to study the effects of “deep” integration through preferential trade agreements combined with infrastructure investment. More generally, our work is also related to the findings of Goldberg & Reed (2022) on the role of demand constraints in national development and, in particular, on how these constraints can be alleviated through international market integration.

The remainder of the paper is organized as follows. Section 1 describes the data used in the analysis. Section 2 presents the ex-post quantification of the trade effects of trade and transport reforms in Africa. Section 3 quantifies the impact of planned infrastructure investments on transport time. Section 4 presents a general equilibrium counterfactual analysis. Concluding remarks follow.

# **1 Data overview**

Analyzing trade and infrastructure in a general equilibrium structural gravity framework requires specific data. The focus of this section is the development and validation of an original dataset on transport times of African exporters. Built using Geographic Information System tools and network analysis, it provides a detailed, finescale perspective on transport times. Given the critical role that transport times play in shaping trade patterns, the production of such data is a contribution in its own right. We also discuss the sources of trade and production data and how we combined them to get an accurate picture of domestic and international trade flows.

## **1.1 Transportation Time**

Transportation times become prohibitive not only when roads are in poor condition, but also when there are delays at borders or operations at ports are inefficient. To get a complete picture of transportation costs for African exporters, we take all these aspects into account and construct a new database of transportation times at the city level. We first map the current infrastructure network to obtain our baseline transport time matrix. We then use the list of investments from the Infrastructure Development Program for Africa (PIDA) in its long-term vision (2012-2040) to create our counterfactual.

We start by mapping the current transportation network to calculate transportation times between large cities in African countries and the rest of the world. The nodes in the global network, which serve as export destinations in the analysis, are all cities (worldwide) with a population over 500,000 and the two most populous cities, data permitting, in each African country. This gives a total of 1,037 cities connected to the transportation network and 32 cities with a population of less than 50,000. The subset for Africa includes 165 connected cities that serve not only as destinations but also as export origins. The final network is solved for each origindestination pair, and routing is determined by the shortest path, adjusting for a maritime preference parameter.

The construction of the transportation network at the city level proceeds as follows.<sup>3</sup> We first build a transportation network for Africa and the rest of the world that includes land and maritime connections. For Africa, we rely primarily on major roads from Open-Street Map, including different classes of roads for which we assume different speeds (see Table A1 in the Appendix). For the rest of the world, we use the land and maritime

> 3To be compatible with trade data, the city-level network is then aggregated to the country level using a theoretically consistent approach. The aggregation procedure, following Hinz (2017), is described in detail below.

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