ESRA 2025 Preliminary Program

Monday 14 July    Tuesday 15 July    Wednesday 16 July    Thursday 17 July    Friday 18 July   

All time references are in CEST

Measuring Discrimination: Methodological Challenges and Insights
Session Organisers	Mr Kien Tran (German Centre for Integration and Migration Research) Mrs Almuth Lietz (German Centre for Integration and Migration Research) Mrs Zaza Zindel (German Centre for Integration and Migration Research)
Time	Tuesday 15 July, 09:00 - 10:30
Room	Ruppert 114

Discrimination based on race, ethnicity, and other characteristics remains a pervasive issue in many societies, contributing to deep-rooted social inequalities. The empirical study of discrimination, especially among racialized and marginalized groups, offers crucial insights into these inequalities but also presents significant methodological challenges. This session will focus on the empirical investigation of discrimination, with a special emphasis on racialized and marginalized populations. The goal is to bring together papers that explore new advancements in survey research related to discrimination reporting.
Survey research is a key catalyst for investigating discrimination empirically. The measurement of discrimination experiences has evolved over time, reflecting shifts in societal attitudes and methodological advancements. How vulnerable group membership is defined and operationalized can influence the prevalence of reported discrimination experiences. Whether individuals are categorized based on self-identification, external perception, or other criteria can significantly affect estimates of discrimination prevalence. Longitudinal trends in discrimination and the development of new techniques for capturing these experiences are critical for understanding how discrimination evolves over time. Experimental designs that simulate discriminatory contexts offer valuable insights into implicit and explicit biases. Furthermore, exploring the relationship between self-reported reasons for discrimination (e.g., race, gender, religion) and individuals' self-identification with vulnerable groups can reveal important discrepancies between perceived and actual experiences of discrimination. These dynamics provide a deeper understanding of how individuals interpret and frame their experiences within broader social contexts.
Relevant topics for this session include, but are not limited to:
• Methodological advances in measuring discrimination, including implicit measures, data-linkage procedures, or comparisons of survey instruments assessing discriminatory experiences.
• Experimental designs simulating discriminatory contexts.
• The influence of operationalization of key characteristics, such as vulnerable group membership and self-identification, on reported discrimination.
• Longitudinal studies of discrimination: trends over time, shifts in prevalence, changes in forms of discrimination.

Keywords: discrimination, racialized groups, measurement, operationalization, racialized categorizations

Papers

The Organizational Context of Discrimination: How Employer Attitudes Shape Ethnic Discrimination in Hiring

Dr Andrea Forster (Utrecht University) - Presenting Author
Professor Martin Neugebauer (Karlsruhe University of Education)

The existence of ethnic discrimination in the hiring process has been repeatedly confirmed using field experiments that confront real employers with fictitious applicants. However, the conditions that exacerbate or alleviate hiring discrimination have received surprisingly little attention by researchers. One reason for this paucity of research is that field experiments do usually not contain the necessary information to study mechanisms behind discriminatory behavior as they solely collect dichotomous answers from employers next to a few general firm characteristics. For example, the diversity attitudes of recruiting personnel and the openness of organizations towards diversity remain in the dark.

Using a unique combination of different data sources, we, attempt to study these attitudes as a source of ethnic discrimination in hiring. First, using data from a nation-wide field experiment in Germany, we confirm previous research on ethnic discrimination in hiring by showing a hiring gap of 7 percentage points between German and Turkish applicants. Second, we study the relationship between anti-immigrant attitudes of employers and ethnic discrimination. As described above, it is difficult to obtain direct information on employers’ attitudes from field experimental data. Therefore, we use three angles to approximate employer attitudes. First, we look at local election results as a proxy for anti-immigrant attitudes. Second, we determine firms’ commitment to diversity by evaluating texts from job advertisements using automated text analysis. And, third, we apply a survey questionnaire to a subset of our field experiment sample that gives us the opportunity to study recruiter attitudes directly.

A Discerning Distinction?: Response Format Effects in Discrimination Measurement

Ms Megan A. Hendrich (Ipsos Public Affairs) - Presenting Author
Professor Randall K. Thomas (Ipsos Public Affairs)

With recent social dislocations and profound polarization of many groups, attention to prejudices and discriminatory behaviors has significantly increased. Accurately measuring experiences of perceived discrimination using standardized methods is essential to track changes and identify impacted groups. The measurement of discrimination has often varied based on the type of discrimination, rendering comparisons across types of discrimination difficult, if not impossible. To standardize measurement, we can focus on simple filter questions commonly used to measure discrimination. Filter questions often use either dichotomous response formats (DRF) with yes-no responses or multiple response formats (MRF) with “Select all that apply.” DRF typically yield higher prevalence results than MRF across a wide range of topics, including donations, health conditions, and importance measures. We sought to evaluate differences in the occurrence of discrimination using these response formats. In our first study, we reported the results of an experiment on discrimination measures using a probability-based sample of 3,193 U.S. adults. In our second study, we sought to replicate and extend the results with 3,344 U.S. adults. We randomly assigned respondents to use either a DRF or MRF to indicate the occurrence of discrimination. We also randomly assigned them to consider the occurrence in one of three recall periods (“Ever,” “Past 5 years,” or “Past year”). We asked about three randomly selected specific forms of discrimination from a set of seven types (e.g., racial, gender, age) within eight different contexts where discrimination may have occurred (e.g., employment, education, housing). If a person indicated they experienced any discrimination, we asked how negative of an impact it had on them (measuring salience). While the magnitude of reports differed somewhat between studies, the general pattern of results showed higher reports of discrimination for the DRF than the MRF.

Measurement Issues on Discriminating Foreigners as Neighbours

Professor Sören Petermann (Ruhr University Bochum)
Mr Daniel Schubert (Ruhr University Bochum) - Presenting Author

The German General Social Survey (ALLBUS) 2016 includes two vignette-based questions measuring views about foreigners as neighbours. For both questions, there are 13 vignettes representing the surrounding 48 households in the immediate neighbourhood. Each vignette differs in the proportion of foreign households. The proportion of foreigners increases evenly from the first vignette (0%) by approx. 8.3 percentage points in each case up to the 13th vignette (100%). Respondents were asked to indicate all neighbourhood vignettes in which they a) want to live and b) do not want to live. The obvious advantage of such a conceptualisation is to determine individual tipping points, which also allow a buffer zone between favoured and unfavoured areas. However, there are three measurement issues with this. Firstly, there is a high proportion of cases with only one specified neighbourhood vignette. Secondly, the results indicate that the underlying linearity assumption (homogeneous neighbourhoods are preferred, heterogeneous neighbourhoods are rejected) does not apply. Thirdly, there is a small but substantial proportion of cases that both like and dislike individual neighbourhood vignettes, i.e. seems to be inconsistent. We address the first two measurement issues by offering solutions that are based on reasonable assumptions about behaviour in the interview situation and from diversity research. Using the ALLBUS 2016 dataset, this presentation analyses the characteristics of preferred and rejected neighbourhood vignettes, offering insights into how tipping points for discrimination preferences can be identified from the vignette results. This work fills a significant gap, as the extensively collected residential preference data in ALLBUS 2016 remain underexplored so far probably because of these measurement issues.

Inconsistencies Between Self-Identification and Perceived External Attribution: Implications for Measuring Discrimination and Racism

Mr Kien Tran (DeZIM (German Center for Integration and Migration Research)) - Presenting Author
Mr Max Thom (DeZIM (German Center for Integration and Migration Research))

In order to effectively capture groups vulnerable to racism and discrimination, it is recommended to
increasingly utilize self-identification in surveys. Complementing this, perceived external attribution
should also be considered. Both aspects are understood as dynamic categories, with the advantage
of being sensitive to temporal and spatial fluctuations (cf. Aikins & Supik 2018). However, when
measuring experiences of discrimination and racism, it is crucial to analyze the relationship between
these categorizations. Inconsistencies between them may result in methodological challenges and
biases, which can hinder the accurate assessment of individuals' actual experiences.
For instance, individuals may be affected by racism or discrimination associated with a particular
group to which they are externally attributed but do not self-identify. Conversely, individuals who
self-identify with a group but are not accordingly externally attributed may experience racism or
discrimination differently, or to a different extent.
Using data from the NaDiRa.panel – a large-scale survey conducted in Germany (2022) with around
20,000 participants – this study aims to explore how the alignment between self-identification and
external attribution contributes to the perception of experiences of discrimination and the perceived
reasons for those experiences.
Specifically, we examine whether statistically significant differences exist in perceived experiences
based on whether individuals:
• are both self- and externally allocated into specific groups
• self-identify but are not externally attributed to specific groups
• do not self-identify but are externally attributed to the specific groups
This study highlights the complexities of identity categorization and suggests potential extensions to
the measurement of racism and discrimination in Germany.

Reflected ascriptions as a further dimension of educational inequalities? A new approach to measuring racial inequalities in Germany

Dr Benjamin Schulz (WZB Berlin Social Science Center) - Presenting Author
Ms Josefine Matysiak (WZB Berlin Social Science Center)

Internationally, there are various approaches to measuring racial inequalities in education. In the United States, researchers use race as a common category for this purpose, whereas in Germany, for historical reasons, measuring race is not recognized. Instead, migration background is a well-established category here. Recent scientific debates in Germany on racism and related inequalities, however, argue that migration background does not include a growing proportion of the German population who experience racial ascriptions, while including others who are hardly affected by them. Therefore, a new categorization approach is needed to distinguish between racism and migration-related dimensions. Building on the U.S. discourse on the multidimensionality of race, this article analyzes whether measuring reflected ascriptions, specifically individuals' perceptions of ascriptions as (non-) German, can close this data gap. Using data from the German National Educational Panel Study (NEPS), we estimate OLS models on educational inequality based on reflected ascriptions. Our results show a significant association between being perceived as non-German and lower mathematical and German competencies - even after controlling for migration background. Reflected ascriptions, therefore, deserve more attention when analyzing educational inequalities in Germany and internationally.