where _t_ indicates the years since the segment quality peak. We fit _b_ 0 and use this value to compute a _δ_ to describe the decay of all roads. When computing the slope of a single segment, we weigh the slope by the number of images in that year. This ensures the model puts more weight in places where there are more measurements. The slopes of each segment ( _b_ 0) are combined into one discount factor using a weighted average, where the weight is the number of images in a particular segment.

To illustrate our data _y_ , Figure 7 zooms into segments 180, 181, and 182 and shows the IRI classifier output for each image for three years. The edge of each segment is marked with a light blue point. Each colored pixel represents one of the tiles fed into the deep-learning classifier, and the color represents the predicted probability, where a darker green tile means a better road and a darker red tile means a worse road. Road quality declines overall. This is visible through the increasing red shades on the right. The exception is segment 180, which is the first segment. This segment first declines in quality from 2012 to 2016 and then increases in quality again in 2021.<sup>8</sup> The resulting data is shown in Appendix Figure A4. From this data, we estimate the road discount factor using the equation above as (0 _._ 735 _±_ 0 _._ 05). This implies that, after three years, 40 percent of a rehabilitated road would still be of the same quality as just after rehabilitation, and 60 percent would be unusable.

# **3.4 Mining and Natural Resources**

As discussed in section 2.2, mining can be an important mediating factor affecting both roads and conflict events. We collected information on the location of both large and artisanal mines in eastern DRC. We used data from the World Bank and S&P Global to compile a list of mining sites, including information on their name, type of commodity extracted, status, and location. We complemented this list with additional research on the company, the type of involvement (Private vs. Public vs. Joint Venture), and the country of ownership. We used IPIS Open Artisanal and Small-Scale Mining in eastern DRC data to map the artisanal mining sites.<sup>9</sup> Mapping the location of mines and donor-funded road rehabilitation projects shows that mining sites cluster near road investment projects (Figure 8). The first reason is colonial path dependency: many of today’s mines exploit deposits first brought up to stream by the Belgian colonial administration, which developed the road network in large part to facilitate mining. However, not all currently exploited deposits existed during colonial times, and many colonial-era mining roads have been overtaken by the jungle in the decades since. The second reason is that miners find a way to reach mines and transport ores if profits exceed the costs of production and transport. In many cases, artisanal miners cut new, improvised footpaths to mining sites; in the case of rich mineral deposits, more capitalintensive roadwork may follow the mining activity, entailing road improvement exclusively with the transport of mining material in mind. There is important variation here between

> 8The resulting data for the three segments is shown in Appendix Figure A3.

> 9 `https://ipisresearch.be/publication/ipis-open-data-dashboard-on-the-artisanal-and-s mall-scale-mining-sector-in-eastern-drc/`

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