# Human Rights Video (22): Landmines

WARNING: this video is disturbing, and meant to be.

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From an advocacy standpoint, this is probably way over the top. Some would call it badvertising and, indeed, I don’t see the need to shock people in this way in order to raise consciousness. More on landmines here. More human rights videos here.

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# Human Rights Maps (75): Military Spending Worldwide, as Percentage of GDP and of World Total

Some data for the year 2008:

Military Spending Worldwide as Percentage of GDP and of World Total

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And this is for the year 2010:

military spending as percentage of GDP

military spending, total

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More on military spending (or “defense” spending) here. Some statistics here. More maps here.

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# Lies, Damned Lies, and Statistics (17): The Correlation-Causation Problem and Omitted Variable Bias, aka “Jumping to Conclusions”

correlation vs causation

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Some more detailed information after my casual remark on the correlation-causation problem. Here’s a fictitious example of what is meant by “Omitted Variable Bias“, a type of statistical bias that illustrates this problem. 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 and in order to avoid another statistical error, 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 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.

correlation and causation

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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.

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

###### (source, the R-square for each test average/income range chart is about 0.95)

[T]he above graph … show[s] that 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, take a look back at this post, which shows that 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. In honor of Professor Mankiw (who does some good work, see here for example), I propose that henceforth we call it the Mankiw Bias.

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# Human Rights Facts (43): Defense Spending in the U.S.

The data on U.S. defense spending (“defense” being of course a euphemism) are here. (I hope the connection to the issue of human rights is obvious and doesn’t need spelling out). The amounts involved are incredible, and yet you can still find national security hawks who believe that it isn’t enough, or who advocate that cutting some of this spending would be extremely dangerous. The Heritage Foundation, for example, has an article out lambasting the Obama administration for some supposed spending cuts. They have this graph for instance:

Obama's plan on defense spending according to the Heritage Foundation

Now, this graph should be used in every textbook on statistics as a classic example of misinformation and manipulation of data. As Benjamin H. Friedman points out:

It’s true that defense spending will probably decline as a percentage of GDP, assuming the economy recovers. But that’s because GDP grows. Ours [GDP] is more than six times bigger than it was in 1950.

The correct way to measure growth or decline in defense spending is to look at the amounts spent on defense in real, inflation adjusted terms. See the solid line in this graph:

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And then it’s clear that the U.S. spends more now than at the height of the Cold War. Friedman again:

By saying that defense spending needs to grow with GDP to be “level”, you are arguing for an annual increase in defense spending without saying so directly. That’s the point, of course. (source)

Defense hawks want military spending to rise together with GDP growth, whatever the international situation, whatever the threats.

As Matthew Yglesias points out:

Since economic growth causes real wages to rise over time, there is some reason for thinking that a military sized appropriately to the strategic environment would need real increases in spending to maintain its level of capabilities. But one way or another, the crucial issue is that the appropriate level of defense spending is determined by the nature of the strategic environment, not by the pace of economic growth. The US economy grew rapidly during the 1990s but the level of military threats facing the country didn’t—thus, a decline in defense expenditures relative to GDP was appropriate.

One interesting trope both in the substance and rhetoric of this argument from Heritage is the idea that 9/11 ought to have touched off a large and sustained increase in defense spending. On the merits, this is a little hard to figure out. It’s difficult to make the case that the 9/11 plot succeeded because the gap in financial expenditures between the U.S. government and Osama bin Laden was not big enough. Would an extra aircraft carrier have helped? A more advanced fighter plane? A larger Marine Corps? Additional nuclear weapons? One of the most realistic ways an organization like al-Qaeda can damage the United States is to provoke us into wasting resources on a far larger scale than they could ever destroy. The mentality Heritage is expressing here is right in line with that path.

More on military spending. And here‘s more on how to lie with statistics.

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# Human Rights Facts (9): Military Spending

This is a follow-up of a previous post on the evolution of war in the world. Whereas the number of wars and their intensity seem to decrease over the last decades, the same cannot be said of the arms trade and the defense budgets. This makes it difficult to hope that the statistics on warfare will continue in the same positive trend for the future. The arms industry, the arms trade and defense budgets have traditionally been a strong driving force of violent conflict.

This post focuses on defense budgets and military spending. (The arms trade will be the topic of another post). Although one should not naively condemn all military spending – to use Bush-speak: there are many “evil” people in the world and we should be able to defend ourselves and our values – it is also the case that arms and weapons are a major factor in war dynamics. Their stocks tend to build up as enemies engage in “arms races”. And such races often cause war. War of course is a massive collection of human rights violations. Arms and weapons are also used in non-war situations, such as police brutality, private use, criminal use etc. where they also lead to rights violations. Arms and weapons that are originally part of the military often wind up in other sectors of society.

According to SIPRI, the Stockholm International Peace Research Institute, the governments of the world spent \$1.4 billion on arms and the military in 2007, an increase of almost 50% compared to a decade ago. This is 2.5% of world gross domestic product (GDP) and \$202 for each person in the world. (Compare: only 0.3% of world GDP is spent on development aid).

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The champion of military spending is the U.S., which accounted for 45 % world total in 2007, followed by the U.K., China, France and Japan, with 4–5 % each. The U.S., however, did not substantially increase its spending over the last decades (the Iraq and Afghanistan wars have led to an increase but not a lot beyond historic levels). In fact, measured as a share of its GDP, its spending decreased somewhat (it’s now about 5% of its GDP, still double of world average):

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Western Europe has seen the lowest increase in spending, but the levels of spending vary a lot between countries:

China is a particular worry given its substantial increases in spending and the lack of transparency in its budgets:

This is a ranking of the biggest spenders:

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The U.S., Russia, China, the U.K., France, India, Pakistan, North-Korea and Israel together have more than 25.000 nuclear arms. Here’s a post on the international arms trade.

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