Abstract
The question of whether childhood mental illness has long term consequences in terms of criminal behavior has been little studied, yet it could have major consequences for both the individual and society more generally. In this paper, we focus on Attention-Deficit/Hyperactivity Disorder (ADHD), one of the most prevalent mental conditions in school-age children, to examine the long-term effects of childhood mental illness on criminal activities, controlling for a rich set of individual, family, and community level variables. The empirical estimates show that children who experience ADHD symptoms face a substantially increased likelihood of engaging in many types of criminal activities. An included “back-of-the-envelope” calculation of the social costs associated with criminal activities by individuals with childhood ADHD finds the costs to be substantial.
Keywords: Mental Illness, ADHD, Crime, Sibling Fixed Effects
Introduction
Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent and fastest growing mental health problems facing children. The prevalence is estimated to be between 2–10% of school-aged children, with 7.4 percent of parents who have children between the ages of 3–17 reporting they were ever told their child had ADHD according to the most recent national survey (1). The rate of treatment for ADHD has also increased dramatically over time—from 0.9 per 100 children in 1987 to 3.4 per 100 children in 1997 (2).1, 2 ADHD is more common among boys (10.7 percent) than girls (4 percent) with few differences by race. While much research has documented the short term consequences of ADHD on school-children, such as increased likelihood of pursuing risky behaviors such as smoking (4) and lower academic performance (5, 6, 7), less research has examined economic outcomes during the transition to adulthood, especially using nationally representative data (6,8).3, 4
This paper contributes to the literature by exploring the link between childhood ADHD and subsequent criminal activity, using nationally representative data that collected retrospective childhood ADHD symptoms as well as early adult outcomes. Existing evidence suggests a link between ADHD symptoms and crime but there has been little research using large longitudinal samples of individuals to test this hypothesis. For example, a recent FBI report shows that adolescents with ADHD symptoms had more adversarial contacts with law enforcement agencies than other adolescents (19:3 odds) and are more likely to be in juvenile justice facilities (5:1 odds) (10) but this study does not include other controls nor does it speak to the commitment of crimes.
In addition to examining the longitudinal link between ADHD symptoms and criminal activities, we also advance the literature by analyzing the tie between differing dimensions of ADHD on crime such as inattentive, hyperactive, and combined typologies. The results show that both inattentive and hyperactive symptoms during childhood increase the likelihood of engaging in numerous types of criminal activities—in many cases the increases in risks for negative behaviors associated with ADHD are substantial. However, there is little evidence that individuals with symptoms of the combined type of ADHD face a multiplicative risk. Since existing research (11) suggests that total social costs of crime may be as high as 4 percent of GDP with a direct average loss to victims of $500 per crime, crime associated with ADHD could have substantial negative effects on GDP and societal well-being more generally. The empirical findings of this paper suggest a substantial payoff to early interventions that either reduce the incidence of ADHD or reduce the consequences of ADHD for the individuals and hence for society.
Background
While much is known about the family and individual level predictors of childhood ADHD, there are still many open questions about its specific causes.5 Four and a half million children ages 3–17 were reported to have ADHD according to data from the 2006 National Health Interview Study (Table 3 (1)). ADHD is more likely to occur in males, children in families with low socioeconomic status, and children with parents who have a high school diploma or GED. Prevalence of ADHD is much higher among close relatives than in the general population, suggesting a genetic tie (14).
Table 3.
Association between Retrospective Ratings of ADHD Symptoms and Stealing
| Outcome | Steal | Steal | Steal | Steal |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.026*** | 0.026*** | 0.018*** | 0.069 |
| (0.018 – 0.034) | (0.018 – 0.034) | (0.004 – 0.031) | (−0.201 – 0.340) | |
| Black | 0.024*** | 0.024*** | ||
| (0.010 – 0.039) | (0.009 – 0.039) | |||
| Hispanic | 0.013* | 0.013* | ||
| (−0.000 – 0.026) | (−0.000 – 0.026) | |||
| Income | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.005*** | −0.005*** | −0.005*** | −0.028 |
| (−0.007 – −0.003) | (−0.007 – −0.003) | (−0.008 – −0.001) | (−0.109 – 0.054) | |
| Mom Education | 0.002** | 0.002* | ||
| (0.000 – 0.003) | (−0.000 – 0.003) | |||
| Intact Family | −0.004 | −0.004 | ||
| (−0.011 – 0.004) | (−0.011 – 0.004) | |||
| Inattentive ADHD | 0.034 | |||
| (−0.007 – 0.075) | ||||
| Hyper ADHD | 0.044** | |||
| (0.004 – 0.085) | ||||
| Combined ADHD | 0.032** | |||
| (0.001 – 0.063) | ||||
| ADHD ANY | 0.035*** | 0.011 | 0.063 | |
| (0.012 – 0.057) | (−0.007 – 0.029) | (−0.180 – 0.306) | ||
| Years of Schooling | −0.000 | −0.000 | −0.000 | −0.011 |
| (−0.002 – 0.002) | (−0.002 – 0.002) | (−0.003 – 0.002) | (−0.055 – 0.034) | |
| PVT Test Score | 0.000 | 0.000 | 0.000 | 0.004 |
| (−0.000 – 0.000) | (−0.000 – 0.000) | (−0.000 – 0.000) | (−0.011 – 0.018) | |
| % in Poverty | −0.001 | −0.001 | ||
| (−0.002 – 0.001) | (−0.002 – 0.001) | |||
| % Black | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Median Household Income | −0.001 | −0.001 | ||
| (−0.002 – 0.001) | (−0.002 – 0.001) | |||
| Unemployment Rate | 0.001 | 0.001 | ||
| (−0.001 – 0.003) | (−0.001 – 0.003) | |||
| % Without Diploma | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Juvenile Serious Crimes | 0.001 | 0.001 | ||
| (−0.002 – 0.003) | (−0.002 – 0.003) | |||
| Total Serious Crimes | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Observations | 12623 | 12630 | 3578 | 214 |
| 1762 | 100 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, high school grade point average, standard deviation of community level household income
Treatments for ADHD still remain somewhat controversial. On one hand, approximately 70% of the patients with ADHD respond to treatment with stimulant medications in the short term and over periods of up to 18 months (2). On the other hand, long-term effects of many treatments are unknown (15). Additionally, pharmacotherapy alone has not yet been shown to improve the long-term outcome for any domain of functioning (16), and treatment has been found to be less effective in adults (17). While the explosion in pharmacological therapy occurred in 1991, as yet there are no long term studies of the consequences of long term use.
The influence of ADHD on children occurs along several dimensions. Children with ADHD have been found to have fewer close friends (18) and exhibit antisocial behavior (12). Poorer educational outcomes may be the most important economic consequence of ADHD (7, 8). In particular, ADHD has been tied to poor concentration and impulsiveness during preschool (19), lower grades and greater retention and suspension (6, 8), poorer perceptions by teachers (20) and lower eventual educational attainment (5, 7). Researchers have also found an increase in risky behaviors, including earlier sexual intercourse and lower rates of contraceptive use (8).
ADHD has also been shown to be linked with several measures of criminal activity, although much of this research has used small convenience samples of individuals or assessed the relationship in a cross-sectional context. Individuals with ADHD have been shown to be more likely to commit both minor offenses such as traffic violations and speeding (21) as well as crimes leading to incarceration (19). In particular, property theft, carrying a concealed weapon, illegal drug possession, and arrests rates have been shown to be positively related to ADHD status (6, 12, 22, 23), as have admission into juvenile justice facilities (10). However, in most cases the reported associations were estimated using cross sectional data on fewer than 300 individuals. Nevertheless these studies all suggest there are economic consequences of ADHD in the form of increased crime.
In addition to direct consequences of ADHD for individuals, there is evidence that families are also penalized by the illness. From the increase in medical expenses (24) of $500 to $1500 per child per year, 6 families with children with ADHD face increased likelihood of disturbances in family and marital functioning (19) and worse maternal mental health (25).7 Crime as an adolescent and young adult is also important because it may have long term economic consequences. Billups, Mocan and Overland (27) have shown that current criminal involvement increases the probability of future crime by increasing what they term criminal human capital and depreciating “legal” human capital. Bound and Freeman (28) and Freeman and Rodgers (29) have shown a particularly strong link between the commitment of crime and subsequent reduced labor force participation and earnings for black youth. Thus society may face both the direct costs of crime and the cost of lower earnings and productivity from those with ADHD symptoms.
Below we explore the tie between ADHD symptoms and various crimes using a nationally representative sample and information on a full range of ADHD symptoms, while controlling for other family and neighborhood characteristics.8 We then estimate a rough calculation of the cost of society of ADHD in terms of increased crime.
Conceptual Framework
The basic model motivating this study stems from Becker’s path-breaking paper (30), extended by Ehrlich (31), which argues that participation in criminal activity is the result of individual’s optimizing response to legal and illegal market opportunities. That is, rational economic agents decide to engage in criminal activity after comparing the financial rewards from crime to those obtained from legal work, taking into account the probabilities of apprehension, conviction and the severity of punishment. ADHD symptoms are likely to influence the perceived rewards from both sectors: perhaps most evidently by lowering the perceived rewards from the legal sector and less evidently by lowering the probability of conviction by a belief that ADHD might be used as an excuse for illegal activity.
The first pathway linking ADHD and crime (lowering the perceived rewards from the legal sector) is consistent with the negative influence of ADHD on school performance. Children with ADHD symptoms tend to be more likely to repeat a grade, complete fewer grades and do less well in terms of grades (see above for relevant literature on this). Thus they will tend to have less education. They may also expect that the same factors that reduce their school performance will limit their formal job market opportunities. The second pathway linking ADHD and crime (using ADHD to reduce the probability of conviction) has been attempted, though generally unsuccessfully, in trials (10).
The factors that underlie these patterns, especially poor school performance and criminal activities, may be linked to the two sets of symptoms measured here. The first set, inattention, is likely to impede the ability to understand materials presented in the classroom, reduce the knowledge of and completion of homework, lower the performance on tests, and reduce the penalties for engaging in illegal activities. All of these factors predict that the youth with inattention symptoms is likely to be far less successful in school and therefore leave school earlier than otherwise expected. The same symptoms may also inhibit the full understanding of the consequences of numerous actions, including various crimes. This pattern may lead the youth to engage in criminal activities, both because labor market activities are limited and because the full consequences of engaging in such activities are not recognized. Easy crimes such as stealing, using and selling drugs, robbery and even burglary may all occur with little attention paid to consequences.9
The second type of ADHD (hyperactive) is generally associated with limited control of impulses or an increase in impulsive behavior. These youth tend to be more disruptive, lose friends and engage in risky behaviors. They too are likely to be less successful in school and in social settings. These youths would seem more likely to engage in crimes that are impulsive and to act in ways that lead to arrest. We expect to see youths with hyperactive symptoms engaging in crimes such as stealing and especially robbery but not selling drugs since this activity requires at least some planning. We also expect them to be more likely to be arrested and convicted of the crimes they do commit. Thus we have clear predictions of differential crimes committed by youth with these two types of ADHD. When we analyze the behavior of youth as a group without differentiation, we expect to see a higher probability of engaging in all of the crimes mentioned above.
Data
The data used in this study come from the restricted version of the National Longitudinal Study of Adolescent Health (Add Health). Add Health is a school-based, longitudinal study of the health-related behaviors of adolescents and their outcomes in young adulthood. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7 through 12 in 1994–95, the study follows up with a series of in-home interviews of students approximately one year and then six years later. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators. By design, the Add Health survey included a sample stratified by region, urbanicity, school type, ethnic mix, and size. Preexisting databases (e.g. census data) provide information about neighborhoods and communities.10
For the present study, several exclusions of the 20,747 adolescents originally sampled are made. First, those who did not complete the three waves of in-home surveys are excluded. This leaves 15,197 eligible respondents. Next, individuals without longitudinal sample weights are excluded, leaving 14,322 individuals. Community-level measures are unavailable for sixty-four individuals. The Peabody Vocabulary Test scores of 650 individuals are unavailable. ADHD symptoms (explained below) are not available for fourteen individuals. These exclusions leave 13,572 (almost 95% of the eligible sample) individuals in the analysis sample. Finally, note that wave 1 family income and mother’s education level are imputed for almost 3,200 and 1,300 individuals, respectively.11
The primary indicator of childhood ADHD symptoms is taken from an eighteen question retrospective rating collected during Wave III of the Add Health study, when respondents were between the ages of 18 and 28 years old. The questions ask respondents to think back to when they were between 5 and 12 years of age and report how often they performed a set of behaviors (e.g. squirmed in their seat, had difficulty sustaining attention in tasks).12, 13 Since nearly all of these children will answer these questions for a period before 1991 when the use of pharmaceuticals to treat ADHD (e.g. Ritalin) became common, we make the assumption that all those who report symptoms were not being treated by pharmaceuticals by that date. To the extent they are subsequently treated, this may influence the outcomes we measure. (We expect that this will lead us to underestimate the effect of ADHD, to the extent the pharmaceuticals reduce the symptoms of ADHD). Unfortunately, we have no information on treatment, so our analysis focuses only on the tie between presence of ADHD symptoms during ages 5 to 12 and subsequent outcomes. We also acknowledge that the retrospective nature of the ADHD questions may lead to measurement error in these variables. While classical measurement error in a linear regression specification will attenuate the coefficients, the effects of measurement error in a non-linear setting such as logistic regression analysis are less clear. In results available upon request, we show that estimating linear models produces very similar results.
Summary statistics for the analysis sample are presented in Table 1. Following Kollins et al. (4) as well as community based samples (34), a symptom is considered present if it was experienced “often” or “very often.” Since there is evidence that the effects of ADHD may vary by whether the symptoms are of the inattentive or hyperactive type (23), we examine the effects of these different domains as well as usual measures of ADHD of any type. In order to examine the consequences of the different dimensions of ADHD, individuals are divided into 1 of 4 groups based on the number of their reported symptoms: (1) 6 or more inattentive (IN) symptoms and fewer than 6 hyperactive (HI) symptoms; (2) 6 or more HI symptoms and fewer than 6 IN symptoms; (3) both 6 or more IN symptoms and 6 or more HI symptoms; and (4) both fewer than 6 HI andfewer than 6 IN symptoms. This 6-symptom cutoff is chosen to be consistent with the DSM-IV ADHD criteria requiring the presence of 6 or more symptoms from either the IN or HI symptom domains (4).14
Table 1.
Summary Statistics: National Longitudinal Study of Adolescent Health
| Variable | Obs | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|---|
| Outcomes | |||||
| Any Crime | 13566 | 0.19 | 0.40 | 0 | 1 |
| Steal | 13449 | 0.03 | 0.18 | 0 | 1 |
| Sell Drugs | 13409 | 0.08 | 0.27 | 0 | 1 |
| Burglary | 13461 | 0.02 | 0.13 | 0 | 1 |
| Robbery | 13456 | 0.02 | 0.14 | 0 | 1 |
| Arrested | 13478 | 0.12 | 0.33 | 0 | 1 |
| Convicted | 13559 | 0.02 | 0.15 | 0 | 1 |
| Individual Characteristics | |||||
| Male | 13572 | 0.51 | 0.50 | 0 | 1 |
| White | 13572 | 0.73 | 0.44 | 0 | 1 |
| Black | 13572 | 0.16 | 0.37 | 0 | 1 |
| Hispanic | 13572 | 0.12 | 0.32 | 0 | 1 |
| Still in School | 13564 | 0.36 | 0.48 | 0 | 1 |
| Age | 13572 | 21.79 | 1.85 | 18 | 28 |
| Peabody Verbal Test Score | 13572 | 101.37 | 14.72 | 14 | 146 |
| Grade Point Average | 13350 | 3.17 | 0.73 | 1 | 5 |
| Education Attainment | 13560 | 13.23 | 2.00 | 6 | 22 |
| Any ADHD | 13772 | 0.09 | 0.29 | 0 | 1 |
| ADHD (Inattentive Type) | 13572 | 0.04 | 0.19 | 0 | 1 |
| ADHD (Hyperactive Type) | 13572 | 0.03 | 0.16 | 0 | 1 |
| ADHD (Combined Type) | 13572 | 0.03 | 0.17 | 0 | 1 |
| Family Characteristics | |||||
| Family Income ($1000s)^ | 13572 | 45.10 | 38.80 | 0 | 990 |
| Mother’s Education Level^ | 13572 | 13.19 | 2.27 | 8 | 18 |
| Married Parents | 13572 | 0.65 | 0.48 | 0 | 1 |
| Community Characteristics | |||||
| % in Poverty | 13572 | 13.98 | 7.06 | 3 | 40 |
| % White | 13572 | 80.92 | 15.43 | 25 | 100 |
| % Black | 13572 | 13.62 | 14.00 | 0 | 75 |
| Median Income | 13572 | 29.52 | 7.84 | 13 | 55 |
| Standard Deviation of Income | 13572 | 29.15 | 5.42 | 18 | 50 |
| Unemployment Rate | 13572 | 6.83 | 2.41 | 3 | 14 |
| % Without HS Diploma | 13572 | 25.04 | 9.26 | 5 | 61 |
| Juvenile Arrests per 100,000 | 13112 | 3.46 | 1.68 | 0 | 23 |
| Total Serious Crimes per 100,000 | 13242 | 56.49 | 26.48 | 0 | 169 |
Imputed Variable
In terms of economic outcome variables, we focus on reports of engagement in criminal activities obtained during wave III of the data collection period, when the average age of the individuals is nearly twenty-two years old. Almost 3.5 percent of the sample reports stealing something worth $50 or more in the previous year, more than 8 percent of the sample reports selling drugs in the last year, 2 percent reported committing burglary in the past 12 months,15 2 percent committed a robbery in the past twelve months.16 Twelve percent of the sample report ever being arrested and two percent reported ever being convicted of a crime. Nearly 20 percent of the sample reported one of these outcomes, which we label as “any crime.”17
Many economic models of crime suggest that persons raised in communities with high rates of poverty and who were themselves poor should be more likely to commit crimes than those raised in higher income areas; persons living in high unemployment areas should also be more likely to commit crimes as the opportunity costs of legal sector employment are lower than when jobs are plentiful. If these living conditions are correlated with the presence of ADHD we might attribute to ADHD the causal influence of poverty and unemployment. In order to take such factors into account, we control explicitly for those we are able to measure. For example, we include the percent in poverty, income characteristics, and education of the community members in our models to take these into account. In addition, since living in high crime areas might lead to lower stigma tied to committing a crime and possibly greater ease of engaging in certain types of crime (and so a higher expected payoff to crime), we include juvenile and total arrests in the community in the estimated model. (These variables may also be tied to differential probability of an individual’s arrest given any actual involvement in crime, which we also seek to control for in our model.) The full list of the individual, family-level and community variables are found in Table 1. Finally, in order to test for whether family-level characteristics might be simultaneously influencing ADHD and criminal activities, we conduct fixed effects analysis where we limit our analysis to families with two or more siblings in the data, where at least one sibling reports ADHD symptoms and one other does not. Since this is a small sample of siblings who are discordant on ADHD status, we also estimate random effects models in a further attempt to capture unobserved factors that might influence the propensity to commit a crime.
Empirical Results
We present associations between childhood ADHD and criminal activities during early adulthood, including stealing, selling drugs, burglary, robbery, being arrested, being convicted, and “any crime” in a series of tables below; all results use robust standard errors clustered at the school-level, longitudinal weights, and report marginal effects from a logistic regression analysis. In particular, we estimate variants of the following specification:
where yic is a criminal activityoutcome for individual i in community c, ADHDi is the characterization of AD/HD symptoms for the individual, Xi is a vector of individual and family background characteristics listed in Table 1, μc is either a vector of community-level characteristics outlined Table 1 or a random/fixed effect at the family level in some specifications18, and εi is an idiosyncratic error term allowed to be arbitrarily correlated for individuals in the same community. As noted above we predict higher rates of particular types of crime, arrest and conviction for those with inattentive/hyperactive ADHD. Thus for example we predict a significant β on ADHD inattentive symptoms for the crime of selling drugs and a significant β on ADHD impulsive symptoms for arrest.
In Table 2, we present results for whether the individual reported to have perpetrated any crime in wave III.19 According to our results and as expected, individuals with all types of ADHD, whom we posit have lower legal human capital, are more likely to report any crime, including a 6.5 percentage point increase for the inattentive type, an eleven point increase for the hyperactive type, and a five point increase for the combined type20. These results are qualitatively similar across several robustness checks, including the exclusion/inclusion of community control variables, education control variables, additional family background variables (public assistance receipt, paternal incarceration, and low birth weight status), stratification by age groups, and the use of lower thresholds for ADHD status (see the appendix). In order to examine the robustness of the results to unobserved family backgrounds we use random and fixed effects specifications. We use an “any ADHD” variable rather than separate the subtypes because of the relatively low prevalence of the subtypes. The baseline results for the full sample show a 7 percentage point increase in any criminal activity, the family random and fixed effects results predict a 11 and 13 point increase in the reporting of any criminal activity, although the family fixed effect is not statistically significant.21 Thus our initial results provide evidence that the observed lower level of human capital (schooling) of those with ADHD found in other studies may translate into a higher probability of committing a crime (or illegal capital). These results are consistent with our hypothesis above.
Table 2.
Association between Retrospective Ratings of ADHD Symptoms and Any Adult Criminal Activity
| Outcome | Any Crime | Any Crime | Any Crime | Any Crime |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.170*** | 0.170*** | 0.142*** | 0.134 |
| (0.150 – 0.189) | (0.151 – 0.189) | (0.117 – 0.168) | (−0.103 – 0.372) | |
| Black | 0.033** | 0.034** | ||
| (0.003 – 0.063) | (0.004 – 0.064) | |||
| Hispanic | −0.006 | −0.006 | ||
| (−0.036 – 0.024) | (−0.036 – 0.024) | |||
| Income | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.011*** | −0.011*** | −0.011*** | −0.006 |
| (−0.017 – −0.006) | (−0.017 – −0.006) | (−0.018 – −0.005) | (−0.015 – 0.003) | |
| Mom Education | 0.010*** | 0.010*** | ||
| (0.005 – 0.014) | (0.005 – 0.014) | |||
| Intact Family | −0.034*** | −0.034*** | ||
| (−0.056 – −0.012) | (−0.056 – −0.012) | |||
| Inattentive ADHD | 0.065** | |||
| (0.003 – 0.128) | ||||
| Hyper ADHD | 0.110*** | |||
| (0.037 – 0.183) | ||||
| Combined ADHD | 0.051* | |||
| (−0.008 – 0.110) | ||||
| ADHD ANY | 0.072*** | 0.107*** | 0.129 | |
| (0.035 – 0.109) | (0.055 – 0.159) | (−0.082 – 0.340) | ||
| Years of Schooling | −0.010*** | −0.010*** | −0.013*** | −0.011 |
| (−0.017 – −0.004) | (−0.016 – −0.004) | (−0.020 – −0.006) | (−0.032 – 0.010) | |
| PVT Test Score | 0.001*** | 0.001*** | 0.001 | 0.000 |
| (0.001 – 0.002) | (0.001 – 0.002) | (−0.000 – 0.002) | (−0.002 – 0.002) | |
| GPA | −0.039*** | −0.039*** | −0.016* | −0.021 |
| (−0.056 – −0.022) | (−0.056 – −0.022) | (−0.031 – 0.000) | (−0.065 – 0.022) | |
| % in Poverty | −0.005** | −0.005** | ||
| (−0.010 – −0.000) | (−0.010 – −0.000) | |||
| Unemployment Rate | 0.007* | 0.007* | ||
| (−0.001 – 0.015) | (−0.001 – 0.016) | |||
| % Without Diploma | 0.001 | 0.001 | ||
| (−0.001 – 0.003) | (−0.001 – 0.003) | |||
| Juvenile Serious Crimes | 0.012*** | 0.012*** | ||
| (0.004 – 0.021) | (0.004 – 0.021) | |||
| Total Serious Crimes | −0.000 | −0.000 | ||
| (−0.001 – 0.001) | (−0.001 – 0.001) | |||
| Observations | 12730 | 12737 | 3638 | 943 |
| Families | 1792 | 449 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, proportion black in community, median and standard deviation of community level household income
In our next set of Tables, we disaggregate the “any crime” variable into its components in order to examine the relationships between ADHD types and each individual criminal activity.
First, in Table 3 we present results for whether an individual reported stealing items worth over $50 in the last year (in wave III). Persons reporting any type of ADHD are more likely to report stealing, including a 3.4 percentage point increase for the inattentive type (not statistically significant), a 4.4 point increase for the hyperactive type, and a 3.2 point increase for the combined type. Results of our series of robust checks are qualitatively similar and are presented in the appendix. Finally, we estimate the specification using sibling differences in ADHD status and reported stealing for the “any ADHD” variable. Results using the full sample indicate that “any ADHD” increases the likelihood of reported stealing by 3.5 points, and using family random and fixed effects models shrinks the estimate to between a 1.1 and 6.3 point increase, though the estimates are not statistically significant. Since we predicted that both types of ADHD symptoms were likely to lead to an increase in stealing these findings are consistent with our expectations.
Next, in Table 4 we present results for whether an individual reported “often” selling drugs in the last year. While those with all types of ADHD symptoms show an increased probability of selling drugs, only the inattentive type is statistically significant—a 4.3 percentage point increase. This result is as predicted above since selling drugs is likely to require planning and hence is less likely for those with impulsive symptoms. The increase in selling drugs among those with inattentive symptoms is consistent with a lack of understanding of the consequences of such behavior. As predicted by the model, school performance, measured by grade point average or test scores reduce the chances of selling drugs. Community-level variables are also important in predicting drug selling behavior. The unemployment rate, the percent in poverty, and juvenile crime rates are associated with the chances of reporting stealing. Like the results for stealing, these results were not very sensitive to a series of robust checks described earlier. Also like previous results, we explore the “any ADHD” variable in the baseline specification using family random and fixed effects. The baseline results show that ADHD increased the chances of selling drugs by nearly 3 percentage points for the baseline specification, a 2.6 percentage point increase in the random effects specification, and a 2.4 percentage point increase in the fixed effects specification with a sample size of approximately 400.
Table 4.
Association between Retrospective Ratings of ADHD Symptoms and Selling Drugs
| Outcome | Sell Drugs | Sell Drugs | Sell Drugs | Sell Drugs |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.070*** | 0.070*** | 0.046*** | 0.069 |
| (0.059 – 0.081) | (0.059 – 0.081) | (0.029 – 0.062) | (−0.166 – 0.304) | |
| Black | 0.009 | 0.010 | ||
| (−0.007 – 0.025) | (−0.007 – 0.026) | |||
| Hispanic | −0.004 | −0.004 | ||
| (−0.024 – 0.016) | (−0.024 – 0.016) | |||
| Income | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.012*** | −0.012*** | −0.003** | −0.003 |
| (−0.016 – −0.009) | (−0.016 – −0.008) | (−0.006 – −0.001) | (−0.010 – 0.004) | |
| Mom Education | 0.004*** | 0.004*** | ||
| (0.002 – 0.007) | (0.002 – 0.007) | |||
| Intact Family | −0.013** | −0.013** | ||
| (−0.025 – −0.001) | (−0.025 – −0.001) | |||
| Inattentive ADHD | 0.043* | |||
| (−0.001 – 0.087) | ||||
| Hyper ADHD | 0.035 | |||
| (−0.013 – 0.083) | ||||
| Combined ADHD | 0.010 | |||
| (−0.026 – 0.046) | ||||
| ADHD ANY | 0.029** | 0.026** | 0.024 | |
| (0.003 – 0.056) | (0.003 – 0.049) | (−0.065 – 0.113) | ||
| Years of Schooling | −0.003* | −0.003* | −0.003** | −0.002 |
| (−0.007 – 0.000) | (−0.006 – 0.000) | (−0.006 – −0.000) | (−0.013 – 0.009) | |
| PVT Test Score | 0.001** | 0.001** | 0.000 | −0.001 |
| (0.000 – 0.001) | (0.000 – 0.001) | (−0.000 – 0.001) | (−0.003 – 0.002) | |
| GPA | −0.015*** | −0.015*** | −0.003 | −0.013 |
| (−0.023 – −0.007) | (−0.023 – −0.007) | (−0.009 – 0.004) | (−0.059 – 0.032) | |
| % in Poverty | −0.003** | −0.003** | ||
| (−0.005 – −0.000) | (−0.005 – −0.000) | |||
| % Black | 0.000 | 0.000 | ||
| (−0.000 – 0.001) | (−0.000 – 0.001) | |||
| Unemployment Rate | 0.004** | 0.004** | ||
| (0.000 – 0.008) | (0.000 – 0.008) | |||
| % Without Diploma | 0.000 | 0.000 | ||
| (−0.001 – 0.001) | (−0.001 – 0.001) | |||
| Juvenile Serious Crimes | 0.007*** | 0.007*** | ||
| (0.003 – 0.010) | (0.003 – 0.010) | |||
| Total Serious Crimes | −0.000** | −0.000** | ||
| (−0.001 – −0.000) | (−0.001 – −0.000) | |||
| Observations | 12588 | 12595 | 3560 | 416 |
| 1753 | 197 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, and median and standard deviation of community level household income
Third, in Table 5 we present results for whether an individual reported burglarizing a home or building during the last year. Although all three subtypes of ADHD are positively related to this outcome, none of the results are statistically significant. Using the “any ADHD” measure for the entire sample and the smaller random effects sample, we estimate small positive ties but they are only statistically significant with our full sample; the sign is negative but not significant for the fixed effects sample. We find few individual, family, or community level results of note—male, black, and younger individuals are more likely to report committing burglaries.
Table 5.
Association between Retrospective Ratings of ADHD Symptoms and Burglary
| Outcome | Burglary | Burglary | Burglary | Burglary |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.016*** | 0.016*** | 0.006* | 0.196 |
| (0.010 – 0.022) | (0.010 – 0.022) | (−0.000 – 0.013) | (−0.070 – 0.462) | |
| Black | 0.021*** | 0.022*** | ||
| (0.009 – 0.034) | (0.009 – 0.035) | |||
| Hispanic | 0.003 | 0.003 | ||
| (−0.007 – 0.012) | (−0.007 – 0.012) | |||
| Income | 0.000* | 0.000* | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.004*** | −0.004*** | −0.001 | 0.011 |
| (−0.005 – −0.002) | (−0.005 – −0.002) | (−0.002 – 0.000) | (−0.064 – 0.087) | |
| Mom Education | 0.001* | 0.001* | ||
| (−0.000 – 0.002) | (−0.000 – 0.002) | |||
| Intact Family | 0.001 | 0.001 | ||
| (−0.004 – 0.006) | (−0.004 – 0.006) | |||
| Inattentive ADHD | 0.017 | |||
| (−0.006 – 0.041) | ||||
| Hyper ADHD | 0.010 | |||
| (−0.010 – 0.030) | ||||
| Combined ADHD | 0.004 | |||
| (−0.013 – 0.021) | ||||
| ADHD ANY | 0.011* | 0.003 | −0.014 | |
| (−0.002 – 0.024) | (−0.005 – 0.011) | (−0.319 – 0.290) | ||
| Years of Schooling | 0.001 | 0.001 | −0.001 | −0.057 |
| (−0.001 – 0.002) | (−0.001 – 0.002) | (−0.002 – 0.001) | (−0.145 – 0.030) | |
| PVT Test Score | −0.000 | −0.000 | −0.000 | 0.000 |
| (−0.000 – 0.000) | (−0.000 – 0.000) | (−0.000 – 0.000) | (−0.012 – 0.012) | |
| GPA | −0.003 | −0.003 | 0.001 | 0.126 |
| (−0.006 – 0.001) | (−0.006 – 0.001) | (−0.002 – 0.004) | (−0.101 – 0.353) | |
| % in Poverty | −0.000 | −0.000 | ||
| (−0.001 – 0.001) | (−0.001 – 0.001) | |||
| % Black | −0.000* | −0.000* | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Unemployment Rate | −0.001 | −0.001 | ||
| (−0.003 – 0.001) | (−0.003 – 0.000) | |||
| % Without Diploma | 0.000 | −0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Juvenile Serious Crimes | 0.001 | 0.001 | ||
| (−0.002 – 0.003) | (−0.002 – 0.003) | |||
| Total Serious Crimes | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Observations | 12635 | 12642 | 3582 | 136 |
| 1764 | 64 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III and median and standard deviation of community level household income
Fourth, in Table 6 we present results for whether an individual committed a robbery in the past year. Using the “any ADHD” measure, we estimate a 1 percentage point increase in the reporting of committing a robbery in our baseline estimates. The estimates vary between 1.7 and 3.6 percentage points with random fixed effects and family fixed effects (N=140). Again the observed pattern is consistent with our expectations.
Table 6.
Association between Retrospective Ratings of ADHD Symptoms and Robbery
| Outcome | Robbery | Robbery | Robbery | Robbery |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.014*** | 0.014*** | 0.013* | 0.064 |
| (0.008 – 0.019) | (0.008 – 0.019) | (−0.002 – 0.028) | (−0.339 – 0.466) | |
| Black | 0.020*** | 0.019*** | ||
| (0.008 – 0.031) | (0.008 – 0.031) | |||
| Hispanic | 0.004 | 0.004 | ||
| (−0.005 – 0.012) | (−0.005 – 0.012) | |||
| Income | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.002*** | −0.002*** | −0.002 | −0.006 |
| (−0.004 – −0.001) | (−0.004 – −0.001) | (−0.005 – 0.001) | (−0.038 – 0.025) | |
| Mom Education | 0.001*** | 0.001*** | ||
| (0.000 – 0.003) | (0.000 – 0.003) | |||
| Intact Family | −0.004 | −0.004* | ||
| (−0.009 – 0.001) | (−0.009 – 0.001) | |||
| Inattentive ADHD | 0.005 | |||
| (−0.017 – 0.028) | ||||
| Hyper ADHD | 0.016 | |||
| (−0.003 – 0.035) | ||||
| Combined ADHD | 0.017 | |||
| (−0.004 – 0.038) | ||||
| ADHD ANY | 0.011* | 0.017 | 0.036 | |
| (−0.001 – 0.024) | (−0.006 – 0.040) | (−0.189 – 0.260) | ||
| Years of Schooling | −0.001* | −0.001* | −0.000 | −0.004 |
| (−0.003 – 0.000) | (−0.003 – 0.000) | (−0.002 – 0.002) | (−0.032 – 0.023) | |
| PVT Test Score | −0.000 | −0.000 | −0.000 | −0.000 |
| (−0.000 – 0.000) | (−0.000 – 0.000) | (−0.001 – 0.000) | (−0.003 – 0.002) | |
| GPA | −0.000 | −0.000 | −0.001 | 0.019 |
| (−0.004 – 0.003) | (−0.004 – 0.003) | (−0.006 – 0.004) | (−0.105 – 0.142) | |
| % in Poverty | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| % Black | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Unemployment Rate | 0.000 | 0.001 | ||
| (−0.001 – 0.002) | (−0.001 – 0.002) | |||
| % Without Diploma | −0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Juvenile Serious Crimes | 0.001 | 0.001 | ||
| (−0.001 – 0.003) | (−0.001 – 0.003) | |||
| Total Serious Crimes | −0.000 | −0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Observations | 12632 | 12639 | 3583 | 140 |
| 1765 | 66 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, and median and standard deviation of community level household income
Fifth, in Table 7 we present results for whether an individual reported ever being arrested by police. Persons with all types of ADHD appear to face an increased chance of being arrested: the inattentive type by 2.2 percentage points (not significant), hyperactive type by 6.4 points, and the combined type by 4.2 percentage points. Our “any ADHD” results showed an increase in the chance of arrest of nearly 4 percentage points in the baseline results, 6.2 points in the random effect results, and over 20 points in the fixed effect results (N=625). The higher probability of arrest of those with impulsive symptoms is expected and may reflect the more “immediate’ crime committed without consideration of the probability of being caught by those with such symptoms. The only community-level variable that is statistically related to being arrested is the rate of juvenile crime.
Table 7.
Association between Retrospective Ratings of ADHD Symptoms and Arrests
| Outcome | Arrest | Arrest | Arrest | Arrest |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.128*** | 0.128*** | 0.090*** | 0.255 |
| (0.112 – 0.143) | (0.112 – 0.144) | (0.068 – 0.112) | (−0.058 – 0.569) | |
| Black | 0.011 | 0.011 | ||
| (−0.011 – 0.033) | (−0.011 – 0.033) | |||
| Hispanic | −0.011 | −0.011 | ||
| (−0.030 – 0.007) | (−0.030 – 0.007) | |||
| Income | 0.000 | 0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.001 | −0.001 | −0.001 | 0.014 |
| (−0.005 – 0.003) | (−0.005 – 0.003) | (−0.005 – 0.003) | (−0.025 – 0.052) | |
| Mom Education | 0.005*** | 0.005*** | ||
| (0.002 – 0.009) | (0.002 – 0.009) | |||
| Intact Family | −0.024*** | −0.024*** | ||
| (−0.038 – −0.010) | (−0.038 – −0.010) | |||
| Inattentive ADHD | 0.022 | |||
| (−0.023 – 0.068) | ||||
| Hyper ADHD | 0.064*** | |||
| (0.016 – 0.111) | ||||
| Combined ADHD | 0.042* | |||
| (−0.001 – 0.086) | ||||
| ADHD ANY | 0.039*** | 0.062*** | 0.245*** | |
| (0.011 – 0.067) | (0.026 – 0.097) | (0.075 – 0.415) | ||
| Years of Schooling | −0.010*** | −0.010*** | −0.013*** | −0.051* |
| (−0.015 – −0.006) | (−0.015 – −0.006) | (−0.018 – −0.008) | (−0.102 – 0.000) | |
| PVT Test Score | 0.001*** | 0.001*** | 0.001*** | 0.001 |
| (0.000 – 0.001) | (0.000 – 0.001) | (0.000 – 0.001) | (−0.004 – 0.006) | |
| GPA | −0.030*** | −0.029*** | −0.013** | −0.033 |
| (−0.042 – −0.018) | (−0.041 – −0.018) | (−0.023 – −0.003) | (−0.103 – 0.037) | |
| % in Poverty | −0.003 | −0.003 | ||
| (−0.007 – 0.001) | (−0.007 – 0.001) | |||
| % Black | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Unemployment Rate | 0.002 | 0.003 | ||
| (−0.004 – 0.009) | (−0.004 – 0.009) | |||
| % Without Diploma | 0.001 | 0.001 | ||
| (−0.000 – 0.003) | (−0.000 – 0.003) | |||
| Juvenile Serious Crimes | 0.007** | 0.007** | ||
| (0.001 – 0.014) | (0.001 – 0.014) | |||
| Total Serious Crimes | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Observations | 12654 | 12661 | 3596 | 625 |
| 1771 | 300 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, and median and standard deviation of community level household income
Finally, in Table 8 we present results for whether the individual reports ever being convicted of a crime. We view this primarily as another indicator of committing a crime rather than tie it to a deterrence effect. We find positive relationships between the inattentive (0.2 point), hyperactivity (nearly 1 point) and combined types (nearly 1 point) and convictions, but the results are not statistically significant. Our specifications using “any ADHD” shows an increase in chances of conviction of 0.5 points in the baseline results, nearly 1 point in the random effects results, and no effect in the family fixed effects results (N=146). Again this pattern is consistent with expectations and likely consistent with the greater probability of arrest and lack of any planning among those with impulsive symptoms.
Table 8.
Association between Retrospective Ratings of ADHD Symptoms and Convictions
| Outcome | Convicted | Convicted | Convicted | Convicted |
|---|---|---|---|---|
| Specification | Sub Types | Any ADHD | Any ADHD | Any ADHD |
| Specification | Baseline | Baseline | Random Effects | Fixed Effects |
| Male | 0.017*** | 0.017*** | 0.011** | 0.000 |
| (0.012 – 0.022) | (0.012 – 0.022) | (0.000 – 0.023) | (−0.002 – 0.003) | |
| Black | 0.004* | 0.004* | ||
| (−0.000 – 0.009) | (−0.000 – 0.009) | |||
| Hispanic | 0.002 | 0.002 | ||
| (−0.003 – 0.007) | (−0.003 – 0.007) | |||
| Income | −0.000 | −0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Age | −0.001** | −0.001** | −0.000 | −0.000 |
| (−0.002 – −0.000) | (−0.002 – −0.000) | (−0.001 – 0.000) | (−0.000 – 0.000) | |
| Mom Education | 0.000 | 0.000 | ||
| (−0.000 – 0.001) | (−0.000 – 0.001) | |||
| Intact Family | −0.005*** | −0.005*** | ||
| (−0.008 – −0.002) | (−0.008 – −0.002) | |||
| Inattentive ADHD | 0.002 | |||
| (−0.006 – 0.010) | ||||
| Hyper ADHD | 0.009 | |||
| (−0.003 – 0.020) | ||||
| Combined ADHD | 0.008 | |||
| (−0.005 – 0.020) | ||||
| ADHD ANY | 0.005* | 0.008 | 0.000 | |
| (−0.001 – 0.011) | (−0.002 – 0.017) | (−0.002 – 0.002) | ||
| Years of Schooling | −0.002*** | −0.002*** | −0.001* | −0.000 |
| (−0.003 – −0.001) | (−0.003 – −0.001) | (−0.003 – 0.000) | (−0.001 – 0.001) | |
| PVT Test Score | 0.000** | 0.000** | −0.000 | −0.000 |
| (0.000 – 0.000) | (0.000 – 0.000) | (−0.000 – 0.000) | (−0.000 – 0.000) | |
| GPA | −0.003** | −0.003** | −0.001 | −0.000 |
| (−0.006 – −0.001) | (−0.006 – −0.001) | (−0.003 – 0.001) | (−0.000 – 0.000) | |
| % in Poverty | −0.001 | −0.001 | ||
| (−0.002 – 0.000) | (−0.002 – 0.000) | |||
| % Black | −0.000 | −0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Unemployment Rate | −0.000 | −0.000 | ||
| (−0.002 – 0.001) | (−0.002 – 0.001) | |||
| % Without Diploma | −0.000 | −0.000 | ||
| (−0.001 – 0.000) | (−0.001 – 0.000) | |||
| Juvenile Serious Crimes | 0.001* | 0.001* | ||
| (−0.000 – 0.003) | (−0.000 – 0.003) | |||
| Total Serious Crimes | −0.000 | −0.000 | ||
| (−0.000 – 0.000) | (−0.000 – 0.000) | |||
| Observations | 12723 | 12730 | 3631 | 146 |
| 1789 | 69 |
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used. Additional Controls: Whether in school during Wave III, and median and standard deviation of community level household income
Conclusion
The evidence presented above leads to a clear conclusion: persons with symptoms of ADHD during the period 5–12 years of age, whether they be of the hyperactivity, inattentive or combined type, are far more likely to report criminal activities as young adults than other individuals. This clear pattern prevails even while other factors that might be expected to influence risk taking are taken into account; these factors include individual characteristics such as gender, race/ethnicity, education of parents, family income, type of family in which raised, and community characteristics such as poverty and income, race/ethnicity, income inequality, the unemployment rate and crime rates. These results are also consistent with the expectations of economic models of crime: persons with ADHD have lower labor market expectations and so are more likely to commit a crime than our other otherwise similar adolescents and young adults.
Turning to results on type of ADHD, the evidence suggests that persons with the inattentive type symptoms are more likely to commit all of the studied crimes except robbery than individuals with no ADHD symptoms. This group was more likely to engage in crimes that might require some planning such as selling drugs or burglary. Individuals with impulsive symptoms had the highest increase in criminal activities of all the ADHD-types and were more likely to be arrested and convicted of a crime. The impulsive nature of their disorder seemed to lead them to engage in more impulsive crimes such as theft and robbery. Interestingly, individuals with the combined-type of ADHD symptoms had the weakest associations with crime; we find very little evidence that these individuals are at a multiplicative risk of criminal activities in comparison with individuals who have only inattentive or only hyperactive symptoms. This result is surprising.
Our results are robust across several specification checks, including lowering the ADHD threshold, controlling for additional variables, and using family fixed and random effects specifications (full results in appendix). The magnitudes of the random and fixed effects results are largely consistent with the results for the entire sample, but due to the smaller sample size, fewer are statistically significant, especially for the fixed effects estimates which are run over the smallest number of observations.
Since the economic model suggests that human capital is the important factor “explaining” gains from legal activity, we also report evidence that ADHD leads to greater participation in crime without controlling for education in our model. That is consistent with other literature that notes a direct effect of ADHD on educational outcomes (5, 7, 36). In results reported in the appendix, the association between ADHD symptoms and risky behavior outcomes is often 10–20% higher when we do not control for education in our specifications.
Clearly then our results provide evidence that there are sizeable economic costs in the form of increased rates of crime for individuals who report symptoms of ADHD as children; costs borne by the individuals, their families, and society more generally. A rough estimate of this cost to victims is between $50 and $170 million dollars per year and our estimate of the total cost to society is between $2 and $4 billion dollars per year; a very sizeable cost to society.22
This evidence suggests that children showing ADHD symptoms should be viewed as a group at high risk of poor outcomes as young adults. As such, a good case can be made for targeting intervention programs on this group of children and conducting evaluations to learn if such interventions are effective in reducing the probability that these children commit a crime. Development of such intervention programs and evaluating them for efficiency could be dollars well spent in terms of crime and drug abuse averted.
The reader should recall however that a disadvantage of the dataset used here is that we do not know whether the individuals were ever treated for ADHD. As noted above, most of the individuals in this dataset were children before special education services were mandated for individuals with ADHD, which occurred in 1991. If few of the individuals in the data were ever treated for ADHD, then the results above provide an upper bound of the effects of ADHD on longer term life outcomes; now that ADHD is treated at much higher rates than when the individuals in this dataset were children the negative consequences may be far less severe. If most of the individuals with ADHD symptoms in the dataset were treated during childhood, then ADHD would still represent a significant cost to individuals and society in terms of adult outcomes. Of course, the costs of medical care, pharmaceuticals and counseling in treatment of ADHD all must be included in any full account of the costs and consequences of ADHD.
Acknowledgments
This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu).
APPENDIX 1. APPENDIX: Robustness of Results across Specifications
| Outcome | Any Crime | Any Crime | Any Crime | Any Crime |
|---|---|---|---|---|
| Specification | Baseline | No Census data | Just Exogenous | Drop PVT/GPA |
| Inattentive ADHD | 0.065** | 0.080** | 0.109*** | 0.092*** |
| (0.003 – 0.128) | (0.019 – 0.140) | (0.054 – 0.165) | (0.035 – 0.150) | |
| Hyper ADHD | 0.110*** | 0.113*** | 0.119*** | 0.117*** |
| (0.037 – 0.183) | (0.042 – 0.184) | (0.048 – 0.191) | (0.045 – 0.189) | |
| Combined ADHD | 0.051* | 0.056* | 0.078** | 0.066** |
| (−0.008 – 0.110) | (−0.004 – 0.115) | (0.018 – 0.139) | (0.006 – 0.126) | |
| Observations | 12730 | 13229 | 14302 | 13744 |
| Outcome | Any Crime | Any Crime | Any Crime | Any Crime |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.072** | 0.085*** | 0.037 | 0.053 |
| (0.001 – 0.143) | (0.029 – 0.141) | (0.027) | (−0.011 – 0.117) | |
| Hyper ADHD | 0.104** | 0.115*** | 0.076*** | 0.109*** |
| (0.023 – 0.185) | (0.042 – 0.188) | (0.023) | (0.035 – 0.182) | |
| Combined ADHD | 0.037 | 0.060** | 0.088*** | 0.038 |
| (−0.027 – 0.100) | (0.002 – 0.117) | (0.026) | (−0.023 – 0.099) | |
| Observations | 9649 | 13753 | 12730 | 12046 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
| Outcome | Steal | Steal | Steal | Steal |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exogenous | Drop PVT/GPA |
| Inattentive ADHD | 0.034 | 0.037* | 0.041** | 0.038* |
| (−0.007 – 0.075) | (−0.004 – 0.078) | (0.002 – 0.080) | (−0.000 – 0.076) | |
| Hyper ADHD | 0.044** | 0.047** | 0.042** | 0.037** |
| (0.004 – 0.085) | (0.004 – 0.089) | (0.003 – 0.081) | (0.001 – 0.073) | |
| Combined ADHD | 0.032** | 0.030* | 0.027* | 0.028* |
| (0.001 – 0.063) | (−0.001 – 0.060) | (−0.001 – 0.056) | (−0.001 – 0.057) | |
| Observations | 12623 | 13120 | 14180 | 13625 |
| Outcome | Steal | Steal | Steal | Steal |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.035 | 0.040* | 0.008 | 0.032 |
| (−0.013 – 0.082) | (−0.000 – 0.080) | (0.011) | (−0.008 – 0.072) | |
| Hyper ADHD | 0.048** | 0.038** | 0.033** | 0.050** |
| (0.002 – 0.093) | (0.002 – 0.074) | (0.013) | (0.006 – 0.094) | |
| Combined ADHD | 0.026 | 0.029** | 0.040*** | 0.028* |
| (−0.007 – 0.058) | (0.000 – 0.058) | (0.014) | (−0.002 – 0.058) | |
| Observations | 9603 | 13634 | 12623 | 11947 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
| Outcome | Sell Drugs | Sell Drugs | Sell Drugs | Sell Drugs |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exogenous | Drop PVT/GPA |
| Inattentive ADHD | 0.043* | 0.056** | 0.065*** | 0.050** |
| (−0.001 – 0.087) | (0.011 – 0.100) | (0.022 – 0.108) | (0.008 – 0.093) | |
| Hyper ADHD | 0.035 | 0.042* | 0.035 | 0.028 |
| (−0.013 – 0.083) | (−0.004 – 0.088) | (−0.010 – 0.081) | (−0.018 – 0.075) | |
| Combined ADHD | 0.010 | 0.013 | 0.022 | 0.016 |
| (−0.026 – 0.046) | (−0.024 – 0.051) | (−0.018 – 0.062) | (−0.022 – 0.053) | |
| Observations | 12588 | 13085 | 14136 | 13581 |
| Outcome | Sell Drugs | Sell Drugs | Sell Drugs | Sell Drugs |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.048* | 0.049** | 0.021 | 0.043* |
| (−0.006 – 0.101) | (0.007 – 0.091) | (0.017) | (−0.005 – 0.090) | |
| Hyper ADHD | 0.035 | 0.028 | 0.026 | 0.034 |
| (−0.018 – 0.089) | (−0.018 – 0.074) | (0.016) | (−0.015 – 0.083) | |
| Combined ADHD | 0.006 | 0.015 | 0.046** | 0.009 |
| (−0.031 – 0.043) | (−0.023 – 0.052) | (0.018) | (−0.028 – 0.046) | |
| Observations | 9577 | 13590 | 12588 | 11913 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
| Outcome | Burglary | Burglary | Burglary | Burglary |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exo | Drop PVT/GPA |
| Inattentive ADHD | 0.017 | 0.021 | 0.020 | 0.016 |
| (−0.006 – 0.041) | (−0.005 – 0.048) | (−0.005 – 0.044) | (−0.006 – 0.038) | |
| Hyper ADHD | 0.010 | 0.011 | 0.009 | 0.008 |
| (−0.010 – 0.030) | (−0.009 – 0.030) | (−0.009 – 0.027) | (−0.010 – 0.026) | |
| Combined ADHD | 0.004 | 0.003 | 0.003 | 0.004 |
| (−0.013 – 0.021) | (−0.013 – 0.020) | (−0.013 – 0.020) | (−0.012 – 0.021) | |
| Observations | 12635 | 13132 | 14191 | 13636 |
| Outcome | Burglary | Burglary | Burglary | Burglary |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.014 | 0.017 | 0.011 | 0.016 |
| (−0.014 – 0.043) | (−0.006 – 0.040) | (0.008) | (−0.009 – 0.041) | |
| Hyper ADHD | 0.006 | 0.008 | 0.018** | 0.011 |
| (−0.010 – 0.023) | (−0.010 – 0.026) | (0.009) | (−0.010 – 0.033) | |
| Combined ADHD | 0.006 | 0.005 | 0.011 | 0.005 |
| (−0.014 – 0.026) | (−0.013 – 0.024) | (0.009) | (−0.014 – 0.024) | |
| Observations | 9613 | 13645 | 12635 | 11959 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
| Outcome | Robbery | Robbery | Robbery | Robbery |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exo | Drop PVT/GPA |
| Inattentive ADHD | 0.005 | 0.009 | 0.011 | 0.004 |
| (−0.017 – 0.028) | (−0.014 – 0.032) | (−0.014 – 0.036) | (−0.016 – 0.025) | |
| Hyper ADHD | 0.016 | 0.021** | 0.019* | 0.013 |
| (−0.003 – 0.035) | (0.000 – 0.042) | (−0.001 – 0.038) | (−0.003 – 0.029) | |
| Combined ADHD | 0.017 | 0.015 | 0.021* | 0.017 |
| (−0.004 – 0.038) | (−0.005 – 0.035) | (−0.003 – 0.046) | (−0.004 – 0.038) | |
| Observations | 12632 | 13129 | 14187 | 13632 |
| Outcome | Robbery | Robbery | Robbery | Robbery |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.005 | 0.004 | −0.001 | 0.006 |
| (−0.019 – 0.029) | (−0.018 – 0.027) | (0.006) | (−0.018 – 0.029) | |
| Hyper ADHD | 0.021* | 0.013 | 0.013** | 0.019* |
| (−0.001 – 0.042) | (−0.004 – 0.029) | (0.006) | (−0.002 – 0.040) | |
| Combined ADHD | 0.018 | 0.018* | 0.015* | 0.015 |
| (−0.005 – 0.040) | (−0.003 – 0.040) | (0.008) | (−0.006 – 0.036) | |
| Observations | 9613 | 13641 | 12632 | 11954 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%
| Outcome | Arrest | Arrest | Arrest | Arrest |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exo | Drop PVT/GPA |
| Inattentive ADHD | 0.022 | 0.020 | 0.049** | 0.048** |
| (−0.023 – 0.068) | (−0.024 – 0.064) | (0.004 – 0.095) | (0.002 – 0.093) | |
| Hyper ADHD | 0.064*** | 0.070*** | 0.083*** | 0.079*** |
| (0.016 – 0.111) | (0.023 – 0.118) | (0.030 – 0.135) | (0.029 – 0.129) | |
| Combined ADHD | 0.042* | 0.047** | 0.074*** | 0.059** |
| (−0.001 – 0.086) | (0.003 – 0.091) | (0.026 – 0.122) | (0.013 – 0.105) | |
| Observations | 12654 | 13149 | 14208 | 13655 |
| Outcome | Arrest | Arrest | Arrest | arrestw3 U25 |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.039 | 0.040* | 0.025 | 0.013 |
| (−0.012 – 0.089) | (−0.004 – 0.083) | (0.019) | (−0.032 – 0.058) | |
| Hyper ADHD | 0.059** | 0.077*** | 0.045*** | 0.065*** |
| (0.006 – 0.113) | (0.026 – 0.128) | (0.016) | (0.016 – 0.114) | |
| Combined ADHD | 0.028 | 0.051** | 0.045** | 0.029 |
| (−0.017 – 0.074) | (0.007 – 0.096) | (0.018) | (−0.015 – 0.072) | |
| Observations | 9635 | 13664 | 12654 | 11974 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
| Outcome | Convicted | Convicted | Convicted | Convicted |
|---|---|---|---|---|
| Specification | Baseline | No Census data | just exog | Drop PVT/GPA |
| Inattentive ADHD | 0.002 | 0.004 | 0.020** | 0.011* |
| (−0.006 – 0.010) | (−0.006 – 0.014) | (0.002 – 0.038) | (−0.000 – 0.022) | |
| Hyper ADHD | 0.009 | 0.013* | 0.019** | 0.013* |
| (−0.003 – 0.020) | (−0.001 – 0.026) | (0.001 – 0.038) | (−0.001 – 0.027) | |
| Combined ADHD | 0.008 | 0.008 | 0.019* | 0.011 |
| (−0.005 – 0.020) | (−0.006 – 0.021) | (−0.003 – 0.041) | (−0.005 – 0.027) | |
| Observations | 12723 | 13222 | 14294 | 13736 |
| Outcome | Convicted | Convicted | Convicted | Convicted |
| Specification | Include Other Xs | Drop Educ/Family Vars | Lower ADHD Bar | Drop Older Kids |
| Inattentive ADHD | 0.004 | 0.009* | 0.004 | 0.002 |
| (−0.005 – 0.013) | (−0.001 – 0.019) | (0.004) | (−0.006 – 0.011) | |
| Hyper ADHD | 0.010 | 0.012* | 0.002 | 0.004 |
| (−0.004 – 0.024) | (−0.001 – 0.025) | (0.003) | (−0.006 – 0.014) | |
| Combined ADHD | 0.004 | 0.008 | 0.004 | 0.008 |
| (−0.010 – 0.017) | (−0.005 – 0.021) | (0.004) | (−0.005 – 0.021) | |
| Observations | 9647 | 13745 | 12723 | 12039 |
Just Exogenous: We drop education attainment, indicator for currently in school, grade point average, PVT score, mother’s education attainment, family income, and family structure
Include other Xs: Welfare receipt, whether father ever jailed, and birth weight
Lower ADHD Threshold: Individuals must have 5 symptoms rather than 6.
Robust 95% confidence intervals in parentheses, +10%,
5%,
1%.
Longitudinal sample weights used.
APPENDIX 2. Appendix: Social Costs of Crime Calculations
We use estimates of the costs of crime from two sources: victim costs estimates are taken from the Uniform Crime Reports (37) for burglary, robbery, larceny, and selling illegal drugs.23 Total social costs are taken from estimates by Boardman et al. (38) for robbery, burglary, larceny, and drugs.24
In order to calculate the effect of ADHD on the increase in criminal activities, we use ranges of coefficients from our previous analyses (odds ratios are shown in Table A1) and use Add Health sample weights to determine the relevant population of individuals affected by ADHD and those reporting criminal activities (shown in Table A1 and A2). The original sample was representative of the US population of 7th–12th graders in 1994–5, and wave 3 included those individuals who were followed in 2000–2001. Finally, since many individuals reported being arrested and/or convicted for crimes either without identifying the crime, or for crimes not used in the analysis in this paper, we include cost estimates for these crimes using a weighted average of the costs of the crimes included in our analysis—these figures are shown in the last row of Tables 3A and 4A.
Table A1.
Values Used to Calculate Costs
| Crime | Prevalence (data) | Lower Bound Odds Ratio | Upper Bound Odds Ratio | Increase in Number of Crimes (Lower Bound) | Increase in Number of Crimes (Upper Bound) |
|---|---|---|---|---|---|
| Steal (Larceny) | 0.0304 | 1.50 | 2.50 | 29,097 | 87,292 |
| Burglary | 0.0174 | 1.30 | 1.90 | 9,989 | 29,967 |
| Robbery | 0.0170 | 1.40 | 2.10 | 13,012 | 35,784 |
| Sell Drugs | 0.0773 | 1.50 | 1.80 | 73,961 | 118,337 |
| Arrested/Convicted | 0.0723 | 1.46 | 2.00 | 49,237 | 90,998 |
Table 2A.
| ADHD Prevalence (data) | 9.2% |
| US Population Represented | 20,800,000 |
| Predicted Population with ADHD | 1,913,600 |
Table A3.
Estimates of Victim Costs of Crime Committed by Persons with ADHD in 2000 dollars
| Crime | Victim Cost/Per Crime | Lower Cost Estimate | Upper Cost Estimate |
|---|---|---|---|
| Steal (Larceny) | $735 | $ 21,386,475 | $ 64,159,425 |
| Burglary | $1,462 | $ 14,603,906 | $ 43,811,719 |
| Robbery | $1,077 | $ 14,014,441 | $ 38,539,713 |
| Sell Drugs | $0 | $ - | $ - |
| Arrested/Convicted | $465 | $ 8,041,162 | $ 23,704,367 |
| Total | $ 58,045,984 | $ 170,215,223 |
Table A4.
Estimates of Total Social Costs of Crime Committed by Persons with ADHD in 2000 dollars
| Crime | Total Cost/Per Crime | Lower Bound Estimate | Upper Bound Estimate |
|---|---|---|---|
| Steal (Larceny) | $7,424 | $ 216,017,945 | $ 648,053,836 |
| Burglary | $16,704 | $ 166,856,122 | $ 500,568,367 |
| Robbery | $36,076 | $ 469,438,228 | $ 1,290,955,128 |
| Sell Drugs | $7,424 | $ 549,083,791 | $ 878,534,066 |
| Arrested/Convicted | $11,988 | $ 421,482,118 | $ 832,270,375 |
| Total | $ 1,822,878,205 | $ 4,150,381,773 |
Footnotes
We thank Daniel Eisenberg, Brett Haberstick, John Mullahy, Jody Sindelar, participants of the 2006 Add Health User Conference and referees for this journal for helpful comments. This research was supported by a grant from the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32 MH18029-20 from the National Institute of Mental Health. We thank Ronice Copeman and Marie Young for excellent editing.
The prevalence within the subpopulation of children receiving special education services in public schools has been reported to be as high as 50% (3).
The largest increases in treatment are from poor, near-poor, and low-income families and children ages 12–18 (2).
This is partly due to data limitations; in order to examine early adult outcomes, information about ADHD is required during childhood and follow up information is needed for adult outcomes of interest that occur much later. Since ADHD is a relatively new diagnosis (for example, the US Department of Education recognized that students with ADHD could be considered disabled and be eligible for special education services in 1991 (2) several important longitudinal datasets that span the relevant years do not contain information on the subject. The Panel Study of Income Dynamics (PSID) only added ADHD measures in 1997 and the National Longitudinal Study of Youth (NLYS) has only a partial measurement for children ages 4–14 by their parents. Several additional data bases that contain information on childhood ADHD are cross-sectional.
One exception to the current focus on childhood ADHD is Kessler et al. (9) where the authors estimate that adult ADHD leads to 120 million days of annual work lost in the US labor force.
Hypotheses about the causes of ADHD have evolved from simple one-cause theories to the view that it is a complex, multifactoral disorder that appears after a threshold of cumulative risk has been met (12, 13). Suggested environmental risks include maternal tobacco and alcohol use and lead exposure.
This is similar in magnitude to the medical costs of having a child with asthma.
Further, the relationship between parents and children with ADHD is often more conflicted and stressful (8, 19) and siblings are more likely to feel victimized by brothers with ADHD (26).
Unfortunately, due to data limitations, we are unable to contribute to knowledge regarding the consequences of treatment of ADHD.
A robbery is a theft in which there is violence, or the threat of violence, Burglary is defined as the entry into a building or part of a building, as a trespasser with the intent to steal anything in the building,
See Udry (32) for full description of the Add Health data set.
Simple single imputation techniques are used; the following variables were used in the imputation: gender, race, high school grade point average and test score, and neighborhood income.
One item asked in the retrospective ADHD section of wave III (“You were spiteful or vindictive”) is not a DSM-IV ADHD symptom and was excluded from analyses; while 1 DSM-IV impulsivity symptom (“Often interrupts or intrudes on others”) was not included in the retrospective ADHD section. Thus, our analyses included responses to 9 inattentive and 8 hyperactive/impulsive symptoms.
Retrospective ratings of previous health should be used with caution when examining adult outcomes. Fortunately, several reviews have concluded that childhood experiences are recalled with sufficient accuracy to provide useful information in retrospective studies (33). Babinski et al. (23) also suggest that since the criteria for ADHD has changed over time; current measures of the illness may be preferred.
We also present results using a lower threshold of five or more symptoms in the appendix.
he question reads, “…how often did you go into a house or building to steal something?” Burglary is defined as “An unlawful entry of a structure to commit a felony or theft” by the FBI.
The question reads, “…how often did you use or threaten to use a weapon to get something from someone?” Robbery is defined as, “The taking or attempting to take anything of value from the care, custody, or control of a person by force or threat of force or violence and/or by putting the victim in fear” by the FBI.
Mocan and Rees (35) use these data in an analysis of links between crime and deterrence. In doing so they compare the self reported frequency of crime by adolescents in this survey to that reported in information from official sources. They find that these self reported rates of assault, robbery and burglary are greater than those from the Bureau of Justice Statistics while those for theft appear to be underreported. (Mocan and Rees (35), table 4.)
Our family fixed effects models are estimated with a conditional logit model.
Results are similar if we instead examine whether the individual reported any crime in any of the waves of the survey. Unfortunately, due to the sample design of the dataset, information was not collected from all individuals the same number of times across the three waves of the sample, so the “ever” variables are not comparable for all individuals in the survey
All other variables are held at their mean level in the marginal effects reported here and below.
These results and those that follow are robust to controlling for educational attainment and high school performance. Results are available upon request.
We provide an appendix that describes these calculations.
The FBI does not calculate the victim costs of selling drugs, so we use $0.
1994$ (in the Boardman et al. article) were converted to 2000$ using the CPI for comparison with the UCR calculations. Unfortunately, the total social cost estimates in Boardman et al. exclude productivity losses and reduction in quality of life estimates and thus are underestimates of true social costs. The cost estimates of crimes reported in Boardman et al. were gathered and updated from Long et al. (39).
Contributor Information
Jason Fletcher, Email: jason.fletcher@yale.edu, Yale University, School of Public Health, 60 College Street #303, New Haven, CT 06520
Barbara Wolfe, Email: wolfe@lafollette.wisc.edu, University of Wisconsin–Madison, Department of Economics, Department of Population Health Sciences, and La Follette, School of Public Affairs, 1180 Observatory Drive, Madison, WI 53726
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