also provide new insights to an established urban literature on agglomeration (Duranton and Puga 2004; Ellison et al. 2010; Combes and Gobillon 2015) and on the relationship between environmental amenities and location choice within the city (Kahn and Walsh 2015).<sup>4</sup> Finally, we contribute to the literature studying the role of firms in driving wage inequality among workers (Song et al. 2019, Card et al. 2013). Recent work (Sorkin 2018, Morchio and Moser 2020) highlights that a large share of welfare inequality across firms is not due to differences in wages but rather to differences in non-monetary compensation, or amenities. We contribute by focusing on one specific dis-amenity - exposure to pollution - and showing how this varies as a function of manager characteristics. Our result shows that, in low-income and informal economies, inequality in worker welfare across firms is likely to be larger than inequality in earnings, due to the negative relationship that we document between exposure to pollution and managerial ability.

The rest of the paper is organized as follows. Section 2 presents background information on the context of our study. Section 3 presents the model. Section 4 describes the sample and data. The empirical strategy is discussed in Section 5 and the results are presented in Section 6. Section 7 concludes. Additional details are in the Appendix.

# **2 Background**

In this section, we provide background information on the context of our study. We discuss trends in urbanization, motorization and pollution emissions in Uganda. Importantly, we highlight key findings from the literature showing that pollution has a negative effect on individuals’ health and, consequently, workers’ productivity. While our empirical work focuses on urban Uganda, the points we make below generalize to other developing countries.

**Pollution levels in Uganda.** Uganda features high levels of pollution: average annual concentration of PM2.5 particulate matter was 50.5 micrograms per cubic meter in 2017.<sup>5</sup> In Appendix Figure A10 we compare the evolution of average pollution levels in Uganda to those in the United States, China and India. The figure shows that pollution levels in Uganda are similar to Chinese levels, and far higher than US levels. Pollution in Uganda is also far above the recommendations by the US Environmental Protection Agency (EPA), set in 2012 and con-

> 4Heblich et al. (2021) document the persistent effects of industrial pollution on neighborhood sorting within English cities. By focusing on location choices within as opposed to across cities, we also relate to a growing literature on how environmental conditions such as pollution or natural resource availability determine the sorting of firms and households across space and migration decisions (Gollin et al. 2021; Liu and Sekhri, 2021; Khanna et al. 2021; Chen et al. 2017).

> 5Source: World Bank: `https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3?name_desc=true& view=map` . PM stands for particulate matter and 2.5 refers to the size of the particles (2.5 micrometers). Due to their small size, these fine inhalable particles pose the greatest risk to health. For additional details see `https://www.epa.gov/pm-pollution/particulate-matter-pm-basics` .

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