Abstract
AbstractIn this paper, we question whether different attitudes of European countries’ policies toward migrant integration correspond to different propensities to hold foreigners in prison. As a first attempt to test the existence of such an association, we cluster 34 European countries for the year 2019, modelling the dimensions of the Migrant Integration Policy Index (MIPEX). Leveraging finite mixtures of multivariate Gaussian, we identify three groups of countries with a similar level of integration. Then, we estimate the relative proportion of foreigners held in prison among clusters, relying on UNODC and UNDESA data and exploiting Fisher’s noncentral hypergeometric (FNCH) model. We aim to introduce the FNCH model on clusters as a new quantitative tool to investigate demographic and social research issues. Results show that, in the less virtuous cluster in terms of migrants’ integration, foreigners are almost twice more exposed to detention than in the other clusters. Moreover, looking at the differences within clusters, we find that foreigners have a different propensity to be held in prison with respect to citizens. The proposed approach adds new valuable insights to the MIPEX and provides a novel perspective on an important and highly debated phenomenon, such as foreigners in prison, through the lenses of migrants’ integration. From a policymaking perspective, there emerges a need for more attention and further investigation on the paths underlying such an association between migrants’ integration and detention. To this aim, it is essential that countries enhance data collection and access, especially on migrant incarceration.
Funder
Università degli Studi di Roma Tor Vergata
Publisher
Springer Science and Business Media LLC
Reference61 articles.
1. Alaimo, L.S., Amato, F., Seri, E.: A longitudinal cross country comparison of migrant integration policies via Mixture of Matrix-Normals. In: A. Balzanella, C.C.M. Bini, R. Verde (eds.) Book of the Short Papers of the 51st Scientific Meeting of the Italian Statistical Society, pp. 1136–1141. Springer (2022)
2. Alaimo, L.S., Amato, F., Maggino, F., Piscitelli, A., Seri, E.: A comparison of migrant integration policies via mixture of matrix-normals. Soc. Indic. Res. 12(3), 327–337 (2021). https://doi.org/10.1007/s11205-022-03024-2
3. Ballerini, V., Liseo, B.: Fisher’s noncentral hypergeometric distribution for population size estimation. In: A. Balzanella, C.C.M. Bini, R. Verde (eds.) arXiv:2210.08346
4. Banfield, J.D., Raftery, A.E.: Model-based gaussian and non-gaussian clustering. Biometrics 49(3), 803–821 (1993)
5. Becker, G.S.: Crime and punishment: An economic approach. In: The Economic Dimensions of Crime, pp. 13–68. Springer, New York (1968)