Course coordinator
Dr Haishan Yuan
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
Analysis of labour markets & its application to contemporary labour market issues, including labour demand & supply issues, unemployment, employment, wage determination & human capital development.
The principal objective of the course is to introduce students to the body of economic theory seeking to explain the workings of labour markets and effects of labour market policies. The course emphasizes bothﾠtheoretical and empiricalﾠaspects of employment and wageﾠsetting behaviour in labour markets. In general, the course relates the subject matter of labour economics to mainstream economic theory rather than providing an institutional, descriptive analysis of the labour market. The course is devoted mainly to microeconomic aspects of labour market issues, although certain major macroeconomic, institutional and policy issues will be covered. In particular, the course illustrates the application of microeconomic methods and models to selected topics, including labour supply, labour demand, wage structure and labour mobility.
Course requirements
Assumed background
It is assumed that students have completed a course of study in both Microeconomics and Macroeconomics at the introductory level.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1010
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON1310
Course contact
School enquiries
School Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed toﾠenquiries@economics.uq.edu.au.ﾠ
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1.
Tutorials commence in Week 2.
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
- Public Holidays: Fri 3 April (Good Friday), Mon 4 May (Labour Day).
- Mid-Semester Break: 6 April - 10 April. Semester 1 classes recommence on Mon 13 April.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.
Aims and outcomes
The principal objective of the course is to introduce students to the body of economic theory seeking to explain the workings of labour markets and effects of labour market policies. The course emphasizes bothﾠtheoretical and empiricalﾠaspects of employment and wageﾠsetting behaviour in labour markets. In general, the course relates the subject matter of labour economics to mainstream economic theory rather than providing an institutional, descriptive analysis of the labour market. The course is devoted mainly to microeconomic aspects of labour market issues, although certain major macroeconomic, institutional and policy issues will be covered. In particular, the course illustrates the application of microeconomic methods and models to selected topics, including labour supply, labour demand, wage structure and labour mobility.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain and evaluate labour market issues, and knowledge and understanding of labour market institutions, contemporary issues and related economic data.
LO2.
Define and analyse problems based on modeling frameworks and an understanding of relevant data.
LO3.
Identify key contemporary labour market issues, both domestic and international, and discuss possible solutions and potential innovations towards improving current policies and practices.
LO4.
Collect, analyse, and organise information related to the labour market in a group setting through the selected articles and to present those ideas clearly and fluently in both written and spoken forms.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz, Tutorial/ Problem Set |
Quizzes
|
40% |
Online through Blackboard 20/03/2026 2:00 pm In-person during lecture 20/04/2026 11:00 am In-person during lecture 25/05/2026 11:00 am |
| Presentation |
Presentation
|
10% |
22/05/2026 2:00 pm |
| Examination |
End-of-semester Exam
|
50% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
Quizzes
- Identity Verified
- In-person
- Online
- Mode
- Written
- Category
- Quiz, Tutorial/ Problem Set
- Weight
- 40%
- Due date
Online through Blackboard 20/03/2026 2:00 pm
In-person during lecture 20/04/2026 11:00 am
In-person during lecture 25/05/2026 11:00 am
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
There are three quizzes, including one online quiz (Quiz 1) to be submitted via Blackboard and two in-person quizzes to be held during classes (Quiz 2 and Quiz 3). The quizzes will potentially cover the materials discussed up to the time of the quiz. They may consist of multiple choice questions, technical/numerical questions, problem solving, and/or short-answer written questions. For Quiz 1, students may consult any materials, physical or electronic. For Quizzes 2 and 3, students may consult any printed or handwritten materials that they bring to the classroom, but must not use any electronic devices except a simple calculator. The online quiz, Quiz 1, is due at 2 PM on March 20, 2026, i.e., the Friday of Week 4, and to be submitted via Blackboard. Quiz 2 is to be completed in person during the lecture of Week 8. Quiz 3 is to be completed in person during the lecture of Week 13. Quiz 1 carries 10% of the total assessment weight. Each of Quizzes 2 and 3 carries 15% of the total assessment weight.
Statement on AI use for Quiz 1 (online): Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Statement on AI use for Quiz 2 & 3 (in-person): This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Quiz 1 will be available 48 hours before its due time and can be accessed via Blackboard. Once released, students may start the online quiz anytime. Once a student starts the online quiz, they have up to 1 hour to complete it (unless the quiz is started less than 1 hour before the due time, in which case the student has until the due time to finish the online quiz). Quizzes 2 and 3 will be handed out at the beginning of the respective lectures and are due 45 minutes after the start.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
Presentation
- Identity Verified
- Online
- Mode
- Activity/ Performance, Oral
- Category
- Presentation
- Weight
- 10%
- Due date
22/05/2026 2:00 pm
- Learning outcomes
- L03, L04
Task description
A selection of topics related to the course materials will be available for student presentations. Students may choose their preferred topic. The default presentation format is a recorded video submission. Both the recorded video and presentation slides must be submitted via Blackboard by 2:00 PM AEST on Friday, May 22, 2026 (Week 12). However, a limited number of in-person presentation slots will be available during the lectures in Week 13. Each in-person slot will have a pre-assigned topic. The complete list of in-person presentation slots and their corresponding topics will be released by the end of Week 4. These slots will be allocated on a first-come, first-served basis.
In-person presentations will be automatically recorded by the lecture recording system.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
End-of-semester Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04
Task description
The end-of-semester exam is designed to cover all learning objectives and to test both depth and breadth of students' knowledge relevant to the course. The exam will cover the course material (including lectures and exercises) of the whole semester. The exam may include multiple choice questions, problem-solving question, short-answer, and/or short essay. The exam is to be submitted online. Students may consult any printed or handwritten materials that they bring to the classroom, but must not use any electronic devices except a simple calculator.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open book examination - any written or printed material is permitted; material may be annotated |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. Course grade description: Complete failure to demonstrate sufficient basic knowledge and understanding of key concepts, relationships and models. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. Course grade description: Failure to demonstrate sufficient basic knowledge of key concepts, relationships and models. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Some basic knowledge is evident, but detail and precision are patently absent at a level sufficient to merit competency and therefore a passing grade. |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: A basic knowledge of core concepts and modelling techniques obtained. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: A more thorough and accurate understanding of modelling analysis displayed through both written and verbal means. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: A thorough, accurate and comprehensive knowledge of concepts and model analysis displayed and some evidence of wider reading. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Accuracy and attention to detail displayed in all analysis together with significant evidence of critical evaluation of material and of wider reading. |
Additional course grading information
A student's final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Modern Labor Economics: Theory and Public Policy, 13th (International) ed., by Ronald G. Ehrenberg & Robert S. Smith | own item needed |
Recommended
| Item | Description | Further Requirement |
|---|---|---|
| Labor Economics, by George J. Borjas, McGraw Hill Education, 7th edition, 2015. | ||
| Mastering 'Metrics: The Path from Cause to Effect, by Joshua D. Angrist & Jörn-Steffen Pischke, Prince University Press | ||
| Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge, South-Western, 5th edition, 2013 | ||
| Mathematics for Economists, by Carl P. Simon and Lawrence E. Blume, 1994 |
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Lecture |
Week 1 Introduction Learning outcomes: L01, L02 |
Week 2 (02 Mar - 08 Mar) |
Lecture |
Week 2 Labor Demand Learning outcomes: L01, L02 |
Week 3 (09 Mar - 15 Mar) |
Lecture |
Week 3 Labor Supply Learning outcomes: L01, L02, L03 |
Week 4 (16 Mar - 22 Mar) |
Lecture |
Week 4 Human Capital Learning outcomes: L01, L02, L03 |
Week 5 (23 Mar - 29 Mar) |
Lecture |
Week 5 Migration & Immigration Learning outcomes: L01, L02, L03, L04 |
Week 6 (30 Mar - 05 Apr) |
Lecture |
Week 6 Labor Market Discrimination Learning outcomes: L01, L02, L03, L04 |
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-sem break |
Week 7 (13 Apr - 19 Apr) |
Lecture |
Week 7 Compensating Wage Differentials Learning outcomes: L01, L02, L03, L04 |
Week 8 (20 Apr - 26 Apr) |
Workshop |
Week 8 Quiz 2 and Presentation Workshop Learning outcomes: L01, L02, L03, L04 |
Week 9 (27 Apr - 03 May) |
Lecture |
Week 9 Inequality in Earnings Learning outcomes: L01, L02 |
Week 10 (04 May - 10 May) |
No student involvement (Breaks, information) |
Week 10 Public Holiday (Labour Day) Learning outcomes: L01, L02 |
Week 11 (11 May - 17 May) |
Lecture |
Week 11 Labor Market Frictions Learning outcomes: L01, L02, L03, L04 |
Week 12 (18 May - 24 May) |
Lecture |
Week 12 Efficiency Wage Learning outcomes: L01, L02, L03, L04 |
Week 13 (25 May - 31 May) |
Workshop |
Week 13 Quiz 3 & Presentations Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.