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Drivers of Income Inequality in China
Chen, Xiao ; Kolář, Daniel (vedoucí práce) ; Semerák, Vilém (oponent)
This thesis uses an empirical approach to examine the drivers of income inequality in China, assuming income inequality to be a linear function of logarithmic per capita income, the educational inequality as well as the percentage of population below secondary, completing secondary, and completing tertiary education. It suggests that growing income inequality is correlated to rising per capita income and educational inequality, utilizing a micro survey data set of 25 provinces from 2010 to 2016 with a one-year gap using the static fixed effects and random effects estimators, as well as a dynamic generalized method of moments estimator. In contrast, the percentage of population completing secondary education has a negative effect. The weighting factors ensure that the data set is representative, and robust and sensitivity tests are applied. The comparison of the EU and China's results shows the features of developed and emerging economies. Keywords Income inequality, Educational inequality, Educational completion, CFPS, Panel data, Weighting factor, Fixed effects, Random effects, GMM-DIFF, GMM-SYS Title Drivers of Income Inequality in China
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Education and Crime: A Panel Data Analysis of the Czech Republic
Lin, Hsin-I ; Břízová, Pavla (vedoucí práce) ; Hlaváček, Jiří (oponent)
This thesis focuses on the relationship between crime and education, as well as macroeconomic and demographic factors such as police efficiency, GDP per capita, employment rate, population density, age and sexual composition of the society. We use the data of fourteen regions of the Czech Republic from 2000 to 2012. First, we apply the fixed-effect model in the data analysis, and further we use GMM for the estimation of new dynamic panel dataset. In addition, taking the possible time effects into account, we also add the time dummies in both regression models. Our finding finds the unexpectedly positive effects of secondary education with A-level exam, GDP per capita and the proportion of population aged 30-59 years old on most of criminal offences. On the other hand, the male ratio in population and the clearance rate are found to influence crimes negatively. Higher education and employment rate are also found to be related negatively with economic crimes. JEL Classification A14, E69, I21, I23, I25, I29, J19, R19 Keywords education, crime, employment, GDP, gender ratio, age, the Czech Republic, panel data, fixed-effect, GMM Author's e-mail cindy1114@livemail.tw Supervisor's e-mail brizova.ies@seznam.cz
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