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 (14): Numbers of Illegal Immigrants

Calculating a reliable number for a segment of the population that generally wants to hide from officials is very difficult, but it’s politically very important to know more or less how many illegal immigrants there are, and whether their number is increasing or decreasing. There’s a whole lot of populist rhetoric floating around, especially regarding jobs and crime, and passions are often inflamed. Knowing how many illegal immigrants there are – more or less – allows us to quantify the real effects on employment and crime, and to deflate some of the rhetoric.

Immigration is a human rights issue in several respects. Immigration is often a way for people to escape human rights violations (such as poverty or persecution). And upon arrival, immigrants – especially illegal immigrants – often face other human rights violations (invasion of privacy, searches, labor exploitation etc.). The native population may also fear – rightly or wrongly – that the presence of large groups of immigrants will lower their standard of living or threaten their physical security. Illegal immigrants especially are often accused of pulling down wages and labor conditions and of creating native unemployment. If we want to disprove such accusations, we need data on the numbers of immigrants.

So how do we count the number of illegal immigrants? Obviously there’s nothing in census data. The Census Bureau doesn’t ask people about their immigration status, in part because such questions may drive down overall response rates. Maybe in some cases the census data of other countries can help. Other countries may ask their residents how many family members have gone abroad to find a job.

Another possible source are the numbers of births included in hospital data. If you assume a certain number of births per resident, and compare that to the total number of births, you may be able to deduce the number of births among illegal immigrants (disparagingly called “anchor babies“), which in turn may give you an idea about the total number of illegal immigrants.

Fluctuations in the amounts of remittances – money sent back home by immigrants – may also indicate trends in illegal immigration, although remittances are of course sent by both legal and illegal immigrants. Furthermore, it’s not because remittances go down that immigrants leave. It might just be a temporary drop following an economic recession, and immigrants decide to sweat it out (possibly supported by reverse remittances for the time of the recession). Conversely, an increase in remittances may simply reflect technological improvements in international payment systems.

Perhaps a better indicator are the numbers of apprehensions by border-patrol units. However, fluctuations in these numbers may not be due to fluctuations in immigration. Better or worse performance by border-patrol officers or tighter border security may be the real reasons.

So, it’s really not easy to count illegal immigrants, and that means that all rhetoric about illegal immigration – both positive and negative – should be taken with a grain of salt.

More posts on this series are here.

Measuring Poverty (6): The Poverty Line in the U.S.

The poverty rate or poverty line in the U.S. is based on a system pioneered by Mollie Orshansky in 1963. In the 1960s, the average US family spend one third of its income on food. The poverty line was calculated by valuing an “emergency food” budget for a family, and then multiplying that number by 3. (Some more data here).

This results in a specific dollar amount that varies by family size but is the same across the U.S. (the amounts are adjusted for inflation annually). To determine who is poor, actual family income is then compared to these amounts. Obviously, if you’re under, you’re poor.

Amazingly, this system hasn’t changed a lot since the 1960s, yet it suffers from a series of measurement problems, resulting in either an over- or underestimation of the number of families living in poverty. The problems are situated both in the calculation of the poverty rates and in the calculation of the income that is subsequently compared to the rates:

  • Obviously, the system should take regional differences in the cost of living, especially in housing, into account. It doesn’t.
  • As already apparent from the image above, a family today spends relatively less on food and more on housing, health care and child care etc. yet the poverty line is still dollars for emergency food times 3. So the question is: should the system take today’s spending patterns into account? We would have to know which it is: 1) Either the increased spending on non-food items has occurred because people can now afford to spend more on such items. 2) Or the increased spending on non-food items has occurred because these items got disproportionately more expensive (housing for instance) or because there wasn’t really any need to buy those items in the old days. Only if 2) is the case should that have an influence on the poverty line. And I think that to some extent it is the case. Child care for instance has become a necessity. In the 1960s, many mothers didn’t go out and work. Now they do, and therefore they have to pay for child care. Those payments should be deducted from income when measuring disposable income and comparing it to the poverty line. The same is true for cars or phones. Today you can’t really have a job without them so they’re no longer luxuries. A society would show very little ambition if it continued to designate the poor as those who have to wash by hand, read with candlelight, and shit in a hole in the floor. In fact, what I’m advocating here is some kind of relative concept of poverty. I’ll come back to that later. All I can tell you now is that this isn’t without complications either.
  • The current poverty measurement doesn’t take into account disproportionate price rises (it merely adjusts for general inflation) and changing needs. An obvious improvement of the U.S. measurement system would be to adjust for exceptional price evolutions (such as for housing) and also to revisit the definitions of basic needs and luxuries. Hence, a better poverty measurement should subtract from income some work-related expenses, child care expenses, and perhaps also some health expenses to the extent that these have become disproportionately more expensive. But that’s not easy:

There is considerable disagreement on the best way to incorporate medical care in a measure of poverty, even though medical costs have great implications for poverty rates. But costs differ greatly depending upon personal health, preferences, and age, and family costs may be very different from year to year, making it hard to determine what exactly should be counted. Subtracting out-of-pocket costs from income is one imperfect approach, but if someone’s expenses are low because they are denied care, then they would usually be considered worse off, not better off. (source)

  • Another problem: the current poverty rate doesn’t take all welfare benefits into account. Income from cash welfare programs counts, but the value of non-cash benefits such as food stamps, school lunches and public housing doesn’t (because such benefits weren’t very common in the 1960s). Those benefits successfully raise the standard of living for poverty stricken individuals. There’s a bit of circular reasoning going on here, because the poverty rate is used, i.a., to decide who gets benefits, so benefits should not be included. But if you want to know how many people are actually poor, you should consider benefits as well because benefits lift many out of poverty.
  • The poverty measure doesn’t include some forms of interests on savings or property such as housing.
  • The poverty measure doesn’t take taxes into account, largely because they didn’t affect the poor very much in the 1960s. Income is counted before subtracting payroll, income, and other taxes, overstating income for some families. On the other hand, the federal Earned Income Tax Credit isn’t counted either, underestimating income for other families.
  • And there’s also a problem counting the effects of cohabitation and co-residency, overestimating poverty because overestimating expenses.

Because the poverty measurement disregards non-cash benefits and certain tax credits, it fails to serve its purpose. Poverty measurement is done in order to measure progress and to look at the effects of anti-poverty policies. Two of those policies – non-cash benefits and certain tax credits – aren’t counted, even though they reduce poverty. So we have a poverty statistic that can’t measure the impact of anti-poverty policy… That’s like measuring road safety without looking at the number of accidents avoided by government investment in safety. Since the 1970s, the U.S. government implemented a number of policies that increased spending for the poor, but the effects of this spending were invisible in the poverty statistics.

This had a perverse effect: certain politicians now found it easy to claim that spending on the poor was ineffective and a waste of money. It’s no coincidence that trickle down economics became so popular in the 1980s. The poverty measurement, rather than helping the government become more effective in its struggle against poverty, has led to policies that reduced benefits. Of course, I’m not saying that poverty reduction is just a matter of government benefits, or that benefits can’t have adverse effects. Read more here.

Fortunately, the US Census Bureau has taking these criticism to heart and has been working on an alternative measure that counts food stamps and other government support as income, while also accounting for child-care costs, geographic difference etc. First results show that the number of poor is higher according to the new measurement system (it adds about 3 million people). For some reason, I think the old system has still some life in it.

Some details of the new measurement:

when you account for the Earned Income Tax Credit the poverty rate goes down by two points. Accounting for SNAP (food stamps) lowers the poverty rate about 1.5 points. … when you account for the rise in Medical Out of Pocket costs, the poverty rate goes up by more than three points. (source)

More posts about problems with poverty measurement are here.