Skill Specificity and Attitudes toward Immigration

I am very happy to announce a second paper published from our SNIS project on attitudes to immigrants: “Skill Specificity and Attitudes toward Immigration” by Sergi Pardos-Prado and Carla Xena out now in AJPS. It develops some of the key tenets of the SNIS project to new levels and provides a clean application.

Similar to what Marco Pecoraro concluded when looking at the risk of unemployment, Sergi and Carla come to the conclusion that economic competition theories cannot be dismissed. Here they focus on skills specificity and the ability to avoid competition with immigrant workers, and highlight that highly educated people are not immune to anti-immigrant attitudes.

Pardos‐Prado, S., & Xena, C. (2018.). Skill Specificity and Attitudes toward Immigration. American Journal of Political Science, Online First. https://doi.org/10.1111/ajps.12406
Pecoraro, M., & Ruedin, D. (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), 628–666. https://doi.org/10.1111/imre.12162

Most important academic skill? English!

The other day I was reviewing a paper that looked quite interesting, but unfortunately was written in such poor English that I could not really understand what was going on. I felt sorry for the author(s). I then recalled a recent discussion with a colleague of mine about how important so-called transferable skills are for students: We know that most of them won’t end up in academia, so stuff like critical thinking, structuring an argument, or reading a regression table a are pretty important. Among these, coherent and comprehensible English must rank very high. For those who stay in academia, I’d argue that it’s the most important skill, because it’s central to communicating with other researchers and having your work understood. Only this way can others build on what we do. Ironically, however, teaching English is typically not a focus at universities, if it is done at all. Like so many things, we just kind of assume students (have to figure out how to) do it.

Image: CC-by-nc Moiggi Interactive

Education and Attitudes — Not So Fast

It’s a very common finding that people with higher levels of formal education are less prejudiced — as captured by their answers in surveys. So common is this assumption that it has almost become an informal benchmark for studies on attitudes to immigrants and other minority groups. If you don’t find an association between levels of formal education and lower prejudice, some will doubt your data or analysis.

There are, however, reasons to doubt that this association is the end of the story. First of all, levels of formal education are not a well specified mechanism. In our IMR article, Marco Pecoraro and I write:

While an association between low levels of education and negative sentiments toward immigrants can be found across countries, the underlying mechanism remains poorly specified.

Most studies use education as a proxy of skills levels, which is a very narrow definition of human capital when we consider the possible competition between citizens and foreigners in the labour market. In our study, we addressed this by using skill levels:

Using these more sophisticated measures of exposure to market competition, we find no evidence that – once values and beliefs are accounted for – workers with low levels of education a priori have more negative attitudes toward foreigners than those with upper secondary education.

A simple reason may be that levels of formal education capture social desirability. An (2014) argues that the relationship between education and attitudes/prejudice is largely driven by social desirability, but others contest this (e.g. Ostapczuk et al. 2009).

There have long been reasons to think more carefully about the relationship between education and prejudice — beyond the lack of a clearly specified mechanism. For instance, Jackman (1978) showed that higher education is associated with being more tolerant on an abstract index, but not on an applied index. Studying support for minority rights in referendums and popular initiatives, Vatter et al. (2014) highlight that the ‘effect’ of education depends on the minority group considered. Similarly, Bansak et al. (2016) show that preferences for certain groups of asylum seekers are same across levels of education. A quite different challenge came from Weil (1985) who showed that the association is weaker or altogether absent in non-liberal countries — if we want to speak of an ‘effect’, it does not appear to be universal. That’s exactly what we have seen in recent years as prejudice and attitudes to minorities are increasingly studied outside Western Europe and North America.

For instance, Bahry (2016) did not find a clear association between levels of formal education and negative attitudes to foreigners in Russia (but Bessudov 2016 did). Diop et al. studied the situation in Qatar and found no association to speak of; Barceló 2016 reports no clear association in Asia. Gordon (2016) reports that higher levels of education mean being less stereotyped in South Africa, but that there is no difference in the opposition to refugees; while Gordon (2015) highlights that xenophobia crosses the class divide in South Africa. Kunovich (2004) finds weaker ‘effects’ of education in Eastern Europe compared to Western Europe, while Dennison & Talò (2017) find no direct ‘effect’ in France — right in Western Europe.

One interpretation of education affecting attitudes to foreigners is the liberalizing effect of education. Most studies use cross-sectional data, so they are in a poor situation to test this. Hello (2002) cast some doubt on this interpretation by showing that the ‘effect’ of education seems to vary across countries. More directly, however, Lancee & Sarrasin (2015) used panel data to follow individuals through education, and they find ‘no effect’ when only modelling within-subject variation: Attitudes change little through education.

So we’ve certainly not seen the end of the story yet.

References

An, Brian P. 2015. ‘The Role of Social Desirability Bias and Racial/Ethnic Composition on the Relation between Education and Attitude toward Immigration Restrictionism’. The Social Science Journal 52 (4): 459–67. doi:10.1016/j.soscij.2014.09.005.

Bahry, Donna. 2016. ‘Opposition to Immigration, Economic Insecurity and Individual Values: Evidence from Russia’. Europe-Asia Studies 68 (5): 893–916. doi:10.1080/09668136.2016.1178710.

Bansak, Kirk, Jens Hainmueller, and Dominik Hangartner. 2016. ‘How Economic, Humanitarian, and Religious Concerns Shape European Attitudes toward Asylum Seekers’. Science 354 (6309): 217–22. doi:10.1126/science.aag2147.

Barceló, Joan. 2016. ‘Attitudes toward Immigrants and Immigration Policy in Asia and the Pacific: A Quantitative Assessment of Current Theoretical Models beyond Western Countries’. Asian Journal of Political Science 24 (1): 87–123. doi:10.1080/02185377.2015.1136228.

Bessudnov, Alexey. 2016. ‘Ethnic Hierarchy and Public Attitudes towards Immigrants in Russia’. European Sociological Review 32 (5): 567–80. doi:10.1093/esr/jcw002.

Dennison, James, and Teresa Talò. 2017. ‘Explaining Attitudes to Immigration in France’. Working Paper. http://cadmus.eui.eu//handle/1814/46245.

Diop, Abdoulaye, Yaojun Li, Majed Mohammmed H. A. Al-Ansari, and Kien T. Le. 2017. ‘Social Capital and Citizens’ Attitudes towards Migrant Workers’. Social Inclusion 5 (1): 66–79. doi:10.17645/si.v5i1.798.

Gordon, Steven Lawrence. 2015. ‘Xenophobia across the Class Divide: South African Attitudes towards Foreigners 2003–2012’. Journal of Contemporary African Studies 33 (4): 494–509. doi:10.1080/02589001.2015.1122870.

———. 2016. ‘Welcoming Refugees in the Rainbow Nation: Contemporary Attitudes towards Refugees in South Africa’. African Geographical Review 35 (1): 1–17. doi:10.1080/19376812.2014.933705.

Hello, Evelyn, Peer Scheepers, and Merove Gijsberts. 2002. ‘Education and Ethnic Prejudice in Europe: Explanations for Cross-National Variances in the Educational Effect on Ethnic Prejudice’. Scandinavian Journal of Educational Research 46 (1): 5–24.

Jackman, Mary R. 1978. ‘General and Applied Tolerance: Does Education Increase Commitment to Racial Integration?’ American Journal of Political Science 22 (2): 302–324.

Kunovich, Robert M. 2004. ‘Social Structural Position and Prejudice: An Exploration of Cross-National Differences in Regression Slopes’. Social Science Research 33 (1): 20–44. doi:10.1016/S0049-089X(03)00037-1.

Lancee, Bram, and Oriane Sarrasin. 2015. ‘Educated Preferences or Selection Effects? A Longitudinal Analysis of the Impact of Educational Attainment on Attitudes Towards Immigrants’. European Sociological Review, March, jcv008. doi:10.1093/esr/jcv008.

Ostapczuk, Martin, Jochen Musch, and Morten Moshagen. 2009. ‘A Randomized-Response Investigation of the Education Effect in Attitudes towards Foreigners’. European Journal of Social Psychology 39 (6): 920–931.

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. doi:10.1111/imre.12162.

Vatter, Adrian, Isabelle Stadelmann-Steffen, and Deniz Danaci. 2014. ‘Who Supports Minority Rights in Popular Votes? Empirical Evidence from Switzerland’. Electoral Studies 36 (December): 1–14. doi:10.1016/j.electstud.2014.06.008.

Weil, Frederick D. 1985. ‘The Variable Effects of Education on Liberal Attitudes: A Comparative- Historical Analysis of Anti-Semitism Using Public Opinion Survey Data’. American Sociological Review 50 (4): 458–74. doi:10.2307/2095433.

Image: CC-by-nc More Good Foundation https://flic.kr/p/8Q5K9r

Are Low-Skilled Minorities Discriminated More?

Today a colleague asked me whether our recent meta-analysis drew any inferences on whether low-skilled minorities are discriminated more than highly-skilled minorities. It does so only at the margins — mostly in the supplementary material (S13). And to be precise, with the data at hand, we can’t say anything about the skills of the applicants, but we’re talking about the skills levels necessary for the job at hand.

What about the average call-back ratios by skills-level of the job? The data are available on Dataverse: doi:10.7910/DVN/ZU8H79.

First we load the data file.

disc = read.csv("meta-clean.csv", header=TRUE, sep=",", fileEncoding="UTF8")

Then we simply average across skills levels (using aggregate). For the meta-analytic regression analysis, refer to the supplementary material. Here we only look at the “subgroup” level, and store the averages in a variable called x.

x = aggregate(disc$relative.call.back.rate[disc$global=="subgroup"], by=list(Global=disc$global[disc$global=="subgroup"], Skills=disc$skills[disc$global=="subgroup"]), mean, na.rm=TRUE)

Since I want a figure, I’m sorting the result, and I don’t include the call-back rate for studies where the skills level was not indicated. Then I add the labels.

p = sort(x[2:4,3])
names(p) = c("high skills", "mixed skills", "low skills")

Finally, here’s the figure. I specify the ylim to include zero so as not to suggest bigger differences as there are.

barplot(p, ylim=c(0,2.2), bty="n", ylab="Average Call-Back Ratio")

The difference between “high” and “low” is statistically significant in a t-test (p=0.002).

occupations
Also on Figshare.

I also looked at the ISCO-88 codes. Now, the level of detail included in the different studies varies greatly, and the data file includes text rather than numbers, because some cells include non-numeric characters. After struggling a bit with as.numeric on factors, I chose a different approach using our good friend sapply.

I create a new variable for the 1-digit ISCO-88 codes. There are 781 rows. For each row, I convert what’s there into a character string (in case it isn’t already), then use substr to cut the first character, and then turn this into numbers.

disc$isco88_1 = sapply(1:781, function(x) as.numeric(substr(as.character(disc$isco88[x]), 0, 1)))

We can again run aggregate to average across occupation levels.

aggregate(disc$relative.call.back.rate[disc$global=="subgroup"], by=list(Global=disc$global[disc$global=="subgroup"], ISCO88=disc$isco88_1[disc$global=="subgroup"]), mean, na.rm=TRUE)

ISCO88 x
2 1.629796
4 1.422143
5 2.142449

I am not including all the output, because there are too few cases for some of the levels:

ISCO-88 Level 1 2 3 4 5 7 8 9
N 3 68 8 36 62 7 11 12

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. Forthcoming. doi:10.1080/1369183X.2015.1133279

Taking Notes on Readings/Papers

4531792759_89882afbe4Here’s something I’ve meant to share for a while now. I use Zotero to manage things I read (articles, books, conference papers, etc.), but what follows is applicable to any similar software. I keep notes on everything I read, and over the years this has evolved into something quite structured (a template in fact). The fact that it is structured is quite useful when I come back to a paper after a while. Here’s the template:

Research question:
Dependent variable:
Explanatory variable:
Data:
Method:
Mechanism:
Results:
Notes:

Obviously, not all papers will have something for each heading. While the heading research question is rather innocuous, unfortunately it’s not always as easy to fill in as it should be. The dependent variable is the quantity of interest; under explanatory variable I include the main explanations. I tend to include control variables here, too, although in brackets.

Data describes the data sources, such as the survey used, the countries covered, population covered, N; experts, ABM, or even “data free”, whatever seems the most adequate description. Method is for methodological details. While usually we are more interested in the results rather than how they were obtained, a quick glance at the methods (and data) can be really helpful in determining how much weight I want to give a particular result.

The heading mechanism is often challenging to fill in, simply because many papers do not state them explicitly, or because the theory section is not tightly connected with the empirical part. I’m not lamenting here; I guess I’m guilty of this, too…

Often my interest is in the results section, where I summarize the main findings. The heading notes takes everything else, namely free notes.

The whole things is (deliberately) rather flexible, but it helps with two things: (1) read papers with some focus, (2) have notes in a format that allow me to retrieve relevant information more quickly (here the advantage of a database over Anki, but obviously only when things can be found).

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