Story idea, interviews and writing: Chiponda Chimbelu
Data analysis and visualisation: Ana Muñoz Padrós
Editing: Gianna-Carina Grün, C Mwakideu
The main data sources for this story have been the EU's Fundamental Rights Agency (FRA), the German Federal Employment Agency (BA, for its initials in German), and a peer-reviewed research article published in Nature, the leading science journal.
Data on the prevalence and experiences of discrimination were mainly taken from the 'Being Black in the EU' report (FRA, 2024), particularly within the range of figures between 28 and 69. The report analyses the responses of over 6,700 people of African descent living in 13 EU countries, building on the results of the upcoming EU-MIDIS III survey (expected to be launched early 2026 at the time of writing), and it can be found in this link.
The EU-MIDIS II survey (FRA, 2016) complemented the main source, see excel file with selected data
The specific figures and details on the process, can be found in the jupyter notebook
These sources also provided data on:
- Awareness of equality bodies
- Reporting of discriminatory events.
Data on employment and unemployment in Germany was taken from the analysis kindly provided by the BA in July 2025. The complete excel file is here.
- Data on unemployment was extracted from the 'Arbeistlosenquote' sheet.
- Data on employement by sectors and demographic groups was extracted from the 'Beschäftigte_Berufssegmente' sheet.
More details on the analysis and the process are in the jupyter notebook
Data on immigrant-native pay gap in Germany was obtained from the peer-reviewed article published in science journal Nature in July 2025: Hermansen, A.S., Penner, A., Boza, I. et al. Immigrant–native pay gap driven by lack of access to high-paying jobs. Nature 644, 969–975 (2025). https://doi.org/10.1038/s41586-025-09259-6
Using the methodology described below, we sourced the following tables from the study's supplementary information :
- Supplementary Table 5. Within-job immigrant–native differences in annual earnings by region of origin and destination country from main analysis reported in Fig. 2b
- Supplementary Table 46. German estimates of immigrant–native differences in annual earnings from main analysis.
For various reasons, not all countries collect data on discrimination, and those that do often use different methods, making comparisons difficult. For more details on data sampling and collection at EU level please refer to the introduction of 'Being Black in the EU' report.
In the context of this story and the data sources informing it, Sub-Saharan African descendents are people either born or who have at least one parent who was born in one of these countries: Angola; Benin; Botswana; Burkina Faso; Burundi; Cabo Verde; Cameroon; the Central African Republic; Chad; Comoros; Congo; Côte d’Ivoire; the Democratic Republic of the Congo; Djibouti; Equatorial Guinea; Eritrea; Ethiopia; Gabon; Ghana; Guinea; Guinea-Bissau; Kenya; Lesotho; Liberia; Madagascar; Malawi; Mali; Mauritania; Mauritius; Mayotte; Mozambique; Namibia; Niger; Nigeria; Réunion; Rwanda; Saint Helena; São Tomé and Príncipe; Senegal; Seychelles; Sierra Leone; Somalia; South Sudan; Swaziland; Tanzania; The Gambia; Togo; Uganda; Zambia; and Zimbabwe.
EU FRA uses 'black person' and 'person of sub-Saharan descendent' interchangeably as "on average, 87 % of the respondents self-identify as ‘a person of African descent or a Black person’ and 13 % do not, with some differences across the 13 countries surveyed."
'Natives' are defined in the Nature study and in our article "as individuals who were born in their country of residence to native-born parents".
The German Federal Employment Agency defines 'Germans' as people with German citizenship.
Since most data on discriminatory experiences was stored in PDF reports, we focused primarily on extracting information and building datasets in a table format.
To this end, we used a python library to find and extract content from PDFs using LayoutLM models, complemented with manual extraction.
The analysis of employment/unemployment data consisted mainly of calculations of rates per cent, shares, and new groups, in order to identify outliers and trends.
To calculate immigrant-native pay gaps, we converted logged annual earnings coefficients with basic adjustments, as found in Nature's research aforementioned supplementary tables, to percentage differences.
We did this conversion using the exponential formula e^x - 1. Therefore, in Google sheets, the calculation was '=(EXP(coefficient) - 1) * 100'. The result shows how far immigrants' pay is from native's pay.
These are the full results, partially shown in the article's visualisations:
Pay gap in Germany, based on Table 46
| Origin | Log coefficient | Pay gap in % |
|---|---|---|
| Asia | -0.241 | -21.41583736 |
| Latin America | -0.17 | -15.63351834 |
| Middle East and North Africa | -0.199 | -18.04501067 |
| Sub-Saharan Africa | -0.393 | -32.49712524 |
| Europe, North America, and Other Western | -0.223 | -19.98851507 |
Sub-Saharan African-natives pay gap in different countries, based on Table 5
| Destination | Log coefficient | Pay gap in % |
|---|---|---|
| Canada | -0.16 | -14.7856211 |
| Denmark | -0.058 | -5.635005256 |
| France | -0.107 | -10.1474327 |
| Germany | -0.113 | -10.68493399 |
| Netherlands | -0.083 | -7.964885278 |
| Norway | -0.079 | -7.596007555 |
| Spain | -0.108 | -10.23724036 |
| Sweden | 0.007 | 0.7024557267 |
| United States | -0.029 | -2.858353553 |