# **5.2 Estimates for other measures of border performance**

In this paper we seek to use the OECD policy data to explain cross-country variation in measures of customs performance. Our primary interest is in estimates of the reported value of time required to clear customs. But we also would like to check three alternate measures of customs performance: the (logged) cost of clearing import and export customs (also taken from the TAB data), and an index measure of country-level performance in customs (taken from the LPI). Since all three variables are continuous we estimate linear specifications with the performance measure on the left hand side, and use the same right hand side variables as in the other regression model. As with the earlier model, we employ multiple imputation techniques to account for missing policy data. The results are reported in Table 7.<sup>36</sup> In column 1, the dependent variable is the log of the TAB measure of the monetary cost of clearing import customs. In this specification the coefficients on the Advanced rulings and Automation variables are significant at the 5% and 10% levels, respectively. Both variables have coefficients that are economically significant - a one point move in each index (on a 0-2 scale) produces a reduction in reported cost of over 30%. Column 2 reports estimates from a log-linear model with the cost of clearing export customs as the outcome variable. In the case of exports none of the policy variables have statistically significant coefficients, on their own. In both specifications the independent variables explain nearly half of the variation in reported cost.<sup>37</sup> Column 3 of Table 7 reports estimates from a linear model with the _customs_ subcomponent of the World Bank’s Logistics Performance Index as the dependent variable.<sup>38</sup> The evidence in this column suggests that the variables that determine variation in the LPI are rather different than those that determine the TAB outcomes. Coefficients on total GDP, the per-capita GDP variables and WGI-effectiveness are all significant for the LPI measure. None of the policy variables have significant coefficients. In the case of the LPI measure it seems that the basic control variables explain most of the variation in the outcome variable.<sup>39</sup>

> 36None of the coefficients associated with the trade infrastructure variables are significant in any of the three specifications. In order to save space we do not report them.

> 37We refer to a Pseudo-R2 measure in the table because the multiple imputation technique requires an alternative version of the R<sup>2</sup> measure. The interpretation of the measure as guide to the share of variation explained by the model remains nonetheless useful as a device for understanding the model results.

> 38This variable associates larger numbers with better performance in customs, so the expected sign pattern differs from the other two columns. The index does not distinguish between import and export customs.

> 39A regression of the LPI measure on the basic control variables alone returns an _R_ 2 of 0.73. Including the WGI variables in the regression increases the _R_<sup>2</sup> to 0.83. It seems that neither the OECD policy data nor the trade infrastructure data explain any meaningful variation in the LPI customs measure once the control variables are included.

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