1. Corruption (“the degree of corruption among government officials”)

2. Rule of law (“law and order tradition”; “the soundness of the established institutions to implement laws and settle claims”) 3. Bureaucratic quality (“efficiency in the provision of government services and the independence of the civil service from political pressures”).

The ratings for these variables range from 0 to 10, with larger values indicating better governance (i.e., less corruption, stronger rule of law, more efficient and independent bureaucracy). We initially included these variables separately in the regression models, but the coefficients on them tended not to be statistically significant because the variables are relatively highly correlated within a country (i.e., the problem of multicollinearity). Significance levels and adjusted goodness-of-fit statistics improved when we instead included aggregate governance variables constructed by summing the three variables and dividing by 3.<sup>11</sup> IM_GOV and EX_GOV are these aggregate measures. We expect the coefficients on these variables to be positive and negative, respectively: stronger governance in the importing country makes discrepancies less likely to be negative, because there is less evasion of import regulations, while stronger governance in the exporting country makes discrepancies less likely to be positive, because there is less evasion of export regulations.

The IM_DUTY×UVAL and IM_DUTY×GOV variables in Equation (6) are interaction terms constructed by multiplying IM_DUTY by IM_UVAL and IM_DUTY by IM_GOV. These interactions are implied by our theoretical model; see Appendix 7. We expect the sign of the former to be negative: the incentive to underreport imports is especially strong when both the import price and the import tariff are high. We have no expectation for the sign of the latter, because IM_DUTY and IM_GOV have opposing effects on the incentive to underreport imports.

# **5.2 Estimation procedures**

We estimated Equation (6) in sequential fashion, beginning with a simple version that included only the measurement-error variables and then adding in the other explanatory variables. For both NC and C sawnwood, we found that the signs, magnitudes, and significance levels of the coefficients changed relatively little as we increased the list of explanatory variables. This provides some confidence in the robustness of the coefficient estimates.

We estimated the model initially using ordinary least squares (OLS). Results of CookWeisberg tests indicated, however, that heteroskedasticity was present: the variance of the error term differed across the bilateral flows. This could lead to two statistical problems, biased estimates of the standard errors of the coefficients and inefficient (imprecise) estimates of the coefficients. In response to the former problem, we reestimated the standard errors of the coefficients using White’s method. We found that they differed little from the OLS estimates.

> 11 Other cross-country studies have used combined versions of the Knack and Keefer variables for similar reasons. Using the first principal component of the three IRIS variables instead of the simple average did not change the econometric results significantly.

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