information problem due to the fact that there might be important firms’ characteristics that simultaneously impact treatment status and the outcome, but cannot be observed in the data<sup>36</sup> (e.g. firms can improve the quality of their management and this may increase their likelihood of receiving the assistance and becoming an exporter at the same time). Apex’s assistance and other programs evaluated in EPA literature I am aware of are mostly non-experimental.<sup>37</sup> Therefore, a critical issue to evaluate EPAs’ programs is potential bias due to self-selection driven by unobservable time-varying characteristics of the firm that can confound the estimator.

I rely on a combination of techniques of propensity score matching (PSM) and differencein-difference (DID) to deal with potential observable and unobservable selection bias, following Heckman et al. (1997).<sup>38</sup> The matching literature suggests we can rely on a set of potential comparison units for those where we observe a similar set of pretreatment covariates, _Xi_ (Rubin, 1977).<sup>39</sup> However, as the number of covariates increases, it gets more difficult to find similar pairs among the untreated units to implement exact matching. An option to deal with this dimensionality issue is by matching units on their vector of pretreatment covariates, _Xi_ , through their propensity score (PS) for receiving the program (Rosenbaum and Rubin, 1983)<sup>40</sup> . To implement the PSM, first I ran a probit to identify the PS of a firm _i_ to receive Apex’s treatment at period _t_ based on _Xi_ ( _t−_ 1 _, ..., t−n_ ) covariates.

I used the PS from equation (1) for matching treated and untreated firms that existed over _t−_ 2, _t−_ 1 and _t_ 0.<sup>41</sup> I analyzed the probability of these firms to receive Apex assistance in _t_ 0 based on their characteristics on _t−_ 2 and _t−_ 1. The option for having firms’ characteristics with two periods before the treatment for matching firms aims to capture available information on the dynamics of both groups of firms, treated and control, which also imposes a parallel trend in the covariates using for matching. The matching procedure adopted was the nearest neighbor following the

> 36A widely accepted solution is to carry a randomized control trial (RCT) in which firms are randomly chosen from the population to be part of treated and control groups (see Duflo and Kremer (2005)). However, this procedure demands ex-ante design that is not always feasible.

> 37In these cases, a standard procedure is relying on econometric techniques geared to deal with this kind of data generated process, such as propensity score matching (PSM), difference-in-difference estimator (DID), Instrumental Variables (IV), Regression Discontinuity Design and possible combinations among them (Blundell and Dias, 2009).

> 38For more examples of the application of this method in applied literature, see Volpe Martincus and Carballo (2008), Volpe Martincus and Carballo (2010a), Chen et al. (2011) and Cadot et al. (2012).

> 39According to Rubin (1977) if for each unit we observe a vector of covariates _Xi_ and _Yi ⊥ Ti|Xi, ∀i_ , then the population treatment effect for the treated, Φ _|T_ =1, is identified: it is equal to the treatment effect conditional on covariates and on assignment to treatment, Φ _|T_ =1 _,X_ , averaged over the distribution _X|Ti_ = 1.

> 40For further details on matching procedure, see Dehejia and Wahba (2002).

> 41The exception are the variables ManagerexpL1, ManagerexpL2 and ManagerexpL3. For those variables I used information from _t−_ 2 in order to identify workers that were in an exporting firm and moved to another firm in _t−_ 1 in an occupation classified as manager.

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