Our meta-analysis on ethnic discrimination in hiring is now available in print. In the meta-analysis we examine 738 correspondence tests in 43 separate studies testing ethnic discrimination in hiring decisions.
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).
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)
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
Eva Zschirnt and I have undertaken a meta-analysis of correspondence tests in OECD countries between 1990 and 2015. It is now available on the website of JEMS. We cover 738 in 43 separate studies conducted in OECD countries between 1990 and 2015. In addition to summarizing research findings, we focus on groups of specific tests to ascertain the robustness of findings, emphasizing (lack of) differences across countries, gender, and economic contexts. Discrimination of ethnic minority and immigrant candidates remains commonplace across time and contexts.
A new paper by David W. Johnston and Grace Lordan shows that self-declared attitudes towards people of other races are more negative during economic downturns (when unemployment is higher). This finding is reminiscent to what Marco Pecoraro and I found with regard to attitudes towards foreigners. While we did not make the link to the context and unemployment levels, our analysis demonstrates that the self-declared risk of unemployment is related to negative attitudes towards foreigners.
Now negative attitudes are not the same as discriminatory behaviour. Interestingly, in our meta-analysis of correspondence tests we found no systematic link between the economic situation and discrimination in the labour market. This would suggest that the impact of the economy is only indirect — or that we’re not doing good enough a job in capturing what’s going on.
Johnston, David W., and Grace Lordan. 2015. ‘Racial Prejudice and Labour Market Penalties during Economic Downturns.’ European Economic Review. doi:10.1016/j.euroecorev.2015.07.011.
Pecoraro, Marco, and Didier Ruedin. 2015. ‘A Foreigner Who Doesn’t Steal My Job: The Role of Unemployment Risk and Values in Attitudes towards Equal Opportunities.’ International Migration Review, 1–53. doi:10.1111/imre.12162.
Zschirnt, Eva and Ruedin, Didier, Ethnic Discrimination in Hiring Decisions: A Meta-Analysis of Correspondence Tests 1990-2015 (April 22, 2015). Available at SSRN: http://ssrn.com/abstract=2597554
I’m happy to announce that a paper I’ve drafted with Eva Zschirnt has won 2nd place for the IMISCOE Rinus Penninx Best Paper Award. In the paper we provide a proper meta-analysis of ethnic discrimination in hiring decision, summarizing the results of 627 correspondence tests carried out between 1990 and 2015. We show that despite the introduction of anti-discrimination legislation, discrimination against ethnic and racial minorities remains prevalent in the hiring process. What is more, there does not appear to be any systematic link between the economic situation and the incidence of discrimination.
Picture credit: © Andreas Perret and nccr – on the move, with permission.