THE SOCIAL ECOLOGY AND SPATIAL DISTRIBUTION OF LETHAL VIOLENCE IN TORONTO, 1988-2003
Sara Kerr Thompson
A thesis submitted in conformity with the requirements
for the degree of Doctorate of Philosophy
Centre of Criminology
University of Toronto
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•+• Canada The Social Ecology and Spatial Distribution of Lethal Violence in Toronto, 1988-2003
Doctorate of Philosophy, 2009
Sara Kerr Thompson
Centre of Criminology University of Toronto
ABSTRACT
This dissertation examines the ways in which neighbourhood demographic, socioeconomic and housing characteristics are related to the risk of homicide in Toronto over the period 1988-2003. This study expands on recent studies of the social ecology of lethal violence in three important ways. First, it provides the first empirical examination of this topic in a large Canadian city, which offers an opportunity to determine the extent to which the ecological covariates of lethal violence identified in the U.S. based literature also predict this violence in the Canadian context. Second, while this dissertation examines total homicide counts, it is also one of a small number of studies that disaggregates these counts into subtypes, in an effort to determine the extent to which the ecological correlates of one type of homicide are specific to, or distinct from, those of another. Finally, this study provides a more nuanced and contextualized analysis of
'neighbourhood effects' and homicide than has been possible with either cross-sectional analyses of neighbourhoods in one city or longitudinal analyses of multiple cities. Spatial analytic methodologies show that, as is the case south of the border, high levels of lethal violence tend to cluster in a small number of inner-city neighbourhoods in Toronto.
ii However, unlike the spatial distribution of homicide in many U.S. cities, this violence also tends to cluster in neighbourhoods outside of the city core. The results of traditional multivariate methodologies show that some of the neighbourhood characteristics included in the multivariate analyses are related to some types of homicide in Toronto, but not to others. Neighbourhoods characterized by economic disadvantage and larger proportions of young male and black residents experienced higher levels of homicide, particularly homicides with young black male victims. This suggests that these types of homicide might be better seen as the result of a similar neighbourhood context. At the same time, however, the differences that emerged in the neighbourhood characteristics associated with different homicide types lend some support to the idea that, to a certain extent, some types of homicide in Toronto may be influenced by distinctive causal factors.
in ACKNOWLEDGEMENTS
I am indebted beyond words to Rosemary Gartner, Anthony Doob, Carolyn Greene,
Anna Pratt, Natasha Madon, Jane Sprott, Peter Kiatipis, Scott Clarke, and Karim Ismaili for helping to bring this dissertation into being with their wisdom, enthusiasm, friendship, and support.
To Peter Sloly, Brian Raybould, Helen Dixon, Mark Saunders and Tom Gage for a once-in-a-lifetime experience with the Toronto Police Service's homicide squad.
To Laine Ruus for her incredible knowledge of all things data-related, as well as for her wicked sense of humour.
And last, but certainly not least, to my family, lifelines through this project. Without your love and encouragement, I'd never have dared to even begin this journey. Over the period that I wrote this dissertation, I also married my best friend and gave birth to my son, a man and a boy who, together, light up my life. My cup runneth over.
iv TABLE OF CONTENTS Abstract ii Acknowledgements iv List of Figures viii List of Tables ix Chapter I. Neighbourhoods and Homicide 1 1.1 Why Neighbourhoods? 4 1.2 Defining Neighbourhoods 5 1.3 Urban Space in Canada and the U.S.: Similarities and Differences 6 1.3.1 Structural Similarities between Canadian and American Cities 8 1.3.2 Structural Differences between Canadian and American Cities 12 1.4 The Scope of This Study 21 Chapter II. Neighbourhoods and Violent Crime in Toronto 24 2.1 The Social Geography of Neighbourhoods in Toronto 24 2.1.1 Poverty by Postal Code: Neighbourhoods and Poverty in Toronto 26 2.2 Trends in the Social and Spatial Distribution of Homicide in Toronto: What We Know 31 2.3 Media Commentaries Over Lethal Violence in Toronto: Implications for this Dissertation 33 Chapter III. Explaining the Spatial Distribution of Homicide 42 3.1 Theoretical Overview 42 3.1.1 Social Disorganization Theory 43 3.1.2 Subcultural Perspectives 48 3.1.3 Strain Perspectives 53 3.1.4 The Routine Activity Perspective 57 3.2 Theoretical Implications: Summary 60 3.3 Disaggregating Homicide by Type 64 Chapter IV. Data Sources and Description 69 4.1 Defining Neighbourhoods 69 4.2 Data Sources and Data Collection 71 4.2.1 The Dependent Variable: Homicide 71 4.2.2 The Independent Variable: Neighbourhood Characteristics 74 4.3 Data Preparation 77 4.4 Data Description 79 4.4.1 Characteristics of Homicide in Toronto, 1988-2003 79 4.4.2 Characteristics of Neighbourhoods in Toronto 80 4.4.3 Bivariate Associations Among Demographic, Economic and Housing Variables in Toronto's Neighbourhoods 81 4.4.4 Bivariate Associations between Neighbourhood Characteristics and Homicide in Toronto's Neighbourhoods 82 4.5 Analytic Techniques 83 4.5.1 Regression Analyses 83 4.5.2 Spatial Autocorrelation 84 Chapter V. Homicide in TorontoNeighbourhoods, 1988-2003 92
v 5.1 Research on the Social Ecology of Lethal Violence 92 5.1.1 Socioeconomic Characteristics 93 5.1.2 Demographic Characteristics 101 5.1.3 Housing Characteristics 111 5.2 The Spatial Distribution of Total Homicide Counts in Toronto's Neighbourhoods, 1988-2003 113 5.3 Principle Components Analysis 114 5.4 Neighbourhood Correlates of Total Homicide Counts in Toronto: Multivariate Analyses 116 5.5 Toronto Neighbourhoods and Homicide: Illustrations of the Multivariate Results 120 5.6 Toronto Neighbourhoods and Homicide: Exceptions to the Multivariate Results 126 5.7 Concluding Remarks 128 Chapter VI. Disaggregated Homicide Types in Toronto's Neighbourhoods, 1988-2003 135 6.1 Relationships Among Types of Homicide in Toronto 135 6.2 The Social Ecology of Black Homicide Victimization 137 6.2.1 Black Homicides in Toronto, 1988-2003: Sample Cases and Descriptive Statistics 142 6.2.2 Characteristics of Homicides Involving Black Victims in Toronto's Neighbourhoods 144 6.2.3 The Spatial Distribution of Black Homicide Victimization 146 6.2.4 Bivariate Associations Between Neighbourhood Characteristics and Black Homicide 147 6.2.5 Neighbourhood Correlates of Black Homicide Victimization in Toronto: Multivariate Analyses 148 6.3 The Social Ecology of Homicide Victimization Among Males Aged 15-34 151 6.3.1 Culture, Masculinity and Lethal Violence Among Young Males 151 6.3.2 Drug Markets, Gangs and the Killing of Young Males 153 6.3.3 Structural Explanations of Youth Homicide: Weakened Informal Controls 154 6.3.4 Homicides Involving Males 15-34 in Toronto: Sample Cases 157 6.3.5 The Spatial Distribution of Homicides Involving Young Males (15-34) in Toronto 158 6.3.6 Characteristics of Homicides Involving Young Males (15-34) in Toronto, 1988-2003 158 6.3.7 Bivariate Associations Between Neighbourhood Characteristics and Homicide Among Young Males 160 6.3.8 Neighbourhood Correlates of Lethal Violence Among Young Males in Toronto: Multivariate Analyses 161 6.4 The Social Ecology of Gun Homicide in Toronto 163 6.4.1 Gun Homicides in Toronto, 1988-2003: Sample Cases 168 6.4.2 The Spatial Distribution of Gun Homicides in Toronto 168
VI 6.4.3 Gun Homicide in Toronto: Descriptive Statistics 169 6.4.4 Bivariate Associations Between Neighbourhood Characteristsics and Gun Homicide 171 6.4.5 Neighbourhood Correlates of Gun Homicide in Toronto: Multivariate Analyses 172 6.5 The Social Ecology of Intimate Femicide 174 6.5.1 Intimate Femicide in Toronto's Neighbourhoods, 1988-2003 Sample Cases 181 6.5.2 The Spatial Distribution of Intimate Femicide in Toronto, 1988-2003 182 6.5.3 Intimate Femicide in Toronto: Descriptive Statistics 182 6.5.4 Bivariate Associations Between Neighbourhood Characteristics and Intimate Femicide 184 6.5.5 Neighbourhood Correlates of Intimate Femicide in Toronto: Multivariate Analyses 184 6.6 Toronto Neighbourhoods and Homicide Types: Summary of Findings 187 Chapter VII. Summary and Conclusions 219 7.1 Summary of Findings 220 7.2 Policy Implications 223 7.3 Directions for Future Research 230 7.3.1 Temporal Trends in the Spatial Distribution and Social Ecology of Serious Violent Crime in Toronto 231 7.3.2 Examining the Micro-Environment of Neighbourhoods with High and Low Levels of Serious Violent Crime 232 7.3.3 Generalizability Issues 233 7.3.4 A Cautionary Note on Fallacies 234 7.4 Limitations and Recommendations 234 References 238
VI1 LIST OF FIGURES
2.1 Total Homicide Victimization Rate, Toronto, 1988-2003 40 2.2 Homicides and Gun-Related Crimes, Toronto, 2005 41 4.1 Map of Toronto Neighbourhoods 87 5.1 Homicide in Toronto's Neighbourhoods, 1988-2003 130 5.2 Highest Homicide Neighbourhoods in Toronto, 1988-2003 131 6.1 Homicide in Toronto's Neighbourhoods Involving Black Victims, 1988-2003 200
6.2 Homicides in Toronto's Neighbourhoods Involving Males (15-34), 1988-2003 203 6.3 Homicides in Toronto's Neighbourhoods Involving Guns 1988-2003 208 6.4 Intimate Femicides in Toronto's Neighbourhoods, 1988-2003 213
vin LIST OF TABLES
4.1 Characteristics of Homicides in Toronto's Neighbourhoods, 1988-2003 88 4.2 Characteristics of 140 Neighbourhoods in Toronto, Averaged Across 4 Censuses (1986,1991,1996, 2001) 90 4.3 Bivariate Correlations for Neighbourhod Characteristics (n=140) and Homicide 91 5.1 Factor Loadings for Measures of Economic Disadvantage 132 5.2 Bivariate Relationships between the Disadvantage Index, Other Neighbourhood Characteristics, and Homicide 132 5.3 Multicollinearity Diagnostics for the Independent Variables 133 5.4 Negative Binomial Regressions: Neighbourhood Characteristics and Homicide Counts 134 6.1 Bivariate Correlations between Homicide Subtypes in Toronto Neighbourhoods 197 6.2 Characteristics of Homicides Involving Black Victims in Toronto's Neighbourhoods 198 6.3 Bivariate Correlations between Neighbourhood Characteristics and the Number of Homicides with Black Victims in Toronto Neighbourhoods 201 6.4 Negative Binomial Regressions: Neighbourhood Characteristics and Homicides with Black Victims 202 6.5 Characteristics of Male (15-34) Homicides in Toronto's Neighbourhoods 204 6.6 Bivariate Correlations between Neighbourhood Characteristics and the Number of Homicides with Male Victims Aged 15-34 in Toronto Neighbourhoods 206 6.7 Negative Binomial Regressions: Neighbourhood Characteristics And Homicides with Young Males Aged 15-34 207 6.8 Characteristics of Gun Homicides in Toronto's Neighbourhoods 209
6.9 Bivariate Correlations between Neighbourhood Characteristics and the Number of Gun Homicides in Toronto Neighbourhoods 211 6.10 Negative Binomial Regressions: Neighbourhood Characteristics and Homicides with Guns 212 6.11 Characteristics of Intimate Femicides in Toronto's Neighbourhoods 214 6.12 Bivariate Correlations between Neighbourhood Characteristics and the Number of Intimate Femicides in Toronto Neighbourhoods 216 6.13 Poisson Regressions: Neighbourhood Characteristics and
IX Intimate Femicide Counts 217 6.14 Summary of Multivariate Results 218 1
CHAPTER I. NEIGHBOURHOODS AND HOMICIDE
Unlike what Cohen (1966) called "kinds of people" explanations of human behaviour, social ecological research focuses on "place-based" explanations - specifically, the effects of neighbourhood characteristics on a host of behaviours, including the risk of criminal victimization. A neighbourhood-level orientation to sociological research began in the 1920s with the work of Shaw and McKay, which was the first to explain crime and delinquency within the context of the changing urban environment and processes of rapid ecological change in the city of Chicago. Unlike many of their contemporaries who subscribed to individualistic and biological theories of crime causation, Shaw and McKay instead proposed that crime was a product of ecological conditions in particular inner-city neighbourhoods characterized by features such as economic deprivation, high residential mobility and racial/ethnic heterogeneity. These features, they argued, operate to disrupt local community social organization, and accounted for variation in rates of crime across neighbourhoods.
In the 1970s, ecological research fell into "temporary dormancy" (Bursik, 1988), largely due to the leveling of several important criticisms concerning a) its denial of human agency; b) its reliance on official definitions of crime and use of official statistics as a measure of crime rates (Baldwin, 1979; Davidson, 1981; Nettler, 1984); c) its tendency to ignore the political and economic influences on crime (Davis, 1975); and d) its inability to account for high crime rates in relatively stable, working-class communities (Bursick & Grasmick, 1993). By the 1980s, with the emergence of more sophisticated reformulations that incorporated victimization data, spatial analysis, a consideration of neighbourhood inequality (Blau & Blau, 1982), and the crucial role that
1 2 political and economic power plays in structuring neighbourhoods with high rates of crime (Blau & Blau, 1982; Bursik, 1988; Bursik & Grasmick, 1993; Kornhauser, 1978;
Sampson & Groves, 1989; Sampson & Wilson, 1995; Stark, 1987), ecological research began anew and in earnest. Indeed, the past two decades have witnessed a proliferation of studies aimed at explaining variation in rates of crime and violence at the neighbourhood level.
The underlying assumption of this brand of research is that neighbourhood characteristics have social effects on crime and violence over and above the effects of characteristics of individual residents - and the goal is to isolate the neighbourhood characteristics that lead to high rates of crime and violence. The recent resurgence in the
'neighbourhood effects' literature has led researchers to examine how and why violence concentrates where it does, and what neighbourhood-level characteristics are associated with its distribution across urban space. This research shows that neighbourhoods differ substantially in their rates of violence, and that the ways in which violence is distributed across urban neighbourhoods is linked to the structural characteristics of those neighbourhoods, including concentrated poverty, residential instability and the racial segregation of minority groups (Bailer, Anselin & Messner, 2001; Lee, 2000; Morenoff
& Sampson, 1997; Parker & Pruitt, 2000; Peterson & Krivo, 1993).
1 There exists a longstanding debate over whether neighbourhood effects truly reflect neighbourhood-level processes, or whether they are instead a reflection of the concentration of certain sorts of individuals in certain sorts of neighbourhoods. Much ecological research on lethal violence cannot empirically distinguish between the two, due to the absence of individual-level data both on those who are victims of homicide and those who are not. There is no doubt that part of the effect of ecological variables that measure neighbourhood disadvantage is due to selection - people who grow up in violent and otherwise unstable homes and neighbourhoods are more likely to end up living in disadvantaged areas due to their inability to hold down a job or to cultivate stable relationships. However, as Miles-Doan (1988) argues, there is strong ethnographic evidence to suggest that part of the neighbourhood effect is due to neighbourhood context itself, which has a profound effect on both social and behavioural outcomes for residents (see, for example, Anderson, 1999 and Patillo-McCoy, 1999). Research that incorporates HLM models provides additional support for this contention.
2 J
This study will follow the ecological tradition by examining how neighbourhood characteristics may be related to the quantity and quality of lethal violence that neighbourhoods experience. The main emphasis will be on spatial variation in, and the ecological predictors of homicide rates in the city of Toronto for the period 1988-2003 ?
The two main questions my research will address are: 1) What neighbourhood-level characteristics are associated with variation in the overall level of lethal violence in
Toronto's neighbourhoods?; and 2) Do the neighbourhood-level correlates of homicide identified in Toronto vary according to the type of homicide, or are the correlates associated with one type of homicide similar to those associated with a qualitatively different type of lethal violence? These and other questions are related to a more general theme that emerges in the literature on neighbourhood effects and violent crime: how does the distribution of lethal violence vary across urban space, and what neighbourhood- level characteristics serve to expose people to or, conversely, insulate them from the risk of homicide victimization?
This chapter proceeds as follows. First, I outline the ways in which researchers have conceptualized and operationalized the concept of 'the neighbourhood', and how I operationalize the concept in this study. Second, I discuss the similarities and differences between urban neighbourhoods in Canada and the United States, and the implications that they may have for the social ecology of lethal violence in this country. The final section provides an overview of the structure of the remainder of this dissertation.
" This study examines factors related to the geographic distribution of lethal violence in Toronto, and does not address issues related to the residential location of either victims or offenders. Instead, the focus is on neighbourhoods as sites of lethal violence and the neighbourhood-level factors that are associated with homicide counts in Toronto's neighbourhoods. As such, conclusions cannot be drawn about the connection between the location of this violence and the residences of either the victim or accused person(s). 4
1.1 WHY NEIGHBOURHOODS?
A number of violence researchers (Bursik 1988; Kubrin, 2003; Rose & McClain,
1990; Sampson & Lauritsen, 2004; Taylor et al., 1984) have claimed that neighbourhoods represent the optimal environment in which to study homicide risk. This is because neighbourhoods possess more "ecological integrity" than larger units of aggregation.
That is, they represent the smallest ecological unit where victims and/or offenders are likely to live or otherwise be exposed to one another and consequently be influenced by the social environment created by the ecological conditions under investigation (Sampson
& Lauritsen, 2004). In other words, neighbourhoods are theorized to be more closely linked to the sorts of proximate causal processes assumed to influence lethal violence.
This is because neighbourhoods are generally conceptualized as important sources of socialization, guardianship, communal social capital, social networks, and identity.
Further, neighbourhood context has implications for the types of services, amenities and opportunities available, which can insulate and protect, or, conversely, enhance the risk of violent victimization (Fagan et al., 2003). For the purposes of this study, then, neighbourhoods are hypothesized to differ in the extent to which they either reduce or increase the potential victimization risk for the people who live, work, pass through, and play in them.
' Researchers differ in their views on the importance of neighbourhood in modern urban centres. For example, Putnam (2000) argues that as a result of the proliferation of mass communication and transportation systems, people have become increasingly disconnected from their local community, both physically and psychically. On the other hand, Bursick & Grasmick (1993) argue that modern urban neighbourhoods continue to provide an important frame of reference for the actions and behaviours of their residents, particularly with respect to criminal offending and control.
4 5
1.2 DEFINING NEIGHBOURHOODS
Interest in the concept of' the neighbourhood' has a long research history that spans the boundaries of a number of disciplines, including sociology, criminology, psychology, political science, geography, history and urban planning. In the sociological literature, definitions of neighbourhoods tend to coalesce around two viewpoints: the neighbourhood as an urban subunit composed of similar macro-level characteristics, and the neighbourhood as a social-psychologically defined spatial unit. The latter approach to neighbourhoods attempts to "get inside people's heads to see how residents make sense of the physical surroundings in which they live" (Stoneall, 1981:121). In this tradition, neighbourhoods have been defined according to the "perceptual similarity" of the population (Bardo, 1998); by symbolic attachment to a particular area (Furstenberg &
Hughes, 1997; Lee & Campbell, 1990); by localized patterns of interaction (Bursick &
Grasmick, 1993; Grannis, 1998); or by geographically relevant symbolic boundaries
(Anderson, 1990; Grannis, 1998; Putnam, 2000). In all cases, it is the perceptions of local residents that are considered key to defining the boundaries that demarcate 'the neighbourhood' and to understanding the salience of the social networks, meanings, and identities contained therein. As such, the degree of consensus that can be reached about any particular set of boundaries is not likely to be large.
The macro-level approach to defining neighbourhoods, on the other hand, was developed largely as a result of the seminal work of Chicago School sociologists such as
Robert Park and Ernest Burgess, who conceptualized neighbourhoods as "natural areas" that developed as a result of competition between population groups for affordable housing and between businesses for land use (Park, 1916). According to this view, a
5 6 neighbourhood is a subsection of, and is nested within, a larger community - a combination of both people and institutions that occupy a spatially bounded area that is shaped by ecological, cultural and political forces (Park, 1916). More modern versions of this social ecological approach to defining neighbourhoods use congruency in the social structure of particular geographic spaces to demarcate "similar" social areas as neighbourhoods (Bardo, 1998). Unlike researchers who use social-psychological definitions that rely on individual perceptions of 'the neighbourhood', social ecologists define neighbourhoods according to similarity of function and population makeup
(Bardo, 1998). Indeed, virtually all American studies examining variation in urban violent crime rates at this level of analysis have relied on geographic boundaries defined by the U.S. Census Bureau, with census tracts or block groups used as proxies for local neighbourhoods (Avakame, 1997; Block & Block, 1992; Crutchfield, 1989; Crutchfield et al., 1999; Kubrin, 2003; Kubrin & Weitzer, 2003; Martinez & Lee, 1998, 2000;
McClain, 1989; Morenoff & Sampson, 1997; Peterson et al., 2000; Rosenfeld et al.,
1999; Sampson et al., 1997).4
1.3 URBAN SPACE IN CANADA AND THE UNITED STATES: SIMIUARITIES AND DIFFERENCES
Given that the cityscape is highly differentiated spatially, it is perhaps not surprising that crime and violence are unevenly distributed across urban space. Empirical
Though census tracts are an imperfect measures of the concept of neighbourhood, a limitation that is generally acknowledged in the literature, researchers are quick to point out that the examination of lethal violence at this "spatially more finite unit of analysis" is an improvement over prior research focused on larger units of aggregation, such as states, Standard Metropolital Statistical Areas (SMSA hereafter) and cities (Titterington et al., 2003: 264). Further, census tracts are typically used to represent neighbourhoods in analyses of urban crime because, by design, they tend to possess reasonably stable boundaries, they are relatively homogeneous in terms of population characteristics, economic status, and living conditions, and they are often the only local measures for which the required data are available (Kubrin, 2003; Peterson et al., 2000).
6 research on the spatial distribution of lethal violence in the U.S. has established that it tends to be concentrated within a small number of neighbourhoods that experience rates well above the national average (Kubrin & Weitzer, 2003; Menard & Huizinga, 2001;
Morenoff & Sampson, 1997; Shaw & McKay, 1942; Wilson, 1987). U.S.-based studies of the social ecology of lethal violence also show that a number of structural predictors appear to be 'the usual suspects' when it comes to understanding the spatial distribution of homicide victimization across urban neighbourhoods. In particular, three categories of structural characteristics have been identified as important predictors of neighbourhood homicide rates: characteristics related to neighbourhood-level demographic composition, socioeconomic composition, and housing conditions. Relevant research on the association between each of these categories and variation in neighbourhood-level rates of lethal violence will be reviewed in Chapter Five.
A major limitation of the existing literature on the ecological correlates of lethal violence is that it is based largely on American data. Canadian cities share a number of structural similarities with their American counterparts that have had a profound impact on the development of and conditions within neighbourhoods, and as such would lead to the expectation that many of the neighbourhood-level correlates identified in the
American literature may also apply to the Canadian context. Yet there are also important differences between cities in Canada and the United States that might lead to distinct patterns in the social ecology of lethal violence in this country. While providing an exhaustive review of the similarities and differences among and between cities on both sides of the border is beyond the scope of this dissertation, the following section will
7 8 provide a review of those factors most commonly cited in the literature on the development and subsequent organization of neighbourhoods in both countries.
1.3.1 Structural Similarities Between Canadian and American Cities
Urbanization, Immigration and Industrial Decline. The growth of many
Canadian urban centres has been similar to that of their U.S. counterparts in a number of respects. One of the most important factors that shaped the character and geography of neighbourhoods in both countries was the 'urban explosion' that began in the post World
War Two period (Bourne, 2000). Between 1951 and 1971, metropolitan Canada nearly doubled its population, a development that was mirrored in urban centres south of the border (Hajnal, 1995). An influx of immigrants prompted much of this rapid growth, with sizable populations of poor immigrants being received by Canadian cities where they were largely concentrated in particular neighbourhoods located in core areas (Statistics
Canada, 1992). By 1986, fully 30 percent of Canada's inner city population was foreign born, a figure almost double that of the national average of 16 percent (Ram et al., 1989); and by 1999, nearly 78% of all immigrants to Canada were destined for one of five urban centres: Toronto, Vancouver, Montreal, Calgary or Ottawa-Carleton (Gertler, 2001). As in the U.S. context, one consequence of high immigration levels over this period was increased geographic differentiation based on class, race, and ethnicity in inner-city neighbourhoods (Gertler, 2001; Hajnal, 1995).5
1 However, as Gertler (2001: 6) points out, one of the most significant phenomena of the second half of the 20"' century was the "marked and sustained outward expansion of the urban population", with the most rapid growth occurring at the relatively low-density suburban edge of Canada's metropolitan regions. Yet unlike the United States, this process, he argues, has not resulted in a "simple spatial dichotomy" of white, native-born affluent suburbs versus central city neighbourhoods comprised of poor, visible-minority immigrants. The social geography of Canadian cities is considerably more complicated. Though Canadian cities certainly experience residential segregation by race/ethnicity, this spatial concentration generally does not reach U.S. levels. Further, many new immigrants are not confined to central city residential locations - Bourne (2000) and Ley & Germain (2000) refer to the "suburbanization of immigration" in
8 9
There is a good deal of evidence to suggest that both Canadian and American cities also experienced similar patterns of industrial movement and decline. Until about the
1950s, heavy manufacturing was the dominant employment-generating activity in many cities on both sides of the border. Its reliance on waterways and railways for freight transportation, and the existence of an available workforce in relative proximity meant that manufacturing activity was strongly centralized within densely developed urban areas (Bunting & Filion, 2000). By the end of World War Two, however, rising wages and a growing middle class - coupled with a post-war housing boom, increased car ownership, and large-scale public investment in roads and highways - led to a shift in the spatial distribution of manufacturing activity to more peripheral parts of the metropolitan region. In Canada, the most pronounced drop in urban manufacturing activity occurred in the Quebec-Windsor axis, which had previously accounted for approximately 80% of this country's manufacturing production (Nader, 1976).
The effects of deindustrialization on urban areas are well documented. In both Canada and the United States, inner-city neighbourhoods suffered deterioration, abandonment and a lack of new capital investment. Many cities also lost selective populations, as the
'move to the suburbs' was largely white and working- or middle-class (Goldberg &
Mercer, 1986; Tey, 1981; Wilson, 1987). As a result of the exodus of industrial capital, many older industrial cities also experienced a concomitant increase in unemployment rates within core areas - and though many of these manufacturing jobs were ultimately replaced by service sector jobs, the wages earned in the service sector were often not sufficient to maintain a family above the poverty line (Hajnal, 1995). As such, to a large
Canada, whereby increasing numbers of new immigrants choose to settle in suburban areas. Nevertheless, it would appear that the least well-off new immigrants to Canada do tend to congregate in inner-city urban neighbourhoods (Ray, 1999).
9 10 extent, the movement of industry to the suburban fringe resulted in the 'hollowing out' of select inner city populations and the loss of decently-paid jobs that would have enabled many of those left behind to pull themselves out of poverty. This resulted in the increased proportion and concentration of poor people living in inner-city neighbourhoods in both countries.
Housing Policies and the Development of Neighbourhoods. Hajnal (1995) argues that a series of governmental programs - most notably housing policies that subsidized suburban home ownership and concentrated low-rent public housing within central cities - expedited inner city flight and exacerbated the class and racial character of this migration in both countries. Public housing in both Canada and the United States emerged as part of a broader reform effort during the post-Second World War reconstruction period, a time when housing shortages vaulted onto the national stage as a very real urban problem. As a result of the post-war baby boom and mass immigration schemes, many cities experienced rapid population increases, but this population growth was not met on the supply side with the construction of adequate numbers of new dwelling units (Purdy, 2003). Of particular concern was the lack of affordable housing for lower-income individuals and families. Consequently, many families lived in overcrowded conditions and/or in substandard dwellings, while single male workers often turned to equally dilapidated urban hostels, boarding houses, or the street (Purdy, 2003).
Following considerable agitation on the part of war veteran and working-class groups for more state action on the housing front, both the Canadian and American governments moved to invest limited funds in public housing projects. However, this direct state provision of housing was initiated with great reluctance; its existence was justified as a
10 11
'stop-gap' measure, a temporary public concession that would alleviate the impact of social and economic instability during the post-war restructuring period. As such, public housing programs and stock were designed to avoid competing with the private housing market (Bratt, 1986; Dreier & Hulchanski, 1993; Hajnal, 1995).
The post-war restructuring period was followed by dramatic social and economic changes that have created a considerably different client base for public housing than the war vets and working poor for whom it was initially constructed. There has been a shift from the independent poor - two parent families who lacked the funds to afford accommodation in the private market and who required temporary assistance in order to do so - to the dependent poor. This shift was instigated by increasing numbers of unemployed or underemployed people and retired and semi-retired people with no or low incomes, of single-parent families, and of visible minorities and/or immigrants who are often discriminated against in the employment and housing markets and are thus in need of low-cost housing. Other factors that have contributed to the shift in the social composition of public housing developments include the deinstitutionalization of psychiatric patients, cutbacks in public spending, social welfare programs and policies that have effectively trapped people in poverty rather than helping them to escape it, and an overall reduction in government expenditures for the construction of new public housing developments (Purdy, 2003). The net result of this combination of factors, argue scholars on both sides of the border (see, for example, Hajnal, 1995; Massey, 2005), has been the increasing marginalization and growing concentration of disadvantaged populations in low-income urban neighbourhoods throughout North America.
11 12
In a number of respects, then, the development of cities in Canada has paralleled that of urban centres south of the border. This has led to similarities in the structural dimensions of neighbourhood organization, especially the geographic concentration of multiple forms of disadvantage - factors that may be relevant to understanding the social ecology of lethal violence in both countries. At the same time, urban centres in Canada and the United States also differ in important respects - and the effects of these differences on urban neighbourhood organization might lead to differences in the ecological distribution and predictors of urban lethal violence between countries.
1.3.2 Structural Differences between Canadian and American Cities
Uneven Development. One significant difference between the Canada and the
United States has to do with what Fong & Shibuya (2000) call "patterns of uneven development" across urban space, in terms of land use and development. In the United
States, following processes of large-scale industrial movement and decline, unused land on the outer rings of cities was "opened up" and developed at relatively low cost (Smith,
1984). The newly developed land attracted residents with social and economic resources, while the less well off remained in inner-city neighbourhoods, which received comparatively little new investment. As such, the movement of industrial, economic and human capital to the suburbs seriously affected the geography of inner-city American neighbourhoods, which suffered from deterioration, abandonment, and a lack of new capital investment (Smith, 1984).
While the movement and subsequent decline of big industry had similar effects on
Canadian inner-city neighbourhoods, cities in this country also experienced somewhat different patterns of land use and development that may have mitigated the effects of
12 13 suburbanization in some neighbourhoods, and exacerbated it in others. For example, compared to their American counterparts, city governments in this country were more actively involved in land use management and more committed to preserving public amenities in inner-city areas. Canadian cities also experienced a more equitable distribution of public services (Ley & Bourne, 1993). As a result of and despite high rates of suburbanization, land and property values in Canadian cities tended to remain highly competitive. This has meant that greater efforts are typically required to attract potential affluent buyers to developing suburban areas, and older inner-city neighbourhoods are often redeveloped instead in an effort to attract middle-class buyers (Fong & Shibuya,
2000; Goldberg & Mercer, 1986; Ley, 1981, 1993; Mercer, 1991). Indeed, since the mid-
1960s, many inner-city neighbourhoods in Canadian cities have been extensively redeveloped, sometimes "beyond recognition" (Bourne, 1991; Ley 1991, 1993). One consequence of the gentrification and revitalization of Canadian urban neighbourhoods has been a substantial increase in the cost of housing, and a concomitant lack of affordable housing (Doucet & Weaver, 1991); low-income residents have been displaced and forced to relocate and cluster in less desirable neighbourhoods. This, many scholars argue, has led to the increased spatial separation of the poor in Canadian cities.
The Spatial Separation of the Poor. A large American literature on the so-called
'ecology of inequality' (Massey and Eggers, 1990) has documented a trend toward the greater spatial separation of the poor (Abramson et al., 1995; Jarkowsky, 1994; Kasarda,
1993; Wilson, 1987). Between 1970 and 1990, the percentage of people living in
"poverty areas" (i.e. census tracts with a poverty rate of over 20%) in the largest 100 U.S. cities increased from 55.1% to 68.8%, and the percentage of people living in "extreme
13 14 poverty" areas (tracts with a poverty rate of over 40%) increased from 16.5% to 28.2%
(Kasarda, 1993).
However, using data from Jarkowsky (2003), Oreopolous (2005) showed that between
1990 and 2000, the U.S. experienced an appreciable decline in the number of high- poverty city census tracts, falling 27%> from 3,414 to 2,510 - a marked reversal of rapidly rising concentrated poverty in inner-city American neighbourhoods noted over the previous two decades. Oreopoulos' analyses also showed that the proportion of households below the poverty line and living in high-poverty American urban neighbourhoods fell sharply over this period, especially for blacks, even while the total poverty rate declined only marginally, from 13.1% to 12.4%. This, he argues, implies significant change in levels of concentrated poverty in U.S. cities during the 1990s, and
"suggests [that] the strong economic growth over this period may have helped reduce city poverty and poverty concentration" (Oreopolous, 2005: 6) . This landmark reversal of decades of increasingly concentrated poverty in the United States was not mirrored in
Canada, where the prevalence of high-poverty neighbourhoods appears to have increased during the 1990s. Over this decade, in all 27 Canadian Census Metropolitan Areas
(CMAs), the percentage of people living in high-poverty neighbourhoods increased from
4 to 6%, and the number of people living below the Low Income Cut Off (LICO hereafter) and in a high-poverty urban neighbourhood increased from 10.6 to 19%.
' However, as Jarkowsky (2003) argues, these gains may have diminished due to the economic downturn since 2000. It should be noted that the Canadian Low Income Cut Off and the U.S. poverty line are not directly comparable. LICOs are established using data from the Survey of Household Spending. The LICO measure is a relative calculation, based on the percentage of income that individuals spend on basic needs and necessities compared to the average household. A household falls below the LICO if it spends more than 20 percentage points above the average comparative household on food, clothing and shelter. For example, if the average Canadian family spends 30% of before-tax income on food, clothing and shelter, a family that spends more than 50% of before-tax income on these items falls below the LICO (see
14 15
Another difference between Canada and the United States has to do with the ecological concentration of race in impoverished neighbourhoods. Most residents in high- poverty American neighbourhoods are poor members of racial and ethnic minority groups, which means that the spatial separation of poor in the United States is an explicitly racialized phenomenon. Sampson & Wilson (1995) found that racial differences in poverty were so strong that in not one city with a population of over
100,000 did blacks live in "ecological equality" to their white counterparts - and the
"worst" neighbourhoods in which whites tend to reside ecologically and economically exceeded the average context of black neighbourhoods.
While Canadian cities also experience substantial levels of neighbourhood segregation by income and ethnicity (Balakrishnan, 1976, 1982; Bauder & Sharpe, 2002; Clarke et al., 1984; Darroch & Marston, 1971; Fong, 1996; Fong & Wilkes, 2003; Murdie, 1994;
Ray & Moore, 1991; White et al., 2003, 2005), there is at least one key difference between the racial composition of disadvantaged neighbourhoods in Canada and the
United States: the trend of ecological inequality by race appears to be less pronounced in
Canada (Oreopoulos, 2005). For example, in 1990, three-quarters of the population of high poverty urban neighbourhoods in the U.S. were racial minorities; half of this population was black, and another 28 percent were other visible minorities, mostly
Hispanic (Jarkowsky, 1997). In Canada, by contrast, 6% of the 1996 population living in
www.ccsd.ca/pubs/archive/fb941 fspovbk.htin, accessed July 21, 2006). The official U.S. poverty line is based mainly on a family's ability to afford food and housing, adjusted to account for such factors as family size, state, family composition and number of children. The Census Bureau updates the poverty line each year, based on changes in the Consumer Price Index (see http:www.census.gov/hhes/poverty/povmeas/ombdirl4.html, accessed July 21, 2006).
15 16 high-poverty neighbourhoods was black, and less than 30% of all visible minorities resided in these neighbourhoods (Jarkowsky, 1997). This is not to say that Canadian urban neighbourhoods do not experience significant levels of the spatial separation of poor racial and ethnic groups. Research has found that visible minorities do indeed experience residential segregation from whites in urban areas (Balakrishnan, 1982; Fong,
1994, 1996; Fong & Gulia, 1996; Fong & Shibuya, 2000), and that blacks are the most segregated of all racial/ethnic groups in this country (Fong & Shibuya, 2000; Fong &
Wilkes, 1999). Yet it would appear that, when compared to the U.S. context, the relative magnitude of the problem is smaller.
Some Canadian research has shown that whereas American inner-city neighbourhoods that experience high levels of poverty concentration also tend to be areas of extensive blight and decay, there is little evidence to suggest that this is also the case in Canadian cities (Walks & Bourne, 2006). Further, the neighbourhoods in which marginalized and racialized populations in Canada reside tend to exist alongside more affluent residential areas (Fong & Shibuya, 2000), and while these areas are, to a certain extent, cut off from their more affluent neighbours, they are a marked departure from U.S. style ghettos that both concentrate and contain socially and economically marginalized populations
(Balakrishnan, 2001; Balakrishnan & Gyimah, 2003; Bauder, & Sharpe, 2002; Myles &
Hou, 2004; Walks & Bourne, 2006).8 As such, the 'pockets of poverty' that exist in many
Canadian inner-city neighbourhoods do not approach the acute levels of residential segregation and concentrated poverty that exist south of the border.
Kazemipur & Halli (2000) argue that this country is currently witnessing the birth of urban underclass ghettos directly linked to growing ethnic communities. Their findings stand in sharp contrast to other studies of ethnic and racial segregation trends that suggest that they do not mimic ghettoisation patterns found in the United States (Balakrishnan, 2001; Balakrishnan & Gyimah, 2003; Bauder & Sharpe, 2002; Fong, 1996; Myles & Hou, 2004; Peters, 2005).
16 17
Part of the explanation for these differences has to do with differences in the design and distribution of public housing in both countries. Though the impetus behind the development of public housing schemes in both countries was similar, the scale and geographic distribution of that housing differs, which has broad implications for the quality of life for lower income households in these developments. As in the United
States, much Canadian public housing stock that was constructed in the 1960s was comprised of large-scale, high-rise projects (Dreier & Hulchanski, 1993). In the United
States, these projects tended to be concentrated within a small number of inner-city neighbourhoods, whereas in Canada, the distribution of public housing developments was
(and is) more geographically spread throughout metropolitan areas (Dreier & Hulchanski,
1993).
When the development of large-scale public housing projects was discontinued in the
1970s in both countries, housing policy responses in Canada and the United States developed along very different lines. In Canada, a permanent stock of good quality, nonprofit social housing was developed. This tends to be smaller-scale, socially mixed, nonmarket housing that is both developed and managed by locally based not-for-profit organizations, including municipal nonprofit housing corporations. The federal government and the private sector are, for the most part, excluded from these roles, though the federal government provides assistance to public and private nonprofit organizations "to construct, acquire, own, and manage housing units for households in core housing need and special purpose groups" (Canadian Mortgage and Housing
Corporation, 1985: 2). The emergence of this 'new' approach to public housing in
Canada is argued to be the result of a political climate open to activist government,
17 18 particularly in the areas of major social spending, such as health insurance, pensions, family allowances, and affordable housing (Guest, 1988; Lightman, 1991; O'Conner,
1989).
The political climate in the United States has traditionally been more hostile to activist government and large-scale spending in the area of government-sponsored public housing. Much of the American public believes that government housing programs do not work, and the term 'public housing' is thought to be synonymous with the perceived failure of liberal policies in an increasingly conservative political climate (Atlas &
Dreier, 1992). This conservative agenda has influenced the development of much public policy in the United States, and has been blamed for the dramatic withdrawal of most forms of federal housing assistance (Atlas & Dreier, 1992). As a result, the private sector is, and has for some time been largely responsible for developing and managing the subsidized rental supply. This heavy reliance on the private sector has created highly unstable and often mismanaged low-rent housing, while years of neglect and disinvestment have rendered older public housing stock "severely distressed", in dire need of repairs and improvements (National Commission on Severely Distressed Public
Housing, 1992).9
Public housing ownership and management styles likely have a trickle-down effect in terms of residents' quality of life. To be sure, public housing conditions are considerably better in Canada than they are south of the border. Further, their smaller scale, better quality and continued government investment have meant that conditions in Canada's
While scholars advocate the transplantation of Canadian-style not-for-profit, community-based housing policy, the nonprofit housing sector faces serious challenges to becoming a major player in the United States. These obstacles have largely to do with the small size of many not-for-profit organizations and inadequate operating funds, both of which have limited ability to achieve economies of scale in terms of development, staffing, management and overall community impact (Dreier & Hulchanski, 1993).
18 19 public housing developments do not match the physical and social deterioration of those located in inner-city American neighbourhoods (Dreier & Hulchanski, 1993). Yet the conditions within Canadian public housing developments alone do not account for the comparatively better quality of life of low-income residents in these neighbourhoods.
Due in large part to differences in state-sponsored welfare policies and programs, including a (marginally) larger public housing supply, universal health and unemployment insurance programs, and a variety of family support programs, some scholars (Banting, 1987; Dreier & Hulchanski, 1993; Hanratty & Blank, 1992; O'Connor,
1989; Wolfe, 1992) have argued that individuals living in low-income urban neighbourhoods in Canada enjoy better housing and urban quality of life relative to their
American counterparts. These differences in the quality of life for lower income households in Canada and the United States, as well as differences in levels of poverty concentration, residential segregation and social welfare policies, may lead to differences in the levels of problem-related and health-compromising behaviours within neighbourhoods, including the risk of violent victimization.
Levels of Lethal Violence. A final significant difference between Canada and the
United States has to do with levels of lethal violence. American cities experience homicide rates that consistently surpass rates in this country. For example, the Canadian
Centre for Justice Statistics reports that in 2003, as in previous years, the homicide rate in
Canada was about one third the rate in the United States (Dauvergne, 2004). Though annual homicide rates in large Canadian cities (i.e. cities with a population of over
500,000 people) demonstrate some variability, with higher rates to the west and lower rates to the east, rates of urban lethal violence in this country, too, are considerably lower
19 20 than rates in American cities. For example, in 2003, Toronto's homicide rate (per
100,000 population) was 1.9. The city of Regina had the highest homicide rate (5.1) among large cities in this country, while Quebec City had the lowest (0.4). By contrast, in
2003, New York City's homicide rate was 7.4 per 100,000 population, and Denver, Los
Angeles, Miami, and Chicago's rates were 11.1, 13.4, 19.4 and 20.6, respectively
(Federal Bureau of Investigation, 2004).
Though Canadian homicide rates have demonstrated relative stability over the past several decades, as discussed in Chapter Two, there is evidence to suggest that in Toronto the risk of homicide victimization is becoming more concentrated among some segments of the population (Gartner & Thompson, 2004). Further, just as there is evidence that the risk of homicide victimization is unequally distributed across social groups in, there is also some evidence to suggest that incidents of serious violent crime tend to concentrate in some of Toronto's neighbourhoods. However, the degree to which the clustering of lethal violence in a handful of inner-city American neighbourhoods is (or is not) mirrored in the Canadian context remains to be seen, and the ecological correlates associated with homicides in Canadian cities have yet to be empirically ascertained.
A major limitation of the existing literature on the ecological correlates of lethal violence is that it is based largely on American data. Though there is a growing literature on neighbourhood effects in Canada, very little of this research concerns the relationship between neighbourhood characteristics and crime, and none of it is specific to the social ecology of lethal violence.10 As the review in the Chapter Five will demonstrate,
To date, only three studies have investigated the social ecology of crime in Canada - all conducted by researchers at Statistics Canada. These studies, conducted in Montreal, Winnipeg and Regina, examine crime data at the neighbourhood level, and the authors argue that the results highlight the diversity among
20 21 homicides in the United States tend to cluster in geographic areas characterized by poverty, the racial segregation of minority groups, and single-parent families. Given the growing concern that acute and concentrated disadvantage may not be a uniquely
American problem, and because research has documented increasing levels of economic inequality and the greater spatial concentration of disadvantaged populations in Canadian cities (Hajnal, 1995; Fong & Shibuya, 2000; Fong & Wilkes, 2003; Kazemipur & Halli,
2000; Ley & Germain, 2000), an empirical understanding of the causes and correlates of lethal violence in this country will contribute to the literature on neighbourhood effects.
1.4 THE SCOPE OF THIS STUDY
This dissertation examines the ways in which neighbourhood demographic, socioeconomic and housing characteristics are related to the risk of lethal violence in
Toronto. This study expands on recent studies of the spatial distribution of homicide in three important ways. First, it provides the first empirical examination of this topic in a large Canadian city, which offers an opportunity to determine the extent to which the ecological covariates of lethal violence identified in the U.S. based literature also predict this violence in the Canadian context. Second, while this dissertation examines total homicide counts, it is also one of a small number of studies that disaggregates these counts into subtypes, in an effort to determine the extent to which the ecological correlates of one type of homicide are specific to, or distinct from, those of another.
Finally, this study provides a more nuanced and contextualized analysis of neighbourhood effects and homicide than has been possible with either cross-sectional
Canadian cities in terms of the spatial distribution and ecological predictors of crime (Fitzgerald et al., 2004; Savoie et al., 2006; Wallace et al., 2006).
21 22 analyses of neighbourhoods in one city or longitudinal analyses of multiple cities. While there is value in the generalizability of results obtained from research on multiple sites, the trade-off with this type of research is a loss of in-depth context framing the research and findings. It is this contextual depth that is an advantage of the current research.
The remainder of this dissertation proceeds as follows. In Chapter Two, I provide an overview of the recent history of Toronto, focusing primarily on the demographic, socioeconomic and political changes that were related to the development of neighbourhoods in this city. This is followed by a discussion of the demographic and socioeconomic composition of Toronto's neighbourhoods during the years 1988-2003.
The final section of Chapter Two will discuss what is currently known about violent crime in Toronto's neighbourhoods, and media commentaries over the social distribution of violent crime and the 'problem neighbourhoods' in which it is thought to disproportionately occur. The purpose of this chapter is to provide some contextual information about crime and neighbourhoods in Toronto within which the results of my statistical analyses can be located.
In Chapter Three, I review the criminological theories that have commonly been advanced to understand neighbourhood variation in rates of lethal violence. I also provide a discussion of the justifications for disaggregating homicide rates by type, and of the ways in which researchers have disaggregated overall homicide rates. This discussion will set the stage for quantitative analyses of the relationship between neighbourhood characteristics and homicide rates (total and disaggregated) in Chapters Five and Six.
The remaining three chapters of this dissertation will provide a discussion of my research methodology (Chapter Four) and the results of my analyses. In Chapter Five, I
22 23 examine the social ecology of total homicide counts in Toronto's neighbourhoods, while
Chapter Six disaggregates total rates into four homicide subtypes: intimate femicide, gun homicides, and the killings of blacks and young men (15-34) in Toronto. I conclude this study in Chapter Seven with a review of this dissertation's major findings, a discussion of its limitations, directions for future research, and of the policy implications that stem from my results.
23 24
CHAPTER II. NEIGHBOURHOODS AND VIOLENT CRIME IN TORONTO
In this chapter, I provide an overview of the socioeconomic and demographic composition of neighbourhoods in Toronto over the period of investigation (1988-2003), what is known about trends in homicide in Toronto, and of media commentaries over the social and spatial distribution of this violence. This serves as a backdrop to the analyses presented in Chapters Five and Six, and aids in the interpretation of those analyses within the larger social context of the city.
2.1 THE SOCIAL GEOGRAPHY OF NEIGHBOURHOODS IN TORONTO
The City of Toronto (population 2.7 million) is part of the Greater Toronto Area
(GTA), one of the fastest growing urban areas in North America. Also one of the most ethnically diverse cities in the world, Toronto is home to people from over 200 nations who speak more than 100 languages and dialects. It is one of Canada's primary immigrant reception centers, receiving nearly 70,000 new immigrants each year. The resulting cultural diversity is reflected in the numerous ethnic neighbourhoods and enclaves in the city; Toronto is often referred to as the "city of neighbourhoods" due to the strength and vitality of its many communities.
Formerly a municipality of 650,000 people, in 1998 the mega-city of Toronto was created through the amalgamation of Metropolitan Toronto with five surrounding municipalities: Etobicoke, North York, Scarborough, York and East York." Each of
" This amalgamation did not alter the jurisdiction of the Toronto Police Service- i.e. these municipalities had all been within the jurisdiction of the Toronto Police Service in the mid-1950s when the City of Toronto was created.
24 25 these former municipalities retains its own distinct identity, and their names continue to be widely used. The "old" City of Toronto is the most densely populated area, and also serves as the business centre of the city. The 'inner ring' suburbs of York and East York are, for the most part, older, ethnically diverse, and predominately middle-class areas.
Many of these neighbourhoods originated as 'streetcar suburbs' whose housing stock is largely comprised of pre-war single-family homes and of high-rises that were constructed in the post-war period. The 'outer ring' suburbs of Etobicoke, Scarborough, and North
York have lower population densities and are generally working- and middle-class.
Residential development consists primarily of single-family dwellings, though certain neighbourhoods in each of these suburbs contain numerous high-density apartment complexes that house large numbers of less affluent residents. These neighbourhoods have been shown to be concentrated areas of poverty (United Way & The Canadian
Council on Social Development, 2004 - UWCCSD hereafter), and some are thought to possess higher rates of crime and violence.
In recent years, evidence has emerged that suggests that all is not well in some of
Toronto's neighbourhoods. In 2004, The United Way of Greater Toronto, together with
The Canadian Council on Social Development published their report Poverty by Postal
Code: The Geography of Neighbourhood Poverty 1981-2001, which detailed rising and intensifying poverty in some of the city's neighbourhoods and a growing concentration of the city's poor in neighbourhoods with high levels of poverty. The following sections outline the report's key findings.
1 The authors of the report argue that up until the early 1980s, most poor families in Toronto lived in mixed-income neighbourhoods, but today, they are "far more concentrated in neighbourhoods with high levels of poverty" (UWCCSD: 3).
25 26
2.1.1 Poverty by Postal Code: Neighbourhoods and Poverty in Toronto
The report shows that there has been a substantial rise in poverty rates among Toronto's families over the last two decades, with almost one in every five families living in poverty in 2001. While Toronto's poverty rate was similar to the national average in 1981 (at 13.3% and 13.0%, respectively), it was considerably higher in 2001 (19.4% in Toronto, compared to a national average of 12.8%). Further, since the early 1990s, the family poverty rate in the City of Toronto has been more than double the rate in the rest of the Toronto Census Metropolitan Area (CMA); in 1991, the poverty rate in the City of Toronto was 16.3%, compared with 7.1% outside of the city. By 2001, the city rate climbed to 19.4%, while the rate for the remainder of the CMA was considerably lower, at 8.8%.
The United Way's analyses also found that by 2001, poor families in Toronto were much more concentrated in neighbourhoods1 where there was a high proportion of families living in poverty compared to twenty years ago (43.2% of poor families were living in 'higher poverty' neighbourhoods, compared with 17.8%o in 1981). " While the ecological concentration of poverty in Toronto appears to have increased significantly over the last two decades, so, too has the concentration of affluence at the upper end of the income scale. Indeed, high-income 'majorities' appear to have expanded in some
'' The Toronto Census Metropolitan Area (CMA) includes the City of Toronto plus 23 surrounding municipalities: Ajax, Aurora, Bradford, West Gwillimbury, Brampton, Caldeon, East Gwillimbury, Georgina, Halton Hills, King Township, Markham, Milton, Mississauga, Mono Township, Newmarket, Tecumseth, Oakville, Orangeville, Pickering, Richmond Hill, Uxbridge, Whitchurch-Stouffville and Vaughan. 14 The United Way's report used census tracts to define neighbourhoods. In 1981, there were 428 neighbourhoods with sufficient data to permit analysis, 476 in 1991, and 522 in 20010 15 The report defined 'Higher Poverty' neighbourhoods as those where 26% or more of neighbourhood families have incomes below Statistics Canada's Low-Income Cut-Off. This definition includes neighbourhoods deemed to be 'high poverty' (where the range of poverty rates falls between 26%-39.9% and 'very high' poverty neighbourhoods (where the level of family poverty in a particular neighbourhood is over 40%).
26 27 areas of the city, especially in mid-and uptown neighbourhoods in the city's core. In the
City of Toronto, anecdotal reports suggest that this ecological juxtaposition by income appears to have been affected, in part, by the 'move to the 905' that has seen large numbers of Toronto's middle-class population taking up residence in suburban areas on the outskirts of the GTA.17
There has also been a dramatic rise in the number of higher poverty
neighbourhoods in the City of Toronto, numbers that "approximately double" every ten years (UWCCSD: 7). In 1981, 30 neighbourhoods were identified as higher poverty
neighbourhoods, but 20 years later, that number had increased to 120. By comparison,
only one postal code in the Greater Toronto Area surrounding the city was identified as
high poverty.
The study also showed that the increase in the number of higher poverty
neighbourhoods was "especially acute" in the former municipalities of Scarborough,
North York, Etobicoke, and East York, where the combined total of such neighbourhoods
rose from 15 in 1981 to 92 in 2001 (UWCCSD: 10).18 Scarborough and North York
16 However, approximately 70% of the city's census tracts are in the "No Majority" category - i.e. they are dominated by neither low-, middle-, nor upper-income majorities. Researchers at the Centre for Urban and Community Studies at the University of Toronto caution against interpreting this to mean that these neighbourhoods are mixed income, and they predict that "these trends and other previous research on income changes across Toronto suggest that several neighbourhoods are now likely to develop either a low- income majority or a high-income majority in the future" (www.urbancentre/utoronto.ca/gtuo, accessed 13 September, 2008.). However, research suggests that middle-income Torontonians are not merely moving to the 905 - they're disappearing altogether. For example, Hulchanski's (2007) analysis of neighbourhood change in Toronto over the last three decades found that poverty in the city has extended to the northeast and northwest of the city core, while an increasingly affluent, predominately white elite holds neighbourhoods in the city centre. In other words, the "looming disappearance" of the average, middle-income neighbourhood has meant that the small number of such neighbourhods that remain are little more than a buffer between an increasingly wealthy core and increasingly impoverished suburbs. This trend toward the concentration of poverty in Toronto's inner suburbs is a marked reversal of trends in the 1970s, when Toronto's low-income population was largely concentrated in the downtown area, while neighbourhoods in the inner suburbs were comprised largely of middle-income majorities (www.urban centre.utoronto.ca/gtuo. accessed 13 September, 2008).
27 28 experienced the most significant increases in the number of higher poverty neighbourhoods over this twenty-year period; North York had more higher poverty neighbourhoods than any of the former municipalities, increasing from 7 in 1981 to 36 in
2001, and in Scarborough, the number of higher poverty neighbourhoods increased from
4 in 1981 to 26 in 2001 (UWCCSD: 10).
Although increases in the number of higher poverty neighbourhoods and concentrations of low income people in these neighbourhoods were most pronounced in
Toronto's inner-suburb areas, the "old" City of Toronto (i.e. the city centre) was not immune to these trends. Between 1981 and 2001, there was a 21% increase in the number of poor families living in the City of Toronto, and across this time period, the two neighbourhoods with the highest poverty rates, Regent Park and Kensington-Chinatown, were located in the city centre. The report's authors also note that unlike the trend in neighbourhood income levels found in the other former municipalities (where the number of higher poverty neighbourhoods increased), the city centre experienced increases in both the number of higher poverty and the number of Tower poverty' neighbourhoods
(i.e. neighbourhoods where up to 12.9% of families have incomes below the LICO).19
This, they argue, "reflects the further widening of income disparity between rich and poor" in the inner city (UWCCSD: 10). The increase in the number of lower poverty neighbourhoods is attributed, in part, to condominium booms of the last 15 years and the continued and ongoing gentrification of downtown neighbourhoods that have displaced less affluent residents to particular kinds of neighbourhoods in the city's core.
19 The number of higher poverty' neighbourhoods increased from 15 in 1981 to 28 in 2001, and the number of Mower poverty' neighbourhoods increased from 49 to 61.
28 29
The United Way's analyses also showed that there has been a profound shift in the resident profile of higher poverty neighbourhoods, with poor, visible minority and immigrant families making up increasingly larger percentages of the total population. For example, the study found that since 1981, there has been a huge increase in the population of poor immigrant families living in higher poverty neighbourhoods, such that by 2001, immigrant families accounted for two-thirds of the total family population living in such neighbourhoods. Further, in 1981, 41,600 visible minority families lived in higher poverty neighbourhoods, but by 2001, this number had increased eightfold, to
333,500. This meant that, in 2001, one-third of Toronto's visible minority population lived in higher poverty neighbourhoods. The analyses conducted by the United Way also found that, between 1991 and 2001, there was a 100% increase in the number of children living in higher poverty neighbourhoods (80,590 compared with 160,590), and a concomitant 60% increase in the number of resident youth (rising from 60,940 to
97,520). Further, between 1981 and 1991, higher poverty neighbourhoods in Toronto experienced a 91.7% increase in the number of lone parent families (21,890 compared with 41,955), and higher unemployment rates than in the city as a whole.
In sum, the United Way's analyses showed that, by 2001, more of the city's poor families were living in geographically concentrated areas of poverty than in the previous two decades, that the number of high poverty neighbourhoods had increased over the last twenty years, that certain neighbourhoods in certain areas of the city had experienced greater increases in poverty than others, and that the social profile of high poverty
29 30 neighbourhoods had changed, with certain groups more vulnerable to living in conditions of poverty in 2001, compared to twenty years earlier.20
The United Way's report also demonstrates that many of the neighbourhood-level characteristics that are typically associated with high rates of homicide in the U.S. literature also appear to be present in some of Toronto's neighbourhoods. American research on the social ecology of lethal violence has consistently shown that neighbourhood characteristics associated with the risk of homicide include poverty and the concentration thereof, racial and ethnic segregation, joblessness, low educational attainment, and large numbers of young people and single parent families (Blau & Blau,
1982; Dobrin et al., 2005; Galea & Ahem, 2005; Peterson & Krivo, 2005; Sampson,
1987, 1995; Sampson & Groves, 1989; Sampson & Wilson, 1995; Tita & Griffiths, 2005;
Wilson, 1987). Given that, as the United Way's analyses also demonstrate, the distribution of demographic and socioeconomic characteristics across neighbourhoods is not random, it would perhaps not be surprising that incidents of lethal violence are similarly non-randomly distributed in Toronto.
Changes in the structure and character of some of Toronto's neighbourhoods likely have much to do with changes over the 1980s and 1990s in social and economic policies and practices that have affected Toronto's social welfare, public health and educational systems, as well as the infrastructure and cohesion of local neighbourhoods. For example, in the mid-1990s, the provincial government instituted a 21.6% cut in welfare payments for families with children, which affected an estimated 1.3 million people. These and other budget cuts were made in the name of reducing the province's 10.6 billion dollar deficit, yet because more than 25% of cuts came through reducing welfare payments, the government's budgetary reductions did not affect middle- and upper-income Ontarians as deeply as they did those less well off. As such, "Things started to go to hell in Toronto" in the mid-1990s (Scrivener, 2006), and disadvantaged young people, their families, newcomers, and visible minorities became increasingly vulnerable to a whole host of negative health outcomes.
30 31
2.2 TRENDS IN THE SOCIAL AND SPATIAL DISTRIBUTION OF HOMICIDE IN TORONTO: WHAT WE KNOW
There is a perception among many that levels of lethal violence in Toronto have been on the rise over the last several decades and that, in more recent years, this violence has reached unprecedented levels. Figure 2.1 charts the city's homicide rate (per 100,000 population) between 1988 and 2003. As this figure shows, apart from a very large spike in 1991, homicide rates were relatively stable, fluctuating between 1.8 and 2.8 per
100,000 (excluding 1991's rate of 4.0).21
While the overall risk of homicide in Toronto has therefore been relatively stable over the period of examination, research suggests that there has been a change in the nature of this violence. For example, in the only examination of homicide in Toronto over an extended period of time, Gartner and Thompson (2004) hypothesized that the risk of homicide victimization may have increased for some Torontonians and declined for others. To examine this possibility, they considered trends in homicide victimization by sex, age and race. Their analyses indicated that over the last three decades, males faced higher risks of homicide victimization than did females, and that this gender gap in homicide victimization grew over time. Gartner and Thompson's analyses also demonstrated that the victims of homicide in Toronto have become younger over the last several decades. In the 1970s, the average age of victims was 37 and 25% of victims were
During this period, the homicide rate in Canada as a whole ranged between 1.8 and 3.0. Toronto's rate has therefore been very close to the national average over the last three decades; and it has consistently been lower than homicide rates in Vancouver, Montreal, Edmonton, and Winnipeg (Gartner & Thompson, 2004). As discussed in the previous chapter, Toronto's homicide rate is also much lower than that of almost any U.S. city of comparable size.
31 32 under the age of 25. Since 1998, however, the average age of homicide victims declined to 33 and 40% of victims were under the age of 25.
A particular concern of many people in Toronto in recent years has been the extent to which the risks of homicide are concentrated among some racial/ethnic groups - particularly the city's black community. Gartner and Thompson's analyses indicated that, since at least 1991, the risk of homicide victimization for Toronto's black population has been substantially higher than the overall risk of homicide in Toronto. Even when excluding 1991, which had an unusually high number of black homicides, the homicide rate per 100,000 blacks in Toronto averaged 10.1 between 1992 and 2003. This was almost five times greater than the average overall homicide rate of 2.4 per 100,000 population.
Gartner and Thompson also tracked changes in weapon use since the 1970s. Their analyses indicated that guns have figured more prominently in Toronto's homicides over time. For example, during the 1970s and 1980s, 25% of homicides in Toronto were committed with guns, but by the 1990s that proportion increased to 32%. And since 2000, fully half of all homicides in Toronto have been committed with guns. The trend for
Canada as a whole is very different. During the 1970s and 1980s, 36% of all Canadian homicides were committed with firearms, and this has decreased to 30% since 2000.
The results of Gartner and Thompson's analyses therefore suggest that a) using the overall homicide rate as an indicator of serious criminal violence in Toronto masks
"~ The average age of homicide victims for the country as a whole is approximately the same as that in Toronto for recent years. Data on the average age of victims for earlier years was not available, thus Gartner & Thompson were unable to determine whether there had been a similar decline in the average age of homicide victims for all of Canada. "J It should be noted that Gartner & Thompson's estimates of the black homicide victimization may be conservative because they were missing data on the race of approximately 20% of homicide victims between 1991 and 2003.
32 different trends for some types of homicide; and b) that there has been a discernable shift in the nature of homicide in Toronto since the mid-1970s. Though the overall risk of lethal violence has been relatively stable over the last thirty years, for males and young people in Toronto, the risks appear to have increased; and black Torontonians face risks of homicide victimization that greatly exceed those faced by non-blacks. Further, homicides in Toronto are now much more likely to be committed with guns than in decades past. Given that the overall risk of homicide in Toronto has been relatively stable over the last thirty years, victimization rates for other types of homicide have necessarily declined. As discussed above, the gender gap in homicide victimization has grown over time - male victimization rates have increased while female rates have declined (Gartner and Thompson, 2004). This trend is driven, in part, by a decline in the risk of intimate femicide in Toronto over this period (Gartner, 2008).
These changes in the nature of homicide in Toronto began to generate considerable public and official concern by the late 1990s. Since that time, media commentaries over issues pertaining to gun, black and young male homicide have both reflected and shaped much of the popular and political discourse on serious violent crime in Toronto. The following section provides a brief overview of some of this commentary, and a discussion of how popular perceptions of homicide in Toronto that stemmed from this coverage shaped my choice of homicide types to examine in this dissertation.
2.3 MEDIA COMMENTARIES OVER LETHAL VIOLENCE IN TORONTO: IMPLICATIONS FOR THIS DISSERTATION
Media commentaries over gun, young male and black homicide victimization typically do not portray these killings as mutually exclusive types. Instead, there tends to 34 be considerable overlap among them. In other words, homicide in Toronto is described as a phenomenon that largely involves young, black males killing other young, black males with handguns (Shephard et al., 2002; Porter & Campbell, 2001). In recent years, the colloquially termed issue of "black on black violence" has received a considerable amount of media attention, and a number of commentaries have attempted to document the scope of the problem. 4 For example, a Toronto Star analysis of non-domestic homicides in 2001 found that 42% of victims were black, that most of these victims were in their teens and early 20s, that blacks constituted the bulk of Toronto's gun homicide victims, and that in the vast majority of cases in which a suspect was ultimately arrested, the alleged perpetrators of these killings were also black (Blatchford, 2003).
In addition to attempts to determine the extent of homicide involving young, often black, men in Toronto, other newspaper commentaries have discussed possible sources of this violence. For example, columnists have cited criminological and other research that points variously to the effects of poverty, discrimination, social alienation, low education levels, limited employment opportunities, a lack of positive role models, and the effects of direct or vicarious exposure to violent crime (Folkes et al., 2002; James, 2005; Lorinc,
2008; Poon, 2006; Scrivener, 2006). High levels of this violence are also often attributed to drug-and gang-related activity (Porter & Campbell, 2001), the importation of a violent
Jamaican subculture (Blatchford, 2003), and/or rap culture that glamorizes and encourages the perpetration of a "thug" lifestyle (Rivers, 2005; Wente, 2005).2=
As will be discussed in Chapter Four, the release of race-based statistics is typically not permitted by police organizations in Canada, which hinders efforts to determine the extent to which the risk of violent victimization may vary across racial/ethnic groups. Media commentaries on black intra-racial violence typically also give voice to members and leaders of the black community. Much of this coverage documents the perceptions of those directly affected by this violence. For example, in a 2001 Toronto Star article, residents lamented the so-called "wall of silence"
34 35
Much of the media discussion of lethal violence among blacks in Toronto also appears to recognize that the risks of homicide among black Torontonians are not uniform, but rather are socially and spatially concentrated in certain places and among certain segments of the black community. For example, one Toronto Star columnist argued that the risk of this violence is skewed toward young black males in Toronto's disadvantaged neighbourhoods: "the boys of the other Toronto, the poor and destitute and suburban"
(James, 2005). And it is among young, black men living in these "separate and unequal" areas of Toronto (James, 2005) that the problem of gun homicide is described to be particularly acute. This commentary is illustrative of what appears to be a more general trend toward acknowledging the importance of'place' in shaping the risk of violent victimization in Toronto. Considerable media attention has been paid to a number of
'problem areas' in which violent crime is thought to occur at higher than average rates.
The neighbourhoods typically referenced in these accounts include Regent Park, Jane and
Finch, Kingston-Galloway, Parkdale, Malvern, Black Creek, St. Jamestown, and
Lawrence Heights. Census data show that these neighbourhoods tend to be inhabited by large numbers of low-income families, and when compared to city-wide averages, they also tend to have larger numbers of visible minorities, young people, single-parent families; and higher rates of unemployment, people living in renter-occupied dwellings
that shrouded the killing of two young men in their neighbourhood, and the obstacles to overcoming it: "There's no love anymore, there's no role models. How many black lives must be taken before this stops? The people who see it happen, why not tell the truth?...The truth shall set you free. That's not informing. That's saving the lives of brothers and sisters" (Porter & Campbell, 2001). This coverage also includes the perspectives of black activists, activist groups and community leaders, who typically concede that "we are in a state of crisis" (James, 2005), and that the black community itself, along with all levels of government, need to "step up" in order to address the problem.
35 36
(particularly in city-owned housing developments), and residents receiving some form of government assistance.
Because these neighbourhoods share a number of demographic characteristics and social problems, media discourses around them tend to be similar, although the alleged sources of these problems vary from reporter to reporter and newspaper to newspaper.
High rates of violent crime that are thought to exist in these neighbourhoods have been attributed variously to "the cultural values of a high-crime urban area" (Blatchford,
2003), including "no snitch" codes that may lead to retaliatory violence in lieu of reporting crime to the authorities (Porter & Campbell, 2001); underfunded schools and community services and facilities that have not kept pace with neighbourhood growth and demographic change (Patrick, 2006); high density public housing developments (Porter
& Campbell, 2001); and racial discrimination and an unequal distribution of opportunities and life chances across neighbourhoods (Wente, 2004). Regardless of the explanatory perspective adopted, however, the tie that binds much media commentary on these neighbourhoods is that they are rife with "the festering conditions that spawn and nurture violent crime" (James, 2005).
While much of the media focus on the topic of neighbourhoods and homicide has been devoted to "the housing projects of the inner suburbs, where gun culture is ingrained and the people carrying the guns are often young men" (Lorinc, 2008: 53), the media has also focused attention on high levels of serious violent crime in neighbourhoods that are zoned more for commercial use. For example, downtown Toronto's Entertainment
26 For a "social profile" of each of these neighbourhoods, see the City of Toronto's "Toronto Neighbourhood Profile" webpage at littp://ww\v.toronto.ca/deinographics/neighbourhoods.htm. accessed 23 June, 2008.
36 37
District has long been portrayed as an area where "stray bullets fly" and "innocent bystanders are getting caught in the crossfire" (Lorinc, 2008: 47). This neighbourhood contains approximately 100 licensed nightclubs with capacity for nearly 50,000 people, and the combination and concentration of alcohol, drugs and large numbers of young men has prompted a heavy police presence in the area, particularly as the clubs close in the early morning hours and thousand of patrons descend upon the streets of the surrounding neighbourhoods. Another area of the city that is perceived to experience higher rates of violent crime is downtown Yonge Street, particularly the strip that runs between Gerrard and Queen Streets. Over the last several decades, this neighbourhood, dubbed a "war zone" (Freeze & Gray, 2003), has been the site of a spate of violent crimes committed by skinheads, rioting youth, revelers at Toronto's annual Caribana parade, and what are believed to be gang-related battles that have resulted in shots fired at and/or the killing of intended and unintended targets alike.
A recent addition to media commentaries on serious violent crime in Toronto involves geographic representations of that violence. For example, in June 2006, the City of Toronto's Social Policy Analysis and Research unit (SPAR) published a map of homicides and gun-realted crimes that occurred in Toronto in 2005, the so-called "Year of the Gun" (see Figure 2.2). The data was collected from a combination of police and
Toronto Community Housing Corporation records, as well as newspaper sources. Though maps such as these typically plot incidents of violent crime for only a short period of time
(for example, a few months or a calendar year), they do provide some preliminary evidence that this violence tends to be concentrated in some areas of the city - and is comparatively absent in others. However, while these maps are instructive in terms of
37 38 providing a visual representation of the ways in which violent crime patterns across the cityscape, they do not provide any contextual analysis of the neighbourhoods within which that violence occurred. One aim of this study is thus to provide a longer-term, empirical understanding of neighbourhood variation in levels of lethal violence in
Toronto, and of the ways in which neighbourhood characteristics may be related to the risk of this violence.
To date, most studies on the spatial distribution and social ecology of lethal violence have focused on total homicide rates. As such, little is yet known about similarities and/or differences in the geography and social ecology of different types of urban homicide, particularly outside of the American context. As I began my thesis research in 2004, gun homicides, the killings of young men and black people were widely commented upon in the media, and had become a source of particular concern for many people in Toronto.
This concern came to the fore in 2005, the so-called "Summer of the Gun". Further, although studies on the social ecology of violent crime have a long and distinguished history, only recently have researchers begun to systematically examine whether 'private' forms of violence, such as much violence against women, and more 'public' forms of violence, such as male-on-male and gun violence, are spatially distributed across urban neighbourhoods in similar ways, and whether they are associated with similar neighbourhood-level characteristics. The need for more research on the social ecology of violence against women is one of the major recommendations of a recent (U.S.) National
Research Council report. What the research on this issue to date suggests is that, at least in the United States, various forms of violent crime do share a similar ecological distribution; they tend to be concentrated in inner-city areas characterized by high rates of
38 39 poverty, family disruption, and residential mobility (Fagan et al., 2003; Lauritsen and
Schaum, 2004).
A second goal of this study, then, is to examine the ways in which different forms of lethal violence are patterned across neighbourhoods in Toronto, whether there are distinct neighbourhood-level correlates associated with these different types of homicide, and the extent to which there may be ecological continuity in the neighbourhoods that experience high levels of each of these types of homicide. The types of homicide under consideration, which are discussed in greater detail in Chapters Five and Six, were selected because of their relevance to popular commentary and concerns over the changing nature of who is at risk of homicide victimization in Toronto.
39 40
Figure 2.1 Total Homicide Victimization Rate (per 100,000) Toronto, 1988 - 2003
88 89 90 91 92 93 94 95 96 97 98 99 0 1 2 3
40 41
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41 42
CHAPTER III. EXPLAINING THE SPATIAL DISTRIBUTION OF HOMICIDE
3.1 THEORETICAL OVERVIEW
Researchers in the social ecological tradition have long studied the question of why neighbourhoods differ in their rates of violent crime. This question has stimulated a good deal of work that examines the relevance of neighbourhood characteristics for understanding the social and spatial distribution of homicide in and across urban neighbourhoods (Bailey, 1984; Block & Block, 1992; Fagan et al., 2003; Kovandzic et al., 1998; Messner & Tardiff, 1985; Morenoff et al., 2001; Parker, 1989; Peterson, Krivo
& Harris, 2000; Sampson, Raudenbush & Earls, 1997). Today, the existence of neighbourhood effects on lethal violence is largely assumed, and the empirical focus has shifted to identifying the specific neighbourhood features that are related to this violence, and explaining the associations between them. In this literature, four main theoretical perspectives have been drawn upon: 1) contemporary versions of social disorganization theory, which highlight the relevance of social structural barriers that impede a neighbourhood's ability to solve common problems and maintain effective social control;
2) subcultural perspectives, which argue that social norms in some inner-city neighbourhoods lead directly or indirectly to the violent resolution of interpersonal conflicts; 3) strain/deprivation perspectives, which emphasize individuals' limited or blocked economic opportunities as the source of feelings of anger, frustration and resentment that can lead to violent behaviour; and 4) the routine activity perspective, which holds that crime and violence are a function of the routine activities of urban life,
42 43 and can be concentrated in particular neighbourhoods because of the presence of targets that are not protected by capable guardians.
Chapter Three is organized as follows: I begin with a discussion of the major theoretical perspectives that have been used to account for neighbourhood-level variation in violent crime. Next, I examine similarities and differences between these perspectives as they pertain to the social ecology of lethal violence, and highlight how they are relevant for understanding different types of homicide. The final section of this chapter provides an overview of the justifications for and the major findings from research that has analyzed disaggregated homicide types, and the form that data disaggregation takes in this study.
3.1.1 Social Disorganization Theory
The social disorganization perspective views the geography of crime as a function of the differing degrees to which neighbourhoods can informally control the nature and amount of local illegal activity. The original formulation of this perspective was developed by Shaw & McKay (1942), who found that high rates of delinquency persisted in certain Chicago neighbourhoods over time, despite changes in the demographic composition of those communities. In seeking to explain this ecological stability in local levels of delinquency, Shaw & McKay focused upon cultural heterogeneity and population turnover in high crime neighbourhoods. Though upward economic mobility was central to the process they described, Shaw & McKay did not posit a direct relationship between economic factors and crime. Rather, areas experiencing economic deprivation and physical deterioration were seen as "transitional zones" that also experienced high population turnover and cultural fragmentation. These factors were
43 44 posited to influence crime and delinquency through a process which they called "social disorganization".
Grounded in the seminal work of Kornhauser (1978), Stark (1987), Bursik (1988),
Sampson & Groves (1989), Bursick and Grasmick (1993), and drawing on social control theory, more contemporary versions of the social disorganization perspective have reformulated the original model into a more sophisticated "systemic model" that incorporates a number of additional neighbourhood mechanisms posited to mediate the relationship between structural conditions and violent crime. For example, social ties, informal social control, social capital, and collective efficacy are hypothesized to either attenuate or exacerbate the effects of neighbourhood characteristics such as poverty, residential instability and ethnic heterogeneity (Bellair, 1997; Bursick & Grasmick, 1993;
Markowitz et al., 2001 ).27 According to this view, the prevalence and density of social networks and the level of participation in community-based organizations generate solidarity and mutual trust - or social cohesion - among neighbourhood residents. In turn, social cohesion promotes the neighbourhood's capacity to monitor and manage criminogenic situations.28 Cohesive neighbourhoods that can effectively mobilize to regulate the behaviour within their boundaries are said to have high levels of collective efficacy (Sampson et al., 1997). So-called'disorganized'neighbourhoods , on the
27 Only very recently has research attempted to measure these intervening processes, and while theoretical and empirical work on the relationship between these variables and crime has lead to important refinements of social disorganization theory, some researchers have identified conceptual and methodological deficiencies which suggest that further consideration of these variables is warranted (see, for example, Kubrin & Weitzer, 2003). Bursick (1988) notes the overlap between the notion of informal social control in social disorganization theory and that of capable guardianship in the routine activity perspective. Both perspectives assume that neighbourhoods are differentially capable of providing the supervisory control necessary to regulate local criminal activity. The routine activity perspective will be discussed in a subsequent section. 29 However, as Bursik and Grasmik (1993) argue, to concentrate solely on these forms of neighbourhood self-control would be to ignore the implications of child socialization on local levels of crime and violence.
44 45 other hand, are theorized to lack the networks, norms, and trust that facilitate coordination and cooperation for mutual benefit among residents. As neighbourhoods lose control of the behaviours within their boundaries and a clear consensus with respect to appropriate conduct dissolves, crime and violence are predicted to increase. The systemic model also suggests that high rates of crime and violence have a reciprocal effect on the structural conditions of neighbourhoods, including their collective capacity for effective socialization and social control, which may ultimately exacerbate levels of crime and violence. These reciprocal effects can be mediated by resident withdrawal from neighbourhood life due to fear of crime and/or each other (Markowitz et al., 2001); abandonment by residents with the means to move out of the neighbourhood (Dugan,
1999); high rates of resident incarceration (Rose & Clear, 1998; Sampson, 1995); and increasing public disinvestment in a neighbourhood as perceptions about high levels of poverty, crime, and violence stigmatize the area as 'problem space' (Venkatesh, 2000).
Numerous empirical analyses have found support for the social disorganization perspective as it pertains to violent crime. Variables that have been taken as indicative of disruption in neighbourhood social organization in these studies include low economic status, measured using various income levels (Bailey, 1984; Baron & Strauss, 1988; Blau
Social disorganization theory is also "centrally concerned with the effectiveness of socialization in preventing deviance" (p.36). As such, the perspective highlights the ways in which factors indicative of social disorganization weaken the effective socialization of young residents, producing a set of negative developmental outcomes that may have implications for rates of crime and violence, such as gang affiliation and other problem behaviours (Sampson, 1997). j0 A number of researchers (Bursick & Grasmick, 1993; Cohen, 1955; Sutherland, Cressey & Luckenbill, 1992; Suttles, 1968) claim that use of the term 'disorganized' to describe urban neighbourhoods is laden with negative connotations and therefore could reflect observer bias. For example, Cohen (1955) has shown that many such neighbourhoods are not 'disorganized' at all (1955: 32-33), arguing that neighbourhoods depicted as socially disorganized are "by no means lacking in social organization," and from the perspective of the people living in them, there is a "vast and ramifying network of informal associations" as opposed to " a horde of anonymous families and individuals." As such, the term is a misnomer, but continues to be used despite widespread acknowledgement that there are differences in degrees and types of social organization, and that these differences have ramifications for local crime rates.
45 46
& Blau, 1982; Martinez, 1996); residential instability, measured using the number or percentage of rental units, percentage of owner-occupied dwellings, etc. (Bachman, 1991;
Breault & Kposowa, 1993; Kennedy et ah, 1991; Miethe et ah, 1991); racial/ethnic heterogeneity, or the degree of racial/ethnic mix (Hansmann & Quigley, 1982; Kposowa
& Breault, 1993; Land et ah, 1990; Miethe et ah, 1991; Sampson & Groves, 1989); and family disruption, generally measured as divorced, single and/or female-parent households (Bachman, 1991; Blau & Blau, 1982; Chamlin, 1989; Huff-Corzine et ah,
1986; Sampson, 1987; Williams & Flewelling, 1988).
Though some research has demonstrated support for the role of social ties (Rose &
Clear, 1998; Rosenfeld et ah, 2001) and collective efficacy (Morenoff et ah, 2001;
Sampson et ah, 1997; Sampson et ah, 1999) in shaping levels of violent crime, the results of other studies appear to run counter to their hypothesized effects. For example,
Villarreal and Silva (2006) found that lower income neighbourhoods in Brazil had
"unusually high" levels of social cohesion among residents, yet many also experienced high rates of violent crime. This contradicts the systemic model, which predicts that a neighbourhood's capacity to control violent crime depends on the density of social networks therein (Bursik & Grasmick, 1993). A growing number of U.S. studies also suggest that, on their own, social networks are not enough to generate social control and prevent crime. For example, Wilson (1996) has argued that, in some disadvantaged neighbourhoods, dense social networks and ties exist in tandem with low levels of informal social control and high levels of violent crime. Patillo-McCoy (1999) also problematizes the notion that strong local ties necessarily lead to lower crime rates. She argues that while dense local ties can "positively affect both the informal and formal
46 47 supervision of youth...at the same time...they also have negative repercussions... for organized criminal enterprises" (p.70). In other words, strong social ties can serve a dual role: they can operate to promote social integration, while at the same time fostering the growth of networks that undermine efforts to rid the neighbourhood of crime and violence. That social ties are complex and vary in character suggests that further
"3 1 conceptual and methodological consideration is necessary.
Researchers have also suggested that the idea of local culture ought to be brought back into explanations of neighbourhood-level variation in violent crime. Though the original formulation of the social disorganization perspective viewed local culture as playing an important role in neighbourhood organization, subsequent work downplayed cultural influences, with most researchers focusing on structural factors instead. Some recent studies, however, have resurrected cultural explanations (see, for example, Kubrin &
Weitzer, 2003; Sampson & Wilson, 1995). This work, which integrates concepts of culture and structure in the social disorganization model, may offer insights into the
'milieu effects' (Lee & Martinez, 2002) influencing different types of lethal violence. For example, some scholars argue that the so-called disorganized conditions in some urban neighbourhoods may give rise to cultural adaptations that include norms that tolerate or even encourage the use of violence, particularly among young, black males (Anderson,
1999; Sampson & Wilson, 1995). In turn, these 'ecologically structured tolerances'
(Sampson & Bean, 2006) may influence rates of violent crime at the neighbourhood- level. While research reveals intra-neighbourhood variation in residents' attachment to
As Kubrin & Weitzer (2003: 378) note: "We need more precise definitions, clearer distinctions, and better operationalizations of these concepts. Social ties may take many forms and thus may vary in their capacity for informal control. Some ties facilitate crime control while others hinder it, yet most writers fail to make this distinction".
47 48 local cultural codes (Anderson, 1999, Fagan & Wilkinson, 1998; Horowitz, 1983), those neighbourhoods in which a larger segment of residents embrace violent codes may experience greater problems with gangs, the drug trade, and violence among young males of the sort that Kubrin & Weitzer (2003) term "cultural retaliatory homicide".
3.1.2 Subcultural Perspectives
The idea that the geographic distribution of lethal violence might be affected by the spatial organization of culture across inner-city neighbourhoods is not specific to the social disorganization perspective. Criminologists have long been interested in identifying behavioural norms and values that are associated with higher levels of violent crime. In the 1950s, Albert Cohen (1955) and Walter Miller (1958) drew upon field observations to identify a core set of oppositional values held by those embedded in criminal social networks. These scholars saw violent crime as a by-product of an alternative set of values held by men and boys within a specific socio-economic location.
Their work, along with that of Wolfgang & Ferracuti (1967), set the stage for
'subcultural' theories of violent crime. The tie that binds this work is the assertion that men and boys of lower socioeconomic standing hold sets of norms and values that are a form of oppositional resistance to the behavioural demands of mainstream culture - values that define violence a means of status attainment and conflict resolution.
Much of this early work was subsequently criticized for its claim that deviant subcultural values and attitudes were ubiquitous among residents of disadvantaged neighbourhoods. Further, crime and violence is not the sole domain of lower-class boys
(Hirschi, 1969); middle-class boys also display some of the same values and attitudes that
J~ Indeed, survey-based research has shown that members of the so-called lower classes do not hold significantly different attitudes from the rest of society (see, for example, Sampson & Bartusch, 1998).
48 49 lead to the use of violence (Cernkovich, 1978). Finally, this work was criticized for ignoring crime and violence committed by females (Chesney-Lind, 1997; Maher, 1997).
As a result of these and other criticisms, subcultural explanations fell out of favour for a time, but recent decades have seen renewed interest in inner-city neighbourhoods and so- called codes of violence.
Contemporary work on cultural codes draws on differential association/social learning theory, and focuses on the 'cognitive landscape' of neighbourhoods (i.e. peoples' perceptions and attitudes toward their environment), and the normative codes that may exist in some that are conducive to violent behaviour. For example, Massey & Denton
(1993) and Bruce, Roscigno & McCall (1998) examined how patterns of racial
segregation have contributed to the development of differential normative codes in black inner-city neighbourhoods. Elijah Anderson's (1990, 1999) ethnographic work has also made an important contribution. He argues that, among some residents of disadvantaged
inner-city neighbourhoods, a 'code of the street' emerges that inverts the value
orientations of mainstream society with a set of values that valorizes the use of
violence. However, he notes that the majority of community residents are 'decent'
families who hold conventional, middle-class values. As such, two poles of value
" Mullins (2006) argues that the renewed interest in (sub) cultural explanations of crime and violence coincided with a decline of urban areas in the United States that saw predominately minority, inner-city neighbourhoods experiencing mass unemployment and increasing rates of poverty, family disruption and violent crime. "Combined with a loss of general community collective efficacy, these trends began to spiral in on themselves, creating a feedback loop of disadvantage that was worsened by the general conservative turn in the funding of social service programs.... in the 1980s. It is within this context that criminologists began exploring criminal subcultures within these urban locales" (p. 14). In terms of measuring "street culture'" and "oppositional values", Anderson suggests things like disregard for neighbours, suspension of legal norms and the use of violence as a means of gaining status and respect. However, Kubrin & Weitzer (2003) argue that greater attention should be devoted to developing appropriate and refined indicators of cultural variables. They also encourage future research that documents the "causal connection" - i.e. the mechanisms that link cultural factors to the use of violence (p.381). They suggest that a return to Shaw & McKay's original model - i.e. ethnographic neighbourhood research - would provide rich data on residents' interpretations of neighbourhood context and the role that culture plays in shaping local levels of violent crime.
49 50 orientation, 'decent' and 'street', socially organize the community, but the aggressive street-oriented minority dominates public space. 3 When navigating the streets, many residents, whether decent or street, "go for bad" - adopting a self-presentation aimed at deterring aggression. This speaks to the idea that neighbourhoods can be culturally heterogeneous, and even those residents who gravitate mainly toward more pro-social mainstream values may 'posture' adherence to competing sets of cultural values, depending upon the situation. Ironically, however, this posturing may invite violence rather than deter it. That is, in adhering to the street code, individuals negotiating certain inner-city neighbourhoods (and each other) may initiate a violent encounter due to the perception that others (who are also abiding by the dictates of the code) will use violence against them. In this sense, the use of violence becomes a pre-emptive strike of sorts, in that "it's either him or me".
The street code also provides a means by which to acquire respect, or 'juice', and avoid humiliating situations of status degradation. If one young man is assaulted by another, the code dictates that he must salvage his self-respect and the respect of his
'running buddies' by avenging himself, or else risk being targeted for violent attack by others. In this sense, violence becomes a means by which to both create and defend one's self-image. Further, the proliferation of guns on the streets of some urban neighbourhoods has implications for the degree of violence that is ultimately meted out:
'5 Because these behaviours are displayed in public space, they may create the impression to outsiders that violence and aggression are the modal form of behaviour in disadvantaged neighbourhoods. While Anderson's work demonstrates that this is not the case, he argues that even residents who do not internalize the code must be familiar with and abide by its rules when moving through the neighbourhood's public spaces. Similarly, Sampson & Bean (2006: 22) argue, "residents may share mainstream cultural values, but these values become existentially irrelevant in certain structural contexts" . Nevertheless, research on subcultural explanations for violence shows that "only a small portion of residents internalize and activate the demands [of such marginal subcultures] (Mullins, 2006: 13).
50 51 guns provide a quick and sometimes final resolution to a dispute, transforming a minor dispute over a 'diss' into a deadly act. Given that the potential for violence is perceived to be ever-present among those who internalize and activate the dictates of the street code, disadvantaged inner-city neighbourhoods where such codes govern interpersonal public behaviours may experience higher levels of violent crime. This may be especially so among young, racialized male residents, who, research suggests, may be more likely than their white counterparts to view the use of violence as acceptable in certain circumstances
(Anderson, 1999; Bailey & Green, 1999; Matsueda et al. 2006; Oliver, 1994). Gun homicides may also be more common in such neighbourhoods, as guns may be viewed as necessary for protection amidst widespread perceptions of danger, and as valued commodities to define social status on the streets. This is because they are "infused with symbols of toughness, power and dominance, and thereby an indication of repute and esteem" (Matsueda et al.2006: 339; see also Tagan & Davies, 2004).
Contemporary work on culture and crime has been criticized on a number of grounds.
For some, the proximity of this position to the victim-blaming tone present in subculture of poverty perspectives has led many to distance themselves from subcultural perspectives more generally. Others have argued that ethnographic methodologies, upon which most research on subcultural perspectives is based, do not meet conventional
'7 Subculture of poverty perspectives are grounded in the work of anthropologist Oscar Lewis, who argued that although the burdens of poverty are systemic and therefore imposed on certain segments of society, they lead to the formation of a subculture as children are socialized into behaviours and attitudes that ultimately perpetuate their life circumstances and render them unable to escape the 'underclass'. Harvey & Reed (1996: 465) argue that "few ideas have been as widely used, or as thoroughly abused, as Lewis' subculture of poverty thesis", and they claim that although Lewis' original theory portrayed the poor as victims whose lives were transformed by poverty, this depiction has been perverted to instead portray the poor as responsible for and deserving of their plight. Such portrayals have been soundly criticized by researchers who cite a biased, middle-class perspective that neglects to consider the effects of social structures and institutions on behaviour, and argue that depictions of the poor as culturally unique hold little explanatory power (Cannon, 1985; Goode & Eames, 1996).
51 52 social science standards of evidence - which suggests the need for a more systematic
TO examination of the concept of the street code. Still others have argued that culture is irrelevant to understanding why some neighbourhoods experience higher levels of violent crime. For example, Kornhauser (1978: 229) posits that criminogenic subcultures "[do] not exist because delinquent activities cannot be collectively endowed with value by human beings whose fate it is to live with one another". Instead of condoning or even valorizing the use of violence, residents of such neighbourhoods may instead possess
"moral cynicism" or fatalism with respect to the prevalence of violent crime in their midst. As such, high crime rates in these neighbourhoods do not result from the presence of oppositional values, but rather because (1) the unequal distribution of opportunities makes it difficult for residents to pursue conventional goals; and (2) residents lack the capacity, the willingness, and the requisite cultural support for exerting social control over others. This perspective thus explicitly links neighbourhood values to the structural realm of labour markets, social networks and human capital; the causal power of culture on is posited to be either weak or nonexistent.
In response, Matsueda et al. (2006) seek to determine whether quantitative survey measures can be created to demonstrate the existence of street codes, and if so, whether these codes are geographically distributed in ways implied by ethnographic observations and as predicted by (sub)-cultural theories. Their findings provide support for the basic propositions of ethnographic research on codes of violence: neighbourhood codes were disproportionately found in Seattle's black and Hispanic neighbourhoods, and in neighbourhoods that experience high rates of violence. Yet Matsueda et al. argue that though they "carefully operationalized the multiple dimensions of violent codes...there remains the question of how such codes operate in concrete situations - a question that qualitative research is better suited to answer" (p.354). As such, while quantitative methods may be appropriate for determining the extent to which codes of violence exist in inner-city neighbourhoods, qualitative field observation may be more adept at exploring the nuances by which these codes are used by residents in real-world situations. 39 Kornhauser's critique notwithstanding, research has suggested that the emergence of oppositional values that condone the use of violence stems from both structural conditions and cultural responses to those conditions that work in tandem to affect local levels of violent crime (Kubrin & Weitzer, 2003; Anderson, 1990, 1999; Fagan & Wilkinson, 1998; Horowitz, 1983).
52 53
3.1.3 Strain Perspectives
Strain perspectives have also been used to explain the spatial distribution of violent crime across urban neighbourhoods. This 'compositional' approach takes individual-level theories and predicts crime rates based on the idea that neighbourhoods with greater numbers of individuals experiencing 'strain' will experience higher rates of violent crime. In other words, this approach argues that neighbourhoods differ in their levels of violent crime partly because they differ in the extent to which they select and retain strained residents (due to, for example, their inability to afford to live in better, less strained neighbourhoods), and in the extent to which the interaction of these residents may generate additional strain and foster criminal responses to that strain (Agnew, 1999:
145).
According to classical strain perspectives, the contradiction between culturally ascribed goals of achieving status and wealth and the unequal distribution of opportunities to achieve those goals can lead to feelings of frustration, resentment and injustice among those who perceive themselves to be unfairly disadvantaged over others in a purportedly egalitarian society (Merton, 1938). As feelings of resentment grow, so too does the potential for crime and violence. This perspective therefore assumes that at the aggregate level, poverty and related structural factors increase the risks for violent interpersonal conduct. The mechanisms explaining this relationship, however, vary according to the particular strand of strain theory employed. Some studies (see, for example, Bailey, 1984; Huff-Corzine et al., 1986; Loftin & Parker, 1985; Messner, 1983;
Sampson, 1987; Williams & Flewelling, 1988) suggest that absolute deprivation (i.e. low income or poverty) corresponds to higher rates of violent crime. Under conditions of
53 54 absolute deprivation, violence may be perceived as one of a limited number of options available to those of limited economic means. That is, criminal activities designed to fulfill instrumental goals, such as the acquisition of money or other forms of calculable personal gain, may require or result in violence. Yet absolute deprivation is also hypothesized to give rise to expressive forms of violence, which may be the consequence of the dehumanizing effects of extreme poverty, along with the "brutal and demoralizing" treatment suffered by the poor (Lynch & Groves, 1989: 63). The likeliest victims of this violence are those in close proximity to the relatively deprived. As Parker (1989: 986) notes:
Perhaps violence is one of the few options available to those without the economic means to deal with problems and crises of everyday life. Absolute deprivation may also produce emotional situations which can escalate into violence, again directed at those close at hand, spouses, children, friends, etc; in other words, violence can occur among such individuals because everyday life is difficult.
Other scholars argue that the concept of relative deprivation is more relevant for explaining geographic variation in lethal violence (Bailey, 1984; Messner, 1989).
Relative deprivation may encourage violent crime because it generates a sense of injustice; perceived deprivation relative to others is posited to generate such feelings due to the disjunction between widely held cultural success goals and the distribution of opportunities to achieve them. Persons experiencing relative deprivation may evaluate their socioeconomic position relative to others, both in their neighbourhoods and in the
As some scholars have pointed out, such comparisons are most likely when advantaged others are highly visible and are perceived to be similar on relevant dimensions (Atkinson, 1986, Passas, 1997). As such, inequality would be most likely to lead to violent crime in those neighbourhoods where deprivation exists in the midst of, or in close proximity to, relative affluence.
54 55 larger population via the dissemination of cultural success goals by the mass media. '
Merton (1938) argued that inequality of opportunity creates situations in which some individuals engage in crime in order to achieve the social and economic goods that others possess. As with conditions of absolute deprivation, this frustration could manifest in the form of expressive violence, as individuals respond to psychological or emotional strain, or in the form of instrumental violence that is used and/or occurs during attempts to acquire material goods that they are unable to attain via more legitimate means.
Research has suggested that high rates of violent crime among some racialized populations may be the result of the relative deprivation and associated frustrations experienced by these groups. For example, Blau & Blau (1982) posit that the social injustices generated by income inequality lead to a state of anomie, which in turn leads to the expression of hostility in the form of violent crime. They further argue that relative deprivation is most acute among some racialized groups, particularly blacks, who suffer disproportionately from the double burden of racial and economic discrimination that
However, individuals may not always compare themselves to advantaged others; they often avoid comparison, or make "downward" or "lateral" comparisons (Major et al., 1991; Olson et al.,1986; Suls & Wills, 1991). Further, comparisons to advantaged persons do not necessarily result in feelings of relative deprivation; the advantaged are often seen to deserve what they have, and/or the comparatively disadvantaged employ other coping strategies to reduce feelings of deprivation (Agnew, 1992; Major et al., 1991). Finally, the end result of relative deprivation is not always criminal; the effect of relative deprivation on crime may be mediated by a number of factors, such as family disruption, the frequency of interaction with angry/frustrated others, the loss of positive stimuli and/or exposure to negative stimuli (for example, verbal or physical abuse) (Agnew, 1992). In addition to feelings of resentment and injustice, relative deprivation is also predicted to have an effect on violent crime because of its socially disorganizing effects (Blau & Blau, 1982; Sampson, 1995; Taylor & Covington, 1988). According to Taylor& Covington (1988: 556), racial and income inequalities widen the gulf between classes and racial/ethnic groups, limit interaction between neighbours, and undermine informal and formal community efforts to control crime. Indeed, the sense of injustice may seem to legitimize violence against the haves by the have-nots. In other words, visibly high levels of inequality can produce resentment that, in turn, disrupts a neighbourhood's social fabric. As a consequence, there is much "resentment, frustration, hopelessness and alienation [which produces] a sense of injustice, discontent, and distrust" (Blau & Blau, 1982: 1 19). The greater the level of distrust in a particular neighbourhood, the less cohesive it is predicted to be. In light of these assertions, some researchers have moved to combine relative deprivation and social disorganization measures in an effort to better predict levels of violent crime (Kawachi et al., 1999; Taylor & Covington, 1988).
55 56 designates them a lower social and socioeconomic position than other groups. As such, the criminogenic effects of interracial inequality are expected to be higher among blacks, and Blau and Blau's theory implies that neighbourhoods with large numbers of poor, black residents will also experience higher rates of violent crime.
Other scholars agree that concept of relative deprivation is well suited to explaining higher rates of lethal violence among certain racial groups (Harer & Steffensmeier, 1992;
Messner & Golden, 1992; Stolzenberg et al., 2006). For example, Stolzenberg et al.
(2006: 304) argue that "race as an ascribed status facilitates the collective awareness of common economic interests, the collective recognition that blacks are disadvantaged relative to whites, and that blacks do not have open access to wealth and economic resources". Consequently, neighbourhoods with higher numbers of the relatively deprived - for example, those with large black populations and high rates of poverty and unemployment - may experience higher rates of violent crime, particularly involving black victims. Further, as with absolute deprivation, feelings of resentment and injustice may lead the relatively deprived to direct their aggression against friends and family members, which may lead to higher rates of this type of violence in some neighbourhoods compared to others where levels of inequality are less pronounced (Zehr,
1976).
The concepts of both absolute and relative deprivation suggest that as poverty and income inequality rise, so too will rates of violent crime. Given the predicted association between race, inequality, and deprivation, measures or indexes that include measures of economic and racial inequality - such as poverty, unemployment, income inequality, racial segregation, and the percentage of black residents - are typically employed as
56 indicators of strain/deprivation. Though a number of studies have examined the theoretical link between some of these measures and lethal violence - particularly poverty and inequality - the empirical evidence to date is mixed. Some studies have found significant positive effects of inequality (Blau & Blau, 1982; Simpson, 1985) and poverty (Bailey, 1984; Messner, 1983; Williams, 1984) on lethal violence, others have found nonsignificant or negative effects of inequality (Bailey, 1984; Messner, 1983;
Messner & Tardiff, 1986; Williams, 1984) and poverty (Blau & Blau, 1982; Messner,
1982; Simpson, 1985), while still others (Kposowa & Breault, 1993) have found both positive and negative effects of inequality and poverty, depending on the size and location of the area. Some criminologists (see for example, Shihadeh & Maume, 1997:
258) also question the legitimacy of using aggregate data to test the individual-level processes that relative and absolute deprivation perspectives focus on. In other words, these scholars question the use of reductionist frameworks that view violent crime as an
"additive function of a series of individual-level processes".
3.1.4 The Routine Activity Perspective
Unlike strain perspectives, which emphasize the conditions that prompt individuals to commit violent crime, the routine activity perspective instead focuses on the characteristics of the criminal event. That is, the geography of crime is explained in terms of the unequal distribution of crime opportunities and targets across space.
Drawing on human ecology theory, Cohen and Felson (1979: 593) posit that the routine
J Kovandzic et al. (1998) suggest that these divergent findings may stem from a number of methodologica issues (for example, different units of analysis across studies, multicollinearity between inequality and poverty and other variables, etc.) - but they also argue that "it may be that the relationship between homicide and inequality and poverty is....historically contingent. The relationship may not be based solely on the fact that inequality and poverty exist but also that economic conditions during certain periods of time, for example, during the 1980s, alter the character of inequality and poverty and, consequently, their impact on homicide" (p.572).
57 58 activities of individuals (i.e. the "recurrent and prevalent vocational and leisure activities individuals undertake on a regular day-to-day basis") facilitate the convergence in time and space of three crime ingredients: a motivated offender, a suitable target, and the absence of a capable guardian.44 The specific 'where' or 'when' of convergence stems from the routine behaviour patterns of each actor involved. Research suggests that demographic and lifestyle characteristics have an impact on the routine activities that people undertake, putting an individual at a greater or lesser risk for criminal victimization (Felson, 1987). As such, the routine activity perspective embeds the concept of opportunity within the parameters of peoples' day-to-day lives, and in so doing, highlights both the spatial and temporal features of crime and violence. Given these parameters, the location of a violent event is expected to be associated in systematic ways with a neighbourhood's sociodemographic characteristics (age and sex structure, racial composition, and marital and employment rates), and with features of the temporal setting (time of day, day of the week, time of year). These associations are expected because sociodemographic and temporal characteristics structure routine activities, which affect the location of victims and offenders in both time and physical space (Messner &
Tardiff, 1985).
Research on the routine activity perspective and violent crime has consistently demonstrated that the risk of violent victimization is associated - both the individual- and
More recent conceptualizations of these three key elements of crime have been refined in significant ways. In the original formulation of the routine activity approach, a constant supply of "motivated" offenders was assumed. In the theory's more recent iteration, the "motivated" offender has been replaced by the "likely" offender - a change that reflects the adoption of a rational choice conceptualization of the "reasoning" offender (Felson, 1998: 53). Felson (1998) recommends measuring target suitability from the offender's perspective, and argues that offenders assess this suitability according to four key considerations: value, inertia, visibility and access to the target. The concept of guardianship has also been modified since its initial description. Originally conceptualized as a single relationship between target and guardian, it has been expanded to consider two additional types of protective relationships: handler/offender (see, for example, Felson, 1995), and manager/place (Eck. 1994; Mazerolle et al., 1998).
58 59 neighbourhood-level - with lifestyle, daily routines and demographic characteristics
(Messner & Tardiff, 1985; Miethe & Meier, 1990; Miethe et al., 1987; Phipps, 2004;
Rountree et al., 1994). Because of differences in population characteristics and land use, different neighbourhoods tend to exhibit different routine activities, leading to criminal victimization being neither randomly nor uniformly distributed across urban neighbourhoods.45 For example, the socioeconomic and demographic composition of the neighbourhood, as well as the structure of routine activities and guardianship at play in a downtown club district differ from the socioeconomic, demographic composition, routine activities and levels of guardianship in an affluent residential neighbourhood, which themselves differ from those in a disadvantaged residential neighbourhood. As such, distinctive patterns of violent victimization, including the risk of homicide, are expected to emerge across urban neighbourhoods.
Though the routine activity perspective has been widely applied in empirical research, problems with the operationalization of key concepts, difficulties in obtaining individual- level data and shortcomings in available statistical techniques have meant that the empirical validity of the perspective is still in question (Akers, 2000; Eck, 1995). The routine activity perspective has also weathered debates over its' proper conceptualization
Recent opportunity models also incorporate the presence of criminal facilitators' -that is, so-called tools of the criminal trade (for example guns, drugs & alcohol) that serve to undermine the capability of guardians, and thus have the capacity to influence rates of crime and violence in the neighbourhood (Clarke, 1997). 46 Miethe et al. (1987: 185) argue that many studies "have examined only the demographic correlates of victimization and its temporal/spatial location, presuming that variation in routine activities or lifestyles must be the underlying cause of differential rates of victimization" . They suggest that more adequate empirical tests develop measures that better capture lifestyles and activities, particularly those outside the home. For example, the identification of modeling tools capable of capturing the dynamic nature of human activities and interactions when individuals converge remains a hurdle. Some researchers have, however, turned to simulation modeling as an alternative approach; recent software developments have made it possible to situate individuals in a particular spatio-temporal environment, and to analyze their interactions and the crime patterns that may result (see, for example, An, Linderman, Qi, Shortridge & Lu, 2005).
59 60 as a micro- or macro-level approach to crime. For example, (Capowich, 1999) argues that multi-level approaches (see for example, Bursick & Grasmick, 1993; Sampson &
Wooldredge, 1987) are more fruitful than micro-level approaches alone. Others argue that the routine activity perspective tends to ignore the research literature that associates crime with offender characteristics - though, as noted, more recent iterations of the theory have moved toward a more realistic conceptualization of the likely offender
(Ekblom & Tilley, 2000; Felson, 1986). Finally, some scholars are critical of post hoc and descriptive research that is guided by routine activity theory (see, for example, Pease,
1997).
3.2 THEORETICAL IMPLICATIONS: SUMMARY
Each of the theoretical perspectives reviewed in the preceding section have attempted to explain neighbourhood differences in levels of violent crime. Economic disadvantage, measured by poverty, unemployment, and welfare receipt, is one of the key neighbourhood characteristics that has been linked to the risk of lethal violence across these perspectives. Empirical research supports this emphasis on economic disadvantage, which is perhaps the most distinguishing characteristic of neighbourhoods experiencing high levels of violent crime (Bursik & Grasmick, 1993; Hannon, 2005; Kubrin, 2003;
Martinez, 2002; Morenoff, et al., 2001; Titterington et al., 2003). However, the specific mechanisms through which economic disadvantage operates to shape this risk are hypothesized to vary.
According to social disorganization theory, the effect of poverty is mediated by the structure of social networks among neighbourhood residents. Disadvantaged
60 61 neighbourhoods are posited to have fewer and weaker social ties among residents, and lower rates of civic and organizational participation. Kubrin & Weitzer (2003: 380) argue that concentrated economic disadvantage "not only deprives neighbourhoods of resources that may be mobilized to control crime, but also increases social isolation among
residents, which impedes communcation and interferes with their capacity to pursue
common values". Such neighbourhoods - which are also typically characterized by
population heterogeneity and large numbers of lone parent families - may be less
equipped to effectively socialize, watch over, and control their young people, which
could allow for the development of delinquent peer groups (Bursik & Grasmick, 1993).
As a consequence, they may experience higher rates of certain types of youth-related
violent crime (for example, gang-related violence). Research also suggests that socially
disorganized neighbourhoods may experience higher levels of intimate partner violence
because the "stigma costs" (Fagan, 1993) associated with this violence may be lower. In
other words, due to the presence of "nonintervention norms" in the neighbourhood more
generally (Browning, 2002), violence among intimate partners may continue unchecked,
with lethal consequences. Such neighbourhoods may also be lacking in supports that
would provide abused women with viable avenues for exiting violent relationships. The
net effect of these factors may be higher rates of lethal violence between intimate
partners. In sum, then, the social disorganization perspective may be applicable across
various homicide types.
Subcultural theory posits a somewhat different explanation for the relationship
between economic disadvantage and violent crime: the ecological concentration of
However, to the extent that intimate relationships are beyond the reach of collective supervision, neighbourhood social organization (or a lack thereof) may be inconsequential with regard to the regulation of intimate partner violence (Browning, 2002).
61 62 disadvantaged populations in urban neighbourhoods may lead to cultural adaptations that invert the value orientations of mainstream society. This is because poverty and related structural features of urban neighbourhoods are hypothesized to impede communication and obstruct the quest for common values among residents, thereby fostering cultural diversity with respect to non-delinquent value orientations (Kornhauser, 1978). One consequence of this cultural fragmentation is the formation and transmission of so-called violent subcultures. Given that some racial/ethnic groups, particularly blacks, are more
likely than whites to live in disadvantaged and 'disorganized' neighbourhoods where codes of violence may be especially prevalent (Anderson, 1999; Bailey & Green, 1999),
the subcultural perspective may be more applicable to understanding the killing of blacks,
particularly young, black males in disputes over status, respect, and/or perceived
transgressions. It is also possible that, due to cultural support for the use of violence more
generally, some disadvantaged neighbourhoods may experience higher rates of violence
against women, including intimate femicide. In other words, neighbourhood-level
normative beliefs and tolerances for the use of violence may also contribute to the
production and tolerance of men's violence against their female intimate partners (Frye &
Wilt, 2001).
Strain theories posit that high-crime neighbourhoods are more likely to select and
retain individuals experiencing economic strain, produce additional strain, and foster
criminal responses to that strain. According to Agnew (1999), disadvantaged
neighbourhoods produce and perpetuate strain in several ways: (1) they influence the
goals that residents pursue and the ability to achieve these goals; (2) they influence
residents' sense of relative and absolute deprivation; (3) they reduce the likelihood that
62 63 residents will be exposed to positive stimuli, and increase the likelihood of exposure to aversive stimuli (such as family disruption, child abuse, and other forms of "vicarious strain"); and (4) they increase the frequency of interaction between large numbers of strained/angry individuals. This contributes to a further increase in levels of neighbourhood strain, negative affect, and violent crime "because these individuals are more likely to mistreat and victimize one another" (Agnew, 1999: 141). Elements of the strain perspective, particularly the concept of relative deprivation, may be especially suited to understanding the social and spatial concentration of lethal violence among some racialized populations. This is because, quite simply, some racialized groups, particularly blacks, suffer from acute levels of racial and economic discrimination.
Feelings of frustration, resentment, and injustice that stem from this inequality are hypothesized to influence aggregated levels of negative affect in neighbourhoods with large numbers of black residents, which in turn may have a direct effect on local levels of violent crime.
Finally, instead of an emphasis on aggregate levels of strain, as above, the routine activity perspective emphasizes aggregate patterns of interaction between potential victims, offenders, and capable guardians that can facilitate or inhibit the opportunity for violence to occur. The latter component of the routine activity model overlaps with the concept of informal social control in the social disorganization perspective, and presumes that neighbourhoods that are unable to effectively supervise the interaction of potential offenders and targets, and/or intervene in violent situations may experience higher rates of violent crime. The increased heterogeneity and attenuated local ties that exist in many disadvantaged inner-city neighbourhoods are hypothesized to result in fewer committed
63 64 informal place managers. It may also be more difficult for place managers to decide who belongs where - or, more importantly, who does not belong in a particular place at a particular time (Eck, 1997; Mazerolle et al., 1998). This implies that the lack of effective guardianship in disadvantaged neighbourhoods may have a generalized positive effect on different types of homicide in the neighbourhood. At the same time, however, the routine activity perspective may also be relevant for understanding specific types of lethal violence in disadvantaged neighbourhoods. For example, "crime facilitators" such as guns may be more prevalent in disadvantaged neighbourhoods, increasing the likelihood of gun-related violence (Clark, 1995).
In sum, then, regardless of the theoretical approach taken, a number of consistent findings have emerged in the literature on neighbourhoods and homicide. First, neighbourhoods differ substantially in their rates of lethal violence. Second, neighbourhoods with high rates of homicide tend to be those characterized by high levels of economic disadvantage. Finally, some theoretical perspectives appear to be more relevant for understanding certain types of lethal violence than others. This is not surprising, given that the legal category 'homicide' includes violence that takes place under a variety of different circumstances. In response to recent charges that aggregate analyses of homicide may not capture the complexity of the mechanisms that link neighbourhood context to violent crime (see, for example, Kubrin 2003), a body of research on neighbourhood effects on different types of homicide is beginning to emerge.
64 65
3.3 DISAGGREGATING HOMICIDE BY TYPE
The core assumption underlying the disaggregation of homicide by type is that a singular focus on overall homicide rates obscures the multidimensional nature of lethal violence. As Block (1993: 368) explains "different homicide syndromes have different characteristics, occur in different areas of the city, [and] affect different segments of the population". For example, a homicide in which a teenage mother smothers her newborn child in an attempt to conceal the birth differs on a number of important dimensions from a homicide in which a young male gang member is shot to death by a rival gang member at a DJ competition. As such, a number of scholars have hypothesized that different types of homicide may have different patterns, causes and correlates. The first systematic attempt to categorize homicides by type was by Marvin Wolfgang (1958) in his book
Patterns of Criminal Homicide. Since then, the idea that homicide should not be treated as though it were a homogeneous category has become generally accepted among violence researchers (Kubrin, 2003; but see Felson, 2002, for a critique of this approach).
Total homicide rates can be disaggregated in a number of ways. To date, most research has focused on the victim/offender relationship (Curtis, 1974; Kovandzic et al.,
1998; Parker & Smith, 1979; Wolfgang, 1958) and/or the motive surrounding the homicide event (Miethe & Drass, 1999; Nielsen et al., 2005; Parker, 1989; Rosenfeld et al., 1999; Titterington et al., 2003; Williams & Flewelling, 1988). These motives are often subclassified to differentiate between two general types of lethal violence: instrumental and expressive (Block & Christakos, 1995; Decker, 1996; Polk, 1994). The instrumental/expressive distinction has been widely used in homicide studies, as researchers have generally considered instrumental acts (those designed to improve one's
65 66 financial or social position) to be qualitatively different from expressive acts (those that are designed not for profit or gain, but rather to vent anger, rage or frustration).
Researchers have also examined the correlates of homicide for different race-, age-, and sex-specific subgroups (Fagan et al., 2003; Harer & Steffensmeier, 1992; Kubrin &
Wadsworth, 2003; McNulty, 2001; Messner & Sampson, 1991; Nielsen et al., 2005;
Peterson & Krivo, 1993; Rennison, 2001; Smith & Brewer, 1992; Steffensmeier &
Haynie, 2000; Titterington et al., 2003). These analyses are justified on the grounds that patterns and correlates of homicide may be race- and age- and sex-specific, and because the killings by and of women have been shown to be distinctly different from those by and of men. Generally speaking, research that disaggregates total homicide rates by type suggests that there are both similarities and differences in the predictors of homicide within and across groups.
Most studies of the ecological correlates of disaggregated homicide rates have been conducted with data from the United States and used states, SMSAs and cities as the unit of analysis. These studies have generally found that some correlates exhibit broad- ranging influence across many types of homicide, whereas others are limited to specific types.4' To date, only a handful of studies have examined smaller units of aggregation, such as census tracts (Kubrin, 2003; Kubrin & Wadsworth, 2003; Kubrin & Weitzer,
2003; Miles-Doan, 1998; Neilsen et al., 2005; Rosenfeld et al., 1999). Like their counterparts at the SMSA and state level, these studies demonstrate that homicide subtypes differ in a number of important respects, and that neighbourhood characteristics
For example, in their examination of disaggregated homicide rates across U.S. cities in 1990, Kovandzic et al. (1998) found that their measures of inequality and poverty had different effects on different types of homicide. Inequality was related to family and stranger but not acquaintance killings, while poverty was only related to acquaintance homicide. Percent black, on the other had, was not only related to all three of their homicide types, but it was also the strongest predictor in the model.
66 67 contribute, in different ways, to this variability. For example, using cluster analyses to
identify four types of lethal violence (general altercation, felony, domestic: male/female,
domestic: female/male), Kubrin (2003) found that neighbourhood disadvantage had positive and significant effects for all homicide subtypes, while other predictors
(residential instability, percent young males, percent divorced males) varied by homicide type. In another study, Kubrin and Weitzer (2003) disaggregated homicides with known
motives into retaliatory and non-retaliatory categories, with the former further refined
into cultural retaliatory (those directly tied to a code of the street) and situational
retaliatory subtypes (those unrelated to a code of the street). Neighbourhood disadvantage
was positively related to cultural retaliatory but not to situational retaliatory killings. In
their study of homicide victimization among African-Americans in St. Louis, Kubrin and
Wadsworth (2003) disaggregated black homicides into six subtypes: gang, intimate,
stranger-robbery, non-stranger robbery, stranger altercation, and non-stranger altercation.
Their results showed that disadvantage was only related to the four non-robbery subtypes.
These studies highlight the importance of disaggregating homicide by type in
neighbourhood-based research. Although some neighbourhood-level predictors appear to
be consistent across homicide types, there are also differences in the ecological covariates
of lethal violence that highlight the value of data disaggregation. In light of and inspired
by these findings, this study examines the spatial distribution of and the neighbourhood-
level characteristics associated with total homicides and with four types of homcide in
Toronto. However, rather than developing mutually exclusive homicide categories based
on victim-offender relationship or motive, the homicide subtypes examined in this study
were chosen because they are meaningful to the Toronto context. That is, they speak to
67 68
some of the media commentaries and public concerns outlined in the preceding chapter.
In Chapters Five and Six, I examine the spatial distribution of and neighbourhood-level characteristics associated with gun-related homicides, as well as the killing of young men
(15-34), black homicide victimization, and the killing of women by their male intimate partners.
50 My examination of intimate femicide stems more from personal research interests than from dominant discourses in Toronto, per se, though it is also guided by the National Research Council's (2004) call for research that estimates the extent of variation in violence against women across census tracts or small neighbourhoods.
68 69
CHAPTER IV. DATA SOURCES AND DESCRIPTION
In this chapter, I provide an overview of the data sources and neighbourhood delineations used in this study. I also provide an introduction to the data preparation and analytic techniques employed in the multivariate analyses presented in Chapters Five and
Six, and some descriptive data on neighbourhoods and homicide in Toronto
4.1 DEFINING NEIGHBOURHOODS
Most research on variation in urban homicide rates uses census tracts as proxies for neighbourhoods (Avakame, 1997; Block & Block, 1992; Crutchfield, 1989;
Crutchfield et al., 1999; Kubrin, 2003; Kubrin & Weitzer, 2003; Martinez & Lee, 1998,
2000; McClain, 1989; Morenoff & Sampson, 1997; Peterson et al., 2000; Sampson et al.,
1997; Rosenfeld et al., 1999). Though researchers generally agree that tracts typically do not correspond to neighbourhoods in a socially meaningful sense, most use them to define neighbourhood boundaries because they are the best local areas for which the required data are generally available.
The neighbourhood delineations that I use in this study were created by the City of
Toronto's Social Policy Analysis and Research unit (SPAR). In collaboration with a variety of stakeholders (see Figure 4.1)51, SPAR constructed a system of consensus perception boundaries^ , designed to be useful to as many users as possible. These neighbourhood boundaries were developed using the following criteria:
1. That they be based on planning areas in former municipalities and existing public
health neighbourhood planning areas;
51 Input from the following stakeholders was received in the creations of Toronto neighbourhood boundaries: Public Health, police, Parks and Recreation, Planning, Library, and a variety of other key community agencies across the city of Toronto. 52 That is, neighbourhood boundaries upon which there is as much consensus as possible.
69 70
2. That no neighbourhood be comprised of a single census tract;
3. That there be a minimum neighbourhood population of at least 7,000 to 10,000;
4. Where census tracts were combined to meet criteria 2 or 3, above, they were to be
joined with the most similar adjacent area according to the percentage of the
population living in low-income households;
5. That they respect existing boundaries such as service boundaries of community
agencies, natural boundaries (rivers) , and man-made boundaries (streets,
highways, etc.) as much as possible;
6. That neighbourhood areas be maintained that are small enough for service
organizations to combine them to fit within their service area;
7. That the final number of neighbourhood areas be 'manageable' for the purposes
CO
of data presentation and reporting.
Practically, the City's neighbourhood boundaries are important for my analyses because they reduce the city's 531 census tracts tol40 neighbourhoods - a more appropriate sample size for analyses of homicide, which is a rare event. These boundaries may also represent a more socially meaningful area, one that residents within each of the city's 140 neighbourhoods would likely identify with more closely than they would with the particular census tract within which their home address is located.
^ Accessed at: http://www.toronto.ca/demographics/neighbourhoods.htm, 2 April, 2007. 54 Indeed, Grannis (1998) argues that the identification of neighbourhoods may be better explained by the 'cognitive maps' of pedestrian street networks that people traverse in their day-to-day activities, and which, for many, constitute the boundaries of their local neighbourhood. These street networks are generally not commensurate with political boundaries as defined by the census. Though the City of Toronto's neighbourhood boundaries are by no means a perfect measure, nor are they ones that all residents of a particular neighbourhood would necessarily identify or agree with, they may represent an improvement over the political/administrative boundaries that are generally used in this kind of research as they more closely approximate the cognitive maps of which Grannis speaks.
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4.2 DATA SOURCES AND DATA COLLECTION
Two types of data were used in this dissertation: homicide data, collected from newspaper reports and police files; and tract-level census data for the years 1986, 1991,
1996 and 2001. Each of these data sources is described below.
4.2.1 The Dependent Variable: Homicide
1 chose to use homicide as my measure of violence and my dependent variable in this study for several reasons. Lethal violence has profound consequences for individuals and neighbourhoods, and in Toronto, it has garnered considerable media, public and political attention over the past several decades. Little is currently known about the ecology of lethal violence in Canada's urban neighbourhoods, despite a wealth of literature on neighbourhood effects on homicide in American cities. This study thus represents a preliminary effort to address this gap in the literature.
Practically, homicide is an excellent measure of violence due to the consistency in the way it has been defined over time (Archer & Gartner, 1984), and because it is more likely to come to the attention of the authorities than are less-than-lethal forms of violent crime.
As such, the 'dark figure' of crime is arguably reduced in research on homicide, and we can be fairly confident that the number of homicides reported and/or detected in Toronto in any given year is more or less consistent with the actual number of homicides that took place.
The homicide data used in this dissertation are part of a larger dataset on homicide in the twentieth century collected by Rosemary Gartner and Bill McCarthy (SSHRC Grant
I acknowledge that there are potential sources of under-reporting in this data set, as in cases where a homicide is erroneously classified as a natural or accidental death, or when a killing is not reported to or detected by the police. Further, police shootings are not systematically covered in this dataset.
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410-94-0756). This dataset includes information on all homicides known to officials in
Toronto between 1900 and 1990. For the purposes of this data collection effort, Gartner and McCarthy defined homicide as "...the intentional use of force that results in a death".
Evidence of intent was required, and in cases where it was difficult to identify, police determinations were relied upon. While the intention of harm was necessary on the part of the accused person, the victim need not have been the intended target, and killings of so-called 'innocent bystanders' were included. To update these data through the early twenty-first century, I collected comparable information on all homicides between 1991 and 2003 from two sources: newspaper accounts and police files. I then pooled the homicide data for this 16-year period in order to have a sufficient number of homicides with which to perform multivariate analyses.
Newspaper Sources
A crime reporter at the Toronto Star provided me with a list of all killings that occurred in Toronto over the period of examination. This list included victims' names and the date of all killings that occurred in Toronto between January 1, 1988 and
December 31, 2003. Using this information, I searched the Toronto Star, the Toronto
Sun, the Globe and Mail and, when possible the National Post, beginning the day after the killing and tracking any coverage for one week thereafter. This was accomplished using the Canadian Newsstand Database, an electronic newspaper search engine available through the University of Toronto's e-resources database. In cases where an accused person was brought before the courts, I also searched newspaper coverage of his/her trial to gather additional information.
For each case, I collected demographic information on the victims and accused persons (including sex, age, employment status, job type, drug/alcohol use at the time of
72 73 the killing, prior criminal record, number of children, marital status, etc.), situational characteristics of the homicide (including the motive, method, and circumstances
surrounding the killing, weapon type, etc.), and case disposition outcomes, where available. For each case, I also wrote a short narrative that summarized the newspaper coverage of the killing.3
Police Sources
Rosemary Gartner and I applied to the Toronto Police Service (TPS) for access
and entered into a research agreement with them that allowed me to review and collect
information from the Chiefs Reports for the years 1998 through 2003, which provide
information on all homicides known to police in Toronto.37 These reports provide the
Chief of Police with a synopsis of the first twenty-four to forty-eight hours of the
homicide investigation. Given that these reports are preliminary accounts of the
circumstances surrounding the killing, they are often missing information, particularly
with respect to characteristics of the accused person(s), motive, cause of death, etc. The
Chiefs Reports are, however, updated as new information is discovered, and as such, the
catalogue typically contains between one and six reports for any given case. The Chiefs
Reports remain, however, preliminary accounts of the homicide incident, thus a data
To validate these data, J obtained annual counts of homicides in Toronto from the TPS's Annual Statistical Report, and compared them with the information supplied by the Toronto Star. The validation revealed a three case difference between sources. That is, the Chiefs Reports indicated that there were three more homicides over the period of examination than did the newspaper sources. However, it was determined that this discrepancy could be accounted for by the fact that two deaths that were initially not ruled to be homicides were later so classified, and one death took place a year after the violent incident, and was missed in my initial newspaper searches. As such, the Toronto Police Service's counts were preferred. 57 The homicide data collection for this dissertation took place over a three-month period between February and May, 2006. I would like to thank Inspector Brian Raybould for providing space within the offices of the Homicide Squad, and for his day-to-day assistance in any number of matters for the duration of the data collection.
73 74 triangulation approach, using newspaper reports, was used to cross-check and supplement police data.
As with the newspaper reports, for each case, I collected demographic information on the victims and accused persons (including sex, age, employment status, job type, drug/alcohol use at the time of the killing, prior criminal record, number of children, marital status, etc.), situational characteristics of the homicide (including the motive, method, address of and circumstances surrounding the killing, weapon type, etc.) and case disposition outcomes, where available. For each case, I also wrote a short narrative that summarized the incident description that was included in these reports.
The homicide dataset, when possible, also includes information on the racial/ethnic background of victims and accused persons. These data were not, however, collected from the Toronto Police Service (TPS) files; the research agreement entered into with the
TPS prohibited the collection of this information from this source. As such, I relied on newspaper reports and photographs of victims and/or accused persons that were printed as part of those newspaper reports. It should be noted that classifying race in this way is fraught with the potential for misclassification and missing data, but given institutional policies that essentially ban the release of data on the race of victims, suspects, or others by the police, there was no other alternative.
4.2.2 The Independent Variables: Neighbourhood Characteristics
I downloaded Canadian census data from the collection of on-line databases and search and retrieval programs maintained by Computing in the Humanities and Social
Sciences (CHASS) at the University of Toronto.58 In particular, I accessed tract-level
581 am grateful to Laine Ruus at the University of Toronto's Data Services Library for her invaluable knowledge and assistance in this part of the project.
74 75 census data for the years 1986, 1991, 1996 and 2001 through the Canadian Census
Analyser, acquired from Statistics Canada through the Data Liberation Initiative consortium.
My independent variables encompass the key correlates of neighbourhood homicide rates discussed in the literature on neighbourhood effects and homicide. They include: (1) percent young males, defined as the average percentage of the neighbourhood population who are young males, aged 15-24; (2) percent lone parent families, defined as the average percentage of the neighbourhood's census families60 living in private households that are headed by either a male or female-lone parent; (3) percent divorced males, defined as the average percentage of the neighbourhood's male residents aged 15 years and older who are divorced; (4) percent residential mobility, defined as the average percentage of the neighbourhood's residents aged five years and older who have changed residences in the past five years '; (5) percent owner-occupied dwellings, defined as the average percentage of all dwellings in a neighbourhood that are lived in by their owners ; (6) percent unemployed, defined as the average percentage of neighbourhood residents aged
15 years and older who were unemployed6 ; (7) median family income, defined as the
Through this program, participating institutions pay an annual subscription fee that allows students and faculty unlimited access to numerous Statistics Canada public use microdata files, databases and geographic files. 60 Statistics Canada's definition of'Census Families' refers to a married couple (with or without children of either or both spouses), a couple living common-law (with or without children of either or both partners), or a lone parent of any marital status with at least one child living in the same dwelling. A couple living common-law may be of the opposite or the same sex. Children in a census family include grandchildren living with their grandparent(s) but with no parents present. 'Movers' are persons who, on census day, were living at a different address than the one at which they resided five years earlier. '" This refers to a private dwelling in which a person or a group of persons is permanently residing. Also included are private dwellings whose usual residents are temporarily absent on Census Day. Unless otherwise specified, all data are for occupied private dwellings, rather than for unoccupied private dwellings or dwellings occupied solely by foreign and/or temporary residents. J This refers to persons aged 15 years and over (excluding institutional residents) who, during the week prior to enumeration, were (a) without work or who actively looked for work in the past four weeks and
75 76 average household income in the neighbourhood ; (8) percent government transfer payments, defined as the average percentage of the neighbourhood's total income that was composed of government transfer payments 3; (9) percent low income, defined as the average percentage of the population classified by Statistics Canada as low income66;
(10) percent black, defined as the average percentage of a neighbourhood's residents who identified their ethnic origin as 'black'67; and (11) recent immigration, defined as the average percentage of the neighbourhood population who came to Canada within the past
3-5 years . 1 have also included the average population size (logged), in order to control for differences in population size across Toronto's neighbourhoods.69
were available for work; or (b) were laid-off and expected to return to their job and were available for work or (c) had definite arrangements to start a new job in four weeks and were available for work. 64 This refers to the weighted mean total income of households. The composition of the total income of a particular neighbourhood refers to the relative share of each income source, expressed as a percentage of the aggregate income of that neighbourhood. Government transfer payments refer to total income from all transfer payments received from federal, provincial or municipal governments during the previous calendar year. This variable is the sum of the amounts reported in the Old Age Security pension and Guaranteed Income Supplement, benefits from the Canada Pension Plan; Employment Insurance benefits; Canada Child Tax benefits; other income from government sources. 66 This refers to the position of an economic family or an unattached individual 15 years of age and over in relation to Statistics Canada's low income cut-offs. This variable defines the total number of low income economic families per geographic area. The low income cut-offs are established based on certain expenditure-income patterns that are differentiated by family size and degree of urbanization. These cut offs are updated yearly by changes in the consumer price index. The comparability of ethnic/racial origin data of the 2001 Census with the data from previous censuses has been affected by several changes in data collection over the past 30 years. In 1986, a check-off for "Black" was added to the questionnaire in response to data requirements resulting from the then-new employment equity legislation. It was included again as a check-off category in 1991. In 1996, a new question was added to the quesionnaire to measure the visible minority population, including Blacks, more directly. The format of the ethnic origin question changed substantially, and the check-off categories that had been provided from 1971 to 1991 were no longer present. Instead, respondents were asked to specify their ethnic origin(s) in four write-in spaces. Further, beginning in the 1986 Census, respondents were specifically instructed to specify as many ethnic groups as they felt were applicable to them; this instruction was retained on the questionnaire in 1991, 1996 and 2001. The number of multiple ethnic origin responses provided by respondents has grown with each census, which has likely affected overall data comparability for the ethnic origin variable. Though 1 have taken into account both single- and multiple- response counts for black respondents, changes in the collection of these data since 1996 have affected data comparability across the 4 censuses. The period of immigration question in the Canadian census refers to groupings of years derived from the year of immigration question. There is not consistency in the most recent period of immigration measure across the 1986, 1991, 1996 and 2001 censuses - the period ranges from 3 to 5 years. This deals with the fact that neighbourhoods with more residents, all else being equal, would be expected to have more homicides. The variable is logged because it is highly skewed.
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4.3 DATA PREPARATION
In order to use the homicide data for my analyses, they first had to be geocoded, which refers to the process of locating an event in geographic space by assigning a code
(i.e. a name, number) to a particular location. This was accomplished by assigning a
70 postal code to the address at which the killing occurred. Though studies on neighbourhood effects and urban homicide generally emphasize the importance and influence of individuals' neighbourhoods of residence in determining their risk of homicide, most research on ecological correlates uses the location of the homicide event as the place identifier, and not the location of the victim's or the offender's residence.
Sampson et al. (2002) point out that research that focuses on characteristics of an individual's neighbourhood of residence is problematic in that many of life's day to day activities and behaviours occur in neighbourhoods other than those in which people reside. Consider the nature of routine activity patterns in modern cities, they argue, where people traverse the boundaries of any number of neighbourhoods on a given day. As such, it makes sense to examine how neighbourhood composition may be related to the nature and type of crime that is experienced there, which is best accomplished by examining the location of the homicide event (Kubrin, 2003).
There are other, more political reasons for examining the neighbourhood in which the homicide takes place, rather than that within which the victim or accused person resides.
Police departments, citing privacy concerns, generally preclude researchers from
Cases for which there was no specific location identifier available that corresponded to a census tract (for example, when police records indicated that a killing occurred in a field "near the intersection of Avenues X and Y"), or where the location of the killing is unknown (i.e. the body was dumped elsewhere after the killing occurred) were excluded from my analyses. Of the 979 homicides that occurred in Toronto over the period of examination, I was unable to geocode 14. As such, my final dataset includes 965 homicides.
77 78
collecting a variety of information on victims and offenders, including their names and
home addresses. This was certainly the case with respect to data collection for this project, which means that, in light of the considerations outlined above, I, too, use the
address at which the homicide took place as my location identifier.
As discussed previously, in this study, information on neighbourhood
characteristics in Toronto was collected using tract-level census data for the years 1986,
1991, 1996 and 2001. Twelve variables were constructed from these censuses to reflect
neighbourhood differences in age and family structure, residential stability, housing
ownership, labour market activity, poverty, racial/ethnic composition, education levels,
and immigration. Given that I am not investigating change over time in either homicide
victimization or neighbourhood characteristics, static values for each of these variables
were used. To construct each variable, I added together the values on a particular
characteristic from each of the four census years within each neighbourhood. 1 then
calculated the mean of this value. Thus, the value of any one variable for each
neighbourhood is an average across four census years. The homicide and census data
were matched on the census tract numbers of the addresses at which these homicides
occurred71, and aggregated from the individual census tract level to the neighbourhood
level. This resulted in a single data set containing 140 neighbourhoods for which both
homicide and sociodemographic information were available.
A popular technique for representing the spatial distribution of violent crime is
geographic boundary thematic mapping. Crime events can be aggregated to these
Statistics Canada has created postal code conversion files that provide a link between postal codes and census tracts. Postal code conversion file data were accessed at the University of Toronto's Data Services Library. These data were combined with my homicide data to create a combined statistical profile for each of Toronto's census tracts.
78 79 geographic units, and counts of events can then be mapped to display the spatial pattern of crime across the area of interest (Eck et al., 2005). For the purposes of this project, homicide data are thematically mapped by neighbourhood.
4.4 DATA DESCRIPTION
4.4.1 Characteristics of Homicide in Toronto, 1988-2003
Table 4.1 provides descriptive statistics for selected characteristics of victims, offenders, and circumstances of the 965 homicides in Toronto's neighbourhoods between
1988 and 2003. Over this period, almost three quarters of the victims of homicide were male, and 6 out of 10 were under the age of 34. In terms of racial background, 39% of victims were white and 34% were black. In addition, just over half of the victims were unmarried and about one third were unemployed.
My dataset also contains information on a variety of incident characteristics, including the relationship between victim and offender (when known), the location of the homicide, and the method of killing. Most victims were killed by someone they knew. For example, approximately 25% of homicides involved friends/acquaintances, 19% involved male or female intimate partners, 14% involved family members, and 8% involved illegal business relationships (for example, drug- or alcohol-, theft- or prostitution-related relationships). Only 21% of victims were killed by strangers.
The most common location for homicides was a residence; in the vast majority of cases, this was the victim's and/or offender's home. About one-fifth occurred in outdoor public places - for example, in streets, parks or parking lots; 15% occurred in stores and places of leisure, such as bars, taverns and restaurants; 7% of victims were killed in semi-
79 80 public places (for example, in elevators, stairways, underground parking lots, and lobbies or hallways of apartment buildings); and 4% of killings took place in a vehicle. Shootings and stabbings each accounted for the victim's death in about a third of the cases. When a shooting occurred, a handgun was typically the weapon of choice.
My ability to document characteristics of homicide offenders in Toronto is greatly constrained by missing data. This is, in part, a function of the fact that 23% (n=228) of
77 homicide cases in my dataset are unsolved. The following statistics on offender sex, age, race and employment status should thus be interpreted with the understanding that a considerable amount of data is missing for each of these variables.
Similar to victims, the vast majority of perpetrators of homicide in Toronto were male, and 8 out of 10 were under the age of 34. This is consistent with virtually all research on homicide which shows that lethal violence is largely the domain of young men. In terms of racial background, the majority of perpetrators were white, while black persons accounted for about a third of the perpetrators of homicide in Toronto. Finally, over half of all perpetrators were unemployed at the time of the killing.
4.4.2 Characteristics of Neighbourhoods in Toronto
Table 4.2 demonstrates that there is substantial variability among neighbourhoods in Toronto on the characteristics I have measured. For example, only 1.4% of residents in one neighbourhood (North St. Jamestown) own their own homes, compared with 95% in
Bridle Path-Sunnybrooke-York Mills. Many of the economic indicators also range widely: 4.5% of residents in Princess-Rosethorn were classified as low income, whereas
70%o in Regent Park were so classified; 3.6% of residents in Lawrence Park North were 72 Compared to data on victims, data on offenders are also more likely to be missing because offenders were often not arrested immediately after the crime, in which case the police data 1 had access to provided less information about the offender.
80 81 unemployed, compared with 17% in Regent Park; and the average household income in
Regent Park, Toronto's poorest neighbourhood was $22,637, compared with $223,232 in
Bridle Path-Sunnybrooke-York Mills, the wealthiest.
An examination of demographic characteristics in Toronto's neighbourhoods reveals a similar pattern of variability. For example, .4% of residents in Kingsway South were black, compared with 20% in Rustic; 7.3% of families in Bridle Path-Sunnybrooke-York
Mills were headed by a lone parent, compared with 45% in Regent Park; and 11% of residents in Bridle Path-Sunnybrooke-York Mills were young males aged 15-24, compared with 26% in Brookhaven-Amesbury. As such, we can see that Toronto, the so- called 'city of neighbourhoods' is indeed a city that encompasses communities that are distinctly and considerably different from each other on a number of dimensions.
4.4.3 Bivariate Associations among Demographic, Economic and Housing Variables in Toronto's Neighbourhoods
Table 4.3 shows the correlations between my measures of neighbourhood characteristics and the number of homicides in each neighbourhood. I first discuss some of the relationships among neighbourhood characteristics, and then discuss their relationships to homicide.
In Toronto's neighbourhoods, certain characteristics are highly correlated, as one would expect. For example, poverty, government transfer payments, percent black residents, unemployment, average household income, lone parent families, and young males are all significantly related across these 140 neighbourhoods. By contrast, my measure of recent immigration is not correlated with any of the other neighbourhood characteristics in my study; this suggests that many of the popular stereotypes about neighbourhoods with large numbers of immigrants do not apply in Toronto.
81 82
To elaborate, neighbourhoods in Toronto with larger percentages of black residents over my period of examination also tended to have lower average incomes, higher percentages of unemployed residents, and more residents living in poverty. These neighbourhoods were also characterized by larger percentages of residents receiving government transfer payments and living in lone parent families. This is consistent with my hypotheses regarding the association between the proportion of black residents and measures of economic disadvantage. These bivariate findings also indicate that the characteristics that typify disadvantaged minority inner-city neighbourhoods in the U.S., as described by Wilson (1987), Sampson & Wilson (1985) and Jarkowsky (1997), also cluster together in Toronto's neighbourhoods. Finally, neighbourhoods in Toronto with high percentages of the population classified as low income tended also to have more unemployed persons and persons receiving government transfer payments, and lower average incomes. Low income neighbourhoods also tended to be neighbourhoods with larger percentages of young male residents (15-24), lone parent families, and recent movers, and lower levels of home ownership.
In sum, then, the associations between my independent variables, shown in Table 4.3, are generally in the expected directions, and provide preliminary evidence of the clustering of particular combinations of characteristics in Toronto's neighbourhoods.
4.4.4 Bivariate Associations between Neighbourhood Characteristics and Homicide in Toronto's Neighbourhoods
At the bivariate level the measures of neighbourhood characteristics in Toronto are associated, in varying degrees, with homicide counts (see the last column in Table
4.3). All of my measures of economic deprivation are related in the expected way to homicide. Low income, the percentage unemployed, and the percent of residents
82 83 receiving government transfer payments are significantly and positively associated with homicide levels in Toronto's neighbourhoods, whereas average household income is negatively associated with homicide. Also consistent with the hypotheses outlined earlier are the relationships between homicide and a number of demographic neighbourhood characteristics. The percentage of residents who are black, young males, lone parents, and have moved in the previous five years are each significantly and positively associated with homicide. Further, home ownership is significantly and negatively correlated with total homicide counts. Thus, neighbourhoods in Toronto that have higher levels of home ownership and larger average incomes have lower levels of homicide overall. The association between these variables and homicide counts is also consistent with expectations of a negative relationship among measures of home ownership, average household income, and homicide at the bivariate level. Contrary to expectations, percent divorced males and logged population are not correlated with total homicides. In addition, percent recent immigrants is also not associated with homicides, a finding that is perhaps not surprising given recent research that suggests that immigration has negative or no effects on levels of violent crime at the neighbourhood-level (Sampson,
2008; Reid et al., 2005; Martinez, 2002; Lee et al., 2001).
4.5 ANALYTIC TECHNIQUES
4.5.1 Regression Analyses
Homicide is a rare event and most neighbourhoods in Toronto have few homicides and even fewer of certain homicide subtypes, despite my pooling the data over a 16-year period. Further, when populations are small relative to incident rates,
83 84 assumptions of ordinary least squares (OLS) regression are likely to be violated, resulting in biased estimates (see Long, 1997; Osgood, 2000). Following past research on the social ecology of lethal violence, then, 1 estimate two types of regression models in my multivariate analyses that are appropriate for modeling rare events: Poisson and negative binomial models (Land et al., 1995).
Poisson models represent the starting point in ecological regression of rare events
(Clayton et al., 1993). Poisson regression assumes equidispersion (i.e. that the variance of the response is equal to the mean). However, the Poisson model is appropriate only if the data are not overdispersed. When the variance is greater than the mean, the distribution is said to display overdispersion. Applying the basic Poisson regression model to such data can produce a substantial underestimation of standard errors, which in turn leads to highly misleading significance tests.
There are a number of ways to allow for the possibility of over-dispersion. The best known and most widely available is the negative binomial regression model, which allows for the variance to differ from the mean (for a complete description, see Osgoode,
2000). This dissertation employs counts for total homicides and each of the homicide types as the dependent variables, and uses either Poisson or the negative binomial estimation, when appropriate, to determine the relationship between neighbourhood characteristics and lethal violence in Toronto's neighbourhoods.
4.5.2 Spatial Autocorrelation
As previously discussed, homicide is not randomly distributed across geographic space, but rather clusters spatially in certain urban neighbourhoods. Spatial autocorrelation refers to the extent to which an event in a particular geographic unit
84 85 constrains, or makes more probable, the occurrence of an event in a neighbouring geographic unit. Positive spatial autocorrelation is said to exist where nearby, or neighbouring areas are more alike, negative spatial autocorrelation describes patterns in which neighbouring areas are not alike, and random patterns indicate no spatial autocorrelation. If there is spatial autocorrelation in the residuals of a regression model, it means that the model is systematically overestimating the parameter estimates in some regions, and underestimating the values in others. Further, the model will produce unrealistic values for the significance levels and confidence limits for the coefficients
(Messner et al., 1999: 427). This is because most regression models assume that the values of each observation in the sample are independent; if there is correlation between them, this is obviously not the case. As such, regression analyses using spatial data must test for spatial dependence in the model.73
There are a number of techniques to measure whether and how much spatial autocorrelation exists, with Moran's I being the de facto standard. Moran's 7 is a weighted correlation coefficient used to detect whether patterns observed in data are clustered, dispersed, or random. In other words, Moran's /measures the correlation between variables according to their spatial location. Similar to regular correlation coefficients, Moran's I values run for 1 to -1, with values near to 1 indicating clustering
(i.e., nearby areas have similar rates) and values close to -1 indicating dispersion (i.e., rates are dissimilar). A value close to 0 indicates a random distribution.
7' If there is evidence of spatial autocorrelation, it can be controlled for in subsequent regression analyses by including a spatial lag variable. The spatial lag variable is computed by running a negative binomial model, saving the predicted homicide counts, and multiplying the homicide counts by a spatial weights matrix (Bailer, personal communication, 30 May, 2007).
85 86
In order to determine the statistical significance of the Moran's /value, Z scores are computed that allow for the testing of the null hypothesis of no spatial clustering. The values of Z-scores are compared to the values of the critical values for the associated alpha level assigned by the researcher. For example, if alpha were set to .05, then a Z- score falling outside the range of-1.96 to 1.96 would be statistically significant.
The program ArcGIS 9.2 was used to compute Moran's /values for each of the outcome variables used in this study. Specifically, inverse distance was used to model the relationship between neighbourhoods. The underlying concept of inverse distance in the context of the present study is that the homicide rates of all neighbourhoods are impacted by the homicide rates in all other neighbourhoods but the greater the distance between neighbourhoods the smaller the impact.
My analysis suggests that, for total homicides and each of the homicide types, the magnitude of the Moran's / statistic is extremely small and close to 0, therefore indicating a random distribution of homicide in Toronto's neighbourhoods. I therefore proceed with regular Poisson and negative binomial regression models, choosing the most appropriate model for each of the homicide types based on the presence or absence of overdispersion in my data.
86 87
FIGURE 4.1
MAP OF TORONTO NEIGHBOURHODS
CITY OF TORONTO Neighbourhoods ns i i» 21 ?4 ', j5 3S » 4S 48 2. -._ 11 it? •• ta 17 131 • 3 7, I M . ^ 5, Sj « * 13? 1 * 1,8 5 m 76 '33 " «
• n i:6 8 . '• 30 303 136 . :-'- 7 t!S. IV ' 10B - W ' - •" 43 175 5S U 13 139 124 en . -, M 13 V. ^ " a • «• S ,"' •• 14 7 17? . ' • « « .,l *;. 6i » „ " •8 •' " ' 8! p /b ., 'fiS4 » 17 \ »l8sr>-iSS: N* :0. Mp "? ' '.'•*»" - X'? '." -'f % r 7 •J!r " I^KWJk- L :43^Jt"™!S» HHMR 87 88 TABLE 4.1 CHARACTERISTICS OF HOMICIDES IN TORONTO'S NEIGHBOURHOODS, 1988-2003 (n=979) Victim Sex (n=979) % Male 72% % Female 28% Victim Age (n= 979) Mean Victim Age 34 %1-15 8% % 16-24 24% % 25-34 27% % 35-44 18% % 45+ 23% Victim Race (n:=671 ) % White 39% % Black 34% % Asian 12% % Other74 15% Victim Marital Status (n=876) % Single (never married) 53% % Married (including common-law) 28% % Separated/Divorced (including common-law) 16% % Other75 3% Victim Employment Status (n=870) % Employed 40% % Unemployed 31% % Student 17% % Other 12% Victim-Offender Relationship (n=728) % Friends/Acquaintances 25% % Strangers 21% % Intimate Partners 19% % Family 14% % Illegal Relationship 8% % Other76 14% This category includes Northern, Southern and Eastern European, South and Latin American, and Middle Eastern. This category includes widowed, living together for a short time (less than one month), or off an on for short periods. 88 89 Percent of cases in which no offender identified 23% Offender Sex (n=751) %Male 91% % Female 9% Offender Age (n=734) % 0-15 1% % 16-24 32% % 25-34 36% % 35-44 16% %45+ 15% Offender Race (n=395) % White 43% % Black 33% % Other 24% Offender Employment Status (n=612) % Employed 29% % Unemployed 54% % Other 17% Location of Killing % Residence 48% % Public 21% % Stores, bars 15% % Semi-public 7% % Car 4% % Unknown 1% % Other 4% Method of Killing (n=971) % Shot 35% % Handgun 75% % Long gun 15% % Other/unspecified gun 10% % Stabbed 31% % Beaten 19% % Strangled/Suffocated 8% % Other77 7% Homicides Committed in the Course of (n=813) % Robbery/theft 12% % Sexual Assault 3% % Disputes over Illegal $ 3% % Other78 82% 76 This category includes housemates/roommates, neighbours, legal business relationships, co-wokers, lovers' triangles, and foster children. 77 This category includes death by poisoning, arson, drowning, thrown or pushed from height, scalding, neglect, hit by car, overdose, and unspecified means. 89 90 TABLE 4.2 Characteristics of 140 Neighbourhoods in Toronto, Averaged Across 4 Censuses (1986,1991,1996,2001) Mean Minimum Maximum Standard Deviation % Black 5.7% .4% 19.8% 4.63 % Low Income 21.2% 4.5% 69.6% 9.43 % Young Males 16.0% 10.7% 25.6% 2.58 % Movers 45.4% 28.2% 74.4% 8.35 % Unemployed 7.2% 3.6% 17.2% 2.02 % Divorced 3.5% 1.4% 7.6% 1.19 Avg. Hhold Income $56,484 $22,637 $223,232 $24,245 % Lone Parents 17.5% 7.3% 44.8% 5.24 %BA 20.6% 4.5% 55.1% 11.93 % Gvt. Transfers 10.9% 2.4% 29% 4.18 % Owners 51.0% 1.44% 94.6% 18.30 % Ret. Immigrants 2.8% .24% 10.9% 1.93 Total Victims 6.9 0 37 6.27 Average Population 16610. 6330. 45559. 7243. (non-logged) This includes defense of person or property (e.g. killing intruder), unspecified, mercy killing, spurned attention, and 'other' motive (i.e. for which a coding category did not exist). 90 91 TABLE 4.3 Bivariate Correlations for Neighbourhood Characteristics (n=140) and Homicide 1 2 4 5 6 7 8 9 10 11 12 13 1. % Low Income 1.00 .768** -.694** .930** .582** .103 .882** .231** .567** -.672** .519** -.022 .719** 2. % Gvt. Transfers 1.00 _ 734** .814** .527** .077 .697** -.036 .223** -.300** -.033 -.046 .452** 3. Av. Household Income 1.00 -.683** _ 455** -.137 -.637** -.201* -.505** .450** -.316** .047 -.407** 4. % Unemployed 1.00 .688** .063 .861** .032 .516** -.510** .385** .003 .688** 5. % Black 1.00 -.008 .730** -.171* .255** -.262** .253** -.062 .473** 6. % Recent Immigrants 1.00 .023 .090 .122 -.128 .113 -. 152 .000 7. % Lone Parents 1.00 .228** .407** -.566** .413** .011 .661** 8. % Divorced Males 1.00 .333** -.587** .474** .010 .135 9. % Young Males 1.00 -.526** .709** -.040 .564** 10. % Owners 1.00 - 692** -.005 -.496** 11. % Movers 1.00 .005 .513** 12. Av. Population Log 1.00 .010 13. Total Homicides 1.00 91 92 CHAPTER V. HOMICIDE IN TORONTO'S NEIGHBOURHOODS, 1988-2003 This chapter examines the relationships between a variety of neighbourhood characteristics and the number of homicides in each of 140 neighbourhoods in Toronto over 16 years. It is organized into four sections. In the first section, I review research on the characteristics associated with neighbourhoods that tend to experience high rates of lethal violence. I then present a map of the spatial distribution of total homicide counts in Toronto's neighbourhoods and the results of multivariate analyses of relationships among neighbourhood characteristics and total homicide counts in Toronto. I find that some of the neighbourhood characteristics typically associated with homicide in American studies - particularly measures of socioeconomic disadvantage and young males in the neighbourhood - are similarly associated with homicide in Toronto's neighbourhoods. However, other neighbourhood characteristics that are hypothesized to be associated with violent crime do not appear to be significantly related to homicide in Toronto. 5.1 RESEARCH ON THE SOCIAL ECOLOGY OF LETHAL VIOLENCE Research conducted in Chicago in the 1940s provided the impetus for contemporary studies on the social ecology of violent crime. Shaw & McKay (1942) described how features of urban neighbourhoods, such as ethnic heterogeneity, residential mobility and low socioeconomic status, accounted for variations in crime and delinquency. Since that time, a major goal of researchers working in the social ecological tradition has been to identify additional correlates of crime and violence at the 92 93 neighbourhood level (Bursik & Grasmick, 1993; Peterson, Krivo & Harris, 2000; Sampson et al, 2002). The following section reviews the major findings of this research. 5.1.1 Socioeconomic Characteristics Economic Disadvantage The significance of urban economic conditions for understanding the spatial distribution of crime was first recognized by researchers at the University of Chicago, who found that poverty surfaced as an important correlate of high rates of crime and delinquency, over and above other measures such as residential mobility and population heterogeneity (Shaw & McKay, 1942). Since then, most studies of the social ecology of violent crime have emphasized various measures of economic disadvantage (Blau & Blau, 1982; Bursik, 1986; Byrne & Sampson, 1986; Messner & Tardiff, 1985; Sampson & Groves, 1989; Land et al., 1990; Bursik & Grasmick, 1993; Krivo & Peterson, 1996; Ousey, 1999). The results of these analyses strongly suggest that socioeconomic disadvantage, as represented by indicators of poverty or income inequality, is among the most consistent ecological predictors of rates of criminal violence, including homicide, at the state, SMSA, city, and neighbourhood levels.79 A growing body of research specific to variation in rates of violent crime in urban neighbourhoods has found that measures of economic disadvantage are associated with the risk of this violence (Hannon, 2005; Bursik & Grasmick, 1993; Kubrin, 2003; Martinez, 2002; Warner, 2003; Morenoff, Sampson & Raudenbush, 2001; Titterington). Some recent research has also suggested that the relationship between poverty and 79 Messner's (1982) analyses of the effect of poverty on homicide rates in 204 SMSAs, which found no relationship between measures of either poverty or income inequality and homicide, is an exception to this general finding. 93 94 violence varies by neighbourhood context, with variation in levels of disadvantage having qualitatively different effects in extremely disadvantaged neighbourhoods (Lauritsen & White, 2001; Krivo & Peterson, 1996, 2000; McNulty, 2001). These studies posit that the relationship between poverty and violent crime is different for so-called "extreme poverty" neighbourhoods, and thus the relationship between poverty and violent crime is nonlinear. That is, the effect of economic deprivation on violence is typically found to be moderate, positive and linear, until disadvantage reaches extreme levels, at which point the slope escalates dramatically. However, the results of these studies differ with respect to the strength of the disadvantage-violence relationship in extremely disadvantaged neighbourhoods. Some neighbourhoods may be particularly vulnerable to the criminogenic effects of extremely high levels of economic deprivation because "the conditions that encourage [violent] criminal behaviour are particularly pronounced and the mechanisms of social control that discourage crime are particularly lacking" (Krivo & Peterson, 1996:642). Other studies suggest that there may be a limit to the impact of economic disadvantage on the social cohesion of a neighbourhood, and thus on the amount of violent crime therein (McNulty, 2001; Krivo & Peterson, 2000). For example, in an examination of racially disaggregated homicide rates across neighbourhoods in Atlanta, McNulty (2001) found that the effect of disadvantage on violence is similar in black and white neighbourhoods characterized by low levels of economic disadvantage, but diminishes substantially at the higher levels of economic disadvantage common to black inner-city neighbourhoods. In other words, variation in levels of economic disadvantage may cease 94 95 to provide meaningful distinctions across neighbourhoods once disadvantage reaches very high levels. There exists considerable debate among researchers with respect to the mechanisms through which economic disadvantage translates into the risk of lethal violence. For example, Blau & Blau (1982: 118-119) argue that where economic inequality - and particularly, economic inequality based on ascribed statuses, such as race - is marked, the rate of violent crime will be high: Socioeconomic inequalities that are associated with ascribed positions, thereby consolidating and reinforcing ethnic and class differences, engender pervasive conflict in a democracy. Great economic inequalities generally foster conflict and violence, but ascriptive inequalities do so particularly. Pronounced ascriptive inequalities transform the experience of poverty for many into the hereditary permanent state of being one of the poor. According to Blau & Blau and others, the widespread resentment, frustration, hopelessness, and alienation engendered by ascriptive inequality may find expression in diffuse aggression; and the cost of inequality is a high rate of violent crime, including homicide. Set in the context of social disorganization theory, most efforts to explain the link between economic disadvantage and neighbourhood violence have focused on the role of informal control as the mediating factor. High rates of economic deprivation are hypothesized to reduce informal control networks among neighbours, which, in turn, lead to heightened rates of violent crime (see, for example, Sampson et al., 1997). Studies also suggest that local institutions matter for controlling neighbourhood violence: disadvantaged neighbourhoods may have difficulty attracting and maintaining conventional institutions - such as stores, banks, libraries, and recreational facilities - that mediate between disadvantage and crime (Peterson, Krivo & Harris, 2000). As such, 95 96 neighbourhoods with a weak institutional base tend to have fewer conventional role models, and fewer formal and informal mechanisms that may serve as a buffer against violent crime. A number of ethnographic studies suggest that the link between poverty and violence may be explained by the development of social norms in response to poverty that are tolerant of violent behaviour as a means of conflict resolution (Anderson, 1999). For example, Ruth Horowitz's (1983) study of a poor inner-city Hispanic neighbourhood in Chicago found that a commitment to a prevailing "code of honour" shaped the values and behaviours of many young residents. Similarly, Fagan and Wilkinson's (1998) study of two inner-city neighbourhoods in New York, and Elijah Anderson's (1999) study of a disadvantaged neighbourhood in Philadelphia identified a "code of the streets" that governs the use of violence, particularly among young, black males. What these studies share is an assertion that living in neighbourhoods characterized by high rates of economic disadvantage gives rise to cultural adaptations that increase the probability of elevated rates of violent crime. In sum, a number of mechanisms have been proposed to explain the relationship between economic disadvantage and lethal violence at the neighbourhood-level. These can be broadly characterized as compositional and contextual perspectives. Those researchers who argue from a non-ecological, or compositional, perspective would suggest that the relationship is a function of the "...personal characteristics of population aggregates" (Byrne, 1986: 79). That is, because the poor are at a higher risk of homicide victimization, larger numbers of poor people living in certain neighbourhoods would This is not to suggest that there is not overlap between the two. For example, poverty can be both a contextual and a compositional characteristic. 96 97 necessarily mean higher levels of lethal violence. Attenuated levels of informal social control that may result from high rates of economic deprivation in the neighbourhood can also be understood as a compositional effect. Contextual perspectives, on the other hand, would suggest that high-poverty neighbourhoods may be characterized by value systems that are tolerant of, and in some instances even prescribe the use of violence as a means of status attainment and/or resolving interpersonal conflict. My analysis does not include measures of any of these intervening mechanisms, so I am not able to adjudicate between these perspectives. As a consequence, in my analysis, economic disadvantage is modeled as having a direct effect on homicide. The following hypothesis can be derived from this discussion: HI: Measures of neighbourhood economic disadvantage will be positively related to homicide counts in Toronto at both the bivariate and multivariate levels. Unemployment At the individual-level, empirical findings on the relationship between unemployment or labour force participation and violent crime have proven contradictory (Gottfredson & Hirschi, 1990; Sampson & Laub, 1990; Uggen, 2002), though when the focus shifts to the neighbourhood-, city- and state-levels, a consistent positive relationship between unemployment rates and rates of violent crime emerges (Crutchfield et al., 2006; Sampson & Lauritsen, 2004; Dobrin et al. 2005; Almgren et al., 1998).8' For example, Almgren et al. (1998) found that homicide rates in 75 Chicago neighbourhoods were predicted by high rates of joblessness, with the strength of this relationship growing 81 It is important to note that the bulk of this literature employs unemployment data (individuals who have not worked for a specified period of time) rather than data on labour force participation (individuals either working or actively looking for work), though some recent studies have looked more broadly at labour force markets, job type and quality, and crime (see, for example, Crutchfield et al., 2006). 97 98 over the period of examination (1970 to 1990). Similarly, Dobrin et al's (2005) analysis of neighbourhood structure and lethal violence in Maryland found that the likelihood of homicide victimization increased in neighbourhoods characterized by larger numbers of unemployed persons. Some scholars have accounted for the positive relationship between unemployment and violent crime by drawing on the routine activity perspective. For example, in their study of social change and crime rate trends, Cohen and Felson (1979) argue that direct- contact predatory violations require an offender willing and able to do the crime, a suitable and accessible target, and the absence of a target protector. When these elements converge in time and space, the risk of victimization is high. They found that those most vulnerable to violent victimization were young, unmarried males who spend time outside of the home engaging in social and employment activities; a public lifestyle creates exposure to the risk of violent victimization. Cohen and Felson argue that high rates of victimization among unemployed persons can be explained by their "residential proximity to high concentrations of potential offenders as well as their age and racial composition" (p.55). This implies that in neighbourhoods with large numbers of unemployed persons sharing similar demographic backgrounds and spending considerable amounts of leisure time in public spaces, local rates of violent victimization should be relatively high. Drawing on the work of Cohen and Felson, Cantor and Land (1985) argue that a complete explanation of the effects of unemployment on crime must incorporate two counterbalancing mechanisms central to the routine activities thesis: criminal motivation and criminal opportunity. With respect to violent crime (homicide, rape, and aggravated 98 99 assault), they argue that the influence of unemployment operates through the types of situations in which potential victims find themselves. Like Cohen & Felson, they argue that a high unemployment rate may lead to an increased concentration of sustenance and leisure activities in the home and neighbourhood of residence, which would suggest that rates of violent victimization in these areas will increase. However, contrary to Cohen and Felson's (1979) thesis, Cantor and Land argue that the concentration of unemployed individuals in these locales may serve to lower violent crime rates because "a substantial fraction of violent crimes involve casual acquaintances or strangers" (p.320). As such, because unemployed individuals are expected to come into less regular contact with those more apt to assault or kill them, the relationship between unemployment and violent crime may be a negative one. Other studies emphasize the indirect effects of unemployment on violent crime. For example, Sampson (1987) argues that the effect of black adult male joblessness on black crime is, in large part, mediated by family disruption. That is, while male joblessness was found to have little or no direct effect on violent crime, it is the major structural determinant of family disruption, which in turn was the strongest predictor of black violence in over 150 U.S. cities. As such, the "labour market marginality" (p.352) of " However, the temporal effects of these two mechanisms - opportunity and criminal motivation - may differ. Cantor and Land (1985) argue that, in the case of the former, the effects are almost immediate, because the unemployment rate is generally considered to be a "coincident indicator" with business cycles. That is, an increase in the unemployment rate may signal a concurrent decline in business activity more generally. Further, an increase in unemployment rates may lead to an increase in the concentration of activities in primary group locations, such as homes and neighbourhoods of residence. As such, Cantor and Land argue "it can be expected that the opportunity impacts of unemployment on crime - through both the system activity and guardianship effects - are contemporaneous" (p.322). By contrast, the impact of unemployment on criminal motivation is less immediate; the newly unemployed person may be covered by government and/or union unemployment benefits for several months after losing his/her job. However, as benefits decline and/or stop altogether, Cantor and Land argue, the unemployed person is more likely to engage in criminal activity. 99 100 American black males may have important implications for explaining high rates of violent crime in urban black communities. Research suggests that the existence of a positive and frequently significant relationship between unemployment and violent crime may also be a function of the contextual effects of different analytic levels. For example, Chiricos (1987: 195) argues that there is less aggregation bias at the lower levels of aggregation (see also Land, Cantor and Russell, 1995): The lower and smaller units of analysis are more likely to be homogeneous, thereby reducing variation within each unit, and allowing for more meaningful variation between units, which is what [unemployment and crime] research is trying to measure. Thus, national-level data may literally cancel out the substantial differences in unemployment and crime that characterize different sections of cities or cities themselves. Given these important areal variations at lower levels of analysis, national data can only serve to 'wash-out' otherwise rich sources of between-unit variation essential to assessing the U-C relationship. Most empirical work to date has taken a unidirectional focus and examined the role of unemployment as a determinant of violent crime (Crutchfield, 1989; Sampson, 1987; Almgren et al., 1998). Some research, however, also considers the reciprocal effects of violent crime on employment opportunities. For example, Staley (1992) argues that the illegal drug market in the United States - and the violence endemic to that market - has caused businesses to abandon inner-city neighbourhoods plagued by drug-related crime and violence. Involvement in the drug trade thus becomes a more attractive option to some remaining residents as legitimate employment opportunities stagnate and/or decline. The increasing drug trade that results discourages new or existing legitimate business ventures, which further undermines employment opportunities in the neighbourhood. As such, though the relationship between labour force participation and violent crime is not necessarily unidirectional, neighbourhoods characterized by high 100 101 levels of unemployment also typically experience high rates of violent crime, including homicide. My analysis does not include measures of some of the intervening mechanisms discussed above, so 1 am unable to assess the routine activity perspective as it pertains to variation in rates of lethal violence at the neighbourhood-level, the contextual effects of different analytic levels, and/or the role that drug markets might play in undermining legitimate local employment opportunities. However, 1 do have measures of family disruption (divorced males, lone parent families), which leads to the following hypotheses: H2a: Unemployment will be positively related to homicide at the bivariate level. H2b: If unemployment has effects on homicide through its effects on family disruption, the effect of unemployment on homicide will be reduced when controlling for measures of family disruption. 5.1.2 Demographic Characteristics Racial Composition A large body of U.S.-based research has established that there is a strong positive relationship between rates of criminal violence and a neighbourhood's racial composition (Crutchfield et al., 2006; Sampson, 1985, 1987; Wilson, 1987; Messner, 1983; Peterson & Krivo, 1993; Shihadeh & Maume, 1997). In particular, African-Americans are disproportionately vulnerable to the risk of violent victimization, and the communities in which they live also tend to exhibit high rates of violent crime (Peterson & Krivo, 2005; Kubrin & Wadsworth, 2003; Sampson, 1987; Wilson, 1987). 101 102 Two general perspectives have informed research on the connection between race and violence: cultural and structural. Cultural perspectives tend to emphasize normative attributes that characterize a particular group. Here, violence is seen as resulting from a culture where criminality in general, and violence in particular, are tolerated or viewed as acceptable forms of behaviour (Wolfgang & Ferracuti, 1967; Curtis, 1974; Sellin, 1938; Sutherland, 1934). For example, based on research conducted in inner-city Philadelphia in the 1950s, Wolfgang & Ferracuti (1967) argued that there exists a subculture of violence among American blacks that transcends geographic and social location. This is because blacks were thought to perceive and interpret interpersonal disputes differently from whites, and as such were more likely to handle situations violently, even for minor or trivial affronts. In the decade following its introduction, the subculture of violence perspective was the primary explanation of the race-violence relationship. More recently, however, this perspective has been criticized for its assumption that members of a particular racial group create and adhere to a distinctive subculture. A number of scholars have disputed this claim, arguing that what are posited to be unique cultural tendencies are, in fact, emergent phenomena - manifestations of local structural conditions and levels of opportunity (Anderson, 1999; Wilson, 1987). In other words, subcultural perspectives have been criticized for their tendency to overlook the relationship between normative processes and the structural features of a given place. Rather than focusing on the alleged pathological or cultural deficiencies associated with blacks and other disadvantaged groups, structural perspectives examine the relationship between material conditions and levels of violence. These perspectives focus 102 103 on a group's status in a class- and race-stratified society; they argue that harsh economic conditions and high levels of residential segregation account for disparate rates of within- group violence (Bursik & Grasmick, 1993; Jencks & Mayer, 1990; Taylor & Covington, 1988). Some studies have also found that, to a large degree, a preponderance of female- headed households in the neighbourhood explains the relationship between race and violent crime in neighbourhoods with large numbers of black residents (Sampson, 1985, 1986; Messner & Tardiff, 1985). However, many of the studies that emphasize the relationship between structural characteristics, race, and violence have tended to neglect cultural processes that may be a response to structural characteristics and mediate their effects on violence (Stewart & Simons, 2006). Instead of viewing structural and cultural perspectives as distinct and incompatible, recent work combines the two. This is due to the assumption that unidimensional approaches may fail to capture the intersection of structural and cultural factors (Kubrin & Weitzer, 2003). For example, Sampson & Wilson (1995) advance an argument that incorporates both structural and cultural elements. Their basic thesis posits that beginning in the 1960s, a variety of forces in the United States (for example, discrimination in the employment market, severe economic disadvantage, inner-city disinvestments, and so- called 'white flight' which further isolated African Americans residents in poorer neighbourhoods over time) undermined the fabric of urban African American neighbourhoods, leading to a situation in which a large proportion of the African American population was and is trapped in extremely structurally disadvantaged urban neighbourhoods. The social isolation and ecological concentration of the "truly disadvantaged", in turn, leads to cultural adaptations that undermine local levels of social 103 104 organization and hence the control of violent crime. As a consequence of a variety of forces, then, poor black neighbourhoods in contemporary American cities are particularly vulnerable to high levels of violent crime, including homicide. The ecological concentration of race in impoverished neighbourhoods is well documented in the American context (Sampson & Wilson, 1995; Jarkowsky, 1994; Wilson, 1987; Kasarda, 1993). Given that Canadian cities also experience substantial levels of neighbourhood segregation by income and race/ethnicity (Fong & Wilkes, 2003; White et al., 2003, 2005), and that blacks in Canada's inner-city neighbourhoods are the most segregated of all racial/ethnic groups (Fong & Wilkes, 1999; Fong & Shibuya, 2000), I hypothesize that: H3a: The proportion of neighbourhood residents who are black will be positively related to homicide counts at the bivariate level. H3b: If this proportion is related to homicide through its association with economic disadvantage and family disruption, its effect on homicide will be reduced when controlling for measures of economic disadvantage and family disruption in a multivariate model. Immigration Early 20n century theories of criminal offending typically posited that there was a positive relationship between immigration and crime (Sellin, 1938; Shaw & McKay, 1942), and that this relationship was driven by the economic deprivation and social dislocation that accompanies the immigration process (Shaw & McKay, 1942). Two key theoretical perspectives are typically advanced to explain the link between immigration status and violent crime: perspectives that focus on the opportunities (or a lack thereof) 104 105 available to newcomers, and those that emphasize criminal subcultures and social disorganization (Bankston, 1998; Martinez, 2002; Martinez and Lee, 2000). Theories that emphasize the differential opportunity structures available to immigrants versus the native-born posit that many immigrants are faced with disadvantages that limit their opportunities for economic success. For example, upon arriving in the host country, many immigrants may experience higher levels of poverty (DeJong & Madamba, 2001) and labor market discrimination (Waldinger, 1993). These limited opportunities may lead to higher levels of criminal offending among immigrants, who turn to illegitimate means of achieving economic success that may include or result in the use of violence (Lee et al., 2001; Merton, 1938). Further, when faced with limited opportunities in the host country, some immigrants may engage in violent crimes out of frustration or a desire for retaliation (Agnew, 1992; Blau & Blau, 1982; Tonry, 1997). Immigrants may also adapt to the limited opportunity structure that some may face upon arrival in the host country by adopting a "criminal immigrant subculture" (Reid et al., 2005: 760): In response to limited legitimate labor market opportunities, criminal immigrant subcultures provide increased opportunities to engage in crime. This crime typically takes the form of organized involvement in property crimes, such as theft, robbery and extortion. Organized property crime subsequently leads to increased violent crime as gangs develop counterculture status markers and systematically protect their territory from competing gangs. Finally, social disorganization theory suggests that the influx of large numbers of culturally heterogeneous and economically disadvantaged immigrant groups change the demographic structure of urban neighbourhoods, weakening local social institutions, and leading to increased levels of violent victimization and offending (Sampson, 1991; Sampson & Groves, 1989; Vessey & Messner, 1999). 105 106 Each of these perspectives suggests that immigration increases crime rates in the host country because immigrants commit more crime than do the native-born. However, while some studies in the 19l and early 201 centuries found an association between immigration and crime (Shaw & McKay, 1942; Park et al., 1925), more recent research suggests that immigrants are generally less involved in crime than are similarly situated native-born groups (Hagan & Palloni, 1998; Tonry, 1997). Nevertheless, popular views and some criminological theory on the immigrant-crime link appear to have been influenced more by older research and/or popular stereotypes than reliable empirical data. To date, the bulk of research examining the relationship between immigration and crime in America has been conducted at the individual-level, though some recent studies have considered how immigration affects rates of violent crime at the neighbourhood level. These studies also suggest that immigration has negative or no effects on patterns of violent crime, even in those immigrant neighbourhoods that are extremely economically disadvantaged (Sampson, 2008; Reid et al., 2005; Martinez, 2002; Lee et al., 2001). This may be a function of the kind of immigrants who are settling in North American cities. As Zhou (2001) argues, there is variability in economic standing among the contemporary immigrant population; many new immigrants find better opportunities and reside in better neighbourhoods than do their less well off native-born counterparts. Even lower-income immigrants may not face the same barriers today that they might An exception is Reid et al. (2005). They argue that immigrants may be no more criminal than the native- born, but that immigration may nevertheless lead to higher rates of violent crime: "One mechanism by which immigration may increase crime, despite little difference in levels of immigrant and native-born offending, is by creating a pool of potential victims possessing fewer resources with which to protect themselves and their property from crime. Beyond increasing vulnerability of individuals, immigration may increase the vulnerability of entire neighborhoods by weakening community ties thereby lessening collective target hardening" (p.761). 106 107 have several decades ago. This is because in addition to ethnic enclaves in some urban areas, many immigrant groups occupy economic niches that provide employment opportunities for newcomers (Logan et al., 1994; Zhou, 1992). Newer immigrants may thus be able to avoid discrimination in native-owned businesses as they have access to employment opportunities in businesses established by co-ethnics who had previously immigrated to the host country (Zhou, 1992). Research in the United States also suggests that the assumption that immigrants face widespread discrimination in the employment sphere may not be accurate; studies have demonstrated that some employers report that they prefer to hire low-skilled immigrants over native-born workers, particularly low-skilled African American workers (Beck, 1996; Waldinger, 1997; Wilson, 1996). As such, because not all immigrants come to North America facing severe economic hardship and/or discrimination in the labour market, increased immigration to certain urban neighbourhoods may actually serve to lessen rates of violent crime therein (Zhou, 2001). A hypothesis derived from more traditional criminological research on immigration and crime is the following: H4a: Measures of recent immigration in Toronto's neighbourhoods will be positively related to homicide counts at the bivariate level. However, H4b: If any association between immigration and homicide is due to the intervening effect of economic disadvantage, then the association between immigration and homicide should be reduced in a multivariate model controlling for measures of economic disadvantage. 107 108 Family Disruption The family is a mechanism of social control fundamental to the control of violence at the local level, an "essential source of community functioning, stability, and supervision that forms a barrier against violence" (Parker & Johns, 2002: 277). In other words, the family is posited to be a mechanism of social control fundamental to the control of violence at the local level. Though early ecological research largely ignored the neighbourhood-level consequences of family disruption, more recent studies tend to incorporate measures of divorce and/or lone parent families with children into their analyses (see Parker, McCall and Land, 1999, for a review of studies). Family disruption is theorized to be associated with neighbourhood crime rates to the extent that it decreases networks of guardianship and informal control among residents (Taylor, Gottfredson & Bower, 1984; Sampson & Groves, 1989). The importance of family structure in understanding neighbourhood-level crime rates has been supported by studies that report a positive relationship between various measures of family disruption and rates of violent crime (Sampson, 1985; Sampson, Raudenbush & Earls, 1997; Knoester & Haynie, 2005; Savoie et al., 2006). For example, Knoester & Haynie (2005) found that neighbourhoods with greater proportions of lone- parent families tended to experience higher levels of youth violence, and Sampson (1986) found that neighbourhood family disruption (i.e. the prevalence of divorce and female- headed households) was an important predictor of the risk of violent victimization. Further, some studies have found that large numbers of female-headed households in the The term "disrupted families" is problematic in that it implies that these families were once together (i.e. non-disrupted) and subsequently disrupted. However, many single parent families were not comprised of two parents to begin with. I acknowledge this issue, and though I use the term "family disruption" in this dissertation, I intend the term to apply to families headed by only one parent, whether "disrupted" or not. 108 109 neighbourhood has, to a large degree, explained the relationship between percent black and violent crime; when the percentage of female-headed families and/or percent divorced is statistically controlled, the relationship between percentage black and neighbourhood-level rates of violent victimization is rendered insignificant (Sampson, 1985, 1986; Messner & Tardiff, 1986; Smith & Jarjoura, 1989). Previous research has also identified marriage as an important factor limiting the involvement of males in criminal victimization and offending (Giordano et al., 2003; Ouimet & LeBlanc, 1996). Divorce is argued to increase rates of violent crime because of the instability that occurs with the disintegration of the family unit, and a concomitant weakening of social control in the neighbourhood, a contextual effect (Parker & Johns, 2002). In the absence of the protective advantages of marriage, large numbers of divorced males in the neighbourhood have been found to be associated with higher rates of crime and violence (Caywood, 1998). My analysis does not include measures of the mechanisms linking family disruption to homicide (such as informal social control). As such, my measures of family disruption — lone parent families and percent divorced males - are modeled as having a direct effect on homicide counts in Toronto's neighbourhoods. The following hypothesis can be derived from this discussion: H5a: Measures of neighbourhood family disruption will be positively related to homicide counts at both the bivariate and multivariate levels. 109 110 Sex-Specific Age Structure The relationship between age and crime is one of the most robust empirical findings in the criminological literature (Hirschi & Gottfredson, 1983), with a disproportionately large share of offending committed by those who are in the age range between mid-adolescence and young adulthood (Farrington, 1986). Research has also linked the sex and age composition of neighbourhoods to economic disadvantage; neighbourhoods with large proportions of children - particularly young males - relative to adults tend also to experience higher rates of economic disadvantage (Brooks-Gunn et al., 1997) which, as discussed previously, is itself associated with crime and violence. This is solely a compositional effect - large numbers of young people, particularly young males, in the neighbourhood means more potential victims for violent victimization. However, there are also contextual reasons to expect a relationship between neighbourhood sex- specific age structure and violent crime: young people are more mobile and less attached to their neighbourhoods than are older people, and this may affect overall levels of informal control in the neighbourhood. As previously discussed, large numbers of young male residents in neighbourhoods characterized by various forms of structural disadvantage may render that neighbourhood more susceptible to the development of local cultural codes that privilege violence as a means of status attainment and/or conflict resolution (Anderson, 1999). The following hypotheses can be derived from this discussion: H6a: Large numbers of young males in Toronto's neighbourhoods will be associated with higher homicide counts at the bivariate level. 110 Ill H6b: If neighbourhoods with large numbers of young males are also economically disadvantaged and/or characterized by high rates of in and out migration, then the association between young males and homicide will be reduced in a multivariate model controlling for economic disadvantage. 5.1.3 Housing Characteristics Owner Occupied Housing Research shows that neighbourhoods with larger numbers of owner-occupied dwellings tend to be neighbourhoods that experience lower crime rates (Sampson et al., 1997; Krivo & Peterson, 2000; Hoff & Sen, 2005). This is because, due to their greater financial investment, homeowners tend to put more effort into the upkeep and appearance of a neighbourhood, and have a greater stake in protecting the neighbourhood from a host of negative outcomes, including crime. These stakes in both the physical and social community may promote activities and behaviour that serve to reduce vandalism, theft and other crimes in the local area, and generally increase social interaction and or responsibility among residents. (Putnam, 2000; Sampson & Groves, 1989). " By contrast, neighbourhoods with higher proportions of renters tend to have higher rates of violent crime (Wallace et al. 2006), as do neighbourhoods that possess large numbers of densely packed renter-occupied dwelling units (Sampson & Lauritsen, 2004; Schuerman & Kobrin, 1986; Roncek & Faggiani, 1986; Roncek, 1981). A preponderance of renter- occupied dwellings is expected to increase violence in neighbourhoods as a function of Overall, the literature demonstrates a negative relationship between home ownership and violent crime (Hoff & Sen, 2005; Krivo & Peterson, 2000; Putnam, 2001), though some research suggests that crime and its consequences (such as fear) may have important reciprocal effects on levels of home ownership in neighbourhoods. That is, higher crime rates may lead to reduced homeownership due to increased withdrawal - both physical (in the form of residential relocation) and psychological (as a function of decreased civic participation and interaction among residents) - from neighbourhood life and/or the neighbourhood itself (White, 2001; Morenoff& Sampson, 1997). Ill 112 anonymity - that is, neighbourhood residents are less likely to know each other, to be concerned for one another's well-being, and to engage in guardianship behaviours (Roncek, 1981). Characteristics related to home ownership, rental density and the concomitant stakes in neighbourhood life that emerge among residents thus appear to be related to the risk of violent crime in the neighbourhood. Given that my analysis does not include measures of mediating mechanisms such as anonymity and the concomitant absence of guardianship behaviours, I hypothesize: H7: Measures of neighbourhood home ownership will be negatively related to homicide counts at both the bivariate and multivariate levels. Residential Instability Residential instability, or high levels of population turnover, has important consequences for levels of violence at the neighbourhood level (Browning et al., 2004; Morenoff et al., 2001; Peterson et al., 2000; Taylor & Covington, 1988; Sampson, 1985; Shaw & McKay, 1942). When the population of a neighbourhood is constantly in flux, residents have fewer opportunities to develop strong, personal ties to one another and to participate in community organizations (Bursik, 1988). As such, informal social structure and control may fail to develop, which in turn makes it difficult to maintain social order, resulting in higher neighbourhood levels of crime and violence. Some researchers also suggest that the positive relationship between residential mobility and high rates of violent crime may be a reciprocal one; high rates of crime and violence may cause fear among the residents, resulting in physical and psychological withdrawal from the neighbourhood, and a weakening of informal control mechanisms. In other words, a feedback loop may operate in some urban neighbourhoods, such that decreases in 112 113 neighbourhood cohesion, instigated in part by high rates of population turnover, lead to increased rates of violent crime (Markowitz et al., 2001; Morenoff & Sampson, 1997; Bursik, 1988). As such, residential instability may be both a cause and effect of elevated rates of crime and violence at the neighbourhood-level. However, my analysis does not include measures of these intervening processes, so I hypothesize that: H8: Measures of neighbourhood residential mobility will be positively related to homicide counts at both the bivariate and multivariate levels. 5.2 THE SPATIAL DISTRIBUTION OF TOTAL HOMICIDE COUNTS IN TORONTO'S NEIGHBOURHOODS, 1988-2003. Figure 5.1 provides a geographic representation of the distribution of total homicide counts in Toronto's neighbourhoods between 1988 and 2003 (n=965). Over this period, 7 of Toronto's 140 neighbourhoods (5%) did not experience any incidents of lethal violence, 32 neighbourhoods (23%) experienced 1-2 homicides, 45 neighbourhoods (32%) experienced 3-6 homicides, and 56 neighbourhoods (40%) experienced 7 or more homicides. This latter category encompasses a broad range of total homicide counts, with the majority of neighbourhoods experiencing between 7 and 17/18 homicide incidents over the period of examination. Further, like the spatial distribution of homicide in American cities, where incidents of lethal violence typically cluster in a small number of inner-city neighbourhoods, high homicide neighbourhoods in Toronto are similarly located in the city's core. Unlike the geography of lethal violence in the United States, however, high homicide neighbourhoods also tend to be located on the outer edges of the city - to the east, the west, and to the north-east and north-west of the city centre. 113 114 A small number of extremely high homicide neighbourhoods (see Figure 5.2) experienced between 21 and 37 homicides. Of these neighbourhoods, Regent Park experienced 37 homicides, the most of any neighbourhood in Toronto, followed by Moss Park and South Parkdale (28 homicides each), Glenfield-Jane Heights (24 homicides), Woburn (22 homicides), and North St. Jamestown (21 homicides). While four of these etremely high neighbourhoods are located in the city core, the remaining two are located in the western and eastern fringe of the city. These neighbourhoods, dubbed "inner cities on the outer edges" (Wente, 2004), are illustrative of the trend toward the increased concentration of poverty in neighbourhoods that ring the city centre, of which one consequence appears to be relatively high levels of lethal violence. 5.3 PRINCIPAL COMPONENTS FACTOR ANALYSIS As discussed earlier, my data set includes a number of measures of socioeconomic disadvantage in Toronto's neighbourhoods. As shown in Table 4.3, these measures are highly correlated with each other: the bivariate correlations between the four measures range from -.64 (median family income and lone parent families) to .93 (low income and unemployed). In addition, a fifth characteristic - the percent of families headed by lone parents - is also very highly correlated with these four measures of socioeconomic disadvantage; the correlations range between -.64 and .86. While conceptually the measure of lone parent families is distinct from these measures of socioeconomic disadvantage, clearly it is not empirically distinct. Including each of these separately in my multivariate models could create problems due to multicollinearity. To I did not include all variables that are strongly correlated with the measures of socioeconomic disadvantage. For example, the percent black measure, which figures prominently in prior research on 114 115 deal with this, I conducted a principal components factor analysis on my measures of socio-economic disadvantage and my measure of lone parent families. Consistent with theory and research on urban neighbourhoods, these variables are not only highly associated, they also load on a single factor (see Table 5.1). Percent low income, percent unemployed, median family income, percent lone parent families, and percent government transfer payment measures each has a high loading on this factor, which I label 'disadvantage'. Including this disadvantage index not only deals with multicollinearity, it also makes my models more parsimonious. For my multivariate analyses, the disadvantage index was constructed by first creating a standardized score for each of the component variables and summing these scores across the four censuses. Bivariate associations between this index and the other independent variables, as well as my dependent variable are shown in Table 5.2. Note that the disadvantage index is highly correlated with percent black, and moderately correlated with percent owners. To ensure that including these variables in my models along with the disadvantage index did not create problems with multicollinearity, I examined diagnostic statistics from an OLS regression in which homicide was the dependent variable. The results are as shown in Table 5.3. None of the tolerance levels are below .2 and none of the variance inflation factors are above 4.0, which indicates multicollinearity should not affect my multivariate results. This index was included in my neighbourhood effects and lethal violence has been excluded from the factor analysis of neighbourhood disadvantage, despite its strong correlations with the five measures of disadvantage, which range from - .455 to .730.U.S. research that has included this measure has found that it loads heavily on factors reflecting high levels of economic deprivation (Messner& Golden, 1992; Land et al., 1990). This is not surprising given the strong correlation between percent black and aggregate measures of disadvantage in the American literature. However, as Rosenfeld et al. (1999) have noted, "[rjace is conceptually distinct from 'disadvantage,' and treating them as attributes of the same dimension confounds attempts at untangling their distinct influences on levels of violence in a community" (p.502). Therefore, I have retained the racial composition of Toronto's neighbourhoods as a separate indicator in my analyses. I have also retained the remainder of my independent variables in their original measurement form. 115 116 multivariate models along with percent black, percent recent immigrants, percent young males (15-24), percent residential mobility, percent divorced males, percent owner occupied housing, and population size. The bivariate correlation between this measure and the total homicide (.69) count is positive and significant, indicating that neighbourhoods that are economically disadvantaged have a higher number of homicides. This is consistent with expectations. 5.4 NEIGHBOURHOOD CORRELATES OF TOTAL HOMICIDE COUNTS IN TORONTO: MULTIVARIATE ANALYSES Homicide is a rare event, and most neighbourhoods in Toronto have small numbers of homicides, even when pooling data over 16 years. When analyzing counts for rare events such as homicide, assumptions of ordinary least squares (OLS) regression are likely to be violated, resulting in biased estimates (Osgood, 2000). As discussed in Chapter Four, the basic Poisson model is appropriate for analyzing counts of rare events, but only if the data are statistically independent and not overdispersed. An examination of the goodness of fit statistics for a Poisson model of total homicide counts revealed evidence of overdispersion. Therefore, for the multivariate analyses of total homicide counts presented below, I estimate a series of negative binomial models - which allow for overdispersion - using annual neighbourhood homicide incident counts summed over my period of examination. This allows me to assess whether the relationship between each neighbourhood characteristic and homicide remains after the effects of other neighbourhood characteristics on homicide have been controlled. The results of these models are presented in Table 5.4.1 begin by estimating a model with the economic disadvantage index as the only predictor of homicide. This is the 116 117 variable that has had the strongest and most consistent relationship with homicide in previous research, and the variable that is often found to mediate the relationships between other neighbourhood characteristics and homicide. (See Hypotheses 3b, 4b, and 6b, which indicate that disadvantage may be responsible for the relationship between homicide and race, immigration, and young males.) Model 1 demonstrates that disadvantage is significantly and positively associated with Toronto homicide counts, which is both consistent with hypothesis HI and not surprising, given the strong correlation at the bivariate level. Thus, neighbourhoods in Toronto with a larger proportion of disadvantaged residents have higher levels of homicide, according to the negative binomial results. In Model 2,1 estimate a model that includes only percent black as a predictor of homicide. This allows me to determine if the bivariate relationship between percent black and homicide is replicated using the more conservative negative binomial analysis, and, if so, the extent to which this relationship is reduced when the disadvantage index is added, in Model 3, as predicted by Hypothesis 3b. As can be seen in the column labeled Model 2 in Table 5.4, there is a significant positive association between percent black and homicide, consistent with Hypothesis 3a. In Model 3, which includes both percent black and the disadvantage index, the coefficient for percent black is reduced to non- significance, whereas the coefficient for the disadvantage index, while reduced somewhat, remains significant and positive. Thus, consistent with Hypothesis 3b, the bivariate association between percent black and homicide is due to the association between percent black and the disadvantage index. In other words, neighbourhoods in Toronto with a high proportion of black residents did experience significantly more 117 118 homicides between 1988 and 2003, consistent with predictions. However, this is because neighbourhoods that have a high percentage of black residents tend also to be economically disadvantaged. It is, thus, neighbourhood poverty, not the race of a neighbourhood's residents, that is related to homicide. Again, both Hypothesis 3a and 3b are supported by these results. These results are also consistent with those of a number of studies in the United States (see, for example, Sampson, 1985; Messner & Tardiff, 1985) that show that percent black is associated at the bivariate level, but not at the multivariate level, with homicide, when measures of poverty are included in the model. In Model 4, which includes the disadvantage index, percent black and percent young males, the coefficient for percent young males is significant and positive, consistent with hypothesis 6a. Thus, neighbourhoods in Toronto with large numbers of young males typically experience higher levels of homicide. In addition, the reduction of the coefficient for the disadvantage index between Models 3 and 4 suggests that part of the effect of disadvantage is due to its association with percent young males, which is consistent with Hypothesis 6b. Nevertheless, the coefficient for the disadvantage index remains positive and significant in Model 4. In Model 5, which includes the disadvantage index, percent black, percent mobility, and percent owners, the disadvantage index remains significant and, of the two new variables, only percent mobility is significantly related to homicide. That is, neighbourhoods in which a large number of residents changed residences in the past five years had higher levels of homicide. This is consistent with Hypothesis 8, which predicts a positive relationship between percent mobility and homicide, and with research that suggests that residential instability has important consequences for local levels of violent 118 119 crime (Browning et al., 2004; Morenoff et al., 2001; Taylor & Covington, 1988; Sampson, 1985). That percent owners is unrelated to homicide in Toronto does not support Hypotheses 7, which predicts a negative relationship at the multivariate level. However, when entered separately in models not reported here, percent movers and percent owners were each significantly related to homicide, which is consistent with expectations. It appears that their strong bivariate association (.802) confounds efforts to ascertain their individual effects on homicide in Toronto's neighbourhoods. The final model (Model 6) is comprised of the disadvantage index, percent black, percent mobility, percent owners, percent young males, percent recent immigrants, percent divorced males, and the population log. In this model, the coefficient for percent movers is reduced to non-significance, while the coefficients for the disadvantage index and percent young males remain significant and positive. Consistent with Hypothesis 4b, percent recent immigrants is not associated with homicide in Toronto. Percent divorced males is also not significantly associated with homicide, which again is contrary to expectations, but consistent with the bivariate results. ' A small number of studies have also investigated the influence of educational attainment on neighbourhood homicide rates. These studies suggest that neighbourhoods with low overall levels of educational attainment tend also to be places with higher rates of violent crime (Savoie et al., 2006; Dobrin et al., 2005). The converse is also true: higher levels of educational attainment have been shown to be associated with lower crime rates (Sampson & Lauritsen, 2004). Several mechanisms have been suggested that link education to violent crime. For example, Lochner's (2004) human capital approach posits that education, as well as job training, develops formal labour market skills, which raises the opportunity costs of crime commission. That is, to the extent that education increases wage rates (and decreases the likelihood of unemployment), it increases the opportunity costs of crime and tends to reduce criminal activity, including violent crime. Further, Lochner (2007) argues that violent crime is associated with higher expected probabilities of arrest, conviction, incarceration and sentence lengths, which is more costly for individuals with better labour market opportunities and wages. In a different approach, Usher (1997) considers education's benefit to society over and above the benefit to the individual to explain its crime- reducing effects: education is hypothesized to produce good citizens, contribute to the creation of common values among individuals, and thus serve as a deterrent to crime. Higher educational attainment has also been shown to produce greater social ties among residents, and more active participation in neighbourhood organizations, and these indicators of social capital can have important effects on rates of lethal violence (Bursik, 1999; Morenoff et al., 2001). My multivariate analyses initially included a % BA measure (i.e. the percent of neighbourhood residents who had attained a university degree), but that variable was not 119 120 5.5 TORONTO NEIGHBOURHOODS AND HOMICIDE: ILLUSTRATIONS OF THE MULTIVARIATE RESULTS The analysis, to this point, has been essentially variable-based, that is, focused on isolating the effects of particular neighbourhood characteristics on homicide. However, neighbourhoods are not simply the sum of a number of variables, they are complex entities with their own histories and identities. To provide a sense of the neighbourhoods in Toronto which share some of the characteristics I have found to be associated with homicide, I describe three below. Regent Park Regent Park is located in downtown Toronto, and is bounded by Queen Street to the south, Parliament Street to the west, Gerrard Street to the north, and Bayview Avenue to the east. Regent Park's residential dwellings consist entirely of social housing, and its physical design — blocks of low and high-rise apartment units built in a 'park-like' setting devoid of through streets - effectively cordons it off from the gentrified, affluent community that exists to the immediate north. This physical isolation is exacerbated by a scarcity of typical businesses and services: the neighbourhood is served by only one laundromat and one convenience store. There are no public telephones or mailboxes. Regent Park is also Toronto's poorest neighbourhood: in 2001, 68 per cent of its families were living below Statistics Canada's Low-Income-Cut-Off rate, compared to a correlated with homicide in my multivariate analyses for total homicide counts or any of the homicide subtypes; it also did not increase the goodness of fit of the models. One likely reason is because %BA is strongly associated (-.6) with the disadvantage index. I therefore did not want to add it to the disadvantage index because, as indicated, previous studies have examined the effects of education on their own, and because I felt that my education measure captured a separate underlying construct. Given the lack of a relationship between this measure and homicide at the multivariate level, I excluded it from my analyses. 120 121 Toronto-wide average of 19 per cent. The neighbourhood is also characterized by high levels of residential mobility. Between 1988 and 2003, on average, over half (53 percent) of Regent Park's residents indicated that they had moved residences in the preceding five years. Regent Park is also home to a large black population. According to the 2001 census, 22 per cent of residents reported their racial/ethnic background as "black", compared with an average of only 8.3% for the city as a whole. In sum, then, residents in Regent Park are, on average, more likely to be poor, young, and black and to have moved in the previous five years than are residents of Toronto's neighbourhoods more generally. Many Torontonians have come to regard Regent Park as a "haven of single mothers, welfare families and deviants...a magnet for crime and drug problems" (Purdy, 2005: 531). The neighbourhood's reputation for high levels of violent crime is, with respect to homicide over the period of examination, warranted: between 1988 and 2003, Regent Park experienced 37 homicides (a rate of 13.85 per 100,000 population), considerably more than any other neighbourhood in Toronto. However, more than half of these killings (n=20) occurred between 1988 and 1992. As such, had my analysis examined a more recent time period, Regent Park may not have had the dubious distinction of being Toronto's most lethal neighbourhood. As discussed in Chapter Seven, the reduction in this violence that began in the mid-1990s may be a function of an influx of resources and services into the neighbourhood. While the United Way's 2004 Poverty by Postal Code report identified Regent Park as Canada's poorest neighbourhood, the city of Toronto did not include it in its list of "priority neighbourhoods" in need of immediate and focused service delivery. This is because relative to many other disadvantaged neighbourhoods in Toronto, a large number of resources and services were made available to residents of 88 According to the City of Toronto's Neighbourhood Profiles website (www.toronto.ca/demographics). 121 122 Regent Park, so much so that since the mid-1990s, the neighbourhood has been described as "resource rich" by city planners. It is thus possible that violent crime rates in Regent Park have been reduced since the mid-1990s by policies and initiatives that attend to broader social issues faced by neighbourhood residents. A variety of aspects of the neighbourhood's built environment have been criticized for contributing to violent crime in Regent Park. For example, enclaves, dead ends, and poorly lit public spaces are argued to have allowed drug and gang-related activity to flourish. Regent Park's crime problem, its physical deterioration following years of neglect, and relentless lobbying on the part of tenants groups prompted the city to develop a plan to demolish and redevelop the neighbourhood. The "revitalization" of Regent Park is slated to be accomplished in six phases over a period of 12 to 15 years. OQ The first phase began in February of 2005. Black Creek Black Creek is a neighbourhood located in the former city of North York in northwestern Toronto, and is bounded by Steeles Avenue West to the north, Highway 400 to the west, the Black Creek to the east, and Finch Avenue West to the south. The neighbourhood includes the north end of the Jane-Finch corridor, an area that has long been associated with high rates of poverty, single-parent families, large immigrant and racialized populations, a high proportion of young people, and high levels of violent crime (between 1988 and 2003, Black Creek experienced 17 homicides, a rate of 13.64 89 Though one of the main goals behind the recent demolition of the housing project has been reducing drug-, gun-, and gang-related violence, there is some preliminary evidence that the redevelopment of Regent Park has instead prompted a surge in this violence. With the demolition of the south-west quadrant of the neighbourhood, drug dealers and gang members have been uprooted from territory that they have controlled for years. Residents report that a "hostile environment" has emerged that has seen Regent Park's main gang - the Point Black Soldiers - being increasingly challenged by a group called the Silent Soldiers, and that these tensions have led to an increase in violent crime in the neighbourhood. (Torstar News Service, November 13, 2006, p. Al). 122 123 per 100,000 population). According to the 2001 census, 75 percent of Black Creek's resident population were immigrants, a higher proportion than the Toronto-wide average of 49 percent. Further, 65 percent of residents self-identified as "visible minorities", and of that figure, 27 percent as "black". This, too, was considerably higher than the city- wide averages of 43 and 8.3 percent, respectively. Much of the housing in Black Creek is comprised of social housing and other high density, low-rent units, though the neighbourhood is also home to a substantial number of socially diverse middle class families. The neighbourhood is also characterized by high levels of residential mobility: on average, fully half of Black Creek's residents indicated that they had changed residences in the previous five years over the period of examination. Black Creek's resident population is also younger than the population of most Toronto neighbourhoods. In 2001, 42 per cent of residents in Black Creek were under 24 years of age, a figure that is well above the average of 29 per cent across Toronto's neighbourhoods. In 2004, the Young Leaders, a local youth led organization, surveyed several hundred local youth, identifying a general feeling of despair among young people, many of whom indicated that they felt increasingly marginalized from school, family and society: Youth, blamed for many of society's ills, are in serious difficulty in the Jane- Finch and surrounding areas. They are dying, languishing in unemployment and despair, victimized by racism, sexism and classism, pushed out of the schools, getting less education than they deserve, acquiring preventable diseases, living unhealthy lives in inadequate or unsafe housing, losing connections with their families and cultures. Cited in the Jane-Finch Neighbourhood Action Plan Report, Prepared by the Griffen Centre, 2005 http://www.twpcommunitvministrv.oro/home/editorfiles/Jane Finch_Neighbourhood_Action_Plan.pdf),. accessed 1 August, 2008. 123 124 In 2003, the Toronto City Summit Alliance's Strong Neighbourhoods Task Force identified Black Creek as one of Toronto's thirteen "Priority Neighbourhoods", in which services and facilities have failed to keep pace with demographic change. As part of the City's Community Safety Plan, a variety of initiatives are currently being established in this neighbourhood, aimed at addressing high social and economic needs, strengthening infrastructure, and reducing violent crime. The City's Plan will be discussed in greater detail in Chapter Seven. South Parkdale South Parkdale is an inner-city neighbourhood located to the west of Toronto's downtown core, bounded by Queen Street West to the north, Altantic Avenue to the east, and the Gardner Expressway to the south and west. The neighbourhood is home to large numbers of economically disadvantaged and otherwise marginalized residents. Simmons (1990) argues that the move toward the deinstitutionalization of the mentally ill that began in the 1980s - along with a retraction of the role of the Welfare State under economic recession and a fiscal crisis at the provincial level - also had profound and lasting effects on South Parkdale's social geography/resident population. In response, numerous social housing projects were established in South Parkdale, including those designed for outpatient psychiatric care, and/or operated by group home agencies. 91 However, South Parkdale suffered from a lack of community care resources and services and housing for the thousands of patients the neighbourhood absorbed in the early 1980s. Slater (2004: 127) argues that by 1981, "between 1,000 to 1,200 ex-psychiatric patients lived in South Parkdale...which, by 1985, contained only 39 official 'group homes' for such patients. The majority of discharged patients gravitated to unofficial boarding homes, or to rooming houses or the even smaller 'bachelorette' apartments in the single -family dwellings of the old South Parkdale which saw prolific (and usually illegal) conversion during the 1970s." In the absence of much needed after-care programming and follow-up, and with respect to finding housing and community-based resources and services, many of the deinstitutionalized patients residing in the neighbourhood were left to their own devices. 124 125 More recently, the neighbourhood has also become home to a number of needle exchanges and drug treatment facilities, including methadone clinics. The cumulative effect of each of these factors has been the concentration of large numbers of socially and economically marginalized residents into this "service-dependent ghetto" (Dear & Wolch, 1987). These include large racialized and immigrant populations, the poor, and people struggling with mental health and/or substance abuse issues. Census data for the years 1996 and 2001 demonstrate that, compared to Toronto neighbourhoods more generally, South Parkdale is characterized by higher representations of racialized populations, low income residents, and residents receiving a government transfer payments. The neighbourhood also experiences higher-than-average levels of lethal violence: over my period of examination (1988-2003), South Parkdale experienced 28 homicides (a rate of 23.04 per 100,000 population). The neighbourhood's high levels of homicide may be the result of compositional (i.e. large numbers of socially and economically marginalized residents, particularly those with mental health and/or substance abuse issues who are drawn to the neighbourhood for a variety of reasons) and/or the contextual effects of neighbourhood poverty on local levels of cohesion and informal control. South Parkdale is also currently undergoing the early stages of residential and commercial gentrification. New residents - many of whom are middle-class families - are attracted to the neighbourhood because of housing affordability, the distinctive housing stock, and its proximity to downtown. Another factor behind the South Parkdale's gentrification has been its growing reputation as an edgy, hip and trendy place to live, which has drawn a large number of artists to its residential and commercial stock. 125 126 Criminological research has shown that the 'in-between' mix of old and new residents can lead to higher rates of violent crime in areas undergoing processes of gentrification MacDonald (1986). For example, MacDonald (1986) argues processes of gentrification can subject neighbourhoods to long transitional periods of economic inequality, which can itself lead to increased rates of criminal violence. 5.6 TORONTO NEIGHBOURHOODS AND HOMICIDE: EXCEPTIONS TO THE MULTIVARIATE RESULTS The three neighbourhoods described above fit the profile described by my multivariate results. However, it is not the case that neighbourhoods that tend to experience high levels of lethal violence will be necessarily be characterized by high levels of economic disadvantage, residential mobility, and/or resident populations that are comprised of large numbers of blacks or young people. In other words, there are neighbourhoods in Toronto that tend to experience high homicide counts but are instead characterized by low levels of economic disadvantage and lower than average numbers of young people and black residents. For example, relative to other Toronto neighbourhoods, the Bay Street Corridor, East Danforth and Islington neighbourhoods all experience high homicide counts of 15 (rate = 18.18 per 100,000), 13 (rate = 14.52 per 100,000), and 11 (rate = 8.95 per 100,000), respectively. High levels of lethal violence in some of these neighbourhoods may be a function of the concentration of leisure activities that provide a context for violence. For example, the Bay Street Corridor and East Danforth neighbourhoods are home to a number of bars and night clubs that draw significant numbers of people - particularly young males - to the neighbourhood at night. Research has demonstrated that the density of bars in a neighbourhood is strongly 126 127 associated with higher rates of violent crime (see, for example, Lipton & Gruenewald, 2008). As such, the concentration of bars in the Bay Street Corridor and East Danforth may, in part, be responsible for the higher homicide counts these neighbourhoods tend to experience. It is also not the case that all neighbourhoods in Toronto that are characterized by high levels of economic disadvantage, residential mobility and large numbers of young and black residents experience high levels of lethal violence. For example, Kennedy Park, located in the former municipality of Scarborough, Mount Dennis and Rustic, both located in the northwestern part of Toronto (the former municipality of North York), all have high levels of economic disadvantage, large numbers of black residents and young people (age 24 and younger), and high levels of residential mobility. Yet despite these characteristics, each of these neighbourhoods tends to experience relatively low levels of homicide: between 1988 and 2003, there were five homicides in Kennedy Park (rate = 4.86 per 100,000), nine in Mount Dennis (a rate of 4.68 per 100,000), and five in Rustic (a rate of 4.39 per 100,000). Further, in all of these neighbourhoods, both the number and the rate of homicide is lower than that for the city as a whole, despite their similarities to other neighbourhoods with high levels of homicide In sum, while a number of neighbourhoods in Toronto experience high levels of lethal violence, others appear to have some sort of a protective advantage that buffers them from high levels of this violence. My analysis is limited to assessing the degree to which certain neighbourhood characteristics may be associated with homicide at the neighbourhood-level. As such, I am unable to speak to the role that other, unmeasured factors may play in shaping the risk of homicide in Toronto's neighbourhoods. For 127 example, in the same way that cultural codes emerge in some neighbourhoods that tolerate and/or condone the use of violence as a means of conflict resolution (Anderson, 1999), local codes that promote the non-violent resolution of interpersonal disputes may develop in other apparently "high risk" neighbourhoods. I would argue that this dissertation both provides an important first step in examining how and why homicide exhibits the patterns it does across neighbourhoods in Toronto, and prompts questions about the extent to which my results can be generalized across neighbourhoods in this city. An important extension to the analyses presented in this dissertation would be an examination of the micro-level processes that operate to shape the risk of homicide victimization in Toronto's neighbourhoods - particularly those neighbourhoods that fit the "high risk" profile, but that do not experience high levels of this violence. Directions for future research will be discussed in greater detail in Chapter Seven. 5.7 CONCLUDING REMARKS To summarize, my bivariate results indicated that certain neighbourhood characteristics are highly correlated with homicide counts in Toronto. For example, measures of economic deprivation, home ownership, residential mobility, and the percentage of residents who are black and young males are all significantly related to homicide counts across Toronto's 140 neighbourhoods at the bivariate level. However, many of the neighbourhood characteristics associated with homicide at the bivariate level do not hold at the multivariate level (for example, percent black, home ownership, and residential mobility). My multivariate analyses demonstrated that disadvantage and percent young males (15-24) are significantly and positively associated with homicide 128 129 counts in Toronto. That is, neighbourhoods with high levels of economic disadvantage and higher percentages of young males had higher levels of homicide between 1988 and 2003. The relationship between economic disadvantage and homicide in Toronto's neighbourhoods is consistent with research on the structural covariates of homicide, in which measures of economic disadvantage typically emerge as important predictors of total homicide rates at varying units of aggregation in U.S.-based studies (Land et al., 1990; Williams & Flewelling, 1988; Huff-Corzine et al., 1986; Sampson, 1986, 1985; Loftkin & Parker, 1985; Simpson, 1985; Bailey, 1984; Williams, 1984; Messner, 1983; Blau & Blau, 1982; Messner, 1982). That percent young males is significantly related to total homicide counts in Toronto is also consistent with research that has found large numbers of young males in the neighbourhood can shape the risk of violent victimization and offending therein (Anderson, 1999; Stewart & Simons, 2006). The questions to which I now turn are whether these characteristics will be significantly related to each of the homicide subtypes, and whether additional characteristics will emerge as important correlates of different homicide subtypes in Toronto. Chapter Six of this dissertation addresses these issues. 129 130 FIGURE 5.1 Homicide in Toronto's Neighbourhoods, 1988-2003 ##mmt Total Homicides |l-2 3-6 7+ 130 131 FIGURE 5.2 131 132 TABLE 5.1 Factor Loadings for Measures of Economic Disadvantage Factor Loading % Low Income .748 % Unemployed .815 Average Household Income -.797 % Lone Parents .770 % Government Transfer Payments .960 TABLE 5.2 Pearson Correlation Coefficients among the Disadvantage Index, Other Independent Variables and Total Homicide Counts % Black .679** % Movers .315** % Owners -.521** % Young Males .394** % Recent Immigrants .042 % Divorced Males .083 Log Average Population -.002 Total Homicide .689** *p< .01 132 133 TABLE 5.3 Multicollinearity Diagnostics for the Independent Variables Tolerance VIF Disadvantage .304 3.293 % Black .378 2.649 % Movers .261 3.826 % Owners .296 J.J) /J % Young Males .413 2.420 % Recent Immigrants .946 1.058 % Divorced Males .521 1.918 Log Average Population .963 1.038 1 JJ> 134 TABLE 5.4 Negative Binomial Regressions: Neighbourhood Characteristics and Homicide Counts (n-140). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Disadvantage .180*** i?2*** .100* .145** .110* (.0352) (.0432) (.0434) (.0521) (.0517) ( % Black .095*** .028 .035 .021 .027 (.0218) (.0266) (.0271) (.0279) (.0298) % Mobility .045** .018 (.0162) (.0202) % Owners .005 .002 (.0087) (.0096) % Young .158*** .127* Males (.0397) (.0544) % Recent -.020 Immigrants (.0508) % Divorced -.028 Males (.1045) Average -.146 Population (.5520) Log Constant 1.789 1.305 1.623 -1.010 -.704 -.644 Log -402.503 -409.144 -401.914 -392.863 -395.470 -392.381 Likelihood *p<.05 **p<.01 **p<.001 Note: Unstandardized coefficient and standard errors (in parentheses). 135 CHAPTER VI. DISAGGREGATED HOMICIDE TYPES IN TORONTO'S NEIGHBOURHOODS, 1988-2003. This chapter examines the relationships between a variety of neighbourhood characteristics and four different types of homicide in Toronto's 140 neighbourhoods for the period 1988-2003: homicides of black people, homicides of males aged 15-34, gun homicides, and intimate femicide. It is organized into four sections. In each section, I review research that pertains to the type of homicide under examination, and then present the results of bivariate and multivariate analyses of relationships among neighbourhood characteristics and each type of homicide in Toronto. I find that some of the neighbourhood characteristics included in my models appear, in varying degrees, to be related to some of these homicide types, but not to others. My results also suggest that a number of neighbourhood characteristics that are hypothesized to be associated with the different types of homicide appear not to be related to lethal violence in Toronto's neighbourhoods. Possible interpretations of these findings will be discussed in the final section of this chapter. 6.1 RELATIONSHIPS AMONG TYPES OF HOMICIDE IN TORONTO It is important to emphasize at the outset that the four types of homicide I examine in this chapter are not mutually exclusive categories. This is because, as noted in Chapter Two, my intention is to examine types of homicide that have been the focus of considerable discourse and debate in Toronto over the last several decades. Given that, for example, gun homicides and the killing of blacks in Toronto - particularly young 135 136 black men - have elicited a great deal of public concern, I elected to examine each of these homicide types separately. However, as we shall see, because many gun homicides over the period of examination were committed by young, black males in Toronto, it is necessarily the case that some of the homicides in my data set will be reflected in the counts for more than one homicide type. There are also theoretical reasons to examine gun homicides, the killing of blacks, and the killings of young males separately. Research suggests that although these homicides may, to a certain extent, be associated with similar neighbourhood-level characteristics (for example, economic disadvantage, racial composition, age structure, residential mobility, and home ownership), there also may be variation in the factors associated with each homicide type (Kubrin, 2000; Kubrin & Wadsworth, 2003). As such, while neighbourhoods characterized by high levels of economic disadvantage and residential mobility, low levels of home ownership, and large numbers of black and young male residents may typically experience higher levels of homicide overall, they may be vulnerable to different types of homicide in varying degrees. One interesting question that stems from the examination of disaggregated homicide subtypes has to do with the extent to which neighbourhoods that experience high levels of one type of homicide also tend to experience high levels of another type of homicide. Table 6.1 shows the bivariate correlations between the four types of homicide examined in this chapter. Given the strong correlations among gun homicides, black homicides and young male homicides in Toronto, there is reason to believe that they will share similar neighbourhood-level correlates. Intimate femicide, on the other hand, is only moderately correlated with the other homicide subtypes, which suggests that the neighbourhood 136 137 characteristics in my models may not be associated with this type of lethal violence to the same extent. 6.2 THE SOCIAL ECOLOGY OF BLACK HOMICIDE VICTIMIZATION Just as homicide is not equally distributed across urban neighbourhoods, it is also not equally distributed across social groups. Although determining racial differences in the Canadian context is difficult due to what amounts to a de facto ban on the release of race-based statistics, as discussed in Chapter Two, some Canadian research has documented that since the early 1990s, the risk of homicide among Toronto's black population has been approximately five times greater than the overall risk of homicide in that city (Gartner & Thompson, 2004). A major explanatory framework for the racial disparity in homicide is derived from a structural perspective that dates back to the work of Chicago School researchers. This perspective assumes that the predictors of urban homicide are the same across racial groups (i.e. racially invariant), and attributes racial differences in lethal violence to differences in the urban milieux in which blacks and whites live. Research shows that blacks and whites do not live in comparable neighbourhood settings in most American cities; the "worst" neighbourhoods in which whites reside are considerably better off than the average neighbourhood in which the majority of residents are black (Wilson, 1987; Sampson, 1987; Krivo & Peterson, 2000). This ecological dissimilarity by race can be linked to structural factors, past and present, including racial segregation, deindustrialization, black male joblessness, the out-migration of middle-class populations from the inner-city, and housing discrimination (Sampson & Wilson, 1995). Segregation 137 138 also appears to be associated with a number of negative social conditions for blacks, including greater poverty, higher levels of unemployment, welfare dependency and single parent families, poorer schools and higher rates of lethal violence (Massey, 1990; Massey, Conton & Denton, 1987). A number of studies in the United States examine total (i.e. not racially disaggregated) homicide rates at the city-, SMSA-, and state-level and are grounded in structural theories that assume that the effects of important structural factors are the same across racial groups. Differences in rates of lethal violence are posited to stem from differences in levels of crime-generating social conditions across groups (Balkwell, 1990; Blau & Blau, 1982; Kposowa & Breault, 1993; Land et al., 1990; Messner 1982, 1983). The assumption is that if white neighbourhoods exhibited the same degree of structural disadvantage that is common to many urban black neighbourhoods, homicide rates would be similar.92 A second body of research, again grounded exclusively in the American context, employs racially disaggregated data to examine the relationship between neighbourhood structural context and homicide. In contrast to studies that assume racial invariance discussed above, this research has demonstrated racial differences in the strength and effects of key structural factors on homicide rates. For example, several measures of socioeconomic deprivation (such as poverty, unemployment, median family income, family disruption, and income inequality) have been found to be more strongly associated with white than black homicide levels (Messner & Rosenfeld, 1999; Messner & " However, such an account denies the importance of racial discrimination - that is, whites, even if subject to the same economic, educational, labour market, and other 'structural' disadvantages can never have the experience of racial discrimination that blacks do. In some respects, then, the 'racial invariance' thesis is race-blind in a very problematic way. 138 139 Sampson, 1991; Ousey, 1999; LaFree et al., 1992; Peterson & Krivo, 1993; Shihadeh & Ousey, 1996; Shihadeh & Steffensmeier, 1994). The notion that some structural characteristics are weaker predictors of homicide in black communities has been interpreted as a challenge to traditional social disorganization theory and the general assumption of racial invariance (Ousey, 1999; McNulty, 2001; McNulty and Bellair, 2003; Wooldredge and Thistlethwaite, 2003). One explanation for these different effects is offered by Krivo & Peterson (2000: 556), who, drawing from research on ecological dissimilarities by race, suggest that: The racially varying effects of predictors may result from the dramatically different social positions of blacks and whites in US cities. In particular, the high level of disadvantage in the African American population may create a situation that actually reduces the importance of these very conditions for generating higher homicide rates for blacks.. .By contrast, whites generally live in communities with a lower prevalence of violence-producing factors. Thus for whites, variation in levels of key theoretical variables should reflect more meaningful differentiation across communities and hence have stronger effects. While this work is instructive with respect to the correlates of black homicide, it does not fully speak to the mechanisms through which structural conditions of poor black inner-city neighbourhoods influence high rates of lethal violence. A number of researchers have argued that residential segregation and the concomitant social isolation it engenders are key to understanding this relationship. For example, Wilson (1987, 1996) argues that black residential segregation is inextricably linked with isolation from mainstream society, which relegates poor blacks to a local context of multiple disadvantages. Weak connections to employment opportunities and conventional role models, family disruption, and a lack of community safeguards and resources undermine social control efforts in these neighbourhoods, which influence the probability of violent outcomes. Consistent with Wilson's thesis, Peterson & Krivo (1993) found racial 139 residential segregation to be associated with higher levels of homicide victimization among blacks, due to the concentration of forms of neighbourhood disadvantage typically associated with violent crime experienced in these communities. Sampson (1987) also emphasized the distinctly high levels of economic disadvantage in black neighbourhoods, arguing that higher rates of family disruption in black communities, often tied to adult male joblessness, lead to higher rates of crime and violence in those neighbourhoods. Other studies addressing this thesis also suggest that homicide rates are higher in cities where blacks experience higher levels of residential segregation from whites, or from more advantaged people of any racial/ethnic background (Lee, 2000; Krivo & Peterson, 2000; Peterson & Krivo, 1999; Shihadeh & Flynn, 1996). The social isolation thesis emphasizes more than just the spatial separation of blacks from whites or the 'haves' from the 'have nots'; it is also a separation of disadvantaged residents from mainstream normative structures. In other words, social isolation from mainstream institutions (for example, churches, schools, and social and civic programs and associations) and the socializing influences they yield "undermines organizational capacity, compromises informal social control, and breeds cultural adaptations that endorse violent behaviour in a variety of situations" (Lee & Ousey, 2005: 48). Drawing on Kornhauser's idea of cultural social disorganization, or the attenuation of social cultural values, recent research has focused on cultural processes that may be a response to (or mediate) the effects of structural characteristics on violence. As discussed in Chapter Three, a number of studies have found support for Anderson's (1999) work describing violence as a cultural adaptation to negative neighbourhood structural conditions - such as high levels of poverty, residential instability, and family disruption. 140 141 These conditions are posited to increase frustration, anger, and despair, and foster a street code among some residents that is conducive to violence. (Anderson, 1999; Bruce, Rosigno & McCall, 1998; Kubrin & Wadsworth, 2003; Stewart and Simons, 2006). In sum, then, empirical research suggests that the structural and cultural factors associated with high rates of black urban violence may differ from those contributing to high rates of white urban violence. My analysis does not include measures of some of the intervening mechanisms described above, so I am unable to assess the role that social isolation and local cultural codes may play in shaping the risk of black homicide victimization in Toronto's neighbourhoods. However, I do have measures of economic disadvantage, racial composition, and family disruption, which lead to the following hypotheses: Hla: Neighbourhood economic disadvantage will be positively related to black homicide counts in Toronto at both the bivariate and multivariate levels. Hlb: My percent black measure will be positively related to black homicide counts at the bivariate and multivariate levels. Hlc: If percent black is related to homicide through its association with economic disadvantage, the effect of percent black will be reduced when controlling for measures of economic disadvantage in a multivariate model. However, percent black is expected to remain significantly related to black homicide counts through a basic compositional effect (i.e the larger the black population in a neighbourhood, the greater the likelihood that homicide victims will be black). 141 142 Hid: Measures of recent immigration in Toronto's neighbourhoods are not expected to be associated with black homicide.93 Hie: Large numbers of young males in Toronto's neighbourhoods will be associated with higher black homicide counts at the bivariate and multivariate levels. Hlf: Large numbers of divorced males in Toronto's neighbourhoods will be positively related to black homicide counts at both the bivariate and multivariate levels, due to the weakening of guardianship networks and levels of informal social control that are hypothesized to stem from high rates of so-called family disruption. Hlg: Neighbourhood home ownership will be negatively related to black homicide counts at both the bivariate and multivariate levels. Hlh: Neighbourhood residential mobility will be positively related to black homicide counts at both the biviariate and multivariate levels. 6.2.1 Black Homicides in Toronto, 1988-2003: Sample Cases and Descriptive Statistics Case #1: The victim, a 21-year old black male, was shot twice in the head in an underground garage in the Jane-Finch area in July, 2001. Three or four black males were seen fleeing the scene on bicycles. Police indicated that the victim was a member of a local street gang. Another man, believed to be a fellow gang member, was wounded while attempting to flee the scene. According to police, the double shooting was rooted in a local conflict between two gangs, the Bloods and the Crips, and was likely the result of a dispute over turf or drugs (Toronto Star, 13 July, 2001; 27 July, 2001). 9j Though I do not expect the percent recent immigrants measure to be associated with black homicide, I have included it in my analyses because it could have an effect and because 1 want to use the same models that 1 used in my analysis of total homicide counts to see if my explanatory variables operate in different ways for the different types of homicide under examination. Given that a large proportion of black homicides involve young males, I expect that neighbourhoods with large numbers of young male and black residents will experience higher levels of this type of homicide. 142 143 Case #2: The victim, an 18-year-old woman, was out with friends at a Rexdale nightclub in March, 2002, when she became involved in an argument with up to six male club patrons. The victim and her friends left the club, but were followed by the others into the parking lot. Police say the men, who were armed with handguns, opened fire on the victim and her friends as they were getting into her car. The victim was shot in the head and died at the scene. A male passenger, who suffered a gun shot wound to the leg, survived. Three black males in their 20s and a young offender were charged with second- degree murder. The victim's death was described as the "second tragedy in this family"; her aunt was shot and killed in 1991. A third tragedy ensued eight months later when the victim's 18-year old brother was ambushed and shot to death as he emerged from a taxi with his girlfriend and her 2-month old child (Toronto Star, 23 March, 2002; Globe & Mail, 26 December, 2002). Case #3: The victim, a 26 year old black male, was shot and killed at an after- hours club in the early morning hours of October 18,1994. Two other men were also shot; both survived numerous gunshot wounds. This incident was the seventh gun-related incident at after-hours clubs in Toronto in the past month, which prompted considerable media coverage and debate about so-called "black on black violence" in Toronto. Police believe that many of the 250 people who were in the club at the time of the shootings saw what happened and either knew or could identify the person(s) involved, but complained that no witnesses would come forward with information. A prominent member of Toronto's black community wasn't surprised by the veil of silence: "There is a long, long thousand year history of people not speaking to occupying armies... There is a strong division between police and many black people, so it's not surprising they won't help 143 144 detectives in the investigation." Police also indicated that they were having difficulty determining a motive for the killing. One investigator was quoted in news reports as saying: "these young kids who are shooting each other, the male blacks, it's not always drugs, it's not always turf battles. Is it male bravado?" However, one community activist argued that killings like this occur for a variety of reasons: "It's a settling of a score or a reflection of the drug trade.. .But a reflection that is worse than those two is a hatred and a self-hatred. Many of these men see no help for themselves and their future" (Toronto Star, 19 October, 1994). 6.2.2 Characteristics of Homicides Involving Black Victims in Toronto's Neighbourhoods Again, data on the racial/ethnic background of homicide victims and offenders were not collected from the Toronto Police Service. As such, I relied on newspaper reports and photographs of victims and/or offenders that were printed as part of those reports to ascertain their racial background. While I acknowledge that classifying race in this way is fraught with the potential for misclassification and missing data, given the institutional policies that preclude the collection of race-based statistics, there was no other alternative. Table 6.2 provides descriptive statistics for selected victim, incident and offender characteristics of black homicides (n=225) in Toronto's neighbourhoods between 1988 and 2003. Over this period, the majority of black homicide victims (83%) were males and young people. For example, 47% of victims were under the age of 24, with an additional 39% in the 25-34 age category. In other words, approximately 90% of all black homicide victims were under the age of 34 (the mean age was 27), which makes them younger than 144 145 victims of homicide in Toronto more generally (see Table 4.1). A large proportion of black victims were also unmarried (61%) and unemployed (37%). My dataset also contains information on a variety of incident characteristics, including the relationship between victim and offender (when known), the location of the homicide, and the method of and apparent motivation for the killing. Approximately one third (34%) of black homicides involved friends/acquaintances, 27% involved strangers, 13% involved male or female intimate partners, and 10% involved illegal relationships. However, when compared to total homicides, black homicide victimization was more likely to involve friends/acquaintances, strangers and illegal relationships, and less likely to involve intimate partner relationships. In terms of location, 34% of black homicides occurred in a private residence. This is a marked difference from total homicides in Toronto, almost half of which occurred in private residences. Further, 29% of black homicides occurred in public spaces - for example, in streets, parks or parking lots; 19% of black homicides occurred in stores and places of leisure, such as bars, taverns and restaurants; and 7% of victims were killed in a vehicle. Total homicides in Toronto, by comparison, were less likely to occur in these places. The use of firearms figures more prominently in black homicides than in total homicides in Toronto: 64% of black homicides involved a gun, compared with 35% of total homicides. Finally, almost 15% of black homicides appear to be motivated by robbery/theft, and 5% by disputes over illegal money. Black homicides are thus more likely to be motivated by robbery and disputes over illegal money than are homicides in Toronto more generally. 145 As discussed in Chapter Five, my ability to document characteristics of black homicide offenders is greatly constrained by missing data. This is, in part, a function of the fact that 33% of the cases involving black victims in my dataset, no information on the offender was available. Once again, then, the following statistics on offender sex, age, race and employment status should be interpreted with the understanding that a considerable amount of data is missing for each of these variables.9" Perpetrators of homicides involving black victims were overwhelmingly young, black males. Of the cases in which an offender was identified and I obtained information on offender age, sex, race and employment status, almost half of the offenders were people under the age of 24 (the mean age was 27), 95% were male, and 92% were black. Compared with homicide offenders in Toronto more generally, then, perpetrators of homicides involving black victims are younger and are more likely to be male and black. This is consistent with the larger literature on homicide, which consistently shows that the characteristics of homicide offenders are typically very similar to those of homicide victims, and that most homicides are intra-racial (Silverman & Kennedy, 2004). In other words, it is to be expected that young, black males tend to be killed by other young, black males. 6.2.3 The Spatial Distribution of Black Homicide Victimization Figure 6.1 provides a geographic representation of the distribution of homicides involving black victims across Toronto's neighbourhoods between 1988 and 2003 (n=225). Over this period, 59 neighbourhoods (42%) did not experience any incidents of this violence, 48 neighbourhoods (34%) experienced 1-2 black homicides, 29 My data set contains information on offender sex, age, race and employment status in 66%, 63%, 47%, and 48% of black homicide cases, respectively. 146 147 neighbourhoods (21%) experienced 3-6 such homicides, and 4 neighbourhoods (3%) experienced 7 or more black homicides. Of these "high homicide" neighbourhoods, Black Creek experienced 12 black homicides, the most of any neighbourhood in Toronto, followed by Thistletown-Beaumond Heights (9 homicides), Regent Park (8 homicides), and Glenfield-Jane Heights (7 homicides). Compared to total, young male (15-34) and gun homicide counts, black homicide victimization in Toronto is concentrated in the smallest number of neighbourhoods, only one of which (Regent Park) is located in the city centre. The remaining high homicide neighbourhoods are located in close proximity to one another, in the northwest section of the city. 6.2.4 Bivariate Associations Between Neighbourhood Characteristics and Black Homicide Table 6.3 shows the bivariate correlations between my measures of neighbourhood characteristics and the number of homicides with black victims in Toronto's neighbourhoods. Black homicide victimization is significantly and strongly associated with the proportion of black residents (.72), and the level of disadvantage in neighbourhoods (.58); and significantly, but only weakly associated with percent young males (.27), percent of residents who have moved in the past five years (.24), and home ownership (-.24). These findings are consistent with Hypotheses la, lb, le, Ig, and lh. Finally, the percent of the population who are recent immigrants and the percent of male residents who are divorced are not significantly related to black homicide victimization. This is contrary to my hypothesis that there would be a positive relationship between this measure of family disruption and black homicide counts in Toronto's neighbourhoods. Thus, neighbourhoods in Toronto characterized by economic disadvantage, large numbers of young and black residents, and high levels of residential mobility tend to 147 148 experience higher levels of black homicide victimization, while those characterized by higher levels of home ownership typically experience lower levels of this type of violence. 6.2.5 Neighbourhood Correlates of Black Homicide Victimization in Toronto: Multivariate Analyses An examination of the goodness of fit statistics for a Poisson model of black homicide counts revealed evidence of overdispersion. For the multivariate analyses of black homicide counts, I therefore estimate a series of negative binomial models, which allow for overdispersion. My dependent variable is the total number of black victims of homicide in each neighbourhood over the period 1988-2003. The results of these models are presented in Table 6.4.1 begin by estimating a model with the economic disadvantage index as the only predictor of black homicide. As discussed previously, measures of economic disadvantage have consistently been identified as important correlates of black homicide in previous research (Messner & Rosenfeld, 1999; Messner & Sampson, 1991; Ousey, 1999; LaFree et al., 1992; Peterson & Krivo, 1993; Shihadeh & Steffensmeier, 1994; Shihadeh & Ousey, 1996). Model 1 demonstrates that disadvantage is significantly and positively associated with black homicide counts in Toronto. This is consistent with Hypothesis la and to be expected, given the strong correlation at the bivariate level. Thus, neighbourhoods in Toronto with a higher proportion of disadvantaged residents have higher levels of black homicide victimization. In Model 2,1 estimate a model that includes only percent black as a predictor of black homicide victimization. This allows me to determine if the bivariate relationship between percent black and black victimization is replicated using the more conservative negative binomial analysis, and, if so, the extent to which this relationship is reduced when the 148 149 disadvantage index is added, in Model 3, as predicted by Hypothesis lc. As indicated in Table 6.4, in the column labeled Model 2, consistent with Hypothesis lb there is a significant positive association between percent black and black homicide victimization. In Model 3, which includes both percent black and the disadvantage index, the coefficient for percent black remains significant and positive, while the coefficient for the disadvantage index is reduced to non-significance. That percent black remained significant and positive is consistent with Hypothesis lb and lc, which predicted such an association, due in part to a compositional effect. However, the coefficient does decrease, suggesting that part of the association between my percent black measure and black homicide victimization in Toronto's neighbourhoods may be accounted for by its relationship to economic disadvantage, which is consistent with Hypothesis lc. In Model 4, which includes the disadvantage index, percent black, and percent young males, the coefficients for percent black and percent young males are significant and positive. Thus, neighbourhoods in Toronto with large numbers of black and young male residents tend to experience higher levels of black homicide victimization, which is consistent with Hypotheses lb and le. Model 5, which includes the disadvantage index, percent black, percent young males, percent mobility, and percent owners, demonstrates that the addition of the last two measures does not reduce the significance of percent black, but does reduce the coefficient for percent young males to non-significance. The final model (Model 6) is comprised of the disadvantage index, percent black, percent mobility, percent owners, percent young males, percent recent immigrants, percent divorced males, and the logged population measure. In this model, the coefficient for percent black remains highly significant and positive, which suggests that the 149 150 relationship between percent black and black homicide is not accounted for by its association with any of the variables in my final model, but may, in part, be a function of a compositional effect (as predicted in Hypothesis lc). None the other measures of neighbourhood characteristics are significantly associated with this homicide type at the multivariate level. The fact that the disadvantage index and percent young males measure are unrelated to black homicide is contrary to Hypotheses la and le, which predicted a significant positive association between these measures and black homicide in Toronto's neighbourhoods. These findings also differ from the multivariate results presented in Chapter Five, where high levels of economic disadvantage and large numbers of young male residents were significantly associated with total homicide victimization in Toronto's neighbourhoods. That percent owners and percent mobility are not associated with black homicide victimization is also contrary to expectations (see Hypotheses lg and lh), but consistent with the results for total homicide counts in Toronto. That percent recent immigrants is not associated with black homicide victimization is consistent with both the bivariate results and Hypothesis Id. Percent divorced males is also not significantly associated with black homicide, which is contrary to expectations, but consistent with the bivariate results. In sum, then, only one of my measures of neighbourhood characteristics (percent black residents) is associated with black homicides in Toronto. As noted earlier, this significant association may, in part, be a function of a compositional effect. However, the fact that the relationship between percent black residents and black homicide counts is not due to its association with my measure of economic disadvantage or any of the other 150 151 measures in my final model is contrary to expectations. This finding also differs from a number of American studies that show that percent black is associated with homicide at the bivariate level but not when measures of poverty are included in the multivariate model (Sampson, 1985; Messner & Tardiff, 1985). Possible interpretations for this finding will be discussed below. Finally, the neighbourhood-level characteristics that were found to be significantly associated with total homicide counts (i.e. the disadvantage index and percent young males measure) do not predict black homicide victimization in Toronto's neighbourhoods. 6.3 THE SOCIAL ECOLOGY OF HOMICIDE VICTIMIZATION AMONG MALES AGED 15-34 Theory and research on lethal and less-than-lethal violence among young people96 have tended to focus on four domains: 1) cultural developments that privilege violence as a means of demonstrating masculinity, cultivating a local reputation, and achieving status within the neighbourhood (Anderson, 1999; Oliver, 1994); 2) the growth of gangs and/or drug markets and the violence associated with these enterprises (Kubrin & Weitzer, 2003; Maxson et al., 1995; Anderson, 1990, 1999; Rosenfeld et al., 1999; Decker & VanWinkle, 1996; Cohen et al., 1998); 3) the concentration of poverty (Strom & MacDonald, 2007); and 4) other social structural factors. Each of these will be discussed in turn. 6.3.1 Culture, Masculinity and Lethal Violence Among Young Males The issue of masculinity and its link to men's behaviour toward others has been identified in both past and present discussions of violent crime. For example, as 96 The bulk of this literature does not differentiate between violence by young males and young females, although the theoretical perspectives drawn on tend to apply violence by males. 151 152 previously discussed, Elijah Anderson argues that the behaviour of many inner-city youth - particularly black youth - is influenced by a street culture or 'code' that prescribes violent reactions to interpersonal attacks and shows of disrespect. At the core of this code is the belief that it is crucial that young males show no tolerance for interpersonal transgressions; failure to do so displays weakness to others and may invite further transgressions (Anderson 1990, 1999). A central aspect of Anderson's thesis is that adherence to the street code is not simply a function of deviant socialization or corrupt values. Instead, it is, in part, an adaptation to desperate structural conditions and a continual threat of violence that is present in some urban neighbourhoods. In these neighbourhoods, many young men - even those committed to conventional values - come to believe that enactment of the code is, at times, necessary for street survival. In other words, while some youth may embrace the street code wholeheartedly and develop a social identity that embraces toughness and violence, those who are less committed to the dictates of the code acquire the ability to 'code switch', or develop a repertoire of posturing behaviours that enable them to "get ignorant" - to act aggressively and demonstrate the capacity for violence if necessary. Anderson details how high rates of poverty, joblessness, family disruption, racial discrimination, isolation, and strained relationships with legal authorities that characterize many disadvantaged neighbourhoods increase the likelihood that the code of the street will govern public encounters. Simply living in a neighbourhood in which the code prevails places youth at greater risk of violent victimization, including homicide. As Stewart & Simons (2006: 6) argue (drawing on Prothrow-Smith, 1991): Youngsters living in [such] inner-city neighbourhoods...are likely to see violence as a way of life. They are likely to be taught violence, to witness violent acts, and 152 153 to have role models who display high levels of aggression and violence. As a consequence, these youngsters take pride in being tough (which usually involves owning a firearm), presenting a violent self-image, and protecting themselves and their 'boys' (a close peer group), which can often end with deadly consequences. Further, the street code and the respect it demands is so entrenched among the hard-core, street-oriented individuals that they are willing to risk dying violently rather than being 'dissed' or victimized by another. Adherence to the code in such neighbourhoods may also be associated with a number of mechanisms that facilitate the transmission of code-related beliefs among youth, including a lack of adequate parental supervision, exposure to violent or aggressive peers, personal and/or vicarious experiences with violence, and the perception that the achievement of status or respect through legitimate avenues is not a realistic option (Brezina et al., 2004).97 6.3.2 Drug Markets, Gangs and the Killing of Young Males Gang-related activity and expanding drug markets have also been offered as explanations for the concentration of homicides involving young males in some inner- city neighbourhoods. A number of economic features of illegal drug and gang activity - which are often inextricably intertwined - are thought to contribute directly to the violence associated with them (Decker & Van Winkle, 1996; Maxson et al., 1985). Cohen et al. (1998: 245) argue that drug markets promote high levels of competition for market shares among dealers, and that this competition is rife with the potential for violence: In the absence of conventional and peaceful means for resolving [drug] market disputes, participants in these enterprises often resort to violence as a means of protecting their market positions. The violence associated with A number of studies have examined the development of code related beliefs and the influence of such beliefs on youth violence (Agnew, 1994; Baron, Kennedy & Forde, 2001; Heimer, 1997; Markowitz & Felson, 1998; Stewart & Simons, 2006). While not all provide direct or complete evaluations of Anderson's thesis, generally speaking, these studies lend support to many of the hypotheses derived from his account. 153 154 consolidating markets is often strategic or instrumental in nature, intended to promote enterprise survival. Dealers may use homicide as a managerial tool, sending an unmistakable message to the remaining underlings. Gang-related homicides have also been shown to be associated with non-drug related turf disputes (Rosenfeld et ah, 1999), and retaliation for previous violence (Kubrin and Wadsworth, 2003). Further, gang homicide victims are overwhelmingly young, black males, and the killings are more likely to take place in public, involve firearms, and involve participants who are close to one another in age (Rosenfeld et al., 1999). They also tend to be highly concentrated in racially isolated, disadvantaged neighbourhoods, which remain "the fundamental social facilitators" of gang-related violence, including homicide (Rosenfeld et al., 1999: 495). 6.3.3 Structural Explanations of Youth Homicide: Weakened Informal Controls A number of macro-level studies on the social ecology of youth homicide suggest that local structural conditions are associated with the risk of this type of violence. For example, in their study of homicide patterns among black and white youth, Strom et al. (2007) found that increases in city-level economic disadvantage and family disruption were positively associated with increases in homicide victimization among black teens (15-19) and young adults (20-24), and among white teenagers (15-19). Similarly, Rosenfeld et al. (1999) found that the killings of young people in St. Fouis neighbourhoods were associated with high levels of racial isolation, poverty, public assistance income, and female-headed households. 98 Many of the structural features identified as predictors of young male homicide in other research are correlated with each other; I have found the same in this study. As discussed in Chapter Five, measures of economic disadvantage, and the percent of residents who are black, young males, recent movers, homeowners, and young males are significantly related at the bivariate level across Toronto's 140 neighbourhoods. 154 155 The tie that binds much of the work on youth homicide is the theoretical link between weakened social control - due to poverty and family disruption - and the ability to effectively socialize neighbourhood residents, exert informal social control, and garner the social and capital resources that may serve as a buffer against violence (Biblarz & Raferty, 1999). Research shows that measures of structural disadvantage, including concentrations of poverty and female-headed families, are linked to attenuated socialization and supervision of neighbourhood youth (Sampson, 1997; Shihadeh & Steffensmeier, 1994), which in turn are related to local levels of violent crime, including homicide (Sampson & Groves, 1989). Residents in such neighbourhoods may also be less able to prevent the proliferation of youth gangs and drug markets, both of which have implications for local homicide rates (Reiboldt, 2001; Cohen et al., 1998; Kubrin & Weitzer, 2003; Blumstein, 1995; Curry & Spergel, 1988; Rosenfeld et al., 1999). In sum, then, a number of intervening mechanisms have been proposed to explain the relationship between neighbourhood economic disadvantage and the killing of young males. These include value systems that are tolerant of, and sometimes even prescribe the use of violence as a means of status attainment and/or conflict resolution, as well as attenuated levels of socialization and supervision that are thought to stem from neighbourhood poverty and a preponderance of lone parent families. My analysis does not include measures of these intervening mechanisms, so I am unable to assess the role that they may play in shaping the risk of homicide involving young males in Toronto's neighbourhoods. As a consequence, in my analysis, economic disadvantage, lone parent families, racial composition, and age structure are modeled as having direct effects on lethal violence among young men. 155 156 The following hypotheses can be derived from this discussion: H2a: Neighbourhood economic disadvantage will be positively related to the number of young male victims of homicide in Toronto at both the bivariate and multivariate levels. H2b: Large numbers of young males in Toronto's neighbourhoods will be associated with higher young male homicide counts at the bivariate level. H2c: Because neighbourhoods in Toronto with large numbers of young males are also economically disadvantaged and/or characterized by high rates of in- and out- migration, the association between percent young males and young male homicide will likely be reduced in a multivariate model controlling for economic disadvantage and mobility. However, percent young males is expected to remain positively related to young male homicide through a compositional effect (i.e. the larger the proportion of young males in a neighbourhood, the greater the likelihood that homicide victims will be young males). H2d: My percent black measure will be positively related to homicides among young males at the bivariate and multivariate level. H2e: If percent black is related to young male homicide through its association with economic disadvantage, the effect of neighbourhood racial composition on young male homicide will be reduced when controlling for measures of economic disadvantage in a multivariate model. However, given that a sizable proportion of young male homicide victims are black, percent black is expected to remain positively related to young male homicide through a compositional effect. 156 157 H2f: Large numbers of divorced males in Toronto's neighbourhoods will be positively related to young male homicides. H2g: Measures of recent immigration will not be related to the killing of young men in Toronto. H2h: Measures of neighbourhood home ownership will be negatively related to young male homicide counts at both the bivariate and multivariate levels. H2i: Measures of neighbourhood residential mobility will be positively related to young male homicide counts at both the bivariate and multivariate levels. 6.3.4 Homicides Involving Males (15-34) in Toronto: Sample Cases Case #1: The victim, a 17-year old male, died after being repeatedly stabbed outside a high school snack bar in May, 1998. Witnesses reported that the killing was sparked by an incident in which the victim accidentally "bumped" the offender, a 15-year old male, in a school hallway. This resulted in a shoving match between the victim and the offender, following which the offender produced a knife and stabbed the victim in front of fifty students. The offender was chased by the victim's friends, but escaped and was arrested sometime later (Toronto Star, 9 May, 1998; 11 May, 1998). Case #2: The victim, a 28-year old gang-involved male, was shot to death outside of a North York housing complex in February, 1991. The killing stemmed from an ongoing territorial war that police indicated was spreading through Toronto's housing projects. The victim was shot in the leg, chest, and back of the head as he fled his attacker following a dispute (Toronto Star, 21 February, 1991). 157 158 6.3.5 The Spatial Distribution of Homicides Involving Young Males (15-34) in Toronto. Figure 6.2 provides a geographic representation of the distribution of young male (15-34) homicide counts across Toronto's neighbourhoods between 1988 and 2003 (n=389). Over this period, 38 of Toronto's 140 neighbourhoods (27%) did not experience any incidents of this violence, 46 neighbourhoods (33%) experienced 1-2 homicides, 40 neighbourhoods (29%) experienced 3-6 homicides, and 16 neighbourhoods (11%) experienced 7 or more such homicides. Of these "high homicide" neighbourhoods, Regent Park experienced 17 homicides, the most of any neighbourhood in Toronto, followed by Thistletown-Beaumont Heights and Glenfield-Jane Heights (14 homicides each). Black Creek (11 homicides), Kensington-Chinatown, L'Amoreaux, and Oakridge (10 homicides each). With the exception of Regent Park and Kensington-Chinatown, which are located in the city-centre, high levels of this kind of homicide appear to cluster in a small number of neighbourhoods, many of which share boundaries and are located on the outer edges of the city. 6.3.6 Characteristics of Homicides Involving Males (15-34) in Toronto, 1988- 2003. Table 6.5 provides descriptive statistics for selected victim, incident and offender characteristics of young male homicides (n=389) in Toronto's neighbourhoods between 1988 and 2003. Over this period, approximately half of the victims were between 16 and 24 years of age (the mean age was 25). Further, the majority of these victims (55%) were black, unmarried (72%) and either unemployed (43%) or attending school (18%). The neighbourhood-based data set used to perform my multivariate analyses contained 388 young male homicides. This is because for one such homicide, information on the location of the killing was not available. 158 159 My dataset also contains information on a variety of incident characteristics, including the relationship between victim and offender (when known), the location of the homicide, and the method of and motivation for the killing. Of the 241 cases in which the relationship between the victim and the offender was known, approximately 47% occurred among people known to one another: 39% of these killings involved friends/acquaintances, 5% involved family members, and 3% involved intimate partners. An additional 32% and 16% of young male homicides involved strangers and illegal business relationships, respectively. Compared to homicide in Toronto more generally (see Table 4.1), then, young male homicide is more likely to involve friends, strangers, and illegal relationships, and less likely to involve family members and intimate partner relationships. In terms of location, 23% of young male homicides occurred in a private residence, 25% occurred in public spaces (for example, in streets, parks or parking lots), and 34% occurred in stores and places of leisure, such as bars, taverns and restaurants. An additional 6% of victims were killed in a vehicle. Young male homicides are thus more likely to take place in public spaces, places of leisure and in vehicles than are total homicides in Toronto. Further, the use of firearms figures more prominently in young male homicides (60%) than in homicide in Toronto more generally (35%). Finally, where a specific motive could be reasonably identified, robbery/theft was the motive in 11 % of cases, and disputes over illegal money accounted for an additional 6% of this type of lethal violence. Thus, young male homicides are more likely to be motivated by disputes over illegal money than are homicides in Toronto more generally.100 Police sources suggest that there has been a rise in gang-related violence, which often includes drug- related activity, in Toronto since the early 1990s. Though my data collection coding sheets included motive 159 160 Again, my ability to document characteristics of the offenders in young male homicides is constrained by missing data, which is, in part, due to the fact that 32% of these homicides were not solved. With this caveat in mind, 1 can provide some information on offender sex, age, race and employment status. The majority of known offenders in these cases were male (95%) and black (51%). Over half (54%) were under the age of 24, while an additional 39% are between the ages of 25 and 34 (the mean age was 27). In other words, the overwhelming majority of known offenders in the killings of young males were themselves young males, and very often they were young, black males. Compared with total homicides in Toronto, then, perpetrators of young male homicides are younger and more likely to be male and black. Unemployment was also common to offenders in these cases (63%), and 11% were students at the time of the killing. 6.3.7 Bivariate Associations Between Neighbourhood Characteristics and Homicide Among Young Males At the bivariate level, my measures of neighbourhood characteristics in Toronto are associated, in varying degrees, with this type of homicide. Table 6.6 shows the correlations between my measures of neighbourhood characteristics and the killings of young men in Toronto's neighbourhoods between 1988 and 2003. Young male homicide is significantly and strongly associated with the proportion of black residents and the level of disadvantage in the neighbourhood; and significantly, but only weakly correlated classifications intended to capture gang- and drug-related activities, the homicide data for my period of examination do not appear to reflect police claims of a rise in this violence. As discussed in Chapter Four, the police files to which I had access were limited to 'Chiefs Reports' that are prepared at an early stage of the homicide investigation. Some homicide investigators indicated a reluctance to classify a homicide as gang- or drug-related early on, due to a concern that such classifications might prove to be inaccurate as the investigation unfolded. Further, because many gang-related killings are retributive in nature, some investigators indicated that they were more comfortable classifying motive as 'revenge' in lieu, at least in the preliminary stages of the investigation, of an official gang classification. Therefore, the homicide data collected from the Chiefs Reports likely represent conservative estimates of the true incidence of these types of killings in Toronto. 160 161 with the proportion of young male residents, the proportion of residents who have moved in the past five years, and levels of home ownership. These findings are consistent with Hypotheses 2a, 2b, 2d, 2h and 2i. Finally, the percent of the population who are recent immigrants, the percent of male residents who are divorced males, and the logged population are not significantly related to the killing of young men in Toronto. That percent divorced males is not associated with young male homicide is contrary to Hypothesis 2f, which predicted that this measure would be significantly associated with this type of violence in Toronto's neighbourhoods. Thus, neighbourhoods in Toronto with higher levels of economic disadvantage and residential mobility, and larger numbers of young male and black residents tend to experience higher levels of young male homicide. 6.3.8 Neighbourhood Correlates of Lethal Violence Among Young Males in Toronto: Multivariate Analyses As was the case with black homicide victimization in Toronto's neighbourhoods, an examination of the goodness of fit statistics for a Poisson model of young male homicide counts revealed evidence of overdispersion. Therefore 1 estimate a series of negative binomial models for the multivariate analyses discussed below. The results of these models are presented in Table 6.7.1 begin by estimating a model with the economic disadvantage index as the only predictor of young male homicide. Model 1 demonstrates that disadvantage is significantly and positively associated with the killings of young men in Toronto's neighbourhoods, which is consistent with Hypothesis 2a and to be expected, given the strong correlation at the bivariate level. As such, neighbourhoods in Toronto with large numbers of disadvantaged residents have higher levels of this type of homicide, according to the negative binomial results. 161 162 In Model 2,1 estimate a model that includes only percent black as a predictor of young male homicide. This allows me to determine the extent to which this relationship is reduced when the disadvantage index is added, in Model 3, as predicted by Hypothesis 2e. As can be seen in the column labeled Model 2 in Table 6.7, there is a significant and positive relationship between percent black and homicides of young males, consistent with Hypothesis 2d. In Model 3, which includes both percent black and the disadvantage index, the coefficient for percent black remains significant, while the coefficient for the disadvantage index, while reduced somewhat, remains positive and significant. In other words, neighbourhoods in Toronto characterized by economic disadvantage and large numbers of black residents experienced more homicides with young male victims between 1988 and 2003, consistent with predictions. In Model 4, which includes the disadvantage index, percent black and percent young males, the coefficient for percent young males is positive and significant, consistent with Hypothesis 2c. Thus, not surprisingly, neighbourhoods in Toronto with large numbers of young male residents tend to experience higher levels of young male homicides. Model 5, which includes the disadvantage index, percent black, percent mobility, and percent home owners, demonstrates that the addition of percent mobility and percent home owner measures does not reduce the significance of the disadvantage index, but does reduce the coefficient for percent black somewhat. Nevertheless, this measure remains positive and significant in Model 5. Further, of the percent mobility and percent owner measures, only percent mobility is significantly related to the killing of young men in Toronto. That is, neighbourhoods in which a large number of residents changed residences in the past five years had higher levels of homicide victimization among young males. 162 163 The final model (Model 6) is comprised of the disadvantage index, percent black, percent mobility, percent owners, percent young males, percent recent immigrants, percent divorced males, and the average logged population. In this model, the coefficient for percent mobility is reduced to non-significance, which suggests that the effect of residential mobility was due to its association with other neighbourhood characteristics included in this model. The coefficients for the disadvantage index, percent black, and percent young males, however, remain positive and significant. This is consistent with Hypothesis 2a and 2c, which predicted an association between measures of neighbourhood economic disadvantage and sex-specific age structure. It is also consistent with Hypothesis 2e, which predicted that the effect of racial composition would be reduced at the multivariate level when controlling for measures of economic disadvantage, family disruption and age structure. The percent divorced males measure is not significantly associated with the killing of young men in Toronto's neighbourhoods, which is contrary to expectations, but consistent with the bivariate results. 6.4 THE SOCIAL ECOLOGY OF GUN HOMICIDE IN TORONTO Criminologists have become increasingly interested in examining reasons for the rise and fall of violent crime rates in many U.S. cities over the last four decades (LaFree, 1999). Factors posited to be responsible for shifts in the quantity of lethal violence over this period include changes in the age structure of the population, the emergence and subsequent decline of crack cocaine markets, changes in policing practices, prison expansion, and escalating handgun use (Blumstein & Wallman, 2000). 163 164 While a number of studies have demonstrated that high rates of lethal violence in some urban neighbourhoods are directly attributable to street gun availability and usage therein (Griffiths & Chavez, 2004; Cohen et al., 1998; Koper, 1995), only one study that I am aware of examines the neighbourhood-level correlates of urban gun homicide (Long- Oonen, 2000). This study, which examines gun homicides in Baltimore, found this violence to be concentrated in neighbourhoods characterized by large numbers of persons living in poverty, families on welfare, female-headed households, minority residents, and high levels of street-level drug trafficking. A number of studies have also found that the presence of gun homicides in one neighborhood increases the likelihood of gun homicide in surrounding neighbourhoods (Fagan and Wilkinson, 1998; Griffiths & Chavez, 2004; Cohen et al., 1998). Fagan and Wilkinson (1998) found that poor neighborhoods and those with demographic characteristics hypothesized to contribute to a lack of social control and stability (for example, large numbers of young persons, particularly males and single parent families) are more susceptible to the diffusion of gun homicide. Similarly, in their examination of gun homicide in Chicago, Griffiths and Chavez (2004) found evidence of a diffusion effect of this violence in neighbourhoods bordering the most violent areas. In an attempt to understand the micro-level processes that may give rise to high levels of gun homicide at the neighbourhood level, Fagan and Wilkinson (1998) offer a situational approach that draws on research emphasizing the social processes involved in violent events, including the use of firearms. Building on Anderson's (1999) concept of "the code of the streets", they point to the importance of violence "scripts" to explain the concentration of gun use among young males in certain inner-city American 164 165 neighbourhoods. Fagan and Wilkinson argue that guns are part of the ecology of inner city neighbourhoods, and that gun violence has become the "ultimate social tool" used to attain status and respect in the inner-city. One subject explains the role of guns in creating the right 'rep' on the streets: You gotta go all out, you go "lace" 'em...; have a fight with duke or whatever, pull out a gun and blast 'em...; you gotta be, niggas ain't gonna fuck with you if you shoot a nigga...; just lace 'em, and niggas will say "yo that nigga don't play, he lace something in a heartbeat (p.79) Fagan and Wilkinson argue that guns have become the preferred symbol for "toughness" and "being the man" on the streets. According to one respondent, guns provide an important means of defining manhood, particularly for young males. When asked how manhood is defined in his neighbourhood, he answered: "If you got a gun you the man (laughing). Ain't no more manhood, it's gunhood." (p.81). Fagan and Wilkinson also identify self-defense as the primary reason for carrying a firearm; all of their subjects spoke of either the "protection" or "defense" of self, family, and/or friends, and felt that safety in their neighbourhoods was uncertain, at best. One subject even deemed his gun "my Visa" for navigating the streets of his neighbourhood in safety (p.83). Gun possession was also common among young men involved in the drug trade, burglaries, and hustling - but firearms' instrumental value existed in concert with their value as symbols of social status, self-worth, and personal power. Fagan and Wilkinson conclude that gun use has become a central part of status and identity formation within the 'street-oriented' world of some inner-city U.S. neighbourhoods. Further, the presence of firearms has changed the 'scripts' for how interpersonal conflict is handled on the 101 'Scripts' are the "cognitive structure or framework that organizes a person's understanding of typical situations, allowing the person to have expectations and to make conclusions about the potential result of a set of events" (Abelson, 1981:719) 165 166 street. Boys in particular learn that guns are part of day-to-day life in some inner-city neighbourhoods, and that gun violence is one of the few options available for resolving interpersonal disputes: .. .carrying firearms seems to enhance feelings of safety and personal efficacy among teenagers. The result is a developmental 'ecology of violence', where beliefs about guns and the dangers of everyday life may be internalized in early childhood and shape the cognitive frameworks for interpreting events and dominating cognitive schema of violence and firearms, creates, shapes and values scripts skewed toward violence (p.86). In sum, then, gun possession, which is often associated with high levels of drug- and gang-related activity (Cohen et al., 1998), increases the risk of violent death in some inner-city neighbourhoods. However, these risks are not limited to drug dealers and gang- involved individuals; for reasons of status and personal protection, gun related violence has also spread to other residents - namely young males - in affected communities (Blumstein, 1995; Grogger & Willis, 1998).102 My analysis does not include measures of the intervening mechanisms - such as "violence scripts" and/or ecologically structured norms in which gun use has come to be an expected and accepted means of establishing street credibility and resolving interpersonal disputes - that have been offered to explain the association between neighbourhood characteristics like economic disadvantage and gun homicide. As a consequence, I am unable to assess the degree to which these influences shape the risk of this violence in Toronto's neighbourhoods. However, I do have measures of economic disadvantage, family disruption, and neighbourhood racial composition, which lead to the following hypotheses: It is important to note that the literature on the social ecology of gun homicide is U.S. specific. Very little is yet known about the social ecology of this violence outside of the American context. 166 167 H3a: Measures of neighbourhood economic disadvantage will be positively related to gun homicide at the bivariate and multivariate levels. H3b: Large numbers of young males in Toronto's neighbourhoods will be associated with higher gun homicide counts at the bivariate level. H3c: Because neighbourhoods in Toronto with large numbers of young males are also economically disadvantaged and characterized by high rates of in- and out- migration, the association between percent young males and gun homicide should be reduced in a multivariate model controlling for economic disadvantage and residential mobility. However, given that a large proportion of gun homicides involve young males, my percent young males measure is expected to remain positively related to gun homicide. H3d: My percent black measure will be positively related to gun homicide at the bivariate and multivariate levels. H3e: If percent black is related to gun homicide through its association with economic disadvantage, the effect of percent black on gun homicide will be reduced when controlling for measures of economic disadvantage in a multivariate model. H3f: Large numbers of divorced males in Toronto's neighbourhoods will be positively related to gun homicides. H3g: Measures of recent immigration will not be related to gun homicide counts in Toronto. H3h: Measures of neighbourhood home ownership will be negatively related to gun homicide counts in Toronto at both the bivariate and multivariate levels. 167 168 H3i: Measures of neighbourhood residential mobility will be positively related to gun homicide counts in Toronto at both the bivariate and multivariate levels. 6.4.1 Gun Homicides in Toronto, 1988-2003: Sample Cases Case #1: In October, 2003, 27-year old twin brothers were sitting at a table with some friends at a Karaoke bar in the city's west end, when four men with guns entered the premises and began firing indiscriminately. The attackers wore bandanas across the lower part of their faces and hats pulled to just above their eyes. One brother was pronounced dead at the scene, while the other succumbed a short time later in hospital; both died of multiple gunshot wounds. Three other men were wounded in the incident. "The people at the table were targeted", said investigators, who added that the shootings possessed "all the hallmarks of gang activity". The killings remain unsolved (Toronto Star, 28 October, 2003). Case #2: In June, 1991, a female victim and her two children were shot and killed by the offender - their husband and father - who then killed himself. Police said that a history of marital problems and a pending separation led to the murder-suicide in Scarborough. The children were shot in the head as they lay together in bed; their mother also died of a gunshot wound to the head. The offender then shot himself in the chest and the head with a .22-calibre handgun (Toronto Star, 18 June, 1991). 6.4.2 The Spatial Distribution of Gun Homicide m Toronto, 1988-2003 Figure 6.3 provides a geographic representation of the distribution of gun homicides across Toronto's neighbourhoods between 1988 and 2003 (n=340). Over this period, 39 of Toronto's 140 neighbourhoods (28%) did not experience any incidents of this violence, 49 neighbourhoods (35%) experienced 1-2 gun homicides, 40 168 169 neighbourhoods (29%) experienced 3-6, and 12 neighbourhoods (9%) experienced 7 or more gun homicides. The neighbourhoods that experienced the highest levels of this violence include Regent Park (16 gun homicides), Thistletown-Beaumont Heights (12 homicides), L'Amoreaux and Glenfield-Jane Heights (11 homicides each), and York University Heights, Weston, and West Humber-Clairville (10 homicides each). With the exception of Regent Park, all of these extremely high homicide neighbourhoods are located on the northwestern, and northeastern edges of the city and, to some extent, in neighbourhoods that share boundaries. 6.4.3 Gun Homicide in Toronto: Descriptive Statistics Table 6.8 provides descriptive statistics for select victim, incident and offender characteristics of gun homicides (n=340) in Toronto's neighbourhoods between 1988 and 2003. Over this period, the majority of victims of gun homicide were male (87%), black (54%), and young. For example, 38% of victims were under the age of 24, with an additional 39% in the 25-34 age category (the mean age was 30). As such, 77% of gun homicide victims were under the age of 34, which makes them considerably younger than were victims of homicide in Toronto more generally. A larger proportion of gun homicide victims were also single (58%) and unemployed (45%). In terms of incident characteristics, the majority of gun homicides involved people known to each other: friends and acquaintances (32%), intimate partners (10%) and family members (6%). An additional 16% of cases involved illegal relationships, and 30% of these killings involved victims and offenders who were unknown to one another. Compared with homicides in Toronto more generally (see Table 4.1), gun homicides ' The neighbourhood-based data set used to perform my multivariate analyses contained 339 gun homicides. This is because for one such homicide, information on the location of the killing was not available. 169 170 were more likely to involve friends and strangers, and less likely to involve family members and intimate partner relationships between victims and offenders. With respect to location, 29% of gun homicides occurred in a private residence, 28% occurred in public spaces - for example, in streets, parks or parking lots, and 25% occurred in stores and places of leisure, such as bars, taverns and restaurants. An additional 9% of victims were killed in semi-public spaces, and 10% in a vehicle. When compared to homicides in Toronto more generally, then, gun homicides were more likely to occur in public spaces and places of leisure, and considerably less likely to take place in private residences. As with black homicide victimization and the killing of young males, handguns appear to be the weapon of choice in gun homicides in Toronto. In 82% of cases, a handgun was used. Finally, where a specific motive could be reasonably identified, robbery/theft accounted for 16% of young male homicides, while an additional 6% involved disputes over illegal money. It would appear, then, that gun homicides are more likely to be Recall that homicides involving guns, black people and young males in Toronto are highly correlated. This is because each tends to be characterized by the others - i.e. gun homicides are typically homicides involving young, often black, males. For example, 77% of black male homicide victims aged 15-34 were killed with a gun, 83% of black victims who were shot were young males aged 15-34, and 66% of young males (whose racial background was known) were shot to death. As such, that each of these homicide subtypes are similar in their correlates and characteristics is to be expected. Further, the degree of overlap among these killings led me to question a) whether or not the killings of young black males with guns constituted a specific subtype with its own predictors, and b) how my results for total homicide counts in Toronto would change if I excluded all homicides involving young black males who were killed with a gun. To answer these questions, I first estimated an additional negative binomial model using the killings of young black males (15-34) who were shot as the dependent variable. None of my measures of neighbourhood characteristics in Toronto was significantly related to this homicide type at the multivariate level. However, given the small number of such homicides (n=120), the absence of any associations may be an issue of a lack of statistical power. In order to determine the extent to which my results for total homicide counts in Toronto may change in the absence of young black male gun homicides, I re-estimated the final model excluding these homicides. The results show that the two neighbourhood characteristics (the disadvantage index and percent young males) that were associated with total homicide counts in Chapter Five are no longer associated with total homicides in Toronto when the killing of young black males with guns are excluded. This suggests that for total homicides in Toronto (excluding the killing of young black men with guns), the typical predictors identified in the larger American literature do not appear to hold in the Canadian context. 170 171 motivated by robbery and disputes over illegal money than are total homicides in Toronto's neighbourhoods. Compared to the information I am able to provide on offenders for the other homicide types in this study, my ability to provide information on offenders in gun homicides is even more constrained. This is because gun homicides have the highest proportion of cases in which no offender was identified (41%). In other words, the problem of missing data is especially pronounced for this type of homicide. I can, however, provide some information based on the data I do have on offender sex, age, race and employment status. Gun homicide in Toronto is an overwhelmingly male phenomenon: in all but one case for which data were available, the offender was male. Further, as was the case with black and young male homicides, offenders in gun homicide cases tend also to be young (the mean age was 28), black (55%) and unemployed (65%>). 6.4.4 Bivariate Associations Between Neighbourhood Characteristics and Gun Homicide At the bivariate level, my measures of neighbourhood characteristics in Toronto are associated with gun homicide in varying degrees. Table 6.9 shows the correlations between my measures of neighbourhood characteristics and gun homicides in Toronto's neighbourhoods between 1988 and 2003. Gun homicides are significantly and strongly associated with the proportion of black residents (.58) and the level of disadvantage (.57) in the neighbourhood, and significantly, but only weakly correlated with the proportion of young male residents (.33), the proportion of residents who have moved in the past five years (.23), and levels of home ownership (-.25). Thus, neighbourhoods with higher levels of economic disadvantage and residential mobility, and larger numbers of young male residents tend to experience higher levels of gun homicide. These findings are 171 172 consistent with Hypotheses 3a, 3b, 3d, 3h, and 3i. Finally, the percent of the population who are recent immigrants, the percentage of male residents who are divorced, and the logged population measure are not significantly related to gun homicide in Toronto's neighbourhoods. 6.4.5 Neighbourhood Correlates of Gun Homicide in Toronto: Multivariate Analyses Once again, an examination of goodness of fit statistics for a Poisson model of gun homicide counts revealed evidence of overdispersion. Therefore, for the multivariate analyses presented below, I estimate a series of negative binomial models using annual neighbourhood gun homicide counts summed over my period of examination. The results of these models are presented in Table 6.10.1 begin by estimating a model with the economic disadvantage index as the only predictor of gun homicide. Model 1 demonstrates that disadvantage is significantly and positively associated with gun homicides in Toronto: neighbourhoods with a larger proportion of disadvantaged residents experience higher levels of this violence. This is consistent with both the bivariate results and Hypothesis 3a, which predicted a positive association between economic disadvantage and gun homicide at the bivariate and multivariate levels. In Model 2,1 estimate a model that includes only percent black as a predictor of gun homicide. This allows me to determine if the bivariate relationship between percent black and gun homicide is replicated at the multivariate level, and, if so, the extent to which this relationship is reduced when the disadvantage index is added, in Model 3. As can be seen in the column labeled Model 2 in Table 6.10, there is a significant positive association between percent black and gun homicide in Toronto, which is consistent with Hypothesis 3d. In Model 3, which includes both percent black and the disadvantage 172 173 index, the coefficient for percent black remains positive and significant, while the coefficient for the disadvantage index, while reduced somewhat, also remains significant and positive. Thus, contrary to Hypothesis 3e, the bivariate association between percent black and gun homicide does not appear to be the result of the association between percent black and the disadvantage index. In Model 4, which includes the disadvantage index, percent black, and percent young males, the coefficient for the disadvantage index is reduced to non-significance, which is contrary to Hypothesis 3a. However, the coefficient for percent black remains positive and highly significant, as does the coefficient for my measure of percent young males. Thus, neighbourhoods in Toronto with large numbers of young male and black residents typically experienced higher levels of gun homicide. That the coefficient for percent young males is positive and significant supports Hypothesis 3c, which predicted such an association between measures of neighbourhood age structure and gun homicide in Toronto's neighbourhoods. Model 5, which includes the disadvantage index, percent black, percent mobility, and percent owners, demonstrates that the addition of the latter two variables does not reduce the significance of the disadvantage index and percent black, and that, contrary to Hypotheses 3h and 3i, neither percent owners nor percent mobility are significantly related to gun homicide in Toronto. The final model (Model 6) includes the disadvantage index, percent black, percent mobility, percent owners, percent young males, percent recent immigrants, percent divorced males, and the population log. In this model, consistent with expectations, the coefficients for the percent black and percent young male measures remain highly positive and significant - again, perhaps due to the fact that 173 174 most gun homicides over my period of examination involved young black males. However, none of the other neighbourhood characteristics are significantly associated with gun homicide in Toronto. This is inconsistent with Hypotheses 3a, 3f and 3i, which predicted a significant and positive relationship between measures of economic disadvantage, divorced males, residential mobility and gun homicide at the multivariate level, but is consistent with predictions that there would be no relationship between measures of recent immigration and this violence. 6.5 THE SOCIAL ECOLOGY OF INTIMATE FEMICIDE To date, much research on intimate partner violence has focused primarily on individual-level characteristics of victims and/or offenders, and event-level or situational characteristics. Comparatively less attention has been paid to the social ecological context within which this violence occurs (Lauritsen & Schaum, 2004). Miles-Doan (1998: 2) argues that the dearth of literature on the social ecology of intimate partner violence is due to a general assumption "that spouse and intimate violence is determined more by interpersonal and situational precipitants than by external agents of control". In other words, potential neighbourhood effects on partner violence have been overlooked because it is often assumed that these effects do not penetrate into intimate settings (Miethe & McCorkle, 1998). This assumption mirrors a tendency to study violence against women in isolation from the larger literature on violence in general. The intellectual separation of research on intimate partner violence stems from the premise that particular features shape the character of lethal conflict between intimate partners in distinctive ways. For example, men tend to kill their female intimate partners 174 175 in response to actual or impending estrangement and/or actual or suspected infidelity (Daly & Wilson, 1988). Studies also show that a couple's age discrepancy is related to the risk of intimate homicide; the greater the difference between the ages of the man and the woman, the higher the risk of lethal violence between them (Breitman, Shackelford & Block, 2004; Daly & Wilson, 1998; Mercy & Saltzman, 1989). The risk of intimate partner violence also varies according to relationship type. Women in common-law relationships are at a greater risk of this violence than are women in marital or dating relationships (Daly & Wilson, 1988), which may be a function of particular demographic characteristics common to common-law relationships. That is, women and men involved in these sorts of relationships tend to be younger, have lower levels of education, and higher levels of poverty and unemployment (Stets, 1991). Further, they are more likely to have children from previous relationships, which itself has been shown to increase the risk of homicide victimization of women (Wilson, Johnson & Daly, 1995). Studies have also found that women who are separated and divorced have heightened risks of intimate partner victimization. Consistent with the 'retaliation thesis' (Campbell, 1992), whereby men become more violent upon separation or divorce from their spouses, some research finds a significant positive relationship between the divorce rate and the killing of female ex-spouses (Stolzenburg & D'Alessio, 2007; Dugan et al., 2003; Wilson & Daly, 1993).105 Dugan et al. (2003) speculate that such patterns result from the escalation of violence between spouses that may occur immediately prior to the ending of the marriage and/or at the point of separation. 3 However, Gillis (1996) found otherwise. His assessment of legal reforms in mid-19th century France that allowed judicial separation to be granted in response to adultery or the threat/occurrence of serious violence found that the overall effect was a decline in rates of domestic homicide 175 176 A variety of individual indicators of economic distress have also been linked to the risk of intimate partner violence. For example, financial problems lead to feelings of stress and frustration that may find violent expression, most notably against female intimates (Macmillan & Gartner, 1999). Further, unemployed males or males earning low wages may be particularly likely to become violent during confrontations with their female partners. The reasons for this may be twofold. First, violence may serve as a substitute for "socioeconomic leverage" (Benson et al., 2003: 212), a means for men to establish their dominance and authority in the home (Macmillan & Gartner, 1999). As Benson et al. (2003: 230) argue: We suspect that male employment instability may increase the risk of intimate violence against women because it reduces men's sense of self-worth and represents a threat to their sense of masculinity. Being repeatedly fired or released from employment may provoke feelings of stigmatization and anger in males, who then may take out their frustrations on their partners. Men's sense of self- worth may be particularly vulnerable when they cannot hold a job, so they may become especially sensitive to affronts to their authority. Thus, the economic aspects of male unemployment may be a less important source of stress than its symbolic aspects. The second reason has to do with opportunity: compared to their employed counterparts, unemployed males may commit more violence in the home as a function of the amount of time they spend there (Cohen and Felson, 1979). Studies examining so-called "neighbourhood effects" on the risk of intimate partner violence have found that economically disadvantaged neighbourhoods also tend to experience higher rates of this violence than do their more advantaged counterparts (Miles-Doan, 1998; Benson et al, 2003; Lauritsen & Schaum, 2004; Van Wyk et al., 2003), as do neighbourhoods with high concentrations of unemployed males and single- parent families (Miles-Doan, 1998; Harries & Kovandzic, 2004). To the extent that 176 177 neighbourhood context reduces the likelihood of finding employment, it may increase the likelihood that some men will express their frustration and/or assert their control in the domestic setting through the use of violence against their less powerful female intimate partners (Messerschmidt, 1993; Miles-Doan, 1998). It may also be the case that neighbourhoods with large numbers of recent immigrants are more vulnerable to higher levels of intimate femicide. Research (see, for example, Chin, 1994; Hondegneu-Sotelo, 1994) suggests that some immigrant communities are characterized by traditional gender arrangements, and that immigrating to a new country, particularly a western one, can introduce fundamental shifts in gender and family relations. In a context where traditional gender expectations predominate but are being challenged (by, for example, exposure to more egalitarian gender roles and expectations), men may resort to violence against their female intimate partners as a means of maintaining their status within the relationship and preserving the traditional gender status quo (Giddens, 1992). Further, due to often tenuous relationships with formal agencies of social control, women - particularly immigrant women and women of colour - in these neighbourhoods may be reluctant to report their victimization to the police. There are several possible reasons for this: (1) the nature of the police in their country of origin may lead them to be distrustful of police organizations in the host country (Crenshaw, 1991; Shirwadkar, 2004; Wachholz & Miedema, 2000); (2) reporting their victimization may be perceived as bringing shame on the larger immigrant community (Crenshaw, 1991; Menjivar & Salcido, 2002; Shirwadkar, 2004); (3) abused immigrant women may be sponsored by their male intimate partners and/or entirely economically dependent upon them. As such, reporting their victimization risks the removal of their 177 178 sole source of sponsorship and/or economic support (Martin & Mosher, 1995; Wachholz & Miedema, 2000). A fourth reason is that there may be language barriers such that immigrant women feel they cannot communicate with agents of the formal legal system (Martin & Mosher, 1995). These and other considerations may mean immigrant women are less likely to report their victimization to legal authorities, which could serve to embolden their male intimate partners, and in some cases, the violence may escalate to homicide. For these reasons, then, neighbourhoods with large numbers of recent immigrants may be more vulnerable to higher levels of intimate femicide. A number of theoretical mechanisms have been proposed that link neighbourhood context with the risk of intimate femicide. According to social disorganization theory, neighbourhood rates of victimization and offending are a function of the strength of informal social controls therein. Neighbourhoods with heterogeneous and unstable populations are less able to realize residents' common values and maintain effective social controls (Bursick & Grasmick, 1993; Sampson & Groves, 1989; Sampson & Lauritsen, 1994). A number of researchers have argued that neighbourhood disadvantage is related to intimate partner violence for reasons identified by the perspective. For Some scholars also argue gender inequality - or inequality in the social status of females relative to males - is an important macro-social factor shaping the geography of female intimate partner homicide victimization. Theories of gender inequality focus on gender roles, gender norms and inequities in social power to explain differences in the use of violence by men and women, and the differing ways in which men and women are victimized. Geographic variation in rates of female homicide victimization, then, is posited to be a function of variation in the degree of gender inequality between men and women. That is, the more inequality there is between men and women in a particular geographic space, the more acceptable it is for men to use violence to control women, and by extension, the higher the expected rate of female homicide victimization (Bailey and Peterson, 1995). City- and state-level studies of the effects of improvements in women's status on homicide victimization, however, have yielded mixed results. Some studies have found a positive relationship between gender equality and violence (Baron and Straus 1988; Peterson and Bailey 1992) - which is supportive of the backlash thesis, while others have found a negative relationship (Bailey 1999). Still others report no statistically significant relationship between inequality and female violence once other structural variables are controlled (Brewer and Smith 1995; Ellis and Beattie 1983; Peterson and Bailey 1992). In this study, I do not incorporate measures of gender inequality because 178 179 example, the concentration of social structural deficits in so-called disorganized neighbourhoods, along with high levels of population turnover, undermine their capacity to invoke and exercise informal social controls on intimate partner violence, due to residents' isolation from one another and a concomitant reluctance to intervene for the common good of the community. Benson et al. (2003: 329) argue that: In neighbourhoods low on collective efficacy, it is not customary for residents to take action for the common good. Hence, no one feels the responsibility to intervene on behalf of victimized women. Violently-inclined spouses may therefore act aggressively against their partners with impunity, feeling that they have little to fear from neighbours either in the form of direct action or social disapproval. Another mechanism through which neighbourhood characteristics may influence intimate partner violence involves cognitive landscapes or "ecologically structured norms (e.g. normative ecologies) regarding appropriate standards and expectations of conduct" (Sampson & Wilson, 1995: 50). Sampson & Wilson posit that in some neighbourhoods, a system of values may emerge that tolerates, and in some cases condones, the use of violence in interpersonal relationships (see also Anderson, 1999). This would lead to the expectation that structurally disorganized neighbourhoods would experience higher rates of intimate partner violence, including homicide, due to cultural support for the use of violence more generally. Disadvantaged neighbourhoods may also be lacking in resources and supports for women in abusive relationships (Browning, 2002), further increasing the likelihood that they will be killed by their partners. In sum, then, while there may be similarities in the macro-level correlates (such as poverty, unemployment, and family disruption) of intimate femicide and other homicide types, some of the hypothesized mechanisms through which these factors translate into the extent to which levels of this inequality vary across small aggregations like neighbourhoods is likely negligible. 179 180 lethal violence among intimate partners are thought to be distinct. It is also important to note Benson et al.'s (2003: 229) claim that "the special characteristics of intimate violence, most notably its location in the home, would suggest that the contextual effect might be weaker". As such, while neighbourhood characteristics may well be associated with this type of violence, the special characteristics of intimacy and privacy may attenuate the role of local context in influencing lethal violence against female intimates. Again, given that my analysis does not include measures of the mediating mechanisms discussed above, such as those indicative of social disorganization (low levels of informal social control, the emergence of local norms that tolerate violence both within and without the home, as well as a lack of resources and supports for women in abusive relationships), I hypothesize: H4a: Measures of economic disadvantage will be associated with intimate femicide in Toronto at both the bivariate and multivariate levels. H4b: Large numbers of divorced males in Toronto's neighbourhoods will be associated with higher intimate femicide counts at both the bivariate and multivariate levels. H4c: Measures of neighbourhood residential mobility will be positively related to intimate femicide at both the bivariate and multivariate levels. H4d: Measures of recent immigration in Toronto's neighbourhoods will be positively related to intimate femicide at the bivariate and multivariate levels. H4e: The percentage of neighbourhood residents who are black will be positively related to intimate femicide counts at the bivariate level H4f: If percent black is related to intimate femicide through its association with economic disadvantage, the effect of neighbourhood racial composition on 180 181 intimate femicide will be reduced when controlling for measures of economic disadvantage in a multivariate model. H4g: The level of neighbourhood home ownership will be negatively related to intimate femicide at the bivariate and multivariate levels. 6.5.1 Intimate Femicide in Toronto's Neighbourhoods, 1988-2003: Sample Cases Case #1: The victim, a 43-year old mother of two, was stabbed to death by her 47-year old husband during an argument in their home in March, 1992. He then called 911 to report the killing. The victim and offender had been married for 25 years. At the time of the killing, the offender had been drinking heavily and was on probation for another offence that involved violence against his wife (Toronto Star, 4 March, 1992). Case #2: The victim, a 17-year old young woman, died of head injuries after her skull was fractured by a blow from an aluminum baseball bat in February, 2003. Her 17- year old ex-boyfriend, who indicated that he killed her "in a jealous rage" was charged with first degree murder (Toronto Star, 25 February, 2003; Toronto Star, 30 March, 2007). Case #3: The victim, a 26 year old woman, was shot to death with a double-barreled shotgun in her apartment by her estranged husband in December, 1991. He then turned the gun on himself. The murder-suicide was witnessed by the couple's two children, aged 4 and 2, who then left the apartment in search of relatives; they were found 2 kilometres away as they tried to cross a busy intersection. At the time of the killing, the offender had been free on bail for assaulting his wife. As a condition of his bail, he had been ordered by the courts to stay away from her and the children (Toronto Star, 3 December, 1991). 181 182 6.5.2 The Spatial Distribution of Intimate Femicide in Toronto, 1988-2003 Figure 6.4 provides a geographic representation of the distribution of intimate femicides across Toronto's neighbourhoods between 1988 and 2003. Over this period, 70 of Toronto's 140 neighbourhoods (50%) did not experience any incidents of this violence, 60 neighbourhoods (43%) experienced 1-2 intimate femicides, and 10 neighbourhoods (7%) experienced three or more intimate femicides. The neighbourhoods that experienced the highest levels of this violence include Woburn (5 intimate femicides), Mount Dennis and Black Creek (4 intimate femicides each), and Flemington- Park, High Park North, Islington-City Centre West, Newtonbrook West, Palmerston- Little Italy, South Riverdale, and Wexford/Maryville (3 intimate femicides each). When compared to the other homicide types examined in this dissertation, the spatial distribution of intimate femicide, is relatively distinct. That is, intimate femicide is more evenly distributed across neighbourhoods in Toronto, does not cluster in neighbourhoods adjacent to one another, and does not tend to occur in neighbourhoods that experience high levels of gun, black, and young male homicide. 6.5.3 Intimate Femicide in Toronto: Descriptive Statistics The killing of women by their male intimate partners is the most distinct of my homicide subtypes, both in its characteristics, as we shall see below, and in its correlates. Table 6.11 provides descriptive statistics for selected victim, incident and offender characteristics of intimate femicide (n=l 18) in Toronto's neighbourhoods between 1988 and 2003. In contrast to gun, young male, and black homicides, which tend to The neighbourhood-based data set used to perform my multivariate analyses contained 116 such homicides. This is because in two cases, information on the location of the killing was not available. 182 183 involve a disproportionate number of young persons, the risk of intimate femicide appears to be somewhat more evenly distributed across age categories. For example, 13% of these killings involved women aged 16-24, 36% involved women aged 25-34, 31% involved women aged 35-44, and in 21% of cases, the victim was forty-five years of age or older (the mean age was 38). Further, in contrast to the homicide types discussed previously, intimate femicide in Toronto tends to involve smaller proportions of black (19%) and unemployed victims (17%). Over half of these killings (56%) involved legally married spouses, with an additional 9% of victims involved in common-law unions. A quarter of intimate femicides involved lovers or ex-lovers, and in 10% of cases, the victim was killed by her ex-spouse. The overwhelming majority of these killings (85%) took place in a private residence, and stabbing was the most common cause of death (47%). In order to determine the relationship between victims of intimate femicide and their offenders, the identity of the offender (obviously) was known. Therefore, I have information on a much larger proportion of offenders than I do for the other homicide subtypes. Unlike perpetrators of gun, black, and young male homicides - who, like their victims, tend also to be young - the majority of perpetrators of intimate femicide in Toronto were age 35 or older. For example, 26% of offenders were between the ages of 35 and 44, with an additional 34% over the age of 45 (the mean age was 40). Consistent with other research, then, the perpetration of intimate femicide in Toronto does not appear to be as age-specific as is the perpetration of my other homicide subtypes. Patterns of unemployment among offenders also appear to differ across homicide types; whereas the majority of offenders involved in gun, black and young male homicides were 183 184 unemployed (65%, 61%, and 63%, respectively), only 35% of offenders in cases of intimate homicide were unemployed at the time of the killing. 6.5.4 Bivariate Associations Between Neighbourhood Characteristics and Intimate Femicide Table 6.12 shows the correlations between my measures of neighbourhood characteristics and the number of intimate femicides in Toronto's neighbourhoods between 1988 and 2003. At the bivariate level, intimate femicide is significantly, though weakly associated with the disadvantage index (.32), the proportion of black residents (.30), and the proportion of residents who changed residences in the previous five years (.28). These findings are consistent with Hypotheses 4a, 4c and 4e. Further, the proportion of residents who are recent immigrants and divorced males are not significantly related to intimate femicide in Toronto. The lack of an association between percent recent immigrants and percent divorced males is contrary to my hypothesis that there would be a positive relationship between these measures and intimate femicide in Toronto's neighbourhoods at the bivariate level. 6.5.5 Neighbourhood Correlates of Intimate Femicide in Toronto: Multivariate Analyses An examination of the goodness of fit statistics for a Poisson model of intimate femicide counts did not reveal evidence of overdispersion. Therefore, for the multivariate analyses of intimate femicide counts presented below, 1 estimate a series of Poisson models. The results of these models are presented in Table 6.13.1 begin by estimating a 108 It should be noted that many of the correlations between neighbourhood characteristics and intimate femicide are weaker in comparison to the other homicide subtypes. For example, the correlation between the disadvantage index and intimate femicide is .32, compared with young male homicides (.63), black homicides (.58), and gun homicides (.57) in Toronto's neighbourhoods. Some scholars (see, for example, Kubrin, 2003: 150) argue that such findings are to be expected: "domestic killings occur in and around the home and are more private in nature; therefore, they are less likely to be influenced by the surrounding community characteristics (identified in social disorganization theory) such as economic disadvantage". 184 185 model with the economic disadvantage index as the only predictor of intimate femicide. Model 1 demonstrates that disadvantage is significantly and positively associated with this type of homicide. This is consistent with Hypothesis 4a and with the larger literature, which suggests that neighbourhoods characterized by high levels of economic disadvantage also tend to experience higher levels lethal violence against female intimates (Miles-Doan, 1998; Benson et al., 2003; Lauritsen & Schaum, 2004; Van Wyk et al., 2003). As previously discussed, research has found a positive relationship between the divorce rate and the risk of intimate femicide (Stolzenburg & D'Alessio, 2007; Dugan et al., 2004; Wilson & Daly, 1993). I therefore estimate a model that includes both the disadvantage index and my percent divorced males measure. Model 2 demonstrates that the coefficient for the disadvantage index remains significant and positive, while there is no significant association between percent divorced males and this type of homicide. This is contrary to Hypothesis 4b, but consistent with the bivariate results.1 9 Recall that so-called "socially disorganized" neighbourhoods that are characterized by a population in flux and low levels of collective efficacy and informal social control are theorized to experience higher levels of intimate femicide, due to the anonymity among neighbours that may undermine the capacity of a neighbourhood to intervene in situations of domestic abuse, coupled the emergence of "ecologically structured norms", or a system of values that may tolerate the use of violence in interpersonal relationships (Sampson & Wilson, 1995; Fagan, 1993). In Model 3,1 estimate a model that includes a 109 It should be noted that, as previously discussed, women tend to be at a heightened risk of intimate partner violence at the point of separation, and this risk declines substantially by the time women are divorced from their partners. Therefore, a measure of "percent separated couples" would more adequately capture the mechanism through which much of this violence takes place. As such, the non-relationship between my percent divorced males measures and intimate femicide is perhaps not surprising. 185 186 block of measures that tap into the central tenets of the social disorganization perspective: the disadvantage index, percent mobility, percent owners, percent young males, and percent recent immigrants. In this model, the coefficient for the disadvantage index is reduced to non-significance, and percent mobility, percent owners, and percent young males are not significantly associated with intimate femicide in Toronto's neighbourhoods. Only percent recent immigrants is significantly and positively associated with this type of lethal violence, which is consistent with Hypothesis 4d. That percent recent immigrants is significantly associated with intimate femicide counts in Toronto may also be a function of the fact that, as previously discussed, some immigrant women may be reluctant to report their victimization to the police. Further, though resources and services for abused ethnic immigrant women existed in Toronto in the late 1980s and early 1990s, they were likely in short supply and may not have been adequately advertised in those communities. As such, many abused immigrant women may not have been aware of the supports that were available to them, which would lead to their under-utilization. The latter years of my period of examination may well have witnessed an expansion of such resources and services, though they may have remained insufficient to address the sometimes distinct needs of immigrant women. For these and other reasons discussed previously, abused immigrant women in Toronto may have been much less likely to report their victimization to the police and/or to have sought assistance from community based supports. In the absence of effective intervention, then, violence against some immigrant women may have escalated to the point that it became lethal. 186 187 The final model (Model 4) is comprised of the disadvantage index, percent black, percent mobility, percent owners, percent young males, percent recent immigrants, percent divorced males, and the population log. In this model, the coefficient for percent recent immigrants is reduced to non-significance. It should be noted that while the coefficient for percent recent immigrants does not decline, the standard error of the coefficient increases, such that the coefficient drops to just below significance. None of the other neighbourhood measures in this model is significantly associated with intimate femicide. As previously discussed, research in the United States on intimate partner homicide - at both the individual- and the neighbourhood-levels - concludes that the killings of intimate partners are empirically distinct in some important respects from other forms of criminal violence. The results of my research are consistent with this conclusion: in Toronto the social ecology of intimate femicide and other forms of lethal violence may be somewhat distinct. 6.6 TORONTO NEIGHBOURHOODS AND HOMICIDE TYPES: SUMMARY OF FINDINGS Discussion: Significant Effects Chapter Five demonstrated that, at the multivariate level, total homicide counts in Toronto's neighbourhoods between 1988 and 2003 are positively and significantly associated with economic disadvantage and large numbers of young male residents. Further, the multivariate analyses demonstrated that the relationship between my percent black measure and total homicide counts is due to the association between percent black and the disadvantage index. In other words, while neighbourhoods in Toronto with large 110 I also ran a negative binomial model and confirmed that there were no effects under this different model specification. 187 188 numbers of black residents experienced significantly more homicides over the period of examination, this is because neighbourhoods with a high proportion of black residents tend also to be economically disadvantaged. As such, it is neighbourhood poverty, not neighbourhood racial composition that is related to total homicide counts in Toronto. These results are consistent with American research on the social ecology of urban homicide that identifies economic disadvantage as an important predictor of homicide in urban neighbourhoods (Messner & Rosenfeld, 1999; Messner & Sampson, 1991). However, a number of neighbourhood characteristics that are often found to be associated with homicide in U.S.-based studies - for example, owner-occupied housing and residential mobility - did not emerge as significant predictors of homicide in the Toronto context. In this chapter, I examined whether the neighbourhood characteristics that are significantly associated with total homicide counts are also significantly related to each of four homicide types, and whether additional characteristics would emerge as important con-elates of homicide in Toronto. The results of my analyses show that some of the neighbourhood characteristics included in my models are related to some of my homicide types, but not to others. For example, as summarized in Table 6.14, the disadvantage index is significantly and positively related to total and young male homicide counts, but not to the killings of blacks, gun homicides, or intimate femicides. Similarly, the percent young male measure is significantly and positively related to total homicide counts, gun homicides and the killing of young men, but not to intimate femicide and black homicide counts. Finally, in the final models for gun homicide, the killings of young males and the 188 189 killings of black people in Toronto, percent black remained positive and highly significant. This last finding raises important questions about why my percent black measure matters in Toronto. Much of the research on the social ecology of lethal violence conducted in the U.S typically finds that so-called 'race effects' are diminished or rendered insignificant once measures of economic disadvantage are controlled for in multivariate analyses. While this was the case with respect to total homicide counts in Toronto's neighbourhoods, the association between percent black and three of my homicide types was not accounted for by its relationship to economic disadvantage or any of the other variables in my final models. These findings beg the question why. One answer is that a compositional effect is operating. In other words, in neighbourhoods with a preponderance of black residents, one would expect that a larger proportion of victims would also be black. Moreover, because gun homicides and the killing of males aged 15-34 in Toronto disproportionately involve black victims, one would also expect that my percent black residents measure will remain significant in the multivariate models for these homicide types. Yet there might also be contextual effects at play that may help to explain why there is an association between the proportion of black residents in a neighbourhood and various types of homicide. As discussed previously, a number of ethnographic studies suggest that high rates of crime and violence in disadvantaged neighbourhoods may be explained with reference to the emergence of local cultural adaptations that privilege violence as a means of status attainment and/or conflict resolution (Anderson, 1999; Horowitz, 1983; Fagan & Wilkinson, 1998). These social norms that develop in response to structural 189 190 disadvantage are especially applicable for understanding violent crime among young, racialized males, who, it is argued, are more likely to internalize and act upon the dictates of these "street codes" in their neighbourhoods. It may be the case that such codes are operating in Toronto's neighbourhoods, and that they represent an intervening mechanism that may give rise to higher risks of homicide among black Torontonians. As also previously discussed, my analyses do not include measures of any of these intervening mechanisms, so 1 am unable to determine whether and to what extent local value systems that are tolerant of, and in some cases prescribe the use of violence, shape the risk of violent victimization among blacks - particularly young black men - in Toronto. A second, and complementary explanation of the relationship between my percent black measure and lethal violence in Toronto's neighbourhoods, involves a consideration of the experience of being poor and black in Canadian society. Studies demonstrate that a high proportion of black youth perceive that Canadian society generally, and the Canadian criminal justice system in particular, discriminate against them (Ruck & Wortley, 2002). In response to this perceived discrimination, some may develop a set of values or norms that justify or excuse the use of violence. To the extent that perceptions of injustice in Canadian society have created a sense of social and legal inequality among young black males in Toronto, violence may also become part of a repertoire of behaviours for coping with the problems in their lives. The perception that one's racial or ethnic group faces discrimination in the school setting and later in the labour market may also prompt some to seek alternative, and illegal, sources of income. Drug markets, which are often controlled by local gangs, may 190 191 offer such an opportunity to people who feel that they have few-to-no options in the legitimate sphere. Further, as previously discussed, both gang- and drug-related activity are associated with high levels of violence among those involved. If it is the case that, due to perceptions of inequality in the opportunities available to them, young black males in Toronto are more likely to become involved in gang- and drug-related activity than are young men from other racial/ethnic groups, levels of violence, including homicide, among this group would necessarily be higher. In sum, then, cultural values that emerge in response to perceptions of injustice in Canadian society may operate in tandem with the rational choice a) to carry a weapon for the purposes of protection, and/or b) to seek alternative sources of income in the illegal economy. These so-called 'cultural adaptations' to structural inequalities may be useful for understanding why neighbourhood racial composition seems to matter for understanding high levels of gun, young male, and black homicide in Toronto's neighbourhoods. Additional research into the micro-level processes that give rise to higher levels of lethal violence among black Torontonians would also be helpful in interpreting these results. To recap, my findings suggest that neighbourhoods in Toronto characteristized by economic disadvantage and larger proportions of young male and black residents experienced higher levels of homicide, particularly homicides with young black male victims, during the period 1988-2003. The literatures on gun, black and young male homicide, reviewed above, identify similar neighbourhood processes associated with the risk of these homicide subtypes. This suggests that they might better be seen as the result of a similar neighbourhood context. At the same time, however, the differences that 191 192 emerged in the neighbourhood characteristics associated with my homicide subtypes lend some support to the idea that, to a certain extent, some types of homicide may be influenced by distinctive causal factors. Discussion: The Absence of Significant Effects My results also suggest that a number of my neighbourhood measures are not related to any of the homicide types. For example, my measures of residential mobility, owner-occupied housing, recent immigration, divorced males, and population size are unrelated to gun homicide, black homicide, young male homicide, and intimate femicide in Toronto's neighbourhoods. This is inconsistent with the findings of a number of studies conducted in U.S. cities. The findings in this chapter are, however, consistent with the results of my analyses in Chapter Five, which also found no association between these measures and total homicide counts in Toronto's neighbourhoods. We now turn to consider possible explanations for these findings. That residential mobility is not associated with lethal violence in Toronto's neighbourhoods may be explained with reference to studies that have found that residents in the neighbourhoods most affected by violent crime are precisely those least able to escape it through residential relocation (Benson et al., 2003). In other words, the poorest urban neighbourhoods may have the lowest levels of in- and out- migration because their residents are effectively 'stuck' there. As discussed in Chapter One, Canadian research has documented an increasing trend toward the geographic concentration of the most disadvantaged populations in some of Toronto's neighbourhoods. These populations tend to be comprised of unemployed or underemployed people, single-parent families, and visible minority and/or immigrant residents who are often discriminated against in the 192 193 employment and housing markets and are thus unable to move to other, better neighbourhoods (Oreopoulos, 2005; Fong & Shibuya, 2000; Kazemipur & Halli, 2000). To the extent that residents of these neighbourhoods are unable to leave, the population may be more stable than it is in flux. Given that the most disadvantaged neighbourhoods in Toronto have low levels of population turnover because racial and economic discrimination prevent residents from leaving, measures of residential mobility may not be associated with high levels of lethal violence in these neighbourhoods. The percent recent immigrants measure was also not significantly associated with any of the homicide subtypes in my final models. This is consistent with recent studies in the U.S. that suggest that immigration has a negative or no effect on patterns of violent crime, even in neighbourhoods characterized by large numbers of immigrants and high levels of economic disadvantage (Sampson, 2008; Reid et al., 2005; Martinez, 2002; Lee et al., 2001). In recent years, Canadian immigration policy has placed growing importance on education, business, and work experience as admission standards to Canada, which has attracted immigrants who are better educated, younger, and more fluent in either English or French than the immigrant population as a whole. Further, the presence of ethnic residential and business enclaves in Toronto's neighbourhoods may mean that some immigrant communities possess high levels of social capital and dense network ties that are helpful in assisting coethnics adapt to the host country. Upon arrival in Toronto, then, those newcomers with few economic resources or opportunities may be able to find employment, sparing them from the severe economic hardship and/or discrimination in the labour market that many new immigrants faced in the past (Logan et al., 1994; Zhou, 1992). Thus, while early social disorganization theorists described 193 194 immigrant communities as disorganized and vulnerable to high rates of violent crime, in some disadvantaged immigrant neighbourhoods, high levels of social capital may instead serve as a buffer that protects residents from this violence. It is also possible that construct validity, measurement error and unrefined measures may be responsible for the absence of effects between my recent immigrant measure and homicide types in Toronto. Recall that this measure captures immigrants who came to Toronto over the previous 3-5 years. As such, it does not capture those who have been in Toronto for longer periods of time. Research suggests that rates of violence among first- generation immigrants are lower than those among second generation immigrants, which are themselves lower than those of third generation immigrants (Sampson et al., 2005). To the extent that the risk of violent victimization and offending increases with length of time in the host country, neighbourhood-level measures of recent immigrants may not capture the complexity of this relationship. My percent recent immigration measure is also crude in the sense that it does not distinguish between specific immigrant groups. Research shows that rates of interpersonal violence vary among immigrant groups (see, for example, Martinez & Nielsen, 2006), and this variation is thought to be, in part, a function of the settings and contexts that immigrant groups move into, the amount of social capital they possess, and the structural constraints they may face upon arrival in the host country. As such, the concentration of some immigrant groups in certain neighbourhoods in Toronto might be associated with higher levels of lethal violence therein, but my immigration measure is not able to capture variation among groups. My percent divorced males measure was also not significantly associated with any of my homicide subtypes. Divorce is hypothesized to increase rates of violent crime at the 194 195 neighbourhood-level because of the instability that occurs with the disintegration of the family unit, and a concomitant weakening of local levels of social control (Parker & Johns, 2002). As previously indicated, however, the percentage of neighbourhood residents who are divorced males may not adequately capture neighbourhood family disruption, which could serve to confound efforts to understand any association between divorce and lethal violence at the neighbourhood level. My measure of neighbourhood home ownership was also not significantly associated with any of my homicide subtypes, though the relationship was a negative one, as expected. Recall that research suggests that neighbourhoods with larger numbers of owner-occupied dwellings tend to be neighbourhoods that experience lower rates of violent crime (Sampson et al., 1997; Krivo & Peterson, 2000; Hoff & Sen, 2005). By contrast, neighbourhoods with high numbers of renters typically experience higher rates of violent crime (Wallace et al., 2006), which is hypothesized to be a function of anonymity among local residents and the concomitant absence of guardianship behaviours that would protect the neighbourhood from high levels of violent crime. My analyses did not include measures of such mediating mechanisms, thus home ownership was modeled as having a direct effect on homicide types in Toronto's neighbourhoods. Had my analyses been able to model the more nuanced mechanisms that have been theorized to link home ownership and local levels of lethal violence, my results may have provided greater insight into a possible relationship between the two. The average logged population measure was also not significantly associated with any of my homicide types, meaning that it is not the case that neighbourhoods with larger populations necessarily experience higher levels of gun, young male and black homicide 195 196 or intimate femicide. Recall that homicides are not randomly distributed across Toronto's neighbourhoods. If they were, it would be expected that neighbourhoods with larger resident populations would have higher levels of homicide.111 As such, the lack of a relationship between the average logged population measure and homicide counts in Toronto - regardless of type - is perhaps not surprising. Finally, intimate femicide counts in Toronto are not significantly associated with any of the neighbourhood characteristics included in my analyses. That is, none of the neighbourhood measures included in my final model are related to the risk of this type of homicide in Toronto. This may be a function of sample size; compared to other homicide subtypes, there were fewer intimate femicides (n=l 16) over the period of examination, which could mean that my models lacked the statistical power to detect relationships between this type of homicide and my neighbourhood measures. Further, as previously discussed, the lack of relationships between the neighbourhood measures in my final model and intimate femicide counts in Toronto may also be a function of the specific etiological nature of this violence. Had my analyses included a broader range of neighbourhood characteristics the results may have provided greater insight into the risk factors for this form of lethal violence in Toronto. This necessarily implies that neighbourhoods with smaller resident populations would be expected to experience lower levels of lethal violence. However, in Toronto, this is not the case. For example, the downtown neighbourhoods that comprise Toronto's 'entertainment district' have small resident populations, due to their location in and around the city's financial district, but relatively high homicide counts. 196 197 Table 6.1: Pearson Correlation Coefficients among Homicide Subtypes in Toronto Neighbourhoods, 1988-2003. 1 2 4 1 Intimate Femicide 1.00 .321** .325** .281** 2 Gun Homicide 1.00 .865** .780** 3 Young Male 1.00 .783** Homicide 4 Black Homicide 1.00 **p<.01 197 198 TABLE 6.2 CHARACTERISTICS OF HOMICIDES INVOLVING BLACK VICTIMS IN TORONTO'S NEIGHBOURHOODS, 1988-2003 (n=225) Victim Sex (n=225) % Male 83% % Female 17% Victim Age (n=979) Mean Victim Age 27 % 1-15 7% % 16-24 40% % 25-34 39% % 35-44 8% % 45+ 6% Victim Marital Status (n=195) % Single (never married) 61% % Married (including common-law) 25% % Separated/Divorced (including common-law) 13% % Other112 1% Victim Employment Status (n=200) % Employed 36% % Unemployed 37% % Student 20% % Other 7% Victim-Offender Relationship (n=136) % Friends/Acquaintances 34% % Strangers 27% % Intimate Partners 13% % Illegal Relationship 10% % Other113 13% Percent of cases in which no offender identified 33% Offender Sex (n-150) % Male 95% % Female 5% " This category includes widowed, living together for a short time (less than one month), or off an on for short periods. J This category includes housemates/roommates, neighbours, legal business relationships, co-wokers, lovers' triangles, family, and foster children. 198 199 Offender Age (n=141) % 0-15 1% % 16-24 46% % 25-34 37% % 35-44 11% % 45+ 6% Offender Race (n=10) % Black 92% % White 6% % Other 2% Offender Employment Status (n=109) % Employed 22% % Unemployed 61% % Other 17% Location of Killing (n=225) % Residence 34% % Public 29% % Stores, bars 19% % Semi-public 8% % Car 7% % Other 3% Method of Killing (n=225) % Shot 64% % Handgun 93% % Long gun 6% % Other/unspecified gun 1 % % Stabbed 24% % Beaten 7% % Strangled/Suffocated 2% % Other114 3% Homicides Committed in the Course of (n=177) % Robbery/theft 14% % Dispute over Illegal $ 5% % Other115 81% 114 This category includes death by poisoning, arson, drowning, thrown or pushed from height, scalding, neglect, hit by car, overdose, and unspecified means. 1,5 This includes defense of person or property (e.g. killing intruder), sexual assault, argument, revenge, unspecified, mercy killing, spurned attention, and 'other' motive (i.e. for which a coding category did not exist). 199 200 FIGURE 6.1 Homicide in Toronto's Neighbourhoods Involving Black Victims, 1988-2003 fc *^P» •ft'- t ••••., * mlmJ- »-**«« Black Homicides -* 200 201 Table 6.3: Pearson Correlation Coefficients among Neighbourhood Characteristics and the Number of Homicides with Black Victims in Toronto Neighbourhoods (n=140), 1988-2003. Disadvantage Index .58** % Black 70** % Mobility .24** % Owners -.24** % Young Males 97** % Recent Immigrants .05 % Divorced Males -.16 Average Population Log -.03 **p 201 202 Table 6.4: Negative Binomial Regressions: Neighbourhood Characteristics and Homicides with Black Victims (n=140) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Disadvantage .224*** .050 .024 .084 .061 Index (.042) (.049) (.048) (.062) (.061) % Black .164*** .168*** 153* * * .146*** (.027) (.034) (.034) (.035) (.037) % Mobility .035 .011 (.019) (.024) % Owners .014 .006 (.010) (.012) % Young .113** .131 Males (.0507) (.070) % Recent .038 Immigrants (.068) % Divorced -.196 Males (.138) Average .117 Logged (.723) Population Constant .256 -.957 -.834 -2.709 -3.066 -3.623 Log -223.753 -211.051 -210.515 -207.941 -208.799 -205.845 Likelihood **p<.01 ***p<.001 Note: Unstandardized coefficients and standard errors (in parentheses). 202 203 FIGURE 6.2 Homicides in Toronto's Neighbourhoods Involving Males (15-34), 1988-2003 i .(?i ••• Young Males 15-34 o 1 -2 3-6 7+ 203 204 TABLE 6.5 CHARACTERISTICS OF MALE (15-34) HOMICIDES IN TORONTO'S NEIGHBOURHOODS, 1988-2003 (n=389). Victim Age (n=389) Mean Victim Age 25 %1-15 2% % 16-24 48% % 25-34 50% Victim Race (n-285) % Black 55% % White 14% % Asian 13% % Other116 18% Victim Marital Status (n=337) % Single (never married) 72% % Married (including common-law) 20% % Separated/Divorced (including common-law) 7% % Other"7 2% Victim Employment Status (n=333) % Employed 36% % Unemployed 43% % Student 18% % Other 3% Victim-Offender Relationship (n=241) % Friends/Acquaintances 39% % Strangers 32% % Illegal Relationships 16% % Family 5% % Intimate Partners 3% % Other118 6% Percent of cases in which no offender identified 32% Offender Sex (n=263) % Male 95% % Female 5% 116 This includes Northern, Southern and Eastern European. South and Latin American, Middle Eastern and Aboriginal Victims. This category includes widowed, living together for a short time (less than one month), or off an on for short periods. This category includes housemates/roommates, neighbours, legal business relationships, co-wokers, lovers' triangles, and foster children. 204 Offender Age (n=253) %0-15 1% % 16-24 53% % 25-34 39% Offender Race (n=141) % Black 51% % White 20% % Other 13% Offender Employment Status (n=199) % Employed 23% % Unemployed 63% % Student 11% % Other119 14% Location of Killing (n=389) % Residence 23% % Public 25% % Stores, bars 34% % Semi-public 9% % Car 6% % Other 1% Method of Killing (n=389) % Shot 60% % Handgun 87% % Long gun 12% % Other/unspecified gun 1% % Stabbed 28% % Beaten 9% % Strangled/Suffocated 2% % Other120 2% Homicides Committed in the Course of (n=315) % Robbery/theft 11% % Dispute over Illegal $ 6% % Other121 83% 119 This includes students and those on welfare/disability This category includes death by poisoning, arson, drowning, thrown or pushed from height, scalding, neglect, hit by car, overdose, and unspecified means. 121 This includes defense of person or property (e.g. killing intruder), sexual assault, argument, revenge, unspecified, mercy killing, spurned attention, and 'other' motive (i.e. for which a coding category did not exist). 205 206 Table 6.6: Pearson Correlation Coefficients among Neighbourhood Characteristics and the Number of Homicides with Male Victims Aged 15-34 in Toronto Neighbourhoods (n=140), 1988-2003. Disadvantage Index .63** % Black .56** % Mobility .34** % Owners -.33** % Young Males .43** % Recent Immigrants .04 % Divorced Males -.02 Average Population Log -.02 206 207 Table 6.7: Negative Binomial Regressions: Neighbourhood Characteristics and Homicides with Young Males Aged 15-34 (n=140) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Disadvantage .221*** .091** 119** (.040) (.048) (.047) (.58) (.057) % Black 135* * * .076** .088*** .063** .077** (.023) (.029) (.030) (.030) (.033) % Mobility .047** .006 (.017) (.022) % Owners .014 .008 (.009) (.011) % Young 159*** .185*** Males (.045) (.062) % Recent .011 Immigrants (.058) % Divorced -.057 Males (.122) Average .194 Population (.630) Log Constant .821 .072 .351 -2.516 -2.463 -4.055 Log -284.621 -286.183 -280.920 -272.828 -276.888 -271.885 Likelihood **p<.01 ***p<.001 Note: Unstandardized coefficients and standard errors (in parentheses). 207 208 FIGURE 6.3 Homicides in Toronto's Neighbourhoods Involving Guns, 1988-2003 •IP «> '%vrK Gun Homicides ;. K' •'*'" •.**.-•.• Mkf ! Ifl i " |1-2 •13-6 ••1 74. 208 209 TABLE 6.8 CHARACTERISTICS OF GUN HOMICIDES IN TORONTO'S NEIGHBOURHOODS, 1988-2003 (n=340). Victim Sex (n=340) % Male 87% % Female 13% Victim Age (n=340) Mean Victim Age 30 % 1-15 2% % 16-24 36% % 25-34 39% % 35-44 12% % 45+ 11% ictim Race (n=266) % Black 54% % White 13% % Asian 14% % Other122 19% Victim Marital Status (n=302) % Single (never married) 58% % Married (including common-law) 30% % Separated/Divorced (including common-law) 10% % Other123 2% Victim Employment Status (n=299) % Employed 36% % Unemployed 45% % Student 16% % Other 3% Victim-Offender Relationship (n=187) % Friends/Acquaintances 32% % Strangers 30% % Illegal Relationships 16% % Family 6% % Intimate Partners 10% % Other124 7% Percent of cases in which no offender identified 41% 1_" This includes Northern, Southern and Eastern European, South and Latin American, Middle Eastern and Aboriginal Victims. ,_J This category includes widowed, living together for a short time (less than one month), or off an on for short periods. 124 This category includes housemates/roommates, neighbours, legal business relationships, co-wokers, lovers' triangles, and foster children. 209 210 Offender Sex (n=199) %Male 100% % Female .5% Offender Age (n=l 88) % 16-24 45% % 25-34 37% % 35-44 8% %45+ 10% Offender Race (n=104) % Black 55% % White 19% % Asian 13% % Other 13% Offender Employment Status (n=143) % Employed 22% % Unemployed 65% % Other125 13% Location of Killing (n=340) % Residence 29% % Public 28% % Stores, bars 25% % Semi-public 9% %Car 10% % Other 1% Gun Type (n=306) % Handgun 82% % Long gun 16% % Other/Unspecified 2% Homicides Committed in the Course of (n=263) % Robbery/theft 16% % Dispute over Illegal $ 6%o % Other126 78% 12" This includes students and those on welfare/disability 126 This includes defense of person or property (e.g. killing intruder), sexual assault, argument, revenge, unspecified, mercy killing, spurned attention, and 'other' motive (i.e. for which a coding category did not exist). 210 211 Table 6.9: Pearson Correlation Coefficients among Neighbourhood Characteristics and the Number of Gun Homicides in Toronto Neighbourhoods, 1988-2003 (n=140). Disadvantage Index .57** % Black .58** % Mobility 23** % Owners -.25** % Young Males .33** % Recent Immigrants .01 % Divorced Males -.09 Average Population Log -.01 **p<.01 211 212 Table 6.10: Negative Binomial Regressions: Neighbourhood Characteristics and Homicides with Guns (n=140) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Disadvantage .091** .054 .108** .072 Index (.039) (.045) (.044) (.055) (.053) % Black .135*** .103*** .096*** (.023) (.029) (.029) (.030) (.033) % Mobility .026 -.012 (.018) (.022) % Owners .008 .004 (.009) (.011) % Young 181 * * * Males (045) (.061) % Recent .007 Immigrants (.063) % Divorced -.074 Males (.128) Average .033 Population (.672) Log Constant .726 -.077 .120 -2.123 -1.461 -2.432 Log -273.222 -269.626 -267.301 -262.504 -266.170 -261.293 Likelihood **p<.01 ***p<.001 Note: Unstandardized coefficients and standard errors (in parentheses). 212 213 FIGURE 6.4 Intimate Femicides in Toronto's Neighbourhoods, 1988-2003. ^ 0 - - % Jk % * Intimate Femicide • 2 i im -3+ 213 214 TABLE 6.11 CHARACTERISTICS OF INTIMATE FEMICIDES IN TORONTO'S NEIGHBOURHOODS, 1988-2003 (n=118). Victim Age (n=118) Mean Victim Age 38 % 16-24 13% % 25-34 36% % 35-44 31% %45+ 21% Victim Race (n=83) % White 29% % Black 19% % Asian 14% % Other127 38% Victim Employment Status (n=105) % Employed 58% % Unemployed 17% % Student 13% % Houseworker (unpaid) 6% % Other128 6% Victim-Offender Relationship (n=118) % Spouse 56% % Ex-Spouse (including common-law) 10% % Common-Law 9% % Lovers, ex-lovers 25% Offender Sex (n=l 18) %Male 100% Offender Age (n=l 17) % 16-24 11% % 25-34 29% % 35-44 26% % 45+ 34% Offender Race (n=74) % White 15% % Black 14% % Asian 7% % East Indian 5% % South American 5% % Other129 53% This includes Northern, Southern and Eastern European, South and Latin American, Middle Eastern and Aboriginal Victims. 128 This includes individuals on welfare or disability pension, and those who were periodically or seasonally employed but not currently working. 214 215 Offender Employment Status (n=106) % Employed 48% % Unemployed 35% % Other130 17% Location of Killing (n=117) % Residence 85% % Public 6% % Semi-public 2% % Car 3% % Other131 4% Method of Killing (n=l 17) % Stab 47% %Beat 17% % Shoot 16% % Strangle 15% % Other132 5% This includes Northern, Southern and Eastern European, Aboriginal and Middle Eastern. This includes students and those on welfare/disability, and those out of the workforce (retired), and those who were periodically or seasonally employed but not currently working. This includes hotel rooms and unknown locations, though victim's body was found in a car, river or lake. J" Refers to sexual assault and mercy killings. 215 216 TABLE 6.12 Pearson Correlation Coefficients among Neighbourhood Characteristics and the Number of Intimate Femicides in Toronto Neighbourhoods, 1988-2003 (n=140). Disadvantage Index 22** % Black 30** % Mobility 2g** % Owners -.26** % Young Males .26** % Recent Immigrants -.09 % Divorced Males .01 Average Population Log -.01 **p<.01 216 217 TABLE 6.13 Poisson Regressions: Neighbourhood Characteristics and Intimate Femicide Counts (n=140) Model 1 Model 2 Model 3 Model 4 Disadvantage QOT*** 094*** .031 -.002 (.023) (..023) (.038) (.050) % Black .034 (.028) % Mobility .005 -.001 (.021) (.024) % Owners -.001 -.002 (.008) (.010) % Young .062 .067 Males (.055) (.058) % Recent .040** .043 Immigrants (.016) (.023) % Divorced -.018 .049 Males (.085) (.152) Average .053 Population (.594) Log Constant -.238 -.175 -2.141 -2.512 Log -169.371 -169.344 -161.850 -160.980 Likelihood **p<.01 ***p<.001 Note: Unstandardized coefficient and standard errors (in parentheses). 217 218 TABLE 6.14 Summary of Multivariate Results Total Young Black Gun Intimate Homicides Male Homicides Homicides Femicides Homicides Disadvantage + + Index % Black + + + % Movers % Owners % Young + + + Males % Recent Immigrants % Divorced Males Average Logged Population 218 219 CHAPTER VII. SUMMARY AND CONCLUSIONS In examining the social ecology of homicide in Toronto, this study has yielded findings that are important for explaining how and why homicide concentrates where it does across the city's neighbourhoods. The literature on neighbourhoods and violent crime has consistently shown that the structural characteristics of neighbourhoods - including demographic, socioeconomic and housing features - influence the levels of violence they exhibit (Bursick & Grasmick, 1993; Krivo & Peterson, 2000; Sampson et al., 2002). To date, however, the bulk of this literature is grounded in the American context. The findings presented in this dissertation build upon the extant literature by examining the social ecology of lethal violence in a large Canadian city. My results indicate that there are some similarities in the spatial distribution and neighbourhood- level correlates of lethal violence in Toronto and U.S. cities, but also some important differences. This chapter proceeds as follows. First, I outline the main findings of the preceding chapters, detailing how my results both confirmed, differed from, and extended the literature on neighbourhoods and violent crime. Second, I discuss the policy implications of my study of neighbourhoods and homicide in Toronto. Third, I describe directions for future research based on the findings of this study. Finally, I review the limitations of this study and make recommendations for remedying those limitations in future research. 219 7.1 SUMMARY OF FINDINGS The analyses presented in Chapters Five and Six demonstrated that, as is the case south of the border, high levels of lethal violence tend to cluster in a small number of inner-city neighbourhoods in Toronto. However, unlike the spatial distribution of lethal violence in many U.S. cities, this violence also tends to cluster in neighbourhoods located outside of the city core. This likely has much to do with the "necklace of high poverty neighbourhoods" (Wente, 2004) that ring Toronto's urban core. These are neighbourhoods that concentrate and contain socially and economically marginalized populations and the social problems that often attend them. My first set of multivariate analyses, presented in Chapter Five, addressed an important question: What neighbourhood-level characteristics are associated with variation in the overall level of lethal violence in Toronto's neighbourhoods? More specifically, the goal was to determine whether key correlates of total homicide rates identified in the largely American literature also apply in the Canadian context. My results indicate that, consistent with research on the structural covariates of total homicide conducted in the United States, neighbourhood economic disadvantage and the percent of residents who are young males are important predictors of this violence (Anderson, 1990; Blau & Blau, 1982; Huff-Corzine et al, 1986; Sampson, 1985, 1986; Steward & Simons, 2006). However, other neighbourhood characteristics that typically are identified in theory and emerge from research on neighbourhood effects and violent crime in the U.S. do not appear to be associated with total homicide counts in Toronto. More specifically, measures of neighbourhood racial composition, housing ownership, and residential instability are not significantly associated with the risk of homicide 220 221 victimization in Toronto's neighbourhoods. These findings lend support to the suggestion that while measures of economic disadvantage may have invariant effects on the risk of lethal violence, the effects of other structural characteristics on homicide may instead be context specific (Land et al., 1990). As discussed in Chapter Three, researchers have become increasingly interested in determining whether and how criminological and sociological theory may be appropriate for explaining homicides disaggregated by victim/offender relationship, by motive and circumstance, by racial group, and by age and sex-specific subgroups, among other characteristics (Curtis, 1974; Fagan et al., 2003; Kovandzic et al., 1998; Kubrin, 2003; Kubrin & Wadsworth, 2003; Miethe & Drass, 1999; Nielsen et al., 2005; Parker & Smith, 1979; Peterson & Krivo, 1993; Titterington et al., 2003; Williams & Flewelling, 1988; Wolfgang, 1958). The few studies to date on "neighbourhood effects" and disaggregated homicide types demonstrate that (1) homicide rates disaggregated by demographic or situational characteristics are associated with different ecological covariates; and (2) the magnitudes of relationships differ across homicide subtypes (Kubrin, 2003; Kubrin & Wadsworth, 2003). Informed and inspired by this research, this dissertation examined the spatial distribution of and the neighbourhood-level characteristics associated with four homicide subtypes. The analyses presented in Chapter Six demonstrate that gun homicides, along with the killings of blacks and young males are, in large part, similarly distributed across Toronto's neighbourhoods. That is, these killings tend to cluster in neighbourhoods adjacent to one another and peripheral to the city core. Further, there is considerable overlap among neighbourhoods with high levels of gun homicides, black homicides, and 221 222 young male homicides, which is to be expected, given the strong correlations among these types of killings and the neighbourhood-level characteristics associated with them. These neighbourhoods include Thistletown-Beaumont Heights, Glenfield-Jane Heights, L'Amoreaux, Black Creek, and Regent Park. With the exception of the Regent Park, all are located on the outer fringes of the city. The spatial distribution of the fourth homicide subtype, intimate femicide, is relatively distinct. That is, intimate femicide is more evenly distributed across neighbourhoods in Toronto, does not cluster in neighbourhoods adjacent to one another, and does not tend to occur in neighbourhoods that experience high levels of gun, black, and young male homicide. This is perhaps not surprising, given that the killing of women by their male partners is the most distinct of my homicide subtypes, both in its individual-level characteristics and its correlates. My multivariate analyses demonstrated that some of the neighbourhood characteristics included in my models are related to some of the homicide subtypes, but not to others. The disadvantage index was significantly and positively related to the killing of young men (15-34) in Toronto, but not to any of the other homicide subtypes. Further, the percentage of neighbourhood residents who are young males was related to young male and gun homicides, but not to black homicides or intimate femicides, while the percent black measure was related to young male, black, and gun homicides in Toronto, but not to intimate femicide. As such, neighbourhoods in Toronto characterized by larger proportions of young male and black residents experienced higher levels of homicide, particularly gun homicides involving young black male victims over the period of examination. 222 223 My results also suggest that several of my neighbourhood measures are not related to any of the homicide subtypes. For example, none of the neighbourhood measures included in my models are significantly associated with intimate femicide counts in Toronto's neighbourhoods. Further, my measures of residential mobility, owner-occupied housing, recent immigration, divorced males, and population size are unrelated to gun homicide, black homicide, young male homicide, and intimate femicide in Toronto between 1988 and 2003. This is inconsistent with the findings of a number of U.S. studies, but consistent with the results of my analyses in Chapter Five, which also found no association between these measures and total homicide counts in Toronto's neighbourhoods. As such, many of the ecological correlates of lethal violence identified in the larger, American literature do not appear to apply in the Toronto context. 7.2 POLICY IMPLICATIONS To borrow from the field of geographic epidemiology, the "ecology of risk" and "geography of intervention" (Andrews, 1985) are important organizing frameworks for considering the policy implications of this study. Having mapped the incidence of homicide across Toronto's neighbourhoods over a 16-year period and examined the neighbourhood-level characteristics associated with this violence, I would suggest that my research offers an empirical base that may aid in targeting neighbourhoods vulnerable to high levels of homicide for anticipatory intervention initiatives. However, one of the most difficult challenges for policy makers interested in reducing violent crime in Toronto may lie in determining what not to do (Doob, 2004). Though the policy implications outlined in this section will challenge some of the current practices in 223 224 Toronto and their underlying beliefs, they are hardly radical in nature. Indeed, given the increasing awareness of the modest or limited effects of many popular interventions, it is my hope that policy makers in Toronto will begin to turn to other crime prevention and reduction initiatives that may more effectively address the structural characteristics of neighbourhoods that render them vulnerable to high rates of lethal violence. A common intervention in high crime neighbourhoods involves intensive, 'targeted' law enforcement strategies. Research, however, suggests while these interventions may sometimes have positive short-term effects, they generally do not reduce crime and violence in the long-term (Cohen et al., 2003; Ludwig, 2005; Rosenfeld et al., 2005). Further, aggressive policing strategies may also lead to increased levels of fear of crime among residents (Rosenbaum, 2006; Kane, 2005; Bidenball & Jesilow, 2005). Researchers argue this is because the very labeling of a neighbourhood as a "hot spot" for crime and violence may raise residents' concerns, as Hinkle and Weisburd (2008: 509) have argued: Walking on the street and observing police may make individuals feel safer, but if they notice much greater police attention on the street block where they live, they are certain to be reminded of the problems that exist on their street. Additionally, seeing a sudden increase in police presence on their block may lead residents to infer that crime has increased and that their block is more dangerous and crime prone than in the past. As such, increased levels of fear among residents that may stem from aggressive police tactics have the potential to offset the benefits of any short-term reductions in crime and violence those tactics may have achieved. Studies also suggest that in neighbourhoods where relationships between residents and the police are already strained, aggressive policing strategies may not only further undermine those relationships, but may also serve to increase local levels of violent 224 225 crime. As Walker (1992) has noted, residents of disadvantaged urban neighbourhoods typically articulate that the police are overly aggressive in their interactions with local residents and with their use of coercive power more generally, which can lead to alienation and conflict between the two groups. As a consequence, neighbourhood residents may choose to bypass agents of the formal system altogether, relying instead on informal methods to redress interpersonal disputes (Anderson, 1999; Kane, 2005; Kubrin & Weitzer, 2003). In addition, studies suggest that decreases in perceived police legitimacy may be associated with reactions of defiance and reduced compliance with the law (Tyler, 1990). As such, increased levels of violent crime in some disadvantaged neighbourhoods may stem, in part, from enforcement activities that undermine the authority of the police as a legitimate institution of formal social control (Kane, 2005; Tyler, 1990; Tyler & Wakslak, 2004). Given the evidence suggesting that police crackdowns and related tactics are unlikely to have beneficial long-term effects, it would appear that caution should be exercised in assuming that no harm can come from such activities. A second common intervention in neighbourhoods that experience high levels of violent crime involves an influx of area-based crime prevention resources and services. This has certainly been the case in Toronto, particularly following the introduction of the City's Community Safety Plan (CSP hereafter) in 2004. Implemented in response to shifts in the social distribution of violent crime in Toronto, as discussed in Chapter Two, the CSP is a toolbox of crime and violence prevention initiatives designed to improve public safety among the groups and neighbourhoods where violent crime is thought to concentrate. In May of 2004, the Strong Neighbourhoods Task Force, a collaborative 225 226 effort between the City of Toronto and the United Way, was established to assess needs in the City's neighbourhoods, and to identify how and where neighbourhood-level investments and interventions should be directed. The Task Force examined indicators of both demographic and physical assets for each of Toronto's 140 neighbourhoods. Subsequently, a number of "priority" neighbourhoods were identified for targeted programs and resources, provided through a broad network of government, community, public and private supports. Though "priority neighbourhoods" were identified in the absence of crime data, many of the initiatives implemented as part of the CSP are aimed at preventing or reducing crime and violence in those neighbourhoods. For example, in Toronto, a variety of gang prevention/exit, anti-violence and anger management programs have been implemented, as have neighbourhood-specific safety audits, whereby 'problem areas' are identified and attended to according to the principles of crime prevention through environmental design. Research, however, suggests that the geographic concentration of crime prevention initiatives may not have an appreciable impact on local crime rates (Savolainen, 2005). This is largely due to the sheer breadth and number of different programs that are often implemented as part of urban community safety plans, which are typically implemented in a manner that makes it difficult, if not impossible, for them to be evaluated. This has been the case in Toronto, and while the desire to respond quickly to changes in the nature and distribution of violence speaks to the level of concern over community safety in the city's neighbourhoods, the speed with which interventions have been implemented limits '"'"' Among the demographic indicators were median household income, percent unemployed, educational attainment, immigrant share of the population, percentage of private households requiring major repairs, and rate of low birth weight babies. Physical assets indicators included the number of schools, community health centers, children and youth services, food banks, and recreations and community centers, as well as the accessibility of those resources and services. 226 227 the ability to put evaluation strategies in place. Criminological research has consistently highlighted the importance of evaluating and monitoring the effects of crime prevention initiatives to ensure that they are having the desired impact. This is, in part, because some well-intentioned interventions have been shown to have negative or harmful effects in the past (Doob, 2004; McCord, 1978, 2002). As such, it is perhaps timely to restate Hope and Murphy's (1983) warning that it not be assumed that the mere implementation of a program will necessarily proceed in a logical sequence toward violence prevention. Careful program evaluation and monitoring are also important in light of the limited resources that are available for investment in neighbourhood-based violence prevention and reduction programs. Thus, an understanding of which interventions are the most effective use of scarce resources is important from a cost-benefit perspective (Doob, 2004). On the whole, violence prevention initiatives that are part of the CSP appear to be one facet of more generalized public policy aimed at empowering and strengthening 'distressed' neighbourhoods, and fostering the growth of healthy and self-governing communities. Studies have shown that concentrating "non-crime" policies in urban neighbourhoods - for example, building neighbourhood-level social and economic capital, increasing levels of community cohesion, and promoting collective action among residents - may, in fact, have important effects on local levels of crime and violence (Sampson, 1995; Bursik & Grasmick, 1993). As such, it may be that more general policies that address social and structural deficits hold the most promise for a long-term reduction in violent crime in Toronto's neighbourhoods. For example, school-based programs that encourage youth to become involved in and committed to school can have 227 228 a number of beneficial effects, including a reduction in levels of violent offending among 'high risk' youth (Smith et al., 1995). Social programs designed to improve pre- and post natal care, enhance parenting skills, and otherwise promote healthy children and families can also have important crime prevention effects (Steinberg, 2000; Howell & Hawkins, 1998; Graham, 1998; Tremblay et al., 2003). In the long run, such programming may also be more cost effective than interventions aimed solely at preventing crime and violence. This is because concentrated crime prevention initiatives, which typically fail to address the structural 'root causes' of crime and violence, tend to focus on one possible benefit: an immediate reduction in crime. Focused public health interventions, however, have the potential to achieve multiple benefits, including but not limited to a reduction in local levels of crime and violence over the long term. One example of targeted social programming that may have resulted in a number of positive effects, including a reduction in lethal violence, is that which occurred in Regent Park. As discussed in Chapter Five, this neighbourhood had the highest homicide count of all of Toronto's neighbourhoods over the period of investigation (n=37). However, more than half of these homicides occurred between 1988 and 1992. The subsequent and precipitous drop in homicide coincided, it would seem, with an influx of resources and services to the neighbourhood. Today, while Regent Park remains Toronto's poorest neighbourhood, it has been described as 'resource rich' relative to other disadvantaged neighbourhoods in the city. As yet, little is known about the changing trajectory of lethal violence in Regent Park, or of the factors that contributed to the decline. It is, however, possible that homicide levels were reduced by the implementation of policies and initiatives that attended to broader social issues faced by neighbourhood residents, 228 229 including poverty, unemployment, education, inadequate housing and a lack of family supports. While a focus on high homicide neighbourhoods is important from a public policy perspective, the Regent Park experience highlights the importance of also focusing attention on those neighbourhoods with declining and/or consistently low violence trajectories. My analyses indicated that neighbourhoods characterized by high levels of economic disadvantage, residential mobility and large numbers of young and black residents tend to experience high levels of lethal violence. However, this is not always the case; some neighbourhoods in Toronto experience levels of lethal violence that are lower than the city average, despite their structural similarity to 'high homicide' neighbourhoods. This suggests that there are 'resilient' neighbourhoods in Toronto that enjoy a protective advantage that buffers them from high levels of lethal violence. Understanding the factors that contribute to neighbourhood resiliency should thus be a priority among researchers and policy makers in Toronto. In sum, criminological research suggests that concentrating targeted, aggressive enforcement activities in disadvantaged neighbourhoods that tend to experience high levels of lethal violence may be counterproductive: such efforts have the potential to increase fear of crime among residents, increase local levels of violent crime, and undermine the perceived legitimacy of the police and the criminal justice system. Further, the concentration of violence prevention programming in Toronto's 'priority' neighbourhoods may not serve to reduce incidents of violence over the long term. This is, in part, because 'priority' neighbourhoods have been identified in the absence of crime data, which means that neighbourhood-specific violence control policies and initiatives 229 230 have been implemented without an empirical understanding of the problems and behaviours they are designed to prevent or control. Further, studies suggest that carefully evaluated and implemented intervention strategies that attend to broader social issues within urban communities may not only be more effective in reducing violent crime than are crime prevention initiatives on their own, but they may also give the public purse more 'bang for its buck' in terms of social benefits. As such, it would seem appropriate that policy makers give preference to interventions that have received support from empirical research, that attend to structural deficits in Toronto's neighbourhoods, and that will help to shift those neighbourhoods onto a healthier trajectory. 7.3 DIRECTIONS FOR FUTURE RESEARCH The findings of this study provide an important first step in understanding the spatial distribution and social ecology of lethal violence outside of the American context. In so doing, the findings prompt both additional questions and new directions for research. In this section, I outline three key directions for future research that build upon, but were beyond the scope of this dissertation. First, I discuss the importance of research that examines the social ecology of violent crime in the context of rapid neighbourhood change in Toronto. Second, I discuss how a dual focus on neighbourhoods that experience high and low levels of violent crime can augment the existing theoretical and empirical literature on micro-level processes that may serve to expose neighbourhoods to, or, conversely, insulate them from the risk of this violence. Third, I suggest that researchers examine the spatial distribution and social ecology of violent crime in other Canadian cities, in order to assess the generalizability of my results. I end this section 230 231 with a caveat about using the findings of ecological research to make inferences about neighbourhood residents and the locality of homicide incidents. 7.3.1 Temporal Trends in the Spatial Distribution and Social Ecology of Serious Violent Crime in Toronto. In this study, I was unable to examine neighbourhood change and homicide over time, due to the relatively small number of homicides that occur in Toronto. As a consequence, I could not assess the degree of ecological stability in the geographic distribution of lethal violence over the period of examination, a time of great change in Toronto's neighbourhoods. As discussed in Chapter Two, in 2004, the United Way of Greater Toronto, together with the Canadian Council on Social Development, published their report Poverty by Postal Code: The Geography of Neighbourhood Poverty, 1981- 2001, which showed that poverty in Toronto not only increased over time, but also became more concentrated within certain neighbourhoods in the city. This sort of "concentrated disadvantage" has been identified in criminological research as one of the most consistent ecological predictors of high rates of violent crime. These shifts in the geographic distribution of poverty across Toronto's neighbourhoods raise the question of whether violent crime is now even more concentrated in the poorest of Toronto's poor neighbourhoods. The pace of neighbourhood change in Toronto and its effects on serious violent crime are also deserving of study. Neighbourhoods that experience relatively rapid turnover in their populations, that undergo substantial alterations in their infrastructure (e.g. through gentrification or zoning changes that change land use patterns), or that experience unusual increases or decreases in the size or composition of their populations may also become more susceptible to serious violent crime (Morenoff & Sampson, 1997). Such 231 changes can disrupt informal social ties among residents that create a sense of mutual responsibility and identification, and, as a consequence, increase an area's vulnerability to violent crime. However, not all neighbourhoods characterized by high levels of poverty or undergoing rapid transformation experience high or increasing rates of serious crime. Typically, however, criminological research has focused on 'problem' neighbourhoods, neglecting those that are more resilient to criminogenic forces. While my dissertation identified a small number of such 'resilient' neighbourhoods, it was beyond the scope of this project to conduct a systematic analysis of the characteristics of these neighbourhoods. A useful extension to the analyses 1 presented in this dissertation would therefore include an examination of (1) how neighbourhood change might have altered the distribution and risk of serious violent crime in Toronto's neighbourhoods; and (2) neighbourhoods with declining and/or consistently low violence trajectories in order to gain insight into the protective factors that buffer them from high rates of serious violent crime. 7.3.2 Examining the Micro-Environment of Neighbourhoods with High and Low Levels of Serious Violent Crime A second avenue involves exploring the micro-level processes that give rise to higher - and lower - levels of lethal violence in Toronto's neighbourhoods. Research suggests that high rates of violent crime in structurally disadvantaged neighbourhoods may be explained with reference to the emergence of local cultural adaptations that privilege violence as a means of status attainment and/or conflict resolution (Anderson, 1999; Horowitz, 1983). It may be the case that such "street codes" are operating in 232 233 Toronto's neighbourhoods and that they represent an intervening mechanism that may give rise to higher rates of violent crime - particularly among young, racialized males who, it is argued, are more likely to internalize and act upon the dictates of these codes. Yet it may also be the case that local cultural codes have emerged in other neighbourhoods that serve to insulate them from high levels of violent crime. 1 therefore recommend that future research examine the micro-environment in neighbourhoods with high and low levels of violent crime in order to understand whether and to what extent local value systems that tolerate or condemn the use of violence may shape local levels of violent crime. 7.3.3 Generalizability Issues Throughout this dissertation, 1 have maintained that an examination of the social ecology of lethal violence in Toronto allowed me to expand the empirical focus outside of the American context. An issue that merits discussion, however, is the extent to which my findings are generalizable to other Canadian cities, during other time periods, and for other types of violent crime. Does lethal violence exhibit similar spatial distributions in other Canadian cities, and is that violence associated with similar neighbourhood characteristics? It is possible that Toronto is not representative of Canadian cities more generally. Gaining a more complete empirical understanding of how neighbourhood characteristics may be related to the quantity and quality of violent crime that neighbourhoods in other Canadian cities experience would thus be a fruitful avenue for future research. 2 o o 234 7.3.4 A Cautionary Note on Fallacies A key problem for social ecological research involves determining whether neighbourhood effects are truly contextual effects that create susceptibilities to violent crime, or whether they stem from compositional effects resulting from the aggregation of residents with particular kinds of characteristics (South & Messner, 2000). A direct relationship between the 'kinds of people' living in Toronto's neighbourhoods and levels of homicide therein cannot be assumed based on the analyses presented in this dissertation. Thus, neighbourhood homicide counts in Toronto indicate only the number of homicides that occurred in the neighbourhoood over the period 1988-2003. My data do not indicate whether local residents are involved as victims or perpetrators of homicide. Thus, I was careful to interpret my analyses such that I limited inferences of individual- level risk factors from aggregate characteristics of neighbourhoods. In other words, from my finding that neighbourhoods with higher levels of economic disadvantage had more homicides, I cannot infer that economically-disadvantaged people are more likely to be victims or offenders in those neighbourhoods. Such an approach is key to avoiding an ecological inference fallacy, whereby inferences about individuals are made based on aggregate-level data. 7.4 LIMITATIONS AND RECOMMENDATIONS I conclude by describing a number of limitations of this study, and offer tentative recommendations for overcoming those limitations in future research. First, relative to other forms of violent crime, homicide is a rare event in Toronto. Even after pooling my homicide data for a 16-year period, the number of homicides in each neighbourhood was 234 235 not of sufficient size to analyze temporal change. This means that 1 was unable to determine if the social ecology of homicide in Toronto changed over the period 1988- 2003, a time of great change in the structure of Toronto's neighbourhoods (Hulchanski, 2006, 2007; UWCCSD, 2004). I would recommend that future research study forms of serious violent crime, in order to obtain a sufficiently large sample of crimes with which to examine changes in the social ecology of serious violent victimization in Toronto over the last several decades. This will allow an examination of the ecological stability in the neighbourhoods that are the most - or least - violent over time, and of the neighbourhood characteristics that are related to the risk of violence. While most studies on the social ecology of lethal violence examine how neighbourhood structural characteristics may be related to homicide rates within neighbourhoods, an emerging area of study examines the diffusion of homicide across neighbourhood boundaries. In other words, researchers are increasingly interested in examining the ways in which neighbourhood homicide rates may be influenced not only by intra-neighbourhood compositional and contextual characteristics, but also by the characteristics of bordering neighbourhoods (Anselin et al., 2000; Bailer et al., 2001; Griffiths, 2006). Again, because of the small number of homicides in Toronto, I was unable to examine the extent to which diffusion processes may be operating in neighbourhoods that share boundaries, particularly those that experience high levels of gun, black, and young male homicides. Future research should examine the extent to which neighbourhoods in Toronto that are geographically proximal to neighbourhoods with high rates of serious violent crime may themselves be at increased risk of this violence. 235 236 Second, I included only the city of Toronto in this study, largely because my homicide data, collected from the Toronto Police Service, included only those killings committed within their jurisdication. Extending social ecological studies to include the so-called "satellite cities" that comprise the Greater Toronto Area (GTA) would be beneficial, particularly because neighbourhoods in the suburbs are distinct from the central city in terms of demographic characteristics and socioeconomic advantages (Hulchanski, 2006, 2007). Further, Toronto may not be representative of large Canadian cities more generally. I would thus recommend that future research examine cities to the west and east - regions that typically exhibit higher and lower (respectively) homicide rates than Toronto and whose neighbourhoods may be organized differently. Third, in this study, I chose a starting point for the analysis that made practical sense. That is, I required a time period that would include a sufficient number of homicides to perform analyses of total and disaggregated homicide counts in Toronto's 140 neighbourhoods, and one that corresponded with several Canadian censuses from which to obtain and average information on neighbourhood characteristics. The end point for this study, however, was determined solely by the availability of homicide data. The start and end points are vitally important because they may alter the patterns that we see. For example, if my starting point had been more recent and extended beyond 2003, Regent Park may not have emerged as the highest homicide neighbourhood in Toronto. Further, the end point of my data precludes an examination of more recent trends in the spatial distribution and social ecology of homicide in Toronto - particularly that of gun homicide, which appears to have continued to increase since 2003. 236 237 Fourth, my analyses were limited to assessing the degree to which select neighbourhood characteristics are associated with different forms of lethal violence in Toronto. The independent variables employed in this dissertation were selected because they encompass the key correlates of total and disaggregated homicide rates discussed in the extant and largely U.S.-based literature. While some of these measures were variously related to some types of homicide in Toronto's neighbourhoods, many were not. As discussed in Chapters Five and Six, the absence of effects may stem from issues pertaining to construct validity, measurement error and/or unrefined measures. However, it is also possible that some neighbourhood characteristics that are typically associated with homicide in U.S. cities simply do not also apply in the Canadian context. As discussed in Chapter One, the development of cities in Canada paralleled that of urban centers south of the border in many ways, but also differed from them in other important respects. The effects of these differences on urban neighbourhood organization may have lead to differences in the ecological correlates associated with lethal violence in Toronto. My final recommendation is drawn from the results of my research on the social ecology and spatial distribution of homicide in Toronto between 1988 and 2003. 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