• spatial econometrics
• misconduct

The Geography of corporate misconduct is a thesis thought to enrich the literature about the corporate misconduct, providing for a different perspective useful to detect and identify the causes of this phenomenon, and that analyze the phenomenon under a data driven approach that assume as the most recent literature that the phenomenon is embedded in the existence of the business, so put the focus on the measurable causes that are external rather than questioning the internal structure of the firms. The first part of the thesis provide a general introduction to what corporate misconduct is and the different ways in which it have been studied.
This part was crucial for it allow to fix a precise definition for what corporate wrongdoing is, that is a central task known the data driven approach the analysis would have followed. After that, a huge database of geo-referenced fines has been used in order to study the evolution of the phenomenon trough the observation period which goes from 2000 to 2020, exploring the evolution of corporate misconduct trough the time, among the US counties, looking at the different types of offenses and at the different types of social control agent involved.
In order to study the corporate misconduct phenomena, two different variable have been chosen: the first was the sum of the cost of the fines issued and then the count of the fines issued. The choice go the variable allowed to detect two particular characteristics of the phenomenon: it's magnitude an it’s diffusion. Trough the georeferenced database it was possible to collect the informations about from which counties the fines come from, so that a spatial pattern could have been used in order to enrich the analysis.
After having grouped the observation variables by counties, the database have been merged with another one containing a series of variables describing different social characteristics off the american counties.
After that, a Spatial autoregressive model have been used in order to detect what are the causes of corporate misconduct that is external to firms organization. Operatively, it has been searched for those variables in the last dataset mentioned have influenced the happening of the observation variables. Moreover Trough The Spatial Autoregressive model it was possible to look at the influences that counties have on their neighbors. The analysis has been reapeted twice, the second time considering “neighbor” not only the counties sharing a border, but trough a second order weight matrix it can also be detected the influence that the counties sharing a neighbour county have on themselves.
The results show how a phenomenon consistent and spread like corporate misconduct, which is growing in magnitude and is currently faced mainly by the federal government, is indeed affected by some characteristics of the social texture of a county, and that that these characteristics can be studied in order to understand, detect and avoid the happening of corporate misconduct and other phenomena, and it could be done in an growing accurate manner if the analyses start we ad hoc data collection.