Replication materials for the paper Optimal Investments in Africa's Road Network, published as a Kiel Working Paper and World Bank Policy Research Working Paper. Please see the authors website for the latest version and publication status updates.

Notes

• The code is organized sequentially, but outputs from each script that are inputs to other scripts are saved under data/ or results/. Thus, each script can be executed on its own, without the need to execute earlier scripts. Also within longer scripts important intermediate results are saved. This is usually indicated at the beginning of a section.

• Each script should be executed in a clean R session, due to global options being set in the scripts.

• Some computations can take long (1-2 days) and require significant memory (>= 30 GB). This pertains particularly to general equilibrium network optimization. I have added a note in the script whenever a computation takes long.

• I do not loop over all possible parameter configurations reported in the general equilibrium section. The total time to obtain all results is approx. 2 months on a strong server. Thus, users should exercise discretion and only replicate GE results of interest by setting the appropriate parameter values.

• For GE simulations install OptimalTransportNetworks.jl (v0.1.5 used, but later versions should work) and OptimalTransportNetworkToolbox (v1.0.4b). I used the MATLAB toolbox for most simulations, but Julia for the Trans-African Network simulations in Section 6.2 of the paper. In theory both libraries are equivalent, the Julia library is just relatively new and the dual model solution and autodifferentiation system is not as efficient in Julia (yet). But all results should be replicable using the Julia library, and a corresponding Julia code is provided for simulations run in MATLAB. Going forward, the Julia library will receive further development and autodifferentiation in JuMP is improving. Its use is therefore encouraged and I'll fix any bugs quickly.

• I do not use any reproducible research solution for R because I believe the packages utilized are quite robust and the specific functions employed will continue to work this way in the near future. In case parts of the code stop working at some point, users may try loading packages through groundhog setting "2024-08-30" as date. Note that I used the development version of tmap (v3.99.9002) not yet available on CRAN. I used R v4.3.0. The full set of R packages and versions used is:

  library(fastverse)
  #> -- Attaching packages --------------------------------------- fastverse 0.3.3 --
  #> v data.table 1.15.4     v kit        0.0.17
  #> v magrittr   2.0.3      v collapse   2.0.16
  fastverse_extend(qs, sf, units, s2, sfnetworks, tidygraph, igraph, cppRouting, geodist, stplanr, tmap, 
                   osrm, ggplot2, ggstar, viridis, africamonitor, dggridR, fixest, xtable, mapview, 
                   dbscan, leaderCluster)
  #> -- Attaching extension packages ----------------------------- fastverse 0.3.3 --
  #> v qs            0.25.5        v osrm          4.1.1    
  #> v sf            1.0.16        v ggplot2       3.5.0    
  #> v units         0.8.5         v ggstar        1.0.4    
  #> v s2            1.1.6         v viridis       0.6.3    
  #> v sfnetworks    0.6.3         v africamonitor 0.2.4    
  #> v tidygraph     1.3.1         v dggridR       3.1.0    
  #> v igraph        2.0.3         v fixest        0.12.0   
  #> v cppRouting    3.1           v xtable        1.8.4    
  #> v geodist       0.0.8         v mapview       2.11.0   
  #> v stplanr       1.2.0         v dbscan        1.1.12   
  #> v tmap          3.99.9002     v leaderCluster 1.5
  #> -- Conflicts ------------------------------------------ fastverse_conflicts() --
  #> x dbscan::as.dendrogram() masks stats::as.dendrogram()
  #> x igraph::decompose()     masks stats::decompose()
  #> x fixest::fdim()          masks collapse::fdim()
  #> x tidygraph::filter()     masks stats::filter()
  #> x tidygraph::replace_na() masks collapse::replace_na()
  #> x igraph::spectrum()      masks stats::spectrum()
  #> x igraph::union()         masks base::union()

  ^{Created on 2024-08-29 with reprex v2.0.2}