We then compute the following variables for each grid cell: (i) the average residual pollution ˆ _em,h,d_ , constructed as described above, for all the observations _m_ recorded within the cell; (ii) the median road size in the cell, where each road dummy is associated an ordinal number, as described in Section 4.4; (iii) the firm density, computed by dividing the number of firms in the cell by the cell area in _km_<sup>2</sup> . To compute (iii) we use our comprehensive initial firm listing (rather than just the firms selected for the survey).<sup>29</sup> Figures 2, 3, and 4, illustrate how our sampled parishes are split into grid cells, and provide suggestive visual evidence that firms are clustered close to major roads (Figure 2), that such roads are more polluted (Figure 3), leading to firm density being higher in grid cells with higher average pollution (Figure 4).

# **5.3 Regression Specifications and Identifying Assumptions**

Equipped with the residualized pollution observations and the grid-cell level variables, we next describe our regression specifications.

**Relationship between road size, pollution and productivity.** Our first objective is to test whether road traffic bundles a bad (pollution) with a good (access to demand). To do so, we study: (i) whether areas near major roads are more polluted and (ii) whether firms near major roads are more profitable, conditional on observables. To provide evidence on the first point, we run the following regression at the grid cell level, for grid cell _j_ in sub-county _s_ in region _r_ :

_ResidPollutionj,s,r_ = _α_ 0 + _α_ 1 _MedianRoadj_ + _δs_ + _θlog_ ( _dist_ ) _r_ + _νj,s,r,_ (3) where _ResidPollutionj,s,r_ is the average residual (log) pollution in the grid cell. _MedianRoadj_ is the median road size in the grid cell. _δs_ are sub-county fixed effects, as we are interested in documenting variation in pollution and firm location choices _within_ urban areas. In addition, we control for log distance to the main city in the region, _log_ ( _dist_ ) _r_ , to make sure that we do not just capture the fact that areas closer to the city center are both more polluted and more productive.<sup>30</sup> To account for spatial correlation, we use Spatial Heteroskedastic and Autocor-

> cells containing these firms, in addition to the grid cells in our sampled sub-counties. In the estimation we control for a dummy for whether the firm falls in this category. Our results are robust to dropping these firms. 29 When computing firm density, we take into account that all grid cells are not exactly 500 _m ×_ 500 _m_ . This may happen because grid cells overlapping two adjacent parishes are split at the parish level, and because parishes are not of rectangular shape. A histogram of grid cell areas can be found in Appendix Figure A18. Besides, in regressions including grid-level variables, we control for whether the grid cell has an area of less than 0.25 square _km_ (dummy), as well as for grid cell size (linear control).

> 30We do not have data on road quality. To the extent that larger roads are of higher quality and road quality reduces pollution (by reducing congestion), then our estimates of the effect of road size on pollution are a lower bound.

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