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CHAPTER 1: IMMIGRANT CONCENTRATION AND HOMICIDE MORTALITY:

1.3 Data and Methods

1.3.2 Control Variables

All data measuring neighborhood structural characteristics, including the measures of immigrant concentration, were taken from the SF3 file of the 2000 U.S. Census. Characteristics were broadly organized into three groups based on the probable mechanism by which they might reduce or enhance homicide probability: (1) poverty or concentrated disadvantage, (2) residential stability, and (3) demographic composition. The number of possible measures is large and collinearity among these variables is likely

to be problematic. Principal component analysis was used to address the potential collinearity and to reduce the total number of regressors in the estimation equations.

The concentrated poverty index, which is expected to have a positive effect on homicide mortality, is composed of the proportion of the tract that is unemployed, the proportion that is below the poverty rate, the proportion non-Hispanic black, the proportion that is receiving public assistance, the natural log of the median family income, and the proportion of the population that does not have a high school diploma.

Residential stability is evaluated as an index of the proportion of individuals who have been in their current home for more than five years, the proportion of housing units that are occupied, and the proportion of occupied housing units that are owner-occupied. Increased residential stability is expected to contribute to a reduction in the level of crime, as longer term residents and homeowners have a greater stake in maintaining a safe neighborhood. However, the effect of residential stability on neighborhood violence has been shown to interact with the effect of concentrated disadvantage (Smith and Jarjoura 1988; also see Sampson and Wilson 1995). For high-poverty or high-violence neighborhoods residential stability may be the consequence of an inability of residents to move to more affluent or safer areas, such that stability could be positively associated with homicide rates.

The proportion of the tract population that is male between the ages of 15 and 24 is used to convey differences in demographic structure among tracts. This variable accounts for the higher likelihood of homicide in those tracts with a greater density of potential homicide offenders and victims.

potential homicide victims and the degree to which individuals come into contact with one another. The census tracts used in this study exhibit wide variation in geographic size, ranging from .04 to 328 square miles, and population size, ranging from 171 to over 12,000 individuals. To account for this variation, the natural log of the total population and the natural log of the population density are included as control variables.

Social capital and collective efficacy theories predict that the existing ethnic composition of a neighborhood may affect the homicide rate within that area. If

immigrants are selecting into a community based on the current prevalence of their ethnic group within that community, it is necessary to separate the immigrant effect from the ethnic group effect. While country-specific ethnicities are not available for the native born population for every census tract, the proportion of the tract population that is native-born Asian and the proportion of the tract population that is native-born Hispanic are included as broad indicators of the potential effect of existing ethnic group

composition.

Neighborhood boundaries are arbitrary constructions and the processes through which immigrant concentration might be expected to affect homicide incidence are not spatially isolated within the borders of a particular census tract neighborhood. The spatial independence of neighborhoods, which is an implicit assumption in traditional regression models, does not reflect the reality that neighborhoods are part of a larger social context where nearby communities may produce effects on individual

neighborhoods (Morenoff, Sampson, and Raudenbush 2001). The discretionary nature of neighborhood boundaries also means that the rate of homicide in an area may be

Research is increasingly focusing on the spatial dynamics of neighborhood violence (Morenoff, Sampson and Raudenbush 2001; Graif and Sampson 2009).

A crucial concept in any spatial methodology is that of the spatial weight matrix, which defines the spatial relationship between each unit of analysis – in this case those tracts that neighbor each other. The weight matrix is chosen based on a theoretical consideration of the social process being modeled. Residents of neighborhoods that are more spatially proximal to those neighborhoods with characteristics predictive of violent behavior may themselves be at a higher risk of violence, as neighborhood boundaries are typically unenforceable. To this end, the spatial weight matrix is defined in this analysis such that tracts which are adjacent (contiguous) to one another are considered neighbors and tracts which do not touch are considered non-neighbors.8

The dependent variable used in this analysis, the count of homicide deaths per tract, is highly clustered in space. The value of the Moran’s I statistic, a frequently used measure of spatial autocorrelation, is 0.567 and the p-value (p<.001) indicates significant spatial clustering.

This spatial analysis is carried out using OpenGeoDa and ArcMap software.

9

Further testing using the OpenGeoDa software suggests that the appropriate model with which to correct for the spatial dependence is a spatial lag model.10

8

Because the choice of a spatial weight matrix is, to some extent, arbitrary, alternative weighting schemes were also considered. The results from the models which follow are robust to these alternative spatial weights, which included identifying neighbors based on queen contiguity (tracts which share a border or a point), 2nd order rook contiguity (tracts which are adjacent to the origin tract, as well as tracts adjacent to those first neighbors), five nearest neighbors (from/to the centroid), and ten nearest neighbors (from/to the centroid).

The spatial lag model includes an endogenous covariate - the neighbor-

9

The Moran’s I p-value is based on the permutation test detailed in Anselin (2005) and carried out in OpenGeoDa.

10

The Lagrange Multiplier test in the OpenGeoda regression diagnostics compares a non-spatial model to a spatial lag model and a spatial error model, and Anselin (2005) provides a decision rule in selecting the

weighted value of the dependent variable. In practice, this is accomplished by averaging the number of homicide deaths in neighboring tracts (as defined by the spatial weight matrix) and then introducing this value as a covariate in the regression model.