(DMSP) Operational Linescan System (OLS) sensors i.e. instruments. These fly on board 6 separate satellites in our 1992-2013 sample, named F-10, F-12, F-14, F-15, F-16, and F-18. For several years, there are OLS observations from two separate satellites; for example, F-12 and F- 14 overlap over 1997-1999. Second, we use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, which has flown on board the single NASA/NOAA SNPP satellite since its launch in 2011. While the available data sample for VIIRS technically begins in 2012, the more reliable stray light-corrected version begins in 2014, which is the sample we utilize.<sup>2</sup> Throughout, we use the acronyms OLS and VIIRS to disambiguate between instruments, which is only one-to-one with satellite in the case of VIIRS.

With this in mind, Table S1 Column HSW is a reference to Henderson et al. (2012)’s baseline result, which appears in their Table 1, Column 1 (p. 1,012) and Table 2, Column 1 (p. 1,015). This estimate used the now dated v. 6.1 of the Penn World Tables (PWT), which is revised between vintages (Johnson et al., 2013). Table 2 Column 2 is our replication of the same specification, but updated using the newest available version of the PWT, v. 10.0. Column 3 uses the same model, but now with since-available inter-calibrated OLS data.<sup>3</sup> Column 4 repeats the same exercise, but extended to the end of the OLS data we include in the analysis, in 2013. This is the first instance of the estimated lights coefficient changing notably, and R-squared also increases. Column 5 repeats this specification, but includes satellite fixed effects. These are advisable since variability across satellite measurements even within the OLS instrument may be substantial, owing to differences in sensor settings across satellites; this approach was followed by Chen and Nordhaus (2011). The sample size increases as a result. Both the estimated coefficient and R-squared decrease against the increases from the larger sample. Column 6 starts the sample in 2005, where our NO2 data begins.

Table S2 Column 1 estimates the same relationship, but using the stray light-corrected VIIRS data set which begins in 2014. VIIRS addresses many of the shortcomings of the DMSPOLS database (Gibson et al., 2020) and has also has been used towards estimating GDP (Chen and Nordhaus, 2019). With this corrected, more accurate data, we observe a comparatively smaller estimated lights coefficient and weaker explanatory power than DMSP-OLS. There are no satellite fixed effects because VIIRS is on board a single satellite. Column 2 compares results from only the brightest lights in city cores. Column 3 compares results with NOAA’s preferable stable (i.e. non-ephemeral) lights product, which increases explanatory power and significance, and we use for the remaining specifications. Column 4 joins the previous DMSPOLS specification from Table S1 Column 5 with Column 3. Column 5 joins the previous

> 2 Stray lights are light which reaches the spacecraft as it observes parts of the Earth that are currently dark. This occurs due to the Earth’s curvature, and typically at high latitudes. Such measurements must be corrected before concluding that a retrieved luminosity is indeed man-made (Elvidge et al., 2017).

> 3Inter-calibration is a statistical method necessary to process the data before it may be used. It is necessary because the digital numbers reported in the OLS data set are, for technical reasons, not directly proportional to actual luminosity. We use the methodology described by Elvidge et al. (2014).

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