It would be interesting to see Dani Rodrik respond to this article (pdf) by Richard Jong-a-Pin and Jakob de Haan in the latest issue of Econ Journal Watch:

Economists treat replication the way teenagers treat chastity—as an ideal to be professed but not to be practiced (Hamermesh 2007, 1).

HPR’s [Hausmann, Pritchett, and Rodrik’s] finding that a political regime change increases the probability of an economic growth acceleration is wrong and the result of a data error. When we correct for this error and stick to the definition of political regime change as a three-unit change in Polity, we find that regime changes do not affect the probability that a growth acceleration occurs. We also find some evidence that economic liberalization increases the probability of a growth acceleration (sustained or otherwise)…

The work represented here was submitted, of course, to the Journal of Economic Growth, although in that version of the paper we had not yet pinpointed the data-description error in the Polity IV manual. The paper was rejected on the basis of the argument that our note is a “welcome correction, however, of limited significance for the main contribution of the original paper.” However, in their abstract, HPR state that one of their main conclusions is that “Political regime changes are statistically significant predictors of growth accelerations.”

Jakob de Haan blogs about the experience:

As our paper was a comment on a previously published paper in the Journal of Economic Growth, it is unlikely to be accepted by another journal. However, a relatively new electronic journal called Econ Journal Watch, recognizes the importance of replication in economics. The editor of that journal, Dan Klein, was therefore happy to publish our paper. It will be published in the first issue of 2008. Of course, HPR get the opportunity to reply to our critique.

Even though I am very happy with this new outlet, I feel that editors of all scientific journals should pay much more attention to replication. A starting point is that authors of published empirical research should commit to make their data available to anyone interested. Unfortunately, even this is not common practice.

Admittedly, the primary achievement of the Growth Accelerations paper was to change how we think about identifying economic growth in a relevant manner. I certainly didn’t recall the regime change finding when I thought of the article. Nonetheless, a data coding error seems like a substantive correction, and I haven’t seen any reply from Hausmann, Pritchett or Rodrik.

I should also note that the Hamermesh paper is interesting in itself.

What use is sources-of-growth accounting?

Aside from all kind of measurement problems, these accounting exercises say nothing about causality, and so are very hard to interpret. Say you found it’s 50% efficiency and 50% factor endowments. What conclusion do you draw from it? You could imagine a story where the underlying cause of growth is factor accumulation, with technological upgrading or enhanced allocative efficiency as the by-product. Or you could imagine a story whereby technological change is the driver behind increased accumulation. Both are compatible with the result from accounting decomposition. Indeed, I have yet to see a sources-of-growth decomposition which answers a useful and relevant economic or policy question…

So here is a contest for economist (or wannabe economist) readers of this blog: can you come up with an interesting question to which a sources-of-growth decomposition is the answer?

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.

I will not hold my breath waiting for the EU, OECD, UNDP, and WB to develop a standard measure of progress in human societies.

Why are people still using the Sachs-Warner index in empirical work? It’s a dummy variable, and it’s not driven by the tariff and NTB components. Surely by now someone must have built and made readily available a cross-country data set that better describes trade policy. And if not, that’s a project worth pursuing, right?

Yuriy Gorodnichenko and Linda Tesar (2006), “Border Effect or Country Effect?“:

This paper reexamines the evidence on the border effect, the finding that the border drives a wedge between domestic and foreign prices. We argue that if there is cross-country heterogeneity in the distribution of within-country price differentials, there is no clear benchmark from which to gauge the effect of a border. In the absence of a structural model it is impossible to separate the “border” effect from the effect of trading with a country with a different distribution of prices. We show that the border effect identified by Engel and Rogers (1996) is entirely driven by the difference in the distribution of prices within the US and Canada.

Via DeLong.

Haiu Looi Kee, Alessandro Nicita & Marcelo Olarreaga describe their efforts to translate theory into practice in measuring the restrictiveness of trade policy. Their VoxEU article is full of interesting details.

[HT: Muse]

“It’s just as important to give US consumers access to cheap foreign goods as it is to make real GDP bigger.” — Robert Hall

The Bureau of Economic Analysis has responded to the Business Week piece criticizing its measurement of productivity gains in the face of outsourcing. I’m not familiar enough with the data and measurement issues to hold an opinion on this topic.