Ricerc@Sapienza

Human migration is the movement of people from one place to another, with the aim of settling, at least temporarily, at a new location. It is today a relevant topic for researchers in Demography, Economics, Political Science, Public Health, Sociology and Statistics.
The quantitative study of migration is however hindered by a frequent lack of available data, especially related to what is commonly defined illegal immigration.

Except for a few developed countries, estimates are constructed from the integration of multiple data sources of varying quality and completeness. In addition, in many countries, the main source, namely the periodical population census, faces an uncertain future, because of funding pressures and the general shift of National Statistics Agencies (NSA) from Census data to the use of various integrated Administrative data sources.

Our project has a statistical flavor and it aims to contribute to the ongoing debate and research in terms of the following points.

1. A more principled methodology in the linkage step, that is the identification of common records in multiple lists. This step is important to avoid to introduce a bias in the estimates
2. The introduction of an explicit model to account for measurement errors in the observed variables. Administrative data are often collected for non statistical purposes and they must be cleaned and re-harmonized before using them into a learning algorithm.
When data are merged from different sources, the relative quality of each data set is different and account for it may be decisive.

3. The exploration of more flexible parametric model in order to improve the already existing model for projection and/or reconstruction of migration flows. In particular, we will consider the Conwey-Maxwell-Poisson model which generalizes the Poisson class, by allowing both under and over dispersion among counts.
Finally, a package implementing the new methodologies will be delivered using the statistical software R.