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Natural Catastrophes and Financial Development
Mikulíková, Pavla ; Horváth, Roman (advisor) ; Jakubík, Petr (referee)
Master's thesis - Natural Catastrophes and Financial Development Pavla Mikul'ıkov'a Academic year 2022/2023 Natural disasters affect lives of many people every year. Using a panel dataset of 214 countries from 1970 to 2021, this thesis analyses the impact of disasters on financial development, namely on depth, efficiency, access, and sta- bility, using fixed effects and system GMM estimators. The main findings imply that depth and stability are negatively affected by disasters, and the impact is more pronounced for lower-income countries. On the other hand, efficiency and access provide no consistent results. There is no type of disasters, e.g., biologi- cal or geophysical, that would have a significant impact on all types of financial development. The effect probably varies due to the different characteristics of disaster types. 1
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Criminality Analysis in the Czech Republic using Self-Organizing Maps
Mikulíková, Pavla ; Cahlík, Tomáš (advisor) ; Palanská, Tereza (referee)
Crime represents one of the most persistent social problems all around the world. To understand the motivation for criminal behaviour, a thorough analysis of its plausible determinants is necessary. This bachelor thesis aims at exploring whether the method of self-organizing maps, a data mining tool, can help in the investigation of the Czech criminal phenomena. To date, no academic study has tried to uncover potential pat- terns in the Czech crime data employing this type of artificial neural network. It is a visualisation method which maps observations based on their multi-dimensional features into a two-dimensional grid, and at the same time, the similarity between observations is preserved by locating similar observations close to each other. For the analysis, the dataset consisting of 75 Czech districts and 18 variables was used. However, the optimal choice of parameters of the model can be seen as a possible limitation of this method. The final outcome of the model consists of six clusters of districts with various levels of crime rates and other characteristics. Our results showed that self-organizing maps can provide an interesting insight into the crime problem, and social sciences can benefit from its application in many research areas. 1
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