variable: import value. For the bulk of our analysis we will aggregate the raw Makstat data to measure that outcome variable at the HS 4-digit-country of origin-semester-year level. The HS 4-digit level of aggregation is chosen to be able to merge the Makstat data set with the list of products under the control of the FVA, which is available at the HS 4-digit level. We will designate products that underwent the FVA reform as treated products for all difference-in-differences and pre-treatment common trends testing regressions. The number of observations per year and some additional basic descriptive statistics based on the Makstat data set are provided in Appendix Table A1. The Makstat data set provides outcomes for 4.5 years of data prior to the reform and 1.5 years after the reform. The Makstat data are aggregated up to the HS4-digit level and concorded to the list of HS4 products overseen by the FVA.

A key challenge to conducting a more detailed analysis of the FVA reforms in terms of sampling and non-compliance is the different product groupings and verbal descriptions that are used to categorize products in the three other data sources: sampling plans, FVA lab sample data set, and EXIM data set. The only way to obtain a data set encompassing the information from the three sources is to define a set of broad product groups that serves as a relatively aggregated common denominator for the three data sets. The list of broad product groups that we define in order to encompass those various product categories and verbal descriptions is shown in Appendix Table A2. We construct several concordances to link across the various data sets, as detailed in Appendix A. Using those concordances, we build a data set which merges across the FVA lab sample data set, the EXIM data set, and the Makstat data set at the broad product group-country of origin-semester-year level, which we use to measure sampling rates and non-compliance rates for the analysis of sampling and compliance in Sections V.1 and V.2. As noted before this data set covers only information corresponding to food of animal origin and animal feeds. The numbers of observations in that merged data set are shown in Appendix Table A3.

An additional source of data we use to construct control variables are the export and import flows from WITS at the annual and monthly levels. We use total exports to the world (except to FYR Macedonia) at the exporting country-HS 4-digit-year level or at the exporting country-HS 4-digit-semester-year level as our foreign supply shock variables and total imports from the world (except from FYR Macedonia) in for each of the four neighbors of FYR Macedonia - Albania, Bulgaria, Greece, and Serbia - at the HS 4- digit-year level or at the HS 4-digit-semester-year level as our import demand shock variables at the HS 4- digit-year and the HS 4-digit-month-year levels, as will be discussed in Section IV.3.<sup>31</sup>

> point of view, our lack of information on inspections by other agencies will raise the possibility of omitted variables bias.  We are unable to address this because we lack the specific data necessary to control for those actions.

> 31 WITS does not report data for Kosovo, another neighbor of FYR Macedonia, thus we focus only on the other four neighbors. As described in Appendix A, WITS data at the country-HS 4-digit product-month-year level is available only from 2010 onwards and for a subset of reporting countries, relative to the data at the country-HS 4-digit product-year level.  Thus, our estimating sample will be smaller in specifications that include the shock variables at the monthly (aggregated to the semester) level.

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