Course coordinator
Professor Christopher O'Donnell
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
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.
A central theme of the course is the distinction between measuring productivity change and explaining productivity change. Students learn that, given data on outputs and inputs, productivity can be measured using index number methods without specifying an economic model of production, whereas explaining productivity change requires explicit models of production and firm behaviour. Within this framework, the course provides a structured, step-by-step guide to selecting and implementing alternative index number and production frontier estimation methods. Students analyse data using R in RStudio.
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
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
ECON2050 + 2320
Course contact
School enquiries
Course 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
Lecturer
Professor Christopher O'Donnell
Tutor
Mr Zane Haster
Mr Marc Vincent Hagan
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. 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 3 April (Good Friday), Mon 4 May (Labour Day).
- Mid-Semester Break: 6 April - 10 April. Semester 1 classes recommence on Mon 13 April.
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
|
30% |
Quiz 1: Technologies 12/03/2026 1:00 pm Quiz 2: Productivity 19/03/2026 1:00 pm Quiz 3: Efficiency 2/04/2026 1:00 pm |
| Examination |
In-semester Exam
|
30% |
Held on 14 April at 12-2pm. Venue to be confirmed. |
| Tutorial/ Problem Set |
Assignment 1: PFA
|
20% |
8/05/2026 4:00 pm |
| Tutorial/ Problem Set |
Assignment 2: DFA and SFA
|
20% |
29/05/2026 4:00 pm |
Assessment details
MCQs
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 30%
- Due date
Quiz 1: Technologies 12/03/2026 1:00 pm
Quiz 2: Productivity 19/03/2026 1:00 pm
Quiz 3: Efficiency 2/04/2026 1:00 pm
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
Each quiz is worth 10%. Quiz 1 will cover production technologies. Quiz 2 will cover measures of productivity change. Quiz 3 will cover managerial behaviour and measures of efficiency.
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.
Submission guidelines
Students will have 30 minutes to complete each quiz. Each quiz will be released on Blackboard at 7am and must be completed by 1pm 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
Held on 14 April at 12-2pm. Venue to be confirmed.
- Other conditions
- Time limited, Secure.
- Learning outcomes
- L01, L02, L03, L04
Task description
This examination will be held on 14 April at 12-2pm. It will cover production technologies, measures of productivity change, managerial behaviour and measures of efficiency.
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 | 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
8/05/2026 4:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L05, L08, L09, L10
Task description
In this assessment, you will apply efficiency and productivity measurement techniques to a real-world dataset by answering a series of structured, numbered questions. The task assesses your skills in data management, quantitative analysis, interpretation, and communication of results. It develops practical skills widely used in performance benchmarking in research and policy settings.
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.
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
- 20%
- Due date
29/05/2026 4:00 pm
- Other conditions
- Longitudinal.
- Learning outcomes
- L06, L07, L08, L09, L10
Task description
In this assessment, you will apply efficiency and productivity measurement techniques to a real-world dataset by answering a series of structured, numbered questions. The task assesses your skills in data management, quantitative analysis, interpretation, and communication of results. It develops practical skills widely used in performance benchmarking in research and policy settings.
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.
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. Course grade description: demonstrates little or no knowledge or comprehension of productivity and efficiency concepts; unable to solve problems; interprets results incorrectly.ﾠ Awarded for scores 0% -29%. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. Course grade description: demonstrates poor understanding of productivity and efficiency concepts; unable to solve problems; interprets results incorrectly.ﾠ Awarded for scores in the range 30% to 46%. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: demonstrates poor understanding of productivity and efficiency concepts; solves problems with many errors; interprets results poorly.ﾠ Awarded for scores in the range 47% to 49%. |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: demonstrates fair understanding of productivity and efficiency concepts; solves problems with some errors; provides satisfactory interpretation of results.ﾠ Awarded for scores in the range 50% to 64%. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: demonstrates good understanding of productivity and efficiency concepts; solves problems with some errors; provides good interpretation of results.ﾠ Awarded for scores in the range 65% to 74%. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: demonstrates excellent understanding of productivity and efficiency concepts; solves problems with few errors; provides very good interpretation of results.ﾠ Awarded for scores in the range 75% to 84%. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: demonstrates mastery of productivity and efficiency concepts; solves problems with minor or no errors; provides excellent interpretation of results.ﾠ Awarded for scores between 85%+. |
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
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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
Library resources are available 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 (23 Feb - 01 Mar) |
Lecture |
Overview Course structure; overview of topics. Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09, L10 |
Week 2 (02 Mar - 08 Mar) |
Lecture |
Production Technologies A Output, input and production possibilities sets; distance functions. Learning outcomes: L01 |
Week 3 (09 Mar - 15 Mar) |
Lecture |
Production Technologies B Cost, revenue and profit functions; other sets and functions. Learning outcomes: L01 |
Week 4 (16 Mar - 22 Mar) |
Lecture |
Measures of Productivity Change Quantity indexes; productivity indexes; other indexes. Learning outcomes: L02 |
Week 5 (23 Mar - 29 Mar) |
Lecture |
Measures of Efficiency A Output-, input-, revenue- and cost-oriented measures. Learning outcomes: L03, L04 |
Week 6 (30 Mar - 05 Apr) |
Lecture |
Measures of Efficiency B Profit- and productivity-oriented measures. Learning outcomes: L03, L04 |
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-semester Break No classes this week. |
Week 7 (13 Apr - 19 Apr) |
Lecture |
Review and In-semester Examination Learning outcomes: L01, L02, L03, L04 |
Week 8 (20 Apr - 26 Apr) |
Lecture |
Piecewise Frontier Analysis A Basic models; models with different assumptions. Learning outcomes: L05, L08 |
Week 9 (27 Apr - 03 May) |
Lecture |
Piecewise Frontier Analysis B Inference; productivity analysis; other models. Learning outcomes: L09 |
Week 10 (04 May - 10 May) |
Lecture |
Deterministic Frontier Analysis A Basic models; least squares estimation. Learning outcomes: L06, L08 |
Week 11 (11 May - 17 May) |
Lecture |
Deterministic Frontier Analysis B Maximum likelihood estimation; productivity analysis. Learning outcomes: L09 |
Week 12 (18 May - 24 May) |
Lecture |
Stochastic Frontier Analysis Basic models; least squares and maximum likelihood estimation; productivity analysis. Learning outcomes: L07, L08, L09 |
Week 13 (25 May - 31 May) |
Lecture |
Review Review of topics; practical considerations. 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 for Students Policy and Procedure
- AI for Assessment Guide
- Recording of Teaching Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.