Do Schools Cause Crime in Neighborhoods? Evidence from the Opening of Schools in Philadelphia

Objectives

To assess the impact of schools on crime in neighborhoods.

Methods

We utilize data of charter and public schools in Philadelphia to estimate the effect of school openings on neighborhood crime patterns between 1998 and 2010. We estimate the change in crime in areas surrounding schools before and after their opening compared to areas where schools were always open with Poisson regression models. We also estimate changes in crime in census tracts as schools are added compared to census tracts that never had schools. Finally, we compare estimates from Poisson regression models to those derived from permutation tests where schools are randomly assigned different opening dates.

Results

We find no evidence that school openings increase crime relative to locations where schools were always open or never had schools. The models generally produce null effects, though there is some evidence for a reduction in property crimes for public school openings and a reduction in violent crimes for charter school openings within certain distances. Estimates at the census tract level show that changes in the number of schools are not associated with any changes in crime relative to tracts with no schools.

Conclusions

Contrary to a large theoretical and empirical literature, the results suggest that school locations play a minimal role in neighborhood crime production in Philadelphia. Future research should investigate specific contexts and mechanisms, such as land-use characteristics and travel patterns to schools, which may interact with specific school settings in ways that are related to crime production.

     

The Murder Mystery: Police Effectiveness and Homicide

Objective

Previous aggregate analyses of the effect of police on crime show that increases in police staffing are especially effective at preventing homicide. This conflicts with evidence that suggests standard police methods should be more effective at preventing robbery, auto theft, and other property crimes. My objective is to reconcile the two.

Methods

Regression of crime rates on uniformed police staffing and other economic and demographic covariates, for a panel of 59 US cities for the period 1970–2013.

Results

Lagged crime rates are strong and statistically significant predictors of both policing staffing and crime rates, particularly homicide. When lags are included in the specification, the apparent effect of police on homicide drops by more than 70 %; there is little change in the effect of police on other crimes. Findings are robust with respect to specification and method.

Conclusions

Previous studies omitted lags and overstated the effectiveness of police on homicide. Because murder accounts for almost 40 % of all costs of crime in US cities, it is no longer clear whether increasing police force size is a cost-effective way to cut crime. Improving police tactics is more likely to work and less expensive.

     

DO NEIGHBORHOODS GENERATE FEAR OF CRIME? AN EMPIRICAL TEST USING THE BRITISH CRIME SURVEY*

For a long time, criminologists have contended that neighborhoods are important determinants of how individuals perceive their risk of criminal victimization. Yet, despite the theoretical importance and policy relevance of these claims, the empirical evidence base is surprisingly thin and inconsistent. Drawing on data from a national probability sample of individuals, linked to independent measures of neighborhood demographic characteristics, visual signs of physical disorder, and reported crime, we test four hypotheses about the mechanisms through which neighborhoods influence fear of crime. Our large sample size, analytical approach, and the independence of our empirical measures enable us to overcome some of the limitations that have hampered much previous research into this question. We find that neighborhood structural characteristics, visual signs of disorder, and recorded crime all have direct and independent effects on individual‐level fear of crime. Additionally, we demonstrate that individual differences in fear of crime are strongly moderated by neighborhood socioeconomic characteristics; between‐group differences in expressed fear of crime are both exacerbated and ameliorated by the characteristics of the areas in which people live.     

What Do Panel Studies Tell Us About a Deterrent Effect of Capital Punishment? A Critique of the Literature

Objectives

We provide a critical review of empirical research on the deterrent effect of capital punishment that makes use of state and, in some instances, county-level, panel data.

Methods

We present the underlying behavioral model that presumably informs the specification of panel data regressions, outline the typical model specification employed, discuss current norms regarding “best-practice” in the analysis of panel data, and engage in a critical review.

Results

The connection between the theoretical reasoning underlying general deterrence and the regression models typically specified in this literature is tenuous. Many of the papers purporting to find strong effects of the death penalty on state-level murder rates suffer from basic methodological problems: weak instruments, questionable exclusion restrictions, failure to control for obvious factors, and incorrect calculation of standard errors which in turn has led to faulty statistical inference. The lack of variation in the key underlying explanatory variables and the heavy influence exerted by a few observations in state panel data regressions is a fundamental problem for all panel data studies of this question, leading to overwhelming model uncertainty.

Conclusions

We find the recent panel literature on whether there is a deterrent effect of the death penalty to be inconclusive as a whole, and in many cases uninformative. Moreover, we do not see additional methodological tools that are likely to overcome the multiple challenges that face researchers in this domain, including the weak informativeness of the data, a lack of theory on the mechanisms involved, and the likely presence of unobserved confounders.     

GHETTOS,THRESHOLDS, AND CRIME: DOES CONCENTRATED POVERTY REALLY HAVE AN ACCELERATING INCREASING EFFECT ON CRIME?*

Theories make varying predictions regarding the functional form of the relationship between neighborhood poverty and crime rates, ranging from a diminishing positive effect, to a linear positive effect, to an exponentially increasing or even threshold effect. Nonetheless, surprisingly little empirical evidence exists testing this functional form. This study estimates the functional form of the relationship between poverty and various types of serious crime in a sample of census tracts for 25 cities, and it finds that a diminishing positive effect most appropriately characterizes this relationship whether estimating the models nonparametrically or parametrically. Only for the crime of murder does some evidence exist of an accelerating effect, although this occurs in the range of 20 to 40 percent in poverty, with a leveling effect on crime beyond this point of very high poverty. Thus, no evidence is found here in support of the postulate of scholars extending William Julius Wilson's (1987) insight that neighborhoods with very high levels of poverty will experience an exponentially higher rate of crime compared with other neighborhoods.     

The Crime Kuznets Curve

Objectives

Criminologists have long studied the relationship between economic conditions and crime. Empirical evidence is inconclusive, pointing at different directions. This may reflect the conflicting theoretical predictions on the relationship between these phenomena, but also the prevailing methodological choice which focuses on linear relationships even though nonlinearities are plausible theoretically.

Methods

In this paper, we revisit the empirical relationship between economic conditions and crime by exploring potential nonlinearities. We look at flexible parametric specifications that include up to a cubic term of per capita income (or one dummy for each income quintile) and nonparametric and semi-parametric specifications (such as General Additive Models). Our results are robust to controlling for the standard socioeconomic, demographic, and policy determinants of crime, as well as to including a lagged dependent variable or state and time fixed effects.

Results

We document the existence of an inverted U-shaped relationship between crime and income within US states for the period 1970-2011. Crime increases with per capita income until it reaches a maximum, and then decreases as income keeps rising. This “Crime Kuznets Curve” (CKC) exists for property crime and for categories of violent crime that can be related to economic appropriation, like robbery, and is less robust for violent crimes not connected to economic incentives. We show that this pattern cannot be explained by correlated changes in economic inequality or by changes in law enforcement.

Conclusions

In addition to providing robust evidence of the existence of a CKC, our findings lay the groundwork for studies exploring the underlying theoretical mechanisms. These should go beyond income inequality or law enforcement, and should explain why the results hold more clearly for property than for violent crime. Our findings and subsequent research to understand the underlying drivers are relevant for policy, as they suggest that violent conflict cannot be tackled solely by the trickle-down forces of economic growth.

     

A NOTE ON THE EFFECTS OF POPULATION DENSITY AND UNEMPLOYMENT ON URBAN CRIME

This note presents a brief review of reported studies analyzing the effects that population density and unemployment may have on urban crime rates. Very few such empirical studies have been conducted. and their Jindings have been quite contradictory and not entirely conclusive. However, for certain types of crimes, the evidence seems to indicate that the unemployment rate has a positive effect on the crime rate, while the effect of population density, if significant at all, is a negative one.     

Crime prevention in a communitarian society: Bang-jiao and tiao-jie in the people's Republic of China

This paper examines two important strategies of community crime prevention in contemporary Chinese society: bang-jiao and tiao-jie. Bang-jiao refers to community efforts to reintegrate offenders into the community. Tiao-jie refers to community groups designed to resolve disputes among neighbors and family members, and in doing so, to reduce crime. We describe these strategies, discuss their philosophical underpinnings, and identify the features of Chinese society that support their implementation. We also explore their effectiveness with survey data from a sample of offenders in Tianjin, China. Our empirical analyses suggest that bang-jiao and tiao-jie may indeed be important structural mechanisms for crime control in a communitarian society.     

Point blank against itself: Evidence and inference about guns,crime, and gun control

This essay considers the empirical foundations for some of the more important and controversial conclusions concerning guns, crime, and gun control advanced in Gary Kleck’s highly influential treatise,Point Blank. We reveal significant flaws in his original data analyses and identify problematic linkages between his evidence and his inferences. We suggest alternative interpretations for some of Kleck’s findings.     

Contrasting simulated and empirical experiments in crime prevention

This paper argues that simulated experiments of crime prevention interventions are an important class of research methods that compare favorably with empirical experiments. It draws on Popper’s demarcation between science and non-science (Conjectures and refutations: the growth of scientific knowledge. Routledge, London, 1992) and Epstein’s principle of generative explanation (Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton, NJ, 2006) to show how simulated experiments can falsify theory. The paper compares simulated and empirical experiments and shows that simulations have strengths that empirical methods lack, but they also have important relative weaknesses. We identify three threats to internal validity and two forms of external validity peculiar to simulated experiments. The paper also looks at the problem of validating simulations with crime data and suggests that simulations need to mimic the error production processes involved in the creation of empirical data. It concludes by listing ways simulations can be used to improve empirical experiments and discussing the differing operating assumption of empirical and simulation experimentalists.

Perception vs. Reality: The Relationship Between Organized Crime and Human Trafficking in Metropolitan Atlanta

Since the enactment of the Trafficking Victims Protection Act of 2000, there has been an increase in both media and scholarly discussions of human trafficking. Although most of these discussions have framed human trafficking as a crime committed primarily by organized crime groups, there has been very little empirical research examining the link between human trafficking and organized crime. In an effort to start to address this gap in the research, we conduct an exploratory study to determine if there is a link between human trafficking and organized crime in one of the Southeast’s human trafficking hubs – Atlanta, Georgia. We collected data on 24 federal human trafficking cases that were indicted in metropolitan Atlanta between 2000 and 2013. Then, we conducted a content analysis of the court documents for each federal human trafficking case and classified the relationship between organized crime and human trafficking using one of three categories: nonexistent, organized criminal network, organized criminal syndicate. For the majority of the human trafficking cases (n = 16), we found that there was no relationship between organized crime and human trafficking. For the cases that did show a relationship between organized crime and human trafficking, we found evidence of organized criminal networks in eight of the cases and evidence of an organized criminal syndicate in only one case.     

CRIMINAL BEHAVIOR AND THE GAINS FROM CRIME

A number of economists recently have applied tools of economics to analysis of crime. The resulting models of criminal activity typically postulate that crime is positively related to gains porn crime and inversely related to the probability of punishment. While empirical studies have confirmed this latter effect, the relation between gains from crime and criminal activity has not been satisfactorily examined. This paper specifically tests the empirical relation between criminal behavior and the gains from crime. The unsettling possibility that decreases in the gains from crime my lead to increases in the number of crimes is discussed.     

The Law of Crime Concentration: An Application and Recommendations for Future Research

Objective

We address four outstanding empirical questions related to the “law of crime concentration” (Weisburd in Criminology 53:133–157, 2015): (1) Is the spatial concentration of crime stable over time? (2) Do the same places consistently rank among those with the highest crime counts? (3) How much crime concentration would be observed if crimes were distributed randomly over place? (4) To what degree does the spatial concentration of crime depend on places that are crime free?

Methods

The data are annual counts of violent and property crimes in St. Louis between 2000 and 2014. Temporal stability in the spatial inequality of crime is measured by computing the fraction of crimes that occur in the 5% of street segments with the highest crime frequencies each year. The spatial mobility of crime is measured by computing the number of years each street segment appears in the top 5% of street segments. Poisson simulations are used to estimate the fraction of crimes that could appear in the top 5% of street segments on the basis of chance alone. The impact of crime-free locales on the spatial concentration of crime is evaluated by comparing results from analyses that include and exclude crime-free street segments from the crime distributions.

Results

The concentration of crime is highly unequal and stable over time. The specific street segments with the highest crime frequencies, however, change over time. Nontrivial fractions of street segments may appear among the 5% with the highest crime frequencies on the basis of chance. Spatial concentration of crime is reduced when crime-free street segments are excluded from the crime distributions.

Conclusions

The law of crime concentration is not a measurement artifact. Its substantive significance, however, should be assessed in future longitudinal research that replicates the current study across diverse social settings.

     

Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed

Objectives

We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations.

Methods

We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement).

Results

There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %.

Conclusions

The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro- or macro- theories may help researchers in exploring this new direction.

     

Police,Crime and the Problem of Weak Instruments: Revisiting the “More Police,Less Crime” Thesis

Objectives

A key question in the general deterrence literature has been the extent to which the police reduce crime. Definitive answers to this statement, however, are difficult to come by because while more police may reduce crime, higher crime rates may also increase police levels, by triggering the hiring of more police. One way to help overcome this problem is through the use of instrumental variables (IV). Levitt, for example, has employed instrumental variables regression procedures, using mayoral and gubernatorial election cycles and firefighter hiring as instruments for police strength, to address the potential endogeneity of police levels in structural equations of crime due to simultaneity bias.

Methods

We assess the validity and reliability of the instruments used by Levitt for police hiring using recently-developed specification tests for instruments. We apply these tests to both Levitt’s original panel dataset of 59 US cities covering the period 1970–1992 and an extended version of the panel with data through 2008.

Results

Results indicate that election cycles and firefighter hiring are “weak instruments”—weak predictors of police growth that, if used as instruments in an IV estimation, are prone to result in an unreliable estimate of the impact of police levels on crime.

Conclusions

Levitt’s preferred instruments for police levels—mayoral and gubernatorial election cycles and firefighter hiring—are weak instruments by current econometric standards and thus cannot be used to address the potential endogeneity of police in crime equations.

     

Acquisitive Crime and Inflation in the United States: 1960–2012

Objectives

Inflation is conspicuous by its absence from recent research on crime and the economy. We argue that price inflation increases the rate of crimes committed for monetary gain by fueling demand for cheap stolen goods.

Methods

The study includes inflation along with indicators of unemployment, GDP, income, consumer sentiment, and controls in error correction models of acquisitive crime covering the period from 1960 to 2012. Both short- and long-run effects of the predictors are estimated.

Results

Among the economic indicators, only inflation has consistent and robust short- and long-run effects on year-over-year change in the offense types under consideration. Low inflation helps to explain why acquisitive crime did not increase during the 2008–2009 recession. Imprisonment rates also have robust long-run effects on change in acquisitive crime rates.

Conclusions

Incorporating inflation into studies of crime and the economy can help to reduce the theoretical and empirical uncertainty that has long characterized this important research area in criminology.

     

Assessing the Impact of Community-oriented Policing on Arrest

Community-oriented policing’s (COP’s) influence and effect on policing has been significant in recent years with evidence suggesting that it increases police satisfaction, partially improves police legitimacy and reduces citizen’s fear of crime. It has also been hypothesized to influence the likelihood of an arrest; however, this possibility presents competing theoretical mechanisms and limited empirical assessment. The current study uses a multilevel analytic approach to merge data from the National Incident Based Reporting System data base with information from the Law Enforcement Management and Administrative Statistics survey to investigate this possibility. Results indicate that agencies engaged in greater and specific COP activities experience an increased likelihood of arrest in violent crime incidents. This effect varies by the type of violent crime and the amount of COP activity undertaken.     

Specifying the Relationship Between Crime and Prisons

There is no scholarly consensus as to the proper functional form of the crime equation, particularly with regard to one critical, explanatory variable—prison population. The critical questions are whether crime and prison rates must be differenced, whether they are cointegrated, and whether they are simultaneously determined—whether crime and prison cause one another. To determine the proper specification, different researchers have applied unit-root, cointegration, and Granger tests to very similar data sets and obtained very different results. This has led to very different specifications and predictably different implications for public policy. These differences are more likely to be due to the methods used, rather than to real differences among the data sets. When the best available methods are used to identify the proper specification for a panel of U.S. states, results are fairly clear. Crime rates and prison populations are close to unit-root; crime and prison are not cointegrated; crime clearly affects subsequent prison populations. Thus the best specification of the crime equation must rely on differenced data and instrumental variables. Alternative specifications run a substantial risk of spurious results.