, we re-estimate it after excluding firms whose annual exports fell below $1,000 in any year. The corresponding IME estimates in Table 3 (core sample) and Online Appendix Table I4 (extended sample) change only slightly.

A separate concern is measurement error. Since total exports is the sum of firm-level exports, classical measurement error in exports per exporter _x_ would bias the IME upward, but classical measurement error in the number of exporters _N_ would bias the IME downward. Depending on their relative importance compared to the true IME, classical measurement error could bias the IME upward or downward. If the measurement error is serially uncorrelated, then instrumenting total exports with its leads and/or lags should yield an unbiased estimate of the IME. As shown in Online Appendix Table I5, the instrumented IMEs are very close to the OLS IME, both economically and statistically.

Our results for the IME could be coming from country differences in industry composition of exports combined with industry differences in average exports per firm. In Figure 2 we plot the (demeaned) intensive and extensive margins against total exports at the origin-industrydestination-year level using HS 2-digit industries. The pattern here is similar to that in Figure 1. Table 4 shows that the IME actually increases when moving to industry-level data. At the lowest level of aggregation available (HS 6-digit), for the core sample of countries the IME is 0.51 with origin-year-industry and destination-year-industry fixed effects. The results also hold in the extended sample, for which we calculated IME disaggregated at HS2 product level. As reported in the Online Appendix Table I6, this IME is also close to 0.52.<sup>12</sup>

# **IME by percentiles**

A positive IME could be due to the presence of export superstars that increase both average exports per firm and total exports for some country pairs, as discussed in Freund and Pierola (2015). We study this possibility by considering separate IME regressions for each exporter size percentile. For each origin-destination-year combination we distribute the exporting firms into percentiles based on the value of their exports. Denoting average exports per firm in percentile

> 11When we allow the IME to differ across origin countries depending on their GDP per capita, we find that IME estimates are close to or larger than 0.4 for any group of countries, as shown in Online Appendix Table I3. 12The presence of large trading firms could lead to both high exports per firm and high total exports and explain our IME estimates. While we are unable to identify large trading firms in the EDD data, we estimate the IME based on a sample including only HS 2-digit industries with low shares of firms exporting via intermediaries, as defined in Chan (2017). The results in Online Appendix Table I7 show an almost unchanged IME at 0.53.