23509 Empirical Methods for Policy Evaluation

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Subject handbook information prior to 2024 is available in the Archives.

UTS: Business: Economics
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): 23571 Introductory Econometrics
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 23572 Applied Microeconometrics

Note

Students who have completed another linear regression subject may apply to have the 23571 Introductory Econometrics prerequisite waived.

Description

Empirical Methods for Policy Evaluation equips students with the modern toolkit of causal inference and rigorous policy evaluation. It covers experimental, quasi-experimental and observational data analysis. These tools are fundamental for estimating the impact of policies, programs and interventions. These methods are used by analysts in the public and private sector. They are also foundational for academic research on causal relationships in economics and many other disciplines.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	explain the aims of empirical impact evaluation alongside other types of evaluations
2.	estimate causal effects using advanced regression techniques including instrumental variable regression, regression discontinuity design, and panel data models
3.	apply quasi-experimental evaluation techniques to real-world scenarios
4.	communicate economic analysis in written form

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the following program learning objectives:

Critically analyse economic problems in Australian and global society using and justifying appropriate economic concepts and frameworks (1.1)
Combine economic analysis, data and econometric techniques to address typical complex problems faced by economists in diverse work environments (4.1)

Contribution to the development of graduate attributes

The subject contributes to the Bachelor of Business and Bachelor of Economics learning goals by providing students with data analytic skills that are in high demand both in the private sector and in the public sector.

This subject specifically contributes to the following graduate attribute(s):

Intellectual rigour and innovative problem solving
Professional and technical competence

Teaching and learning strategies

This subject will be delivered through a combination of lectures and active learning experiences where ongoing feedback is provided in weekly tutorials / laboratories. It is imperative that students prepare for and attend all classes. These classes will be face-to-face and/or through remote access.

Subject delivery is based on a problem-based strategy, designed to develop students’ understanding of and ability to apply the content covered in the lectures.

The assessment structure emphasizes collaborative work, enabling students to work together and learn from each other.
The subject will make use of an advanced econometrics package for the purpose of analysing data. Students are also challenged to develop their skill in using the econometric software in order to complete problem sets.

In addition to feedback provided in each interactive session, further feedback for the problem sets will be made available.
Students are expected to visit the UTS Learning Management System web site of the subject, which provides all the relevant information and materials, including lecture notes, problem sets, and papers for reading assignments.

In the second half of the semester, students are expected to work on their end-of-term research project, which will allow students to master the methods taught in the class by learning-by-doing rather than simply solving ready-made problem sets. For this reason, students will work on their original research topic.

Content (topics)

Types of evaluations
Review of linear regression – CEFs, regression anatomy, dummy variables, interactions
Directed Acyclic Graphs (DAGs)
Randomised experiments in theory and in practice
Difference-in-differences
Panel data methods
Instrumental variables regression
Regression Discontinuity Design

Assessment

Assessment task 1: Problem Sets (Individual)

Objective(s):	This addresses subject learning objective(s): 2, 3 and 4 This addresses program learning objectives(s): 1.1 and 4.1
Weight:	20%
Criteria:	Critical discussion of key concepts in the realms of program evaluation and econometrics Competent use of the econometric package and application of econometric techniques to produce relevant output for set problems Provide working out to explain the derivation of solutions Writing (organisation, focus, readability, and exposition) Each assignment question must be addressed. The answers do not necessarily have to be completely correct to receive full marks. Figures and tables (readability and effectiveness)

Objective(s):

This addresses subject learning objective(s):

2, 3 and 4

This addresses program learning objectives(s):

1.1 and 4.1

Weight:

20%

Criteria:

Critical discussion of key concepts in the realms of program evaluation and econometrics
Competent use of the econometric package and application of econometric techniques to produce relevant output for set problems
Provide working out to explain the derivation of solutions
Writing (organisation, focus, readability, and exposition)
Each assignment question must be addressed. The answers do not necessarily have to be completely correct to receive full marks.
Figures and tables (readability and effectiveness)

Assessment task 2: Research Project (30% Group and 10% Individual)

Objective(s):	This addresses subject learning objective(s): 2, 3 and 4
Weight:	40%
Length:	Your research paper should not exceed 15 pages of 1.5 spaced text, which includes key figures and tables. An Appendix should follow, containing a snapshot of your data and your R codes; this Appendix does not count towards the page limit. You may also add extra figures and tables in this Appendix. Additionally, the title page, table of contents, and references are also excluded from the page limit.
Criteria:	Quality of the methodological design and of the discussion on its suitability (potential bias, causal argument) Other aspects in econometric work such as thoroughness, careful data check, discussion of summary stats, choice of statistical tests, analysis of alternative specifications Completeness of the literature review and depth of discussion of the existing literature Relevance and significance of the topic Quality of the exposition (writing, organisation, focus, conciseness, readability) Choice of figures and tables (readability and effectiveness)

Assessment task 3: Final Exam (Individual)

Objective(s):	This addresses subject learning objective(s): 1, 2 and 4
Weight:	40%
Criteria:	Ability to critically discuss key concepts in the realms of program evaluation and econometrics Competent application of econometric techniques to set problems Writing (organisation, focus, readability, and exposition)

Minimum requirements

Students must achieve at least 50% of the subject’s total marks

Required texts

Cunningham, Scott (2021) Causal Inference: The Mixtape, Yale University Press

References

Stock, James & Watson, Mark (2019) Introduction to Econometrics, 4th ed., Global Edition, Pearson
Angrist, J & Pischke, J (2014) Mastering 'Metrics: The Path from Cause to Effect, Princeton
Wooldridge, J.M. (2019) Introductory Econometrics: A Modern Approach, 7th Edition, Cengage