The literature suggests that neighborhoods with organized criminal networks would have lower crime rates than other neighborhoods or communities, because of the social control their organization exerts on residents and visitors. The strictly organized Italian-American Mafia seems to have characteristics that would translate throughout the neighborhood: People will not participate in overt illegal behaviors because they do not know who is watching, and the fear of what the Mafia might do keeps residents and visitors to the neighborhood relatively well-behaved. Using crime statistics from the NYPD and census data for neighborhood characteristics, four linear regressions were calculated. The results indicate that low socioeconomic status is the main factor explaining neighborhood crime rate variations in New York City. The percent of the population under 18 and density were also listed as influential factors for some variables. The percent of foreign-born Italians was noted as significant in the correlation models, though it is not yet clear what this might truly indicate. The proxy variable for Mafia presence was not significant, and this can either be due to inaccuracies of the measurement of the variable or a true decrease in the influence of Mafia presence after the string of RICO arrests in the 1980s and 1990s. The results imply that Mafia presence does not influence neighborhood social control, but they do reinforce social disorganization theory. The foundation of this theory is neighborhood stability; the more unstable a neighborhood is, the more susceptible the neighborhood is to crime and dysfunction. Factors like low socioeconomic status and density influence neighborhood stability. Future research should attempt to have more accurate representations of Mafia presence and neighborhood characteristics.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5136 |
Date | 01 January 2009 |
Creators | Marshall, Hollianne |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations |
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