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.

Visualizing asylum statistics

Note: of potential interest to R users for the dynamic Google chart generated via googleVis in R and discussed towards the end of the post. Here you can go directly to the graph.

02alessandro-penso
An emergency refugee center, opened in September 2013 in an abandoned school in Sofia, Bulgaria. Photo by Alessandro Penso, Italy, OnOff Picture. First prize at World Press Photo 2013 in the category General News (Single).

The tragic lives of asylum-seekers make for moving stories and powerful photos. When individual tragedies are aggregated into abstract statistics, the message gets harder to sell. Yet, statistics are arguably more relevant for policy and provide for a deeper understanding, if not as much empathy, than individual stories. In this post, I will offer a few graphs that present some of the major trends and patterns in the numbers of asylum applications and asylum recognition rates in Europe over the last twelve years. I focus on two issues: which European countries take the brunt of the asylum flows, and the link between the application share that each country gets and its asylum recognition rate.

Asylum applications and recognition rates
Before delving into the details, let’s look at the big picture first. Each year between 2001 and 2012, 370,000 people on average have applied for asylum protection in one of the member states of the European Union (plus Norway and Switzerland). As can be seen from Figure 1, the number fluctuates between 250,000 and 500,000 per year, and there is no clear trend. Altogether, during this 12-year period, approximately 4.5 million people have applied for asylum, which makes slightly less than one percent of the total EU population. Of course, this figure only tracks people who have actually made it to the asylum centers and filed an application – all potential refugees who have perished on the way, or have arrived but been denied the right of formal application, or have remained clandestine are not counted.

asylum_applications_small

Figure 1 also shows the annual number of persons actually recognized as ‘refugees’ under the terms of the Geneva Convention by the European governments: a status which grants considerable rights and protection. This number is quite lower with an average of around 40.000 per year (in the EU+ as a whole) which makes for less than half-a-million in total for the 12 years between 2001 and 2012. While the overall recognition rate remains between 7% and 14%, there is considerable variation between the different European states both in the share from the asylum flows they receive, and in the national asylum recognition rates.

Who takes the brunt of the asylum burden?
Both the asylum flows and the recognition rates are in fact distributed highly unequally across the continent, and in a way that cannot be completely accounted for by the wealth of destination countries, former (colonial) ties between asylum sources and destinations, nor geographical distance. To compare the shares of the total European pool of asylum applications and recognitions that a destination country gets, I create the so-called ‘burden coefficient’. The ‘burden coefficient’ compares the actual share of asylum applications a country received in a year to its ‘fair’ share which is defined as its relative share of the annual  total EU+ GDP. Simply put, if a country accounts for 10% of the European GDP, it would have been expected to receive 10% of all asylum applications filed in Europe that year. Taking account of GDP adjusts the raw asylum application shares in view of the expectation that richer and more populous countries should bear a proportionally higher share of the total European asylum ‘burden’ than poorer and smaller states.

asylum_applications_burden

Figure 2 shows the (logged) burden coefficient for asylum application shares for each EU+ country, averaged over the period 2010-2012. The solid line at zero indicates an asylum applications share perfectly proportional to a  country’s GDP share (a ‘fair’ burden). Countries with positive values receive a higher share of all applications than implied by their GDP level, and countries with negative values receive a lower than their implied share. (The dotted lines show where a country that is doing twice as much / twice as little as expected would be). Clearly, Spain, Portugal, Italy and many (but not all) of the East European countries underdeliver while Cyprus, Malta, Greece, and several West European states (notably Sweden, Belgium, and Norway) take a disproportionately high  share of the total pool of asylum applications filed in Europe over the last few years. Note that these comparisons already take into account (correct for) the fact that most of the Southern and Eastern European countries are poorer (have lower GDP) than the ones in the Western and Northern parts of the continent.

asylum_recognitions_burden

The picture does not change much when we focus on actual asylum recognitions (under the terms of the Geneva Convention) instead of applications. Figure 3 shows the burden coefficient (again averaged over 2010-2012) for full status refugee recognitions in Europe. The country ranking is similar with a few important exception – Greece grants much fewer asylum recognitions than expected even after we account for the state of its economy; Austria and Switzerland join the ranks of states which do much more than their implied share; and, sadly, many more countries in fact underdeliver when it comes to full refugee status grants. (Note that some states offer alternative protection to those denied the full ‘Geneva Convention’ status but the forms and level of this protection differs significantly across the continent).

Are asylum application shares responsive to the recognition rate?
Given these rather significant discrepancies across Europe in how many asylum applications countries get, and how much protection they offer, it is natural to ask whether the applications shares and the recognition rates are in fact related. Do asylum seekers flock at the gates of the European states which are most generous in their recognition policy? Do low recognition rates deter potential refugees from applying in certain countries? Can the strictness of asylum policy be an effective policy tool shaping future application flows? A comprehensive statistical analysis shows that while application shares and recognition rates are associated, their responsiveness to each other is rather weak. Simply put, manipulating the recognition rates is unlikely to have big practical effects on the asylum application share a country receives, and changes in the applications rates only weakly affect state recognition rates. The details of the analysis are rather technical and can be found here, but a dynamic visualization can help illustrate the patterns.

The dynamic interactive chart linked here shows the relationship between asylum applications and asylum recognition rates for each EU+ country over the last 12 years (the chart cannot be embedded in this post due to WordPress policy, but there is a screenshot below). When you press ‘Play’ each dot traces the experience of one country over time. You can choose to observe all, select a single state to focus upon, or tick a couple to compare their experiences.

dynamic-asylum-1

A movement of a dot (and the trace in leaves) in a horizontal direction means that the number of asylum applications received by a country increases while the recognition rates remains the same. Similarly, a vertical move implies a change in the recognition rate but a stable asylum application flow. A trajectory that follows a diagonal suggests a link between applications and recognition rates.

When paused, the state of the chart at each year shows the cross-sectional association between applications and recognition rates: it is easy to see that there is a (rather stable) weakly-strong positive relationship. But the trajectories of individual countries over time do not suggest that there is a temporal link between the two aspects of asylum policy for particular countries. For example, in the UK between 2001 and 2004 both the recognition rates and the applications fall, which would suggest strong responsiveness, but then the recognition rate moves up from 4% to almost 30% without any significant increase in applications. The trajectory of Denmark (try it out) exhibits something close to a dynamic link with rates depressing applications initially but then when they rise again, applications seem to pick up as well. Of course, asylum flows are driven by many other factors as well, so while suggestive, the patterns in the chart should be interpreted with care.

dynamic-asylum-2

More comprehensive analyses of asylum policy in Europe addressing these questions and more are available in my published articles accessible here and here. The original data comes from the UNHCR annual reports. The dynamic chart is generated using Google Chart Tools through the googleVis library in R, you can find the code here. I found it useful to generate a simple version, adjust the settings manually, and then copy the final settings via the Google Chart’s Advanced Panel back to R.

Scatterplots vs. regression tables (Economics professors edition)

I have always considered scatterplots to be the best available device to show relationships between variables. But it must be even better to have the regression table and a full description of the results in addition, right? Not so fast:

A new paper shows that professional economists make largely correct inferences about data when looking at a scatterplot, but get confused when they are shown the details of the regressions next to the scatterplot, and totally mess it up when they are shown only the numbers without the plot! Wow! If you needed any more persuasion that graphing your data and your results are more important than those regression tables with zillions of numbers, now you have it.

P.S. The authors of this research could have done a better job themselves in communicating visually their findings…

[via Felix Salmon]

 

The illusion of predictability: How regression statistics mislead experts
Emre Soyer& Robin M. Hogarth
Abstract
Does the manner in which results are presented in empirical studies affect perceptions of the predictability of the outcomes? Noting the predominant role of linear regression analysis in empirical economics, we asked 257 academic economists to make probabilistic inferences given different presentations of the outputs of this statistical tool. Questions concerned the distribution of the dependent variable conditional on known values of the independent variable. Answers based on the presentation mode that is standard in the literature led to an illusion of predictability; outcomes were perceived to be more predictable than could be justified by the model. In particular, many respondents failed to take the error term into account. Adding graphs did not improve inferences. Paradoxically, when only graphs were provided (i.e., no regression statistics), respondents were more accurate. The implications of our study suggest, inter alia, the need to reconsider how to present empirical results and the possible provision of easy-to-use simulation tools that would enable readers of empirical papers to make accurate inferences.

Visualizing left-right government positions

How does the political landscape of Europe change over time? One way to approach this question is to map the socio-economic left-right positions of the governments in power. So let’s plot the changing ideological  positions of the governments using data from the Manifesto project! As you will see below, this proved to be a more challenging task than I imagined, but the preliminary results are worth sharing nonetheless.

First, we need to extract the left-right positions from the Manifesto dataset. Using the function described here, this is straightforward:

lr2000<-manifesto.position('rile', start=2000, end=2000)

This compiles the (weighted) cabinet positions for the European countries for the year 2000. Next, let’s generate a static map. We can use the new package rworldmap for this purpose. Let’s also build a custom palette that maps colors to left-right values. Since in Europe red traditionally is the color of the political left (the socialists), the palette ranges from dark red to gray to dark blue (for the right-wing governments).

library (rworldmap)
op <- palette(c('red4','red3','red2','red1','grey','blue1', 'blue2','blue3', 'blue4'))

After recoding the name of the UK, we are ready to bind our data and plot the map. You can save the map as a png file.

library(car)
lr2000$State<-recode(lr$State, "'Great Britain'='United Kingdom'")

lrmapdata <- joinCountryData2Map( lr2000,joinCode = "NAME", nameJoinColumn = "State", mapResolution='medium')

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
png(file='LR2000map.png', width=640,height=480)
mapCountryData( lrmapdata, nameColumnToPlot="position",colourPalette=op, xlim=c(-9,31), ylim=c(36,68), mapTitle='2000', aspect=1.25,addLegend=T )
dev.off()

The limits on on the x- and y-axes center the map on Europe. It is a process of trial and error till you get it right, and the limits need to be co-ordinated with the aspect and the width and height of the png file so that the map looks reasonably well-proportioned. Here  is the result (click to see in full resolution):

It looks a bit chunky but not too bad. Next, we have to find a way to show developments over time. We could show several plots for different years on one page, but this is not very effective:

A much better way would be to make the maps dynamic, or, in other words, to animate them. But this is easier said than done. After searching for a few days for tools that can accomplish the job, I settled for producing individual maps for each month, importing the series into Adobe Flash, and exporting a simple animation movie. The R code to produce  the individual  maps:

lr<-manifesto.position('rile', start=1948, end=2008, period='month')
lr$State<-recode(lr$State, "'Great Britain'='United Kingdom'")
u.c<-unique(lr$Year.month)
for (i in 1:length(u.c)){
     lr.temp<-subset(lr, lr$Year.month==u.c[i])
     lrmapdata <- joinCountryData2Map( lr.temp,joinCode = "NAME", nameJoinColumn = "State", mapResolution='medium')
     plot.name<-paste('./maps/map',i,'.png', sep='') 

     par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
     png(file=plot.name, width=640,height=480)
     mapCountryData( lrmapdata, nameColumnToPlot="position",colourPalette=op, xlim=c(-9,31), ylim=c(36,68), mapTitle=u.c[i], aspect=1.25,addLegend=T )
     dev.off() }

And here is the result (opens outside the post):

Flash video of Left-Right positions (slow)

It kind of works, it has buttons for navigation, but it has one major flow – it is damn slow. It should be 12 frames (maps) per second, and it is 12 fps inside Flash, but once exported, the frame rate goes down (probably because my laptop’s processor is too slow). In fact, I can export a fast version, but only if I get rid of the control buttons. Here it is (right-click and press play to start):

Flash video of Left-Right positions (fast)

You can also play the animation as an AVI video (uploaded on YouTube), but somehow, through the mysteries of video-processing, a crisp slideshow of 8mb ended up as a low-res movie of 600mb.


The results resemble my initial idea, although none is perfect. Ideally, I would want a fast movie with controls and a time-slider, but my Flash programming skills (and my computer) need to be upgraded for that. Meanwhile, the Manifesto project could also update their data on which the animation is based.

Altogether, the experience of creating the visualization has been much more painful than I anticipated. First, there doesn’t seem to be an easy way to get a map of Europe (or, more precisely, of the European Union territories) for use in R. The available options are  either too low resolution, or too outdated (e.g. featuring Czechoslovakia), or require centering a world-map using ylim and xlim which is a problem because these coordinates are connected to the dimensions and the resolution of the output plot. For the US, and for individual European states, there are tons of slick and easy-to-find maps (shapefiles), but for Europe I couldn’t find anything that doesn’t feature huge tracts of land east to the Urals, which are irrelevant and remain empty with political data (which is usually available for the EU+ states only). Any pointers to good, relatively high-res maps (shapefiles) of the EU will be much appreciated.

Second, producing an animation out of the individual maps is rather difficult. Currently, Google Charts offer dynamic plots and static maps, I hope in the future they include dynamic maps as well. Especially because the googleVis package makes it possible to build Google charts from within R. I also found a new tool called StatPlanet which seems relevant and rather cool, but still relies on Adobe Flash and has no packaged Europe/EU maps. The big guns in visualization software are most probably up to the task but Tableau is prohibitively expensive and Processing is said to have a steep learning curve. Again, any help in identifying solutions that do not require proprietary software to produce animated maps would be much appreciated. I hope to be able to post an update on the project soon.

Creating Data Maps

There are several online tools for data visualization including IBM’s ManyEyes and Google’s Chart Tools. For a recent post on the other blog to which I contribute I wanted to map the distribution of a variable on a geographical map of Europe. I decided that’s a good opportunity to try a site called Target Map which promises free, high-quality, customizable data maps. The result of my efforts can be seen below:

The link to the map is here.

Altogether, I can’t say that I am too happy with the mapping utility. My main quibble is that there are no default color palettes that translate well continuous variables into color hues. By default, the program offers highly contrasting color choices for the different categories but ones that don’t suggest the ranking of categories. And I couldn’t find an easy way to customize the color palette.

Data entry is OK, although once you select Europe as the geographical scope of your data, you can’t have any values for Turkey, for example, even if you try to supply them manually. Altogether, Target Map might be useful for some very small and inconsequential projects but for serious staff one should bite the bullet and get familiar with R’s map utilities (something I have been planning to do for a while).