Immigration and voting for the radical right in Andalusia

I wrote a short text for the European Politics and Policy (EUROPP) blog on the link between immigration presence and voting for Vox, a relatively young radical right party, in the Spanish region of Andalusia.  Full text is here, see also this post from 2015 about a similar link with Euroscepticism in the UK. The most important graph is below. Here is an excerpt:

 
To sum up, the available empirical evidence suggests that the relative size of the non-Western foreign-born population at the municipal level is positively, and rather strongly, related to the share of votes cast for Vox, the first Spanish radical right party to get in parliament since the end of Franco’s regime. Immigration might be responsible to a considerable extent for the resurgence of the radical right in Andalusia.

The political geography of human development

The research I did for the previous post on the inadequacy of the widely-used term ‘Global South’ led me to some surprising results about the political geography of development.

Although the relationship between latitude and human development is not linear, distance from the equator turned out to have a rather strong, although far from deterministic and not necessarily causal, link with a country’s development level, as measured by its Human Development Index (HDI). Even more remarkably, once we include indicators (dummy variables) for islands and landlocked countries, and interactions between these and distance from the equator, we can account for more than 55% of the variance in HDI (2017). In other words, with three simple geographic variables and their interactions we can ‘explain’ more than half of the variation in the level of development of all countries in the world today. Wow! The plot below (pdf) shows these relationships.

 

 

In case you are wondering whether this results is driven by many small counties with tiny populations, it is not, When we run a weighted linear regression with population size as the weight, the adjusted R-squared of the model remains still (just above) 0.50. On a sidenote, including dummies for (former) communist countries and current European Union (EU) member states pushed the R-squared above 0.60. Communist regime or legacy is associated with significantly lower HDI, net of the geographic variables, and EU membership is associated with significantly higher HDI.

The next question to consider is whether the relationship between geography and development has grown weaker or stronger over time. There are many plausible ideas we might have about the influence of globalization, the spread of information and communication technologies, wars, and financial crises on the links between geography and development. When we look at the data, however, it turns out that the strength of the link has remained roughly the same since 1990. Wow! Despite of all global social and political transformations over the past 30 years, geography still play the same, rather larger role in constraining and enabling human development. The gif below shows the same plots for 1990, 2000, 2010, and 2017. While overal development grows over time, the relationship with distance from the equator remains roughly the same, as indicated by the slopes of the linear regression lines.

 

 

Note that the way the HDI is constructed (HDI) makes changes in development over time not quite comparable (the index is capped at 1.0, so if you are an already highly developed country, there is not much scope to improve further your index). Also, the sample of countries for which there is available data is smaller in 1990 (N=144) than in 2017 (N=191).

Since we mentioned population size, let’s consider the link between the population size of a country and its level of HDI. Are small countries more successful? Does it pay off to be a large state? Maybe countries with populations that are neither too big nor too small perform best?

As the plot below (pdf) shows, there is no clear relationship between population size and HDI. The linear regression line slopes slightly downwards but the ‘effect’ is not significant and it is not really linear. The loess fit meanders up and down without a clear pattern. It turns out there is no sweet spot for population size when it comes to human development. Small populations can be just as good, and just as bad, and bigger ones. There are tiny states that are successful, and ones that do pretty badly. The same for mid-sized, big, and enormous countries (not in terms of area, but population).

 

 

This lack of relationship is quite remarkable, but there is another surprise when we look at the change in development between 2000 and 2017. As the plot below (pdf) shows, more populous countries have been more successful in improving their HDI over the past 18 years. It is not a huge difference, but given the overall small scale of the observed changes, it is significant and important.

 

 

To sum up, while in general population size is not related to development, during the past two decades more populous countries have been more successful in improving their development index. This is of course good news, as it means that more people live longer, study longer, and enjoy higher standards of living.

For now, this concludes my exploits in political geography, which turned out to harbor more insights that I expected, even when I have only explored a total of five variables. If you want to continue from here on your own, the R script for the figures is here and the datafile is here.

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.

What’s a demockracy?

– What’s a democracy?

– Democracy means that people rule and the government respects the opinions of the citizens.

– So the government should do what the people want?

– In principle, yes, but…

– Can a majority of the people decide to abolish the parliament?

– No, the basic institutions of the state are usually set in the Constitution and constitutional rules are not to be changed like that. Everything that is in the constitution is off limits.

– OK, I can see why. Can the people decide different groups deserve different pay for the same job?

– No, even if this is not outlawed by the Constitution, there is the Universal Declaration of Human Rights, and fundamental human rights are not be changed by democratic majorities.

– Makes sense. Can the people decide on gay marriage? That’s not in the Declaration.

– Well, there are certain human rights that are not yet in constitutions and universal declaration, but we now recognize them as essential so they are also not subject to majorities.

– OK, so in democracies the government does what the people want, but not when it comes to constitutional issues, recognized fundamental human rights, and other very important norms.

– Yes.

– So can the people decide to change the interest rate?

– Oh, no! Not even politicians can do that. Monetary policy is delegated to independent central banks.

– But people can decide on regulating tel…

– Nope, regulation is basically all delegated to independent agencies, so that’s out.

– Hm, ok, so can the people decide to change the terms of foreign trade?

– Not really, these are set in international treaties so people cannot change anything that is in international treaties just like that.

– Got it. But people surely can decide if their country goes to war or not?

– Well, foreign policy is tricky, there is a lot of secret information involved, complex strategies to be made and it needs rapid responses, so, no.

– OK, can people decide on pensions, then?

– Pensions affect the future lives of those who can’t vote yet, so current majorities can’t really decide.

– OK, so in democracies the government does what the people want, but not when it comes to constitutional issues, recognized fundamental human rights and other very important norms, and not on anything that is in international treaties, and not on monetary policy or any regulatory issues, and not on foreign policy, and not on pensions. But for the rest the government should do what the majority of people want?

– Well, not really. It might not be clear what people want: there could be cyclical majorities among policy alternatives. And it might not be clear how to respond: respecting majorities on particular issues might lead to disrespecting a majority of the people overall.

– That sounds complicated. But if there are not cyclical majorities and one can satisfy a majority of people on a majority of the issues, then one should do what the people want?

– Nope. People might not want what’s good for them. People don’t understand policy and don’t follow political developments close enough. And people are duped by politicians and the media.

– Hard to disagree. I think I got it now: Democracy is a political system in which the government does what the people want, but not when it comes to constitutional issues, recognized fundamental human rights and other very important norms, and not on anything that is in international treaties, and not on monetary policy or any regulatory issues, and not on foreign policy, and not on pensions, and not on anything where it is unclear what the majority wants or how to satisfy a majority of people on majority of issues, and then only if the people want what’s right for them, to be decided by some experts in government or outside. Now that’s what I call a real demockracy!

Books on data visualization

Here is a compilation of new and classic books on data visualization:

 

Scott Murray (2017) Interactive Data Visualization for the Web 

Elijah Meeks (2017) D3.Js in Action: Data Visualization with JavaScript 

Alberto Cairo (2016) The Truthful Art: Data, Charts, and Maps for Communication 

Andy Kirk (2016) Data Visualization 

David McCandless (2014) Knowledge is Beautiful 

 

Edward Tufte (2006) Beautiful Evidence  

Edward Tufte (2001) The Visual Display of Quantitative Information 

Edward Tufte (1997) Visual Explanations: Images and Quantities, Evidence and Narrative 

Edward Tufte (1990) Envisioning Information 

The Discursive Dilemma and Research Project Evaluation

tl; dr When we collectively evaluate research proposals, we can reach the opposite verdict depending on how we aggregate the individual evaluations, and that’s a problem, and nobody seems to care or provide guidance how to proceed.

Imagine that three judges need to reach a verdict together using majority rule. To do that, the judges have to decide independently if each of two factual propositions related to the suspected crime is true. (And they all agree that if and only if both propositions are true, the defendant is guilty).

The distribution of the judges’ beliefs is given in the table below. Judge 1 believes that both propositions are true, and as a result, considers the conclusion (defendant is guilty) true as well. Judges 2 and 3 consider that only one of the propositions is true and, as a result, reach a conclusion of ‘not guilty’. When the judges vote in accordance with their conclusions, a majority finds the defendant ‘not guilty’.

 

Proposition 1 Proposition 2 Conclusion
Judge 1 true true true (guilty)
Judge 2 false true false (not guilty)
Judge 3 true false false (not guilty)
Majority decision TRUE TRUE FALSE (not guilty)

However, there is a majority that finds each of the two propositions true (see the last line in the table)! Therefore, if the judges vote on each proposition separately rather than directly on the conclusion, they will have to find the defendant ‘guilty’. That is, the judges will reach the opposite conclusion, even though nothing changes about their beliefs, they still agree that both propositions need to be true for a verdict of ‘guilty’, and the decision-making rule (majority) remains the same. The only thing that differs is the method through which the individual beliefs are combined: either by aggregating the conclusions or by aggregating the premises.

This fascinating result, in which the outcome of a collective decision-making process changes depending on whether the decision-making procedure is premise-based or conclusion-based, is known as the ‘discursive dilemma‘ or ‘doctrinal paradox‘. The paradox is but one manifestation of a more general impossibility result:

There exists no aggregation procedure (generating complete, consistent and deductively closed collective sets of judgments) which satisfies universal domain, anonymity and systematicity.” (List and Pettit, 2002).

Christian List has published a survey of the topic in 2006 and keeps an annotated bibliography. The paradox is related but separate from Arrow’s impossibility theorem, which deals with the aggregation of preferences.

After this short introduction, let’s get to the point. My point is that the collective evaluation of scientific research proposals often falls victim to the discursive dilemma. Let me explain how.

Imagine three scientific experts evaluating an application for research funding that has three components. (These components can be about three different aspects of the research proposal itself or about three different parts of the application, such as CV, proposal, and implementation plan). For now, imagine that the experts only evaluate each component as of excellent quality or not (binary choice). Each expert uses majority rule to aggregate the scores on each section, and the three experts reach a final conclusion  using majority rule as well.

The distribution of the evaluations of the three experts on each of the three components of the application are given in the table below. Reviewer 1 finds Parts A and C excellent but Part B poor. Reviewer 2 finds Parts B and C excellent but part A poor. And Reviewer 3 finds Parts A and B poor and part C excellent. Overall, Reviewers 1 and 2 reach a conclusion of ‘excellent’ for the total application, while Reviewer 3 reaches a conclusion of ‘poor’. By aggregating the conclusions by majority rule, the application should be evaluated as ‘excellent’. However, looking at each part individually, there is a majority that finds both Parts A and B ‘poor’, therefore the total evaluation should be ‘poor’ as well.

 

Part A Part B Part C Conclusion
Reviewer 1 excellent poor excellent EXCELLENT
Reviewer 2 poor excellent excellent EXCELLENT
Reviewer 3 poor poor excellent POOR
Majority decision POOR POOR EXCELLENT ?

So which one is it? Is this an excellent proposal or not, according to our experts?

I do not know.

But I find it quite important to recognize that we can get completely different results from the evaluation process depending on how we aggregate the individual scores, even with exactly the same distribution of the scores and even when every expert is entirely consistent in his/her evaluation.

But before we discuss the normative appeal of the two different aggregation options, is this a realistic problem or a convoluted scenario made up to illustrate a theoretical point but of no relevance to the practice of research evaluation?

Well, I have been involved in a fair share of research evaluations for journals, publishing houses, different national science foundations as well as for the European Research Council (ERC). Based on my personal experience, I think that quite often there is a tension between aggregating expert evaluations by conclusion and by premises.  Moreover, I have not seen clear guidelines how to proceed when the different types of aggregation lead to different conclusions. As a result, the aggregation method is selected by the implicit personal preferences of the one doing the aggregation.

Let’s go through a scenario that I am sure anyone who has been involved in some of the big ERC evaluations of individual research applications will recognize.

Two of the three reviewers find two of the three parts of the application ‘poor’, and the third reviewer finds one of the three parts poor and the other two parts ‘good’ (see the table below).

Part A Part B Part C Conclusion
Reviewer 1 poor poor good POOR
Reviewer 2 good poor poor POOR
Reviewer 3 good poor good GOOD
Majority decision GOOD POOR GOOD ?

Thus a majority of the final scores (the conclusions) indicate a ‘poor’ application. However, when the reviewers need to indicate the parts of the application that are ‘poor’, they cannot find many! There is a majority for two  out of the three parts that finds them ‘good’. Accordingly, by majority rule these cannot be listed as ‘weaknesses’ or given a poor score. Yet the total proposal is evaluated as ‘poor’ (i.e. unfundable).

There are three ways things go from here, based on my experience. One response is, after having seen that there is no majority evaluating many parts of the application as ‘poor’ (or as a ‘weakness’), to adjust upwards the overall scores of the application. In other words, the conclusion is brought in line with the result of the premise-based aggregation. A second response is to ask the individual reviewers to reflect back on their evaluations and reconsider whether their scores on the individual parts need be adjusted downwards (so the premises are brought in line with the result of the conclusion-based aggregation). A third response is to keep both a negative overall conclusion and a very, very short list of ‘weaknesses’ or arguments about which parts of the proposal are actually weak.

Now you know why you sometimes get evaluations saying that your project application is unfundable, but failing to point out what its problems are.

Again, I am not arguing that one of these responses or ways to solve the dilemma is always the correct one (although, I do have a preference, see below). But I think (a) the problem should be recognized, and (b) there should be explicit guidelines how to conduct the aggregation, so that there is less discretion left to those doing it.

If I had to choose, I would go for conclusion-base aggregation. Typically, my evaluation of a project is not a direct sum of the evaluations of the individual parts, and it is based on more than can be expressed with the scores on the application’s components. Also typically, having formed a conclusion about the overall merits of the proposal, I will search for good arguments to make why the proposal is poor, but also add some nice things to say to balance the wording of the evaluation. But it is the overall conclusion that matters, and the rest is discursive post hoc justification that is framed to fit to requirements of the specific context of the evaluation process.

Another argument to be made in favor of conclusion-based aggregation is the idea that reviewers represent particular ‘world-views’ or perspectives, for example, stemming from their scientific (sub)discipline. Therefore, evaluations of individual parts of a research application should not be aggregated by majority, since the evaluations are not directly comparable. If I consider that a literature review presented in a project proposal is incomplete based on my knowledge of a specific literature, this assessment should not be overruled by two assessments that the literature review is complete coming from reviewers who are experts in different literatures than I am: we could all be right in light of what we know.

In fact, the only scenario in which premise-based aggregation (with subsequent adjustment of the conclusions) makes sense to me is one where all reviewers know, on average, the same things and they provide, on average, scores without bias but with some random noise. In this case, majority aggregation of the premises filters the noise.

But I am sure that there more and different arguments to be made, once we realize that the discursive dilemma is a problem for research evaluations and that currently different aggregation practices are allowed to proliferate unchecked.

I suspect that many readers, even if they got this far in the text, would be unconvinced about the relevance of the problem I describe, because they think that (a) research evaluation is rarely binary but involves continuous scores, and (b) because aggregation is rarely based on majority rule.

The first objection is easier to deal with: First, sometimes evaluation is binary, for example, when the evaluation committee needs to list ‘strengths’ and ‘weaknesses’. Second, even when evaluation is formally on a categorical or continuous scale, it is in practice binary because anything below the top end of the scale is ‘unfundable’. Third, the discursive dilemma is also relevant for continuous judgements.

The second objection is pertinent. It is not that majority rule is not used when aggregating individual scores: it is, sometimes formally, more often informally. But in the practice of research evaluation these days, having anything less than a perfect score means that a project is not going to be funded. So whatever the method of aggregation, any objection (low score) by any reviewer is typically sufficient to derail an application. This is likely a much bigger normative problem for research evaluation, but one that requires a separate discussion.

And since we have to spend a lot of time preparing comprehensive evaluation reports, also of  projects that are not going to be funded, the discursive dilemma needs to be addressed so that the final evaluations are consistent and clear to the researchers.