The World Bank may have unwittingly succeeded in shaving half a percentage point from global economic growth and in acquitting China’s yuan from charges of massive undervaluation.
It has also managed to make the fast growing wonderland of “Chindia”—China and India—suddenly look somewhat less important to global commerce and slightly poorer than before. With the bank last month announcing new benchmarks to compare the local purchasing power of various currencies with what a dollar buys in the US, China’s share in the world economy is now 10 %, one-third lower than previously estimated.
India’s share has fallen by almost the same magnitude to 4%, which means it’s yet to overtake Germany as the fourth biggest economy.
So, how did the World Bank accomplish all this by tweaking a single data series?
Purchasing power parity,or PPP, isn’t just any number. Though the world’s biggest statistical exercise takes a long time to complete and is rarely updated with fresh survey data—the revised data set refers to 2005; the previous one was for 1993—it’s nonetheless a crucial metric.
The indicator captures differences in relative price levels. It’s important because many goods and services aren’t internationally traded and their costs to the consumers, say, in China and India, can’t be calculated by multiplying US prices by the relevant market exchange rate. An understanding of the local currency’s true purchasing power is important for marketers to get their pricing right in emerging markets.
Guesstimate to estimate
Some, like Ratan Tata, the Indian businessman behind the much-hyped $2,500 (Rs1 lakh) car, Tata Nano, may rely on experience and intuition. Others, especially Western multinational executives, who lack first-hand knowledge of developing-country buyers, need data.
The average price level in China and India in relation to the US has been almost purely in the realm of conjecture so far; now, there are some hard figures supporting the estimates.
China participated for the first time in the World Bank- coordinated survey, and India rejoined the project after a 20-year hiatus.
The results are dramatic.
The new statistics show that Chinese prices are higher than assumed until now. PPP exchange rate is 3.4 yuan to the dollar, which is a lot closer to the market rate of 7.24 than the bank’s previous estimate of about 1.9.
As a result, the models of yuan misalignment built around deviation of the market exchange rate from the value suggested by PPP have suddenly begun to show little or no evidence of undervaluation in China’s currency.
So far at least, economists aren’t willing to accept absence of proof as proof of absence. And perhaps they are right.
But the new statistics have forced the International Monetary Fund (IMF) to do some reconciling of its own. IMF last week lowered its estimate of global economic growth in 2007 to 4.7 % from its October assessment of 5.2 %.
The other change that’s bound to take place is in the estimates of global poverty, and especially in China and India.
The World Bank is yet to do the math. But there already are indications that the picture that may emerge won’t be pretty.
Albert Keidel, an economist at the Carnegie Endowment for International Peace in Washington, DC, had in September estimated the impact of a very similar revision in China’s PPP exchange rate on poverty in Hunan, a landlocked province south of the Yangtze river basin.
Estimates of poverty
According to Keidel’s calculations, the adjustment will see the number of people living on the equivalent of $1 a day or less in Hunan rising to almost 12%, compared with 2% estimated now.
There may be similar increases for India, too.
It isn’t that the bank has changed its mission from eradicating poverty to creating it. It’s just that millions of people in China and India who were assumed to have escaped dire poverty because of high economic growth may still be vulnerable.
Unless of course the bank has got it wrong.
To arrive at the new estimate, China’s National Bureau of Statistics surveyed households in 11 big cities; later the Asian Development Bank and the World Bank devised a way to extrapolate the data for all of the mainland, including villages. Was the sample representative or did errors get magnified?
The problem is that we don’t really know. And we must.
Purchasing power parity, after all, tells us a lot about affordability that market exchange rates don’t. Unless we have a measure of what people in China and India really can afford, how do we know how cheap a new, clean source of energy has to be to wean them off coal?
Venture capitalist Vinod Khosla, founder of Sun Microsystems Inc., calls it the “Chindia price,” which must be met, he says, by any new technology that hopes to be a solution to climate change. That’s all the more reason to bolster the statistics.
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