Capital Punishment, What Can We Hope For?

Emile Friant; Capital Punishment; 1908
Emile Friant; Capital Punishment; 1908

The death penalty is going out of style. Many people have many good reasons to be happy about that, and I’m among those who consistently advocate against this inhuman punishment. (Without losing sight of the many other forms of injustice perpetrated against – duly or unduly – convicted criminals).

However, it’s not going away fast enough. One can wonder why it’s still practiced at all, and hasn’t gone the road of slavery, torture, human sacrifice or similar remnants of the Middle Ages which, to the extent that they still exist, are mostly hidden in shame.

Instead of trying to answer this question, I’m going to ponder an easier one: when can we hope the death penalty will be abolished altogether? No way of knowing for sure, of course, but we can extrapolate some data sets. For instance, there’s the hopeful evolution of the numbers of countries that have outlawed the practice. So-called abolitionist countries can be divided into two groups: abolitionist in law or in practice. Depending on the source, there are about 100 countries that have no death penalty in their laws, and about 35 to 40 that still have laws but no longer apply them. That leaves about 60 so-called “retentionist” countries (some would prefer a less flattering qualifier). (Again according to the sources, there are about 195 independent countries in the world today).

Venezuela was the first country still existing in the world to abolish the death penalty for all crimes, doing so by Constitution in 1863. Among the last countries were Russia, Argentina and Latvia (you can find the complete list here). The rate of abolition accelerated quickly over the course of the last 3 decades, as I show in this graph:

death penalty, number of abolitionist countries

As this is a case of exponential growth, we can extrapolate:

death penalty, projected abolition worldwide

If the trend over the last 4 or 5 decades continues, and the most recent flattening of the curve is just a glitch – two big “ifs”, I admit – then the death penalty will be illegal everywhere sometime around 2022.

Another, and probably more relevant set of data are the actual numbers of executions. It’s hard to get your hands on a long time series of reliable numbers for the word as a whole, partly because data on executions in China – by far the biggest killer – are notoriously incomplete. Still, if we graph the available numbers for some of the worst countries, and assume that secrecy isn’t becoming more of a problem over time (again a big “if”), then we get a similarly hopeful evolution for most countries:

numbers of executions China Iran Saudi Arabia United States

(source)

Were we to add a trend line as in the graph on abolition, then China would stop executing in 2017, which seems a bit optimistic. Saudi Arabia would stop in 2027. The US only in 2034. This neatly illustrates the limits of statistical analysis, since I’m ready to bet that the order will be exactly the reverse.

Declining popular support is a further indication of the demise of the death penalty:

popular support for the death penalty in the US

And not just in the US. According to some sources, a majority of Chinese think their government executes too many people, although a clear majority still favors the punishment in principle. (Needless to say that public opinion is difficult to measure in authoritarian countries, and may be affected by authoritarian practices and the relative paucity of public debate). Less than half of Britons, French and Australians support the reintroduction of the death penalty. On the other hand, 55% of Brazilians and a stunning 85% of Japanese are in favor. (Source).

Capital Punishment (42): The Stupidity of Deterrent Statistics, Ctd.

The so-called deterrent effect is one of the main arguments in favor of capital punishment. I’ve argued many times before that the data we have don’t support the existence of this effect. Some of the data even suggest the possibility that instead of a deterrent effect, capital punishment has a brutalization effect (because it sends out the normative message that violent retaliation is the normal response to ill-treatment and that the sanctity of life is a naive moral ideal).

The following quote nicely summarizes the difficulty of proving the deterrent effect:

I would like to know how a statistical study, no matter how sophisticated, can possibly tell us the subjective motives for acts that were never taken and, moreover, how it can do so with the specificity of telling us approximately how many people did not do what they otherwise would have done under different circumstances. Where are these people? And, more importantly, how would we recognize one if we happened across him or her? (source)

Of course, people who want to disprove the deterrent effect also face this difficulty, but I assume we can agree that the burden of proof is on those who want to use the effect as an argument in favor of capital punishment. And that turns out to be a very heavy burden in this case.

Anyway, even if deterrence could be proven and even if we could establish with some certainty that every execution saves n lives – as some have argued, oblivious of the difficulties pointed out in the quote above – then we would still have good reasons to reject capital punishment.

More here.

Why Do Countries Become/Remain Democracies? Or Don’t? (20): Education Again

It’s a common assumption that democracy is driven by levels of education:

  • Less educated people are – supposedly – easier to oppress and more willing to accept extreme and simplistic ideologies that authoritarian rulers can exploit. They are also said to be less tolerant, and therefore less willing to accept freedoms and rights that protect outgroups.
  • Once people become more educated, they start earning more. And because they earn more, they have more leisure time. And because they have more leisure time, they have more opportunities to engage in various activities. And because they have these opportunities, they start to demand the freedoms they need to take up these opportunities. Better education itself, irrespective of the higher earning potential that goes with it, opens up opportunities to do things, and hence drives the demand for the freedom necessary to do things.
  • More educated people are also more aware of the ways in which their governments oppress them and of the liberties enjoyed in other countries, and they are better able to organize and mobilize against their governments.
  • Maslow’s theory about the hierarchy of needs also plays a part: when lower needs – such as food, clothing and shelter – are met, then the preconditions are fulfilled for the appearance of higher needs. Higher education levels, because they help to fulfill lower needs, assist the appearance of needs such as self-actualization, self-esteem and belonging, needs that require freedom for their realization.
  • Democracy requires a certain level of education among citizens in order to function properly. Of course, it’s not because B requires A that A results in B; claiming that education results in democracy because democracy needs education would mean committing a logical error. However, the fact that democracy needs education does probably increase the likelihood that democracy will follow from more education. At least the absence of some level of education will diminish the chances of democracy.
  • And, finally, more education improves the capacity to make rational choices, and democracy is essentially a system of choice. Democracy will therefore intrinsically appeal to the higher educated.

And indeed, there is a correlation – albeit not a very strong one – between levels of education and degrees of democracy.

The correlation may be due to the fact that democracies are better educators, but there are some reasons to believe that part of the causation at least goes the other way. Anecdotal evidence is provided by the recent Arab Spring: education levels in Arab countries have risen sharply in recent decades.

More posts in this series are here.

Measuring Human Rights (13): When More Means Less and Vice Versa

Human rights violations can make it difficult to measure human rights violations, and can distort international comparisons of the levels of respect for human rights. Country A, which is generally open and accessible and on average respects basic rights such as speech, movement and press fairly well, may be more in the spotlight of human rights groups than country B which is borderline totalitarian. And not just more in the spotlight: attempts to quantify or measure respect for human rights may in fact yield a score that is worse for A than for B, or at least a score that isn’t much better for A than for B. The reason is of course the openness of A:

  • Human rights groups, researchers and statisticians can move and speak relatively freely in A.
  • The citizens of A aren’t scared shitless by their government and will speak to outsiders.
  • Country A may even have fostered a culture of public discourse, to some extent. Perhaps its citizens are also better educated and better able to analyze political conditions.
  • As Tocqueville has famously argued, the more a society liberates itself from inequalities, the harder it becomes to bear the remaining inequalities. Conversely, people in country B may not know better or may have adapted their ambitions to the rule of oppression. So, citizens of A may have better access to human rights groups to voice their complaints, aren’t afraid to do so, can do so because they are relatively well educated, and will do so because their circumstances seem more outrageous to them even if they really aren’t. Another reason to overestimate rights violations in A and underestimate them in B.
  • The government administration of A may also be more developed, which often means better data on living conditions. And better data allow for better human rights measurement. Data in country B may be secret or non-existent.

I called all this the catch 22 of human rights measurement: in order to measure whether countries respect human rights, you already need respect for human rights. Investigators or monitors must have some freedom to control, to engage in fact finding, to enter countries and move around, to investigate “in situ”, to denounce etc., and victims should have the freedom to speak out and to organize themselves in pressure groups. So we assume what we want to establish. (A side-effect of this is that authoritarian leaders may also be unaware of the extent of suffering among their citizens).

You can see the same problem in the common complaints that countries such as the U.S. and Israel get a raw deal from human rights groups:

[W]hy would the watchdogs neglect authoritarians? We asked both Human Rights Watch and Amnesty, and received similar replies. In some cases, staffers said, access to human rights victims in authoritarian countries was impossible, since the country’s borders were sealed or the repression was too harsh (think North Korea or Uzbekistan). In other instances, neglected countries were simply too small, poor, or unnewsworthy to inspire much media interest. With few journalists urgently demanding information about Niger, it made little sense to invest substantial reporting and advocacy resources there. … The watchdogs can and do seek to stimulate demand for information on the forgotten crises, but this is an expensive and high risk endeavor. (source)

So there may also be a problem with the supply and demand curve in media: human rights groups want to influence public opinion, but can only do so with the help of the media. If the media neglect certain countries or problems because they are deemed “unnewsworthy”, then human rights groups will not have an incentive to monitor those countries or problems. They know that what they will be able to tell will fall on deaf ears anyway. So better focus on the things and the countries which will be easier to channel through the media.

Both the catch 22 problem and the problems caused by media supply and demand can be empirically tested by comparing the intensity of attention given by human rights monitoring organizations to certain countries/problems to the intensity of human rights violations (the latter data are assumed to be available, which is a big assumption, but one could use very general measures such as these). It seems that both effects are present but not much:

[W]e subjected the 1986-2000 Amnesty [International] data to a barrage of statistical tests. (Since Human Rights Watch’s early archival procedures seemed spotty, we did not include their data in our models.) Amnesty’s coverage, we found, was driven by multiple factors, but contrary to the dark rumors swirling through the blogosphere, we discovered no master variable at work. Most importantly, we found that the level of actual violations mattered. Statistically speaking, Amnesty reported more heavily on countries with greater levels of abuse. Size also mattered, but not as expected. Although population didn’t impact reporting much, bigger economies did receive more coverage, either because they carried more weight in global politics and economic affairs, or because their abundant social infrastructure produced more accounts of abuse. Finally, we found that countries already covered by the media also received more Amnesty attention. (source)

More posts in this series are here.

Measuring Poverty (12): The Experimental Method

The so-called experimental method of poverty measurement is akin to the subjective approach. Rather than measuring poverty on the basis of objective economic numbers about income or consumption the experimental method uses people’s subjective evaluation of living standards and living conditions. But contrary to the usual subjective approach it’s aim is not to ask people directly about what poverty means to them, about what they think is a reasonable minimum level of income or consumption or a maximum tolerable level of deprivation in certain specific areas (food, health, education etc.). Instead, it uses experiments to try to gather this information.

For example, you can set up a group of 20 people from widely different social backgrounds and some of them may suffer from different types of deprivation, or from no deprivation at all. The group receives a sum of money and has to decide how to spend it on poverty alleviation (within their test group or outside of the group). The decision as to who will receive which amount of funding targeted at which type of deprivation has to be made after deliberation and possibly even unanimously.

The advantage of this experimental approach, compared to simply asking individual survey respondents, is that you get a deliberated choice: people will think together about what poverty means, about which types of deprivation are most important and about the best way to intervene. It’s assumed that such a deliberated choice is better than an individual choice.

More posts in this series are here.

Racism (14): Race and Consumer Behavior

There’s strong evidence of racist sorting by people looking for a job (white job seekers often avoid working for black managers, and white workers quit their jobs more rapidly when a white manager is replaced with a black manager). A similar phenomenon is race discrimination by buyers.

Do buyers discriminate based on race? This column describes an experiment in the US that advertised iPods online from black and white sellers. Black sellers received fewer offers at lower prices, doing better in markets with competition amongst buyers and worse in high-crime markets. The authors find evidence of both statistical and taste-based discrimination. … [I]t appears that discrimination may not “survive” in the presence of significant competition among buyers. Furthermore, black sellers do worst in the most racially isolated markets and markets with high property crime rates, suggesting a role for statistical discrimination in explaining the disparity. (source)

The important question is indeed to what extent this “sorting” on the part of buyers is motivated by statistical discrimination or by taste-based discrimination:

  • Statistical discrimination means that race is used as a proxy for unobservable negative characteristics, maybe in this case a judgment about the probability that black sellers will be happy with a marginally lower sales price, given their statistically higher rates of poverty. Or perhaps there’s distrust based on unclear statistical judgments about the risk of buying fake or stolen goods, meeting sellers in an inconvenient or dangerous neighborhoods, or dealing with unreliable sellers who might not complete the transaction.
  • Taste-based discrimination occurs when people just don’t like dealing with black people for no particular reason apart from the difference in race.

The study cited above uses a number of clever ways to disentangle these two effects. For instance, the inclusion of white tattooed sellers, who also received fewer and lower purchase offers, suggesting that part of the differences are due to statistical discrimination. Another part, however, is just plain racism. Black sellers are at a significant disadvantage on average, and that’s due to both statistical and taste-based discrimination.

More on statistical discrimination here.

Why Do Countries Become/Remain Democracies? Or Don’t? (13): Prosperity

I already mentioned in a previous post how democracy is correlated with prosperity. There’s a much higher proportion of democracies among rich countries than among poor countries. The level of national income is the most important factor explaining inter-country variations in the degree of democracy. If we assume from this correlation that there is a causal link from prosperity to democracy, then low income is the most important barrier to democracy. But the causal link probably goes in both directions. Countries aren’t just democratic – or remain so – because they prosper (among other reasons), but it’s also the case that countries prosper to some extent because they are democratic (disproving the often heard claim that economic development requires authoritarian government).

The correlation between democracy and prosperity is obvious from this paper (at least for non-Muslim countries).

The stronger one of the causal links seems to be the one going from prosperity to democracy rather than vice versa. If you accept that, there’s an additional question (it’s one made famous by Przeworski and Limongi): are there more democracies among rich countries than among poor countries

  • because economic development increases the likelihood that countries will undergo a transition to democracy (this is often called modernization theory), or
  • because economic development makes democracies less likely to fall back into dictatorship?

Przeworski and Limongi found that affluence makes it very unlikely that a shift from democracy to dictatorship occurs, while Boix and Stokes find that there is an effect of affluence on the likelihood of a shift to democracy. Both effects are visible in this study.

It’s likely that the economic effect on transition towards democracy is a bit smaller than the effect halting the opposite transition. The reason is probably the fact that the transition from democracy to authoritarianism is in se much easier than the other way around. Some even say that democracy is inherently suicidal. Whatever the merits of that claim, it’s obvious that an authoritarian leader has the resources and the necessary lack of scruples to cling to power. Especially when his country becomes more prosperous. He can then use this prosperity to bribe the population into submission, and buy the arms and security forces when this doesn’t work.

Again, economic development isn’t a sufficient or even necessary prerequisite for democracy to appear or to survive. Things are more complicated than that and many other factors are in play, including conscious human activity and volition. People can decide to make or destroy a democracy at any level of economic development.

Religion and Human Rights (28): Is Religion Particularly Violent?

9/11 and other terrorist attacks apparently motivated by Islamic beliefs has led to an increased hostility towards Islam, but also towards religion in general. Perhaps in an effort to avoid the charge of islamophobia, many anti-jihadists have taken a new look at the violent history of other religions, particularly Christianity, and concluded that religion per se, because of the concomitant belief in the absolute truth of God’s words and rules, automatically leads to the violent imposition of this belief on unwilling fellow human beings, or – if that doesn’t work – the murderous elimination of persistent sinners. This has given rise to a movement called the new atheists. The charge of fanatical and violent absolutism inherent in religion is of course an old one, but it has been revitalized after 9/11 and the war on terror. I think it’s no coincidence that many of the new atheists are also anti-jihadists (take Christopher Hitchens for example).

There are many things wrong with question in the title of this blogpost. (And – full disclosure – this isn’t part of a self-interested defense of religion, since I’m an agnostic). First of all, it glosses over the fact that there isn’t such a thing as “religion”. There are many religions, and perhaps it can be shown that some of them produce a disproportionate level of violence, but religion as such is a notoriously vague concept. Nobody seems to agree on what it is. Even the God-entity isn’t a required element of the definition of religion, except if you want to take the improbable position that Buddhism isn’t a religion. All sorts of things can reasonably be put in the container concept of “religion” – the Abrahamic religions as well as Wicca and Jediism. The claim that “religion is violent” implies that all or most religions are equally violent, which is demonstrably false.

That leaves the theoretical possibility that some religions are more violent than others. If that claim can be shown to be true, islamophobia may perhaps be a justified opinion, but not the outright rejection of religion inherent in new atheism (which, of course, has other arguments against religion besides religion’s supposed violent character). However, how can it be shown empirically and statistically that a certain religion – say Islam – is relatively more violent than other religions? In order to do so you would need to have data showing that Islam today (or, for that matter, Christianity in the age of the crusades and the inquisition) is the prime or sole motive behind a series of violent attacks. But how do you know that the violent actor was motivated solely or primarily by his religious beliefs? Because he has a Muslim name? Speaks Arabic? Looks a certain way? Professes his religious motivation? All that is not enough to claim that he wasn’t motivated by a combination of religious beliefs and political or economic grievances for instance, or by something completely unconnected to religion, despite his statements to the contrary.

Now let’s assume, arguendo, that this isn’t a problem, and that it is relatively easy and feasible to identify a series of violent attacks that are indisputably motivated solely or primarily by certain religious beliefs. How can you go from such a series to a quantified comparison that says “the religion behind this series of attacks – say again Islam – is particularly violent”? That seems to be an unwarranted generalization based on a sample that is by definition very small (given the long history of most religions and the lack of data on motivations, especially for times that have long since passed). Also, it supposes a comparison with other causes of violence, for example other religions, other non-religious belief systems, character traits, economic circumstances etc. After all, the point of this hypothetical study is not to show that (a) religion can lead to bad things. That’s seldom disputed. Everything can lead to bad things, including fanatical atheism (and don’t tell me communism and fascism were “really” religions; the word “religion” is vague, but probably not as vague as that – which doesn’t mean that there aren’t any religious elements in those two world-views). The claim we’re discussing here is that (a) religion – because of its fanatical absolutism and trust in God’s truth – is particularly violent, i.e. more violent than other belief systems, and hence very dangerous and to be repudiated.

I think it’s useless, from a purely mathematical and scientific point of view, to engage in such a comparative quantification, given the obvious problems of identifying true motivations, especially for long periods of time in the past. There’s just no way that you can measure religious violence, compare it to “other violence”, and claim it is more (or less) violent. So the question in the title is a nonsensical one, I think, even if you limit it to one particular religion rather than to religion in general. That doesn’t mean it can’t be helpful to know the religious motives of certain particular acts of violence. It’s always good to know the motives of violence if you want to do something about it. What it means is that such knowledge is no reason to generalize on the violent nature of a religion, let alone religion as such. That would not only obscure other motives – which is never helpful – but it would also defy our powers of quantification.

Income Inequality (22): Social Mobility in Anglo-Saxon Economies, Ctd.

After completing my older post on the subject – in which I argued that Anglo-Saxon economies don’t do a very good job promoting social mobility despite the focus on individual responsibility and policies that (should) reward merit (e.g. relatively low tax rates) – I found this graph which I thought would illustrate my point.

Although the US and other Anglo-Saxon countries aren’t in the graph, the UK is. And the effect of parental education on child earnings in the UK is particularly large. The children of the well-off and well-educated earn more and learn more than their less fortunate peers in all countries in the world, and that’s hardly surprising given the importance of a head start, both financially and intellectually. What is surprising is that this is less the case in countries which pride themselves on their systems that offer people incentives to do well (low taxes, minimal safety nets etc.).

So one wonders which fact-free parallel universe David Cameron, the new UK Prime Minister, inhabits:

The differences in child outcomes between a child born in poverty and a child born in wealth are no longer statistically significant when both have been raised by “confident and able” parents… What matters most to a child’s life chances is not the wealth of their upbringing but the warmth of their parenting. (source, my emphasis)

Extolling the virtues of good parenting can never hurt, except if you have a low boredom threshold because it’s so goddamn obvious. But making it sound like parents’ wealth or education are “insignificant” is truly grotesque and an insult to those poor parents who have children that aren’t doing very well. And even for those living in the alternative reality where only bad parents keep children back, the Conservative leader’s position in fact, and unwittingly, should lead to left-wing policies, as Chris Dillow points out:

Because of bad parenting – which begins in the womb – some people do badly in school and therefore in later life; they are less likely to be in work, and earn less even if they are. However, we can’t choose our parents; they are a matter of luck. It’s quite reasonable to compensate people for bad luck, so there’s a case for redistributing income to the relatively poor, as this is a roundabout way of compensating them for the bad luck of having a bad upbringing.

High levels of social mobility can compensate for high levels of income inequality: if people can be socially mobile, and if their earnings and education levels don’t depend on who their parents are but on their own efforts and talents, one can plausibly claim that the existing inequalities are caused by some people’s lack of effort and merit. However, the UK and the US combine two evils: low mobility and high inequality, making it seem that whatever effort you invest in your life, you’ll never get ahead of those rich lazy dumb asses. So why would you even try? Low mobility solidifies high inequality.

Just to show that the U.S. isn’t better than the U.K.:

Parental income is a better predictor of a child’s future in America than in much of Europe, implying that social mobility is less powerful. Different groups of Americans have different levels of opportunity. Those born to the middle class have about an equal chance of moving up or down the income ladder, according to the Economic Mobility Project. But those born to black middle-class families are much more likely than their white counterparts to fall in rank. The children of the rich and poor, meanwhile, are less mobile than the middle class’s. More than 40% of those Americans born in the bottom quintile remain stuck there as adults. (source)

Gender Discrimination (22): Gendercide

The Economist has a front page story this week on “gendercide”, the millions of girls missing in the world, especially in India and China. Perhaps as many as 100 million girls have disappeared in the last decades because of

  • selective abortions encouraged by new medical technology (ultrasounds and fertility technology)
  • childhood neglect of girls (nutritional, educational neglect and neglect in health care)
  • prejudice, preference for male offspring and
  • population policies such as the “one child policy” in China.

Interestingly, the skewed sex ratios that result from gendercide (in some areas of China, 130 boys are being born for every 100 girls) are coming back to haunt the men that are responsible (although many mothers probably aren’t without fault either). Because of their relative scarcity, women have found an unlikely source of power. They have a competitive advantage in the marriage market, and can demand more in marriage negotiations, or at least be more selective when choosing a mate.

Causes

In my view, the word “gendercide” is somewhat overwrought because, contrary to genocide, the word that inspired the neologism of gendercide, there’s no centralized plan to exterminate women. Femicide would be a better term since it’s obviously only one of two genders that’s targeted, but it still sounds like a government organized campaign of extermination. Gendercide is the result of a combination of causes:

  • individual choices based on
  • plain prejudice against girls
  • cultural and legal traditions, or
  • economic incentives that have been formed by historical prejudice.

Perhaps girls still need a dowry, and poor parents may find it difficult to save enough and hence prefer a boy. Or perhaps they prefer a boy because the law of their country or tribe – inspired by age-old prejudice – says that only boys can inherit land or the family business. Again, the parents may prefer a boy for this reason, not because they dislike girls. Or perhaps tradition holds that girls marry off into their husbands families, and parents simply want to be sure to have someone in their home to care for them when they are old (“raising a daughter is like watering your neighbor’s garden”, is a Hindu saying).

Consequences

The consequences of gendercide are mixed. It’s obviously horrible to the girls that are aborted or neglected to death. But, as in the “boomerang” case cited above, gendercide may ultimately empower women. However, the skewed sex ratios also spell trouble: the presence of armies of men who can’t find wives and have children (“bare branches” or “guanggun” they are called in China) may result in more sexual violence, depression, suicide, human trafficking etc. It’s estimated that in 10 years time, one in five young Chinese men won’t be able to find a bride. On the other hand, a shortage of women will encourage immigration, and immigration may help some women escape poverty, and perhaps will also result in more intercultural tolerance.

Solutions

Economic development won’t stop it. In China and India, the regions with the worst sex ratios are wealthy ones, with educated populations. Even in some population strata in the U.S. sex ratios are skewed. When people escape poverty, fertility rates drop, and when families have fewer children, the need to select for sex only becomes more important in order to realize their son preference. In poor societies with high fertility rates, families are almost destined to have a boy at some point. Female children will suffer relative neglect and may die more often and more rapidly (skewing the sex ratios), but selective abortions aren’t much of a risk: families don’t really feel the need to limit the number of children (on the contrary often, because children are a workforce), and ultrasound technology for sex determination of fetuses isn’t as readily available as in rich countries or regions. When families want few children – as they do in more developed regions – or are forced by the government to limit their number of children (as in China), they will abort female fetuses in pursuit of a son.

Ultimately, only a cultural change will help. The son preference has to die out. Education probably will help, as it always does. Ending pernicious policies such as the one child policy will also help, but then overpopulation hysterics will have to be dealt with. This policy didn’t help stop population growth anyway. Other East Asian countries reduced population pressure as much as China without brutal policies.

Old customs and discriminating laws should also be abolished. Think of the dowry system, or inheritance rights. Stigmatizing abortion, especially sex selective abortion, will also help.

Why Do Countries Become/Remain Democracies? Or Don’t? (9): The Resource Curse

If we value democracy, then it’s interesting to know

  • how societies have achieved the transition from authoritarian forms of government to more democratic ones
  • why other societies have failed
  • and how democracies have avoided the opposite transition.

This knowledge will help us to promote and sustain democracy in the future. Something we already know is that this isn’t simple. There are a huge number of factors at play and there’s no silver bullet. Some of the most widely discussed factors are economic development, levels of education, and religion and culture.

I’ll bracket two important issues here: what kind of democracy are we talking about, and how do we measure transition or development towards democracy? If you want to know what promotes or inhibits democracy and act on this knowledge in order to further the cause of democracy, you can’t avoid these questions, but discussing them here would take us too far.

What I want to focus on here is the so-called resource curse. This curse is believed to be a phenomenon that blocks countries’ development towards democracy. Promoting democracy means lifting the curse. Now, what is this curse, and is it real or just another simplistic explanation of the course of history?

Countries which own lots of natural resources such as diamonds, oil or other valuables that are found in the ground, are often relatively poor, badly governed, violent and suffering from gross violations of human rights. Resource wealth can trigger corruption and grabbing, can give autocrats the means to retain power by buying off opposition or building a repressive state apparatus, or can tempt democratically elected leaders to cling to highly beneficial positions of power.

This sounds good but even a cursory glance at reality reveals some counter-indications. There are many resource rich countries that are governed very well and are pinnacles of democracy (take Norway). Still, that may only disprove part of the resource curse. It may be the case that democracies benefit from resources and are able to solidify themselves, while non-democracies are doomed to remain as they are because of resource abundance. Resources then only create a curse when democratic institutions are absent. So we shouldn’t worry about democracies failing because of resources, but about autocracies failing to transform because of them.

However, there’s an article here claiming that

resource wealth is positively associated with both economic growth and institutional quality.

Much depends, it seems, on how to measure resource abundance. There also is a reversal of the direction of causation, a common mistake in statistics:

There is no evidence that resource-dependent countries end up with slow growth and bad institutions. Rather, countries with bad institutions attract little investment, and as a result they grow more slowly and remain dependent on exports of commodities.

Racism (10): Race and Health

African Americans in the U.S. are more likely to die of cancer. It seems that a similar disparity exists for strokes and lead poisoning.

Many ethnic groups have a higher death rate from stroke than non-Hispanic whites. Although the death rate from stroke nationwide dropped 70% between 1950 and 1996, minorities the decline was greatest in non-Hispanic whites. The greatest number of stroke deaths compared to whites occurred in African-Americans and Asians/Pacific Islanders. Excess deaths among racial/ethnic groups could be a result of a greater frequency of stroke risk factors, including obesity, hypertension, physical inactivity, poor nutrition, diabetes, smoking and socioeconomic factors such as lack of health insurance. (source)

Lead poisoning causes, i.a., cognitive delay, hyperactivity, and antisocial behavior.

The Causes of Poverty (30): Lack of Economic Growth

When a country achieves a certain level of economic growth – or, more precisely, rising levels of GDP per capita because economic growth as such can be the result of rising population levels – it is assumed that this reflects a higher average standard of living for its citizens. Economic growth is therefore seen as an important tool in the struggle against poverty. If a country is richer in general, the population will also be richer on average. On average meaning that GDP growth isn’t necessarily equally distributed over every member of the population. That is why GDP growth isn’t sufficient proof of poverty reduction. Separate measurements of poverty and inequality are necessary.

So in theory, you can have GDP growth and increasing levels of poverty, on the condition that GDP growth is concentrated in the hands of a few. However, that’s generally not the case. GDP growth benefits to some extent many of the poor as well as the wealthy, which is shown by the strong correlation between poverty reduction and levels of GDP growth (always per capita of course). It’s no coincidence that a country such as China, which has seen strong GDP growth over the last decades, is also a country that has managed to reduce poverty levels substantially.

Unfortunately, growth isn’t a silver bullet. Poverty is a complex problem, requiring many types of solutions. Promoting economic growth will do a lot of the work, but something more is required. In a new paper, Martin Ravaillon gives the example of China, Brazil and India. The levels of poverty reduction in these three countries, although impressive, do not simply mirror the levels of economic growth. Although half of the world’s poor live in these three countries, in the last 25 years China has reduced its poverty level from 84% of the population in 1981 to just 16% in 2005. China is exceptional, but Brazil also did well, cutting its rate in half over the same period (8% of Brazilians still live on less than $1.25 a day). Regarding India, there are some problems with its statistics, but whichever statistic you use, there’s a clear reduction.

Ravaillon points out that the intensity of poverty reduction was higher in Brazil than in India and China, despite lower GDP growth rates.

Per unit of growth, Brazil reduced its proportional poverty rate five times more than China or India did. How did it do so well? The main explanation has to do with inequality. This (as measured by the Gini index, also marked on the chart) has fallen sharply in Brazil since 1993, while it has soared in China and risen in India. Greater inequality dampens the poverty-reducing effect of growth. (source)

Which is rather obvious: lower levels of income equality means a better distribution of the benefits of growth. So the “pro-growth strategy” against poverty is important but not enough, and should be combined with Brazilian type anti-inequality measures (focus on education, healthcare and redistribution).

Capital Punishment (24): The Probability of Capital Punishment in the U.S., by Race

The U.S. population is about 300,000,000. Whites represent about 80%, or roughly 240,000,000. If you check the numbers of executions in the U.S., you’ll see that there were about 1,000 in the period from 1977 to 2005. 584 of those executions were of whites. That’s about 20 executions per year on average, meaning that whites have a chance of 1 in 12,000,000 of being executed.

There are about 40,000,000 African Americans, representing roughly 13 % of the U.S. population. 339 executions in the 1977-2005 period were of African Americans. That’s about 12 a year, meaning that blacks have a chance of 1 in 3,300,000 of being executed.

A black person in the U.S. is therefore almost 4 times more likely to be executed. Even if we assume that this higher probability of being executed correctly reflects a higher probability of being involved in crime that comes with capital punishment – and that’s something we shouldn’t assume, because it’s likely that there are injustices involved, e.g. inadequate legal representation and such – that shouldn’t put our minds at ease. We then still have to ask the question: why are blacks more likely to be involved in capital offences? Surely not because of their race. Something happens in society that leads to this outcome, and it’s likely that there are injustices involved: for example, inadequate education, poverty levels, discrimination etc.

Lies, Damned Lies, and Statistics (24): Mistakes in the Direction of Causation

Suppose you find a correlation between two phenomena. And you’re tempted to conclude that there’s a causal relation as well. The problem is that this causal relation – if it exists at all – can go either way. It’s a common mistake – or a case of fraud, as it happens – to choose one direction of causation and forget that the real causal link can go the other way, or both ways at the same time.

An example. We often think that people who play violent video games are more likely to show violent behavior because they are incited by the games to copy the violence in real life. But can it not be that people who are more prone to violence are more fond of violent video games? We choose a direction of causation that fits with our pre-existing beliefs.

Another widely shared belief is that uninformed and uneducated voters will destroy democracy, or at least diminish its value (see here and here). No one seems to ask the question whether it’s not a diminished form of democracy that renders citizens apathetic and uninformed. Maybe a full or deep democracy can encourage citizens to participate and become more knowledgeable through participation.

A classic example is the correlation between education levels and GDP. Do countries with higher education levels experience more economic growth because of the education levels of their citizens? Or is it that richer countries can afford to spend more on education and hence have better educated citizens? Probably both.

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 (22): Objects in Statistics May Appear Bigger Than They Are

From a news report some weeks ago:

French Finance Minister Christine Lagarde Thursday voiced her support for France Telecom’s chief executive, who is coming under increased pressure from French unions and opposition politicians over a recent spate of suicides at the company.

Ms. Lagarde summoned France Telecom CEO Didier Lombard to a meeting after the telecommunications company confirmed earlier this week that one of its employees had committed suicide. It was the 24th suicide at the company in 18 months.

In a statement released after the meeting, Ms. Lagarde said she had “full confidence” that Mr. Lombard could get the company through “this difficult and painful moment.”

The French state, which owns a 27% stake in France Telecom, has been keeping a close eye on the company, following complaints by unions that a continuing restructuring plan at the company is putting workers under undue stress.

The suicide rate among the company’s 100,000 employees is in line with France’s national average. Still, unions say that the relocation of staff to different branches of the company around France has added pressure onto employees and their families.

On Tuesday, a spokesman for France’s opposition Socialist Party called for France Telecom’s top management to take responsibility for the suicides and step down. Several hundred France Telecom workers also took to the streets to protest against working conditions.

In the statement released after Thursday’s meeting, France’s Finance Ministry said Mr. Lombard had set up an emergency hotline aimed at providing help to depressed workers. The company has also increased the number of psychologists available to staffers, according to the statement. (source)

More on the problems caused by averages is here.

Lies, Damned Lies, and Statistics (21): Misleading Averages

Did you hear the joke about the statistician who put her head in the oven and her feet in the refrigerator? She said, “On average, I feel just fine.” That’s the same message as in this more widely known joke about statisticians drowning in a pond with an average depth of 3ft. And then there’s this one: did you know that the great majority of people have more than the average number of legs? It’s obvious, really: Among the 57 million people in Britain, there are probably 5,000 people who have only one leg. Therefore, the average number of legs is less than 2. In this case, the median would be a better measure than the average or the mean.

But seriously now, averages can be very misleading, also in statistical work in the field of human rights. Take income data, for example. Income as such isn’t a human rights issue, but poverty is. When we look at income data, we may see that average income is rising. However, this may be due to extreme increases at the top 1% of income. If you then exclude the income increases of the top 1% of the population, the large majority of people may not experience rising income. Possible even the opposite. And rising average income – even excluding extremes at the top levels – is perfectly compatible with rising poverty for certain parts of the population.

Averages are often skewed by outliers. That is why it’s necessary to remove outliers and calculate the averages without them. That will give you a better picture of the characteristics of the general population (the “real” average income evolution in my example). A simple way to neutralize outliers is to look at the median – the middle value of a series of values – rather than the average (or the mean).

An average (or a median for that matter) also doesn’t say anything about the extremes (or, in stat-speak, about the variability or dispersion of the population). A high average income level can hide extremely low and high income levels for certain parts of the population. So, for example, if you start to compare income levels across different countries, you’ll use the average income. Yet country A may have a lower average income than country B, but also lower levels of poverty than country B. That’s because the dispersion of income levels in country A is much smaller than in country B. The average in B is the result of adding together extremely low incomes (i.e. poverty) and extremely high incomes, whereas the average in A comes from the sum of incomes that are much more equal. From the point of view of poverty average income is misleading because it identifies country A as most poor, whereas in reality there are more poor people in country B. So when looking at averages, it’s always good to look at the standard deviation as well. SD is a measure of the dispersion around the mean.

More posts in this series.

Capital Punishment (23): The Truth About the Deterrent Effect

Some more data to support the claims expressed in this post, and this one. There’s a paper here presenting the results of a survey among leading criminologists regarding their opinion on the deterrent effect of capital punishment in the U.S.

The findings demonstrate an overwhelming consensus among these criminologists that the empirical research conducted on the deterrence question strongly supports the conclusion that the death penalty does not add deterrent effects to those already achieved by long imprisonment.

Of course, it’s not because experts believe something that this corresponds to the truth, but at least it’s ammunition that can be used against those proponents of the death penalty who like to claim that there is a “scientific consensus” in favor of the deterrent effect. There is no such thing. On the contrary, if there’s a consensus, it’s for the opposing view.

Another point: this kind of statistic on expert opinion, together with the data offered in the posts I linked to above, is much more convincing than the data comparing murder rates in capital punishment states and abolitionist states.

At first sight, this graph also undermines the deterrent argument, but it’s not as solid as it appears. It’s always important to control your data for other variables which can explain a difference. Maybe there are other reasons why states without the death penalty have lower murder rates, e.g. less poverty, more gun control etc. And maybe the murder rate in states with capital punishment would be even higher without capital punishment.

Lies, Damned Lies, and Statistics (18): Comparing Apples and Oranges

Before the introduction of tin helmets during the First World War, soldiers only had cloth hats to wear. The strange thing was that after the introduction of tin hats, the number of injuries to the head increased dramatically. Needless to say, this was counter-intuitive. The new helmets were designed precisely to avoid or limit such injuries.

Of course, people were comparing apples with oranges, namely statistics on head injuries before and after the introduction of the new helmets. In fact, what they should have done, and effectively did after they realized their mistake, was to include in the statistics, not only the injuries, but also the fatalities. After the introduction of the new helmets, the number of fatalities dropped dramatically, but the number of injuries went up because the tin helmet was saving soldiers’ lives, but the soldiers were still injured.

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.

Lies, Damned Lies, and Statistics (16): Measuring Public Opinion in Dictatorships

Measuring human rights requires a certain level of respect for human rights (freedom to travel, freedom to speak, to interview etc.). Trying to measure human rights in situations characterized by the absence of freedom is quite difficult, and can even lead to unexpected results: the absence of (access to) good data may give the impression that things aren’t as bad as they really are. Conversely, when a measurement shows a deteriorating situation, the cause of this may simply be better access to better data. And this better access to better data may be the result of more openness in society. Deteriorating measurements may therefore signal an actual improvement. I gave an example of this dynamic here (it’s an example of statistics on violence against women).

Measuring public opinion in authoritarian countries is always difficult, but if you ask the public if they love or hate their government, it’s likely that you’ll have higher rates of “love” in the more authoritarian countries. After all, in those countries it can be pretty dangerous to tell someone in the street that you hate your government. They choose to lie and say that they approve. That’s the safest answer but probably in many cases not the real one. I don’t believe for a second that the percentage of people approving of their government is 19 times higher in Azerbaijan than in Ukraine, when Ukraine is in fact much more liberal than Azerbaijan.

In the words of Robert Coalson:

The Gallup chart is actually an index of fear. What it reflects is not so much attitudes toward the government as a willingness to openly express one’s attitudes toward the government. As one member of RFE/RL’s Azerbaijan Service told me, “If someone walked up to me in Baku and asked me what I thought about the government, I’d say it was great too”.

Lies, Damned Lies, and Statistics (12): Generalization

An example from Greg Mankiw’s blog:

Should we [the U.S.] envy European healthcare? Gary Becker says the answer is no:

“A recent excellent unpublished study by Samuel Preston and Jessica Ho of the University of Pennsylvania compare mortality rates for breast and prostate cancer. These are two of the most common and deadly forms of cancer – in the United States prostate cancer is the second leading cause of male cancer deaths, and breast cancer is the leading cause of female cancer deaths. These forms of cancer also appear to be less sensitive to known attributes of diet and other kinds of non-medical behavior than are lung cancer and many other cancers. [Health effects of diet and behavior should be excluded when comparing the quality of healthcare across countries. FS]

These authors show that the fraction of men receiving a PSA test, which is a test developed about 25 years ago to detect the presence of prostate cancer, is far higher in the US than in Sweden, France, and other countries that are usually said to have better health delivery systems. Similarly, the fraction of women receiving a mammogram, a test developed about 30 years ago to detect breast cancer, is also much higher in the US. The US also more aggressively treats both these (and other) cancers with surgery, radiation, and chemotherapy than do other countries.

Preston and Hu show that this more aggressive detection and treatment were apparently effective in producing a better bottom line since death rates from breast and prostate cancer declined during the past 20 [years] by much more in the US than in 15 comparison countries of Europe and Japan.” (source)

Even if all this is true, how on earth can you assume that a healthcare system is better because it is more successful in treating two (2!) diseases?

Another example: the website of the National Alert Registry for sexual offenders used to post a few “quick facts”. One of them said:

“The chance that your child will become a victim of a sexual offender is 1 in 3 for girls… Source: The National Center for Victims of Crime“.

Someone took the trouble of actually checking this source, and found that it said:

Twenty-nine percent [i.e. approx. 1 in 3] of female rape victims in America were younger than eleven when they were raped.

One in three rape victims is a young girl, but you can’t generalize from that by saying that one in three young girls will be the victim of rape. Perhaps they will be, but you can’t know that from these data. Like you can’t conclude from the way the U.S. deals with two diseases that it “shouldn’t envy European healthcare”. Perhaps it shouldn’t, but more general data on life expectancy says it should.

These are two examples of induction or inductive reasoning, sometimes called inductive logic, a reasoning which formulates laws based on limited observations of recurring phenomenal patterns. Induction is employed, for example, in using specific propositions such as:

This door is made of wood.

to infer general propositions such as:

All doors are made of wood. (source)

More posts in this series.

Lies, Damned Lies, and Statistics (11): Polarized Statistics as a Result of Self-Selection

One of the most important things in the design of an opinion survey – and opinion surveys are a common tool in data gathering in the field of human rights – is the definition of the sample of people who will be interviewed. We can only assume that the answers given by the people in the sample are representative of the opinions of the entire population if the sample is a fully random subset of the population – that means that every person in the population should have an equal chance of being part of the survey group.

Unfortunately, many surveys depend on self-selection – people get to decide themselves if they cooperate – and self-selection distorts the randomness of the sample:

Those individuals who are highly motivated to respond, typically individuals who have strong opinions, are overrepresented, and individuals that are indifferent or apathetic are less likely to respond. This often leads to a polarization of responses with extreme perspectives being given a disproportionate weight in the summary. (source)

Self-selection is almost always a problem in online surveys (of the PollDaddy variety), phone-in surveys for television or radio shows, and so-called “red-button” surveys in which people vote with the remote control of their television set. However, it can also occur in more traditional types of surveys. When you survey the population of a brutal dictatorial state (if you get the chance) and ask the people about their freedoms and rights, many will deselect themselves: they will refuse to cooperate with the survey for fear of the consequences.

When we limit ourselves to the effects of self-selection (or self-deselection) in democratic states, we may find that this has something to do with the often ugly and stupid “us-and-them” character of much of contemporary politics. There seems to be less and less room for middle ground, compromise or nuance.

Lies, Damned Lies, and Statistics (9): Too Small Sample Sizes in Surveys

So many things can go wrong in the design and execution of opinion surveys. And opinion surveys are a common tool in data gathering in the field of human rights.

As it’s often impossible (and undesirable) to question a whole population, statisticians usually select a sample from the population and ask their questions only to the people in this sample. They assume that the answers given by the people in the sample are representative of the opinions of the entire population. But that’s only the case if the sample is a fully random subset of the population – that means that every person in the population should have an equal chance of being chosen – and if the sample hasn’t been distorted by other factors such as self-selection by respondents (a common thing in internet polls) or personal bias by the statistician who selects the sample.

A sample that is too small is also not representative for the entire population. For example, if we ask 100 people if they approve or disapprove of discrimination of homosexuals, and 55 of them say they approve, we might assume that about 55% of the entire population approves. Now it could possible be that only 45% of the total population approve, but that we just happened, by chance, to interview an unusually large percentage of people who approve. For example, this may have happened because, by chance and without being aware of it, we selected the people in our sample in such a way that there are more religious conservatives in our sample than there are in society, relatively speaking.

This is the problem of sample size: the smaller the sample, the greater the influence of luck on the results we get. Asking the opinion of 100 people, and taking this as representative of millions of citizens, is like throwing a coin 10 times and assuming – after having 3 heads and 7 tails – that the probability of throwing heads is 30%. We all know that it’s not 30 but 50%. And we know this because we know that when we increase the “sample size” – i.e. when we throw more than 10 times, say a thousand times – we will have heads and tails approximately half of the time. Likewise, if we take our example of the survey on homosexuality: increasing the sample size reduces the chance that religious conservatives (or other groups) are disproportionately represented in the sample.

When analyzing survey results, the first thing to look at is the sample size, as well as the level of confidence (usually 95%) that the results are within a certain margin of error (usually + or – 5%). High levels of confidence that the results are correct within a small margin of error indicate that the sample was sufficiently large and random.

Lies, Damned Lies, and Statistics (7): “Drowning” Data

Suppose we want to know how many forced disappearances there are in Chechnya. Assuming we have good data this isn’t hard to do. The number of disappearances that have been registered, by the government or some NGO, is x on a total Chechen population of y, giving z%. The Russian government may decide that the better measurement is for Russia as a whole. Given that there are almost no forced disappearances in other parts of Russia, the z% goes down dramatically, perhaps close to or even below the level other comparable countries.

Good points for Russia! But that doesn’t mean that the situation in Chechnya is OK. The data for Chechnya are simply “drowned” into those of Russia, giving the impression that “overall”, Russia isn’t doing all that bad. This, however, is misleading. The proper unit of measurement should be limited to the area where the problem occurs. The important thing here isn’t a comparison of Russia with other countries; it’s an evaluation of a local problem.

Something similar happens to the evaluation of the Indian economy:

Madhya Pradesh, for example, is comparable in population and incidence of poverty to the war-torn Democratic Republic of Congo. But the misery of the DRC is much better known than the misery of Madhya Pradesh, because sub-national regions do not appear on “poorest country” lists. If Madhya Pradesh were to seek independence from India, its dire situation would become more visible immediately. …

But because it’s home to 1.1 billion people, India is more able than most to conceal the bad news behind the good, making its impressive growth rates the lead story rather than the fact that it is home to more of the world’s poor than any other country. …

A 10-year-old living in the slums of Calcutta, raising her 5-year-old brother on garbage and scraps, and dealing with tapeworms and the threat of cholera, suffers neither more nor less than a 10-year-old living in the same conditions in the slums of Lilongwe, the capital of Malawi. But because the Indian girl lives in an “emerging economy,” slated to battle it out with China for the position of global economic superpower, and her counterpart in Lilongwe lives in a country with few resources and a bleak future, the Indian child’s predicament is perceived with relatively less urgency. (source)

Migration and Human Rights (21): China’s Demographic Aggression and Provocation of Racism, The Cases of Tibet and Xinjiang

If only Han Chinese inhabit Tibet, what is the meaning of autonomy? Dalai Lama (source)

The recent protests and violence by Uighurs in China’s Xinjiang province are reminiscent of the March 2008 protests in Tibet. Like the Tibetans, the Uighurs believe that they are colonized by Han Chinese who have settled in the Tibetan and Uighur provinces in large numbers, and continue to do so. (92% of Chinese are Han). As a result, the ethnic Turkic Muslim Uighurs now make up less than half of the 20m population in their province, and probably less given the tendency of official Chinese statistics to underestimate internal migration flows. This is compared to 75% in 1949. (In Tibet, the indigenous population is still the majority according to official statistics, but this is likely to change with the new train link to the province).

It is widely accepted that these migration flows are part of official Chinese government policy. Populating border regions with Han Chinese is believed to lessen separatist tensions and demands for autonomy, and is handy when it comes to expropriating the local resources. The local populations however see this as demographic aggression and an attack on their culture. If their land is taken over, so will their culture, language, traditions and religion. In Xinjiang, evidence of this is the prohibition on headscarves, the languages used in schools etc.

Not surprisingly, these policies of demographic aggression – which the Dalai Lama has called a form of cultural genocide – combined with other authoritarian policies, provoke a reaction, and unfortunately, this reaction often takes the form of anti-Han racism. (Most victims of the recent clashes in Tibet and Xinjiang were Han, although – as usual – the victims of the government’s reaction don’t get mentioned).

Lies, Damned Lies, and Statistics (6): Statistical Bias in the Design and Execution of Surveys

Statisticians can – wittingly or unwittingly – introduce bias in their work. Take the case of surveys for instance. Two important steps in the design of a survey are the definition of the population and the selection of the sample. As it’s often impossible (and undesirable) to question a whole population, statisticians usually select a sample from the population and ask their questions only to the people in this sample. They assume that the answers given by the people in the sample are representative of the opinions of the entire population.

Bias can be introduced

  • at the moment of the definition of the population
  • at the moment of the selection of the sample
  • at the moment of the execution of the survey (as well as at other moments of the statistician’s work, which I won’t mention here).

Population

Let’s take a fictional example of a survey. Suppose statisticians want to measure public opinion regarding the level of respect for human rights in the country called Dystopia.

First, they set about defining their “population”, i.e. the group of people whose “public opinion” they want to measure. “That’s easy”, you think. So do they, unfortunately. It’s the people living in this country, of course, or is it?

Not quite. Suppose the level of rights protection in Dystopia is very low, as you might expect. That means that probably many people have fled the country. Including in the survey population only the residents of the country will then overestimate the level of rights protection. And there is another point: dead people can’t talk. We can assume that many victims of rights violations are dead because of them. Not including these dead people in the survey will also artificially push up the level of rights protection. (I’ll mention in a moment how it is at all possible to include dead people in a survey; bear with me).

Hence, doing a survey and then assuming that the people who answered the survey are representative for the whole population, means discarding the opinions of refugees and dead people. If those opinions were included the results would be different and more correct. Of course, in the case of dead people it’s obviously impossible to include their opinions, but perhaps it would be advisable to make a statistical correction for it. After all, we know their answers: people who died because of rights violations in their country presumably wouldn’t have a good opinion of their political regime.

Sample

And then there are the problem linked to the definition of the sample. An unbiased sample should represent a fully random subset of the entire and correctly defined population (needless to say that if the population is defined incorrectly, as in the example above, then the sample is by definition also biased even if no sampling mistakes have been made). That means that every person in the population should have an equal chance of being chosen. That means that there shouldn’t be self-selection (a typical flaw in many if not all internet surveys of the “Polldaddy” variety) or self-deselection. The latter is very likely in my Dystopia example. People who are too afraid to talk won’t talk. The harsher the rights violations, the more people who will fail to cooperate. So you have a perverse effect that very cruel regimes may score better on human rights surveys that modestly cruel regimes. The latter are cruel, but not cruel enough to scare the hell out of people.

The classic sampling error is from a poll on the 1948 Presidential election in the U.S.

On Election night, the Chicago Tribune printed the headline DEWEY DEFEATS TRUMAN, which turned out to be mistaken. In the morning the grinning President-Elect, Harry S. Truman, was photographed holding a newspaper bearing this headline. The reason the Tribune was mistaken is that their editor trusted the results of a phone survey. Survey research was then in its infancy, and few academics realized that a sample of telephone users was not representative of the general population. Telephones were not yet widespread, and those who had them tended to be prosperous and have stable addresses. (source)

Execution

Another reason why bias in the sampling may occur is the way in which the surveys are executed. If the government of Dystopia allows statisticians to operate on its territory, it will probably not allow them to operate freely, or circumstances may not permit them to operate freely. So the people doing the interviews are not allowed to, or don’t dare to, travel around the country. Hence they themselves deselect entire groups from the survey, distorting the randomness of the sample. Again, the more repressive the regime, the more this happens. With possible adverse effects. The people who can be interviewed are perhaps only those living in urban areas, close to the residence of the statisticians. And those living there may have a relatively large stake in the government, which makes them paint a rosy image of the regime.

Measuring Human Rights (6): Don’t Make Governments Do It

In the case of dictatorial governments or other governments that are widely implicated in the violation of the rights of their citizens, it’s obvious that the task of measuring respect for human rights should be – where possible – carried out by independent non-governmental organizations, possibly even international or foreign ones (if local ones are not allowed to operate). Counting on the criminal to report on his crimes isn’t a good idea. Of course, sometimes there’s no other way. It’s often impossible to estimate census data, for example, or data on mortality, healthcare providers etc. without using official government information.

All this is rather trivial. The more interesting point, I hope, is that the same is true, to some extent, of governments that generally have a positive attitude towards human rights. Obviously, the human rights performance of these governments also has to be measured, because there are rights violations everywhere, and a positive attitude doesn’t guarantee positive results. However, even in such cases, it’s not always wise to trust governments with the task of measuring their own performance in the field of human rights. An example from a paper by Marilyn Strathern (source, gated):

In 1993, new regulations [required] local authorities in the UK … to publish indicators of output, no fewer than 152 of them, covering a variety of issues of local concern. The idea was … to make councils’ performance transparent and thus give them an incentive to improve their services. As a result, however,… even though elderly people might want a deep freeze and microwave rather than food delivered by home helps, the number of home helps [was] the indicator for helping the elderly with their meals and an authority could only improve its recognised performance of help by providing the elderly with the very service they wanted less of, namely, more home helps.

Even benevolent governments can make crucial mistakes like these. This example isn’t even a measurement error; it’s measuring the wrong thing. And the mistake wasn’t caused by the government’s will to manipulate, but by a genuine misunderstanding of what the measurement should be all about.

I think the general point I’m trying to make is that human rights measurement should take place in a free market of competing measurements – and shouldn’t be a (government) monopoly. Measurement errors are more likely to be identified if there is a possibility to compare competing measurements of the same thing.

Measuring Democracy (3): But What Kind of Democracy?

Those who want to measure whether countries are democratic or not, or want the measure to what degree countries are democratic, necessarily have to answer the question “what is democracy?”. You can’t start to measure democracy until you have answered this question, as in general you can’t start to measure anything until you have decided what it is you want to measure.

Two approaches to measuring democracy

As the concept of democracy is highly contestable – almost everyone has a different view on what it means to call a country a democracy, or to call it more or less democratic than another – it’s not surprising to see that most of the research projects that have attempted to measure democracy – such as Polity IV, Freedom House etc. – have chosen a different definition of democracy, and are, therefore, actually measuring something different. I don’t intend to give an overview of the differences between all these measures here (this is a decent attempt). What I want to do here is highlight the pros and cons of two extremely different approaches: the minimalist and the maximalist one. The former could, for example, view democracy as no more than a system of regular elections, and measure simply the presence or absence of elections in different countries. The latter, on the other hand, could include in its definition of democracy stuff like rights protections, freedom of the press, division of powers etc., and measure the presence or absence of all of these things, and aggregate the different scores in order to decide whether a country is democratic or not, and to what extent.

When measuring the democratic nature of different countries (and of course comparing them), should we use a minimalist or maximalist definition of democracy? Here are some pros and cons of either approach.

Differentiation

A minimalist definition makes it very difficult to differentiate between countries. It would make it possible to distinguish democracies (minimally defined) from non-democracies, but it wouldn’t allow to measure the degree of democracy of a given country. I believe an ordinal scale with different ranks for different levels of quality of democracy in different countries (ranging from extremely poor quality, i.e. non-democracies, to perfect democracies) is more interesting than a binary scale limited to democracy/non-democracy. The use of a maximalist definition of democracy would make it possible to rank all types of regimes on such an ordinal scale. A maximalist definition of democracy would include a relatively large number of necessary attributes of democracy, and the combination of presence/absence/partial development of each attribute would almost make it possible to give each country a unique rank in the ordinal scale. Such a wide-ranging differentiation is an advantage for progress analysis. A binary scale does not give any information on the quality of democracy. Hence, it would be better to speak of measuring democratization rather than measuring democracy. And democratization not only in the sense of a transition from authoritarian to democratic governance, but also in the sense of progress towards a deepening of democratic rule.

A minimalist definition of democracy necessarily focuses on just a few attributes of democracy. As a result, it is impossible to differentiate between degrees of “democraticness” of different countries. Moreover, the chosen attributes may not be typical of or exclusive to democracy (such as good governance or citizen influence), and may not include some necessary attributes. For example, Polity IV, perhaps the most widely used measure of democracy, does not sufficiently incorporate actual citizen participation, as opposed to the mere right of citizens to participate. I think it’s fair to say that a country that gives its citizens the right to vote but doesn’t actually have many citizens voting, can hardly be called a democracy.

Acceptability of the measurement vs controversy

A disadvantage of maximalism is that the measurement will be more open to controversy. The more attributes of democracy are included in the measure, the higher the risk of disagreement on the model of democracy. As said above, people have different ideas about the number and type of necessary attributes of a democracy, even of an ideal democracy. If the only attribute of democracy retained in the analysis is regular elections, then there will be no controversy since few people would reject this attribute.

Balancing

So we have to balance meaning against acceptability: a measurement system that is maximalist offers a lot of information and the possibility to compare countries beyond the simple dichotomy of democracy/non-democracy, but it may be rejected by those who claim that this system is not measuring democracy as they understand the word. A minimalist system, on the other hand, will measure something that is useful for many people – no one will contest that elections are necessary for democracy, for instance – but will also reduce the utility of the measurement results because it doesn’t yield a lot of information about countries.

Capital Punishment (18): The Stupidity of Deterrent Statistics, Ctd.

Some more data on the supposed deterrent effect of capital punishment:

In 2003, there were [in the U.S.] 16,503 homicides (including nonnegligent manslaughter), but only 144 inmates were sentenced to death. Moreover, of the 3374 inmates on death row at the beginning of the year, only 65 were executed. Thus, not only did very few homicides lead to a death sentence, but the prospect of execution did not greatly affect the life expectancy of death row inmates. Indeed, Katz, Levitt, and Shustorovich have made this point quite directly, arguing that “the execution rate on death row is only twice the death rate from accidents and violence among all American men” and that the death rate on death row is plausibly lower than the death rate of violent criminals not on death row. As such they conclude that “it is hard to believe that in modern America the fear of execution would be a driving force in a rational criminal’s calculus.” John J. Donohue III and Justin Wolfers (source)

Proponents of capital punishment may answer this in two ways:

1. It proves their point: if all these data are correct, we need more capital punishment, and then the deterrent effect will kick in. Capital punishment as it is used now may indeed not deter significantly, but that’s no reason to abolish it; it’s a reason to step up the production of corpses.

But this reasoning leads to a reductio ad absurdum: if deterring crime is so important, and if we should do more to deter crime, then why don’t we change the execution methods: burn criminals alive at the stake. That should deter. But this, of course, brings home the point that we simply can’t do what we want to people in order to achieve some beneficial aggregate social good. If proponents of the death penalty shy away from this ultimate implication of the deterrent argument – and I think most of them will – then there’s no reason why opponents cannot have good reasons to reject killing criminals in other, less cruel ways. If propopents concede the point that there are certain things we can’t do to people, regardless of who they are or what they’ve done, then opponents can make the case that these “certain things” do not only include burning people alive but also killing them in a way which is less cruel but which nevertheless implies instrumentalizing people for the benefit of others with whom they have no relationship and who may not have been born yet. This instrumentalization is perhaps not physically cruel, but it is dehumanizing. People are no longer viewed as humans but as tools for the maximization of social wellbeing.

2. The calculating criminal is a myth. Murderers don’t look at death row statistics or other statistics mentioned in the quote above in order to decide whether or not to actually kill someone. They are deterred, not by numbers, but by the general vivid image of the horror of capital punishment. That may be true in the case of some types of murderers (e.g. the uneducated ones, or those motivated by passion), but not in the case of other types (some people may indeed look at the data and calculate that the risk of being killed for their crimes is so low that it’s ok to go ahead*).

But even if it is true and people don’t calculate, the “burning at the stake” implication still holds. If it’s the vivid nature of the punishment that counts as a deterrent, not the statistical likelihood of actually receiving this punishment (which is very low as a matter of fact), then let’s make it as “vivid” as possible and bring back the Middle Ages.

* I’m thinking of professional criminals for example.

Why Do Countries Become/Remain Democracies? Or Don’t? (6)

Democracy is a human right. If we want to promote universal respect for this right, we have to know how societies have achieved the transition from authoritarian forms of government to more democratic ones, and how democracies have avoided the opposite transition. Once we know this, we can promote the future emergence of democracies, and we can counteract the breakdown of existing ones.

Unfortunately, this is a very murky area of political science. The only thing that’s clear is that there is no silver bullet. There isn’t one thing we can do to transform societies once and for all into democracies. Things aren’t easy or simple. A huge number of factors have been identified as causes of or obstacles to democratic transitions, and existing democracies need constant nurturing and protection. A few of the factors that have been named as either promoting or inhibiting democracy are:

  • economic growth or GDP per capita
  • protestant culture versus catholic culture (a catholic culture is believed to be more hierarchical)
  • levels of education and literacy
  • income or wealth inequality (in very unequal societies, the wealthy have a lot to lose with democracy)
  • levels of employment in agriculture versus industry (industrial societies are believed to more more urban and less attached to traditional and authoritarian social relationships)
  • the presence/absence of neighboring democracies
  • export diversity (countries with one major export product such as oil tend to be “resource cursed”)
  • is a country a former U.K. colony or not? (former U.K. colonies are believed to be more sympathetic to democracy given their British colonial heritage)
  • is there a large middle class or not?
  • etc.

Statistical analysis to pinpoint which ones of these many variables really determine democracy – and which ones are merely guesses – has yielded contradictory results, not surprisingly given the low numbers of observations (societies or countries don’t change their political systems very often) and the relative lack of long time series (most classifications of regime types haven’t started earlier than a couple of decades ago). One interesting analysis is here.

So don’t expect me to have an opinion here. What I wanted to focus on in this post is the first in the list. There are two radically opposing views on the effect of economic development on democracy. One view is called modernization theory. Basically, the idea is that as countries develop economically, people will switch to other, higher needs, such as self-government, self-control, and political activity in general. Poverty, on the contrary, forces people to focus on survival and makes democracy seem like a luxury.

However, the opposite view is also persuasive. Countries that do well economically are less likely to become democratic because the population is quite pleased with how things are going and will not revolt. The authoritarian rulers can claim that it’s thanks to them that things are going well. It’s not unlikely that economic collapse rather than success causes authoritarian regimes to break down.

So even if you isolate one of dozens of possible factors causing regime transition, things aren’t very clear. Should we starve dictatorships, or help them develop economically? As a result of this lack of clarity, it’s very difficult to frame foreign policy in such a way that it favors the development of democracies around the world. This may go some way to explain the traditional lack of ambition in diplomatic circles.