To test or not to test: do workplace drug testing programs discourage employee drug use?

SocialScience

Social Science Research 33 (2004) 45–63

www.elsevier.com/locate/ssresearch

RESEARCH

* Corresponding author. Fax: 1-843-792-1358.

E-mail address: [email protected] (M.T. French).

0049-089X/$ - see front matter � 2003 Elsevier Science (USA). All rights reserved.

doi:10.1016/S0049-089X(03)00038-3

To test or not to test: do workplace drugtesting programs discourage employee drug use?

Michael T. French,a,* M. Christopher Roebuck,b

and Pierre K�ebreau Alexandrec

a Department of Health Administration and Policy, Medical University of South Carolina,

19 Hagood Avenue Suite 408, P.O. Box 250807, Charleston, SC, USAb AdvancePCS, 11350 McCormick Road, Executive plaza II, 9th Floor, Hunt Valley, MD 21031, USA

c Department of Epidemiology and Public Health, University of Miami, FL, USA

Abstract

Workplace drug testing programs are often met with intense criticism. Despite resistance

among labor and consumer groups and a lack of rigorous empirical evidence regarding effec-

tiveness, drug testing programs have remained popular with employers throughout the 1990s

and into the current century. The present study analyzed nationally representative data on over

15,000 US households to determine whether various types of workplace drug testing programs

influenced the probability of drug use by workers. The study estimated several empirical spec-

ifications using both univariate and bivariate probit techniques. The specification tests favored

the bivariate probit model over the univariate probit model. Estimated marginal effects of drug

testing on any drug use were negative, significant, and relatively large, indicating that drug test-

ing programs are achieving one of the desired effects. The results were similar when any drug

use was replaced with chronic drug use in the models. These results have important policy im-

plications regarding the effectiveness and economic viability of workplace anti-drug programs.

� 2003 Elsevier Science (USA). All rights reserved.

Keywords: Drug testing; Workplace programs; Employee drug use

1. Introduction

Workplace anti-drug policies or programs can take several forms with

occasionally conflicting objectives. The most intrusive and emotionally charged

mail to: [email protected]

46 M.T. French et al. / Social Science Research 33 (2004) 45–63

policy/program for individuals is a drug testing program, which can be administered

through pre-employment screening (e.g., requiring urine samples from all job appli-

cants) or post-employment surveillance (e.g., requiring urine samples from existing

employees on a random, comprehensive, or suspicion basis) (Lange et al., 1994).

Drug testing programs can be very costly for a company1, and their effectivenessin mitigating employee drug use is often uncertain (Normand et al., 1994).

The full costs and benefits of drug testing programs remain largely unknown be-

cause most drug testing programs are relatively new and empirical work is sparse in

this area. For example, the Bureau of Labor Statistics estimated that 45.9% of work-

sites with more than 250 employees had a drug testing program in 1990, up from

31.9% in 1988 (Hayghe, 1991). Using a national probability sample of worksites, a

survey by the Research Triangle Institute estimated that 48.4% of all non-govern-

mental worksites with 50 or more full-time employees had a drug testing programin 1993 (Hartwell et al., 1996). The prevalence increases dramatically (i.e., over

60%) when considering only worksites with more than 250 employees.

Trice and Steele (1995) provide an excellent historical perspective on the growth

of Employee Assistance Programs (EAPs) and drug testing programs at contempo-

rary worksites. It is interesting to note that the growth of worksite anti-drug pro-

grams has accelerated during the 1980s and early 1990s at the same time that

national surveys show a declining rate of drug use in the US. Trice and Steele

(1995) conjecture that rising conservatism in the US during this period can be linkedwith the simultaneous reduction in drug use and increase in anti-drug programs.

Whether EAPs and drug testing have independently contributed to this decline in

drug use is unclear, but companies are now spending a considerable amount of

money on anti-drug programs aimed at a shrinking population of working-age drug

users.

Despite this rapid growth, not all worksites have anti-drug policies or programs,

and the majority of worksites do not perform drug testing. Some potential reasons

for not adopting a drug testing program include threats of legal action, privacy pro-tection concerns, cost, inaccuracy of drug tests, and questions about the link between

drug use and poor work performance (Axel, 1989; Trice and Steele, 1995). Neverthe-

less, many firms have established these programs with little evidence that they are

effective or money-saving for the company money currently or in the long run.

Some observational field studies have published findings that tend to support the

use of drug testing programs, but the findings are sometimes inconsistent or ambig-

uous (e.g., Normand et al., 1990; Ryan et al., 1992; Zwerling et al., 1990; Zwerling

et al., 1992; National Research Council-Institute of Medicine (NRC-IOM), 1994). Inone of the more convincing studies, Lange et al. (1994) found that positive drug

screens of job applicants to a large teaching hospital declined from 11% in 1989,

when a drug testing program was implemented, to 6% in 1991, after continuous op-

eration of the program. While changes in the prevalence of drug use among the US

1 Studies show that the direct cost per drug test can range from $30 to $150, with most estimates

clustered around $40–60. These values, however, do not include any legal costs or related activities

associated with a drug testing program (Thompson et al., 1991).

M.T. French et al. / Social Science Research 33 (2004) 45–63 47

population could have explained some of the decline in the rate of positive drug

screens in this study, the authors contended that the time frame was too short and

that the changes were not dramatic enough to diminish the significance of the results.

McDaniel (1988) used self-reported drug use information for over 10,000 young

adults to determine their suitability for military employment. The intent of the studywas to ascertain whether a drug testing program could be justified based on follow-

up employment suitability differences (i.e., discharge from the military based on

behavioral or performance criteria) between drug users and non-drug users. The

findings showed that employment suitability rates varied with drug use patterns,

and that the age at first drug use was significantly related to being classified as

unsuitable. The results were somewhat compromised, however, by the low

prevalence rates for most illicit drugs.

Sujak et al. (1995) approached the drug testing issue from a different perspectiveby examining the relationships between program characteristics and potential appli-

cants� attitudes and intentions to apply for employment in organizations with such

programs. If potential applicants have a negative reaction to a particular drug testing

program (regardless of their drug-using preferences), they may self-select out of the

applicant pool and impair the competitiveness of the organization. Using a sample of

undergraduate students at a Midwestern university, Sujak et al. (1995) found that

test result confidentiality was the most important factor in student attitudes, but also

that program type did not have a significant effect on attitudes or intent to apply.The authors suggest that the latter result may be driven by the fact that the sample

in general had very negative attitudes toward drug use and few participants reported

ever using drugs in their lifetime. It would be interesting to see whether these results

would endure with a more representative sample of working individuals (and drug

users).

McGuire and Ruhm (1993) published one of the few studies on workplace drug

abuse programs in the economics literature. They developed a labor market model

with asymmetric information to examine the policy implications of drug abuse treat-ment (i.e., EAPs) and workplace drug testing. Their overall conclusion was that la-

bor market incentives will generally lead to too little treatment and too much testing.

One reason for this result stems from the fact that firms have incentives (based on

performance differences) to test workers for drug use in order to reduce the number

of drug abusers whom they employ. This behavior, however, creates a negative ex-

ternality for non-testing firms by diminishing the quality of their applicant pool

(McGuire and Ruhm, 1993). The findings of McGuire and Ruhm rest on the as-

sumption that drug users do not work for firms that test because they either self-se-lect out, or they are denied employment due to a positive drug screen.

The present paper adds to the drug testing literature by examining the relation-

ships between worksite-based drug testing programs and employee drug use. Specif-

ically, a key research question is whether the presence of different types of drug

testing programs negatively influences the probability and frequency of drug use

by existing employees. Using pooled data from the 1997 and 1998 National House-

hold Surveys on Drug Abuse (NHSDA), the analysis employed univariate and bivar-

iate probit techniques to estimate the relationships between employee drug use and


firms� decisions to operate a drug testing program. The drug use measures included

any drug use and chronic drug use. The drug testing measures included any, pre-em-

ployment, suspicion-based, and random.

2. Empirical model

If workplace anti-drug programs are effective in dissuading drug use, then a neg-

ative relationship should be found between the presence of a program and employee

drug use. Similarly, drug users in general—particularly chronic drug users—would

sort into jobs that are less likely to be governed by these programs (McGuire and

Ruhm, 1993). It seems plausible that, all else equal, drug users are less likely to seek

a job that has pre-employment or random drug testing relative to an identical jobthat does not involve a drug testing program. The same argument could be made

for a job that is subject to a formal, written policy regarding disciplinary action

for the use of illicit substances. All of these sanctions-oriented programs presumably

have a negative impact on drug use.

Some workplace programs, however, are more treatment-oriented than sanctions-

oriented. Such programs distribute literature on drug abuse, assist employees in con-

tacting community-based treatment providers, or in some cases offer direct support

in the form of structured employee assistance programs (EAPs). Because treatment-oriented programs are not directly threatening or penalizing, it is possible that drug

users do not differentially select away from worksites that implement them. On the

other hand, drug-using employees may view these programs as indicative of negative

management sentiment toward drug use and may shy away for this reason.

To empirically examine the relationships between drug testing programs and em-

ployee drug use, consider a standard univariate probit model such as Eq. (1) below:2

2 T

for Pr(

practic

norma3 G

drug p

STRID

low rel

PrðD ¼ 1Þ ¼ Uðb0 þ bZZ þ bI I þ bRRþ bHH þ bDT DT Þ; ð1Þ
where D is the dichotomous choice of consuming any amount of illicit drugs; Uð�Þ isthe cumulative normal distribution; Z is a vector of demographic, environmental,
and occupational variables (i.e., proxies for personal tastes and preferences); I is

personal income; R is a series of regional indicators (i.e., proxy measures for the

prices of illicit drugs);3 H is labor supply during the past year; DT is a dummy

variable for any type of drug testing program at the individual�s primary place of

employment; and b is a vector of parameters to estimate.

he probit technique estimates maximum likelihood probit models based on the normal distribution

D ¼ 1). Some analysts are fonder of logit models, which are based on the logistic distribution. In

e, the results from the logit and probit tend to be similar, as the logistic distribution is similar to the

l distribution except in the tails.

iven the nature of the illegal drug markets, it is very difficult to obtain credible information on illicit

rices. One of the few sources currently available is the US Drug Enforcement Administration�sE data. However, several recent studies in the economics literature have found these data to have

iability (e.g., Chaloupka et al., 1999; Farrelly et al., 1999; Horowitz, 2000).


Estimation of Eq. (1) can be performed across the sample of working individuals

to determine whether employment at a drug testing workplace has a negative impact

on the probability of using any illicit drug. In addition, the marginal effect of DT on

the probability of illicit drug use can be estimated.4

Several extensions of the simple binary probit model in Eq. (1) are possible. First,the model can account for the fact that drug testing programs are not homogeneous;

some program features are more invasive than others. In particular, the survey ques-

tioned respondents about the presence of any drug testing program (DTa), a pre-em-

ployment program (DTp), a suspicion-based program (DTs), and a random program

(DTr) at their place of employment. The binary probit model in Eq. (1) can be broad-

ened by adding and replacing each one of these right-hand variables to the core

specification.

The next extension recognizes that drug demand is more complex than a dichot-omous choice between a positive consumption amount and zero consumption. Un-

fortunately, the survey questionnaire lacks the necessary measures to create a

continuous index of drug consumption because respondents only reported the fre-

quency of use for each drug in a categorical format, and consumption across drug

types is not additive. Instead, a measure for chronic drug use (CDU) was created

based on criteria specified by the Office of National Drug Control Policy (ONDCP,

1996). Eq. (1) can then be estimated with CDU as the dependent variable and all

other variables (including the drug testing variables) as previously defined.A possible criticism of the univariate probit models described thus far is that the

worker�s decision to use illicit drugs and work for a company that performs drug

tests is determined jointly with the firm�s decision to ‘‘offer’’ a drug testing program.

This simultaneity is not captured in the univariate probit model (Eq. (1)). To address

this issue and improve efficiency, a bivariate probit model is specified where D and

DT are jointly determined.5 This specification allows for correlated disturbances

across the two equations in a fashion similar to the seemingly unrelated regressions

model (Greene, 2000).

4 T

variab

the pro

univar

a work

D (Gre

where5 In

standa

possib

variab

D� ¼ b01 þ bZ1Z1 þ bI I þ bR1R1 þ bHH þ bDT DT þ e1; ð3Þ

he ‘‘marginal effect’’ in a probit model is the change in the probability pertaining to the dependent

le for an infinitesimal change in each continuous independent variable, and the discrete change in

bability for each binary or dummy variable. Because DT is a binary independent variable in the

iate probit model, the following formula is used to calculate the marginal effect of being employed at

place that has any type of drug testing program, DT, on the probability of consuming illicit drugs,

ene, 2000):

oE D=Z;I;R;H ;DT½ �oDT

¼Pr½D¼ 1j �Z;�I ; �R; �H ;DT ¼ 1��Pr½D¼ 1j �Z;�I ; �R; �H ;DT ¼ 0�

¼U½b0þbZ�ZþbI

�IþbR�RþbH

�H þbDT ��U½b0þbZ�ZþbI

�IþbR�RþbH

�H �; ð2Þ�Z;�I; �R, and �H are the mean values for the Z, I, R, and H variables.

principle, the joint (employers and employees) selection process can also be modeled through a

rd selection model or an instrumental variables (IV) approach. These options, however, were not

le in the present study due to the lack of appropriate and reliable identifying or instrumental

les (e.g., illicit drug prices) in the data set.

6 T


D ¼ 1 if D� > 0 and D ¼ 0 if D�6 0;

DT � ¼ b02 þ bZ2Z2 þ bSS þ bINDINDþ bR2R2 þ e2 ð4Þ

DT ¼ 1 if DT � > 0 and DT ¼ 0 if DT �6 0;

ðe1; e2Þ � Nð0; 0; 1; 1; qÞ:
All variables are the same as previously defined except that Z2 represents demo-
graphic characteristics for workers at a particular workplace (i.e., proxy measures

for the firm�s perception of illicit drug use among employees), S represents the size of

the organization measured as the total number of employees at the workplace and

the total number of employees with the company, IND is a series of dummy variables

for the type of industry, R2 is a series of dummy variables for the region of the US

where the workplace is located, and q ¼ Cov½e1; e2� 6¼ 0. A recent national survey

found that organization size, industry, and location were the most important pre-dictors of drug testing prevalence (Hartwell et al., 1996).

2.1. Data

The choice of a data set for these analyses is limited by the number of studies that

collect confidential and sensitive information on individuals� use of illicit drugs. An-

other important component of information for this study is workplace policies and

programs directed at substance abuse prevention and/or treatment. Lastly, it is im-portant to analyze a sample that is relatively large, nationally representative, and re-

cently constructed. A data set that best meets all of these criteria is a pooled sample

from the 1997 and 1998 National Household Surveys on Drug Abuse (NHSDA)

(Substance Abuse and Mental Health Services Administration SAMHSA, 1999a,b,

2000a,b).

The 1997/1998 NHSDA is the seventeenth/eighteenth survey in a series that began

in 1971. The sample design is a nationally stratified multistage area probability sam-

ple of the noninstitutionalized household population in the 50 contiguous UnitedStates, aged 12 years and older. Various segments of the population were oversam-

pled, including youth, minorities, and current smokers, aged 18–34. A total of 50,005

interviews were completed in 1997 and 1998 with an overall response rate of 78%.

Since 1994, the NHSDA questionnaire has included additional measures for health

status, health care, access to care, and mental health. Questions pertaining to work-

place anti-drug policies and programs were also added to the NHSDA in 1994 and

became standard questions in 1997. This information makes the 1997 and 1998

NHSDAs the most recent nationally representative information on workplace drugtesting programs.6

Although the NHSDA is one of the largest surveys of drug use undertaken in the

US, it has certain limitations that may affect the analyses (SAMHSA, 1999a, 2000a).

Most importantly, the data are self-reported, which raises questions regarding

he public-use file for the 1999 NHSDA was not available at the time this paper was written.


validity and reliability. NHSDA procedures were designed to maximize honesty and

recall, but ultimately the value of the data depends on respondents� truthfulness andmemory. Although debate continues as to the reliability of self-reporting, a few stud-

ies have examined the validity of self-reported drug use information among a variety

of different populations (e.g., community-based, drug abusing, and treatment seek-ing) and have found the measures to be quite accurate (e.g., Falck et al., 1992; Har-

rison, 1995, 1997; Hersh et al., 1999; Rouse et al., 1985; Turner et al., 1992; Weiss

et al., 1998; Zanis et al., 1994).

Another limitation of the NHSDA is its cross-sectional design. It would be useful

to analyze and report longitudinal changes in drug use patterns and how these

changes are related to drug testing exposure for specific individuals. Unfortunately,

because a new cohort is sampled every year, the NHSDA cannot examine these

issues.Lastly, a small segment of the US population (about 1%) is excluded from the

sampling frame because they are not part of the target population. The excluded sub-

populations are active members of the military and persons in institutional group

quarters (e.g., hospitals, prisons, nursing homes, and treatment centers). It is un-

likely, however, that the exclusion of these subpopulations would have significantly

impacted our workplace policy analysis. For additional details on sample design, re-

sponse rates, main findings, and other technical details of the 1997 and 1998

NHSDA, refer to SAMHSA (1999a,b, 2000a,b).Table 1 reports sample means for all variables that are used in the empirical mod-

els. The presentation is segmented by drug-using status. A subject is classified as a

drug user (DU) if he/she used any illicit drug during the past year. CDUs include

all individuals who used one or more illicit drugs at least once a week during the past

year. Finally, a non-drug user (NDU) did not use any illicit substance during the

past year. Individuals under the age of 18 or over the age of 65 were excluded from

the analysis given their unique employment choices (e.g., full-time students, retirees).

Statistically significant differences between DUs and NDUs (using the Kruskal–Wal-lis equality of populations rank–sum test) are noted in the table.

Looking first at the drug testing variables, almost 47% of these employees worked

for a company that had any type of testing program.7 The most popular type of pro-

gram was pre-employment (38.8%), followed by reasonable suspicion (30.6%) and

7 Given that all data from the NHSDA are self-reported, one can question whether survey respondents

are truly informed or aware of their employer�s policy regarding drug testing and other anti-drug

programs. Another way to look at this issue, however, is the importance of perception versus knowledge.

That is, individuals who directly report either the presence or absence of a drug testing program at their

workplace are probably using this perception (whether accurate or not) to influence their drug-using

decisions vis-�a-vis such programs. On the other hand, a small percentage of respondents reported ‘‘don�tknow’’ to the drug testing questions. Similarly, one could argue that a ‘‘don�t know’’ response is actuallyequivalent to a ‘‘no’’ response because lack of knowledge or awareness implies that these individuals are

not influenced (at least in a drug-using sense) by drug testing programs. Thus, those respondents who

reported ‘‘don�t know’’ to the drug testing questions were recoded at ‘‘no’’ for the present analysis on the

basis that drug testing perceptions may be more important than reality when analyzing program

effectiveness.

Table 1

Variable means by drug-using status

Variable NDU DU CDU Total

ðN ¼ 13; 711Þ ðN ¼ 1689Þ ðN ¼ 544Þ ðN ¼ 15; 400Þ

Socio-demographics

Age�� 36.77 33.42 33.58 36.40

Male (%)�� 44.91 59.56 64.71 46.51

White (%) 72.86 72.11 70.77 72.78

Black (%) 22.71 23.86 24.82 22.84

Hispanic (%)�� 25.18 18.29 18.75 24.43

Married (%)�� 60.52 37.77 37.13 58.03

Highest grade completed�� 13.03 13.01 12.68 13.03

Rural (%)�� 15.36 12.20 12.13 15.01

New England and Mid-Atlantic (%)�� 14.06 10.95 10.48 13.72

East and West North-Central (%)�� 15.70 18.65 17.65 16.03

South Atlantic (%) 17.26 15.16 14.89 17.03

East and West South-Central (%)�� 18.98 14.33 15.44 18.47

Mountain (%)�� 13.83 17.82 18.20 14.27

Pacific (%)�� 20.17 23.09 23.35 20.49

Very religious (%)�� 32.07 19.09 16.45 30.65

Number of moves past year�� 0.28 0.50 0.55 0.30

Personal income past year ($1000)�� 28.28 26.34 25.12 28.06

Full-time employed (%) 85.52 84.43 82.54 85.40

Weeks worked past year�� 48.12 46.85 46.36 47.98

Employer characteristics

Company size: less than 10 people (%) 16.77 18.90 21.97 17.00

Company size: 10–24 people (%)�� 9.19 12.40 12.48 9.54

Company size: 25–99 people (%) 13.09 14.63 14.53 13.26

Company size: 100–499 people (%) 15.39 14.21 13.59 15.26

Company size: 500 people or more (%)�� 45.56 39.86 37.43 44.94

Location size: less than 10 people (%)�� 24.52 27.86 30.35 24.88

Location size: 10–24 people (%)�� 15.59 18.77 18.62 15.94

Location size: 25–99 people (%)�� 22.43 22.38 22.35 22.42

Location size: 100–499 people (%) 19.97 18.29 18.62 19.79

Location size: 500 people or more (%)�� 17.49 12.70 10.06 16.96

Agriculture, forestry, and fisheries (%) 2.33 2.03 2.05 2.30

Mining (%) 0.40 0.42 0.37 0.41

Construction (%)�� 6.12 9.58 11.36 6.49

Manufacturing (%) 16.48 14.24 14.53 16.24

Transport., communications, and pub.

utilities (%)

7.03 7.30 6.70 7.06

Wholesale trade (%) 3.96 4.13 3.54 3.98

Retail trade (%)�� 13.80 19.87 23.09 14.46

Finance, insurance, and real estate (%) 6.31 5.15 4.84 6.19

Services (%)�� 38.57 33.99 30.73 38.07

Public administration (%) 4.80 3.05 2.61 4.61

Employee occupations

Executive, administrative,

and managerial (%)

13.01 12.78 11.36 12.98

Professional specialty (%)� 14.78 12.24 10.61 14.50

Technicians, and related support (%) 4.32 4.36 3.54 4.33

Sales (%) 8.98 10.75 10.80 9.18


Table 1 (continued)

Variable NDU DU CDU Total

ðN ¼ 13; 711Þ ðN ¼ 1689Þ ðN ¼ 544Þ ðN ¼ 15; 400Þ

Administrative support (%)�� 15.01 11.88 9.31 14.67

Private household, protective, and other

service (%)

15.84 15.88 18.44 15.84

Farming, forestry, fishing (%) 2.30 1.85 2.05 2.25

Precision production, craft, and repair (%)�� 9.73 13.37 15.27 10.13

Operators, fabricators, and laborers (%) 15.82 16.36 18.25 15.88

Drug testing programs

Any drug testing at workplace (%)�� 47.26 42.72 40.64 46.76

Pre-employment drug testing (%)�� 39.50 33.27 31.20 38.82

Suspicion-based drug testing (%) 30.64 29.78 28.63 30.55

Random drug testing (%)�� 24.62 20.64 17.70 24.19

Note. Data were pooled from the 1997 NHSDA (N ¼ 7319) and the 1998 NHSDA (N ¼ 8081) to form

the full sample (N ¼ 15; 400). NDU, non-drug user past year; DU, drug user past year; CDU, Chronic

drug user past year.

Statistically significant differences between DUs and NDUs were calculated using Kruskal–Wallis

equality of populations rank test: �p < :10; ��p < :05; ��p < :01.


random (24.2%). The only measure that did not show a significant difference between

DUs and NDUs was suspicion-based drug testing.

The data in Table 1 indicate that drug-using status was significantly related to nu-

merous socio-economic variables, including age, gender, Hispanic ethnicity, marital

status, education, region of residence, religious beliefs, number of moves, personal

income, and annual weeks worked. For example, DUs were younger, more likely

to be male, non-Hispanic, unmarried, and less religious than NDUs. DUs also

moved more often, had less personal income, and worked fewer weeks during thepast year.

DUs were less likely to work for very large companies or in the services industry,

but more likely to work in the construction and retail trade industries. The occupa-

tion groups that showed significant differences between DUs and NDUs were profes-

sional specialty, administrative support, and production, craft, and repair.

3. Results

Following the empirical strategy outlined earlier, we first conducted a specifica-

tion search to determine whether it was appropriate to estimate a standard binary

probit or a bivariate probit.8 The key parameter in this search is the estimate for

q, the correlation between the disturbances in the drug use and drug testing equa-

tions (Greene, 2000). If q is significantly different from zero, then it is appropriate

to employ the bivariate probit model. Otherwise, the univariate probit model is more

8 Although the bivariate probit is a relatively new technique in the social science literature, several

published studies have employed a similar strategy (e.g., Feng et al., 2001; Rees et al., 2001).


appropriate. Since the estimate for q was significantly different from zero in each

model, all results that follow will pertain to the bivariate probit models. It should

be noted, however, that the estimated marginal effects for the drug testing variables

were very similar under both models, bivariate probit and univariate probit. All re-

sults from the univariate probit specifications are available upon request from thecorresponding author.

The first set of results (Table 2) corresponds to the bivariate probit models of any

drug use (DU) and drug testing (DT). Table 3 reports the estimation results for the

bivariate probit models of CDU and drug testing. In addition, marginal effects were

calculated and reported for all drug testing measures to determine the incremental

effect of each testing program on the probability of drug use.

The coefficient estimates for drug testing in the DU equations (Table 2) were neg-

ative and statistically significant in all four specifications (DTa, DTp, DTs, and DTr).Similarly, the coefficient estimates for drug testing in the CDU equations (Table 3)

were also negative and statistically significant. The quantitative interpretation of

these findings can be determined from the estimated marginal effects. For example,

the marginal effect for any drug testing in the DU specification was )0.0265, suggest-ing a 24.16% ()0.0265/(1689/15,400)) lower rate of drug use among employees at

worksites with any drug testing program relative to worksites without any drug test-

ing program. Similarly, the marginal effect for any drug testing in the CDU specifi-

cation was )0.0136, suggesting that employees at worksites with any drug testingprogram were 38.5% ()0.0136/(544/15,400)) less likely to be chronic drug users.

Coefficient estimates for many of the other variables in Tables 2 and 3 were sig-

nificant, and the signs seem plausible. For example, age was significantly related to

drug use in a nonlinear fashion. Hispanic ethnicity, marriage, education, religiosity,

and labor supply were negatively related to drug use. Drug use was positively asso-

ciated with being male and moving often. In the drug testing equations, employee

age, gender, race, and education were significantly related to whether a person would

work at a company that performed drug testing. In addition, drug testing was signif-icantly related to worksite location, company size, and workplace size.

One might argue that the CDU specifications described above are somewhat mis-

leading because CDUs are compared to a combined group of NDUs and non-chronic

drug users. An alternative specification is to drop non-chronic drug users from the

sample and directly compare CDUs and NDUs. We performed this sample reduction

exercise and then re-estimated the bivariate probit models. The results were quite

similar to the estimates reported in Table 3. Namely, all of the drug testing coeffi-

cients were statistically significant, the estimated marginal effects were negativeand greater in magnitude than the earlier estimates, and the estimates for q were po-

sitive and significant. Thus, these additional findings support the earlier results.

4. Discussion and conclusion

Considerable public attention has recently descended on drug abuse policy and

program debates given the vast array of consequences that can be traced to the abuse

Table 2

Bivariate probit estimation results for drug use (DU)

Explanatory variable Dependent variable

Any drug use Any drug use Any drug use Any drug use

0.31��

Any drug testing (DTa) (0.06) — — —

[)0.03])0.34��

Pre-employment drug testing

(DTp)— (0.07)

[)0.02]— —

)0.35��

Suspicion-based drug testing

(DTs)— — (0.08)

[)0.02]—

)0.38��

Random drug testing (DTr) — — — (0.10)

[)0.01]

1998 Survey )0.007 )0.004 )0.002 )0.007(0.03) (0.03) (0.03) (0.03)

Age 0.08�� 0.08�� 0.08�� 0.08��

(0.02) (0.02) (0.02) (0.02)

Age2 )0.001�� )0.001�� )0.001�� )0.001��

(0.0002) (0.0002) (0.0002) (0.0002)

Male 0.38�� 0.38�� 0.39�� 0.39��

(0.03) (0.04) (0.03) (0.04)

White 0.14� 0.16�� 0.16�� 0.16��

(0.07) (0.08) (0.08) (0.08)

Black 0.15� 0.17�� 0.17�� 0.17��

(0.08) (0.08) (0.08) (0.08)

Hispanic )0.35�� )0.36�� )0.36�� )0.37��

(0.04) (0.04) (0.04) (0.04)

Married )0.45�� )0.44�� )0.44�� )0.44��

(0.03) (0.03) (0.03) (0.03)

Highest grade completed )0.02�� )0.02�� )0.02�� )0.02��

(0.007) (0.007) (0.007) (0.007)

Rural )0.03 )0.04 )0.02 )0.01(0.05) (0.05) (0.05) (0.05)

New England and Mid-Atlantic )0.29�� )0.29�� )0.30�� )0.29��

(0.06) (0.06) (0.06) (0.06)

East and West North-Central )0.03 )0.04 )0.04 )0.05(0.05) (0.05) (0.05) (0.05)

South Atlantic )0.14�� )0.15�� )0.14�� )0.14��

(0.05) (0.05) (0.05) (0.05)

East and West South-Central )0.20�� )0.20�� )0.20�� )0.17��

(0.05) (0.05) (0.05) (0.05)

Mountain 0.05 0.05 0.06 0.06

(0.05) (0.05) (0.05) (0.05)

Very religious )0.30�� )0.30�� )0.30�� )0.30��

(0.04) (0.04) (0.04) (0.04)

Number of moves in past year 0.15�� 0.15�� 0.15�� 0.15��

(0.02) (0.02) (0.02) (0.02)


Table 2 (continued)


Any drug use Any drug use Any drug use Any drug use

Personal income past year

($1000)

)0.001(0.0009)

)0.001(0.0009)

)0.001(0.0009)

)0.001(0.0009)

Full-time employed )0.04 )0.04 )0.05 )0.05(0.05) (0.05) (0.05) (0.05)

Weeks worked past year )0.005�� )0.005�� )0.005�� )0.005��

(0.002) (0.002) (0.001) (0.002)

Constant )1.58�� )1.66�� )1.61�� )1.68��

(0.34) (0.34) (0.34) (0.34)

Any drug

testing

Pre-employment

drug testing

Reasonable

cause drug

testing

Random drug

testing

1998 Survey 0.06�� 0.07�� 0.09�� 0.06��

(0.02) (0.02) (0.02) (0.03)

Age 0.03�� 0.03�� 0.02 0.04��

(0.01) (0.01) (0.01) (0.01)

Age2 )0.0003�� )0.0004�� )0.0002 )0.0005��

(0.0001) (0.0002) (0.0002) (0.0002)

Male 0.21�� 0.21�� 0.19�� 0.23��

(0.03) (0.03) (0.03) (0.03)

White )0.04 )0.05 0.02 )0.003(0.06) (0.06) (0.06) (0.07)

Black 0.26�� 0.31�� 0.27�� 0.30��

(0.06) (0.07) (0.07) (0.07)

Hispanic )0.01 0.04 )0.03 )0.09��

(0.03) (0.03) (0.03) (0.04)

Married 0.0001 0.01 )0.03 )0.03(0.02) (0.03) (0.03) (0.03)

Highest grade completed )0.005 )0.01�� )0.02�� )0.02��

(0.005) (0.005) (0.005) (0.006)

Rural 0.11�� 0.04 0.19�� 0.24��

(0.04) (0.04) (0.04) (0.04)

New England and Mid-Atlantic )0.13�� )0.13�� )0.17�� )0.02(0.04) (0.05) (0.05) (0.05)

East and West North-Central 0.26�� 0.22�� 0.23�� 0.26��

(0.04) (0.04) (0.04) (0.05)

South Atlantic 0.38�� 0.36�� 0.34�� 0.45��

(0.04) (0.04) (0.04) (0.05)

East and West South-Central 0.35�� 0.29�� 0.34�� 0.62��

(0.04) (0.04) (0.04) (0.04)

Mountain 0.37�� 0.30�� 0.40�� 0.55��

(0.04) (0.04) (0.04) (0.05)

Company size: less than 10

people

)1.48��

(0.06)

)1.39��

(0.06)

)1.32��

(0.07)

)1.23��

(0.07)

Company size: 10–24 people )0.84��

(0.05)

)0.85��

(0.06)

)0.64��

(0.06)

)0.65��

(0.06)

Company size: 25–99 people )0.55�� )0.53�� )0.47�� )0.38��

(0.04) (0.04) (0.04) (0.05)


Table 2 (continued)

Explanatory variable Any drug

testing

Pre-employment

drug testing

Reasonable

cause drug

testing

Random drug

testing


(0.04) (0.04) (0.04) (0.04)

Location size: less than 10

people

)0.46��

(0.05)

)0.49��

(0.05)

)0.38��

(0.05)

)0.25��

(0.06)

(0.05) (0.05) (0.05) (0.06)

Location size: 10–24 people )0.39�� )0.40�� )0.33�� )0.20��

(0.05) (0.05) (0.05) (0.05)


(0.04) (0.04) (0.04) (0.04)


(0.04) (0.04) (0.04) (0.04)

Constant )0.34 )0.55�� )0.60�� )1.53��

(0.25) (0.25) (0.25) (0.27)

Correlation among the

equations (q)0.14�� 0.13�� 0.22�� 0.17��

Note. Standard errors reported in parentheses; selected marginal effects reported in brackets; coefficient

estimates for eight occupation categories not reported in the drug use equations; coefficient estimates for

nine industry categories not reported in the drug testing equations; statistically significant: �p < :10;��p < :05; ��p < :01.


of illicit drugs (e.g., Harwood et al., 1998; Rice et al., 1991). Recent studies have eval-

uated the costs and effectiveness of drug abuse treatment (French et al., 2000; French

et al., 2002; Zarkin et al., 2001); drug abuse treatment relative to source country con-

trols, interdiction, and law enforcement (Rydell and Everingham, 1994); and ex-

tended prison stays for drug law violators (Caulkins et al., 1997). Anti-drug abuse

programs at the workplace have also been reviewed recently, but the literature is

sparse and the findings are inconclusive (e.g., National Research Council-Institute

of Medicine NRC-IOM, 1994; National Research Council, 2001).One aspect of workplace anti-drug programs that has not received much attention

in the empirical literature is the effectiveness of these programs in mitigating drug use

among current employees and in discouraging job search from drug-using appli-

cants. Intuition would suggest that drug-using individuals would prefer to work

for employers without an anti-drug policy to avoid any disapproval or sanctions re-

lated to their continued drug use. Similarly, the quantity and frequency of drug use

may be an important factor in this relationship in that heavier drug users may have a

greater distaste for worksites with anti-drug programs relative to casual drug usersand non-drug users. For instance, chronic drug users would have a greater likelihood

of having their drug use detected at worksites with random drug testing due to their

higher frequency of use.

If all worksites in the US had identical drug testing programs, then we might see a

decline in the overall level of drug use among workers; differential effects across

worksites, however, would be unlikely. But not all worksites have programs in place,

Table 3

Bivariate probit estimation results for chronic drug use (CDU)


Chronic

drug use

Chronic

drug use

Chronic

drug use

Chronic

drug use

)0.38��

Any drug testing (DTa) (0.09) — — —

[)0.01])0.40��

Pre-employment drug testing (DTp) — (0.10) — —

[)0.01])0.43��

Suspicion-based drug testing (DTs) — — (0.10) —

[)0.01])0.51��

Random drug testing (DTr) — — — (0.14)

[)0.01]

1998 Survey 0.03 0.03 0.03 0.02

(0.04) (0.04) (0.04) (0.04)

Age 0.07�� 0.07�� 0.07�� 0.07��

(0.02) (0.02) (0.02) (0.02)

Age2 )0.001�� )0.001�� )0.001�� )0.001��

(0.0003) (0.0003) (0.0003) (0.0003)

Male 0.38�� 0.39�� 0.39�� 0.39��

(0.05) (0.05) (0.05) (0.05)

White 0.03 0.07 0.07 0.07

(0.10) (0.11) (0.11) (0.11)

Black 0.12 0.17 0.17 0.16

(0.11) (0.12) (0.12) (0.12)

Hispanic )0.29�� )0.29�� )0.30�� )0.31��

(0.06) (0.06) (0.06) (0.06)

Married )0.36�� )0.35�� )0.35�� )0.36��

(0.05) (0.04) (0.04) (0.04)

Highest grade completed )0.03�� )0.03�� )0.04�� )0.04��

(0.01) (0.01) (0.01) (0.01)

Rural )0.02 )0.02 0.003 0.01

(0.07) (0.07) (0.07) (0.07)

New England and Mid)Atlantic )0.25�� )0.25�� )0.25�� )0.24��

(0.08) (0.08) (0.08) (0.08)

East and West North-Central )0.01 )0.03 )0.03 )0.04(0.07) (0.07) (0.07) (0.07)

South Atlantic )0.10 )0.10 )0.10 )0.09(0.07) (0.08) (0.08) (0.08)

East and West South-Central )0.12 )0.12� )0.11 )0.07(0.07) (0.07) (0.07) (0.08)

Mountain 0.09 0.08 0.09 0.11

(0.07) (0.07) (0.07) (0.08)

Very religious )0.33�� )0.34�� )0.34�� )0.34��

(0.05) (0.05) (0.05) (0.05)

Number of moves in past year 0.14�� 0.14�� 0.14�� 0.14��

(0.03) (0.03) (0.03) (0.03)


Table 3 (continued)


Chronic

drug use

Chronic drug

use

Chronic

drug use

Chronic

drug use

Personal income past year ($1000) )0.001 )0.001 )0.001 )0.001(0.001) (0.001) (0.001) (0.001)

Full-time employed )0.0545 )0.0516 )0.0620 )0.0623(0.06) (0.07) (0.06) (0.07)

Weeks worked past year )0.004�� )0.004�� )0.004�� )0.004��

(0.002) (0.002) (0.002) (0.002)

Constant )1.87�� )1.92�� )1.86�� )1.96��

(0.47) (0.48) (0.47) (0.48)


testing

Pre-employment

drug testing

Reasonable

cause drug

testing

Random

drug

testing

1998 Survey 0.06�� 0.07�� 0.09�� 0.06��

(0.02) (0.02) (0.02) (0.03)

Age 0.03�� 0.03�� 0.02 0.04��

(0.01) (0.01) (0.01) (0.01)

Age2 )0.0003�� )0.0004�� )0.0002 )0.0005��

(0.0001) (0.0002) (0.0002) (0.0002)

Male 0.21�� 0.21�� 0.19�� 0.23��

(0.03) (0.03) (0.03) (0.03)

White )0.04 )0.05 0.02 )0.01(0.06) (0.06) (0.06) (0.07)

Black 0.26�� 0.31�� 0.27�� 0.29��

(0.06) (0.07) (0.07) (0.07)

Hispanic )0.01 0.04 )0.03 )0.09��

(0.03) (0.03) (0.03) (0.04)

Married )0.0001 0.01 )0.03 )0.03(0.02) (0.03) (0.03) (0.03)

Highest grade completed )0.01 )0.01�� )0.02�� )0.02��

(0.01) (0.01) (0.01) (0.01)

Rural 0.11�� 0.04 0.19�� 0.24��

(0.04) (0.04) (0.04) (0.04)

New England and Mid-Atlantic )0.12�� )0.13�� )0.17�� )0.02(0.04) (0.05) (0.05) (0.05)

East and West North-Central 0.26�� 0.23�� 0.23�� 0.27��

(0.04) (0.04) (0.04) (0.05)

South Atlantic 0.38�� 0.36�� 0.34�� 0.44��

(0.04) (0.04) (0.04) (0.05)

East and West South-Central 0.35�� 0.30�� 0.34�� 0.62��

(0.04) (0.04) (0.04) (0.04)

Mountain 0.37�� 0.30�� 0.41�� 0.55��

(0.04) (0.04) (0.04) (0.05)

Company size: less than 10 people )1.48�� )1.39�� )1.33�� )1.23��

(0.06) (0.06) (0.07) (0.07)


(0.05) (0.06) (0.06) (0.06)


(0.04) (0.04) (0.04) (0.05)


Table 3 (continued)


testing

Pre-employment

drug testing

Reasonable

cause drug

testing

Random

drug

testing


(0.04) (0.04) (0.04) (0.04)

Location size: less than 10 people )0.46�� )0.49�� )0.38�� )0.25��

(0.05) (0.05) (0.05) (0.06)


(0.05) (0.05) (0.05) (0.05)


(0.04) (0.04) (0.04) (0.04)


(0.04) (0.04) (0.04) (0.04)

Constant )0.34 )0.55�� )0.60�� )1.54��

(0.25) (0.25) (0.25) (0.27)

Correlation among the equations (q) 0.17�� 0.16�� 0.25�� 0.20��

Note. Standard errors reported in parentheses; selected marginal effects reported in brackets; coefficient

estimates for eight occupation categories not reported in the drug use equations; coefficient estimates for

nine industry categories not reported in the drug testing equations; statistically significant: �p < :10;��p < :05; ��p < :01.


however, and one potential result of differential adoption is that drug-using workers

are avoiding worksites with drug testing programs and instead seeking employment

at worksites without programs. If this is not the case, then drug testing companies

with a relatively small number or proportion of drug-using employees may be invest-

ing large sums of money in ineffective pursuits. Furthermore, empirical support for

the self-selection hypothesis is not necessarily socially desirable either. Assuming that

drug-using workers are less productive than their non-drug using counterparts (cete-

ris paribus), firms that invest resources in anti-drug programs, which merely shiftsthe costs of drug-using workers to companies that have not implemented a program,

do so without ever addressing the root cause of the productivity differences (McGu-

ire and Ruhm, 1993).

The present study used pooled data from two nationally representative surveys of

households to examine the relationships among drug use (any drug use, chronic drug

use) and workplace drug testing programs (any program, pre-employment, suspi-

cion-based, random) for employed individuals between the ages of 18 and 65. The

specification search included both univariate models of drug use and bivariate mod-els of drug use and workplace testing programs. Marginal effects were estimated for

the drug testing measures in every model.

Viewed together, the empirical results were significant, consistent, and policy rel-

evant. Namely, estimated marginal effects of drug testing on drug use from the bivar-

iate probit specifications (the most reliable model based on specification tests) were

negative, statistically significant, and relatively large in all cases. These results

emerged regardless of whether any drug use or chronic drug use was entered as

one of the dependent variables. Indeed, this pattern persisted even when chronicdrugs users were analyzed apart from non-chronic or casual drug users.


Employers and drug policy officials should find these results interesting and some-

what reassuring. As discussed earlier in the paper, drug testing programs are rela-

tively expensive for employers to administer, and the legal ramifications associated

with improper testing could be quite serious for careless or inattentive organizations.

Besides the legal ramifications, drug testing worksites may discourage highly produc-tive employees and lower the overall morale of the workforce. From a narrow effec-

tiveness perspective, the present findings suggest that these programs have achieved a

desirable result by deterring some potential drug-using employees. On the other

hand, the deterrent effect often comes at a high cost in the form of drug testing ex-

penses, employee turnover, and additional recruitment efforts. Thus, workplaces

with any type of drug testing program should carefully examine the full range of

costs and outcomes of these programs and then determine whether they can be jus-

tified from both an economic and a societal perspective.

Acknowledgments

Financial assistance for this study was provided by the Robert Wood Johnson

Foundation (Grant No. 035152). Sara Markowitz, Kathryn McCollister, Kerry

Anne McGeary, Helena Salom�e, Silvana Zavala, two anonymous reviewers, and

participants at the 2001 American Public Health Association conference, the 2001Western Economic Association conference, the 2000 Pacific Rim Allied Economics

Association conference, and the labor seminar series at Pompeu Fabra University

in Barcelona, Spain, provided helpful suggestions on earlier versions of the paper.

Carmen Martinez, Suzanne Gresle, and William Russell offered excellent administra-

tive support. This research was initiated while Michael French was a faculty member

in the Department of Epidemiology and Public Health at the University of Miami.

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Documents

To test or not to test: do workplace drug testing programs discourage employee drug use?