differences-in-differences approach that dynamically uses areas not yet treated as a control group, (2) a longdifference approach that focuses on understanding the impact of road upgrades on incidentally connected localities, and (3) a market access approach.

First, we leverage temporal and spatial variation of road upgrades to estimate a differences-in-differences model, restricting our sample to units that received an upgrade at some point over the study period. In doing so, we control for unobserved time-invariant heterogeneity in locations, as well as common temporal trends throughout Ethiopia. Moreover, we recover average treatment effects by length of exposure to improved roads. Treatment effects in post-treatment periods allow an understanding of how long impacts took to materialize after improvements, whereas treatment effects in pre-treatment periods allow us to test whether treatment and control units had parallel trends in outcome variables before treatment.

Our second empirical approach focuses on understanding the impact of RSDP on areas that incidentally benefited from the program; that is, on localities that were not targeted by RSDP but were near an upgraded road that connected two targeted localities. Localities that were incidentally connected (because they happen to lie between two targeted localities) provide a possible source of exogenous variation in network connections. We rely on a long-difference framework, comparing non-targeted localities near an upgraded road to those far from an upgraded road. To further address endogenous road placements, we instrument road upgrades with a hypothetical road network that represents what would have been constructed if connecting all targeted locations in a least-cost manner was the chief concern.

For our third empirical approach, we construct a standard measure of market access that computes, for each unit, the size of the total market that can be reached, weighted by the inverse transportation cost of traveling to different markets. While the previous two approaches consider the treatment as an improvement of any type, the market access measure allows us to use the changes in speed limits associated with road upgrades. We use a long-difference strategy to understand how changes in market access are associated with changes in outcome variables. To uncover the causal impact of market access on outcome variables, we control for baseline levels of market access, local economic activity, and pre-trends in selected variables. In addition, to further account for non-random road placement, we instrument market access with a version of market access that excludes immediately surrounding localities when constructing the market access measure. As a robustness check on these results, we also implement a panel data model that controls for time-invariant characteristics.

These different empirical approaches each come with their own set of advantages and limitations. For example, the difference-in-difference approach enables us to leverage the granular nature of both the satellite imagery and the road data and to test whether locations were endogenously targeted based on pre-trends. However, to temporally fix the time of “treatment,” this approach considers treatment to begin the first year 3