first stage from two stage least squares, lights, CO2, electricity, and surveys are all relevant.

Table 5 Column 1 presents an OLS estimate of _β_ = 0 _._ 168. This baseline estimate is presumably due to errors-in-variables. Our hypothesis is that night lights are the most promising instrument, since they are also available for the full sample, but always measured at a different time from NO2, and so there is reason to believe their measurement errors are uncorrelated. Column 2 proceeds using night lights as an instrument, finding a larger estimate of _β_ = 0 _._ 262, and corroborating the hypothesis of attenuation in the OLS estimate. Tests for relevance and weak identification both indicate lights are both relevant and not weak.

**Threats to Identification.** Our primary identifying assumption is that the measurement errors in NO2 and lights are uncorrelated. At face value this seems likely, as the two sensors always make measurements at different times: NO2 during the day, and lights at night. However, there is room to critique any exogeneity assumption, which is inherently untestable. In the present case, there may be some concern that meteorological factors such as cloud cover are a confounding factor, since neither NO2 or lights are measurable if cloud cover is too dense. We defend our exogeneity assumption in three ways. First, as previously noted, OMI makes NO2 readings around 1:30 PM local time. Perhaps by coincidence, the VIIRS instrument makes night lights readings around 1:30 AM (Elvidge et al., 2013). Therefore, readings could not be further apart, and there is more than ample opportunity for random changes in cloud cover due to constant, wind assisted movement. Second, there are a variety of other sources of significant measurement error which affect one sensor, but not the other because of their different timing. For example, auroral activity is a significant concern for night lights retrievals but not NO2.

Third, there are other potential instruments available which corroborate the lights-based estimate. In Table 5 Column 3, we appeal to the results of Pinkovskiy and Sala-i Martin (2016), who showed that measurement errors in PovcalNet survey means are uncorrelated with those of lights. Thus, these represent another potential instrument, in which we can have some particular confidence. Survey means cover a smaller sample of countries than satellite-based signals, so they are perhaps not the best instrument for our purposes. However, by including both lights and survey means as instruments, we now have an overidentified model, and can test those overidentifying restrictions with a standard Sargan (1958)-Hansen (1982) _J_ -statistic. We find that it is not possible to reject the null hypothesis that both overidentifying restrictions are valid, with a _p_ -value of 0.273. Of course, validity of overidentifying restrictions does not necessarily imply instrument exogeneity (e.g. Deaton (2010)). But we may also see that the estimate of _β_ changes only marginally with the inclusion of this additional instrument.

**Robustness Checks.** In Table 5, Columns 4 and 5, we further scrutinize our lights-based estimate in Column 2 by including electricity and CO2 as additional instruments. Again, we may not reject the validity of overidentifying restrictions. Moreover, all instruments are jointly

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