National Study on Ethnic Discrimination in the Housing Market

Today I’m giving you the first national field experiment on ethnic discrimination in the housing market. Financed by the Swiss Office for Housing and the NCCR on the move, we examined to what extent one’s name affects the likelihood to be invited to view an apartment. We covered the entire country, across language regions and across urban and rural areas.

Between March and October 2018, our diligent research assistants sent more than 11,000 enquiries to over 5,700 landlords in all parts of Switzerland. We varied the name of the person sending an enquiry (stimulus sampling) along with other features such as politeness or the family situation. Overall over 70% of the enquiries were answered positively in the sense of an invitation to view the apartment or steps in this direction.

We find no clear differences between commercial and private landlords. The response rate for women was around 1 percent higher, while highly qualified people had a 2 percent higher response rate, especially academics who use their doctoral title (we dind’t expect this to make such a big difference when we designed the study). As previous field experiments have shown, the quality of the message we sent affected the probability of a response: Compared to a standard text, the response rate for friendlier queries is about 5 percent higher, while queries with the default text from online portals show a 10 percent lower response rate.

We find evidence of ethnic discrimination in the sense of unequal treatment based on the name. Enquiries with names from neighbouring countries (Germany, Italy, France) were even invited somewhat more frequently to view apartments than those from Switzerland, but people with Kosovar (response rate just under 3 percent lower) or Turkish names (response rate about 5 percent lower) have significantly fewer chances of being invited for a viewing. Whether those interested were naturalised with foreign-sounding names or stated that they had a permanent residence permit was hardly a factor. The rate of discrimination we observe is similar in order of magnitude to that found in comparable studies in other Western countries.

With the national coverage, we can also observe variation in responses by local context where the property is located. In municipalities with higher rental prices, the positive response rate is higher for everyone, and a higher vacancy rate in the municipality is associated with a higher response rate, except for people with Kosovar names. In urban areas the probability of discriminating against people with foreign names is lower. We also find that people with foreign-sounding names are less likely to be invited in municipalities with restrictive political attitudes towards immigration (as measured in the results of popular initiatives and referendums).

Auer, Daniel, Julie Lacroix, Didier Ruedin, and Eva Zschirnt. 2019. ‘Ethnische Diskriminierung auf dem Schweizer Wohnungsmarkt’. Grenchen: BWO. https://www.bwo.admin.ch/bwo/de/home/Wohnungsmarkt/studien-und-publikationen/diskriminierung-auf-der-schweizer-wohnungsmarkt.html.

Ethnic discrimination in hiring: UK edition

The BBC report on a large correspondent test in the UK carried out by the excellent GEMM project. It’s good to see this reach a wider audience; it’s sad to see the results from our meta-analysis confirmed once again.

British citizens from ethnic minority backgrounds have to send, on average, 60% more job applications to get a positive response from employers compared to their white counterparts

What I really like about this short report by the BBC is that the essentials are covered. Yes we see discrimination, but no, it’s not so bad that none of the minority applicants would ever succeed. They also start the piece with an example of someone changing their name on the CV as a strategy to counter expected (or experienced) discrimination — and they highlight that discrimination has not declined despite policy changes, and indeed that discrimination affects native citizens who happen to have a ‘foreign’ name: they pay for an action of their parents or grandparents.

Are employers in Britain discriminating against ethnic minorities?, GEMM project: PDF of report

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. https://doi.org/10.1080/1369183X.2015.1133279.

Planning a Field Experiment? Read These First

Are you planning to run a field experiment, perhaps even on discrimination in hiring decisions? As a complement to our meta-analysis of existing field experiments on ethnic discrimination in hiring, you could do worse than reading two recent working papers by my co-author Eva Zschirnt: In one, she outlines the history of field experiments in much more detail than a journal article can do, in the other she collects ethical considerations in a single place.

Zschirnt, Eva. 2016a. ‘Measuring Hiring Discrimination – A History of Field Experiments in Discrimination Research’. NCCR On the Move Working Paper Series 7 (May): 1–32.
———. 2016b. ‘Revisiting Ethics in Correspondence Testing’. NCCR On the Move Working Paper Series 8 (May): 1–26.
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. doi:10.1080/1369183X.2015.1133279.

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