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Long-Term Dynamics of Neighborhoods and Crime: The Role of Education Over 40 Years

• Original Paper
• Published: 01 October 2021
• Volume 39 , pages 187–249, ( 2023 )

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• Adam Boessen   ORCID: orcid.org/0000-0001-5907-327X 1 ,
• Marisa Omori 1 &
• Claire Greene 1  

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A Correction to this article was published on 09 November 2021

This article has been updated

Over the last 40 years, considerable changes have occurred in both education and crime, and in this study, we examine the longer-term consequences of education for violence in communities. We argue that the impact of education on crime depends on the temporal and spatial context of educational levels. Specifically, we focus on whether the type of educational attainment matters and the broader historical context. We also examine whether these patterns are robust for different regions of the city and racial/ethnic compositions of neighborhoods.

Using longitudinal neighborhood data over 40 years in St. Louis, Missouri, we test whether education has consequences for violent crime with a series of two-way fixed effects models.

Neighborhoods with more college degrees in more recent time periods are generally associated with reductions in violent crime, especially in the white, southern region of the city. In contrast, neighborhoods with greater reliance on high school degrees were associated with violence reduction in the past, especially in the Black, northern part of the city, but the relationship no longer holds in the modern era. Both time and place therefore matter for education’s association with crime in neighborhoods.

The findings provide evidence that educational attainment has important consequences for neighborhood crime, but this relationship depends on the kind of education, historical temporal period, and region of the city. Overall, communities with more college degrees are consistently associated with reductions in violence in more recent decades.

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A Correction to this paper has been published: https://doi.org/10.1007/s10940-021-09537-2

Nevertheless, a challenge has emerged in the literature in regards to these systemic and social disorganization theories of neighborhood crime, which suggest that poor communities are organized (e.g., see Sánchez-Jankowski 2008 ; Whyte 1943 ), and other work argues that many places with strong kin and friendship ties can also have higher crime rates (Pattillo 1998 ; Sampson 2012 ).

Moreover, even amongst different kinds of universities (public vs. private), there is evidence of inequality in the spatial distribution of social friendships among students who attend schools (e.g., private school students often have a much broader spatial footprint in where students come from who attend, see Spiro et al. 2016 ).

We use 2005–2009 ACS data rather than the 2010 census since the ACS data are already in year 2000 tracts.

Census tracts are also a useful approach since neighborhood census data are not available in micro units at earlier decades but tract data is available, and tracts’ boundaries can be standardized between decades.

As of the 2000 census, there were 113 tracts for St. Louis, but four census tracts had zero population or low population (e.g., a tract with a large cemetery) and were omitted from the analyses. We use the same analytical sample for St. Louis that Peterson and Krivo 2010 used in their study. The sample sizes in the tables are 544 rather than 545, and this is because one tract in 1970 had missing data, and this observation was omitted from the analysis.

Specifically, blocks are mostly contained within the named neighborhoods, although tracts are relatively similar too. We first intersected the blocks with the named neighborhoods in ArcGIS, and we then merged in the block population data, and used it to apportion the crime data. To assess this procedure, we used our crime data from 2010 that has x–y coordinates. We put our 2010 data into the named neighborhood units and apportioned them into year 2000 units. As a comparison, we used the x–y coordinates of the 2010 crime data and aggregated them directly to 2000 census tracts. We then correlated the violent crime measures using the apportioned units and the crime data aggregated directly to 2000 census tracts. The correlation was .93, which suggests that this apportionment approach is reasonable.

As an assessment of the quality of these data, we compared the distributions of the counts of crime at each decade with the reported Uniform Crime Reports that are available at the city level for each time point. All data were quite similar with the reported uniform crime report data, and this gives us confidence in the quality of these data. Of course, these data also are subject to the issues of official police data with not all crimes being reported or recorded (Lynch and Addington 2007 ; MacDonald 2001 ). Yet, Baumer ( 2002 ) noted that these reporting practices are not related systematically to neighborhood characteristics, therefore suggesting that the coefficients are unbiased. We also do not focus on rape or sexual assault given well-known issues with this crime type.

We tested whether there were crime type differences amongst the different crime categories of violent crime by estimating separate models for homicide, aggravated assault, and robbery. The results were nearly identical and given the abundance of tables, we only show the results for our general measure of violent crime. Future research might also examine the consequences of education for other types of crime (i.e., property crime), as well as heterogeneity within various crime types (Kubrin and Herting 2003 ; Kubrin and Weitzer 2003 ).

For 1970 and 1980, the census asked about the number of years of school each person completed (e.g., high school 1 year, high school 2 years, high school 3 years, etc.). For 1990, 2000, and 2010, the census question changed to reflect the level of school completed and type of degree. For the 1970 and 1980 census, we follow the Census’ approach to compare educational attainment over time by only focusing on high school for 4 years completed (or higher) or college degree 4 years (or higher). While some prior work has examined ‘years of education’ (e.g. See Lafree et al. 1992 ), we do not use this approach for a few reasons. First, the census does not ask about years of education in 1990, 2000, or 2010, and abandoned this practice for the 1990 census (see Kominski and Adams 1994 ; Kominski and Siegel 1993 ). Second, we are primarily interested in the qualitative distinction and credential of a college degree (not number of years educated), and this implies nonlinear (categorical) change. Third, many students, particularly in the modern era, likely go to school for many years (e.g., 5 years to obtain a college degree) or part time, and thus they may have more years of education but without a degree, suggesting more measurement error if we used this approach.

As another approach, some research has created an economic disadvantage factor. We did not use this approach because we are interested in within neighborhood change over time, and thus we would be comparing relative standardized within unit changes over time. As such, these analyses would allow for possibility that changes in other neighborhoods driving some of the within unit change. Thus, a neighborhood could appear to be changing simply because of its position in the distribution of a neighborhood factor score. It is also more conceptually challenging to interpret over time, and there are few measures available to compute over the 40 year time period. We did test some ancillary models that created a factor score with poverty, unemployment, and single parent families, and the inclusion of this measure did not alter our main findings, giving us further confidence in our models. Finally, we also estimated models using only unemployment, and the results were substantively the same.

For 1970, the heterogeneity measure is only based on 4 categories since there is not a measure of the percent Asian in the Census for this decade. This group is thus combined with the other category.

Rather than focusing on heterogeneity, another approach would include measures of individual racial/ethnic groups. We tested this possibility, and given that Black and white residents are by far the largest groups in St. Louis, we estimated a series of ancillary models that included the % Black residents. The results were the same, and the % Black measure was not significant for any of the models. We also note that this measure is correlated with our disadvantage measure (.76 on average over time), and overall, this gives us further confidence in our results. Finally, We also test models in the paper that examine differences by racial/ethnic composition of the neighborhood.

Recent work on immigration shows it is protective for communities, particularly during the 1990’s crime decline (Martinez et al. 2010 ; Ousey and Kubrin 2018 ). One explanation for these findings would suggest that they are due in part to high educational achievement among many immigrants, and thus we control for immigration.

We account for the binning nature of the data (i.e., the census only asks about income categories) using the Pareto-linear procedure with the prln04.exe program (Nielsen and Alderson 1997). This measure is correlated with poverty on average over decades at .23.

We also estimated a series of ancillary models that did not include neighborhoods with less than 1000 residents. The results from these models were essentially identically to the models shown in the tables.

The random effects model is another approach to understanding change in neighborhoods over time, and this approach theoretically would be quite different since it assesses differences between neighborhoods. While we think this is an interesting research question, our focus is on within neighborhood change and the fixed effects have the added benefit of accounting for time stable unobservables. Nonetheless, we did perform as Hausman test that allows for testing systematic differences between random and fixed effects models. The test was significant, suggesting that fixed effects is both theoretically and empirically the better approach.

As ancillary models, we also estimated multilevel mixed effects models that included random effects for neighborhoods and time. For these models, we included a random intercept for each neighborhood and the effect of time was allowed to vary across each neighborhood as a random slope. The results from these models were substantively the same as those presented in the text, which further strengthens our findings.

It is well-known that Stata’s “xtnbreg, fe” command is not a true fixed effects model, and one approach to estimate fixed effects is to use dummy variables (Allison 2009 ). One challenge with this approach is what is often referred to as the ‘incidental parameters problem’. To assess this issue, we estimated models without using neighborhood dummy variables, but we included our neighborhood entity fixed effects through Stata’s estimation commands (i.e., xtpoisson, fe) and added time dummies, and the results were substantively the same, giving us further confidence in the results.

As another set of ancillary models, we included a lagged violent crime rate with all of our models. This measure did not alter the substantive pattern of results, and this further strengthens our findings.

We also tested models with a 65% majority threshold and the results were similar.

Other researchers have used a similar modeling strategy when using states as units to understand punishment over decades (Campbell et al. 2015 ; Greenberg and West 2001 ; Jacobs and Carmichael 2001 ). We did estimate ancillary models for each decade separately and the results were similar. Also, we note that one study used time series models of crime trends in cities from 1960’s to 2000 suggests that there is little evidence that crime trends are historically contingent (LaFree 1999 ; McDowall 2002 ; McDowall and Loftin 2005 ; see also Parker et al. 2017 ). We are not aware of any research to date that has examined the historically contingent nature of different neighborhood predictors on crime.

To study neighborhoods or micro places over time, some studies have employed group based trajectory models (Stults 2010 ; Weisburd et al. 2004 ). While this approach is reasonable for some research questions (see also Bauer 2007 ; Bollen and Brand 2010 ; Kreager et al. 2011 ; Martinez et al. 2010 ; Nagin and Tremblay 2005 ), we are interested in within neighborhood processes, but the studies that employ those trajectory models nearly always focus on between neighborhood differences in the types of trajectories. The group based trajectory models employed most often in the literature also do not account for potentially important time stable unobserved characteristics (which we do with our fixed effects), as well as baseline differences among neighborhoods. Finally, the fixed effects models allow for easily testing differences between discrete historical contexts. We are aware of no work testing period change with group based trajectory models in that the change is assumed to operate in the same way across the entire time period of study. In this paper, we explicitly test whether neighborhoods experienced a discrete change in different decades for education.

Collinearity was tested with Philip Ender’s Stata ado file: ‘collin’. All variance inflation factors were under 4, thus there is no evidence of an issue in regards to collinearity. We tested for outliers using studentized residuals from models estimated as linear regressions (the outcome was converted to a rate). We then estimated models without observations with studentized residuals greater than or less than 2, and the results were the same.

One possibility for future research is that many of the high crime neighborhoods are located on the boundary (Delmar Blvd) separating the north and south regions of St. Louis. As such, the position of a neighborhood within a region in tandem with the placement of boundaries may be important for crime patterns.

As one comparison between the map for violent crime (Fig.  1 A) and racial/ethnic composition (Fig.  1 B), as well as considering the average plots in “Appendix Fig. 7 ”, we see that Black and mixed neighborhoods generally have higher crime rates than white neighborhoods on average. But, the cold spots on the extreme end of the distribution are always in white neighborhoods over time, while the ‘hot spots’ are most often in majority white neighborhoods in the 1970’s, but in more recent decades they are located in majority Black or mixed neighborhoods. Moreover, many of the cold spots are largely surrounded by other white neighborhoods, while the Black and mixed neighborhood hot spots are often sharing a border between different racial/ethnic compositions (i.e., Black neighborhoods next to white neighborhoods or mixed neighborhoods next to Black neighborhoods), suggesting future research more explicitly consider these patterns as a part of a larger socio spatial process (see also Boessen and Hipp 2015 ). Further, some of the areas with no population near downtown (i.e., railroad tracks) and key road boundaries (i.e., Delmar Blvd.) effectively shape high crime hot spots.

As noted in Appendix, the correlation between poverty and bachelor’s degrees is − .19, suggesting these while correlated as would be expected, it is relatively modest.

Because the regions are time-invariant, these differences were previously differenced out in our fixed effects models (see also Bollen and Brand 2010 ). Although not shown in the tables, we estimated ancillary models that included interactions between region and each decade timepoint to assess whether the regions were statistically different over time in their consequences for crime. These models indicated that the most recent decades (2000 and 2010) were significantly different from earlier decades (1970, 1980, and 1990), suggesting that we are theoretically and empirically justified in assessing differences by region. As another approach, we removed both sets of fixed effects from the models, and estimated separate models for each timepoint. With these models we could include our indicator for region, and it was significant in all of the models. We also tested these models with indicators for various racial compositions of neighborhoods with a series of N-1 dummy variables (white, Black, or mixed), and the results showed differences over time by racial composition of neighborhood. Taken as a whole, these ancillary models indicate we are theoretically and empirically justified for our models by region and racial/ethnic composition of neighborhood.

We also estimated models without racial/ethnic heterogeneity in the models, and the results were the same.

We show additional models with 1990 and 2010 as the reference groups in Appendix Tables 12 and 13 , comparing the third time point (i.e., the 1990’s) and the last time point (i.e., the 2010’s).

Although we are interested in each individual period effect, we used Stata’s ‘testparm’ command to jointly test each set of interactions as a Wald test (see also Paternoster et al. 1998 ). The result of these tests are consistent with the results presented, suggesting overall differences in the effects in different time periods.

We also briefly point out that these race/ethnicity majority neighborhood analyses are essentially comparisons between other similar (i.e., within group) neighborhoods (i.e., comparing white majority neighborhoods with other white majority neighborhoods), but this approach does allow for seeing their effect when plotted.

It’s also worth keeping in mind that there are only 5.2% (N = 11) mixed neighborhoods in 1970 and 12.8% (N = 14) in 1980, but this grows to 33% (N = 36) by 2010 and the crime rates in these areas are still relatively high when compared to white neighborhoods.

We also tested models using unemployment (rather than poverty), and the results were substantively similar.

We also tested whether high school degrees act as a mediator between poverty and violence, and there was no evidence of any indirect effects of poverty on violence being mediated by high school degrees.

We also estimated these models without the lagged violent crime measure, and the results were substantively the same.

The data are available at ICPSR: https://www.icpsr.umich.edu/icpsrweb/RCMD/studies/27501 .

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Acknowledgements

Funding for this study was obtained from the University of Missouri St. Louis through the School of Public Policy’s Creating Whole Communities Fellowship and though the College of Arts and Sciences Research Grant Program. We also thank Lee Slocum for insightful comments on this manuscript.

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Violent crime rate by region and racial composition over time

Figures %bachelor’s degrees on violent crime by racial composition 1970–2010

Figures %high school degrees only on violent crime by racial composition 1970–2010

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Boessen, A., Omori, M. & Greene, C. Long-Term Dynamics of Neighborhoods and Crime: The Role of Education Over 40 Years. J Quant Criminol 39 , 187–249 (2023). https://doi.org/10.1007/s10940-021-09528-3

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Education and crime.

Although the topic of education and crime may seem straightforward, there are many different viewpoints from which it can be examined. Researchers have studied this topic from many different perspectives. As a result of this research, several connections between education and crime have been introduced into the literature and are widely accepted. (adsbygoogle = window.adsbygoogle || []).push({});

I. Introduction

Ii. general perspectives on education and crime, iii. definitions, iv. education’s impact on crime, v. crime’s impact on education.

VI. Conclusion and Bibliography

The purpose of this research paper is to provide an overview of the topic of education and crime. Although at first glance this appears to be a simple task, there is an inherent complexity to examining such a broad subject. There are many different perspectives from which a discussion of education and crime could develop. Criminologists might assume that a discussion of education and crime would comprise an overview of the impact that an individual’s education level may have on his or her criminal or antisocial behavior. Alternatively, parents might assume it is a discussion of the impact of school violence and crime on the safety and learning of their children, and legislatures might assume it to be a comparison of the monies spent on fighting crime in the United States versus those spent to improve American schools. A novice might be expecting all or none of these approaches. This research paper attempts to address all of these views, albeit briefly.

The research paper begins with an overview of the generally accepted views about the relationships between education and crime. Given the volume of research on this topic, researchers have generally agreed on several basic specifics that they believe reflect the true relationship between crime and education. Next, this research paper attempts to clarify several points that need to be addressed initially. First, several general terms are defined (e.g., education, educational attainment, intelligence, street smarts, and crime) and then discussed as they are used in the study of the connections between education and crime. Finally, a discussion of how these terms intermingle is offered.

In order to develop a comprehensive framework from which to examine the concept of education and crime, two overall perspectives are addressed: (1) education’s impact on crime and (2) crime’s impact on education. It is hoped that through a discussion of these two general perspectives readers can develop an appreciation for the complexity of such a broad research area.

The concept of education’s impact on crime is examined first. In this examination, education is in essence discussed as a definite inverse correlate between its attainment and criminal behavior; that is, as one (education) increases, the other (crime) decreases. A discussion of education’s preventative nature is also presented, with a focus on its repressive nature in regard to initial criminal behavior and eventual recidivism rates. This examination involves a brief discussion of the connection between intelligence (IQ) and crime.

Crime’s impact on education is also discussed as the second overall perspective in examining education and crime. In this discussion, crime is identified as a potential barrier to educational opportunity and attainment. Strong evidence supports the belief that criminal behavior and crime often block many people from beginning the educational process. That many others are prevented from educational attainment due to arrests, periods of incarceration, and past convictions/criminal histories also has strong empirical support. Finally, violence and safety issues in schools are briefly discussed in regard to the way they influence these subjects.

Although the topic of education and crime may seem straightforward, there are many different viewpoints from which it can be examined. Researchers have studied this topic from many different perspectives. As a result of this research, several connections between education and crime have been introduced into the literature and are widely accepted. The following are a few of the empirically supported beliefs about the connections between education and crime:

• A person’s lack of education often increases the likelihood that he or she will become involved in crime and antisocial behavior. The opposite is considered true as well: The more education an individual has, the increased likelihood that he or she will live a crime-free life.
• The lack of educational attainment generally decreases one’s future employment opportunities because of increasing hiring standards in society, thus leading to possible criminal behavior for those individuals who cannot obtain viable employment.
• The lack of education and educational attainment generally limits one’s IQ, thus making him or her more vulnerable to others for exploitation and potential secondary criminal involvement.
• The more educated a community is, the less crime it experiences.
• The more educated a person is, the less he or she fears crime, and the less it significantly affects his or her life.
• It is generally believed that increases in one’s criminal behavior decrease his or her ability (and motivation) to complete higher levels of education (i.e., dropping out of school, getting expelled).
• History has demonstrated that increases in crime rates will almost always drain valuable resources from a community’s educational needs and require that those resources be directed toward crime control efforts.
• History has also shown that an increase in local neighborhood crime very often decreases the effectiveness of local schools’ educational programs and even student attendance.
• African Americans and Hispanics, overall, have less educational attainment than other racial groups. They also have a higher dropout rate than other racial groups. African Americans and Hispanics who drop out of school have a much higher rate of incarceration than those who do not. Research has empirically supported the theory that African Americans and Hispanics have higher rates of criminal behavior, and many scholars argue that there is a definite correlation between race and crime.
• On a practical level, one need only look at the fact that on days when school is in session, the level of property crime committed by juveniles decreases drastically.

Given these findings, it is difficult for many people to believe that, given that the United States has one of the highest incarceration rates in the industrialized world, its rate of spending on educational systems is among the lowest. Many consider this to be one of the major catalysts for the ongoing increases in delinquent and violent behavior in America.

To understand the possible connections and correlations between education and crime, one must first have an understanding of the essential parts of this discussion. These essential parts are actually definitions of several basic terms that people often use without giving much thought to their proper connotation. These terms may seem universally understood, but, as with many seemingly basic concepts, they have many different interpretations. In the sections that follow, definitions are provided for several key terms: education, educational attainment, intelligence, street smarts, and crime.

A. Education

The word education encompasses both the teaching and instruction and the learning of knowledge and information. This could involve the learning of proper social conduct and/or the absorption of technical competency. Simply put, education is one’s ability to know something and his or her ability to then do something with this information. It very often focuses on the development of one’s skills to work effectively in various trades or professions. It also involves the development of one’s mental capacity, moral development, and global understanding.

Formal education consists of methodical instruction, teaching, and training by professional teachers, instructors, trainers, and professors, whereas informal education generally consists of instruction from parents, families, peers, or social interactions. The former consists of the application of pedagogy (i.e., strategies and/or styles of instruction) and the development of curricula (i.e., a set of instructional activities to offer instruction), whereas the latter consists of the social learning that a person gains from interactions with his or her intimate peer groups.

In evaluations of the topic of education and crime, education is most often viewed as something that one is given, has, or accepts, that influences his or her future behavior; that is, education is something that changes how a person views himself or herself and his or her environment. Education is generally viewed as a positive influence on one’s behavior and life. It is widely accepted that the more education a person has, the more social that person’s behavior will be, and the more opportunities he or she will have; he or she ultimately will have a better quality of life. A basic assumption in the field of criminology is that the higher a quality of life one experiences, the less likely he or she will be motivated to be involved in criminal or antisocial behavior.

B. Educational Attainment

Educational attainment is generally viewed as a measure of the amount of education a person has completed at any given point in his or her life. This usually involves a listing of the highest level of education a person has successfully completed (e.g., high school diploma, college degree). The term also can refer to any other type of technical learning that one may have, such as a technical certification or professional license.

In discussions of education and crime, educational attainment often is seen as an accomplishment that is believed to have a positive immediate or long-term impact on a person’s prosocial behavior and success in life. The general view is that higher levels of educational attainment allow people more options for higher levels of employment. In turn, higher levels of employment generally lead to more income. The logic in this line of thinking is that the more income one has, the less likely he or she will be to seek criminal behavior or be interested in antisocial behavior.

C. Intelligence

Intelligence (also often referred to as intellect) is an all encompassing term used to describe the capacity of one’s mind and its associated abilities, including such human capabilities as the ability to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn.

There are, of course, many ways to define intelligence. This is especially true when one is applying this trait to animal behavior, or even to plants. Some scholars argue that the concept of intelligence also includes such traits as creativity, personality, character, knowledge, and/or wisdom. Some have also argued that traditional measures of intelligence such as IQ tests, for example, are inadequate, because people can demonstrate intelligence in many ways. Some arguments claim that people can demonstrate their intelligence in eight different ways: (1) linguistic intelligence (“word smart”), (2) logical–mathematical intelligence (“number/reasoning smart”), (3) spatial intelligence (“picture smart”), (4) bodily– kinesthetic intelligence (“body smart”), (5) musical intelligence (“music smart”), (6) interpersonal intelligence (“people smart”), (7) intrapersonal intelligence (“self smart”), and (8) naturalist intelligence (“nature smart”).

In examinations of education and crime, intelligence often takes on several interesting perspectives. Some people argue that extremely high and extremely low levels of intelligence often lead to criminal and antisocial behavior. Individuals with very high levels of intelligence can use their intellect to mastermind large criminal efforts, and those with very low levels of intelligence are victimized and often the pawns of these more highly educated individuals. Higher levels of intellect are often found in people who are involved in organized and white-collar crime (e.g., embezzlement), whereas lower levels of intellect are often found in disorganized and blue-collar crime (e.g., street crime).

D. Street Smarts

Although street smarts is not a very technical or academic term (some people consider it to be a slang term), many use it to describe the unique abilities possessed by many individuals. It often is used to describe a person who does not have much formal education (i.e., educational attainment), or a great deal of mental capacity or ability (i.e., intelligence), but who has a great or cunning ability to survive in almost any environment (especially in dangerous ones). The skills and abilities often demonstrated by people who have street smarts are things such as a unique ability to read others’ body language and behavior. Such individuals also have the ability to understand the complexities of human behavior, drives, and motivations.Very often, these abilities are developed by people who need to survive in impoverished and dangerous neighborhoods that provide very little assistance or support to their inhabitants. Some people also call these skills common sense, that is, the ability to figure out what works and what does not work in any given situation without any formal instruction or study.

In examinations of education and crime, street smarts often are viewed as behaviors or abilities that lead a person toward criminal or antisocial behavior. Much of this view originates from the belief that most crime is street-level, or blue-collar crime; thus, it is activity most often engaged in by people living on the street who are either unemployed or employed in blue-collar positions. Many people would argue that common sense is something possessed by most law-abiding citizens but that street smarts are possessed only by the so-called criminal element.

Crime is most often defined as any breach of an established rule, regulation, or law committed by someone for whom a punishment may ultimately be prescribed by some governing authority or law enforcement body. Crime is also often defined as any deviant behavior that violates prevailing norms, specifically, cultural standards prescribing how humans ought to behave normally.

Academics often approach this topic through efforts to identify the complex realities surrounding the concept of crime. They seek to understand how changing social, political, psychological, and economic conditions may affect the current definitions of crime. Criminologists understand that this will affect the form of the legal, law enforcement, and penal responses made by any given state.

There are many different ways to classify crimes. A very basic method is to separate them into two types: (1) mala prohibita and (2) mala in se. Mala prohibita (“evil prohibited”) crimes are those that are illegal because legislatures label and identify them as such. These are crimes such as seat belt laws, helmet laws, or gambling laws. The other type of crime is labeled mala in se (“evil in itself ”). These acts, such as murder and sexual assault, are almost universally deemed harmful and negative.

In examinations of education and crime, crime often is viewed as acts committed by people who lack education; lack any educational attainment; and, most often, lack any higher level of intelligence. However, crime is a much more complex human experience and behavior than this view represents.

The topic of education and crime can be approached from many different perspectives, so a framework for a basic understanding must be developed. The first area of discussion is education’s impact on crime and criminal behavior. Although this issue is debatable, there is an overwhelming consensus among public officials, academics, teachers, and parents that postsecondary education is one of the most successful and cost-effective methods of preventing crime. Much of this consensus has been derived from the volumes of empirical research that has examined educational attainment as it relates to crime trends and public safety. Comparisons of state-level education data and crime and incarceration rates have consistently supported the fact that states that have focused the most on education (in general, financial support) tend to have lower rates of violent crime and incarceration. Although education can never be viewed as a “cure all” or magic bullet that will guarantee reductions in criminal activity or crime rates, research suggests that increased investments in quality education can have a positive public safety benefit.

A. Education as Crime Prevention

One of the most dominant ideas under the umbrella concept of education’s impact on crime is the belief that a reduction in crime can most often be achieved by increased crime prevention and that the most effective form of crime prevention is achieved through education. Most people would argue that education can be an important element in preventing individuals from engaging in criminal behavior. Given the previous discussions in this research paper, increased levels of education generally lead to many other characteristics that are viewed as positive correlates of lessening one’s criminal or antisocial behavior.

The literature generally offers two explanations for the preventive force of education on crime and antisocial behavior. The first is that education may change individuals’ preferences (and, in turn, their breadth of choices). The second explanation is that education contributes to a lower time preference (i.e., learning the consequences of one’s actions often make that individual postpone the direct satisfaction of needs). Some scholars argue that education leads to a lower time preference for consumption in the present (teaching one the potential negative aspects of immediate gratification) and a higher time preference for consumption in the future (teaching one the benefits of working in the present to prepare for the future).

Many researchers argue that formal education (i.e., educational attainment) has a very strong impact on teaching students (through the study of history, sociology, and other subjects) on which they should focus more of their attention in the future. Formal schooling and instruction can communicate images of the situations and difficulties of adult life, which are inevitable future issues for all adolescents. Thus, educated people should be more productive at reducing the remoteness of future pleasures.

Many researchers also argue that the more education an individual has, the more heavily he or she will weigh the future consequences (i.e., punishment) of his or her current criminal or antisocial actions. If more education leads individuals to understand the benefit of delayed gratification, then people with a higher education should be deterred from committing criminal acts. It is believed that higher levels of education will make the immediate gratification of an individual’s preferences and desires through criminal activities less important.

Most empirical studies have addressed the relationship between education and crime. Some have found that adolescents who are involved in paid employment or attend K–12 education are less likely to engage in criminal behavior. This suggests that a reduction in criminal behavior contributes largely to the social rate of return for the monies spent on education in the United States. There is much debate on the correlation between the money spent on education and the quality of education and its resultant overall impact on criminal behavior.

Not all studies find that more highly educated people are less likely to engage in criminal behavior, however. Some researchers argue that a country’s average education level does not necessarily have a statistically significant effect on the number of violent crimes (e.g., homicides and robberies). As discussed earlier, many have also argued that increased levels of education actually facilitate the criminal behavior in some individuals because of their increased abilities and knowledge (e.g., computer fraud, pyramid schemes).

The following is a list of empirically supported findings about the connections between crime prevention and education:

• Most studies have found that graduation rates are generally associated with positive public safety outcomes and lower crime rates for communities.
• States with higher levels of educational attainment also have crime rates lower than the national average.
• States with higher college enrollment rates experience lower violent crime rates than states with lower college enrollment rates.
• States that make more significant monetary investments in higher education experience more positive public safety outcomes and lower crime rates.
• The risk of incarceration, higher violent crime rates, and low educational attainment are concentrated among communities of color, whose members are more likely to suffer from barriers to educational opportunities.
• Disparities in educational opportunities contribute to a situation in which communities of color experience less educational attainment than whites, are more likely to be incarcerated, and are more likely to face higher violent crime rates.

For most people, the connection between education and crime prevention is easy to see. Criminologists have spent centuries trying to determine the causes of criminal and antisocial behavior. A central component that emerges over and over is the idea of individual motivation and desire. Human motivation and desire are very complex natural occurrences, and they are difficult to understand, although most people would argue that it is easy to understand the connection between these traits and criminal behavior.

B. The Connection Between Intelligence (IQ) and Crime

Many trends have been supported by contemporary research that has examined possible connections between education and criminal behavior. That levels of education (higher and lower) are significant in the manifestation of criminal behavior has received empirical support, as has the notion that individuals with learning disabilities (and thus with lower education, intelligence, and coping skills) are more prone to violent behavior.

The major reason for these connections is the interrelated causal pattern of events that occur in learning, with education at the center. School achievement is generally predictive of prosocial behavior, designated as upholding the moral values of a society. Most people would argue that school achievement predicts prosocial behavior because in most societies academic achievement is interrelated with several other variables, such as financial success, high self-esteem, and an internal locus of control. This particular model may account for the reasoning behind the general idea that individuals with a high IQ generally have fewer tendencies for criminal behavior than individuals with a low IQ.

Investigations of the connection between criminal behavior and IQ often are based on the general hypothesis that having a higher IQ results in easier achievement in school. As stated earlier, doing well academically is associated with several societal factors as well. Individuals with a lower IQ may not succeed as much academically, which would result in lower self-esteem and not as much financial success, resulting in an increased disposition toward criminal behavior. This would seem to highlight the importance of stressing education and addressing issues of learning disabilities at an early age to prevent, or at least mitigate, these negative attributes, thus preventing future criminal behavior and the resulting increased crime rates.

The connection between one’s intelligence level and his or her criminal behavior is a very complicated and controversial area. Empirical research most often finds that IQ and crime are actually negatively correlated; that is, as one increases, the other decreases. Explanations for this generally fall into three approaches: (1) IQ and crime are spuriously, not causally, correlated; (2) low IQ increases criminal behavior; and (3) criminal behavior actually decreases IQ.

There are also popular arguments against IQ as a cause of crime. Some scholars argue that standardized IQ tests measure only middle-class knowledge and values instead of innate human intelligence. As a result, the fact that most minority groups and impoverished populations score lower on IQ tests simply reflects their diverse cultural backgrounds. These same groups also commit proportionately more crime because they suffer structural disadvantages, such as poverty and discrimination. Consequently, the same people who score low on IQ tests also tend to commit more crime, and so IQ and crime are empirically correlated. Thus, this correlation is not causal but reflects only culturally biased testing of intelligence (see Gardner, 1993).

A variation of this argument holds that the structural disadvantages that increase crime rates also reduce educational opportunities, thus lessening individuals’ ability and motivation to score well on IQ tests. Many researchers argue that the IQ–crime correlation occurs only because both are rooted in structural disadvantage, which, in statistical terms, represents a spurious correlation at best. Although these discrimination-type hypotheses have wide appeal, they have received fairly little support in empirical studies, because IQ and crime are significantly correlated within race and class groups as well as when one statistically controls for race, class, test-taking ability, and test-taking motivation.

Another argument against IQ as a cause of crime holds that schoolteachers and administrators treat students differently according to their perceptions of the students’ intelligence, thus giving negative labels and fewer educational opportunities to those whom they see as less intelligent. These labels and constrained opportunities in turn produce feelings of alienation and resentment that lead students toward delinquent peers and criminal behavior. As such, society’s reaction to intelligence, and not any property of intelligence itself, increases criminal behavior. Unfortunately, few studies have adequately tested this labeling-type hypothesis (i.e., that deviance is derived from the labeling and mistreatment of certain individuals).

C. Education and Recidivism

Given the various aspects of this discussion, many people argue that the U.S. government should resume its long-standing policy of releasing a portion of Pell Grants (student educational grants) and other types of financial aid to qualified incarcerated individuals. They argue that the benefits of such a practice (reductions in recidivism rates) will always far outweigh the public protests against such efforts (arguing that this reduces the funds available to nonincarcerated individuals).

The focus of the pro-grant arguments is that resuming this policy would drastically decrease rates of recidivism and save individual states millions of dollars each year. Again, there seems to be overwhelming consensus among many people that postsecondary education is the most successful and cost-effective method of preventing crime. However, this often becomes controversial when one starts applying these ideas to people who have already committed criminal acts. More than 1.5 million individuals are housed in adult correctional facilities in the United States. The U.S. Department of Justice generally portrays offenders as impoverished and uneducated prior to incarceration. Inside American prisons, many adult inmates are illiterate, and many more are functionally illiterate.

Most researchers would argue that social, psychological, and demographic factors correlate strongly with recidivism. Most persons are released from prison into communities unskilled, undereducated, and highly likely to become reinvolved in crime. Rates of recidivism in the United States are extraordinarily high. Although prison-based education has been found to be the single most effective tool for lowering recidivism, today these programs are almost nonexistent. Many would also argue that prison education is far more effective at reducing recidivism than are boot camps, shock incarceration, or vocational training.

In response to the American public’s growing fear of crime and the call for more punitive measures to combat such fear, many legislators and policymakers have promoted building more prisons, enacting harsher sentencing legislation, and eliminating various programs inside prisons and jails. With rearrest rates increasing almost daily, it is clear that incarceration alone is not working in the United States. In fact, the “get tough” philosophy (originating in the mid-1980s), which pushes for more incarceration, punishment, and limitations of the activities available to prisoners, has often resulted in the elimination of strategies and programs that seek to prevent or reduce crime. As has been discussed repeatedly in this research paper, research has consistently shown that quality education is one of the most effective forms of crime prevention and that educational skills can help deter young people from committing criminal acts as well as greatly decrease the likelihood that people will return to crime after release from prison.

Despite this evidence of their extraordinary effectiveness, educational programs in correctional facilities have in many cases been completely eliminated. As of 2008, more than 1.6 million individuals were housed in adult correctional facilities in the United States, and at least 99,682 juveniles are in custody. The majority of these individuals will be released into communities unskilled, undereducated, and highly likely to become reinvolved in criminal activity. With so many ex-offenders returning to prison, it would seem clear that the punitive, incarceration-based approach to crime prevention has not worked as a basis for criminal justice policy in America. Therefore, it should not be surprising that so many people argue that the country needs to promote policies and procedures that are successful. Education, particularly at the college level, can afford individuals with the opportunities to achieve and maintain productive and crime-free lives and help to create safer communities for all.

A second overall perspective on the concept of education and crime is to examine the impact of crime on education. As with education’s impact on crime, crime’s impact on education has several directions from which it can be approached. The following sections discuss crime as a barrier to educational opportunity and attainment as well as briefly consider school safety issues.

A. Crime as a Barrier to Educational Opportunity

One of the major areas in which crime’s impact on education can be found is in how crime very often serves as a barrier to educational opportunity for many people. This barrier status can appear from two directions: (1) the negative mobility patterns for some groups in terms of traditional and nontraditional criteria for upward movement and educational achievement and (2) individuals’ lack of opportunity for educational attainment due to their own criminal behavior (e.g., incarceration, dropping out of school, and expulsions).

For many people, going to college or achieving higher levels of education is an unrealistic goal because of financial constraints or living conditions; instead, daily survival is of utmost concern. Many of these individuals have had to drop out of school at an early age to help support their families and/or take care of younger siblings; for others, their own criminal behavior became a barrier to their future educational attainment. Incarcerated individuals obviously have very few opportunities (if any) above remedial instruction that generally leads to a GED. Others, because of their behavior, have been forced out of their local schools by suspensions and/or expulsions. As state budgets become more and more restrictive, educational programs in general have been eliminated or greatly decreased.

B. Crime’s Connection to High School Graduation

As stated previously, many individuals are forced to drop out of traditional K–12 educational programs because of their own criminal or delinquent behavior. These individuals usually start off with in-school suspensions, which evolve into out-of-school suspensions and, ultimately, to expulsions. In most states where the compulsory education age is 16, these individuals often find themselves forced to attend alternative educational programs. Research has supported the belief that the majority of these youth do not seek any postsecondary educational opportunities; many do not finish high school or GED programs.

Most, if not all, of the typical criminal or delinquent school behaviors, such as skipping school, drug use, violent behavior, and engaging in property crime, correlate strongly with a lack of high school graduation. Many educational systems across the United States have adopted a zero-tolerance policy stance when it comes to any type of negative student behavior. The primary result of these policies is expulsion from school of the delinquent child, and the primary result of most expulsions is that the individual never returns to school. Thus, lacking the proper educational attainment (and, possibly, intellect), he or she is not able to be competitive in most job markets. As stated earlier, a lack of employment is a major factor in an individual’s decision to turn to criminal behavior to meet his or her financial needs.

C. School Safety Issues

A final area of discussion is the very practical impact that crime can have on education. The scope of this research paper does not allow a full examination of the issues related to school violence and its results, but it would be improper not to mention this issue at least briefly. Readers would be well advised to seek further information about the various impacts of school violence on students and teachers. There are volumes of research dealing with the most common forms of school violence: sexual harassment and bullying. These two issues alone, many people would argue, are responsible for a great deal of high school dropouts, assaults, and even school shootings.

School safety and the proper protection of students are very strongly connected to crime. The more crime a school has, the less safe the students are going to feel, and the less secure they feel, the less they will learn. When students have to worry about their safety on a daily basis at a school, the academic experiences very often get left behind. Most people would agree that learning becomes secondary very quickly when a child has to worry more about death then failure in the classroom.

Many of the connections that crime has with K–12 education relate to incidents that occur between students. There is a significant problem with bullying and sexual harassment on the campuses of many American schools. These acts, although not obviously violent, many times go unnoticed and can have an extremely negative impact on the victims. As previously stated, such treatment has been connected to high dropout rates, failing grades, and even juvenile suicides.

VI. Conclusion

It is extremely difficult to argue against the philosophy that substantial savings on the social costs of crime could be obtained by investing in education. Empirical research repeatedly has supported the theory that the likelihood of a person committing a criminal act decreases with years of education, although research also has found that the probability of committing some types of acts (e.g., tax fraud and embezzlement) actually increases with years of education.

It is also interesting to find that more highly educated people very often have more permissive attitudes and social norms toward criminal behavior. One possible reason for this is that they are confronted less frequently with criminality and are less likely to be victims of a violent crime. It is a known fact that criminality tends to be higher in areas where less-educated people live. A second reason for more permissive attitudes and social norms toward criminality might be that more highly educated people have a more liberal worldview in general. It also is a known fact that people with higher education generally earn more than less educated people and thus have a better, and safer, quality of life.

The potential benefits of, and access to, certain types of criminal behavior simply increase as one’s earnings increase. Activities such as money laundering and insider trading often do not concern people who have no or very little funds. A second explanation is that more highly educated people are simply more knowledgeable and more informed about the possibilities of committing certain types of white-collar crimes. Thus, criminologists often point out that the key to white-collar or upper class criminal behavior is access (i.e., to funds, to inside information).

This is also true with blue-collar types of criminal behavior (e.g., shoplifting, vandalism, and violent street crimes). Research has supported the realization that most often these types of acts are committed by people with lower levels of education. One explanation is that people with less education have a “higher time discount”—that they see the future and calculate it differently than do people with more education. Moreover, they very often take into account the future consequences of their actions (punishment and sentencing) less than more highly educated people.

A final few notes on this subject should be pointed out from the discussion earlier about views on time consumption. It is argued that education leads to a lower time preference for consumption in the present and a higher time preference for consumption in the future and that, in turn, education very often teaches people to control their emotions (restraint and self-control). Most scholars hope that higher education attainment will lead to more intelligence, which will lead to more understanding of the consequences of one’s actions, whether positive or negative.

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Education Policy and Crime

This paper discusses the relationship between education and crime from an economic perspective, developing a human capital-based model that sheds light on key ways in which early childhood programs and policies that encourage schooling may affect both juvenile and adult crime. The paper first discusses evidence on the effects of educational attainment, school quality, and school enrollment on crime. Next, the paper discusses evidence on the crime reduction effects of preschool programs like Perry Preschool and Head Start, school-age programs that emphasize social and emotional development, and job training programs for low-skill adolescents and young adults. Finally, the paper concludes with a broad discussion of education policy and its potential role as a crime-fighting strategy.

For their comments and suggestions, I thank David Card, Phil Cook, David Deming, Jens Ludwig, and participants at the NBER Economics of Crime Control Conferences in Boston, MA, and Berkeley, CA. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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Education Policy and Crime , Lance Lochner. in Controlling Crime: Strategies and Tradeoffs , Cook, Ludwig, and McCrary. 2011

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Preschool Education, Educational Attainment, and Crime Prevention: Contributions of Cognitive and Non-Cognitive Skills

Arthur j. reynolds.

1 Institute of Child Development, Minneapolis, MN 55455

Judy A. Temple

2 Humphrey Institute of Public Affairs and Department of Applied Economics, Minneapolis, MN 55455

We investigated the extent to which cognitive and noncognitive skills accounted for the measured links between participation in preschool intervention and high school completion, highest grade completed, and incarceration history in early adulthood. Using data from the Chicago Longitudinal Study, an on-going 20-year investigation of the effects of the school-based Child-Parent Center early intervention program for over 1,500 children, we assessed the contribution of school readiness and achievement test scores up to age 14 and remedial education as well as measures of social adjustment, motivation, educational expectations, problem behavior, and juvenile arrest to the estimated direct effect of preschool. Hierarchical regression analysis indicated that when assessed separately, cognitive factors accounted for 42% of the preschool effect on high school completion, 37% on highest grade completed, and 23% on incarceration history by age 24 while noncognitive factors accounted for, respectively, 36%, 45%, and 59%. Together, cognitive and noncognitive factors explained 46%, 51%, and 59% of the main effect of preschool participation. The set of cognitive skills made greater value-added contributions to educational attainment while noncognitive skills made greater value-added contributions to incarceration history. Our findings support the important role of test scores, school performance, and social and motivational factors in explaining the effect of preschool participation on economically important indicators of well-being.

Studies from many disciplines report high rates of return to investments in high quality preschool programs, especially for children from low-income families. Temple and Reynolds (2007) , Rolnick and Grunewald (2003) , and Karoly, Kilburn, and Cannon (2005) describe and synthesize this literature. Enriched preschool programs, which broadly include home visitation programs by nurses and educational daycare as well as preschool education programs for 3- and 4- year olds, have demonstrated significant long-term effects in terms of reduced need for school remediation, higher educational attainment, greater economic well-being, and reduced crime (e.g., Barnett and Masse, 2006 ; Nores, Barnett, Belfield, and Schweinhart, 2005 ; Reynolds, Temple, Robertson and Mann, 2002 ; Currie, 2001 ). Results of cost-benefit analyses of early childhood programs suggest benefit-cost ratios in the range of 4 to 10 or higher ( Reynolds & Temple, 2008 ).

RESEARCH CONTEXT

Economists, psychologists, and preventionists conducting research on early childhood interventions have focused on two lines of inquiry. First, they have investigated the timing of program investments and have concluded that certain types of early investments are likely to have greater rates of return or levels of effects than later investments. When discussing the cumulative process of skill formation and the relative importance of early human capital investments, Heckman (2000) described this as “skill begets skill.” This is consistent with the developmental and educational literature on the cumulative advantages of positive early experiences ( Zigler & Berman, 1983 ; Woodhead, 1988 ) and the generative mechanisms of direct and indirect effects (Berrueta-Clement et al., 1984; Consortium for Longitudinal Studies, 1983 ; Reynolds, 1993; 2000 ; Schweinhart, Barnes, & Weikart, 1993 ).

Second, and consistent with objective of this paper, economists and developmental researchers are investigating the pathways through which early investments in education lead to higher educational attainment and general well-being in adulthood. Historically, early interventions were designed to provide educational enrichment to children with special needs or to children from economically-disadvantaged families (Ramey & Ramey, 1992). The major goal was to enhance cognitive skills, as measured by achievement tests or language assessments. As described in Consortium for Longitudinal Studies (1983) and Campbell et al. (2002) , early studies demonstrated that participation in intervention was linked to higher cognitive skills at school entry. The cognitive advantage created by participation in intervention was then found to account for longer-term effects on school achievement and need for school remedial services. Later research on the well-known Perry Preschool Program suggests that participation in preschool intervention caused lower rates of delinquency and crime and improved economic well-being in adulthood. Through path analysis, Perry researchers have consistently found that these results are primarily due to the process of cognitive advantage (Berrueta-Clement et al., 1984; Schweinhart et al., 2000; 2005 ). Noncognitive skills such as motivation and social adjustment contributed only as a consequence of the initial cognitive advantage. The focus on the broader program goal of social competence was being established ( Zigler & Tricket, 1978 )

More recent studies have tested a wider array of cognitive and noncognitive factors that explain early intervention effects. In the first empirical test of alternative explanations for the long-term effects of preschool intervention, Reynolds, Ou, and Topitzes (2004) , using structural modeling with data in the Chicago Longitudinal Study, found that while cognitive advantage, measured by kindergarten achievement test scores, independently accounted for about a quarter of the total effect of preschool on educational attainment and juvenile arrest, family behavior and school support factors each accounted for similar proportions of the total effect of preschool. Social adjustment and motivation factors, which are most closely linked to noncognitive skills, accounted for very little of the total effect of preschool, especially on educational attainment.

Nevertheless, Heckman (2000) and Heckman and Masterov (2004) argue that the longer-term effects of the Perry Preschool Program and other successful early interventions are due primarily to the effect of the interventions on noncognitive skills. In contrast to developed abilities such intellectual aptitude or achievement test scores, noncognitive skills include motivation, commitment, socio-emotional attributes, and attitudes.

To strengthen their argument that noncognitive skills are an important determinant of adult outcomes, Heckman, Stixrud, and Urzua (2006) examine a wider array of cognitive and noncognitive factors that are associated with educational attainment and other adult outcomes such as crime or earnings. Using data from the National Longitudinal Survey of Youth (NLSY), Heckman et al. estimate the relative importance of cognitive skills as measured by an Armed Forces Qualifications Test score and noncognitive skills as represented by a measure of local control and a measure of self-esteem. They argue that cognitive and noncognitive skills both are important in explaining a large number of economic outcomes in adulthood, although their relative importance varies according to outcomes and individual characteristics. Heckman et al. suggest that the findings regarding noncognitive skills in particular have important policy implications because they believe that these skills can be greatly affected by early childhood interventions.

CURRENT STUDY

The research in this paper builds upon the Heckman et al. (2006) study and other prior research ( Consortium for Longitudinal Studies, 1983 ; Reynolds, 2000 ; Schweinhart et al., 1993 ) in two ways. First, we employ a larger number of both cognitive and noncognitive measures which are measured at different points in time. We use achievement test scores, need for remedial education, and a comprehensive set of noncognitive factors, including social and emotional learning, motivation, and attitudes. Second, because we use a longitudinal data set that was designed to study the impact of an early childhood intervention, we are able to examine the effects of early intervention of the wide array of cognitive and noncognitive skills and then see how these skills are related to educational attainment and crime. While Reynolds, Ou and Topitzes (2004) investigated several competing hypotheses of preschool effects in the Chicago Longitudinal Study (CLS), several limitations restrict interpretations. First, measures of social adjustment and motivation were restricted to classroom social adjustment as rated by teachers and self-perceptions of school commitment. Second, the temporal order of influences, based on established theory, was presumed to be from cognitive to noncognitive and not the reverse. Moreover, the outcomes of educational attainment and crime were measured no later than age 20, leaving open the question of whether identified paths continued to be influential for early adult well-being.

In this paper, we use data from the Chicago Longitudinal Study ( CLS, 2005 ) to investigate the importance of many measures of cognitive and noncognitive skills that have been hypothesized to account for the effects of preschool participation. Two major questions are addressed:

• Does participation in the Child-Parent Center preschool program affect measures of cognitive and noncognitive skills above and beyond child and family background factors?
• To what extent, both separately and together, do measures of cognitive and noncognitive skills account for the estimated direct effects of Child-Parent Center preschool participation on high school completion and incarceration in young adulthood?

The CLS has followed a cohort of children born to families living in high-poverty neighborhoods in Chicago between 1979 and 1980. In this quasi-experimental study, almost a thousand children participated in the Child-Parent Center early education program and a matched group of children from randomly selected schools participated in full-day kindergarten programs, which was the usual educational treatment at the time for low-income children. The study has collected a rich set of cognitive and noncognitive measures beginning in kindergarten and continuing on a yearly basis up to age 15 from teachers, parents, children, and from standardized tests of reading and math skills given annually by the school district. These data are an unusually rich source of information of the various skills students acquire as they progress through school.

As the students in the CLS have reached adulthood, a number of important student outcomes have been collected including measures of educational attainment and criminal behavior. These data are well-suited for examining the pathways through which participation in an enriched program of preschool intervention affects early adult outcomes. Our study investigates the effects of preschool participation on various cognitive and noncognitive skills and suggests the relative importance of these skills in determining educational attainment and crime.

We consider cognitive skills as represented by test scores but also by special education placement for specific learning disabilities and by grade retention. The achievement test scores include school readiness scores on the Iowa Tests of Basic Skills (ITBS), word analysis and math achievement at the end of kindergarten, and reading comprehension and math achievement up to eighth grade. Children’s noncognitive skills are defined as the social, emotional, and attitudinal aspects of learning and development. Through self-assessments and ratings by teachers and parents between third and tenth grades, we included school commitment, achievement motivation, expectations for educational attainment, classroom social adjustment, problem behaviors in schools and official juvenile arrest. Other related skills and assets were not assessed in the study, including school and community attributes, peer influences, and family socialization and parenting behavior. However, parent involvement in children’s education was used to test the robustness of the effects on cognitive and noncognitive factors. See Reynolds and Temple (2005 , 2008 ) for additional perspectives on contributing factors.

Sample and Data

The study sample was drawn from the Chicago Longitudinal Study ( CLS, 2005 ), an ongoing investigation of a panel of low-income minority (93% African American; 7% Hispanic/Latino) children growing up in high-poverty neighborhoods in Chicago. The original sample (N=1,539) included 989 children who attended the CPC preschool program and 550 children who participated in alternative public programs, all of whom entered matriculated kindergarten in the Chicago Public Schools in 1986.

Data were collected since birth from various sources, such as study participants, parents, teachers, and administrative records from schools and other agencies ( Reynolds, 2000 , Reynolds et al., 2001 ; Reynolds et al., 2007 ). Educational data were gathered from elementary, secondary, and post-secondary schools attended by participants. Administrative records of county-level adult arrest and incarceration histories were collected from four states in the Midwest. Incarceration records were obtained state departments of corrections and county level court data. The sample size for the present study included 1,368 participants for whom educational attainment (892 program and 480 comparison cases) and 1,413 participants with known incarceration status (913 and 500 comparison cases). Recovery rates for the adult outcomes were roughly 90% for the program and comparison group with no suggestion of differential or selective attrition.

CPC Program Description

Since the CPC program is fully described in previous reports ( Reynolds, 2000 ; Reynolds et al., 2004 ), we provide a summary of the main features. Located in or close to elementary schools in the Chicago public school system, the preschool program provides educational and family-support services to children beginning at age 3 or 4. Within a structure of comprehensive services similar to Head Start, the acquisition of basic skills in language arts and math is emphasized through relatively structured but diverse learning experiences that include teacher-directed, whole-class instruction, small-group and individualized activities, and frequent field trips. Literacy experiences involving word analysis, oral communication, and listening skills are highlighted. All teachers in the half-day preschool program have bachelor’s degrees and are certified in early childhood education. Classes include 17 children and 2 staff members (teacher and aide). Each center is run by a head teacher, and a parent resource teacher implements the parent room activities in cooperation with the school-community representative. After a half- or full-day kindergarten, school-age services also are provided up to third grade in the elementary schools. Consequently, we include such participation as a covariate in the analysis. Since the comparison group had all-day kindergarten, this component of the program is not assessed. Families in CPC neighborhoods participated at a high rate (over 80%) which helps ensure that findings are representative of eligible children rather than sample selective.

Adult Outcomes

Two types of educational and social outcomes are examined in this paper for the students in the Chicago Longitudinal Study. These include educational attainment by age 24 and adult incarceration status from ages 18 to 24.

Educational Attainment

Two measures of educational attainment were used: high school completion and highest grade completed. High school completion is a dichotomous variable indicating whether youths completed their secondary education with an official diploma or were awarded a General Education Development (GED) credential by August 2004 (N=1,372). Highest grade completed is an ordinal criterion ranging from 7 to 16 (N=1,368). Participants who obtained a GED were assigned a value of 12 while college attendance was coded depending on the number of credits earned. Thirty credits were treated as one year of college attendance.

Adult crime

Crime was measured through a dichotomous indicator: any history of incarceration or jail from age 18 to 24.. The adult incarceration sample (N=1,413) includes all participants who completed the CLS adult survey, whose criminal arrest or incarceration records were available, or who were verified as residents of Illinois after age 18 based on school records, project tracking, and public aid records. Most incarcerated cases served their sentence in state correctional institutions. Only participants who were in jail 30 days or more were coded as incarcerated.

Measures of Preschool Participation

Child-parent center participation.

CPC program participation was measured through two dichotomous measures: preschool participation and follow-on participation . Follow-on participation indicates whether the student received school-age services from first to third grades, primarily consisting of small class sizes in special classes offered in the CPCs. Because the focus of the present study is to examine the relationship between preschool participation and youth outcomes, this additional participation in the grade school years is included in the analysis as a covariate. The program variables were measured from school records and verified by project investigators school by school.

Measures of Cognitive and Noncognitive Skills

Cognitive skills/school achievement.

Entering kindergarten cognitive readiness (age 5) was measured by the Iowa Test of Basic Skills (ITBS; Hieronymus, Lindquist, & Hoover, 1980 ). The test measures a broad array of readiness skills, including listening, word analysis, vocabulary, language, and mathematics. Each subscale had approximately 30 pictorial items. Internal consistency reliability was .94. As with the measures below, ITBS developmental standard scores were used in the analysis and they have equal-interval scale properties.

Word analysis in kindergarten

Word analysis in kindergarten was measured by the ITBS scale at age six. The scale consists of 35 items evaluating prereading skills, such as letter-sound recognition and rhyming. The math subtest scale in kindergarten included 33 items assessing numbering, classification, and quantification. Internal consistency reliability was .87 for word analysis, and .82 for math test. Research has confirmed the measure’s predictive validity for later achievement ( Reynolds, 1991 , 2000 ).

ITBS reading and math scores at age 12 and 14 were also included as measures of school achievement. At age 12, the ITBS reading comprehension subtest includes 54 items on interpreting test passages. The mathematics total subtest contains 101 items on computation, concepts, and problem-solving skills. Both subtests have high reliability (KR-20s > .90). At age 14, the ITBS reading scale in a continuous measure comprised of 58 items that emphasized understanding of text passages (alpha = .92). The math test contained 117 items assessing conceptual domains, computation, and problem solving. The reliability was .95 ( Reynolds, 2000 ).

Grade retention

Grade retention reflects all individuals who ever repeated a grade between ages 6 and 14. Any learning disability special education placement reflects all individuals who received special education due to learning disability between ages 7 and 18. Children who were made to repeat a grade typically did so because of poor academic performance, and so grade retention and special education placement are used here as additional ways of measuring cognitive abilities.

Classroom adjustment

Classroom adjustment was measured on a six-item scale rated by teachers from first grade through sixth grade (ages 7 to 12). The scale asks teachers to rate children from poor/not at all (1) to excellent/very much (5) on items including “concentrates on work,” “follows direction,” “is self-confident,” “participates in group discussion,” “gets along well with others,” and “takes responsibility for actions” (alpha = .91). Three composite measures were created from the annual scores: average of ages 7 to 9 (grades 1 to 3), average of ages 10 to 12 (grades 4 to 6), and average of ages 9 to 12 (grades 3 to 6).

Perceived competence

Perceived competence was measured through a self-concept scale including task persistence on a 10 to 12-item (slightly different from year to year) scale coded from strongly disagree (1) to strongly agree (4). Average of internal consistency of the scale is .75. Examples of the items are like “my classmates like me”, “I get along well with others”, “I am smart”, and “I try hard in school”. The scores were transformed into z -scores first, and 2 average scores were used: average of ages 9 and 10 and average of ages 9 to 12.

Intrinsic motivation

Intrinsic motivation between ages 9 and 12 was measured through student ratings on 14 items, such as “I get bored in school”, “learning is fun”, and “I like to learn things”. Items vary year by year. A total score for each year was calculated by summing the ratings for all available items. The total scores were then transformed into Z-scores, and averaged across ages 9 through 12.

Troublemaking behavior

Troublemaking behavior between ages 9 and 12 was measured through student ratings on 4 items (“I get in trouble at school”, “I get in trouble at home”, “I follow class rules”, and “I fight at school”) related to their behavior at school and home. Each item was measured on a three-point scale (1= not much, 2= some, 3= a lot) in ages 9 and 10 and a four-point scale (from 1 = strongly agree to 4 = strongly disagree) in ages 11 and 12. A total score for each year was calculated by summing the ratings for all four items. The total scores were then transformed into Z-scores, and averaged across ages 9 through 12.

Acting out behaviors

Acting out behaviors between ages 12 and 13 were measured through a teacher-rated 6-item scale from the Teacher-Child Rating Scale (T-CRS), including “disruptive in class”, “fidgety, difficulty sitting still”, “disturbs others while they are working”, “constantly seeks attention”, “overly aggressive to peers (fights)”, and “deviant, obstinate, stubborn. Response categories ranged from (1) not at all to (5) very well. The internal reliability was .94. The continuous measure was recoded into a dichotomous variable indicating if one is 1 standard deviation above average score.

Assertive social skills

Assertive social skills between ages 12 and 13 was measured through a teacher-rated 5-item T-CRS scale, including “defends own views under group pressure”, “comfortable as leader”, “participates in class discussions”, “expresses ideas willingly”, and “questions rules that seem unfair/unclear”. The internal reliability was .87.

Teachers rated participants on peer social skills on a 5-item T-CRS scale (e.g. “has many friends”, “is friendly towards peers,” and “well liked my classmates”) at age 12 (alpha = .93) and age 13 (alpha = .92). The scale ranged from not at all (1) to very well (5). The average score was used.

Task orientation

Task orientation between ages 12 and 13 also was measured through a 5-item T-CRS scale, including “completes work”, “well organized”, “ functions well even with distractions”, “works well without supervision”, and “a self-starter”. The internal reliability was .93.

Student expectations

Student expectations is a dichotomous variable indicating whether students expected to go to college or not. This measure is based on the item, “How far in school do you think you will get?” from student survey at age 10. If students were missing at age 10, an age 16 response on the same item was used.

School commitment

School commitment was measured through a 12-item scale rated by students at ages 11, 12, and 16. Same items were included in the scale (e.g., “I try hard in school”, “I like school”, “I give up when school work gets hard”). Two items were asked only at age 16 (10 th grade): “I learn a lot at school” and “there are many things about school I don’t like”. The items were rated on a four-point scale coded from strongly disagree (1) to strongly agree (4) (alpha= .74 for age 11, alpha = .78 for age 12, alpha= .79 for age 16). The average score of ages 11 and 12 was used, and if one was missing from both, the score at age 16 was used. Overall, the scores ranged from 25 to 64.5 with a higher score indicating greater motivation.

Any juvenile

Any juvenile arrest codes participants 1 if they had an official delinquency petition before age 18; otherwise they were coded 0. Data came from administrative records of the Cook County Juvenile Court in Illinois and a few other locations in Illinois and Wisconsin. To be included in the analysis, youth had to be living in Chicago at any time between ages 10–18.

Parent involvement

Parent involvement between ages 7 and 12 measures the frequency of parent participation in school from grades 1 through 6. Teachers rated “parent’s participation in school activities” from poor/not at all (1) to excellent/much (5). The total scale ranged from 0 to 6, reflecting the number of times that parents were given “average or better” ratings (average or better is a score of 3 or higher; Min. = 0, Max. = 5) for parent involvement. We dichotomized the ratings to minimize the possibility of halo effects or other method artifacts from teacher reports.

A number of dichotomous sociodemographic measures were included in all analyses as covariates, including race/ethnicity, gender, maternal education, free lunch eligibility, single parent status, teen parent status, family size, public aid receipt (AFDC), and status of child welfare case history by child’s age 4. For several explanatory variables, participants with missing values were imputed through multiple imputation procedures using the Expectation-Maximization algorithm (Schaefer, 1997), and a dummy variable equal to one when these data are missing is included as an additional control in the regression analyses. Only one outcome variable, kindergarten cognitive school readiness, had a significant number of cases imputed. Intercorrelations among variables as well as program and comparison group mean differences were similar before and after imputation suggesting missing data were relatively unsystematic in the study sample. These covariates were included in the estimation of main effects and to assess the contribution of the mediators.

Data analysis

We used multiple regression to analyze highest grade completed and probit regression to analyze the dichotomous dependent variables high school completion and incarceration status. Analyses were conducted in STATA (Stata Staff, 2003). To address the first question (Does CPC preschool participation affect cognitive and noncognitive skills?), the measures of skills were regressed on preschool participation, school-age participation, and the set of demographic control variables. To address the second question (Do cognitive and noncognitive skills mediate or help explain the estimated main effect of CPC preschool participation on high school completion and incarceration status?), we conducted a hierarchical analysis that included cognitive and noncognitive mediators separately and together in four models. First, only the cognitive measures were included and they were included one by one. Second, only the noncognitive measures were included, also one by one. Third, the cognitive measures were entered first, and then the noncognitive measures. Finally, the noncognitive measures were entered and then the cognitive measures. This change of order allows an assessment of the added contributions of one set above and beyond the other. Consistent with the difference in difference meditational approach ( MacKinnon, 2008 ), the change in the preschool coefficients were examined in percentage terms. For other approaches, such as structural equation modeling see MacKinnon (2008) and Joreskog (1996) . Reynolds et al. (2004) used the structural modeling approach. Because previous studies ( Reynolds et al., 2001 , 2007 ) have established that preschool participation is significantly and independently associated with educational attainment, arrest and incarceration, we emphasize program links to the cognitive and noncognitive mediators and the explanation of this overall main effect.

Descriptive Statistics

Table 1 presents the descriptive statistics of the key measures described in the previous section. We analyze Iowa Test of Basic Skills (ITBS) test scores at various ages. We have test score information available at both kindergarten entry and at the end of kindergarten. We also focus on later test scores for math and reading for students aged 12 and 14. These ages generally correspond to grades 6 and 8. Table 1 also shows the means and other statistics for the large number of noncognitive measures we employ in our analysis. At the bottom of the table, the descriptive statistics for educational attainment and crime are presented. Approximately 75% of the sample either graduated from high school or received the GED as of age 24. Almost a quarter of the sample had spent more than 30 days in jail.

Descriptive Statistics for Explanatory and Outcome Measures

Table 2 shows the unadjusted means on cognitive skills/school achievement and noncognitive skills by groups. There are significant differences between the preschool group and the comparison group on all measures of cognitive skills/school achievement. The preschool group has higher test scores than the comparison group. The preschool group also has lower rates of grade retention and special education placement for learning disability than the comparison group. In terms of noncognitive skills, the group differences are significant for most of the measures except perceived competence, intrinsic motivation to learn and student expectations of attending college. The preschool group has higher scores on classroom adjustment, assertive social skills, peer social skills, task orientation, and school commitment than the comparison group, and has fewer troublemaking behavior and lower rates of acting out and delinquency than the comparison group.

Unadjusted Means of Cognitive and Noncognitive Skills by CPC Preschool Participation

Note . Sample sizes are from the original study sample. Sample sizes for high school completion, highest grade completed, and incarceration were, respectively, 1,372 (892 and 480), 1,368 (889 and 479), and 1,413 (913 and 500).

Program Estimates for Cognitive and Noncognitive Skills

We first estimate examine the relationship between preschool participation and the accumulation of cognitive and noncognitive abilities measured in the years after preschool. Tables 3 and ​ and4 4 show these results. In the quasi-experimental design of the CLS, preschool children were matched with very similar children from equally poor (actually, slightly poorer) neighborhoods that did not contain the Child-Parent Center intervention sites. The comparability of these groups been extensively examined in previous studies and no evidence of selection bias has been found (e.g., Temple and Reynolds, 2007 and Reynolds and Temple, 1995 ). As a result, we interpret the results in Tables 3 and ​ and4 4 as suggesting the causal effects of preschool participation on the cognitive and noncognitive abilities.

Adjusted Program Group Means and Marginal Effects for Cognitive Outcomes

Note. Adjusted for gender, race, single parent status, maternal education, mother was a teen parent, number of children, TANF, free lunch, mother unemployment, income60, missing of individual indicators, child welfare history by age 4, and follow-on participation. Kindergarten achievements were not adjusted for follow-on participation.

Adjusted Program Group Means and Marginal Effects for NonCognitive Outcomes

Note. Adjusted for gender, race, single parent status, maternal education, mother was a teen parent, number of children, TANF, free lunch, mother unemployment, income60, missing of individual indicators, child welfare history by age 4, and follow-on participation.

Table 3 reports the effect of preschool participation on 9 different cognitive outcomes. Seven OLS regressions were employed for the continuous test scores measures, and two probit regressions were run to analyze the outcomes of any retention and any special education placement. For all the regressions reported in this paper, specifications accounting for within-site intervention correlations in errors are not shown because the within-site correlations are low and the results with robust standard errors differ little from the OLS results. In Table 3 , the adjusted group means show the mean level of the cognitive outcome for the preschool group and the non-preschool group, controlling for 12 or 13 socio-demographic variables listed in the note to the table. The p-value reported for the estimated marginal effects of preschool indicates that all group differences are significant at the 5% level or below. Most of these results are similar to those reported in previous studies of the CLS such as Reynolds et al. (2005) and Reynolds and Temple (1995) , but some differences may exist due to the fact that some of the socio-demographic control variables have only been recently added to the study. Temple and Reynolds (2007) discuss some of the new information on family backgrounds that have been added to the CLS in recent years as more administrative data have become available.

In Table 3 , the effects of the preschool program on test scores suggest that preschool participants have scores that are 3 to 5 points higher than non-participants. This roughly corresponds to roughly a 5 or 6 month difference in kindergarten and a 4 or 5 month difference as of grade 8. Students who participated in the preschool program have almost a 6 percent point reduction in the probability of being made to repeat a grade. Preschool participants have a 3 percentage point reduction in the probability of being placed in special education, which corresponds to almost a one-third reduction in this probability (from 9.5% to 6.3%).

The findings that preschool participation can affect academic performance is not surprising because the preschool is an educational intervention with a focus on early literacy activities. In Table 4 , however, we examine the effect of preschool on over a dozen measures of non-cognitive abilities. Table 4 reports the results of 15 separate regressions. Twelve of the outcomes are continuous and were measured using OLS, while the remaining dichotomous measures were analyzed using probit analysis. The effects of preschool participation on these non-cognitive skills are not as strong overall as the academic results reported in Table 3 . However, many of the effects of preschool on these skills are significant at the 10% level or below. Importantly, teacher ratings of classroom adjustment for ages 7–9 appear to be significantly affected by preschool participation. Other strong effects were found for peer social skills, school commitment, delinquency, and parental involvement. Note that all signs on the estimated effects of preschool are in the expected direction. Taken as a whole, the results in Table 4 suggest that a program of enriched preschool participation for children from disadvantaged families has an effect on a wide range of non-cognitive abilities observed over both the early and later years of schooling. In the tables that follow, the role of both the cognitive and non-cognitive skills in determining important adult outcomes of educational attainment and crime will be examined.

Initial Contributions of Cognitive and Noncognitive Skills

Since CPC preschool participation is significantly associated with adult well-being, we use a hierarchical regression approach to estimate the contributions of cognitive and noncognitive skills to effect of preschool. A key metric in this approach is the percentage reduction in the estimated main effect of preschool associated with a particular indicator or set of indictors of cognitive/noncognitive skills. A cognitive/noncognitive indicator would completely account for the preschool effect if after inclusion, the preschool coefficient was reduced close to 100% or at a minimum the included cognitive/noncognitive skill would change the preschool coefficient from statistically significant to nonsignificant.

Table 5 shows the estimated effects of preschool participation before and after inclusion of cognitive and noncognitive skills hypothesized to account for the observed effect. As shown in the first row of Table 5 , preschool participants had an adjusted rate of high school completion that was 8.7 percentage points higher than the comparison group. The preschool group also completed about a third of a year more of schooling, and had a 5 percentage point lower rate of adult incarceration.

Estimated Effects of CPC Preschool Participation after Including Cognitive and Noncognitive Skills One at a Time

Note. Coefficients are the estimated preschool effect after including one and only one indicator of cognitive or noncognitive skills.

The second and third blocks of Table 5 show the marginal effect of preschool after including cognitive and non cognitive skill indicators one at a time. Inclusion of variables one at a time provides a relative index of variable contributions without the construct confounding of simultaneous entry of all variables. Among cognitive skills, ITBS reading and math achievement at age 14 were associated with the largest reductions in the main effect of preschool on the three adult outcomes. This indicates they accounted for the largest share of the preschool main effect. Grade retention and special education for learning disabilities were generally associated with the smallest reductions in main effect, indicating they alone contributed the least to the explanation of preschool effects.

Among noncognitive skills, school commitment and juvenile arrest were associated with the largest reduction in preschool main effects. School commitment accounted for the largest share of the effect on high school completion while juvenile arrest accounted for the largest share of effects on highest grade completed and incarceration. Teacher ratings of classroom adjustment by third grade and self-reports of trouble making behavior by age 12 accounted for the smallest reduction in the main effect of preschool on high school completion whereas peer social skills and classroom adjustment accounted for the smallest reduction in effects on highest grade completed and incarceration, respectively.

Value-Added Contributions of Cognitive and Noncognitive Skills

As shown in Table 6 , we estimated the independent effects of cognitive skills on the relation between CPC preschool and adult outcomes controlling for noncognitive skills as well as the independent effects of noncognitive skills controlling for cognitive skills. Within each skill area, the sequential effects of different indicators also were assessed based the temporal order of measurement.

Hierarchical Estimates of Effects of CPC Preschool Participation by Skills Area and Sequentially Across Skill Area

Cognitive skills

In the first block of Table 6 , each cognitive indicator contributed to the explanation of preschool effects. Note that inclusion of only kindergarten achievement measures did not substantially reduce the size of the preschool coefficient. The largest reduction in effects occurred when all five cognitive measures were included in the model. For example, the set of cognitive indicators accounted for 42% of the main effect of preschool participation on high school completion, 37% of the main effect on highest grade completed, and 23% of the main effect of incarceration.

Noncognitive skills

Similarly, the full set of noncognitive skills accounted for the largest reduction in the size of the preschool coefficient. For example, the noncognitive skills accounted for 36% of the main effect of preschool on high school completion, 45% of the main effect on highest grade completed, and 59% of the main effect on incarceration. Not surprisingly, juvenile arrest status explained the largest percentage of the preschool main effect, especially for adult incarceration.

Table 6 , block 4 also shows that cognitive skills uniquely contributed to preschool effects on high school completion and highest grade completed above and beyond the influence of noncognitive skills. In contrast, the set of noncognitive skills contributed most to the explanation of the preschool effect on incarceration history (see block 3). Noncognitive skills made smaller unique contributions to educational attainment.

Together, the set of cognitive and noncognitive skills accounted for roughly one-half of the direct main effect of preschool on adult outcomes, including 46% of the effect on high school completion, 51% of the effect on highest grade completed, and 59% of the effect on incarceration.

In summary, both cognitive and noncognitive skills accounted for sizable shares of the link between preschool participation and adult outcomes. The inclusion of both sets of skills in the model made the largest contribution to the explanation of main effects. Nevertheless, 40 to 50% of the estimated direct effect of preschool was left unaccounted for by cognitive and noncognitive skills.

A number of studies in recent years have demonstrated that high-quality early intervention programs have long lasting effects into adulthood. Understanding how early education can produce long lasting benefits in terms of educational attainment, income, and other economic outcomes is an important area of current study. While there is a long history research in psychology and education of attention to the mechanisms of early education effects, cognitive explanations have predominated. The identification of the social-emotional, motivational, and family processes have been less developed ( Reynolds, 2000 ; Zigler & Berman, 1983 ). Heckman et al. (2006) and Heckman (2000) argue that the effects of early education on cognitive abilities only represents a relatively small portion of the overall effects of early education, and suggest that more research should be conducted on the effects of early education on noncognitive abilities. This focus has been apparent in some studies of long-term effects ( Reynolds, 2000 ; Schweinhart, Barnes, & Weikart, 1993 ).

As one of very few studies investigating the differential contributions of cognitive and noncognitive skills to preschool effects, our findings support the important role of test scores, school performance, and social and motivational factors in explaining the effect of enriched preschool on economically important indicators of well-being.

Using data from an ongoing investigation of the effects of preschool intervention for a large sample of children from low-income urban families, we find that participation in an enriched preschool program generates increases in both cognitive and noncognitive abilities throughout the school years. A strength of our study is the availability of a large number of noncognitive and cognitive measures obtained at different points in time. We find that while noncognitive skills are important for educational attainment, these abilities are especially important in explaining criminal activity as measured by incarceration history. Given the large benefits to society of policies that can reduce crime, it appears that high-quality preschool intervention can be an effective tool in the fight against crime by not only increasing educational attainment but also addressing deficits in noncognitive skills that are correlated with criminal activity.

Contributions to Knowledge

The identification of the cognitive and noncognitive skills that account for long-term effects helps advance the field in three important respects. First, given the consistency and strength of the mediators in accounting for long-term effects,, confidence that preschool programs like the CPCs promote youth and adult well-being is substantially increased. The theory of the CPC program, which was empirically corroborated, is that early enrichment of language and literacy through center-based education within a family supportive environment strengthens school success leading to greater well-being. One implication is that increasing access to preschool and other early intervention programs deserves higher priority. Although states have substantially increased investments in preschool, nationally only 20% of 4-year-olds attended state-funded programs in 2006 ( Barnett et al., 2007 ). Moreover, compared to the evidence on interventions for older children, evidence on the effects of preschool intervention is strong. Extensive longitudinal studies of other social programs are rare and those that have been conducted have not investigated processes of impact as thoroughly as early intervention ( Reynolds & Temple, 2008 ). Findings of this study show the benefits of high-quality programs and provide a framework for documenting and understanding long-term effects for other programs.

The second contribution is that the study identifies and clarifies the initiators of the impact of preschool that lead to later well-being. Initiating influences are those outcomes that are directly impacted by intervention and that affect other intervening outcomes culminating in better well-being. The literature on human capital formation emphasizes cognitive-academic skills and socio-emotional development (noncognitive skills) which determine later well-being ( Heckman, 2000 ) while ecological models incorporate family and school factors that contribute independently or in interaction with individual attributes ( Bronfenbrenner, 1989 ). Our study supports the important role of both cognitive and socio-emotional factors in accounting for the impact of preschool on adult well-being. To strengthen programs and sustain their effects, a focus on both sets of factors is necessary.

Finally, study findings provide further support for the generalizability of the identified mediators from preschool to well-being. In the CLS, key elements of the model have been found to be consistent between adolescence and adulthood and for different outcome domains spanning achievement to educational attainment, and criminal behavior. Moreover, other interventions that impact these processes would be expected to contribute to enhanced well-being. These could be independent or complementary of preschool. For example, interventions that prevent child maltreatment may have longer-term effects on health and well-being through impacting juvenile delinquency and school performance. Combined with other studies of pathways of influence ( Reynolds, 2000 ; Consortium for Longitudinal Studies, 1983 ; Schweinhart et al., 1993 ), the cognitive advantage hypothesis, for example, is a consistent explanation of long-term effects. More generally, early cognitive and scholastic advantages contribute to social and motivational gains that culminate in enhanced well-being.

Sources of CPC Impacts

As a high-quality program, CPC has many features that contribute to its effectiveness. Unlike most other early childhood programs, CPCs provide comprehensive services to children and families over ages 3 to 9 in a school-based setting. This context facilitates continuity and integration of services from preschool into the early grades. Second, as a public-school program, all teachers have 4-year degrees, are certified in early childhood, and are well-paid. Teachers have aides in each class and classrooms are limited to 17 children in preschool and 25 in the elementary grades. The parent program is the third key element. With a staffed parent resource room in each center, comprehensive family services and resources are provided and tailored to parent’s needs. Compared to other programs, parent involvement in children’s education is high. Indeed, this is one source of effects on school success ( Reynolds, 2000 ). Finally, with an instructional philosophy emphasizing school success, the activity-based curricula in literacy intensive within a teacher-directed classroom structure.

Based on the accumulated evidence, greater investments are warranted in ensuring that programs and interventions strategies are high in quality following the key principles of effectiveness found in the CPC and other cost-effective programs. Among these are the provision of services that (a) are of sufficient length or duration, (b) have high intensity, (c) have low class sizes and ratios of children to teachers, (d) are comprehensive in scope, and (e) are implemented by well-trained and compensated staff. Although adhering to these principles increases program costs, the benefits that follow in improved child well-being can be considerable. As found in this study, preschool participation was linked to higher educational attainment and lower rates of crime as a function of promoting both cognitive and noncognitive skills. Further studies are needed to identify complementary interventions and practices that strengthen the breadth of effects of preschool and enhance children’s learning experiences as they progress through the formative years.

Acknowledgments

Funding for this project has been provided by NIH grant RO1HD034294, the University of Wisconsin Graduate School, and the Doris Duke Charitable Foundation.

Coefficients of Explanatory Variables in the Cognitive, NonCognitive, and Combined Models

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Education and Crime Research Paper

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In modern societies, an individual’s life trajectory—including an individual’s involvement in criminal activity—has become increasingly determined by his or her educational experiences. Over the past few centuries, schools have in many ways come to challenge families as the primary site for childhood socialization. The expanding role of formal education in the lives of youth has many causes. Economic production has become more dependent on cognitive skills taught in schools. Work has become typically set off from home life, limiting parents’ ability to monitor and train children informally. Increasing female labor participation rates in recent decades have accelerated this trend, with over two thirds of mothers with children under age eighteen now currently employed. At the same time that work responsibilities have increasingly separated parents from their children, public education has been expanded to command greater portions of a youth’s time. At the beginning of the nineteenth century only about ten percent of U.S. individuals age fourteen to seventeen attended high school; by the end of the century, only about ten percent of young adults failed to complete high school. As recently as in the 1940s, less than ten percent of individuals attained a bachelor’s degree; by the end of the century, almost one-third of young adults were expected to attain such degrees. Not only have the number of years an individual is involved in a formal education system increased, but the amount of time per year has also dramatically expanded. The length of the school day has grown and the days in an academic school year have roughly doubled over the past century.

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Research has clearly demonstrated how an individual’s educational outcomes structure a wide range of adult life-course outcomes. Given the prominent role of education in an individual’s life, educational experience has both significant direct and indirect effects on criminality. Over the past decade, educational experience has come to mediate the influence of social background on occupational destinations. By the end of the twentieth century, educational attainment had come to replace social origins as the primary determinant of occupational status, earnings, and even one’s choice of marital partners. It is not surprising, therefore, that educational attainment plays a prominent role in explaining who is likely to commit criminal acts or subsequently to become incarcerated. Individuals who are incarcerated are less likely to have had previous success either in labor or marriage markets: about half of jail and prison inmates have never been married, close to half were unemployed prior to incarceration, and more than half had been living in poverty. More direct effects of educational experience are apparent when one examines the educational characteristics of those who are incarcerated. Only about 28 percent of incarcerated individuals in state and federal prisons have successfully graduated from high school (U.S. Department of Justice).

Schools play such a critical role in adult lifecourse outcomes because they affect individuals through several important social mechanisms. Schools are responsible for the socialization of youth. Schools work to train individuals for different roles in society and thus determine the selection of individuals for the allocation of scarce resources. Schools also structure an individual’s interpersonal interactions and associations. The criminological significance of these distinct educational functions will first be explored and then connected to the relationship between crime and variation in educational performance and the structure of schooling. Lastly, conclusions and implications about the relationship between education and crime will be identified.

Mechanisms Producing Education-Crime Associations

As youth increasingly spend time in educational (rather than family) settings, the role of schools in the socialization of children and adolescents increases. Schools provide the context where much of the drama of the maturation process now unfolds. Children and particularly adolescents struggle—often in interaction with school authority—to define themselves as individuals with distinct identities. Identity formation involves challenges in many social psychological domains, including moral development. Educational psychologists have long argued that a critical stage in the process of moral development occurs during adolescence. Youths struggle to create their own definitions of right and wrong, as well as their own place in such a moral order (see Gilligan; Kohlberg).

Émile Durkheim, one of the founding influences on modern sociology, devoted a significant portion of his writings to how schools contribute to this socialization process. In Moral Education: A Study in the Theory and Application of the Sociology of Education (1903), Durkheim argued that schools confront individual students as the embodiment of society’s moral authority. Youths learn in schools to respect society’s moral authority if the rules they confront do not appear arbitrary, unenforceable, or unjust. Durkheim argued that discipline is needed in education ‘‘to teach the child to rein in his desires, to set limits on his appetites of all kinds, to limit and, through limitation, to define the goals of his activity’’ (p. 43). Essential to Durkheim’s conception of the role of school discipline in the socialization of youth is his attention to the Hobbesian problem of order. The philosopher Thomas Hobbes argued that since individuals are governed by passions and desires, the threat of sanctions from a greater authority was necessary to constrain individual actions and promote social order. Durkheim countered that the strength of external sanctions was ultimately dependent on individuals internalizing these restrictions as normative rules. Durkheim argued that schools provide social settings whereby individuals are able to develop attachments to and integration with a larger societal moral order.

Durkheim’s insights were most effectively introduced into contemporary criminological research by Travis Hirschi. Following Durkheim’s insights, Hirschi was instrumental in developing criminological control theory , which has argued that individuals are subject to greater likelihood of criminal involvement when they have less attachment and integration with conventional authority. Since control theory owes its intellectual origins to earlier explorations of the role of schools in moral development, it is not surprising that—given the dramatic expansion of the role of schools in the lives of youth—much of the contemporary research from this perspective has emphasized the relationship between educational experience and criminality. Hirschi in later work with Michael Gottfredson argued that schools in fact were in many respects better situated than families to control and properly socialize youth. School personnel were argued to have a greater ability than family members to monitor, assess, and sanction youth misbehavior. School personnel were also claimed to have a greater incentive and need to control youthful behavior because of the large concentration of children and adolescents in close proximity to each other. Regardless of whether it has in any way replaced family-based socialization, involvement in schooling also serves an important role in the socialization of individuals. Schools provide youth with forms of attachment to conventional activities and thus increase an individual’s ability to resist the temptations of criminal behavior.

While socialization of youth is one of the primary mechanisms whereby a causal relationship develops between educational experience and crime, the role of the education system in training, selection, and allocation is also critical. Sociologists Max Weber and Pitrim Sorokin, writing in the first third of the twentieth century, highlighted the fact that schools not only were responsible for training individuals for specific occupational tasks, but more importantly schools also served as closure mechanisms preventing individuals from gaining access to lucrative subsequent occupational positions. A second primary function of schools is thus ‘‘to sort and sieve’’ students for either success or failure. Schools directly determine through grades and promotions which students will have access to privileged advanced training leading to coveted occupational positions in a society and which will instead face the greatest risk of economic hardship.

Criminologists have argued that since schools are involved in selection and the allocation of scarce resources, they are sites where individuals confront obstacles to their aspirations for upward social mobility. Social scientists such as Richard Cloward, Lloyd Ohlin, and Arthur Stinchombe have developed strain theories of delinquency that link criminal behavior to blocked and frustrated status attainment. To the extent that schools produce resistance and misbehavior associated with institutional barriers to adult occupational success, a second mechanism underlying an association between crime and education is identified.

In addition to socialization and selection, schools also function to structure patterns of individual interpersonal interactions and associations. Social scientists, such as George Simmel and George Herbert Mead, argued early in the twentieth century that interpersonal interactions and associations were critical dimensions of how individuals came to understand and act in society. Criminologists have applied these insights by focusing on two processes. First, researchers such as Edwin Sutherland argued that delinquency could result from patterns of differential association . Since schools can structure youth interaction through a variety of mechanisms, the likelihood of youth misbehavior could be increased or dampened through such a structuring process. Second, schools provide settings where individual interactions occur. Researchers have argued that personnel within formal institutions often engage in a labeling process . Students are argued to have negative labels applied to them, which carry social stigmas. Since this research tradition assumes that individual meanings are the product of the dynamics of social interactions, often students will accept the negative labels assigned to them by authority figures. Rather than labels being easily rejected by students as being erroneous, they instead are argued to often become self-fulfilling prophecies.

Crime and Educational Performance

Given the multiple mechanisms whereby schools can influence adult life-course outcomes, it is not surprising that researchers repeatedly and consistently have demonstrated that educational performance and commitment are both negatively associated with adolescent delinquency, adult criminality, and incarceration. The more education an individual has the lower the risk of both criminal behavior and penal sanction. The higher the score on standardized cognitive tests, which partially reflect school learning, the lower the risk of criminality. High grade point averages and positive student attitudes toward school also have repeatedly been demonstrated to reduce the likelihood of adolescent delinquency and presumably adult criminality. Youth records of school sanction for student misbehavior, such as expulsion and suspension, are also clearly associated with adult criminality (Laub and Sampson; Gottfredson and Hirschi; Wilson and Herrnstein). These patterns are consistent with various criminological theoretical expectations discussed above. Students who are successful in terms of test score, grade point average, and years of education, are: defined as ‘‘bright’’ and ‘‘good’’ (labeling theory); have generally high degrees of attachment to conventional school activities (control theory); face easier success in pursuit of their ambitions (strain theory); and often are segregated off from students who are disruptive (differential association).

Several important research efforts have documented the relationship between school performance and crime. In 1950, Sheldon and Eleanor Glueck published an influential study of delinquency that documented the early onset of delinquent behaviors. Nearly half the delinquent youth had identifiable behavior problems before entering the fourth grade. Individuals who demonstrate early onset of serious identifiable misbehavior are likely to have entered school predisposed to failure as a result of the absence of early childhood family socialization. Even for these students, however, it is likely that schools can serve to either reinforce or dampen their preexisting tendencies for misbehavior. In 1969, Travis Hirschi published a seminal study of delinquency that focused much greater attention on educational behavior than did the earlier study by the Gluecks. Hirschi surveyed over five thousand junior and senior high school students in the San Francisco Bay area. He found systematic evidence that school performance and attachment (as measured by cognitive test scores, grades, and attitudes toward school) each had significant effects on the number of self-reported delinquent acts. Hirschi attributed this pattern of results to variation in the extent to which students formed positive attachments to school authority and activities. In the early 1990s, criminologists John Laub and Robert Sampson extended Hirschi’s work, demonstrating that school attitudes and performance (as measured by grades) affect delinquency rates.

Variation in The Structure of Schooling and Crime

Years of educational attainment, cognitive test score, student grades, and attitudes toward school, however, are only a small part of how schools structure adolescent experience. Educational research demonstrates that other school factors—such as curriculum, resources, and school peer climates—also strongly influence a student’s life chances. While numerous studies have examined the overall effect of schooling on deviance and crime, much of the existing criminological research has largely ignored the actual character of schooling. Criminological research has only begun to provide a more pedagogically sensitive examination of an adolescent’s involvement with educational institutions. Such an examination requires a more complete elaboration and specification of the high school context that serves to diminish or increase the probability of criminality. Educational research has begun to inform criminological investigation by focusing on the role of vocational education, educational resources, and peer climates in affecting the incidence of delinquency, crime, and incarceration.

Vocational Education

Vocational programs were instituted and expanded in high schools based on proponents’ claims that occupational course work would reduce unemployment, crime, and deviant behavior in young adults. Criminological research has suggested mixed evidence on whether these programs have actually served to reduce individual propensity for criminal behavior. Because vocational education can function to segregate lowachieving students in particular courses either within a school or actually in a separate school within a larger district, many criminologists are skeptical that any positive effects of the programs can emerge. Setting vocational students off from academic students could lead to detrimental patterns of differential association or the labeling of vocational students as ‘‘less able’’ or as ‘‘youthful troublemakers.’’

It is important to note, however, that such negative effects are conditional on the actual structure of how vocational programs are organized. In many European countries such as Germany, for example, vocational programs and adolescent apprenticeships are an integral part of a socially validated educational system. In these settings, there is neither great stigma nor profound social segregation associated with these programs. In the United States, many schools in recent years have attempted to adopt an academy model for their vocational programs, where vocational education is integrated into both academic course work and the world of work: in these programs significant stigma or segregation is less likely. In 1971, Ahlstrom and Havighurst published what became a prominent skeptical evaluation of the role of vocational education in reducing the prevalence of delinquency. Ahlstrom and Havighurst investigated a specialized vocational work-study program designed for four hundred inner-city, maladjusted youth. The program was shown to have little effect on crime rates during student teen years.

Vocational education, however, has been demonstrated to have positive effects on student reports of satisfaction with school and positive perceptions of their teachers. Positive adolescent work experience is also related to psychological feelings of mastery, internal control, and self-competence. Given the significance of these factors in predicting criminality, it is likely that under certain circumstances vocational education can significantly discourage criminality. Recent criminological research has demonstrated that vocational education course work significantly reduces the likelihood of adult incarceration, if the course work occurs in an educational setting that does not concentrate and segregate high proportions of economically disadvantaged youth (Arum and Beattie).

Educational Resources

Few criminological studies have attempted to estimate the effects of educational resources on individual delinquency and propensity for criminal behavior. One exception is Gary Gottfredson and Denise Gottfredson’s Victimization in Schools (1985). The Gottfredsons argue that rates of student and teacher victimization in schools are a product of a range of school characteristics, including school resources, peer composition, and vocational curricular emphasis. Educational resources are likely important in that they can allow schools to reduce class size and thus increase a student’s opportunities for learning from, and relating to, their teachers—that is, their likelihood of attachment to conventional activities. Educational resources can also be used to ensure greater monitoring of youth.

Educational resources likely affect a school’s ability to influence positively an individual’s life course, since schools with greater resources are better able to provide more positive enriched educational experiences for adolescents (such as costly vocational education programs). Recent noncriminological research has identified a clear pattern of the effects of educational resources on a range of socioeconomic outcomes including growth in test scores, increased years of educational attainment, and higher lifetime earnings. These socioeconomic outcomes have all been related to individual criminality and incarceration risk. It is therefore not surprising that high school student-teacher ratios have also been demonstrated to affect adult incarceration risk (Arum and Beattie).

Peer Climates

Peer climates can affect criminality in a number of ways, including differential association and altering social norms for acceptable behavior. Peer climates emerge in school as a product of both ecological and institutional factors. While peer climates are partly a reflection of peer composition, they are also structured by institutional factors. School practices in general and school disciplinary practices in particular define the parameters in which specific peer climates emerge and flourish. In the United States, significant variation in disciplinary practices exist: many public schools still practice corporal punishment, while in other schools often little is done to control student misbehavior and gang activity.

Peer composition has been demonstrated to be clearly associated with delinquency and subsequent incarceration in a large number of studies. Peer climates characterized by higher dropout rates and students of lower socioeconomic origins provide settings that make conventional school attachment more difficult. Research by James Coleman has emphasized, however, that schools have a role in structuring the manner in which peer climates exist. Work by Émile Durkheim also suggests the importance of school disciplinary practices in the socialization of youth. Punishment is necessary, according to Durkheim, because it unequivocally communicates that a normative rule has been broken.

Challenges to school disciplinary practices, regardless of whether they are from external environmental or internal organizational sources, would be particularly unsettling to the normative order of the school. Conservatives argue that due to administrative and legal challenges to school authority, students no longer view school rules as inviolate (Toby). At a practical level, school discipline works to generate student compliance and academically focused peer cultures. Peer climates have long been associated with student academic performance. In recent work, Coleman and his colleagues have argued that private schools outperform public schools in part because they are able to maintain stricter disciplinary climates with lower rates of student absenteeism, vandalism, drug use, and disobedience. Sociologists have also found that rates of misbehavior during the senior year are lower in schools that have higher rates of disciplining of sophomore students (Diprete et al.). Misbehaving students also have lower levels of educational achievement as measured by change in grades and test scores. Conservatives claim that without proper order and discipline, schools are unable to function properly and effective socialization is impossible.

Progressive educators, however, have countered that as traditional authoritarian disciplinary practices are eliminated from public schools, students will be less alienated from their educational environments, and more likely to remain in school and apply themselves to their studies. Support for this is suggested by the fact that the use of strict disciplinary practices, such as corporal punishment, leads to lower educational achievement and higher rates of delinquency. Researchers also argue that these school practices can lead to the formation of oppositional peer groups that resist formal education.

Conclusions and Implications

Criminologists who believe that propensity for adult criminality is established in early childhood attempt to dismiss empirical research that identifies significant school effects on delinquency and crime. These critics argue that selection bias accounts for education-crime associations. That is, some criminologists will argue that both educational and criminal trajectories are set at a very early preschool age. By the time that children enter school, the argument goes, families (or genetics) have already produced ‘‘bad kids.’’ Individuals fail in school because they lack social control: failure in school thus reflects individuallevel socialization problems that underlie criminal propensity; poor educational performance itself therefore does not produce criminal behavior. While some criminologists might still argue this position, it is fundamentally inconsistent with the larger social scientific research community’s understanding of the role of education in life course development. At least since the late 1960s, social scientists have recognized that educational experience has come to mediate the relationship between social origins and adult lifecourse outcomes. While poorly socialized youth certainly are less likely to do well in terms of educational attainment, schools—if properly structured—can successfully counter these tendencies. Schools are institutions that can serve as ‘‘turning points’’ in individual lives. As the criminologists John Laub and Robert Sampson have argued: ‘‘despite the connection between childhood events and experiences in adulthood, turning points can modify life trajectories—they can ‘redirect paths.’’’

Since schools play a critical role in determining the likelihood of delinquency, crime, and incarceration, policymakers historically have turned to educational reform to address social problems associated with adolescent delinquency and adult criminality. The last two decades of the twentieth century, however, were exceptional in U.S. history in terms of both educational and criminological policy. In unprecedented ways, policymakers have relied on incapacitation by the penal system to address the crime problem in society. Concurrently, educational policy has lost its focus on designing programs to integrate and socialize economically disadvantaged youths to become productive members of society. Instead, educational policymakers have become fixated on the narrow task of improving school performance and efficiency in terms of measurable student gains on cognitive standardized tests. While prison rolls have more than doubled in the last two decades of the twentieth century, high school vocational education enrollments have plummeted as the programs have been dismantled due to their high cost. While the penal system has demanded an increasing portion of local, state, and federal finances, educational budgets have struggled just to keep up with inflation and demographic growth in school age populations. While government officials increasingly threaten to sanction schools for the lack of student progress on cognitive tests, schools as institutions have become legally constrained from applying disciplinary sanctions to maintain peer climates conducive to learning and socialization. How policy reformers reconcile these tensions and contradictions in educational and social policy will determine the character of the educationcrime relationship in the future.

Bibliography:

• AHLSTROM, WINTON, and HAVIGHURST, ROBERT. 400 Losers: Delinquent Boys in High School. San Francisco: Jossey-Bass Publishers, 1971.
• ARUM, RICHARD, and BEATTIE, IRENE. ‘‘High School Experience and the Risk of Adult Incarceration.’’ Criminology 37, 3 (1999): 515– 538.
• CLOWARD, RICHARD, and OHLIN, LLOYD. Delinquency and Opportunity. New York: Free Press, 1960.
• COLEMAN, JAMES, and HOFFER, THOMAS. Public and Private High Schools: The Impact of Communities. New York: Basic Books, 1987.
• COLEMAN, JAMES; CAMPBELL, ERNEST; HOBSON, CAROL; MCPARTLAND, JAMES; MOOD, ALEXANDER; WEINFELD, FREDERICH; and YORK, ROBERT. Equality of Educational Opportunity. Washington, D.C.: Department of Health, Education and Welfare, 1966.
• DIPRETE, THOMAS; MULLER, CHANDRA; and SHAEFFER, NORA. Discipline and Order in American High Schools. Washington, D.C.: Government Printing Office, 1981.
• DURKHEIM, ÉMILE. Moral Education: A Study in the Theory and Application of the Sociology of Education (1903). New York: Free Press, 1961.
• GILLIGAN, CAROL. In a Different Voice: Psychological Theory and Women’s Development. Cambridge, Mass.: Harvard University Press, 1982.
• GLUECK, SHELDON, and GLUECK, ELEANOR. Five Hundred Criminal Careers. New York: Knopf, 1930.
• GOTTFREDSON, GARY, and GOTTFREDSON, DENISE. Victimization in Schools. New York: Plenum Press, 1985.
• GOTTFREDSON, MICHAEL, and HIRSCHI, TRAVIS. A General Theory of Crime. Stanford, Calif.: Stanford University Press, 1990.
• HIRSCHI, TRAVIS. Causes of Delinquency. Berkeley: University of California Press, 1969.
• KOHLBERG, LAWRENCE. Essays on Moral Development. San Francisco: Harper and Row, 1981.
• LAUB, JOHN, and SAMPSON, ROBERT. ‘‘Turning Points in the Life Course: Why Change Matters to the Study of Crime.’’ Criminology 31 (1993): 301–325.
• POLK, KENNETH, and SCHAFER, WALTER. Schools and Delinquency. Englewood Cliffs, N.J.: Prentice Hall, 1972.
• RUTTER, M.; MAUGHAN, B.; MORTIMORE, P.; and OUSTON, J. Fifteen Thousand Hours: Secondary Schools and Their Effects on Children. Cambridge, Mass.: Harvard University Press, 1979.
• SAMPSON, ROBERT, and LAUB, JOHN. Crime in the Making: Pathways and Turning Points Through Life. Cambridge, Mass.: Harvard University Press, 1993.
• SOROKIN, PITRIM. Social and Cultural Mobility. New York: Free Press, 1927.
• STINCHOMBE, ARTHUR. Rebellion in a High School. Chicago: Quadrangle Books, 1993.
• SUTHERLAND, EDWIN. Principles of Criminology. Philadelphia: Lippincott, 1937.
• TOBY, JACKSON. ‘‘The Schools.’’ In Edited by James Q. Wilson and Joan Petersilia. San Francisco, Calif.: Institute for Contemporary Studies, 1995.
• S. Department of Justice. Report to the Nation on Crime and Justice. Washington D.C.: Bureau of Justice Statistics, 1988.
• WEBER, MAX. ‘‘The Rationalization of Education and Training.’’ Max Weber: Essays in Sociology. New York: Oxford University Press, 1946.
• WILSON, JAMES, and HERRNSTEIN, RICHARD. Crime and Human Nature. New York: Simon and Schuster, 1985.

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Gambino Crime Family Research Paper

This essay about the current status of the Gambino crime family explores how this once-dominant force in the American Mafia has adapted to changes in the landscape of organized crime. The Gambino family, historically involved in a wide array of criminal activities, has faced significant disruptions due to law enforcement actions, particularly under the Racketeer Influenced and Corrupt Organizations Act (RICO). Despite numerous high-profile indictments and the shifting tactics of law enforcement, the family continues to operate, focusing on less conspicuous activities that include white-collar crimes like credit card fraud and cybercrime. Leadership has also evolved, with a move towards more low-profile figures to avoid the intense public and legal scrutiny of past decades. The essay details how the Gambino family’s current operations reflect broader trends in organized crime, where visibility is minimized and activities are more subtly integrated into legitimate sectors. This shift demonstrates the ongoing evolution and resilience of organized crime families in response to heightened law enforcement capabilities and technological advancements.

How it works

The Gambino crime family, once among the most powerful criminal organizations in the U.S., has experienced significant transformations from its heyday under the leadership of infamous bosses like Carlo Gambino and John Gotti. Today, while the landscape of organized crime has shifted dramatically due to various factors including law enforcement tactics and changes in the economy, the Gambino family still operates, albeit in a more subdued manner compared to its past prominence. This essay explores the current status of the Gambino crime family, examining its activities, leadership, and challenges in the modern era.

Historically, the Gambino family was a pivotal component of the American Mafia or La Cosa Nostra, involved in a wide range of criminal activities such as racketeering, extortion, loan sharking, illegal gambling, and murder. During the 20th century, particularly under the leadership of Carlo Gambino and later John Gotti, the family was known for its significant influence not only in New York City but across the United States. The dramatic trials and public persona of Gotti during the 1980s and early 1990s brought immense media attention and public scrutiny to the family.

However, the intense focus from law enforcement agencies including the FBI and significant legal challenges such as the Racketeer Influenced and Corrupt Organizations Act (RICO) have severely impacted the Gambino family’s operations. Numerous high-profile indictments and convictions have plagued the family over the years, leading to a decline in their public visibility and power. Despite these setbacks, the family has not dissolved but has rather adapted to the new realities of organized crime.

In recent years, the Gambino family has reportedly involved itself in more subdued and less conspicuous criminal activities. This includes infiltrating legitimate industries such as construction, waste disposal, and the waterfront docks, as well as engaging in white-collar crimes including credit card fraud and cybercrime. These activities suggest a strategic shift from their more overtly violent past to operations that might attract less attention from law enforcement and the media.

Leadership within the Gambino family has also seen changes, with a move towards younger and more low-profile bosses. These leaders are believed to maintain a lower public profile as a strategic decision to avoid the law enforcement scrutiny that famously toppled their predecessors. The current alleged boss, Frank Cali, was murdered in 2019, indicating that while the family may have lowered its profile, it still deals with significant internal and external pressures and violence.

The adaptation strategies of the Gambino crime family reflect broader trends in organized crime, where many groups have moved towards more covert operations and away from the traditional rackets that defined the Mafia of the past. Today’s environment, marked by advanced surveillance technologies and more sophisticated law enforcement tactics, forces criminal organizations to continually evolve.

In conclusion, the Gambino crime family of today bears little resemblance to the ostentatious and fiercely powerful organization it once was. While still engaged in illegal activities, their operations are more discreet, and their involvement in white-collar crime represents a significant tactical shift. The Gambino family’s ability to adapt to the new conditions of organized crime demonstrates the enduring, although transformed, presence of traditional organized crime families in America’s criminal landscape. This evolution speaks to broader shifts within organized crime, highlighting the ongoing challenge for law enforcement and society in addressing these hidden enterprises.

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AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

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Write down your thoughts and shred them to relieve anger, researchers say

Writing negative reactions on paper and shredding it or scrunching and throwing in the bin eliminates angry feelings, study finds

Since time immemorial humans have tried to devise anger management techniques.

In ancient Rome, the Stoic philosopher Seneca believed “my anger is likely to do me more harm than your wrong” and offered avoidance tips in his AD45 work De Ira (On Anger).

More modern methods include a workout on the gym punchbag or exercise bike. But the humble paper shredder may be a more effective – and accessible – way to decompress, according to research.

A study in Japan has found that writing down your reaction to a negative incident on a piece of paper and then shredding it, or scrunching it into a ball and throwing it in the bin, gets rid of anger.

“We expected that our method would suppress anger to some extent,” said Nobuyuki Kawai, lead researcher of the study at Nagoya University. “However, we were amazed that anger was eliminated almost entirely.”

The study, published in Scientific Reports on Nature , builds on research on the association between the written word and anger reduction as well as studies showing how interactions with physical objects can control a person’s mood. For instance, those wanting revenge on an ex-partner may burn letters or destroy gifts.

Researchers believe the shredder results may be related to the phenomenon of “backward magical contagion”, which is the belief that actions taken on an object associated with a person can affect the individuals themselves. In this case, getting rid of the negative physical entity, the piece of paper, causes the original emotion to also disappear.

This is a reversal of “magical contagion” or “celebrity contagion” – the belief that the “essence” of an individual can be transferred through their physical possessions.

Fifty student participants were asked to write brief opinions about an important social problem, such as whether smoking in public should be outlawed. Evaluators then deliberately scored the papers low on intelligence, interest, friendliness, logic, and rationality. For good measure, evaluators added insulting comments such as: “I cannot believe an educated person would think like this. I hope this person learns something while at the university.”

The wound-up participants then wrote down their angry thoughts on the negative feedback on a piece of paper. One group was told to either roll up the paper and throw it in a bin or keep it in a file on their desk. A second group was told to shred the paper, or put it in a plastic box.

Anger levels of the individuals who discarded their paper in the bin or shredded it returned to their initial state, while those who retained a hard copy of the paper experienced only a small decrease in their overall anger.

Researchers concluded that “the meaning (interpretation) of disposal plays a critical role” in reducing anger.

“This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away,” said Kawai.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as hakidashisara ( hakidashi sara refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, just outside Nagoya. Hakidashisara is an annual festival where people smash small discs representing things that make them angry. The study’s findings may explain the feeling of relief that participants report after leaving the festival, the paper concluded.

More on this story

Fridge magnets can be cool aid to holiday memory recall, study finds

Want to skip that Christmas party? The host probably won’t mind, study shows

‘Succession syndrome’ prevalent among wealthy households, psychiatrists warn

Drugs and alcohol do not make you more creative, research finds

Why being rude to the waiter (or other staff) is the worst strategy

Authors of original dating profiles rated more attractive, research finds

I fear my children are overexposed to technology. Experts say I’m right to worry

I used to be ashamed of being a fangirl. Now I see how joyous and creative it was

Autistic scholar Temple Grandin: ‘The education system is screening out visual thinkers’

Human neurons transplanted into rats to help study brain disorders

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Trump Trial Illustrates Jury Challenges in the Social Media Age

FILE PHOTO: Former U.S. President and current Republican presidential candidate Donald Trump enters his trial after a lunch break at Manhattan Criminal Court in New York, New York, USA, 19 April 2024. Trump is facing 34 felony counts of falsifying business records related to payments made to adult film star Stormy Daniels during his 2016 presidential campaign. SARAH YENESEL/Pool via REUTERS/File Photo

By Jody Godoy

NEW YORK (Reuters) - Donald Trump's criminal trial in New York has barely begun but one of the highest-profile court cases in U.S. history has already highlighted the challenges of insulating a jury from social media.

As opening statements are set to begin in New York on Monday, the salacious case involving a hush-money payment to a porn star -- the first criminal trial of a former U.S. president -- will test the limits of what a judge can control.

To keep the trial fair and jurors safe from intimidation or influence schemes, the court aims to maintain the secrecy of their identities, shield them from online attacks and ensure they are not swayed by coverage and social media comments. 

But Justice Juan Merchan has virtually no ability to police what is posted by most users on social media. According to

Pew Research in 2023, 90% of U.S. adults own smartphones, and the same percentage say they are online every day.

"In some ways the social media aspect of the case makes those concerns even more serious," Manhattan criminal defense attorney Michael Bachner said.

Trump has millions of online followers, and some were behind death threats to election workers after he lost the White House in 2020.

Merchan sought to keep prospective jurors' identities concealed during jury selection. Their names were not disclosed except to Trump, his lawyers and prosecutors.

Merchan soon prohibited media outlets from reporting the potential jurors' employment after excusing a juror who said she felt intimidated because some personal details were made public.

One person whose online speech Merchan believes he should be able to control is the defendant himself. He has ordered Trump not to make public statements about jurors, prosecutors and court staff or their families, though he is free to air his thoughts on the judge and district attorney. 

Prosecutors have accused Trump of violating the order multiple times, including in a Truth Social post on the jury pool. "They are catching undercover Liberal Activists lying to the Judge in order to get on the Trump Jury," Trump posted.

Merchan has scheduled a Tuesday hearing on those claims. Trump has said it would be an "honor" to be jailed for violating the order.

AVOIDING COVERAGE

As the trial progresses, jurors also must try to comply with a court order to avoid coverage of the case, including on social media and mobile devices.

"To be honest, my generation is on social media a lot," one potential juror told Merchan. "So, if I'm scrolling, I usually see it." But she assured the judge that she could avoid reading headlines about the case that popped up on her phone.

In a recent civil case involving former Republican vice presidential candidate Sarah Palin, jurors learned of a decision by the judge to dismiss the case from news alerts.

Christina Marinakis, chief executive at trial consulting firm Immersion Legal, said the barrage of headlines and social media notifications on jurors' phones has been a huge problem in her cases.

"This is another reason we have alternates, because somebody is going to see something during the course of the trial that may cause them to get dismissed," she said.

Six alternate jurors were selected for the Trump criminal trial.

The court could turn to a tactic most famously used in trials of organized crime figures to prevent jury tampering: sequestering jurors. 

While technically an option in this case, it's not one judges invoke lightly given the extreme disruption it would mean for jurors to live in a hotel under court supervision. 

"It's kind of a dire thing to do. You have to really have a good reason to do it," Bachner said.

(Reporting by Jody Godoy in New York; editing by Tom Hals and Cynthia Osterman)

Copyright 2024 Thomson Reuters .

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

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About half of americans say public k-12 education is going in the wrong direction, what public k-12 teachers want americans to know about teaching, what’s it like to be a teacher in america today, race and lgbtq issues in k-12 schools, most popular.

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