period _t-1_ and _t_ for each firm in the matched sample, as a robustness check. Results seem robust for different specifications (the propensity increases from 1.8% to 4.0%). Another robustness check is based on a sample that excludes firms that received assistance through a specific and relatively new program (Peiex) that is targeting smaller firms to improve competitiveness to export.<sup>53</sup> When excluding Peiex’s treated firms and their matched pairs from the sample, Apex’s treatment increases by approximately 1.07 times the export propensity for non-exporters at _t+1_ (column 4), from about 5% to 10%. The effect is larger for the specification without additional covariates at firm level (see column 3). In this case, the propensity to export in _t+1_ goes from 3.5% to 11.3%.

|Dependent variable:||Export du|mmy (t+1)|||Export d|ummy (t)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Apex|0.023***<br>(0.005)|0.020***<br>(0.005)|0.078***<br>(0.015)|0.067***<br>(0.015)|0.022***<br>(0.004)|0.020***<br>(0.004)|0.062***<br>(0.013)|0.056***<br>(0.013)|
|lfrmssize||0.009*||0.042***||0.002||0.000|
|||(0.005)||(0.015)||(0.008)||(0.020)|
|frmsage||0.033||0.061||0.094||-0.012|
|||(0.038)||(0.099)||(0.060)||(0.037)|
|frmsage2||0.000**||0.000||0.000*||-0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lwage||-0.001||0.002||0.003||0.011|
|||(0.006)||(0.018)||(0.005)||(0.013)|
|lschool||0.021||0.120**||0.024||0.098|
|||(0.014)||(0.059)||(0.016)||(0.063)|
|shexpsrnet||0.002||0.003||0.010***||0.031***|
|||(0.003)||(0.007)||(0.003)||(0.007)|
|ManagerexpL1||0.207**||0.188||0.125||0.065|
|||(0.089)||(0.119)||(0.086)||(0.100)|
|ManagerexpL2||-0.036||-0.117||0.104||-0.023|
|||(0.100)||(0.135)||(0.095)||(0.115)|
|ManagerexpL3||0.010||0.051||-0.015||0.044|
|||(0.100)||(0.133)||(0.067)||(0.106)|
|Import||0.001||-0.014||0.025||0.039|
|||(0.025)||(0.056)||(0.023)||(0.054)|
|layer||0.003||0.013||-0.001||-0.009|
|||(0.004)||(0.012)||(0.003)||(0.011)|
|Fixed efects|.|.|.|.|.|.|.||
|Firms|YES|YES|YES|YES|YES|YES|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|YES|YES|
|Number of id|4,888|4,888|1,128|1,128|4,888|4,888|1,128|1,128|
|Observations|9,776|9,776|2,256|2,256|9,776|9,776|2,256|2,256|

Note: Linear Probability Model (LPM) using panel fixed effects. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 6: Impact of Apex on the probability to export (matched sample) Due to the exclusion restriction used to define the population of non-exporting firms in periods _t-1_ and _t-2_ , the dependent variable does not vary in period _t-1_ . Hence, Apex’s treatment coefficients for the specifications in columns (1), (3), (5) and (7) in table 6 are equivalent to the marginal effect at the mean from a probit using a cross-section for the period of treatment

> _(t)_ .<sup>54</sup> Based on a probit estimation with sector-year fixed effects, we can compare the expected

> 53Peiex started at the end of 2008 and has a large number of firms that began to receive the program in 2009. 54Results for Apex’s treatment are also relatively similar for the specification with covariates used as robustness

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