Under the DID assumption the within FE estimator identifies Apex’s effect on the propensity of non-exporting firms to become exporters in period _t+1_ - columns (3) and (4) - and period _t_ - columns (7) and (8). It is noticeable that under this condition, Apex’s effect in _t+1_ is substantially smaller than its contemporaneous effect. In section 4 I explained the reason why the within estimator for the period _t+1_ may be downward biased due to the contemporaneous effect of firms that enter into the program in different years.

|Dependent variable:||Export du|mmy (t+1)|||Export d|ummy (t)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Apex|0.034***<br>(0.004)|0.020***<br>(0.004)|0.009***<br>(0.003)|0.009***<br>(0.003)|0.028***<br>(0.003)|0.017***<br>(0.003)|0.023***<br>(0.003)|0.022***<br>(0.003)|
|lfrmssize||0.001***||0.003***||0.000**||0.004***|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|frmsage||-0.000***||-0.000||-0.000||0.001|
|||(0.000)||(0.001)||(0.000)||(0.001)|
|frmsage2||0.000||0.000***||-0.000||0.000***|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lwage||0.001***||-0.000||0.001***||0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|lschool||0.002***||-0.001||0.002***||-0.001|
|||(0.000)||(0.001)||(0.000)||(0.001)|
|shexpsrnet||0.064***||-0.110***||0.073***||0.283***|
|||(0.005)||(0.018)||(0.004)||(0.022)|
|ManagerexpL1||0.222***||0.032||0.116***||-0.030|
|||(0.019)||(0.032)||(0.017)||(0.029)|
|ManagerexpL2||0.172***||0.039||0.140***||0.076**|
|||(0.020)||(0.035)||(0.019)||(0.032)|
|ManagerexpL3||0.366***||0.063***||0.276***||0.072***|
|||(0.012)||(0.024)||(0.011)||(0.023)|
|Import||0.073***||0.020***||0.063***||0.033***|
|||(0.003)||(0.004)||(0.002)||(0.004)|
|layer||0.003***||0.001**||0.002***||0.000|
|||(0.000)||(0.000)||(0.000)||(0.000)|
|Fixed Efects|.|.|.|.|.|.|.|.|
|Firms|-|-|YES|YES|-|-|YES|YES|
|Year*Sector|YES|YES|YES|YES|YES|YES|YES|YES|
|State|YES|YES|-|-|YES|YES|-|-|
|Number of id|270,969|270,354|270,969|270,354|270,969|270,354|270,969|270,354|
|R-squared|0.010|0.159|0.004|0.007|0.007|0.114|0.011|0.022|
|Observations|655,021|653,267|655,021|653,267|655,021|653,267|655,021|653,267|

Note: Linear Probability Model (LPM) using pooled OLS and panel fixed effects. Treated firms in 2007 are excluded from the sample. Standard errors clustered at firm level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 5: Impact of Apex on the probability to export (full sample without matching) Now I show the results using the matched sample based on the PSM method and a linear probability model (LPM) with firm and time-sector fixed effects to identify the impact of Apex’s assistance on the probability of becoming an exporter (see table 6). Apex’s treatment increases by approximately 2.3 percentage points (pp) the probability of a non-exporting firm to become an exporter (see column (1) in table 6). What does this effect mean? The propensity to export in period _t+1_ for an average matched firm in the control group is 1.75%. Apex’s treatment increases this probability to 4.05%. Therefore it more than doubles the chances of a non-exporting firm to become an exporter (it increases its propensity by 130%). I also added other covariates for the 18