This map shows how easily White Europeans in Europe associate black faces with negative ideas. Each country's colour reflects the average Implicit Association Test (IAT) score for that country using data from Harvard's Project Implicit. Overall we have scores for 288,076 White Europeans, collected between 2002 and 2015, with sample sizes for each country shown inset.
The IAT The IAT is a measure of implicit racial attitudes. Because of the design of the test it is very difficult to deliberately control your score . Many people, including those who sincerely hold non-racist or even anti-racist beliefs, demonstrate positive implicit bias on the test. Scores on the test are not reliable, and so don't allow predictions of individuals' true implicit attitudes or behaviour. However, when many scores are collected together definite patterns emerge. The most striking of these is that the average score on the racial bias IAT is non-zero. Both in the US, where this measure was developed, across Europe (shown here for the first time), the test shows that people are slower to associate Blackness with positive words such as "Good" or "Nice" and faster to associate Blackness with negative concepts such as "Bad" or "Evil".
One idea is that implicit attitudes, such as measured by the IAT, reflect the automatic associations we hold in our minds, and that these develop over years of immersion in the social world. Although we, as individuals, may not hold racist beliefs, the ideas we associate with race may be constructed by a culture which describes people of different ethnicities in consistent ways, and ways which are consistently more or less positive. Looked at like this, the IAT - which is a weak measure of individual psychology - may be most useful if individuals' scores are aggregated to provide a reflection on the collective social world we inhabit.
The results shown in this map give detail to what we already expected - that across Europe racial attitudes are not neutral. Blackness has negative associations for White Europeans, and there are some interesting patterns in how the strength of these negative associations vary across the continent.
*open data, open tools* This new map is possible because Project Implicit release their data via the Open Science Framework (osf.io). This site allows scientists to share the raw materials and data from their experiments, allowing anyone to check their working, or re-analyse the data (as we have done here). The data analysis and map were done in R, an open source statistical programming language, and we collaborated using github.com, a platform for software projects. Now the data and code to produce the map are shared on Figshare.com, a site which allows data and graphics to be given stable digital object indentifiers (DOIs) and so integrated into the scholarly literature like other publications. We believe that open tools and publishing methods like these are necessary to make science better and more reliable.
*Sample limitations* The data comes from Europeans who visited the US Project Implicit website, which is in English. Language specific IAT data may be available in the near future. For now we can be certain that the sample reflects a subset of the European population which are more internet-savvy than typical, probably younger, and probably more cosmopolitan (both because they are both comfortable using a website in English, and from the sheer fact that they were interested in taking a test of implicit racism). These factors are likely to underweight the extent of implicit racism in each country.
This data reflects scores on just one IAT (the classic White-Black/Positive-Negative IAT). Other dimensions of social attitudes can be assessed by different IATs. You can explore these at Project Implicit https://implicit.harvard.edu/implicit/
*Acknowledgements* George Gittu collated and cleaned the data, coded and refined the map. Tom Stafford helped with some analysis decisions and wrote this text. Tom Stafford was part funded by a Leverhulme Trust grant on implicit bias 2014-2017, and is grateful both to the Trust and his project partners, Jules Holroyd (PI) and Robin Scaife for introducing him to the literature on implicit bias. Thanks also to Frank Xu, Brian Nosek and Colin Smith at Project Implicit.