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Understanding Crime Trends: Workshop Report 5 An Empirical Assessment of the Contemporary Crime Trends Puzzle: A Modest Step Toward a More Comprehensive Research Agenda Eric P. Baumer The main purpose of this chapter is to report findings from an original analysis that aims to add a heretofore missing element to the extant crime trends literature: a comprehensive assessment that includes most of the major factors that have been identified as potential keys to resolving the recent crime trends puzzle. I begin with an overview of the state of the existing research and then outline the ways in which the present study goes beyond previous efforts. I then describe the sample and data used in the empirical analysis, summarize the key empirical findings and assess them in comparison to conclusions drawn in other recent studies, and close with a call for additional research that can build on the analysis to help establish the kind of research agenda needed to make significant progress in developing a more definitive portrait of the determinants of recent crime trends. THE CRIME TRENDS PUZZLE The basic portrait of U.S. crime trends during the past three decades is now well known. There were steep increases in rates of robbery, motor vehicle theft, and overall homicide from the mid- to late 1980s through the early 1990s. The patterns for homicide during the 1980s varied by age and method, with youth firearm homicide rates following the trend shown for overall homicide, but adult homicide rates and nongun homicide rates falling modestly throughout the 1980s, much like the observed trends in burglary (e.g., Blumstein and Rosenfeld, 1998; Cook and Laub, 1998). Crime patterns were much more consistent across crime types in the 1990s, as all forms of crime declined considerably, a trend that showed signs of slowing
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Understanding Crime Trends: Workshop Report only during the early years of the present decade (Blumstein and Wallman, 2006a; Zimring, 2006). In the past few years, attention has turned to increases in violence observed in some cities across the United States (e.g., Police Executive Research Forum, 2006). What explains these recent shifts in crime rates? Are they the result primarily of modifications to the quantity and quality of policing and incarceration? Were shifts in abortion laws, demographics (e.g., age structure and immigration), or the economy (e.g., unemployment and wages) important? Have changes in illicit drug (e.g., crack cocaine) involvement or alcohol consumption played a role? Do these or other factors help explain the substantial degree of variability in crime trends observed across places? And, ultimately, which factor or set of factors has contributed most to shaping recent crime trends? These are the questions of primary interest in this volume and the ones that I have been asked to examine in this chapter. Addressing these questions is difficult but vitally important for shaping perceptions of public safety among citizens, informing public policy debates about how best to respond to crime, and identifying conditions that are most apt to produce or prevent major shifts in crime. THE STATE OF KNOWLEDGE Policy makers, the media, and other citizens have rightly pressed for answers to the puzzling changes in U.S. crime rates over the past three decades. However, the existing empirical research on recent crime trends is in the early stages of development and is not at the point of sufficient breadth or depth to provide definitive evidence on which factors mattered a lot, which mattered relatively little, and, importantly, which mattered the most. The research community appears to have been reluctant to admit this apparent fact, concluding instead that the available evidence supports either the conclusion that virtually all of the dozen or so factors implicated in the theoretical literature played some role in shaping recent crime trends or the conclusion that one or more specific factors were very important and others mattered little. In my view, neither of these conclusions is supported strongly by the available evidence, which consists primarily of inconclusive circumstantial patterns and empirical evidence based on limited data and models that, as elaborated below, simply do not permit strong conclusions one way or another. Despite bold claims about which factors mattered and which did not (e.g., Levitt, 2004), as Travis and Waul (2002, p. iii) summed up after a national forum on the subject, although a good deal has been learned from prior research, there are no “definitive answers to the questions raised by the recent crime [trends]—that would require more research, new data, and a sustained effort to reconcile every competing claim” (see also Blumstein and Wallman, 2006b; Rosenfeld, 2004).
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Understanding Crime Trends: Workshop Report The uncertainty associated with identifying more clearly the primary sources of recent crime trends is not due to a scarcity of ideas about why crime probably changed in the manner it has or to the absence of sophisticated empirical investigations. Within just the past decade, four books (Blumstein and Wallman, 2006a; Conklin, 2003; LaFree, 1998; Zimring, 2006), a National Institute of Justice (NIJ) symposium published in the Journal of Criminal Law & Criminology (Travis, 1998), a major NIJ intramural research project (Lattimore et al., 1997), and several articles and conference panels have been devoted to explaining the crime trends observed in the United States during the 1980s and 1990s. As illustrated in Figure 5-1, this attention has generated a rich and creative set of plausible hypotheses for recent crime trends. Surely, one or more of the factors identified in the figure was highly influential in shaping recent crime trends in the United States. But if the hypothesized causes have been identified, why FIGURE 5-1 Heuristic model of hypothesized main effects on recent crime trends.
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Understanding Crime Trends: Workshop Report does one not know something more definitive empirically about the sources of recent crime trends, and specifically about which factors mattered or were most influential? When criminology and criminal justice scholars are asked why crime rates have taken their observed path since the early 1980s, why is the modal response something along the lines of “it is unclear, but probably several things worked together to bring crime down” (see, e.g., Tierney, 2007)? In my view, there are two major impediments to providing definitive responses to key theoretical and policy questions associated with recent crime trends: There simply has not been a sufficient amount of empirical research on the matter, and the existing body of research is largely unsystematic along a variety of dimensions. In a vibrant research agenda, a diversity of data, measures, and methods brought to bear on a problem often are welcomed, because a high volume of research makes it is possible to see meaningful patterns emerge from a collective effort that helps to establish an overall sense of the issue at hand, net of the different ways in which the issue has been studied. But the volume of empirical research in this area does not seem sufficient at present to generate a landscape ripe for producing enough information to glean emergent patterns across the diverse approaches currently taken among those studying crime trends. What is more, the relatively barren landscape of crime trends research has provided fertile ground for advocates of the importance of particular factors (e.g., policing, incarceration, crack, abortion, immigration) to draw relatively strong conclusions in an empirical vacuum in which the various factors that also may have mattered (and possibly mattered more) are rarely considered simultaneously. There are several excellent empirical papers on recent crime trends that provide persuasive evidence that a given factor was critical to shaping recent trends, yet most of the factors identified in Figure 5-1 have not been evaluated empirically very often or in systematic ways and, even when they are examined, the overall body of relevant empirical research on crime trends suffers from two major limitations that have served as impediments to establishing a more definitive body of knowledge on why crime rates have taken their observed path since the early 1980s: (1) a narrow research focus on only a few of the factors believed to be important for shaping recent crime trends and, consequently, a high degree of empirical misspecification and (2) substantial variability in the analytical methods applied across studies, including the explanatory variables considered, units of analysis employed, and the types of models estimated. Regarding the first limitation, although most observers seem to agree that several factors probably coalesced to shape recent crime trends (e.g., Blumstein and Wallman, 2006a; Rosenfeld, 2004; Travis and Waul, 2002; Zimring, 2006), the empirical literature generally has focused narrowly on
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Understanding Crime Trends: Workshop Report a small subset of the potentially relevant factors, most typically police force size, drug arrest rates, and incarceration. City- and county-level studies of recent crime trends (e.g., Baumer et al., 1998; Gallup-Black, 2005; Levitt, 1997; Lott, 1998; Ousey and Lee, 2002; Phillips, 2006) have focused on the first two of these factors, but they typically exclude time-varying indicators of nearly all of the other factors shown in Figure 5-1 and, perhaps most importantly, they rarely incorporate any indicators of incarceration or jail confinement rates, which have emerged as central in many other studies. Similarly, although state-level studies of recent crime trends routinely include indicators of overall incarceration rates (e.g., Levitt, 1996; Liedka, Piehl, and Useem, 2006; Marvell and Moody, 1994, 1997), they have not considered age- or crime-specific incarceration rates, average sentence length, time served, or prisoner release rates, even though these factors have been highlighted as potentially important in theoretical discussions and are available as data elements in the public domain. It is also noteworthy that none of the existing studies of recent crime trends has included indicators of recent immigration flows and, surprisingly, only a few have incorporated direct indicators of changes in the nature of policing across multiple geographic units (MacDonald, 2002; Messner et al., 2007; Rosenfeld, Fornango, and Rengifo, 2007). A corresponding story emerges for many of the economic and demographic conditions emphasized as potentially important in the theoretical literature on recent crime trends. Although the occasional study examines wages (Gould, Weinberg, and Mustard, 2002), levels of “domesticity” (Dugan, Nagin, and Rosenfeld, 1999), and indicators relevant to assessing the role of abortion law changes (Donohue and Levitt, 2001, 2006), most studies do not consider these factors, despite evidence suggesting that they may be very important. In short, a common theme in the extant research on recent crime trends is that most studies have a limited scope, focusing on a few select factors and ignoring many other potentially important ones. The high level of empirical misspecification makes the findings reported in much of the prior research on crime trends open to charges of spuriousness, including studies often cited as the primary regression-based evidence of a significant link between recent crime trends and such factors as crack cocaine involvement and incarceration (e.g., Baumer et al., 1998; Ousey and Lee, 2002; Stemen, 2007). Some studies minimize the serious omitted variables bias concern by incorporating “fixed effects,” but this strategy does not say anything about the specific role of the omitted factors for which the fixed portions of the model are serving as a surrogate. Overall, despite much speculation about the explanatory potential of several factors, the limited scope of existing research and differences across studies in the variables considered make it difficult to draw definitive conclusions
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Understanding Crime Trends: Workshop Report about the role of given factors, much less to make concrete determinations about the relative impact of specific factors. A second major impediment to establishing from existing research a more definitive body of empirical evidence and knowledge about the factors that have shaped recent crime trends is the substantial variability in analytical methods applied across studies (see also Spelman, 2008). Beyond the significant differences in model specification already noted, the extant research also is hard to pin down systematically because of (a) the use of different units of analysis across studies and (b) the reliance on different types of statistical methods to estimate key parameters. With respect to the former issue, research on recent crime trends has been conducted across multiple units of analysis, most often states, counties, cities, neighborhoods, and police precincts. It is not necessarily important that a particular unit of analysis be identified, a priori, as superior for studying crime trends, for the reality is that each of these units has conceptual merit, and there are important trade-offs in the choice of unit. Nonetheless, it would be useful to know the empirical implications of using different units of analysis, something that cannot be deciphered easily from existing research. There also appears to be little consensus on the type of methodological approach that is best suited for studying crime trends, especially in subnational (states, counties, cities, neighborhoods) studies, and consequently there is a tremendous degree of variation across studies in the methods applied. Some studies have used methods geared toward identifying classes of crime “trajectories” (e.g., Weisburd et al., 2004), while most have applied different versions of two suitable analytical strategies: pooled time-series cross-sectional panel models (e.g., Donohue and Levitt, 2001; Gould, Weinberg, and Mustard, 2002; Phillips, 2006) and multilevel growth curve models (e.g., Baumer et al., 1998; Kubrin and Herting, 2003; Ousey and Lee, 2004; Rosenfeld, Fornango, and Rengifo, 2007). The specific choice between the latter two approaches is not very important, for they can be made to be equivalent with proper modifications, but it is critical to recognize that the two approaches typically are implemented in ways that are likely to generate different findings and conclusions even when applied to the same data (see, e.g., Phillips and Greenberg, 2008). It is also important to note that there are some important differences in how each of the two most common approaches is implemented. For instance, some studies of recent crime trends employ econometric panel models appropriately by testing for stationarity in the variables (e.g., Phillips, 2006), but most others do not do this (e.g., Donohue and Levitt, 2001; Gould, Weinberg, and Mustard, 2002). Some estimate models in both levels and differences (Moody and Marvell, 2005), while most focus on levels only (for a review, see Moody, 2007). And some include unit-specific trends (e.g., Raphael and Winter-Ebmer, 2001), while most studies do not.
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Understanding Crime Trends: Workshop Report In many instances there are legitimate disagreements about which of the many possible specifications is most appropriate under particular circumstances, but it is important to recognize that the different specifications used are likely to generate different results and conclusions even when applied to the same data (Spelman, 2008). Recognizing this is, of course, key to compiling a systematic body of knowledge that may or may not point to particular answers to questions about crime trends. RESEARCH NEEDS AND THE PRESENT STUDY Recent crime trends represent a major social phenomenon and a fundamental research issue for which criminological researchers should provide concrete answers. We should and can do better than “a lot of things mattered,” but doing so will require a more vibrant research agenda that focuses on modeling recent crime trends in a much more comprehensive and systematic way. Ideally, this research agenda would incorporate a comprehensive set of measures that mirror as closely as possible the factors emphasized in theoretical and policy discussions of crime trends. It would also apply appropriate methodological techniques uniformly across multiple units of analysis (or a common unit) while attending to important econometric issues (e.g., stationarity, spatial dependence, endogeneity) that are critical for the inferences drawn from temporal data. As Spelman (2008) has recently demonstrated, attending to these issues even for a single crime rate covariate—incarceration rates—in a bivariate context is a highly complex enterprise, so extrapolating the effort to a larger scale will not be easy. Nonetheless, this kind of systematic effort is feasible and would help to better pinpoint empirical patterns in available data and minimize the degree to which conclusions are confounded by differences across studies in specification, unit of analysis, or method. Developing the research agenda just described will take a concerted effort by a community of scholars who share data and ideas to build a knowledge base on crime trends incrementally and systematically. The goal here is to make a modest contribution to this effort by significantly broadening the scope of empirical research to incorporate not only the factors often considered (e.g., police force size, drug use and market activity, age structure, incarceration) in the literature, but also various others that have been highlighted in theory but only occasionally considered in empirical studies (e.g., immigration, wages, alcohol consumption, levels of domesticity, and youthful cohort “quality”). The main contribution, then, lies in an expansion of the empirical specification typically applied in research on recent crime trends—it is the first study of which I am aware that examines simultaneously each of the major factors shown in Figure 5-1 that have been emphasized in theoretical and policy discussions of recent crime trends.
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Understanding Crime Trends: Workshop Report This strikes me as a logical starting point for establishing a more definitive knowledge base about recent crime trends. I must emphasize that the analysis described below is relatively modest and only an initial step in moving toward a more definitive knowledge base from which to draw answers to the important questions that have emerged about recent crime trends. As mentioned above and elaborated below, there are also several unresolved and complicated methodological issues that should be tackled in a comprehensive research agenda on crime trends. In an exhaustive analysis, for instance, the econometric properties of each of the variables considered should be evaluated (e.g., Are the variables stationary or nonstationary?) and appropriate transformations should be made to the data and estimation techniques (e.g., Should the variables be differenced? Are the variables cointegrated?). Furthermore, the many instances of possible endogeneity in models of crime trends should be addressed, spatial dependence should be assessed and, if necessary, modeled appropriately, and there should be a systematic approach to model selection so that one can make informed choices about the factors that emerge as most relevant. These matters are not merely statistical exercises; they have important implications for the inferences drawn about crime trends. Although there is important work being done on these issues both in criminology and other disciplines, collectively they represent a challenging set of issues that is more complex than often portrayed in the literature and which cannot be addressed satisfactorily here given the space constraints of this volume.1 Instead, although the present work expands on recent crime trends research by considering a much larger set of the factors most commonly emphasized in theoretical discussions, it applies the same econometric tools used in many existing subnational studies. Consequently, the conclusions that can be drawn must be viewed as tentative; the important methodological issues noted earlier will need to be addressed to assess the validity of the results reported below. 1 National-level studies of crime and other social phenomena have addressed these issues, and some panel studies have entertained these issues as well, but in general they have not been dealt with effectively in the criminological literature. Among other things, most panel studies that employ panel unit root tests have used so-called second-generation tests that do not account for spatial dependence, which is likely to be present in most of the subnational data used to study crime trends and could bias results. Also, there are many questions about how to handle heterogeneous panels, some of which contain unit roots and others that do not, that have not been adequately resolved. Differencing is a common solution when nonstationarity is found in panel studies, but, without testing for cointegration, it is not clear whether this is an appropriate solution or one that produces valid results. Finally, assessments and corrections for endogeneity vary wildly in the literature, so its impact remains unclear, and there is little guidance from the literature about how best to approach such issues in practice.
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Understanding Crime Trends: Workshop Report BROADENING THE SCOPE OF CRIME TRENDS RESEARCH This study expands the scope of research on recent crime trends by taking a more comprehensive approach to measuring and modeling effects of factors that have been well represented in prior research (e.g., such commonly considered factors as policing, incarceration, and illicit drug activity), by incorporating measures of factors that are irregularly included in the extant research (e.g., largely neglected factors, such as alcohol consumption, legal wages, levels of domesticity, immigration, and cohort quality indicators) and, more generally, by estimating models in which all of these factors are considered simultaneously. Many of these factors have been discussed extensively elsewhere, including prior chapters in this volume, so they do not need to be reviewed in detail here. But I elaborate on some relatively neglected issues and outline the ways in which I go beyond past work. Reconsidering Commonly Addressed Factors Two factors that have received a particularly high level of attention in public discourse on recent crime trends, and especially the 1990s crime decline, are changes in policing and incarceration. With respect to the former, Eck and Maguire (2006) provide an excellent treatment of the various changes in the quantity and quality of policing that have occurred in the past several decades, focusing especially on (1) increases in the number of police officers devoted to helping address crime problems and (2) enhancements to the manner by which police agencies have approached their work, particularly a move toward a targeted policing focus on behaviors thought to facilitate crime, such as levels of public disorder and the prevalence of weapon carrying. Several studies examining the link between crime trends and police size have generated inconsistent results (see Eck and Maguire, 2006, for an exhaustive review). A relatively large body of research also has examined the effects of different policing approaches on levels of crime (e.g., Sampson and Cohen, 1988; for a review, see MacDonald, 2002), but only a small handful of studies have assessed explicitly the role of recent changes in the nature of policing on contemporary crime trends. Overall, the existing research suggests that policing efforts that targeted public order violations and weapon carrying may have had a modest effect on crime trends during the 1990s (Braga et al., 2001; Kennedy et al., 2001; Messner et al., 2007; Piehl et al., 2003; Rosenfeld, Fornango, and Rengifo, 2007), but the limited attention to this issue precludes more definitive conclusions being drawn. I build on existing work by examining whether a changing police focus on public order crimes and weapons offenses—as measured by the number of arrests per 100,000 residents for weapons
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Understanding Crime Trends: Workshop Report violations, vandalism, prostitution, gambling, liquor laws, drunkenness, disorderly conduct, vagrancy, curfew violations, loitering, and suspicion (for similar measures, see Messner et al., 2007; Rosenfeld, Fornango, and Rengifo, 2007)—is associated with recent crime trends across a relatively large sample of cities. The other major criminal justice factor emphasized in the literature on recent crime trends is the well-documented substantial increase in incarceration rates, which have more than tripled in the United States since the early 1970s (Zimring, 2006). There is a long history of linking incarceration to crime rates through incapacitation and/or deterrent processes (e.g., Zimring and Hawkins, 1973) and a relatively large and growing empirical literature. Yet, despite the substantial attention devoted in prior research to the role of overall incarceration rates and what appears to be some consensus on the overall impact of incarceration on crime trends (Stemen, 2007; but see Spelman, 2008), two aspects of the link between incarceration and crime have been neglected in prior work and warrant additional consideration: (1) the analysis of both “stock” (i.e., the overall number of persons per capita in prison at a given point in time) and “flow” (i.e., the number of persons per capita admitted to prison and released from prison in a given year) measures of incarceration and (2) the analysis of temporal variability and scale effects for incarceration. Discussions of incarceration effects tend to emphasize the crime reduction that may result from relatively immediate sentencing actions, such as the recent removal from the street of each additional offender. Most studies of incarceration effects estimate how stock incarceration rates for a given year affect crime in that year or the next. However, during periods of sentence enhancements, such as the 1980s and 1990s, the stock incarceration rate may not be a very good indicator of how many offenders were removed from the streets and placed in prison in a particular year (it will reflect admissions in that year and many before it). Thus, modeling its contemporaneous or one-year lagged effect on crime rates may yield a misleading picture of the effect of recent incarceration practices on crime rates. Annual flow indicators of the number of persons admitted to prison (less the number of persons released), of course, are well suited for gauging such effects. The present study therefore estimates models of crime for both stock and flow measures of incarceration to evaluate in a more comprehensive manner the role of incarceration in shaping recent crime trends. Considering the prison flow measures not only provides a more precise look at incarceration effects than relying solely on the stock incarceration rate, but it also permits an independent assessment of prison releases on recent crime trends. There has been a lot of attention recently to the consequences and challenges of a large volume of prisoners moving from prison back to
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Understanding Crime Trends: Workshop Report their home communities (e.g., Travis and Visher, 2005) yet very little direct empirical investigation of whether and how trends in prison releases may have affected crime trends during the past few decades. Only one of the regression-based studies of recent crime trends has considered the quantity or quality of prison releases (Kovandzic et al., 2004). No significant impact of prison release rates was observed in that study, but the focus was exclusively on homicide, a relatively rare crime, and it is uncertain whether similar findings would emerge for more common offences. Recent research on incarceration rates and crime trends also challenges the assumptions shared in most previous studies of linear and time-invariant incarceration effects. Some have argued that the elasticity of incarceration has changed over time, although there is disagreement about the direction of this change (see Liedka, Piehl, and Useem, 2006; Spelman, 2000). Spelman’s (2000) research suggests that the effectiveness of prisons may have increased over time because of growth in the scale of imprisonment, the proportion of crime committed by adults, and the selectivity of law enforcement efforts (i.e., the degree to which serious offenders are imprisoned). More recently, Liedka, Piehl, and Useem (2006) suggest that the crime reduction benefits of incarceration are likely to be reduced as the scale of incarceration reaches very high levels and may even reverse, such that very high levels of incarceration may actually increase crime. Their state-level panel analysis of data of 1972-2000 reveals evidence consistent with this claim. Furthermore, although they do not examine the issue directly, they suggest that in contrast to Spelman’s argument about the increasing effectiveness of incarceration over time, one implication of their findings may be that the elasticity of incarceration has probably declined (i.e., become less negative) as incarceration rates in many states have approached and surpassed an effective deterrent or incapacitation level. These recent studies point to the need for additional refined analyses of incarceration effects that move beyond estimating elasticities under the assumption of temporal invariance and that are attentive to potential variability in elasticity by changes in scale and the composition of the prison-bound population. I explore some of these issues by examining not only the main effect of the overall prison admissions rate, but also the possibility of a nonlinear response for this variable and whether the estimated effects have changed over time. The role of illicit drug use and market activity, especially with respect to crack cocaine, also has received a good deal of attention in the theoretical and empirical literature on recent crime trends. Although there are some doubters (e.g., Zimring, 2006), there seems to be a fairly strong consensus that the rise and fall of crack use and crack markets are important pieces of the crime trends puzzle over the past 25 years (Blumstein and Wallman, 2006a; Johnson, Golub, and Dunlap, 2006; Levitt, 2004). The central
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Understanding Crime Trends: Workshop Report indicators used did not decline substantially during the 1990s, rendering their ability to account for the observed changes in crime to be minimal, in the neighborhood of 1-2 percent. Some of the other factors, like changes in policing focus (e.g., arrest rates for public order and weapons offenses), levels of domesticity, immigration rates, alcohol consumption, and firearm prevalence may emerge as more relevant in analyses that better attend to issues of measurement error, unit measure mismatch, stationarity, and simultaneity, but for now I would conclude that their contributions to the 1990s crime decline do not appear to be substantial. Out-of-Sample Predictions Thus far I have focused on attempting to explain what happened with respect to recent crime trends. Another objective of the NRC crime trends workshop was to assess the capacity for existing models to provide forecasts or predictions of subsequent crime rates beyond the period covered in the study (i.e., post-2004). Although it is unclear whether and how decent crime forecasts would be put to use, the idea of knowing what is coming is enticing. For instance, if it was known sooner in the mid-1980s what may have been on the horizon, perhaps law enforcement and other public policy agents could have responded in ways that could have reduced the dramatic rise in violence that occurred. Furthermore, in a time of tight local budgets, high levels of anxiety among the public, and claims makers who tend to sensationalize highly visible violent incidents, it might be useful to have some sense of whether crime is likely to continue the descent seen for most of the 1990s and beyond, whether it is likely to rise significantly, or whether it might maintain a steady state. Despite some courageous and sophisticated previous efforts, crime forecasting is highly undeveloped at the present time and, in part because of this, I chose to focus most of my efforts in this chapter on developing explanatory models of recent crime trends rather than forecasting crime beyond the study period. Nevertheless, part of the value in assessing the factors that contributed to recent crime trends lies in what these models say about the likely direction of crime beyond the period under study. So what do the models outlined above, which focus on trying to explain crime trends between 1980-2004, say about the path of crime in subsequent years? To address this question, I used the coefficients from Table 5-2, which includes city and year fixed effects and city-specific time trends, to estimate crime levels (rates of homicide, robbery, burglary, and auto theft) for 2005. I first obtained, or in some cases estimated, city-specific values for each explanatory variable included in the models shown in Table 5-2. Since the focus was on estimating levels of crime in 2005, for the one-year lagged variables in the model (i.e., police force size, incarceration rates,
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Understanding Crime Trends: Workshop Report public order and weapons arrest rates, and serious arrest certainty) the city-specific values reflect 2004 conditions, and for all other variables, which were measured contemporaneously, the city-specific values reflect 2005 conditions derived from additional data collection. I then multiplied these city-specific covariate observed values by their corresponding coefficients from the models shown in Table 5-2 to generate a predicted crime level for 2005. This process yielded predicted 2005 crime levels for each of the cities included in the study. I next obtained from the UCR the observed crime levels for the 50 largest of these cities and used the observed values to compute the percentage change between 2004 and 2005 in observed crime levels and the predicted percentage change between 2004 and 2005 using the predicted values for 2005 derived from the procedures just outlined. Table 5-5 summarizes the results of the exercise. The UCR data reveal that the 50 largest cities included in the study experienced an average (median) increase of about 3.79 percent in logged homicide rates between 2004 (the last year of the study period) and 2005. This figure represents the observed change in homicide over the one-year period. Applying the procedures outlined above, my analysis predicted an average (median) decrease of 2.74 percent over this one-year period based on the results of the panel crime regression model displayed in Table 5-2 (including city and year fixed effects and city time trends) and the city-specific values on the explanatory variables. For robbery, the observed change was a 1 percent increase, and the prediction was for a .40 percent decline. As Table 5-5 shows, the predicted and observed change values are closer for burglary and especially motor vehicle theft; in the latter case, the model predicted a slight decline (−.27 percent) that was very close to the observed change (−.34 percent). The gaps here are not large in absolute terms, but at least for homicide, robbery, and burglary they probably are unacceptable, given the high stakes associated with crime prediction. In each of these three cases, for instance, one would have predicted decreases TABLE 5-5 Predictions of 2005 Crime Levels for 50 Largest Cities from Regressions of Recent Crime Trends, 1980-2004 Homicide Robbery Burglary Auto Theft % Change 2004-2005 Predicted −2.74 −.40 −.39 −.27 Observed 3.79 1.02 .01 −.34 % Cities predicted in right direction 44 40 46 48 NOTE: Figures for predicted and observed change represent median values for the 50 largest cities in the sample for which this information could be computed.
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Understanding Crime Trends: Workshop Report in crime when, in fact, there were increases. Indeed, another way to look at forecasting is to ask more simply about predictions in the direction of crime changes. In other words, is crime going to increase or decrease next year? The bottom row of Table 5-5 presents results relevant to this question. The procedures outlined above yielded an accurate prediction compared to the observed change in 44 percent of the cities in the case of homicide, 40 percent for robbery, 46 percent for burglary, and 48 percent for motor vehicle theft. Overall, the model based predictions that emerge from my study do not appear to be very good—one could do better in predicting the direction of changes in these crimes by flipping a coin. It is possible that 2005 was an aberrant year. When I replicated Table 5-5 predicting changes in crime for 2003-2004, the predicted and observed robbery and burglary rates were closer and the record for predicting the direction of crime changes improved dramatically, with the right direction predicted in two-thirds to three-quarters of the cities (results not shown). However, the gap between observed and predicted homicide rates in this supplementary analyses were no better, and when I repeated the process yet again for predicting changes in crime for 2002-2003, the results were mixed (not shown), with some estimates outperforming those shown in Table 5-5 and others doing less well. Maybe 2005 was aberrant in the degree of unexplained change, maybe one or more of the other years was, maybe the types of model-based predictions summarized here are not well suited for predicting crime, or maybe crime trends are not highly predictable, save for periods of major shifts, such as the 1990s, when a predicted decline in crime would have been a good bet (as it turns out) for many years. Overall, like crime trends research in general, the issue of forecasting crime is in the early stages of development and more work needs to be done to better understand the nuances of making future crime predictions, to outline the best approaches to take given the reality of existing data, and to define acceptable parameters of prediction performance. There are several reasons why the regression models used to predict crime in this study probably did not fare better, including measurement mismatches between the city-level outcome variables and, in some cases, state-level explanatory variables, other sources of measurement error, methodological limitations of the models used to generate the estimates (e.g., no attention to stationarity and little formal attention to endogeneity), and simpler things, like the assumption of linearity and temporal invariance in the estimated effects of the explanatory variables. With respect to the latter, as noted earlier, one of the more important factors in explaining recent crime trends—incarceration—appeared in this study to exhibit some nonlinear and temporally variable effects, and these were not captured in the prediction exercise summarized above (the pooled estimates were used instead). Other studies, too, have shown that incarceration effects could vary by
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Understanding Crime Trends: Workshop Report location (e.g., DeFina and Arvanites, 2002), another issue not explored here that could have introduced some error into the effort of predicting crime from the models estimated. Overall, the main story that emerges from the prediction exercise is that better predictions will probably require a more comprehensive modeling strategy that attends to the fundamental methodological issues and various analytical nuances mentioned above. CONCLUSION In this chapter I have attempted a more comprehensive approach to the measurement and modeling of contemporary crime trends. Most of the extant literature has focused on a small number of potentially relevant factors, even though the theoretical literature highlights numerous other factors that may have been important for shaping recent crime trends. Taking a more comprehensive measurement approach required, in some cases, the use of state-, metropolitan area-, and county-level explanatory variables to explain city-level crime rates (cities were the unit of analysis chosen by the NRC). Future research should assess the implications of this type of unit mismatch by replicating the models developed here with crime rates measured at levels that match the explanatory variables. However, given the measures and methods used in this research, one can conclude that the findings generated from a more comprehensive approach affirm some of the results reported elsewhere with respect to incarceration, drug market conditions and drug use, lagged birth cohort conditions, and the economy, but also point to some additional factors that show relevance in shaping recent crime trends, including changes in levels of cohabitation, prisoner release rates, and the drug market age structure. As summarized in Table 5-5, the overall conclusions diverge somewhat from two widely cited reviews of prior work (Levitt, 2004; Zimring, 2006), a result that underscores the importance of simultaneously considering the various factors emphasized in the theoretical literature rather than focusing on a select few factors. In essence, it seems important to take a comprehensive measurement approach to studying crime trends, especially if the goal is to assess which of the various factors hypothesized to shape recent trends actually matter (and if so, how much). I close by reiterating that, in some respects, it is highly premature to draw definitive conclusions from this or most previous work about the factors that were mostly responsible for shaping recent crime trends. Although the public and the media are anxious to know what happened and why with respect to contemporary crime trends, the reality is that empirical literature on crime trends is in the early stages of development. Much more research is needed to develop answers in which there can be a high degree of confidence. I have argued that increasing the breadth of empirical studies to
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Understanding Crime Trends: Workshop Report incorporate measures of each of the major factors emphasized in theoretical discussion and policy debates is important, but there are other fundamental issues that require more attention as well before strong inference can be drawn. For one, the magnitude of the effects of criminal justice factors and other variables may be misestimated in most studies, including the present research, because of possible simultaneous relationships between these indicators and crime rates. One of the trade-offs of doing a comprehensive study is that it is very difficult to deal with these issues adequately, but doing so is an important next step. A second, perhaps even more fundamental issue, which needs to be addressed more systematically in research on contemporary crime trends before definitive conclusions can be drawn, concerns the time-series properties of crime rates and the factors thought to be important for shaping crime trends. As noted at the outset, most of the extant research on contemporary crime trends and the present study assume stationarity in the variables and proceed by estimating panel regression models in levels. Although this may be an appropriate approach, if crime rates or the explanatory variables (or both) are nonstationary, the results that emerge could be spurious, which obviously has important implications about the most important factors in shaping recent crime trends. A variety of methodological issues need to be sorted out to satisfactorily address the time-series properties of variables considered, many of which currently are or have been explored (Baumer and Rapach, 2007; Moody, 2007; Spelman, 2008) but are not yet resolved. Pursuing such research more vigorously should better clarify the methods most appropriate for drawing valid inferences from panel studies of crime trends. In conclusion, this work takes some small but necessary steps toward addressing the two questions around which the NRC crime trends workshop has been organized: (1) Which factors were most important for explaining city-level crime trends observed between 1980-2004? (2) What might one reasonably expect for city crime levels in the years following this period? Tentative answers to these questions have been generated from a database supplied by the NRC, to which I added several measures. A more definitive resolution to these issues would be valuable, but achieving that goal will require a much larger and elaborated effort that retains the comprehensive approach to measurement and modeling outlined here, but also attends to fundamental methodological issues with respect to time-series estimation, simultaneity, model selection, spatial dependence, forecasting, and other issues. Although the NRC workshop on crime trends is a good start, much more research is needed to gain a full understanding of the past and future path of crime in the United States.
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