Revolving Roles in Drug-Related Crime: The Cost of Chronic Drug Users as Victims and Perpetrators

Revolving Roles in Drug-Related Crime: The Cost of

Chronic Drug Users as Victims and Perpetrators

Michael T. French,1,3 Kathryn E. McCollister,2 Pierre Kebreau

Alexandre,2 Dale D. Chitwood,1 and Clyde B. McCoy2

Numerous studies have established a strong connection between the use of illicitdrugs and the commission of other illegal activities, including both predatory andpropertycrimes.Nostudy,however,has examined the costof crimesassociatedwithdrugusers both as victims andas perpetrators. In the present study, recent datawereanalyzed from a targeted sample of chronic drug users (CDUs) and a matchedsample of non-drug users (NDUs) inMiami-Dade County, Florida, to estimate theincremental cost of crime associated with CDU. Two separate models wereemployed to estimate (1) the probability of being a victim or a perpetrator of crimeand (2) the cost of crime for both situations. The costmeasureswere transformed toreduce the influence of extreme outliers, and a smearing technique was used tocompare the cost of crime for CDUs relative to NDUs. The findings illustrate thatcriminalactivityamongCDUs is circular, extensive, andcostly. Implications for lawenforcement, criminal justice policy, and substance abuse treatment are discussed.

KEY WORDS: cost of crime; chronic drug users.

1. INTRODUCTION

Numerous studies suggest that the use of illicit drugs is strongly related tothe commission of criminal acts (Benson et al., 1992; Chaiken and Chaiken,1990; French et al., 2000b; Inciardi and Pottieger, 1994; Nurco, 1998). Localand national surveys indicate that drug users are more likely to have a con-nection with the criminal justice system (CJS, through arrests and incarcera-tions) than non-drug users (NDUs, e.g., Anglin and Speckart, 1988; Inciardiand Pottieger, 1995; Lightfoot and Hodgins, 1988). CJS data indicate that a

1Department of Sociology (2030), University of Miami, P.O. Box 248162, 5202 University

Drive, Merrick Building, First Floor, Coral Gables, FL 33124-2030.2Department of Epidemiology and Public Health (D93), University of Miami, P.O. Box

016069, 1801 NW 9th Avenue, Third Floor, Miami, FL 33101.3To whom correspondence should be addressed. Phone: 305-284-6039; E-mail: [email protected].

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0748-4518/04/0900-0217/0 � 2004 Plenum Publishing Corporation

large percentage of arrestees test positive for illicit drug use at the time of theirarrest (U.S. Department of Justice, NIJ, 1998). Furthermore, data from theFederal Bureau of Investigation (U.S.Department of Justice, FBI, 1999) showthat many predatory crime victims believe that their perpetrators were underthe influence of illicit drugs or alcohol when the crime was committed (U.S.Department of Justice, BJS, 1997). Consequently, criminal justice agencies inmany states recognize that prisons and jails may be an effective setting forproviding treatment to substance-abusing offenders (Hiller et al., 1999; Inc-iardi et al., 1997; Martin et al., 1999; Wexler et al., 1999a, b). Some states(e.g., California) have decided to bypass incarceration with legislation thatdirects non-violent drug offenders to probation and community-based treat-ment instead.

Despite the strong correlation between drug use and crime reportedthroughout the literature, most studies are careful to emphasize that empiricalevidence of causality running from drug use to criminal behavior is difficult toobtain due to research design constraints and data limitations (e.g., Chaikenand Chaiken, 1990; Fagan, 1990; Goldstein, 1985; Johnson and Belfer, 1995;Nurco et al., 1995). Drug use may be the catalyst for criminal activity, but theetiology involving drug use and crime is complicated to articulate and difficultto measure. For example, both drug use and crime may be a reaction topoverty/low income or the loss of family structure (e.g., Chaiken andChaiken,1990; Hunt, 1991; Zhang, 1997). Alternatively, the systemicmodel of drug useand crime postulates that drug use is an underlying cause of criminal activity(Goldstein, 1985). Another explanation might postulate a reverse causality,whereby a criminal lifestyle leads to experimentation with illicit drugs andpossibly to abuse (Chaiken and Chaiken, 1990). Although the drug use/criminal activity connection has spurred lively debate and much scientificinvestigation, the causality issue is difficult to resolve empirically and will notbe directly examined in this paper.

The economic model of crime was developed conceptually (e.g., Blockand Heineke, 1975; Ehrlich, 1973) and tested empirically (e.g., Cornwell andTrumbull, 1994; Meyers, 1983; Witte, 1980, 1983), beginning with Becker’sseminal study (1968). Few studies, however, have estimated the economicmodel of crime with a focus on drug users (e.g., Kim et al., 1993; Markowitzand Grossman, 1998).4 As an alternative line of research, economists have

4A notable recent study by Levitt and Venkatesh (2000) analyzed unique data from the records of

a drug-selling street gang. Although gang members in the upper echelon of the organization

usually earned wages that exceeded their potential in the legitimate labor market, these risk

premiums did not fully compensate for the dangers of drug dealing and gangmembership. Thus,

Levitt and Venkatesh conclude that social/non-pecuniary factors may play a larger role in gang

formation and behavior than economic considerations. This result suggests that the economic

model of crime may require some extensions to fully explain the criminal behavior of drug users.

French et al.218

recently developed models to estimate the tangible and intangible costs ofindividual criminal acts (Cohen, 1988, 1998; Miller et al., 1993, 1996; Raj-kumar and French, 1997). These estimates have subsequently been used toestimate the economic benefits of drug abuse interventions (French et al.,2000c; French et al., 2002a, b, c).

The current paper uses community-based information from a targetedsample of chronic drug users (CDUs) and NDUs to estimate the incre-mental cost of crime associated with CDU.5 It is both an improvement andan extension of earlier studies on the prevalence and cost of drug-relatedcrime (e.g., Grogger and Willis, 2000; Miller et al., 1993; Rajkumar andFrench, 1997). The methods and findings are a meaningful contribution tothe addiction and criminology literature for several reasons. First, the dataare unique and current. Community-based targeted samples of CDUs andNDUs are rarely available to social science researchers. Second, detailedinformation on the number of specific crimes committed is reported, asopposed to general measures of criminal activity or arrests. Third, CDUsand NDUs can be analyzed both as victims and as perpetrators of crimes toexamine the revolving nature of drug-related crime. The perspective thatCDUs assume revolving roles as perpetrators and victims of crime has neverbeen considered in the previous research on the drugs–crime nexus. Fourth,recent statistical methods are employed to estimate the total (tangible andintangible) cost of crime, including models for both dichotomous andcontinuous dependent variables, data transformations for outliers, regres-sion diagnostics, and smearing factors for statistical inferences. And finally,the cost estimates have direct law enforcement and criminal justice policyimplications regarding potential interventions for problematic drug users.

The next section briefly reviews the theoretical background on the tan-gible and intangible costs of criminal activity. Section 3 motivates and spec-ifies the empirical models. Descriptive statistics for the sample and data arethen presented in Section 4. The estimation results are presented in Section 5.The paper concludes with a discussion of main findings, study limitations,legal and policy implications, and recommended extensions to this research.

2. THEORETICAL BACKGROUND ON THE COSTS OF CRIME

Crime imposes costs on society in a number of ways, all of which mustbe properly quantified to accurately measure the full social cost (Rajkumar

5CDUs included individuals who had consumed an illicit drug(s) at least once a week during the

previous 12 months, tested positive for cocaine and/or opiates on a urine screen, but never

injected illicit drugs and therefore had no evidence of track marks. NDUs had never used

cocaine or opiates, tested negative on a urine screen for both drugs, and had no visible track

marks.

Revolving Roles in Drug-Related Crime 219

and French, 1997). For simplicity, cost elements can be divided into fourmain categories (Harwood et al., 1998; Rajkumar and French, 1997; Riceet al., 1990):

Victim costs. These elements include the medical costs, lost wages, reducedproductivity, and property damage suffered by crime victims as well as thepain and suffering they endure as a result of crime. The personal loss of lifesuffered by a homicide victim should also be included. In the case of stolenproperty, unless it is damaged or destroyed, it is typically not counted as asocial loss because it is transferred to another member of society, namely,the criminal (Becker, 1968; McChesney, 1993). Although this assumption iscertainly debatable, property loss would only amount to a small percentageof the total cost of crime.

Costs of crime protection and law enforcement. These include police protec-tion costs, costs of running the CJS, private legal costs, costs of drugtrafficking (for drug-related crime), and correctional costs (includingincarceration). This category is typically labeled CJS costs.

Crime career/productivity losses. This category refers to the value of lostproductivity of law-abiding citizens who turn to crime rather than pursue alawful career that could directly benefit society.

Other external costs. The effects of crime touch many segments of society(Anderson, 1999). As crime escalates across a community, residents whowere not personally victimized are beset by fear and psychologicaldistress about becoming victims. In addition to this emotional toll,individuals may participate in more overt activities, such as purchasinglocks, weapons, security alarms, and other devices (see Clotfelter andSeeley, 1979, for a detailed examination of the private costs of crime,including purchased goods and services and protective behavior).Casual observation of the proliferation of these devices in today’ssociety indicates the importance of avoidance behaviors in the overallcost of crime.

Many of the items listed above are directly measurable because they areobservable. For example, short-term medical expenses, lost wages of vic-tims, property damage, protection and policing costs, and, to a certainextent, lost productivity can all be estimated through victimization surveysand criminal justice records (Jones and Vischi, 1979; McPheters, 1979).These costs are labeled ‘‘direct costs’’ by some (Harwood et al., 1998; Rice

French et al.220

et al., 1990), but Rajkumar and French (1997) suggest the term ‘‘tangiblecosts.’’

Most studies on the cost of drug-related crime, such as Harwood et al.(1984), Harwood et al. (1998), and Rice et al. (1990), have focused only ontangible costs.6 Several tangible costs, such as victims’ long-term medicalexpenses, the cost of crime-prevention programs, and averting behaviors aredifficult to measure and generally not quantified. Furthermore, many‘‘intangible costs’’ can only be measured indirectly, such as the crime vic-tims’ pain and suffering, the loss of life suffered by homicide victims, andpotential victims’ fear of crime. For a discussion of these issues, see Grayand Joelson (1979), Rajkumar and French (1997) and Cohen (1988, 1998).

The intangible costs to victims of crime are difficult to measure becauseindividual well-being or utility is a theoretical concept that does not easilytranslate to income or monetary equivalents (French, 2000; Kenkel, 1997;Phelps, 1997). Losses in utility are indirectly translated into monetary valuesby using the concept of either victim compensation or victim willingness topay (Rajkumar and French, 1997; Zarkin, et al., 2000).

In the victim-compensation approach, the cost of a crime is measuredby how much money would be necessary to compensate the crime victim.This award should ideally cover all losses incurred by the victim, includinghis/her pain and suffering. The alternative approach is to measure the dollaramount a potential victim is willing to pay to reduce the risk of a particularcrime occurring in the future. By measuring this risk reduction and dollarpayment, one can calculate the cost of the crime to the potential victim. Avariation of this approach is to link a particular crime to the injury or deaththat results and ask how much an individual is willing to pay to reduce therisk of that injury or death. Most methods for estimating the victim cost ofcrime are based on either the victim-compensation or the willingness-to-payconcepts.7

Rajkumar and French (1997) advocate using the cost-of-illnessapproach to estimate the tangible costs of crime and the jury compensationmethod to estimate intangible costs (Cohen, 1988, 1998; Miller et al.,1993,1996). Rajkumar and French derived full cost estimates for the followingcrimes: aggravated assault, robbery, burglary, theft, auto theft, forgery andembezzlement, fencing, gambling, pimping and prostitution, and drug law

6Several critical reviews of the cost-of-illness methodology for estimating the social cost of

substance abuse have been published, including Heien and Pittman (1989), Anderson (1992),

Anderson (1992), Reuter (1999), and Cohen (1999).7The victim-compensation concept is more commonly referred to in the literature as the

‘‘willingness to accept’’ or the ‘‘willingness to accept compensation’’ concept. Willingness to

pay and willingness to accept are different but related concepts. For a discussion, see Mishan

(1959) or Lankford (1988).


violation. Costs were estimated for each individual type of crime and weredivided into three categories: crime victim costs, CJS costs, and crime-careercosts.

Rather than undertaking the daunting task of developing new unit costestimates, we relied on existing estimates in the literature. Specifically, thecrime-specific cost estimates reported by Rajkumar and French (1997) andMiller et al., (1996) are used in the empirical models that follow to estimatethe total cost of crime for the average CDU as a victim of criminal activity, aperpetrator of criminal activity, and overall.

3. EMPIRICAL MODELS

To estimate the cost of crime for CDUs and NDUs, the analysis fol-lowed a sequential series of steps, which involved simplifying assumptions,data transformations, and statistical estimation. Much of the analysisextended earlier studies and already-established techniques (e.g., Chitwoodet al., 1999; French et al., 2000b, 2001c; Rajkumar and French, 1997).Listed below are the sequential steps in the estimation process along with abrief description of each phase.

3.1. Convert Criminal Activity Events to Cost Estimates

As discussed later in the paper (Section 4), data from the Health Ser-vices Research Center questionnaire (Chitwood et al., 1999; French et al.,2000a) includes information on four different types of crime.8 Since theanalysis is concerned with multiple crimes, each of which involves a differentsocietal cost, these crimes must be converted to a normalizing factor(dollars) so that each measure can be aggregated and compared. As notedearlier, Rajkumar and French (1997) and Miller et al., (1996) report unitcost estimates for a variety of different crimes. For our purposes, costestimates for the following crimes were converted to 1997 dollars (the year

8This subset of FBI index crimes was selected during the questionnaire design phase to provide

variation in criminal activity, to emphasize predatory acts, and to minimize survey adminis-

tration time. The act of homicide as a perpetrator was also included in the questionnaire.

Although this event was relatively rare (13 total reported acts; seven subjects with one act each,

one subject with two acts, and one subject with four acts), the cost is very high and inclusion of

homicide in the total perpetrator cost estimate could distort comparisons with total victim cost.

Thus, the core analysis that follows does not include the cost of the 13 reported acts of

homicide. However, this decision had little effect on the key findings reported in Tables II and

III. The estimated marginal effect of CDU on any perpetrator cost is 0.2335 without homicides

included (Table II) and 0.2313 with homicides included. The smearing estimate for CDU in the

perpetrator cost equation is 11.55 with homicides included (Table III) and 10.62 without

homicides included.

French et al.222

of survey administration) using the consumer price index (Bureau of LaborStatistics, 1997).

Cost of Assault and Battery¼$10,4889

Cost of Aggravated Assault¼$58,31210

Cost of Robbery¼$25,15511

Cost of Sexual Assault/Rape¼$97,06612

The unit cost estimates above were then multiplied by the corre-sponding number of crimes committed by each individual in the sample, andthe results were summed across all crime types to create a ‘‘total annualizedcost of crime’’ variable. In addition, the total cost of crime estimates weresubdivided into victim cost and perpetrator cost.

3.2. Calculate Mean Cost Estimates

With access to unit cost estimates for individual crimes, mean annu-alized cost estimates can be calculated for the full sample, for CDUs andNDUs, and for victims and perpetrators. Significant differences in mean costestimates can then be determined across drug-using groups for victims,perpetrators, and overall. Analysis of mean differences in variables acrosstwo or more groups provides a general indication of central tendency, butmultivariate analysis is necessary to adjust for other important covariates inthese relationships.

3.3. Estimate the Incremental Prevalence and Cost of Crime (Victim and

Perpetrator) for CDUs Relative to NDUs

The cost of crime for each individual as a victim (perpetrator) wasdetermined by summing the cost of crimes that involved the individual as avictim (perpetrator). The total annual cost of crime associated with eachindividual was determined by adding the cost of crime as a victim to the costof crime as a perpetrator. These calculations resulted in some observations

9Miller et al., (1996) estimated the cost of ‘‘Other Assault or Attempt’’ as $9400 in 1993

dollars. This value was inflated to 1997 dollars using the CPI (http://www.westegg.com/

inflation/).10Rajkumar and French (1997) estimated the cost of ‘‘Aggravated Assault’’ as $50,743 in 1992

dollars. This value was inflated to 1997 dollars using the CPI (http://www.westegg.com/

inflation/).11Rajkumar and French (1997) estimated the cost of ‘‘Robbery’’ at $21,890 in 1992 dollars. This

value was inflated to 1997 dollars using the CPI (http://www.westegg.com/inflation/).12Miller et al., (1996) estimated the cost of ‘‘Rape & Sexual Assault’’ as $87,000 in 1993 dollars.

This value was inflated to 1997 dollars using the CPI (http://www.westegg.com/inflation/).


with positive values for the cost of crime and some observations with zerovalues (i.e., non-victims or non-offenders).

As discussed earlier, several studies have found that drug users areengaged in criminal activity at rates that significantly exceed those of NDUs(Chaiken and Chaiken, 1990; Grogger and Willis, 2000; Inciardi and Pot-tieger, 1994, 1998). Furthermore, chronic or problematic drug users usuallyhave higher crime rates than casual or experimental drug users (Frenchet al., 2000b; Inciardi et al., 1997). To investigate these relationships sta-tistically, we estimated two models. The first specification examined thedichotomous criminal activity event and was estimated with the probittechnique. The second specification examined the cost of crime and wasestimated with Ordinary Least Squares (OLS).13 Prior to executing OLS,however, we transformed the crime cost estimates by winsorizing the mea-sures at the 5% tails and then taking the natural logarithm (adding 1 to allobservations so that the transformation was defined for zero values). Thispractice has been used by several other criminology studies when faced witha skewed dependent variable with a few extreme outliers (e.g., Nagin andSmith, 1990).14

To estimate these models, we first created three dichotomous variablesfor any victim cost, any perpetrator cost, and any victim or perpetrator cost.We then used the probit technique to estimate the relationships between anycrime cost (victim, perpetrator, and overall) and a series of explanatoryvariables:

PrðCi ¼ 1Þ ¼ UðCDUbcdu þ XcÞ ð1Þ

where Ci is an indicator variable for any positive crime cost of type i (victim,perpetrator, or overall); CDU is an indicator variable for chronic orproblematic drug use; X is a vector of other variables that influence criminal

13Since the cost of criminal activity resembles a censored (from below) dependent variable, we

considered the use of the Tobit technique, which has been employed recently in the crimi-

nology literature (Keane and Arnold, 1996; Osgood et al., 2002a, b). However, research has

cast doubt on the Tobit model because of the strict assumption of normality (Heckman, 1990;

Manski, 1989). If the error term is not normally distributed, the estimator based on the Tobit

regression is inconsistent. One approach is to adopt alternative distributional assumptions

(e.g., Kalbfleish and Prentice, 1980), but often assuming some other specific distributions may

not necessarily solve the problem and may make it worse (Greene, 2000). Thus, we opted for

OLS rather than Tobit.14As an example, the mean combined (victim + perpetrator) cost was $163,183, the median was

$0, the 95th percentile started at $457,950, the skewness was 9.73, and the kurtosis was 106.69.

Furthermore, the most criminally active CDUs (i.e., top 10%) were responsible for 90.65% of

the CDU victim costs, 90.49% of the CDU perpetrator costs, and 85.71% of the CDU

combined costs.

French et al.224

activity including demographic characteristics (e.g., age, race, education,marital status), labor market measures, and neighborhood controls (cityindicator variables); bcdu and c are parameters to estimate; and U is thecumulative normal distribution. Additionally, marginal effects (includingadjusted standard errors) were calculated for CDU and all other explana-tory variables.

Second, after winsorizing each cost measure, the natural log of cost wasregressed (OLS) against the same explanatory variables noted above:

LnðTCi þ 1Þ ¼ CDUdcdu þ Xw þ e ð2Þ

where TCi is the total cost of crime type i (victim, perpetrator, or overall);CDU and X are the same as defined above; dcdu and w are parameters toestimate; and e is a random error term.15

Finally, the estimation results for Ln(TCi + 1) in Eq. (2) were re-transformed to linear arithmetic space using smearing factors (Duan, 1983;Manning, 1998; Mullahy, 1998), which permitted direct comparisons bet-ween CDUs and NDUs. Using the standard retransformation approach(i.e., exponentiating the predictions of the log-transformed variable) oftendoes not produce the conditional mean in the original metric although thepredictions are consistent estimators. The smearing technique consistentlyand efficiently addresses the potential problem of retransformation bias,which arises when unbiased and consistent quantities from the transformedscale are retransformed into the original scale (Duan, 1983).

4. SAMPLE AND DATA

The sample design and data collection procedures for this study weredeveloped by the Health Services Research Center at the University ofMiami. The primary objective was to administer a lengthy health servicesquestionnaire to approximately 1800 African-American, Hispanic, and non-Hispanic white individuals who were demographically similar, but differ-entiated by their drug-using status. Sample accumulation was closelymonitored to ensure that roughly equivalent sample sizes were obtained for

15Ideally, the analysis would also test whether CDU was exogenous in both the probit and OLS

specifications and, if necessary, correct for the endogeneity of drug use through an instru-

mental variables (IV) technique (Davidson and MacKinnon, 1993; Norton et al., 1998).

Unfortunately, we were unable to identify any plausible instrumental variable(s) that were

significantly correlated with drug use, but orthogonal to criminal activity.


CDUs, CDUs that were also injection drug users (IDUs), and NDUs. Inaddition, representation similar to community demographics by gender andrace/ethnicity was also monitored. This ambitious data collection effort tookabout 2 years to complete and resulted in a final analysis sample of 1480individuals. Several researchers at the University of Miami have describedthe data collection methods and conducted independent analyses of theseunique data (Alexandre and French, in press; Chitwood et al., 1998,1999;French et al., 2000a,2001c). Thus, the material below is a brief overview ofthe survey design, the sample, and the data.

4.1. Subjects

Separate inclusion criteria were established for the three groups ofsubjects: CDUs, IDUs, and NDUs. CDUs included individuals who hadconsumed an illicit drug(s) at least once a week during the previous12 months, tested positive for cocaine and/or opiates on a urine screen, butnever injected illicit drugs and therefore had no evidence of track marks.Active IDUs were distinguished from other CDUs by the fact that the mostcommon route of drug ingestion was through a hypodermic needle. NDUshad never used cocaine or opiates, tested negative on a urine screen for bothdrugs, and had no visible track marks. Individuals who had used marijuanafewer than 13 times during the past year were also eligible for the NDUgroup. Consequently, NDUs included individuals who (1) had never usedcocaine or opiates, (2) had never injected drugs, but (3) may have usedmarijuana during the past 12 months on a ‘‘casual’’ basis. The eligibilitycriterion for a CDU was consistent with the definition that was developed bythe Office of National Drug Control Policy (ONDCP, 1995). For the presentresearch, IDUs and other CDUs were combined to form a single CDUsample.

4.2. Recruitment

A targeted sampling strategy was used to reduce the bias that could beproduced by recruiting a convenience sample of drug users from institu-tionally based settings such as drug treatment programs or the CJS (Kaplanet al., 1987; Watters and Biernacki, 1989). This technique is an adaptationof aspects of theoretical sampling (Glaser and Strauss, 1967), stratifiedsurvey sampling (Babbie, 1999), and network sampling (Biernacki andWaldorf, 1981). Targeted sampling is a strategy used to obtain systematicdata when true random sampling techniques are not possible and conve-nience sampling is not rigorous enough to meet the demands of the research

French et al.226

design. Targeted sampling is the preferred technique for identifying andsampling members of elusive community-based populations such as CDUs.Such populations are socially invisible in that many of their activities, beingclandestine in nature, are hidden from the open view of mainstream society.Although institutional settings provide relatively easy access to persons whouse illicit drugs, there is limited generalizability from institutionally basedgroups to the larger, community-based population of drug users (Chitwoodet al., 1993; Chitwood and Morningstar, 1985).

Three strategies were employed to develop the sample that was re-cruited into this study. The first was to designate a recruitment territoryencompassing the entire area of metropolitan Dade County, Florida.Among the largest cities in Dade County are Miami, Miami Beach, NorthMiami, Hialeah/Opalocka, and South Miami, including Homestead. Thesecond strategy was to identify high-risk areas within Dade County byusing geo-coding procedures based on indicator data from drug treatment,criminal justice, and street outreach databases (Rivers et al., 1999), andthen to mobilize recruitment efforts in these areas. A high-risk designationwas based on above-average scores for crime, drug use, poverty, and othersocial indicators. Once high-risk communities were identified, the studyconducted further ethnographic mapping to delineate the locations,interaction styles, and social organization of both drug users and non-users who were located in the sample areas. Based upon this knowledge,the third strategy was to identify potential study participants and to de-velop specific plans for recruiting in each community. The two primarygoals of this sampling technique were to identify a broad spectrum ofstudy-eligible participants and to develop a trust level that would mini-mize potential selection bias resulting from a high rate of refusal toparticipate.

Subjects were recruited from an area that spanned 78 zip codes. NDUswere recruited from the same zip codes as CDUs. Three full-time outreachworkers visited these neighborhoods to screen potential participants. Drugusers and non-users alike were approached on the street by these staffmembers. Consenting individuals who appeared to be eligible were providedround-trip transportation to a central assessment center for a more com-prehensive screening. The outreach workers recruited subjects at all times ofday and every day of the week to obtain a representative sample. Afterpassing the full eligibility criteria, the subjects were escorted to a privateroom to complete the questionnaire with the assistance of an experiencedsurvey administrator. Total participant time (including transportation)generally ranged from 1.5 to 2.5 h. Each participant was paid 25 dollars.Less than 5% of the participants who were transported to the assessmentsite were found to be study-ineligible or refused to participate in the study.


Recruitment was completed in December 1997 with 1570 individualsenrolled in the study. The study sample was stratified by drug use group,ethnicity, and gender. After cleaning the data and eliminating unusableobservations, the total sample included 926 CDUs (including 540 IDUs) and553 NDUs. A two-stage quota sampling design was used to insure inclusionof adequate numbers of women and ethnic minorities. Subsamples bygender and race/ethnicity included 842 men, 638 women, 557 African-Americans, 481 Hispanics, and 442 non-Hispanic whites.

4.3. Comparability of CDUs and NDUs

The primary goal of the recruitment strategy was to form samples ofCDUs and NDUs that were similar in all respects except for drug use. Asnoted earlier, we employed several quality control steps to meet this goal.This required that both classification bias and selection bias be minimizedto the greatest possible extent. Our major concern was assuring that thesampling process did not confound the independent variable of interest,drug use, with other variables. Although this was not a case–control study,we used criteria for sample selection that are similar to those used in case–control studies in order to maximize comparability between the drug-usingand non-using subsamples (Goodman et al., 1988; Hennekens and Buring,1987; Schlesselman, 1982). For this study the objective was to assure thatthe NDU subsample was comprised of persons who would have beenselected for the CDU subsample had they been users of illicit drugs. Wesought to minimize selection bias in a number ways. For instance, all studyparticipants were recruited within the same communities—those known tobe areas of heavy drug use. All participants were recruited on the streets ofthese communities in non-institutional settings. These communities werecharacterized by persons of relatively low socioeconomic status. Allparticipants were recruited by the same team of recruiters within the sametime period. Identical screening procedures were followed for allparticipants.

Subsample comparability in all areas except drug use also is maximizedwhen mis-classification on the drug use variable is minimized. Studies thatinvestigate illicit drug use can be vulnerable to under-reporting of drug useby respondents, which can in turn result in mis-classification (Weatherbyet al., 1994). To minimize such classification bias in this study, establishedbiologic markers were used to validate self-reported drug use and non-use.All participants in both Subsamples participated in the same protocol tovalidate their drug use status.

French et al.228

4.4. Instrumentation

Due to the broad aims and objectives of the research project, it was notpossible to locate a single data collection instrument that addressed allinformation needs. Thus, questions from several of the leading criminaljustice and health services instruments (e.g., Dennis et al., 1996; McLellanet al., 1985; Substance Abuse and Mental Health Services Administration(SAMHSA), 1996; Weisner and Schmidt, 1995) were reviewed and selected.Since health services information—especially with regard to drug users—hasnot been widely explored in the literature, many new questions weredesigned to obtain important information on demographics, health status,morbidity, health care utilization, barriers to utilization, drug use, route ofdrug ingestion, and related lifestyle behaviors. The final questionnaire,which contained well over 300 questions, was divided into seven sections:screening, general, medical, satisfaction, alcohol and drug use, demo-graphics, and safety. A complete version of the Health Services ResearchInstrument is available from the corresponding author.

4.5. Sample Statistics

Table I presents mean values for all of the variables used in theempirical analysis. In addition to overall sample means, values are reportedby drug using status, including CDUs (N ¼ 926) and NDUs (N ¼ 553). Theaverage age of the sample was just over 37 years. CDUs were less likely tobe female and working than NDUs. At the time of the interview, only 10%of the sample was working full-time, and only 9% was working part-time.Fifty-eight percent of the full sample, however, were employed at least partof the time during the past 12 months. Average legal income during the pastyear was $6215 for CDUs and $8296 for NDUs (P<0.01).

Dummy variables were created for the five largest cities in the sample:Miami, Miami Beach, Hialeah, North Miami, and South Miami. Themajority of the sample resided in Miami (52%), with considerably less livingin South Miami (4%) and North Miami (8%). CDUs and NDUs, however,did not differ in city representation.

Regarding criminal activity, 58% of CDUs were involved in crime as avictim and/or perpetrator during the past year, compared to 34% of NDUs.The average annual victim cost of crime was $141,864 for CDUs and$34,866 for NDUs ($25,897 and $13,718 when winsorized at the 5% level).Similarly, the average annual perpetrator cost of crime was $95,377 forCDUs and $4308 for NDUs ($29,205 and $3697 when winsorized). Finally,the combined (victim plus perpetrator) annual cost of crime was $237,241for CDUs and $39,174 for NDUs ($77,168 and $21,130 when winsorized).


All of the mean values for the prevalence of criminal activity and crime cost(i.e., victim, perpetrator, and total) were significantly different betweenCDUs and NDUs.

5. RESULTS

The estimation results are reported in Tables II and III. Looking first atthe probit results for any criminal activity as a victim, a perpetrator, andeither a victim or a perpetrator (Table II), the coefficient estimates (marginaleffects) clearly indicate that CDU is positively related to all types of crime(P<0.01). The estimated marginal effect of CDU on the probability of being

Table I. Variable Means, by Drug Using Status

VariableCDU

(N=926)NDU

(N=553)Total

(N=1479)

DemographicsAge 37.410 37.235 37.345Male* 0.595 0.533 0.572Black 0.390 0.353 0.376Hispanic 0.323 0.331 0.326Married 0.066 0.107 0.081Highest grade completed 11.059 11.210 11.116

Employment and incomeFull-time employed** 0.066 0.161 0.101Part-time employed 0.071 0.112 0.087Legal income past year ($)** 6215 8296 6993

CitiesHialeah 0.126 0.180 0.146Miami 0.521 0.514 0.518North Miami 0.103 0.042 0.080Miami Beach 0.222 0.220 0.221South Miami 0.028 0.045 0.035

Criminal activityAny victim cost** 0.452 0.286 0.390Any perpetrator cost** 0.333 0.110 0.250Any total cost** 0.584 0.338 0.492Victim cost ($)** 141,864 34,866 101,857Winsorized (5%) victim cost ($)** 25,897 13,718 21,343Perpetrator cost ($)** 95,377 4308 61,326Winsorized (5%) perpetrator cost ($)** 29,205 3697 19,667Total cost ($)** 237,241 39,174 163,183Winsorized (5%) total cost ($)** 77,168 21,130 56,215

Note: CDU = Chronic drug use, including injection drug use. NDU = non-drug user.*Statistically significant differences across groups, P<0.05.**Statistically significant differences across groups, P<0.01.

French et al.230

a victim of crime is 0.1620, suggesting that CDUs are 56.64% more likely(0.162/0.286) to be crime victims during the past year than are NDUs. Theestimated marginal effects for CDU and the probability of being a perpe-trator of a crime (0.2335) and the probability of being a victim or perpe-trator (0.2471) have similar interpretations.

The OLS results for the magnitude of crime costs are reported in TableIII. It should be recalled that the dependent variable in each specification isthe natural log of crime cost after first winsorizing each measure at the 5%level. Similar to the univariate probit estimates reported above, CDU ispositive and significant in each of the three crime cost specifications.16

Retransformation of the coefficient estimates from a log-linear speci-fication, however, is not always straightforward, especially not if hetero-scedasticity is present in the estimated residuals. Duan (1983) proposed a‘‘smearing estimate’’ to retransform a log-linear model back to theuntransformed scale when the distribution of the error term is unknown,and several studies have employed and sometimes even extended this tech-nique (Ai and Norton, 2000; Andersen et al., 2000; Manning, 1998; Mull-ahy, 1998). The smearing estimates for the proportional effect of CDU were

Table II. Probit Results for Cost of Crime

Variable Victim cost Perpetrator cost Total cost

CDU 0.1620* (0.0272) 0.2335* (0.0213) 0.2471* (0.0280)Age )0.0016 (0.0087) )0.0256* (0.0080) )0.0180** (0.0091)Age squared )0.00004 (0.0001) 0.0002 (0.0001) 0.0001 (0.0001)Male )0.0144 (0.0277) 0.1054* (0.0229) 0.0349 (0.0289)Married )0.0389 (0.0482) )0.0360 (0.0413) )0.0478 (0.0507)Black )0.1898* (0.0314) 0.0137 (0.0286) )0.1472* (0.0344)Hispanic )0.0945* (0.0335) )0.0397 (0.0290) )0.0782** (0.0365)Highest grade completed )0.0048 (0.0052) )0.0084 (0.0045) )0.0047 (0.0054)Employed full)time )0.0280 (0.0461) )0.0412 (0.0374) )0.0597 (0.0482)Employed part)time )0.0541 (0.0458) 0.0074 (0.0409) )0.0462 (0.0487)Legal income 1.93e)08 (1.40e)06) )4.01e)07 (1.23e)06) )1.64e)06 (1.50e)06)Hialeah 0.2584* (0.0894) 0.0854 (0.0897) 0.2270* (0.0810)Miami 0.2635* (0.0781) 0.1347 (0.0713) 0.2870* (0.0774)North Miami 0.3486* (0.0848) 0.1259 (0.0989) 0.3377* (0.0688)Miami Beach 0.2635* (0.0864) 0.1456 (0.0884) 0.2853* (0.0764)

Notes: All coefficient estimates are marginal effects (constant term not reported). Standarderrors are reported in parentheses.CDU = Chronic drug use, including injection drug use.*Statistically significant, P � 0.01; **Statistically significant, P � 0.05.

16The OLS estimate in Table III includes White standard errors because the Cook–Weisberg

test indicated the presence of heteroscedasticity in all three specifications.


calculated as the ratio of expected crime costs for CDU and NDU(Andersen et al., 2000; Manning, 1998):

EðTCcduÞEðTCnduÞ

¼ edcduScdu

Snduð3Þ

where E(TCcdu) is the expected crime cost for CDU, E(TCndu) is the expectedcrime cost for NDU, dcdu is the estimated coefficient from the log-linearspecification of crime cost and CDU, Scdu is the smearing factor for CDU,and Sndu is the smearing factor for NDU. The smearing factors for CDUand NDU are the average of the exponentiated residuals for each group.

Retransformations of the crime cost estimates with the heteroscedasticsmearing factors suggest that CDUs have 1.69 times higher victim costs of

Table III. Regression Results for Natural Log of Crime Cost

Variable Victim cost Perpetrator cost Total cost

CDU 1.7078* (0.2822) 2.5320* (0.2177) 2.8257* (0.2941)Smearing estimate

for CDU1.69 11.55 3.46

�2 value forCook–Weisberg testfor heteroscedasticity

18.49* 175.49* 8.73*

Age )0.0275 (0.0864) )0.3047* (0.0675) )0.1965** (0.0865)Age squared )0.000 (0.0011) 0.0026* (0.0008) 0.0014 (0.0011)Male )0.2102 (0.2813) 1.0163* (0.2406) 0.3518 (0.2917)Married )0.3708 (0.4748) )0.2224 (0.3831) )0.3532 (0.5190)Black )2.0869* (0.3340) 0.0326 (0.2977) )1.6037* (0.3529)Hispanic )1.0953* (0.3686) )0.2753 (0.3116) )0.8143** (0.3740)Highest grade

completed)0.0501 (0.0538) )0.0942** (0.0449) )0.0591 (0.0551)

Employed full-time )0.2522 (0.4565) )0.5111 (0.3895) )0.6056 (0.4774)Employed part-time )0.5523 (0.4609) )0.1296 (0.3921) )0.5791 (0.4770)Legal income )1.25e)06 (1.20e)05) 2.46e)06 (0.00001) )1.71e)05 (1.19e)05)Hialeah 2.1190* (0.6817) 0.4412 (0.6097) 1.8769** (0.7727)Miami 2.3139* (0.6256) 1.0451 (0.5788) 2.551* (0.7217)North Miami 3.2151* (0.7767) 0.7451 (0.7000) 3.3822* (0.8607)Miami Beach 2.2647* (0.6640) 1.0855 (0.6131) 2.6487* (0.7570)Constant 4.1056** (1.7498) 8.2883* (1.4438) 7.9925* (1.7696)

Notes: All crime cost variables were winsorized (5% level) prior to taking the natural log. Whitestandard errors are reported in parentheses. CDU= Chronic drug use, including injection druguse. The smearing estimates for CDU were calculated as the ratio of expected crime costs forCDU and NDU: (E(TCcdu)/E(TCndu)) = e�cdu (Scdu/Sndu), where E(TCcdu) and E(TCndu) are theexpected crime costs for CDU and NDU,�cdu is the estimated coefficient from the log-linearspecification of crime cost and CDU, and Scdu and Sndu are the smearing factors for CDU andNDU. The smearing factors for CDU and NDU are the average of the exponentiated residualsfor each group.*Statistically significant, P � 0.01; **Statistically significant, P � 0.05.

French et al.232

crime than NDUs, 11.55 times higher perpetrator costs, and 3.46 timeshigher combined costs.17 Thus, even after controlling for other covariates inthe relationship between drug use and crime cost, the empirical evidencestrongly supports the position that chronic drug use has a significant effecton the probability of crime involvement and the cost associated with being avictim or offender of one or more criminal acts.

Besides CDU, several other variables were significant predictors of thecrime cost measures. As reported in Tables II and III, age was significantlyrelated to the probability and cost of being an offender. Men were morelikely to be offenders and had higher associated costs than women. Blacksand Hispanics were less likely to be victims and had lower victim costs, andlegal income was negatively related to the magnitude of victim cost.Somewhat surprisingly, neither education nor employment status were sig-nificantly related to criminal activity.

6. DISCUSSION

6.1. Legal and Policy Implications

CDUs generate significant social costs through their involvement incriminal activity as both victims and perpetrators. The results of this studyoffer qualitative and quantitative evidence that, relative to NDUs, CDUsare significantly more likely to be involved in all types of crime. Forexample, CDUs are 73.11% more likely to be associated with any crimerelative to NDUs, and the total cost of crime is 3.46 times higher for theaverage CDU. The estimated differential in the cost of crime for CDUsinvokes important public safety and public policy concerns. Many jails andprisons across the U.S. are overflowing with drug users (approximately onequarter of all inmates in most states) who were convicted of minor drugoffenses and/or nonviolent drug-related crimes. This popular ‘‘get tough’’policy for drug users has led to jail and prison overcrowding, and substantialcost increases for all levels of criminal justice. Thus, persons who use illicitdrugs not only contribute disproportionately to the societal costs of crime,but also add to the overburden upon correctional facilities (Rajkumar andFrench, 1997). An effective policy is needed that will reduce both the cost ofcrime and the cost of incarceration of persons who use illicit drugs.

17It is worthwhile noting that the smearing corrections in these data generated estimates that

were markedly different than the unadjusted (eb � 1) estimates for victim costs and combined

costs, but similar for perpetrator costs. Specifically, the unadjusted estimates indicate that

CDUs have 4.52 times higher victim costs of crime than NDUs, 11.48 times higher perpetrator

costs, and 15.87 times higher combined costs.


CDUs also pose a particular challenge to society because of their fre-quent and risky drug use (Bruneau et al., 1997; Holtgrave, 1998), utilizationof expensive health care services (French et al., 2000a; McGeary andFrench, 2000), and employment problems (Alexandre and French, in press;French et al., 2001a, b). Given this abundance of empirical data, the com-pelling questions for criminal justice agencies, legal scholars, public healthorganizations, and public policy officials should focus on (1) how to developeffective and cost-effective interventions (e.g., substance abuse treatmentalternatives to incarceration through cross-system collaborations) for thisrelatively small yet problematic segment of the population, (2) how to effi-ciently identify and divert CDUs to these programs, and (3) how to retainthem for optimal exposure and successful outcome. Effective interventionscould generate social benefits such as reduced inmate crowding and incar-ceration costs, increased treatment opportunities for drug users, avoidedcosts of future criminal activity, and improved employment opportunitiesand corresponding reductions in welfare costs.

These findings also support the need for greater awareness of the cycleof crime and victimization within the chronic drug using population. Forlaw enforcement, this awareness is important for effective policing andapprehension of criminal offenders. In particular, understanding that withinthe CDU population, the perpetrator today may be the victim tomorrow.From the criminal justice perspective, these results offer evidence of the highprevalence of drug abuse within the offender population and reinforce theneed for prison-based and post-release substance abuse treatment programsfor criminal offenders. Finally, these findings support a need for substanceabuse interventions to incorporate treatment components that addresscriminal behaviors in addition to substance abuse. In an era where pre-vention is an important public health theme, prevention of chronic drugabuse and criminal activity will require a strong alliance between treatmentproviders, criminal justice agencies, and local communities to effectivelyaddress the drug–crime nexus.

6.2. Limitations

Study limitations should be considered within the context of the re-search and policy implications. The magnitude, significance, and consistencyof the estimated results suggest that any shortcomings in the estimationprocedures may impact the precision of the estimates; but the qualitativefindings are unlikely to change. Nevertheless, the following limitationsshould be noted. First, the data are self-reported from voluntary subjects,which introduces possible concerns about honesty and recall, especially

French et al.234

regarding drug use and criminal activity (Harrison and Hughes, 1997).Second, the research results apply to CDUs and NDUs in Miami-DadeCounty, Florida, but the national implications are uncertain.18 Third,quantity/frequency thresholds are one of several different criteria used tocharacterize a problematic drug user or a drug abuser (French et al., 2001c).Fourth, the estimates for CDU in Tables II and III may be biased if CDU isan endogenous variable (Greene, 2000). Unfortunately, we were unable totest or correct for possible endogeneity bias due to the unavailability ofacceptable instrumental variables. Fifth, these estimates should not be usedto estimate the total social cost of drug-related crime in this sample ofindividuals due to potential double counting of crimes across victims andperpetrators. Future analyses of problematic drug use and the cost ofcriminal activity can address some of these shortcomings by obtainingnationally representative data, verifying some of the self-reported infor-mation with abstracted data from criminal justice agencies, and acquiringdata on criminal justice policies that can be matched to respondents.

7. CONCLUSION

The present study demonstrates that CDUs generate significant socialcosts through their involvement in criminal activity as both victims andperpetrators. This research analyzed unique and recent community-baseddata on CDUs and NDUs recruited through targeted street outreachactivities in Miami-Dade County, Florida. The analysis examined whetherCDUs were more likely to be victims of crime, perpetrators of crime, andeither victims or perpetrators relative to NDUs. After estimating the cost ofcriminal involvement for all victims and perpetrators, the analysis thenexamined the cost of criminal activity for CDUs compared to NDUs. Theresearch employed a smearing technique to calculate the relative cost ofcrime from the log-linear specifications. Identifying CDUs as victims and/orperpetrators of crime and estimating the associated costs is an original andinnovative contribution to the criminal justice and addiction literature(Chaiken and Chaiken, 1990; Cohen, 1998; Martin et al., 1999; Nurco,1998).

18The 2000 U.S. Census reports that the population of Miami-Dade County, Florida, was 2.25

million people, placing this county among the eight largest in the country. In addition, Miami-

Dade County surpassed Bexar County, Texas, as the county with the highest percentage of

Hispanics (57%) of any large county in the nation. Source: The Miami Herald, March 31,

2001, page 2B.


ACKNOWLEDGEMENTS

This research was supported by grants (P50 DA10236, R01 DA11506,and P50 DA07705) from the National Institute on Drug Abuse. Thismanuscript was partially completed while Michael French was a visitingprofessor at the Research Center for Health and Economics, Department ofEconomics, Pompeu Fabra University, Barcelona, Spain. An earlier versionof this paper was presented at the Western Economic Association annualmeeting in June 2000 and at the annual conference of the American PublicHealth Association in November 2000. We gratefully acknowledge KennethGoodman, Sara Markowitz, Ann Morales, and Michael Prendergast forsuggestions on an earlier draft; Brandon Boswell, Michael Kunz, ChrisRoebuck, Helena Salome, and Silvana Zavala for programming andresearch assistance; and William Russell, Carmen Martinez, and SuzanneGresle for administrative support.

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Revolving Roles in Drug-Related Crime: The Cost of Chronic Drug Users as Victims and Perpetrators