Criminological Highlights Vol. 21, No. 6 - November 2024

Item 2

Young people (age 14-17) who had been found guilty of a criminal offence were tracked for 7 to 10 years. Self-reported offending and arrests by police were recorded periodically throughout this period. Compared to Whites with exactly the same arrest records, Blacks had committed fewer offences.

Criminal records – arrests and convictions – are used for many purposes. These include the decision to proceed with a criminal prosecution if a person is suspected to have committed an offence. Similarly, a criminal record is routinely used in sentencing those found guilty (e.g., to determine whether a person should be imprisoned). The working assumption is that a specific criminal record (e.g., a record showing that a person has a certain number of previous arrests) has the same meaning for all people. Even though it is understood that an official record does not describe all wrongdoing, it is assumed that two people with the same record have similar criminal histories. This paper examines this assumption and “validates a longstanding worry that many decision points in the criminal process are influenced by racial biases in criminal records” (p. 491).

Neighbourhoods that vary in their racial makeup may also vary in terms of how intensely they are policed. In addition, police officers may be more likely to lay formal criminal charges for some suspects than for other suspects. It is plausible, therefore, that the ratio of formal charges to offences committed would vary in systematic ways. But these factors are often ignored later in a person’s life when some new decision (e.g., whether to charge, whether to imprison, whether to employ) is being made. At that point, the criminal record itself is seen as a simple unbiased indicator of past misbehaviour. This paper demonstrates that, as a record of misbehaviour, a criminal record is not unbiased.

To determine if an “official” record of offending reflects actual offending equally across racialized groups, we need to see whether a measure of “actual offending” – in this case self-report offending – is equally accurate across groups. In this study, self-reported offending by Blacks, Hispanics, and Whites was examined. The concern is simple: Is there, in self-reports, differential reporting rates across racial/ethnic groups? One way to examine whether there is any differential self-reporting is to look at changes in reporting rates between reports for the month immediately before self-report offending data were collected and the reports for the month immediately following a data collection. (These latter data were collected at the next data collection, months later.) The assumption is that if there is systematic under-reporting, it would be most likely to appear when the questions dealt with the most recent period. For property and violent offences, there was no evidence of systematic differences in reporting. For drug trafficking, there was some evidence of under-reporting by Black respondents.

The data for property and violent offending, therefore, appear to be free of systematic reporting errors. The findings were quite clear on the central issue being examined: A Black respondent with a specific number of arrests (e.g., two arrests) had fewer actual offences than did a White respondent with an identical number of arrests. The data suggest that “Black subjects committed 53%, 30%, 23%, and 56% fewer offences than White subjects with the same number of arrests for property, violent, drug, and [drinking-driving] crimes, respectively.” [Data for Hispanic respondents were also collected and showed a bias in criminal records, although it was not as consistent as that for Blacks.]

Conclusion: The data suggest that the meaning – in terms of actual offending – of a criminal record is biased against Blacks. A Black person in this study had committed fewer actual offences than a White person who had exactly the same criminal record. In certain ways, this is not surprising. If those making decisions on the deployment of police officers or whether to charge someone apprehended for an offence are at all likely to see Blacks as more “criminal” one can easily imagine that a Black person would be more likely to be charged than a White person who had done the same thing. The possible biasing effects could also affect decisions about Blacks and Whites in other areas (e.g., access to housing, jobs, etc.).

Reference: Grunwald, Ben (2024). Racial Bias in Criminal Records. Journal of Quantitative Criminology, 40, 489-531.

Item 3

Undocumented immigrants in the American state of Texas “have substantially lower crime rates than native-born citizens and legal immigrants across a range of felony offences” (p. 32340).

Donald Trump, as well as the current Texas governor, Greg Abbott, have often made evidence-free assertions that immigrants – especially undocumented immigrants – are responsible for a disproportionate amount of crime. The data on legal immigrants is quite consistent (see Criminological Highlights 18(5)#1, 18(6)#6): Immigrants to the US and Canada are not disproportionately responsible for crime. If anything, they are less likely to be involved in offending than native born residents. This paper takes these analyses one step further and looks at the crime rates of undocumented immigrants in the State of Texas.

The challenge in estimating a “crime rate” for undocumented immigrants is that estimating both the numerator (number of crimes they are responsible for) and the denominator (a reasonable estimate of their number in the community) is not straightforward. Texas, however, is unusual. It checks the immigration status (native born, legal immigrant, illegal immigrant) for all people arrested (using both state and federal data). The Texas population is estimated as being 17% foreign born. Census data as well as peer-reviewed data sources from the Center for Migration Studies were used to make estimates of the total “foreign born” population as well as the number of government authorized immigrants. This allowed the researchers to estimate the residual group: undocumented immigrants. Texas is unique among US states in requiring knowledge of the immigration status of everyone in its criminal record system.

The study examines felonies – violent and property crime as well as drug violations and traffic violations – during the period 2012-2018. “Relative to native-born citizens and legal immigrants, undocumented immigrants have the lowest felony arrest rates across all four crime types. For violent, property, and drug offences, legal immigrants occupy a middle position between undocumented immigrants and US-born citizens” (p. 32342). For traffic violations, the rate for undocumented immigrants was the lowest, but legal immigrants had the highest rate.

When the broad categories of crime were broken down into more specific categories, it was found that “Without exception, undocumented immigrants have the lowest crime rates” (p. 32343) when looking at homicide, assault, robbery, sexual assault, burglary, theft and arson. “For most crimes, the criminality of legal immigrants tends to be less than that of native-born citizens” (p. 32343). In each of the 7 years (2012-2018) in which crime rates were calculated, native born US citizens achieved the highest overall crime rates. Undocumented immigrants had the lowest overall crime rate (measured by felony arrests).  The results are “consistent with research on the selective nature of migration, which suggests that immigrants tend to fare better on multiple social indicators than would be expected by their level of socioeconomic disadvantages” (p. 32346).

Conclusion: “These findings clearly run counter to some of the basic assumptions behind strict immigration enforcement strategies… The results presented [in this paper] undermine the claims that undocumented immigrants pose a unique criminal risk. In fact, [the] results suggest that undocumented immigrants pose substantially less criminal risk than native US citizens” (p. 32345). Unlike what is proposed by some politicians, it is clear that “removing those with relatively low felony crime rates [undocumented immigrants] is unlikely to reduce the overall risk of criminal victimization” (p. 32345). 

Reference: Light, Michael T., Jingying He, and Jason P. Robey (2020). Comparing Crime Rates Between Undocumented Immigrants, Legal Immigrants, and Native-Born US Citizens in Texas. Proceedings of the National Academy of Sciences, December 22, 2020, 117, 32340-32347.

Item 4

In the US, when people either live in or move into locations where there is a high concentration of foreign-born residents, their arrest rate becomes lower than that of people who do not have the benefits of having foreign-born neighbours.

“Public opinion has historically linked immigration with increases in crime and social disorder…. Yet what is different today… is that… the findings emerging from this research – both at the individual and macrolevel – are that immigrants are less criminally involved than their native-born peers and that the influx of foreign-born residents into the community generally exerts an inverse or null effect on aggregate crime” (p. 561).

This study examines whether changes in the immigrant concentration in the county in which people live has an effect on the arrest rates of individual people.  These decreases in crime could occur in two ways. First, an increased concentration of immigrants in a neighbourhood might decrease its crime rate. Second, if a person were to move into a county with a higher concentration of immigrants, that person’s likelihood of arrest might decrease. [This study also examines self-report offending, using a measure of the number of different types of offences the person reported engaging in. And it also looked at the children of immigrants. Some results using these measures are slightly different, but the overall results are much the same as described here.]

The study uses data from a US sample of 7,897 youths born between 1980 and 1984 who were interviewed annually between 1997 and 2011. The concentration of immigrants in each respondent’s county was estimated from US census data. Various time-varying (e.g., age, school dropout, concentrated disadvantage of the neighbourhood) and time-invariant (e.g., sex, race, parental education) factors were controlled for.

Within individuals, increases in immigrant concentration in the area in which they live reduces the likelihood of arrest. Furthermore, the effect on arrest of the change in the percent foreign-born in a person’s neighbourhood does not depend on whether individuals moved into a county with a different concentration of foreign-born residents, or whether the country itself changed in its proportion of immigrant residents.

During the period of time in which data were collected for this study, there was, generally, an increase in the proportion of foreign-born people in the various counties in which respondents lived. The “crime reduction” effect of a higher concentration of immigrants in a person’s county tended to reduce arrests for other immigrants, their children, and others.

Conclusion: The fact that living in, or moving to, a county with a high concentration of immigrants reduces the likelihood of a person being involved in crime is a fairly simple reminder of how the presence of immigrants benefits more than just those immigrants. This study does not attempt to explain why a high concentration of immigrants reduces offending. But it is clear that “Immigration serves as a pro-social force for the community that promotes within-individual reductions in criminal offending and that these effects influence a wide range of living situations and people” (p. 585).

Reference: Widdowson, Alex O., Javier Ramos, Kayla Alaniz, and Kristin Swartz (2024). The Within-Individual Effects of US Immigration on Individual-Level Offending During Adolescence and Early Adulthood. Journal of Research in Crime and Delinquency, 61(4), 560-593.

Item 5

When a person violates a condition of probation, revoking their probation and incarcerating them does little, overall, to affect the likelihood that they will eventually be sent to jail for a new offence. However, for low-risk offenders, revoking their probation for a technical violation increases substantially the likelihood that they will subsequently commit a new offence and be jailed.

Probation is a very common penalty for criminal offences. In Ontario, Canada, for example, 41% of adults found guilty in criminal court have probation as the most serious component of their sentence. But given that probation sentences often involve many conditions, and complying with all of these conditions is often a serious challenge, it is not surprising that there are a fair number of cases in which probation is revoked and the person is sent to custody.

This paper looks at the impact of probation revocations that involve sending the offender to jail. Specifically, to determine the impact of imprisoning those who do not comply with probation conditions, this study takes advantage of the fact that in many cases offenders do not comply with their probation conditions but are not incarcerated. In the US, probation terms average around 2 years and involve an average of about 15 separate conditions. Non-compliance with any of these conditions puts the person in jeopardy of being incarcerated. Other research (e.g., see Criminological Highlights collection on The Effects of Imprisonment ) suggests that being sent to prison might, if anything, increase the likelihood of reoffending, at least for some types of offenders.

This paper follows a sample of 1,873 probationers in Indiana who completed their probation between 2014 and 2016. About half were serving a sentence for a misdemeanor; few (6.8%) had been found guilty of a violent offence. Most (64%) were seen as low-risk offenders. They had all apparently violated at least one probation condition, though only about 12% were imprisoned as a result of this violation. The focus was on whether revoking probation and sending the offender to jail had different criminal justice consequences than simply re-releasing the offender on probation (perhaps with modified conditions). The follow-up period was 5 years from the end of the original probation term or from the end of the revocation of the original term of probation.

Focusing largely on whether the offender re-appeared in the Indiana jail system for a new criminal offence within 5 years, those who had their earlier probation revoked were considerably more likely to be charged with new offences, returned to jail, and have technical violations.  This is not surprising because the two groups (those whose probation was revoked and those who were re-admitted to probation) were very different on such things as their “risk of reoffending” profiles (e.g., prior arrests, prior imprisonment, etc.). However, after controlling for a wide range of background characteristics, “revocation was not a significant predictor of any of the five [criminal justice outcomes that were examined]” (p. 5).  Said differently, the revocation of probation and the placement of the offender in jail did not, in the long run, affect the likelihood of reoffending.

There was, however, an indication that for a subset of probationers – those who were low risk and had their original probation revoked for a purely technical violation (rather than a new offence) – revocation was responsible for an increase in the likelihood that, during the five-year follow-up period, they would be jailed for a new offence.

Conclusion: When offenders violate conditions of probation – especially if they are initially low risk for reoffending – it would appear that public safety is not enhanced by sending them to prison. Instead, these data would suggest that it would make more sense to examine the reasons for the non-compliance with the original probation order to see if conditions could be modified in a manner that would be more likely to increase public safety without the potentially harmful impact of imprisonment.

Reference: Diaz, Carmen L., E.M. Lowder, M.N. Bohmert, & M. Ying (2024). A Retrospective Study of the Role of Probation Revocation in Future Criminal Justice Involvement. Journal of Criminal Justice, 93, 1-9.

Item 6

Publicly funded improvement of deteriorated neighbourhood vacant land can reduce crime.

“Vacant and blighted urban land is a widespread environmental condition encountered by millions of people each day” (p. 2946). This study examines what happens when a municipality intervenes and makes inexpensive improvements to vacant lots. These improvements involved cleaning them up and planting grass in a way that made them attractive for informal use. The goal of the study was to determine what effect, if any, these improvements had on reported crime and on residents’ views of their neighbourhoods.

Vacant lots in Philadelphia, Pennsylvania that were described as having blighted conditions were identified. 110 clusters of vacant lots were identified containing, in total, 541 vacant lots. Perhaps one of the most remarkable aspects of this civic improvement experiment is that the City allowed these clusters of vacant lots each to be randomly assigned to one of three conditions. The main intervention involved removing trash and debris, grading the land, planting new grass and a few trees. A low wooden fence (with multiple ungated entrances) was included in the design.  A “basic” upgrade for another group of vacant lots involved cleaning up the debris and mowing whatever grass was there. A third group of lots was left unchanged. The cost of implementing the “main intervention” was about $5 per square metre ($4000 for 20m by 40m lot).

Data on police reported crime as well as data on the perceptions of fear and safety of residents who lived near these vacant lots were collected and analyzed over a 3-year period (1.5 years before and 1.5 years after the changes were made). In all, 445 residents were interviewed before and after the intervention (147-150 per condition). All people who were interviewed within each of the 110 clusters lived within a 0.4 km. radius of one another. Crime events were counted in the immediate vicinity of the lots chosen for this study.

Interventions – both “full” and “basic” – each had the effect of reducing crime, compared to the “no intervention” condition. Specifically, there was less total crime, as well as fewer gun assaults, burglaries and events described by the police as “nuisances” in the areas where interventions had taken place. In neighbourhoods that were below the poverty line, the effects were similar, but more pronounced.

Residents’ views were quite consistent with the crime data. Residents of neighbourhoods that had received the improvements in the vacant land were less likely to report that there was a lot of crime. Compared to those who lived in neighbourhoods in which no intervention had occurred, residents also reported that there was less vandalism. They also reported that they were less likely to avoid going out because of safety concerns and were more likely to spend time relaxing and socializing outside.

It is worth remembering that these were not huge interventions. They typically took about 2 months to complete (in April and May) and the improvements were neither extensive nor expensive.

Conclusion: “Structural dilapidation and blight can be key causes of negative outcomes in terms of people’s safety – both their perceptions of safety and their actual, physical safety. The physical components of neglected and impoverished urban environments can be changed in inexpensive and sustainable ways as a direct treatment strategy for violence and fear in cities…. [These interventions] can be key in spurring people-focused urban connectivity and the reestablishment of vibrant, busy streets” (p 2950-1).

Reference: Branas, Charles C., Eugenia South, Michelle C. Kondo, Bernadette C. Hohl, Philippe Bourgois, Douglas J. Wiebe, and John M. MacDonald (2018). Citywide cluster randomized trial to restore blighted vacant land and its effects on violence, crime, and fear. Proceedings of the National Academy of Sciences, March 20, 2018, 115(12), 2946-2951 & supporting information online.

Item 7

The presence of urban greenspace – trees and other vegetation – in urban areas is associated with lower rates of crime, even when controlling for a range of well-established predictors of crime.

Previous research (Criminological Highlights 21(4)#3) suggests that prisons that contain high levels of greenspace (vegetated landcover) are associated with lower rates of prisoner self-harm, less violence targeting other prisoners or prison staff, and higher rates of prisoner well-being. This paper takes these findings outside the prison walls and looks at the relationship between crime and the presence of urban greenspace in Washington, D.C. neighbourhoods (see also Criminological Highlights 21(6)#6).

There are several theories about how urban greenspace (UGS) might be associated with reductions in crime. For example, the presence of UGS might offer attractive gathering space where people would gather, thus increasing guardianship. Alternatively, UGS might be associated with increased social ties and opportunities for interaction in the neighbourhood.

This paper looks at the effects of two forms of UGS – tree canopy coverage and noncanopy vegetation coverage (measured using satellite imagery) – on both violent and property crime in Washington, D.C., a city that is ethnically diverse with a relatively high poverty rate. Measures were obtained for each “census block group” (average population 1,534). Various characteristics of these block groups were measured and controlled statistically. These included poverty rates, the type of employment engaged in by the residents, ethnic heterogeneity and racial makeup, age, presence of businesses selling alcohol or cashing cheques, and violent and property crimes in contiguous block groups.

The census block areas had an average of 31% tree canopy and 13% noncanopy vegetation. Neighbourhoods with higher percents with tree canopy coverage had lower violent and property crimes. High rates of noncanopy vegetation were associated with lower violence rates but were not associated significantly with property crime. Other characteristics of the neighbourhoods that were controlled for – e.g., the presence of businesses selling alcohol, cheque cashing businesses – were, as expected, linked to violent and property crime rates. But the effect of UGS on crime was above and beyond these effects.  

The nature of the relationship between tree canopy coverage and violent crime varied across types of neighbourhoods. It held for low and average poverty groups, but not for high poverty neighbourhoods. Similarly, this effect was not found in neighbourhoods with low home ownership. The effects of tree canopy coverage were similar when examining property crime: High rates of tree canopy coverage are not associated with lower property crime rates in high poverty areas. More generally, it may be that “in communities characterized by social disorganization, tree canopy coverage may not reduce crime” (p. 256).

Conclusion: Although these findings are consistent with some earlier studies showing a reduction in crime associated with urban greenspace, these effects “seem to depend, in part, on the type of greenspace, the type of crime, and the socioeconomic and land use characteristics of neighbourhoods” (p. 256). Most notably, UGS may not have any effect on crime in severely disadvantaged communities. It is important, therefore, that “city officials and urban planners should take care to evaluate [UGS programs] rather than assume crime reductions” (p.257).

Reference: Wo, James C. and Ethan M. Rogers (2024). Urban Greenspace and Neighborhood Crime. Criminology, 62, 236-275.

Item 8

When using a “risk assessment” tool to predict recidivism by youths, it is better to use the scale’s actual score than to allow “over-rides” based on the intuitions of those who think they understand the youth.

Predictions of reoffending by youths are used for many purposes, among them to determine what kind of treatment, if any, the youth should receive. In some jurisdictions, risk assessments may be used to aid in the sentencing process.

The challenge is that predictions of reoffending by youths are often wrong. The best available assessment tools are usually better than chance, but not by an enormous margin. If a high score means that a person is likely to reoffend, a typical finding in this area would be similar to what was found in this study: that a randomly chosen person who has reoffended will have a higher score than a randomly chosen person who has not reoffended only around 71% of the time. Given that pure chance is 50%, this improvement over chance, though statistically significant, is not impressive when thinking about predicting for individual cases.  A coin that is flipped and comes out heads 71% of the time is clearly ‘biased’ but a prediction of “heads” is far from perfect.

This paper looks at the use and impact of the discretion that probation officers in Ontario (Canada) have to override an actual risk assessment of youths. Youths supervised by Ontario probation officers were assessed for the purpose of matching the intensity of the intervention with the likelihood that a youth would reoffend. In this study, one of the “most established and well-researched” (p. 176) risk assessment tool, the Youth Level of Service/Case Management Inventory was used. It consists of 42 items that are summed to create an overall score. This continuous score is frequently divided into four risk groups.  Previous research suggests that the frequency of the use of the “clinical override” varies considerably. However, most of the overrides are in the direction of increasing the apparent “risk” of the youth.

A total of 1259 youths, age 12-19 (average age 16.3 years), were followed for a minimum of a year after they had been assessed. Any conviction within 24 months of being assessed constituted evidence of recidivism. Indigenous youths’ scores described them as having a higher risk of reoffending than non-Indigenous youths. Those whose original offence was sexual in nature had significantly lower risk scores than those whose original offence was non-sexual in nature. 10.8% of the youths received over-rides of the original test score. In every case, the “clinical” (subjectively determined) override was used to increase risk-level classification. Over-rides were more likely for those whose original offence was sexual in nature. 

The original statistically created risk score was significantly related to recidivism and was the same magnitude for those who did and did not have their final scores over-ridden. However, when one examines the recidivism predictions for those cases in which there was a risk override, the prediction was almost exactly at chance level. These findings are very similar to risk overrides with many other prediction scales: Empirically based risk assessments are generally superior to clinical judgements. The problem is that those in the field appear to want to be allowed to use their clinical intuition to override statistical predictions even though clinical overrides have repeatedly been shown to be less accurate.

Conclusion: The findings in this study with youths are similar to those of other studies: Clinical overrides are much more likely to be in the direction of increased prediction of risk of reoffending. The reason for this may be simple: “the occurrence of a false positive [a prediction of reoffending that does not occur] is less concerning [to the probation officer] than underestimating the risk of a youth who re-offends” (p. 187). But the consequence of these overrides is important: What started as a modestly accurate prediction ended up being no better than an unbiased coin flip.

Reference: Schmidt, Fred, Amy Killen, Dilys Haner, and Elaine Toombs (2024). Clinical Override Use with the Youth Level of Service/Case Management Inventory. Criminal Justice & Behavior, 51(2), 175-193.

This issue of Criminological Highlights was prepared by Anthony Doob, Rosemary Gartner, Maria Jung, Tyler King, Jihyun Kwon, Katharina Maier, and Jane Sprott