JBNST - Vol. 244/4 - 2024

The goal of the paper is to propose an abstract but formalised model of how Schwartz higher order values may influence individual decisions on sharing an individual effort among alternative economic activities. Subsequently, individual decisions are aggregated into the total (collective) economic output, taking into account interactions between the agents. In particular, we explore the relationship between individual higher order values: Self-Enhancement, Self-Transcendence, Openness to Change and Conservation – measured according to Schwartz’s universal human values theory – and individual and collective economic performance, by means of a theoretical agent based model. Furthermore, based on empirical observations, Openness to Change (measured by the population average in the case of collective output) is positively associated with individual and collective output. These relations are negative for Conservation. Self-Enhancement is positively associated with individual output but negatively with collective output. In case of Self-Transcendence, this effect is opposite. The model provides the potential explanations, in terms of individual and population differences in propensity for management, willingness to change and skills (measured by an educational level) for the empirically observed relations between Schwartz higher order values and individual and collective output. We directly calibrate the micro-level of the model using data from the ninth round of the European Social Survey (ESS9) and present the results of numerical simulations.

This paper illustrates a behavioral mixed frequency macro-finance model where both real and financial variables are generated on a daily basis. Further, while financial sector data is collected at the same frequency as it is generated (i.e. daily), real data can only be collected on a quarterly basis. Under these circumstances, output and inflation, upon which data is available with a significant delay, become unsuitable as the sole information guide for monetary policy. We suggest that policy makers can deal with this information problem by reacting to the variable on which data is collected on high frequency basis: the stock price.

We explore the impact of fake news on asset price dynamics within the asset-pricing model of Brock and Hommes (Brock, W. A., and C. H. Hommes. 1998. “Heterogeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model.” Journal of Economic Dynamics and Control 22 (8): 1235–74). By polluting the information landscape, fake news interferes with agents’ perception of the dividend process of the risky asset. Our analysis reveals that fake news decreases the steady-state price of the risky asset by making it even more risky. Moreover, fake news increases the market share of agents who use the destabilizing technical trading rule by rendering fundamental trading more difficult and costly. Instead of converging toward its steady state, the risky asset’s price may thus be subject to wild fluctuations. As it turns out, these fluctuations are concentrated below the risky asset’s steady-state price. We also show that fake news campaigns may allow certain agents to realize fraudulent profits.

Models involving human decision-makers often include idealized assumptions, such as rationality, perfect foresight, and access to relevant information. These assumptions usually assure the models’ internal validity but, at the same time, might limit the models’ power to explain empirical phenomena. This paper addresses the well-known model of the hidden action problem, which proposes an optimal performance-based sharing rule for situations in which a principal assigns a task to an agent and the task outcome is shared between the two parties. The principal cannot observe the action taken by the agent to carry out this task. We introduce an agent-based version of this problem in which we relax some of the idealized assumptions. In the proposed model, the principal and the agent only have limited information access and are endowed with the ability to gain, store and retrieve information from their (finite) memory. We follow an evolutionary approach and analyze how the principal’s and the agent’s decisions affect their respective utilities, the sharing rule, and task performance over time. The results suggest that the optimal (or a close-to-optimal) sharing rule does not necessarily emerge in all cases. The results indicate that the principal’s utility is relatively robust to variations in memory. On the contrary, the agent’s utility is significantly affected by limitations in the principal’s memory, whereas the agent’s memory appears to only have a minor effect.

This article describes the processing and accessibility of the person and establishment fixed wage effects in German administrative data. These effects have been estimated following the approach of Abowd, J., Kramarz, F., and Margolis, D. (1999. High wage workers and high wage firms. Econometrica 67: 251–333) and Card, D., Heining, J., and Kline, P. (2013. Workplace heterogeneity and the rise of West German wage inequality. Q. J. Econ. 128: 967–1015). They can be linked to most of the available administrative datasets provided by the Research Data Center (FDZ) of the German Federal Employment Agency at the Institute for Employment Research (IAB). They are available for different time intervals from 1985 until 2021. These effects have been used in numerous articles that deal with the contributions of workers and establishments to earnings inequality.

Access to high quality microdata is a precondition for the empirical investigation of many interrelationships in the economic and social sciences. Therefore, well-functioning research data infrastructure is a cornerstone of a successful science location. While other countries in Europe, such as Denmark and the Netherlands, have had microdata centres at their respective National Statistical Offices for quite some time, microdata access for research purposes in Austria was very limited for a long time. Established in 2022, the Austrian Micro Data Center (AMDC) at Statistics Austria enables researchers of accredited research institutions to work with pseudonymized microdata on individuals and firms. The available microdata includes not just microdata of Statistics Austria but also registry data of the Austrian federal government. The main novelty is that microdata can be linked deterministically to each other via unique pseudonymized identifiers among data sets of Statistics Austria, administrative registers, and also to microdata brought in by the researchers themselves. The AMDC is operated by Statistics Austria and its services are open to research institutions worldwide.