**Pollution is due to road traffic.** Next, we bring evidence to argue that the geographic variation in pollution is mainly due to road traffic rather than the firms themselves. First, we study the cyclicality of pollution across hours of the day: if we find that pollution peaks at rush hour rather than when most firms are open for business, then this indicates that the main source of pollution emissions is road traffic. Figure 5 uses data from our stationary monitors and information on time use within the firm from our survey to suggest that the main source of pollution in these urban areas is road traffic rather than the firms themselves. The top panel shows that pollution peaks between 6-9am and 7-9pm. These times correspond to rush hour in Uganda. On the bottom panel, we plot the share of workers that report being at the firm premises working by hour of the day. The figure shows that production activity peaks between 10am to 3pm, that is, when pollution is _lowest_ .

We conduct one further test in Figure 6: we split the stationary monitors by whether they fall in a grid cell where there is at least one firm, or whether they have no firms nearby. If firms are a source of pollution themselves, we would expect the cyclicality in pollution emissions throughout the day to be different in these two areas. The figure shows that the cyclicality across the two sets of monitors is almost identical. This provides one more piece of evidence in favor of the interpretation that pollution originates from traffic and is exogenous to firms. Appendix Figure A19 displays the cyclicality of pollution during the day when restricting to grid cells containing roads and is almost identical to Figure 6.

**Firms benefit from locating near larger roads.** We next study whether locating near larger roads provides profitability benefits to firms. While we do not observe firms moving across locations, we can study, following the specification shown in equation 4, the cross-sectional relationship between proximity to large roads and measures of profitability, conditioning on a rich set of controls. The results are shown in Table 3.

Columns 1-4 show that there are clear benefits from being located on large roads, as shown by an increase in total revenues and profits, as well as per worker. Importantly, note that we control for manager quality, which is a very strong predictor of revenues and profits, as expected given the prior literature on returns to management skills (Bloom and Van Reenen 2007, 2011; McKenzie and Woodruff 2017). Therefore, our results do not just capture the fact that more productive managers locate in areas with larger roads (and that are more polluted). In fact, as we show in column 8, high skilled managers are _not_ more likely to locate near larger roads. This attenuates the concerns that the documented relationship between profits per worker and road size is driven by omitted variables, as long as sorting on unobservables and observables are related. Rather, these results are in line with there being actual productivity benefits from locating near roads with high traffic (and high pollution).

Columns 5 and 6 show that these productivity benefits pass through to workers in the form 22