Chronic Drug Use and Crime

Substance Abuse, Vol. 21, No. 2, 2000

Chronic Drug Use and Crime

Michael T. French, Ph.D.,1,6 Kerry Anne McGeary, Ph.D.,2 Dale D. Chitwood,Ph.D.,3 Clyde B. McCoy, Ph.D.,1 James A. Inciardi, Ph.D.,4 and Duane McBride,Ph.D.5

This paper used bivariate and multivariate analyses to estimate the relationshipsbetween chronic drug use and various measures of criminal activity. The data forthese analyses were derived from the 1993 (1) and 1995 (2) National HouseholdSurveys on Drug Abuse (NHSDA). Measures of criminal justice system contact andcriminal activity included ever arrested, arrested during the previous year, commissionof a predatory crime (e.g., assault, fighting) during the previous year, and commissionof a property crime (e.g., theft, property damage, car theft, breaking and entering)during the previous year. The analysis was conducted separately for males, females,and age groups, and it distinguished between chronic drug users, nonchronic drugusers, and nondrug users. The results consistently showed a significant linear relation-ship between criminal activity and frequency of drug use. These findings have implica-tions regarding the potential reduction in predatory and property crime that couldoccur from a decrease in drug use. Significant differences in criminal behavior betweenchronic drug users and other cohorts may signal a critical need to develop targetedinterventions for this particular type of drug user.

KEY WORDS: chronic drug use; crime; evaluation.

INTRODUCTION

Numerous studies have established that the use of illicit drugs is strongly relatedto the commission of criminal acts (3–9). Local and national surveys showed thatdrug users were more likely to be arrested and have a connection with the criminaljustice system compared to nondrug users (10–12). Criminal justice system data

1Department of Epidemiology and Public Health, University of Miami, Miami, Florida.2Department of Economics, University of Miami, Miami, Florida.3Department of Sociology, University of Miami, Miami, Florida.4University of Delaware Research Center, New York, New York.5Behavioral Science Department, Andrews University, Berrien Springs, Michigan.6Corresponding author: Michael T. French, Ph.D., Department of Epidemiology and Public Health(D93), University of Miami School of Medicine, P.O. Box 016069, 1801 N.W. 9th Avenue, Third Floor,Miami, Florida 33136. e-mail: [email protected]

95

0889-7077/00/0600-0095$18.00/1 2000 Association for Medical Education and Research in Substance Abuse

96 French, McGeary, Chitwood, McCoy, Inciardi, and McBride

indicated that a large percentage of arrestees tested positive for illicit drug use atthe time of their arrest (U.S. Department of Justice, NIJ) (13). Furthermore, datafrom the Federal Bureau of Investigation (U.S. Department of Justice, FBI) (14)showed that many predatory crime victims believed that their perpetrators wereunder the influence of illicit drugs or alcohol when the crime was committed (U.S.Department of Justice, BJS) (15).

Despite the strong correlation between drug use and crime reported throughoutthe literature, most studies were careful to point out that empirical evidence ofcausality running from drug use to criminal behavior was more difficult to discerndue to data limitations (6, 16–19). Drug use may be the catalyst for criminal activity,but the etiology involving drug use and crime is more complicated to articulate anddifficult to measure. For example, both drug use and crime may be a reaction topoverty/low income or the loss of familial structure (6, 7, 20). Alternatively, thesystemic model of drug use and crime postulates that drug use is an underlyingcause of criminal activity (16). Another explanation might reverse the direction ofcausality whereby a criminal lifestyle leads to experimentation with illicit drugs andpossibly abuse (6).

The economic model of crime was developed conceptually (21–23) and testedempirically beginning with Becker’s (24) seminal study (25–28). However, fewstudies have estimated the economic model of crime with a focus on drug users(29–31). The present paper was not able to estimate the economic model of crimebecause price data for illicit drugs, geographic differences in law enforcement effort,and criminal justice sanctions were not available on the NHSDA surveys. Further-more, restrictions on geographic identifiers for NHSDA participants prevented theanalysis from combining data sources to acquire the necessary variables. Conse-quently, similar to previous studies, methodologic challenges and data limitationshave hindered an investigation of causality between drug use and crime. However,the current study presents new and different information on the relationships be-tween drug use and criminal activity.

Putting aside the issue of causality, the majority of studies in the criminologicliterature have examined relationships between any substance use and the probabil-ity of being arrested or committing a criminal act (12, 32–40). Some studies havealso analyzed these relationships for particular types of drugs such as heroin orcocaine (7, 11, 41–45). A study by Chaiken and Chaiken (6) offered a comprehensivereview of the literature on the relationships between drugs and predatory crime.The authors noted that the relationship between episodes of drug use and crimewas complex, with many offenders initiating crime before they initiated drug use.Although the rate of drug use was strongly related to the rate of criminal activity,most adolescents and adults who used illicit drugs did not commit predatory crimes.However, the authors emphasized that persistent and frequent use of multiple drugswas strongly associated with higher rates of predatory crimes.

Chaiken and Chaiken (6) encouraged more empirical research that investigatedthe specific relationships between quantity and frequency of drug use and severityof crime. In their review of the literature, Nurco et al. (5) cautioned against treatingdrug users as a homogeneous group when analyzing relationships between druguse and crime. To address these research priorities and following the recommenda-

Chronic Drug Use and Crime 97

tions of earlier studies, the present paper estimated the relationships between threecategories of drug use and four measures of criminal activity using the 1993 and1995 waves of the NHSDA. The analysis was guided by the hypothesis that drugusers had higher rates of criminal activity relative to nondrug users and, amongdrug users, chronic drug users had more criminal activity compared to casual drugusers. Findings from this study provide the first nationally representative informa-tion on the association between chronic drug use and crime.

SAMPLE AND DATA

The choice of a dataset for these analyses was limited because of the numberof surveys that collected confidential and sensitive information on individuals’ useof illicit drugs. In addition to illicit drug use, another important component for thisstudy was self-reported information on a range of criminal activities and contactwith the criminal justice system. Finally, it was desirable to analyze a sample thatwas relatively large, nationally representative, and recently constructed. One datasetthat met all of these criteria was the 1995 NHSDA (SAMHSA) (2). To determinethe stability of the findings in 1995, the drug use/crime relationships were alsoestimated using data from the 1993 NHSDA (SAMHSA) (1).

The 1995 NHSDA was the 15th and most recently available survey in a seriesthat was begun in 1971. The sample design was a nationally stratified multistagearea probability sample of the noninstitutionalized household population in the50 United States who were 12 years of age and older. Various segments of thenoninstitutionalized household population were oversampled (i.e., based on the1990 Census), such as youth, minorities, and current smokers between the ages of18 and 34, inclusive. A criminal activity supplement was added to the questionnairein 1993 and then repeated in 1995. The added information on criminal activity andcriminal justice status was ideal for our purposes. The response rates in 1993 (79.2%)and 1995 (80.6%) were comparable and relatively high for voluntary self-reportedsurveys (46).

For the present study, drug-using status was divided into three categories (twocategories for users and one category as a nonuser comparison group) based oncriteria defined by the Office of National Drug Control Policy (ONDCP) (47).Chronic drug users (CDUs) included all individuals who used one or more illicitdrugs weekly or more often during the previous year. Nonchronic drug users(NCDUs) were individuals who used an illicit drug during the previous year, butdid not meet the weekly or more often condition to be classified as a CDU. Finally,a nondrug user (NDU) did not use any illicit substance during the previous year.

Classifying drug users into ‘‘chronic’’ and ‘‘casual’’ categories was statisticallyconvenient and policy relevant as suggested by ONDCP (47). In addition, relatedstudies found that chronic drug use was significantly related to health servicesutilization (48) and employment status (49,50). The most important shortcomingof this strategy, however, was the inability to distinguish between types of chronicdrug users (e.g., marijuana users, crack smokers, heroin injectors). Investigation ofboth the 1993 and 1995 NHSDAs revealed that the majority of CDUs were multidrug


users, which leant support to the present classification scheme, but further analysesof these relationships are necessary and planned.

The crime supplements to the 1993 and 1995 NHSDA offered numerous possi-bilities for measuring criminal behavior and criminal justice contact. To increasethe precision of the estimates and economize on data reporting, two broad measuresof criminal behavior were constructed: one for property crime and the other forpredatory crime. The indicator variable (i.e., yes � 1; no � 0) for property crimeincluded stealing from a store, stealing something from other than a store, damagingproperty, stealing a car, or breaking into a home or building. Using force to getmoney or other things from someone, getting into a physical fight, hurting someonebadly enough to require medical attention, or armed robbery were used to constructthe indicator variable for predatory crime. Finally, indicator variables for ‘‘everarrested’’ and ‘‘arrested during the previous year’’ were included in the bivariateanalysis, but not in the multivariate analysis.

METHODS

The initial strategy of this investigation was to examine bivariate relationshipsbetween categories of drug use and measures of crime. The analysis was segmentedby gender, survey year, and age (i.e., 12–17, 18–24, 25–34, and 35–65). Followingthe bivariate analysis, probit regressions were estimated to determine the partialrelationships between rates of drug use and criminal activity measures. Both thebivariate and multivariate analyses used sampling weights to account for the complexsurvey design of the NHSDA and generate national estimates (2, 51). The probitspecifications took the following general form:

C*i � �Zi � �Si � �Ei � �CDUi � �NCDUi � �i

Ci � 1 if C*i � 0 and Ci � 0 otherwise (1)�i � N(0, 1)

where C*i was individual i’s unobserved probability of criminal activity during theprevious year (e.g., committed a predatory crime, committed a property crime), Ci

was the discrete observed crime variable, Zi was a vector of unambiguous exogenousdemographic variables (e.g., age, race/ethnicity, gender) that influenced criminalbehavior for individual i, Si represented potentially endogenous demographic vari-ables (e.g., family income, education, enrolled in school, number of times moved,married, number of times married, number of jobs in the previous 5 years, numberof drinks past month) that may have influenced criminal behavior, Ei representeddummy variables for region of residence (and possibly a proxy for criminal justiceresources), CDU was an indicator variable for chronic drug use, NCDU was anindicator variable for nonchronic drug use, �, �, �, �, and � were parameters toestimate, and �i was a normally distributed random error term with a mean of 0and a variance of 1. Numerous variations of this general model were estimatedsuch as gender- and age-specific regressions, interactions between demographicsand regions, demographics–drug use interactions, and separate equations for a


group(s) of explanatory variables (e.g., excluding the potentially endogenous demo-graphic variables).

The coefficient estimates and standard errors from models such as Eq. (1)indicated whether a particular independent variable was significantly related to ameasure of criminal activity. However, unlike ordinary least squares (OLS) regres-sion, the coefficient estimates from a probit regression do not represent the marginaleffects for the independent variables (52). Because CDU and NCDU were binaryindependent variables in the probit model, the following formula was used tocalculate marginal effects (52):

�E[C�Z, S, E, CDU, NCDU]�CDU

� Pr[C � 1�Z, S, E, NCDU, CDU � 1] � Pr[C � 1�Z, S, E, NCDU, CDU � 0]

� [�Zi � �Si � �Ei � �NCDUi � �] � [�Zi � �Si � �Ei � �NCDUi] (2)

where all variables were defined as before and was the cumulative normal distribu-tion. The marginal effects for CDU and NCDU were estimated for each of theprobit regressions to determine the quantitative influence of drug use on predatoryand property crime.

RESULTS

Tables I and II report national estimates of variable means for all variablesused in the statistical models. Table I presents information for males between theage of 12 and 65. Data were included for both the 1993 and 1995 NHSDA and thevariable means were organized by drug-using status. Table II presents the sameinformation for females. Appendix Tables A1 through A4 (available on requestfrom the corresponding author) present variable means by drug-using status andage categories for males and females. Statistically significant differences in criminalactivity variables across the drug-using categories (p 0.05; Kruskal–Wallis test)were noted in each table.

The data in Tables I and II indicated that drug-using status was significantlyrelated to age, race, ethnicity, marital status, and education (p � 0.05). CDUswere younger, more often African-American, less often married, and less educatedrelative to NDUs. In addition, CDUs reported more moves and jobs relative toNDUs, they were less often employed full time, and they reported lower familyincome (p � 0.05). The same general relationships held for CDUs relative toNCDUs, but the differences tended to be narrower. Few significant differenceswere found in the distribution of drug users within geographic regions. However,given their drug-using preferences, it was not surprising that CDUs reported greateralcohol consumption than did NDUs, and they reported alcohol consumption tothe point of intoxication more often (p � 0.05).

The demographic profile of drug users was interesting, but the essential vari-ables in Tables I and II were measures of criminal activity and arrests. Thesemeasures showed the widest and most significant differences between CDUs,


Table I. National Estimates of Variable Means, by Drug-Using Status: Males, Age 12–65

1993 NHSDA 1995 NHSDA

CDU1 NCDU2 NDU3 CDU1 NCDU2 NDU3

Variable (N � 967) (N � 1,460) (N � 9,419) (N � 666) (N � 841) (N � 5,996)

Agea,b 27.7072 28.9101 36.8341 30.7136 28.0680 36.7835Whitea 0.7957 0.8524 0.8421 0.7888 0.8445 0.8418African-Americana 0.1733 0.1127 0.1096 0.1731 0.1271 0.1129Other race 0.0309 0.0349 0.0483 0.0381 0.0285 0.0454Hispanica,b 0.0933 0.1104 0.0998 0.0989 0.0789 0.1078Marrieda,b 0.2735 0.3663 0.6358 0.3084 0.3060 0.6231Divorced 0.1164 0.1030 0.0756 0.2083 0.1058 0.0921Singlea,b 0.6100 0.5307 0.2744 0.4775 0.5882 0.2824Times marrieda,b 0.5159 0.5775 0.9140 0.7352 0.5300 0.9021Enrolled in schoola,b 0.2780 0.2937 0.2340 0.1984 0.3907 0.2234Highest gradea,b 12.0839 12.8893 12.3857 12.1283 12.3059 12.5995Moves4,a,b 2.1931 1.7728 1.1179 0.6492 0.6169 0.2811New England 0.0381 0.0351 0.0421 0.0342 0.0764 0.0509Mid-Atlantic 0.1743 0.1231 0.1716 0.1595 0.0919 0.1439East North Central 0.1050 0.1463 0.1334 0.1051 0.1752 0.1498West North Central 0.0843 0.1064 0.1013 0.0712 0.1170 0.0851South Atlantica 0.2267 0.2219 0.2239 0.2064 0.1437 0.1764East South Central 0.0268 0.0144 0.0256 0.0565 0.0677 0.0644West South Central 0.0756 0.0656 0.0898 0.0808 0.0963 0.0949Mountaina 0.0622 0.0465 0.0620 0.0693 0.0837 0.0640Pacifica 0.2069 0.2408 0.1502 0.2168 0.1481 0.1706Employed full-timeb 0.5411 0.6135 0.6567 0.5631 0.5343 0.6592Employed part-timea,b 0.1436 0.1459 0.0820 0.1388 0.1640 0.0859Total family income5,a 30,621 36,920 41,360 0.7862 0.8172 0.8365Jobs6,a,b 3.3333 2.6014 1.8801 1.2788 1.2730 0.9592Property crime7,a,b 0.3022 0.1782 0.0532 0.1877 0.1548 0.0349Predatory crime8,a,b 0.2807 0.1872 0.0760 0.1990 0.1823 0.0497Ever arresteda,b 0.4927 0.3433 0.1298 0.5125 0.2599 0.1201Arrested previous yeara,b 0.2967 0.1481 0.0428 0.1243 0.0655 0.0145No. drinks previous mo.a,b 71.3435 46.4007 22.0626 47.8812 33.5743 14.5388Days drunk previous yr.a,b

�300 0.0140 0.0077 0.0036 0.0380 0.0070 0.0032201–300a 0.0889 0.0271 0.0034 0.0175 0.0143 0.0032101–200a,b 0.1884 0.0604 0.0186 0.0560 0.0335 0.00781–100a,b 0.4269 0.6132 0.2163 0.5941 0.6853 0.2832Nevera,b 0.2398 0.2593 0.6949 0.2774 0.2394 0.6847

1CDU � chronic drug user during the previous year.2NCDU � nonchronic drug user during the previous year.3NDU � nondrug user during the previous year.41993 NHSDA measures moves in the previous 5 years; 1995 NHSDA measures moves in the previ-ous year.

51993 NHSDA total income variable is continuous. 1995 NHSDA total income variable is binary, 0 if� $20,000 and 1 if � $20,000.

61993 NHSDA measures jobs in the previous 5 years; 1995 NHSDA measures jobs in the previous year.7Property crime includes stealing from a store, stealing something from other than a store, damagingproperty, stealing a car, or breaking into a home or building.

8Predatory crime includes using force to get money or things from someone, getting into a physicalfight, hurting someone bad enough to require medical attention, and armed robbery.

aStatistically significant differences in variable means across the drug-using categories in 1993, p 0.05(Kruskal–Wallis rank–order test).

bStatistically significant differences in variable means across the drug-using categories in 1995, p 0.05(Kruskal–Wallis rank–order test).


Table II. National Estimates of Variable Means, by Drug-Using Status: Females, Age 12–65

1993 NHSDA 1995 NHSDA

CDU1 NCDU2 NDU3 CDU1 NCDU2 NDU3

Variable (N � 553) (N � 1,388) (N � 12,117) (N � 458) (N � 805) (N � 8,518)

Agea,b 28.4260 28.2353 37.1354 28.4801 26.1549 37.3265Whitea 0.7622 0.8627 0.8223 0.8401 0.8579 0.8139African-Americana 0.2060 0.1030 0.1286 0.1446 0.0975 0.1355Other race 0.0318 0.0343 0.0491 0.0153 0.0447 0.0506Hispanica,b 0.1293 0.0698 0.0969 0.0643 0.0633 0.1023Marrieda,b 0.3532 0.3152 0.6137 0.2546 0.3018 0.6068Divorced 0.1889 0.1912 0.1285 0.2112 0.1288 0.1381Singlea,b 0.4516 0.4706 0.2213 0.5290 0.5645 0.2221Times marrieda,b 0.7049 0.6997 0.9666 0.6818 0.5248 0.9696Enrolled in schoola,b 0.2704 0.3493 0.2286 0.2522 0.4230 0.2198Highest gradea 11.6394 12.4759 12.4514 11.9349 12.4263 12.4926Moves4,a,b 2.2818 2.5199 1.0710 1.0767 0.5607 0.2686New England 0.0969 0.0552 0.0502 0.0299 0.0429 0.0523Mid-Atlantic 0.1005 0.1345 0.1544 0.1482 0.1362 0.1723East North Central 0.1297 0.1442 0.1502 0.1301 0.1606 0.1412West North Central 0.0556 0.0669 0.1012 0.0776 0.0866 0.0901South Atlantica 0.2201 0.2163 0.2425 0.1549 0.1228 0.1745East South Central 0.0279 0.0083 0.0177 0.0559 0.0836 0.0628West South Centralb 0.1267 0.0859 0.0898 0.0706 0.0807 0.1073Mountaina 0.0461 0.0878 0.0483 0.1037 0.0692 0.0476Pacific 0.1967 0.2008 0.1454 0.2291 0.2173 0.1519Employed full-timea,b 0.3883 0.3906 0.4345 0.3433 0.4182 0.4517Employed part-timea,b 0.1685 0.2128 0.1663 0.1811 0.2280 0.1618Total family income5,a 25,952 32,350 36,315 0.6885 0.7696 0.7907Jobs6,a,b 2.7932 2.7026 1.6623 1.1720 1.1273 0.8239Property crime7,a,b 0.2843 0.1567 0.0263 0.2440 0.1483 0.0193Predatory crime8,a,b 0.2509 0.1393 0.0284 0.2060 0.1200 0.0190Ever arresteda,b 0.2596 0.1226 0.0281 0.3327 0.1162 0.0238Arrested previous yeara,b 0.1531 0.0576 0.0163 0.0974 0.0198 0.0044No. drinks previous mo.a,b 44.2773 28.2546 7.2385 23.8742 12.9579 4.7889Days drunk previous yr.

�300 0.0195 0.0088 0.0031 0.0099 0.0021 0.0008201–300 0.0351 0.0033 0.0007 0.0110 0.0012 0.0007101–200a 0.1119 0.0365 0.0048 0.0448 0.0216 0.00251–100a,b 0.4139 0.5388 0.1409 0.6265 0.6875 0.1622Nevera,b 0.3462 0.3726 0.7906 0.2927 0.2747 0.8113

1CDU � chronic drug user during the previous year.2NCDU � nonchronic drug user during the previous year.3NDU � nondrug user during the previous year.41993 NHSDA measures moves in the previous 5 years; 1995 NHSDA measures moves in the previ-ous year.

51993 NHSDA total income variable is continuous. 1995 NHSDA total income variable is binary, 0 if� $20,000 and 1 if � $20,000.

61993 NHSDA measures jobs in the previous 5 years; 1995 NHSDA measures jobs in the previous year.7Property crime includes stealing from a store, stealing something from other than a store, damagingproperty, stealing a car, or breaking into a home or building.

8Predatory crime includes using force to get money or things from someone, getting into a physicalfight, hurting someone bad enough to require medical attention, and armed robbery.

aStatistically significant differences in variable means across the drug-using categories in 1993, p 0.05(Kruskal–Wallis rank–order test).

bStatistically significant differences in variable means across the drug-using categories in 1995, p 0.05(Kruskal–Wallis rank–order test).


NCDUs, and NDUs. Focusing on males in 1995 as an example, 18.8% of CDUscommitted a property crime compared to 15.5% of NCDUs and only 3.5% of NDUs.Looking at females in 1993, 25.1% of CDUs committed a predatory crime comparedto 13.9% of NCDUs and only 2.8% of NDUs. Almost 30% of female CDUs werearrested in 1993, but only 2.8% of female NDUs were arrested during that year.These types of hierarchical and statistically significant relationships between druguse and crime were present for every measure of crime, regardless of gender, age,or survey year.

Given the strength and consistency of the bivariate relationships, one wouldexpect to estimate a positive and statistically significant partial effect between druguse (either CDU or NCDU) and crime measures through the probit regressions.Tables III (males) and IV (females) display the coefficient estimates for the propertycrime and predatory crime regression models.7 As expected, all of the coefficientestimates for CDU and NCDU were positive and statistically significant at the 99%level or higher.

To interpret the magnitude of these coefficient estimates, Eq. (2) was used tocalculate the marginal effects of CDU and NCDU on the probability of committinga property crime or a predatory crime during the past year. The estimated marginaleffects are reported in Table V. All of the marginal effects were less than thecoefficient estimates, but they were still significantly different from zero in all cases.In addition, the marginal effects for CDUs were always larger than those forNCDUs. As an example, being a female CDU was associated with a 17.7% increasein the probability of committing a predatory crime in 1995 relative to female NDUs.The increase in the probability of committing a predatory crime was only 4.7%higher for female NCDUs relative to female NDUs. Similar results were reportedfor males and across survey years. These findings offered convincing evidence thatany type of drug use was associated with a higher probability of committing propertyor predatory crimes relative to NDUs. Moreover, CDUs had a higher probabilitythan NCDUs of committing both types of crimes.

It is also interesting to note some of the coefficient estimates for other variablesin Tables III and IV. Alcohol use was strongly related to criminal activity for males,but not for females. Being married was negatively related to the probability ofcommitting a predatory crime for males, but the results were mixed for females.Surprisingly, education was a significant predictor of criminal activity in only oneof the four probits for males. The reverse was true for females, as education displayeda significant negative relationship with crime in three of the four regressions. An-other surprising result was the general lack of significance between current employ-ment status (either full- or part-time job) and crime involvement. This result wasconsistent for both males and females. However, the number of different jobs arespondent had during the previous 5 years (previous year for the 1995 NHSDA)was positive and statistically significant for all regressions except property crime in

7Regression statistics for ‘‘ever arrested’’ and ‘‘arrested during the previous year’’ were not reported inthe paper to conserve space and because the results were similar to the criminal activity models. Inaddition, regression models were estimated for four age groups (i.e., 12–17, 18–24, 25–34, and 35–65),with similar results to those presented in Tables III and IV. The full regression output is available fromthe corresponding author.

Chronic Drug Use and Crime 103T

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Table V. Estimated Marginal Effects for CDUs andNCDUs

Dependent Variable

Property PredatoryMeasure Crime Crime

1993 (Males)CDU 0.2359 0.1212NCDU 0.1129 0.0770

1993 (Females)CDU 0.3061 0.2185NCDU 0.0999 0.0803

1995 (Males)CDU 0.1011 0.1544NCDU 0.0647 0.0803

1995 (Females)CDU 0.1913 0.1767NCDU 0.0516 0.0466

Note: Estimated marginal effects are relative to NDUs.

1993 and 1995 for males. One interpretation of the labor market findings was thatjob turnover was a more important predictor of criminal activity than employmentstatus per se.

DISCUSSION

The past two decades have spawned a variety of empirical and theoreticalstudies demonstrating a strong relationship between drug use and crime (4–9). Thepresent study contributes to this body of literature by estimating the relationshipsbetween chronic drug use and various types of criminal activity with nationallyrepresentative data. Few studies have used nationally representative data on druguse and crime. In addition, the research was unique in that it considered threecategories of subjects with different drug use frequencies and focused primarily onproperty and predatory crimes. The results corroborated the positive drug use/crime relationship, but more importantly, they showed that severity of drug usewas significantly related to the probability of committing a property or predatorycrime. Bivariate analysis indicated that CDUs were involved in crime 10–30%more often than NDUs, and probit regressions estimated marginal effects of crimeinvolvement for CDUs that were 2–3 times higher than the marginal effects forNCDUs. The results were consistent across gender, age groups, and survey years.

Although the NHSDA was one of the largest surveys of drug use ever under-taken in the United States, it contained certain limitations (SAMHSA) (2). Mostimportantly, its data were self-reported, which raised questions regarding validityand reliability. NHSDA procedures were designed to maximize honesty and recall,but data quality still relied largely on respondents’ truthfulness and memory. Onthis point, however, a few studies (36, 53–56) have examined the validity of self-reported information in this context and have found the measures to be quite good.


A second limitation of the NHSDA is its cross-sectional design. Although itwould be useful to analyze and report longitudinal changes in drug-use patternsand how these changes were related to criminal behavior for specific individuals,the NHSDA did not permit such analysis because a new cohort was sampledevery year.

Third, some small segments of the U.S. population (about 1%) were excludedfrom the sampling frame because they were not part of the target population. Theseincluded members of the active-duty military and persons in institutional settings(e.g., hospitals, prisons, nursing homes, treatment centers). Because individuals incorrectional facilities were one of these excluded groups, such exclusions may haveaffected our results. For additional details on sample design, response rates, mainfindings, and other technical details of the 1993 and 1995 NHSDA, refer toSAMHSA (1, 2).

Finally, data from the NHSDA did not permit structural modeling to determinecausality. Other studies have struggled with data limitations when estimating struc-tural equations such as in the economic model of crime (25, 27, 57) or when takingon the challenging task of estimating a variant of this model with drug users.Research projects on this topic are important because examining and understandingthe causality between drug use and crime is useful for developing targeted interven-tions with a lasting social impact. Perhaps future data collection efforts will beginto assemble measures (e.g., illicit drug prices, enforcement measures for drug laws)that allow a more rigorous analysis of the structural connection between chronicdrug use and crime.

In conclusion, public attention toward crime and drug abuse issues has sharp-ened, as revealed by public opinion polls consistently ranking them among the topfive national problems (58). Despite the data limitations noted, the present findingsmay have important public policy implications. The analyses consistently showedthat CDUs were significantly more involved in criminal activities compared toNCDUs and NDUs. Other research found that many arrestees had drug-relatedproblems, but less than 10% of inmates with substance abuse problems actuallyreceived treatment (59). Given the strong connection between drug use and crime,criminal justice system efforts could address both problems simultaneously by ex-panding and enhancing drug abuse treatment in criminal justice settings (42, 60–62).Indeed, investing public resources in drug abuse treatment has money saving poten-tial largely as a result of benefits associated with crime reduction (63–68).

ACKNOWLEDGMENTS

This work was supported by a grant (P50 DA10236) from the National Instituteon Drug Abuse, Public Health Service, U.S. Department of Health and HumanServices. We gratefully acknowledge David Brown for his programming and re-search assistance, Robert Anwyl, Kathryn McCollister, and Helena Salome for theireditorial assistance, and Carmen Martinez for her administrative support. NaciMocan, participants at the Pacific Rim Allied Economic Organizations conference,


and two anonymous reviewers provided helpful suggestions on an earlier draft ofthis manuscript.

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