Priority fields for this application are:
- Machine learning applications in commodity market analysis
- Taxes, Inequality, and Sustainability
- Empirical analysis of the internal organization of business firms and institutions
- Consumer Behavior and Sustainable Food Consumption
All topics are described below.
Topic 1: Machine learning applications in commodity market analysis
The School of Economics and Business is seeking a PhD candidate to study machine learning applications in commodity market analysis. Commodities make up an important part of the Norwegian economy. Globally, Norway is currently the eight largest producer of oil and the third largest producer of gas. Our significant hydropower production is processed downstream into fertilizer, aluminum, ferrosilicon, zinc, and various alloys. Furthermore, Norway exported seafood for a total value of NOK 107.3 billion in 2019.
Machine learning (ML) is currently a hot topic in economics, especially related to prediction and forecasting, but also for policy targeting. It can be viewed as a set of computational techniques and algorithms, applied to big data sets in order to identify concealed patterns and information. The PhD candidate can study the topic from a number of viewpoints, including, but not limited to, prediction versus causal inference, how machine learning techniques can be used to improve forecasting, structural analysis of commodity markets, and/or market performance analysis.
Structural analysis of commodity markets: Traditional econometric models are designed to make inference about causal relationships, while machine learning algorithms focus on producing accurate granular predictions. There is potentially much to be gained from implementing machine learning techniques alongside a more traditional econometric framework. Machine learning may help the econometrician improve, expand or even uncover new structural models. The PhD-candidate may focus on how machine learning techniques can change and improve structural econometric modeling for commodity market analysis, with emphasis on markets that have a strong impact on the Norwegian economy like e.g. energy and/or seafood.
Prediction/forecasting: There is currently much research activity in using machine learning and similar techniques for forecasting electricity spot prices, and it is likely that machine learning will play an important role in improving price prediction in other commodity markets as well. One possible focus for the PhD candidate could be on improving point and probabilistic forecasts of prices and quantities in commodity markets using machine learning techniques.
Market performance: New empirical industrial organization (NEIO) has been important to bring quantitative analysis closer to the analysis of non-competitive behavior in a variety of markets. Increased access to dis-aggregated market data allows for more detailed and realistic modeling of market behavior and performance. Identification and estimation of market regime switches, including, but not limited to, market manipulation and exercise of market power, are important for understanding of price formation and for accurate price forecasts in commodity markets. The availability of detailed high quality data for electricity markets makes these markets well suited for experimental modeling and analysis using machine learning techniques.
Candidate: We seek PhD applicants with a Master’s degree in Economics, Business Administration, or a related field. Strong data science skills are required.
Contact information: Olvar Bergland, +47 67231119, email@example.com
Topic 2: Taxes, Inequality, and Sustainability
The School of Economics and Business is seeking a PhD candidate in the field of public economics, and in topics related to Taxes, Inequality, and Sustainability at Skatteforsk - Center for Tax and Behavioral Research (https://www.skatteforsk.no).
The design of the tax system can affect individuals’ and firms’ behavior by providing incentives to avoid and evade taxes and by altering relative prices and returns, ultimately affecting the level and composition of savings, investments, employment, and production in the economy. There is a need to bridge macro and micro related topics when analyzing behavior of agents and how this should be taken into account when designing policy.
One main future challenge is to raise tax revenue to finance the welfare system in a small, open economy like Norway. This is particularly relevant following the covid-19 crisis, with a global economic downturn, mass unemployment and increased budget deficits following large public crisis packages to counteract the crisis.
Tax avoidance and tax evasion erodes the tax base and challenges the sustainability of universal welfare state policies and the ability to redistribute through the tax system, which then also has implications for inequality. Increased inequality may affect political stability and reduce political support for transition into more environmental and sustainable solutions.
In order to design a robust tax system that reduces both incentive and ability for agents to participate in tax avoidance and evasion without reducing investment and labor supply incentives, it is necessary to understand agents’ responses to tax changes in detail. This project aims to do this by analyzing rich Norwegian register data.
As a PhD-student at Skatteforsk you will be working on topics of direct policy relevance with an active and international research group and be included into ongoing projects. You will get access to a large micro data base with a broad range of variables on Norwegian firms, individuals, as well as ownership information that links firms and individuals, working on policy relevant questions, such as effects of the covid-19 crisis measures.
Candidate: We seek PhD applicants with a Master’s degree in Economics, Business Administration, or a related field. The candidate should have an interest in topics related to behavioral responses to taxation and inequality, preferably with prior knowledge and interest in the field of taxation. Strong interest in econometrics/statistics, coding and empirical analysis is required, and prior experience with Stata and/or R is a definitive advantage.
Contact information: Annette Alstadsæter: +47 91697877, firstname.lastname@example.org.
Topic 3: Empirical analysis of the internal organization of business firms and institutions
Our society is dependent upon the effectiveness of our business firms and governmental institution. No business goal – or no governmental ambition – can be reached without the mobilization of people and teams within business firms and governmental institutions. How firms and institutions are organized, led and managed is thus of critical importance.
A large number of theories exist to explain how organizations function – and some theories also suggest how organization should function in order to maximize performance. Yet we still have an insufficient understanding of organizational functioning, and there is also a need to convert existing theoretical knowledge into actionable tools and solutions, and evaluate the outcome from efforts at improving organizational effectiveness.
The purpose of this project is to collect and analyze data related to strategies and goals of firms or institutions (e.g., governmental agencies), and consider how the organization of these firms/institutions has been designed to meet these goals and strategies and/or how they are led and managed. The data may be collected both at the organizational level and at the sub-unit level (e.g., divisions and departments within a firm or institution). Data may also be collected about change efforts or decision processes in order to evaluate how changes are introduced and effect of introducing changes in the organization.
Candidate: We seek PhD applicants with a Master’s degree Economics, Business Administration, or a related field. The candidate should have an interest in strategic management, leadership and/or organizational theory/design., some experience with data collection and statistical analysis, and ideally also some work experience from large organizations.
Contact information: Nicolay Worren, +47 67231124, email@example.com
Topic 4: Consumer Behavior and Sustainable Food Consumption
The School of Economics and Business is seeking a PhD candidate to study consumer behavior and food consumption in relation to current societal developments. The food industry is Norway’s second largest industrial sector in terms of employment. The food industry is partly based on agricultural products and partly seafood products. The agricultural based food industry is primarily based on domestically produced agricultural products for the domestic market. The seafood sector is mainly export driven and it is the second largest sector in terms of export value. Given the importance of the food sector, food consumption in Norway as well as in main export markets are of crucial importance for the sustainability of Norwegian food production and consumption.
The project opens up for studies related to consumer behavior and food consumption either in the domestic or export market. Changes in consumer behavior and food consumption is directly linked to many of the Sustainability Development Goals of the United Nations, for example, responsible consumption and production, decent work and economic growth, climate action, life below water, life on land, good health and well-being, and zero hunger. Changes in consumer behavior and food demand is affected by different factors such as economic (e.g., prices and income), socioeconomic (e.g., age, sex, education, children, and place of living), attributes of the food (e.g., used production technology, taste, safety, convenience, and nutrition), the political environment (e.g., climate change, public health issues, self-sufficiency, and the environment), disasters (e.g., the ongoing coronavirus pandemic), and other factors. Many of these factors can be affected by companies in the Norwegian food industry.
The purpose of this project will be to collect and analyze data related to consumer behavior and food consumption within the framework discussed above. The PhD student will in cooperation with the supervisors develop the specific research topics.
Candidate: We seek PhD applicants with a Master’s degree in Economics, Agricultural Economics, Consumer Studies, Business Administration, or related fields. The candidate should have a strong interest in econometrics/statistics, coding, and empirical analysis. Substantial prior experience with Stata, R, and/or similar packages is a definitive advantage.
Contact information: Kyrre Rickertsen, +47 4006 6049, firstname.lastname@example.org