probit specification. If the marginal effects at the means for different industries are compared, it is noticeable that the impact is heterogeneous and seems to be larger and for more industries among medium firms. Also the effect seems positive for some industries that are highlighted as their main target by Apex.<sup>65</sup>

|Dependent Vari|able: Expor|t status (t+|1)||||||
|---|---|---|---|---|---|---|---|---|
|Variable|M|icro|S|mall|Med|ium||Large|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|apex|0.014***|0.013***|0.024**|0.024***|0.067***|0.048**|0.038|0.035|
||(0.004)|(0.004)|(0.010)|(0.009)|(0.022)|(0.019)|(0.052)|(0.050)|
|lfrmssize||0.007||0.024**||-0.002||0.083*|
|||(0.006)||(0.012)||(0.026)||(0.048)|
|frmsage||-0.007||-0.013||0.166||0.109|
|||(0.005)||(0.018)||(0.164)||(0.085)|
|frmsage2||0.000||-0.000||0.000||-0.000|
|||(0.000)||(0.000)||(0.001)||(0.001)|
|lwage||-0.003||-0.017||-0.049*||0.172***|
|||(0.004)||(0.028)||(0.028)||(0.040)|
|lschool||0.012||0.033||0.103||-0.121|
|||(0.014)||(0.039)||(0.096)||(0.334)|
|shexpsrnet||0.003||-0.002||-0.008||0.027*|
|||(0.002)||(0.007)||(0.013)||(0.015)|
|ManagerexpL1||0.036||0.069||0.422***||0.256**|
|||(0.277)||(0.203)||(0.139)||(0.121)|
|ManagerexpL2||-0.445***||0.087||0.220||-0.258*|
|||(0.169)||(0.250)||(0.175)||(0.136)|
|ManagerexpL3||0.330***||-0.005||-0.152||-0.050|
|||(0.119)||(0.189)||(0.164)||(0.208)|
|Import||0.010||-0.027||0.129**||-0.192**|
|||(0.038)||(0.031)||(0.054)||(0.092)|
|layer||-0.001||0.003||0.028**||-0.143*|
|||(0.004)||(0.007)||(0.013)||(0.075)|
|Fixed efects|||||||||
|Firms FE|YES|YES|YES|YES|YES|YES|YES|YES|
|SectorYear FE|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.053|0.101|0.106|0.114|0.197|0.284|0.264|0.417|
|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). In the alternative specification of large firms that includes all firms in the matched sample (treated and untreated firms) above 150 employees, instead of using the definition based on treated firms and using their respective matched pairs as a counterfactual, the coefficient for “Import” is not significant. In addition, many large firms are importers in both periods and many in this sample changed their importing status from _t_ to _t+1_ in this sample. A similar issue happens with ManagerexpL2 in the sample of micro Table 8: Heterogeneous effect: Impact of Apex treatment on the probability to export in _t+1_ according to firms’ size (baseline) I used the same procedure to estimate the contemporaneous effect. Again, the effect of Apex is positive and statistically significant at 5% for micro, small and medium firms (see table 9), but not for large firms. Furthermore, the contemporaneous effect is significant only at 10% for small<sup>66</sup>

> 65See information on sector projects available on www.apexbrasil.com.br. These sectors include: food, beverage, agribusiness, bulding, furniture, culture (e.g. musical instrument, art), design, machinery and equipment, fashion industry (e.g. wearing apparel, leather, shoes), health devices, among others.

> 66Regarding micro firms, the contemporaneous (export in period _t_ ) marginal effect at the means (MEM) are statistically significant for wearing apparel (1.3 pp), leather (7.0 pp), chemicals (4.4 pp), machinery (5.0 pp) and other manufacturing products (5.3 pp) - this group of products is equivalent to division 32 of the ISIC Rev.4, which includes jewellery, bijouterie, musical instruments, sports goods, games, toys and medical instruments. With respect to small firms, there is no statistically significant difference between industries. Among medium

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