relies on the assumption of parallel trends and does not control for selection on observables (firm-level covariates).

The DID method can be improved by matching that controls for observed firm characteristics correlated with both program participation and performance. The key assumption for the impact estimated by this method to be unbiased is that selection into the program is based only on observable firm characteristics.<sup>30</sup> The matching procedure evolves in two steps. First, firm-level covariates are used to predict the probability of getting (or enrolling into) the program using a probit or logit regression. This predicted probability is called a propensity score. Second, the control group is formed by picking, for each treated firm, the untreated firms with the closest propensity score. For each treated firm, depending on the method, there can be either one matched control firm or several, using a weighted scheme.<sup>31</sup> Average outcomes in first differences are then compared between the treatment group and the matched control group. The propensity score matching DID estimator allows for time-invariant unobserved firm heterogeneity to affect selection and outcomes. But it does not address the problem that selection - as well as outcomes - may depend on unobserved time-varying firm heterogeneity, as will be discussed below as well as in section 6.3.

The studies surveyed in Volpe (2011) are good illustrations of the use of quasi-experimental methods in the evaluation of trade assistance. These studies, recently carried out at the Integration and Trade Sector of the Inter-American Development Bank, use DID and matchingDID methods to assess the effectiveness of export promotion activities of PROMPEX/PROMPERU (Peru), PROCOMER (Costa Rica), URUGUAY XXI (Uruguay), PROCHILE (Chile), EXPORTAR (Argentina), and PROEXPORT (Colombia). They use rich and unique datasets for the six Latin American countries that combine firm-level customs data with covariates drawn from other national firm-level data sources, and constitute the first rigorous micro-based evidence of the effects of export promotion.<sup>32</sup> The picture emerging from Christian Volpe’s survey is that export promotion was effective in facilitating export expansion for firms in the LAC region, but primarily along the extensive margin. Firms exporting differentiated goods benefit more than those selling more homogeneous goods. Small and relatively inexperienced companies benefit more than larger and already established exporters. Finally, bundled services that provide support to firms throughout the export development process appear to be more effective than isolated actions.

> 30 This assumption is designated as ―ignorable treatment assignment‖ by Rosenbaum and Rubin (1983) which is the seminal study on propensity score matching estimation. The assumption means that program participation and outcomes are independent, conditional on a set of observed attributes.

> 31 The single-match method is called ―nearest-neighbor.‖ Alternatively, one can use _n_ nearest neighbors, or the entire sample of untreated firms with weights that decrease with distance from the treated firm’s propensity score. This latter method is called ―kernel matching.‖ Many other refinements are possible. See Caliendo and Kopeinig (2005) for details on propensity score matching estimators.

> 32 An alternative, more traditional route to the evaluation of export-promotion’s effectiveness is the aforementioned cross-country study of Lederman et al. (2010).

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