I am a Postdoctoral Fellow at the Department of Economics at the University of Regensburg. My primary research field is behavioral labor economics. I am interested in the interaction between the current trends in the labor market and human psychology. I am currently studying the behavioral effects of automation in the workplace and how they shape workers’ welfare and inequality. Among other topics I find exciting are what is the role of ability and motivation in success in life, and what are the determinants of effort. I am also interested in choice under risk, stochastic choice, and methodology of experimental economics.
I take a comprehensive approach to research that includes behavioral insights, economic modeling, experimental design, and structural econometric methods. My work has been published in such journals as Experimental Economics, Journal of Economic Behavior and Organization, Journal of the Economic Science Association, Annals of Finance, and Mathematical Social Sciences.
Ph.D. in Economics, 2018
Georgia State University
M.A. in Economics, 2011
European University at St. Petersburg
Specialist (B.A. equivalent) in Economics (Summa Cum Laude), 2009
St. Petersburg State University
I study the effect of task difficulty on workers’ effort. I find that task difficulty has an inverse-U effect on effort and that this effect is quantitatively large, especially when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. The inverse-U pattern of effort response to difficulty is inconsistent with many popular models in the literature, including the Expected Utility models with the additively separable cost of effort. I propose an alternative mechanism for the observed behavior based on non-linear probability weighting. I structurally estimate the proposed model and find that it successfully captures the behavioral patterns observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and for the models of effort provision.
I study the effect of task difficulty on workers’ effort. I find that task difficulty has an inverse-U effect on effort and that this effect is quantitatively large, especially when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. The inverse-U pattern of effort response to difficulty is inconsistent with many popular models in the literature, including the Expected Utility models with the additively separable cost of effort. I propose an alternative mechanism for the observed behavior based on non-linear probability weighting. I structurally estimate the proposed model and find that it successfully captures the behavioral patterns observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and for the models of effort provision.
I show how using response times as a proxy for effort can address a long-standing issue of how to separate the effect of cognitive ability on performance from the effect of motivation. My method is based on a dynamic stochastic model of optimal effort choice in which ability and motivation are the structural parameters. I show how to estimate these parameters from the data on outcomes and response times in a cognitive task. In a laboratory experiment, I find that performance on a digit-symbol test is a noisy and biased measure of cognitive ability. Ranking subjects by their performance leads to an incorrect ranking by their ability in a substantial number of cases. These results suggest that interpreting performance on a cognitive task as ability may be misleading.
An important methodological issue in experimental research is the extent to which one should use context-rich or abstract language in the instructions for an experiment. The traditional use of abstract context in experimental economics is commonly viewed as a way to achieve experimental control. However, there are some advantages to using context-framed instructions, such as “employer and worker” instead of “player 1 and player 2.” Meaningful context can enhance understanding of an environment and reduce confusion among participants, particularly when a task requires sophisticated reasoning, and hence may yield responses of better quality. In emotionally-charged research questions, such as pollution or bribes, contextual instructions may affect behavior in the experiment, but this effect may be appropriate as it relates to the research question. Our review of the evidence from the literature indicates that in the great majority of cases meaningful language is either useful or produces no change in behavior. Nevertheless, a few important considerations are worth keeping in mind when using rich context. Finally, we see the choice of context as being an expansion of the experimenter’s toolkit and a factor to consider in experimental design.
Benchmarking is a universal practice in portfolio management and is well-studied in the optimal portfolio selection literature. This paper derives axiomatic foundations of the relative return, which underlies a benchmark-based evaluation of portfolio performance. We show that the existence of a benchmark naturally arises from a few basic axioms and is tightly linked to the economic theory. Our method relies on the use of both axiomatic and economic approaches to index number theory. We also analyze the problem of optimal portfolio selection under complete uncertainty about a future price system, where the objective function is the relative return.
This paper develops an axiomatic theory of an economic variable average growth rate (average rate of change) measurement. The obtained structures generalize the conventional measures for average rate of growth (such as the difference quotient, and the continuously compounded growth rate) to an arbitrary domain of the underlying variable and comprise various models of growth. These structures can be described with the help of intertemporal choice theory by means of parametric families of time preference relations on the “prize-time” space with a parameter representing the subjective discount rate.
The course provides students with a rigorous introduction into the fundamental concepts and models of the microeconomic theory. The course consists of two parts. The first part on the Mathematical Methods of Microeconomics is a part of a two–week math boot camp at the beginning of the semester. This part introduces students to the mathematical methods that are essential for the analysis of microeconomic models. The second part of the course introduces students to the central concepts of game theory, incentives and contract theory, and behavioral economics.
This course provides an introduction to mathematical techniques that are frequently used in economic analysis. Topics covered include differential and integral calculus and matrix algebra. Emphasis is placed on the applications of mathematics to topics in economic theory.
This course provides a systematic study of human and firm behavior within the context of the production, distribution, and consumption of goods.
This class reviews the major insights of behavioral economics, which is the application of insights from psychology and experimental …
The course covers the theory and practice of modern corporate finance. It explains why companies and financial markets behave the way …
Experimental economics is a relatively new field in which decision making is examined in a controlled laboratory setting. The data from …
The course provides a comprehensive overview of the modern issues in labor economics. The topics include minimum wages, discrimination …
The course covers modern issues in the public finance, with the emphasizes on public expenditures. The topics include market failures …
Data and code for my “Give Me a Challenge or Give Me a Raise” paper.
A web app that lets you take the Big Five
personality test.