Priority topics for applications for PhD Research Fellowships 2025

Topics for doctoral dissertation projects at the School of Economics and Business 2025

1. 1. Behavioral Economics and Taxation: Insights from Developing Economies

Climate and environmental change pose an increasing threat to people across the world. Who is affected, how strongly, and their ability to adapt varies across regions. Critically, people’s ability and willingness-to-adapt depends on their risk and time preferences because both the costs and benefits of adaption arise at different points in time often with costs being more immediate and benefits delayed into the future. A complicating matter in adaption is trust and attitudes towards the state and authorities because individuals need to know that they can reap the benefits of early investments in adaption. If property rights are weak and trust is low, then this erodes willingness-to-invest. It is clear then that successful climate adaption presupposes trust and patience, and often requires behavior driven by a concern for the “common good.” The PhD project focuses on the interplay between the four factors: Climate risk (perceived or actual), risk preferences, time preferences, and trust. The successful candidate will have the opportunity to work on existing extensive datasets from Ethiopia and Malawi. The data comprises incentivized risk and time preference experiments along with socio-economic variables. There may be an opportunity to gather new primary data through an emerging collaboration in Ghana.

Project goals:

• Explore the relationship between climate risk (perceived and actual), risk preferences, time preferences, and trust to understand their combined impact on successful climate adaptation strategies. 
• Utilize rich datasets from Ethiopia and Malawi and conduct complementary field experiments, potentially in Ghana, to analyze behavioral responses to climate risk under varying socio-economic and environmental conditions. 
• Identify how trust in governments and institutions influences individual and collective climate adaptation efforts, emphasizing behavioral changes required for the "common good" and the role of patience and cooperation in such processes.

The project will utilize experimental data on behavioral preferences (e.g., risk, time, and trust) and survey data on taxation attitudes and practices. Potential collaboration with ongoing projects in BEE (Behavioral Economics) and Skatteforsk (Tax Research Group) will provide access to existing datasets and opportunities for fieldwork. 

Desired qualifications:

• The successful candidate will hold a master’s degree in economics, behavioral economics, or a related field.  
• Experience with experimental methods in economics (e.g., risk and time preferences) is desirable.  
• Interest or experience in development economics or taxation in developing countries will be an advantage.
• Proficiency in data analysis tools and experimental design is essential.

Supervisor(s): Dag Einar Sommervoll (dag.einar.sommervoll@nmbu.no).

2. 2. Financial Forecasting and Risk Management

Decisions are made based on forecasts. An investor chooses a portfolio of assets based on their anticipated returns; a firm budgets its cash flows using predicted sales and costs; forecasts of financial derivative prices underpin risk management decisions. The topics of financial forecasting and risk management accommodate a broad range of problems that can be addressed using financial econometrics and machine learning. Whether you have a certain business problem you are trying to crack, a concrete market you would like to learn more about, or a general passion for quantitative analysis in finance, this is your opportunity to pursue it with the support of like-minded researchers. With experience in the financial sector and at top-tier universities (Cornell, KU Leuven), the main supervisor offers a unique blend of practical insight and academic rigor to support your work.

Project goals are to develop efficient forecasting algorithms and risk management techniques on the way to reaching the targets set by key decision makers.

• Price Forecasting. Forecasting asset and derivative prices is exciting but also quite tough. You may or may not be able to generate superior point forecasts, but a reasonable density forecast will always be appreciated by risk managers and sophisticated investors. The goal is (1) to develop and apply forecasting algorithms that efficiently extract information from historical data to yield accurate density forecasts and (2) to extract optimal (user-specific) point forecasts from them.
• Asset Pricing and Hedging. Consider an exchange-listed company such as Equinor that is exposed to oil price risk. To hedge some of it, Equinor uses futures and options contracts. In the context of an asset pricing model such as the CAPM or a multifactor one, what hedge ratio would maximize Equinor’s share price? How sensitive is the ratio to the choice of the asset pricing model? If you like salmon/electricity better than oil, consider SalMar/Statkraft and salmon/power derivatives instead. The goal is to develop a hedging strategy that maximizes the company’s market value (for an exchange-listed company) or another metric that the company’s management is targeting. Collaboration with the company is desirable.
• Open research problems in financial forecasting and risk management. The PhD candidate may suggest their own topic in the subject area (broadly defined).

Data sources/type: Financial time series from open sources and/or Datastream (as well as company’s private data for Topic 2).

Desired qualifications:

• The successful candidate will have a master’s degree in econometrics, economics, finance or a related field.
• Strong quantitative skills.
• Interest in applied financial econometrics, forecasting, applied machine learning and/or risk management.
• A healthy relationship with programming and data analysis in R, Python or similar.

Supervisor(s): Daumantas Bloznelis (daumantas.bloznelis@nmbu.no).

1. 3. Role of uncertainty in green transition

The ongoing thrust has coined words like ESG (Environmental, social and governance), responsible & sustainable investing, and green and dirty investments to reflect the embedding sentiment towards a sustainable future. Despite this euphoria, experts argue that crude oil cannot be ignored soon, even in the Net Zero scenario by 2050 (IRENA, 2018). A significant number of research has been conducted in the field of climate finance and green finance with a focus on firm performance, stock return volatility and credit risk.  We are interested in the following questions: How do the various forms of uncertainty affect renewable energy investments? Do the impacts differ depending on the source of the uncertainty or shock or depending on the characteristics of the different clean energy sectors? For example, do financial market shocks have a different impact than real-economy shocks in different sectors? What are the main differences between the effects from Geopolitical Risks, Climate Risks, Economic Uncertainty, and other uncertainty measures? This study aims to provide empirical evidence on these unexplored avenues of research and contributes to financial literature.

Project goals:

This study will be an important step in understanding the financial viability of a financial instrument that can help mitigate the negative impacts of climate change. We will examine the systemic risk in the energy market. This issue is highly important for developed and developing economics. We will develop network tropology tools that are suited for exploring and identifying the nature of demand and supply side characteristics in the different sources of energy sectors. In addition, we will then develop financial instruments (derivatives and swaps) to mitigate climate risk and test their empirically usefulness.

Data sources/type: Financial and economic data. Most data are publicly available or can be bought.

Desired qualifications:

• The successful candidate will have a master’s degree in economics, finance or a related field.
• Strong quantitative and analytical background
• Experience with programming

Supervisor(s): Muhammad Yahya (muhammad.yahya@nmbu.no), Espen G. Haug (espen.haug@nmbu.no), Gazi Uddin (gazi.salah.uddin@liu.se)

1. 4. Economics of managing biological invasions – Valuing ecosystem service impacts from marine invasive alien species

Invasive alien species can be both a benefit and a nuisance. They can provide economic opportunities for local communities while at the same time negatively affect invaded ecosystems. How to manage these underlying trade-offs is complex and requires inter- and transdisciplinary approaches to ensure optimal outcomes for biodiversity, the economy, and society.

The candidate will have the opportunity to work on the BIODIVERSA+ project “Biological Invasions Resolved through Adaptable, Versatile, and Engaging Nature Based Solutions” (BRAVE). The project assesses nature-based solutions for managing biological invasions and restoring affected ecosystems through active stakeholder engagement and participatory valuation methods recognizing the multiple and plural values of nature and the ways these translate to human well-being. 

The PhD candidate will primarily be working on the Norwegian Case Studies of the Pacific Oyster and the Pacific Pink Salmon but will be part of a larger consortium with case studies in Denmark, Sweden, Italy, and Portugal.

The overarching goal of the project, which the successful PhD candidate will contribute to, is to assess the value of nature-based solutions for ecosystem restoration through both revealed and stated preference methods when the invasive species is also a (potentially) economically valuable species.

Data sources/type: Revealed and stated preference data.

Desired qualifications:

The successful candidate will have a master’s degree in economics, environmental and resource economics, or a related field. Familiarity with revealed and stated preference methods is desirable. Proficiency in a Scandinavian language is desirable to actively participate in all aspects of data gathering.

Supervisor(s): Ståle Navrud (stale.navrud@nmbu.no), Erlend Dancke Sandorf (erlend.dancke.sandorf@nmbu.no).

1. 5. Efficient and sustainable use of energy and natural resources

The candidate will work within the research areas of the NMBU research group Climate, resources, energy and environment (KREM). The exact research topics and methods will be discussed with potential KREM supervisors.

The KREM research covers a wide range of topics, including energy markets, valuation of natural environments, consumer behaviour, use of petroleum resources, carbon markets, management of agricultural land tropical forest, and land markets. Methodologically, the research includes analytical methods, simulation models, econometrics, institutional analysis, valuation studies and field experiments.

Geographically, the research can be divided into three main areas:
(i) Norwegian environmental and climate policies,
(ii) land and resource use in developing countries, and
(iii) international climate policy.

The goal is to perform research of high quality, with the aim of publishing results in respected scientific outlets.

Desired qualifications:

The candidate should have a master’s degree in economics, preferably within environmental, energy or resource economics, or a related field.

Supervisor(s): Arild Angelsen (arild.angelsen@nmbu.no), Ståle Navrud (stale.navrud@nmbu.no), Knut Einar Rosendahl (knut.einar.rosendahl@nmbu.no), Erlend Dancke Sandorf (erlend.dancke.sandorf@nmbu.no).

1. 6. AI-driven research in strategy and/or organization science

The advent of AI and, in particular, large language models (LLMs) means that it is possible to develop new research methods.

Simulated environments can be set up with scenarios that represent real-world challenge and dilemmas. The scenarios can be systematically manipulated to perform experiments to test social science theories and/or assess individual skills. It is also possible to introduce new methods for representing organizations analytically and quantitatively and develop new practitioner tools, such as organizational digital twins (ODTs) or “augmented organization charts” that represent the organization’s roles and activities and are automatically updated.

The project goals will be adapted to the candidate’s background and interests, but may include to…:

• Develop and test LLM-based simulations for collecting and analyzing data from experiments with human participants
• Develop and test new methods for extracting organizational data from e.g., email traffic or event logs
• Test classic organizational theories related to e.g., centralization/decentralization, hierarchy, collaboration, coordination costs, etc. by using LLM-based simulations
• Test the application of AI-driven methods for strategy development and/or organization design

Data sources/type:

• Data gathered from participant interaction with AI-based simulations
• Internal data gathered from companies or institutions (e.g., email traffic, event logs, etc.)
• Perceptual data gathered from questionnaires (e.g., assessments by participants in the studies)

Desired qualifications:

• Master’s degree in business administration; organizational theory; economics; computer Science, or related field
• Knowledge and interest in strategy and organizational theory
• Interest in AI, quantitative and experimental research methods
• Ideally, some work experience from industry or consulting

Supervisor(s): Nicolay Worren (nicolay.worren@nmbu.no), Frida Feyer (frida.karine.feyer@nmbu.no), Sverre Ubisch (sverre.ubisch@nmbu.no).

1. 7. Open Topic

The School will also consider applications from well-qualified candidates who are interested in joining the program based on a research proposal unrelated to any of the above topics.