A few words about me ...

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My name is Vadim. I finished my M. Sc. studies in Economics in 2013 and obtained a Ph.D. in Economics (Dr. oec.) in 2017 at the university of Hohenheim [my CV].

I am proud to work at one of the largest German state-owned banks, the
​L-Bank, as a Data Analyst.

Previously I worked at the at the Gambling Research Center and at the Chair for  Economic Growth and Inequality at the University of Hohenheim.  Together with my coauthors  I was actively working on many other research questions, including the following ...

• Can scarce time-series data on human behavior be similated using generative adversarial networks? Our novel aplication of time GANs to online gambling data was presented at the Current Advances in Gambling Research conference in London in June 2023.

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• Analysis and identification of pathological gambling behavior using machine learning methods and neural networks. In this fascinating project we identify features important for prediction of exclusion events in online casinos and evaluate different machine learning models according to their performance. Preliminary results were presented at the  Current Advances in Gambling Research conference in Cardiff in May 2022, at the EASG conference in Oslo in September 2022 and at the German Congress on Addiction in Berlin in September 2023.

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• Which estimation methods and under which conditions perform best /worst? There are thousands of empirical papers using various estimation tools and similar datasets, yet obtaining completely different results. This question can be approached by model-based Monte-Carlo simulations, which allow for a broad range of scenarios: why not creating obstacle racing for the estimators and see which one wins? For example: Kufenko and Prettner (2017) and Kufenko (2020).

 

• ​Modeling and measuring convergence and divergence across countries: are we doing it right? Testing the robustness of the history augmented Solow model, confroting it with data and analysing the implications of measuring income per capita, per household and per adult equivalent.  For example: Kufenko, Prettner and Geloso (2018) and Kufenko, Geloso and Prettner (2020).

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• Health, education or infrastructure? Which target variables for economic policies are most growth-promoting? Here is our contribtuion on that topic: Bloom, Khoury, Kufenko and Prettner (2021).

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• What are the reasons and consequences of inequality? Political economy of inequality is a fascinating topic: Throughout history one can recognise a pattern of inefficient institutions amplifying inequalities. These inequalities later manifest themselves in income and health disparities, which are easy to measure, but hard to overcome. For example: Geloso, Kufenko and Arsenault Morin (2023); Arsenault Morin, Geloso and Kufenko (2017); Hagemann and Kufenko (2016). 

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• Institutions and political economy of rent-seeking: the good, the bad and the ugly institutions. Analysing examples from economic history and the recent past, it is important to highlight the far-reaching implications of rent-seeking.  For example: Ivanovic, Kufenko, Begovic, Stanisic and Geloso (2018).​

 

• Business cycles and cycles in economic literature: how do economists react to crises? A fascinating topic on the long-memory effects in economic literature. For example: Kufenko and Geiger (2016).

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• My other interests lie in the spheres of economic growth and development, automation, digitalization and  inequality and income polarization,  spectral and frequency analysis, demography and health economics and political science.