Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
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 char- acteristics 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 neigh- borhood 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 âeffectsâ: 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 Âanalysis that affect estimates of why and how neighborhood crime rates change, including size and definition of spatial units, mutual and reciprocal rela- tions between units, the endogeneity of criminal justice enforcement and neighborhood ecology, the influences of macro-changes (i.e., the politi- cal 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 81
82 UNDERSTANDING CRIME TRENDS 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 neighbor- hoods 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 perspec- tive 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 maltreat- ment, 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 hous- ing, 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). But fewer studies have recognized that neighborhoods are dynamic â 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 e Â ffects of growing up in a low-poverty neighborhood on school dropout and teen pregnancy.
CRIME AND NEIGHBORHOOD CHANGE 83 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, B Â ellair, 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 neighbor- hoods (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 neighbor- hoods change (see, also, Ludwig and Kling, 2007). Taylor and ÂCovington (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 differ- ences 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
84 UNDERSTANDING CRIME TRENDS 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 neighbor- hood 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 ânatural social areasâ 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 experiencesâboth direct and vicarious victimizationâdegrade residentsâ 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 citi- zens that the neighborhood had approached a racial âtipping pointâ 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
CRIME AND NEIGHBORHOOD CHANGE 85 of cross-sectional analyses to identify three distinct stages of neighborhood changeâemerging, transitional, and enduringâthat characterized the natu- ral history of neighborhood evolution from a stable low-crime area into a high-crime area. 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 man- slaughter) 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 âunderclassâ 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 increas- ing 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 Chicagoâs 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 spa- tially concentrated homicide and patterns of diffusion to other neighbor- hoods 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, â hanges signaling neighborhood deterioration and rising crime rates include a shift from C 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.
86 UNDERSTANDING CRIME TRENDS 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 iden- tify turning points in the natural history of neighborhood development to pinpoint when crime rates change and grow. Bursik and Webb (1982) updated Shaw and McKayâs (1943) original data on juvenile court refer- rals Chicagoâs 74 local community areas to show that ecological shifts in neighborhoods were associated with deflections in a neighborhoodâs 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 neighborhoodsâ 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 crimeâ s Â pecifically, homicideâin 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 donât link changes in homicide to changes in neighborhoods and are silent on the contemporane- ous changes in neighborhood and crime. Although each of these studies offers important clues about neighbor- hood 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 boundariesâsuch as street segments in Weisburdâs Seattle analysisâare 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 residentsâ 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 configu- rations may yield more accurate units to specify social processes, but these
CRIME AND NEIGHBORHOOD CHANGE 87 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 inter- ested 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 donât 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 speci- ficity 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 Kellingâs (1982) theory of âbroken windowsâ 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) hypoth- esized 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 neces- sarily 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
88 UNDERSTANDING CRIME TRENDS 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 toâor inoculate them fromâharm. Accordingly, these potential disparities raise the stakes in advancing the science of studying crime and neighbor- hood 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 struc- tural 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 influ- ence both the substantive claims of research and their compatibility with other Â 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 ques- tion. 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 neigh- borhood studies begs the question of how area size affects the estimation
CRIME AND NEIGHBORHOOD CHANGE 89 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 mat- ters 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 dimen- sions 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 âplacedâ 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 realâtheoretically meaningfulâdifferences 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 com- mon (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 âhot spotsâ of crime and the prevention poten- tial 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
90 UNDERSTANDING CRIME TRENDS 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. While precincts may have had social meaning at one time, they now are socially and economi- cally heterogeneous areas whose value for testing theories of social control is contested (see, e.g., Wooldredge, 2002). The limitations on administra- tive 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 dif- ferences 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 vio- lence 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 concen- trated. They examined crime trends over 84 police beats in 4 of Chicagoâs 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 includ- ing a measure of spatial dependence (Moranâs I). Fagan et al. (2003) and others (e.g., Corman and Mocan, 2000; M Â essner et al., 2007; Rosenfeld et al., 2007) examined the effects of Âpolicing p Â oliciesâorder maintenance policing, drug enforcementâon crime rates in New Yorkâs 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 popu- lations. Police resources are allocated in precincts based on crime trends and patterns, and within precincts, specific beats are resourced in real time. â owever, H New York City added a precinct in 1993, at the outset of the crime decline that lasted a decade. â or example, the 22nd precinct is Central Park, where there is no population and little F crime overall.
CRIME AND NEIGHBORHOOD CHANGE 91 These differences matter. When Fagan et al. (2003) further disÂaggregated 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. ÂCovariates 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 p Â roblemsâif crime trends are a function of local social area or neighbor- hood 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 âjourneyâ 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 neighbor- hoods in New York, solve these problems in terms of articulating ânaturalâ 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 histori- cal 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 rela- tionship between neighborhoods and precincts and also precincts and cen- sus tracts. These differences in area size and specification matter in the identifica- tion of crime trends. Figure 4-2 shows the results of semiparametric mixture ( Â 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-
92 UNDERSTANDING CRIME TRENDS FIGURE 4-1aâ Shape file for New York City police precincts and census tracts. SOURCE: Data obtained from http://www.infoshare.org. cent of the units that are theFigure 4-1a, groups. Cross-tabulations of tracts lowest rate bitmapped and their neighborhood membership (not shown) show that these in fact are the same 45 percent. Although ANOVA tests using measures of social disor- ganization and economic deprivation showed similar predictors for the tract and neighborhood analyses, one difference did emergeâmeasures of spatial dependence (Moranâs I) were not significant in the neighborhood models, but they were significant predictors in the tract models. The implications of this finding for conceptualizing âneighborhoodâ are discussed below. Defining and Bounding Neighborhoods The definition of âneighborhoodâ and the articulation of its spatial size and boundaries affect our estimates of crime trends. Definitions of neigh- borhood in sociology, geography, and criminology have varied over time, in part reflecting the process of development of the city itself. Definitions of
CRIME AND NEIGHBORHOOD CHANGE 93 FIGURE 4-1bâ Shape file for New York City neighborhoods and police precincts. SOURCE: Data obtained from http://www.infoshare.org. Figure 4-1b, bitmapped ânatural areasâ over a century ago were based on the interplay of business competition and the growth of housing for workers near workplaces (Park, 1916; Park, Burgess, and McKenzie, 1925). Accordingly, neighborhoods included business, residences, and religious and social institutions that were part of the fabric that bound residents together. These areas also were con- nected toâand nested inâlarger subdivisions of cities as well as to each other (Sampson et al., 2002; Shaw and McKay, 1943; Suttles, 1970). As cities grew and changed both commercially and demographically, the popu- lation composition of neighborhoods often changed, leading to changes in both its internal identity and cohesion as well as its relations to adjacent areas. At times, externalities imposed change, through the construction of public housing (Marcuse, 1995), or the replacement of slums with other housing reforms (Harcourt, 2005), or the construction of highways or other public works projects (Jacobs, 1961). As a result, administrative boundaries sometimes became Âhistorical arti-
94 9.00 8.00 Group 4 - 36 Neighborhoods (13.2%) 7.00 6.00 5.00 4.00 Group 3 - 65 Neighborhoods (23.9%) Homicides per 10,000 3.00 2.00 Group 2 - 117 Neighborhoods (43.0%) 1.00 Group 1 - 54 Neighborhoods (19.9%) 0.00 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year FIGURE 4-2aâ Trajectory models for homicides in New York City neighborhoods (N = 292), 1985-2000. Figure 4-2a, landscape
0.80 0.70 Group 3 - 437 Tracts (22.5%) 0.60 0.50 0.40 0.30 Group 2 - 779 Tracts (40.1%) Homicides per 10,000 0.20 0.10 Group 1 - 725 Tracts (37.4%) 0.00 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year FIGURE 4-2bâ Trajectory models for homicides in New York City census tracts (N = 2,217), 1985-2000. 95 Figure 4-2b, landscape
96 UNDERSTANDING CRIME TRENDS Homicide Trajectories High Homicide Moderate Homicide Low Homicide FIGURE 4-2câ Homicide trajectory for New York City census tracts (N = 2,217), 1985-2002. Figure 4-2c, bitmapped facts as neighborhoods changed. In addition to internal changes, Âconcurrent changes in adjacent but administratively distinct areas could create social and economic ties that span those older boundaries and create cross-Âboundary social interactions or markets that complicate neighborhood analyses. So, a personâs local environment may be influenced more by nearby locations that span administrative boundaries than by more distant locations in the
CRIME AND NEIGHBORHOOD CHANGE 97 same unit. A local environment thus needs to be defined as the proximity- weighted average of all surrounding locations in which a person interacts; in this formulation, proximity itself is a variable that needs both empirical and theoretical definition. Accordingly, to understand what a neighborhood is and how it influ- ences individuals, one needs to theorize the relevant contextual environment for a person or small local areas. For local social ties, the relevant context may be pedestrian-scale contexts (immediate block or blocks) (Grannis, 1998) or small location-based crime environments (Weisburd et al., 2004), which are most relevant for understanding neighborly interactions, social contact, etc. Or, if one conceptualizes relevant patterns of social interac- tion as based in economic or social institutions, then institutional-scale contexts (school attendance zones, shopping, churches, etc.) may be most relevant for the types of neighborhood (social) effects that are mediated through social institutions. Normalized or routinized economic activity also is defined in this context. Finally, these scales or contexts also are likely to have age-specific effects, so that the proscribed boundaries of child or adolescent interactions may differ in locus and scale from that which affects the social ties and behaviors of adults. Figure 4-3 from Lee et al. (2008) illustrates these issues. Persons 2, 3, and 4 may share social ties, economic interests (either legal or illegal), and institutional affiliations. Yet they are separated for analytic purposes by the administrative boundary between Tracts A and B and (more important) are thought to be affected equally by either the structural or dynamic character- istics of their respective tract memberships. Person 6 in Tract C also shares Tract C characteristics with Persons 7 and 8, but the reality of her everyday interaction patterns is more likely to be influenced by the tract adjacent to the left (B). The difficulties of attributing in part or whole neighborhood effects equally to all three persons in Tract A or all three in Tract C are obvious. And, since no one in this example is living in Tracts D, E, and F, their influences may not be included analytically at all. Yet their proximity to Tracts A, B, and C suggests that its residents are likely to share economic, cultural, social, and institutional space with their neighbors, and have some influence on the behavior of nearby neighbors in adjacent tracts. Can spatial autocorrelationâeither of dependent or independent measuresâaccount for this? Not very well and only partially at best. Lee et al. (2008), suggest an alternate strategy, in which each personâs local environment is measured and aggregated across persons to estimate area effects as a continuous distribution that incorporates the shared influ- ences of persons in âlocal communitiesâ that span administrative bound- aries. They suggest algorithms to estimate local area effects of relevant characteristics (e.g., policing, air quality, population density) in the local environment through careful aggregation of these characteristics in the
98 UNDERSTANDING CRIME TRENDS FIGURE 4-3â Proximity and local environments. SOURCE: Lee et al. (2008). surrounding areas weighted by proximity, density, or even network prop- erties. This alternative rejects the notion of administrative boundaries to understand neighborhood and its effects, substituting both perceptions of persons in areas based on reciprocal influences on them and their neighbors who are in close proximity. Grannis (1998) defined neighborhood by examined residential and street patterns and compared it with two measured dimensions of residentsâ lives: their social networks and cognitive maps of their areas. This approach is similar to the methods used by Jackson and Manbeck (1998) and Coulton, Korbin, and Chow (1995). Both of these fit well with Lee et al.âs notions of proximity, although Grannis focuses more closely on local residential inter- actions and their effects on micro-areas. Grannisâs model produced good similarity in the boundaries drawn by the different individuals in the same areas and was especially efficient in explaining neighborsâ efforts at social control. Which, then, matters more: the perceived local environment, which may vary across developmental phases and particular social or economic contexts, or the structured environment, in which individuals cognitively
CRIME AND NEIGHBORHOOD CHANGE 99 structure their neighborhoods and each assigns contextual effects to those boundaries? Figure 4-3 shows the potential variability in the span of loca- tional proximities. Estimates of neighborhood effects on crime may profit from using cognitively drawn boundaries to capture guardianship of specific areas as well as the allocations of formal (legal) and informal controls (see, for example, Sampson and Raudenbush, 2004). Developmental studies that track behavioral changes over the life courseâwhether in crime or other social interactionsâmay benefit more from capturing the multiple influ- ences that shape behaviors and the varying combinations of influences at particular developmental stages. The idea of direct measurement of neighborhood as a substitute for observational data is conceptually attractive but practically difficult. The methods to compute these effects are still developing, and such questions as the frequency of measurement, sampling frames, methods, and aggregation procedures require some experimentation. Because physical characteristics also are important features of neighborhood, direct measurement requires multiple methods, including social observation and interviews with residents. But the potential advantages of this strategy for understanding local crime trends and other social and institutional processes make a strong case for its importance as an alternative to the artificiality of current spatial thinking that often is a prisoner of arcane and incompatible administrative boundaries. Theories of Change Conceptualizing neighborhood is the central theoretical task in under- standing neighborhood crime trends. A preliminary question is whether neighborhood change is implicated at all in changing crime trends. The answer depends, of course, on how one thinks about neighborhood. Until recently, studies of neighborhood effectsâsimilar to city-level analysesâ focused on the traditional characteristics of social disorganization, con- centrated poverty and deprivation, segregation, and other social structural attributes and characteristics linked to the capacities of neighborhoods to exert social control. These characteristics were useful in differentiating which neighborhoods had higher risks of crime and violence over time, but they were less helpful in explaining change. One reason is that these characteristics may not change as quickly as changes in crime rates, or the scale of their changes may poorly match the rate or magnitude of changes in crime rates. Poverty, perhaps the most salient marker of neighborhood position, is stubbornly persistent over time (Sampson and Morenoff, 2006), and neighborhoods seldom change their ordinal ranking in disadvantage in a city even as their material conditions â ee, S Bursik and Grasmick, 1993, for an exception.
100 UNDERSTANDING CRIME TRENDS may improve over time. Sampson and Morenoff (2006) show that the initial starting point for each place in a panel study of neighborhoods is the best predictor of where a neighborhood will rank over time. Thus, they char- acterize poor neighborhoods as poverty traps of âdurable inequalityâ for which, beyond a tipping point, poverty will only ratchet up (Massey, 2007). For example, research on New Yorkâs crime decline typically controls for social structural attributes and characteristics at one time point (usually at the outset or midpoint of a panel) to isolate effects of neighborhoods or precincts on crime (Fagan and Davies, 2004; Rosenfeld et al., 2007). Others claim that the changes are too small and slow to account for sudden spikes or drops in crime (see Zimring, 2006) and that change need not be taken into account to understand crime trends. That may be true for larger aggregates, such as police precincts or zip codes, perhaps because those aggregates are compositionally heterogeneous and smaller group-specific or small-area changes are hidden. But smaller units sometimes do change, usually in response to an external shock, such as deindustrialization (Galster and Mincy, 1993; Sampson and Wilson, 1995; Wilson, 1991), school desegregation (Weiner, Lutz, and Ludwig, 2006), or the passage of fair housing laws (Bursik, 1988). Recent studies show that the sudden influx of immigrants can also animate changes in crime. Saiz and Wachter (2006) suggest that housing prices grow more slowly in neighborhoods with higher rates of immigration, as a function of white flight and increased racial segregation. Sudden increases in numbers of immigrants can change the risks and rates of crime in either direction, often for the better (Massey, 1995, 2007; Massey and Denton, 1993). For example, MacDonald, Hipp, and Gill (2008) show that the succession of Mexican immigrants in poor neighborhoods in Los Angeles accounts for a significant portion of the crime decline in those areas. Martinez (2002) reports the same for Latino immigration and homicide in several cities. But sudden increases in immigration also can destabilize neighbor- hoods, with crime increases following in short order. For example, the white population in the four census tracts in Washington Heights, in north- ern Manhattan, declined from 73 percent in 1970 to less than 25 percent in 1980, much of it replaced by Hispanic immigrants primarily from the Dominican Republic (Fagan, 1992). While crime rose across the city in this era, it rose more quickly in the Washington Heights neighborhood than in many other places where population characteristics were stable. The combination of rapid demographic change, access to transshipment routes, and a strategic location at the intersection of major highways connecting the city with the nearby suburbs from three states helped fuel the growth of a dynamic and violent drug market in this neighborhood that persisted for nearly two decades (Fagan, 1992). These rapid and significant changes, in a broader setting in which most areas change slowly and more modestly,
CRIME AND NEIGHBORHOOD CHANGE 101 show the need to decompose change and examine the effects of different levels and forms of change over time. Beyond the pace and size of change the question remains as to what types of change to measure. A good starting point is to assume that the factors that typically explain neighborhood effects statically also will exert influences on crime rates as both crime and neighborhoods change. While the candidates are as broad as the literature on neighborhoods, one can identify three broad categories or domains of effects: social interactional mechanisms, political economy and institutional forces, and legal interven- tions. These three dynamics also may be reciprocal and over time become tightly wound in a social-institutional ecology of neighborhood. Social Interactions and Social Organization Social interactional mechanisms generally include the dynamic pro- cesses of what sociologists have traditionally termed informal social control. These include such factors as social ties, mutual trust, shared norms, social networks, and routine activities (see Sampson et al., 2002, for a review). These social processes and the forms of social organization that they influ- ence or even produce become part of the dynamics of social regulation in neighborhoods, a process identified as collective efficacy by Sampson and colleagues (1997). The regulatory behaviors include willingness to intervene when wrongdoing takes place or to advocate for solutions to neighborhood problems, guardianship, and institutional participation (e.g., school boards, citizen groups) that can leverage services and resources. But adverse neigh- borhood change, such as increasing segregation and poverty concentration, can erode social control and social regulation, leading to a rejection of the social and moral norms underlying law and legal institutions (see Sampson and Bartusch, 1998, for an illustration in Chicago neighborhoods). The weaker social position of a neighborhood can also erode its leverage for essential services, launching a downward spiral in its social capital and regulatory efficacy. For now, there is limited evidence on whether changes in these mechanisms over time influence changes in crime rates, a product of the limited availability of neighborhood-level data on social interactions over sufficient periods to detect such effects. Criminal groups and networks also are features of the social organi- zation of neighborhoods that may exert strong influences on crime, and they may have variable presence and influence over time. The presence of gangs, for example, affects the developmental trajectories of adolescents and increases their risks for involvement in serious delinquency Â(Thornberry et al., 2004), sustain illegal markets in drugs (Levitt and Venkatesh, 2001; Venkatesh, 2000, 2006), and perpetuate lethal violence through recurring disputes (ÂPapachristos, forthcoming). Like illegal markets, gangs are located
102 UNDERSTANDING CRIME TRENDS in specific neighborhoods. Some gangs endure over time, others arise in specific eras and then dissipate (Klein, 1997). Drug-selling networks also are often location-based and themselves influence neighborhood social organization. They often are the targets of law enforcement, but they also exert their own brand of social influence and control on neighborhoods, a form of influence that can have the perverse effect of reducing crime to protect income-Âgenerating illegal activities (Fagan, 1992). Political Economy The broad category of political economy includes both institutional forces and the effects of physical structures in the neighborhood. Changes in the structure and composition of housing exerts an effect. For example, Schwartz, Susin, and Voicu (2003) linked changes in housing prices with changes in violent crime rates in New York; they show that in police pre- cincts, declining crime rates through the 1990s stimulated a housing boom and increases in housing values. Fagan and Davies (2007) found the oppo- site: changes in housing prices stimulated changes in crime rates, and these effects were most salient in neighborhoods experiencing tipping points in crime. Looking back at Figure 4-1a, they show that the housing-crime rela- tionship was strongest in Groups 3 and 4, while in wealthier areas, housing values rose while crime remained stable. Other potentially important domains of housing include the locations of public housing and the potential leverage on crime either of policies designed to reduce collateral crime problems, such as drug dealing, or to aggressively monitor illegal occupancies. Construction of new housing and the replacement of dilapidated and condemned housing also can alter the social and economic landscape of communities by strengthening the social capital of local residents and increasing their capacities for local control (Saegert, Winkel, and Swartz, 2002). In contrast, public housing developments maintain the concentration of poor families without altering the housing or social landscapes of their immediate social context. Public housing sites in New York are sited in the neighborhoods with the highest concentrations of homicides, regardless of whether the era was one with a high (1990) or low (2000) homicide rate (Fagan et al., 2007). Fagan and Davies (1999) illustrate the contagion effects of violence and other crime in and around public housing sites in New York. In a later analysis of the effects of drug enforcement in public housing, Fagan et al. (2007) show that policies targeting drug markets in and around public housing have crime reduction benefits for the surrounding neighborhood but limited benefits in the public housing projects. Accordingly, as housing and legal interventions improved in the areas surrounding public housing sites, the inability to transform either the physical features of public housing, to
CRIME AND NEIGHBORHOOD CHANGE 103 alter the mix of families and ameliorate concentration effects, or to change the perceptual frames of their residents, through either of these mechanisms, seemed to contribute to persistent crime problems over time. What one sees, then, in such neighborhoods as the South Bronx and Red Hook in Brooklyn is that repairing or replacing poor housing with new developments potentially reduces the effects of physical disorder, and it may have a secondary effect on social disorder (Fagan et al., 2007; Geller, 2007). Physical disorder and social disorder are highly correlated, and âbroken windowsâ theories posit that they combine to signal to would-be criminals that social control is weak. There have been several cross-sectional studies showing mixed results for this theory, but until recently there have been no panel studies to show whether changes in disorder lead to changes in crime. The few studies that do examine changes in disorder rely on observational data, including police-generated measures of disorder (e.g., Rosenfeld et al., 2007), which are less often based on citizen complaints of âviolations,â such as loud music or graffiti, than on police-initiated interventions. The one panel design to test the influence of neighborhood disorder on crime was recently completed by Geller (2007), based on a longitudinal study of disorder in 55 subboroughs in New York from 1991 to 1999. Subboroughs (or subboros) are administrative boundaries designed by the Census Bureau to capture broad trends in housing. Geller used data from the Housing and Vacancy Survey (HVS), a survey conducted at three year intervals with subboros as the primary sampling unit. The HVS both rep- licates census variables for the person-level survey and measures housing characteristics for households. The HVS household data are used to index rent stabilization (i.e., rent control) rates in New York. Geller measured physical disorder using an index that includes housing conditions (broken windows, dilapidated walls and stairwells) and other neighborhood condi- tions (boarded-up buildings in the surrounding area) and compared it with crime rates in the subboros. Figure 4-4 shows a strong decline in crime, with the sharpest decline taking place in the most disorderly neighborhoods (in terciles). However, in a panel analysis in which she lagged disorder by one survey period and used fixed effects to account for unmeasured factors in the subboros plus a rich set of covariates, she found no effects of chang- ing disorder on crime. The inability to detect disorder effects on crime comports with the observations of Sampson and Raudenbush (2004) that disorder may be a social construction tied to the structural position of the residents of an area and their social position (Hipp, 2007). These studies raise doubts about whether physical and social disorder exerts an independent effect on crime that is separable from the poverty that almost always surrounds it. The most disorderly neighborhoods in New York also are the poorest (Fagan and Davies, 2000; Geller, 2007). If the links between disorder and crime
104 UNDERSTANDING CRIME TRENDS 40003000 violentrate 2000 1000 0 1990 1995 2000 (mean) year least disorder midrange disorder most disorder Figure 4-4a, fully editable Figure 4-4b, bitmapped, not editable FIGURE 4-4â Physical disorder (broken windows) and felony violent crime rate per 10,000 persons, New York City subboros (N = 55), 1987-2002. SOURCES: Geller, 2007; New York City Police Department, Statistical Report, Complaints and Arrests, various years; New York City Department of City Plan- ning. Available: http://www.nyc.gov/html/dcp/pdf/census/cdsnar.pdf; New York City Housing and Vacancy Survey, various years, available: http://www.census.gov/hhes/ www/Âhousing/nychvs/nychvs.html. Figure 4-4c, bitmapped, not editable
CRIME AND NEIGHBORHOOD CHANGE 105 are at best tenuous, then the decline in crime in these poorestâand most disorderlyâneighborhoods may have less to do with disorder than with the forces impelling a broader and secular decline in crime that reflects more complex neighborhood changes in their social organization and political economy. Immigration is one such change. The political economy and social organization of poor neighborhoods has been transformed by the rise in immigrant populations in New Yorkâs poorest neighborhoods (Fagan and Davies, 2006), and also in Los Angeles (MacDonald et al., 2008). Sociolo- gists are now beginning to identify the secondary effects of the influx of immigrants on the social ties and economic activities in urban neighbor- hoods. In some cases, immigrants can increase risks of crime, as in the case of Washington Heights, discussed earlier. But there also is evidence from several cities, including Chicago, New York, Miami, and others, that the arrival of immigrants is associated with lower crime rates (Fagan and Davies, 2006; Martinez, 2002; Papachristos et al., 2007). Immigrants often seek neighborhoods that they can afford, and where people share racial or ethnic characteristicsâthat is, where people look like them. So they settle in areas that may have elevated crime risk, but their influence may alter that risk. They also may attract or develop commercial activity to provide essential services to newcomers, stimulating the creation of new institu- tions, such as churches and neighborhood self-help groups (Martinez and Valenzuela, 2006). The causal mechanisms through which immigrants exert a protective effect on crime in neighborhoods that have concentrations of social struc- tural risks are as yet unknown. Also, other studies of second and third gen- erations of these immigrant families suggest that the protective effects may dissipate over time, with generational mixing and replacement that dilutes the selection effects of the first-generation settlers. But these processes vary by immigrant group. Smith (2005, p. 121) shows that most immigrants in the United Kingdom have low crime rates, but offending rates accelerate for second-generation Afro-Caribbeans but not among immigrants from South Asia. There is much that is not yet understood about this phenomenon, including its constancy across racial and ethnic groups, covariation with the characteristics of the landing neighborhoods, and the effects of human capital that new immigrants bring with them that advantages them in both in legal and informal workplaces. Immigration illustrates a more general theoretical point: the movement of persons into and out of neighborhoods can alter the social composition, stability, and social organization of neighborhoods, affecting the social ties among neighbors and, in some cases, the networks of individuals through which crimes can occur or through which it is regulated and controlled. For example, exogenous shocks, such as court-ordered busing or economic
106 UNDERSTANDING CRIME TRENDS downturns, have led to âwhite flightâ in some places, producing sudden sharp and often adverse compositional changes. The churning effects of such population shifts tend to resegregate the abandoned neighborhoods as places where minority populations live in conditions of concentrated poverty, which tend to attenuate their life chances and the life chances of their children (see Frey, 1979, 1994). Such concentration effects sharpen the risk factors for crime (for a review, see Sampson and Wilson, 1995). Or, in the case of gentrification, these changes can homogenize neighborhoods but skew them toward less poverty. Gentrification draws better resourced persons who displace poorer long-term residents, often creating contrasts and tensions with surrounding areas that animate violent crime (see Taylor and Covington, 1988). Segregation and resegregation seem to be the rule; race and class integration of neighborhoods following population shifts are rare (Sampson and Sharkey, 2008; Sobel, 2006a). Recent policy experiments tested the effects of housing vouchers as policy instruments to deconcentrate poverty and improve the well-being and safety of poor inner-city residents. Court-ordered desegregation of public housing in Chicago, for example, created the methodological condi- tions to test a different question: What are the effects on neighborhoods of moving disadvantaged persons living in poor neighborhoods with high crime rates to places that are more integrated, where poverty rates are lower and far less concentrated, and where schools and work opportunities are improved? These experiments and quasi-experiments produced inconsistent findings about the effects of such moves on individual families and on the neighborhoods to which they moved. Results from the Gautreaux program in Chicago, where 7,100 families used housing vouchers to relocate to pri- vate housing in Chicago and its suburbs, suggest positive effects on school outcomes and employment for the children in those families (Rosenbaum, 1995) and modest income and employment gains for the adults (Popkin, Rosenbaum, and Meaden, 1993). The Moving to Opportunities (MTO) program, a randomized experiment compared with the quasi-experimental design of Gautreaux, showed that many families moved to neighborhoods that were better off in terms of poverty, crime, and disorder (see, for example, Keel et al., 2005; Kling et al., 2004), but the effects on families were not as positive as in the Gautreaux program. In comparing the outcomes of the Gautreaux and MTO initiatives and taking into account longer term neighborhood effects, including social ties, economic resources, and other services, Clampet-Lundquist and Massey (2008) show that neighborhoods exerted an independent and positive effect on the employment and earnings of MTO participants (but see Kling et al., 2004, and Ludwig et al., 2008). But the relocation of families from poor high-crime places raised the disturbing potential for criminogenic effects in the neighborhoods in which voucher recipients settled. Citing unpublished
CRIME AND NEIGHBORHOOD CHANGE 107 research by criminologists at the University of Memphis, Rosin (2008) claims that crime rates increased in the areas of that city in which families from high-crime neighborhoods relocated, while the neighborhoods they left experienced sharp drops in crime. No such effects were found in MTO, and Kling and Ludwig (2007) explicitly reject such âcontagionâ argu- ments. Sampson and Sharkey (2008) suggest that when families relocate from poor places, there remains a stratification of incomes with virtually nonoverlapping income distributions and little exchange between minority and white areas. In other words, the interaction of selection effects and political economy produce racially configured hierarchies and equlibria of neighborhood inequality (Sampson and Sharkey, 2008). Crime patterns in new places reflect this inherent stability in reconstituted places, both neigh- borhood effects and their consequences endure, and these poverty traps appear to have their own perverse form of mobility. Legal Interventions The effects of policing and incarceration on crime have been examined in a variety of studies, and there is ample evidence that legal interventions can affect crime in both positive and negative ways. The question here is the relationship between legal interventions, neighborhood change, and crime. The few studies on this rely on observational data on both policing and crime, neither of which is unbiased. The usual research paradigm to estimate these effects is to examine a lagged measure of policing (arrests, expenditures, personnel) or incarceration rates (jail or prison admissions) on crime rates, with controls for the social structural conditions at the unit of analysis. Spatial dependence is not a factor, since most of these studies use larger spatial aggregatesâsuch as police precinctsâfor which spatial dependence may be less influential on crime rates. The analyses may include fixed effects for both neighborhood units and time to isolate the effects of the policing or other legal variables. The conceptual and analytic challenge in these designs is the identifica- tion of policing or other legal variables that interact with neighborhood structures or dynamics to shape the behavior of offenders or of neighbors who choose whether to participate in social regulation (see, for example, Fagan and Meares, 2008; Patillo, 1998). The âstandardâ paradigm is chal- lenged to avoid the selection effects of how and where police and enforce- â Ludwig and Kling find no evidence of contagion. Instead, Kling and Ludwig show that neighborhood racial segregation is the strongest predictor of variation between neighborhoods in arrests for violent crimes in the MTO sample. They speculate that factors such as drug market activity are more common in poorer neighborhoods with concentrations of minority residents.
108 UNDERSTANDING CRIME TRENDS ment are allocated. For example, Fagan and Davies (2000) showed that order maintenance policing in New York was concentrated in the cityâs poorest neighborhoods and that poverty and disorder were isomorphic in these analyses. One solution may be to use instrumental variables, but the selection of a valid instrument is difficult since many eligible candidates (e.g., health indicators, such as tuberculosis rates) are poorly measured over time locally. Also, changing neighborhoods may narrow the list of eligible instruments, since they also may be changing over time. A second challenge in legal interventions is the relationship between policing and law enforcement generally and the reactions of local residents both to styles of policing and to the quality of interactions they and their neighbors have with police (National Research Council, 2004). Weitzer (2000) and Tyler and Fagan (2008) show that citizens react negatively to disrespectful policing and tend to withdraw from the social regulatory mechanisms that are an important of neighborhood controls on crime; they show that these effects are strongest in poorer neighborhoods and neigh- borhoods with high concentrations of minority citizens. These structural characteristics of policing, with the accompanying process dimensions, and the reactions of citizens are another type of neighborhood social interaction that is central to understanding neighborhood effects. Research has yet to capture these effects in panel designs to allow for tests over time of how changes in policing styles affect neighborhoods and crime. Incarceration also affects neighborhoods (Clear, 2007). The movement of persons between prisons and neighborhoods is a dynamic process that unfolds over time and affects these areas in several ways. Returning inmates often place strains on their families and potentially weaken their participa- tion in social control, both at home and among their neighbors. The con- centrations of inmates in specific neighborhoods may attenuate property values, attract heightened surveillance by police, adversely affect child and adolescent development to increase risks of youth crime, and stigmatize the neighborhood and its residents in ways that could disadvantage them eco- nomically. If disenfranchised from electoral participation, for example, their political capital is weakened, and residents may have weaker influence and leverage to influence institutions and services in their areas. Returning pris- oners also may bring with them mental health problems that can adversely affect the âpsychological capitalâ of a neighborhood (Petersilia, 2003). These processes may also reverse neighborhood fortunes at some tip- ping point. Crime may increase in response to changes in housing prices, for example, as neighborhoods change and newcomers come into conflict with long-time residents (Taylor and Covington, 1988). But crime may tip downward at some threshold of compositional change, even as prices con- tinue to rise. The possibility of discrete eras separated by abrupt processes
CRIME AND NEIGHBORHOOD CHANGE 109 of neighborhood change suggests the need for analytic models that can account for contiguous but quite distinct ecological effects over time. Endogeneity and Simultaneity It is no surprise that neighborhood factors collapse into each other and into crime. That is, poverty, poor health, bad housing, weak social control, and other neighborhood deficits are highly correlated with each other and with crime, and their effects multiply to produce what Wilson (1987) termed âconcentration effects.â The interdependence of these factors in shaping the trajectory of neighborhood ecologies challenges researchers to identify or isolate specific effects of any single factor. These factors often are endogenous, meaning that they are linked in complex relationships where they affect each other reciprocally and simultaneously. Panel Â studies of neighborhoods further complicate endogeneity: the longer the time series, the more complicated the analysis, since different eras may experience different patterns and sources of change. Simultaneity raises parallel chal- lenges in panel studies, with effects both sustained over closely spaced time intervals, and also exerting influences on other factors in the neighborhood ecology. Issues of endogeneity and simultaneity arise at the starting point of panels or time series and sustain over time (see, for example, Fagan et al., 2003, on the endogeneity of crime, neighborhood social structure, enforce- ment, and incarceration). The social selection and self-selection of individuals to neighborhoods also raises the risk of aggregation biases that may affect our understand- ing of how these effects work in neighborhoods (Hipp, 2007; Wooldredge, 2002). Selection effects complicate inferences that might distinguish aggre- gation effects from structures and dynamic processes that are unique to neighborhoods, beyond the persons who live there (see Jencks and Mayer, 1990, for a discussion). Harding (2003) demonstrates a useful approach to resolving the problems of selection bias, confounding, unobserved het- erogeneity, and omitted variable bias that complicate the estimation of neighborhood effects. Using a counterfactual causal framework based on propensity score matching and sensitivity analysis, he addresses the inher- ent endogeneity of adolescent development and neighborhood (see also MacDonald et al., 2007, on neighborhood contexts and citizen evaluations of police). The selection challenge is further complicated by the reality of changing neighborhoods, and these propensities must be recomputed periodically. And, there may be serial correlation or autoregression in the propensity scores themselves, due to relatively slow but measurable changes in neighborhood context. Yet these problems are often ignored. Instrumental variables, or instru- ments, have some promise to address endogeneity (see, for an example,
110 UNDERSTANDING CRIME TRENDS Clear, 2007). Instruments can produce a consistent estimate of a causal effect when the predictors are correlated with the error terms. This often happens as a result of endogeneity (see, Levitt, 1998, for an example). In panel designs of neighborhood change, there are risks with instru- ments: they too may change over time, and after a lengthy period of influence in a neighborhood, they may no longer be uncorrelated with the dependent variable at some tipping point. For example, crime may at the outset influence election cycles and put a more conservative party in office, but the relationship between crime and that party becomes endogenous over time. Or police may be assigned to high-crime areas, but they soon become part of the social fabric of the area and their presence endures over time. So instruments can be of help, but their selection is difficult and conceptually demanding. Also, the weaker the instrument, the larger the standard error and the more difficult it is to identify specific neighborhood effects. One analytic solution is to use cross-lags, in which simultaneous regres- sions are estimated with reciprocal causal factors, each lagged simultane- ously (Ferrer-Caia and McArdle, 2004). But the measurement constraints on cross-lag modelsâin terms of the number of predictors or Âcovariatesâare significant. Other solutions include using random effects for time to account for serial correlation or estimating (benchmark) endogeneity at the outset of a panel using simple ordinary least squares (OLS) regressions of the crime- neighborhood relationship. Returning to Harding, the counterÂfactual model offers a useful strategy for disentangling otherwise confounded effects pro- duced by both endogeneity and simultaneity. One final complexity in estimating the causal effects of neighborhoods is the inherent reliance on the stable unit treatment value assumption (SUTVA). Thinking about neighborhoods as a treatment for both individual and family, a basic neighborhood theory would demand homogeneity of treatment and no transference or interference among the residentsâthat is, one assumes that they are independently and identically distributed (Sobel, 2006a). This seems unreasonable, because of the network aspects of neigh- borhood life and the density of urban neighborhoods in particular, and also because of the complexity and heterogeneity of neighborhood components. But it is exactly that interference that may be the mechanism through which neighborhood acts (Sobel, 2006b), in turn making it inherently difficult to estimate neighborhood effects. When neighborhoods themselves are complex and changing contexts, the estimation of an average âtreatmentâ effect becomes quite difficult. Sobel (2006b) warns that when interference is present among residents, there is a cross-level interaction of a structural or aggregate neighborhood effect that changes the meaning of the contextual effect estimate. Thus, one cannot empirically identify neighborhood effects when SUTVA is violated, but SUTVA is violated if one believes in neighbor- hood effects (Sobel, 2006a,b). This is a serious conundrum.
CRIME AND NEIGHBORHOOD CHANGE 111 Data Limitations Most studies use observational data to measure both crime and neigh- borhood characteristics, a matter of convenience and often necessity. Regres- sion models with observational data can produce good fits, but they also risk biases in the regression coefficients models because of selection effects (Berk, Li, and Hickman, 2005). While one compensates for the fact that people are not randomly assigned to neighborhoods nor are crime-control policies randomly distributed to neighborhoods, with propensity score models and other statistical accommodations, the success of these strate- gies depends on the nomination of, and data availability for, theoretically sensible components of a selection model. In considering neighborhood change, these complexities multiply. Neighborhood data often are limited to observational measures rep- resenting compositional characteristics (e.g., income, ethnicity, unemploy- ment rate, household structure, renter status) as proxies for the social mechanisms through which neighborhood effects are thought to operate (Pebley and Sastry, 2006). The Neighborhood Change Database (Tatian, 2003) illustrates the promise and limitations of neighborhood indicators that rely solely on structural features. Such limitations raise two important problems. First, neighborhood measures often are aggregated into admin- istrative units that do not comport well with natural neighborhood bound- aries or even with other administrative units. In New York, for example, precincts, tax collections, school districts, election districts, health service areas, mental health catchment areas, and census tracts are poorly aligned. The HVS sampling units (subboros) also are not aligned with any of the above, and census tracts often overlap the HVS units. The solutions to align and reconcile can be expensive and challenging. One solution is to obtain individual records by person or household, perhaps by student or recipient of key public services, and individually geocode each record. That would be prohibitively expense and raise privacy issues, particularly for children and in health settings. Another strategy is to use geographic information systems to generate comprehensive and compatible templates that can reconcile measures across bounded areas based on population weighting. Second, compositional neighborhood data lack information on the neighborhood processes that connect structure to the moving parts of theory. For example, while many studies show a strong correlation between neighborhood poverty rates and crime, they rarely analyze data about the moving parts of a causal model of neighborhood effects to identify the mechanisms through which poverty influences neighborhood life: skewed â n contrast, a very simple regression model for a properly implemented randomized experi- I ment may not fit the data very well, but it is far more likely to produce unbiased estimates (Berk et al., 2005).
112 UNDERSTANDING CRIME TRENDS social networks, weak social organization, low levels of social ties and interactions among neighbors, levels of institutional participation, or the elasticity of social ties beyond the neighborhoodâs boundaries. Rarely are data available, either at a static point or in a panel design over time, to measure what Sobel (2006a) terms the interference of neighborhood effects (but see Grannis, 1998; Sampson and Raudenbush, 2004; Sastry, Ghosh- Dastidar, Adams, and Pebley, 2006). Causal modeling of neighborhood effects under these circumstances is analytically risky. But the creation of these data is essential to developing a data infra- structure to study neighborhood dynamics and neighborhood changes over time. Data on residentsâ social interactions and networks within ânaturalâ neighborhood boundaries require systematic data collection across neigh- borhoods on samples of residents (see, for example, Grannis, 1998; the Los Angeles Family and Neighbors Study in Sastry et al., 2006). These data can be combined with administrative data and resident surveys to develop rich datasets on the development of communities and their change over time. The frameworks suggested by Lee et al. (perceived environments) and Grannis (social interactional spaces) could be combined with observational (Sampson and Raudenbush, 1999) and administrative data to measure the types of densities and proximities to local institutions and networks that comprise the dynamic component of neighborhood effects. This would be a resource-intensive effort: the data must be collected by researchers them- selves through interviews or direct observation (Pebley and Sastry, 2006; Sampson and Raudenbush, 1999). Similar problems are evident in the measurement of crime and the availability of crime data. Not all cities make crime data available in a sufficiently flexible form to allow for spatial disaggregation in small units of resolution. In New York, for example, only complaints and arrests are reported, and only for precincts. Data on police beats or other data with spatial coordinates are not reported. Contrast this with the micro-data analyzed by Weisburd et al. (2004). Depending on the theoretical question, data on crime event locations and circumstances, together with victim and offender characteristics and residential information, are needed to answer important questions about neighborhoods and crime. Calls-for-service data also are indicators about crime and neighborhood. Calls for service reflect the propensity of residents in different neighborhoods to report crime to the police; they can represent social disorder or social disorganization, and they can address questions about the utilization of police services and the character of informal social control (Black, 1983, 1989) or the perceptions of citizens of the legitimacy of law and legal actors (Tyler and Fagan, 2008). Some researchers have analyzed call data to show crime hot spots to guide the allocation of police resources (Weisburd et al., 2004). There are some technical problems in calls data, including duplications (multiple reports of
CRIME AND NEIGHBORHOOD CHANGE 113 the same crime), errors (e.g., confusion of gunshots with a car backÂfiring, erroneous reports of weapons being brandished, cars that are used by one family member unknown to others who then report it as stolen), and inconsistencies in aggregation and reporting by type of crime (e.g., where the gunshot was heard may be some distance from where the gun was fired). These issues require data cleaning and quality checks to ready them for analysis of neighborhood effects. Homicide records are the most stable measure over time and are avail- able from multiple sourcesâboth police and public health sources. The comparative validity of data from these two sources may vary from city to city and year to year. For example, there may be discrepancies in which fatalities are classified as homicide versus accidental deaths or unclassified deaths whose cause is not determined. Public health data on nonfatal inju- ries also has proven to be a valuable source as an alternative to subjective criminal legal categories, such as assault (see, for example, Zimring and Hawkins, 1997). For crimes other than violence, particularly property crimes, alternatives to criminal justice data are needed to capture crime trends that may be unreported to the police. In this regard, alternate sources, such as insurance records for theft and burglary losses, are impor- tant stopgaps to data gaps in administrative sources on crime. Insurance rate data also provide an alternate framework to assess neighborhood risk, particularly for property crimes, including residential burglary and property theft. The availability of these series over time is a distinct advantage for the measurement of neighborhood change. CONCLUSIONS Neighborhood influences on crime have been an enduring and cen- tral theme in criminological research for over a century. Theoretical and research attention on neighborhoods has been tied to broader interests in understanding how social influences contribute, either directly or in conjunction with individual influences, to the causes and control of crime. Interest in neighborhood influences transcends particular subareas in the study of crime, with important contributions to the study of crime causa- tion or motivation, mechanisms of formal and informal social control, and now, at the conclusion of a full epidemic cycle of rising and falling violent crime rates, its influence on long-term temporal crime trends. In recent years, the study of neighborhood effects has evolved from static to dynamic effects: interest in life-course studies of individuals now parallels studies of neighborhood change, and the interaction of these two dynamics is the focus of this chapter. The robust research activity on neighborhoods and neighborhood change has faced down serious challenges and continues to advance. We
114 UNDERSTANDING CRIME TRENDS have now reached a tipping point in modern community research at which the evidence is more conclusive than it was in the Chicago era, nearly a century ago. And the Âmethods and measures are much improved as well. Neighborhood studies and approaches have limitations, but the logical connections among them suggest a cumulative body of evidence that has madeâand will continue to makeâimportant contributions to theory and knowledge. The confluences among studies suggest new directions to dis- entangle the dynamics of neighborhood change and crime. Accordingly, beyond responding to the challenges identified in this chapter, a research agenda to advance the study of dynamic change in neighborhoods and crime trends requires two separate streams of thought. One is a set of research questions that can establish basic facts about change and its importance. The other is the design of an infrastructure of data and analytic tools that can sustain the science of studying neighborhood change. Essential Questions Neighborhoods do change, some more quickly or slowly than others, apart from any changes in crime. And crime is part of the neighborhood landscape or ecology, and so crime change is, in fact, neighborhood change. This leads to several essential questions about crime and neighborhood change. First, what proportion of change in crime rates, up or down, is attributable to change in neighborhood contexts? Some portion or com- ponents of the variance in crime change is attributable to neighborhood change, but other parts of it may be part of secular trends or other unob- served exogenous factors. Understanding the leverage that neighborhoods have over crime rates is an important part of understanding both crime trends and neighborhood ecology. Second, what are the causal paths? In other words, what is leading what? Since these changes may be closely spaced temporally, are the simul- taneity problems insurmountable? Are there nonrecursive, reciprocal pro- cesses that make crime change and neighborhood change parts of a systemic process that perhaps is better understood not through multivariate models but through models and paradigms of equilibrium (see Persico, Todd, and Knowles, 2001, for an example)? Each of these causal arrangements raises difficult identification problems that will require analytic tools that are not part of the historically comfortable package of regression solutions. Third, neighborhoods exist in conjunction with one another, as part of a larger urban ecology. At a minimum, they may be mutually influential, or the influence may be skewed, with one area dominating the other. What, then, is the reciprocity between neighborhoods? What are the processes of exchange and mutual influence or even unilateral influence? Why do some
CRIME AND NEIGHBORHOOD CHANGE 115 neighborhoods change faster or in a different direction than the adjacent areas, and is this important for a neighborhood that is relative stable but surrounded by dynamically changing areas? When policies target a specific area, can one isolate mutual or spatial influence of the surrounding areas from the effects of external shocks or policy instruments? And at some point in the evolution of neighborhoods, do those shocks eventually become internalized into neighborhood ecology? Another domain of questions focuses on between-neighborhood differ- ences. Crime trends in cities are very local: the largest changes, whether up or down, are limited to a relatively small group of neighborhoods within the larger city landscape (see, for example, Figures 4-2a and 4-2b). Even with rapid change and sharp crime declines, the relative position of most neigh- borhoods at any point in time remains the same (Sampson and Morenoff, 2006), suggesting that neighborhoods themselves exist in a larger political economy of the city. The enduring nature of their relative poverty in the face of material neighborhood improvements (including better housing and lower crime rates) raises a particular challenge: there is little chance that poor neighborhoods will change places with their wealthier counterparts. Given the spatial dependence of poverty concentrations, positive neighbor- hood change may in fact be fragile and at risk for reversing if broader social and economic conditions worsen (Sampson and Morenoff, 2006, p. 200). Thus, change can be curvilinear, with neighborhood fortunes improving and declining at different points in their life cycle. An important research ques- tion, then, is where the tipping point is for positive neighborhood change to be sustainable, and when it might be more fragile and reversible. Card, Mas, and Rothstein (2007) suggest that the tipping point for racial segrega- tion is between 5 and 20 percent white (see Sampson and Morenoff, 2006), with predictably adverse consequences in terms of rents, housing prices, and other neighborhood characteristics. Thus, neighborhoods have trajectories of change, which are likely to vary among neighborhoods (see, for example, Figures 4-2aâc). Research should test various theoretical propositions about factors that distinguish neighborhoods in the magnitude of increase and decline in their crime rates, and why these factors do not lead to more extensive changes in the social position of neighborhoods relative to the whole. In other words, what is it, when crime rates decline, that maintains the social order of neighborhoods, leaving the same ones vulnerable to crime epidemics in subsequent eras? The final set of questions addresses the policy levers that induce neigh- borhood change in a way that can influence crime trends. In some cases, these policies were designed to alter conditions with no attention to crime, but their effects on crime, however incidental, can be salient and beneficial. Recent studies suggest, as discussed earlier, two domains of urban policy that can be analyzed in a search for effects on crime trends: housing and
116 UNDERSTANDING CRIME TRENDS 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 Âcollateral 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 dif- ferent 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 agen- cies 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 dif- ficulties are compounded by the diverse theoretical interests that are identi- fied 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 admin- istrative 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 neighbor- hood research that focuses on changes in rates of crime or social structural and other ecological parameters in areas over time. For example, neighbor- hood studies are likely to rely on observational data that often is deidenti- fied 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 stan- dards of researchers and their professional associations also can buttress respect for privacy and confidentiality. For example, the identities of dis-
CRIME AND NEIGHBORHOOD CHANGE 117 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 vari- able 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 cityâs neighborhoods. These additional elements could include direct observa- tions of neighborhood interaction data that are coupled with surveys and local administrative datasets on compositional characteristics of neighbor- hoods 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 individ- ual and neighborhood change is the Program in Human Development in C Â hicago Neighborhoods, in which the sampling design explicitly anticipates a Â nalyses of both individuals and neighborhood effects as well as their multiÂ level 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 i Ânteractions of neighborhoods to better understand the moving parts of
118 UNDERSTANDING CRIME TRENDS 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 indi- cators (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 neighbor- hood change and crime change that would determine the right frequency of observations? Some changes are slow, as in changes in the built envi- ronment, 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 Bureauâs 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 neighbor- hoods to sustain research. 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 cityâs neighborhoods. â ee, S for example, Floridaâs Open Records Law, FL Statutes Â§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.
CRIME AND NEIGHBORHOOD CHANGE 119 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. Criminol- ogy, 38, 137-169. Berk, Richard A. (2005). Knowing when to fold âem: 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 determin- ing 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: Cam- bridge 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 neighbor- hoods. 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 neighbor- hood 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 disadvan- taged 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.
120 UNDERSTANDING CRIME TRENDS 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 drop- ping 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 eco- nomic 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 Âeffects in surrounding neighborhoods. In Victor Goldsmith, Philip G. McGuire, John ÂMollenkopf, 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 âbroken windowsâ: 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. Jour- nal 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 Advance- ment 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. Depart- ment of Justice. Fagan, Jeffrey, Valerie West, and Jan Holland. (2003). Reciprocal effects of crime and incarcer- ation 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 multiÂ ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ variate 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.
CRIME AND NEIGHBORHOOD CHANGE 121 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. Â arvard C.L.-C.R. Law Review, 26(1), 113-184. H Gostin, Lawrence O., Scott Burris, and Zita Lazzarini. (1999). The law and the publicâs 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 Associa- tion, 263(15), 2051-2052. Gould, Madelyn S., Sylvan Wallenstein, Marjorie H. Kleinman, Patrick OâCarroll, 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.âs 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. Wash- ington, 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.
122 UNDERSTANDING CRIME TRENDS 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 âneighborhood effectsâ 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- Â ufficiency and health from a randomized housing voucher experiment. (Princeton Uni- s versity 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 disÂ advantage and neighborhood culture. Social Problems, 50, 157-180. Land, Kenneth, Patricia McCall, and Lawrence Cohen. (1990). Structural covariates of homi- cide rates: Are there any invariances across time and social space? American Journal of Sociology, 95(4), 922-963. Lee, Barrett A.,Â Sean F. Reardon, Glenn Firebaugh, Chad Farrell, Stephen A. Matthews, and David OâSullivan. (2008). Beyond the census: Patterns and determinants of racial segre- gation 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 ÂEconomics, 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).Â Interpreting âpublic housingâ history. Journal of Architectural and Planning Research, 12(3), 240-258. Martinez, Ramiro, Jr. (2002). Latino homicide: Immigration, violence, and community. L Â ondon: Routledge Press. MartÃnez, Ramiro, Jr., and AbÃ©l Valenzuela (2006). Coming to America: Immigration, ethnic- ity, and crime. New York: New York University Press.
CRIME AND NEIGHBORHOOD CHANGE 123 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: H Â arvard 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 homi- cide 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. Com- mittee 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 ho- micide. 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).Â Suggestions for the investigations of human behavior in the urban en- vironment. 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 childrenâs well- b Â eing. In David B. Grusky and Szonja SzelÃ©ny (Eds.), The inequality reader: Contem- porary 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 experi- ences 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.
124 UNDERSTANDING CRIME TRENDS 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 segrega- tion at multiple geographic scales. American Sociological Review, 83(October), 771. Reardon, Sean F., and David OâSullivan. (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: Tool- kit 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 residen- tial choice: Lessons from the Gautreaux program. Housing Policy Debate, 6, 231-269. Rosenfeld, Richard, Robert Fornango, and Andres F. Rengifo. (2007). The impact of Âorder- maintenance policing on New York City homicide and robbery rates: 1988-2001. Crimi- nology, 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 Cityâs 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 âBroken Windows.â Social Psychology Quarterly, 67(4), 319-342. Sampson, Robert J., and Patrick Sharkey. (2008). Neighborhood selection and the social r Â eproduction 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.
CRIME AND NEIGHBORHOOD CHANGE 125 Sampson, Robert J., Jeffrey Morenoff, and Thomas Gannon-Rowley. (2002). Assessing âneigh- borhood effectsâ: 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 d Â esign of a multilevel longitudinal survey of children, families, and communities: The Los A Â ngeles 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 cityâs $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 Cityâs real estate boom? Journal of Housing Research, 14(1), 101-135. Shaw, Clifford R., and Henry D. McKay. (1943). Juvenile delinquency and urban areas. Chi- cago: 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 Associa- tion, 101, 1398-1407. Sobel, Michael E. (2006b). Spatial concentration and social stratification: Does the clustering of disadvantage âbegetâ 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: Cam- bridge 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. Cam- bridge, 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 âhollowed out.â Journal of Quantitative Crimi- nology, 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].
126 UNDERSTANDING CRIME TRENDS 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. Criminol- ogy, 42(2), 283-322. Weitzer, Ronald. (2000). Racialized policing: Residentsâ perceptions in three neighborhoods. Law and Society Review, 34, 129-155. Wilkinson, Deanna L., and Jeffrey Fagan. (1996).Â The role of firearms in violence âscriptsâ: 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 homi- cide: A comparative study of disaggregated rates in American cities. American Sociologi- cal Review, 53, 421-431. Wilson, James Q.,Â andÂ George L. Kelling. (1982). Broken windows: The police and neighbor- hood safety. The Atlantic Monthly, March, 29-37.Â 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.