Panel Data Research on Corruption. Russia's perspective
| Autor: | Bc. Ksenia Pogodina |
|---|---|
| Rok: | 2015 - letní |
| Vedoucí: | doc. Ing. Tomáš Cahlík CSc. |
| Konzultant: | |
| Typ práce: | Bakalářská |
| Jazyk: | Anglicky |
| Stránky: | 50 |
| Ocenění: | |
| Odkaz: | https://is.cuni.cz/webapps/zzp/detail/138251/ |
| Abstrakt: | The thesis assesses causes and consequences of public sector corruption, using panel data specification. The model presented in this work extends and updates the existing model, where new studies are incorporated and new methods for empirical evidence are used. Moreover, the analysis is expected to be more accurate and comprehensive than the existing one, since cross-sectional analysis is substituted by panel data analysis, which captures unobserved heterogeneity and country specific effects. The main correlations I am interested in are between the level of public corruption in a country and three other important variables: level of market competitiveness within economy, level of education in a country and the extent of democracy there. Hence, the topic is covered from both points of view: theoretical and empirical. Additionally, the model is applied for different samples of countries (developing and developed) in order to investigate if global tendencies hold for specific groups of countries or not. Furthermore, the work includes an example of Russian economy, where it is studied from theoretical and graphical perspective and only after that the proper inference is made, applying my general model for sample of developing countries. Empirical research shows that corruption and competition are negatively related. In addition, higher secondary education and more political rights (democracy) have depressing effect on corruption in a country. On the other hand, increase in percentage of people with tertiary education leads to higher corruption. However, when the full sample was divided for samples of developing and developed countries, the support for all above mentioned hypotheses was not found, since some variables were insignificant. The methods implemented in current work are as follows: G2SLS random-effect IV and Fixed-effects (within) IV regressions. That is a combination of Fixed effects and Random effects models with Instrumental Variable technique. Additionally, OLS and 2SLS methods are used. |
