The Soviets matched the US only by spending up to 20% of GDP on the military during the Cold War. This column argues that, in stark contrast to this example, China has the potential to match the US in certain military spheres with similar burden on its economy. Using exchange rates comparisons significantly understates the Chinese military spending. A much more realistic assessment is obtained using PPP terms. If both countries spent the same fraction of their GDP on the military, the relative size of China’s military machine would be more than 90% of the US one.
Peter Robertson, 30 March 2015
Michele Ca'Zorzi, Jakub Mućk, Michał Rubaszek, 13 February 2015
Notwithstanding the progress made in the field of exchange rate economics, we still know very little of what drives major currencies. This column argues that the best that one can do is to assume that currencies move to gradually restore (relative) purchasing power parity. Contrary to widely held beliefs, this is in general a much better strategy than to just assume that the exchange rate behaves like a random walk.
Jeffrey Frankel Frankel, 09 May 2014
Many claim that China will soon overtake the US. This column argues that this claim is based on a misuse of statistics. ICP price data is necessary to compare living standards, since a dollar’s worth of yuan buys more in China than a dollar buys in the US. But the fact that rice and clothes are cheap in rural China does not make the Chinese economy larger. What matters for size in the world economy is how much a yuan can buy on world markets. Using the correct prices, the US remains the world’s largest economic power by a substantial margin.
Martin Ravallion, 26 March 2010
The World Bank’s estimate of China’s real GDP per capita was revised down by 40% in 2005. This column explains how economic growth impacted price structures in developing countries -- impacts that had not been factored into how old PPPs were updated prior to new price surveys. It argues that large revisions could be avoided by using better economic models for predicting PPPs.