The Causes of Poverty (58): Low Average Intelligence in Poor Countries?

The claim that poverty is caused by the stupidity of the poor has an international equivalent: some people look at the fact that most wealthy countries in the world are mainly populated by white people, combine this fact with the claim that non-Western countries have lower average IQ, and conclude that they have found the reason why poor countries are poor.

This is of course a nasty piece of victim blaming on a global scale. It’s also borderline racist. Moreover, if successful, this view will make poverty reduction impossible, given the genetic determinism that is often paired with IQ analysis. If kids get their IQ from their parents, if IQ determines wealth, and if nothing else causes poverty, then why bother doing anything at all?

For example, a book by Richard Lynn and Tatu Vanhanen titled “IQ and the Wealth of Nations” suggests that the average IQ in Africa is around 70, much lower than in East Asia or the West. They also claim that lower average IQ scores are the cause of low levels of development, income, literacy, life expectancy etc.

There are many problems with this theory. First, most of their data are made up. IQ score aren’t available for many countries. At best, the scores are extrapolated on the basis of tiny samples. Second, the theory confuses cause and effect. It’s poverty that drives down IQ rates. The Flynn effect suggests that factors such as improved nutrition, health care and schooling improve IQ test performance. IQ determinism is simply wrong.

Even if the data could tell us that poor countries have indeed relatively low average IQ rates, that’s no reason to assume that low IQ causes poverty. Causation may go the other way, and it’s also possible that there’s something else, a third element that causes both poverty and low IQ, for example the experience of colonialism. The colonizers were no more interested in creating education institutions than in fostering sustainable, non-extractive economies. Don’t forget about the omitted variable bias. However, now we’re assuming that the data can tell us about IQ, and they currently can’t.

Other posts in this series are here.

Lies, Damned Lies, and Statistics (33): The Omitted Variable Bias, Ctd.

I discussed the so-called Omitted Variable Bias before on this blog (here and here). So I suppose I can mention this other example: guess what is the correlation, on a country level, between per capita smoking rates and life expectancy rates? High smoking rates equal low life expectancy rates, right? And vice versa?

Actually, and surprisingly, the correlation goes the other way: the higher smoking rates – the more people smoke in a certain country – the longer the citizens of that country live, on average.

Why is that the case? Smoking is unhealthy and should therefore make life shorter, on average. However, people in rich countries smoke more; in poor countries they can’t afford it. And people in rich countries live longer. But they obviously don’t live longer because they smoke more but because of the simple fact they have the good luck to live in a rich country, which tends to be a country with better healthcare and the lot. If they would smoke less they would live even longer.

Why is this important? Not because I’m particularly interested in smoking rates. It’s important because it shows how easily we are fooled by simple correlations, how we imagine what correlations should be like, and how we can’t see beyond the two elements of a correlation when we’re confronted with one that goes against our intuitions. We usually assume that, in a correlation, one element should cause the other. And apart from the common mistake of switching the direction of the causation, we often forget that there can be a third element causing the two elements in the correlation (in this example, the prosperity of a country causing both high smoking rates and high life expectancy), rather than one element in the correlation causing the other.

More posts in this series are here.

Why Do Countries Become/Remain Democracies? Or Don’t? (11): The Relative Cost of Freedom and Dictatorship

When dictatorial governments come under international pressure to improve the human rights situation in their countries, they often react by stating that they govern developing countries and don’t have the resources that are necessary to make improvements. Such statements have some plausibility. A judiciary, a well-trained police force, a functioning system of political representation etc. all require funding.

However, to some extent this explanation is no more than an excuse: you don’t need money to stop persecution of dissidents, to lift restrictions on the media, to allow demonstrations etc. On the contrary, you save money by doing so. You don’t need a large police force or paramilitary force; you don’t need strong government controls of every aspect of society and the economy; you don’t need to bride your citizens into acceptance of the state etc. But obviously the goal of dictators isn’t to save money and make the country better off by investing that money in the economy.

On the other hand, it remains true that the adequate defense of freedom, rights and democracy requires money, which is probably why rich countries usually score higher in freedom indexes. And, consequently, governments can save money by limiting freedom and by oppressing people.

So, both oppression and freedom cost money, and both a reduction of oppression and a reduction freedom save money. The question is then: what is, overall, the cheapest? A dictatorship or a democracy? And how can we know? Well, one possible indicator could be government spending as a percentage of GDP. If democracies have a systematically higher percentage, one could say that freedom costs more than oppression (on the condition that there isn’t a third variable explaining why democracies spend more).

However, one look at the data tells you that there isn’t much of a correlation between freedom and government spending, or between oppression and government spending. There are some countries that oppress a lot with not a lot of money – “not a lot” in relative terms compared to GDP. China and Saudi Arabia for example. And there are others that do need a lot of money (a large share of the economy) to keep the bosses in place. Cuba and Zimbabwe for example. But perhaps that is because their GDP is so low, not because they need a lot of money to oppress. In other cases, such as Saudi Arabia we may think they don’t spend a lot on oppression but we are fooled because their GDP is relatively high. And anyway, even dictatorships use some part of their state budget for things that aren’t quite so bad.

Likewise for freedom: freedom comes “cheap” in the U.S., and is “expensive” in Sweden. Between quotation marks because government spending over GDP is a very imprecise measure of the cost of freedom or oppression, for the reasons just given. It’s not because a country’s GDP doubles thanks to higher oil prices that the cost of freedom also doubles. Freedom (like oppression) costs money but not money as a fixed percentage of GDP.

Alternatively, you can also look at the tax burden. Here, the data show that countries that impose the highest taxes are also the ones that are most free (Scandinavia obviously ranks high on both accounts). But is that because freedom costs so much more than oppression? Perhaps the answer is “yes” if you include in “freedom” the things that make freedom possible, such as good healthcare, education etc.

But perhaps a more interesting and useful question would be: what cost considerations or economic incentives would produce a move towards democracy or away from democracy? It’s clear that a crisis of some sort – 9/11, a war, or, more appropriately in the current context, an economic recession or depression (see the Roosevelt cartoon below) – encourages democratic leaders to abridge certain rights, freedoms and democratic procedures. In the case of an economic crisis, the claim is that freedom and proper democratic procedures are just too expensive economically. A swift resolution of the crisis requires strong centralized intervention.

It’s also widely accepted that one of the causes of the demise of the Soviet Union was the unbearable cost of oppression. I think it’s better foreign policy to try to make oppression as costly as possible, rather than trying to make freedom as cheap as possible. Freedom tends not to be very cheap, I guess. And when it is, it’s probably not really freedom.

Migration and Human Rights (26): The “Criminal Immigrant” Stereotype, Ctd.

Contrary to right-wing rhetoric and popular belief (examples here and here), there isn’t much of a correlation between Latino immigration in the U.S. and crime rates. There’s an interesting new article about this here confirming my previous claims (to make it even more interesting: it’s from a conservative magazine).

Nearly all of the most heavily Latino cities have low or even extremely low crime rates, and virtually none have rates much above the national average. Eighty percent Latino El Paso has the lowest homicide and robbery rates of any major city in the continental United States. This is not what we would expect to find if Hispanics had crime rates far higher than whites. Individual cities may certainly have anomalously low crime rates for a variety of reasons, but the overall trend of crime rates compared to ethnicity seems unmistakable.

Maybe we should assume that the numbers are bit too rosy because of the tendency of illegal immigrants to underreport crime (although the article tries to correct for underreporting by comparing homicides – almost no underreporting – to overall crime). Also, the likelihood of underreporting by illegal immigrants can be offset by a possibly equal effect of criminal restraint on the part of illegal immigrants: for the same reasons that they underreport crime – fear of contacting the authorities and being identified as illegal immigrants – they stay out of trouble with the police and try to act decently.

However, if we look at it from another side, we see that incarceration data show somewhat higher levels for Hispanics or immigrants (although most Hispanics are American-born, the vast majority still comes from a relatively recent immigrant background):

the age-adjusted Hispanic incarceration rate is somewhat above the white rate—perhaps 15 percent higher on average. (source)

Still, one can’t simply conclude from this that crime is more rampant among Hispanics or immigrants. It’s still possible that instead of higher criminality we simply witness the result of harsher treatment of those sections of the population by the judicial system. Also, incarceration rates are inflated because many immigrants are in jail not because of ordinary crimes, but because of infractions of immigration law; you should exclude the latter if you want to compare Hispanic and white criminality (unless you consider infractions of immigration law as essentially equivalent to ordinary crime, which is not altogether insane; but the point of this post is to examine the claim that there are more ordinary criminals among Hispanic immigrants than among [longtime] citizens).

In addition, you should correct incarceration rates for age and gender: in general, most criminals are young men, and it happens to be the case that most immigrants are also young men. So the likelihood that immigrants end up in prison is – slightly – higher compared to the general population, not because they’re Hispanics but because they are young men. Any other, non-immigration related influx of young men in a certain area – e.g. military demobilization or a huge construction project – would have an effect on crime. (If you don’t correct for this, you’re making a common statistical mistake: see here for other examples of the “omitted variable bias”).

Finally, immigrants are relatively poor and there is a link between poverty and crime. So that can also explain the higher incarceration rate for immigrants. If you link the higher probability of poor people engaging in crime with the fact that poor people have lower quality legal representation, you have a double explanation. So, again, if Hispanics do end up in jail more often, perhaps it’s because they’re relatively poor, not because they are Hispanics and somehow racially prone to crime.

All this is limited to the U.S. People can still make the case that immigration in other countries promotes crime, but that case is made harder by the false claims about the U.S. (At least in France there’s no proof of the share of immigrants in the population having a significant impact on crime rates). These false claims are always based on anecdotes, and you’ll always be able to find criminals with foreign sounding names in order to whip up a frenzy against immigration, thereby satisfying your racist hunger and building a political following of ill-informed voters. Again a clear demonstration of the usefulness of statistical analysis in human rights issues and the danger of anecdotal reasoning.

Bonus paper here. Quote:

We examine whether the improvement in immigrants’ relative incarceration rates over the last three decades is linked to increased deportation, immigrant self-selection, or deterrence. Our evidence suggests that deportation does not drive the results. Rather, the process of migration selects individuals who either have lower criminal propensities or are more responsive to deterrent effects than the average native. Immigrants who were already in the country reduced their relative institutionalization probability over the decades; and the newly arrived immigrants in the 1980s and 1990s seem to be particularly unlikely to be involved in criminal activity.

More on migration.

Lies, Damned Lies, and Statistics (23): The Omitted Variable Bias, Ctd.

You see a correlation between two variables, for example clever people wear fancy clothes. Then you assume that one variable must cause the other, in our case: a higher intellect gives people also a better sense of aesthetic taste, or good taste in clothing somehow also makes people smarter. In fact, you may be overlooking a third variable which explains the other two, as well as their correlation. In our case: clever people earn more money, which makes it easier to buy your clothes in shops which help you with your aesthetics.

Here’s an example from Nate Silver’s blog:

Gallup has some interesting data out on the percentage of Americans who pay a lot of attention to political news. Although the share of Americans following politics has increased substantially among partisans of all sides, it is considerably higher among Republicans than among Democrats:

The omitted variable here is age, and the data should be corrected for it in order to properly compare these two populations.

News tends to be consumed by people who are older and wealthier, which is more characteristic of Republicans than Democrats.

People don’t read more or less news because they are Republicans or Democrats. And here’s another one from Matthew Yglesias’ blog:

It’s true that surveys indicate that gay marriage is wildly popular among DC whites and moderately unpopular among DC blacks, but I think it’s a bit misleading to really see this as a “racial divide”. Nobody would be surprised to learn about a community where college educated people had substantially more left-wing views on gay rights than did working class people. And it just happens to be the case that there are hardly any working class white people living in DC. Meanwhile, with a 34-48 pro-con split it’s hardly as if black Washington stands uniformly in opposition—there’s a division of views reflecting the diverse nature of the city’s black population.

Lies, Damned Lies, and Statistics (17): The Correlation-Causation Problem and Omitted Variable Bias

Suppose we see from Department of Defense data that male U.S. soldiers are more likely to be killed in action than female soldiers. Or, more precisely, the percentage of male soldiers killed in action is larger than the percentage of female soldiers. So there is a correlation between the gender of soldiers and the likelihood of being killed in action.

One could – and one often does – conclude from such a finding that there is a causation of some kind: the gender of soldiers increases the chances of being killed in action. Again more precisely: one can conclude that some aspects of gender – e.g. a male propensity for risk taking – leads to higher mortality.

However, it’s here that the Omitted Variable Bias pops up. The real cause of the discrepancy between male and female combat mortality may not be gender or a gender related thing, but a third element, an “omitted variable” which doesn’t show up in the correlation. In our fictional example, it may be the type of deployment: it may be that male soldiers are more commonly deployed in dangerous combat operations, whereas female soldiers may be more active in support operations away from the front-line.

OK, time for a real example. It has to do with home-schooling. In the U.S., many parents decide to keep their children away from school and teach them at home. For different reasons: ideological ones, reasons that have to do with their children’s special needs etc. The reasons are not important here. What is important is that many people think that home-schooled children are somehow less well educated (parents, after all, aren’t trained teachers). However, proponents of home-schooling point to a study that found that these children score above average in tests. However, this is a correlation, not necessarily a causal link. It doesn’t prove that home-schooling is superior to traditional schooling. Parents who teach their children at home are, by definition, heavily involved in their children’s education. The children of such parents do above average in normal schooling as well. The omitted variable here is parents’ involvement. It’s not the fact that the children are schooled at home that explains their above average scores. It’s the type of parents. Instead of comparing home-schooled children to all other children, one should compare them to children from similar families in the traditional system.

Greg Mankiw believes he has found another example of Omitted Variable Bias in the data plotting test scores for U.S. students against their family income:

Kids from higher income families get higher average SAT scores. Of course! But so what? This fact tells us nothing about the causal impact of income on test scores. … This graph is a good example of omitted variable bias … The key omitted variable here is parents’ IQ. Smart parents make more money and pass those good genes on to their offspring. Suppose we were to graph average SAT scores by the number of bathrooms a student has in his or her family home. That curve would also likely slope upward. (After all, people with more money buy larger homes with more bathrooms.) But it would be a mistake to conclude that installing an extra toilet raises yours kids’ SAT scores. … It would be interesting to see the above graph reproduced for adopted children only. I bet that the curve would be a lot flatter. Greg Mankiw (source)

Meaning that adopted children, who usually don’t receive their genes from their new families, have equal test scores, no matter if they have been adopted by rich or poor families. Meaning in turn that the wealth of the family in which you are raised doesn’t influence your education level, test scores or intelligence.

However, in his typical hurry to discard all possible negative effects of poverty, Mankiw may have gone a bit too fast. While it’s not impossible that the correlation is fully explained by differences in parental IQ, other evidence points elsewhere. I’m always suspicious of theories that take one cause, exclude every other type of explanation and end up with a fully deterministic system, especially if the one cause that is selected is DNA. Life is more complex than that. Regarding this particular matter, education levels are to some extent determined by parental income (university enrollment is determined both by test scores and by parental income, even to the extent that people from high income families but with average test scores, are slightly more likely to enroll in university than people from poor families but with high test scores).

What Mankiw did, in trying to avoid the Omitted Variable Bias, was in fact another type of bias, one which we could call the Singular Variable Bias: assuming that a phenomenon has a singular cause.