Ricerc@Sapienza

Most empirical work in the social sciences is based on observational data that are often both incomplete, and therefore unrepresentative of the population of interest, and affected by measurement errors. These problems are very well known in the literature and ad hoc procedures for parametric modeling have been proposed and developed for some time, in order to correct estimate’s bias and obtain consistent estimators. However, to our best knowledge, the aforementioned problems have not yet been jointly considered.

Tax evasion is a matter of huge concern for all countries as it undermines the public finance and can lead to a very inefficient resource allocation. Therefore it is of great interest to develop models that can help targeting the units (firms or individuals) at risk of noncompliance. Our focus is on a particular facet of tax evasion: undeclared work. We use an ad hoc data set by linking information on the inspections carried out in Italy in 2005 by the National Institute of Social Security on building and construction companies and a vast set of firms characteristics.