catch up on YouTube
Let’s stop calling them ‘second generation’ immigrants
It’s common to differentiate between ‘first generation’ immigrants (i.e. people who moved to live in a different country), and ‘second generation’ immigrants (i.e. their descendants). It might look like a systematic (hence scientific?) approach, but it’s not appropriate. Typically, we use the term generation like this: “The bakery is owned and operated by fifth-generation baker Sylvain Chaillout and his parents.” Here we have baker after baker, and supposedly this makes Sylvain Chaillout (the firth-generation baker) more of a baker than any Johnny-come-lately baker. Contrast this with the ‘second generation’ immigrant, a person who is by most people’s definition not an immigrant him or herself, and if anything less of an immigrant than any Johnny-come-lately immigrant who has just arrived.
Image: cc-by-nc-nc open-arms
Schrödinger’s Immigrant
Currently the notion of Schrödinger’s immigrant is going around the internet (again). Referring to Schrödinger’s cat, a Schrödinger immigrant is one lazing around on (undeserved) social benefits, while simultaneously stealing your job. So far, so funny: It’s a good laugh at the UKIP and other parties politicizing against immigration.
The joke — Schrödinger’s immigrant — only works, however, because of outgroup homogeneity: the tendency to regard out-groups as homogeneous (while drawing fine distinction within the in-group). It only works, because it refers to a single immigrant, collapsing all immigrants into a single type. Obviously with two or more immigrants the ‘paradox’ is readily resolved. Put differently, the joke is only funny if we fall into the same trap as those we are laughing at.
With such simplified thinking, however, we miss the opportunity to use proper evidence to counter the overblown fears some members of society seem to have, but also the opportunity to take these fears seriously, and acknowledging that there are different means of competing with immigrants so that some individuals may be affected by the arrival of immigrants more than others.
Image credit: Modified from https://flic.kr/p/dK7dSa CC-by-sa by Joe Szilagyi
Use Interpolated Median Values to Measure Brain Waste
For a while now, I have been coordinating an IMISCOE research group on brain waste with Marco Pecoraro. Brain waste — not my choice of term — is the underutilization of education and skills in their country of destination, a specific form of educational mismatch also referred to as over-education, over-qualification, over-schooling. The stereotypical case is an immigrant scientist working as a taxi driver.
One way to enumerate brain waste is to look at the average educational or skills level in a specific occupation or occupational group, and then check whether an individual has higher or lower levels of education or skills. That’s quite neat, until it comes to choosing the average. Typically we measure skills and education using ordered scales, and depending on the researcher the mean, median, or mode is used (or sometimes a mix of them). None of them is really appropriate, but with interpolated median values, there is a more appropriate measure out there.
Interpolated median values are generally the most adequate measure of central tendency when there is a limited number of response categories, such as Likert scales or the level of education. To calculate interpolated median values, each response category is understood as a range with width w, and within the median response, linear interpolation is used. In principle, we could estimate any quantile, but we’re interested in the median (q=0.5).
In addition, when comparing groups rather than individuals (which is what we typically do), superimposed kernel distributions would be quite helpful: once for the majority population, and once for the immigrant group studied. The interpolated median could readily be added to give a good sense of how much of a difference there is between the groups in substantive terms.
Now, if you were thinking that the measure of central tendency does not matter, here’s a bunch of distributions (as histograms because of the small number of observations in these examples, say of levels of education), along with their mean (blue line), median (red line), and interpolated median (dashed black line). We can see that in some configurations the choice of central tendency makes no difference at all, in others there is a small difference, and in others still the differences are substantive. It’s these substantive differences we should be worried about.
While I’m at it, here are some other challenges to enumerating brain waste. Typically we do not (attempt to) adjust for quality differences of education, but take diplomas at their face value. Differences in quality may occur across countries, but also within countries across universities etc. Typically we do not distinguish between over-skilled and over-educated, even though conceptually the two are different. Here the lack of adequate questions in the data is a major limitation. Finally, we often should also consider the counterfactual: Would this over-qualified immigrant have been able to realize their potential in the country of origin (or elsewhere)? While being over-qualified is generally a problem, for the individual in question it may still be the ‘optimal’ outcome.
No, nationality is not a mechanism
This post might serve as a reminder to myself and others doing research on immigrants and their descendent that nationality is not a mechanism. Put differently, if you discover that people with nationality A differ from people with nationality B in a given characteristic, you have not explained anything at all.
It feels rather obvious when put this way, but it’s usually harder when it comes to multiple regression models. So often we throw in a control variable like “foreign national” or “foreign born” without thinking why we do so, what alternative explanation we think we are capturing. Obviously, a person’s passport or place of birth is used as a shorthand or proxy of something else, but what exactly?
Let’s consider the commonly used variables of migration background or migration origin. Short of calling a particular section of society different in essence (which we probably don’t want to), there are a range of concepts we might be trying to capture, like the experience of (racial) discrimination, having a different skin colour, having a different religion, holding different values, having poor language skills, being of the working class, having additional cultural perspectives and experiences, transnational ties, or a combination of these.
Knowing what we’re after is essential for understanding. Sometimes it is necessary to use proxies like immigrant origin, but we need to specify the mechanism we’re trying to capture. Depending on the mechanism, who should be counted as of immigrant origin, for example, can be quite different, especially when it comes to children of immigrants, individuals of “mixed” background, and naturalized individuals. Having poor language skills, for example, is something most likely to affect (first generation) immigrants; but likely experience of racial discrimination is probably not disappearing just because it was my grandparents rather than me who came to this country.
Didier Ruedin 