Outline

Overview

The purpose of this unit is to study some of the major issues in modern labour markets. Trends such as the increase in part-time work, the growing inequality in income and earnings, changes in the returns to education, and the simultaneous increase in hours of work and unemployment are addressed. The material consists of both empirical facts relating to the labour markets and the theories which are used to understand these facts. Part of the unit is devoted to the study of wage and employment contracts in the presence of uncertainty and other information problems. Imperfect information will have implications for the level of employment and unemployment, the structure of wages, and the use of particular forms of compensation such as bonuses, trust funds, and performance bonds.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ECON6001 or ECON6701 or ECON6002 or ECON6702
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Katrien Stevens, katrien.stevens@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Paper-based exam	45%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	4x Online quizzes Variety of question types	10%	Multiple weeks	Four quizzes	AI allowed
Outcomes assessed:
In-class quiz	Quiz student presentations Quiz questions based on student presentations.	4%	Multiple weeks	10 mins each presentation week	AI allowed
Outcomes assessed:
Written work	Review article Written report about an assigned research article	8%	Multiple weeks	max. 500 words (TBC)	AI allowed
Outcomes assessed:
Presentation	In-class Presentation Presentation of assigned research article and leading of class discussion	8%	Multiple weeks	30 minutes	AI allowed
Outcomes assessed:
Written test	In-semester test Paper-based test	25%	Week 08 Due date: 20 Apr 2026 at 14:30	1 hour	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Online quizzes: 4 online quizzes, which will run throughout the semester and will be accessible for multiple days.
In-semester test: based on the material covered in Weeks 1 to 5.
Final exam: covers material from the entire unit but with an emphasis on material discussed after and not included in the in-semester test.
Review article: Students will write a report in which they review some of the key ideas of an assigned research article. The article will be chosen from a list of assigned readings and is related to topics discussed in lectures. This is aimed at deepening and extending insights into the material studied. NO simple extensions for this assignment as it is due shortly before the (group) presentation of the research article.
In-class quiz student presentations: students will respond to quiz questions after each student presentation.
Group presentation: In groups, students will present an overview of the research article (also discussed in their review article). The group presenting will also prepare questions for and lead class discussion related to the article. These student presentations are planned for Weeks 9, 12 and 13 (TBC).

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a High Distinction indicates work of an exceptional standard, a Distinction a very high standard, a Credit a good standard, and a Pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75 - 84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65 - 74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50 - 64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to labour economics	Lecture (3 hr)
Week 02	Individual labour supply	Lecture (3 hr)
Week 03	Dynamic labour supply (life-cycle)	Lecture (3 hr)
Week 04	Education and human capital	Lecture (3 hr)
Week 05	Labour demand and adjustment costs	Lecture (3 hr)
Week 06	Labour market equilibrium and compensating wage differentials	Lecture (3 hr)
Week 07	Discrimination	Lecture (3 hr)
Week 08	In-semester test	Lecture (1 hr)
Week 09	Student presentations 1	Lecture (3 hr)
Week 10	Contract models: incentives and insurance	Lecture (3 hr)
Week 11	Unemployment	Lecture (3 hr)
Week 12	Student presentations 2	Lecture (3 hr)
Week 13	Student presentations 3	Lecture (3 hr)

Attendance and class requirements

Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold. If a unit of study has a participation mark, your attendance may influence this mark.
Lecture recordings: Most lectures (in recording-equipped venues) will be recorded and made available to students on Canvas. However, you should not rely on lecture recording to substitute your classroom learning experience.
Preparation: Students should commit to spend approximately three hours preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

The key textbook for this unit is R.G. Ehrenberg, R.S. Smith and K.F Hallock Modern Labor Economics: Theory and Public Policy (International Edition), Routledge: 15th Edition (2025), 14th Edition (2023) or 13th Edition (2021). This textbook is available for purchase online as either an e-book or a hard copy. The Uni library also offers limited online access to this textbook. [Previous editions of this textbook can be used, but students are responsible for studying the correct chapters/materials.]

The textbook will be supplemented with a number of published research articles. These articles are required reading and will form the basis for class discussion. Knowledge of these papers will be assumed and tested in an exam. These additional readings can be accessed through the Reading List on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. understand the economic concepts, theories, and approaches used in the study of labour economics
LO2. apply appropriate economic tools to analyse decision-making by suppliers and demanders of labour
LO3. evaluate underlying theories, concepts, assumptions, limitations, and arguments in labour economics
LO4. understand the main econometric problems and empirical techniques used in analysing labour markets, and in evaluating alternative theories
LO5. present coherent arguments on current issues in labour markets and labour market policies
LO6. communicate effectively, both orally and in writing, through assessment tasks and tutorial participation.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Student feedback has confirmed that student learning is enhanced by (1) students preparing for and being actively involved in tutorials; and (2) active participation in class, incl. in discussions after student presentations. A set of 4 online quizzes has been introduced to support student learning throughout the semester. In addition, students will be assigned to tutorial groups to prepare and present solutions to a problem set in class. Further feedback is welcome.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ECON6009: Economics of the Labour Market

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links