AØKK08457U Seminar: Economics of Human Capital

Volume 2026/2027
Education

MSc programme in Economics

The seminar is primarily for students at the MSc of Economics.

Content

The purpose of the seminar is to train the students' abilities to understand human capital as an economic concept and to carry out empirical analyses on micro data using relevant econometric techniques.

 

Human capital is the stock of skills, knowledge and health that individuals accumulate over the life cycle, and which determines their productivity, earnings and well-being. Following Becker and Grossman, the seminar treats education and health not as separate fields but as two forms of the same underlying investment problem.

 

The default is that students use the Survey of Health, Ageing and Retirement in Europe (http://www.share-project.org/) to carry out the empirical projects. SHARE is a cross-national panel of individuals aged 50 and over I multiple European countries. It observes education, retrospective childhood conditions, income, employment, health, health behaviors and cognitive test scores for the same individuals across nine waves. For those more interested in schooling and adult skills, other publicly available datasets are PISA ( https://www.oecd.org/en/about/programmes/pisa/pisa-data.html), and PIAAC ( https://www.oecd.org/en/about/programmes/piaac/piaac-data.html). However, students are encouraged to conduct analyses in datasets they find themselves and have access to. They are also encouraged to creatively merge information across different sources.

 

If students have an alternative and appropriate micro dataset set up by the beginning of the course, they should contact the teacher by the course start, in order to determine the data’s relevance, as it must be within the scope of the seminar.

 

Generally, the topics are bounded to empirical strategies that can be pursued within the SHARE, PISA, or PIAAC databases. These include inequalities in health, schooling, and human capital accumulation; risky health behaviors; effects of compulsory schooling, health, and retirement policies. For further inspiration students are encouraged to view the long list of publications carried out on SHARE data ( http://www.share-project.org/share-publications/journalarticles00.html).

 

A central part of the course will be to apply methods on real data. Therefore, the students will be obliged to meet data access requirements laid out by SHARE ( http://www.share-project.org/data-access/user-registration.html). SHARE has made the survey ” easySHARE” available for teaching purposes. PISA and PIAAC data are freely available

Learning Outcome

After completing the seminar the student is expected to be able to fulfill the learning outcome specified in the Master curriculum and to be able to:

 

Knowledge:

  • Explain central human capital concepts related to individual behavior;
  • Reflect on the counterfactual problem in econometric applications;
  • Reflect on underlying assumptions for estimating

 

Skills:

  • Be able to choose among econometric models for different applications and argue for the choice;
  • Formulate testable research questions related to casual relationships in health and human capital;
  • Assess not only the advantages of different techniques, but also their pitfalls;
  • Be able to write clearly about data, econometric analyses and results
  • Interpret empirical results within a labor, health, or economics of education theoretical framework.

 

Competences:

  • Professionally, being able to understand and apply empirical strategies to answer specific research questions.

Human Capital: A Theoretical and Empirical Analysis (1994), with Special Reference to Education, Gary S. Becker. Chicago University Press.

 

Health Economics (2013), Jay Bhattacharya, Timothy Hyde and Peter Tu. Palgrave Macmillan.

 

Economics of Education: An Introductory Textbook (2025), Antonio Cabrales and Ismael Sanz. Palgrave Macmillan.

 

Human Capital for Humans: An Accessible Introduction to the Economic Science of People (2025), Pablo A. Peña. Chicago University Press.

 

Introductory Econometrics: A Modern Approach (8thed, 2025), Jeffrey M. Wooldridge. South-Western.

 

Mostly Harmless Econometrics (2009), Joshua Angrist and Jörn-Steffen Pischke. Princeton University Press.

Before taking the seminar, students would benefit from taking Development Economics and Econometrics I and II or Applied Econometric Policy Evaluation.

Students will also benefit from previous or concurrent participation in courses on Advanced Development Economics – Micro Aspects and Advanced Development Economics – Macro Aspects.
Students receive individual guidance from the instructor.
Students prepare a draft assignment, which they present to the teacher and the other students. The students take turns acting as opponents during each other’s presentations. The feedback should especially focus on the written presentation in the draft assignment, with particular emphasis on the introduction.
Exact dates will be available in the seminar’s course room no later than 14 days before the start of the semester

• Kick-off meeting: Week 6 / 36. See exact date in Absalon.

• Additional meetings/introductory teaching/guidance: Optional. See Absalon.

• Deadline for submission of commitment paper/project description:
No later than February 28 / September 30.

• Deadline for uploading seminar paper draft in Absalon: No later than one week before the presentations. See exact date in Absalon.

• Presentations: In the period November 20 – December 11 for the autumn semester and May 1 – 23 for the spring semester.
See exact dates in Absalon.
  • Category
  • Hours
  • Project work
  • 186
  • Seminar
  • 20
  • Total
  • 206
Oral
Individual
Collective
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)

 

Brief written feedback given on commitment papers by supervisor.

Peer written feedback given to early paper drafts half-way through the course.

Collective feedback is given as projects are being presented.

Each student receives oral feedback on the presentation from peers and supervisor.

The supervisor gives the students individual guidance during the seminar.

Credit
7,5 ECTS
Type of assessment
Home assignment
Type of assessment details
Individual or in groups of up to 3.
A seminar paper of 15 standard pages for one person, 22.5 standard pages for 2 and 30 standard pages for 3 students.
See further exam information in the Masters Programme Curriculum.
Examination prerequisites

Attendance in all seminar activities as stated in the Master curriculum.

Reexam: Hand in and have approved a synopsis.

Aid
All aids allowed

Use of AI tools is permitted. You must explain how you have used the tools. When text is solely or mainly generated by an AI tool, the tool used must be quoted as a source.

Marking scale
7-point grading scale
Censorship form
External censorship
Exam period

The seminar paper must be uploaded in Digital Exam.

Common submission date for all seminars: June 1 at 10:00 for the spring semester.

For enrolled students more information about examination, rules, aids etc. is available at the intranet for  Master (UK) and  Master (DK ).

Re-exam

Individual seminar paper of 15 standard pages. See further exam information in the Masters Programme Curriculum.

Deadline and more information is available at  MSc in Economics - KUnet

More information about reexam etc. is available at  Master(UK) and  Master(DK).

Criteria for exam assesment

Students are assessed on the extent to which they master the learning outcome for the seminar and can make use of the knowledge, skills and competencies listed in the learning outcomes in the Curriculum of the Master programme.

Course information

Language
English
Course code
AØKK08457U
Credit
7,5 ECTS
Level
Full Degree Master
Duration
1 semester
Placement
Spring
Course capacity
One class of up to 20 students
Study board
  • Department of Economics, Study Council
Contracting department
  • Department of Economics
Contracting faculty
  • Faculty of Social Sciences
Course Coordinators
  • Gonçalo da Silva Lima   (12-6e76756a6873763573707468476c6a767535727c356b72)
Saved on the 25-09-2026