Theories of discrimination

Whether you’re new to research on (ethnic) discrimination or have a couple of studies under your belt, you could do worse than reading Lauren Rivera‘s review article on employer decision-making.

While I’m not entirely convinced about the ostensible lack of studies on employers, the review really shines on summarizing different reasons for discrimination. Oddly enough, most of the article s I come across seem to highlight the distinction between taste-based discrimination and statistical discrimination (yes, our meta-analysis included), and largely neglect other theories. Lauren Rivera solves this by zooming out and drawing a distinction between competency-based, status-based, and social closure–based approaches. Much of the literature focuses on competency-based arguments, in which employers are almost absent other than picking the ‘best fit’ or the ‘most competent’.

In the review, Lauren Rivera highlights important differences within these three broad categories. For instance, within competency-based approaches, we have:

  • human capital theory — disparities are the result of skills and educational mismatches
  • signalling theory — disparities stem from not using the right signals of competence
  • social capital — disparities because workers do not have the same networks and ties to organizations that are valued by the companies
  • statistical discrimination — perceptions or actual average group performance is taken as a proxy, leading to disparities

We also have status-based approaches where disparities are the result of implicit or explicit views of the characteristics of groups, like warmth or ‘worth’. I guess we could speak of stereotypes here, but perhaps this is not helpful. Whatever we call these perceptions, they act as filters that disadvantage groups with low status. In this context, I missed a clearer position on the fact that some descriptions of ‘statistical discrimination’ in the literature are rather inaccurate.

There are also theories drawing on social closure. These include the ‘taste-based’ (overt or covert) dislike of minority groups. While this theory is very often invoked in publications, I have not come across many contributions that make explicit the link to social closure. This link is useful, because it allows us to see other approaches drawing on opportunity hoarding and preservation (these arguments seem more common in the literature on anti-minority attitudes than on discrimination): the desire to keep existing privileges and resources for the in-group, so employers exclude members of minority groups.

I liked the part on emotions, and decisions by ‘similarity’ in the review, but missed a more explicit link to the comparison of theories in table 1.

Rivera, Lauren A. 2020. ‘Employer Decision Making’. Annual Review of Sociology 46 (1): 215–32.

Zschirnt, Eva, and Didier Ruedin. 2016. ‘Ethnic Discrimination in Hiring Decisions: A Meta-Analysis of Correspondence Tests 1990–2015’. Journal of Ethnic and Migration Studies 42 (7): 1115–34.

We have no idea — same analysis, different results

In a recent paper, Akira Igarashi and James Laurence look at anti-immigrant attitudes in the UK and Japan. Like my 2019 paper in JEMS, they and how they highlight the limited research on non-Western countries, but they analysis they do is much more similar to what Sjoerdje van Heerden and I did in Urban Studies. Them like us relied on panel data to get a better handle on changing attitudes to immigrants. Them like us looked at the share of foreigners in the area (this relates to theoretical expectations that individual attitudes to immigrants reflect changes in the share of foreigners in the area; we refer to the same theories). We both used fixed-effect panel models. They find that “increasing immigration harms attitudes towards immigrant”, while we report that “a larger change in the proportion of immigrant residents is associated with more positive views on immigrants among natives” — yes, the exact opposite!

Need another example? Several studies examine the impact of sudden exposure to refugees on attitudes to immigrants and votes for radical-right parties. Such sudden exposure happened for example in Austria and Germany in 2015. In separate analyses, Andreas Steinmayr 2020 finds a clear increase in support for the radical-right, as we find in the work by Lukas Rudolph and Markus Wagner. Max Schaub, Johanna Gereke and Delia Baldassarri, by contrast “record null effects for all outcomes”. Same situation, same strategy to obtain the results.

We could now start the detective work, examining the small differences in modelling, ponder about the impact of how we define neighbourhoods, invoke possible differences between the countries (are the Netherlands an expectation, when the UK and Japan yield the same results? — not likely). Or we could admit how little we know, how much uncertainty there is in what we do, how vague our theories are in the social sciences that we can come to quite different conclusions in quite similar papers. I guess what we can see here is simply a scientific search for answers (it’s not like our research output would otherwise disagree so clearly). It’s probably also a call for more meta-level research: systematic analyses that synthesize what we do and don’t know, because even though individual papers sometimes contradict, we know quite a lot!

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): 317–34.

Igarashi, Akira, and James Laurence. 2021. ‘How Does Immigration Affect Anti-Immigrant Sentiment, and Who Is Affected Most? A Longitudinal Analysis of the UK and Japan Cases’. Comparative Migration Studies 9 (1): 24.

Rudolph, Lukas, and Markus Wagner. 2021. ‘Europe’s Migration Crisis: Local Contact and Out‐group Hostility’. European Journal of Political Research, May, 1475-6765.12455.

Ruedin, Didier. 2019. ‘Attitudes to Immigrants in South Africa: Personality and Vulnerability’. Journal of Ethnic and Migration Studies 45 (7): 1108–26.

Schaub, Max, Johanna Gereke, and Delia Baldassarri. 2020. ‘Strangers in Hostile Lands: Exposure to Refugees and Right-Wing Support in Germany’s Eastern Regions’. Comparative Political Studies, September, 001041402095767.

Steinmayr, Andreas. 2020. ‘Contact versus Exposure: Refugee Presence and Voting for the Far-Right’. The Review of Economics and Statistics, May, 1–47.

Zschirnt, Eva, and Didier Ruedin. 2016. ‘Ethnic Discrimination in Hiring Decisions: A Meta-Analysis of Correspondence Tests 1990–2015’. Journal of Ethnic and Migration Studies 42 (7): 1115–34.

Out now: Politicising Immigration in Times of Crisis — or how to measure the impact of a crisis when we don’t agree when the crisis was

I’m happy to announce a new publication in JEMS on politicizing immigration in times of crisis. Especially so, as it is the ‘first one’ for two of my excellent co-authors!

The basic setup is quite simple, we look at data on the politicization of immigration — our update on the SOM project. It’s a broad understanding of politicization, looking at how different actors (broadly defined) talk about immigration and immigrant integration. We use claims-analysis using printed newspapers as the basis, which allows us to compare the situation over time. We then examine how the nature of politicization differs during times of crisis compared to non-crisis periods.

We have N=2,853 claims to examine, the oil crisis of the 1970s and the financial crisis of the late 2000s as two external crises not directly related to immigration. Theoretical considerations provide us with expectations of how claims-making during periods of crisis differs qualitatively: we look at salience (how many claims are made), polarization (the positions taken in claims), actor diversity (who makes the claims), and frames (how claims are justified).

And then you sit down to define the crisis periods… we started with discussions in the team, soon realizing that we don’t agree. Then we went to the literature, trying to find a more authoritative definition of when these crises started and ended. And then we fully embraced uncertainty: basically there is no agreement on when these crises stared or ended. The solution is also relatively simple: we just used all the possible definitions (a bit of combinatorics there…) and run separate regression models. 7,524 of them to be precise. The nice thing with that is that you really have to embrace uncertainty, and that graphs really are more intuitive than any arbitrary measure of central tendency.

Yes, you get things that are fairly obvious (we can quibble about effect size):

Sample effect size; grey dashed line on right indicates zero; blue dashed line on the left indicates the median coefficient across all the regression models.

and you get things that are simply unclear, with values around zero quite credible, but would you bet against en effect size of +0.05 or -0.05?

Sample effect size; grey dashed line on left indicates zero; blue dashed line on the right indicates the median coefficient across all the regression models.

What I really like about this kind of presentation is that it naturally embraces our uncertainty about the state of things. Yes, “crisis” is vague as a concept, yes, it is difficult to operationalize it (otherwise we would not run 7,524 regression models), but we still can discern systematic patterns of how the politicization of migration in times of crisis differs from non-crisis moments.

Bitschnau, Marco, Leslie Ader, Didier Ruedin, and Gianni D’Amato. 2021. “Politicising Immigration in Times of Crisis: Empirical Evidence from Switzerland.” Journal of Ethnic and Migration Studies. Online First. doi: 10.1080/1369183X.2021.1936471. [ Open Access]

The impact of Covid-19 on Migration and Transnationalism

Roxanne Gerber and Philippe Wanner have nicely summarized the impact of the first wave of Covid-19 on the Swiss migrant population.

  • effect on labour-market participation similar to general population
  • greater difficulties by entrepreneurs and self-employed
  • greater impact on low-skilled workers
  • international mobility (unsurprisingly) down a great deal — more than twice as many as in 2018 never (could) visit their country of origin