| 001 - ETIQUETA DE REGISTO |
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| Identificador do registo |
394683 |
| 100 ## - DADOS GERAIS DE PROCESSAMENTO |
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| Dados gerais de processamento |
20170823d2014 y0pory50 ba |
| 101 ## - LÍNGUA DA PUBLICAÇÃO |
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| Língua do texto, banda sonora, etc. |
Inglês |
| 200 ## - TÍTULO E MENÇÃO DE RESPONSABILIDADE |
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| Título próprio |
Predicting crime through incarceration |
| Indicação genérica do tipo de material |
Recurso eletrónico |
| Informação de outro título |
the impact of rates of prison cycling on rates of crime in communities |
| Primeira menção de responsabilidade |
Todd R. Clear ...[et al.] |
| 210 ## - PUBLICAÇÃO, DISTRIBUIÇÃO, ETC. |
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| Lugar da publicação, distribuição, etc. |
[Rockville, MD] |
| Nome do editor, distribuidor, etc. |
National Criminal Justice Reference Service (NCJRS) |
| Data da publicação, distribuição, etc. |
2014 |
| 215 ## - DESCRIÇÃO FÍSICA |
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| Indicação específica do tipo de material e extensão do item |
1 CD ROM |
| Dimensões |
12 cm |
| 300 ## - NOTAS GERAIS |
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| Texto da nota |
Project supported by Award No. 2009-IJ-CX-4037 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. Ficheiro de 6,25 MB em formato PDF (141 p.). |
| 330 ## - SÚMULA OU RESUMO |
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| Texto da nota |
The purpose of this project has been to estimate the impact of "prison cycling"-the flow into and out of prison--on crime rates in communities, with special concern about areas that have high rates of prison cycling. ln this work, we explicitly hypothesized that: (1) there would be a positive impact of neighborhood reentry rates on neighborhood crime rates, controlling for neighborhood characteristics; (2) there would be a positive effect of neighborhood removal rates (admissions) on neighborhood crime rates, controlling for neighborhood characteristics; (3) the effect of the rate of both removal and reentry on the neighborhood crime rate would depend upon the level of removal and reentry (tipping point); and (4) the effect of the rate of both removal and reentry on crime the neighborhood crime rate would depend upon the level of concentrated disadvantage in the neighborhood (interaction effect). To complete the proposed work, we compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix ofurban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2012. Census tract characteristics come from the 2000 Census Summary File 3. The crime, release, and admission data were individual levei data that were then aggregated from the individual incident levei to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects ofrates ofprison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey. Our analytic strategy, was one of analytic triangulation. Through the data collection associated with this project, we amassed a uniquely comprehensive crime and incarceration dataset over time - arguably one ofthe most comprehensive assembled to date. This dataset allowed us to model the relationship between crime and incarceration using a range of techniques (fixed effects panel models, Arrellano-Bond estimations, and vector auto-regression) taking advantage of each and being partially freed of the limitations of any one. We gave considerable attention to the problem ofmodeling. As might be expected, different models often provide different results. The most parsimonious models provide small standard errors with significant results, but there are sometimes sign changes when new control variables are added, suggesting instability in the modeling strategy. By contrast, the most stable results are provided by fixed effects models that, while intuitively attractive, have the disadvantage of large standard errors. When we use this analytic approach, we achieve results that, we believe, are more reliable. Overall, our work finds strong support for the impact of prison cycling on crime. It seems that such cycling has different effects in different kinds of neighborhoods, consistent with the idea of a "tipping point" but more clearly expressed as an interaction between crime policy and type of neighborhood. The results in Tallahassee, Boston, and Trenton provide consistent support for this idea. In Newark, as a result ofthe city's limited variability in neighborhood disadvantage, we failed to find the same pattern. Further research will investigate whether this neighborhood interaction holds in other sites. It will also enable us to think about how neighborhood change over time affects the prison cycling-crime relationship. Do neighborhoods that improve start to benefit from incarceration policy? In contrast, does current incarceration policy become a factor that inhibits neighborhood improvement? |
| 606 ## - NOME COMUM USADO COMO ASSUNTO |
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| Elemento de entrada |
PREVISÃO CRIMINAL |
| ID Autoridade |
346660 |
| 606 ## - NOME COMUM USADO COMO ASSUNTO |
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| Elemento de entrada |
CRIMINALIDADE |
| ID Autoridade |
224783 |
| 606 ## - NOME COMUM USADO COMO ASSUNTO |
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| Elemento de entrada |
DETIDO |
| ID Autoridade |
326042 |
| 606 ## - NOME COMUM USADO COMO ASSUNTO |
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| Elemento de entrada |
ESTADOS UNIDOS |
| ID Autoridade |
334103 |
| 606 ## - NOME COMUM USADO COMO ASSUNTO |
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| Elemento de entrada |
ESTABELECIMENTO PRISIONAL |
| ID Autoridade |
551 |
| 710 ## - NOME DE COLECTIVIDADE - RESPONSABILIDADE PRINCIPAL |
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| Palavra de ordem |
PREDICTING CRIME THROUGH INCARCERATION |
| ID Autoridade |
358519 |
| 955 ## - |
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| -- |
Oferta |
| 995 ## - LOCALIZAÇÃO E INFORMAÇÃO DE EXEMPLARES |
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| -- |
0 |
| -- |
PJCD_258_000000000000000 |
| -- |
2022-06-06 |
| -- |
2022-06-06 |
| -- |
0 |
