In mobility markets – especially vehicle for hire markets – drivers offer individual transportation by car to customers. Drivers individually decide where to go to pick up customers to increase their own utilization (probability of carrying a customer) and utility (profit). The utility drivers retrieve from customers comprises both costs of driving to another location and the revenue from carrying a customer and is thus not shared between different drivers. In this thesis, I present the Vehicle for Hire Problem (VFHP) as a generalization of the Kolkata Paise Restaurant Problem (KPRP) to evaluate different strategies for drivers in vehicle for hire markets. The KPRP is a multi-round game model presented by Chakrabarti et al. (2009) in which daily laborers constitute agents and restaurants constitute resources. All agents decide simultaneously, but independently where to eat. Every restaurant can cater only one agent and agents cannot divert to other resources if their first choice is overcrowded. The number of agents equals the number of resources. Also, there is a ranking of restaurants all agents agree upon, and no two resources yield the same utility. The VFHP relaxes assumptions on capacity and utility: Resources (customers) are grouped in districts, agents (drivers) can redirect to other resources in the same district. As the distance between agent and resource reduces the agent’s utility and the location is not identical for all agents, the utility of a given resource is not identical for all agents. To study the impact of the different assumptions, I build four different model variants: Individual Preferences (IP) replaces the shared utility of the KPRP with uniformly distributed utilities per agent. The Mixed Preferences (MP) model variant uses the utility assumption of the VFHP, but the capacity of all districts remains 1. The Individual Preferences with Multiple Customers per District (IPMC) model variant groups customers in districts, and uses the uniform utilities introduced in the IP model variant. Mixed Preferences and Multiple Customers per District (MPMC) implements all assumptions of the VHFP. In this thesis, I study different strategies for the KPRP and all variants of the VFHP to build a foundation for an incentive scheme for dynamic matching in mobility markets. The strategies comprise history-dependent and utility-dependent strategies. In history-dependent strategies, agents incorporate their previous decisions and the utilization of resources in previous iterations in their decision. Agents adapting utility-dependent strategies choose the resource offering the highest utility with a given probability.
Keywords: vehicle for hire markets; distributed decision making; agent-based modelling; congestion game; limited rationality.
In this bachelor thesis, I first introduce the machine learning methodology of text classification with the goal to describe the functioning of neural networks. Then, I identify and discuss the current development of Convolutional Neural Networks and Recurrent Neural Networks from a text classification perspective and compare both models. Furthermore, I introduce different techniques used to translate textual information in a language comprehensible by the computer, which ultimately serve as inputs for the models previously discussed. From there, I propose a method for the models to cope with words absent from a training corpus. This first part has also the goal to facilitate the access to the machine learning world to a broader audience than computer science students and experts. To test the proposal, I implement and compare two state-of-the-art models and eight different word representations using pre-trained vectors on a dataset given by LogMeIn and on a common benchmark.
I find that, with my configuration, Convolutional Neural Networks are easier to train and are also yielding better results. Nevertheless, I highlight that models that combine both architectures can potentially have a better performance, but need more work on identifying appropriate hyperparameters for training. Finally, I find that the efficacy of word embedding methods depends not only on the dataset but also on the model used to tackle the subsequent task. In my context, they can boost performance by up to 10.2% compared to a random initialization. However, further investigations are necessary to evaluate the value of my proposal with a corpus that contains a greater ratio of unknown relevant words.
Keywords: neural networks; machine learning; word embedding; text classification; business analytics.
In this thesis, I examine how corporate taxes, dividend taxes, personal income taxes, and consumption taxes affect corporate payout behaviour. Using rich international panel data that consist of 40,609 firms across 115 countries from 1999 to 2013, I run linear regressions of each of the four tax rates on three payout variables which measure frequency and magnitude of regular cash dividends distributed by firms. In my baseline model, I find that the predictions of the new view – one of the two views in neoclassical theory – on short-run payout responses only partially hold true. Inconsistent with initial hypotheses, corporate taxes on average do not impact a firm’s dividend payout behaviour in the short run. Regarding dividend taxes, my results show that the hypothesised dividend tax neutrality only holds true for the relative amount of dividends but not for a firm’s likelihood to distribute, increase, and initiate dividends. Consistent with initial hypotheses, personal income taxes and consumption taxes trigger mostly large payout responses in terms of frequency and magnitude of dividend payouts. In my two model extensions, in which I focus on payout behaviour of cash-rich firms and employ a more flexible definition of the time horizon characterising short-run payout, my findings are again only partially in line with predictions of the new view on short-run payout responses. With these results, this thesis not only analyses well-investigated tax rates – corporate taxes and dividend taxes – for which current literature shows mixed empirical evidence but also examines hitherto scarcely considered tax rates – personal income taxes and consumption taxes – in the neoclassical framework and determines their impact on corporate payout.
Keywords: corporate payout; corporate tax; dividend tax; personal income tax; consumption tax.
In this thesis, I present empirical evidence on the effect of personal taxes on firm-level investment. Exploiting a cross-country panel that consists of 40,608 firms from a total of 115 countries in the period 1999-2013, I employ a linear regression model in which I regress five different definitions of the personal tax wedge against capital investment of firms. I find that the average investment response of firms strongly depends on the definition of the personal tax wedge. My baseline regression reveals that, if the pure personal tax rate increases, firms on average show a positive capital investment response. That is, if firms cannot shift the economic burden of personal taxes to other stakeholders, an increase in personal taxes, ceteris paribus, increases the factor price of labour and thus exerts higher pressure on corporate profits. Profit-maximising firms therefore counteract this pressure by (partially) substituting the more expensive input factor labour by capital, increasing their capital investment. This effect, however, does not hold true for alternative definitions of the personal tax wedge that additionally include social security contributions. Likewise, I obtain mixed results when testing for cross-sectional variation in capital investment responses arising from differences in relative market power, the ability to substitute input factors, and financial constraints. In this context, my thesis provides empirical evidence on the effect of personal taxes on investment behaviour at the firm level and thus adds to current literature, which mainly considers the effect of personal taxes on aggregate investment, economic growth, and total factor productivity.
The issue of avoiding information about the consequences of one’s own actions is discussed intensively. Acting that way, makes it harder to be judged for one’s decisions. My bachelor thesis deals with strategic ignorance and self-serving behaviour. This paper aims to explore if people really avoid information to a high degree and whether there are certain situations or circumstances which influence these behaviour patterns. Four different experimental studies were used and compared to a large amount of literature. It is found that intransparency in situations allows for a moral “wiggle room” which makes people’s actions more egoistic. Also, people like to be seen as altruistic. By analyzing the Bayesian signaling model which introduces an agent caring about his self-image, his economic advantages and who has the opportunity to find out about social benefits and the cost of acting social, the findings show that willful ignorance can be an excuse for selfish behaviour and helps maintain the idea that they act up to their ideals. Looking at situations where people have to bring an effort, ignorance shows better outcomes because people work harder when they don’t know about the negative consequences.
Keywords: strategic ignorance, moral wiggle room, dictator games, self-serving behavior.
In response to the intensification of economic crises in the euro area, the European Central Bank (ECB), along with other central banks, has conducted both conventional and unconventional monetary policy. The most recent unconventional measure has been outright asset purchases under the corporate sector purchase programme (CSPP) targeting euro-denominated investment-grade bonds issued by non-financial corporations in the euro area. Using a Difference-in-Differences (DID) approach on a sample of euro-zone data I find that the CSPP initiative has consistently contained credit risk. In contrast, spillover effects to firms not subject to the CSPP policy are limited.
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