Economics honours students
The Honours Program features a supervised thesis on an agreed topic, providing students with a comprehensive introduction to economic research.
The following projects have been proposed by School of Economics staff for 2026. Please note that this is not an exhaustive list; additional projects will be added as they become available.
Students who are enrolled in, or considering the Bachelor of Economics (Honours) program for 2026 are encouraged to contact the potential supervisors listed to express their interest if there is a particular topic they would like to explore for their thesis.
Project | Design of environmental markets such as pollution permits, carbon offsets, and biodiversity markets |
Supervisor(s) | A/Prof Lana Friesen and Prof Ian MacKenzie |
| Description | The thesis would examine how the design aspects of environmental markets influence the outcomes of those markets (e.g., initial permit allocations, price collars, trading rules). The thesis would likely use a lab experiment to collect empirical evidence, supported by a relevant theoretical framework. |
Relevant Literature | Friesen et al., 2022, “Mind your Ps and Qs: An Experimental Investigation of Variable Permit Supply in the US Regional Greenhouse Gas Initiative”, Journal of Environmental Economics and Management, 112, 10220. |
Project | Biased Social Learning in Pricing and Negotiations |
Supervisor(s) | Dr Zachary Breig and A/Prof Antonio Rosato |
| Description | In many markets, when the quality of an asset or product is unknown, consumers often rely on the choices of others who may be better informed and try to glean information from them. However, correctly performing this "signal extraction" task requires a great deal of sophistication on the part of consumers. Indeed, a host of evidence from behavioural and experimental economics suggests that people are usually unable to correctly extract information from others' actions. This Honours project will investigate the properties of firm pricing and bargaining negotiations when consumers hold biased beliefs that prevent them from achieving correct learning. The results of the project will provide guidance for the regulation of markets with incomplete information, the prime example of which is the housing market. The project will also evaluate the impact of different negotiation protocols and disclosure policies for buyer welfare and market efficiency. The project will involve either a theoretical analysis or an experimental one (or a combination of both). The ideal candidate should have a solid foundation in microeconomics and game theory and be interested in subsequently applying for postgraduate research degrees. |
Relevant Literature | BANERJEE, A. V. (1992): “A simple model of herd behavior,” The Quarterly Journal of Economics, 107, 797–817. BOSE, S., G. OROSEL, M. OTTAVIANI, AND L. VESTERLUND (2006): “Dynamic Monopoly Pricing and Herding,” RAND Journal of Economics, 37, 910–928. EYSTER, E. AND M. RABIN (2010): “Naive Herding in Rich-Information Settings,” American Economic Journal: Microeconomics, 2, 221–243. NGANGOUE, M. K. AND G. WEIZSACKER (2021): “Learning from unrealized TAYLOR, C. (1999): “Time-on-the-Market as a Sign of Quality,” Review of Economic Studies, 66, 555–578. |
Project | Primary Care Networks: Formation and Impact |
Supervisor(s) | Dr Christiern Rose |
| Description | Primary Care Networks are collaborative groups of primary care providers. They have recently been rolled out in several developed countries. This project uses data from England. Conceived in the NHS Five Year Forward View in 2014, Primary Care Networks are collaborative groups of GP practices that work closely with other health and social care providers. By sharing resources, PCNs aim to improve the quality and coordination of primary care services, enhance access to a wider range of healthcare professionals, and ultimately, deliver better patient outcomes. Despite their extensive rollout, their impact on patient outcomes is poorly understood. We aim to address this first by modelling how these networks form, and second by using causal inference techniques to infer their impact on patients. |
Relevant Literature | Largely related to literatures on econometric models of network formation and impact of healthcare policy on patient outcomes. |