ASOA22213U Discrimination: Conceptualization, Causes, Measurement and Counteraction

Volume 2025/2026
Education

BA and MA elective course


Specialisation line/course package:

Welfare, inequality, and mobility

 

The course is open to:

  • Exchange and Guest students from abroad
  • Credit students from Danish Universities

 

Full-degree students enrolled at the Faculty of Social Science, UCPH 

  • Bachelor and Master Programmes in Political Science
  • Bachelor and Master Programmes in Psychology 
  • Bachelor and Master Programmes in Economics
  • Master Programme in Social Data Science
  • Master Programme in Global Development
  • Master Programe in Security Risk Management
Content

What is discrimination, how do we measure it, and can it be countered? Are women discriminated against as political candidates? Do minority members face exclusion in the housing market or public services? Even though formal rules and informal norms against discrimination are strong, evidence shows that it continues to affect individuals’ chances of success. Understanding why discrimination occurs—and why it persists despite legal prohibitions—is crucial for building critical awareness and informing policies.

 

This course offers students a rigorous introduction to the field of discrimination by using evidence from across the social sciences. The course is structurally divided into two parts: Week 1 focuses on the theoretical foundations, moving from standard definitions to advanced sociological critiques. Week 2 focuses on empirical research, examining causal inference and the effectiveness of anti-discrimination interventions.

 

We begin by establishing the dominant paradigms in the field. We will explore Taste-Based and Statistical Discrimination models used in economics to explain labor market outcomes, as well as recent refinements. We then shift to perspectives prominent in Critical Race and Feminist Theories that view discrimination not as a pathology but as a strategic tool for social closure. We will examine how groups use discrimination to monopolize advantages and maintain boundaries. This block covers Systemic, Indirect, and Cumulative Discrimination. Finally, we discuss counter-intuitive findings that challenge standard models, like the Integration Paradox. We conclude the theoretical week by debating the theory of Contested Discrimination, asking why the very definition of discrimination is a subject of societal struggle rather than a settled objective fact.

 

The second week focuses on research design, with a particular emphasis on the “gold standard” of discrimination research: field experiments and audit studies. We will examine how researchers isolate causal mechanisms in real-world settings, such as healthcare and public administration, and how organizational, political, and social contexts can mitigate or inflate discrimination. Finally, we will reflect (and debate!) on the policies and initiatives that aim to counteract discrimination, including affirmative action policies.

 

This seminar aims to be interactive and there will be debates, student presentations, among other interactive tasks. Active class participation is thus expected.

 

Learning Outcome

Knowledge

  • Develop an understanding of discrimination, including its relevance, key theoretical frameworks, and counteraction initiatives.
  • Demonstrate an understanding of the measurement of discrimination and the use of different designs in studies of discrimination.
  • Discuss the latest experimental evidence on discrimination in various areas, such as gender, class, ethnicity, sexual orientation and partisanship.

 

Skills

  • Critically evaluate designs used in measuring discrimination, identifying their strengths, weaknesses, and potential biases.
  • Analyse and evaluate the initiatives aiming to counteract discrimination. 
  • Being able to synthesize knowledge and information from the course and to independently formulate an accompanying research design to study discrimination.

 

Competences

  • Apply theoretical and methodological knowledge in analysing instances of discrimination across various social contexts.
  • Integrate course knowledge to formulate research designs that address different aspects of discrimination.

Lippert-Rasmussen, K., 2013. ‘What is Discrimination’ (chapter 1). In Born Free and Equal? Oxford University Press. https:/​/​academic.oup.com/​book/​8942/​chapter/​155252515

 

Tilcsik, A., 2021. ‘Statistical Discrimination and the Rationalization of Stereotypes’. American Sociological Review 86 (1). SAGE Publications Inc: 93–122. doi:10.1177/0003122420969399.

Fiske, S. T., Cuddy AJC, and Glick P., 2007. ‘Universal dimensions of social cognition: Warmth and competence.’ Trends in cognitive sciences 11 (2): 77-83.

Galos, D.R., 2023. Social Media and Hiring: A Survey Experiment on Discrimination based on Online Social Class Cues. European Sociological Review, 1-13.

Olsen, A.L., Kyhse‐Andersen, J.H., and Moynihan, D., 2022. The unequal distribution of opportunity: A national audit study of bureaucratic discrimination in primary school access. American Journal of Political Science, 66(3): 587-603.

Paluck, E. L., Porat, R., Clark, C. S., and Green, D. P., 2021. Prejudice reduction: Progress and challenges. Annual review of psychology, 72, 533-560.

Schaeffer, M., & Kas, J. (2024). The integration paradox: a review and meta-analysis of the complex relationship between integration and reports of discrimination. International Migration Review58(3), 1384-1409.

Schaeffer, Merlin, Krzysztof Krakowski, and Asmus Leth Olsen. 2024. “Correcting Misperceptions about Ethno-Racial Discrimination: The Limits of Evidence-Based Awareness Raising to Promote Support for Equal-Treatment Policies.” American Journal of Political Science n/a(n/a). doi:10.1111/ajps.12933.

Schaeffer, Merlin, Jutta Höhne, and Celine Teney. 2016. “Income Advantages of Poorly Qualified Immigrant Minorities: Why School Dropouts of Turkish Origin Earn More in Germany.” European Sociological Review 32(1):93–107. doi:10.1093/esr/jcv091.

The course will cover both contemporary sociological theory and empirical research. This means students should be familiar with descriptive and multivariate statistics (e.g. multiple OLS regression).
Lectures, presentations, exercises and debates
  • Category
  • Hours
  • Class Instruction
  • 42
  • Preparation
  • 164
  • Total
  • 206
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Home assignment
Type of assessment details
The students are required to formulate their own exam questions based on pre-defined guidelines provided by the teacher. Students will receive the exam guidelines for formulating exam questions during the ongoing semester. The teacher is required to provide at least two exemplary exam questions that adhere to the guidelines.

The exam can be written individually or in groups of max. 4 students.
Length of the exam is 10 pages + 5 pages pr. extra group member.
Examination prerequisites

To get qualified to the exam, the students must have completed a classroom presentation.

Aid
Only certain aids allowed (see description below)

The Department of Sociology prohibits the use of generative AI software and large language models (AI/LLMs), such as ChatGPT, for generating novel and creative content in written exams. However, students may use AI/LLMs to enhance the presentation of their own original work, such as text editing, argument validation, or improving statistical programming code. Students must disclose in an appendix if and how AI/LLMs were used; this appendix will not count toward the page limit of the exam. This policy is in place to ensure that students’ written exams accurately reflect their own knowledge and understanding of the material. All students are required to include an AI declaration in their exam submissions regardless of whether they have used generative AI software or not. This declaration should be placed as the last page of the exam submission. Please note that the AI statement is not included in the calculation of the overall length of your assignment. The template for the AI statement can be found in the Digital Exam system and on the Study Pages on KUnet under “Written exam”. Exams that do not declare if and how AI/LLMs were used will be administratively rejected and counted as one exam attempt.

Marking scale
7-point grading scale
Censorship form
No external censorship
Exam period

Exam information:

The examination date can be found in the exam schedule   here

The exact time and place will be available in Digital Exam from the middle of the semester. 

Re-exam

Reexam info:

The reexamination date/period can be found in the reexam schedule   here

 

Same as the ordinary exam.

Criteria for exam assesment

See learning outcome