In our second exercise we consider an outcome that has not yet been investigated in the literature on impact evaluation of trade facilitation reforms: rates of compliance with import regulations. Specifically, we investigate the outcomes of laboratory tests ordered by the FVA. One of the objectives of the risk-based sampling reform was to more closely align testing activity with the likelihood of non-compliance. We design econometric tests of this hypothesis.

Let ������� be an indicator variable that takes the value of one if a sample taken from shipment _s_ of product group _k_ from country _i_ at time _t_ is found to violate FVA standards and zero if it is compliant.<sup>37</sup> For example, _salmonella_ might be found in a shipment of imported chicken. We shall call ������� a noncompliance indicator. Next, we calculate sampling rates ����� as the ratio of the number of shipments that were sampled to the number of shipments arriving for import clearance at the _kit_ level. Under the principles of risk management, sampling rates should be higher when probabilities of non-compliance are higher.<sup>38</sup> Our initial model takes the form: 2) ������� = �� + ������� + �����, to which we add product group, country of origin, and time-period fixed effects in succession. Our initial test simply asks whether the probability of non-compliance is higher when the sampling rates are higher, i.e., �� > 0. Sampling rates can vary across product groups for reasons unrelated to the probability of noncompliance. Most notably the level of harm can vary substantially across products, and we use product group fixed effects to capture this variation. It could also be that products imported from distant countries or countries with low levels of per capita income are more likely to be non-compliant, and the country fixed effects are intended as controls for such variation. Time-period fixed effects may soak up time-based variation in threats, though the source of such variation is not as obvious as in the case of product group and country effects. We estimate the model on pre-reform data, i.e., up to June 30, 2014.

In order to understand whether the FVA reform affected relationships between non-compliance and sampling rates we estimate a model with interaction terms. We augment Equation (2) in the following way: 3) ������� = �� + ������� + ���� + ��������� + ����� where �� is an indicator variable that takes the value of 1 in the period from July 2014 onwards when the reform is in place and 0 otherwise. An effective reform should have strengthened the relationship between

> 37 In principle, more than one sample can be taken from a single shipment, but our data do not specify systematically the number of samples taken from each shipment.  Thus, we use only the information on whether or not the shipment was subject to sampling and laboratory testing.

> 38 A literature on racial profiling in policing considers a similar exercise when testing for racial discrimination in police search activity.  Knowles et al. (2001) build a model in which compliance rates are responsive to policing, and propose an “outcome test” which posits police bias if the searches find drugs in the same proportions across races.  The focus is on outcomes rather than search activity.  The assumption of fully responsive compliance rates is probably not suitable for our exercise (i.e., food spoilage may be mechanically higher for shipments that travel larger distances).  We focus instead on the question of whether or not inspectors are searching more among groups of shipments that are more likely to be non-compliant.  This is one of the goals of the reform effort, and it may or not be true of inspection activity prior to reform.

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