# **2 Assessing Trade Impacts of PTAs and Infrastructure Investments**

In this paper, we employ a four-step strategy to investigate the trade implications of the AfCFTA and of the improvements in transportation infrastructure. The results of the first two stages are outlined in this section: i) estimating a structural gravity model where the ambition of _signed_ trade agreements explains bilateral trade intensity; ii) giving a functional form to the _unobserved_ bilateral frictions in relations between African countries and trading partners by making them dependent on transport time as measured in our data set. The third stage, which quantifies the impact of planned infrastructure investments on transport time, is presented in the section 3. The final stage, simulating the general equilibrium effects of policy and infrastructure integration in Africa, is discussed in section 4.

## **2.1 The Effects of Different Types of PTAs on Trade**

In this section, we estimate the trade impact of the three clusters of PTAs identified in Fontagn ́e et al. (2023) in a gravity framework in order to recover the partial effect on trade of existing PTAs associated with the different clusters.This estimation is performed on the worldwide sample of countries and the obtained elasticity is used in the trade policy counterfactuals addressing the economic impact of integration among African countries.<sup>22</sup> To this end we estimate the following structural gravity equation with PPML on panel data: Where X _ij,t_ includes both intra-national and international manufacturing plus agricultural trade flows on 1-year intervals from 1986 to 2018.<sup>23</sup> Bilateral flows are normalized with total absorption at destination (X _.j,t_ ), as to mitigate the differences in the penalization of large and small trade flows of the PPML estimator (Eaton, Kortum & Sotelo 2013, Head & Mayer 2014).

Our main variable of interest, PTA _ij,t_ , is sliced across clusters: PTA<sup>_z_</sup> _ij,t_<sup>.Thedummy</sup><sup>_INTL BRDRij_takes</sup> the value of one for international trade flows. We interact this dummy with decades indexed by _T_ (leaving the first period as reference). Exporter-time and importer-time fixed effects control for time-varying Multilateral Resistance Terms, whereas bilateral fixed effects control for time invariant unobserved characteristics of the country pairs, that can potentially lead to self-selection into PTAs (Baier & Bergstrand 2007). As an additional control, we also include the variable _Opted Out PTAij,t_ , to control for agreements that are no longer in force.

The results presented in Table 2 present the elasticity of bilateral trade to the different types of PTAs (as classified by the clustering procedure) in column 1 to 3 for manufacturing and agriculture, manufacturing only, and agriculture only respectively. Two results emerge. First, PTAs of different ambition have a different impact on trade, with PTAs in cluster # 1 having the largest impact and the PTAs in cluster # 3 the lowest respectively.

> 22The estimated elasticity captures the world average effect of trade integration. African countries register only two agreements within the “deep” class: Common Market for Eastern and Southern Africa (COMESA) and Economic Community of West African States (ECOWAS). Estimating a regional specific elasticity would likely be affected by such scanty sample.

> 23Although results are robust to the use of longer intervals, e.g. 4-year intervals as in Anderson & Yotov (2016), we privilege a specification on sequential data as suggested by Egger, Larch & Yotov (2022).

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