Sixth, another potential concern is that results might be driven by inspectors specializing in clearing different goods. This concern is mitigated by the fact that, formally, there is no specialization across different inspectors: they all clear the same set of goods. However, one may nonetheless wonder whether the IT department staff who are manipulating assignment are systematically assigning declarations containing certain products to unwitting inspectors that do not have the requisite expertise to adequately evaluate them; they may be seeking out inspectors that are the worst at detecting fraud for particular sets of products. To address this concern, Appendix Table A18 presents regressions where the unit of observation is an item (recall that a declaration can contain multiple items). The dependent variables are the log of the initially declared unit price, adjustments in that unit price, the finally registered unit price, the adjustment in weight (finally registered - initially declared) and an item-specific measure of the hypothetical tax revenue loss. The main regressor of interest is still the excess interaction share and the set of controls now includes HS 8-digit product-inspector fixed effects, broker fixed effects, source country fixed effects, month-year fixed effects, and a vector of both declaration characteristics (the risk score, a dummy for the red channel, a dummy for being a mixed shipment, a dummy for GasyNet’s valuation advice) and the item-specific tax rate. The HS 8-digit product-inspector fixed effects capture the comparative advantage of the inspector in detecting fraud in different types of products. The item-level initially declared unit price is significantly negatively correlated with excess interaction (column (1)). Excess interaction is associated with a lower item-level initial unit price but also with significantly lower adjustments to the unit price. As a result, the final unit price is even more negatively correlated with excess interaction. Excess interaction is also associated with lower weight adjustment and higher potential tax revenue losses, but these associations are not statistically significant at conventional significance levels.

Seventh, evidence of heterogeneity in the differential treatment of deviant declarations is hard to reconcile with explanations other than corruption for our main results. We estimate Equation (5) allowing the excess interaction share to be interacted with the tax rate. Differential treatment by inspectors that interact excessively frequently with a given broker appears particularly pronounced for declarations subject to higher taxes: these are especially less likely to be deemed fraudulent and exhibit significantly higher tax revenue losses, as seen in in Appendix Table A19.

Some final evidence consistent with corruption is provided by the analysis of inspector re-assignments made by the customs port manager. Such re-assignments are substantially more likely when declarations are initially assigned to an inspector with whom the broker is not interacting excessively frequently (see Appendix Table A20). This is inconsistent with re-assignments being random. Moreover re-assigned declarations typically yield higher fraud findings, value and tax adjustments. This is especially the case if they are taken away from inspectors with initial excess interaction, suggesting that these non-randomly assigned declarations were more risky to start with. By contrast, re-assigned declarations from inspectors 24