The ‘Global South’ is a terrible term. Don’t use it!

The Rise of the ‘Global South’

The ‘Global South‘ and ‘Global North‘ are increasingly popular terms used to categorize the countries of the world. According to Wikipedia, the term ‘Global South’ originated in postcolonial studies, and was first used in 1969. The Google N-gram chart below shows the rise of the ‘Global South’ term from 1980 till 2008, but the rise is even more impressive afterwards.

Nowadays, the Global South is used as a shortcut to anything from poor and less-developed to oppressed and powerless. Despite this vagueness, the term is prominent in serious academic publications, and it even features in the names of otherwise reputable institutions. But, its popularity notwithstanding, the ‘Global South’ is a terrible term. Here is why.

 

There is no Global South

The Global South/Global North terms are inaccurate and misleading. First, they are descriptively inaccurate, even when they refer to general notions such as (economic) development. Second, they are homogenizing, obscuring important differences between countries supposedly part of the Global South and North groups. In this respect, these terms are no better than alternatives that they are trying to replace, such as ‘the West‘ or the ‘Third World‘. Third, the Global South/Global North terms imply a geographic determinism that is wrong and demotivational. Poor countries are not doomed to be poor, because they happen to be in the South, and their geographic position is not a verdict on their developmental prospects.

 

The Global South/Global North terms are inaccurate and misleading

Let me show you just how bad these terms are. I focus on human development, broadly defined and measured by the United Nations’ Human Development Index (HDI). The HDI tracks life expectancy, education, and standard of living, so it captures more than purely economic aspects of development.

The chart below plots the geographic latitude of a country’ capital against the country’s HDI score for 2017. (Click on the image for a larger size or download a higher resolution pdf). It is quite clear that a straight line from South to North is a poor description of the relationship between geographic latitude and human development. The correlation between the two is 0.48. A linear regression of HDI on latitude returns a positive coefficient, and the R-squared as 0.23. But, as is obvious from the plot, the relationship is not linear. In fact, some of the southern-most countries on the planet, such as Australia and New Zealand, but also Chile and Argentina, are in the top ranks of human development. The best summary of the relationship between HDI and latitude is curvilinear, as indicated by the Loess (nonparametric local regression) fit.

 

 

 

You can say that we always knew that and the Global South was meant to refer to ‘distance from the equator’ rather than to absolute latitude. But, first, this is rather offensive to people in New Zealand, Australia, South Africa and the southern part of South America. And, second, there is still far from a deterministic relationship between human development and geographic position, as measured by distance from the equator. The next plot (click on the image for a larger size, download a pdf version here) shows exactly that. Now, overall, the relationship is stronger: the correlation is 0.64. And after around the 10th degree, it is also rather linear, as indicated by the match between the linear regression line and the Loess fit. Still, there is important heterogeneity within the South/close to equator and North/far from equator countries. Singapore’ HDI is almost as high as that of Sweden, despite the two being on the opposite ends of the geographic scale. Ecuador’s HDI is just above Ukraine’s, although the former is more than 50 degree closer to the equator than then latter. Gabon’s HDI is higher than Moldova’s, despite Gabon being 46 degrees further south than Moldova.

 

 

This is not to deny that there is a link between geographic position and human development. By the standards of social science, this is a rather strong correlation and fairly smooth relationship. It is remarkable that no country more the 35 degrees from the equator has an HDI lower than 0.65 (but this excludes North Korea, for which there is no HDI data provided by the UN).  But there is still important diversity in human development at different geographic zones. Moreover, the correlation between geographic position and development need to be causal, let alone deterministic.

There are good arguments to be made that geography shapes and constraints the economic and social development of nations. My personal favorite is Jared Diamond’s idea that Eurasia’s continental spread along an East-West axis made it easier for food innovations and agricultural technology to diffuse, compared to America’s continental spread along a North-South axis. But geography is not a verdict for development, as plenty of nations have demonstrated. Yet, the Global South/Global North categories suggest otherwise.

 

What to use instead?

OK, so the Global South/Global North are bad words, but what to use instead? There is no obvious substitute that is more descriptively accurate, less homogenizing and less suggestive of (geographic) determinism. But then don’t use any categorization that is so general and coarse. There is a good reason why there is no appropriate alternative term: the countries of the world are too diverse to fit into two boxes: one for South and one for North, one for developed and one for non-developed, one for powerful, and one for oppressed.

Be specific about what the term is referring to, and be concrete about the set of countries that is covered. If you mean the 20 poorest countries in the world, say the 20 poor countries in the world, not countries of the Global South. If you mean technologically underdeveloped countries, say that and not countries of the Third World. If you mean rich, former colonial powers from Western Europe, say that and not the Global North.  It takes a few more words, but it is more accurate and less misleading.

It is a bit ironic that the Global South/Global North terms are most popular among scholars and activists who are extremely sensitive about the power of words to shape public discourses, homogenize diverse populations, and support narratives that take a life of their own, influencing politics and public policy. If that’s the case, it makes it even more imperative to avoid terms that are inaccurate, homogenizing and misleading on a global scale.

If you want to look at the data yourself, the R script for the figures is here and the datafile is here.

Inclusive institutions and economic development

Francis Fukuyama reviews Why Nations Fail, the new book by Daron Acemoglu and James Robinson, at his blog. The review is fairly critical. Fukuyama agrees that institutions are of paramount importance for development (as you would expect given his own recent book) but is unsatisfied with the vague (or even missing) definitions of the two central concepts of the book – ‘inclusive institutions’ and ‘extractive institutions’. This conceptual stretching allows the labels to be applied quite arbitrarily to fit the argument of the book.

In substantive terms the critique boils down to the question whether democratic (inclusive) institutions are necessary for stable economic development. In Fukuyama’s view they are not (think contemporary China) and might even be counterproductive (following Huntington). In Acemoglu and Robinson’s view, democratic political institutions and inclusive economic institutions are indispensible for sustained long-term development. Fukuyama’s quibble with Why Nations Fail fits into a line of argumentation he is in the midst of constructing which can be summarized as ‘good governance is necessary for development but democracy is not necessary for good governance’. His latest project, for example, is to develop a new conceptualization and measurement of governance which moves away from the traditional indicators of (Western-style) rule of law and democratic accountability. Here is a characteristic quote from the project’s announcement:

One can think of many ways in which greater democratic participation actually weakens the quality of governance.  

Acemoglu and Robinson respond to Fukuyama’s review at their own blog. But in my opinion Fukuyama’s general critique (and his smaller points about misinterpretations of historical episodes) remains. Irrespective of one’s normative convictions, one has to admit that economic development has been possible throughout history and space in the absence of inclusive, democratic institutions (unless one stretches the definition of democratic institutions to include 17-th century England or contemporary Singapore). Whether growth without political democratization is sustainable in the long term remains an open question (China).

Both Fukuyama and Acemoglu & Robinson focus on macro-level institutions but it is instructive to look at the meso- and micro-levels of institutions as well (taking the work on the management of common pool resources by Elinor Ostrom and others as a guide). In my reading, the message of this literature about inclusiveness, democracy and governance is the following: Successful management of common resources needs some form of participation and voice by the people within the community but also restricted access to the resource. Effective governance needs institutions that are inclusive for ‘insiders’ and exclusive for ‘outsiders’. For example, early community-based institutions for managing marine resources throughout the world provided for some influence by ordinary members of the community but at the same time they strictly defined who can and cannot fish and enforced these boundary rules. Of course, who is an outsider and who is insider is in itself a political question. And we don’t know whether these lessons from the micro-level generalize to society-wide institutions. 

Finally, although I remain skeptical whether democratic (in the narrow sense) institutions are necessary (in the strong sense) for economic development, the recent experience of Central and Eastern Europe (CEE) suggest a strong link between the two. Even for those with only cursory knowledge of the region would be clear that the countries that installed the most open, democratic and inclusive political regimes are also the most economically successful ones. In the early phases of post-communist transitions after the fall of the Berlin Wall many advocated economic development before political liberalization. In line with Fukuyama’s reasoning, it was feared that democratization prior to, or together with, economic reforms would impede development and lead to the implosion of these countries. Fortunately for the region, these opinions did not prevail and most of the CEE states initiated political and economic reforms simultaneously (in some cases with the additional burden of nation-building). Looking back, we can ascertain that those states which experienced the earliest and most far-reaching political liberalization were also the ones to achieve the greatest economic development (Poland, the Czech Republic, one hesitates to add Hungary). Whether economic reforms led or followed political liberalization or whether they were all predetermined by pre-communism legacies, political culture, etc. might be still an unresolved issue. Nevertheless, at the very least we can say that in CEE the establishment of democratic political institutions did not halt economic development.

Slavery, ethnic diversity and economic development

What is the impact of the slave trades on economic progress in Africa? Are the modern African states which ‘exported’ a higher number of slaves more likely to be underdeveloped several centuries afterwards?

Harvard economist Nathan Nunn addresses these questions in his chapter for the “Natural experiments of history” collection. The edited volume is supposed to showcase a number of innovative methods for doing empirical research to a broader audience, and historians in particular. But what Nunn’s study actually illustrates is the difficulty of making causal inferences based on observational data. He claims that slave exports contributed to economic underdevelopment, partly through impeding ethnic consolidation. But his data is entirely consistent with a very different interpretation: ethnic diversity in a region led to a higher volume of slave exports and is contributing to economic underdevelopment today. If this interpretation is correct, it could render the correlation between slave exports and the lack of economic progress in different African states spurious – a possibility that is not addressed in the chapter.

The major argument of Nunn’s piece is summarized in the following scatterplot. Modern African states from which more slaves were captured and exported (correcting for the size of the country) between the XVth and the XIXth centuries are associated with lower incomes per capita in 2000 (see Figure 5.1 on p.162, the plot reproduced below is actually from an article in the Quarterly Journal of Economics which looks essentially the same):

The link grows only stronger after we take into account potential ‘omitted variables’ like geographical location, natural openness, climate, natural resources, history of colonial rule, religion and the legal system. Hence, the relationship seems more than a correlation and Nunn boldly endorses a causal interpretation: “the slave trades are partly responsible for Africa’s current underdevelopment” (p.165).

Not being a specialist in the history of slavery, my initial reaction was one of disbelief – the relationship seems almost too good to be true. Especially when we consider the rather noisy slave exports data which attributes imperfect estimates of slave exports to modern states which didn’t exist at the time when the slaves were captured and traded. While it is entirely plausible that slave exports and economic underdevelopment are related, such a strong association several centuries apart between the purported cause and its effect invites skepticism.

It seemed perfectly possible to me that the ethnic heterogeneity of a territory can account for both the volume of slave exports, and current economic underdevelopment. In my layman’s worldview, people are more likely to hunt and enslave people from another tribe or ethnicity than their own. At the same time, African countries in which different ethnicities coexist might face greater difficulties in providing public goods and establishing the political institutions conductive to economic prosperity. So I was a bit surprised that the analysis doesn’t control for ethnic diversity, in addition to size, climate, openness, etc.

But then towards the end of the essay, the relationship between slave exports and ethnic diversity is actually presented and the correlation at the country level turns out to be very high. But Nunn decides to interpret the relationship in the opposite direction: for him, slave exports caused ethnic diversity by impeding ethnic consolidation (which in turn contributes to economic underdevelopment today). He doesn’t even consider the possibility of reverse causality in this case, although the volume of slave exports could easily be a consequence rather than a cause of ethnic diversity in a region.

Of course, data alone cannot give an answer which interpretation is more likely to be correct. And this is exactly the point. When the assignment of countries into different levels of slave exports is not controlled by the researcher or randomized by nature, it is imperative that all possible interpretations consistent with the data are discussed and evaluated; especially in a volume which aims to bring research methodology lessons to the masses.

And finally, if my suggestion that ethnic diversity is more likely to be a cause rather than an effect of slave exports is correct, can ethnic diversity explain away the correlation between slave exports and economic performance? While Nunn doesn’t test this conjecture, he has the data available on his website, so why don’t we go ahead and check: while I can’t be entirely sure I replicate exactly what the original article is doing [there is no do-file online], a regression of income on slave exports with ethnic diversity included as a covariate takes the bulk of the significance of slave exports away.