This research note critically evaluates conventional methods for allocating homicides with an unknown victim/offender relationship to meaningful categories, and it proposes an alternative approach. We argue that conventional methods are based on a problematic assumption, namely, that the missing data mechanism is “ignorable.” As an alternative to these methods, we propose an imputation algorithm derived from a log‐multiplicative model that does not require this assumption. We apply this technique to estimate levels of homicides disaggregated by victim/offender relationship using the Federal Bureau of Investigation's Supplementary Homicide Report (SHR) data for 1996 and 1997, and we compare the resulting estimates with those obtained from the application of conventional procedures. Our results yield a larger proportion of stranger homicides than are obtained from the conventional methods.
The relationship between racial residential segregation and AfricanAmerican crime rates has thoroughly been explored within the literature on race and crime. However, predictions are mixed when addressing the relationship between racial residential segregation and white crime. According to Massey (2001), whites should be expected to benefit from segregation. This paper explores the research and the literature related to racial residential segregation and crime from the perspective of white advantage. Specifically, it is postulated that racial residential segregation may benefit whites economically, politically, and culturally via several key pathways: by removing them from residential areas of concentrated disadvantage, by distancing them from criminogenic subcultures and areas of higher victimization, and by maintaining political stability and/or reinvestment in white neighborhoods. The methods by which these relationships may be empirically tested are discussed. We further explore different measures of racial segregation, as well as potential intervening variables that may mediate the relationship between racial residential segregation and crime, and discuss the benefits and implications for their inclusion in future research.
Researchers commonly include a measure of the level of divorce among the standard covariates in macro-level studies of homicide, justifying this practice with reference to social disorganization theory. We review the underlying logic for a divorce/homicide relationship, distinguishing between a “cultural/normative conflict” variant advanced by the classical Chicago School theorists and a “structural/control” variant associated with the neosocial disorganization perspective. We suggest that the cultural/normative conflict variant implies that the effects of divorce will become attenuated over time, whereas the structural/control variant implies stability in effects. We then assess the degree to which the effects of levels of divorce on homicide rates have changed with panel data for a sample of large U.S. cities during the period 1960-2000.The results of seemingly unrelated regression (SUR) analyses reveal considerable stability in the effects of a measure of divorce on homicide rates, especially if the divorce measure is combined with a “sibling” measure of family disorganization—the percentage of children not living with two parents. Our analyses suggest that the commonly observed positive effect of measures of divorce on homicide rates over recent decades is most plausibly interpreted with reference to the “structural/control” arguments associated with the neosocial disorganization perspective.
Our research revisits prior work by Neapolitan (2005) on the quality and use of race-specific homicide data. Neapolitan reported that correlations between Black homicide offending rates based on arrest data and rates based on data from the Supplementary Homicide Reports (SHR) for samples of large U.S. cities are only moderately strong. He proposed that, given these findings, the respective rates cannot be regarded as valid indicators of the same concept. We extend Neapolitan's research by estimating regression models to determine the extent to which conclusions about the structural covariates of Black homicide offending rates differ depending on the specific measure of the dependent variable. In addition, we have computed three different Black homicide offending rates with the SHR data: (1) A rate based on single victim/single offender incidents; (2) a rate based on all offenders of known race; and (3) a rate based on the number of Black offenders when the race of offender has been imputed. Our analyses reveal that, consistent with Neapolitan's findings, the correlations between the Black offending rate based on the arrest data and the various SHR-based rates are only moderately strong. In the regression analyses, explained variance is comparatively low in the model with the Black offending rate based on arrest data. However, the regression coefficients do not diverge much across models. Overall, our results suggest that empirical findings and substantive conclusions about city-level covariates of Black offending rates might be less sensitive to the selection of data source than is often assumed. Article by guest on December 3, 2014 hsx.sagepub.com Downloaded from 152 Homicide Studies 18 (2)
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