Immigration, socio-economic conditions and crime: a cross-sectional versus cross-sectional time-series perspective
| Autor: | Luigi M. Solivetti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
media_common.quotation_subject Immigration Instrumental variables Affect (psychology) Social Sciences (all) Crime Crime determinants Longitudinal analysis Natives Political science mental disorders Development economics Immigration and crime health care economics and organizations 0505 law media_common Pace 05 social sciences Instrumental variable Perspective (graphical) General Social Sciences social sciences Synchronic analysis 050501 criminology population characteristics human activities |
| Popis: | This study purpose is to verify if there is an association between foreign immigration and crime. In doing this, the study investigates also some satellite aspects revolving around this possible association: the range of offences affected by immigration, the relationship between immigrant and native crime, and whether the immigration impact on crime is direct or indirect. The present study has addressed these issues by both a cross-sectional and a longitudinal analysis, the latter including an instrument. The study is based on data of the Italian provinces. Italy represents a critical case for studying the migration–crime relationship, because in this country the rise in foreign immigration has been sudden and its pace feverish. The cross-sectional analysis findings show that crime intensities are affected by time-invariant factors and marginally by immigration. On the contrary, the longitudinal analysis shows that variations in immigration had a positive impact on both the most serious and the most common offences, on property crimes as well as on crimes of violence. There is no evidence of indirect effects of immigration on crime or of a link with native crime. In contrast to previous literature regarding the U.S., Canada, and Australia, these results suggest that a spiralling immigration can affect crime. In terms of methods, these findings show that the standard synchronic analysis models can be biased by non-observed factors and that therefore cross-sectional time-series models can offer significant advantages. |
| Databáze: | OpenAIRE |
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