(2007). Table 1 reports the results of the two-step validation exercise.

First we aim at identifying the tariff equivalent of one additional day of shipment for African exporters with the rest of the world by regressing the log of trade in 2018 on the number of days ( _days_<sup>#</sup> _ij_<sup>)neededtoreachany</sup> of the destination markets in the sample.<sup>15</sup> The estimated equation reads: where _i_ is the exporter country, _j_ the importer country, _Dij_ a vector of bilateral trade frictions (excluding distance), with _i_ being restricted to African exporter countries while _j_ being any destination in the sample.

Results are shown in column 1 and 2 of Table 1. In column 1 we focus on the shortest path in the transportation matrix without restrictions while in column 2 we add, whenever possible, a maritime preference to the algorithm searching for the shortest path. The estimated coefficients are then translated into tariff equivalent using an elasticity of substitution of _σ_ = 6, according to the following formula: Tariff Equivalent = ( _exp_ [( _β_<sup>_days_</sup> _/ − σ_ ) _−_ 1]) _∗_ 100.<sup>16</sup> In our preferred specification, column 1, an additional day of transport is equivalent to a 2.4 percent tariffs _for African exporters_ , a plausible value considering that Hummels & Schaur (2013) estimate _for the United States_ a tariff equivalent in the range of 0.6 to 2.1 percent. With a preference for maritime routes in the calculation of the optimal transport time, we obtain a tariff equivalent of 1.8 percent (in column 2).

In the second step, that is, in column 4 of Table 1, we regress the number of days against distance (without maritime preferences) and then use the equivalent tariff estimates from column 1 to infer the elasticity of trade costs (which we do not observe) to distance as in Hummels (2007), using _exp_ [( _β_<sup>_days_</sup> _/ − σ_ ) _−_ 1]) _∗ βdist_ . The results in column 6 replicate the analysis on data with maritime preferences, using the equivalent tariffs derived from column 2. The estimated equation reads: Column 3 and 5 show the estimation using the log number of days as a dependent variable by sake of comparison. In both columns 3 and 5, the estimated elasticity is around one: the number of days is proportional to the distance, as expected.

The regression results on the number of days indicate an elasticity of trade costs to distance of 0.180 when transportation time is calculated without the maritime preference (column 4) and 0.178 when more weight is given to the maritime route (column 6). The implied elasticity for African exporters is therefore slightly higher than 0.151, the one estimated for the United States in Hummels (2007). The evidence presented here suggests that transport time data can be used with some confidence to quantify the economic impact of trade reforms and infrastructure investments in Africa.<sup>17</sup>

> 15Note that the trade data used for the transport time validation also include domestic sales. The construction of the trade data is detailed in Section 1.2.

> 16The average of elasticities of substitution significant at the 1 percent level is 6.3 in Fontagn ́e, Guimbard & Orefice (2022).

> 17Note that the estimates presented to date are for 2018, and repeating the analysis for 2014 produces virtually unchanged results. The corresponding tables are available on request.

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