I’m happy to announce that my article on attitudes to immigrants/foreigners in South Africa has finally made it into print. Most of the academic literature on the topics focuses on the Western world; here I show that the same mechanisms seem to apply more generally.
Part of the motivation for this article is quite topical at the moment: the common view in South Africa that we cannot discern patterns in who is more opposed to immigrants, and the view that South Africa is somehow an exceptional case. Another motivation was to test the validity of the work we do on Western countries.
Heerden, Sjoerdje van, and Didier Ruedin. 2019. ‘How Attitudes towards Immigrants Are Shaped by Residential Context: The Role of Neighbourhood Dynamics, Immigrant Visibility, and Areal Attachment’. Urban Studies 56 (2): 317–334. https://doi.org/10.1177/0042098017732692.
Pecoraro, Marco, and Didier Ruedin. 2016. ‘A Foreigner Who Does Not Steal My Job: The Role of Unemployment Risk and Values in Attitudes toward Equal Opportunities’. International Migration Review 50 (3): 628–66. https://doi.org/10.1111/imre.12162.
Pecoraro, Marco, and Didier Ruedin. 2019. ‘Occupational Exposure to Foreigners and Attitudes towards Equal Opportunities’. Migration Studies. https://doi.org/10.1093/migration/mnz006.
Ruedin, Didier. 2019. ‘Attitudes to Immigrants in South Africa: Personality and Vulnerability’. Journal of Ethnic and Migration Studies 43 (7): 1108–26. https://doi.org/10.1080/1369183X.2018.1428086.
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:
I am happy to announce that my paper on attitudes to immigrants in South Africa is now available at the Journal of Ethnic and Migration Studies (JEMS). It all started with a literature on xenophobic violence I could not quite believe. This quote sums it up quite nicely: “All South Africans appear to have the same stereotypical image of Southern Africans.” (Mattes et al. 1999, p.2). It went across what I knew about attitudes to foreigners elsewhere, and crucially I did not come across an explanation why South Africa would be such an exceptional case. Having churned the numbers, I come to quite a different conclusion. Not only are there discernable patterns in South African attitudes to immigrants, but indeed:
When implemented to reflect the specific context, research on attitudes to immigrants appears to generalise to non-Western contexts.
So this paper serves a dual purpose. On the one hand, it shows that what we have learned in Western Europe and North America does indeed seem to apply elsewhere. This is an important test of validity. On the other hand, it presents research on an under-researched country and indeed continent! In a context where xenophobic violence is a recurring phenomenon, I demonstrate that we do not have to tap entirely in the dark.
Supplemental material on OSF, where I also linked a short summary of the research.
An old post of mine on using JFreq and Wordscores in R still gets frequent hits. For some documents, the current version of JFreq doesn’t work as well as the old one (which you can find here [I’m just hosting this, all credit to Will Lowe]). For even longer documents, we have a Python script by Thiago Marzagão archived here (I have never tried this). And then there is quanteda, the new R package that also does Wordscores.
Having said this, a recent working paper by Bastiaan Bruinsma, Kostas Gemenis heavily criticize Wordscores. While their work does not discredit Wordscores as such (merely the quick and easy approach Wordscores advertises — which depending on your view is the essence of Wordscores), I prefer to read it as a call to validating Wordscores before they are applied. After all, in some situations they seems to ‘work’ pretty well, as Laura Morales and I show in our recent paper in Party Politics.
It’s the time of the year I make my students read codebooks (to choose a data set). It often strikes me how complex survey questions can be, especially once we take into account introductions and explanations. The quest is clear: precision, ruling out alternative understandings. Often, these are (or seem to be) the sole tools we have to ensure measurement validity.
Against this background, a paper by Sebastian Lundmark et al. highlights that minimally balanced questions are best for measuring generalized trust: asking whether “most people can be trusted or that you need to be very careful in dealing with people” (fully balanced) is beaten by questions that limit themselves to whether it is “possible to trust people.”
Lundmark, Sebastian, Mikael Gilljam, and Stefan Dahlberg. 2015. ‘Measuring Generalized Trust An Examination of Question Wording and the Number of Scale Points’. Public Opinion Quarterly, October, nfv042. doi:10.1093/poq/nfv042.