countries to create two new global rankings of GDP accuracy.

_Objective Data Quality._ Existing rankings of GDP data quality are at least partially subjective. We assemble a new world ranking of GDP misstatement by country over 2005-2020, which ranges from 0.25 to 5 percentage points per annum on average, and is in line with Henderson et al. (2012)’s estimate of trend growth errors up to 3 percentage points. We find that existing subjective rankings are misleading, and our objective ranking implies a significant reshuffling.

_GDP Errors during the 2020 Pandemic._ We reconsider reported GDP during the 2020 COVID19 crisis and create a ranking of misstatement in this year specifically. We find that even for countries with very high-quality statistical systems including the United Kingdom, the unprecedented official GDP statistics reported during 2020 seem unlikely to be entirely accurate. We ultimately conclude that 2020 GDP systematically understated economic activity, globally.

Finally, we close by discussing other avenues for research we have no space for in this proofof-concept work, including subnational and sub-annual estimates, and nowcasting.

**Related Literature.** There is a wealth of economic research using satellite data, as summarized by Donaldson and Storeygard (2016). The primary signal which has been used towards measuring economic activity is the radiance of night lights. Levin et al. (2020) provides a comprehensive review of this literature.

Alternative satellite data such as high-resolution imagery has been used to measure world poverty (Jean et al., 2016) and detect urban markets (Baragwanath et al., 2019). Moreover, there have been many examples of using satellite air pollution data in the environmental and health literature (Jayachandran, 2009). However, to our knowledge the only previous study using air pollution data to measure cyclical economic activity is Morris and Zhang (2018), which uses NO2 to infer economic activity in China. The current study represents a more comprehensive and independent effort to carry out this seed of an idea to its full fruition, globally, with a distinct methodology, and in new applications.

Our research design builds on scientists’ long-held observation that there is a correlation between NO2 and economic activity. For example, several scientific studies observed reductions in NO2 during the 2009 Financial Crisis (Lin and McElroy, 2011; Castellanos and Boersma, 2012; Russell et al., 2012; Itahashi et al., 2014). It should come as no surprise that scientists also measured an abrupt downturn in NO2 during the 2020 pandemic (Gkatzelis et al. (2021) and references therein). While the reduction in economic activity causing these reductions is often implicit (e.g. Keller et al. (2021)), none of the scientific literature has attempted to measure a formal statistical relationship between NO2 and economic activities globally.<sup>2</sup>

> 2Our search revealed just two scientific studies quantifying correlations between NO2 and official measures of economic activity, and the focal point of these analyses are particular places or times. First, Montgomery and Holloway (2018) present correlations between trend economic growth and NO2 in a study of 38 cities from 2005 to 2011. Second, Wei et al.

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