Measuring Human Rights (34): The Golden Age of Human Rights Ended in 1970?

The purpose of human rights measurement, as I’ve stated many times before in this blog series, is to get some idea about progress. Are human rights better protected now than they were before, or vice versa? There are different ways to measure respect for human rights, and therefore also different outcomes. However, most measurements indicate that there is some progress, at least for certain human rights.

Now, here’s something strange: if we look (again) at Google Ngrams*, then it seems as if the Golden Age of human rights ended somewhere in the 1970s:

Or perhaps we shouldn’t over-interpret this. It’s possible that until the 70s people had other ways of describing cruelty and oppression and that human rights only became lingua franca after 1970 (another ngram seems to confirm this).

However, I still find this rather hard to understand. Almost no mentions of human rights violations in the millions of books written before 1970, and then a steady and steep rise. After all, human rights were “invented” in the 18th century, and their seeds were planted long before that. Also, it’s not as if the last 3 of 4 decades saw a large increase in the number or gravity of human rights violations. On the contrary.

So, why the increase in mentions? Perhaps it is simply a matter of increased consciousness. Or perhaps we’re seeing the famous Tocqueville effect again: Tocqueville has famously argued that the more a society liberates itself from injustices the harder people find it to bear the remaining injustices. And the harder they find it, the more they talk about it. Vice versa, when injustice is widespread and and permanent it may feel like destiny and then there’s no use even mentioning it.

* If you don’t know what the Ngram viewer is, go here first.

Measuring Human Rights (33): Measuring Racial Discrimination

The measurement of racial discrimination may seem like a purely technical topic, but in reality it comes with a huge moral dilemma. In order to measure racial discrimination, you have to categorize people into different racial groups (usually in your national census). On the basis of this you can then collect social information about those groups, and compare the average outcomes in order to detect large discrepancies between them. For example, do blacks in the US earn less, achieve less in school etc. Only then can you assume that there may be racism or discrimination and can you design policies that deal with it.

Now, categorizing people into different racial groups is not straightforward. You need to do violence to reality. Racial classifications and categorizations are not simply a reflection of factual reality, of “real group identities”. Instead they are social constructions or even fantasies influenced by centuries of prejudice, stereotypes and power relations. If we want to use racial classifications to measure discrimination, then we give people labels that may have little or nothing to do with who or what they are and how they identify themselves. Instead, these labels perpetuate the stereotypes and power relations that were the basis of the racial classifications when they were first conceived centuries ago. For example, “black” or “African-American” is not a simple descriptive label of a well-defined and existing group of people; instead it’s an ideological construction that was once used to segregate certain groups of very different people and subordinate them to a lower station in life. (Evidence for the claim that race is a social construct rather than a natural fact can be found in biology and in the fact that racial classifications differ wildly from one country to another).

In other words, the “statistical representation of diversity is a complex process which reveals the foundations of societies and their political choices” (source). In this particular case, the foundation of society was racism and the political choices were segregation and discrimination. If today we use the same racial and ethnic classifications that were once used to justify segregation and discrimination, then we run the risk of perpetuating racist social constructions. As a result, we may also help to perpetuate stereotypes and discrimination, even as we try to go in the opposite direction. It’s a form of path dependence.

Statistics are not just a reflection of social reality, but also affect this reality. Statistical categories are supposed to describe social groups, but at the same time they may influence people’s attitudes towards those groups because they contain memories of older judgments that were once attached to those groups. The dilemma is the following: the use of racial classifications to measure discrimination means giving people labels that have little or nothing to do with who they are or what they are; but they have something to do with how others treat them. It’s this treatment that we want to measure, and we can’t do so without the use of classifications. Using such classifications, however, can help to perpetuate the treatment we want to measure and avoid.

More posts in this series are here.

Measuring Human Rights (32): Assessing Advocacy and Policy by Way of Counterfactual Thinking

(source unknown)

Human rights measurement is ultimately about levels of respect for human rights, but it can also be useful to try to measure the impact of human rights advocacy and policy on these levels. Both advocacy and policy (the difference being that the former is non-governmental) aim at improving levels of respect for human rights. Obviously, those levels don’t depend solely on advocacy or policy, but it’s reasonably to assume that they are to some extent dependent on those types of action. It’s hard – although not impossible – to imagine that millions of people and dozens of governments and international institutions would engage in pointless activity.

The question is then: to what extent exactly? How much do advocacy and policy help? The problem in answering this question is that we won’t necessarily learn a lot by simply looking at the levels and how they evolve. Not only is there the difficulty of comparing different possible causes; a flat trend line – or even a declining trend line – may cover up how much more awful things would have been without advocacy and policy. Levels of respect may very well stay as they are or even worsen while advocacy and policy are relatively successful because the levels without advocacy and policy would have been even lower.

Of course, it’s very hard to quantify this. If there’s improvement, you can at least try to sort out the relative contribution of different causes. If things don’t improve or even worsen, then the only way to measure the effect of advocacy and policy is the use of counterfactual thinking. And that’s a problem. How bad (or good?) would things have been without advocacy and policy? We can’t redo a part of a country’s history to test what would have happened with other choices. We can speculate about the answer to “what if” questions but since we can’t experiment we’re left with a lot of uncertainty. What if Hitler had won the war? Or had been admitted to art school? Fun questions to try and answer, but the answers won’t tell us much about the real world, unfortunately. If they did, we would know what to do.

More posts in this series are here.

Measuring Human Rights (31): Which Changes in the Spatial Pattern of Human Rights Are Most Likely?

One result of human rights measurement is a spatial pattern of human rights, a pattern that of course changes over time: countries with lower or higher levels of respect for human rights show up on a world map and this world map shows a certain spatial pattern.

The current spatial pattern of human rights is, somewhat simplistically, like this: wealthy and developed “Western” countries, although by no means free from human rights violations, show on average higher levels of respect for human rights than most developing nations. This is no reason to distribute praise or blame: developed countries share responsibility for human rights violations in developing countries, and high levels of respect for certain human rights in developed countries may be partly a matter of luck or perhaps even the direct consequence of the exploitation of developing regions. It’s also the case that rights cost money, hence wealthier countries can be expected to show higher levels of respect for rights.

Just take it as a fact rather than a judgment, admittedly a stylized fact (one can argue that human rights are better protected in Italy than in the US even though the latter is much wealthier; the same is true if you compare Botswana en China). Here’s an example of one human rights index that confirms this spatial pattern:

(source)

Given this current spatial pattern, what’s our best guess about the future? The dynamics of human rights are poorly understood: unfortunately, we don’t really know which actions or events are most likely to change levels of respect for human rights, at least not in the positive sense. We know that war, genocide, authoritarian rule and poverty bring levels down, but we don’t know quite as well how to bring levels up. We assume that different types of forces may play a role:

• bottom-up forces such as popular revolts, changes in cultural practice etc.;
• top-down forces such as coups d’états, government policies, national legislation, international law, international institutions etc.;
• horizontal forces such as peer pressure among states, conditional bilateral development aid, pay-offs, military intervention, naming-and-shaming etc.

Incentives also play a role, and maybe even forces beyond human control such as climate, geography etc. However, the exact result and impact of these forces is unclear and controversial, so we don’t really know what to do and kinda grope in the dark hoping something is successful.

Given the fact that many people and many institutions actually try to do something in order to raise levels of respect for human rights, it’s indeed likely that some actions will be somewhat effective. Hence the spatial pattern of human rights may change in the future. Here are my guesses as to how it may change:

1. Those areas of the world where respect for rights is already relatively high are most likely to see additional improvements. I agree that low hanging fruit is easiest to pick, and that is why we may see spectacular progress in some countries where respect is currently low: the removal of an oppressive regime can, in theory, bring rapid and large improvements in levels of respect, but in practice there are very few cases (often the overthrow of an oppressive regime is followed by civil war or a successor regime that is only slightly better or even worse). Conversely, sometimes high hanging fruit is, paradoxically, easier to pick. Countries with a reasonably high level of respect often have a history of struggle for rights as well as a culture of rights resulting from that struggle. Rights are part of the ethos of the common man. Remaining rights violations will therefore be more jarring, and existing institutions necessary to tackle them are in place. Another reason to believe that improvements in human rights will first take place in those countries that are already relatively good is the dynamic of bilateral aid: aid donors are likely to give more to countries that already have a certain level of respect, not just because donors like aid conditionality but also because of things such as the “bottomless pit syndrome”. Badly governed countries just take the aid and spend it for the rulers’ personal profit. Donors understandably don’t like this and therefore tend to give to countries that are better governed.
2. Those areas of the world adjacent to areas where respect for rights is already relatively high are likely to see additional improvements. Countries tend to see rights violations in neighboring countries as more urgent than rights violations far away. The former violations can have spillover effects: a civil war in the country next door can cause refugee flows into your own country or other types of spillovers, hence you have an incentive to do something about the war. The same is true for other types of rights violations. Rights violations in a country far away don’t create the same incentives to act. Additionally, the EU and other regional organizations insist that candidate member countries – almost always adjacent countries – first respect human rights before they can become members. These candidate countries therefore have a powerful incentive to raise levels of respect, since membership is often profitable. And there are also other, non-spatial types of proximity among adjacent countries: they may share a language – or their languages may belong to the same family – or a religion. This kind of cultural proximity makes bilateral intervention more likely and more acceptable. If one of two adjacent countries has a high level of respect for human rights, it may find it easier to intervene in the other country in order to foster human rights. It may offer effective institutional assistance for instance, assistance that is more effective – because more acceptable and easier – than assistance from a country far away, “far away” both spatially and culturally. Another reason to believe that proximity plays a role: a country that exists in the proximity of other countries that perform better in the field of human rights is in direct competition with those other countries; competition for workers, international investment etc. Both workers and companies will prefer to invest in countries that are free. Hence the underperformers in a certain region will have the incentive to do better.

If these two claims are correct, then we’ll see increasing polarization among two groups of countries. Not the optimal outcome, but perhaps the most likely one. Time will tell.

More posts in this series are here.

Measuring Human Rights (30): Distortions Caused by the Exclusion of Prisoners

A 12-year-old boy in a detention center in Biloxi, Mississippi, that is operated by a private security firm

(source, source)

I’ve already cited one example of human rights measurement gone wrong because of the exclusion of the prison inmate population: violent crime rates seem to go down in many countries, but a lot of the decrease only happens because surveys and databases exclude the crimes that take place inside of prisons. Crime may not have gone down at all; perhaps a lot of it has just been moved to the prisons.

I’ll now add a few other examples of distortions in human rights measurement caused by the exclusion of the prisoner population. The cases I’ll cite result in distortions because the exclusion of the prison population is the exclusion of a non-representative sample of the total population. For example, it’s well-known that African-Americans make up a disproportionate share of the inmate population in the U.S. Becky Pettit, a University of Washington sociologist, argues in her book “Invisible Men” that we shouldn’t take for granted some of the indicators of black progress in the U.S.:

For example, without adjusting for prisoners, the high-school completion gap between white and black men has fallen by more than 50% since 1980 … After adjusting … the gap has barely closed and has been constant since the late 1980s. (source)

We see similar results when counting or better recounting voter turnout numbers, employment rates etc.

(source)

It should be rather easy to include prisoners in most of these measurements – certainly compared to the homeless, illegal immigrants and citizens of dictatorships. The fact that we almost systematically exclude them is testimony to our attitude towards prisoners: they are excluded from society, and they literally don’t count.

More posts in this series are here.

Measuring Human Rights (29): When More Means Less, and Vice Versa, Ctd.

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Take the example of rape measurement: better statistical and reporting methods used by the police, combined with less social stigma and other factors result in statistics showing a rising number of rapes, but this increase is due to the measurement methods and other effects, not to what happened in real life. The actual number of rapes may have gone down.

This is a general problem in human rights measurement: more often means less, and vice versa. The nature of the thing we’re trying to measure – human rights violations – means that the more there is, the more difficult it is to measure; and the more difficult, the more likely that we wrongly conclude that there is less. (See here). When levels of rights violations approach totalitarianism, people won’t report, won’t dare to speak, or won’t be able to speak. It’s not social stigma or shame that prevents them from speaking, as in the case of rape, but fear. Furthermore, totalitarian governments won’t allow monitoring, and will have managed to some extent to indoctrinate their citizens. Finally, the state of the economy won’t allow for easy transport and communication, given the correlation between economic underdevelopment and totalitarian government.

Conversely, higher levels of respect for human rights will yield statistics showing more rights violations, because a certain level of respect for human rights makes monitoring easier.

More on measuring human rights.

Lies, Damned Lies, and Statistics (40): The Composition Effect

Take the evolution of the median wage in the US over the last decades. The trend is nearly flat and one would therefore naturally assume that there have been hardly any income gains for the average US citizen. However, some have argued that this conclusion is wrong because it ignores the composition effect. In this example, the composition of the labor force has obviously changed over the last decades, and has changed dramatically. More women and immigrants have entered the workforce and those tend to be lower income groups, especially at the moment of entry. When they enter the labor force, their incomes go up, obviously, but they bring the average and the median down. When, at the same time, the wages of white men go up, the aggregate effect may be close to zero. And yet, paradoxically, all groups have progressed. The conclusion that the average citizen did not progress would only hold if the composition of the population whose wages are compared over time had not changed.

Now, it seems to be the case that in this particular example there is really no large composition effect (see here). However, this effect is always a possibility and one should at least consider it and possibly rule it out before drawing hasty conclusions from historical time series. If you don’t do this, or don’t even try, then you may be “lying with statistics”.

More posts in this series are here.

Measuring Human Rights (28): Countries Hit Hardest by Judgments of the European Court of Human Rights

If you have a reasonably effective international court that monitors human rights violations in a number of countries, then the number and severity of judgments of this court can be used to measure respect for human rights in those countries and compare levels of respect.

The ECHR was set up in Strasbourg by the Council of Europe in 1959 to deal with violations of the 1950 European Convention on Human Rights in the 47 member European states that are members of the Council (not to be confused with the European Union). It’s probably the most effective human rights court in the world today – which doesn’t mean that all its judgments are respected. Citizens in all member states have more or less equal access to the ECHR and the court applies the human rights convention equally to all member states. So this should give a useful and internationally comparable measurement of respect for human rights, at least for this subset of 47 countries.

It’s clear from the map below which countries most commonly fall foul of the ECHR:

(source, where you can find an interactive version)

In 2011, the European Court of Human Rights found 159 violations against Turkey, 121 against Russia and 105 against Ukraine. Of 1157 judgements pronounced in 2011, the human rights violations most frequently found by the court were in the length of proceedings (341), the right to liberty and security (241) and the right to a fair trial (211).

One can expect more judgments against more populous countries, of course, but there’s no clear link between population size and number of judgments, which confirms the intuition that some European countries are more free than others.

More posts in this series are here.

Update: a more complete database is here.

Measuring Poverty (15): A Common Misconception About Relative Poverty

Yesterday, I had a short email exchange with Tim Harford, in which I reacted to one of his claims in this article, more specifically the claim that the use of a relative notion of poverty in poverty measurement implies that poverty will always be with us:

Eurostat, the European Union’s statistics agency, … defines the poverty line as 60 per cent of each nation’s median income. (The median income is the income of the person in the middle of the income distribution.)

This has an unfortunate consequence: poverty is permanent. If everyone in Europe woke up tomorrow to find themselves twice as rich, European poverty rates would not budge. That is indefensible. Such “poverty” lines measure inequality, not poverty.

This argument against relative poverty is as common as it is mistaken. Here’s my email to Tim:

I read your article on poverty measurement a moment ago, and I wanted to object. You say that using a relative poverty measurement of income below 60% of median income makes poverty “permanent”. It does not. True, someone with an income of 61% of the median does not suddenly become poor because the median person receives a pay rise. But it’s also true that it’s perfectly doable – mathematically if not in reality – to raise every single poor person’s income above 60% of the median without changing the median. Poverty is only permanent when one would use 60% of the average as threshold, but no one proposes such a foolish thing, fortunately.

In fairness to Tim, his article does list some advantages of relative poverty and he qualified his views in our email correspondence.

More posts in this series are here.

Lies, Damned Lies, and Statistics (39): Availability Bias

example of availability bias on a newspaper’s frontpage

(source)

This is actually only about one type of availability bias: if a certain percentage of your friends are computer programmers or have red hair, you may conclude that the same percentage of a total population are computer programmers or have red hair. You’re not working with a random and representative sample – perhaps you like computer programmers or you are attracted to people with red hair – so you make do with the sample that you have, the one that is immediately available, and you extrapolate on the basis of that.

Most of the time you’re wrong to do so – as in the examples above. In some cases, however, it may be a useful shortcut that allows you to avoid the hard work of establishing a random and representative sample and gathering information from it. If you use a sample that’s not strictly random but also not biased by your own strong preferences such as friendship or attraction, it may give reasonably adequate information on the total population. If you have a reasonably large number of friends and if you couldn’t care less about their hair color, then it may be OK to use your friends as a proxy of a random sample and extrapolate the rates of each hair color to the total population.

The problem is the following: because the use of available samples is sometimes OK, we are perhaps fooled into thinking that they are OK even when they’re not. And then we come up with arguments like:

• Smoking can’t be all that bad. I know a lot of smokers who have lived long and healthy lives.
• It’s better to avoid groups of young black men at night, because I know a number of people who have been attacked by young black men (and I’ll forget that I’ll hardly ever hear of people not having been attacked).
• Cats must have a special ability to fall from great heights and survive, because I’ve seen a lot of press reports about such events (and I forget that I’ll rarely read a report about a cat falling and dying).
• Violent criminals should be locked up for life because I’m always reading newspaper articles about re-offenders (again, very unlikely that I’ll read anything about non-re-offenders).

As is clear from some of the examples above, availability bias can sometimes have consequences for human rights: it can foster racial bias, it can lead to “tough on crime” policies, etc.

More posts in this series are here.

Lies, Damned Lies, and Statistics (38): The Base-Rate Fallacy

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When judging whether people engage in discrimination it’s important to make the right comparisons. Take the example of an American company X where 98 percent of employees are white and only 2 percent are black. If you compare to (“if your base is”) the entire US population – of which about 13 percent are African American – then you’ll conclude that company X is motivated by racism in its employment decisions.

However, in cases such as these, it’s probably better to use another base rate, namely the number of applicants rather than the total population. If only 0.1 percent of job applications where from blacks, then an employment rate of 2 percent blacks actually shows that company X has favored black applicants.

The accusation of racism betrays a failure to point to the real causes of discrimination. It’s a failure to go back far enough and to think hard enough. The fact that only 0.1 percent of applicants were black – instead of the expected 13 percent – may still be due to racism, but not racism in company X. Blacks may suffer from low quality education, which results in a skill deficit among blacks, which in turn leads to a low application rate for certain jobs.

The opposite error is also quite common: people point to the number of blacks in prison, compare this to the total number of blacks, and conclude that blacks must be more attracted to crime. However, they should probably compare incarceration rates to arrest rates (blacks are arrested at higher rates because of racial profiling). And they should take into account jury behavior as well.

More about racism. More posts in this series.

Measuring Human Rights (27): Measuring Crime

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A number of crimes are also human rights violations, so crime rates can tell us something about the degree of respect for human rights. Unfortunately, as in most cases of rights measurement, crime measurement is difficult. I won’t discuss the usual difficulties here – underreporting by victims or relatives, lack of evidence, corrupt or inefficient police departments etc. Instead, I want to mention one particularly interesting problem that is seldom mentioned but possibly fatal for crime rate statistics: most reductions in crime rates are not really reductions, especially not those reductions that come about as a result of tougher law enforcement and higher incarceration rates. When we imprison criminals, rather than bringing crimes rates down, we just move the crime from society towards the prisons:

the figures that suggest that violence has been disappearing in the United States contain a blind spot so large that to cite them uncritically, as the major papers do, is to collude in an epic con. Uncounted in the official tallies are the hundreds of thousands of crimes that take place in the country’s prison system, a vast and growing residential network whose forsaken tenants increasingly bear the brunt of America’s propensity for anger and violence.

Crime has not fallen in the United States—it’s been shifted. Just as Wall Street connived with regulators to transfer financial risk from spendthrift banks to careless home buyers, so have federal, state, and local legislatures succeeded in rerouting criminal risk away from urban centers and concentrating it in a proliferating web of hyperhells. (source, source)

And there’s no way to correct for this and adjust overall crime rate statistics because quality statistics on crime rates inside prison are even harder to get than statistics on “normal” crime rates – given the quasi lawlessness of prison life.

More on prison violence here and here.

Economic Human Rights (40): How Do Poor People Live?

floodwater in Srinagar, Kashmir, India

(source unknown)

The poor tend to become a number, a statistic, an undifferentiated mass, especially here on this blog. Talk of the “bottom billion” and the one-dollar-a-day people only makes things worse. Of course, it’s important to know the numbers, if only to see how well we are doing in the struggle against poverty. But to actually know what we have to do, we need to know what poverty actually means to poor people. How do these people live? Which problems do they face? Who are they? None of this can be captured in numbers or statistics. Pure quantitative analysis doesn’t help. We need qualitative stories here, and these stories will necessarily differentiate between groups of people because poverty means different things to different people.

Keeping in mind the caveat that poverty is “multidimensional” and that it varies with the circumstances, is it possible to give a more or less general impression of the “lives of the poor”? There’s an interesting attempt here. Banerjee and Duflo analyzed survey data from 13 countries in order to distill a picture of the way people live on less than one dollar a day, of the choices they have and the limits and challenges they face.

The countries are Cote d’Ivoire, Guatemala, India, Indonesia, Mexico, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, South Africa, Tanzania, and Timor Leste. Obviously, the lives of the poor are very different in these different countries, and vary even for different groups within each country. Still, some general information can be extracted:

• The number of adults (i.e. those over 18) living in a family ranges from about 2.5 to about 5, with a median of about 3, which suggests a family structure where it is common for adults to live with people they are not conjugally related to (parents, siblings, uncles, cousins, etc.). When every penny counts, it helps to spread the fixed costs of living (like housing) over a larger number of people. Poverty has consequences for family structure, and vice versa.
• Poor families have more children living with them. The fact that there are a large number of children in these families does not necessarily imply high levels of fertility, as families often have multiple adult women.
• The poor of the world are very young on average. Older people tend to be richer simply because they have had more time to accumulate resources.
• Food typically represents from 56 to 78 percent of consumption expenses among rural households, and 56 to 74 percent in urban areas.
• The poor consume on average slightly less than 1400 calories a day. This is about half of what the Indian government recommends for a man with moderate activity, or a woman with heavy physical activity. As a result, health is definitely a reason for concern. Among the poor adults in Udaipur, the average “body mass index” (that is, weight in kilograms divided by the square of the height in meters) is 17.8. Sixty-five percent of poor adult men and 40 percent of adult women have a body mass index below 18.5, the standard cutoff for being underweight. Eating more would improve their BMI and their health, and yet they choose to spend relatively large amounts on entertainment. Which just shows that the poor have the same desires as anyone else and choose their priorities accordingly.

collecting water from holes in the ground, Udaipur, India

(source unknown)

• The poor see themselves as having a significant amount of choice, and choose not to exercise it in the direction of spending more on food. The typical poor household in Udaipur could spend up to 30 percent more on food than it actually does, just based on what it spends on alcohol, tobacco, and festivals. Indeed, in most of the surveys the share spent on food is about the same for the poor and the extremely poor, suggesting that the extremely poor do not feel the need to purchase more calories. This conclusion echoes an old finding in the literature on nutrition: Even the extremely poor do not seem to be as hungry for additional calories as one might expect.
• Tap water and electricity are extremely rare among the poor.
• Many poor households have multiple occupations. They may operate their own one-man business, sometimes more than one, but do so with almost no productive assets. They also have jobs as laborers, often in agriculture. And they cultivate a piece of land they own. Yet, agriculture is not the mainstay of most of these households. Where do they find non-agricultural work? They migrate. The businesses they operate are very small, lacking economies of scale and without employment opportunities for people outside the family. That’s a vicious circle because it means that few people can find a job and are forced to start petty businesses themselves. This circle makes economies of scale very difficult.
• The poor tend not to become too specialized, which has its costs. As short-term migrants, they have little chance of learning their jobs better, ending up in a job that suits their specific talents or being promoted. Even the non-agricultural businesses that the poor operate typically require relatively little specific skills. The reason for this lack of specialization is probably risk spreading. If the weather is bad and crop yields are low, people can move to another occupation.
• The poor don’t save a lot, unsurprisingly. Some of it has to do with inadequate access to credit and insurance markets. Banks and insurers are unwilling to give access to the poor and saving at home is hard to do; it’s unsafe and the presence of money at home increases the temptation to spend (that’s true for all of us by the way).
• In 12 of the 13 countries in the sample, with the exception of Cote d’Ivoire, at least 50 percent of both boys and girls aged 7 to 12 in extremely poor households are in school. Schooling doesn’t take a large bite from the family budget of the poor because children in poor households typically attend public schools or other schools that do not charge a fee.

Related Articles

• Victorian poor records go online (bbc.co.uk)
• What about the pursuit of happiness? | Mukoma Wa Ngugi (guardian.co.uk)

Measuring Human Rights (26): Measuring Murder

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Murder should be easy to measure. Unlike many other crimes or rights violations, the evidence is clear and painstakingly recorded: there is a body, at least in most cases; police seldom fail to notice a murder; and relatives or friends of the victim rarely fail to report the crime. So even if we are not always able to find and punish murderers, we should at least know how many murders there are.

And yet, even this most obvious of crimes can be hard to measure. In poorer countries, police departments may not have the means necessary to record homicides correctly and completely. Families may be weary of reporting homicides for fear of corrupt police officers entering their homes and using the occasion to extort bribes. Civil wars make it difficult to collect any data, including crime data. During wartime, homicides may not be distinguishable from casualties of the war.

And there’s more. Police departments in violent places may be under pressure to bring down crime stats and may manipulate the data as a result: moving some dubious murder cases to categories such as “accidents”, “manslaughter”, “suicide” etc.

Homicides usually take place in cities, hence the temptation to rank cities according to homicide rates. But cities differ in the way they determine their borders: suburbs may be included or not, or partially, and this affects homicide rates since suburbs tend to be less violent. Some cities have more visitors than other cities (more commuters, tourists, business trips) and visitors are usually not counted as “population” while they may also be at risk of murder.

In addition, some ideologies may cause distortions in the data. Does abortion count as murder? Honor killings? Euthanasia and  assisted suicide? Laws and opinions about all this vary between jurisdictions and introduce biases in country comparisons.

And, finally, countries with lower murder rates may not be less violent; they may just have better emergency healthcare systems allowing them to save potential murder victims.

So, if even the most obvious of human rights violations is difficult to measure, you can guess the quality of other indicators.

More posts in this series are here.

Statistical Jokes (40): Analyst v Normal Person

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Other peculiar behavior by statisticians is described here. More statistical jokes are here.

Measuring Poverty (14): Measuring Income Inequality

"prosperity is just around the corner", but unfortunately not the corner where these unemployed people are headed

(source)

Income inequality may or may not be the best definition of poverty,  but it’s certainly one that is often used. In many European countries, you’re counted as poor when your income is below 50% or so of the median income. Maybe this is the wrong way to measure poverty, but if you use absolute measures for poverty (such as a basic income, minimum consumption etc.) you’ll also face some problems. So it’s worthwhile to examine some of the usual methods for measuring income inequality and see how they hold up, while at the same time bracketing the discussion about poverty as either absolute deprivation or unequal distribution.

Methods for measuring income inequality

The Gini coefficient is the most widely used. It’s based on the proportion of the total income of a population that is cumulatively earned by a % of the population; a value of 0 expresses perfect equality where everyone has equal shares of income and a value of 1 expresses maximal inequality where only one person has all the income. A low Gini coefficient indicates therefore a more equal distribution. (The complete formula is here).

A disadvantage of the Gini measure is that it doesn’t capture where in the distribution the inequality occurs: is a society unequal because the top 1% has astronomically high incomes, because the poor are very poor, because there is practically no middle class, or because of some other reason?

Other measures are

• the ratio of the incomes of the top 10% (best paid) to the bottom 10% (worst paid)
• the proportion of a population with income less than 50% of the median income
• a population may be split into segments, e.g. quintiles or deciles, and each segment’s income share is then compared to each other segment’s (for example, the top 10% of the population – “top” in income terms – has x % of total income)
• some other measures are here.

These different measures can give contradictory numbers: two societies with the same Gini score can have different ratios of top-bottom, top-middle or middle-bottom incomes (see an example here). Hence, no single measure will tell us the last word about inequality in a society.

What is income?

The focus of all these measurement systems is income, but we should first decide what to count as income. Income doesn’t have to be cash or currency. A farmer in a poor country who grows his own products has non-cash income. Perhaps public services such as healthcare or education should count as income. And how about tax reductions, tax refunds, government benefits such as unemployment insurance, food stamps and various vouchers?

All those forms of non-cash or non-labor income are important when measuring income inequality because the poor profit disproportionately from those non-cash or non-labor related forms of income. Hence, including them in total income can make a large difference in income inequality numbers. (Higher income groups may have less or different tax refunds and their education may represent a smaller portion of their total income – the returns of their education may of course be higher, but those returns are typically cash based in the sense that they lead to higher labor compensation).

We should also decide if we want to use income before or after taxation; depends if we want to measure the effectiveness of redistribution or simply gross inequalities. And what about capital gains, imputed house rents from home ownership, inheritance etc. In general, how should wealth be included in income? Or shouldn’t it be?

How do we measure income?

Once we’ve solved the difficult problem of defining income, we’re still left with the practical problem of measuring it. Most cash income is captured in tax return data, but not all, and not equally well in all countries. Sometimes, you’ll need to use consumption data as a proxy for income data, or surveys about living standards. “Informal” income typically does not show up in tax data, but does in consumption data.

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Another problem with measures of inequality is that they may be contaminated by notions of fairness. Some deliberately design their measurement system in such as way that inequalities look bigger than they actually are. For example, they use pre-tax inequalities because those are often larger than post-tax inequalities – a lot of tax systems are redistributive towards the poor (e.g. progressive taxation systems). Or they focus on income inequality when consumption inequality may have diminished. Others may mistakenly deduce evaluations of fairness or injustice from the simply fact of income distributions and forget that measures of income inequality are silent about who is on which side of the divide. If person A in a two person economy has twice the income of person B, then the measurement of inequality would be absolutely the same when B switches places with A. Measures of income inequality say nothing about who deserves what, about how income has been acquired, about whether some occupations should yield higher compensation (for example because we want the right incentives), or about how income should ideally be distributed.

And then there is the opposite mistake: assuming that income inequality is always necessary and just because it’s the automatic result of the fact that people have different levels of human capital and productive abilities. This is a mistake because it ignores a number of facts: no one has ever been able to prove that some abilities or occupations deserve higher wages from a moral point of view, and a lot of inequality is the result not of different abilities or efforts but of differences in luck and connections. Hence, fairness remains a legitimate concern. Contrary to the “left-wing mistake”, the “right-wing mistake” will not distort the measurement of inequality: if you believe inequality is not a problem you hardly have a reason for measuring it, let alone distort the measurement.

What I want to stress is how difficult it is to measure income inequality and how many mistakes we can make. This doesn’t mean that the numbers are rubbish. We should just be careful when drawing sweeping conclusions, that’s all.

Something more about the causes of income inequality, rather than the measurement of it, is here.

Statistical Jokes (37): Significant Digits

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But please, have some sympathy for the statistician. He’s not really a nihilist and he does care about people dying. All he wants is that the nihil regains its place at the right side of estimated numbers.

More on fake accuracy here. More statistical jokes here.

Measuring Human Rights (25): Measuring Hunger

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First, and for those in doubt: hunger is a human rights violations (see article 25 of the Universal Declaration). Second, before we discuss ways to measure this violation, we have to know what it is that we want to measure. It’s surprisingly difficult to define hunger.

Definition of hunger

The word “hunger” in this context does not refer to the subjective sensation that we have when lunch is late. We’re talking here about a chronic lack of food or a sudden and catastrophic lack of food (as in the case of a famine). We measure a lack of food by measuring dietary energy deficiency, which in turn is computed based on average daily calorie intake. The FAO estimates that the average minimum energy requirement per person is 1800 kcal per day. The global average per capita daily calorie intake is currently about 2800 kcal. This average obviously masks extreme differences between the obese and the chronically undernourished.

The FAO minimum energy requirement per person of 1800 kcal is also an average. The minimum calorie need depends on many things: age, climate, health, height, occupation etc.

Usually, the concept of “hunger” as it is defined here is different from “malnutrition“. Hunger is a lack of food defined as a lack of calorie intake. Malnutrition is a lack of quality food, of micronutrients such as vitamins and minerals, and of a divers diet. Hence, people may have access to sufficient quantities of food and still be malnourished.

Hunger and famine are also different concepts. Hunger is a chronic and creeping lack of food, while a famine results from the sudden collapse of food stocks. A famine implies widespread starvation during a limited period. It can’t go on forever because it must stop when everyone has died or when food supplies are restored. Chronic hunger on the other hand can go on forever because it doesn’t imply widespread starvation. Of course, people do die of chronic hunger, and on a global level hunger kills more people than famines do. But whereas in the case of famine people die of starvation, the victims of chronic hunger usually don’t starve to death. When we say that hunger kills someone every 3.6 seconds we usually mean that this person dies from an infectious disease brought on by hunger. Hunger increases people’s vulnerability to diseases which are otherwise nonfatal (e.g. diarrhea, pneumonia etc.). In fact, most hunger related deaths do not occur during famines. Chronic hunger is much more deadly – it’s just not as noticeable as a famine. When and where famines occur, they are more deadly and catastrophic. But they occur, thank God, only exceptionally. Hunger on the other hand is a permanent fixture of the lives of millions and ubiquitous in many countries.

"dying child" by Jac Saorsa

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Measurement of hunger

Given this definition, how do we go about and measure the extent of chronic hunger? (The measurement of famine is a separate problem, discussed here). There are different possible methods:

• So-called food intake surveys (FIS) estimate dietary intake and try to relate this to energy needs determined by physical activity. Calorie intake below a minimum level means hunger. The problem here is that minimum calorie intake thresholds are somewhat arbitrary and do not always take people’s different calorie requirements into account. Even for a single individual, this threshold can vary over time (depending on the climate, the individual’s age, occupation and health etc.). Moreover, when trying to measure calorie intake, you’re faced with the problem of hunger due to imperfect absorption: it’s not because someone in a sample buys and consumes x number of calories that he or she actually absorbs those calories. The widespread incidence of diarrhea and other health problems often mean that only a fraction of calories eaten are absorbed by the body.
• In order to bypass this, some propose a measurement method based on revealed preferences. The greater the share of calories people receive from the cheapest foods available to them, the hungrier they are; and, conversely, the more they buy expensive sources of calories, the less hungry they are. Their choice of foods reveals whether they have enough calories. This method therefore eliminates the threshold and absorption problems.

Our approach derives from the fact that when a person is below their nutrition threshold, there is a large utility penalty due to the physical discomfort associated with the body’s physiological and biochemical reaction to insufficient nutrition. At this stage, the marginal utility of calories is extremely high, so a utility-maximizing consumer will largely choose foods that are the cheapest available source of calories, typically a staple like cassava, rice or wheat. However, once they have passed subsistence, the marginal utility of calories declines significantly and they will begin to substitute towards foods that are more expensive sources of calories but that have higher levels of non-nutritional attributes such as taste. Thus, though any individual’s actual subsistence threshold is unobservable, their choice to switch away from the cheapest source of calories reveals that their marginal utility of calories is low and that they have surpassed subsistence. Accordingly, the percent of calories consumed from the staple food source, or the staple calorie share (SCS), can be used as an indicator for nutritional sufficiency. (source, source)

• Still another method consists of measuring hunger’s physical effects on growth and thinness. Instead of measuring calorie intake, hunger or revealed preferences, you measure people’s length, their stunted growth and their body mass index. However, this is very approximative since length and weight may be determined by lots of factors, many of them unrelated to hunger.
• And finally there are subjective approaches. The WFP does surveys asking people how often they ate in the last week and what they ate, how often they skip meals, how far they are away from markets, if their hunger is temporary or chronic etc. Gallup does something similar.

More on hunger here. More data here. And more posts in this series here.

Measuring Democracy (8): A Multidimensional Measurement

scene outside a UK polling station

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Any attempt to measure the degree of democracy in a country should take into account the fact that democracy is something multidimensional. It won’t suffice to measure elections, not even the different aspects of elections such as frequency, participation, fairness, transparency etc. It takes more than fair and inclusive elections to have a democracy. Of course, the theoretical ideal of democracy is a controversial notion, so we won’t be able to agree on all the necessary dimensions or elements of a true democracy. Still, you can’t escape this problem if you want to build a measurement system: measuring something means deciding which parts of it are worth measuring.

You would also do best to take a maximalist approach: leaving out too many characteristics would allow many or even all countries to qualify as fully democratic and would make it impossible to differentiate between the different levels or the different quality of democracy across countries. A measurement system is useful precisely because it offers distinctions and detailed rankings and because it makes it possible to determine the distance to an ideal, whatever the nature of the ideal. Obviously, a maximalist approach is by definition more controversial than a minimal one. Everyone agrees that you can’t have a democracy without elections (or, better, without voting more generally). Whether strong free speech rights and an independent judiciary are necessary is less clear. And the same is true for other potential attributes of democracy.

Once you’ve determined what you believe are necessary attributes you can start to measure the extent at which they are present in different countries. Hence, your measurement will look like a set of sliding scales:

With all the markers on the right side in the case of a non-existing ideal democracy, and all the markers on the left side in the unfortunately very real case of total absence of democracy.

(The aggregation of these scales into a total country score is another matter that I’ve discussed elsewhere).

Some candidates of attributes are:

• Does a country include more or less people in the right to have a democratic say? How high is the voting age? Are criminals excluded from the vote, even after they have served their sentence? Are immigrants without citizenship excluded? Are there conditions attached to the right to vote (such as property, education, gender etc.)?
• Does a country include more or less topics in the right to a democratic say? Are voters not allowed to have a say about the affairs of the military, or about policies that have an impact on the rights of minorities? Does the judiciary have a right to judicial review of democratically approved laws?
• Does a country include more or less positions in the right to a democratic say? Can voters elect the president, judges, prosecutors, mayors, etc., or only parliamentarians? Can they elect local office holders? Does a country have a federalist structure with important powers at the local or state level?
• Does a country impose qualified majorities for certain topics or positions? Do voters have to approve certain measures with a two-thirds supermajority?
• Does a country provide more or less ways to express a democratic say? Can voters only elect officials or can they also vote on issues in referenda?
• Does a country impose more or less restrictions on the formation of a democratic say? Are free speech rights and assembly and association rights respected?
• Does a country accept more or less imbalances of power in the formation of a democratic say? Are there campaign financing rules?
• Does a country show more or less respect for the expression of a democratic say? How much corruption is there? Is the judiciary independent?

A “more” score on any of these attributes will push up the total “democracy score” for a country. At least it seems so, if not for the conclusion that all these complications in the measurement system are still not enough. We need to go further and add additional dimensions. For example, one can argue that we shouldn’t define democracy solely on the basis of the right to a democratic say, not even if we render this right as complex as we did above. A democracy should, ideally, also be a stable form of government, and allowing people to decide about the fundamental rights of minorities is an expression of the right to a democratic say but it is not in the long term interest of democracy. Those minorities will ultimately rebel against this tyranny of the majority and cause havoc for everyone.

More posts in this series are here.

Measuring Human Rights (24): Measuring Racism, Ctd.

(source, more about the watermelon stereotype here)

Measuring racism is a problem, as I’ve argued before. Asking people if they’re racist won’t work because they don’t answer this question correctly, and understandably so. This is due to the social desirability bias. Surveys may minimize this bias if they approach the subject indirectly. For example, rather than simply asking people if they are racist or if they believe blacks are inferior, surveys could ask some of the following questions:

• Do you believe God has created the races separately?
• What do you believe are the reasons for higher incarceration rates/lower IQ scores/… among blacks?
• Etc.

Still, no guarantee that bias won’t falsify the results. Maybe it’s better to dump the survey method altogether and go for something even more indirect. For example, you can measure

• racism in employment decisions, such as numbers of callbacks received by applicants with black sounding names
• racism in criminal justice, for example the degree to which black federal lower-court judges are overturned more often than cases authored by similar white judges, or differences in crime rates by race of the perpetrator, or juror behavior
• racial profiling
• residential racial segregation
• racist consumer behavior, e.g. reluctance to buy something from a black seller
• the numbers of interracial marriages
• the numbers and membership of hate groups
• the number of hate crimes
• etc.

newspaper clipping about an interracial marriage; February 10, 1955

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A disadvantage of many of these indirect measurements is that they don’t necessarily reflect the beliefs of the whole population. You can’t just extrapolate the rates you find in these measurements. It’s not because some judges and police officers are racist that the same rate of the total population is racist. Not all people who live in predominantly white neighborhoods do so because they don’t want to live in mixed neighborhoods. Different crime rates by race can be an indicator of racist law enforcement, but can also hide other causes, such as different poverty rates by race (which can themselves be indicators of racism). Higher numbers of hate crimes or hate groups may represent a radicalization of an increasingly small minority. And so on.

Another alternative measurement system is the Implicit Association Test. This is a psychological test that measures implicit attitudes and beliefs that people are either unwilling or unable to report.

Because the IAT requires that users make a series of rapid judgments, researchers believe that IAT scores may reflect attitudes which people are unwilling to reveal publicly. (source)

Participants in an IAT are asked to rapidly decide which words are associated. For example, is “female” or “male” associated with “family” and “career” respectively? This way, you can measure the strength of association between mental constructs such as “female” or “male” on the one hand and attributes such as “family” or “career” on the other. And this allows you to detect prejudice. The same is true for racism. You can read here or here how an IAT is usually performed.

Yet another measurement system uses evidence from Google search data, such as in this example. The advantage of this system is that it avoids the social desirability bias, since Google searches are done alone and online and without prior knowledge of the fact that the search results will be used to measure racism. Hence, people searching on Google are more likely to express social taboos. In this respect, the measurement system is similar to the IAT. Another advantage of the Google method, compared to traditional surveys, is that the Google sample is very large and more or less evenly distributed across all areas of a country. This allows for some fine grained geographical breakdown of racial animus.

More specifically, the purpose of the Google method is to analyze trends in searches that include words like “nigger” or “niggers” (not “nigga” because that’s slang in some Black communities, and not necessarily a disparaging term). In order to avoid searches for the term “nigger” by people who may not be racially motivated – such as researchers (Google can’t tell the difference) – you could refine the method and analyze only searches for phrases like “why are niggers lazy”, “Obama+nigger“, “niggers/blacks+apes” etc. If you find that those searches are more common in some locations than others, or that they become more common in some locations, then you can try to correlate those findings with other, existing indicators of racism such as those cited above, or with historic indicators such as prevalence of slavery or lynchings.

More posts in this series are here.

Measuring Democracy (7): Some Technical Difficulties

Suppose you want to construct a democracy index measuring the level or lack of democracy in different countries in the world. The normal thing to do is to select some supposedly essential characteristics or attributes of democracy and try to measure the level or presence of those. So, for example, you may select free speech, elections, judicial independence and a number of other characteristics. Some of those are perhaps already measured and you can simply take those measurements. For others, you may have to set up your own measurement (e.g. a survey, analysis of newspapers or official documents etc.), or use a proxy.

In any case, you’ll end up with different datasets on different attributes of democracy, and you’ll have to bring those datasets together somehow in order to make your overall index, you single country-level democracy score. The problem is that the datasets contain different kinds of scales which cannot as such be aggregated into a global index. The scales and the values in the scales have to be normalized, i.e. translated into a common metric.

normalized value = raw value/maximum raw value

First, however, you have to rescale some existing scales so that they start at 0 – in other words, so that the lowest score is 0 (instead of starting at 1 for example, or at -10 such as the Polity IV scale). This way, all scales will have a normalized range from 0 to 1; 0 being the negation or total absence of the attribute; 1 being the complete and perfect protection or presence of the attribute.

What about weighting the different attributes? Some may be more important for a democracy than others. However, introducing weights in this way inevitably means introducing value judgments. While value judgments can’t be avoided (they’ll pop up at the moment of the selection of the attributes as well, for example), they can be minimized. If you choose not to use weighting, you consider all attributes to be equally important, which is a view that can be defended given the often interdependent nature of the attributes of democracy (an independent judiciary for example will likely not survive without a free press).

Once the different data sources are translated into normalized scales and, if necessary, weighted appropriately, they have to be aggregated in order to calculate the global index of quality of democracy. One possible aggregation rule would be this:

global index = source 1 * source 2 * ... * source n.

So a simple multiplication. But that would mean that a value of 0 for one attribute results in labeling the country as a whole as having 0 democratic quality. This is counter-intuitive, even with the assumption of equal importance of all attributes. Hence, a better aggregation rule is the geometric or arithmetic mean (or perhaps the median).

However, there’s also a problem with averages: low scores on one attribute can be compensated by high scores on another. So very different democracies can have the same score. Also, within one country, a high score on suffrage rights but 0 on actual participation would give a medium democracy score, whereas in reality we wouldn’t want to call this country democratic at all (the score should be 0 or close to 0). Perhaps we can’t avoid weights after all.

More posts in this series are here.

(image source)

Statistical Jokes (34): Another Venn Diagram

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Other Venn jokes are here, here and here. More statistical jokes here.

Lies, Damned Lies, and Statistics (37): When Surveyed, People Express Opinions They Don’t Hold

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It’s been a while since the last post in this series, so here’s a recap of its purpose. This blog promotes the quantitative approach to human rights: we need to complement the traditional approaches – anecdotal, journalistic, legal, judicial etc. – with one that focuses on data, country rankings, international comparisons, catastrophe measurement, indexes etc.

Because this statistical approach is important, it’s also important to engage with measurement problems, and there are quite a few in the case of human rights. After all, you can’t measure respect for human rights like you can measure the weight or size of an object. There are huge obstacles to overcome in human rights measurement. On top of the measurement difficulties that are specific to the area of human rights, this area suffers from some of the general problems in statistics. Hence, there’s a blog series here about problems and abuse in statistics in general.

Take for example polling or surveying. A lot, but not all, information on human rights violations comes from surveys and opinion polls, and it’s therefore of the utmost importance to describe what can go wrong when designing, implementing and using surveys and polls. (Previous posts about problems in polling and surveying are here, here, here, here and here).

One interesting problem is the following:

Simply because the surveyor is asking the question, respondents believe that they should have an opinion about it. For example, researchers have shown that large minorities would respond to questions about obscure or even fictitious issues, such as providing opinions on countries that don’t exist. (source, source)

Of course, when people express opinions they don’t have, we risk drawing the wrong conclusions from surveys. We also risk that a future survey asking the same questions comes up with totally different results. Confusion guaranteed. After all, if we make up our opinions when someone asks us, and those aren’t really our opinions but rather unreflected reactions we give because of a sense of obligation, it’s unlikely that we will express the same opinion in the future.

Another reason for this effect is probably our reluctance to come across as ignorant: rather than selecting the “I don’t know/no opinion” answer, we just pick one of the other possible answers. Again a cause of distortions.

(image source)

Measuring Human Rights (23): When “Worse” Doesn’t Necessarily Mean “Worse”, Ctd.

Benny Hill

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Just because nobody complains does not mean all parachutes are perfect. Benny Hill

A nice illustration of this piece of wisdom:

Using state-level variation in the timing of political reforms, we find that an increase in female representation in local government induces a large and significant rise in documented crimes against women in India. Our evidence suggests that this increase is good news, driven primarily by greater reporting rather than greater incidence of such crimes. (source)

The cited “increase in female representation in local government” resulted from a constitutional amendment requiring Indian states to have women in one-third of local government council positions.

Since then, documented crimes against women have risen by 44 percent, rapes per capita by 23 percent, and kidnapping of women by 13 percent. (source)

This uptick is probably not retaliatory – male “revenge” for female empowerment – but rather the result of the fact that more women in office has led to more crime reporting. Worse is therefore not worse. A timely reminder of the difficulties measuring human rights violations. Measurements often depend on reporting, and reporting can be influenced, for good and for bad. Also, a good lesson about the danger of taking figures at face value.

Similar cases are here and here. More posts in this series are here.

Lies, Damned Lies, and Statistics (36): Manipulating the X-axis Scale in Graphs

Although less common than its sister lie – manipulating the y-axis in graphs – manipulation of the x-axis does occur.

But first a technical note: “bins” are clusters of subpopulations for which the frequency of some characteristic is measured. Together, the bins form a histogram or a graphical representation showing the distribution of a characteristic for an entire population (like a survey group). Here’s an example:

A survey of 31 black cherry tress revealed that three of them had a height between 60 and 65 feet; 8 had a height between 70 and 75 feet etc. There are 6 bins on this graph’s x-axis, probably because the person analyzing the survey data thought that 6 would be an adequate number. And indeed, dividing a population of 31 into 20 or 2 subgroups would probably not result in interesting numbers, at least not in this case.

Working with bins means that the x-axis shows a split of the surveyed population into smaller groups according to certain ranges of the characteristic that was surveyed (height in this case), making it possible to see how many individuals (trees in this case) belong to a certain range or subgroup. Notice that in this example the bins are

• not too numerous
• not too few
• of equal size (always a range of 5 feet)
• consecutive and
• non-overlapping.

As they should be. (The size shouldn’t always be equal, but often is).

Many histograms have a “bell-shape” like in this example (in which case they show what is called a “normal distribution“), but they can also have other shapes, depending on the population and the characteristic surveyed. A survey of the frequency of a certain disease among the population of a country, with the population divided into bins according to individuals’ age, would be skewed to the left since older people – on the right – may suffer more frequently from the disease.

Since all this is probably old news to most of you, let’s go straight to an example of manipulation of bins. Such manipulation often involves tinkering with the ranges of certain bins, so that the different bins are no longer of the same size. The following example is about income shares across the population of the U.S. Technically, the graph below is not a histogram because the y-axis shows cumulated income for ranges of income groups rather than frequencies, but for our purposes it’s equivalent:

wsj graph of income distribution

(source, source)

This graph is then used by the Wall Street Journal to argue against increased taxation of the rich as a means to close the budget deficit, because supposedly that’s not where the money is. Or, better, the money is there, otherwise they wouldn’t be rich, but there are just not enough of them; taxing the middle class would be better according to the WSJ because it’s they who have all the money … at least if you believe their graph. The problem is that the highest bar in their graph is for people making $100-$200K, whereas the bar immediately to the left of this one is for the income range of $75K to $100K – an income range only one-quarter the size. No surprise that the bar for $100-$200K is so much larger than the rest…

If you want to argue that taxing the rich does make it possible to bring in a lot more revenue, then you could use this alternative graph, made from the same data:

wsj graph of income distribution alternative

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Or this one:

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More alternative presentations of the same data are here.

It all depends what you mean by “rich” and “middle class”, but claiming -  as does the WSJ – that $200K is still “middle class” is stretching the point.

More posts in this series are here.

Human Rights Facts (64): Estimates of a Dramatic Decrease in the Number of Poor People

Using the internationally accepted poverty threshold of $1.25 a day, around 900 million people lived in extreme poverty in 2010; that’s down from 1.4 billion in 2005 and 1.8 billion in the early 1990s. Estimates for 2015 therefore are very optimistic: by that year, there may be less than 600 million people living below $1.25 a day:

(source)

Given the global population increase over the same period, that’s all the more remarkable. It means that the poverty rate (the proportion of humanity living in poverty) dropped even more quickly:

(source, MDG1a is short for Millennium Development Goal 1a)

Here a geographic breakdown of those estimates (in millions of people):

(source)

Measuring Human Rights (22): When Can You Call Something a “Famine”?

Scene from the famine of 1921-1923 in Soviet Russia

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With yet another famine in the Horn of Africa, perhaps it’s a good time for a few words about famine measurement.

People have a right to adequate nourishment and to be free from chronic hunger (see article 25 of the Universal Declaration). Starvation is an extreme form of violation of this right (and is obviously also a violation of the right to life). So we obviously want to know the existence and extent of cases of starvation. There are individual cases of starvation – a elderly person who has lost her mobility and social network may starve abandoned in her flat – but most cases involve large scale famines. Let’s focus on the latter.

The problem is that death by famine or starvation is difficult to identify. People suffering from extreme malnutrition often don’t die of hunger but of diseases provoked by malnutrition, such as pneumonia or diarrhea. Since those are diseases that can have other causes besides malnutrition, it’s often difficult to count the number of people who have died from malnutrition. Their body weight may tell us something, but you can’t go about weighing corpses on a large scale.

Hence it’s difficult to determine whether or not a famine has occurred or is occurring. When does widespread suffering of hunger become a famine? Not every food crisis or widespread occurrence of malnutrition leads to famine-type starvation. A famine is obviously characterized by mortality caused by malnutrition. So we must look at mortality rates, but given the difficulty of establishing whether deaths are caused by malnutrition or other factors, how do we decide that a certain mortality rate is caused by malnutrition and is therefore the symptom of a famine? It’s difficult.

And yet, it’s common to find newspaper reports about “an outbreak of famine” is this or other part of the world. Ideally, we only want to declare a famine when a famine is actually occurring or about to occur. False alarms are not only silly but they create indifference. Fortunately, people seem to have overcome some of the difficulties and have agreed on a non-arbitrary way to determine that there is a famine going on:

• when overall mortality rates in a region are extremely high, or high compared to the baseline – which may itself be high already, perhaps because of a war (a mortality rate of at least two people per 10,000 per day is usually considered part of the evidence of famine conditions)
• when this is combined with survey indicators about low food availability and malnutrition (a rate of malnutrition – ratio of weight to height – among children age six months to five years above an average of 30% is the usual measure here)
• when there is anecdotal evidence (perhaps also from surveys)
• and when there are proxy measures such as below average rainfall

then you can build a useful measurement and a more or less scientific way of ascertaining that a food crisis has passed the famine threshold.

the McAfrica, not one of McDonalds' best decisions

(source, source)

None of this should be understood as implying that food crises which don’t reach the famine threshold are unimportant and don’t deserve attention or assistance. It only means that it’s a good thing to distinguish real famines from lesser crises and to avoid crying wolf.

One problem with the measurement system presented above is that it’s no help in preventing a famine. It’s difficult to turn it into a probability index rather than a threshold index. It tells you when a famine has occurred or is ongoing, not when there’s a risk of famine. When mortality rates are high, you’re already late, perhaps too late.

More posts in this series are here. More on famine here.

Measuring Human Rights (21): Perceptions of Domestic Violence

Lithography of a "Drunk father"

Asking people if they think some types of human rights violations are acceptable is one way to measure levels of respect for human rights. Although not all those who think such violations are acceptable will actually engage in them, it’s clear a certain number of them will; and almost all of them will tolerate violations and fail to report them. Hence, perceptions of acceptability of violations – captured by way of surveys – are a good indication of prevalence of violations, and while they cannot provide exact figures on the numbers of violations, they can yield interesting cross-country comparisons.

Here’s an example about domestic violence:

(source, click image to enlarge)

Domestic violence – which in most cases means men physically harming women (either their wives or daughters) – has a number of human rights implications. It is obviously a violation of the right to bodily integrity, and possibly the right to life. But it also serves to maintain a patriarchal system of gender inequality and discrimination.

Matters are made worse by the reluctance of many governments to interfere in families’ private affairs, perhaps on account of some misunderstood respect for the right to privacy – although the more likely reason is gender prejudice among those who legislate and make policy. For example, most countries have no legislation outlawing marital rape, and those who have often fail to enforce it:

(source, click image to enlarge)

Legislation can apparently make a difference:

(source)

Less people think domestic violence is acceptable in countries that have legislation against it. However, it’s not clear which way the causation goes: a widely shared belief that domestic violence is unacceptable can be caused by legislation, but legislation can also be the effect of beliefs. And if less women report domestic violence in countries that have legislation, it may be due to the fact that legislation deters violence, but it may also be the case that countries that have legislation had a prior culture that is less accepting of domestic violence.

By the way, the numbers of women reporting domestic violence isn’t necessarily a better indicator or measurement basis of domestic violence. After all, when domestic violence is widespread, it will deter reporting. So less reporting of domestic violence can paradoxically indicate a higher prevalence.

More posts about problems with human rights measurement are here.

Statistical Jokes (31): Prosperity Just Around the Corner

Alfred Frueh’s classic, published in The New Yorker 0n 16 January 1932, commenting on President Hoover’s statement that prosperity is just around the corner

More statistical jokes here.

Statistical Jokes (30): No Way to Bias a Coin Flip

(source)

This excerpt from a scientific paper is not a joke, but it’s funny nonetheless, at least to me:

Dice can be loaded — that is, one can easily alter a die so that the probabilities of landing on the six sides are dramatically unequal. However, it is not possible to bias a coin flip — that is, one cannot, for example, weight a coin so that it is substantially more likely to land “heads” than “tails” when flipped and caught in the hand in the usual manner. Coin tosses can be biased only if the coin is allowed to bounce or be spun rather than simply flipped in the air. …

The law of conservation of angular momentum tells us that once the coin is in the air, it spins at a nearly constant rate (slowing down very slightly due to air resistance). At any rate of spin, it spends half the time with heads facing up and half the time with heads facing down, so when it lands, the two sides are equally likely (with minor corrections due to the nonzero thickness of the edge of the coin) … Jaynes (1996) explained why weighting the coin has no effect here (unless, of course, the coin is so light that it floats like a feather): a lopsided coin spins around an axis that passes through its center of gravity, and although the axis does not go through the geometrical center of the coin, there is no difference in the way the biased and symmetric coins spin about their axes. (source)

More on bias here; more statistical jokes here.

Measuring Human Rights (20): What is More Important, the Number or Percentage of People Suffering Human Rights Violations?

I hate mondays, by Banksy

(source, more Banksy here)

Take just one human right, the right not to suffer poverty: if we want to measure progress for this human right, we get something like the following fact:

[N]ever in the world have there been so many paupers as in the present times. But the reason of this is that there have never been so many people around. Indeed never in the history of the world has been the percentage of poor people been so low. (source)

Percentage of world population in extreme poverty as defined by the World Bank ($1.25 per day)

So, is this good news or bad news? If it’s more important to reduce the share of the world population suffering a particular type of rights violation, then this is good news. On the other hand, there are now more people – in absolute, not in relative numbers – suffering from poverty. If we take individuals and the distinctions between persons seriously, we should conclude that this is bad news and we’re doing worse than before.

Thomas Pogge has argued for the latter view. Take another example: killing a given number of people doesn’t become less troubling if the world’s population increases. If we would discover that the real number of the world’s population at the time of the Holocaust was twice as large as previously assumed, that wouldn’t diminish the importance of the Holocaust. What matters is the absolute number of people suffering.

Thomas Pogge

On the other hand, if we see that policies and interventions lead to a significant lowering of the proportion of people in poverty – or suffering from any other type of rights violation – between times t and t+n, then we would welcome that, and we would certainly want to know it. The fact that the denominator – total world population – has increased in the mean time, is probably something that has happened independently of those policies. In the specific case of poverty, a growing population can even make a decrease in relative numbers of people suffering from poverty all the more admirable. After all, many still believe (erroneously) in the Malthusian trap theory, which states that population growth necessarily leads to increases in poverty in absolute numbers.

More posts in this series are here.

Human Rights Maps (132): Democracy, the Difference Between Self-Identification and Reality

First, if you doubt that democracy is a human rights issue, go here. The following map shows the countries of the world that self-identify as a democracy in green, and the tiny minority that doesn’t in red (Vatican, Saudi Arabia, Myanmar, Fiji, Tonga and Brunei):

countries that self-identify as democracies (green) or not (red)

(source, click image to enlarge)

Now, compare this to the latest Freedom House scores, which helpfully but completely coincidentally have the same color codes:

Freedom House scores for 2001 and 2009

(source)

This raises two related questions: why is there a difference between self-identification and reality, and why do countries think it is important to claim that they are democracies, even when the facts clearly belie this claim and the governments making the claim probably know better? Self-delusion can’t be excluded. Some governments probably have an excessively optimistic view of their country’s institutions and achievements. Some may have an excessively minimalistic view of democracy (but then again, Freedom House makes the same mistake…). Some may believe to have the support of the people and think that this is a sufficient condition. Some may hope that claiming the support of the people will allow them to get away with more on the international scene, or to get some beneficial treatment from other countries. And some may hope for a self-fulfilling prophecy effect.

What we can take away from this is that the idea of democracy seems to be very powerful. I just wish it was more than merely the idea that is powerful.

More maps about democracy are here. More about democracy measurement is here. More human rights maps are here.

Human Rights Facts (61): The Inadequacy of the U.S. Poverty Line

I’ve mentioned some of the problems with the U.S. system of poverty measurement before, but this is much more eloquent:

poverty line and basic needs in the US

(source, source; the “savings” requirement covers retirement and emergencies and is included because the study wanted to capture economic stability rather than mere survival, and lifelong economic security rather than day-by-day security, which is quite appropriate given the instability of our economic system)

More on poverty measurement here.

Measuring Human Rights (19): Measuring Racism

This blog contains numerous statements similar to this one:

There are large and important differences between blacks and whites in nearly every facet of life – earnings, unemployment, incarceration, health, and so on. (source)

We can assume that racism and discrimination are at least partially to blame for such discrepancies in life prospects between races, and that these discrepancies are therefore indicators of racism. If we want to measure racism, we’ll look at those discrepancies: are they becoming more important, then there’s more racism, and vice versa.

Of course, there are other explanations for such discrepancies (e.g. the stigma of acting white and the war on drugs) and there are also other, perhaps better indicators of racism.

For an example of other indicators, one can look at the frequency of the use of expressions of racism, such as racial epithets. This is an overview of the use of “nigger” in Google Books (via the Ngram tool):

(click to enlarge)

It seems the epithet is used much less than it used to be, but there’s no real pattern. Why did it increase in the late sixties and why did it go down again in the late seventies? Was that a reaction to the Civil Rights Movement? One can only speculate. And the same is true for the increase during the Great Depression and the decrease following the abolition of slavery. Also, when you use this as a measure of racism, it can only be a measure of overt racism. Political correctness may hide racism.

Another possible measure of racism is the frequency of discussions about racism. Again, an Ngram:

(click to enlarge)

Talk about racism only became common very recently, in the 1970s. Now, is that because there’s more racism since the 1970s? Most likely not. Frequent talk about racism can also be a sign of increased opposition to racism.

Another indicator of racism are the numbers of interracial marriages and interracial dating. If this becomes more common, one can assume that there’s less racism.

Residential or educational segregation may indicate racial animus on the part of people avoiding black neighborhoods or schools, but it may also be merely an economic issue or it may be motivated by worries about the quality of education.

More posts in this series are here.

Statistical Jokes (28): Learning Statistics

Learning Statistics is like taking a Mediterranean cruise.

You gain a new appreciation for the vastness of the world; you meet dozens of new characters, half of them with Greek names; and after 2 hours you feel utterly seasick.

More statistical jokes here.

Measuring Poverty (13): The Inadequacy of the U.S. Poverty Line

I’ve mentioned some of the problems with the U.S. system of poverty measurement before, but this is much more eloquent:

(source, source; the “savings” requirement covers retirement and emergencies and is included because the study wanted to capture economic stability rather than mere survival, as well as lifelong economic security rather than day-by-day security, which is quite appropriate given the instability of our economic system)

More on poverty measurement here.

Measuring Human Rights (18): Guerrilla Polling in Dictatorships

OH ALRIGHT by Roy Lichtenstein

Measuring respect for human rights is most important in societies where respect is a rare commodity. The problem is that it’s not only most important in such societies, but also most difficult. You need a certain level of freedom to measure respect for human rights. And regimes that violate rights also have the means to cover up those violations (see here for example). I’ve called that the catch 22 of rights measurement. One problem is public opinion: a lot of human rights measurement depends on public opinion polls, but such polls are notoriously unreliable in repressive regimes, for obvious reasons: the public in those countries is either misinformed, indoctrinated or afraid to speak out, or all of the above.

Hence, good quality human rights measurement requires some creative polling. Political scientists Angela Hawken and Matt Leighty have come up with a new strategy, called guerrilla polling. Here’s an example:

Kim Eun Ho is a former police officer from North Korea who defected to the South in 2008. … With the aid of a friend and a smuggled cell phone, he is circumventing North Korea’s leadership to solicit opinions from its citizens.

Kim conducts a nightly public-opinion poll of North Korean residents, the first poll of its kind and illegal in North Korea. Here’s how it works: Kim calls his friend in North Korea on a smuggled cell phone. The friend then uses a North Korean land line to call a subject and presses the cell phone against the handset of the landline phone, allowing Kim to conduct a brief interview.

If the interviewee were discovered by the police, they would almost certainly be punished — perhaps severely. To circumvent the North Korean police, Kim has tailored his questions so that they take about 90 seconds to answer. He tapped phones himself as a North Korean police officer, and he estimates that it takes about two to three minutes for the police to trace a call. (source)

More posts about human rights measurement are here.

Related Articles

• Measuring Human Rights (13): When More Means Less and Vice Versa (filipspagnoli.wordpress.com)
• “Guerrilla Polling” in Repressive Societies (volokh.com)
• North Korea’s undercover journalists reveal misery of life in dictatorship (telegraph.co.uk)

Lies, Damned Lies, and Statistics (35): Sample Sizes, Ctd.

cherry picking

(source)

This isn’t the first time I mention sample sizes as a common problem in statistics. Usually, the problem is one of survey design: insufficiently large sample sizes for respondents produce unreliable survey results.

However, the same error – or fraud, when the error is willful – can occur in data interpretation. Take a look at this graph by John Taylor:

(source)

The problem?

Taylor’s conclusion: The data on spending shares show that the most effective way to reduce unemployment is to raise investment as a share of GDP. But why begin the scatter plot in 1990? There’s no good reason. In fact, most folks typically download the entire history of available macro data. … The chart below goes back to 1948:

(source)

This is a form of cherry-picking data that allows you to “prove” a strong correlation where there’s actually none at all. In this way, you’ll find a correlation in almost all data sets, as long as you pick a sufficiently small sample of the set. In this example, you can only limit the selection to the last two decades if you have a good argument about why the economy is different now compared to some decades ago, and why there’s a correlation now when there wasn’t before. However, that argument – which would be interesting – seems to be lacking. And if it’s lacking,  there’s no excuse for cherry picking the last two decades.

Other examples of cherry-picking are here. More posts about lies and errors in statistics are here.

Statistical Jokes (27): Averages

Patient: “Will I survive this risky operation?”

Surgeon: “Yes, I’m absolutely sure that you will survive the operation.”

Patient: “How can you be so sure?”

Surgeon: “9 out of 10 patients die in this operation, and yesterday died my ninth patient.”

More statistical jokes here.

Statistical Jokes (26): Control Variable

the twins from the movie The Shining

(source)

A statistician’s wife had twins. He was delighted. He rang the minister who was also delighted.

“Bring them to church on Sunday and we’ll baptize them,” said the minister.

“No,” replied the statistician. “Baptize one. We’ll keep the other as a control.”

And a bonus joke:

(source)

More statistical jokes here.

Statistical Jokes (25): Dangerous Neighborhood

“Why are you moving? You have arrived to this lovely neighborhood just a few weeks ago.”

“Yes, but I read in the local paper a bit of statistics that said, ‘most auto accidents happen within eight miles of your home’.”

More statistical jokes here.

Measuring Human Rights (17): Human Rights and Progress

We’re all aware of the horrors of recent history. The 20th century doesn’t get a good press. And yet, most of us still think that humanity is, on average, much better off today  than it was some centuries or millennia ago. The holocaust, Rwanda, Hiroshima, AIDS, terrorism etc. don’t seem to have discouraged the idea of human progress in popular imagination. Those have been disasters of biblical proportions, and yet they are seen as temporary lapses, regrettable but exceptional incidents that did not jeopardize the overall positive evolution of mankind. Some go even further and call these events instances of “progressive violence”: disasters so awful that they bring about progress. Hitler was necessary in order to finally make Germany democratic. The Holocaust was necessary to give the Jews their homeland and the world the Universal Declaration. Evil has to become so extreme that it finally convinces humanity that evil should be abolished.

While that is obviously ludicrous, it’s true that there has been progress:

• we did practically abolish slavery
• torture seems to be much less common and much more widely condemned, despite the recent uptick
• poverty is on the retreat
• equality has come within reach for non-whites, women and minorities of different kinds
• there’s a real reduction in violence over the centuries
• war is much less common and much less bloody
• more and more countries are democracies and freedom is much more widespread
• there’s more free speech because censorship is much more difficult now thanks to the internet
• health and labor conditions have improved for large segments of humanity, resulting in booming life expectancy
• etc.

So, for a number of human rights, things seem to be progressing quite a lot. Of course, there are some areas of regress: the war on terror, gendercide, islamism etc. Still, those things don’t seem to be weighty enough to discourage the idea of progress, which is still quite popular. On the other hand, some human rights violations were caused by elements of human progress. The Holocaust, for example, would have been unimaginable outside of our modern industrial society. Hiroshima and Mutually Assured Destruction are other examples. Both nazism and communism are “progressive” philosophies in the sense that they believe that they are working for a better society.

Whatever the philosophical merits of the general idea of progress, progress in the field of respect for human rights boils down to a problem of measurement. How doe we measure the level of respect for the whole of the set of human rights? It’s difficult enough to measure respect for the present time, let alone for previous periods in human history for which data are incomplete or even totally absent. Hence, general talk about progress in the field of human rights is probably impossible. More specific measurements of parts of the system of human rights are more likely to succeed, but only for relatively recent time frames.

Bonus joke:

(source)

More posts in this series are here.

Related Articles

• Human Rights and Utopia (filipspagnoli.wordpress.com)

Statistical Jokes (24): Probability

drawing from a British driving manual

There was this statistics student who, when driving his car, would always accelerate hard before coming to any junction, whizz straight over it , then slow down again once he’d got over it. One day, he took a passenger, who was understandably unnerved by his driving style, and asked him why he went so fast over junctions. The statistics student replied, “Well, statistically speaking, you are far more likely to have an accident at a junction, so I just make sure that I spend less time there.”

More statistical jokes here.

Measuring Human Rights (16): The Right to Healthcare

health check in school

(There’s a more theoretical post here about the reasons why we should call health care a human right. But even if you think those are bad reasons, you may find the following useful).

The right to health care is one of the most difficult rights to measure. You can either try to measure people’s health directly and assume that good health means good health care, or you can measure the provision of health care and assume that there will be good health with a good health care system. Doing the latter means, for example:

• measuring the number of health workers per capita for countries
• measuring the quality of hospitals
• measuring health care spending by governments
• measuring the availability and affordability of health care
• measuring the availability and affordability of health insurance
• etc.

Doing the former means:

• measuring life expectancy
• measuring infant mortality
• measuring maternal mortality
• measuring calorie intake
• measuring the incidence of certain diseases
• measuring the survival rates for certain diseases
• etc.

Needless to say that every single one of these measurements is fraught with problems, although some more so than others. Even if you’re able to have a pretty good measurement for a single indicator for a single country, it may be difficult to compare the measurement across countries. For example, health insurance is organized in so many different ways that it may be impossible to compare the level of insurance across different countries.

But let’s focus on another measure. Life expectancy is often used as a proxy for health. And indeed, when people live longer, on average, we can reasonably assume that they are healthier and that their health care system is better. It’s also something that is relatively easy to measure, compared to other indicators, since even developing countries usually have reasonably good data based on birth and death certificates. And yet, I say “relatively” because there are some conceptual and definitional problems:

• Exceptional events such as a natural disasters or a war can drag down life expectancy numbers, but those events need not influence health in general or the quality of health care.
• Wealthy countries may have more deaths from car accidents than poorer countries, simply because they have more cars. This will pull their relative life expectancy down somewhat, given that younger people are more likely to die in car accidents. And if you use life expectancy to measure health you’ll get a smaller health gap compared to poorer countries than is the case in reality (at least if life expectancy is not corrected for this and if it’s not supplemented with other health indicators).
• How are miscarriages counted? If they are counted as child mortality, they drag down life expectancy rates compared to countries where they are not counted.
• What about countries that have more homicides? Or suicides? Although the latter should arguably count since suicides are often caused by bad mental health. If a country’s life expectancy rate is pulled down by high suicide rates, life expectancy rates are still a good indicator of health and of the quality of health care, assuming that health care can reduce suicide rates and remove, to some extent, the underlying health causes of suicide. However, homicides are different: a country with a very good health care system, a very high level of health and a high murder rate can have its health rating pulled down artificially when only life expectancy is used to measure health.
• Differences in diet and other types of risky behavior should also be excluded when comparing health and life expectancy across countries. It’s wellknown, for instance, that obesity is more of a problem in the U.S. than in many countries that are otherwise comparable to it. Obesity drags down life expectancy and reduces the average level of health, so life expectancy rates which are not corrected for obesity rates are still a good measure for health, but they are not a good measure for the quality of the U.S. health care system. If you want to use life expectancy rates to compare the quality of health care systems you’ll have to correct for obesity rates and perhaps for other types of risky behavior such as smoking or the absence of exercise. Maybe the U.S. health care system, even though it “produces” somewhat lower life expectancy rates than in comparable countries, is actually better than in other countries, yet still not good enough to offset the detrimental effects of high average obesity.

Hence, uncorrected life expectancy rates may not be such a good indicator of national health and of the quality of a national health care system. If we return to the case of the U.S., some of this may explain the strange fact that this country spends a lot more on health and yet has somewhat lower life expectancy rates than comparable countries:

(click image to enlarge)

Or maybe this discrepancy is caused by a combination of some misuse and waste at the spending side – more spending on health doesn’t necessarily result in better health – and some problems or peculiarities with the measurement of life expectancy. Let’s focus on the latter. As stated above, some cultural elements of American society, such as obesity, pull down life expectancy and worsen health outcomes. But there are other peculiarities that also pull down life expectancy, and that have nothing to do with health. I’m thinking of course of the relatively high levels of violence in the U.S. (see here for example). Death by assault is 5 to 10 times higher in the U.S. than in comparable countries (although those numbers tend to go down with the passing of time). This affects younger people more than older people, and when more young people die, life expectancy rates drop sharper than when more old people die.

However, even if you correct U.S. life expectancy rates for this, the rates don’t move up a lot (see here). The reason is that the numbers of deaths caused by homicide pale in comparison to other causes. Obesity levels, for instance, are a more important cause. But correcting life expectancy rates for obesity levels doesn’t seem appropriate, because we want to measure health. If you leave out all reasons for bad health from life expectancy statistics, your life expectancy rates go up, but your average health doesn’t. Obesity isn’t the same as homicide. Correcting life expectancy statistics for non-health related deaths such as homicide makes them a better indicator of health. Removing deaths from obesity doesn’t. If you have life expectancy rates without obesity, they may be a fairer judgment of the health care system but not a fairer judgment of health: a health care system in a country with a lot of obesity may be equally good as the one in another country and yet result in lower life expectancy. The former country does not necessarily have lower life expectancy because of its underperforming health care system – we assumed it’s of the same quality as elsewhere – but because of its culture of obesity.

However, if you really want to judge health care systems, you could argue that countries plagued by obesity should have a better quality system than other countries. They need a better quality system to fight the consequences of obesity and achieve similar life expectancy rates as other countries that don’t need to spend so much to fight obesity. So, life expectancy is then reinstituted as a good measure of health.

More on health, health care, and life expectancy. More posts in this series.

Statistical Jokes (23): Coin Toss

Apple's coin toss application, for all those occasions when a coin is hard to find

A statistics major was completely hung over the day of his final exam. It was a True/False test, so he decided to flip a coin for the answers. The stats professor watched the student the entire two hours as he was flipping the coin…writing the answer…flipping the coin…writing the answer. At the end of the two hours, everyone else had left the final except for the one student. The professor walks up to his desk and interrupts the student, saying:

“Listen, I have seen that you did not study for this statistics test, you didn’t even open the exam. If you are just flipping a coin for your answer, what is taking you so long?”

The student replies bitterly, as he is still flipping the coin: “Shhh! I am checking my answers!”

More statistical jokes here.

A Killer Argument Against the Quantitative Approach to Human Rights?

Joseph Stalin wasn’t a very nice man. Among his lesser sins was his disdain for statistics: “kill a man and it’s a tragedy, kill a million and it’s a statistic”. What he meant of course was not just that it’s a statistic, but also that it’s not very important. Who cares if Stalin or anyone else killed one million, 10 million or 6,321,012? People care about actual persons, not numbers. (Actually, it’s a misquote; he never really said it).

Regular readers of this blog immediately recognize this as a frontal attack on our main project, the quantitative approach to human rights. I believe that it’s very important to have statistics and other quantitative data on human rights violations if we want to measure progress on human rights. In other words, I do care about numbers. We need to know how many people die of hunger, how many live in poverty etc. so that we can assess the quality and impact of our policies.

Now, I have to admit that Stalin was on to something. Numbers don’t carry a lot of meaning and don’t engender empathy. Powerful anecdotes about the fate of individual persons, testimonies and other narratives about concrete cases make it more likely that people start to care. If you tell school children for example that an estimated 850,000 people died during the Rwandan genocide or that less than 20% of China;s citizens now live on less than $1 a day compared to 80% 30 years ago, they will probably register this information, but they will only really start to care about genocide or poverty when they read about stories like this one for example. If you focus on human rights violations as quantities you may end up viewing human beings as quantities as well, and then you lose the motivating power of the individual story. There’s no room for differences between cases if you focus on numbers, and there are no individual and motivating stories without differences between cases.

indistinguishable skulls of victims of the genocide in Rwanda

(source)

The same argument against the abstraction and lack of meaning in numbers can be used against human rights talk in general, and not just quantitative talk. Human rights talk, like number talk, is abstract, devoid of specific personal stories. It’s talk about a biological species and the rights that it has, not about persons. The lists of human rights in treaties and declarations are very general and abstract sentences separated from specific circumstances and people, as they have to be. Human rights make differences between people morally irrelevant, and they have to do so otherwise you end up with privileges instead of human rights. However, we may end up not with the desired equality of rights but with sameness and interchangeable specimens of a biological species. And then we lose the motivating power of very specific and personal stories about suffering and oppression.

The answer to this challenge against number talk and rights talk is obvious, however: one approach doesn’t exclude the other. Numbers and abstractions may not be very motivating but they can help to assess the success of people who are otherwise motivated. And some of us may be motivated by numbers after all.

Statistical Jokes (22): The Original Venn Diagram

More Venn diagrams here. More statistical jokes here.

Measuring Democracy (6): Three Waves of Democratization According to Polity IV and Google Ngrams

(source)

Following Samuel Huntington, many political scientists believe that there have been three waves of democratization in recent history. The first wave of democracy began in the early 19th century when suffrage was gradually extended to disenfranchised groups of citizens. At its peak, however, there were only about 20 democracies in the world during this first wave. After WWI, with the rise of fascism and communism, the wave started to ebb, and this ebb lasted until the end of WWII. The second wave began following the Allied victory in World War II. This wave culminated in the 1960s with around 30 democracies in the world. The third wave started in the 1970s and really took off in the late 1980s, with the democratization of Latin America and the fall of the Berlin Wall. Today there are some 60 democracies in the world.

Maybe recent events in the Maghreb and the Middle East are the start of a fourth wave, now focused on Arab countries.

Those numbers I cited above come from one of the two major democracy indexes, namely Polity IV. Polity IV gives countries a score ranging from -10 to +10; the numbers above are of countries achieving the rather ambitious score of +8 or higher (in other interpretations of the Polity IV score, +6 is already a democracy). Freedom House, the other index, usually gives a higher number of democracies, but is only available for the most recent decades. I don’t want to discuss the relative merits of either measurement system in the current post. Let’s just assume, arguendo, that Polity IV is a good measure (Freedom House probably measures something a bit different). In the graph below, the green line represents the Polity IV score (number of countries with a score of +8 or more):

(source, source; click image to enlarge)

The three waves are clearly visible in the green line. Although some have expressed doubts about the quality of Huntington’s work and the reality of the three waves (see here for instance), there does seem to be at least some truth in the metaphor.

I’ve also included in the graph above the results of a search in Google’s Ngram tool. I searched for “democracy” (blue line) and “democratic” (red line) (democratic without a capital D because I don’t want results including mentions of the Democratic Party). As you may know, this tool allows you to calculate the frequency of keywords in the millions of books available in Google’s book collection. Such frequencies can be thought of as approximations of the general use and popularity of a word at a certain time. One can assume that when there’s a wave of democratization there’s also an uptick in the frequency of the use of word such as “democracy”.

I find it interesting that both the first and the third wave of democratization are reflected in a rising popularity of the words “democracy” and “democratic”, but not the second wave. When the number of democracies was at its lowest point in the 30s and 40s, talk about democracy was most common, more common even than today. And the interest in democracy decreased steadily from the 50s until the 80s, while the number of democracies rose during those decades.

More posts in this series here. Another post on Ngrams is here.

Measuring Human Rights (15): Measuring Segregation Using the Dissimilarity Index

"At the bus station in Durham, North Carolina." May 1940, Jack Delano

If people tend to live, work, eat or go to school together with other members of their group – race, gender etc. – then we shouldn’t automatically assume that this is caused by discrimination, forced separation, restrictions on movement or choice of residence, or other kinds of human rights violations. It can be their free choice. However, if it’s not, then we usually call it segregation and we believe it’s a moral wrong that should be corrected. People have a right to live where they want, go to school where they want, and move freely about (with some restrictions necessary to protect the property rights and the freedom of association of others). If they are prohibited from doing so, either by law (e.g. Jim Crow) or by social pressure (e.g. discrimination by landlords or employers), then government policy and legislation should step in in order to better protect people’s rights. Forced desegregation is then an option, and this can take various forms, such as anti-discrimination legislation in employment and rent, forced integration of schools, busing, zoning laws, subsidized housing etc.

There’s also some room for intervention when segregation is not the result of conscious, unconscious, legal or social discrimination. For example, poor people tend to be segregated in poor districts, not because other people make it impossible for them to live elsewhere but because their poverty condemns them to certain residential areas. The same is true for schooling. In order to avoid poverty traps or membership poverty, it’s better to do something about that as well.

In all such cases, the solution should not necessarily be found in physical desegregation, i.e. forcibly moving people about. Perhaps the underlying causes of segregation, rather than segregation itself, should be tackled. For example, rather than moving poor children to better schools or poor families to better, subsidized housing, perhaps we should focus on their poverty directly.

However, before deciding what to do about segregation, we have to know its extent. Is it a big problem, or a minor one? How does it evolve? Is it getting better? How segregated are residential areas, schools, workplaces etc.? And to what extent is this segregation involuntary? The latter question is a hard one, but the others can be answered. There are several methods for measuring different kinds of segregation. The most popular measure of residential segregation is undoubtedly the so-called index of dissimilarity. If you have a city, for example, that is divide into N districts (or sections, census tracts or whatever), the dissimilarity index measures the percentage of a group’s population that would have to change districts for each district to have the same percentage of that group as the whole city. Formally:

Where bi is the number of blacks in district i, B is the number of blacks in the city as a whole, wi is the number of whites in district i, and W is the number of whites in the city as a whole. This formula gives you a number between 0 and 1, 0 for no segregation and 1 for complete segregation (similar to the Gini coefficient).

The dissimilarity index is not perfect, mainly because it depends on the sometimes arbitrary way in which cities are divided into districts or sections. Which means that modifying city partitions can influence levels of “segregation”, which is not something we want. Take this extreme example:

(source)

The image shows the same city twice, with two different partitions, A and B situation. No one has moved residency between situations A and B, but the district boundaries have been altered radically. In situation A with the districts drawn vertically, there is no segregation (dissimilarity index of 0). But in situation B, with the districts drawn horizontally, there is complete segregation (index = 1), although no one has physically moved. That’s why other, complementary measures are probably necessary for correct information about levels of segregation. Some of those measures are proposed here and here.

More on segregation is here. More posts in this series are here.

Human Rights Maps (118): World Giving Index, a Map of Charity

(source, this is the version of 2010, click image to enlarge)

The World Giving Index offers a view of charitable behavior worldwide reflecting the fact that being charitable is about more than simply giving money. (In case you’re wondering about the link between charity and human rights, go here).

The Index is based on three types of charitable behavior – giving money to an organization, volunteering time to an organization and helping a stranger. The map above reflects the way the world looks based upon the charitable behavior of each country’s population and shows their ranking in the Index. The size of the circle reflects the World Giving Index percentage score and the number is its ranking on the World Giving Index.

The top 21 most generous countries, in order, are:

1. Australia
1. New Zealand
3. Ireland
3. Canada
5. Switzerland
5. United States
7. Netherlands
8. Britain
8. Sri Lanka
10 Austria
11. Lao People’s Democratic Republic
11. Sierra Leone
13. Malta
14. Iceland
14. Turkmenistan
16. Guyana
16. Qatar
18. Hong Kong
18. Germany
18. Denmark
18. Guinea

The incidence of giving money to charity ranges from as low as 4% in Lithuania to as high as 83% in Malta. Incidence of volunteering lies in a range from 2% in Cambodia to 61% in Turkmenistan. Each country has its own way to give. In Liberia, less than one tenth (8%) of the population give money to charity every month. Yet over three-quarters (76%) of Liberians help a stranger every month, more than any other country in the world. Overall, 20% of the world’s population had volunteered time in the month prior to interview, 30% of the world’s population had given money to charity, and 45% of the world’s population had helped a stranger.

Giving money to charity increases with age, largely explained by changes in disposable income. Women are generally more likely to give than men, but only just barely – 30 percent versus 29 percent. Religious affiliation is correlated with higher levels of giving. And within countries, those with higher wealth tend to give relatively less than those with lower wealth, paradoxically.

More on charity and poverty. More human rights maps.

Related Articles

• Canada ranked second most charitable in the world (theglobeandmail.com)
• Ireland comes third in World Giving Index (politics.ie)
• The Most Generous Countries on Earth (economix.blogs.nytimes.com)
• Australia, NZ top ‘giving’ index (bbc.co.uk)
• ‘World Giving Index’ measures charity in 153 nations (theglobeandmail.com)
• “US Fifth Most Giving Country in the World, China 147th” and related posts (tonic.com)