How to Measure the Integration (of Immigrants)

Just back from the annual IMISCOE conference, I was struck once again how often we talk about (civic) integration (of immigrants) without a clear notion what we actually mean by it. What is more, it’s become a mantra to insist on integration being a two-way process, while this is not a logical necessity. A while ago, I have written up my position in a COMPAS working paper.

I argue that integration should be understood as proximity, and suggest that we can talk about individuals being integrated as well as groups being integrated. An individual or group is considered integrated if it cannot be distinguished in relevant dimensions (the working paper is full of graphs to illustrate the argument). This is equivalent to saying that they are assimilated in relevant dimensions.

It is possible to use standard statistical methods to determine integration: it’s a matter of determining whether two groups differ in relevant dimensions, or whether the position of an individual is within the typical range of values.

By drawing a distinction between individuals and groups, we can have integrated individuals who belong to groups that are not integrated, and groups that are integrated as a whole, while some of their members are not.

Where the working paper ends is the political question: what dimensions are relevant? To answer this question, it would be necessary to map out specific visions of the society we aspire. Clearly there’s no single (objective) answer to that one.

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.

More theory to make social sciences more interesting

Richard Swedberg urges us to theorize more to make social sciences more interesting. His recent article in BJS summarizes Swedberg’s 2014 book in a short and accessible manner. While we’re more used to seeing the article first followed by a longer book, I’m happy to see this article as Swedberg’s message deserves to be heard. Contrary to what I chose as the title of this post, Swedberg actually doesn’t call for more theory as such, but for more of the right kind of theory. Good theory isn’t abstract and empirically irrelevant (i.e. much of what passes as ‘theory’ today). Interestingly Swedberg focuses on observation and creativity, and not formal modelling as it is done in economics (which he regards as mechanistic).

Swedberg, Richard. 2016. ‘Before Theory Comes Theorizing or How to Make Social Science More Interesting’. The British Journal of Sociology, February. doi:10.1111/1468-4446.12184.

Swedberg, Richard. 2014. The Art of Social Theory. Princeton: Princeton University Press.

MIPEX Results for Switzerland

I have been writing about MIPEX quite a bit recently, not least since we published how the MIPEX scores changed over time since 1848. The latest (official) MIPEX results for Switzerland are now out (along with a new look), although it’ll be a while until all country scores are available.

New is the addition of “health”, a policy area we didn’t consider in the SPSR article. It happens to be an area where Switzerland does comparatively well — also an area where there was a great deal of effort in the past few years. The way new policy areas can be added to the MIPEX on a whim illustrates that the overall score should always be treated with a grain of salt. On the other hand, more indicators are great news for those willing to spend a little bit of time to re-combine the individual items into theoretically sound combinations, as I suggest we should do more often.

MIPEX as Measure of Citizenship Models

MIPEX are currently launching their latest release (with a shiny new website), and their data are often used in academic research. Earlier I have shown that the MIPEX can indeed be used as scales — as it is often done –, although there is scope for improving these scales. Put differently, from a statistical point of view, the dimensions and sub-dimensions in the MIPEX data are not optimal. There are two approaches to this: First, we can reduce the data complexity by removing items that are not strongly associated. Second, we can use the redundancy in the data, and pick and mix the data.

In a paper just published in the SSQ, I demonstrate this by recombining bits and pieces of the MIPEX to create citizenship scores that closely match those in Koopmans et al. On the one hand, this is a demonstration that we can easily create more valid constructs when recombining existing data sources like the MIPEX. On the other hand, I have gained classifications of citizenship models in many more countries than previous endeavours — with less effort. As a side product, I can validate the citizenship typology presented by Koopmans et al. by showing the existence of ethnic-pluralistic citizenship models (segregationism), previously only predicted on a theoretical basis.

Koopmans, Ruud, Paul Statham, Marco Giugni, and Florence Passy. 2005. Contested Citizenship: Immigration and Cultural Diversity in Europe. Minneapolis: Minnesota University Press.
Ruedin, Didier. 2015. “Increasing Validity by Recombining Existing Indices: MIPEX as a Measure of Citizenship Models.” Social Science Quarterly. doi:10.1111/ssqu.12162.