restricting our analysis to other types of violence (one-sided violence in UCDP or battles in ACLED). In addition, increased gender-based violence due to the influx of workers might be considered as orthogonal to the reasons for local conflicts in DRC. While it is possible for other countries and other non-World Bank projects, none of the World Bank ICR documents lists the eruption of local violence as a reason for stopping or delaying the project. While World Bank projects are only a subset of all projects, their completion would be more likely to be disrupted in case of increased violence compared to projects led by the government. Therefore, it is unlikely that the completion date of such long and often delayed projects is explained by a sharp decrease in violence.

# **3.3 Using Satellite Images to Infer Road Quality**

The road project data detailed in the previous section gives us a start and end year for the project. We will maintain the assumption that between these dates, the project is ongoing, delivering improved road quality only towards the end, where implementation tends to concentrate. What cannot be said, however, is how long the increased road quality is maintained after the end of the project–what one could call the perishability of the peace dividend of road rehabilitation. This matters to identify the impacts of road rehabilitation on conflicts. We expect the average quality on rehabilitated roads to increase until the completion of the project and then gradually fall again, given the harsh tropical conditions reducing the lifespan of newly rehabilitated unpaved earth roads (see discussion in section 2.1).

We use machine learning models for assessing the quality of roads using satellite imagery (Cadamuro et al. 2018; Thegeya et al. 2022; Conner et al. 2023). We first classify the images as described in Muhebwa et al. (2023) to then find a parameter that describes the decay of road quality over time in a particular region of the DRC. Each image is given predicted quality labels from a 2-class and 5-class classifier. We encountered two main problems. First, extensive road quality data is not available to train the machine learning models. We had to use road quality data from other African contexts (Kenya and Liberia) to train the model and then use satellite imagery for the DRC (Muhebwa et al. 2023).

Second, getting remote sensing data for the entire sample period and road network in DRC is not possible due to the limited image availability. We, therefore, focus on estimating a road discount factor from three large road projects taken from our database that were completed after 2010. These projects were large road rehabilitation projects in conflictridden regions in eastern DRC. Muhebwa et al. (2023) provides a road quality classification for 7,121 images of project roads.<sup>5</sup> Each image is a 256 by 256-pixel satellite image at 60 cm/px resolution. We, therefore, obtain a road quality class index for short road segments of 150m by 150m based on a classifier. The classifier was trained on groupings based on the International Roughness Index (Sayers et al. 1986), and we use these evaluations to classify

> 5We start with 55,719 images from the project areas. To clean the data, we first filtered out any roads that were not the three main project roads. This leaves 7,121 images. Of these images, 6,061 are classified as ‘bad’ roads by the binary classifier.

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