There is widespread agreement among both market participants and China’s policymakers that China’s economic growth slowed in 2018. However, there is much less consensus on the magnitude of the slowdown and even on when it started. Similar disagreements over the magnitude and timing of Chinese business cycles have occurred periodically since at least the early 1990s. In contrast to earlier years, these issues are now of major importance for policymakers in other large economies because China’s role in the global economy has increased dramatically. Indeed, on the eve of China’s accession to the World Trade Organization (WTO) in 2001, China accounted for 3.6 percent and 7.3 percent of global GDP and merchandise trade, respectively. Those shares have now increased to 16 percent and 23.8 percent as China has become the world’s second largest economy and largest trading country. Moreover, China plays a dominant role in world demand for many key energy, metal, and agricultural commodities, and possesses one of the world’s largest financial systems, which is poised to become more globally integrated as its domestic markets gain inclusion in important global benchmark indexes (Sin 2019). 

 Disagreements on China’s business cycle stem from differing views on the reliability and accuracy of China’s official economic statistics and on differing approaches to addressing  perceived shortcomings in the official data. In this article, we seek to add some alternative indicators to policymakers’ toolbox for measuring China’s cyclical fluctuations, which, in turn, can be used as inputs for making relevant policy decisions. In contrast to much of the previous academic literature, we focus almost exclusively on relatively high-frequency (monthly)  indicators of changes in China’s growth rate, as opposed to growth-rate levels. However, we offer some observations on what the indicators say about cyclical fluctuations in longer-term trend growth. We group our alternative indicators into two buckets. The first revolves around satellite nighttime lights (NTL), based on a methodology described in Clark, Pinkovskiy, and Sala-iMartin (2020). That article focused on growth-rate levels through the fourth quarter of 2015 and found no convincing evidence that the growth rate at the end of 2015 had been slower than officially reported, though it was noted that there was evidence that the change in the growth rate (marking a slowdown) had been more than reported. In this article, we focus entirely on the changes in growth, at monthly frequency from 2001 or 2006 (depending on data availability) through the middle of 2019. The second set of indicators we refer to as “factor based.” This includes an indicator based on principal component analysis (PCA) and a novel approach using sparse partial least squares regression (SPLS), which is discussed in detail in  a companion article in this special issue (Groen and Nattinger 2020). Our results suggest that China’s economic growth has been more volatile over the past five years than portrayed in the official GDP statistics. By our measures, growth slowed by  substantially more than reported over the course of 2014 and 2015 and then staged a rebound in 2016, to peak in early 2017, a pattern that was scarcely evident in the official data. During the most recent cycle, growth slowed beginning in 2017, but may have been more stable in 2018 and the first half of 2019 than portrayed in the financial press at the time. Our analysis also suggests that cyclical growth upturns (accelerations) have become significantly shorter-lived in the period after the global financial crisis, while growth slowdowns have become much longer. These fluctuations have occurred around a trend growth rate that has been slowing, and which is likely to slow substantially in coming years. The rest of this article is organized as follows: Section 1 provides some background on long-standing controversies over the accuracy of China’s GDP data. Section 2 provides a high-level overview of methodologies most frequently employed to calculate alternative growth indicators, and then introduces the methods used in this article. Section 3 discusses  the results, focusing on what they say about the contours of China’s business cycle and growth  performance since the beginning of 2014. Section 4 broadens the focus to how the alternative indicators correlate with global data and provides additional analysis focused on which  alternative indicators provide the best fit to the global data. Section 5 takes a longer-term view on the cyclical fluctuations around China’s longer-term trend. Section 6 concludes. The  appendixes provide details on the satellite nighttime light methodology used in two of our  alternative indicators and the data employed in our analysis.