ECOS3997: Semester 1, 2020

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Units ECOS3997 Semester 1 2020 [Normal day]

Unit outline_

ECOS3997: Interdisciplinary Project in Economics

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit of study is concerned with the application of economic principles to problems in an interdisciplinary context. It builds on theoretical knowledge acquired in previous studies and introduces methods of applied economic analysis to real-world problems. Initially, a research problem will be presented by a guest lecturer. Supporting lectures will be delivered by the unit coordinator on the nature of research, appropriate theoretical concepts, quantitative methods and communication. Students will have an opportunity to define a research problem, conduct a literature review, analyse data, and present research results in an interdisciplinary context.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	12 credit points at 2000 level from one of the following majors: Economics; Econometrics; Financial Economics; Environmental, Agricultural & Resource Economics
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Shauna Phillips, shauna.phillips@sydney.edu.au
Lecturer(s)	Nicolas de Roos, nicolas.deroos@sydney.edu.au
	Zoe Alderton-flett, zoe.alderton-flett@sydney.edu.au
	Christian Gillitzer, christian.gillitzer@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Final Report written assignment	60%	Week 11 Due date: 15 May 2020 at 23:00	3,000 words
Outcomes assessed:
Assignment	Media Presentation media presentation	40%	Week 13 Due date: 29 May 2020 at 23:00	1,500 words
Outcomes assessed:

Assessment summary

Final Report – written economic analysis report
Media Presentation – communicate economics concepts and ideas to a non economics audience

Assessment criteria

Final Report – written assignment
Media Presentation – communicate economic concepts and ideas to a non-economics audience

For more information see guide to grades.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Penalty: 5% per calendar day late. If work is submitted more than 10 days after the due date, or is submitted after the return date, the mark will be 0.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Stream 1 Lecture 1: Introduction	Lecture (2 hr)
	Stream 2 Lecture 1 Introduction	Lecture (2 hr)
	Stream 3 Lecture 1: Introduction	Lecture (2 hr)
Week 02	Stream 1 Lecture 2: Interdisciplinary lecture; inequality in Australia	Lecture (3 hr)
	Stream 2 Lecture 2 Interdisciplinary lecture; pollution	Lecture (3 hr)
	Stream 3 Lecture 2: Interdisciplinary lecture: Edgeworth cycles	Lecture (2 hr)
Week 03	Stream 1 Lecture 3: Income taxation	Lecture (3 hr)
	Stream 2 Lecture 3 Non-renewable resource extraction (I)	Lecture and tutorial (3 hr)
	Stream 3 Lecture 3: Edgeworth cycles	Lecture (3 hr)
Week 04	Stream 1 Lecture 4: Inequality and mobility	Lecture (3 hr)
	Stream 2 Lecture 4 Non-renewable resource extraction (II)	Lecture and tutorial (3 hr)
	Stream 3 Lecture 4: Collusion	Lecture (3 hr)
Week 05	Stream 1 Lecture 5: Attitudes toward inequality	Lecture (3 hr)
	Stream 2 Lecture 5 Non-renewable resource extraction and pollutions	Lecture and tutorial (3 hr)
	Stream 3 Lecture 5: Obfuscation and collusion	Lecture (3 hr)
Week 06	Stream 1 Lecture 6: Wealth taxation; evasion	Lecture (3 hr)
	Stream 2 Lecture 6 Policy, mining model	Lecture and tutorial (3 hr)
	Stream 3 Lecture 6: Empirical evidence	Lecture (3 hr)
Week 07	Stream 1 Lecture 7: Complete remaining topics	Lecture (3 hr)
	Stream 2 Lecture 7 Mining model	Lecture and tutorial (3 hr)
	Stream 3 Lecture 7: Edgeworth cycle summary	Lecture (3 hr)
Week 08	Stream 1 Lecture 8: Written communication: policy brief	Lecture (3 hr)
	Stream 2 Lecture 8 Written communication: policy brief	Lecture (3 hr)
	Stream 3 Lecture 8: Written communication policy brief	Lecture (3 hr)
Week 09	Stream 1: Free - students work on assignments	Lecture (3 hr)
	Stream 2 Free - students work on assignments	Lecture (3 hr)
	Stream 3 free: students work on assignments	Lecture (3 hr)
Week 10	Stream 1 Lecture 9: Principles of clear communication	Lecture (3 hr)
	Stream 2 Lecture 9 Principles of clear communication	Lecture (3 hr)
	Stream 3 Lecture 9: Principles of clear communication	Lecture (2 hr)
Week 11	Stream 1 Lecture 10: Advanced research techniques	Lecture (3 hr)
	Stream 2 Lecture 10 Advanced research techniques	Lecture (3 hr)
	Stream 3 Lecture 10 Advanced research techniques	Lecture (2 hr)
Week 12	Stream 1 Lecture 11: Assembling a professional product	Lecture (3 hr)
	Stream 2 Lecture 11 Assembling a professional product	Lecture (3 hr)
	Stream 3 Lecture 11 Assembling a professional product	Lecture (2 hr)
Week 13	Stream 1 Free - students work on final assignment	Lecture (3 hr)
	Stream 2 Free - students work on final assignment	Lecture (3 hr)
	Stream 3 Free - students work on final assignment	Lecture (3 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.

Required readings

Please refer to eReserve on the ECOS3997 Canvas site for a list of readings by stream

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 economic principles to problems in an interdisciplinary context.
LO2. Critically review relevant literature.
LO3. Apply appropriate quantitative and analytical techniques to generate and interpret results for interdisciplinary research problems.
LO4. Demonstrate an understanding of insights provided by economics as they apply to real-world problems in an interdisciplinary context.
LO5. Communicate research in an interdisciplinary audience.
LO6. Demonstrate an understanding of the nature and implications of assumptions and value judgements involved in interdisciplinary research.

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.

This is the first time this unit will run

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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