the mechanism at work and the characteristics of those firms for which an intervention is more effective, in case there are heterogeneous effects. My previous estimations suggest that an increase in the number of employees is positively correlated to the propensity of a firm to export. This section aims to evaluate if there is heterogeneity in the effect of Apex according to different firms’ size cohorts.

In the case of EPAs, Volpe Martincus and Carballo (2010a) find evidence of heterogeneity with respect to export performance based on different groups defined according to their export value distribution.<sup>61</sup> Also, Volpe Martincus et al. (2012) show that EPAs’ impact differs by firm’s size categories.<sup>62</sup> However, the heterogeneous effect of EPAs according to different size categories of firms on promoting new exporters is still an open question.

In order to evaluate the heterogeneous effect of Apex I ran the baseline model using different sub-samples, according to different categories of firms’ size, based on the number of employees. Firms were classified as follows: Micro ( _size <_ 20), Small (20 _< size <_ = 50), Medium (50 _< size <_ = 150) and Large ( _size >_ 150), where size refers to the number of employees. Due to the fact that I matched firms using their characteristics ex-ante treatment, I classified the firms according to their number of employees in period _t-1_ . Although size of the firm was among the covariates in the matching procedure and their average and distribution was very similar between the treated and control groups (see table 12, in appendix), the number of treated and control group firms in each size cohort can differ. I used as a baseline sample a definition based on treated firms (number of employees in period _t-1_ ). Then I added their respective matched-pairs in the same (size cohort) group.<sup>63</sup> I also used as an alternative sample a classification based on the number of employees for all firms in period `t-1` . Results are relatively similar among these two procedures.

Table 8 shows the results regarding the impact of Apex’s treatment on export status in period _t+1_ . The effect is positive and statistically significant for micro, small and medium firms. However, the effect is not significant for large firms. Similar results are obtained if large firms are classified as being above 250 employees.<sup>64</sup> As mentioned in the previous section, the results for columns (1), (3), (5) and (7) are equivalent to the marginal effect at the means under a

> 61Using Chilean firm level data.

> 62Using Argentinian firm level data.

> 63Those are firms in the control group that were matched with the respective firm in the treatment group.

> 64For micro firms the effect is positive and significant for wearing apparel (1.1 pp), leather (4.3 pp), machinery (6.2 pp). For small firms the effect is positive for rubber and plastic (17.0 pp) and machinery (7.3 pp). For medium firms the effect is positive for food (9.0 pp), beverage (18.3 pp), rubber and plastic (18.3), basic metals (11.5 pp), metal products (10.8), computer (23.8 pp), electrical (12.2 pp), machinery (12.8 pp) and motor vehicles (17.9 pp). If the alternative definition of firms’ size is used, also leather, printing, chemicals, pharmaceutical and furniture become significant, while electrical becomes insignificant. I have not found heterogeneous effect between industries for large firms.

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