The Evasion of Emotions

Since 1950s, starting with Schmölders’ publication, it has been ascertained that behavioural aspects have consequences on tax evasion, tax compliance and tax morale. Most of the researches in this field have focused on two aspects: explaining how tax behaviours are affected by individuals’ situation and by their social contest, and creating the microstructured datatsets required for such analysis. Following the tradition, this thesis will firstly presents an analysis on the psychological framework used to explain tax related behaviours, in particular with respect to tax evasion reporting by fellow citizen. In this study, citizens’ sentiment towards the local and national level administration is proxied through sentiment indexes based on geolocated twitter data. Secondly, it uses a dataset from the website evasori.info: who gathers anonymous reports from people which record illegal fiscal events as, for example, not emitted fiscal documents. It also records tax evaded amounts, economic sector, geolocalization of the event and even if the report ended with a signalling to the Italian tax police called Guardia di Finanza. The aim of the thesis is to investigate if there exists dependencies between spatial, social, demographic variables and the probability to report the aforementioned facts given that the illicit has actually happened and the reporter knew the existence of the website. This study will highlight the active role played by citizens tax behaviour and explain which factors affect their choice of signalling the event to the tax police. In addition, this study will explore changes in the reported tax evasion events, and their amounts, through time and space. To catch the essence of cultural and economic differences among different areas of Italy, three italian provinces will be analysed: Rome, Milan and Naples. The timespan taken in consideration brings together three governments: Monti’s, Renzi’s and Conte I’s ones. The empirical model will consist of a logit regression, in order to obtain indicators of how much individuals of different areas have different propensions to report illegal tax evasion behaviours to the tax police.