A quantitative investigation of immigrants integration and detention in Europe

Author:

Ballerini Veronica,Seri EmilianoORCID

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)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3