Summer Research Scholarship

Supporting Business Entry and Growth via Tax and Transfers

This project aims to characterise the optimal tax treatment of business income for insurance and efficiency purposes. Using new firm-level data for Australia, the project expects to first identify key determinants of businesses creation, growth and exit, before and after COVID-19. In light of those determinants, the project expects to develop original macroeconomic models integrating firm dynamics into optimal taxation frameworks. Expected outcomes include formulating fiscal policies that provide adequate stimulus to businesses, by balancing public insurance and income inequality.

Applicants would gain valuable skills in areas such as data collection, Australian micro-level data, macroeconomic modelling, and numerical computing.

This project is open to applications with students with the following qualities:

• Background in Economics
• Outstanding quantitative and writing skills
• High level of English proficiency
• Econometric skills, and some experience with econometrics software (such as Stata or R)
• Some experience with computational software (such as Matlab or Phython)

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 January – 21 February 2025).

Hours of engagement: between 20 - 36hrs per week.

On-site and remote.

Primary Supervisor and further information:

Prof Begoña Domínguez: b.dominguez@uq.edu.au

Dr Antonio Andrés Bellofatto: a.bellofatto@uq.edu.au

Supervisors can be contacted by students prior to submitting an application.

Formulation and Simulation of Algorithmic Search Models

Background:

Over the last decade, firms have increasingly used algorithmic pricing to maximise their profits. The impacts of algorithmic pricing on equilibrium prices, quantities, and welfare have received considerable attention in the economics literature in the last few years. More recently, some websites have started offering consumers new AI tools to search products, prices, and even bargain deals. We refer to the use of online AI tools by consumers as algorithmic search.  

Aim:

The aim of this project is to analyse the impact of algorithmic search on equilibrium prices, quantities, and welfare. At this stage, the main objective of the project is the formulation, solution, analysis and, if pertinent, simulation of a theoretical model.

Approach:

The approach of the project is theoretical, based on both Microeconomic Theory and Game Theory.

The student is expected to:

• Review academic economic literature on algorithmic pricing.
• Investigate how algorithmic search tools work. For instance students will study how consumers may benefit from using tools such as HAGGLE IT and Comparison.
• Formulate, solve and simulate a model/game that can be used to analyse an economy with sellers using algorithmic pricing and buyers using algorithmic search.

Preliminary readings:

Hill, Gregory “Until now, sellers have used AI to get the best deal for themselves – those tables are about to turn.” The Conversation, January 5, 2024

Johnson, Justin P., Andrew Rhodes, and Matthijs Wildenbeest. "Platform design when sellers use pricing algorithms." Econometrica 91.5 (2023): 1841-1879.

Calvano, Emilio, et al. "Artificial intelligence, algorithmic pricing, and collusion." American Economic Review 110.10 (2020): 3267-3297.

Brown, Zach Y., and Alexander MacKay. "Competition in pricing algorithms." American Economic Journal: Microeconomics 15.2 (2023): 109-156.

Hedlund, Jonas, and Carlos Oyarzun. "Imitation in heterogeneous populations." Economic Theory 65.4 (2018): 937-973.

The students will gain experience in applications of models in game theory. Moreover, at the end of this project, students will also have a clear understanding of and experience in modelling games and rigorous techniques in game theory and microeconomics. In particular, the student is going to:  

• have a deeper understanding of the role of theoretical models,
• study welfare implications of algorithmic pricing and algorithmic search, and
• provide policy implications on the efficiency of markets where algorithmic pricing and algorithmic search are prevalent.

This experience will be useful to offer the students the possibility to develop a research agenda.

Students will be asked to review academic literature.

Students will be asked to give weekly oral presentation of their progress.

Students will be asked to fully formulate and analyse economic models.

Where suitable, students will be asked to produce simulations of economic models.

Students will also investigate how different services on the web support algorithmic search and how sellers use algorithmic pricing.

This project is open to applications from students with a background in economics and or mathematics, at an intermediate level, ideal for 3^rd – 4^th year students, especially those aspiring to study in the Economics Honours.

Skills for working with mathematical models and performing simulations is indispensable.

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 Jan – 21 Feb 2025).

Hours of engagement must be between 20 – 36 hrs per week

The project can be completed under a remote working arrangement.

Primary Supervisor and further information:

Carlos Oyarzun: c.oyarzun@uq.edu.au

Metin Uyanik: m.uyanik@uq.edu.au

The applicants can contact Carlos Oyarzun or Metin Uyanik via email for further information on the project.

Bayesian Structural VARMAs

The aim of this project is to develop a Bayesian Structural Autoregressive Moving Average (SVARMA) specification, where priors can be implemented directly on impulse responses up to some fixed horizon. The key objectives are to (a) translate existing theory-driven restrictions used in the literature to priors on impulse responses at relevant horizons, and (b) derive the implied priors on VARMA coefficients.

Students will have the opportunity to develop and analyse a multivariate time-series econometric model that is at the forefront of academic and practical empirical work. A key skill that will be gained is translating economic theory into quantitative probabilistic components that may be used to construct simulation algorithms to extract information from data.

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 January – 21 February 2025).

Hours of engagement must be between 20 - 36hrs per week

The project can be completed under a remote working arrangement, if necessary, but on-site attendance is preferred.

Primary Supervisor and further information:

Eric Eisenstat: e.eisenstat@uq.edu.au

Students with further questions about the project are welcome to contact the supervisor prior to submitting an application.

Bargaining and Coalition Formation under Incomplete Information

A buyer is negotiating with a seller, or a monopolist selling multiple goods to a set of buyers, or multiple parties forming a coalition are examples of bargaining and collation formation.

In many settings, players lack knowledge of the other players' valuations (types) but they know their own valuations. This information asymmetry creates challenges in negotiation processes, hindering the overall efficiency and success of the bargaining even though all players may gain from negotiation.  

This project aims to conduct a comprehensive study on bargaining and coalition formation under incomplete information. The primary focus is the formulation and analysis of a game-theoretic model suitable to study these problems. Asymmetric information and welfare implications of different legislative methods for bargaining will be considered. Specifically, the 100% agreement and quota method will be analysed, providing recommendations for policy improvements.

Preliminary readings:

Ali, S. N., Kartik, N., & Kleiner, A. (2023). Sequential veto bargaining with incomplete information. Econometrica, 91(4), 1527-1562.

Keniston, D., Larsen, B. J., Li, S., Prescott, J. J., Silveira, B. S., & Yu, C. (2021). Fairness in incomplete information bargaining: Theory and widespread evidence from the field (No. w29111). National Bureau of Economic Research.

Dragu, T., & Laver, M. (2019). Coalition governance with incomplete information. The Journal of Politics, 81(3), 923-936.

The students will gain skills in data collection and applications of Game Theory models. Moreover, at the end of this project, the students will also have a clear understanding of how to use Game Theory to analyse asymmetric information in bargaining models, leveraging rigorous game theory techniques and microeconomics concepts. In particular, the student is going to:  

• conduct an in-depth review of existing literature on bargaining and coalition formation under information asymmetry.
• have a deeper understanding of the role of and models under information asymmetry in game theory and their implications in special bargaining problems such as strata sales, government formation and monopoly sales.   
• study welfare implications of different legislative methods, contributing to informed policy decisions.

This knowledge will be useful to offer the students the possibility to develop a research agenda.

Students will be asked to produce weekly reports, and a final document at the end of the project. Students will be asked to review literature, work with basic examples, collect data, write algorithms and read proofs when necessary.

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 January – 21 February 2025).

Hours of engagement: Between 20 - 36hrs per week

Attendance is preferred, but the project can be completed under a remote working arrangement.  

Primary Supervisor and further information:

Dr. Metin Uyanik: m.uyanik@uq.edu.au

Dr. Carlos Oyarzun: c.oyarzun@uq.edu.au  

The Effect of Parental Retirement on Marriage Rate

This project aims to study the effect of parental retirement on their children’s marriage rate by analysing survey data in China. We plan to use exogenous variations coming from China’s age requirements on retirement in a regression discontinuity design. Previous literature has found evidence on delayed parental retirement in explaining low fertility rate of the second generation. In China’s setting, where marriage is sometimes nudged, it will also be interesting to examine the effect of parental retirement on marriage rate. This study contributes to our understanding on the unintended social consequences of delayed retirement.

Scholars are expected to review literature, evaluate suitability of various survey data, conduct data analysis using Stata/R/Python, and write summary reports under the guidance of Dr Yaying Zhou and Prof Brenda Gannon.

Suitable for students in Economics, with strong data analysis skills and econometric backgrounds, using Stata preferably, ability to write .do files and ability to work in teams. Students should be available for in-person or online meetings during the summer research program.

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 January – 21 February 2025).

Hours of engagement must be between 20 - 36hrs per week

Primary Supervisor and further information:

Dr Yaying Zhou (Primary): yaying.zhou@uq.edu.au 

Prof Brenda Gannon: b.gannon@uq.edu.au 

Please send in complete application in order to be considered.

This project seeks to evaluate the use and benefits of RiPPLE as an assessment task and to identify ways to improve the use of RiPPLE for summative assessment in this and other courses.

The UQ-developed digital tool RiPPLE has been used as an innovative, student-centred and inclusive assessment task in ECON7021 (The Macroeconomy) since the second semester of 2021. The RiPPLE assessment is set up with multiple rounds, focusing on different topics, requiring students to create one study resource (e.g. multiple-choice or multiple-answer questions), evaluate five peer resources, and correctly answer ten questions on the first attempt in each round.

RIPPLE benefits active learning by providing continued interaction opportunities with educators and peers, fostering students’ sense of belonging in line with inclusive pedagogy (Thomas, 2021). Since one of the learning and student experience strategies of UQ is to provide “rich and varied educational experiences that are designed to foster a sense of belonging”, the RiPPLE assessment task aligns closely with the UQ Strategic Plan 2022-2025. An added benefit of the tool is its adaptive learning capabilities and alignment with UQ’s graduate attributes, with the assessment component requiring students to exercise critical thinking and give and receive feedback (Abdi et al., 2021; Gyamfi et al., 2022; Khosravi et al., 2017, 2019). The interactive and continuous nature of the assessment task further affords students many opportunities to improve their communication skills. Student feedback captured in the Student Evaluation of Course and Teaching (SECaT) course reports has provided anecdotal evidence that RiPPLE increases engagement in the course, is inclusive and allows for innovative knowledge application.

We aim to evaluate the impact of RiPPLE by analysing the usage and experience data (quantitative and qualitative) collected in RiPPLE over seven semesters. The analysis will enable us to illustrate and document the benefits of RiPPLE and identify ways to improve its use as an assessment task. In addition to benefiting the students for a large postgraduate course in intermediate macroeconomics (ECON7021), the project will benefit other stakeholders interested in implementing or trialling the model in their teaching environments.

References

Abdi, S., Khosravi, H., Sadiq, S., & Demartini, G. (2021). Evaluating the quality of learning resources: A learnersourcing approach. IEEE Transactions on Learning Technologies, 14(1), 81-92.

Gyamfi, G., Hanna, B. E., & Khosravi, H. (2022). The effects of rubrics on evaluative judgement: A randomised controlled experiment. Assessment & Evaluation in Higher Education, 47(1), 126-143.

Khosravi, H., Cooper, K., & Kitto, K. (2017). RiPPLE: Recommendation in peer-learning environments based on knowledge gaps and interests. Journal of Educational Data Mining, 9(1), 42–67.

Khosravi, H., Kitto, K., & Williams, J. J. (2019). RiPPLE: A crowdsourced adaptive platform for recommendation of learning activities. Journal of Learning Analytics, 6(3), 91–105.

Thomas, L. (2021). Inclusive teaching: Becoming an effective facilitator of learning. In L. Hunt & D. Chalmers (Eds.), University teaching in focus: A learning-centred approach (2nd ed.) (pp. 217-242). Routledge.

The project will provide an explanatory graphical representation and statistical analysis of quantitative and qualitative data to explore the use and benefits of RiPPLE from Semester 1, 2022, to Semester 2, 2024, and identify potential improvements to enhance the use of RiPPLE as an assessment task in a large postgraduate intermediate macroeconomics course.

This project suits a student with a background in economics, applied econometrics, statistics or education. The successful candidate must be proficient in quantitative data analysis, have some knowledge of conducting qualitative data analysis and literature reviews, and be able to work independently and in collaboration with the project supervisor.

Project duration, hours of engagement & delivery mode

6 weeks, 13 January – 21 February 2025

Between 20 – 36 hours per week

Both on campus and remotely

Primary Supervisor and further information:

State Capacity and Economic Development in Asian Countries

State capacity is a fundamental concept in political science and economics, essential for understanding economic development. It refers to a government's ability to effectively implement its policies.

This project involves a comparative analysis of state capacity across various Asian countries. The selected candidate will be responsible for collecting and cleaning data to support this research. The project expands on the analysis presented in the paper "State Capacity and Economic Development: A Network Approach" by Acemoglu, García-Jimeno, and Robinson, published in the American Economic Review (2015, 105(8)).

After completing this task, a student will gain skills in data collection and cleaning up data.  A student will also understand how to use software like R, Python or Matlab.  Further, a student will be able to gain skills in data integration.

In terms of economic theory and quantitative techniques, a student will learn how to utilize network analysis tools and methods to study the interactions and relationships between different components of state capacity.

This project is open to applications from students with a background in economics or statistics, and 3^rd – 4^th year (or above) students only.

Project duration, hours of engagement & delivery mode

6 weeks (13 January – 21 February 2025).

30 hrs per week

An applicant will be required on-site for the project.

Primary Supervisor and further information:

Shino Takayama: s.takayama1@uq.edu.au

It is recommended to contact the supervisor before submitting an application

Football Crowd Bias: Behavioural Economics of Home-Field Advantage under Brazil's Exclusive Fan Law

The game of football (soccer) is an ideal setting to study human decision-making, as it involves many similarities to economic choices, such as risks, biases, and game theory. Just like in the stock market, decisions are made in high-stakes, incentivised environments with real consequences. Consequently, it has been a popular domain for economists to study behaviour, with one of the most common research topics being the home-ground advantage. To date, these research papers have compared matches with spectators to matches without spectators to investigate the differences in the behaviour of players, teams, and referees. This research project will contribute to this research literature by looking at a special case in Brazil, which passed an unusual policy regarding spectator attendance.
 
The objectives for this Summer research project are as follows:
WEEK 1: Background research on the Brazilian football policy, and search for the football data sources.
WEEKS 2-3: Collect the data, Clean the data, Produce a final dataset ready for analysis
WEEKS 4-5: Statistical analysis of the data
WEEK 6: Final report
 
This project is most suitable to a student with basic web-scraping skills, as these will be critical for sourcing the data. A student with basic language skills in Portuguese, or an interest in sports/behavioural economics research, would also find this project interesting.  

The main skills expected from this project are the planning and execution of efficient data collection and subsequent statistical analysis, as befits empirical economics research. The student will learn how to best source, scrape, clean, and analyse secondary data. If time permits, the final report will contain a data description and analysis plan, together with hypotheses and the main results.    

This project is open to students from any background (economics is not a requirement; computer science skills, especially web scraping, are highly desirable). It would best suit students with an interest in behavioural economics research, especially related to sports. A basic level of Portuguese would be desirable, though given that the sources are all in text form, this is not essential. Knowledge of statistics and econometrics is desirable, but not essential.

Project duration, hours of engagement & delivery mode

Duration of the project, 6 weeks (13 January – 21 February 2025).

36hrs per week

Primary Supervisor and further information:

Dr. David Smerdon: d.smerdon@uq.edu.au