4 and 9. The definition of all variables is provided in Appendix Table A1. Here we briefly describe the declaration-level customs outcomes on which we will estimate the impacts of corruption. These are clearance time (measured as the log number of hours from the time the declaration was (last) assigned to an inspector to her assessment of the declaration), a dummy for whether or not fraud was recorded, the change in log value (finally registered - initially declared), tax adjustment, and hypothetical tax revenue losses described below. As additional declaration-level customs outcomes used in robustness exercises we consider the change in log weight (finally registered - initially declared) and the gap between the port authority weight and the initially declared weight (for simplicity called weight gap).

Hypothetical tax revenue losses for a declaration are computed based on the difference between hypothetical tax yield and actual tax yield. Measuring hypothetical tax yield is notoriously challenging given that it is unobserved. Our baseline measure of a declaration’s hypothetical tax yield considers as a reference price for each of its items the median unit price (ratio of value to weight) reported across Malagasy importers for the same origin country and year. For each item included in the declaration the relevant reference price is multiplied by the item’s weight and the item’s tax rate. Summing the resulting hypothetical item-level tax yield across all items included in the declaration yields the declaration-level hypothetical tax yield. This is a conservative measure, for it assumes that the median unit price is not itself under-reported. Our alternative measure of a declaration’s hypothetical tax yield considers as a reference price for each of its HS 6-digit products the unit price reported by the exporting country in that year in UN COMTRADE multiplied by the products’ weights and by the products’ actual tax rates and sums these across all products in the declaration.<sup>18</sup> This measure has the advantage of using prices that are more likely to be exogenous to tax evasion in Madagascar.<sup>19</sup> Two additional measures of hypothetical tax revenue losses are constructed for two subsets of declarations. For declarations for which port authority weight data is available, hypothetical tax yield is constructed also correcting for underreporting of quantities assuming that the measured port authority weight is correct.<sup>20</sup> For declarations for which GasyNet’s valuation advice was issued, hypothetical tax yield is constructed as the declaration’s advised value multiplied by the average tax rate. As determinants of corruption (and subsequently as controls for evasion risk) we rely on the following _ex-ante_ risk characteristics of the import declaration: the tax rate (tariffs and other taxes), the risk score, a dummy for the red channel, a dummy for being a mixed shipment (i.e., one that includes different items), the share of differentiated products as per Rauch (1999)’s classification, and a dummy for

> 18A HS 6-digit product’s weight is obtained by summing across the weights of all corresponding items. A HS 6-digit product’s tax rate is obtained as the ratio between the sum of actual taxes and the sum of finally declared import value across all corresponding items.

> 19Firms behind a given export flow might conspire with importers in Madagascar issuing fake invoices for them to minimize their tax liabilities. In addition, export unit prices may be downward biased since they are typically recorded as Free On Board (FOB) whereas import prices are recorded Cost Insurance Freight (CIF) and hence include transportation and insurance costs.

> 20We cannot correct quantities declared at the item level since port authority weight is available only for the declaration as a whole. By implication we are assuming that the weight of all items in a declaration is underreported to the same extent.

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