distance with two alternative measures, the driving distance along the best route (to adjust for quantity) and the time traveled along the best route (to adjust for quality), both computed using Google Maps. Using these alternative measures of distance changes the coefficients differently for different countries, without a clear pattern.

The methodology for recovering trade costs that we employ relies heavily on precisely identifying the source location of goods. One concern is that the source locations of imported goods included in our sample are not precisely identified for every period. That is, we may be incorrectly assigning the border or the port of entry. Alternatively, it may be that some of the distribution or logistic costs are already included in the origin price that we observe, because we may not be measuring these goods exactly at the border, so that trade costs for these goods may be underestimated.

For these reasons, we rerun steps 1 and 2 of our estimation procedure considering locally-produced goods only. Results are included in Panel B of Table 5, for all distance measures. It turns out that focusing on locally produced goods significantly changes the results. Coefficients estimated using geodesic distance increase markedly for Georgia, Rwanda, and Tanzania, where the share of imported products in the sample is above 30%, and for Madagascar, where only one product is imported, but for which we have only 9 products. In Georgia, the coefficient turns from zero to 0.084 and in Madagascar, Rwanda and Tanzania the coefficients increase by a factor of 2.8, 4.5 and 3, respectively. The coefficient for Kenya remains unchanged, since there are no imported goods, and the coefficients for Nigeria decrease 20%. The implied _ad valorem_ equivalents change in the direction of the change in the coefficients, and are included in Figure 3.<sup>9</sup> For example, in Georgia the _ad valorem_ transport cost is now 15.2%, and in Madagascar and Rwanda it is above 20%.

# **5.1 Robustness checks**

In this section, we perform a number of robustness checks to study the sensitivity of the results in section 4.3 to the assumptions regarding the estimation of pass-through rates and intra-national trade costs. The estimates of the effect of distance on trade costs obtained under these alternative specifications are presented in Table 6, which reproduced the baseline estimates in the first row. In all cases, we use our preferred sample of locallyproduced goods only. We explain each of the robustness checks in what follows.

We begin by exploring alternatives to the estimation of pass-through rates, which are important to control for the effect of imperfect competition, in rows 2-4. The baseline specification bottom-codes estimates of pass-through rates by setting _ρ_ ˆ<sup>_k_</sup> _od_<sup>= 0</sup><sup>_._2 if</sup><sup>_ρ_ˆ</sup><sup>_k_</sup> _od_<sup>_<_0</sup><sup>_._2,</sup> since the theoretical framework imposes that pass-through rates be positive. Results are sensitive to using the raw estimates, as can be seen from row 2 of Table 6. The effect

> 9To compute the _ad valorem_ equivalents, we now use the average destination price of locally-produced goods.

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