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• W2979423248 abstract "This dissertation is part of a growing specialism within Applied Empirical Economics. The specialism leverages novel sets of trace data originating from the recent trend in digitalization across public and private sectors. Trace data originates from the natural usage of (digital) products or services and offers a sharp contrast to directly collected data. The greatest strength of trace data is that it is unobtrusive and non-reactive. The collection of trace data does not interfere with the natural flow of behavior and events in the given context.The first chapter gives an overview of the challenges and opportunities that these new datasets present to economic research and how they are utilized in the dissertation. From challenges concerning privacy to the potential to explore novel heterogeneity in economic behavior. It also introduces the datasets that are utilized, namely, the population of Russian interbank contracts, client-level financial data from a large European bank, and data on inhabitants of the EVE Online virtual world.The second chapter analyses the population of Russian interbank contracts with a layered stochastic block model rooted in Bayesian inference and Network Science, a novel feat in this literature. We find that loan maturity details, up till now rarely included in studies of interbank networks, are informative of the lending and borrowing patterns and economic functions present in the interbank lending market. For the third chapter, we obtained access to client-level data of over 3 million Belgian clients of a large European bank covering the period 2006-2016. We utilize this data to explore possible mechanisms contributing to the perpetuation of wealth inequality. We find evidence for a transmission channel via human capital allocation, in the form of higher quality job-matchings.The fourth chapter also utilizes the client-level bank data but focuses on consumption dynamics. We find an asymmetric consumption response to anticipated income changes which the different theories in the literature are unable to explain. We propose the well-known myopic loss-aversion model from behavioral economics as an explanation. The fifth chapter details my contribution and yet to be explored possibilities on a dataset containing the behavior of individuals in a virtual world (EVE Online). I explain the findings of two other projects I co-authored and collaborated on in a more supporting and advisory role. For these projects, I provided my skills and expertise in data collection and processing, and advised on the research and the methodology. The first project detailed is published in PLOS ONE, and the second project in Physica A: Statistical Mechanics and its Applications and are on testing and extending Social Balance Theory (SBT) utilizing a statistical physics approach. SBT is the political science theory behind the principle of ``the enemy of my enemy is my friend''. I end by discussing future possibilities with such virtual world trace data." @default.
• W2979423248 created "2019-10-18" @default.
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• W2979423248 date "2019-01-01" @default.
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• W2979423248 title "Addressing socioeconomic challenges with micro-level trace data" @default.
• W2979423248 hasPublicationYear "2019" @default.
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