Research areas

Research at the PLUS Department of Economics focuses on understanding behavior of individuals, groups and markets in different economic contexts. The department is committed to contribute to the highest level of research. Our current research is supported financially by leading research foundations and agencies.

Research Areas and projects

Research areas

Behavioral and Experimental EconomicsAlexander K. Wagner, Lisa Windsteiger
Health EconomicsKlaus Nowotny, Jörg Paetzold, Hannes Winner
Labor EconomicsKlaus Nowotny, Jörg Paetzold
MacroeconometricsNiko Hauzenberger, Paul Hofmarcher, Florian Huber
MacroeconomicsNiko Hauzenberger, Paul Hofmarcher, Florian Huber
MicroeconometricsKlaus Nowotny, Jörg Paetzold, Hannes Winner
Public Economics and Political EconomyKlaus Nowotny, Jörg Paetzold, Alexander K. Wagner, Hannes Winner, Lisa Windsteiger


Featured research projects

Human-algorithm interactions in economic decision making

Funding agency: Jubiläumsfonds of the Austrian National Bank
Project duration: 2023-2027
Project members: Alexander K. Wagner (PI), Alexander Guggenberger

Project summary: Human-algorithm interactions are increasingly shaping the economic behavior of consumers, firms, and therefore society as a whole.  This project aims to advance our understanding of the fundamental changes that human-AI collaborations bring to economic decision-making by investigating AI-supported human decision-making in two important economic domains: pricing decisions and demand planning decisions. This will allow us to gain insights into the potential benefits and pitfalls of AI-based decision making in both non-strategic and strategic settings.

 How to foster lifelong learning: evidence from a large and generous educational leave program

Funding agency: Jubiläumsfonds of the Austrian National Bank
Project duration: 2022-2025
Project members: Jörg Paetzold, Klaus Nowotny and Jakob Losert

Project summary: Lifelong learning is of increasing importance for many economies around the globe to combat structural change and rising labor market dynamics. This has been exposed even more by the disruptive effects of the COVID-19 crisis, which reinforced trends towards digitalization, automation and technological change. Both labor market and education policies have to keep pace with these structural changes, and finding the right tools to foster lifelong learning is essential. In this research project we will analyse the impact of the Austrian educational leave program (Bildungskarenz) on labor market outcomes. First, we will use administrative data of all private sector employees to study the effect of the generosity of the leave program on program take-up. Furthermore, we will investigate whether participation in the lifelong learning program has positive wage and career effects on the participants.

Analysis of central bank communication using advanced text modeling methods

Funding agency: Jubiläumsfonds of the Austrian National Bank
Project duration: 2022-2025
Project members: Paul Hofmarcher and Niko Hauzenberger

Project summary: Transparent communication strategies are recognized as important tools to effectively implement monetary policy. In the aftermath of the financial crisis with many central banks being bound by the effective lower bound (ELB), forward guidance and well designed communication strategies have been used to offset this constraint. In this research project we analyze central bankers’ speeches to identify different communication strategies and their impact on macroeconomic and financial quantities, considering also differences over time, region and actors. State-of-the-art models for text analysis such as topic models and the text-based ideal point models are applied and extended to suitably capture these strategies and gain relevant insights. The methodological advances will also be accompanied by open-source software implementations to facilitate uptake and dissemination.

Decision power in committees: theory and experiments

Funding agency: Diligentia Stiftung
Project duration: 2021-2024
Project members: Alexander K. Wagner and Georg D. Granic (Erasmus University Rotterdam)

Project summary: Committee decision making is ubiquitous in private and public organizations, including corporate boards, juries at court, and political committees. This project investigates behavior and outcomes in voting committees in which there is both asymmetric formal voting power and asymmetric information between committee members. Using controlled lab and field experiments, we test the main assumptions and behavioral predictions of the game theoretic analysis developed in the project.

High-dimensional statistical learning: New methods to advance economic and sustainability policies

Funding agency: FWF
Project duration: 2019-2023
Project members: Niko Hauzenberger, Michael Pfarrhofer and Anna Stelzer

Project summary: Recent years have seen a tremendous surge in the availability of socio-economic data characterized by vast complexity and high dimensionality. However, prevalent methods employed to inform practi- tioners and policy makers are still focused on small to medium-scale datasets. Consequently, crucial transmission channels are easily overlooked and the corresponding inference often suffers from omitted variable bias. This calls for novel methods which enable researchers to fully exploit the ever increasing amount of data. In this project, we aim to investigate how the largely separate research streams of Bayesian econometrics, statistical model checking, and machine learning can be combined and integrated to create innovative and powerful tools for the analysis of big data in the social sciences. Thereby, we pay special attention to properly incorporating relevant sources of uncertainty. Albeit crucial for thorough empirical analyses, this aspect is often overlooked in traditional machine learning techniques, which have mainly been centered on producing point forecasts for key quantities of interest only. In contrast, Bayesian statistics and econometrics are based on designing algorithms to carry out exact posterior inference which in turn allows for density forecasts. Our contributions are twofold: From a methodological perspective, we develop cutting-edge methods that enable fully probabilistic inference of dynamic models in vast dimensions. In terms of empirical advances, we apply these methods to highly complex datasets that comprise situations where either the number of observations, the number of potential time series and/or the number of variables included is large. 

Health of elderly parents and their children’s labor market activity and well-being

Funding agency: Max-Kade Foundation
Project duration: 2021-2022
Project members: Jörg Paetzold

Project summary: Many societies around the world are aging rapidly, facing the prospects of a population pyramid turning upside down. One of the key challenges of an aging population is that severe illnesses and the subsequent need of care will become more widespread. This development already results in expanding long-term care (LTC) budgets in almost all OECD countries. However, not only public budgets are strongly affected by this increasing need of long-term care for the elderly in our society, but also their family members. Adult children are often important providers of care when parents develop caring needs, which in turn may affect their own well-being as well as labor market activity. Understanding the impact of providing care to a sick relative is crucial when designing long-term care policies. This research project will employ high-quality administrative health and social security data from Austria to address this issue.


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