non-production employment (Panel B, column 1), but, conditional of total firm employment, these associations become statistically insignificant (Panel B, column 2).

These correlations may be driven by industry trends, such as industry-specific growth processes. To account for those trends, we add in column 3 interactions between year dummies and industry dummies. In column 4, we also add initial conditions, the level of log sales in 2001 interacted with year dummies, to account for firm-specific trends (Brambilla, Lederman, and Porto, 2012). In all these specifications, the results are robust and the magnitudes of the coefficients are stable across models.

In terms of employment categories (Panel C), we find that exporters hire more engineers and administrative service workers and hire less maintenance service workers than non-exporters. There is no statistically discernible difference in managerial and unskilled blue-collar employment.

While these correlations are very robust, they are still correlations, not necessarily causal effects. This is because, for instance, there might be omitted variables creating biases. More productive firms are, for example, more likely to export and, at the same time, to be more efficient in the use of skilled labor. There might also be measurement errors (Brambilla, Lederman, and Porto, 2012). To get to these causal effects, we instrument the variable _Eijt_ with the average firm-level bilateral exchange rate. This strategy follows Revenga (1992), Bastos, Silva and Verhoogen (2017), Brambilla, Lederman and Porto (2012), Brambilla and Porto (2016), and Park, Yang, Shi and Jiang (2010), among others. Intuitively, the argument is that exogenous export opportunities for a firm are likely to arise when its foreign export markets expand and this will happen when exchange rate changes make Chilean exports relatively cheaper. Given these exogenous changes, a firm will be more likely to take advantage of these export opportunities if it is exposed to those markets. A natural measure of destination exposure in this case is the share of a firm’s exports to that destination in total firm sales. Access to the administrative customs records allows us to do this for the Chilean ENIA firms. Formally, we define the instrument _z_<sup>0</sup> as where _sdij_ is the share of exports of firm _i_ (in industry _j_ ) to export destination _d_ at the initial time period (year 2001), and _rdt_ is the bilateral exchange rate between Chile and country _d_ at _t_ . Hence, _zijt_<sup>0isaweightedaverageoftherealexchangeratefacedbyChileanexporters,</sup> where the firm-specific weights are the initial shares of exports in sales. As in Brambilla, Lederman and Porto (2012), we also interact _z_<sup>0</sup> with initial firm sales (i.e., log sales in 2001) to include any firm advantages in profiting from export opportunities based on firm size. Averages for the value of these instruments are reported in panel E) of Table 1.<sup>2</sup> The statistical validity of the instrument can be checked with first-stage results for the

> 2In all our specifications, we use both _z_ 0 and its interaction with initial sales as instruments. We do this to maintain consistency with the literature that utilizes a similar approach as ours. However, all the results in the paper remain unaltered by a specification that keeps only the average firm-level exchanger rate, _z_<sup>0</sup> , and drops the interaction with initial sales. These results are reported in an Online Appendix available at the authors’ websites.

5