non-compliance rates and sampling rates, which we test by evaluating the hypothesis that �� > 0. We conduct this test controlling for product group and country of origin fixed effects.

The need to control for various sets of fixed effects combined with the very small number of shipments found to be non-compliant (as will be shown in Section V) leads us to estimate Equations (2) and (3) by OLS as linear probability models, acknowledging the shortcoming that the estimated predicted probabilities may not be meaningful since they can lie outside of the [0,1] interval. But this is not a limitation for the qualitative predictions from the regressions which are our focus.<sup>39</sup> One important challenge for Equation (2) and (3) tests is the endogeneity of the sampling rate. A responsive border agency will react to evidence of non-compliance by raising the sampling rates, even within the current time period. This is a feedback effect that would strengthen the coefficient on �� in cases where the non-compliance rate is unexpectedly high.<sup>40</sup> Another possibility for an increase in sampling activity in a given product group-country pair could be due to a notification of non-compliance somewhere else in the world, for example via the EU’s RASSF. We will estimate Equations (2) and (3) excluding some products where such feedback effects might be stronger and using lagged sampling rates to address this endogeneity concern.

# **_IV.3 Effect on imports_**

One of the central questions in the literature on impact evaluation of trade facilitation reforms concerns the effects of reforms on trade. Even in the enviable situations where complete and integrated data sets are available from a customs agency, estimating this effect is challenging. In cases like ours, where an integrated data set is not available, the challenge is even greater. The policy reform that we consider lacks the specific aspects that are useful for evaluation (such as randomized selection of shipments for inspection). While the identification of trade effects of this reform is considerably more difficult, we undertake it anyway because reforms of this kind have not been studied in the literature and our evidence can begin to fill in this important gap.

Our baseline model is a simple difference-in-differences (D-i-D) model that relies on variation across products in their exposure to the reform. Using the Makstat data, we ask whether the products for which the FVA has oversight responsibilities saw faster import growth than other products in the period after the FVA reform, relative to the period before the reform. Consider as a baseline, the following regression model: 4) ln (����) = ������� + ��� + ��� + ����,

> 39 We were unable to obtain results for Equations (2) and (3) defined as logit regressions due to convergence problems. 40 Note that a within-year feedback effect would be the correct management response by the border agency.  Insofar as we are evaluating the link between non-compliance rates and sampling rates as a correlation, the feedback effect is not a problem so long as we do not interpret the coefficient as a causal effect of sampling rates on non-compliance rates.

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