Over at the BI team, there’s a nice summary of the lack of evidence on unconscious bias and diversity training. Note in particular the difference between perceived “effectiveness” and the lack of evidence that behaviour actually changed. As usual, the focus is really on application and the question what should be done. Discrimination is too serious an issue that we can leave it to feel-good check-box exercises!
Here a few hard facts from the NCCR on the move.
- Job applicants with Black skin colour on their picture and a name from Cameroon have to send 30% more job applications to get invited for a job interview. They are Swiss citizens. Blog.
- Job applicants with a name indicating Kosovan ancestors have to send up to 50% more job applications to get invited for a job interview. They are Swiss citizens. Blog.
- 18% of the Swiss population entitled to vote are of ‘immigrant origin’. In 2015, 13% of the candidates for the National Council had a name suggesting ‘immigrant origin’ — only 6% got elected. Blog.
- If your name suggests Turkish or Kosovan ancestry, you’re 3-5 percentage points less likely to be invited to view an apartment: There are landlords who do not want to meet you. Blog.
Image credit: CC-by-sa Quinn Dombrowski
There were no real statistical tests presented and discussed in the paper. We don’t know whether the differences are significant.
Did the reviewer mean no outdated p-values? Well spotted! That discussion of the substantive meaning of the coefficients and those credibility intervals — apparently not spotted.
Image: CC-by Chase Elliott Clark
I’ve long been critical of population estimates as ‘evidence’ of racism, but now there is no reason left to do so. The basic ‘evidence’ is as follows: There are say 5% immigrants in country X, you ask the general population, and their mean estimate is maybe that there are 15% immigrants in the country. Shocking, they overestimate the immigrant population, which is ‘evidence’ that the general population is generally racist (I enjoyed this phrase). I’ve been critical of this because of three reasons. First, we don’t generally tell survey participants what we mean by ‘immigrants’, but use a specific definition (foreign citizens, foreign born) for the supposedly correct answer. Second, why should members of the general population have a good grasp of the size of the immigrant population? We might be able to estimate the share of immigrants in our personal network, but that’s not the same as estimating population shared. Third, if we see this as evidence of racism, we assume that the threat perspective is dominant.
It turns out, however, that there is a general human tendency to overestimate the population share of small groups: immigrants, homosexuals, you name it. David Landy and colleagues demonstrate that this tendency to overestimate small groups comes hand in hand with a tendency to underestimate large groups — a pull towards the average. There’s nothing particular about immigrants there, and nothing about racism either.
Landy, D., B. Guay, and T. Marghetis. 2017. ‘Bias and Ignorance in Demographic Perception’. Psychonomic Bulletin & Review, August, 1–13. https://doi.org/10.3758/s13423-017-1360-2.
Photo: CC-by-nc-nd by IceBone
In their 2014 article, Leslie Schwindt-Bayer and Peverill Squire show that the political power of legislatures can affect gender representativeness of legislatures. In the article they discuss likely mechanisms and suggests that the same result applies to ethnic groups. The argument is that in a legislature with more professional power, need to provide representatives with incentives to compensate for their investments like long sessions. These incentives, in turn, encourage incumbents to preserve their seats and discriminate against under-represented groups. Sounds reasonable enough, but ever since collecting information on the ethnic composition of legislatures worldwide, I have been keen to empirically check such claims.
I did so using the spreadsheet from the DICE Database and my own data on ethnic representation. This gives me 35 countries to have a quick look at the claim: there is no such correlation among the countries examined.
Ruedin, Didier. 2009. ‘Ethnic Group Representation in a Cross-National Comparison’. The Journal of Legislative Studies 15 (4): 335–54. doi:10.1080/13572330903302448.
———. 2010. ‘The Relationship between Levels of Gender and Ethnic Group Representation’. Studies in Ethnicity and Nationalism 10 (2): 92–106. doi:10.1111/j.1754-9469.2010.01066.x.
———. 2013. Why Aren’t They There? The Political Representation of Women, Ethnic Groups and Issue Positions in Legislatures. Colchester: ECPR Press.
Schwindt-Bayer, Leslie, and Peverill Squire. 2014. ‘Legislative Power and Women’s Representation’. Politics & Gender 10 (4): 622–658. doi:10.1017/S1743923X14000440.