A colleague recently commented that he is confused where I stand with regard to the academic use of MIPEX data. Apparently I have been rather critical and quite enthusiastic about it. I guess this sums it up quite well. I’ve always been critical of the (historical) lack of a theoretical base for the indicators used, and the often uncritical use of the aggregate scores as indicators of ‘immigration policy’ in the literature. I’m enthusiastic about its coverage (compared to other indices), the effort to keep it up-to-date, and the availability of the detailed data.

A few years back, I verified that it is OK to use the MIPEX as a scale (as is often done), highlighting redundancy in the items and that such scales could be improved:

In the context of the SOM project, we have demonstrated that it is feasible to expand the MIPEX indicators back in time. We did so for 7 countries back to 1995. I refined these data by using the qualitative descriptions provided to identify the year of the change, giving year-on-year changes since 1995 for the 7 SOM countries. These data are experimental in that they rely on the documentation and not original research. If that’s not enough, Camilla and I have then created a complete time series of the MIPEX indicators in Switzerland since 1848. This showed that we definitely can go back in time, but also that quite a few of the things MIPEX measures were not regulated a century ago.

Even with the short time in the SOM data, these data are quite insightful:

Later I provided a different approach: re-assembling! The idea is generic and does not apply to the MIPEX alone: make use of the many indicators in the database, but use your own theory to pick and choose the ones you consider most appropriate (rather than be constrained by the presentation in the MIPEX publications). I have demonstrated that the MIPEX data can be used to closely approximate the Koopmans et al. data, but immediately cover a wider range of countries and observe changes over time. Now we can have theory and coverage!

And yes, we can apply these data to gain new insights, like the nature of the politicization of immigrant groups:

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.

New Report on Sans-Papiers in Switzerland

Just a few days ago, a new report on sans-papiers in Switzerland to which I contributed was published. You can read typical news coverage here (in English), but contrary to the “10 facts about Switzerland’s illegal immigrants“, the report tries hard to present a nuanced picture. I’m particularly happy that the press release (to which we did not contribute) includes a bandwidth alongside the “best” estimate. It’s been a struggle at times to convince co-authors and others involved that aggregating expert estimations will never yield a precise number and particularly that we should communicate this uncertainty. Perhaps one day this kind of numbers will be reported using graphical representation.

More disappointing is that the press release compares the current “best” estimate with that a decade ago: with the provision of free movement of persons between Switzerland and countries of the EU a significant group of sans-papiers have become (potentially) legal residents: the comparison is not meaningful.

The report also provides a nuanced portrait of the sans-papier population; their number is just one aspect…

Morlok, Michael, Harald Meier, Andrea Oswald, Denise Efionayi-Mäder, Didier Ruedin, Dina Bader, and Philippe Wanner. 2016. “Sans-Papiers in der Schweiz 2015.” Bern: Staatssekretariats für Migration (SEM). Report available in German and French.