the ease with which trade occurs between countries _i_ and _j_ at time _t_ .<sup>24</sup>

# **2.2 The Role of Transportation in Shaping Bilateral Trade Frictions**

In this section we want to quantify to what extent transportation – as revealed by the network analysis above – is a source of bilateral frictions in trade relations between African countries and their trading partners. To proceed, we start from _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_,theestimatedtradefrictionmatrixfromthepreviousstepandregressthemon</sup> transportation time. In the regression, we include the existing PTAs (i.e. the baseline) and additional controls that capture geography and historical bilateral determinants of trade (i.e. language proximity, contiguity and colonial ties) and control for unobserved exporter and importer characteristics using fixed effects. The estimation is performed in cross section for the year 2018 because we do not have time variation in our analysis of transport networks.<sup>25</sup> Having identified the functional form for bilateral frictions, we will be able in the next section to build counterfactuals using the estimated elasticity of _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> to changes in transportation time to model our counterfactual scenarios. Table 3 presents the results of these estimates across specifications. Column 1 shows the average impact of transportation time. Column 2, on the other hand, distinguishes between domestic trade (namely _i_ = _j_ ) and cross-border trade, and shows that the elasticity of cross-border trade frictions with respect to transport time is significantly larger than that of domestic trade. In column 3 we introduce a maritime preference in the choice of the optimal route, which has a limited impact on the measured elasticity, as shown by the standard errors. Our preferred estimates are reported in columns 2 and 3. These coefficients are used in the next section to translate changes in transport time into changes in bilateral trade frictions.

In our preferred specifications, we include the PTA variable as a control variable. This ensures that the elasticity of trade frictions with respect to transport time is orthogonal to the trade agreement in question. Omitting PTAs from the equation would have a limited effect on the estimate of the cross-border elasticity, while the effect on the estimate of the domestic elasticity would be slightly reduced. 26 However, it could be argued that there is an interdependence between PTAs and transportation time. Columns 4 and 5 examine this relationship by introducing an interaction between these two variables. The resulting interaction term is negative and statistically significant. For example, in column 4, the elasticity of cross-border trade frictions increases slightly when the interaction with PTAs is taken into account ( _−_ 1 _._ 836 _−_ 0 _._ 581 = _−_ 2 _._ 417). Interestingly, the elasticity of domestic trade frictions also increases when the interaction term is included ( _−_ 1 _._ 024). These results suggest that the expected benefits associated with improved connectivity and synchronized trade policies

> 24All other things being equal, an increase in one of the determinants of trade frictions, such as distance or transportation time, implies a decrease in the ease of trade. The underlying values of _β_<sup>�</sup> and _μ_ � _ij_ are calculated over the entire period and are therefore time-invariant, although both the baseline and the counterfactual are calculated in a cross-section (either 2018 or 2014, depending ondata availability). Since 0.6% of the estimated _μ_ � _ij_ are unidentified, we replace missing trade costs with standard gravity regression predictions using log distance, common border, colonial ties, and language proximity as explanatory variables.

> 25When we use year 2014, although the regional coverage increases to 30 African countries, the estimated elasticity of _τ_<sup>�</sup> _ij,t_<sup>1</sup><sup>_−σ_</sup> to transportation time hardly changes. In 2014, without maritime preferences (corresponding to column 4 of Table 3) the coefficient for log(Transportation Time) _i_ = _j_ is -2.369 (se 0.119); whereas the estimated coefficient for log(Transportation Time) _i_ = _j_ is -0.606 (se 0.157). With maritime preferences (corresponding to column 5 of Table 3) the estimated coefficient for log(Transportation Time) _i_ = _j_ is -2.029 (se 0.101); whereas the estimated coefficient for log(Transportation Time) _i_ = _j_ is -0.651 (se 0.157).

> 26Our preferred specification in column 2 of Table 3 without the PTA variable reports an estimated coefficient of -2.454 (se 0.130) for cross-border trade and -0.408 (se 0.169) for domestic trade. The corresponding general equilibrium tables are available upon request.

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