Supervisors info:
Ευαγγελία Παπαπέτρου, Καθηγήτρια Τομέα Ι, Τμήμα Οικονομικών Επιστημών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Summary:
The aim of this thesis is to study for the first time in the literature, the impact of card payments on the income taxes, by using a unique sample of annual data for nineteen euro-area countries, from 2000 to 2016. In doing so, it applies panel data analysis, fixed and random effects methodology and choosing the most appropriate using the Hausman test. Moreover, it applies k-means machine learning methodology, in order to classify the euro area countries in clusters of common characteristics, which are determined by the structure of selected macroeconomic indicators, such as GDP and taxes. The findings obtained, are important and provide further insights for government and tax administration, in order to improve tax collection efficiency and fair the competition within enterprises.
The data for the card payments (value of card payments, number of card payments, number of POS) were obtained from the European Central Bank Statistical Data Warehouse, for the income taxes, unemployment GDP and VAT revenues, from the Eurostat database and for the PIT and CIT rates from European Commission. The analysis performed using the data analysis and statistical software: STATA version 13, Microsoft Office Excel 2016 and R statistical programming language.
Finally, this thesis consists of six chapters. The chapter one is the introduction, presenting the purpose of this thesis. The second chapter presents the literature review and provides evidence on the relationship between card payments and tax revenues, shadow economy and economic growth. The third chapter describes the data. The fourth chapter, describes the methodology, the fifth chapter presents the model and the empirical estimation, while the sixth chapter, presents the conclusions of the research.
SUBJECT AREA: Panel Analysis, Data Analytics.
