and 26.8%/24%/19.5% under frictions. These packages are globally macroeconomically feasible under frictions if they can raise Africa’s aggregate growth rate (4.1%) by 0.33%/0.2%/0.09%. For the _>_ $ 1 ( $ 60.9B) package, this would require that growth in MA translates into economic growth at a rate of _≥_ 81:1. This is plausible in light of empirical results suggesting economic returns to MA gains as high as 3:1, but there are several factors that could raise the cost of these packages considerably and lower the macroeconomic returns, especially if they are financed through debt.

Calibrating a general equilibrium framework by Fajgelbaum & Schaal (2020) permitting global optimization over the space of networks, I also derive optimal investments in spatial equilibrium, letting a welfare-maximizing social planner spend a fixed infrastructure budget. My calibration accounts for trade in multiple goods and through international ports. I consider cases with/without inequality aversion, cross-border frictions, and increasing returns to infrastructure. I also simulate with different values for the elasticity of substitution ( _σ_ ). Planners with a baseline budget of $ 35B optimally connect large cities and ports with each other and surrounding smaller cities, focusing on populated/productive areas. An inequality-averse planner invests slightly more in the poorer Central African and the Horn of Africa regions. About 30% of the investments are new/proposed links, built particularly around major cities to limit congestion. With increasing returns to infrastructure (low congestion forces), the investments become less concentrated around large cities, and the planner upgrades more roads (building few new ones). This increases welfare gains. With inequality aversion, the gains again become more equitable at minor aggregate losses, and the planner upgrades more roads in Central Africa. Overall, due to concave utility of consumption, remote cities gain more welfare despite receiving less infrastructure.

To focus spending on important roads, I additionally consider a trans-African planner that optimally invests in continental transport roads connecting 47 major (port-)cities. The planner, endowed with $ 15B, also mostly improves regional connectivity under standard assumptions of _σ_ = 3 _._ 8. Much is invested along the West African coastline, but also in better connecting Dakar to Bamako, Khartoum to Egypt, Kampala to Mogadishu via Mombasa, and Kinshasa to Mbuji Mayi. An inequality-averse planner fully connects Kinshasa to Bukavu/Rwanda and further to Mombasa, Mogadishu, and Dar es Salaam. Thus, the planner upgrades a corridor crossing populated Central Africa. To include truly pan-African investments with roads across sparsely populated Northern Central Africa (e.g., Goma to Bangui) and the Sahara within an optimally spent $ 15B budget would require an unrealistically low elasticity _σ_ = 1 _._ 5 coupled with inequality aversion and increasing returns. Further, activating border frictions reduces equilibrium investments along these links again, indicating that such investments should be coupled with trade facilitation measures.

In conclusion, the paper characterizes optimality for road network investments at different spatial scales and economic objectives from a pan-African perspective. It thus provides broad guidance for policymakers interested in improving Africa’s road infrastructure. An overarching finding is that trans-African roads are neither MA nor welfare maximizing compared to benefits from improved local connectivity in populated areas or between nearby cities and coastal ports. This is especially true under border frictions, which reduce the value of trans-African links. Yet, these links are essential for trans-African trade under AfCFTA to pick up. A general policy recommendation is thus to prioritize regional connectivity improvements, reduce border frictions, and invest in trans-African roads along specific high-yield links/corridors. The graphical results provided in the paper and appendix should be helpful in prioritizing continental and regional investments, with detailed data and results available on GitHub. The multi-level approach combining routing engines, PE, and GE analysis can also be adapted to examine specific regions/investments— such as, recently, for the CEMAC region as part of a World Bank regional infrastructure assessment program. Towards this end, the Julia library and reproducibility package are utile.

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