China inflates its GDP statistics


A customer looks at items displayed on shelves at a supermarket in Shenyang, Liaoning province, April 11, 2014. China’s consumer inflation rate increased in March as fresh food prices jumped, but persistent deflation in the industrial sector was another signal of weak demand and slowing growth in the world’s second-largest economy.

New World Bank figures, based on Purchasing Power Parity measurements, suggest that China’s GDP has caught up with America’s. A forthcoming article by Jeremy Wallace in the British Journal of Political Science suggests that neither China’s subnational nor national GDP statistics are particularly reliable at moments of political turnover. However, this doesn’t mean that these indicators are biased over the longer run.

China, like other authoritarian regimes, has strong incentives to gimmick its growth numbers. National leaders may want to maintain public support, while their underlings may want to keep the favor of their bosses. This leads to systematic pressures to overreport growth in times of crisis. Wallace argues that electricity and consumption data are less likely to be manipulated, since they get less attention. This provides us with some indication of when China is likely to play with the numbers. For example:

National-level Chinese electricity and GDP statistics diverged conspicuously in the wake of the collapse of the US investment bank Lehman Brothers in September 2008. Many believed that the Chinese government was propping up its GDP growth rate estimates to ease concerns that its economy was grinding to a halt as demand for exports dropped with the Global Financial Crisis. The divergence between the GDP growth figure and electricity production growth suggests that the regime manipulated the closely-watched, headline figure to boost confidence while faithfully reporting the less-examined electricity series.

By examining the relationship between GDP and electricity and consumption patterns, it’s possible to spot divergences that are plausibly the result of political manipulation. Wallace’s theory would suggest that these divergences are especially likely to happen when there’s turnover in local leadership, as outgoing leaders spar for promotion to higher ranks, while incoming leaders look to make an immediate impression. And indeed that’s the pattern he finds. Wallace concludes that:

Politically sensitive data such as GDP at the national and sub-national levels are likely targets of manipulation. When possible, researchers should use a close correlate that is less likely to be manipulated, such as electricity consumption or production, as a robustness check to ensure that their findings are not driven by artifacts of political games rather than economic realities.

Of course, the problem is that if any alternative economic statistic becomes politically salient (i.e. not only researchers but politicians start paying attention to it), it, too, will surely become a target for manipulation.

[post updated following communication with author of paper].

Henry Farrell is associate professor of political science and international affairs at George Washington University. He works on a variety of topics, including trust, the politics of the Internet and international and comparative political economy.
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