U.S. flag

An official website of the United States government, Department of Justice.

NCJRS Virtual Library

The Virtual Library houses over 235,000 criminal justice resources, including all known OJP works.
Click here to search the NCJRS Virtual Library

Crimes of Poverty: Economic Marginalization and the Gender Gap in Crime (From Gender and Crime: Patterns in Victimization and Offending, P 115-136, 2006, Karen Heimer and Candace Kruttschnitt, eds., -- See NCJ-214516)

NCJ Number
214521
Author(s)
Karen Heimer; Stacy Wittrock; Halime Unal
Date Published
2006
Length
22 pages
Annotation
This study compared the gender gap in arrests with the economic well-being of men and women in order to examine the link between economic deprivation and crime among women.
Abstract
Overall, the results support an economic marginalization explanation for the variation across time and city in the gender rate ratios of arrest for violent and property crimes. As women’s unemployment rates increase relative to those of men, women account for a larger share of arrests for most crimes examined. Even after controlling for other relevant factors, such as population size and decade, the findings indicated that the gender rate ratio of aggravated assault was largest when women’s rates of unemployment were higher relative to men’s unemployment rates. Other findings revealed that the gender rate ratio of arrests for robbery was largest when poverty was concentrated among female-headed households and that the gender rate ratios of both burglary and larceny arrests increased as women’s relative unemployment rates increased. The findings underscore the importance of viewing women’s economic circumstances in comparison with men’s, which is consistent with recent popular commentary regarding female poverty. Data were drawn from the 100 largest United States cities in 1970 for each decennial census year from 1970 through 2000. The data included city-level measures of female and male arrests for 1970, 1980, 1990, and 2000 and an array of sociodemographic data. General linear mixed models were specified and computed with the use of the statistical software package, SAS. Models were estimated using Residual Maximum Likelihood (REML) equations. While the findings suggest that patterns of female offending across time and cities are linked to women’s economic deprivation, more research is necessary to fully understand the complexities involved with this phenomenon. Figures, table, notes