costs of armed actors, which then leads to reductions in violence. This result may add reservations to the idea behind labor-intensive road rehabilitation projects, which are funded as ‘quick impact’ projects, that an influx of cash for work into conflict areas may affect violence (Bachmann and Schouten 2018). However, such question would require a more precise data collection effort to precisely identify when works are performed and workers getting paid.

In the appendix, we provide robustness checks. Generally, we find that the road stock results hold up better - the semi-structural model of road stock decay leads to statistically significant and robust findings regarding violence reduction. Importantly, the road stock results are robust, considering the inclusion of province/year fixed effects. This means that effects are identified within a province, i.e. they are not driven by changes at the province level like other policies that were implemented in these larger political units. We will further discuss robustness to different violence types and data sources.

**Prediction Matched Difference-in-Difference** As discussed earlier, a key problem for identification is the endogeneity of road placement. Even if, as we argue in section 3.2, the start and completion of projects are not directly a function of how violent a region is, the completion of projects could be affected by violence in the sense that project implementation is affected by unobserved factors (such as e.g., local capacity) correlated with violence. This is a hard identification problem to address with observational data.

Mueller and Rauh (2023) develop a method that we call Prediction Matched Differencein-Difference (PreMDiD) to address this issue. The idea is simple: matching observations on the pre-treatment risk evaluation from `conflictforecast.org` allows us to control for the violence outlook in the year before the treatment.<sup>10</sup> We take the December forecast of the year before road completion to construct a control group and then hold three years before and after road completion in treated cells next to three years before and after a placebo treatment in control groups with the same risk forecast. The resulting data of real and placebo treatment windows is then analyzed through the cutting-edge difference-in-difference estimation method by Callaway and Sant’Anna (2021). A key advantage of this method is that it allows for a study of violence dynamics in treatment cells compared to untreated cells. This allows us to study pre-trends and violence dynamics after road completion.

This takes care of a main challenge to identification because it allows us to control for conflict risk when analyzing the effect of road on conflict events. What we are asking is: Holding risk at the cell-level constant, did road projects reduce the realized violence? Figures 12 and 13 show results for the log of UCDP events and fatalities. Period 0 indicates the year in which the treated cells receive a completed road project. The year -1 is the year before roads are completed. Year -3 is the omitted category. Point estimates show the difference between the treatment and control groups. If roads reduce violence, we expect these to fall with completion in year 0. There is a notable reduction of violence intensity (measured by log events and fatalities) with the completion of a road - even if we control for

> 10For a description of the risk forecast see Mueller and Rauh (2022).

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