# **1 Introduction**

This paper uses open access, daily, global NASA satellite measurements of nitrogen dioxide (NO2) to provide estimates of business cycle movements that redress long recognized shortfalls in national accounts and other satellite estimates such as night light measures. Over the last decade, global satellite readings of night lights have revolutionized our ability to estimate economic activity in situations where government data is unreliable, or where subnational data is simply unavailable (Henderson et al., 2012). However, these readings only offer information content beyond GDP in countries with the most inaccurate statistical systems (Chen and Nordhaus, 2011), they collect data at night when most economic activity is minimal, and have notoriously misestimated important cyclical movements. For instance, during the financial crisis when on the ground data estimated GDP in the US to fall 8.9% below trend, night lights were 1% _above_ trend.<sup>1</sup> In contrast, US NO2 was 7 _._ 6% below trend at the end of 2009, clearly reflecting the recession.

In fact, we will show that NO2, a direct byproduct of combustion that stays near its source for relative long periods and is always measured during the daytime, when most economic activities occur, has explanatory power for annual economic fluctuations in addition to both lights and all of the most prominent ground level signals available. Moreover, while each of these signals and GDP are only available at a lag, NO2, is published online just a few days after measurement. Finally, we show that NO2’s uniquely explanatory power applies to all countries, including advanced economies.

Our methodological contribution is an objective, data-driven identification strategy for the error-measurement framework utilized by Chen and Nordhaus (2011) and Henderson et al. (2012). Error-measurement frameworks typically require the analyst to impose prior beliefs concerning data accuracy to overcome bias from errors-in-variables. We exploit the fact that NO2 and night lights are measured at different times to achieve identification through exogeneity in measurement errors. We scrutinize this identifying assumption at length, leveraging overidentifying restrictions from household surveys (Pinkovskiy and Sala-i Martin, 2016). Ultimately, we show the growth elasticity of NO2 is constant across nations. Finally, we substantiate our approach by showing that an NO2 composite estimate for economic activity is close to GDP in the U.S., a placebo in which data is widely thought to be accurate. However, it rebuts GDP from China, where official data has long been considered suspect (Rawski, 2001).

From the outset, we stress that NO2 is not infallible, and there are other key complementarities between NO2 and lights. In particular, NO2 captures a specific type of economic activity associated to the burning of fossil fuels. While combustion is responsible for about 84% of

> 1While there are instances where lights reflect abrupt downturns (Elvidge et al., 2020; Beyer et al., 2021), we show that such instances are the norm rather than the exception, as lights only predict annual fluctuations in 20% of countries, and have no additional explanatory power when ground level signals are available.

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