by an unskilled operative at a non-exporting firm.

From the detailed employment records of the ENIA, we define the following production activities or tasks: management (directors), administrative services (accountants, lawyers), production services (engineers, blue-collar workers), and general maintenance services (unskilled non-production workers). We define skills according to formal education. We assume that directors, accountants, lawyers, engineers comprise skilled labor (because these workers need to obtain a college degree). To enrich the analysis, we also define a highly-skilled group, which includes managers and engineers. Unskilled workers are blue-collar, non-specialized and general maintenance workers (assuming these type of workers typically have lower formal educational levels). Finally, we let production workers include engineers and blue-collar operatives, while non-production workers include managers, administrative workers and maintenance workers.

Table 1 presents some key summary statistics. We report the unconditional averages as well as averages for exporting firms and non-exporting firms. On average, 39% of workers in Chilean firms are skilled workers and 61% are unskilled workers. As expected, exporters utilize a higher share of skilled workers (41%) than non-exporters (39%). Exporters are much larger and they hire, on average, more workers in all skilled and unskilled categories than non-exporters. Production workers account for 73% of employment of all Chilean firms, but 70% of the employment of exporters. In addition, exporters employ more managers, engineers and administrative services workers than non-exporters. Employment of unskilled blue-collar workers is less, while maintenance employees are slightly more, among exporters. Finally, the average exporter ships around 32% of its sales abroad. Among all firms, exports accounts for only 5% of total firm sales.

# **2.1 Regression Analysis**

We begin our empirical analysis with the following regression model where _i_ indices firms, _t_ , time, and _j_ , an industry. Outcomes are denoted by _yijt_ and export intensity (the ratio of exports to total sales) is _Eijt_ . We add a control vector **x** , which includes firm level variables such as log total employment and initial conditions (initial sales) interacted with year dummies to account for firm-specific trends. The regression includes firm fixed effects, _φi_ and industry-year effects, _φjt_ .

Results from OLS-FE models are presented in Table 2. In column 1, we show the basic correlation of log employment and export intensity (conditional only on firm fixed-effects and year-effects). In column 2, we add log employment to control for size. This means we compare firms of equal size, with different export intensity. As it can be seen in these models, the correlation between exports and highly-skilled and skilled employment is positive and statistically significant. The results show that a firm with 10 percentage points higher export intensity hires 1.9% more highly-skilled workers and 1.6% more skilled workers than a similar-size firm. Exporters tend to hire less unskilled labor, but this coefficient is weak statistically. Exports are positively correlated with both production and 4