|Dependent Vari|able: Expor|t status (t)|||||||
|---|---|---|---|---|---|---|---|---|
|Variable|Mi|cro|Sm|all|Me|dium|La|rge|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|apex|0.019***|0.016***|0.015**|0.013*|0.046**|0.035**|0.051|0.056|
||(0.004)|(0.004)|(0.007)|(0.007)|(0.019)|(0.017)|(0.051)|(0.052)|
|lfrmssize||0.008*||0.005||0.010||-0.076|
|||(0.005)||(0.008)||(0.025)||(0.067)|
|frmsage||-0.006||-0.002||0.177||0.107|
|||(0.004)||(0.013)||(0.147)||(0.087)|
|frmsage2||-0.000||0.000||-0.000||-0.000|
|||(0.000)||(0.000)||(0.000)||(0.001)|
|lwage||0.000||-0.026||-0.018||0.060|
|||(0.004)||(0.020)||(0.023)||(0.045)|
|lschool||0.008||0.051*||0.097||0.141|
|||(0.011)||(0.027)||(0.075)||(0.356)|
|shexpsrnet||0.008***||0.006||0.024*||0.036*|
|||(0.003)||(0.007)||(0.014)||(0.021)|
|ManagerexpL1||0.097||-0.015||0.208||0.179|
|||(0.258)||(0.191)||(0.156)||(0.141)|
|ManagerexpL2||0.163||-0.063||0.186||0.084|
|||(0.203)||(0.187)||(0.160)||(0.151)|
|ManagerexpL3||0.166||0.011||-0.036||-0.121|
|||(0.175)||(0.044)||(0.116)||(0.192)|
|Import||0.004||0.002||0.156***||-0.106|
|||(0.033)||(0.009)||(0.059)||(0.097)|
|layer||-0.007*||0.004||0.020*||0.083|
|||(0.004)||(0.006)||(0.011)||(0.068)|
|Fixed efects|||||||||
|Firms FE|YES|YES|YES|YES|YES|YES|YES|YES|
|SectorYear FE|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.057|0.077|0.078|0.086|0.178|0.246|0.185|0.276|
|Number of id|3,104|3,104|1,062|1,062|566|566|156|156|
|Observations|6,208|6,208|2,124|2,124|1,132|1,132|312|312|

Notes: Linear Probability Model (LPM) using panel fixed effects and the baseline definition of firms’ size categories. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The number of id refers to the total number of firms (treated and untreated in each size cohort).

Table 9: Heterogeneous effect: Impact of Apex on the probability to export in _t_ according to firms’ size (baseline) Tables 8 and 9 show the LPM for the subsamples defined according to the size of the firm in t-1 (micro, small, medium and large). Overall, it is noticeable that the effect is positive and statistically significant for firms smaller than 150 employees. The effect seems to be larger for medium firms (between 50 and 100 employees), but it is not statistically different than zero for large firms.<sup>67</sup> These results are in line with the findings of Volpe Martincus et al. (2012) that show evidence of more effectiveness of EPAs’ activity for smaller firms.

firms, the marginal effect at the means (MEM) are significant for wearing apparel (4.8 pp), pharmaceuticals (20 pp), rubber and plastic (16 pp), computer (20 pp) and machinery (15 pp). If the definition of size does not take the matched pairs into account, then non-metalic and other manufacturing products become significant. Although the MEM seems very large the propensity to export in these industries is larger. For example, the treatment raises the propensity to export from 17% to 33% in the rubber and plastics industry. With respect to the large firms I have not found differences between industries that are statistically significant. Also, Apex’s effect is significant at 5% in the alternative definition of firms’ size.

> 67The effect is not statistically significant when considering other thresholds for large firms (e.g. 250 employees or more).

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