Greedmtch matching algorithm (see Parsons (2001)), discarding control observations with PS smaller (or larger) than minimum (or maximum) PS value of treatment group.

Matching requires a clear definition of population of interest and outcome (Caliendo and Kopeinig, 2008). In the case of APEX, firms join the program in different years and this decision, as well as its outcome might be strongly affected by macro variables, such as exchange rate and economic growth, among others. In addition, as we see in the descriptive statistics there is sectoral heterogeneity, which results in different economic environments. Heckman et al. (1997) suggest that two important features to reduce bias in nonexperimental programs are to use the same questionnaire for both groups and having participants and controls placed in a common economic environment.<sup>42</sup> In order to deal with this issue I adopted two different procedures. First I used a propensity score as a partial balancing score complemented by exact matching on year and sector (see Lechner (2002) and Todo (2011)).<sup>43</sup> Second, I followed Heckman et al. (1997) and matched the sample, year by year. Both procedures generated similar results. I kept the first one due to the fact that I have a unique covariate’s balance analysis. Under this procedure, the baseline model<sup>44</sup> was a pooled probit from 2007 to 2009 using firms’ characteristics from _t-1_ and _t-2_ as covariates. I used exact matching for year and sector (CNAE 2 digits). For example, a firm belonging to sector _j_ at period _t_ was matched with another firm (control group) at sector _j_ with the nearest PS in period _t_ . Furthermore, I used firms that appear for the first time in the sample as receiving Apex treatment.<sup>45</sup> The results of the probit and the descriptive statistics of the matched sample is available in section 7.2 of the Appendix.<sup>46</sup> A critical assumption in the PSM procedure is the fact that it controls for potential bias relying on observable covariates _Xi_ . However, the performance of exporters as well the probability of firms’ self selection into the program might be lead by unobservable firms’ characteristics. Assuming they are time-fixed characteristics, we can identify the average treatment effect by DID. I used a panel fixed effects model<sup>47</sup> (using log for continuum covariates) to obtain a DID

> 42The dataset used in the paper was obtained through a similar questionnaire between firms (e.g. RAIS).

> 43I also tested exact matching for year, sector and region which provides similar results.

> 44I tested different matching approaches. The mainly difference is regarding the ratio (control firms/treated firms), variables for exact matching was implemented (i.e. year, region, sector) and time lag for the covariates. The results are consistent for different matching procedures and are available under request.

> 45Results using all apex treated firm are available under request.It means that if a firm was receiving apex for 2 years without becoming an exporter it was considered as treated both period.

> 46I also provide further assessment of the matching quality in section 5 and in section 7.2 of the Appendix.

> 47Lechner (2011) suggests the use of a linear specification as an alternative of identification for the non-linear outcome such as exporting status.

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