Course coordinator
Professor Christopher O'Donnell
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
This course provides a comprehensive coverage of modern methods for analysing the productivity and efficiency of different types of decision-making units (e.g., individuals, firms, industries, regions, economies). Students learn how different assumptions concerning technologies, markets and firm behaviour can be used to guide the construction of proper productivity indexes. They then learn how these indexes can be exhaustively decomposed into measures of technical change, environmental change, and various types of efficiency change. Students learn how to estimate these components using data envelopment analysis (DEA), deterministic frontier analysis (DFA) and stochastic frontier analysis (SFA) methods. Students gain an understanding of why the estimation of these components is critically important for public policy-making. The course has a strong applied focus.
The focus of the course is on measures of firm performance that are of practical relevance to policy makers (e.g., output per employee, technical efficiency). The course explains how various assumptions concerning production technologies and managerial behaviour can be used to inform the computation (or estimation) of many of these measures. For example, under very strong assumptions, changes in total factor productivity (TFP) can be estimated using the well-known growth accounting method of Solow (1957). Under much weaker assumptions, changes in TFP can be measured using the regression methods taught in ECON2300 (Introductory Econometrics). The course provides students with a step-by-step guide on how to choose between and implementﾠthese types of methods. Students analyse different datasets using RStudio. RStudio is a free integrated development environment (IDE) for R. In turn, R is a free open-source software environment that is widely used for statistical computing and graphics in academia, government and industry.
Course requirements
Assumed background
Before attempting this course, you are advised that it is important to complete the appropriate prerequisite course(s) listed on the front of this course profile. No responsibility will be accepted by UQ School of Economics, the Faculty of Business, Economics and Law or The University of Queensland for poor student performance occurring in courses where the appropriate prerequisite(s) has/have not been completed, for any reason whatsoever.ﾠﾠ Knowledge of theﾠmaterial taught in ECON2050 (Mathematical Economics) and, to a lesser extent, ECON2320 (Business and Economic Decision Techniques) would certainly beﾠuseful but is not required.ﾠﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON2300 or 7310
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
(ECON2050 + 2320) or (ECON7250 + 7322)
Incompatible
You can't enrol in this course if you've already completed the following:
ECON3340
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 to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Professor Christopher O'Donnell
Tutor
Professor Christopher O'Donnell
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 1. 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.
Important Dates:
· Public Holidays: Fri 18 April (Good Friday), Mon 5ﾠMay (Labour Day).
· Mid-Semester Break: 21ﾠApril - 25ﾠApril. Semester 1 classes recommence on Mon 28ﾠApril.
Aims and outcomes
The aims of this course are to
- show how various assumptions concerning production technologies and managerial behaviour can be used to inform the measurement of efficiency and productivity change;
- give students experience estimating various measures of technical change, environmental change and efficiency change;
- explain the different types of government policies that can be used to improve managerial performance; and
- provide the skills necessary to critically evaluate papers appearing in the mainstream productivity literature.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
explain the most common assumptions made about production technologies (e.g., inactivity, no free lunch).
LO2.
compute measures of output and input quantity change (and therefore productivity change) in ways that are consistent with measurement theory.
LO3.
explain the situations under which different firm managers may choose to maximise output, minimise input, maximise revenue, minimise cost, maximise profit and/or maximise productivity.
LO4.
define various measures of technical, mix, scale, allocative, revenue, cost and profit efficiency.
LO5.
explain the main assumptions that underpin piecewise frontier analysis and use DEA and FDH methods to estimate a piecewise-linear frontier
LO6.
explain the main assumptions that underpin deterministic frontier analysis and use GA, LS and ML methods to estimate the parameters of a deterministic frontier model
LO7.
explain the main assumptions that underpin stochastic frontier analysis and use LS and ML methods to estimate the parameters of a stochastic frontier model
LO8.
use DEA, LS and ML methods to estimate levels of technical, cost, revenue and profit efficiency.
LO9.
use DEA, LS and ML methods to decompose proper TFP indexes into measures of technical change, environmental change and various types of efficiency change.
LO10.
identify the policies that governments might use to target the different drivers of productivity change.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
MCQs
|
20% |
Quiz 1: 21/03/2025 4:00 pm Quiz 2: 4/04/2025 4:00 pm |
| Examination |
In-semester Exam
|
30% |
End of the Week 8 tutorial. |
| Tutorial/ Problem Set |
Assignment 1: PFA
|
20% |
2/05/2025 4:00 pm |
| Tutorial/ Problem Set |
Assignment 2: DFA and SFA
|
30% |
30/05/2025 4:00 pm |
Assessment details
MCQs
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 20%
- Due date
Quiz 1: 21/03/2025 4:00 pm
Quiz 2: 4/04/2025 4:00 pm
- Other conditions
- Student specific, Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
Quiz 1 will cover production technologies and measures of productivity change. Quiz 2 will cover managerial behaviour and measures of efficiency. These assessment tasks evaluate student's abilities, skills and knowledge without the aid of Artificial Intelligence (Al). Students are advised that the use of Al technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Each quiz is worth 10%. Students will have 30 minutes to complete each quiz. Each quiz will be released on Blackboard at 10am and must be completed by 4pm on the due date.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
In-semester Exam
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of the Week 8 tutorial.
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
This examination will be held in the Week 8 tutorial. It will evaluate student's abilities, skills and knowledge without the aid of Artificial Intelligence (Al). Students are advised that the use of Al technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Answer sheets/booklets will be collected at the end of the examination.
Deferral or extension
You may be able to defer this exam.
Assignment 1: PFA
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
2/05/2025 4:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L05, L08, L09, L10
Task description
This assessment task evaluates student's abilities, skills and knowledge without the aid of Artificial Intelligence (Al). Students are advised that the use of Al technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This assignment must be submitted electronically via Blackboard.
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.
Assignment 2: DFA and SFA
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 30%
- Due date
30/05/2025 4:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L06, L07, L08, L09, L10
Task description
This assessment task evaluates student's abilities, skills and knowledge without the aid of Artificial Intelligence (Al). Students are advised that the use of Al technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
This assignment must be submitted electronically via Blackboard.
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.
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. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
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
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies and Procedures. Please refer to the Academic Integrity Modules (AIM). It is strongly recommended that you complete the AIM if you have not already done so.
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 |
|---|---|---|
| Computer | Tutorials will be held in a Bring Your Own Device teaching space. | own item needed |
| R and RStudio | Used for all computer work. | own item needed |
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 (24 Feb - 02 Mar) |
Lecture |
Overview Course structure; overview of topics; introduction to R and RStudio Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10 |
Week 2 (03 Mar - 09 Mar) |
Lecture |
Production Technologies Output sets; distance functions; other sets and functions Learning outcomes: L01 |
Week 3 (10 Mar - 16 Mar) |
Not Timetabled |
No Classes |
Week 4 (17 Mar - 23 Mar) |
Lecture |
Measures of Productivity Change Quantity indexes, productivity indexes, other indexes Learning outcomes: L02 |
Week 5 (24 Mar - 30 Mar) |
Lecture |
Managerial Behaviour and Measures of Efficiency Output maximisation; cost minimisation; other types of behaviour; output- and cost-oriented measures of efficiency; other measures Learning outcomes: L03, L04 |
Week 6 (31 Mar - 06 Apr) |
Lecture |
Piecewise Frontier Analysis A Basic models; models with different assumptions Learning outcomes: L05, L08 |
Week 7 (07 Apr - 13 Apr) |
Lecture |
Piecewise Frontier Analysis B Inference; productivity analysis; other models Learning outcomes: L09 |
Week 8 (14 Apr - 20 Apr) |
Lecture |
Review and In-semester Examination Learning outcomes: L01, L02, L03, L04 |
Week 9 (28 Apr - 04 May) |
Lecture |
Deterministic Frontier Analysis A Basic models; least squares estimation Learning outcomes: L06, L08 |
Week 10 (05 May - 11 May) |
Lecture |
Deterministic Frontier Analysis B Maximum likelihood estimation; productivity analysis Learning outcomes: L09 |
Week 11 (12 May - 18 May) |
Lecture |
Stochastic Frontier Analysis A Basic models; least squares and maximum likelihood estimation Learning outcomes: L07, L08 |
Week 12 (19 May - 25 May) |
Lecture |
Stochastic Frontier Analysis B Productivity analysis Learning outcomes: L09 |
Week 13 (26 May - 01 Jun) |
Lecture |
Review Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10 |
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 - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.