ECOS3037: Semester 1, 2026

Units ECOS3037 Semester 1 2026 [Normal day]

Unit outline_

ECOS3037: Behavioural Insights for Public Policy

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

Overview

This unit focuses on the application of behavioural economics to policy issues. Students will gain a solid understanding of behavioural economics and apply it to key policy areas like early childhood development, education, health, labour markets, and tax and transfer systems. Questions explored include: Why do many people set ambitious goals but fail to follow through? How does poverty affect decision-making? How should we choose between nudges and paternalistic policies? The unit develops essential skills for careers in public policy or other sectors that require an in-depth understanding of daily decision-making and its long-term effects.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	(ECOS2030 or ECMT1020) and (ECOS2001 or ECOS2901 or ECOS2002 or ECOS2902)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Juliana Silva Goncalves, juliana.goncalves@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	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Interactive oral	Participation In-class participation throughout the semester and Q&A following presentation assignment	10%	Multiple weeks	N/A	AI prohibited
Outcomes assessed:
In-person written or creative task	Policy brief In-person written assessment - 500 words	10%	Week 07	1 hour	AI prohibited
Outcomes assessed:
In-person written or creative task	Research proposal In person written assessment- 1000 words	20%	Week 11	2 hours	AI prohibited
Outcomes assessed:
Interactive oral	Presentation In-class presentation of research proposal	10%	Week 13	15-20 minutes (750 words equivalent)	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

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	Mark	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 Behavioural Economics	Lecture (2 hr)
Week 02	Behavioural Economics Meets Public Policy	Lecture (2 hr)
Week 02	Tutorial 1	Tutorial (1 hr)
Week 03	Self-Control	Lecture (2 hr)
Week 03	Tutorial 2	Tutorial (1 hr)
Week 04	Self-Control, Locus of Control, and the Labour Market	Lecture (2 hr)
Week 04	Tutorial 3	Tutorial (1 hr)
Week 05	Principal-Agent Model, Motivation, and Non-standard Preferences	Lecture (2 hr)
Week 05	Tutorial 4	Tutorial (1 hr)
Week 06	Attention & Poverty	Lecture (2 hr)
Week 06	Tutorial 5	Tutorial (1 hr)
Week 07	Introduction to Experimental Economics	Lecture (2 hr)
Week 07	Tutorial 6: Policy Brief	Tutorial (1 hr)
Week 08	Behavioural Insights for Early Childhood Policy	Lecture (2 hr)
Week 08	Tutorial 7	Tutorial (1 hr)
Week 10	Behavioural Insights for Education Policy	Lecture (2 hr)
Week 10	Tutorial 8	Tutorial (1 hr)
Week 11	Behavioural Insights for Health Policy	Lecture (2 hr)
Week 11	Tutorial 9: Research Proposal	Tutorial (1 hr)
Week 12	Social Forces, Misperceptions & Policy	Lecture (2 hr)
Week 12	Tutorial 10	Tutorial (1 hr)
Week 13	Group Presentations	Lecture (2 hr)

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.

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. Apply behavioural economics insights to make sense of human behaviour and policy issues
LO2. Design policy interventions that leverage behavioural economic insights to improve their effectiveness
LO3. Synthesise and critically analyse behavioural economics research relevant for public policy
LO4. Effectively communicate behavioural economics insights to key stakeholders in academia, government, private or non-profit sectors

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.

The quantity of the materials covered in class will be adjusted to reflect feedback about the overall volume of learning.

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links