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Understanding Crime Trends: Workshop Report 4 Crime and Neighborhood Change Jeffrey Fagan There is broad agreement in both popular culture and social science that rates of crime and delinquency vary across neighborhoods. Yet researchers and citizens disagree on whether these differences are attributable to characteristics and relationships among of persons who live in neighborhoods, or if there are factors about the neighborhoods themselves that influence crime rates independently of the persons who live there. The question becomes further complicated as neighborhoods change over time, since both the composition of the neighborhoods and the broader features of those neighborhoods are changing simultaneously. The challenge in this chapter is to assess research on the influence of neighborhood change on changes in crime rates and to determine the unique knowledge that neighborhood studies contribute to the understanding and control of crime. Accordingly, this chapter reviews research on factors that influence changes in crime rates between and within neighborhoods in cities over time. First is a brief review of local area studies of neighborhood and crime, focusing on neighborhood “effects”: the structures and processes in neighborhoods that are thought to affect trajectories of crime over time. Next the chapter identifies challenges in theory, measurement, and analysis that affect estimates of why and how neighborhood crime rates change, including size and definition of spatial units, mutual and reciprocal relations between units, the endogeneity of criminal justice enforcement and neighborhood ecology, the influences of macro-changes (i.e., the political economy of cities) on local crime rates, constraints of observational and administrative data, theoretical specifications of neighborhood and measurement and analytic strategies. Illustrations from recent research
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Understanding Crime Trends: Workshop Report on crime trends in New York City highlight the challenges of estimating neighborhood influences on crime trends. INTRODUCTION For several decades, research on neighborhood and community variation in crime and delinquency focused on identifying cross-sectional between-area differences in rates of violent or property crime. Often constrained by data limitations, these studies have adopted a static view of community or neighborhood, assuming that differences in crime rates between neighborhoods were stable over time, and that these differences reflected differences in the characteristics of communities that were stable over time (see, for example, Bursik, 1984). Shaw and McKay (1943), for example, showed that crime rates were predictably higher in socially disorganized communities, independent of the residents of those areas. More recently, Land, McCall, and Cohen (1990) suggested that the social and economic correlates of crime were stable over time and across different spatial aggregations. More recent studies have adopted a dynamic, developmental perspective to the study of social and economic behaviors in communities and neighborhoods. Recent interest in neighborhood effects has produced new research on small-area variations in child development and child maltreatment, teenage sexual behavior and childbearing, school dropout, home ownership, and several indicators of health, suicide, disorder, drug use, and adolescent delinquency (see, e.g., Brooks-Gunn et al., 1993; Coulton, Korbin, Su, and Chow, 1995; Crane, 1991; Gould, 1990; Gould et al., 1990; Harding, 2003; Wilkinson and Fagan, 1996). These studies make strong claims that growing up in neighborhoods characterized by concentrated socioeconomic disadvantage has enduring consequences on child and adolescent development. These disadvantages are thought to affect adults as well, attenuating their access to decent housing, job networks that provide access to stable family-sustaining wages, and quality education to prepare them for changing labor markets (Jargowsky, 1997; Massey and Denton, 1993; Wilson, 1987).1 But fewer studies have recognized that neighborhoods are dynamic 1 Not everyone agrees, however, citing weak evidence that there are neighborhood effects independent of the consequences of growing up in poor families on individuals that are net of the aggregate effects on poor people concentrated in poor places (Jencks and Mayer, 1990; see, generally, Raudenbush and Sampson, 1999). Indeed, just how important neighborhoods are can be gauged by the relative contributions of neighborhood effects and individual factors in multilevel studies of covarying change over time (Raudenbush and Sampson, 1999). Recent work by Harding (2003) suggests that after adjusting for the selection biases that produce the concentration effects of poor people in specific neighborhoods, there are important negative effects of growing up in a low-poverty neighborhood on school dropout and teen pregnancy.
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Understanding Crime Trends: Workshop Report entities that change over time (like people), and that these transformations are likely to produce complex and changing outcomes in several indicators of social and economic life, including crime (see Sampson, Morenoff, and Gannon-Rowley, 2002, for a review). This perspective reflects a large body of research that recognizes that rates of social and health behaviors vary in communities over time and that the characteristics of communities influence those rates. That is, communities have natural developmental histories that parallel changes in social behaviors of persons over the life course. And while it naturally follows that neighborhood effects would also influence crime, there has been less attention in recent studies to the question of how changing neighborhood contexts influence crime (see, for exceptions, Bellair, 2000; Fagan and Davies, 2004). The few studies thus far on crime and neighborhood change point to complex interactions and (nonrecursive) feedback processes between crime and the social dynamics and compositional characteristics of neighborhoods (Bellair, 2000). Other studies (e.g., Fagan et al., 2007; Morenoff, Sampson, and Raudenbush, 2001) suggest that processes of diffusion and contagion explain changes over time in homicides and violence as neighborhoods change (see, also, Ludwig and Kling, 2007). Taylor and Covington (1988) examined crime rates in Philadelphia neighborhoods to show how neighborhood change, including gentrification, increased both relative deprivation in stable but poor areas and created new crime opportunities that raised the risks of crime in the improving adjacent ones. Schwartz (1999) linked changes in housing prices to declines in violent crime across New York City police precincts, net of changes in social indicators, and Tita, Petras, and Greenbaum (2006) tied violent crime to weaker housing prices. And some researchers discount the importance of changes in social ecology, whether citywide or in specific neighborhoods, in explaining recent changes in crime (Zimring, 2006). But these studies are relatively rare data points that offer limited answers to the larger question of the relationship between neighborhood change and crime. The science of studying crime and neighborhood change is still developing, both conceptually and methodologically. Since the early Chicago School work (described below), few studies have applied a developmental perspective to chart the natural history of neighborhood change and crime in different areas of modern cities. While neighborhood change is not a necessary condition to produce changes in crime, the broad fact of differences within and between neighborhoods in crime rates over time challenges theories that are built on cross-sectional, time-limited differences in violence rates from one area to the next. This chapter reviews research on neighborhood change and crime and identifies challenges in theory, measurement, and methods. The study of neighborhoods over time has created a rich body of sociological theory
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Understanding Crime Trends: Workshop Report to conceptualize space and its effects on people, both individually and as collectives or aggregates. But studies of neighborhood change have been rare and usually limited to a few neighborhoods in single cities. Most rely on one of two types of research enterprises: qualitative methods that focus on social organization and exchanges between persons and groups, or observational data on social, economic, or health indicators. Few have been prospective, and most have limited their theoretical questions to social structure. Analytic methods that model within-person change can be applied to neighborhoods, but the translation is not simple, and it may be the case that methods have not yet developed to address the complicated questions of endogeneity of crime and area change, the spatial dependence of neighborhoods and the shared and diffused processes of change across natural or administrative borders, or the simultaneity of crime changes and neighborhood changes. After a review of studies on crime and neighborhood change, the chapter discusses five challenges that confront research in this area. These challenges are illustrated with data from a panel study of violent crime in New York City neighborhoods for the period 1985-2000. The chapter concludes by outlining an agenda for building an infrastructure of data that will sustain research on neighborhood differences in crime. CRIME AND NEIGHBORHOOD CHANGE Interest in neighborhood change as a predictor of changing crime rates can be traced to the Chicago School traditions of studying “natural social areas” whose identities are the products of complex social and economic factors, sometimes endogeneous (Park, Burgess, and McKenzie, 1925) and sometimes imposed from the outside by political economic dynamics (Logan and Molotch, 1988; Suttles, 1970). Despite this interest, there have been surprisingly few longitudinal studies of neighborhood change and changes in crime rates. The good news is that these few studies converge in several areas to inform theory and research. Physical and social deterioration is a persistent theme of neighborhood change in these studies. Taub, Taylor, and Dunham (1984) used survey and archival data and physical observations to weave a story about crime and neighborhood change in eight Chicago neighborhoods. They report on a reciprocal dynamic in which crime experiences—both direct and vicarious victimization—degrade residents’ investments in social control and upkeep. These visual cues of deterioration, together with subjective evaluations about the likelihood of crime and other adverse events, in turn cued citizens that the neighborhood had approached a racial “tipping point” that would trigger a sharp spike in crime, motivating some residents to move away. Schuerman and Kobrin (1986) also implicated physical deterioration in the shift of a neighborhood from low to high crime. They used a series
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Understanding Crime Trends: Workshop Report of cross-sectional analyses to identify three distinct stages of neighborhood change—emerging, transitional, and enduring—that characterized the natural history of neighborhood evolution from a stable low-crime area into a high-crime area.2 Harrell and Gouvis (1994) also used a residual change analysis over two decennial censuses to predict increases in crime associated with changes in neighborhood ecology. Their predictions weakened in areas where residential mobility increased, a response to deterioration similar to the narratives voiced by respondents in the Taylor et al. (1984) survey. A second thread in these studies is the reciprocal influence of adjacent neighborhoods to increase crime rates. Taylor and Covington (1988) used residual change scores in census variables (1970 and 1980) to assess two indicators of violence (aggravated assault, murder, and nonnegligent manslaughter) in 277 Baltimore neighborhoods. Their study used two time points, not the 10 between the decennial censuses. The two most salient neighborhood changes during the decade were the emergence of a large number of gentrifying neighborhoods and the descent of several older, minority neighborhoods into an “underclass” status. They focused on the process of gentrification, located neighborhood change in both relative deprivation and social disorganization theories, and identified components of violence attributable to each process. As neighborhoods became more homogeneously poorer and socially isolated, they experienced increasing violence. In the gentrifying neighborhoods, violence increased as their status and stability increased relative to the increasingly poor adjacent neighborhoods. Morenoff and Sampson (1997) also examined this dynamic, focusing on violent crime over three decades in Chicago’s 862 census tracts as a function of population loss and the concentration of socioeconomic disadvantage. Using residual changes in the decennial census to measure neighborhood ecology, they identified a dynamic process in which homicide animated population loss, and the replacement process induced higher rates of spatially concentrated homicide and patterns of diffusion to other neighborhoods experiencing similar changes. They identified race-specific effects in homicide, spatial proximity to homicide, and socioeconomic disadvantage associated with African American population gains and white population loss. Heitgerd and Bursik (1987) also examined neighborhood change from 1960-1970 and analyzed juvenile court referrals to show that even stable, 2 Changes signaling neighborhood deterioration and rising crime rates include a shift from single to multiple-family dwellings, as well as increases in residential mobility, unrelated individuals and broken families, the ratio of children to adults, minority group populations, women in the labor force, and nonwhite and Spanish-surname population with advanced education, structural domains long associated with social area theories of crime.
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Understanding Crime Trends: Workshop Report well-organized communities could have high rates of delinquency when the adjacent neighborhoods experienced rapid racial change. Finally, several studies have analyzed neighborhood change to identify turning points in the natural history of neighborhood development to pinpoint when crime rates change and grow. Bursik and Webb (1982) updated Shaw and McKay’s (1943) original data on juvenile court referrals Chicago’s 74 local community areas to show that ecological shifts in neighborhoods were associated with deflections in a neighborhood’s crime rates. Analyzing these data once again, Bursik (1984) identified correlates of neighborhood crime rates in each decade from 1940 to 1970. The sharp change in correlates in 1950 suggested an ecological shift that was linked to a turning point in neighborhoods’ crime rates. Bursik and Grasmick (1992, 1993) used hierarchical linear models to estimate crime rate change from 1930 to 1970, again identifying an ecological shift in 1950 that preceded increases in crime. More recent work has charted variation in trajectories of crime—specifically, homicide—in neighborhoods over time (e.g., Fagan and Davies, 2007; Griffiths and Chavez, 2004; Kubrin and Weitzer, 2003; Weisburd et al., 2004). The empirical solutions identify numerous patterns of rise and fall in homicide rates over time in neighborhoods in cities, using initial starting points of social structural characteristics of neighborhoods at the outset of the panel as predictors. But these studies don’t link changes in homicide to changes in neighborhoods and are silent on the contemporaneous changes in neighborhood and crime. Although each of these studies offers important clues about neighborhood change and crime, they also are limited in some important ways. First, most have used census tracts to bound and characterize neighborhoods. The older Chicago studies are an exception, but the 74 areas are large, heterogeneous aggregates of several smaller neighborhoods, a strategy that might mask important influences in smaller corners of these larger areas. For smaller areal units, there is no consensus whether census block groups or tracts or other boundaries—such as street segments in Weisburd’s Seattle analysis—are either socially meaningful or theoretically appropriate to study either community structure or social processes (see Bursik, 1988). There are alternatives to using either administratively drawn boundaries or micro-units. For example, Fagan and Davies (2004), as well as Fagan, West, and Holland (2003), use boundaries drawn in New York that integrated residents’ perceptions of the natural boundaries of their neighborhoods, proscribed by their attribution of shared belonging among residents, with census and other administrative boundaries that provide data conveniences for consistent measurement and comparability across studies (see Jackson and Manbeck, 1998). Research with these alternate social-spatial configurations may yield more accurate units to specify social processes, but these
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Understanding Crime Trends: Workshop Report may run into other types of data problems and limit comparability between studies. Defining the appropriate space is a conceptual as well as empirical challenge, as illustrated later on. Second, because census data are collected decennially, researchers interested in neighborhood change have limited their study periods to these fixed 10-year intervals. Other studies use much shorter time windows, limiting their analyses to shorter periods in which the window for estimating change may be artifactually short. Yet crime trends usually don’t cooperate with the attributes and characteristics of the decennial censuses. Crime trends can be quite volatile within a decade or even span decades, and inferences about changes in crime rates at a decade apart can be quite misleading (see, for example, Fagan and Davies, 2004, and Fagan, Davies, and Holland, 2007, on the roller coaster of crime rates in New York from the early 1980s through 2000). The nonlinear patterns of these changes demand not only more frequent and disaggregated measurement of local conditions, but also more complex functional forms for analysis, including quadratic terms for time parameters to allow for curvilinear changes in crime rates as well as the predictors of crime. Third, studies of neighborhood change in crime rates vary in the specificity of the crime form and the theoretical linkages that would predict changes in specific types of crime. Some studies specify linkages to violence based on carefully specified theories, and others measure changes in more global measures of crime without disaggregating crime into dimensions that might be differentially predicted by alternate theories. For example, Wilson and Kelling’s (1982) theory of “broken windows” suggested that signs of disorder launched a contagious process that signaled to would-be criminals that there was no guardianship in an area, in turn leading to higher crime rates. Their general theory had no correspondence to any specific crime type, and subsequent empirical tests showed quite limited predictive power for any specific form of crime (Harcourt, 2001; Sampson and Raudenbush, 1999). In contrast, Taylor and Covington (1988) hypothesized and confirmed that the juxtaposition of contrasting trajectories of change may accelerate violence by creating targets of robbery opportunity in newly gentrified areas adjacent to chronically poor ones, but not necessarily other crimes. These studies provide robust evidence of variation in the rates of change over time in crime between spatial units in cities, variation that cannot be explained simply by aggregating the social attributes and characteristics of individuals in these areas. They also contain lessons for theory and policy. Making ecological claims about factors that have variable effects risks theoretical error and possibly policy missteps. For example, cities experiencing steep crime declines may in fact have localized crime trends that either oppose the aggregate trend across areas, or that may mask more
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Understanding Crime Trends: Workshop Report complex if not conflicting results in local areas, results that may challenge the broader citywide claim when viewed as a function of policy instruments (e.g., policing) or theoretically salient factors (e.g., immigration, the siting and form of public housing). Also, the benefits and burdens of declining crime in cities may not be shared by all citizens of a city. If the rise and fall in crime trends over time between neighborhoods varies by gender, age, or race, there may be local conditions that expose these population groups to—or inoculate them from—harm. Accordingly, these potential disparities raise the stakes in advancing the science of studying crime and neighborhood change, especially when crime rates are rising and falling at different rates and in different directions in neighborhoods in a city, and when other cities are experiencing similar volatility at the same time. A parallel question is the extent of covariation between neighborhood change and crime trends. There is good evidence linking neighborhood differences in social structure and other ecological factors to differences in crime rates and, more recently, to the growth and contraction of crime (Fagan and Davies, 2004). But there is less evidence about whether structural or other types of changes in neighborhoods are causally linked to changes in neighborhood crime rates. And little is known about whether the pace of changes in neighborhoods itself can influence crime rates. So conceptualizing and measuring neighborhood change on these putative predictors of neighborhood crime trends also raise research challenges. FIVE CHALLENGES On both ends of this question, our understanding of patterns and trends in neighborhoods and crime trends is influenced by our choices of spatial units, crime specifications, theoretical perspectives, and analytic methods, as well as the limitations of measurement. These decisions influence both the substantive claims of research and their compatibility with other studies. There also are larger conceptual questions about how one thinks about space within cities and the interdependencies of these spatial units. Different spatial units matter in different way, depending on the question. In this section, these challenges are identified and illustrated. What Spatial Resolution? One simple empirical fact emerging from neighborhood studies is that the extent of observed heterogeneity in patterns over time in cities depends on the size of the spatial area studied. The size of the area and its spatial resolution depend on the question at hand, and the selection of a spatial unit thus becomes a theoretical question. But the variation of units in neighborhood studies begs the question of how area size affects the estimation
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Understanding Crime Trends: Workshop Report of neighborhood effects. Bursik and Grasmick (1993) argue that findings are robust across units of different sizes, whereas Coulton, Korbin, Su, and Chow (1995) say that unit size makes a difference. Whether unit size matters because of aggregation biases or because of the theoretical question at hand is difficult to disentangle. Weisburd et al. (2004, p. 291) analyzed changes in crime rates over 14 years in street segments, which are two or more faces on both sides of a street between two intersections. Using group-based trajectory modeling with a poisson distribution (Nagin, 2005; Nagin and Land, 1993), they identified 18 distinct trajectories of crime, using aggregate counts of crime incidents. No tests were reported to distinguish the 18 groups on dimensions of neighborhood social structure or social organization. Weisburd et al. (2004) also reported temporal heterogeneity among the street segments: Eight trajectories were stable (accounting for 84 percent of the total street segments), three were increasing, and seven were decreasing over time. Although several factors may explain the high degree of heterogeneity in the Seattle study, two stand out. First, the fine resolution of the spatial unit and the use of general (multidimensional) crime categories yielded numerous and complex micro-trends over time. There were 29,849 street segments in Seattle, and over 2 million crime incidents over the 14-year period that were linked to specific geographic coordinates and “placed” on a block face. Nearly one in five was eliminated because they occurred at street intersections and could not be assigned to a street segment. With this many data points and observations, complex and diverse patterns are not surprising, especially over a lengthy period of observation. Whether these distinct patterns reflect real—theoretically meaningful—differences or noisy data is hard to sort out. Second, five crime categories were used to characterize incidents. The most frequent were Uniform Crime Reports index crimes (11.4 percent), and nontrivial traffic violations the least common (4.7 percent). If different neighborhood configurations and social ecologies are associated with different crime categories, the Seattle study captured four dimensions at once: time, ecological risk, temporal change, and crime type. Fine resolution in trends might be expected when the four dimensions are collapsed. Weisburd et al. were interested in street segments because of their concern for identifying the “hot spots” of crime and the prevention potential for focusing limited legal resources on places where crime risks are highest. Other studies also are concerned with the effects of policing on crime trends but use larger spatial aggregations, such as police precincts in New York (Fagan, West, and Holland, 2003; Corman and Mocan, 2000; Rosenfeld, Fornango, and Rengifo, 2007) or smaller police units such as beats and districts in Chicago (Papachristos, Meares, and Fagan, 2007). These are administratively defined areas that reflect the units where police
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Understanding Crime Trends: Workshop Report resources are allocated and managed and are conveniences for compiling data and examining variation in how police deploy resources. They also have the advantage of remaining stable over time.3 While precincts may have had social meaning at one time, they now are socially and economically heterogeneous areas whose value for testing theories of social control is contested (see, e.g., Wooldredge, 2002). The limitations on administrative borders may be most important in studies that attempt inferences in administrative areas where distinct population subgroups regularly interact with legal actors. Studies using police district aggregations often control for the differences in their social makeup by including both covariates for relevant population characteristics and fixed effects for the districts or precincts. For example, Papachristos et al. (2007) examined the effects of a gun violence suppression program using police beats in Chicago. Chicago police departments are organized into 28 police districts, and each district is then subdivided into beats. The beats were more homogeneous and socially meaningful than the larger districts, and Papachristos et al. were able to focus on specific areas where police efforts and crime both were concentrated. They examined crime trends over 84 police beats in 4 of Chicago’s 28 police districts, showing strong downward trends for all beats but steeper slopes for the experimental group. They used propensity scores to identify treatment effects in a quasi-experimental design, controlling also for trends in other areas of the city. The use of beats struck a compromise between the artificiality of police boundaries and the scale of area that would be most likely to capture networked offenders in small social spaces. Papachristos controlled for the mutual influences of these spaces by including a measure of spatial dependence (Moran’s I). Fagan et al. (2003) and others (e.g., Corman and Mocan, 2000; Messner et al., 2007; Rosenfeld et al., 2007) examined the effects of policing policies—order maintenance policing, drug enforcement—on crime rates in New York’s 75 police precincts. These precincts are large, with an average population of over 110,000 persons in 2000, and variable in size (standard deviation = 50,194). Some precincts are more racially and economically diverse than others and often include several smaller, more homogeneous neighborhoods. Other precincts include commercial areas that were virtually empty at night (Wall Street) or with different daytime and nighttime populations.4 Police resources are allocated in precincts based on crime trends and patterns, and within precincts, specific beats are resourced in real time. 3 However, New York City added a precinct in 1993, at the outset of the crime decline that lasted a decade. 4 For example, the 22nd precinct is Central Park, where there is no population and little crime overall.
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Understanding Crime Trends: Workshop Report These differences matter. When Fagan et al. (2003) further disaggregated precincts into neighborhoods to reestimate local area affects of policing on crime, they reported different predictors of crime patterns in neighborhoods over time compared with the predictors at the precinct level. Spatially smaller micro-trends, such as the ones detected by Weisburd et al. (2004) in Seattle, or the neighborhood models identified by Fagan et al. (2003) and Fagan and Davies (2004) in New York, may be masked in larger spatial aggregations, such as precincts or police districts. Covariates that control for compositional differences between precincts usually are computed from aggregations of census tract data. These aggregations of multiple neighborhoods in police districts raise risks of identification problems—if crime trends are a function of local social area or neighborhood effects (crime markets, population concentrations), these smaller area effects may be masked when heterogeneous, multineighborhood police districts or precincts are the unit of analysis. The most common spatial unit used in analyzing crime trends (and many other neighborhood effects) is the tract (Hipp, 2007a,b; see also Sampson et al., 2002, for more detail). Tracts are smaller in both area and population and have the advantage of greater social homogeneity. But they also raise problems of spatial dependence since neighborhoods may span several tracts (this is discussed and illustrated in the next section). Tracts also change over time, multiplying as populations grow in a tract. Tracts in commercial areas have low populations, requiring the use of “journey” files that estimate the daytime and nighttime populations of tracts based on a complex algorithm using commuting times. Other aggregations, such as planning districts in Chicago and neighborhoods in New York, solve these problems in terms of articulating “natural” boundaries that encompass areas with social meaning to their residents. For example, New York has defined neighborhood boundaries based on the work of Kenneth Jackson and John Manbeck (1998). Using historical data, tract boundaries, and interviews with local residents, they drew 330 neighborhood areas, each encompassing about 7 census tracts and between 25,000 and 45,000 people. Figures 4-1a and 4-1b show the relationship between neighborhoods and precincts and also precincts and census tracts. These differences in area size and specification matter in the identification of crime trends. Figure 4-2 shows the results of semiparametric mixture (trajectory) models to identify trends in homicides over time in New York from 1985-2000. The top figure shows that we can identify four trajectory groups for neighborhoods, while three are identified for tracts in the bottom graph. The highest risks are concentrated in about one in nine neighborhoods, but one in five tracts. For neighborhoods, there is a second trajectory with more modest increases and declines. Each analysis shows stability in 45 per-
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Understanding Crime Trends: Workshop Report immigration. Other domains of urban policy, including family and child support or child care, public assistance experiments, and mental health interventions, can also be mined to see if there are unintended or collateral effects on crime. The design of these initiatives often falls short of the standards of social experimentation, yet there is much to be learned from a series of quasi-experiments that can be run on neighborhoods with different paths of change that have experienced one or more of these social interventions. Building a Data Infrastructure for Understanding Neighborhoods and Crime Research on neighborhoods and crime often begins anew with each project. Researchers reach into archives of existing data and approach agencies for updates and supplements to bring the measures up to date. Rarely is updating routinized in agencies unless there are institutional norms or legislative mandates to do so. (Crime may be an exception, based on both reporting mandates and needs for good data to support investigations.) Compiling reliable measures of the complex dimensions of neighborhoods over a period of time necessary to identify changes is a difficult challenge (see, for example, Tatian, 2003). Data are maintained separately by agency, rarely aggregated to similar spatial units, and (in the extreme) in languages that are better suited to administrative needs than for research. These difficulties are compounded by the diverse theoretical interests that are identified in this chapter. An infrastructure for neighborhood data in cities is needed to support research on neighborhoods and crime, and such an infrastructure should be maintained in archives that are accessible to users with minimal administrative burdens. The Neighborhood Change Database is one such effort. Privacy concerns are limited in these proposals, since crime data often are aggregated administratively, as are data on attributes and characteristics of neighborhood ecology. Risks to human subjects are mitigated in neighborhood research that focuses on changes in rates of crime or social structural and other ecological parameters in areas over time. For example, neighborhood studies are likely to rely on observational data that often is deidentified to reduce risks from accidental disclosure. But privacy concerns may arise in the study of the social organization of neighborhoods and networks in them. Here, we can emphasize the importance of the regulatory functions in universities and research institutes that are charged with the protection of human research subjects from social risk and psychological harm. Beyond these regulatory strategies, the social norms and ethical standards of researchers and their professional associations also can buttress respect for privacy and confidentiality. For example, the identities of dis-
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Understanding Crime Trends: Workshop Report tressed neighborhoods should be guarded whenever possible, to prevent stigmatization in the form of redlining or other deinvestments. Yet this raises a tension when spatial analyses of neighborhoods are employed, and the results are often displayed using maps. One could argue plausibly that there is value in both national archives and local or regional ones. My preference is for the local. Archives of cross-city data are challenged to construct files with similar elements so as to avoid measurement errors arising from inconstancy in the underlying meaning of variables that may be based on different metrics across variable local contexts. Local data archives should feature data drawn from in the city or region and contributed by institutions and agencies under local working agreements and data-sharing arrangements. For example, within the Neighborhood Change Database project are more than 15 local supplemental archives. Locally designed archives have the advantage of building on national templates for both observational and survey data and then enriching these through measures that capture the texture of each city’s neighborhoods. These additional elements could include direct observations of neighborhood interaction data that are coupled with surveys and local administrative datasets on compositional characteristics of neighborhoods as well as social outcomes across a range of behavioral dimensions (Raudenbush and Sampson, 1999). A useful example of a dataset design that addresses both individual and neighborhood change is the Program in Human Development in Chicago Neighborhoods, in which the sampling design explicitly anticipates analyses of both individuals and neighborhood effects as well as their multilevel or hierarchical effects (Raudenbush and Sampson, 1999; Sampson, Raudenbush, and Earls, 1997). In Los Angeles, the Los Angeles Family and Neighborhoods Study is a similar effort that has produced a rich dataset paralleling the structure and interests of the Chicago study (Sastry et al., 2006; Pebly and Sastry, 2006), although with emphasis on developmental outcomes and less focus on antisocial behavior. In the Los Angeles study, neighborhood appears to have independent effects on child development net of individual and family characteristics, and the explained variance of neighborhood factors well outweighs the other effects (Pebley and Sastry, 2006). There are a number of administrative datasets, ongoing surveys, and other data massing and integration projects that can be incorporated into these local archives or that can serve as templates for the design of a local archive. For example, the New York HVS, the Youth Risk Behavior Survey, the National Longitudinal Survey of Youth 97 (NLSY97), and others in progress all have local components that could be expanded and designed into local ongoing efforts. Surveys should also delve into the social interactions of neighborhoods to better understand the moving parts of
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Understanding Crime Trends: Workshop Report neighborhood social control. In the health care system, vital statistics data in most cities and states provide addressable data on fatalities that can supplement police records. Most cities maintain zoning and housing indicators (sale prices, rent indices, etc.) to allow for measurement of the built environment in neighborhoods. School, health care, and public assistance records all can provide important information on composition that can supplement surveyed and observed data. The final consideration is the pace of change and the schedule and spacing of data points. What are reasonable assumptions about neighborhood change and crime change that would determine the right frequency of observations? Some changes are slow, as in changes in the built environment, and others may be relatively quick, as in the case of the sudden population shift in Washington Heights reported by Fagan (1992). This pace itself can churn neighborhoods in a way to quickly change both patterns of social interaction and other neighborhood barometers such as crime rates. This would suggest more frequent observations, certainly more frequent than the decennial census and closer in timing to the Census Bureau’s American Community Survey. A second consideration is the lag time that is theorized between change in a causal factor and the observed change in a social outcome. These lag times will vary by outcome domain: school test scores may improve more slowly than will changes in the crime rate. The design of such archives and the infrastructure that is created will require both resources and political will to set institutional incentives for agencies to contribute. Crime data in particular may be a political question; there are risks in transparency that inelasticity in crime rates will be seen as political failure. What incentives are there for police to create stronger and more accessible crime data with local addressability, incentives that can offset the political risks that some departments may fear? There are two ways to address these requirements. Open records laws often provide the institutional aegis for the release of information on crime and neighborhoods to sustain research.8 One way to address this is by shifting social and professional norms toward more open and transparent data systems to monitor changes in local crime rates that mirror changes in each city’s neighborhoods. 8 See, for example, Florida’s Open Records Law, FL Statutes §119 (http://www.leg.state.fl.us/statutes/index.cfm?app_mode=display_statute&url=ch0119/ch0119.htm), describing the requirements and procedures for publicly available information while setting forth privacy restrictions that safeguard sensitive information about individuals.
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Understanding Crime Trends: Workshop Report REFERENCES Anderson, Elijah. (1994, May). The code of the streets. The Atlantic Monthly, 81-94. Anderson, Elijah. (1999). Violence and the code of the street: Decency, violence and the moral life of the inner city. New York: Norton. Bellair, Paul. (2000). Informal surveillance and street crime: A complex relationship. Criminology, 38, 137-169. Berk, Richard A. (2005). Knowing when to fold ‘em: An essay on evaluating the impact of Ceasefire, Compstat, and Exile. Criminology and Public Policy, 4(3), 451-465. Berk, Richard A., Azusa Li, and Laura J. Hickman. (2005). Statistical difficulties in determining the role of race in capital cases: A re-analysis of data from the state of Maryland. Journal of Quantitative Criminology, 21(4), 365-390. Black, Donald. (1983). Crime as social control. American Sociological Review, 48(1), 34-45. Black, Donald. (1989). Sociological justice. New York: Oxford University Press. Blumstein, Alfred, and Joel Wallman. (2000). The crime drop in America. New York: Cambridge University Press. Brooks-Gunn, Jeanne, Gregory J. Duncan, Pamela K. Klebanov, and Naomi Sealand. (1993). Do neighborhoods influence child and adolescent development? American Journal of Sociology, 99, 353-395. Browning, Christopher R., Seth L. Feinberg, and Robert D. Dietz. (2004). The paradox of social organization: Networks, collective efficacy, and violent crime in urban neighborhoods. Social Forces, 83(2), 503-534. Bursik, Robert J., Jr. (1984). Urban dynamics and ecological studies of delinquency. Social Forces, 63(2), 393-413. Bursik, Robert J., Jr. (1988). Social disorganization and theories of crime and delinquency: Problems and prospects. Criminology, 26, 519-551. Bursik, Robert J., Jr., and Harold G. Grasmick. (1992). Longitudinal neighborhood profiles in delinquency: The decomposition of change. Journal of Quantitative Criminology, 8(3), 247-263. Bursik, Robert J., Jr., and Harold G. Grasmick. (1993). Economic deprivation and neighborhood crime rates, 1960-1980. Law and Society Review, 27(2), 263-283. Bursik, Robert J., Jr., and Jim Webb. (1982). Community change and patterns of delinquency. American Journal of Sociology, 88(1), 24-42. Card, David, Alexandre Mas, and Jesse Rothstein. (2007). Tipping and the dynamics of segregation. (NBER Working Paper No. 13052.) Cambridge, MA: National Bureau of Economic Research. Available: http://www.nber.org/papers/w13052 [accessed August 2008]. Clampet-Lundquist, Susan, and Douglas S. Massey. (2008). Neighborhood effects on economic self-sufficiency: A reconsideration of the moving to opportunities experiment. American Journal of Sociology, 114(1), 67-93. Clear, Todd R. (2007). Imprisoning communities: How mass incarceration makes disadvantaged neighborhoods worse. New York: Oxford University Press. Cook, Philip J., and John H. Laub. (1998). The unprecedented epidemic in youth violence. In Michael Tonry and Mark H. Moore (Eds.), Youth violence. Chicago: University of Chicago Press. Cook, Philip J., and John H. Laub. (2001). After the epidemic: Recent trends in youth violence in the United States. In Michael Tonry (Ed.), Crime and justice: A review of research (pp. 1-37). Chicago: University of Chicago Press. Corman, Hope, and Naci Mocan. (2000). A time-series analysis of crime, deterrence, and drug abuse in New York City. American Economic Review, 90(3), 584-604.
OCR for page 120
Understanding Crime Trends: Workshop Report Coulton, Claudia J., Jill E. Korbin, M. Su, and J. Chow. (1995). Community-level factors and child maltreatment rates. Child Development, 66, 1262-1276. Crane, Jonathan. (1991). The epidemic theory of ghettos and neighborhood effects on dropping out and teenage childbearing. American Journal of Sociology, 96(5), 1226-1259. Donohue, John J., and Justin Wolfers. (2005). Use and abuses of empirical evidence in the death penalty debate. Stanford Law Review, 58(4), 791-845. Eckberg, Douglas L. (1995a). Estimates of early twentieth-century U.S. homicide rates: An econometric forecasting approach. Demography, 32(1), 1-16. Eckberg, Douglas L. (1995b). What homicide epidemic? Long-term vital statistics trends. Presented at the annual meeting of the American Association for the Advancement of Science, Atlanta, February. Fagan, Jeffrey. (1992). Drug selling and licit income in distressed neighborhoods: The economic lives of street-level drug users and dealers. In George E. Peterson and Adelle V. Harrell (Eds.), Drugs, crime and social isolation: Barriers to urban opportunity (pp. 99-142). Washington, DC: Urban Institute Press. Fagan, Jeffrey, and Garth Davies. (1999). Crime in public housing: Two-way diffusion effects in surrounding neighborhoods. In Victor Goldsmith, Philip G. McGuire, John Mollenkopf, and Timothy Ross (Eds.), Analyzing Crime Patterns: Frontiers of Practice (pp. 121-136). Thousand Oaks, CA: Sage Publications Fagan, Jeffrey, and Garth Davies. (2000). Street stops and “broken windows”: Terry, race, and disorder in New York City. Fordham Urban Law Journal, 28, 457-504. Fagan, Jeffrey, and Garth Davies. (2004). The natural history of neighborhood violence. Journal of Contemporary Criminal Justice, 20(2), 127-149. Fagan, Jeffrey, and Garth Davies. (2006). Crime and immigration. Presented at the annual meeting of the American Society of Criminology, Los Angeles. Fagan, Jeffrey, and Garth Davies. (2007). The political economy of the crime decline in New York City. Presented at the annual meeting of the American Association for the Advancement of Science, San Francisco, February. Fagan, Jeffrey, and Tracey L. Meares. (2008). Punishment, deterrence and social control: The paradox of punishment in minority communities. Ohio State Journal of Criminal Law 6(2), 173-229. Fagan, Jeffrey, Franklin E. Zimring, and June Kim. (1998). Declining homicide in New York: A tale of two trends. Journal of Criminal Law and Criminology, 88, 1277-1324. Fagan, Jeffrey, Juanjo Medina-Ariza, and Susan Wilt. (2002). Social and ecological risks of domestic and non-domestic violence against women in New York City. (Final Report, National Institute of Justice, Grant #1999-WT-VW-0005.) Washington, DC: U.S. Department of Justice. Fagan, Jeffrey, Valerie West, and Jan Holland. (2003). Reciprocal effects of crime and incarceration in New York City neighborhoods. Fordham Urban Law Journal, 30, 1551-1602. Fagan, Jeffrey, Garth Davies, and Jan Holland. (2007). Drug control in public housing: The paradox of the drug elimination program in New York City. Georgetown Journal on Poverty Law and Policy, 8(3), 415-460. Ferrer-Caia, Emilio, and John J. McArdle. (2003). Alternative structural models for multivariate longitudinal data analysis. Structural Equation Modeling, 10, 493-524. Frey, William H. (1979). Central city white flight: Racial and nonracial causes. American Sociological Review, 44(3), 425-448. Frey, William H. (1994). The new white flight: New migration patterns are creating three separate, unequal Americas. American Demographics, 16(4), 1-40. Galster, George C., and Ronald B. Mincy. (1993). Understanding the changing fortunes of metropolitan neighborhoods, 1980 to 1990. Housing Policy Debate, 4(3), 303-352.
OCR for page 121
Understanding Crime Trends: Workshop Report Geller, Amanda B. (2007). Neighborhood disorder and crime: An analysis of Broken Windows in New York City. Ph.D. dissertation, School of Social Work, Columbia University. Gladwell, Malcolm. (1996). The tipping point. Boston: Little, Brown. Gostin, Lawrence. (1991). The interconnected epidemics of drug dependency and AIDS. Harvard C.L.-C.R. Law Review, 26(1), 113-184. Gostin, Lawrence O., Scott Burris, and Zita Lazzarini. (1999). The law and the public’s health: A study of infectious disease law in the United States. Columbia Law Review, 99(1), 59-64. Gould, Madelyn S. (1990). Teenage suicide clusters. Journal of the American Medical Association, 263(15), 2051-2052. Gould, Madelyn S., Sylvan Wallenstein, Marjorie H. Kleinman, Patrick O’Carroll, and James Mercy. (1990). Suicide clusters: An examination of age-specific effects. American Journal of Public Health, 80(2), 211-212. Grannis, Rick. (1998). The importance of trivial streets: Residential streets and residential segregation. American Journal of Sociology, 103(6), 1530-1564. Griffiths, Elizabeth, and Jorge M. Chavez. (2004). Communities, street guns, and homicide trajectories in Chicago, 1980-1995: Merging methods for examining homicide trends across space and time. Criminology, 42(4), 941-978. Harcourt, Bernard. (2001). Illusion of Order: The false promise of broken windows policing. Cambridge, MA: Harvard University Press. Harcourt, Bernard. (2005). Policing L.A.’s skid row: Crime and real estate development in downtown Los Angeles (an experiment in real time). Chicago: University of Chicago Legal Forum. Harding, David J. (2003). Counterfactual models of neighborhood effects: The effect of neighborhood poverty on dropping out and teenage pregnancy. American Journal of Sociology, 109(3), 676-719. Harrell, Adelle, and Caterina Gouvis. (1994). Predicting neighborhood risk of crime. Washington, DC: The Urban Institute. Heitgerd, Janet L., and Robert J. Bursik. (1987). Extracommunity dynamics and the ecology of delinquency. American Journal of Sociology, 92(4), 775-787. Hipp, John R. (2007a). Block, tract and levels of aggregation: Neighborhood structure and crime and disorder as a case in point. American Sociological Review, 72(5), 659-680. Hipp, John R. (2007b). Income inequality, race, and place: Does the distribution of race and class within neighborhoods affect crime rates? Criminology, 45, 665-697. Jackson, Kenneth, and John Manbeck. (1998). The neighborhoods of Brooklyn. New Haven, CT: Yale University Press. Jacobs, Jane. (1961). The death and life of great American cities. New York: Random House. Jargowsky, Paul A. (1997). Poverty and place: Ghettos, barrios, and the American city. New York: Russell Sage Foundation. Jencks, Christopher, and Susan E. Mayer (1990). The social consequences of growing up in a poor neighborhood. In National Research Council, Inner-city poverty in the United States (pp. 111-186). Laurence E. Lynn and Michael G. H. McGeary (Eds.), Committee on National Urban Policy, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press. Kahan, Dan M. (1997). Social influence, social meaning, and deterrence. Virginia Law Review, 83(2), 349-395. Keels, Micere, Greg Duncan, Stefanie DeLuca, Ruby Mendenhall, and James Rosenbaum. (2005). Fifteen years later: Can residential mobility programs provide a long-term escape from neighborhood segregation, crime, and poverty? Demography, 42(1), 51-73.
OCR for page 122
Understanding Crime Trends: Workshop Report Kelling, George, and Catherine Cole. (1996). Fixing broken windows. New York: Free Press. Kirk, David S., and Andrew V. Papachristos. (2007). Legal cynicism and the framing of neighborhood violence: Implications for “neighborhood effects” research. Available: http://ssrn.com/abstract=1081894 [accessed August 2008]. Klein, Malcolm W. (1997). The American street gang: Nature, prevalence, and control. New York: Oxford University Press. Kling, Jeffrey R., Jeffrey B. Liebman, Lawrence F. Katz, and Lisa Sanbonmatsu. (2004). Moving to opportunity and tranquility: Neighborhood effects on adult economic self-sufficiency and health from a randomized housing voucher experiment. (Princeton University Department of Economics, Industrial Relations Section Working Paper #481.) Princeton, NJ; Princeton University. Available: http://ideas.repec.org/p/pri/indrel/860.html [accessed August 2008]. Kling, Jeffrey R., Jens Ludwig, and Lawrence F. Katz. (2005). Neighborhood effects on crime for female and male youth: Evidence from a randomized housing voucher experiment. Quarterly Journal of Economics, 120, 87-130. Kling, Jeffrey R., Jeffrey B. Liebman, and Lawrence F. Katz. (2007). Experimental analysis of neighborhood effects. Econometrica, 75(1), 83-119. Knowles, John, Nicola Persico, and Petra Todd. (2001). Racial bias in motor vehicle searches: Theory and evidence. Journal of Political Economy, 109(1), 203-229. Kubrin, Charles E., and Ronald Weitzer. (2003). Retaliatory homicide: Concentrated disadvantage and neighborhood culture. Social Problems, 50, 157-180. Land, Kenneth, Patricia McCall, and Lawrence Cohen. (1990). Structural covariates of homicide rates: Are there any invariances across time and social space? American Journal of Sociology, 95(4), 922-963. Lee, Barrett A., Sean F. Reardon, Glenn Firebaugh, Chad Farrell, Stephen A. Matthews, and David O’Sullivan. (2008). Beyond the census: Patterns and determinants of racial segregation at multiple geographic scales. American Sociological Review, 73(5), 766-791. Levitt, Steven D. (1998). Why do increased arrest rates appear to reduce crime: Deterrence, incapacitation, or measurement error? Economic Inquiry, 36(3), 353-372. Levitt, Steven, and Sudhir Alladi Venkatesh. (2001). Growing up in the projects: The economic lives of a cohort of men who came of age in Chicago public housing. American Economic Review, 91(2), 79-84. Logan, John R., and Harvey L. Molotch. (1988). Urban fortunes: The political economy of place. Berkeley: University of California Press. Ludwig, Jens, and Jeffrey R. Kling. (2007). Is crime contagious? Journal of Law and Economics, 50(3), 491-518. Ludwig, Jens, Jeffrey B. Liebman, Jeffrey R. Kling, Greg J. Duncan, Lawrence F. Katz, Ronald C. Kessler, and Lisa Sanbonmatsu. (2008). What can we learn about neighborhood effects from the Moving-to-Opportunity experiment? A comment on Clampet-Lundquist and Massey. American Journal of Sociology, 114(1), 114-188. MacDonald, John M., John R. Hipp, and Charlotte Gill. (2008). Neighborhood effects of immigrant succession on crime: Did immigrants cause the crime drop in Los Angeles? Presented at the 2008 Annual Workshop on Crime and Population Dynamics, Population Research Center, University of Maryland at College Park. Marcuse, Peter. (1995). Interpreting “public housing” history. Journal of Architectural and Planning Research, 12(3), 240-258. Martinez, Ramiro, Jr. (2002). Latino homicide: Immigration, violence, and community. London: Routledge Press. Martínez, Ramiro, Jr., and Abél Valenzuela (2006). Coming to America: Immigration, ethnicity, and crime. New York: New York University Press.
OCR for page 123
Understanding Crime Trends: Workshop Report Massey, Douglas S. (1995). Getting away with murder: Segregation and violent crime in urban America. University of Pennsylvania Law Review, 143(5), 1203-1232. Massey, Douglas S. (2007). Categorically unequal: The American stratification system. New York: Russell Sage Foundation. Massey, Douglas S., and Nancy Denton. (1993). American apartheid. Cambridge, MA: Harvard University Press. McArdle, J.J., and F. Hamagami. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. Collins and A. Sayer (Eds.), New methods for the analysis of change (pp. 139-175). Washington, DC: American Psychological Association. Messner, Steven A., Sandro Galea, Kenneth J. Tardiff, Melissa Tracy, Angela Bucciarelli, Tinka Markham Piper, Victoria Frye, and David Vlahov. (2007). Policing, drugs and the homicide decline in New York City in the 1990s. Criminology, 45(2), 385-414. Morenoff, Jeffrey. (2003). Spatial dynamics of birth weight. American Journal of Sociology, 108, 976-1017. Morenoff, Jeffrey, and Robert J. Sampson. (1997). Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970-1990. Social Forces, 76, 31-64. Morenoff, Jeffrey, Robert J. Sampson, and Stephen W. Raudenbush. (2001). Neighborhood inequality, collective efficacy and the spatial dynamics of homicide. Criminology, 39(3), 517-560. Nagin, Daniel S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press. Nagin, Daniel S., and Kenneth C. Land. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed poisson model. Criminology, 31(2), 327-362. National Research Council. (2004). Fairness and effectiveness in policing: The evidence. Committee to Review Research on Police Policy and Practices, Wesley Skogan and Kathleen Frydl (Eds.). Committee on Law and Justice, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. Papachristos, Andrew V. (forthcoming). Murder by structure: A network theory of gang homicide. American Journal of Sociology (in press). Papachristos, Andrew V., Tracey L. Meares, and Jeffrey Fagan. (2007). Attention felons: Evaluating Project Safe Neighborhoods in Chicago. Journal of Empirical Legal Studies, 4(2), 223-272. Park, Robert. (1916).Suggestions for the investigations of human behavior in the urban environment. American Journal of Sociology, 20(5), 577-612. Park, Robert, Ernest W. Burgess, and Roderick D. McKenzie. (1925). The city. Chicago: University of Chicago Press. Patillo, Mary E. (1998). Sweet mothers and gang bangers: Managing crime in a black middle class neighborhood. Social Forces, 76(3), 747-774. Pebley, Anne R. and Narayan Sastry. (2006). Neighborhoods, poverty, and children’s well-being. In David B. Grusky and Szonja Szelény (Eds.), The inequality reader: Contemporary and foundational readings in race, class, and gender (pp. 165-178). Greenwich, CT: Westview Press. Popkin, Susan, James E. Rosenbaum, and Patricia Meaden. (1993). Labor market experiences of low-income black women in middle-class suburbs: Evidence from a survey of Gautreaux program participants. Journal of Policy Analysis and Management, 12(3), 556-573. Raudenbush, Stephen W., and Robert J. Sampson. (1999). Ecometrics: Toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. Sociological Methodology, 29(1), 1-41.
OCR for page 124
Understanding Crime Trends: Workshop Report Reardon, Sean F. (2007). Measuring spatial context: Race, space, and scale in the metropolis, 1990-2000.Presented at Workshop on Wealth and Inequality, Institute for Economic and Social Research and Policy, Columbia University, New York. Reardon, Sean F. (2008). Beyond the census tract: Patterns and determinants of racial segregation at multiple geographic scales. American Sociological Review, 83(October), 771. Reardon, Sean F., and David O’Sullivan. (2004). Measures of spatial segregation. Sociological Methodology, 34(1), 121-162. Ridgeway, Greg. (2006). Assessing the effect of race bias in posttraffic stop outcomes using propensity scores. Journal of Quantitative Criminology, 22(1), 1-30. Ridgeway, Greg, Dan McCaffrey, and Andrew Morral. (2006). The TWANG package: Toolkit for weighting and analysis of non equivalent groups. Unpublished technical report, RAND Corporation, Santa Monica, CA. Rosenbaum, James E. (1995). Changing the geography of opportunity by expanding residential choice: Lessons from the Gautreaux program. Housing Policy Debate, 6, 231-269. Rosenfeld, Richard, Robert Fornango, and Andres F. Rengifo. (2007). The impact of order-maintenance policing on New York City homicide and robbery rates: 1988-2001. Criminology, 45(2), 355-384. Rosin, Hanna. (2008). American murder mystery. Atlantic Monthly, (July/August), 40-54. Rowe, David C., and Joseph L. Rogers. (1994). A social contagion model of adolescent sexual behavior: Explaining race differences. Social Biology, 41, 1-18. Rubinowtiz, Leonard S., and James E. Rosenbaum. (2000). Crossing the class and color lines: From public housing to white suburbia. Chicago: University of Chicago Press. Saegert, Susan, Gary Winkel, and Charles Swartz. (2002). Social capital and crime in New York City’s low income housing. Housing Policy Debate, 13(1), 189-226. Saiz, Albert, and Susan M. Wachter. (November 2006). Immigration and the neighborhood. (FRB of Philadelphia Working Paper #06-22.) Available: http://ssrn.com/abstract=931733 [accessed August 2008]. Sampson, Robert J. (2008) Moving to inequality: Neighborhood effects and experiments meet social structure. American Journal of Sociology, 114, 189-231. Sampson, Robert J., and Dawn Jeglum Bartusch. (1998). Legal cynicism and (subcultural?) tolerance of deviance: The neighborhood context of racial differences. Law and Society Review, 32, 777-804. Sampson, Robert J., and Jeffrey Morenoff. (2006). Durable inequality: Spatial dynamics, social processes, and the persistence of poverty in Chicago neighborhoods. In Samuel Bowles, Steven S. Durlauf, and Karla Hoff (Eds.), Poverty traps (pp. 176-203). Princeton, NJ: Princeton University Press. Sampson, Robert J., and Stephen W. Raudenbush. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105, 603-651. Sampson, Robert J., and Stephen W. Raudenbush. (2004). Seeing disorder: Neighborhood stigma and the social construction of “Broken Windows.” Social Psychology Quarterly, 67(4), 319-342. Sampson, Robert J., and Patrick Sharkey. (2008). Neighborhood selection and the social reproduction of concentrated racial inequality. Demography, 45(1), 1-29. Sampson, Robert J., and William Julius Wilson. (1995). Toward a theory of race, crime, and urban inequality. In John Hagan and Ruth D. Peterson (Eds.), Crime and inequality. Stanford, CA: Stanford University Press. Sampson, Robert J., Stephen W. Raudenbush, and Felton Earls. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918-924.
OCR for page 125
Understanding Crime Trends: Workshop Report Sampson, Robert J., Jeffrey Morenoff, and Thomas Gannon-Rowley. (2002). Assessing “neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology,28, 443-478. Sastry, Narayan, Bonnie Ghosh-Dastidar, John Adams, and Anne R. Pebley. (2006). The design of a multilevel longitudinal survey of children, families, and communities: The Los Angeles Family and Neighborhood Survey. Social Science Research, 35, 1000-1024. Schuerman, Leo, and Soloman Kobrin. (1986). Community careers in crime. In Albert J. Reiss, Jr., and Michael Tonry (Eds.), Communities and crime (pp. 67-100). Chicago: University of Chicago Press. Schwartz, Alex. (1999). New York City and subsidized housing: Impacts and lessons of the city’s $5 billion capital budget housing plan. Housing Policy Debate, 10(4), 839-877. Schwartz, Amy Ellen, Scott Susin, and Ioan Voicu. (2003) Has falling crime driven down New York City’s real estate boom? Journal of Housing Research, 14(1), 101-135. Shaw, Clifford R., and Henry D. McKay. (1943). Juvenile delinquency and urban areas. Chicago: University of Chicago Press. Smith, David J. (2005). Ethnic differences in intergenerational crime patterns. Crime and Justice, 32, 55. Sobel, Michael E. (2006a). What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. Journal of the American Statistical Association, 101, 1398-1407. Sobel, Michael E. (2006b). Spatial concentration and social stratification: Does the clustering of disadvantage “beget” bad outcomes? In Samuel Bowles, Steven S. Durlauf, and Karla Hoff (Eds.), Poverty traps (pp. 204-229). Princeton, NJ: Princeton University Press. Suttles, Gerald. (1970). Social order of the slum: Ethnicity and territory in the inner city. Chicago: University of Chicago Press. Tatian, Peter A. (2003). Neighborhood change database, data users guide. Washington, DC: The Urban Institute. Available: http://www2.urban.org/nnip/ncua/ncdb/NCDB_LF_DataUsersGuide.pdf [accessed October 2008]. Taub, Richard P., Garth D. Taylor, and Jan D. Dunham. (1984). Paths of neighborhood change: Race and crime in urban America. Chicago: University of Chicago Press. Taylor, Ralph B., and Jeanette Covington. (1988). Neighborhood changes in ecology and violence. Criminology, 26, 553-589. Thornberry, Terence P., Marvin D. Krohn, Alan J. Lizotte, Carolyn A. Smith, and Kimberly Tobin. (2004). Gangs and delinquency in developmental perspective. New York: Cambridge University Press. Tita, George E., Tricia L. Petras, and Robert T. Greenbaum. (2006). Crime and residential choice: A neighborhood level analysis of the impact of crime on housing prices. Journal of Quantitative Criminology, 22(3), 299-317. Tyler, Tom R., and Jeffrey Fagan. (2008). Legitimacy, compliance and cooperation: Procedural justice and citizen ties to the law. Ohio State Journal of Criminal Law 6(2), 231-275. Venkatesh, Sudhir A. (2000). American project: The rise and fall of a modern ghetto. Cambridge, MA: Harvard University Press. Venkatesh, Sudhir A. (2006). Off the books: The underground economy of the urban poor. Cambridge, MA: Harvard University Press. Wallace, Roderick. (1991). Expanding coupled shock fronts of urban decay and criminal behavior: How U.S. cities are becoming “hollowed out.” Journal of Quantitative Criminology, 7, 333-356. Weiner, David A., Byron F. Lutz, and Jens Ludwig. (2006). The effects of school desegregation on crime. Unpublished working paper, University of Chicago. Available: http://www.aeaweb.org/annual_mtg_papers/2007/0107_1300_0303.pdf [accessed August 2008].
OCR for page 126
Understanding Crime Trends: Workshop Report Weisburd, David, Shawn Bushway, Cynthia Lum, and Sue-Ming Yang. (2004). Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology, 42(2), 283-322. Weitzer, Ronald. (2000). Racialized policing: Residents’ perceptions in three neighborhoods. Law and Society Review, 34, 129-155. Wilkinson, Deanna L., and Jeffrey Fagan. (1996). The role of firearms in violence “scripts”: The dynamics of gun events among adolescent males. Law and Contemporary Problems, 59(1), 55-90. Williams, Kirk R., and Robert L. Flewelling. (1988). The social production of criminal homicide: A comparative study of disaggregated rates in American cities. American Sociological Review, 53, 421-431. Wilson, James Q., and George L. Kelling. (1982). Broken windows: The police and neighborhood safety. The Atlantic Monthly, March, 29-37. Wilson, William J. (1987). The truly disadvantaged. Chicago: University of Chicago Press. Wilson, William J. (1991). Studying inner-city social dislocations: The challenge of public agenda research. American Sociological Review, 56, 1-14. Wooldredge, John. (2002). Examining the (ir)relevance of aggregation bias for multi-level studies of neighborhoods and crime with an example comparing census tracts to official neighborhoods in Cincinnati. Criminology, 40(4), 681-710. Zimring, Franklin E. (2006). The great American crime decline. New York: Oxford University Press. Zimring, Franklin E., and Gordon Hawkins. (1997). Crime is not the problem: Lethal violence in America. New York: Oxford University Press.