relation Consistent (SHAC) standard errors a la Conley (1999) using the routine developed by Hsiang (2010).<sup>31</sup> We also run a version of this regression at the individual pollution measurement level. To do that, we replace the median road size in the cell with a variable capturing the size of the road closest to the pollution measurement.

Our key coefficient of interest is _α_ 1 _._ A positive estimate would indicate that areas closer to larger roads are more polluted. To interpret _α_ 1 as the causal effect of road traffic on pollution, we need two identifying assumptions. The first is that the location (and size) of roads is predetermined relative to contemporaneous sources of pollution emissions, such as large factories. As discussed in Section 2, the core of the road infrastructure in Uganda was built in the 1960s. This alleviates potential concerns related to the endogenous placement of roads based on the current layout of local economic activity. The second identifying assumption is that the firms in our sample are not the sources of PM2.5 pollution themselves. We note that this is unlikely, given that our sample includes small firms in carpentry, metal fabrication and grain milling, and so the nature of their production process is such that they do not produce substantial emissions of PM2.5. In addition, as discussed above, Figure 1 shows that pollution peaks at rush hour rather than when firms are open, which is consistent with pollution coming from traffic rather than the themselves.<sup>32</sup> To study whether firms next to major roads are more profitable, we estimate the following regression for firm _i_ in grid cell _j_ in sub-county _s_ and region _r_ : where _yi,j,s,r_ is the outcome of firm _i_ , such as log profit per worker. We regress this on the median road size in the cell and the firm-level standardized index of managerial quality _ManScorei_ , controlling for sector fixed effects _λl_ , sub-county fixed effects and distance from the major city in the region, as in equation 3. Standard errors are adjusted for spatial correlation.

Our main coefficient of interest is _β_ 1. A positive estimate would indicate that firms located near major roads are more profitable. Similarly to equation 3, our identifying assumptions are that roads are pre-determined with respect to firm location. As shown in Table 1, the average firm has been in business for 10 years, while the main road network was built in the 1960s. Therefore, concerns about the validity of this assumption are not first order. A second identifying assumption is that conditional on sub-county and sector fixed effects and on our

> 31 When including sub-county fixed effects, we first demean both left- and right-hand side variables.

> 32Note that if firms cluster on major roads and this creates agglomeration externalities that increase traffic (e.g., demand externalities leading to more customers driving to the firm cluster) then this would only refine the interpretation of our results. Our main result – firms choose to locate in areas with high pollution since road traffic bundles pollution and firm demand – would be unaffected. The counterfactuals, however, would vary, as changing the firms’ location choice would also change the geographical distribution of traffic and demand.

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