Anamaria Nicolae, together with Michael Nower, led an Impact Case Study that was submitted by the Business School to REF2021. Their research has had significant impact on Bank of England analysis and parliamentary scrutiny of Government activity and policy, as well as having impacted on the business community, regarding the implications of various Brexit related trade scenarios for the United Kingdom’s economy. Specifically, the research has: (1) provided a bespoke macroeconomic model that was used in the Bank of England’s ‘EU withdrawal scenarios and monetary and financial stability: A response to the House of Commons Treasury Committee’ report, November 2018, offering unique insights into the effects of various No-Deal/no-transition scenarios on the short-run dynamic pattern of GDP, which informed and influenced monetary policy making; (2) impacted the voting decisions of MPs in their votes on the “indicative vote on No Deal” and “Benn Bill” in the House of Commons, as well as influencing the work of the International Trade Select Committee and other parliamentary bodies on a number of Brexit and trade-related publications; (3) informed local MEP and business organisations on possible trade agreements.
The research of Riccardo Scarpa is mostly based on the estimation of robust random utility models based on discrete choice data, revealed or stated, and the effectiveness of advanced experimental designs. Applications have spanned various fields and explored attributes of choice alternatives, such as therapies by patients (1, 2, 3), job preferences by junior doctors, environmental disclosures of listed firms by shareholders, nutritional labels by food consumers (1, 2), land use by farmers, high schools preferences by parents, electricity contracts by households, and livestock market facilities by shepherds in LDCs. Other studies looked at determinants of membership of international environmental agreements (1, 2), and recreationists' demand for outdoor recreation (1,2), diversity of preference within couples and real vs stated choice, effectiveness of nudging in milk and wine choice and discrete-continuous models consistent with random utility.
Empirical findings of his studies inform various dimensions of environmental sustainability in food choice, electricity sources, patient care and the willingness to pay (WTP) for non-market goods. They also provide guidance to practitioners about how to obtain robust WTP estimates, test WTP differences, and improve experimental design of treatments.
Habib Rahman, together with Michael Naef, led the FADE-IN project, which developed a comprehensive framework to support 24 developing countries across Africa, Asia, the Middle East, and the Pacific in implementing their Nationally Determined Contributions (NDCs) under the Paris Agreement. Delivered in partnership with Curtin University and the Asian Disaster Preparedness Center (Thailand), and supported by the Australian Government, the project was based on a series of country-level and regional consultations. It identified critical gaps in climate finance, institutional capacity, and technology needs.
FADE-IN has provided strategic guidance to developed countries (specifically Annex II nations under the UNFCCC) on how to effectively direct support in areas such as capacity building, climate financing, technology transfer, and institutional development. As an immediate impact, the initiative has strengthened international cooperation and directly informed Australia’s climate diplomacy—including the delivery of capacity-building trainings on carbon market mechanisms in Viet Nam and the design of innovative climate finance tools in Africa, targeted at high- and mid-level government officials.
Laura Marsiliani, together with Ashar Aftab and Habib Rahman, participate in the interdisciplinary JusTN0W initiative spearheaded by Durham University Strategic Research Fund. Strand 3 of the project is led by the Economics Department and developed in collaboration with other disciplines at Durham University and external partners. It will provide workable environmental, economic, and business scenarios to simulate carbon control policies and mechanisms and ascertain their costs and benefits.
Objectives include to:
The aim of this project by Morten Lau is to improve the decision-making process by patients and doctors in relation to knee surgery and more specifically revisions of primary knee arthroplasty, which is a highly specialized procedure. More than 150,000 primary knee arthroplasty procedures have been performed over the past 20 years in Denmark, and approximately 13% of those procedures are revised within the first 20 years of the operation. The specific purpose of the project is to understand the dualism between patient psyche and revision needs. How do (multiple) revisions affect the quality of life and psyche of patients? Are certain psychological conditions a contributing factor to repeated revisions? What is the importance of risk willingness for repeated revisions? Is it possible to formulate a unified theory of how multiple revisions depend on the severity of knee problems, life wishes and willingness to take risk? The project is led by Professor Anders Odgaard (University Hospital Copenhagen, Rigshospitalet), and the steering group also includes Professor Morten I. Lau (Copenhagen Business School and Durham University) and Professor Hong Il Yoo (University of Loughborough). The cohort study involves patients from five university hospitals across Denmark, and the group have so far obtained 2 million kroner in funding from a private donor (Sundhedsdonationer) to cover expenses for the first three years of the project, as well as financial support from Rigshospitalet to cover expenses for a PhD student.
Spyros Galanis and Christos Ioannou (University Paris 1 Panthéon-Sorbonne) have developed calimantic.com, a platform that runs private prediction markets with firms and organisations. They secured around £1 million through a UK and a French grant, they are running markets with various organisations, and they have published several papers on the topic, for example on the ability of prediction markets to aggregate information when participants have imprecise beliefs and they are ambiguity averse. The aim of the project is to understand under which conditions the prediction markets are an effective tool of decision making by aggregating the information that is dispersed among the employees in a firm or organisation.
Prediction markets leverage the wisdom of the crowd, by aggregating information that is dispersed among individuals. The mechanism is intuitive. Traders buy and sell securities, which pay £1 only if a specific event occurs (e.g. the firm's sales revenue in the next quarter surpasses £X, or whether a new product will be successful) and £0 otherwise. On the one hand, if the security price is low and some employees have private information that the event is highly likely, they will buy the security; consequently, its price will go up. On the other hand, if the security price is high and some employees have private information that the event is highly unlikely, they will sell the security, hence its price will go down. Such price movements could reveal to an employee information that others might have, prompting her to update her beliefs and either buy or sell the security, thus, further revealing to other employees some of her own private information. The final price, normalized to be between 0 and 1, is interpreted as the employees' probability of the event occurring. Information gets aggregated if the final price (or probability) is close to the true outcome (0 or 1). Public prediction markets, where everyone can participate, have been growing significantly in recent years, some examples are Polymarket, with a trading volume of $9 billion in 2024, and Kalshi.
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