Facts matter: On PPP GDP estimates


The recent revisions of PPP GDP estimates have been widely discussed, which puzzles Daniel Altman:

[P]urchasing power figures are not, by themselves, a good measure of a country’s economic importance in the world. They are only an interesting abstract notion. So, when the World Bank said that China’s purchasing power was smaller than it had previously thought, the announcement didn’t change anyone’s lives, nor did it affect the timeline for China becoming the world’s dominant economy. Just ask the people in French’s article; they were poor before, and they’re still poor now.

Now, I rarely dabble in epistemology, but I think that people care about what they believe. Though poor people exist even if you forget to count them, people care when we find out that millions more persons are in poverty than previously estimated. Some lives ought to be affected by these estimates, because when the facts change, some people change their actions. One has to be strangely captivated by the thing-in-itself to believe that it’s no big deal whether we accurately perceive it or not.

An easy example of why this matters is that some people use the number of people exiting poverty during the last 25 years as a measurement of globalisation’s success. For example, see Surjit Bhalla’s Imagine There’s No Country: Poverty Inequality and Growth in the Era of Globalization.

Arvind Subramanian has a very insightful post on why the facts matter (over at Rodrik’s blog):

The reductions in GDP per capita imply a large increase in measured poverty, especially in China and India. Is this a problem? Yes, the new numbers are going to be awkward for the Bank because China and India cannot suddenly have hundreds of millions more poor people because new data have been produced. We are not quite in a Heisenberg quantum world where measurement affects underlying realities.

But the problem is less big than it appears. First, it should be emphasised that the new revisions change poverty rates according to the international one-dollar-a-day standard. But most researchers and policy-makers place far more faith in nationally determined poverty benchmarks and estimates. India’s poverty rate will always be determined by the NSS surveys (fraught and contentious though even they are) not by international measurements.

The international standard was created to facilitate cross-country comparisons. But it was always recognised that setting this standard was hazardous because of the difficulties in comparing poverty across borders and time. The new revisions have merely served to expose these difficulties, and it is going to be very interesting to see how the Bank extricates itself out of this problem…

The new data suggest that renminbi undervaluation is about 16%, which is not only substantially lower than most analysts’ estimates (of about 30-40 per cent) but also implausibly lower than the estimates for other countries, including India’s (undervaluation of about 26 per cent)…

The broader policy question that India and the world community should be asking is why nearly 15 years had to elapse before GDP data were updated. Had the Bank devoted more time, effort, and financial resources to doing more such exercises in the past, there would be fewer surprises today…

[A] large share of the Bank’s resources — substantially larger than currently foreseen — should be channelled to activities that produce global public goods. A great example of such goods is knowledge produced by the Bank, including the knowledge embodied in the new GDP data generated by the Bank’s statisticians…

We should therefore raise a toast to these humble folk, the bean counters, who beaver away at such unsexy but invaluable tasks. But as we do so, we should not shy away from asking this question: can the loanwallahs at the World Bank (and elsewhere) make comparable claims of adding value to the world.