A new report by the Federal Reserve Bank of San Francisco finds that Chinese GDP numbers do tend to reflect data provided by more independent sources—namely the exports and imports of China’s trading partners, which are immune from any Beijing manipulation. The Fed study first studied trade with China and its biggest trading partners: the US, the EU and Japan
Some commentators have questioned whether China’s economy slowed more in 2012 than official gross domestic product figures indicate. However, the 2012 reported output and industrial production figures are consistent both with alternative Chinese indicators of the country’s economic activity, such as electricity production, and trade volume measures reported by non-Chinese sources. These alternative domestic and foreign sources provide no evidence that China’s economic growth was slower than official data indicate.
To test the accuracy of Chinese GDP data, we compared them with a range of alternative domestic indicators available over a long time span that seem less subject to manipulation. We grouped these alternative indicators into two sets. The first set, labeled “Li” in Figure 1, was based on the preferences of Vice Premier Li, as reported in the 2007 U.S. diplomatic cable. He said he got a better read on the pace of economic activity by looking at electricity production, rail cargo shipments, and loan disbursements. In his view, those data were relatively accurate. The second set of alternative indicators, labeled “Broad” in Figure 1, included an index of consumer sentiment, construction of new floor space, an index of raw materials usage, air passenger volume, and the nominal value of new residential real estate construction.
We then examined year-over-year values for GDP and our two sets of alternative indicators. For each of the sets, we performed a statistical exercise known as principal components. This method synthesized the fluctuations in these indicators into two indexes, one for each of the two sets. Creating summary indexes rather than using the eight indicators individually simplified the analysis.
Next, we looked at the statistical relationship between the two alternative indicator indexes and China’s GDP over a sample period from the first quarter of 2000 through the third quarter of 2009. We then used the information on the statistical relationship between GDP and the indexes during this period to project GDP growth from the fourth quarter of 2009 through 2012. This allowed us to check whether recent data are consistent with the relationship observed from 2000 through 2009.
We found that reported Chinese output data are systematically related to alternative indicators of Chinese economic activity. These include alternative indicator indexes of Chinese activity composed of variables that are less susceptible to official manipulation, as well as externally reported trade volume measures. Importantly, these models suggest that Chinese growth has been in the ballpark of what official data have reported. We find no evidence that recently reported Chinese GDP figures are less reliable than usual.
Chinese provinces collectively reported economic output that was significantly larger (paywall) in aggregate than the official figure statisticians produced for the country as a whole— 57.6 trillion yuan vs. 51.9 trillion yuan, respectively. This led to jokes in the Chinese and Hong Kong media that China had a “missing province” somewhere that was obviously doing very well.
Standard Chartered economist Stephen Green has said that China’s official GDP figures were boosted by government statisticians underestimating inflation, which results in an overestimate of actual growth. And the New York Times recently discovered that some Chinese power plant managers were inflating output figures to hide the extent of a big slowdown in usage from the central government.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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