DO COUNTRIES often get trapped betwixt poverty and prosperity? The concept of a middle-income trap was popular long before it was thoroughly tested, or even very clearly defined. The debate has thus benefited a lot from an inquiry into middle-income slowdowns by Barry Eichengreen, Donghyun Park and Kwanho Shin (EPS). Their 2013 paper is here. The authors define their terms precisely and test them rigorously. They trawled through the historical record, looking for fast-growing economies (enjoying average per-capita growth of at least 3.5% over seven years, to be precise) that suffered steep slowdowns (of at least 2 percentage points in average growth over the next seven years).

In their most recent paper, using the latest data, they argue that such slowdowns tend to occur in countries with per-person incomes of $10,000-11,000 and $15,000-16,000. Figure 1 opposite is their map of the middle-income minefield.

As I mentioned in a previous post, the authors chose not to look for slowdowns among poorer countries. They're interested in finding out which middle-income brackets are most treacherous, not whether the middle-income range is more treacherous than other stages of development.

We tried to extend their methods to the full range of countries. In the previous post I described these efforts in inordinate detail. The upshot is that when slowdowns in poor countries are added to the chart, the middle-income range no longer stands out as particularly mine-filled. (See chart 2.*)

If middle-incomes are not especially treacherous, what is? What other factors help to explain sharp slowdowns? The economic era is an obvious one. In chart 3 below, we again report the number of years that qualify as slowdowns. But this time we count the number in each calendar year rather than the number in each income bracket.

The 1970s distinguish themselves as the most perilous decade, entrapping dozens of countries. And within that benighted decade, the years around the oil-price shocks of 1973 and 1979 stand out as particularly nasty.

This is, of course, not entirely surprising: the 1970s were bad years for the global economy, and slowdowns are more likely in bad years. But the chart does suggest that the kind of slowdowns showcased in the Eichengreen, Park and Shin paper reflect the global conjuncture at least as much as they reflect an individual country's level of development.**

It also just so happens that an unusual number of countries had incomes per head between $10,000 and $16,000 in the 1970s. What difference does this make? To answer that question, I tried to replicate Figure 1 (shown above) from the most recent Eichengreen, Park and Shin paper but with one difference. Unlike them, I counted slowdowns by year rather than by income level. Like Figure 1, the chart only includes countries with per-capita incomes greater than $10,000.***


Whereas the original Figure 1 suggested that income levels around $10,000 and $15,000-16,000 were a minefield, lending credence to the idea of a middle-income trap, this chart has a simpler message: 1974 was a very bad year.




* The chart counts all the years that qualify as slowdowns. It also offers a separate count of the "key" years. What is meant by "key"? Many countries suffer slowdowns, as EPS define them, for several years in a row. This is partly because the authors look at growth averaged over seven years. So a single bad year will affect this average for seven years. The authors use a statistical test, called a Chow test, to pick out one key year in any string of consecutive slowdown years. I did the same, although my results differed a little from theirs. The chart shows my results both with and without the Chow test.

** Introducing time fixed-effects would presumably help distinguish the influence of the year from the influence of the level of development. In investigating why slowdowns occur, the authors do compare slow-growers with fast-growers in the same year, thereby eliminating the year's common impact. But in identifying the income levels at which slowdowns occur, they do not control for the year, as best I can tell.

*** In chart 4, I did not rely on my own Chow tests to identify the key slowdown years. I instead simply counted the number of key years listed in Table 1 of Eichengreen, Shin and Park 2013. Chart 4 is not, then, affected by any minor differences between my method and theirs. Their 2013 paper did categorise a few of the cases (shown in dark blue in the chart) ambiguously (these were mostly two-year slowdowns in which neither year was identified as the most significant year). But as chart 4 makes clear, this handful of ambiguous cases doesn't change the gist of the story.