Summer and winter research projects

Retirees often avoid purchasing life annuities. Adverse selection, where healthier individuals are more likely to buy annuities, can lead to thin private markets and justify a strong public pension system to provide longevity insurance. A key driver of low annuity demand is that retirees may value leaving bequests or retaining flexible access to savings.

This project aims to study the implications of these problems, generating key insights to inform policy and regulation, with an especial focus on Australia’s retirement.

Information design techniques applied to this problem may be part of this project.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 – 23 hours per week.
• Applicant is not required on-site for the project.

Scholars may gain knowledge in the literature of annuities and adverse selection (see references below).

They will be encouraged to generate novel models, with the perspectives to generate publications from their research.

Students may be asked to produce a final report summarising their contribution at the end of their project.

References:

• Coates, Brendan, Joey Moloney, and Esther Suckling. Simpler super: taking the stress out of retirement. Grattan Institute, 2025.
• Lockwood, Lee M. "Bequest motives and the annuity puzzle." Review of economic dynamics 15.2 (2012): 226-243.
• Davidoff, Thomas, Jeffrey R. Brown, and Peter A. Diamond. "Annuities and individual welfare." American Economic Review 95.5 (2005): 1573-1590.
• Dworczak, Piotr, and Giorgio Martini. "The simple economics of optimal persuasion." Journal of Political Economy 127.5 (2019): 1993-2048.
• Hosseini, Roozbeh. "Adverse selection in the annuity market and the role for social security." Journal of Political Economy 123.4 (2015): 941-984.

3rd- and 4th-year students with a background in economics and/or mathematics, particularly:

• microeconomics
• macroeconomics
• game theory
• probability.

• Dr. Andres Bellofato
• Dr. Carlos Oyarzun

To find out more about the project, email any of the supervisors (or both):

• a.bellofatto@uq.edu.au
• c.oyarzun@uq.edu.au

This project examines the economic impact of consumers delegating purchasing decisions to autonomous artificial intelligence (AI) agents. As digital markets evolve, it is critical to evaluate whether these agents enhance market efficiency by improving the quality of consumer choices, or whether they create new inefficiencies.

The student will contribute to the development of a standardised benchmarking index for commercial AI shopping agents, evaluating their performance across various market structures.

The project builds on preliminary work that has surveyed the current landscape of AI shopping agents and existing benchmarks, and piloted an evaluation of leading commercial agents; the student will extend this foundation.

References:

• Wang, J., Xiao, K., Sun, Q., Zhao, H., Luo, T., Zhang, J. D., & Zeng, X. (2026). ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents. https://arxiv.org/abs/2508.04266
• Du, Z., Li, T., Zhang, Y., & Zhang, H. (2026). EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent. https://arxiv.org/abs/2606.17698
• Allouah, A., Besbes, O., Figueroa, J., Kanoria, Y., & Kumar, A. (2025). What Is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic E-Commerce. Columbia Business School Research Paper No. 381574. https://arxiv.org/abs/2508.02630
• Shahidi, P., Rusak, G., Manning, B. S., Fradkin, A., & Horton, J. J. (2025). The Coasean singularity? Demand, supply, and market design with AI agents. In A. K. Agrawal, A. Korinek, & E. Brynjolfsson (Eds.), The Economics of Transformative AI. University of Chicago Press.

• 6 weeks (11 January – 19 February 2027).
• 20 – 23 hours per week.

The student will gain a foundational understanding of behavioural industrial organisation and experimental economics.

They will develop skills in evaluating algorithmic decision-making and understanding how market complexity impacts choice quality.

Expected deliverables include: 

• a literature review
• a preliminary benchmarking report comparing the decision accuracy of 3–5 commercial AI agents.

Economics students with an interest in microeconomics, or maths/science/engineering students with some economics background.

Dr Kenan Kalaycı

To find out more about the project, email Dr Kenan Kalaycı:

• k.kalayci@uq.edu.au.

The summer project forms part of a larger study exploring the relationship between student wellbeing, bullying and victimisation, using PISA (Programme for International Student Assessment) data.

The project focuses on school autonomy as a potential determinant of student wellbeing and behaviour.

• 6 weeks (11 January 2027 – 19 February 2027).
• 22 hrs per week.
• The applicant can work from home, but will be required to attend some meetings with supervisors in person.

Under the guidance of their supervisors, the student will be involved in data processing, econometric analysis, reporting results and visualisation.

They will also be asked to present their work in the form of short reports.

This project will give students aspiring to or considering an Honours, Masters, or PhD degree a taste of research.

This project is open to applications from students who have:

• an intermediate level of training in econometrics and statistics, who are also proficient in STATA or R
• completed ECON2300 Introductory Econometrics or ECON7310 Elements of Econometrics with a grade of 6 or higher.

Completion of ECON3360/ECON7360 Causal Inference for Econometrics and received a grade of 6 or higher is desirable.

Professor KK Tang 

To find out more about the project, email Professor KK Tang:

• kk.tang@uq.edu.au.

Wealthy Australians can be taxed as individuals (top personal rate 47%) or through the companies and trusts they control (corporate rate 25 – 30%). This project builds the empirical groundwork for understanding how that choice shapes who actually bears the tax burden.

Using 2 publicly available sources:(i) the ATO’s de-identified Individuals Sample File (a 2% sample of individual tax returns) and (ii) the ATO’s Corporate Tax Transparency data and company/trust statistics; the student will construct a set of stylized facts on income concentration and effective tax rates across both the personal and corporate tax bases and document the gap (“wedge”) between the 2.

This is the first stage of a potential larger research project that will ultimately link individuals to the corporations they own using confidential tax microdata (ABS BLADE/PLIDA); the public-data facts assembled here motivate and guide that future work.

Core tasks:

• Assemble and clean the ATO Individuals Sample File, Corporate Tax Transparency data, and relevant ATO Taxation Statistics tables (with the World Inequality Database for historical and international context).
• Compute effective average tax rates across the personal income distribution (including a dividend-imputation / franking-credit adjustment) and effective corporate tax rates from company data.
• Document income concentration, the corporate-versus-personal tax-rate wedge, and their evolution over time.
• Produce clear figures and tables in a documented, reproducible code repository (Python, Matlab, R, or Stata).

• 6 weeks (11 January – 19 February 2027).
• Approximately 20 hours per week.
• Delivery mode: on-site with flexibility.

The student will gain hands-on experience with real tax microdata and official tax statistics, learn to measure inequality and tax progressivity, and produce reproducible empirical research.

Deliverables include:

• A cleaned dataset and reproducible code repository.
• A short written report of the stylized facts (and the stretch component, if undertaken).
• A brief oral presentation at the end of the program.

Strong quantitative students (Honours, coursework Masters, or high-performing 3rd- or 4th-year undergraduates), who:

• are comfortable programming in Python, Stata or R

and have completed:

• intermediate macroeconomics
• public economics or
• econometrics

and are interested in taxationinequality and public policy.

Prior data-handling experience is an advantage.

Dr Jose Alvarado

This project can accommodate one student.

To find out more about the project, email Dr Jose Alvarado:

• j.alvarado@uq.edu.au.

Special interest groups seek to influence public policy through lobbying, political donations, organisational ties and coordinated activity. These relationships are often only partially observed in conventional datasets.

This project will investigate whether machine-learning and network-analysis methods can identify and map potentially latent networks among special interest groups in the United States.

The student will:

• help assemble and clean relevant public data,
• construct measures of connections between organisations and political actors
• and apply suitable methods such as community detection, link prediction, clustering or representation learning.

The analysis will emphasise validation, transparent interpretation and careful communication of uncertainty.

• 6 weeks, 11 January 2027 – 19 February 2027.
• 20 – 23 hours of engagement per week during the official program dates.
• Hybrid, with on-campus and online meetings at UQ and independent work that may be completed remotely.

The student will gain practical experience in data collection and cleaning, Python-based data analysis, machine learning, network analysis, research design, reproducible workflows and the communication of empirical findings.

Working with the supervisor, the student will produce a research report that documents the data and methods, presents maps or visualisations of the identified networks, summarises the findings, discusses validation and limitations, and outlines promising directions for further research.

Students with an interest in:

• applied economics
• political economy
• public policy
• data science
• or a related field.

who are:

• curious, careful with data and willing to learn new computational methods.

Basic familiarity with Python would be helpful. Prior experience with machine learning or network analysis is welcome but not required.

The project is particularly well suited to students considering Honours, an MPhil or a PhD who would like experience with an independent research project.

Dr Haishan Yua

Current UQ students are welcome to apply to UQ School of Economics.

There is no need to contact the supervisor before submitting an application.

Economics remains underrepresented among many groups of young Australians, particularly female students and students from lower socioeconomic backgrounds. While publicly available data have helped document these patterns, much less is known about Indigenous students' access to, and participation in, senior secondary economics.

In 2025, the Queensland Curriculum and Assessment Authority (QCAA) released subject enrolment data by Indigenous status for the first time, creating a unique opportunity to investigate this important issue. This project will use these newly available data to examine both the availability of economics in Queensland secondary schools and the extent to which Indigenous students choose to study the subject when it is offered.

The project contributes directly to the School's objective of understanding and addressing barriers to Indigenous students' engagement with economics. The project also aligns with broader Faculty and University priorities to strengthen Indigenous participation and success in higher education by generating evidence that can inform future outreach and engagement initiatives.

The analysis will be based on existing datasets and is well suited to a third-year (or more advanced) economics student with an interest in applied data analysis, education, and public policy.

The project consists of 2 main components:

1. Supply-side analysis: This component examines whether Indigenous students have equitable access to economics in Queensland secondary schools. Using QCAA data on schools offering economics together with ACARA school profile data, the project will map the geographic distribution of schools offering economics and compare this with the proportion of Indigenous students across Queensland. The resulting visualisations will help identify whether economics is systematically less available in areas with larger Indigenous student populations, providing evidence of potential structural barriers to participation.
2. Demand-side analysis: This component investigates whether Indigenous students choose economics at the same rate as non-Indigenous students when the subject is available. Combining school-level access data with the newly released QCAA enrolment data, the project will estimate and compare economics enrolment rates for Indigenous and non-Indigenous students. Any differences in participation may highlight opportunities to strengthen engagement with economics among Aboriginal and Torres Strait Islander students.

Depending on progress, the project could be extended to compare participation patterns across other senior subjects and/or examine changes over time, providing broader insights into subject choice and educational participation in Queensland.

• 26 January 2027 – 19 February 2027.
• 20 hrs per week.
• Onsite 3 days per week for the duration of the project.

The student will gain skills in data analysis using STATA, producing descriptive statistics and writing a report on a policy relevant topic.

This may lead to a publication, oral presentations at the University.

3rd- and 4th-year students who are familiar with the use of STATA, and have a background in:

• applied economics
• statistics
• econometrics.

Associate Professor Julie Moschio

To find out more about the project, email Associate Professor Julie Moschio:

• j.moschion@uq.edu.au.

The scholar will be involved in learning by doing activity in the field of Productivity and Efficiency Analysis, e.g., through conducting replication exercises, data preparation and analysis, literature review, etc.  

Exact responsibilities may vary depending on the active project at the time of the placement as well as the individual interests, knowledge and skills of the scholar.

View examples of finished projects.

Learn more about the field of productivity and efficiency analysis.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 – 23hrs per week.
• Majority of the project can be completed remotely. Meetings could be in-person or online.

Expected learning outcomes include:

• gaining knowledge and skills of practicing various methods of econometric and economic analysis, including real data analysis and Monte Carlo simulations
• preparing literature review reports using classical and modern approaches.

Expected deliverables include:

• reports from replication exercises
• reports from economics analysis
• data preparation and data analysis
• literature review reports.

Masters students and 3rd–4th year undergraduate students who are studying economics or a related field, such as:

• statistics/econometrics
• operations research
• applied mathematics
• computer science, etc.

Professor Valentin Zelenyuk

To find out more about the project, email Professor Valentin Zelenyuk :

• v.zelenyuk@uq.edu.au.

This project forms part of a broader research agenda on political finance and the use of securities-like instruments in electoral competition. The theoretical component of the project studies how public funding rules tied to electoral performance can shape parties' incentives, entry decisions, campaign effort, and fiscal costs.

The Summer Research Scholar will contribute to the empirical and data-building component of the project. The main goal is to investigate whether comparable data on political campaign spending and public campaign finance can be obtained for a selected sample of countries, focusing on the most recent presidential election in each country where data are available.

The scholar will work from an existing country-level dataset and help extend it by searching official electoral authority websites, government portals, legislation, campaign finance reports, public datasets, and other reliable sources.

The work will involve recording data availability, source links, institutional details, definitions, coverage, and limitations, and, where possible, collecting campaign spending figures in a structured and reproducible format.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 hours per week.
• Core tasks can be completed remotely, with regular online meetings with the supervisor/research team. In-person meetings may be arranged where useful and feasible.

By participating in the project, the scholar can expect to gain experience in the early stages of building a cross-country dataset for academic research, such as:

• collecting, verifying and documenting cross-country political economy data
• identifying and assessing official and secondary sources on campaign finance and electoral spending
• working with spreadsheets, research logs, README files and structured documentation for reproducible empirical research
• understanding institutional differences in campaign finance systems across countries
• evaluating data quality, comparability, missingness and source reliability.

Expected deliverables include:

• a completed country-level spreadsheet documenting the availability of campaign spending or public campaign finance data for each country in the sample
• source links and concise notes explaining where the information was found and how the data can be accessed
• a README document describing the workflow, file structure, data sources, key variables, coding decisions, and instructions for future users
• a research log recording search steps, source checks, unresolved issues, and country-specific follow-up notes
• cleaned and well-organized databases, where available, with consistent variable names and clear distinctions across spending categories
• a short summary report describing data availability, comparability issues, limitations, and cases requiring further follow-up.

This project is suitable for students with an interest in:

• applied economics
• political economy
• public policy
• comparative politics, or
• data-driven research.

who enjoy:

• careful empirical work
• online source searches
• institutional research
• organizing information clearly.

And have:

• strong attention to detail and be comfortable working with spreadsheets such as Excel or Google Sheets
• reliability and organisation skills
• patience, precision, and good judgment in assessing the credibility and comparability of different data sources.

Because the project involves cross-country research, the ability to work with translation tools carefully, would be valuable.

Experience with R, Python, Stata or another statistical software package would be an advantage but is not required.

The project does not require advanced theoretical modelling.

Dr Allan Hernandez Chanto

To find out more about the project, email Dr Allan Hernandez Chanto:

• a.hernandezchanto@uq.edu.au.

Hot-briquetted iron (HBI) is a compacted form of direct reduced iron that may play an important role in the transition to low-emissions steel production. Australia has abundant iron ore resources and exceptional renewable energy potential, raising the possibility that it could become a globally competitive producer of HBI.

This project will undertake a high-level economic assessment of the conditions under which HBI production could be internationally competitive in Australia.

The student will:

• review the HBI production process and value chain
• identify the main drivers of competitiveness
• examine factors such as ore quality, renewable electricity and hydrogen costs, capital costs, infrastructure, transport and access to export markets.

Using publicly available literature and data, the student will develop a simple analytical framework or spreadsheet model to illustrate how these factors influence Australia's competitiveness relative to alternative production locations.

The project will conclude by identifying the broad conditions under which HBI production in Australia could become commercially attractive and highlighting key uncertainties and priorities for future research.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 hours per week.
• The project will commence with an in-person kick-off meeting, followed by weekly online meetings, and will otherwise be undertaken remotely.

Through this project, the student will gain an understanding of the economics of industrial decarbonisation, green iron production and international competitiveness.

They will also develop skills in:

• literature review
• economic analysis
• spreadsheet modelling
• communicating technical concepts to both specialist and non-specialist audiences.

The student will produce:

• a plain-language explainer (approximately 2 – 3 pages) describing how hot-briquetted iron is produced, its role in low-emissions steelmaking, and the key requirements for producing it competitively in Australia
• a short research report (approximately 5 – 10 pages) summarising the literature, identifying the main drivers of competitiveness, presenting a simple analytical framework and outlining the broad conditions under which HBI production in Australia could be commercially competitive.

• Undergraduate economics student in their 3rd-year or later.
• Postgraduate economics students who have successfully completed at least one semester of coursework.

Professor Flavio Menezes

The references below provide a useful starting point for the project and are intended to guide the successful applicant's initial literature review. The student will be encouraged to identify and incorporate additional relevant academic, industry and government sources as the project progresses.

General background references:

• International Energy Agency (IEA). Iron and Steel Technology Roadmap.
• International Energy Agency (IEA). Net Zero Roadmap
• Mission Possible Partnership. Making Net-Zero Steel Possible (PDF, 17.7 MB).

Australian context references:

• CSIRO publications on green iron, hydrogen-based ironmaking and the NEOSMELT project.
• Australian Renewable Energy Agency (ARENA). Reports on green iron, hydrogen and industrial decarbonisation projects.
• Australian Government Department of Industry, Science and Resources. Publications relating to the Future Made in Australia initiative and green metals.
• OECD. Green Iron Opportunities in Australia.

Industry and academic literature references:

• Fortescue, BHP, Rio Tinto and BlueScope publications on green iron and low-emissions steel pathways.
• World Steel Association reports on direct reduced iron and electric arc furnace technologies
• Academic papers on hydrogen-based direct reduction, industrial decarbonisation and the economics of green steel.

To find out more about the project, email Professor Flavio Mendez:

• f.menezes@uq.edu.au

Social dilemmas are widely used to measure cooperation because they create a conflict between private material incentives and collective welfare. Their interpretation generally assumes that participants understand both what cooperation achieves for the group and what it costs them personally. Existing evidence from the supervisor's studies indicates a substantial and systematic asymmetry: more cooperative participants often recognise the collective benefits of cooperation but are less likely to identify its private material cost. From a standard economic-incentives perspective, this pattern can be described as misunderstanding or misperception of the personal cost of cooperation.

However, the same responses may reflect a different construal of the social dilemma rather than a simple failure to understand incentives. Participants may apply internalised cooperation heuristics, engage in team reasoning, treat collective welfare as part of the relevant objective, or interpret the task through moral and normative expectations. Under these accounts, participants may understand the monetary consequences but answer or choose from a social rather than narrowly individual perspective. Distinguishing economic misperception from alternative task construals is therefore a central objective of the project.

The project will examine whether this asymmetry is consistent across social-dilemma contexts, what personal and situational factors predict it, and how strongly it shapes observed cooperation. The central questions are whether the same people underweight or reinterpret their private costs across different dilemmas, whether the pattern generalises across samples and cultural settings, and whether cognitive ability, numeracy, beliefs, social preferences, perceived norms, team identification, moral judgments, response times, or task framing help distinguish competing explanations.

The project will begin with existing social-dilemma datasets held by the supervisor. Across these datasets, the scholar will audit available measures of private-payoff understanding, collective-payoff understanding, beliefs, preferences, perceived appropriateness, cognitive reflection, response times, demographics, task framing, group identification, and incentivised cooperation. Particular attention will be given to variables that can separate limited understanding of monetary incentives from deliberate or intuitive social construals of the dilemma.

The first empirical stage will identify the determinants and behavioural consequences of understanding asymmetries within these datasets. The scholar will test whether cooperative participants systematically underestimate the personal costs of cooperation, whether this tendency is stable across provision, maintenance, public-goods, prisoner's-dilemma, and related social-dilemma settings, and whether it is better explained by a general comprehension deficit, a directional misperception of incentives, internalised cooperation heuristics, team reasoning, or moralised task construal.

The second stage will systematically identify publicly available social-dilemma datasets containing both comprehension responses and behavioural decisions. Open-science repositories, published data supplements, and the Cooperation Databank will be used to locate relevant studies and datasets. The Cooperation Databank will serve as a study-discovery and evidence-mapping resource; empirical analyses will rely on datasets in which participants' understanding and cooperation are directly observed.

For every eligible dataset, the scholar will code the social-dilemma structure, comprehension measures, sample characteristics, task framing, incentives, repeatedness, group size, cultural setting, and available individual-difference variables. Where participant-level data are accessible, the project will conduct an individual-participant-data meta-analysis, estimating the average asymmetry in understanding and testing whether its determinants and behavioural consequences replicate across studies. Where only published summary statistics are available, compatible study-level effect sizes may be incorporated in a supplementary conventional meta-analysis.

The intended scholarly output is a co-developed research paper presenting the first systematic meta-analysis of asymmetric understanding in social dilemmas and its determinants. The paper will assess whether cooperative participants consistently underappreciate private costs, whether this tendency generalises across dilemma types and populations, and whether the evidence is more consistent with economic misperception or with socially oriented task construal. The project will contribute to the measurement and interpretation of cooperation in experimental economics and identify the most informative directions for subsequent experimental research.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 hours per week.
• The core tasks can be completed remotely, with regular online meetings with the supervisor and, where useful, members of the wider research team. In-person meetings may be arranged where feasible.

By participating in the project, the scholar can expect to gain experience in:

• behavioural and experimental economics, especially cooperation, social dilemmas, comprehension and task construal
• auditing, cleaning and harmonising variables across multiple experimental datasets
• systematic study identification using open-science repositories, published supplements and the Cooperation Databank
• coding social-dilemma designs, comprehension measures, incentives, samples and potential moderators
• using regression, multilevel, predictive and meta-analytic methods to distinguish competing explanations of understanding asymmetries
• conducting individual-participant-data meta-analysis and, where appropriate, conventional study-level meta-analysis
• testing cross-study consistency, heterogeneity and out-of-sample predictive performance
• reproducible research workflows and transparent interpretation of exploratory and meta-analytic evidence.

Expected deliverables include:

• a complete audit of the supervisor's relevant social-dilemma datasets, documenting games, comprehension measures, cooperation outcomes, beliefs, preferences and contextual variables
• a preregistered or otherwise transparent study-selection and coding protocol for identifying additional social-dilemma datasets
• a study database recording dilemma type, incentive structure, comprehension measures, sample characteristics, cultural setting and available moderators
• reproducible cleaned datasets and analysis files for the selected internal and external studies
• meta-analytic estimates of the asymmetry between understanding collective benefits and private cooperation costs.
• meta-analytic tests of cognitive, motivational, demographic, cultural and design-related determinants of the asymmetry
• analyses assessing whether the evidence is more consistent with limited incentive understanding, directional misperception, cooperation heuristics, team reasoning or moral and normative task construal
• a paper-ready set of tables, forest plots, heterogeneity analyses and robustness checks
• a substantial draft of a co-developed research manuscript suitable for further development and submission
• a final presentation to the supervisor and where appropriate, the wider research group.

Undergraduate or postgraduate students studying:

• economics
• behavioural science
• psychology
• statistics
• data science
• or a related discipline.

who are interested in:

• cooperation
• social preferences
• decision-making
• experimental methods or
• reproducible quantitative research.

who possess:

• careful reasoning
• attention to detail
• reliability
• intellectual curiosity 
• willingness to document positive, null and inconsistent findings transparently.

Prior coursework in econometrics or statistics is desirable.

Experience with R, Stata, Python, or another statistical package would be advantageous, but the project can accommodate a motivated student with strong quantitative foundations and a willingness to learn.

Familiarity with experimental economics, multilevel modelling, latent-class analysis, machine learning or systematic evidence synthesis would be useful but is not required.

The project can accommodate one student.

Dr Ozan Isler

To find out more about the project, email Dr Ozan Isler:

• o.isler@uq.edu.au.

Mine closure can require substantial expenditure on land rehabilitation, environmental remediation, infrastructure removal and long-term monitoring. Because these costs arise late in a mine’s life, regulators must address uncertainty about future costs, operator insolvency and incentives to delay or underperform rehabilitation.

This project will investigate how mine closure and rehabilitation are organized in Australia and selected international jurisdictions. Beginning with Queensland and Western Australia, the student will compare how different systems:

• establish closure obligations
• calculate financial assurance
• allocate responsibility for rehabilitation
• monitor performance
• deal with abandoned mines or insolvent operators.

The comparison will focus on the incentives created by alternative institutional arrangements, including bonds, pooled funds, insurance, government provision and contracting with specialist firms.

The research will provide the institutional foundation for a longer-term project examining whether alternative procurement, auction, contracting or financial-assurance mechanisms could improve mine rehabilitation.

• 6 weeks (11 January 2027 – 19 February 2027).
• 20 hours per week.

• A structured comparison table describing mine-closure arrangements across the jurisdictions studied.
• A review of:
  • relevant legislation
  • government guidance
  • policy reports
  • industry standards
  • academic research.
• Several short case studies illustrating how closure arrangements operate in practice, including at least one case involving operator failure, insolvency or an abandoned mine.
• A research report of approximately 15 – 20 pages that:
  • explains the principal institutional models currently in use
  • compares their strengths and weaknesses
  • identifies the central incentive and information problems
  • highlights gaps in the available evidence
  • identifies promising questions for future economic and policy research.

Undergraduate or postgraduate students studying:

• economics
• public policy
• or a related discipline.

Dr Zachary Breig

To find out more about the project, email Dr Zachary Breig:

• z.breig@uq.edu.au.