obtain 144 million OSM routes connecting all points and compute distance-weighted local measures of network quality and continental market access (MA). Using data on trading across borders, which I translate into road distance/travel time, I characterize how cross-border trade frictions impede cell-MA. A trade across the median African border is equivalent to _∼_ 1000km of national roads or 3.2 road days. My results imply that particularly cells across the border from major markets such as Nigeria suffer _≤_ 80% MA losses due to frictions.

In the second part of the paper, I identify 447 large cities with populations above 100,000 and 52 international ports with a quarterly outflow above 100,000 TEU and obtain routes between them. I process these routes into a realistic representation of the African transport network comprising 1379 nodes and 2344 edges representing 315,000km of road segments. The network connects 444 million people or 86% of the African urban population. I parameterize the network with fresh routing information along all links and develop an algorithm to find 481 new links (104,000km), yielding route efficiency gains of _≥_ 50%. Using this network graph, I simulate partial equilibrium MA returns from extending and/or upgrading the entire network or individual links. I find that continental MA, denominated in travel time, can be improved by _≤_ 78% through eliminating crossborder frictions. Upgrading the entire network to allow travel speeds of 100km/h yields a 42% frictionless MA gain and a 27% MA gain under current frictions. MA gains from new roads are lower and also decrease under frictions. Spatially, including frictions favors investments in national links, significantly reducing the value of transnational and remote links. I also find sizeable differences in the marginal values and cost-benefit ratios of links and propose three investment packages containing high-return links with and without border frictions. These packages cost between 17 and 61 billion 2015 USD and generate MA gains between 20% and 38%. They are macroeconomically feasible if they can raise the aggregate economic growth rate by _≤_ 0.54%.

The final part of the paper moves this to a general equilibrium setting following Fajgelbaum & Schaal (2020) and Graff (2024). I let cities trade with each other and with port cities, computing optimal investments into the full continental scale transport network. To simulate optimal investments in trans-African roads, I then reduce the network to links connecting the 47 largest (port-)cities. On both networks, I let welfare maximizing social planners spend budgets covering _∼_ 1/3 of all work—15 and 35 billion USD’15, respectively. I simulate cases with/without frictions, increasing returns to infrastructure, and inequality aversion in the planner’s objective.

The $ 35B planner on the full network optimally connects large cities with ports and surrounding cities. Investments are redistributional as in Gorton & Ianchovichina (2021). Under increasing returns to infrastructure, this planner upgrades many more roads and expands into remote regions, yielding higher and more equitable welfare gains. Similarly, the trans-African $ 15B planner primarily upgrades road segments within different populated regions, enhancing connectivity between major cities and with ports. An inequality-averse planner also upgrades an entire corridor crossing southern central Africa from Luanda/Kinshasa to Mombasa/Dar es Salaam. Lowering the elasticity of substitution between different goods to a value of 1.5 creates incentives to also build corridors spanning northern central Africa, better connecting Rwanda/Bukavu and Khartoum to Western Africa. Under current border frictions, investments in connections through Congo and into Nigeria are reduced. Prioritizing the central African infrastructure gap thus requires inequality-averse planners, unrealistic trans-African trade elasticities, and/or efforts to reduce border frictions.

Methodologically, in this paper, I develop a rigorously quantitative approach to characterize economically optimal investments into large road networks. I demonstrate the use of geospatial big data and routing engines to algorithmically create large and accurate network graphs, including potential new roads, and simulate investments on them via established quantitative frameworks. I am also the first to account for cross-border frictions and trade through ports to study their implications for optimal spatial allocations. Detailed estimation of road construction/upgrading costs facilitates interpretation and enables link-level cost-benefit analysis. A corollary of the work is an easily accessible Julia implementation<sup>2</sup> of Fajgelbaum & Schaal (2020)’s MATLAB toolbox.<sup>3</sup>

> 2https://github.com/OptimalTransportNetworks/OptimalTransportNetworks.jl

> 3https://github.com/OptimalTransportNetworks/OptimalTransportNetworkToolbox

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