Young Men and Drugs in

Manhattan:

A Causal Analysis

Richard R. Clayton, Ph.D.

Harwin L. Voss, Ph.D.

NIDA Research Monograph 39

September 1981

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Alcohol, Drug Abuse, and Mental Health Administration

National Institute on Drug Abuse

Division of Research

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Rockville, Maryland 20857

For sale by the Superintendent of Documents, U.S. Government Printing Office

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The NIDA Research Monograph series is prepared by the Division of Research

of the National Institute on Drug Abuse. Its primary objective is to provide

critical reviews of research problem areas and techniques, the content of

state-of-the- art conferences, integrative research reviews and significant

original research. Its dual publication emphasis is rapid and targeted

dissemination to the scientific and professional community

Editorial Advisory Board

Avram Goldstein, M.D.

Addiction Research Foundatron

Palo Alto, California

Jerome Jaffe, M.D.

College of Physicians and Surgeons

Columbia University, New York

Reese T. Jones, M.D.

Langley Porter Neuropsychiatric lnstitute

University of California

San Francisco, California

Jack Mendelson, M.D.

Alcohol and Drug Abuse Research Center

Harvard Medical School

McLean Hospital

Belmont, Massachusetts

Helen Nowlis, Ph.D.

Office of Drug Education, DHHS

Washington, D.C.

Lee Robins, Ph.D.

Washington University School of Medicine

St. Louis, Missouri

NIDA Research Monograph series

William Pollin, M.D.

DIRECTOR, NIDA

Marvin Snyder, Ph.D.

DIRECTOR, DIVISION OF RESEARCH, NIDA

Robert C. Petersen, Ph.D.

EDITOR-IN-CHIEF

Eleanor W. Waldrop

MANAGING EDITOR

Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
Young Men and Drugs in

Manhattan:

A Causal Analysis
The project upon which this monograph is based was supported by Grant No. DA-01121 from the National Institute

on Drug Abuse to the University of Kentucky. Drs. Clayton

and Voss are professors in the Department of Sociology.

COPYRIGHT STATUS

The table on page 70 is adapted from copyrighted material

and therefore may be under copyright. It is reproduced

with the permission of Marcel Dekker, New York. Before

reprinting, readers are advised to determine the copyright status of this material or to secure permission of

the copyright holder. All other material in this volume

except quoted passages from copyrighted sources is in

the public domain and may be used or reproduced without

permission from the Institute or the authors. Citation

of the source is appreciated.

Opinions expressed are those of the authors and do not

necessarily reflect the opinions or official policy of

the National Institute on Drug Abuse; Alcohol, Drug

Abuse, and Mental Health Administration; Public Health

Service; or the Department of Health and Human Services.

Library of Congress catalog card number 81-600121

DHHS publication number (ADM) 81-1167

Printed 1981

NIDA Research Monographs are indexed in the Index

Medicus. They are selectively included in the coverage of BioSciences Information Service, Chemical

Abstracts, Psychological Abstracts, and Psychopharmacology Abstracts.

iv
Foreword

Young Men and Drugs in Manhattan: A Causal Analysis examines the

etiology and natural history of drug use with a special focus on

heroin. This study required a sample distinctly different from

those used in nationwide surveys, which yield relatively small

percentages of heroin users. To enhance the probability of

selecting men who had used heroin, New York City was chosen, in

part because its Narcotics Register could be used to plot high

opiate use areas. The sample was selected from those areas.

A Lifetime Drug Use Index (see chapter 5) is one outstanding

element of the study. The authors believe that this index and the

procedures used in its construction meet all of the essential criteria for a composite index of illicit drug use that reflects the

extent and frequency of use of various substances. In addition,

it is easy to construct and is based on the type of information

that is usually obtained in surveys of drug use.

The present report form a companion piece to NIDA Research Monograph 5, Young Men and Drugs--A Nationwide Survey, by John O'Donnell

and his associates (including Richard Clayton and Harwin Voss),

published in 1976. In the foreword, Robert DuPont described that

report as a "landmark study" and an "important piece of social

history." He said that the study "captured the right group at the

right time." These descriptions are still valid. The National

Institute on Drug Abuse is fortunate to be able to add anew

dimension to that report with this monograph, both a complement

and a compliment to the earlier work.

Marvin Snyder, Ph.D.

Director

Division of Research

National Institute on Drug Abuse

v
In Memoriam

John A. O'Donnell

In the fall of 1981 ten years will have passed since the serviceman

studied by Lee Robins (1973) left Vietnam for home. During that

time our knowledge of the epidemiological parameters and psychosocial correlates and predictors of drug use and abuse has expanded

geometrically. As the drug field has expanded it has also matured.

There are numerous indices of that maturity. First, there now exists

a series of on-going national surveys designed to chart trends in

the use of various drugs. Second, a number of prospective longitudinal studies have been/are being conducted that shed light on the

factors that influence the initiation, progression, continuation,

and cessation of drug use. Third, then methodological rigor and

statistical sophistication exhibited. in many recent studies reflect

a substantive specialty that is at the cutting edge of the scientific

investigation of human behavior. Fourth, increasing attention is

being given to the development and testing of theoretical models

to account for drug use. Perhaps the most important sign that the

drug field has come of age is the attempt by Lettieri et al. (1980)

to "synthesize" the extant theories on drug abuse.

The Young Men and Drugs studies span this period of phenomenal growth

and, in many ways, represent a unique chapter in the maturation process

that has occurred in the study of drug use and abuse. The nationwide

study provided unambiguos evidence of the drug epidemic that occurred

in the United States fran the mid-1960s to the early 1970s. The

Manhattan study provided an objectively derived composite index of

illicit drug use and has shed new light on the factors that account

for the use of illicit drugs among men who grew up in areas of the

city known to be high in drug use.

The uniqueness of the Young Men and Drugs studies and more than a few

of the insights gleaned from them can be attributed to a truly remarkable and unique human being, John A. O'Donnell. Jack died on 17

October 1979. He was a distinguished colleague, a fellow researcher,

and a dear friend who has left an indelible mark on the drug field

and on our personal and professional lives. Without John A. O'Donnell,

the Young Men and Drugs studies would not have been possible.

vi
Contents

Foreword .......................... v

In Memoriam ........................ vi

List of Tables ....................... viii

Summary of Major Findings ................. xiii

Chapter 1

Introduction ....................... 1

Chapter 2

Lifetime Prevalence and Correlates ........... 6

Chapter 3

Incidence and the Drug Epidemic ............. 25

Chapter 4

Multiple Drug Use .................... 46

Chapter 5

A Composite Index of Illicit Drug Use .......... 65

Chapter 6

Drug Sales ........................ 99

Chapter 7

Predictors of Illicit Drug Use ............. 114

Chapter 8

Causal Models to Explain Illicit Drug Use ........ 129

Chapter 9

Toward Theoretical Synthesis .............. 157

Appendix A

Sampling Procedures ................... 173

References ......................... 177

List of NIDA Research Monographs .............. 188

vii
List of Tables

TABLE

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

2.9

2.10

3.1

Lifetime Prevalence of Drug Use by Race in the

Manhattan and National Samples (Percentages)

Lifetime Prevalence of Drug Use Among Black

Males in the New York Sample and Black Males

in Brunswick's Harlem Sample (Percentages)

Lifetime Prevalance of Drug Use by Race and Age

in the Manhattan and National Samples

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalance of Drug Use by Race and

Education in the Manhattan Sample (percentages)

Lifetime Prevalence of Drug Use by Race and

Father's Occupation, National Sample

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalence of Drug Use by Race and

Father's Occupation, Manhattan Sample

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalence of Drug Use by Race and

Respondent's Occupation, National Sample

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalence of Drug Use by Race and

Respondent's Occupation, Manhattan Sample

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalence of Drug Use by Race and

Current Family Status, Manhattan Sample

(Percentages) . . . . . . . . . . . . . . . . .

Lifetime Prevalence of Drug Use by Race and

Current Employment Status, Manhattan Sample

(Percentages) ..............

Number of New Cases of Use by Drug and First

Year of Use . . . . . . . . . . . . . . . . . . . . . 

PAGE

. . . . . 7

. . . . . 9

. . . . . 10

. . . . . 13

. . . . . 15

. . . . . 17

. . . . . 18

. . . . . 19

. . . . . 21

. . . . . 22

. . . . . 26

viii
TABLE

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

3.10

3.11

4.1

4.2

4.3

4.4

4.5

4.6

PAGE

New Cases of Use by Drug and First Year of

use in Percentages . . . . . . . . . . . . . . . . . . . 27

Median Age at First Use of Drug Among Users

by Year of Birth . . . . . . . . . . . . . . . . . . . . 29

Median Age at First Use and Lifetime Prevalence

of Use of 8 Drugs by Age Group . . . . . . . . . . . . . 31

Median Age at First Use of Drug Among Nonexperimental Users by Age Group. . . . . . . . . . . . . 32

Year of First Use of Alcohol by Birth Cohorts

(Cumulative Percentages of Total Sample). . . . . . . . . 34

Year of First Use of Marijuana by Birth Cohorts

(Cumulative Percentages of Total Sample). . . . . . . . . 35

Year of First Use of Opiates by Age Groups

(Cumulative Percentages). . . . . . . . . . . . . . . . . 38

Annual Prevalence of Heroin-Opiate Use by

Age Groups (percentages). . . . . . . . . . . . . . . . . 40

Frequency of Heroin-Opiate Use by Year,

Manhattan Sample . . . . . . . . . . . . . . . . . . . . 42

Use by Friends, Availability, and Chance of

Future Use of Drugs by Extent of Marijuana

Use (Percentages) . . . . . . . . . . . . . . . . . . . . 43

Item Marginals for Manhattan and National

Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Guttman Scale Patterns, Manhattan and National

Samples . . . . . . . . . . . . . . . . . . . . . . . . . 49

Use and Nonuse Patterns for Pairs of Drugs:

Manhattan Sample(Percentage). . . . . . . . . . . . . . 50

Pair-Wise Relationships of Marijuana, Heroin,

and Cocaine and Other Drugs: Manhattan and

National Samples Compared (Percentages) . . . . . . . . . 53

Time-Order of Onset of Use Among Pairs of

Drugs, Manhattan and National Samples Compared . . . . . 56 &

57

Time-Order of Onset of Drug Use by Race in

the Manhattan Sample . . . . . . . . . . . . . . . . . . 59

ix
TABLE PAGE

4.7

4.8

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

5.10

5.11

5.12

5.13

5.14

Extent of Use of Three Gateway Drugs

(Tobacco, Alcohol, and Marijuana) by use of

Other Drugs (percentages and Correlation). . . . . . . . 61

Lifetime Extent of Use of Marijuana and Heroin,

National and Manhattan Samples . . . . . . . . . . . . 63

Lu Index Weights for Each Category of Extent

of Use in the National Study of Young Men . . . . . . . 69

Lu Index Weights for Extent of Use in the

Study by Pandina, White, and Yorke. . . . . . . . . . . . 70

Estimated Number of Users of Drugs. . . . . . . . . . . 73

1977 CODAP Admissions by Primary Drug of

Abuse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Emergency Room Mentions of Drugs in DAWN System,

May 1976-April 1977 . . . . . . . . . . . . . . . . . . 76

Medical Examiner Mentions of Drugs in DAWN

System, May 1976-April 1977 . . . . . . . . . . . . . . 77

Regression Coefficients for Three Drug Indices

and Three Dependent variables: The Young Men

and Drugs Nationwide Sample . . . . . . . . . . . . . . 79

Combination of the Relative Scale Values for

the Young Men and Drugs Nationwide Sample . . . . . . . 80

Retail Price of Illicit Drugs . . . . . . . . . . . . . 81

Regression Coefficients for Drugs Based on

Number of Arrests from the Supported Work

National Sample . . . . . . . . . . . . . . . . . . . . 82

Combination of the Relative Scales Into an

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83

Concordance of Rankings on Scales Used in

the Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85

Correlations, Means, and Standard Deviations

for the Five Alternative Indices Of Illicit

Drug Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Analysis of Variance: Lifetime Drug Use Index

by Race and Age Group for the Manhattan

Sample. . . . . . . . . . . . . . . . . . . . . . . . . 92

x
TABLE

5.15

PAGE

5.16

5.17

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

7.1

7.2

7.3

7.4

8.1

Analysis of Variance: Lifetime Drug Use Index

by Race andEducational Attainment of the

Manhattan Sample . . . . . . . . . . . . . . . . . . . . 94

Test of Difference Between Means: Lifetime

Drug Use Index by Peer Behavior at Age 13 for

the Manhattan Sample . . . . . . . . . . . . . . . . . . 95

Test of Difference Between Means: Lifetime

Drug Use Index by 13 Self-Reported Criminal

Activities for the Manhattan Sample . . . . . . . . . . 97

Number and Patterns of Drug Sales, National 100&

Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 101

Patterns of Drug Sales, Manhattan Study . . . . . . . . 103

Patterns of Drug Sales in the Manhattan 104&

Study: Specific Drugs Sold . . . . . . . . . . . . . . 105

Drugs Sold in the National and Manhattan

Studies: A Comparison . . . . . . . . . . . . . . . . . 107

Drug Sales Index Scores by Race and Cohort . . . . . . 110

Drug Sales Index Scores by Race and Educational

Attainment . . . . . . . . . . . . . . . . . . . . . . . 111

Drug Sales Index Scores by Participation in

10 Specific Criminal Acts . . . . . . . . . . . . . . . 112

Correlations (r) of Drug Sales Index with Age

at First Use of Marijuana, Educational Attainment, and the Lifetime Index of Drug Use

within Racial Categories . . . . . . . . . . . . . . . . 113

Inter-Item Correlations (Pearson's r) for the

Family Influence Scale.. . . . . . . . . . . . . . . . 118

Inter-Item Correlations (Pearson's r) for the

Peer Influence Scale . . . . . . . . . . . . . . . . . . 123

Analysis of Variance: Mean Scores on the Six

Scales by Race and Age Group . . . . . . . . . . . . . . 126

Analysis of Variance: Mean Scores on the Six

Scales by Race and Educational Attainment . . . . . . .127

Correlations (Pearson's r) Among the 8 Variables

in the Model: Blacks Above the Diagonal and

Whites Below the Diagonal . . . . . . . . . . . . . . . 137

xi
TABLE PAGE

8.2 Stepwise Multiple Regression: Model for

Blacks . . . . . . . . . . . . . . . . . . . . . . . . . 139

8.3 Stepwise Multiple Regression Model for

Whites . . . . . . . . . . . . . . . . . . . . . . . . .140

8.4 Partial Correlation Equations: Paths to be

Deleted from Model I for Blacks . . . . . . . . . . . .146

8.5 Partial Correlation Equations: Paths to be

Deleted from Model I for Whites . . . . . . . . . . . .149

xii
Summary of Major Findings

1. In the national study reported in Young Men and Drugs and in the

Manhattan study of young men, data were obtained regarding 9 drugs

or drug classes. The 3 drugs for which there are sizable differences

between the samples in lifetime prevalence are: marijuana (55 percent

in the national and 75 percent in the Manhattan sample), heroin (6

percent in the national and 26 percent in Manhattan), and cocaine

(14 percent in the national and 37 percent in Manhattan).

2. In the national and Manhattan samples, blacks were more likely

than whites or others to have used marijuana, heroin, and cocaine.

In the Manhattan sample, the lifetime prevalence of use of these

drugs for blacks, whites and others, respectively, are: marijuana,

82, 74, and 53 percent; heroin, 39, 11, and 21 percent; and cocaine,

56, 26, and 30 percent.

3. In the Manhattan sample, there are major differences in educational

attainment by race. The distribution in percentages for blacks and

whites, respectively, are: high school dropout, 31 and 9 percent;

high school graduate, 31 and 12 percent; some college, 27 and 32

percent; college graduate, 9 and 47 percent. Some 60 percent of the

black high school dropouts had used heroin, but only one of the 11

black college graduates reported use of heroin.

4. Among whites in the Manhattan study, for each drug class, the

highest percentage of users is found among the unemployed men.

Students rank second or are tied for second for all illicit drugs

other than heroin. In contrast with the findings regarding drug use

and the occupational prestige of the respondents' fathers, the

highest percentages of illicit drug use are found among the men in the

occupations with the least prestige, and the lowest percentages are

found among the men with the most prestigious occupations. The patterns

of drug use by occupational prestige among blacks in the Manhattan

study are not as clear as those for whites.

5. In 1966, all of the men in the oldest age group, those born

between 1944-46, had reached the age of twenty, and 27 percent of them

had used opiates by that time. For the group born in 1947-49, some

35 percent had used opiates by 1969, when they had attained the age

of twenty. The comparable figures for the two youngest age groups

(born in 1950-52 or 1953-54) are 40 and 35 percent, respectively.

Thus, a higher, not a lower, proportion of the men in the younger

age groups had used opiates by the age of 20. The age at first use

xiii
of opiates, as well as marijuana,was lower in the younger cohorts

and produced a peaking of cases. The data suggest that there was an

epidemic of drug use in Manhattan in the late 1960s and early 1970s.

However, the epidemic appeared about 2 years earlier in Manhattan

than in the nation as a whole.

6. In the national sample, the annual prevalence of heroin use never

exceeded 2 percent, and the annual prevalence for opiates other than

heroin reached 10 percent in only one year, 1974. In contrast, the

annual prevalence of heroin-opiate use in Manhattan reached a peak

of 20 percent in 1970. Thus, in the late 1960s and early 1970s,

the annual prevalence of heroin-opiate use was substantially higher

in Manhattan than in the rest of the country.

7. In 1968 in Manhattan, 59 percent of the opiate users reported

using opiates (including heroin) daily or several times a day,

whereas, in 1973, only 37 percent of the users reported daily use.

Similarly, in 1968, only 20 percent of the users said they used opiates

once or twice a month or less than once a month, but in 1973, 41 percent of the users selected these categories to describe their use of

opiates.

8. Almost two-fifths of the nonusers of marijuana reported that more

than a few of their friends currently used marijuana, and almost onehalf of the men who had used marijuana most frequently reported that

they had friends who were currently using cocaine. The rather

widespread use of illicit drugs on the part of the respondents'

friends means that transnission of the drug use through friendship

networks is likely to continue in Manhattan.

9. Marijuana may be the key gateway drug for understanding multiple

drug use. Some 37 percent of the men who had used marijuana had also

used psychedelics, while none of the nonusers of marijuana reported

using psychedelics. The comparable figures for stimulants, sedatives,

heroin, and other opiates are 34 to 36 percent for the users of

marijuana in comparison with 1 to 5 percent for the nonusers. Fortynine percent of the marijuana users in the Manhattan sample reported

having used cocaine in comparison with none of the men who had not

used marijuana.

10. The marijuana users in the Manhattan sample were more likely to

have used heroin and cocaine than their counterparts in the national

sample. Thirty-four percent of the men in the Manhattan sample who

had used marijuana reported use of heroin in comparison with only 11

percent in the national sample. In Manhattan, 49 percent of the

marijuana users had used cocaine. The comparable figure for the

national sample is 25 percent.

11. Use of heroin is strongly correlated with the extent of use of

marijuana. In the national sample, only 1 man (0.1 percent) who had

not used marijuana had used heroin. The percentages who had used

heroin increase progressively to 33 percent among the men who had

xiv
used marijuana 1000 times or more. The same type of progression occurs

in the Manhattan sample. The percentage of heroin users ranges from

1 percent among men who had never used marijuana to 56 percent of those

who had used marijuana 1000 times or more.

12. A composite index of illicit drug use was constructed on the

basis Of 7 national data sets. Weights for each drug according to

extent of use were established. On this measure,the overall mean

for the national sample is 105 in comparison with 470 in Manhattan.

There are sizable differences by race. In Manhattan, the mean for

whites is 223 and for blacks it is 737.

13. Seventeen percent of the national sample had sold drugs at some

time. Projected to the 19 million men born in the years 1944-54

represented by the national sample, about 3.2 million of these men

have distributed drugs illegally. Of those who had sold drugs, 54

percent had only sold one drug, and this was usually marijuana. In

the Manhattan sample, 33 percent of the respondents had sold drugs.

14. The most dramatic differences in drug sales occur for heroin

and cocaine. Only 2 percent of the men in the national sample report

the sale of heroin, and 10 percent of the sellers had dealt in

heroin. In Manhattan, 8 to 9 percent of the men had sold heroin.

Among the drug sellers, the figures are: sale of heroin as a favor,

24 percent; sale of heroin to purchase one's own supply, 27 percent;

and sale of heroin for profit, 28 percent.

15. The technique of path analysis was used to test causal models

of illicit drug use that were derived from theories of deviance.

The key variable in the explanation of illicit drug use is selling

drugs.

xv

Chapter 1

Introduction

In response to the unprecedented epidemic of drug use that began

in the late 1960s (DuPont and Greene 1973), the Special Action

Office for Drug Abuse Prevention (SAODAP) was established. The agency

concentrated primarily on expansion of treatment facilities across

the nation. However, reports that many soldiers in Vietnam were

using opium and heroin led to concern that existing treatment facilities in the United States might be inadequate. This idea arose

because follow-up studies of persons treated for addiction to heroin

and other opiates showed that nearly all of them relapsed within a

short period of time (O'Donnell 1965). As a result, SAODAP funded

Robins to study a sample of Vietnam veterans who were returning to the

United States in September of 1971. She found that 29 percent of her

sample of enlisted men had used opium or heroin more than 10 times

and more than once a week; further, 20 percent of the men reported

that they had been addicted to narcotics in Vietnam. However, in

interviews conducted eight to twelve months after their return to

the United States, only 1 percent of then men indicated that they

had been readdicted to opiates. Robins' (1973, 1974) investigation showed that relapse was not inevitable.

The earlier follow-up studies were based on samples of treated

addicts, and one explanation for the discordant findings was that

persons who sought treatment or were placed in treatment programs

by the courts were not a representative sample of opiate users.

Apparently they were a residue of that population and excluded those

persons who quit using opiates rather easily. While the Vietnam

veterans did not constitute a random sample of young American men,

they ware more representative of young persons than any sample of

treated heroin addicts. One implication was that the widely held

belief about the high probability of relapse was not necessarily

true for heroin users in general.

Young Men and Drugs: A Nationwide Survey

Robins' findings suggested that a study of drug use in the general

population might increase knowledge appreciably. Such a study was

discussed by the staff of SAODAP and sociologists in the Department

1

354-188 0 - 81 - 2
Of Sociology, University of Kentucky, and the School of Public Health,

University of California, Berkeley. In June 1973, a grant was awarded

to the University of Kentucky in which the late John A. O'Donnell

was Principal Investigator. A contract was established with the

University of California at Berkeley to share in the design and execution of the study, and later, the Institute for Survey Research

at Temple University was selected to collect and to edit the data.

The primary goal of the study was to obtain national estimates of

the incidence and prevalence of use of illicit drugs among young

men, but four areas of focus were listed in the grant application:

(1) the natural history of nonmedical drug use; (2) the incidence

and prevalence of illicit drug use; (3) evidence concerning the belief that an epidemic of drug use occurred in the 1960s; and

(4) the correlates and determinants of drug use. Each of these

areas implied different and, to some extent, contradictory considerations in the research design. A national sample of men born

in the years 1944 through 1954 was obtained from Selective Service

records, and data pertaining to the incidence, prevalence, and

correlates of drug use, as well as the drug epidemic, were obtained

in the national survey (O'Donnell et al. 1976).1

Young Men and Drugs: The Manhattan Study

It was recognized that the national sample would produce too few

heroin users to provide adequate information about the natural

history of heroin use. To examine the etiology and natural history

of drug use with a special focus on heroin required a distinctly

different sample. A sample of young men who lived in areas in which

the rate of drug use was known to be high was needed to enhance the

probability of selecting men who had used heroin.

While high drug use areas might be found in any major metropolitan

center, attention was focused on New York City because the city

maintains a Narcotics Register in which opiate users--primarily

heroin users--are listed on the basis of reports from the police

and treatment agencies (Fishman et al. 1971). The register does

not include all heroin users, but it is the most extensive listing

available.

The information in the register was used to compute rates of opiate

use for the years 1964 through 1967 for the 347 health areas in

New York City (Koval 1969).2 Of the five boroughs, Manhattan had

the highest rate of users, and within the 82 health areas in Manhattan, regular opiate users were even more concentrated. For example, five health areas in Harlem including only 1.5 percent of

New York's 15-44-year-old population contained almost 12 percent Of

the regular opiate users. These five health areas had rates of

opiate use some seven to ten times the rate for the city as a whole.

Although some health areas on the West Side and other health areas

in the borough had relatively high rates, the health areas with the

highest rates werel located in Harlem, East Harlem, and other nearby

areas.

2
A target sample of 540 men was chosen randomly from Selective Service

Boards serving health areas in Manhattan that had high rates of opiate

users to represent the men born in the years 1944 through 1954.

(See appendix A for a detailed discussion of sampling procedures.)

The interviews with men in the Manhattan sample were initiated early

in 1975. It was more difficult to locate the men in the Manhattan

sample than in the national sample. Many of the prospective respondents had moved, and many of the men had moved several times. In

some cases, entire neighborhoods in which the men had resided had

been demolished in urban renewal projects; consequently, former

neighbors and storekeepers were not available to provide information

to help trace them. In addition, priority, both in terms of effort

and expenditures, was given to the national sample because the

researchers wanted to achieve the highest possible rate of completed interviews. As a result, when field work had to be terminated

in May of 1975, only 294 of the 540 men had been interviewed, for a

completion rate of 54 percent.

While the target sample was a probability sample or a collection of

eleven probability samples of birth cohorts, a claim of representativeness cannot be made on the basis of a completion rate of 54

percent. However, it should be noted that such a claim would be of

dubious value had the completion rate approached 100 percent because it is not at all clear what group of people the sample could

be viewed as representing. The geographical areas fran which the

sample was drawn are not contiguous and do not correspond to any

meaningful geopolitical unit. Further, a few of the respondents

had not resided in New York City from birth to the age of eighteen

(see appendix A). Most, but not all, of the respondents were raised

in or near high drug use areas, and almost all of the men lived in

large metropolitan areas during most of their childhood. An accurate description of the original target sample would be that it was

a sample of men who registered with Selective Service Boards serving moderate or high drug use areas in Manhattan.

It is important to repeat the purpose for which the Manhattan sample

was selected--to study the etiology and natural history of drug use,

especially use of heroin. A random sample is essential for an accurate description of the incidence and prevalence of drug use. To

test causal models, adequate measurenent of the relevant variables

is more important than random sampling (Camillieri 1962). The

Manhattan sample is valuable because it was drawn from the general

population of young men, most of whom resided in moderate or high

drug use areas. It is not a sample selected from a treatment

population or from those who have been arrested for drug use or

other criminal behavior; yet, it is one in which use of illicit

drugs is likely to be extensive. It cannot be claimed that the

sample is representative of any population. The primary source of

bias is the failure to locate many of the men in the target sample.

It is generally assumed that the more deviant individuals are the

more difficult to locate. To some extent, this is counterbalanced

by the fact that the most deviant individuals are likely to have
prison or treatment records which make it easier to locate them.

On the whole, then, the sample is one that should permit comparison

of users and nonusers of drugs and a search for possible causal

factors.

The Interview Schedule

It was anticipated that an adequate number of drug users, especially

heroin users, would be identified so that it might be possible to

ascertain the early stages in the natural history of drug use and to

determine why some men had and some men had not become drug users.

A sufficient number of questions from the national interview schedulewere retained to permit comparison of the Manhattan and national

samples. The major change in the interview schedule involved the

detailed questions about drug use; specifically, the questions from

the national interview about the quantity and frequency of use of

each of nine drugs or drug classes on a year-by-year basis were

deleted. These were replaced by items suggested by several sociological and social psychological theories--differential association,

anomie, differential opportunity, social control, and labeling.

This does not mean that there is a formal test of any of these

theories; none of the theories is, as yet, sufficiently specific to

be tested formally. Because each theory suggests the importance of

some early experiences in the family or with peers and legal authorities that might be relevant to drug use, items ware designed

to tap these concepts.

There was a question as to how the items should be phrased. Because the interviews were to be conducted with 20-to 30-year-old

men, it was believed that more recent experiences would be remembered

more accurately than early childhood experiences. Also, for some

relationships, such as those with peers and others outside the family,

the early teen years are likely to be the most important ones.

Therefore, the items concerning early influences were generally

phrased as of the time "when you were 13 to 15." In two short,

self-administered questionnaires, there were some double items that

asked about experiences at age 13 and age 16. Other items, referring only to the family, were phrased in terms of "when you were

growing up."

In the following chapters, attention is focused on a comparison of

the national and Manhattan samples. After the similarities and differences have been described, the data obtained in Manhattan are

examined from an etiological perspective.

4
FOOTNOTES

1

Of the 2,981 potential interviewees, 2,510 men (84 percent) were

interviewed. The sample was a multistage stratified random sample.

Technically, there were eleven random samples of men born from

1944 through 1954 who registered with the Selective Service System

when they reached the age of eighteen. Residents of Alaska and

Hawaii excluded from the target population.

2

A health area is an aggregation of contiguous census tracts that

are fairly homogeneous in terms of socioeconomic characteristics;

each health area in New York contains approximately 22,000 persons.

5
Chapter 2

Lifetime Prevalence and

Correlates

Measures of lifetime prevalence (defined as any use in the person's

lifetime) are crude, and they are presented only as an initial way

of describing the respondents experiences with drugs. In chapter

5, a composite measure of drug use is presented, and extent of use

is taken into account in development of the index. For each of the

drugs or drug classes, experimental use (less than 10 times) is included in the data on lifetime prevalence. In some of the tables,

data obtained in the national survey are shown for comparative purposes. In the first table, the data are presented separately for

blacks, whites, and others because these groups differ according to

a number of important variables. In subsequent tables, the others

are excluded. While there are a sufficient number of blacks and

whites in each sample to permit meaningful comparisons, there are

only 71 men classified as other in the Manhattan sample, and these

men are not comparable with the others in the national sample. In

Manhattan, most of the men in the other category are Puerto Rican.

In the national sample, only 48 of the 104 others are Spanish, and

many of these men are Mexican or Cuban rather than Puerto Rican.

For this reason, the analysis is essentially restricted to blacks

and whites.

Lifetime Prevalence

The data on lifetime prevalence obtained in the Manhattan and

national samples are presented in table 2.1. With the exception of

tobacco, the figures for lifetime prevalence are higher in the

Manhattan sample than in the national sample. In both samples, 88

percent of the men reported that they had used tobacco. There are

negligible differences for alcohol and stimulants, and moderate

differences for psychedelics, sedatives, and opiates. These differences do not result from uniformly higher rates of use of these

drugs in the three racial groups. The prevalence of psychedelics in

the two samples is almost identical among blacks; the others and

particularly the whites contribute substantially to the higher rate

of psychedelic use in Manhattan. In Manhattan, one-third of the

whites reported use of psychedelic drugs. The higher figure for

sedatives in Manhattan is accounted for by more extensive use of

this class of drugs by whites, while each group contributes to the

higher figure for opiates. The three drugs for which there are sizable differences are marijuana, heroin, and cocaine. For these drugs,

lifetime prevalence is 20 to 23 percentage points higher in Manhattan than in the national population of young men. The lifetime

6
TABLE 2.1 Lifetime Prevalence of Drug Use By Race in the Manhattan and National

Samples (Percentages)

Total Black white Other

National Manhattan National Manhattan National Manhattan National Manhattan

Tobacco 88 88 87 89 88 92 86 82

Alcohol 97 99 94 100 97 98 96 99

Marijuana 55 75 65 82 54 74 51 63

Psychedelics 22 28 25 26 22 33 19 25

Stimulants 27 28 25 19 28 42 25 25

Sedatives 20 27 24 25 20 35 17 18

Heroin 6 26 14 39 5 11 5 21

Opiates 31 37 34 42 31 38 23 27

Cocaine 14 37 24 50 13 26 11 30

N 2,510 294 303 125 2,103 98 104 71
prevalence of these drugs in the national sample is highest among

blacks, and this is also the case in Manhattan. However, the whites

and others also contribute to the higher prevalence figures for these

drugs in Manhattan.

In 1975-76, Brunswick (1979) studied a sample of 277 black males who

were 18 to 23 years old. Her data on lifetime prevalence among

these residentsof Harlem maybe compared with the data obtained in

Manhattan. Use of heroin and sedatives is more extensive in the

Manhattan sample than in Brunswick's Harlem sample. Otherwise, the

figures are similar, and the differences may be attributed to the

younger age range in her sample (see table 2.2).

Among the men who had ever used illicit drugs, the respondents in

the Manhattan sample had used these drugs more extensively than was

the case in the national sample, with the sole exception of the psychedelics. In the national sample, sizable proportions of the men who

had used a drug experimental users in that they had used it

less than 10 times. The comparable proportions are smaller in the

Manhattan sample. For example, 31 percent of the marijuana users in

the national sample had used it less than 10 tines, but this was

true for only 19 percent of the marijuana users in Manhattan. In

Manhattan, 15 percent of the blacks and 19 percent of the whites

had only used marijuana experimentally, but this was true of 25 and

32 percent, respectively, of the blacks and whites in the national

sample. Almost 50 percent of the men in the national sample who had

used heroin had done so only experimentally in comparison with 21

percent of the users in the Manhattan sample. Again, there are

differences among the racial groups in Manhattan; percent of the

blacks, 55 percent of the whites, and 20 percent of the others were

experimental users of heroin. In the national sample, 64 percent

of the users of cocaine had used it less than 10 times in comparison

with 40 percent in Manhattan. The percentages for experimental use

of cocaine in the three racial groups are: blacks, 29; whites, 64;

and others, 43. The whites do not differ from the natiohal figure

for whites (64 percent), while blacks differ considerably (29 versus

47 percent). In contrast, the percentage of experimental users of

psychedelics is slightly lower in Manhattan than in the national

sample, but the difference is minimal--53 and 56 percent, respectively.

For the other drugs, there are no notable differences.

Age. In the national sample there is a strong inverse relationship

between age and lifetime prevalence of use for all drugs except

tobacco and alcohol. The percentage of users is generally higher in

the younger than in the older cohorts. However, this is accounted

for primarily by the whites, who constitute 84 percent of the sample.

This can be seen in table 2.3, in which the percentage of users of

each drug is shown for whites and blacks by age group. In the national

sample, the percentages are consistently higher as one moves from the

older to the younger age groups, with the exception of a few minor

reversals in the youngest age category.

8
TABLE 2.2 Lifetime Prevalence of Drug Use Among Black Males in the

New York Sample and Black Males in

Brunswick's Harlem Sample (Percentages)

Brunswick Sample1Manhattan Sample

Alcohol 88 100

Marijuana 86 82

Psycehdelics 25 26

Stimulants 12 19

Sedatives 11 25

Heroin 18 39

Cocaine 42 50

N

1

Brunswick (1979:449)

277 125

9
TABLE 2.3 Lifetime Prevalence of Drug Use by Race and Age in the Manhattan and

National Samples (Percentages)

Born in Born in Born in Born in

1944-1946 1947-1949 1950-1952 1953-1954

National Manhattan National Manhattan National Manhattan National Manhattan

White

Tobacco 93 96 88 91 88 91 83 90

Alcohol 98 100 98 100 97 91 96 100

Marijuana 38 71 52 75 63 77 60 70

Psychedelics 9 17 16 34 29 36 33 45

Stimulants 18 50 27 28 35 46 31 50

Sedatives 13 29 17 34 23 36 27 40

Heroin 2 12 3 9 7 14 7 10

Opiates 25 38 27 28 35 46 38 45

Cocaine 5 21 9 25 18 27 19 30

N 467 24 581 32 503 22 452 20

Black

Tobacco 94 100 91 100

Alcohol 98 100 92 100

Marijuana 47 82 69 94

Psychedelics 12 18 34 29

Stimulants 10 4 33 29

Sedatives 14 18 29 29

Heroin 12 32 16 42

Opiates 16 41 42 61

Cocaine 16 46 30 58

84 84 83 77

93 100 95 100

72 84 60 71

27 32 19 20

29 27 21 11

26 35 21 14

20 43 2 34

33 38 37 29

28 60 17 34

N 49 22 86 31 105 37 63 35
The highest percentages of lifetime use of marijuana, heroin, and cocaine are found among blacks in the national sample. This is also the

case in the Manhattan sample, and percentages are higher than in

the national sample. However, the pattern across the age groups is

different in Manhattan. In the national sample, the percentage of

users of marijuana, heroin, and cocaine is lower for blacks among the

men born in 1953-1954 than among the men born in 1950 to 1952. While

it did not constitute overwhelming evidence, this suggested that

nationally the rates of drug use for blacks might be declining while

the rates for whites increasing. In the youngest age group in

Manhattan, the figures for lifetime prevalence of use of marijuana

and cocaine are similar for blacks and whites, but for heroin the

figure is higher for blacks than whites. On the other hand, the

figures for psychedelics, stimulants, sedatives, and opiates are

higher for whites than for blacks. Data from a more recent survey

show that the rates of drug use are similar among black and white

youth (12 to 17 years old), but for young adults, the rates are

lower among blacks than whites (Fishburne et al. 1980:45-47, 85-87).

Fishburne and her co-workers present data for lifetime prevalence by

age and race for only five drugs--marijuana/hashish, stimulants, sedatives, tranquilizers, and analgesics. Except for analgesics, the

percentage of users is lower among blacks than whites in the category

of young adults (18 to 25 years old). Among the respondents who

were 26 years of age or older, the figure for marijuana is higher

for blacks than for whites; for the other drugs, there are only

minimal differences between whites and blacks.

In Manhattan, the pattern among whites is similar to the one observed

whites, the increase in the percentage of drug users from the older

in the national sample, but this is not the case for blacks. Among

to the younger age groups is observed for psychedelics, sedatives, and,

to a lesser extent, cocaine. The percentage of users of marijuana

and heroin is similar in the four age groups, and this is also the

case for stimulants with the exception of the lower figure for the

1947-1949 age group. There are only 20 to 32 men in the four age

groups for whites; consequently, sizable differences are not statistically significant. There are only 22 men in the 1944-1946 age

group for blacks, but with this exception there are a sufficient

number of respondents in each age group to permit comparisons among

the blacks. If one ignores tobacco and alcohol, there is a definite

pattern for use of the other drugs among blacks. In both samples,

the pattern resembles a U-shaped curve; the percentages are higher

in the two middle age groups than in the oldest or youngest age

group. The only exception in the national sample is the figure for

the opiates in the youngest age group; in Manhattan the only exception is for the opiates in the oldest age group. The similarity in

the pattern across age groups in the two samples is remarkable in

view of the fact that the lifetime prevalence of drug use is considerably higher in the Manhattan sample than in the national sample.

It would appear that a general decline in drug use among blacks was

even reflected in an area in which illicit drug use was extensive.

This suggests that some--or all--of the factors that accounted for

11
the decrease in drug use among blacks were influential in the areas

of highest drug use. This is a tentative suggestion because the decrease between the last two age groups is statistically significant

in the Manhattan sample for only three drugs--stimulants, sedatives,

and cocaine. Finally, it must be emphasized that the apparent decline involves only the youngest blacks in the two samples. Overall,

the lifetime prevalence of illicit drug use among blacks is higher

than among whites. This is true for most drugs and especially for

three illicit drugs--marijuana, heroin, and cocaine. In the national

sample, the lifetime prevalence figures are higher for whites than

blacks only for stimulants. The comparable figures are higher for

whites than blacks in the Manhattan sample for psychedelics, stimulants, and sedatives (see table 2.1).

Education. In the interviews, the respondents were asked to indicate

the last year of school they had completed. As in the national

study, the responses were classified as: less than high school,

high school graduate, some college, and college graduate. However,

it should be noted that there are only 9 white high school dropouts

and only 11 blacks who are college graduates (table 2.4). The

percentage of blacks who had attended college is similar in the

Manhattan and national samples: Manhattan, 36 percent and natiohal,

31 percent. This is not the case among the whites. In the Manhattan sample, 79 percent of the whites had attended college and 47 percent of thewhites are college graduates. The comparable percentages

in the national sample for whites are 50 and 21. In view of the natureof the areas from which the Manhattan respondents were selected,

these figures were totally unexpected. They undoubtedly reflect the

ease of access to higher education in New York City which has an extensive system of college campuses. The disparity in educational

attainment between the whites and blacks suggests the importance of

analyzing the data separately rather than without a control for race.

With respect to education, the only clear trend is for heroin; among

whites and blacks this drug was used less frequently by the man with

nwre education. There is a strong linear relationship between level

of education and use of heroin. Some 60 percent of the black high

school dropouts had used heroin, but only one (9 percent) of the 11

black college graduates reported any use of heroin. In general,

the lowest percentages of use are found among the college graduates.

The only exceptions are psychedelics and stimulants in which the lowest figure is found among blacks who did not complete high school,

and the latter are differences of only 3 to 5 percentage points.

among the whites, the men who had completed some college have the

highest lifetime prevalence figures for psychedelics, sedatives, and

opiates, whereas the high school dropouts, who are few in number, have

the highest prevalence of marijuana, stimulants, and cocaine. The

high school graduates have the highest figure for heroin. Among the

blacks, the high school graduates are more likely to have used every

illicit drug except heroin than the men in the other categories.

Occupational Prestige. In its first report, the Marihuana Commission

stated that: "Use (of marijuana) is found in all socioeconomic groups

12
TABLE 2.4 Lifetime Prevalence of Drug Use by Race and

Education in the Manhattan Sample (Percentages)

Less than High School Some College

High School Graduate College Graduate

Tobacco 89

Alcohol

Marijuana

Psychedelics

Stimulants

Sedatives

Heroin

Opiates

Cocaine

N 9 12 31 46

Tobacco

Alcohol

Marijuana

Psychedelics

Stimulants

Sedatives

Heroin

Opiates

Cocaine

N

100

89

33

56

44

22

44

44

92 92

100 100

80 88

22 28

15 22

25 28

60 40

38 48

58 60

40 40

White

83 94 94

92 97 100

58 77 72

33 52 20

42 52 33

33 48 24

25 13 4

33 52 28

25 36 15

Black

88

100

85

26

21

24

2 1

4 4

38

34

64

100

64

27

18

18

9

27

18

11

13
and occupations, though slightly more predominant among persons with

above-average incomes" (National Commission on Marihuana and Drug

Abuse 1972:39). Citing a New York survey, the Commission concluded

that the prevalence of marijuana, whether measured in terms of ever

use or regular use, is similar across occupational categories. In its

second report, the Marihuana commission asserted that "the drug

problem of the 1950s was clearly identified with the children of the

middle and upper classes" (National Commission on Marihuana and Drug

Abuse 1973:17). This assertion suggests that use not only of marijuana but of all illicit drugs was more extensive among middle and

upper class yeah. This may be the case, but it has never been documented adequately. In fact, in the national studies conducted for the

Marihuana Commission, the only possible indicator of social class is

education. One looks in vain for a measure of occupational prestige

or a more complex measure of social class (Fishburne et al. 1980).

This is also true of the Johnston et al. (1980) survey of high school

seniors.

In the national study, no data pertaining to occupational prestige

were analyzed. The occupational data were initially coded with the

three-digit census codes to preserve as much detail as possible.

This meant that extensive recoding of the data was required for

translation into the Duncan Socioeconomic Index (see Voss 1981).

Four levels of occupational prestige were created with approximately

equal numbers of respondents in each category.1

In table 2.5, the national data on lifetime prevalence of drug use

are presented according to the occupational prestige of the respondents' fathers. The men who provided insufficient information about

their father's occupation for coding are categorized separately.

Among whites, the highest figures for use of illicit drugs other than

marijuana are found among these men. The only exception occurs for

sedatives. If these men are excluded, some slight but nevertheless

discernible patterns emerged. First, the percentages for heroin are

unique as there is little or no difference among the prestige categories. For the other illicit drugs, the lowest percentages are consistently found among the men whose fathers had jobs with the lowest

occupational prestige, and the men whose fathers had jobs with the

most prestige consistently show the highest percentages. For each

of the drugs other than marijuana and heroin, the percentage of users

in the highest prestige category is 9 to 12 percentage points higher

than the figures for the men in the lowest category. For marijuana

the difference is 24 percentage points. There is also a discernible

trend in the percentages as one moves from the lowest to the highest

level of occupational prestige. There is one minor reversal for

opiates.

The pattern is somewhat different among blacks. First, the men who

provided insufficient information about their father's occupation

show lower percentages of users than is the case for men in the highest

three categories. The pattern for all of the illicit drugs, including heroin, is similar, but stronger than the one for whites. The

14
TABLE 2.5 Lifetime Prevalence of Drug Use by Race and Father's

Occupation, National Sample (Percentages)

No Info. 1 (low) 2 3 4(high)

Tobacco 92

Alcohol 96

Marijuana 62

Psychedelics 35

Stimulants 38

Sedatives 23

Heroin 6

Opiates 38

Cocaine 19

N 48

Tobacco 95

Alcohol 100

Marijuana 68

Psychedelics 22

Stimulants 24

Sedatives 10

Heroin 15

Opiates 29

Cocaine 17

N 41

White

89 90

96 97

39 51

16 18

23 24

15 16

5 5

26 32

8 9

469 487

Black

83 88

94 90

58 65

14 31

19 30

20 28

6 22

28 39

14 40

127 83

89 85

98 98

59 63

23 27

28 35

21 26

5 4

30 35

13 18

546 553

88 95

91 100

73 90

39 47

30 37

36 42

18 21

36 58

30 32

33 19

15
highest percentages are found among the men whose fathers had the most

prestigious jobs, and the differences between the high and low categories range from 15 to 32 percentage points.

Comparable data for the Manhattan sample are shown in table 2.6. The

cutting points established for the national sample are again utilized

to permit comparison of tables 2.5 and 2.6. As a result, the number

of whites in the lower categories are limited. Except for heroin and

opiates the white males in the lowest occupational prestige category

had the lowest percentages of users, as was the case in the national

sample. However, comparable upward trends are not apparent. In fact,

except for heroin, the highest percentages are found in categories

2 and 3. Among blacks, the lowest figures for stimulants and sedatives

are found among the 24 men who did not describe their fathers' occupations. In contrast with the national sample, the highest percentages

for marijuana, psychedelics, opiates, and cocaine are found in category 3. The most impressive numbers in the table pertain to heroin.

One-third of the black males who did not describe their father's

occupation or whose father had a low prestige job had used heroin,

whereas almost one-half of the men in the other prestige categories

had tried heroin.

In table 2.7, the national data pertaining to prevalence of drug use

are shown in relation to the prestige of the respondent's current

occupation. Among whites, for each drug or drug class, the highest

percentage of users is found among the unemployed men. Students

rank second or are tied for second for all illicit drugs other than

heroin. In contrast with the findings regarding drug use and the

occupational prestige of the respondents' fathers, the highest

percentages of illicit drug use are found among the men in the occupations with the least prestige, and the lowest percentages are

found among the men with the most prestigious occupations. Except

for a few minor reversals, there is a slight downward trend in illicit

drug use as one moves from the low to the high prestige categories.

The greatest difference--12 percentage points--between the lowest

and highest prestige categories involves use of psychedelics. These

data suggest that men who enter the most prestigious occupations are

somewhat less likely to report any use of illicit drugs, particularly

heroin. On the other hand, illicit drug use is moderately higher

among the unemployed men than among those in any of the occupational

prestige categories.

Among blacks, the highest figures for sedatives and cocaine are observed among the 55 unemployed men (table 2.7). For heroin, the

highest figure is found among the students. For the other illicit

drugs, the blacks in category 3 have the highest figures. As a

result, there are no clear trends comparable to those for whites.

The pattern for whites in Manhattan is similar to the one observed

in the national sample (table 2.8). The numbers are limited and

may be unstable, but there appears to be an inverse relationship

between drug use and occupational prestige. Again, among blacks, the

unemployed men do not have the highest percentages except for heroin.

16
TABLE 2.6 Lifetime Prevalence of Drug Use by Race and Father's

Occupation, Manhattan Sample (Percentages)

No Info. 1 (low) 2 3 4 (high)

Tobacco 100

Alcohol 100

Marijuana 100

Psychedelics 75

Stimulants 50

Sedatives 25

Heroin 0

Opiates 50

Cocaine 50

N 4

Tobacco 92

Alcohol 100

Marijuana 83

Psychedelics 21

Stimulants 12

Sedatives 17

Heroin 33

Opiates 46

Cocaine 54

N 24

White

85 90

100 100

46 80

15 40

23 30

23 40

15 20

31 50

15 30

13 10

Black

90 87

100 100

84 84

18 29

16 16

18 32

32 45

34 36

42 52

38 31

91 94

96 98

73 78

32 33

68 37

41 35

14 8

50 31

36 20

22 49

88 87

100 100

88 67

35 33

29 33

29 33

47 47

65 47

59 47

17 15

17

354-188 0 - 81 - 3
TABLE 2.7 Lifetime Prevalence of Drug Use by Race and Respondent's

Occupation, National Sample (Percentages)

Unemployed Student 1 (low) 2 3 4 (high)

Tobacco 93 83

Alcohol 96 98

Marijuana 74 63

Psychedelics 46 28

Stimulants 44 30

Sedatives 36 21

Heroin 17 5

Opiates 48 33

Cocaine 30 15

N 149 145

Tobacco

Alcohol

Marijuana

Psychedelics

Stimulants

Sedatives

Heroin

Opiates

Cocaine

N

87 70

94 80

69 55

27 30

29 20

33 20

20 25

36 30

33 30

55 20

White

91 8 8 88

96 9 8 9 7

55 5 4 5 1

24 2 2 1 9

30 2 8 2 4

20 1 9 2 0

7 4 4

30 3 2 3 1

15 1 2 1 1

433 451 442

Black

88 9 1 88

96 9 5 9 5

60 61 7 8

20 2 1 3 5

21 2 3 38

16 20 2 8

10 9 22

31 3 0 4 0

16 2 5 30

97 56 4 0

86

99

46

12

24

16

1

26

7

483

89

94

69

26

23

23

6

37

23

36

18
TABLE 2.8 Lifetime Prevalence of Drug Use by Race and Respondent's

Occupation, Manhattan Sample (Percentages)

Unemployed Student 1 (low) 2 3 4 (high)

Tobacco 89 88

Alcohol 100 100

Marijuana 72 76

Psychedelics 56 18

Stimulants 50 53

Sedatives 61 35

Heroin 17 6

Opiates 50 29

Cocaine 50 12

N 18 17

Tobacco 100 80

Alcohol 100 100

Marijuana 84 80

Psychedelics 20 40

Stimulants 14 30

Sedatives 25 30

Heroin 59 30

Opiates 46 50

Cocaine 61 40

N 44 10

White

88 100

100 100

88 100

50 100

50 100

50 100

25 100

50 100

50 100

8 1

Black

72 85

100 100

83 85

39 23

39 15

28 23

44 38

56 46

56 62

18 13

88 93

88 100

71 70

41 19

59 22

35 16

18 3

47 27

29 11

17 37

92 81

100 100

79 81

12 38

8 25

17 31

17 19

21 44

25 44

24 16

19
That three-fifths of the unemployed blacks had used heroin is,

however, a notable finding. Among blacks, the only apparent downward

trend involves heroin. In fact, for psychedelics and sedatives the

percentages for categories 1 and 4 are uite similar.

Family Status. Another variable that was found to be related to lifetime prevalence of drug use in the national study was current family

status. Family status was categorized as: married, coupled, living

in parental home, and living independently. Coupled is the term

employed for men who were living with a woman to whom they were not

married (see Clayton and Voss 1977). For current family status, the

pattern in Manhattan is comparable to the one found in the national

study (table 2.9). Among the whites, the highest percentage is found

among the men who were coupled for each drug except heroin. The

white males who were still living in their parental home have the

highest prevalence of heroin. The next highest percentage was

reported by the men living independently. It may be noted that 24

percent of the men in Manhattan were living with their parents in

comparison with 11 percent of the respondents in the national study.

by race, the percentages in Manhattan are: white, 20; black, 26.

among whites, the lowest percentage of users is found among the married men for six of the seven illicit drugs. For stimulants, the

lowest percentage of users is observed among the men still living

with their parents. Among blacks, the highest percentage for each

drug is found among the men who were coupled. The men who were living independently rank second on psychedelics and opiates. In

contrast with the whites, the blacks who were married rank second on

marijuana and stimulants.

Employment. In the national study, employment was related to lifetime prevalence of drug use. The lowest percentage of users was

found among the men who were employed, and drug use was most extensive among the unemployed. The following categories were used for

employment: working full-time, working part-time, student, and

unemployed. Full-time was defined as working 30 hours or more per

week. This variable was constructed from items in which questions

were asked about current employment. Because there are so few parttime workers and students in the Manhattan sample, these categories

were combined. Another important difference between the samples involves the extent of unemployment. Only 8 percent of the men in the

national sample were unemployed, whereas 35 percent of the white respondents and 43 percent of the black respondents in Manhattan were

not currently employed. The data on lifetime prevalence and current

employment status are presented in table 2.10. Among whites, the

percentage of respondents in Manhattan who had used marijuana, stimulants, and sedatives is highest among the unemployed. It is noteworthy that the lifetime prevalence of heroin use among the men employed full-tine is essentially the same as the figure for the men

without jobs. These figures are, however, substantially lower than

the comparable ones for blacks. The highest prevalence for sedatives,

heroin, opiates, and cocaine is observed. among the unemployed blacks,

but the highest figure for marijuana is found among the men employed

20
TABLE 2.9 Lifetime Prevalence of Drug Use by Race and Current

Family Status, Manhattan Sample (Percentages)

Married Coupled Parental Home Independent

Tobacco 93

Alcohol 97

Marijuana 62

Psychedelics 14

Stimulants 38

Sedatives 17

Heroin 3

Opiates 28

Cocaine 10

N 29

Tobacco 78

Alcohol 100

Marijuana 87

Psychedelics 13

Stimulants 22

Sedatives 22

Heroin 26

Opiates 35

Cocaine 35

N 23

White

100

100

100

56

56

56

11

56

56

9

Black

100

100

100

50

50

50

58

75

75

12

80 95

95 100

70 78

30 42

35 45

35 42

20 12

35 42

25 30

20 40

85 93

100 100

76 81

21 28

15 14

24 21

48 35

36 42

52 49

33 57

21
TABLE 2.10 Lifetime Prevalence of Drug Use by Race and Current

Employment Status, Manhattan Sample (Percentages)

Full-time Part-time and Student Unemployed

Tobacco 93

Alcohol 97

Marijuana 72

Psychedelics 27

stimulants 35

Sedatives 28

Heroin 10

Opiates 33

Cocaine 20

N 60

Tobacco 85

Alcohol 100

Marijuana 85

Psychedelics 25

Stimulants 21

Sedatives 25

Heroin 30

Opiates 41

Cocaine 44

N 61

White

75

100

75

75

50

25

25

75

50

4

Black

70

100

60

40

20

20

20

30

40

10

91

100

76

38

53

47

12

41

32

34

96

100

83

24

17

26

54

44

57

54

22
full-time. There is a sizable difference between the employed and

unemployed men in the prevalence of heroin, and, to a lesser extent,

cocaine, but there are only limited. or negligible differences for

the other drugs.

Summary Comparison of the Manhattan and national samples reveals

that the lifetime prevalence of drug use is higher in Manhattan for

every drug or drug class other than tobacco. The differences are

most extensive for marijuana, heroin, and cocaine. For blacks and

whites, at least twice as many men in Manhattan had used heroin and

cocaine than was the case in the national sample. For the others,

the comparable figures are three to four times higher in Manhattan

than in the national sample. Among the men who had ever used. illicit

drugs, the respondents in the Manhattan sample had used these drugs

more extensively than was the case in the national sample, with

the sole exception of psychedelics.

In the national sample there is a strong inverse relationship between age and lifetime prevalence for all drugs, except tobacco

and alcohol. For whites, the pattern across age groups in Manhattan

is similar to the one observed in the national sample--the percentage

of drug users increases from the older to the younger age groups for

psychedelics, sedatives, and, to a lesser extent, cocaine. However,

among blacks there is a U-shaped curve in both the Manhattan and

national samples; the percentages are higher in the two middle age

groups than in the oldest or youngest age group. In Manhattan, tobacco and heroin were used less frequently by the men with more education. There are no consistent patterns for the other drugs, but

except for minimal differences for psychedelics and stimulants, the

lowest percentages of use are found among the college graduates.

There is little difference in lifetime prevalence of heroin in the

national sample according to the occupational prestige of the respondents' fathers. For the other illicit drugs, the lowest percentages are found among the men whose fathers had jobs with the lowest

occupational prestige, and the men whose fathers had jobs with the

most prestige consistently show the highest percentages. This pattern is found among both whites and blacks. Among whites in the

Manhattan sample, the lowest prevalence of illicit drugs other than

heroin and opiates was found in the lowest prestige category but

upward trends comparable to those in the national sample were not

observed. A notable finding for blacks was that almost one-half of

the men whose fathers did not have a low prestige occupation had

used heroin. In contrast with the findings regarding drug use and

the occupational prestige of the respondents' fathers, in the national sample the highest percentages of illicit drug use are found

among the men in the occupations with the least prestige, and the

lowest percentages are found among the men with the most prestigious

occupations.

The pattern for whites in Manhattan is similar to the one observed

in the national sample. Among blacks the percentages across the

23
occupational prestige categories are similar. A notable finding is

that three-fifths of the unemployed blacks had used heroin.

The findings regarding family status are similar in the two studies.

With the exception of heroin use among whites, the men who were

coupled have the highest lifetime prevalence of all illicit drugs.

For whites, the lifetime prevalence of heroin use among the men

employed full-time is essentially the same as the figure for the men

without jobs. Among blacks, there are notable differences between

the employed and unemployed men in the prevalence of heroin and

cocaine,

FOOTNOTE

1

The SEI scores for the respondents' fathers were divided as follows:

(1) under 16; (2) 16 to 31; (3) 32 to 61; and (4) 62 to 96. For

the respondents, the groupings were: (1) under 18; (2) 18 to 33;

(3) 34 to 61; and (4) 62 to 96. The respondents who were in the

armed forces presented a problem. They were coded as (1) armed

forces, unspecified (N = 48); (2) armed forces, enlisted (N = 65);

and (3) armed forces, officers (N = 17). The men in the first two

categories were assigned a score of 15, while the officers were

assigned a score of 44. These categories were selected on the

basis of our distributions for the entire national sample. Thus,

differences among whites, blacks, and others were not considered.

24
Chapter 3

Incidence and the Drug Epidemic

In the report on the national Young Men and Drugs study, a chapter was

devoted to the issue of whether or not the United States had experienced

a heroin epidemic, as had been suggested by knowledgeable individuals

(Jaffe 1973; DuPont and Greene 1973). It was noted that the question

of an epidemic of drug use refers to an historical issue, not one

of the respondents' maturation. As in the national study, both historical and maturational changes are reflected in the Manhattan data,

because the sample also includes eleven birth cohorts. In this

chapter, data are analyzed to assess the incidence and prevalence of

drug use and the timing of changes within the sample of men interviewed in the Manhattan study. While the national data suggested

that there was an epidemic of drug use in the latter part of the 1960s

it was anticipated that the New York data might reflect endemic drug

use rather than providing additional evidence of the occurrence of

a drug epidemic.

Incidence of Drug Use

Thedataon the incidence of new users are presented in the first

two tables in this section. The number of new users appearing in

each year from 1956 or earlier to 1975 is shown separately for the

drug classes in table 3.1. It may be seen that three men reported

use of tobacco in 1956 or earlier, and two more did so in 1957.

These data are presented as percentages in table 3.2; the base for

the percentages in each column is the total number of who reported

any use of the drug or drug class in their lifetime.

The zero entries in these tables do not mean that these drugs were

not being used in New York. Nor do the low figures for the early

years mean that use of these drugs was restricted in Manhattan in

those years. Rather, the zeros and the low incidence figures reflect

the restricted age range in the sample. Thus, the increases in the

number of new users of tobacco and alcohol in the 1960s occurs primarily because this was the time period when the men were reaching the

age at which use of these drugs is common in American society.

For the reader's convenience, the largest figure in each column is

underlined in table 3.1. with the exception of tobacco and alcohol,

these figures are found in the four-year period, 1967 to 1970. The

peak year for psychedelics, sedatives, and opiates was 1969; for marijuana the number of new cases peaked in 1968. The peak years for

these drugs occurred approximately two years later in the national

25
TABLE 3.1 Number of New Cases of Use by Drug and First Year of Use
TABLE 3.2 New Cases of Use by Drug and First Year of Use in Percentages
sample. This apparently reflects an earlier introduction of illicit

drugs in Manhattan than was the case nationally. The difference is

not a function of different age distributions in the national and

Manhattan samples; in fact, when the respondents are grouped into

four age categories, there is a remarkable similarity in the percentage distributions in the two samples.

The five consecutive years in which the largest percentage of new

users appeared in each drug category are bracketed in table 3.2. Of

the men who ever used tobacco, 44 percent are included in these

peak years, 1963-1967. For alcohol, the peak years are 1962-1966,

and 45 percent of the new cases appeared in this period. The comparable figures for cigarettes and alcohol in the national sample

are both 45 percent. Therefore, for the legal drugs, there is

striking consistency in the findings of the two studies. Between

54 and 70 percent of the new users of the other drug classes are

found in the peak five-year periods in Manhattan. These peak years

are essentially the same--1966 to 1970 or 1967 to 1971--for all of

these drugs. The new users of marijuana, stimulants, and sedatives

are concentrated in the years 1966-1970, and the peak period was

1967-1971 for the psychedelics, opiates, and cocaine. A somewhat

greater concentration in the peak periods appeared approximately

two years later--in 1968-1972 or 1969-1973--in the national sample.

In all of the drug categories, there is a decline in the number of

new users in the four or five years following the peak year, as may

be seen in table 3.1. However, the number of new users of tobacco

was the same in 1973 and 1974, and one more man tried cocaine in 1974

than the number who had done so in the previous year. Few new users

of alcohol can be anticipated because nearly all of the men had

used it. For the other drugs, including tobacco, some additional

users might be expected in the years after the interviews were completed.

The median age at first use of each drug or drug class is shown in

table 3.3 for each birth cohort. There is little variation in the

median age at which tobacco and alcohol were first used. In each

cohort, one-half of the men who ever used alcohol did so for the first

time between the ages of 14 and 16. The comparable medians in the

national sample were 15 or 16, or within the range in the Manhattan

sample. There is some variation among the cohorts in the median

age at first use of tobacco, but with the exception of the 1945 birth

cohort, the range is restricted to the ages of 15 to 17. In the national sample, the comparable question was restricted to cigarettes,

but the range of medians was identical. While the median age at

which initial use of tobacco and alcohol occurred is remarkably

similar for the eleven birth cohorts, this does not appear to be

the case for the other drugs or drug classes. Rather, there appears

to be a marked downward trend in the medians from the oldest to the

youngest man, but it should be noted that some of these medians are

based on a small number of cases. To provide a more adequate basis

for comparison, four age groups were defined by birth year. As may

28
TABLE 3.3 Median Age at First Use of Drug Among Users by Year of Birth

Year of

Birth Tobacco Alcohol Marijuana Psychedelics stimulants Sedatives Heroin/Opiates Cocaine

1944 15 15 17 23*

45 13 14 18 26**

46 17 16 21 17*

47 17 16 20 22

48 17 14 18 21*

49 17 15 18 19

1950 15 14 16 20*

51 15 15 17 18*

52 15 15 16 18

53 15 14 16 17

54 15 14 16 16

21*

21*

20*

21*

18

18

18

17*

18

17

18

24* 20* 22*

21** 18* 22*

22** 23 22

21 19 23

18* 17 21

20 17 20

17* 20* 18

17* 17 20

18 17 18

16 16 18

19* 15 18

**These medians are based on 2 to 4 cases and may, therefore, be unstable.

*These medians are based on 5 to 7 cases and may, therefore, be unstable.
be seen in table 3.4, the medians are similar for tobacco and alcohol,

but for the other drugs, there is a clear downward trend in the medians from the oldest to the youngest age groups. A similar trend

was observed in the national study.

The data in tables 3.3 and 3.4 cannot be taken as proof of an earlier

age of onset of drug use in the younger cohorts because limits are

set on median age by the age of the cohorts. The men born in 1954

were 20 years old when they were interviewed in 1974; consequently,

the median age for them could not exceed 20. The higher median ages

in the older age groups may reflect only the fact that at the time

they were interviewed, these men were older, and this permitted

higher medians to appear. The data in table 3.4 are relevant to

this point. For most of the drugs, the percentages of users in the

two oldest age categories are similar. In contrast, a lower percentage of the men in the youngest age group had used each of the

drugs, other than the opiates, than was the case for the other age

categories. If, by the age of 30, the percentage of users, for example, of marijuana, increases to the level observed in the other age

categories, the median age at first use for the youngest men will

necessarily be higher. While somewhat higher medians may be anticipated for marijuana, psychedelics , stimulants, and sedatives, the

percentage of users in the four age groups was similar for opiates

and cocaine. For these drugs, the median age at first use is lower

among the younger men. Thus, the median age at first use among the

younger age groups would match the medians for the older men only if

higher proportions of the younger men subsequently use opiates and

cocaine.

In table 3.5, the men who had used a drug only experimentally, that

is, less than 10 times, are excluded. Among the nonexperimental

users, there is no evidence of a decline in the age at first use of

tobacco, alcohol, or marijuana. The medians for psychedelics and

sedatives are based on limited numbers, and this may account for the

apparent decline. For the opiates and cocaine, the medians clearly

are lower in the younger age groups, but, again, this may be due to

differences in years at risk, as previously noted. It is only for

cocaine in the oldest age group that one finds a higher median in

table 3.5 than in table 3.4. This means that men who used a drug

experinentally did so at a somewhat later age than the men who used

it more extensively.

As was indicated in the report on the national study (O'Donnell

et al. 1976), the peaking of new cases, as seen in table 3.1, could

occur in a number of ways which are not mutually exclusive:

1. An increase in new cases could be produced by higher proportions

of users in the younger cohorts, even if the size of the cohorts

remained constant. As was shown in table 3.4, this factor is not

present in the Manhattan sample. The increase observed in table 3.1

is not the result of higher percentages of drug users in the younger

cohorts than in the older cohorts.

30
TABLE 3.4 Median Age at First Use and Lifetime Prevalence of Use of 8 Drugs By Age Group

Median Age at First Use

Drugs

of 8 Drugs 8 Drugs

Age Group 1944-46 1947-49 1950-52 1953-54 1944-46 1957-59 1950-52 1953-54

n (62) (79) (81) (72) (62) (79) (81) (72)

Tobacco 15 17 15 16 74 76 67 57

Alcohol 14 15 14 14 98 100 99 96

Marijuana 20 19 16 16 76 78 76 69

Psychedelics 23 21 18 17 35 35 27 27

Stimulants 21 19 17 17 32 32 27 27

Sedatives 20 20 17 17 30 34 24 24

Opiates 19 20 18 16 29 39 28 30

cocaine 22 21 18 18 33 43 38 30

Percentage Who Ever Used
TABLE 3.5 Median Age at First Use of Drug Among Nonexperimental Users By Age Group

Age

Group Tobacco Alcohol Marijuana Psychedelics Sedatives Stimulants Opiates Cocaine

1944-46 15 15 16 21* 16* 20* 19 23

1947-49 17 15 18 19 20 18 19 19

1950-52 15 14 16 17* 16 16 17 20

1953-54 15 14 16 16* 18* 16 16 18

*Median is based on only 9 or fewer cases.
2. An increase in new cases could be produced if succeeding cohorts

were larger in number, even if the age at first use of a drug did

not change from one cohort to another and the proportion of users

in the cohorts remained constant. As may be noted in table 3.4,

the number of men in the oldest age group is somewhat smaller, but

overall the numbers of men in the age categories are quite similar.

3. If the number of persons in successive cohorts is identical and

the proportion using drugs remains the same, but the age at first use

drops between succeeding cohorts, there will be an increasing number

of men entering the ranks of users in a given year or at least within

a brief span of years. If, for example, each cohort usually produces

a given number of new users by the age of 16, but then in a subsequent cohort that number is produced by age 15, there will be a year

or a span of a few years in which many more new users appear; in one

brief period the persons who used a drug by the age of 15 in one

cohort would be added to those who used it by the age of 16 in the

previous cohort. The data in tables 3.3 and 3.5 suggest, but do not

demonstrate, that this factor may have been operating for some drugs.

4. The same peaking could be produced if the age at first use increased

between succeeding cohorts, with the peak occurring later than in the

first case. There is no evidence to suggest that this is the case

in the Manhattan sample.

5. The first four possibilities involve differences among cohorts.

Regardless of such differences, an increase in new cases could be

produced by historical changes that have a similar effect on all

persons who are living at the time such changes occur, or at least

for all persons who have entered the age of risk and have not as

yet left it.

There are, then, five possibilities insofar as explanation of the

observed increase in new cases of drug use in this sample is concerned.

In the national study, only the fourth possibility could be discounted. In this sample, the first and second possibilities could

also be eliminated. The data on median age suggest that the third

factor (a drop in the age at first use between succeeding cohorts)

was influential, and the possibility of historical change cannot be

discounted. The occurrence of riots in the ghettos of many large

cities, the Vietnam War, and anti-war protests on college campuses

all suggest that historical changes had an important impact on young

men. It is not possible to assess the relative importance of these

historical influences and the changing age at onset of drug use.

However, the effect of the changing age at onset can be clarified

by comparing and contrasting the year of onset of use for alcohol

and marijuana.

The cumulative percentages for cases of alcohol use, based on the

total number of men in the sample, are presented in table 3.6.

Comparable percentages for new cases of marijuana use are shown in

table 3.7. The tables may be compared with each other. Within each

33

354-188 0 - 81 - 4
TABLE 3.6 Year of First Use of Alcohol by Birth Cohorts (Cumulative Percentages of Total Sample)

Year of Birth

1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954

Year of

First Use n 23 15 24 25 31 23 3 1 23 27 35 37

1953

54

55

56

57

58

59

1960

61

62

63

64

65

66

67

68

69

1970

71

72

73

74

4 7 4 -- 3 -- -- -- 4 -- --

4 20 4 4 3 -- -- -- 4 -- --

9 20 4 4 3 -- -- -- 4 -- --

26 20 8 4 3 4 -- -- 4 -- --

44 27 12 4 3 4 -- -- 4 -- --

44 33 17 8 6 4 -- -- 4 -- --

56 60 38 16 13 4 3 -- 4 3 3

74 73 58 28 23 9 3 9 8 3 8

78 73 67 36 32 13 3 9 12 3 8

87 87 83 40 55 30 6 22 12 9 14

87 9 3 96 60 64 48 26 30 23 14 14

87 93 100 68 74 65 52 48 27 20 16

100 93 100 8 4 81 83 71 48 35 20 22

100 93 100 84 8 4 87 81 70 50 29 40

100 100 100 92 84 100 87 74 65 60 46

100 100 100 96 90 100 9 4 83 85 69 65

100 100 100 100 94 100 94 100 96 80 89

100 100 100 100 97 100 94 100 9 6 89 90

100 100 100 100 97 100 97 100 96 91 92

100 100 100 100 97 100 97 100 96 91 92

100 100 100 100 100 100 97 100 96 94 97

100 100 100 100 100 100 97 100 100 94 97
TABLE 3.7 Year of First Use of Marijuana by Birth Cohorts (Cumulative Percentages of Total Sample)

Year of Birth

1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954

Year of

First Use n 23 15 24 25 31 23 31 23 27 35 37

1956

57

58

59

1960

61

62

63

64

65

66

67

68

69

1970

71

72

73

74

9

-- -- --

13 -- -- --

17 -- 8 --

22 13 8 --

35 20 12 4

39 27 25 8

3 9 27 29 8

44 4 0 38 16

44 60 3 8 28

52 67 38 28

52 73 42 36

61 73 42 48

61 73 67 64

61 73 67 72

61 73 83 84

70 73 83 84

70 73 83 88

70 73 83 88

70 73 83 88

-- --

-- --

-- --

-- --

-- --

10 4

13 4

19 4

26 4

32 4

42 30

42 5 2

4 8 6 5

52 7 0

61 7 4

6 1 74

64 74

64 78

64 78

-- --

-- --

-- --

-- --

-- -- --

-- -- --

-- -- --

-- -- --

-- --

3 4

26 9

36 9

52 22

58 35

6 4 5 6

6 8 6 5

6 8 65

74 74

74 74

74 74

77 78

-- -- --

4 -- --

4 -- 3

7 -- 3

18 6 8

22 9 11

37 11 11

44 29 14

56 43 24

63 51 46

67 6 6 54

67 69 6 0

70 71 65

74 71 65
table, either cohorts or years of first use can be compared, and it

should be noted that comparisons of diagonals are legitimate and

useful. To illustrate, the diagonals for age 17 are underlined in

each table. This enables one to compare the cohorts in terms of

the percentage who had used alcohol or marijuana at or before that

age. Similar comparisons can be made for other ages; one simply

has to subtract the year of birth from the year of first use. In

table 3.6, adjacent columns should be compared. If one allows for

sampling fluctuation, any two adjacent columns are essentially similar,

except that the one on the right has been displaced one space downWard. In other words, if each cell in a column is compared with the

cell immediately below and to the right of it, the percentages are

approximately equal. This means that the cohorts are similar to

each other with respect to first use of alcohol. By age 10, from

3 to 20 percent of the men in each cohort had used alcohol, and the

percentages increase fairly regularly in the succeeding years. By

the age of 17, which is highlighted by the underlined diagonal in

the table, the percentages of men in each cohort who had used alcohol range between 68 and 96.

The peak year for new cases of alcohol use in table 3.1 was 1963.

If the row for 1963 is examined in table 3.6, it will be observed

that men in the 1945 and 1946 cohorts furnished a few new cases, at

ages 17 and 18, and that the other cohorts were contributing sizable

numbers, as evidenced by the fact that the percentages for 1963 average about 10 points higher than for 1962. Table 3.7 is not subject

to the potential weakness of the tables in which median ages are

presented, because comparisons can be made in term of ages that

all of the cohorts have reached. In each column in table 3.6, the

first cell that shows 50 percent marks the age at which one-half

of the cohort, not merely one-half of the users, had begun alcohol

use. These cells fall roughly on a diagonal. For the youngest

cohorts, these cells are slightly above the diagonal suggested by

the oldest cohorts. This indicates that first use of alcohol occurred

somewhat earlier in the younger cohorts. As in the national sample,

the peaking of new cases of alcohol use in table 3.1 is, to a slight

extent, due to the earlier use of alcohol in the younger cohorts.

There is no hint of any historical change that affected all cohorts.

Since there is no question of an epidemic of alcohol use, the similarity across cohorts establishes that the increase in new cases of

alcohol use observed earlier in table 3.1 is almost entirely a maturational effect. A certain proportion of new cases could be anticipated

at each age, and as each cohort moved through that age, it contributed

its expected number of new cases. The same maturational effect, then,

can be expected to account at least partially for the increases

observed in table 3.1 for the other drugs.

New cases of marijuana use by cohort are shown in table 3.7, and it

is apparent that the maturational effect, which applies to all cohorts, was not the only cause of the increase in marijuana use noted

in table 3.1, If the underlined. figures for the age of 17 are examined,

36
one finds two distinctive patterns. In the 1944 through 1949 cohorts,

one finds that from 27 to 39 percent of the men had used marijuana

by the age of 17. The comparable figures for the 1950 through 1954

cohorts range from 51 to 58 percent. In view of the data on median

age in tables 3.4 and 3.5, it wouuld appear that in the older cohorts

experimental use of marijuana occurred rather late, because there

was little difference between the age groups when the median age at

first use was examined among the nonexperimental users. If one exmines the location of the cells in which at least one-half of the

men in each cohort had used marijuana, it is apparent that these

figures would lie on a diagonal, comparable to the one for alcohol,

if the 50 percent figure for the 1944, 1946, 1947, and 1948 cohorts

would be four cells higher in their respective columns. In other

words, experimental users in these cohorts tried marijuana at a

later age than their counterparts in the younger cohorts.

In the national study, the peak year for new cases of marijuana use

was 1969, and men from each of the eleven cohorts contributed to the

peak. The peak year for the appearance of new users of marijuana

occurred a year earlier in Manhattan. However, the men in the two

oldest cohorts did not contribute to this peak (table 3.7). On the

other hand, a few new cases of marijuana use did occur in these older

cohorts after the peak year. The men in the two youngest cohorts

contributed to the peak in 1968, although they were only 14 and 15

years of age at that time. The increase in marijuana use in the late

1960s partly resembles the increase in alcohol use in that it reflects

maturational effects. However, an additional factor is observed for

marijuana--a higher proportion of the men in the younger cohorts had

used marijuana by the age of 17. Thus, the figures in table 3.3 on

median age of onset are not misleading, but reflect a real change.

The peak year for new cases of opiate use was 1969, or the middle

year in the peak five-year period of 1967-1971 (table 3.1). As the

number of opiate users in each cohort varies fran 30 to 52 percent

and provides too few cases for analysis on a cohort-by-cohort basis,

the year of first opiate use is shown for four age groups in table

3.8. It is apparent that the men in each age group contributed to

this peak period, although few of the youngest men used opiates prior

to 1969. These data reflect the lower age of onset of opiate use

among the younger men (table 3.3).

The lower cumulative percentages in the two younger age groups undoubtedly reflect their shorter period of risk. A crude control for

the greater period of risk in the older age groups can be made by

examining the percentages when the men in each age group reached

twenty years of age. In 1966, all of the men in the oldest age group

had reached the age of twenty, and 27 percent of them had used opiates by that time. For the 1947-49 group, 35 percent had used

opiates by 1969, when they had all attained the age of twenty. The

comparable figures for the two younger age groups are 40 and 35 percent, respectively. Thus, a higher, not a lower, proportion of the

men in the younger age groups had used opiates by the age of twenty.

37
TABLE 3.8 Year of First Use of Opiates by Age Groups

(Cumulative Percentages)

Age Groups

Year of 1944-46 1947-49 1950-52 1953-54

First Use n (62) (79) (81) (72)

1958 or earlier

59

1960

61

62

63

64

65

66

67

68

69

1970

71

72

73

74

3

10

10

13

14

14

21

24

27

29

34

39

42

42

42

45

45

1

2

2

2

4

6

8

14

16

23

32

35

40

44

44

44

46

--

--

--

--

--

--

1

4

7

15

21

28

32

35

40

43

43

1

1

1

1

1

1

1

1

3

4

6

21

26

29

33

36

39

38
In terms of the different ways that a peaking of cases could occur,

as noted earlier in this chapter, the data suggest that the third

possibility occurred in Manhattan. That is, the age at first use of

opiates, as well as marijuana, declined in the younger cohorts and

produced a peaking of new cases. The data suggest that there was a

drug epidemic in Manhattan in the late 1960s and early 1970s. However

a caveat is in order. The data do not permit assessment of the fifth

possibility, which involves historical changes. Further, to this

point, the analysis has been based on the number of new users appearing in each calendar year. Yet, this is only part of the issue

regarding the existence of an epidemic; other relevant questions

concern the length of time those who used a drug continued to use it,

as well as the extent of their use.

Annual Prevalence

In the national study, data on annual prevalence--the percentage

of the total sample who used each drug in each calendar year--were

obtained for the years 1957 through 1974, and heroin and other opiates

were differentiated. In the Manhattan study, comparable questions

were posed only about opiates, and use of heroin was not distinguished

from use of other opiates. Although the questions were phrased in

terms of use "on your own," or nonmedical use, a few of the men may

have reported medical use in response to the questions. One indication

that this may be the case is the fact that three of the 127 men who

had used opiates reported their use at the age of eight or earlier.

There is little difference across age groups in the percentage of

users of opiates (table 3.8).

The data pertaining to the annual prevalence of heroin-opiate use

are presented in table 3.9 for the years 1957 through 1974. In

addition to annual prevalence for the entire sample, the percentage

of men in each age group who reported heroin-opiate use each year is

shown. A man is counted in a given year if he used opiates at least

once in that year. Experimental use of heroin-opiates is not reflected

in these data because the question pertaining to pattern of use yearby-year was posed only to nonexperimental users. For comparative

purpose, the lifetime prevalence of opiate use, or the percentage of

men in each age category who ever used opiates, is also presented.

The experimental users are included in the figures for lifetime prevalence. For the four age categories, the percentages for lifetime

prevalence are one and one-half to two times higher than the figures

shown in table 3.9 for annual prevalence. In the national sample,

the annual prevalence of heroin never exceeded 2 percent, and the

annual prevalence of other opiates reached 10 percent in only one

year, 1974. In contrast, the annual prevalence of heroin-opiate use

in Manhattan reached a peak of 20 percent in 1970. Thus, in the late

1960s and early 197Os, the annual prevalence of heroin-opiate use was

substantially higher in Manhattan than in the rest of the country.

The peak five-year period in terms of annual prevalence is bracketed

in table 3.9; it spans the years 1969-1973, and men in each of the

four age categories contributed to this peak period. Also bracketed

39
TABLE 3.9 Annual Prevalence of Heroin-Opiate Use by Age

Groups (Percentages)

40
are the peak periods within each age group. These periods show

considerable overlap. In fact, they are identical for the two younger

age groups. Again, this shows how the lower age of onset of heroinopiate use in the younger age categories contributed to the peaking

of heroin-opiate use in a restricted span of time.

In the interivews, a man who reported use of heroin-opiates more

than ten times as was given a card showing eight statements describing

different frequencies of heroin-opiate use. The respondent was asked

to select the statement that best described his use of opiates during

each of the years he used them. The resulting data, shown in table

3.10, reveal a definite shift in the frequency of heroin-opiate use

from daily use to use once or twice a month or less than once a month.

For example, in 1968, 59 percent of the users reported use at least

daily or several times a day, whereas in 1973, only 37 percent of the

users reported daily use. Similarly, in 1968, only 20 percent of the

users said they used opiates once or twice a month or less than once

a month, but in 1973, 41 percent of the users selected these categories to describe their use of opiates.

The data in tables 3.9 and 3.10 show not only a substantial decline

in the annual prevalence of heroin-opiate use in Manhattan, but also

a shift from daily use to occasional use of heroin-opiates. In the

national study, the only evidence of a decline in annual prevalence

involved psychedelics and stimulants. While speculative, this difference in the findings may be a reflection of the fact that illicit

drug use flourished in Manhattan several years before the drug epidemic

became a nationwide phenomenon. If the trend nationally follows the

one in Manhattan, then the findings in the national study would have

been different had the study been conducted two or three years later.

In other words, 1974 was too early to detect a decline in the annual

prevalence of heroin or other opiates.

The Future

In the analysis of the data in the national study, it was suggested

that marijuana was the key drug in the drug epidemic of the 1960s

(O'Donnell et al. 1976:59; O'Donnell and Clayton 1981). The arguments

concerning the importance of marijuana in the drug epidemic will not

be reviewed in this chapter. Rather, the presumed importance of marijuana is noted to explain the presentation of the data in table 3.11

in terms of the extent of marijuana use.

It is generally recognized that drug use is transmitted largely

through friendship networks, and it may be seen in table 3.11 that

use of marijuana by at least a few of the respondent's friends was

almost universal when the men in the Manhattan sample first used

marijuana.

The respondents were also asked: "What would you say the chances are

that you will be using each drug, even occasionally, three years from

now? Would you say there was no chance, a slight chance, a good

41
TABLE 3.10 Frequency of Heroin-Opiate Use by Year, Manhattan Sample
TABLE 3.11 Use by Friends, Availability, and Chance of Future Use of Drugs by

Extent of Marijuana Use (Percentages)
chance, or a very good chance? How about (Drug)?" While these are

subjective estimates of future use, 64 percent of the men indicated

that there was at least a slight chance they would be using marijuana

three years later. The respondents' estimate of the likelihood of

future use of each drug or drug class is directly related to the extent

of their marijuana use. This is particularly noticeable for marijuana;

16 percent of the nonusers said there was some chance of use in three

years, but the figures increase rapidly with extent of use to 96

percent for the men who had used marijuana most frequently. In table

3.11, those who indicated there was even a slight chance of using

are included as possible future users. If only those who reported

the chance of marijuana use as "good" or "very good" are counted,

the percentages in terms of extent of marijuana use would be nonusers, 1; used less than 10 tines, 2; used less than 100 times, 30;

used 100 to 999 times, 61; and used 1,000 or more times, 78. Therefore, one can infer that a sizable proportion of the men who have

used marijuana intend to continue or resume use, and even 1 percent

of the men who have never used it recognize some chance of future use.

The findings in the national study were similar. The implication is

that there will continue to be a large reservoir of marijuana users,

and nonusers may learn to use the drug from them. The possibility

of such learning is apparent in the responses to the question: "How"

many of your current friends and acquaintances use each drug?" (table

3.11). Again, it may be observed that the extent of prior use of

marijuana is related to the likelihood that a man will report having

friends who currently use marijuana. In general, this holds true for

the other drugs, although there are some minor reversals. Almost

two-fifths of the nonusers reported that more than a few of their

friends currently used marijuana, and almost one-half of the men

who had used marijuana most often reported that they had friends who

were currently using cocaine. The rather widespread use of illicit

drugs on the part of the respondents' friends means that transmission

of drug use through friendship networks is likely to occur.

The men were also asked. how difficult it would be for them to obtain

each of the drugs within a day if they wanted to do so and had

sufficient funds. Overall, 75 percent of the men reported it would

be easy to obtain marijuana, and approximately 40 percent of the

respondents indicated that they could easily obtain the other drugs.

It must be remembered that the increased drug use in Manhattan, as

well as in the nation, has necessarily changed the social climate

surrounding drug use. In the years when few people were using marijuana, one had to overcome numerous obstacles prior to trying the

drug. Today, a man who knows that more than one-third of this friends

are currently using marijuana can readily justify experimenting with

the drug. This is also true, to a lesser extent, for the other drugs.

The only data in table 3.11 that suggest drug use will not be more

widespread in this sample in the future are the figures for chances of

use in three years for nonusers and experimental users of marijuana.

However, the men who had used marijuana more then experimentally

indicated that they might well try the other illicit drugs in the

44
future. At least some of the conditions conductive to the spread of

drug use existed at the time the men in the Manhattan sample were

interviewed. Whether the existence of these conditions did, in

fact, lead to more widespread use is not known. It is evident that

the data do not provide any basis for an optimistic prediction that

the prevalence of drug use in Manhattan will decline in subsequent

years.

45
Chapter 4

Multiple Drug Use

The purpose of this chapter is to compare patterns of multiple drug

use in the national and Manhattan samples. While multiple drug use

can be defined in many different ways, in this chapter it will be

defined simply and broadly as the use of two or more drugs at any

tine in an individual's lifetime. Use of such abroad definition

will allow us to examine an idea that has received considerable attention in the drug field. This is the hypothesis that drug use is

patterned and that individuals pass through developmental stages

as they become more involved with drugs. Kandel (1975) has identified these stages as use of (1) beer or wine, (2) cigarettes or hard

liquor, (3) marijuana, and (4) other illicit drugs. A number of

measurement strategies and techniques have been utilized to study

the form, content, and significance of the stages of drug involvement. Regardless of the strategy one selects, an analysis aimed at

identification of developmental stages requires examination of

multiple drug use.

Patterns of Drug Use

One technique that has been used. to study multiple drug use is

Guttman scaling (Kandel and Faust 1975; Single et al. 1974). It

offers a way to discover patterns of use across drug classes. Use

of this technique is based on the assumption that drug use may be

conceptualized along a single dimension. The order of the drug

classes along that dimension is established by the proportion of

respondents who report use of each of them. Relevant data are

shown in table 4.1 and indicate several important differences between the Manhattan and national samples. First, a larger proportion of the men in the Manhattan sample reported use of each drug

class. This is particularly true for cocaine, heroin, and marijuana

with 23, 19, and 20 percentage point differences, respectively,

between the two samples. Second, the prevalence rates for stimulants, sedatives, and other opiates in the national sample were

surprisingly high. Therefore, criteria were developed to differentiate quasi-medical use from use that was exclusively nonmedical in

nature (see O'Donnell et al. 1976:135-37). As the data in column

1 of table 4.1 show, the lifetime prevalence rates for stimulants,

sedatives, and other opiates in the national sample are substantially

lower when quasi-medical use is counted as nonuse. When similar

criteria were applied to the Manhattan sample the percent who could

be classified as quasi-medical users was negligible. Therefore,

nonmedical use of opiates and sedatives is substsantially higher in

46
TABLE 4.1 Item Marginals for Manhattan and National Samples

Drug Class

National Manhattan Percentage

Percent Rank Percent Rank Point

Used Used Differences

Alcohol 97 1 99 1 + 2

Marijuana 55 2 75 2 +20

*Stmulants 23 (27) 3 28 5.5 + 5 (+1)

Psychedelics 22 4 28 5.5 + 6

*Other Opiates 20 (31) 5 36 4 +15 (+5)

*Sedatives 16 (20) 6 26 7 +10 (+6)

Cocaine 14 7 37 3 +23

Heroin 6 8 25 8 +19

*For stimulants, sedatives, and other opiates, criteria were applied

to determine whether use was quasi-medical or nonmedical. The figures in parentheses in column 1 indicate the percent who had used

the three drugs with "quasi-medical use counted as use." The figures not in parentheses represent quasi-medical use counted as no

use. In the Manhattan sample the percent who met the criteria of

quasi-medical use was negligible.

47
the Manhattan sample than in the national sample. Third, the order

of the drugs, according to lifetime prevalence, is different in the

two samples. Only alcohol, marijuana, and heroin occupy comparable

ranks. Cocaine (37 percent) is ranked third in the Manhattan sample,

but it is seventh in the national sample (14 percent).

The data on patterns of nonmedical use across the drug classes are

shown in table 4.2 in a Guttman scaling format. Several conclusions

can be drawn from these data. First, a majority of the men, 62 percent in the Manhattan and 78 percent in the national sample, fall

intooneof the nine pure scale type patterns. This is especially

interesting because there are 256 possible patterns of use with

eight drugs, and the order of the drugs in table 4.1 differs in the

two samples. Hence, the clustering into the nine pure scale types

demonstrates clearly the existence of nodal patterns of progression

across drug classes. A second conclusion is that multiple drug use

is more prevalent in the Manhattan sample. In both samples, 22

percent of the men had used only alcohol and marijuana. On the

other hand, 40 percent of the men in the national sample had only

used alcohol in comparison with 21 percent in the Manhattan sample.

This difference, as well as the more extensive use of the illicit

drugs in the Manhattan sample, reflects in part the selection

criterion for the Manhattan sample, namely, registration with a

Selective Service Board serving an area with a high rate of drug

use. Third, twice as many men in the Manhattan sample (9 percent)

reported use of all eight drug classes than was the case in the

national sample (4 percent). These findings demonstrate that multiple illicit drug use is more prevalent in Manhattan than in the

national population.

Use of Pairs of Drugs

A second tactic that has bean used to study multiple druguseis to

examine the percentage of persons who report use or nonuse of one

drug in terms of reported use of another drug. However, before a

detailed analysis of the data in table 4.3 is presented, it is

appropriate to offer a general interpretation. An examination of

the pairs of columns for each drug, in which users of the drug are

compared with nonusers, confirms that use of any drug is associated

with use of all other drugs. This can be illustrated by examining

the data for users and nonusers of tobacco and alcohol and tobacco

and marijuana. Nearly all of the tobacco users have used alcohol

but so have most of the nonusers; hence, knowledge of tobacco use or

nonuse does not help predict use or nonuse of alcohol. However,

81 percent of the tobacco users report having used marijuana, in

comparison with only 26 percent of those who never used tobacco.

This finding is interesting from two perspectives. First, use of

tobacco is correlated with marijuana use, and it may be noted that

the percentage of tobacco users who have used marijuana (81 percent)

is higher in the Manhattan sample than was the case in the national

sample (59 percent). Second, as was found in the national study,

approximately one-fourth of those who have never used tobacco report

48
TABLE 4.2 Guttman Scale Patterns, Manhattan and National Samples
TABLE 4.3 Use and Nonuse Patterns for Pairs of Drugs: Manhattan Sample (Percentage)
having used marijuana. This is contradictory to what would be predicted by Kandel's (1975) model of developmental stages of drug use.

In these two samples, prior use of tobacco is not a necessary condition for use of marijuana.

Two pointS should be mentioned about the users and nonusers of alcohol: almost all of the respondents have used alcohol and use of

alcohol is associated with use of the other drugs. some one-fourth

to one-third of the users of alcohol have used other drugs. In

contrast, only one of the three nonusers of alcohol had used tobacco,

and no use of other drugs was reported by these abstainers.

An important inference that can be made on the basis of the data in

table 4.3 is that marijuana may be the key gateway drug for understanding multiple illicit drug use. Evidence for this can be found

in two places in the table. The first is in the column indicating

the percentages of users and nonusers of marijuana who reported using

other drugs. The second location is in the third row in which the

percentages of users and nonusers of the other drugs who had also

used marijuana are shown. Some 37 percent of the men who had used

marijuana had also used psychedelics while none of the nonusers

reported psychedelic use. The comparable figures for stimulants,

sedatives, heroin, and. opiates are 34 to 36 percent for the users of

marijuana in comparison with 1 to 5 percent for the nonusers.

Finally, 49 percent of the marijuana users reported having used

cocaine in comparison with none of the men who had not used marijuana.

If, in the next section of this chapter, it is established that use

of marijuana predates use of the other drugs, a plausible hypothesis would be that use of marijuana, in conjunction with other factors,

greatly facilitates a parson's progression into use of other illicit

substances.

An examination of the figures in row 3 of table 4.3 also suggests

this conclusion, though from a slightly different perspective. Of

those who had ever used psychedelics or cocaine, 100 percent had also

used marijuana; this was also true for 96 to 99 percent of the men

who had used stimulants, sedatives, heroin, or other opiates. Among

the nonusers of these drugs, the percentages reporting use of marijuana ranged from 60 to 66 percent. The findings in the national

study were almost identical, except that lower percentages of the

nonusers of stimulants, sedatives, heroin, and other opiates reported use of marijuana (see O'Donnell et al. 1976:100).

There are several reasons for suggesting marijuana rather than alcohol as a key to the understanding of multiple drug use. First, all

of the users of marijuana used alcohol, and almost all of them used

tobacco. Thus, to know that a man has used marijuana is to know

that he has used at least alcohol and probably has used tobacco. If

tobacco and alcohol are defined as drugs, marijuana use almost

automatically implies multiple drug use. Second, the associations

between marijuana use and use of other drugs are stronger than the

comparable associations for alcohol. Third, marijuana use is a

better predictor of use of other illicit drugs than is the licit

51
drug alcohol. For nonusers of either drug, one can predict with a

high probability of being correct that they will not have used

other illicit drugs. However, with respect to nonusers of alcohol,

one is making a prediction about 1 percent of the sample, but with

respect to marijuana, 25 percent of the men in the Manhattan sample

and 45 percent in the national sample are involved. Finally, some

of the differences between users and nonusers are larger for drugs

other than marijuana. For example, the difference between users

and nonusers of psychedelics is 60 percentage points for cocaine use,

and for heroin use, this difference is a dramatic 75 percent age

points. It should be noted that higher percentages of the nonusers

of psychedelics and heroin had used cocaine than was the case for

men who had not used marijuana, but this is not the reason marijuana use is emphasized. Rather, it will be shown that in terms of

temporal order, use of alcohol and marijuana generally preceded use

of other drugs. Because of the temporal order, it is appropriate to

predict from marijuana use to use of the other illicit drugs, but

it is only in a statistical sense that one can predict marijuana use

from use of drugs such as the psychedelics.

To facilitate comparison, data from the Manhattan and national

samples are shown in table 4.4. With respect to use and nonuse of

marijuana by use of the other illicit drugs, there are clear differences between the samples. Thirty-four percent of the men in the

Manhattan sample who had used marijuana reported use of heroin, in

comparison with only 11 percent in the national sample. In Manhattan, 49 percent of the marijuana users indicated that they had used

cocaine. The comparable figure for the national sample is 25 percent.

In the columns pertaining to heroin use, there are substantial differences in the use of psychedelics, stimulants, sedatives, and other

opiates. In each case, a higher percentage of the men in the national sample who reported use of heroin had used these four classes

of drugs than was the case in the Manhattan sample. In the analysis

of the national data, it was concluded that heroin use represents

the deepest possible involvement in the drug milieu (O'Donnell et

al. 1976:1O1). Heroin use was also relatively rare, as only 6

percent of the respondents had used the drug. On the other hand,

two-thirds of the heroin users had used all eight classes of drugs.

In contrast, in Manhattan, 25 percent of the men had used heroin.

Their use of heroin undoubtedly reflects extensive involvement in

the drug milieu, but it is important to recognize that heroin use is

not as rare an occurrence in Manhattan. As previously noted., a larger

proportion of the men in Manhattan reported experience with all of

the classes of drugs than was the case in the national study. In

contrast, the data on use and nonuse of heroin and use of psychedelics, stimulants, sedatives, and opiates suggest- that these drugs

were either less readily available or the Manhattan subjects were

more selective in the drugs they used. While marijuana use means

multiple drug use in the sense that it reflects use of tobacco and

alcohol, these data suggest that use of psychedelics, stimulants,

and sedatives may be more extensive nationally than it is in socalled "high drug use" areas. On the other hand, multiple drug use

52
TABLE 4.4 Pair-Wise Relationships of Marijuana, Heroin, and Cocaine and Other Drugs: Manhattan

and National Samples Compared (Percentages)
in such areas may involve marijuana, heroin, and cocaine to a greater

extent than is true in other parts of the country. At least this is

the case in the area from which the Manhattan sample was selected.

Evidence in support of the latter point can be found in the last

four columns in table 4.4. The men in the national sample who had

used cocaine were more likely to report use of psychedelics, stimulants, sedatives, and, to a limited extent, opiates, than their

counterparts in the Manhattan sample. However, the reverse was true

for heroin. Nationally, 38 percent of the cocaine users reported

use of heroin in comparison with 63 percent of the men in Manhattan.

There is thus a much stronger linkage between use of heroin and

cocaine among the men who grew up in high drug use areas than among

men in the general population, who presumably were raised in areas

in which illicit drugs were less commonly used.

Two precautionary comments are appropriate at this point. First,

the data in tables 4.1 through 4.4 are based on crude measurements

of drug use and thus must be interpreted cautiously. Second, the

data in these tables do not shw nor are they intended to suggest

that use of any one drug is a necessary condition for progression

to use of other drugs. The concept of necessary conditions is less

useful in an examination of the issue of progression through stages

of drug use than is the probabilistic conception of causality. The

probabilistic conception of causality employs criteria such as found

in the Surgeon General's Smoking and Health reports (statistical

association, the consistency, strength, specificity, coherence, and

time-order of the association, and tests of the association for

spuriousness). These criteria are quite similar to those articulated for the social sciences by Hirschi and Selvin (1967). According to Hirschi and Selvin, three criteria must be met to establish

a causal relationship: (1) the presumed cause and presumed effect

must be statistically correlated; (2) the presumed cause must occur

temporally prior to the presumed effect; and (3) the relationship

must not be spurious (i.e., the observed statistical relationship

between the indicators of the cause and effect must not be the product

of factors antecedent to both of them).

Year of Onset

one of the most interesting problems in the drug field concerns

the temporal order of onset of use of various classes of drugs.

The problem is interesting because (1) it relates to the issue of

progression through stages of drug use and (2) it is one of the

criteria that must be met in the exploration of cause-effect relationships between use of one drug and use of other drugs. The problem of temporal order is difficult to establish from a methodological

standpoint because most studies are cross-sectional in nature and

rely on retrospective data to determine the year of onset. Even in

longitudinal studies, the order of events is, to some extent, based

on retrospective reconstruction. In the initial measurement the

respondent is asked to reconstruct the order of events in his or

54
her life to that point in time. The problem of retrospective reconstruction can be minimized in a longitudinal study by anchoring the

data collection points to a calendar year or to a shorter time span

(Elliott and Ageton 1976). The problem of temporal order is also

a complex one conceptually because involevement in deviant behavior,

including drug use, is usually embedded in a number of related lifeevents. For example, the initial onset of marijuana use may be less

salient than the movement of the initiator from membership in a conventional peer group to one in which many of the members are actively

involved not only in drug use, but in other forms of deviance as

well. In addition, initial use of an illicit substance such as

marijuana may be less salient an event in one's life than onset of

regular or daily use. In other words, it is possible that initial

use may be almost a chance event, fostered less by rational and

deliberate choice than by situationally-based opportunity. In contrast, regular use of a substance may involve an explicit decision

and my be the event that is most important in explaining progression

to other drugs.

In the Manhattan study, data on the order of onset of drug use were

obtained in two ways. First, specific questions were posed regarding alcohol. The question on alcohol use was: "In what year did

you have your first drink of wine, beer, or liquor on your own,

or with friends?" For the other drugs or drug classes, questions

about year of onset were asked only of persons who had previously

indicated that they had used a class of drugs on at least 10 separate occasions. For marijuana or hashish, psychedelics, stimulants,

sedatives, the opiates (including heroin), and cocaine, the question was phrased: "In what year did you first try ? "

The data in table 4.5 show, for each pair of drugs, the order in

which they were used in the Manhattan and national samples. As

noted previously, quasi-medical use of stimulants, sedatives, and

opiates was treated as nonuse in the national sample. Quasi-medical

use of these substances was negligible in the Manhattan sample and

was not included. Therefore, these data essentially reflect onset of

nonmedical use. In the national study, when a respondent indicated

that he had first used two drugs in the same year, the order of use

of these drugs could usually be established by reference to the month

of onset for the pair of drugs. The month in which a drug was first

used was not obtained in the Manhattan study, and ties on year of

onset could not be resolved. As shown in table 4.5, there are more

men listed as using a pair of drugs in the same year in the Manhattan

sample than in the national sample. Consequently, only percentage

differences in the third set of columns that are greater than 10

points will be noted.

Alcohol use is clearly antecedent to use of the other drugs in

both samples. The only notable difference in the first part of

table 4.5 is that 15 percent of the Manhattan respondents who had

used both alcohol and marijuana reported that their initial use of

these drugs occurred in the same year. However, this must be viewed

in light of the comments in the previous paragraph.

55
TABLE 4.5 Time-Order of Onset of Use Among Pair of Drugs, Manhattan and National Sample Compared
TABLE 4.5 (cont.)
If one ignores alcohol, it is apparent that marijuana is a key

gateway drug for use of other drugs. For at least three-fourths of

the men in both samples who reported use of marijuana and one of the

other drugs, marijuana use occurred first. For more than 90 percent

of the men who had used both marijuana and cocaine, use of marijuana

cccurred first.

Several key differences between the two samples are apparent when

the onset of psychedelics is examined in relation to onset of other

drugs, excluding alcohol and marijuana. First, more of the Manhattan

respondents report initial use of psychedelics and stimulants in the

same year, but there does not appear to be a consistent temporal

order of onset of these drugs in either sample. The same conclusion

is appropriate for psychedelics and sedatives as well as for psychedelics and opiates. Second, psychedelic use is clearly antecedent

to the use of cocaine in the national sample, while the order of

onset for these drugs is not as clear in the Manhattan sample.

In the national sample, 73 percent of the men who had used stimulants

and heroin, as well as 75 percent of the respondents who had used

stimulants and cocaine, used stimulants first. Again, the time order

for stimulants and cocaine is not nearly as clear in the Manhattan

sample. Some 52 percent of the men in the Manhattan sample who

reported use of stimulants and sedatives indicated that their first

use of both drugs occurred in the same year. Nor is there a clear

pattern of onset for stimulants and opiates.

Use of sedatives generally preceded use of cocaine and heroin among

the men in the national sample who reported use of both pairs of

drugs. The time order of onset is less clear in the Manhattan sample;

38 percent of the men reported the same year of onset for sedatives

and opiates and 21 percentof the users of sedatives and cocaine

initially used both drugs in the same year. Use of opiates is generally antecedent to the use of cocaine in the national sample,

but not in manhattan. In fact, one-third of the men who reported

use of opiates and. cocaine indicated that they initially used these

drugs in the same year.

The second way in which the time order of onset of drug use was

measured in the Manhattan study was straightforward. For the drug

classes the respondent had ever used, he was asked: "Which of the

drugs did you use first? Which was second?" As many as eight drugs

or drug classes were ranked; the number depended upon the number of

drugs the respondent had ever used. This question was not asked

in the national survey. These data are presented separately for

whites, blacks, and others in table 4.6 because there are some

differences among these groups.

For at least one-half of the respondents, the first drug used was

tobacco. A majority of the men in each of the three racial groups

who had used two or more of the drug classes indicated that alcohol

was the second drug they had used. While 20 percent of the blacks

and 7 percent of the whites and others named marijuana as the first

58
TABLE 4.6 Time-Order of Onset of Drug Use by Race in the

Manhattan sample

FIRST DRUG SECOND DRUG THIRD DRUG

Black White Other Black White Other Black White Other

No Use (n) 0 1 1 8 4 12 23 26 24

Use (n) 125 97 70 117 94 59 102 72 47

Tobacco 63.2 51.5 52.9 18.8 37.2 30.5 3.9 6.9 4.3

Alcohol 30.4 42.3 44.3 60.7 53.2 59.3 8.9 5.6 2.1

Marijuana 3.2 2.1 1.4 17.1 5.3 5.1 70.6 75.0 76.6

Psychedelics 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Stimulants 0.0 1.0 0.0 1.7 3.2 1.7 1.0 5.6 4.3

Sedatives 0.0 2.1 0.0 0.0 0.0 0.0 2.0 2.8 4.3

Opiates 3.2 1.0 1.4 1.7 0.0 3.4 7.8 4.2 6.4

Cocaine 0.0 0.0 0.0 0.0 0.0 0.0 4.9 0.0 2.1

FOURTH DRUG FIFTH DRUG SIXTH DRUG

Black White Other Black White Other Black White Other

No Use (n) 54 49 42 72 61 51 89 69 55

Use (n) 71 49 29 53 37 20 36 29 16

Tobacco

Alcohol

Marijuana

Psychedelics

Stimulants

Sedatives

Opiates

cocaine

59
or second drug used, more than 70 percent of the men each group

who had used three or more of the drugs named marijuana as the third

drug used.

These findings are not surprising in that they essentially replicate

the findings in the national study. Tobacco, alcohol, and marijuana

are clearly the drugs that are most widely used by youth and young

adults in this society, and the order in which they are used reflects

a general movement from licit to illicit drugs. This confirm the

domain assumption in Kandel's (1975) model of developmental stages.

Because there is a rather consistent order of onset, tobacco, alcohol, and marijuana are often labeled "gateway," drugs.

The order of onset differs by race for the men who used four, five,

and six drugs. Among blacks, the most commonly used fourth drug

or drug class is the opiates (including heroin), the fifth is cocaine,

and the sixth is sedatives. Among whites, the most commonly used

fourth drug or drug class is the psychedelics, the fifth is stimulants, and the sixth is sedatives. Among the others, the fourth

drug used is most often cocaine, the fifth drug is either opiates or

cocaine, and the sixth drug or drug class is the sedatives.

Overall, the data on tine order of onset of drug use in the Manhattan study coincide with the findings in the national study for

tobacco, alcohol, and marijuana. There are differences by race in

the order of onset beyond these gateway drugs, but the order of onset

forwhites in the Manhattan sample is quite similar to the one found

in the national study.

Extent of use

To this point, measurement of drug use has involved a crude distinction between use and nonuse. However, a five-category ordinallevel measure is available in both the national and Manhattan

studies. The respondents were asked to indicate how often they had

used each drug in terms of the following categories: never, under

10 times; under 100 times; under 1000 times; 1000 times or more.

Cross-tabulation of these data provides another perspective on multiple drug use. Shown in table 4.7 are the percentages of men

who had used the other drugs in terms of the extent to which they

had used the three gateway drugs, tobacco, alcohol, and marijuana.

The value of gamma, a nonparametric correlation coefficient, was

calculated on the basis of 5x5 tables showing the extent of use of

each pair of drugs.

As expected, the relationship between extent of use of the gateway

drugs and use of the other drugs is usually linear. Stated differently, there is a stairstep increase in the percentage figures as

one moves from left to right and the gamma values are moderately

strong. For example, while only 28 percent of the men in the Manhattan study had ever used psychedelics, 52 percent of the men who

had used marijuana between 100 and 999 times had used psychedelics.

60
TABLE 4.7 Extent of Use of Three Gateway Drugs (Tobacco, Alcohol,

and Marijuana) by Use of Other Drugs (Percentages and Correlation)

EXTENT OF USE Value

use times times times times Gamma

No 1-9 10-99 100-999 1000+ of

Tobacco n = (35) (42) (20) (33) (164)

Percent Alcohol Users 94.3 100.0 100.0 100.0 99.4 .528

Percent Marijuana Users 25.7 66.7 75.0 81.8 86.0 .491

Percent Psychedelic Users 2.9 26.2 15.0 18.2 37.2 .459

Percent Stimulant Users 8.6 14.3 10.0 21.2 39.6 .553

Percent Sedative Users 5.7 14.3 10.0 18.2 37.8 .584

Percent Heroin Users 5.7 9.5 0.0 21.2 37.8 .643

Percent Opiate Users 5.7 38.1 15.0 27.3 48.2 .444

Percent Cocaine Users 5.7 26.2 5.0 39.4 49.4 .561

ALCOHOL n =

Percent Marijuana Users

Percent Psychedelic Users

Percent Stmulant Users

Percent Sedative Users

Percent Heroin Users

Percent Opiate Users

Percent Cocaine Users

MARIJUANA n = (74) (42) (54) (56) (68)

Percent Psychedelic Users 0.0 4.8 14.8 51.8 63.2 .811

Percent stimulant users 4.1 14.3 13.0 46.4 60.3 .703

Percent Sedative Users 2.7 11.9 13.0 42.9 58.8 .709

Percent Heroin Users 1.4 16.7 20.4 32.1 55.9 .632

Percent Opiate Users 5.4 21.4 29.6 53.6 73.5 .683

Percent Cocaine Users 0.0 14.3 24.1 58.9 82.4 .805

(3)

0.0

0.0

0.0

0.0

0.0

0.0

0.0

(18) (50) (99) (124)

27.8 66.0 71.7 89.5 .523

5.6 16.0 28.3 36.3 .366

0.0 14.0 26.3 40.3 .490

0.0 18.0 27.3 33.9 .335

11.1 18.0 23.2 33.1 .267

5.6 24.0 36.4 48.4 .398

11.1 32.0 30.3 48.4 .353

61
For men who had used. marijuana 1000 times or more 63 percent had used

psychedelics. A similar pattern is found for use of marijuana and

the other drugs. Although only 36 percent of the men in the total

sample had used cocaine, the percentages for use of cocaine in terms

of extent of marijuana use are, respectively, zero, 14.3, 24.1,

58.9, and 82.4. At least one-half of the men who had used marijuana

1000 or more times had used each of the other illicit drugs. These

data suggest that extensive use of marijuana is strongly predictive

of other illicit drug use.

Historically, much of the controversy about marijuana use and subsequest use of other illicit drugs has focused on heroin. The socalled steppingstone hypothesis suggests a link between marijuana

and heroin. For this reason, the data on the extent of use of marijuana and heroin in both studies are shown in table 4.8.

The association between use of marijuana and use of heroin is apparent by mere visual inspection of table 4.8. In the national sample,

147 (10.6 percent) of the 1,382 marijuana users had used heroin.

Of the 1,128 men who had not used marijuana, only 1 man (less than

0.1 percent) had used heroin. Examination of the percentage of heroin

users within each category of marijuana use reveals the

linearity of the relationship. The range is from 0.1 percent to 33.2

percent. The value of gamma is .876 and the value of Pearson's r

is .342.

A similar relationship is apparent in the Manhattan sample. Only 1

(1.4 percent) of the 74 men who had not used marijuana had used

heroin in comparison with 74 (33.6 percent) of the 220 marijuana

users who reported use of heroin. Again, the relationship is linear

across the categories of marijuana use. The range is from 1.4

percent among nonusers to 55.9 percent among the men who used marijuana 1000 or more times. The value of gamma is .634, and the

value of Pearson's r is .408.

The temporal order of onset of use is equally clear. Among the 147

men in the national sample who had used marijuana and heroin, 90

percent used marijuana first, 7 percent used heroin first, and the

time order was indeterminate for 3 percent. In the Manhattan sample,

76 of the men had used both marijuana and opiates (including heroin).

Of these 76 men, 79 percent used marijuana first, 13 percent used

the opiates first, and for 9 percent it was not possible to determine

the time order of onset. According to Hirschi and Selvin (1973),

the criterion of temporal order is satisfied if, in a clear majority

of the cases, the presumed cause is antecedent to the presumed effect.

The data presented in table 4.8 show that two of the three criteria

of causality outlined by Hirschi and Selvin (1973), association and

temporal order, are met with regard to the marijuana-heroin relationship. The third criterion, lack of spuriousness, is considerably

more difficult to establish (see O'Donnell and Clayton 1979, 1981).

62
TABLE 4.8 Lifetime Extent of Use of Marijuana and Heroin,

National and Manhattan Samples

NATIONAL SAMPLE

Extent of Marijuana Use

1-9 10-99 100-999 1000 or

Extent of Heroin Use Zero times times times more times TOTAL

Zero 1127 418 346 296 117 2362

1-9 times 1 4 6 22 39 72

10-99 times 0 0 2 14 25 42

100-999 times 0 0 4 4 9 17

1000 times or more 0 0 1 2 15 18

TOTAL 1128 423 356 338 265 2510

Percent Heroin Users 0.1 0.9 3.7 12.4 33.2 5.9

MANHATTAN SAMPLE

1-9 10-99 100-999 1000 or

Extent of Heroin Use Zero times times times more times TOTAL

Zero 73 3 5 43 38 30 219

1-9 times 0 2 1 5 8 16

10-99 times 0 1 3 7 4 15

100-999 times 1 3 4 3 9 20

1000 times or more 0 1 3 3 17 24

TOTAL 74 4 2 54 56 68 294

Percent Heroin Users 1.4 16.7 20.4 32.1 55.9 25.5

63
Conclusions

In this chapter the purpose has been to compare patterns of multiple

drug use in the national and Manhattan studies. This has been

accomplished by an exmaination of Guttman scale patterns, use of

pairs of drugs, order of onset of use of pairs of drugs and onset

patterns for use of up to six drugs, and by an examination of the

percent of men reporting use of other drugs within 5 categories of

the extent of use of tobacco, alcohol, and marijuana. It is clear

that: (1) use of any drug is strongly related. to use of other drugs,

and (2) extent of use of marijuana is a strong predictor of use of

other illicit drugs.

These two conclusions suggest a number of steps that should be taken

in subsequent analyses of these data. First, a composite index is

needed to measure multiple drug use, and an effort to construct a

logically consistent index is discussed in the next chapter.

Second, there is a need to identify and measure variables that might

intervene between the use of marijuana and use of other illicit

drugs. The sale of illicit drugs may be such an intervening variable, and an index of drug sales has been constructed and tested

(chapter 6). Third, the development and testing of causal models

is also needed. In Chapter 8 causal models are tested in which

the drug sales index and the composite index are used. Data from

the Manhattan study have already been used to develop and test

theoretical models designed to predict early marijuana use (O'Donnell

and Clayton 1979).

64
Chapter 5

A Composite Index of

Illicit Drug Use

In a recent comprehensive review of the literature, Kandel (1980)

suggests that researchers in the drug field should assign a high

priority to the development of a composite index of illicit drug

wuse. An index that reflects not just the use, but the extent or

frequency of use of various substances, has been needed for a long

time. Unfortunately, the conceptual and methodological problems

associated with constructing such an index have been formidable.

Lacking a logically consistent and empirically sound method of

combining all of a person's drug use into a single score, researchers

generally examine each drug class separately, analyzing patterns of

use within, rather than across, drug classes.

The purpose of this chapter is to describe the procedures used in

developing a composite index of illicit drug use and to illustrate

its utility by applying it to the Manhattan sample. Seven drugs

or drug classes are included in the index--marijuana, psychedelics,

stimulants, sedatives, heroin, other opiates, and cocaine. The

weights assigned according to the extent of use of each drug or

drug class were derived from analyses of data from seven different

sources:

(1) The national survey (Abelson et al. 1977) of drug use in the

general population sponsored by the National Institute on Drug

Abuse(NIDA);

(2) Admissions to treatment in federally funded drug programs that

are part of the Client Oriented Data Acquisition Process (CODAP

1977);

(3) Mentions of drug-related emergency room visits recorded by the

Drug Abuse Warning Network (DAWN 1977);

(4) Drug-related deaths according to information provided to DAWN

by medical examiners (1977);

(5) The evaluation of the national Supported Work study of samples

of ex-addicts, ex-offenders, AFDC mothers, and unemployed minority

youth (Mathematica Policy Research, Inc. 1978);

(6) Data fran the Drug Enforcement Administration (DEA 1978) regarding the prices paidbyagents inpurchasing illegal drugs to

obtain evidence;

65

354-188 0 - 81 - 6
(7) The nationwide survey of drug use among young men (O'Donnell

et al. 1976).

Actually, after the weights were derived for each drug class, several

alternative indices of illicit drug use were constructed for the

national sample of young men. Then, one of these indices, the

Lifetime Drug Use Index, was applied to the smaller parallel sample

of young men from Manhattan. Use of this independent sample overcomes a major limitation of earlier efforts to develop such an

index in which the data set from which weights are derived is

used to test the index.

PREVIOUS EFFORTS

Ordinal Scales

There have been several attempts to develop a composite drug use

index, but each of the available indices has only limited utility.

For example, in their longitudinal study of predominantly white,

middle-class students in the Boston suburbs, Smith and Fogg (1975)

created a simple ordinal scale. The categories and the percentage

of the sample included in each category were: (1) no illicit

drug use, 41 percent: (2) marijuana only, 40 percent; and (3) use

of one or more of the other illicit drugs, 19 percent. With similar

data from a large, nationally representative sample of high school

seniors, Johnston, O'Malley, and Eveland (1978) constructed an

ordinal scale consisting of five categories: (1) no use of illicit

drugs: (2) use of marijuana only; (3) experimental use of one, two

or three pills; (4) more than experimental use of one or two pills;

and (5) more than experimental use of three pills or any use of

heroin. Such simple classifications may be satisfactory for a

study of young persons who are not heavily involved in drug use.

However, a simple ordinal scale is inadequate if one's subjects

report use of a large number of drugs or diverse patterns of use.

Expert Judges

Another way to construct a composite drug index is to have a panel

of judges assign a weight to each drug class. Bucky and his coworkers (1974) asked twenty physicians to "rate the severity of the

major psychotropic drugs." A similar approach was used by Hoffman

and his colleagues (1975) who asked four drug experts to rate

eight drugs on a scale from 0 to 100 with respect to the "hazard of

each for users." The weights obtained in these two studies are:

Bucky et al. (1974) Hoffman et al. (1975)

marijuana 1.8 marijuana 35.0 methamphetamines 75.8

LSD 3.7 cigarettes 41.5 hallucinogens 83.8

amphetamines 4.7 alcohol 51.3 narcotics 91.3

barbiturates 6.5 stimulants 52.5

heroin 7.4 depressants 70.8

66
The obvious limitation of this approach is lack of reliability, as

the weights reflect the arbitrary judgment of the experts, and different panels of experts will produce different weights. For

example, LSD is ranked lower than amphetamines in the study by

Bucky and his co-wrokers, while hallucinogens are ranked much

higher than stimulants and somewhat higher than methamphetamines

in the study conducted by Hoffman and his colleagues. In Bucky's

study, the weight for barbiturates is almost four tines larger

than the weight for marijuana. In Hoffman's study, the weight for

depressants is only twice as large as the weight for marijuana.

Another problem with reliance on a panel of experts is that many

researchers would object to any set of weights suggested by experts.

Distribution of Use in a Sample

Recently, there have been several attempts to develop intervallevel, composite indices of drug use on the basis of the frequency

or extent of use of various drugs in a sample. Douglass and Khavari

(1978) obtained information on 19 drugs or drug classes. For each

class, a score of 0 to 7 was assigned to the responses of having

never used it, using none now, using it less than monthly, and so on

to using it several times a day. The responses were treated as

if they constituted an interval scale. The mean and standard

deviation were computed for each drug, and a normal score was computed

for each level of use. The difference between levels for each drug

is a constant, and it is used as a weight for that drug. For each

drug a person had used, the level was multiplied by the weight, and

the sum of these products formed the drug use index.

There are several problem with this approach. First, the scores for

levels are treated as if the intervals between them are equal, but

it is clear that they are not. For example, if one were to estimate

the number of times a drug was used in a month on the basis of the

following response categories--about once a month, about once a

week, several times a week, daily, several tines a day--the result

would be something like 1, 4, 10, 30, 75 or more, not the numbers 1

through 7 that were actually used. With a wider range of numbers,

the men and standard deviation, and therefore the drug weights,

would be quite different. Second, it is questionable whether the

standard deviation should be used to measure dispersion for skewed

data. Data on drug use are frequently skewed because a majority of

the respondents fall in the categories reflecting no use or infrequent use. With the exception of marijuana in studies of the general

population, relatively few subjects will have used a drug a sufficient number of times to be classified in the higher levels. The

writers of a standard text suggest: "As a measure of dispersion,

the standard deviation has meaning only insofar as the pattern of

variation is normal" (Mueller et a1.1977:169). A more serious

criticism of Douglass and Khavari's approach is that, even if the

compoite index ware satisfactory, it could only be used with the

single sample on which it was based. Therefore, the weights could

not be used in other samples.

67
Lu (1974) constructed his index by assigning weights to the categories of extent of use of each drug. These weights are not assigned

arbitrarily; rather, they are determined by the proportion of

cases in the total sample that are found in the various categories.

Essentially, the weights reflect the frequency or rarity of a given

level of drug use in a sample. In Lu's approach, a distinctive

weight is produced for each level of use of each drug. Although

Lu incorrectly states that the variance is always .083, the mean

for each drug class as well as the composite index is always .5.

Lu's composite index has been used in two large-scale studies. In

the national sample of young men, (O'Donnell et al.1976), weights

were assigned according to categories of lifetime extent of use for

three drugs (psychedelics, heroin, and cocaine) and to the categories of constructed typologies for five drugs (alcohol, marijuana,

stimulants, sedatives, and opiates). These weights are shown in

table 5.1. Pandina et al. (1981) decided to avoid evaluative labels

such as light, moderate, and heavy. They constructed the weights

for their sample on the basis of self-reported extent, frequency,

and recencyof use. The index weights for the three dimensions for

each drug (wine, beer, liquor, marijuana, amphetamines, barbiturates,

hallucinogens, cocaine, inhalants, opiates) were added and averaged

to produce the composite index score. Their weights for extent of

use are shown in table 5.2.

The Lu index has several advantages. First, the weights are based

on the distribution of responses for each drug, and they are normalized

to produce a mean score of .5. Second, the weights are lower for

drugs that are used most frequently and higher for drugs that are

rarely used. Third, for drugs that are widely used, such as alcohol and marijuana, the differences in the weights for different

levels of use appear to be reasonable (see tables 5.1 and 5.2).

On the other hand, the Lu index has serious deficiencies. A major

limitation of the Lu index involves the weights assigned to drugs

that are not widely used; the weights for minimum and maximum use

are quite similar. For example, in the O'Donnell study, use of

heroin less than 10 times was assigned a score of .955 in comparison

with a score of .996 for use of heroin 1,000 times or more (table

5.1). Comparable results were obtained in Pandina's study in which

use of opiates one or two times received a score of .961 and use

of opiates more than 50 times was scored .999. Similarly, it is

difficult to justify a weight of .471 for non-use of heroin;

this weight is almost as high as the one for heavy alcohol use, .509

(table 5.1). Further, construction of the Lu index entails extensive

computations. However, this effort does not appear to be justified

as a subject's score on the index is highly correlated with the

number of different drugs the person ever used. In the O'Donnell

study, the Pearsonian correlation between the Lu index and a simple

count of the number of drugs ever used is .971. Finally, the Lu

index can only be used with the single sample on which it is based

because the drug weights reflect the variations in use of each drug

68
TABLE 5.1 Lu Index Weights for Each Category of Extent of Use in the National Study of Young Men1
TABLE 5.2 Lu Index Weights for Extent of Use in the Study by

Pandina, White, and Yorke

Drugs

Percent

Never Number of Times Ever Used Ever

used 1-2 3-9 10-19 20-50 50+ used

Wine .052 .210 .409 .588 .758 .921 89.6

Beer .059 .212 .379 .506 .641 .858 88.2

Liquor .082 .277 .477 .640 .795 .936 83.4

Marijuana .255 .548 .622 .687 .752 .824 49.0

Amphetamines .381 .800 .871 .923 .955 .985 23.8

Barbiturates .388 .820 .897 .945 .972 .993 22.4

Hallucinogens .408 .856 .929 .967 .985 .996 18.4

Cocaine .448 .925 .968 .988 .996 .999 10.4

Inhalants .451 .929 .970 .987 .993 .998 9.8

Opiates .471 .961 .986 .994 .997 .999 5.8

Source: Adapted from Robert J. Pandina, Helene Raskin White and

Joseph Yorke. Estimation of substance use involvement: Theoretical

considerations and empirical findings. The International Journal

of the Addictions, 16(1):1-24, 1981. © 1981, Marcel Dekker.

70
in that sample; therefore, the weights cannot be used in other samples.

For example, if the Lu index was used in a sample of long-term heroin

addicts, heroin would have one of the lowest weights, and it would

contribute the least to the total index. Nor would the unique weight

for heroin that was derived from a sample of addicts be useful in a

study involving a sample of high school or college students.

In effect, previous efforts to develop a composite index of drug use

have been largely unsuccessful. In some cases simple ordinal scales

have been used to represent the complex reality of multiple drug use.

In other instances, the opinions of experts about the relative danger

associated with use of various drug classes have served as the source

of weights for the drugs. Finally, several researchers have developed

seemingly complex computational schemes to derive weights for each

level of use of various drugs and for combining these weights into a

single score. As noted above, these efforts have been inadequate

because the procedures utilized to derive weights are too elementary,

based on unreliable judgments, or mathematically or substantively

flawed.

RATIONALE AND CRITERIA

In each of the efforts to develop a composite index, it may be inferred

that the researchers accepted the assumptions that (1) the different

drugs or drug classes should be assigned different weights, and

(2) within each drug or drug class, the weights should vary according to some common dimension such as the frequency or extent of use.

Another appropriate assumption is that use of heroin is more

serious than use of marijuana, and use of other illicit drugs,

such as sedatives or psychedelics, falls somewhere between these

two boundary drugs. Regardless of how seriousness is defined,

this assumption is consistent with the perceptions of researchers,

policy makers, and the general public regarding the social costs

of drug use at both the individual and societal levels. At the

individual level, costs include such things as effects on health,

including the possibility of death; effects on employment and job

performance; and effects on freedom, including the possibility of

arrest, conviction, and confinement in a correctional institution.

In each of these areas--health, employment, and contacts with criminal

justice agencies--negative effects may occur. The latter area

deserves an additional comment. Although the research to date has

not firmly established the causal order, there are available an

impressive number of studies that clearly demonstrate that drug use

and crime are highly correlated (Research Triangle Institute 1976;

Inciardi 1979; Ball et al. 1980, 1981; Gandossy et al. 1980). At

the societal level, costs include such things as expenditures for

medical care that may be attributed to drug use; direct economic

costs stemming from absenteeism, lowered productivity, and jobrelated accidents; and the processing, service delivery, and systemic

costs in the drug treatment and criminal justice arenas. The provision

of treatment services for drug users involves the expenditure of

millions of dollars annually. While the presumed ill effects on

drug users which caused them to seek or to be forced into treatment

71
are difficult to measure quantitatively, it is inherently easier

to calculate these costs at the individual level than at the societal

level. This is the case because cost-units are more manageable,

and consequences or effects can be traced more directly to specific

drugs or drug-using episodes. At the societal level, it would be

difficult, if not impossible, to estimate the amount of money

expended annually in response to illicit drug use, and it would

Clearly be impossible to allocate these expenditures to the various

drug classes.

Consequently, one criterion employed in the construction of a composite index of drug use was that the index should, at least in a

general way, reflect the different social costs involved in use of

the various drug classes. In addition, a composite index of drug

use should be comprehensive in that it must include the major

classes, and it must be sensitive to the dimensions that differentiate

patterns of use of these major classes of drugs. Some of the

relevant dimensions are quantity, frequency, dosage, route of

administration, price, and context of use. A composite index should

also be composed of weights derived objectively, rather than by

arbitrary judgment. Finally, a composite index should be developed

on the basis of more than a single sample, and it should be easy to

construct and utilize.

DERIVATION OF WEIGHTS

An important criterion for the development of a composite index of

drug use is comprehensiveness. Therefore, a decision was made to

focus on seven drugs or drug classes, including marijuana, psychedelics, stimulants, sedatives, heroin, other opiates, and cocaine.

In the development of weights for each drug or drug class, seven

data sets were used.

1. NIDA National Survey, 1977. A key ingredient in the construction

of a composite drug use index is an estimate of how many persons

have used each of the drug classes. The rates of use in table 5.3

constitute the denominators for each drug class and are drawn from

the NIDA National Survey (Abelson et al. 1977).

2. CODAP Admissions, 1977. Included. in CODAP are data on admissions and discharges, as well as other characteristics of all clients

in federally funded drug treatment programs. In 1977, there were

approximately 1,600 clinics in the CODAP system. The clients in

these clinics represented approximately one-half of all persons in

treatment for drug abuse in the United States. Technically, CODAP

does not provide random sample of all patients in treatment

for drug abuse, but it certainly is the largest set of data available

regarding persons in treatment programs. In practical terms, it

is clearly the most important data set for the derivation of weights

for a composite index of drug use, because treatment is a major cost

of drug use, both at the individual and the societal levels.

72
TABLE 5.3 Estimated Number of Users of Drugs

Percentage Who Used in the Past Year Users,

Age 12-17 Age 18+ to nearest thousand1

Marijuana 21.8 12.8 24,357,OOO

Psychedelics 3.1 1.5 2,990,000

Stimulants 3.7 2.7 4,914,000

Sedatives 2.0 2.0 3,455,000

Heroin 0.6 0.42 741,000

Opiates 3.4 1.2 2,622,000

Cocaine 2.6 2.8 4,787,000

1

These estimates are to the nearest thousand. The estimated populations represented by the sample segments are 24,938,OOO for youth,

between 12 and 17 years old, and 147,819,000 for persons 18 and

older.

Source: Percentages are derived from tables 8 and 9 in Abelson et

al. (1977).

73
While the CODAP system permits the recording of as many as three

drugs, only the primary drug of abuse at admission is used in the

construction of a composite drug use index. Shown in the first

column in table 5.4 is the estimated number of admissions to CODAP

in 1977 by the primary drug of abuse at admission. Estimates of

the number of users of the seven drugs were derived from the NIDA

National Survey: these figures are shown in the second column in

table 5.4. Shown in column 3 is the ratio of CODAP admissions to

the total number of users in the population. To reduce these

ratios to a common scale, each ratio was divided by the highest

ratio. These figures are shown in the column labeled "relative

scale." In this scale, heroin has a value of 1.0, and for each of

the other drug classes, the relative scale value is between zero

and 1.0. In the last column in table 5.4, each drug class is ranked

on the basis of the relative scale.

3. DAWN Emergency Room Mentions. DAWN is a reporting system, cosponsored by the Drug Enforcement Administration and the National

Institute on Drug Abuse, that is designed to gather data on drug

abuse from selected locations within the United States. It serves

to identify drugs that are currently being abused, changing patterns

of abuse, relative hazards to health and the relative abuse potential

of drugs, and data that are used for the control and scheduling

of old and new drugs. For the period of May 1976 to April 1977,

the DAWN system obtained reports from 456 emergency rooms in 21

Standard Metropolitan Statistical Areas, as well as 147 emergency

rooms in Chicago, Los Angeles, and New York, and 193 in other cities.

Each visit to an emergency room which involved any drug is reported,

and as many as five drugs per contact are counted as a mention. It

is not known whether the drug mentioned is the cause of the emergency room visit, but for the purpose of constructing an index, it

will be assumed that each drug played some part in the visit. The

procedures followed earlier with the CODAP data were repeated, and

the results for the emergency room data obtained through DAWN are

shown in table 5.5.

4. DAWN Medical Examiner Mentions. The Medical Examiner Mentions

are drawn from the same locations as the DAWN Emergency Room Mentions. In these areas, the county medical examiners and coroners

report all drug-involved deaths; in the remainder, the drug may be

a contributing factor, but not the only factor, in causing death.

in table 5.6, the data for Medical Examiner Mentions are presented;

the raw data were manipulated according to the procedures used

earlierwiththe CODAP and DAWN Emergency Room data.

5. Young Men and Drugs: A Nationwide Survey. Three types of

variables were constructed with the national data to assess social

costs. First, official criminal record was defined as an ordinal

variable with four categories ranging from never arrested to served

time. These categories were assigned scores from 0 to 3. Second,

self-reported criminal involvement was defined on the basis of

responses to questions about 12 separate offenses; on this measure,

74
TABLE 5.4 1977 CODAP Admissions by Primary Drug of Abuse

Users Relative

Admissions (Thousands) Patio Scale Rank

Marijuana1

13,389 24,357 .000550 .00521 6

Psychedelics2 5,228 2,990 .001748 .01655 4

Stimulants1 7,544 4,914 .001535 .01453 5

Sedatives3 9,813 3,455 .002840 .02689 2

Heroin4 78,259 741 .105613 1.00000 1

Opiates4

4,999 2,622 .001907 .01806 3

Cocaine1 2,398 4,787 .000501 .00474 7

1

The figure for admissions is the sum of the figures shown in each

table 2 of the Quarterly Reports. The CODAP figure for "Amphetamines" is used for the "Stimulants."

2

No exact figure is given, but in table 5.1 the percentage for

"Hallucinogens" is reported. This percentage was applied to total

admissions to obtain the estimate for "Psychedelics."

3

An exact figure for barbiturates is given in table 5.2. In table

5.1 a percentage for "Other Sedatives or Hypnotics" is provided.

These figures were combined to produce the estimate for "Sedatives."

4

In table 5.2 a figure for all opiates is given. In table 5.1

percentages for "Heroin" and "Other Opiates" are shown. The figure

for opiates was divided between heroin and opiates proportionately

according to the percentage for heroin and opiates.

75
TABLE 5.5 Emergency Room Mentions of Drugs in DAWN System,

May 1976-April 1977

Users Relative

Mentions (Thousands) Ratio Scale Rank

Marijuana 3,602 24,357 .000148 .00860 7

Psychedelics 3,500 2,990 .001171 .06804 5

Stimulants18,012 4,914 .001630 .09471 3

Sedatives 27,982 3,455 .008099 .47057 2

Heroin2

12,753 741 .017211 1.00000 1

Other Opiates 6,633 2,622 .002530 .14700 3

Cocaine 1,420 4,787 .000297 .01726 6

1

This iS the sum of TC 11 (Psychostimulants) and TC 12A (Amphetamines). TC 13 (Antiobesity Preparations) are not separately

listed and are not included.

2

Heroin is metabolized to morphine; consequently it may be the

metabolite that is detected. DAWN reports "Heroin/Morphine"

as one drug, and some of the instances of use undoubtedly were

morphine. It seems safe to assume that the vast majority of

mentions represent heroin use, and all of them are counted as

heroin.

Source: From table 2.1, DAWN Phase V Report, page 18.

76
TABLE 5.6 Medical Examiner Mentions of Drugs in DAWN System,

May 1976-April 1977

Rate per Relative

Mentions (Thousands) million Scale Rank

Marijuana 4 24,357 0.164 .000096 7

Psychedelics 33 2,990 11.037 .006481 6

Stimulants1455 4,914 92.598 .054367 4

Sedatives 1,693 3,455 490.014 .287718 2

Heroin22,262 741 1703.104 1.000000 1

Other Opiates 456 2,622 173.913 .102115 3

Cocaine 5 4 4,787 11.281 .006624 5

1See fn. 1, table 5.5.

2

See fn. 2, table 5.5

Source: Fromn table 4.1, DAWN Phase V Report, page 50.

7 7
scores range from 0 to 34.1

 Third, unemployment was defined in terms

of the number of months the respondent was not employed since his

last job, or since attaining the age of 18, to the date of the

interview. Any period in which a respondent was a student was

excluded. In regression analyses these three measures of drug use

were used for each of the seven drug classes: (a) number of years

in which a drug was used; (b) maximum frequency of use in any given

year; and (c) an ordinal measure of extent of lifetime use in which

the categories were: none, under 10 times, 10-99 times, 100-999

times, and 1,000 times or more. The three sets of regression

coefficients for each dependent variable were combined as simple

averages, as shown in tables 5.7 and 5.8, to provide a single scale

for the data derived from this survey. Essentially the same

procedures as those employed earlier with the CODAP and DAWN data

sets were used.

6. Illicit Drug Prices. Illicit drug prices are clearly relevant

for a composite index. At a minimum, they represent cost to the

user. Higher prices presumably create more pressure to commit

crimes to obtain money to purchase drugs. Higher prices may also

produce a temptation for sellers to adulterate drugs, and in doing

so they may introduce substances that present health hazards.

Price also has weaknesses as an indicator, particularly for those

drugs that can be purchased legally. In table 5.9, data obtained

from the Drug Enforcement Administration concerning illicit drug

prices are presented. There is considerable error inthesedata

because they are based largely on purchases made by agents to obtain

evidence. They do not provide an adequate sampling of prices

nationwide or for any specific location. Yet, even allowance for

gross errors could not eliminate the differences in price per gram

shown in table 5.9.

7. Supported Work Study. In this nationwide study, large samples

of ex-addicts, ex-offenders, unemployed youth, and AFDC women were

randomly assigned to a Supported Work program or to a control

group. Baseline data were obtained from persons in both groups,

and they were reinterviewed after 9 months, 18 months, 27 months,

and 36 months. Data on arrests ware regressed against the seven

drug variables for a sample in which ex-addicts and ex-offenders

were combined, with sex, race, age, educational attainment, and

marital status statistically controlled. The regression coefficients,

relative scale, and rank are shown in table 5.10.

CONSTRUCTION OF A COMPOSITE INDEX

The figures in table 5.11 are the relative scale values shown in

tables 5.4 through 5.10. The relative scale values for each drug

class were averaged across the various data sets to produce an

overall average value, and the drug classes were then ranked according to the overall mean values. A weighting system that reflects

this relative scale is shown in the last column in table 5.11.

Marijuana receives a might of 1, and the other drugs have proportionately larger weights. The maximum weight, 24, is for heroin.

78
TABLE 5.7 Regression Coefficients for Three Drug Indices and Three

Dependent Variables: The Young Men and Drugs

Nationwide Sample

Years of Maximum Lifetime Relative

Use Frequency Use Mean Scale Rank

Marijuana .268

Psychedelics .000

Stimulants .000

Sedatives .726

Heroin 2.316

Opiates .000

Cocaine .000

A. Months Unemployed

.424 .411 .368

.000 .000 .000

.000 .000 .000

1.420 .750 .965

2.220 3.165 2.567

.000 .000 .000

1.911 1.958 1.290

B. Official Criminal Record

.143 4

.OOO 6

.OOO 6

.376 3

1.000 1

.000 6

.503 2

Marijuana .045 .094 .088 .076 .633 3

Psychedelics .033 .066 .067 .055 .458 4

Stimulants .037 .050 .000 .029 .242 5

Sedatives .000 .000 .000 .000 .000 6.5

Heroin .129 .096 .135 .120 1.000 1

Opiates .000 .000 .000 .000 .OOO 6.5

Cocaine .000 .130 .107 .079 .658 2

Marijuana

Psychedelics

Stimulants

Sedatives

Heroin

Opiates

Cocaine

.262

1.023

.499

.274

1.242

.150

.000

C. Self-Reported Crime

.806 .493 .520

1.402 1.047 1.157

.939 0.112 .850

.792 .940 .669

1.292 1.631 1.388

.474 .634 .419

1.106 1.013 .706

.375 6

.834 2

.612 3

.482 5

1.000 1

.302 7

.509 4

79
TABLE 5.8 Combination of the Relative Scale Values for the Young Men and Drugs Nationwide Sample
TABLE 5.9 Retail Price of Illicit Drugs

1

Estimates of dosage units were obtained from pharmacologists and pharmacists. These refer to

medical usage and may be underestimates of dosage among drug abusers. The estimate for barbiturates is, in particular, dubious, as different preparations range from 15 to nearly 100

mg. LSD is manufactured illicitly and what is sold may not be LSD. Dosage cannot be

ascertained.

2

Since almost all studies group marijuana and hashish users, a single price is desirable. The

estimate in the Price/gm column was obtained by weighting the marijuana price by 1372, the

hashish price by 719. These are the proportions of users of these drugs found in the survey

of young men.

3

As methamphetamine and amphetamine are both included in the class of stimulants, a single

estimate is desirable. Methamphetamine was given a weight of 245, amphetamine was given a

weight of 361. These are the frequencies with which these drugs were reported in the survey

of young men.

L.S.D. is not included in the scale, because its cost does not represent the cost of all

psychedelics. However, this argument does not hold for marijuana, stimulants, sedatives

and heroin, and they are given a relative scale value.

Source: Drug Enforcement Statistical Report: Statistics Compiled Through March 1978, pp.33, 35.
TABLE 5.10 Regression Coefficients for Drugs Based on

Number of Arrests from the

Supported Work National Sample

Coefficient

Relative

Scale Rank

Marijuana .00 .00 6.5

Psychedelics .00 .00 6.5

Stimulants 1.36 .76 3

Sedatives .49 .27 5

Heroin 1.79 1.00 1

Opiates .54 .30 4

Cocaine 1.56 .87 2

Source: Personal communication from Mathematica Policy Research,

Inc., 1978.

82
TABLE 5.11 Combination of the Relative Scales Into an Index
This implies that use of heroin is 24 times more serious than use of

marijuana.

A question that deserves attention at this point is: How consistent

is the ranking of the various drug classes across the six data sets?

As may be seen in Section A, table 5.12, the ranks of the seven drugs

on the basis of the data derived from CODAP,the two DAWN sources,

the survey of youn men, and the Supported Work study are consistent.

Kendal's Coefficient of Concordance, W, a measure of the association

among k samples of ranks, is .619, which is significant at the .01

level. There is, then, a significant degree of agreement across the

five data sets regarding the relative rank of the seven drug classes.

The Illicit Price Index, which was derived from DEA data, includes

only five drugs. Comparison of these drugs, ranked according to

price, with ranks fran the other data sets produces a value of W of

.645, which is significant at the .05 level.

While there is statistically significant concordance among the ranks

for the drugs in the various data sets, it is also clear that there

are some differences. The ranks for the three health-related data

sets--CODAP, DAWN Emergency Room Mentions, and DAWN Medical Examiner

Mentions--closely resemble each other and are somewhat different from

the ranks obtained from the national survey of young men and the

data from the Supported Work project. The latter two sets of data

included measures of employment and criminality, not health. Cocaine

provides a good illustration. In the Young Men and Supported Work

samples, cocaine ranks second. This is also the case for the Illicit

Price Index. This suggests the possibility of an association between

use of cocaine and criminal behavior, perhaps, in part, as a result

of the cost of the drug. In contrast, cocaine is ranked seventh in

the CODAP data and fifth in the DAWN Emergency Room Mentions. This

implies that at current levels and patterns of use, insufficient

numbers of users of cocaine have sought treatment or emergency room

assistance to give cocaine a higher rank in the CODAP and DAWN data

sets. The differences in the ranking of the drugs, as illustrated

by cocaine, suggest that these data sets may be tapping at least

two distinct aspects of the social costs of drug use, the healthrelated costs and the costs associated with criminality or unemployment.

OTHER POSSIBILE INDICATORS OF SOCIAL COST

In the derivation of drug weights, two other potential indicators

were considered, but rejected. The five drug schedules established

by Congress were considered as possible indicators of social cost.

According to Federal law, drugs in Schedule I and II have a high

potential for abuse. Drugs in schedule I have no currently accepted

medical usage in the United States, but the ones in Schedule II do.

The other three schedules reflect decreasing potential for abuse.

The inclusion of heroin, marijuana, LSD, mescaline, peyote, and

psilocybin in Schedule I reduces the utility of the schedules, but

for purposes of constructing an index, one might argue that their

84
TABLE 5.12 Concordance of Rankings on Scales Used in the Index

E.R. M.E. Young Supported

CODAP DAWN DAWN Men Work

Marijuana

Psychedelics

Stimulants

Sedatives

Heroin

Opiates

Cocaine

Marijuana

Stimulants

Sedatives

Heroin

Cocaine

A. Rankings of All Seven Drugs

6 7 7 5 6.5

4 5 6 4 6.5

5 4 4 6 3

2 2 2 3 5

1 1 1 1 1

3 3 3 7 4

7 6 5 2 2

W = .619 P .01

B. Comparison With Illicit Price Scale

E.R. M.E. Young Supported Illicit

CODAP DAWN DAWN Men Work Price

4 5 4 4 5 5

3 3 5 5 3 3

2 2 3 3 4 4

1 1 1 1 1 1

5 4 2 2 2 2

W = .645 P .05

85
inclusion in the same schedule reflects the judgment of Congress

that these drugs are, in some way, equivalent. However, the drug

schedules present a practical problem in that drugs in the same

pharmacological class are often found in several schedules. For

example, various opiates are found in all five schedules. Consequently,

the schedules cannot be used unless drugs are treated singly, rather

than by class.

Another potential indicator was the penalty structure for illegal

possession and sale of drugs. Levine (1974) provides a compilation

of the state laws, as of 1970. At best, only four classes of drugs

can be distinguished in most states, and these classes do not coincide with pharmacological classes. Sedatives and stimulants are

often classified as "dangerous drugs." In all of the states except

Wisconsin, cocaine is classified as a narcotic; in Wisconsin it is

classified as a dangerous drug. Marijuana is usually classified as

a narcotic, but in some states it is treated as a hallucinogen. A

few states provide a separate classification for marijuana for

purposes of penalties. Many states, but not all of them, distinguish

among possession, possession with intent to sell, and the sale of

drugs. Many states also specify different sentences for first,

second, and subsequent offenses for each of these categories. Decause of the differences in the classificatory schemes, it is difficult to make state-by-state comparisons. In those instances in which

specific comparisons can be made, it is apparent that the penalties

for the same offense differ widely from state to state. For example,

for a first offense involving possession of marijuana, the penalties

as of 1970 ranged from 7 days in Nebraska to 1-10 years in Arizona,

and 2 years to life in Texas. In view of the wide variety of penalties

attached to the same offense, any average that might be constructed

would not reflect the actual penalties used in the 50 states. Consequently, the legal penalties for drug violations are not useful as

indicators of social cost.

FIVE ALTERNATIVE INDICES OF ILLICIT DRUG USE

While a number of weighting schemes may be intuitively appealing,

the goal was to create a composite index of illicit drug use that is

comprehensive, sensitive to differences between drug classes, yet

sensitive as well to patterns of use within drug classes, objectively

and empirically sound, easy to construct and use, and able to discriminate between groups known to differ on illicit drug use. On

the basis of the extensive data available for the national sample of

young men, five alternative indices of drug use were constructed.

These indices vary primarily in terms of the complexity of the

information utilized in their construction.

1. Illicit Drug Use Index. In the national study of young men, the

respondents provided information about their lifetime use of cigarettes,

alcohol, marijuana, psychedelics, stimulants, sedatives, heroin;

other opiates, and cocaine. Specifically, a respondent was asked to

indicate the month and year in which he first used each drug, as well

86
as his most recent use by month and year He was

then asked to indicate whether he had used the drug in the intervening years and the extent to which he used it. The interval

covered was from 1956, when the oldest men in the study would

have been 12 years old, to 1975, when the last interviews were

conducted. For each year in which some use was reported, the respondent was asked how frequently he used the drug. The response

categories were (1) less than once a month; (2) about once or twice

a month; (3) about once or twice a week; and (4) almost every day

or daily.

While these are not precise counts of the number of times a drug

was used, it is apparent that to assign the numbers 0 to 4 to the

responses would not adequately reflect the interval implicit in

them. For convenience, weights are restricted to integers. A

value of 1 is assigned to less than once a month, and 2 is appropriate for once or twice a month. Once or twice a week is assigned

a value of 9, and almost every day, which often meant daily, is

given a value of 40. At this point, then, two sets of weights are

available:

Drugs Frequency

Marijuana 1 Less than once a month 1

Psychedelics 2 Once or twice a month 2

Opiates 3 Once or twice a week 9

Stimulants 5 Almost every day 40

Sedatives 6

Cocaine 7

Heroin 24

For each year that a drug was used, the weight assigned to the drug

was multiplied by the weight for frequency of use. The sum of

these products for as many as 20 years and for as many as seven

drugs constitutes the Illicit Drug Use Index. The scores could

range from a low of zero to a maximum of 38,400. It was anticipated

that the men score for this index would not be high for a sample

drawn from the general population because the drugs with high weights

are not widely used.

The Illicit Drug Use Index was constructed to utilize the maximum

amount of the data available in the national study, and it includes

data on the frequency of use of seven classes of drugs over a

period covering twenty years. It is unlikely that such detail

would be obtained in most studies, particularly those in which

data on drug use are obtained by means of self-administered questionnaires. The Illicit Drug Use Index is the most comprehensive

measure that could be constructed using the weights derived earlier,

and it may be the most sensitive to variations in patterns of illicit

drug use.

2. Extent of Drug Use Index. In a screening question, the men were

asked to indicate how many times they had used each of the illicit

87
drug classes. The responses were: never; less than 10 times;

10-99 times; 100-999 times; and 1,000 times or more. In constructionof the Extent of Drug Use Index, these responses were assigned

weights of 0 to 4 and ware summed across the seven drug classes.

The possible range on this measure is 0 to 28.

3. Lifetime Drug Use Index. In this index, the sane response

categories are used as in the Extent of Drug Use Index, but the

numbers that are assigned reflect the probable number of times the

drug was used. The numbers are not precise because the response

categories lack precision, but they are more accurate than the

simple weights of 0 to 4 employed in the Extent of Drug Use Index.

The weights are based on assumptions about the average frequencies

each response probably reflects.

Assumed Mean Number

Interview Response of Times for Response Weight

Never Used 0 0

Less than 10 times 5 1

Under 100 times 40 8

Under 1,000 times 200 40

1,000 times or more 600 120

For the response of less than 10 tines, it was assumed that some

men may have minimized their use, while other men may have only used

the drug once or twice. Therefore, the average was set slightly

above the midpoint of the interval. For the other responses, the

average frequencies are assumed to be below the midpoints, and. as

the amount of use reported increases, a lower mean was selected.

on the assumption that the responses might reflect sane exaggeration.

The weights were derived by division of the assumed mean by 5,

the lowest mean other than zero. The Lifetime Drug Use Index, then,

is the sum of seven products; each weight for extent of use was

multiplied by the weights for the seven drug classes. The range

for this measure is 0 to 5,760.

4. Number of Drugs Used Index. The final measure constructed for

the national sample is a simple count of the number of illicit drug

classes which the person reported ever using, and the range is from

0 to 7.

5. Total Drug Use Index (Lu Weights). With the exception of alcohol, the weiqhts found in table 5.1 were applied to the national

sample of young men to obtain scores for the Total Drug Use Index.

This index reflects the weighting scheme devised by Lu (1974).

INTERCORRELATIONS AMONG THE FIVE INDICES

The question as to which of these five measures of drug use is

preferable may be answered by examining the intercorrelations among

them and by determining how sensitive the indices are to differences

between groups known to differ in terms of their drug use. The

88
means, standard deviations, and intercorrelations among the five

indices are shown in table 5.13. It is apparent that all of the

indices are highly skewed measures of drug use. The standard

deviations are considerably larger than the means. In fact, for

the Illicit Drug Use Index, the standard deviation is almost four

and one-half times larger than the mean. For each index, the mean

is near the zero end of the scale. However, this is to be expected

because only a minority of the young men in the national sample

used any of the seven drugs, other than marijuana, and only a part

of that minority used any of the drugs extensively. Therefore, the

scores are necessarily clustered at the low end of each index, and

this would be the case for any other index one might construct. A

transformation could be used, but this does not appear to be essential or desirable.

All of the correlations in table 5.13 are significantly different

fran zero at the .001 level. However, this is partially due to the

number of cases, 2,510. All of the indices meet the criteria of

comprehensiveness and objectivity. Because it is based on the most

detailed information, the Illicit Drug Use Index may be most sensitive to other important dimensions of drug use such as number of

years of use. On the other hand, this index may be of limited.

utility in many surveys because it requires answers to a large

number of specific questions. If the Illicit Drug Use Index is

used as the benchmark because of its comprehensiveness, then the

Lifetime Drug Use Index would be ranked second on the basis of its

correlation of .767 with the Illicit Drug Use Index. If the

requisite detailed data more available, it would not be reasonable

to use the less adequate measure. Yet, in terms of ease of obtaining

the required data, the Lifetime Drug Use Index is preferable to the

Illicit Drug Use Index because it is based on responses to a single

question about the number of times the person had used each drug.

The item used to construct this measure, or a modification of it,

has been asked in numerous surveys (see Elinson and Nurco 1975, for

examples and recommendations; see Nehemkis et al. 1976, for various

operationalizations of drug use).

Two indices, Extent of Drug Use Number of Drugs Used, are highly

correlated with each other (r = .940), but have relatively low

correlations with the Illicit Drug Use Index (r = .560 and .448,

respectively) and the Lifetime Drug Use Index (r = .687 and .506,

respectively). The significance of these relatively low correlations is apparent when one notes the high correlation of the Lu

index with the Extent of Drug Use and Number of Drugs Used. Indices

(r = .970 and .947, respectively). As one might expect, the Lu

index is not strongly correlated with either the Illicit Drug Use

Index (r = .452) or the Lifetime Drug Use Index (r = .530). It is

apparent that the weighting scheme used in developing the Illicit

Drug Use Index and the Lifetime Drug Use Index yields measures of

drug use that are superior to: (1) a count of the number of drugs

used: (2) an ordinal scale based on the extent of use; and (3) the

Lu index.

89
TABLE 5.13 Correlations, Means, and Standard Deviations for the Five

Alternative Indices of Illicit Drug Use
APPLICATION OF THE LIFETIME DRUG USE INDEX TO THE MANHATTAN SAMPLE

To test the Lifetime Drug Use Index with the data obtain in the

national sample of young men is problematic because the weights

are derived, in part, from regression analyses performed on the

data obtained from that sample. A satisfactory test of the utility

and differentiating power of the index requires application of

the index to a sample that was not used in the derivation of the

weights used in the index. Therefore, a Lifetime Drug Use Index

was constructed for the Manhattan sample. This measure was chosen

because data regarding use of all of the drug classes on a yearby-year basis were not obtained in the Manhattan sample. In

construction of the index, the drug weights (marijuana = 1,

psychedelics = 2, opiates = 3, stimulants = 5, sedatives = 6,

cocaine = 7, heroin = 24) were multiplied by the weights for the

extent of lifetime use (never used = 0, used less than 10 times = 1,

used less than 100 times = 8, used less than 1,000 times = 40,

used 1,000 times or more = 120) and summed.

DIFFERENTIATING POWER OF THE LIFETIME DRUG USE INDEX

An analysis of variance was completed in which race and age group

were the independent variables and the Lifetime Drug Use Index

was the dependent variable. The data in table 5.14 indicate that

there are statistically significant differences in illicit drug

use by race in the Manhattan sample. The average score on the

Lifetime Drug Use Index is 737.21 for blacks, in comparison with

339.80 for others and 223.20 for whites. These differences are,

at least in part, a reflection of the ecology of drug use in Manhattan. Among the areas from which the respondents were chosen

are Central Harlem and East Harlem, and residents of these areas

have high levels of opiate use, according to data in the Narcotics

Registry. The Registry data were used to classify the health

areas in Manhattan in terms of rates of opiates use. The goal

was to choose respondents who had spent all or most of their

formative years in high drug use areas. Each respondent provided

an address for each house or apartment in which he had lived from

birth to the age of 18, and these were coded by health areas. In

the overwhelming majority of cases, the health areas in which these

men grew up were high in drug abuse in comparison with the rest

of Manhattan and New York City, but there are significant and

measurable differences among the health areas in Manhattan that

are classified as high drug use areas. The men in the Manhattan

study reflect these differences. The rates of opiate use within

the health areas with high rates were classified on a three-point

continuum of high, higher, and highest. For example, a higher

proportion of the blacks, in comparison with the whites and others,

had spent all or most of their formative years residing in areas

known to have the very highest rates of opiate use. These are

ecological areas that are saturated with drugs and drug abusers.

In contrast, the whites in the Manhattan sample were clearly

clustered at the lower end of the continuum, while the others were

concentrated in the middle category (Ritter 1981).

91
TABLE 5.14 Analysis of Variance: Lifetime Drug Use Index by

Race and Age Group for the Manhattan Sample

Lifetime Drug Use Index

Independent Standard

Variables n Mean Deviation

TOTAL (294) 469.90 1036.77

BLACK (125) 737.21 1188.43

1944-46 22 594.14 1183.20

1947-49 31 942.55 1169.91

1950-52 37 733.30 1239.15

1953-54 35 649.40 1180.33

WHITE (98) 233.20 789.46

1944-46 24 520.00 1512.68

1947-49 32 90.78 175.36

1950-52 22 216.82 410.86

1953-54 20 85.95 120.30

OTHER (71) 339.80 950.61

1944-46 16 519.75 1092.19

1947-49 16 761.25 1551.91

1950-52 22 100.50 348.75

1953-54 17 83.47 243.16

F for Race 8.636 p. .001

F for Age Group 1.185 p. .316

F for Race-Age Group Interaction 1.173 p. .321

92
Although there were no statistically significant differences by

age group, some of the differences in terms of age group by race

are worthy of note (table 5.14). The mean scores on the Lifetime

Drug Use Index were highest in the 1947-1949 age group for blacks

and others, whereas the highest mean value on this index for

whites occurred in the oldest (1944-1946) age group. The 1953-1954

age group had the lowest mean values on the Lifetime Drug Use

Index for whites and others in comparison with blacks, among whom

the lowest mean values on this measure occurred in the oldest age

group (1944-1946).

In another analysis of variance (table 5.15) in which race and

educational attainment served as the independent variables,

statistically significant differences were observed for both of

these variables. The relationship between education and illicit

drug use is clearly linear for blacks and whites. In these groups,

the men with less than a high school education have the highest

scores. Among the others, who are primarily Puerto Rican, college

graduates have the lowest scores on the Lifetime Drug Use Index,

while the men with some college education have the highest scores.

In addition to race, age, and educational attaimment, there are

numerous variables that could be used to test the sensitivity of

the Lifetime Drug Use Index with respect to group differences.

For illustrative purposes, the results of only two of these

tests will be presented--behavior exhibited by friends of the

respondent when he was 13 years old and self-reported involvement

in 13 criminal offenses.

A key assumption in the drug field is that an individual's use of

drugs often reflects the forms of behavior, drug related and nondrug related, exhibited by his or her friends. In a self-administered

questionnaire the Manhattan respondents were asked: Think about

your friends, the people you spent time with and did things with.

when you were 13, and them when you were 16, were at least some of

your friends: (a) sometimes in trouble at school; (b) sometimes

in trouble with the police; (c) arguing a lot with their parents;

(d) drinking beer, wine, or liquor at times; (e) smoking marijuana;

(f) using other drugs; and (g) people your parents didn't approve

of?" To insure that this perceived peer behavior was temporally

antecedent to illicit drug use for the bulk of the sample, only

the responses for age 13 are included in table 5.16.

As expected, the average scores on the Lifetime Drug Use Index

were higher for those respondents who reported at least some of

their friends at age 13 were doing the things listed in table 5.16.

While this was true for all seven items, differences between the

average scores were not statistically significant for three of

the items (arguing with parents; drinking beer, wine, or liquor;

and people your parents didn't approve of). It is interesting that

the highest scores on the Lifetime Drug Use Index occur for those

men who, when they ware 13 years old, had at least some friends

93
TABLE 5.15 Analysis of Variance: Lifetime Drug Use Index by

Race and Educational Attainment of the Manhattan Sample

Independent

Variables

Lifetime Drug Use Index

Standard

n Mean Deviation

TOTAL (294) 469.90 1036.77

BLACK

Less than high

school

High school

graduate

Some college

College graduate

WHITE

Less than high

school

High school

graduate

Some college

College graduate

(125) 737.21 1188.43

40 1176.80 1304.44

40

34

11

(98)

9 1282.44 2342.60

12 249.00 480.58

31 151.16 231.57

46 57.78 139.65

806.58 1325.09

352.03 823.32

77.00 127.97

223.20 789.46

OTHER

Less than high

school

High school

graduate

Some college

College graduate

(71) 339.80

18 418.11

25 234.40

22 477.09

6 40.67

F for Race 3.477 p. .032

F for Education 7.548 p. .001

F for Race-Education Interaction 1.593 p. .149

950.61

908.82

964.34

1102.75

62.84

94
TABLE 5.16 Test of Difference Between Means: Lifetime Drug

Use Index by Peer Behavior at Age 13

for the Manhattan Sample

At Age 13 At

Least Some

Friends Were:

Lifetime

Drug Use

Value of Significance

n Mean t Level

a) Sometimes in trouble

at school

YES

NO

b) Sometimes in trouble

with the police

YES

NO

c) Arguing with

parents

YES

NO

d) Drinking beer, wine

or liquor

YES

NO

e) Smoking marijuana

YES

NO

f) Using other drugs

YES

NO

g) People your parents

didn't approve of

YES

NO

169 612.92

122 251.66

50

242

775.80

410.08

63 588.22

229 441.28

123 543.63

170 419.32

70 831.20

221 360.70

26 1075.04

266 413.80

83

209

596.87

423.97

3.02 .003

2.28 .023

.99 NS

1.01 NS

3.35 .001

3.14 .002

1.28 NS

95
using other drugs (mean = 1075.04). The next highest score occurred

for the group having friends smoking marijuana (mean = 831.20).

The data lend support for the proposition that the earlier an

individual associates with peers who are involved in deviant

activity, the greater is the likelihood that the person will

subsequently exhibit deviant behavior, including use of illicit drugs

(Jessor and Jessor 1977; O'Donnell and Clayton 1979, 1981).

A second test of the sensitivity of the Lifetime Drug Use Index to

known group differences concerns self-reported involvement in 13

criminal activities. The positive relationship between illicit

drug use and criminality has been thoroughly documented (Voss 1976;

McGlothlin et al. 1978; Nurco and DuPont 1977; Ball et al. 1980,

1981; Clayton and Tuchfeld, unpublished). The data in table 5.17 confirm the relationship between drug use and crime. With the exception

of public drunkenness and driving while intoxicated, the average

score on the Lifetime Drug Use Index is markedly higher for the

men in the Manhattan sample who reported criminal acts; the value of

t is significant at the .001 level.

SUMMARY AND CONCLUSIONS

The purpose of this chapter was to describe the development of a

composite index of illicit drug use and its application to a set

of data that was not employed in the construction of the index.

Creation of such an interval-level index has been assigned a high

priority in the drug field. Previous attempts at construction of

a composite drug use index have suffered from reliance on subjective

evaluations, the generation of weights from a single sample, or

scales that only meet the criterion of ordinality. A composite

index of drug use must be sufficiently comprehensive to include

the major drug classes, sensitive to differences in how various

drugs are used, objectively constructed, and not limited to any

one particular sample. It should also reflect the differential

social costs associated with each drug and should be easy to use.

The Lifetime Drug Use Index meets all of these criteria. It is

based on data from (1) the 1977 NIDA National Survey, (2) CODAP

admissions for 1977, (3) 1977 DAWN-Emergency Room drug mentions,

(4) DAWN-Medical Examiner drug mentions for 1977, (5) DEA's Illicit

Price data, (6) the Supported Work study, and (7) the national

survey of young men. Through the procedures described in detail in

this chapter, these data sets were used to derive (1) weights for

seven major drug classes and (2) five separate composite indices

of illicit drug use. All of these meet the essential criteria for

a composite measure of drug use: each distinctive pattern of drug

use is assigned a unique, single score. Correlations among the

indices were examined and in terms of parsimony and ease of

construction, the Lifetime Drug Use Index was selected as the most

functional index. This index was then applied to the 294 men in the

Manhattan sample. Analyses of differences in mean scores on the

index according to race and age group, race and educational

attainment, peer behavior when the respondent was 13 years old,

96
TABLE 5.17 Test of Difference Between Means: Lifetime Drug Use

Index by 13 Self-Reported Criminal

Activities for the Manhattan Sample

Criminal

Activity

1 . Public Intoxication

YES

NO

2 . Driving while

intoxicated

YES

NO

3 . Auto theft

YES

NO

4. Breaking and entering

YES

NO

5 . Armed robbery

YES

NO

6 . Shoplifting

YES

NO

7 . Stealing (face-to-face)

YES

NO

8 . Illegal gambling

YES

NO

9 . Bad checks

YES

NO

10. Forged prescriptions

YES

NO

11. Pimping

YES

NO

Lifetime

Drug Use Value of Significance

n Mean t Level

153 472.37

141 467.22

76 222.50

213 556.15

16 1510.81

276 409.70

41 1366.15

253 324.66

19 1991.37

275 364.78

127 758.67

167 250.30

32 1572.84

262 335.19

28 1215.50

266 391.42

18 1379.83

276 410.56

3 2532.00

291 448.64

9 2598.44

285 402.68

12. Breaking into automobiles

YES 19 1859.42

NO 275 373.90

13. Running con games

YES 25 1539.16

NO 269 370.53

.04 NS

-2.44 .015

4.24 .001

6.36 .001

7.16 .001

4.29 .001

6.86 .001

4.11 .001

3.94 .001

3.53 .001

5.71 .001

6.44 .001

5.67 .001

97

354-188 0 - 81 - 8
and 13 self-reported criminal activities demonstrated the sensitivity

of the index to known group differences. In conclusion, the authors

believe that the Lifetime Drug Use Index and the procedures used

in its construction met all of the essential criteria for a composite

index. In addition, the index is easy to construct and is based

on the type of information that is usually obtained in a survey of

drug use. In chapter 8 of this report, the Lifetime Drug Use

Index will be used as a dependent variable in testing causal

models concerning use of illicit drugs.

FOOTNOTE

1

Ten questions about involvement in criminal activity were posed.

At another point in the interview, each respondent was asked if

he had ever sold drugs and, if so, what drugs. Positive responses

were scored as follows: "Have you ever . . ."

1. Been drunk in a public place

2. Shoplifted something from a store

3. Driven a car while drunk

4. Stolen a car

5. Run numbers, or had a job which involved illegal gambling

6. Broken into a house, school, or place of business

7. Forged, or passed bad checks

8. Forged prescriptions or passed script

9. Stolen anything from a person face-to-face

10. Been armed with or used a weapon of any kind while

committing a theft or robbery

If ever sold marijuana

If ever sold one or more of the following drugs:

heroin, cocaine, barbiturates, amphetamines,

psychedelics, or other opiates

1

1

2

2

2

3

3

3

4

5

5

5

98
Chapter 6

Drug Sales

Social research pertaining to drugs has been devoted almost exclusively to the following topics: (1) the incidence and prevalence

of use of specific drugs; (2) the social and health consequences of

drug use; (3) the causes and correlates of drug use; and (4) the association of drug use with other forms of deviance, particularly

criminality. While the relationship between drug use and crime has

received considerable attention, most of the research has focused

on heroin addicts and their involvement in nondrug crime (McGlothlin

et al. 1977, 1978; Demaree and Neman 1976; Johnston et al. 1979; Ball

etal. 1980, 1981; Inciardi 1979). Only a few studies have dealt

explicitly with the phenomenon of drug sales. For example, in

Inciardi's (1979) study of 239 active male heroin users in Miami,

92 percent had been involved in drug sales in the preceding 12 months

and drug sales accounted for 51 percent of the 80,644 criminal

offenses committed by these men in 1978. Single and Kandel (1978)

studied the relationship among marijuana use, drug sales, friends'

use of drugs, and use of other illicit drugs; testing the subculture

of drug use hypothesis posited by Johnson (1973). Of the 1617 adolescents they studied, 14 percent reported selling marijuana only,

while 12 percent reported selling other illicit drugs. Drug sales

was strongly correlated with the frequency of marijuana use (gamma =

.93) and use of other illicit drugs (gamma = .91).

While these two studies underscore the significance of drug sales as

an indicator of involvement in the drug subculture, they also reflect the dearth of descriptive information about this phenomenon.

In this chapter, data will be presented from both the national and

Manhattan samples concerning "patterns" of drug selling. After a

presentation of these descriptive data, the development of an interval

level measure of drugs sales is discussed.

Drug Sales

In the national study, the topic of drug selling was introduced by

the following question: "have you ever sold any drugs illegally--

even as a favor or just to pay for your own supply?" If the respondent answered positively, he was asked to indicate which drugs he had

sold. Shown in table 6.1 are the number of drugs sold and the patterns

of drug sales. A total of 427 young men, or 17 percent of the national

sample, had sold drugs at some time. Projected to the 19,000,000 men

born in the years 1944 through 1954, some 3,230,000 young men have

distributed drugs illegally. Of the 427 men who had sold drugs, 232

99
TABLE 6.1 Number and Patterns of Drug Sales, National Study

+

+

+

+

+

+

+

+

+

+

+

NATIONAL STUDY: Patterns of Drug Sales

Number of Mari- Psyche- Stimu- SedaDrugs Sold n juana delics lants tives Heroin Opiates Cocaine

1

1

1

1

1

1

1

TOTAL

2

2

2

2

2

2

2

2

2

TOTAL

3

3

3

3

3

3

3

3

3

3

3

TOTAL

4

4

4

4

4

4

4

TOTAL

196

10

12

3

a

1

2

232

25

40

5

a

3

5

3

1

2

92

21

2

1

1

3

9

5

1

1

5

1

50

11

1

5

2

1

1

1

2 2

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+ +

+ +

+

+

+

+

+

+

+ +

+

+

+

+

+ +

+ +

+ +

+ +

+ +

+ +

+

+ +

+

+

+ +

+

+ + +

+

+

+

+

+ +

+

+

+

+ +

100
TABLE 6.1 (continued)

5

5

5

5

5

5

5

5

5

5

5

5

5

TOTAL

8 +

8 +

4 +

1 +

1 +

1 +

1 +

1 +

2 +

1 +

1 +

1 +

+

+

+ +

+ +

+ +

+ + +

+ + +

+ + +

+ + +

+ + +

+ + +

1 + + +

3 1

GRAND

TOTAL 427 384 113 141 51 41 30 36

Percent of

Total

Sample 17 15

Percent of

Sellers 90

+ +

+ +

+ +

+ +

+ +

+ +

+

+

+

+

+

+

+ +

5 6 2 2 1 1

26 33 12 10 7 8

+

+

+

+

101
(54 percent) had sold only one drug, and this was usually marijuana;

92 men (22 percent) had sold two drugs; and 103 men (24 percent) had

sold three or more drugs. Calculated on the basis of the total

sample of 2510 men, the percentages are: one drug, 9 percent; two

drugs, 4 percent; and three or more drugs, 4 percent. Of the men

who had sold one drug, 84 percent of them had sold marijuana. Nearly

all (96 percent) of the men who had sold two or more drugs had sold

marijuana.

The figures in the last line in table 6.1 show that marijuana is the

drug most commonly sold. Fifteen percent of the men in the entire

sample had sold marijuana. While 5 and 6 percent, respectively, had

sold psychedelics and stimulants, no more than 1 or 2 percent of the

men had sold other illicit drugs.

The fact that a relatively large proportion of all young men in this

country have violated the laws prohibiting the sale of illicit drugs

is important from the perspective of policy. In President Carter's

message to Congress in August 1977, he recommended the elimination of

all Federal criminal penalties for the possession of up to one ounce

of marijuana (Federal Strategy 1979:67). However, he advocated retention of Federal penalties for trafficking. While there has been

a movement to decriminalize marijuana, the changes in State laws

have pertained to possession for use. The laws regarding sales have

not been altered in most of the States that have removed criminal

penalties for possession. A few States exclude "casual transfers"

fran the laws regarding sales. In conjunction with decriminalization of possession, a distinction needs to be made between sales to

friends as a favor or simply to finance one's own supply, and sales

to friends or strangers for profit. The line between sales as a

favor and sales for a profit is not an easy one to draw.

In the Manhattan study, three questions about selling were asked:

(1) "Have you ever sold drugs illegally, just as a favor to friends?"

(2) "Have you ever sold drugs illegally, not for profit, but just

to pay for your own supply of the drugs?" (3) "Have you ever sold

any drugs illegally for profit?" If the respondent answered in the

affirmative, he was asked to name the drugs he had sold for each

purpose. Shown in table 6.2 are the patterns of drug sales among

the Manhattan respondents. A total of 94 men, 33 percent of the

sample, had sold drugs for one or more of the three purposes mentioned. In comparison, 17 percent of the men in the national sample

had sold drugs. In New York, a slightly higher proportion of the

men had sold drugs as a favor, rather than to pay for their own

supply or for profit. Nevertheless, it is important to note that

almost one-fifth of the entire Manhattan sample reported selling

drugs for each of these purposes.

The number of drugs that were sold, as well as the specific drugs

involved, is shown in table 6.3. Eleven percent of the respondents

had sold one drug as a favor to a friend; 12 percent of the men had

sold two or more drugs for this reason. The sale of one drug to pay

102
TABLE 6.2 Patterns of Drug sales, Manhattan Study

PATTERNS OF DRUG SALES

AS Favor For Own For

to Friend Supply Profit n Percent

No

Yes

No

Yes

No

Yes

No

Yes

TOTAL Yes 67 (23.2)

TOTAL No 220 (76.7

No No 193 67.2

No No 16 5.6

Yes No 6 2.1

Yes No 18 6.3

No Yes 14 4.9

No Yes 12 4.2

Yes Yes 7 2.4

Yes Yes 21 7.3

52 (18.1) 54 (18.8) 94 (32.8)

235 (81.9) 233 (81.2) 193 (67.2)

103
TABLE 6.3 Patterns of Drug Sales in the Manhattan Study: Specific Drugs Sold
TABLE 6.3 (continued)
for one's own supply was reported by 10 percent of the men, and an

additional 10 percent of the respondents sold two or more drugs for

this purpose. Similarly, 9 percent of the men sold one drug for profit, and 9 percent of the sample had sold two or more drugs for profit.

The data in table 6.4 permit comparison of the national and Manhattan

samples with regard to the specific drugs that were sold. overall,

the two samples appear to be similar with respect to the sale of marijuana. In the national sample, 15 percent of the men had sold marijuana, and in the Manhattan sample, 20 percent of the respondents

had sold marijuana as a favor; 12 percent of the men had sold it for

profit. If the number of men in the Manhattan sample who had sold

drugs is used as the base, the figures are: sold marijuana as a

favor, 64 percent; sold marijuana to purchase one's own supply, 38

percent; and sold marijuana for profit, 48 percent. In comparison,

in the national sample, 90 percent of the men who had sold drugs had

sold marijuana. However, the apparent difference between the samples

disappears when the data are examined according to the purpose for

which drugs were sold. Of the men who sold drugs as a favor to a

friend, 91 percent had sold marijuana. The comparable figures for

the other two purposes are: sold marijuana to pay for one's own

supply, 70 percent; and sold marijuana for profit, 83 percent. It

appears that marijuana is an important commodity among those who

sell drugs, whether one focuses on the national population of young

men or young men who grew up in high drug use areas in Manhattan.

There is little difference between the samples in the sale of psychedelics, as only 5 percent of the men in the national sample and 2

percent of the Manhattan respondents had sold these drugs. There are,

however, substantial differences in terms of the percentage of drug

sellers who had sold psychedelics. In the national sample, 26 percent of these men had sold psychedelics. In comparison, 5, 4, and 5

percent, respectively, of the drug sellers in the Manhattan sample had

sold psychedelics as a favor, for their own supply, or for profit.

These differences are puzzling in view of the fact that extensive

use of psychedelics, and presumably distribution of these drugs, has

been reported among college students, and slightly higher proportions

of the Manhattan respondents had attended or graduated from college

than was the case in the national sample. Among whites, the difference in educational attainment is substantial; 21 and 47 percent,

respectively, of the men in the national and Manhattan samples are

college graduates.

In terms of the percentage of the total sample who reported sale of

stimulants, sedatives, and opiates other than heroin, the differences

between the national and Manhattan samples are not extensive. However,

differences emerge for stimulants when attention is focused on drug

sellers. In the national sample, 33 percent of the drug sellers had

sold stimulants in comparison with 10 percent of them in the

Manhattan sample who had sold drugs as a favor, 3 percent who had sold

drugs to purchase their own supply, and 4 percent who had sold stimulants for profit.

106
TABLE 6.4 Drugs Sold in the National and Manhattan Studies: A Comparison
The most dramatic differences in drug sales occur for heroin and

cocaine. Only 2 percent of the men in the national sample report the

sale of heroin, and 10 percent of the sellers had dealt in heroin.

In Manhattan, 8 to 9 percent of the men had sold heroin. Among the

drug sellers, the figures are: sale of heroin as a favor, 24 percent;

sale to purchase one's own supply, 27 percent; and sale for profit,

28 percent. Among the men who sold drugs as a favor, 36 percent

report selling heroin, as do 52 percent of the men who sold drugs

for other purposes. These figures are one indication of the importaut role that heroin plays in the economy in areas in which its

use is endemic. Some four to five times as many of the Manhattan

respondents had sold cocaine than was the case in the national sample,

and approximately twice as many of the drug sellers in Manhattan had

dealt in cocaine than was the case among their counterparts in the

national sample.

An Index of Drug Sales

One of the key indicators of involvement in the drug subculture is

participation in the drug distribution network through the sale of

drugs (see Johnson 1973). Recognition of the important role of drug

sales in the perpetuation of drug use is reflected in the drug laws.

The sale of drugs outside of medical-pharmacological channels is

illegal, regardless of the reason for which drugs are sold, and

severe penalties may be imposed upon a person convicted of illicit

drug sales.

In the interview schedules in both the national and Manhattan studies,

questions about drug sales were designed to identify the specific

drugs that a respondent had sold. On the other hand, questions about

the number of sales, the number of customers, or the sales total in

dollars were not posed. The questions were simply designed to measure

whether or not a respondent had sold drugs and, if so, which drugs

he had sold. In the national study, the names of as many as five

specific drugs were obtained from the men who reported selling drugs.

An Index of Drug Sales was constructed by summing the weights for the

drugs the respondent had sold. The weights developed in chapter 5

in the construction of the Lifetime Drug Use Index were utilized for

this purpose. The range on this measure is from 0 to 45. In the

Manhattan study, as many as three drugs ware recorded for each of the

three qeustions about selling drugs. Selling drugs as a favor was

assigned a multiplier of 1, while weights of 2 and 3, respectively,

were used for sales to purchase one's own supply and for selling

drugs for profit. Combined with the weights for the drugs, the weights

used in the construction of an Index of Drug Sales are:

Favor Supply Profit

Marijuana 1 2 3

Psychedelics 2 4 6

Other Opiates 3 6 9

Stimulants 5 10 15

Sedatives 6 12 18

Cocaine 7 14 21

Heroin 24 48 72

108
Because the Indices of Drug Sales for the national and Manhattan studies

are not directly comparable, some descriptive data regarding racecohort differences within the two samples are presented. Correlations

between the Drug Sales Index for the Manhattan sample and other pertinent indices are then examined.

The Drug Sales Index: Differences and Correlations

While few would reject the unidimensional and developmental assumptions upon which the Drug Sales Index is founded, a "good" index is

one that (a) differentiates between groups that would be expected to

differ on that phenomenon and (b) is correlated with, predicted from,

and predictive of other phenomena-variables that are part of the

same theoretical model. In the remainder of this chapter we will

examine the differentiating and correlational power of the Drug Sales

Index for the Manhattan sample. The location and salience of Drug

Sales in causal models concerned with illicit drug use will be discussed in chapter 8.

The data in table 6.5 indicate differences between the three racial

groups on Drug Sales that are statistically significant beyond the

.001 level (F = 7.97 for race). These differences are in the direction that would be predicted from our knowledge of scores of the

three groups on the Lifetime Drug Use Index--blacks highest with a

Drug Sales score of 26.98, whites lowest with a score of 5.95, and

the others in between with a score of 13.37 on the Drug Sales Index.

The differences by cohort are not statistically significant. However,

it should be noted that the relative sizes of scores on the Drug

Sales Index by cohort parallel those found when the dependent variable

was the Lifetime Drug Use Index.

The data in table 6.6 indicate that education is more efficacious as

a differentiating factor for drug sales than is race; the F value is

significant at the .01 level compared to the .04 level for race.

For all three racial groups, those with less than a high school education, the dropouts, are highest on drug sales. among blacks, the

dropouts and high school graduates are quite high on drug sales, whereas

among whites, only the dropouts are high. Among the others there is

not a great deal of visible difference in drug sales unless the

respondents are college graduates.

The Drug Sales Index was examined for those who were or more not

involved in 10 different criminal activities (see table 6.7). The

t value was statistically significant for each criminal act except

being drunk in public and driving while drunk. For the latter act,

the mean scores were not in the predicted direction. What is important is the magnitude of the differences in drug sales for the other

criminal acts, particularly those that one would expect to be associated with illicit drug use and involvement in the drug subculture.

The Drug Sales Index is correlated (table 6.8) strongly with the

Lifetime Drug Use Index for all three racial groups, moderately with

109
TABLE 6.5 Drug Sales Index Scores by Race and Cohort

Lifetime Drug Use Index

Independent Standard

Variables n Mean Deviation

TOTAL

BLACK

1944-46 22 17.23 44.59

1947-49 31 34.55 57.30

1950-52 37 28.14 54.25

1953-54 35 25.17 44.16

WHITE

1944-46 24 15.79 43.26

1947-49 32 3.53 9.88

1950-52 22 2.05 4.46

1953-54 20 2.30 6.06

OTHER

1944-46 16 13.94 30.09

1947-49 16 22.50 52.72

1950-52 22 9.59 35.22

1953-54 17 9.12 36.58

(294) 16.68 41.38

(125) 26.98 50.52

( 98) 5.95 22.77

( 71) 13.37 39.56

Main Effects 3.30 p. .007

F for Race 7.97 p. .000

F for Cohort .40 p. .750

Interactions .71 p. .639

Explained 1.89 p. .041

110
TABLE 6.6 Drug Sales Index Scores by Race and

Educational Attainment

Independent

Variables

Lifetime Drug Use Index

Standard

n (%) Mean Deviation

TOTAL (294) 16.68 41.38

BLACK (125) 26.98 50.52

Less than high school 40(32) 37.98 59.14

High school graduate 40(32) 32.15 53.10

Some college 34(27) 16.38 40.05

college graduate 11( 8) .91 2.07

WHITE ( 98) 5.95 22.77

Less than high school 9( 9) 34.89 63.43

High school graduate 12(12) 2.67 4.56

Sane college 31(31) 5.26 16.28

College graduate 46(46) 1.61 5.55

OTHER ( 71) 13.37 39.56

Less than high school 18(25) 15.33 44.85

High school graduate 25(35) 12.48 34.98

Some college 22(30) 14.41 45.21

College graduate 6( 8) 7.33 13.00

Main Effects 5.324 p. .000

F for Race 3.381 p. .035

F for Education 3.589 p. .014

Interactions .927 p. .475

Explained 2.926 p. .001

111
TABLE 6.7 Drug Sales Index Scores by Participation in

10 Specific Criminal Acts

Criminal

Activity

Lifetime

n

Drug Use Value of Significance

t Level

1.

2.

3.

4.

.337

5.04

5.

9.15

6.

7.

8.

7.11

4.37

5.70

9.

10.

Drunk in public

YES

NO

Driven while drunk

YES

NO

Stolen a car

YES

NO

Broken into a house,

school or business

YES

NO

Armed while committing

a theft or robbery

YES

NO

Shoplifted

YES

NO

Stolen something

face-to-face

YES

NO

Run numbers or involved

in illegal gambling

YES

NO

Forged or passed

bad checks

YES

NO

Forged prescriptions or

passed bad script

YES

No

153 18.91

141 14.26

76 10.67

218 18.78

16 65.50

276 13.84

41 58.26

253 9.94

19 90.89

275 11.55

127 28.20

32 62.06

262 11.14

28 48.25

266 13.36

18 67.83

276 13.34

3 95.67

291 15.87

.96

-1.47

6.25

4.28

3.38

.142

.000

.000

.000

.000

.000

.000

.000

.001

112
TABLE 6.8 Correlations (r) of Drug Sales Index With Age at First

Use of Marijuana, Educational Attainment,

and the Lifetime Index of Drug Use Within Racial Categories

BLACKS WHITES OTHERS

Age at First Use of Marijuana -.23 -.24 -.33

Education -.32 -.32 -.03

Drug Index .42 .84 .63

education for blacks and whites but negligibly for others, and moderately with age at onset of marijuana use for all three groups.

Conclusions

The data presented in this chapter are unique for two reasons.

First, they concern the sale and distribution of drugs, a topic on

which little systematic research has been conducted. Second, they

allow us to compare the prevalence of drug sales for both a national

representative sample and a sample from areas of Manhattan known to

be high in drug use.

Several conclusions and implications can be drawn from these data.

First, a relatively large proportion of the youth and young adults in

this country have sold drugs. In the national sample the prevalence

figure is 17 percent, representing over 3.2 million young men between

the ages of 20 and 30. In the Manhattan study, 1 in 3 in the sample

had sold drugs. Second, marijuana is a common medium of exchange

among youth and young adults, whether in the national population or

in the high drug use areas. Third, heroin and cocaine are popular

mediums of exchange in the Manhattan study but are only rarely sold

by young men in the national study.

While these data and conclusions are interesting and provocative,

they underscore how little is known about a behavior that is central

to the drug abuse problem in the United States. While there are many

research questions about drug selling that deserve immediate attention, some of the more important ones are listed below.

1)

2)

3)

4)

5)

What are the salient contextual and psychosocial mechanisms

involved in a decision to sell drugs?

Are there thresholds of drug use that, once attained, virtually

insure that a person will begin selling drugs?

At what point in the developmental sequence of drug use does one

"usually" move into drug sales?

What role does fear of apprehension and certainty of detection

play in the decision to sell or not sell drugs?

Given the prevalence of marijuana use and the evident propensity

of users to sell drugs, particularly marijuana, what can be done

from statutory and enforcement perspectives to reduce the sales

of illicit drugs?

113

354-188 0 - 81 - 9
Chapter 7

Predictors of Illicit Drug Use

The primary objective of the Manhattan study was to examine the natural

history and etiology of illicit drug use. In this chapter, attention

will be focused on the item and scales used to measure etiological

factors that may be predictive of illicit drug use. Manhattan is an

appropriate site for etiological research because it contains areas in

which the rate of illicit drug use is high. Thus, one could expect to

obtain a sizable number of regular opiate users in a small sample.

Residence in Manhattan does not, however, imply that the subjects were

necessarily exposed to the same sociocultural environment during their

formative year.

Definitions of Sociocultural Environment

The concept of a sociocultural environment is a complex one that can

be defined in various ways. Three definitions were utilized in the

Manhattan study. First, the concept of a sociocultural environment

traditionally has been linked to geographical criteria. This definition

influenced the sampling design. The respondents were selected randomly

fran the files of Selective Service boards located in whole or in part

in health areas with high rates of opiate use. The second definition

of the sociocultural environment also has a geographical connotation.

In Manhattan, as in other large cities, there are cultural enclaves

or neighborhoods, such as Spanish Harlem, that are identified by the

ethnic composition of the population. The assmuption underlying definition of the sociocultural environment in terms of ethnicity is that

blacks, whites, and Hispanics have different experiences in the process

of growing up because they reside in different cultural settings.

Thus, knowledge of a person's ethnic background and the area in which

he was raised provides a researcher with valuable information about the

kinds of influences that have affected his life. Consequently, in this

report, ethnic status is used as a control variable. A third definition

of sociocultural environment is based on a person's perception of the

forces influencing him at various times in his life. It is assumed

that the respondent's subjective perception of the environment may be

as important in explaining his behavior as the objective characteristics of that environment.

Problems in Measuring Sociocultural Environment

Efforts to assess subjective perceptions of the sociocultural environment create numerous measurement problems. These problems were partially resolved by measuring experiences occurring in different periods

of time. Because the objective of the study was to explain the use

114
of marijuana as well as use of other illicit drugs, it was crucial to

obtan measurements of the respondent's perception of the environmental

forces operative in his life at the time he was most vulnerable to

begin use of illicit drugs. Herein lies the difficulty. The respondents were going to be 20 to 30 years old when they were to be interviewed and they were to be interviewed only once. Therefore, it was

necessary to link the items designed to measure etiological variables

to a specific time in each man's life that would be prior to his use

of illicit drugs. Use of the qualifier, when you were 13 to 15 years

old, and the phrasing of the questions with reference to the neighborhood in which the respondent was living in those years, was designed

to focus on a period antecedent to his use of illicit drugs. The

decision to link the predictive items to a specific age range and to

the neighborhood in which the respondent was then living raises a

general question about the validity of retrospective life history

data. However, this is a question that critics of survey research

are more likely to raise than are persons who have questioned drug

users about their past experiences. Experience in the national survey,

as well as the experience of researchers who have studied heroin

addicts, suggests that most people accurately remember events that

were salient in their lives, even if they occurred 10 or 15 years

earlier (Ball 1967; Maddux and Desmond 1979; McGlothlin et al. 1978;

Inciardi 1979). However, our goal was not simply to measure highly

salient events such as graduation from high school or one's first

arrest. Rather, many items in the interview schedule were relational

in nature in that they pertained to what the respondent was doing,

thinking, experiencing, or feeling with regard to significant others

in his neighborhood when he was 13 to 15 years old. If subsequent

experiences strongly influence one's recollection of earlier periods

in one's life, then data obtained retrospectively may be distorted.

The credibility of retrospective data can only be evaluated in terms

of consistency and predictive power. To insure maximum consistency

and credibility, the researchers conducted numerous pretest interviews

with inmates at the Federal Correctional Institution in Lexington,

Kentucky. Many of these inmates had grown up in ghettos in large

metropolitan areas and they did not find it difficult to answer the

questions. Once a reasonably complete interview schedule had been

constructed, a member of the research team reviewed each item in the

schedule with three ex-addict interviewers employed by the New York

State Office of Drug Abuse Services. The purpose of this lengthy

session was to insure that the item would be understood by the subjects in Manhattan. Changes in the wording of some items were made

as a result. In addition, several of the early interviews with respondents were conducted by members of the research team.

Adequate measurement of the respondent's subjective perception of his

sociocultural environment required identification of the etiological

factors that might be predictive of illicit drug use. Our solution

to this important problem was to rely on extant theories in the field

of deviance. While a number of theoretical perspectives have been

proposed, none of them has been shown to be superior to the others in

terms of explanatory power. The task of selection of relevant variables

115
was minimized by the extensive conceptual overlap among the various

theories. Unfortunately, there is little consensus regarding how these

concepts should be operationalized. Further, none of these theoretical

perspectives was developed specifically to explain drug use. Consequently, the investigators had to design items to measure the key

concepts in the various theories in an effort to predict use of marijuana and other illicit drugs. The theories that were used to identify

key concepts were Merton's (1957) theory of anomie, Sutherland's

theory of differential association (Sutherland and Cressey 1974),

Hirschi's (1969) social control theory, the labeling perspective (Decker

1963; Schur 1971), and the theory of deviant subcultures (Cohen 1955;

Cloward and Ohlin 1960; Johnson 1973).

Etiological Factors in Illicit Drug Use

While the list of factors that might predict illicit drug use is

extensive, seven key concepts were identified in the existing theories.

Family influence and school adjustment were two factors that might

insulate a person from use of illicit drugs. Two factors that may

facilitate involvement with illicit drugs are the behavior exhibited

by a person's peers when he was 13 to 15 years old (i.e., their

involvement in deviant activities and their use of drugs) and the

person's own early involvement in deviant behavior. Labeling may also

be predictive of illicit drug use. This factor was measured in terms

of the perception that significant others (parents, teachers, friends)

believed the respondent was headed for trouble with the law. Finally,

two factors measure involvement in the drug subculture: the availability of illicit drugs in the respondent's neighborhood, and the

respondent's self-reported involvement in selling drugs.

Family Influence

The first--and perhaps the most enduring--source of influence on a

person comes from the family in which he is raised. This is true

regardless of where one grows up--a suburb in middle America, a small

rural town, or a ghetto in Manhattan. In an overwhelming majority of

cases, the individual family unit successfully carries out its primary

function: nurturant socialization. There is little doubt that the

family exerts a powerful influence on one's life; however, there is

considerable debate concerning how the family influences the behavior

of its members. Thus, the appropriate way to measure family influence

is controversial. Some scholars argue that the crucial variable is

structural. They emphasize the presence or absence of certain kinds

of adults and a child's ordinal position within the family, regardless

of its composition (Moynihan 1965; Kellam et al. 1977; Zajonc 1976).

In contrast, others, such as Hirschi (1969) and Clayton (1979), argue

from a social psychological perspective. They suggest that in order

to understand how the family influences behavior, one must examine

the affectional bonds between parents and the child. These bonds are

not static. They change over time as the developmental needs of the

child and the dynamics of family relationships change. Changes in

the bond between parents and child can be seen most clearly in the

116
degree of parental control over the child's time and associates, the

child's identification with his parents, the degree to which the child

sees the parents as role models, and the amount and quality of

parent-child communication. The early teen years are crucial ones not

only because youth undergo marked physiological changes, but also

because they are years in which youth may experiment with alcohol,

tobacco, and marijuana, especially in neighborhoods in which drug use

is extensive. As youth seek independence and autonomy, they are often

caught between the demands from parents for conventional actions and

fran peers for risk-taking behavior.

Family influence was initially defined in terms of five dimensions:

family control, closeness to mother, closeness to father, use of the

father as a role model, and communication with parents. There were

12 items in the interview schedule that ware designed to tap these

dimensions. In testing the Family Influence scale for reliability or

internal consistency, 3 item were eliminated to increase its reliability. Excluded from the scale were items regarding: (1) whether

the parents set a definite time for the respondent to be home when he

was 13 to 15 years old and whether he usually met it; (2) how close

the respondent felt to his father; and (3) whether the respondent used

his father as a role model. Table 7.1 contains a correlation matrix

(Pearsons' r) for the 9 items in the Family Influence scale. The scale

has a high degree of internal consistency or reliability as indicated

by the Cronbach's (1951) alpha value of .81. A brief summary of the

responses to the various items will give the reader an understanding

of the distribution of the responses. Seventy-three percent of the

men said their parents knew where they were when not at home and 38

percent said their mothers knew all of their friends. Fifty-six

percent of the men said they felt very close and 30 percent described

themselves as fairly close to their mother. The following percentages

said that they often discussed with their parents things related to:

school (54 percent); friends (32 percent); future plans (35 percent);

leisure time activities (22 percent); girls (13 percent); and news or

national events (29 percent). The range of possible scores on the

Family Influence scale is 9 to 31. The overall men is 23.97 with a

standard deviation of 4.71. In chapter 8, Family Influence is one of

the variables in the causal model designed to predict illicit drug use.

An inverse relationship between Family Influence and the use of illicit

drugs is hypothesized. That is, high scores on Family Influence are

associated with low scores on the measure of use of illicit drugs.

Early Deviance

While it is generally acknowledged that early involvement in delinqluent

activities is predictive of later deviance, precocity is not included

as a basic concept in any of the existing theories. There are at

least two reasons why precocity has generally been ignored. First,

inclusion of early deviance in a theoretical model as a predictor of

later deviance would leave a theorist open to the criticism of circularity. Second, most of the theoretical models in the field of

deviance do not refer to developmental processes. Nevertheless, study

117
TABLE 7.1 Inter-Item Correlations (Pearson's r) for the Family Influence Scale
after study of adults, whether criminals, drug users, or both, has

noted the strong correlation between youthful involvement in delinquent activities and deviance in adulthood (Glaser 1960; Robins

1966).

The strength of this correlation and the consistency with which it

occurs in the literature led to inclusion of items in the Manhattan

interview schedule that would provide information regarding involvement in unconventional or deviant activities. The question was

phrased: "Think now about how you spent your leisure time when you

were 13 to 15 years old. For each of these activities, tell me if

you spent a total of a couple of hours or more a week doing that

activity." This question was followed by the inquiry, "Which of these

would you say you spent the most time on?" Listed below are the

activities and the marginal distribution of positive responses.

The second set of figures reflects the number of men who indicated

that a particular activity occupied the greatest amount of their

time.

YES MOST

Unconventional Activities Number Percent Number

a. Hanging around street 202 69 67

b. Playing pool 89 30 7

c. Pitching pennies 90 31 0

d. Shooting craps 49 17 3

e. Playing con games or stealing 47 16 2

To construct a scale, each respondent was assigned a score of 1.

Points were added to his score as follows: 1 pint for hanging

around the street, playing pool, or pitching pennies; 2 points for

shooting craps; and 3 points for running con games or stealing.

If items a-c were named as the activity on which most of his leisure

time was spent, 1 point was added: if items d or e were so named, 2

points were added. In addition, in a self-administered questionnaire,

the respondents were asked if they had ever (a) run away from home,

(b) been suspended or expelled from school, or (c) had sexual intercourse. Two points were added for each of these three activities if

they occurred at age 14 or earlier.

Correlations among the items dealing with unconventional activities

are moderate and range from .258 (playing con games or stealing with

playing pool) to .436 (pitching pennies with shooting craps). The

average inter-item correlation is .326. Because of the nature of the

scale, an alpha value was not constructed. However, the range of

scores on the Early Deviance scale is 1 to 17 with a mean of 3.39

and a standard deviation of 2.51.

School Adjustment

Like the family, the school attempts to instill societal norms and

values in students. Not only does it foster skills that may be

119
useful in obtaining a job, but success in school is also the key that

opens the gates to tracks that might otherwise be closed to youth

from an urban ghetto. This is apparent in the Manhattan sample.

Forty-seven percent of the whites in comparison with 9 percent of the

blacks are college graduates. As noted previously, the high proportion of white collage graduates is remarkable in view of the fact

that these men were raised in areas of Manhattan that rank high on

most indicators of social pathology, particularly rates of opiate

use (Inciardi 1981).

Several theorists have suggested that lack of success in school is

related to involvement in unconventional or deviant activities.

Cohen (1955) sees the school and its teachers as responsible for

inculcating in youth societal norms and values and for providing

youth with the skills necessary to achieve success as an adult.

According to Hirschi (1969) and Elliott and Voss (1974), youth who

are attached to school are less likely to be involved in delinquency

than are youth with little attachment to school.

The concept of attachment to school implies positive affect, but only

one item, which refers to how much the respondent liked school,

clearly measures attachment. However, two other items (grades in

school at ages 13 to 15 and the frequency with which the respondent

got into trouble with teachers at age 13) are relevant to the

respondent's school experiences. These three items were combined to

form a scale which reflects School Adjustment.

Only 14 percent of the respondents indicated they disliked or really

hated school when they were 13 to 15 years old, while another 15 percent said they did not really care, one way or another, about school

at that age. Twenty-seven percent of the men indicated that their

grades were either fair or poor at ages 13 to 15, while 21 percent

described them as excellent and 51 percent said they were good. A

total of 47 percent of the respondents said that they got into

trouble with teachers and school officials all of the time when they

were 13 and an additional 42 percent said they were in trouble quite

often. The range of scores on the School Adjustment scale is 3 to

13 with a men of 10.03 and a standard deviation of 1.82 for the

total sample. While the alpha value for the School Adjustment scale

is quite low, .451, the scale is later used as an independent variable

in explaining use of illicit drugs because of its salience in various

theories of deviance.

Peer Influence

Peer influence is a key concept in nearly all theories of deviance,

and it has proven to be a strong predictor of drug use among adolescents and young adults. The items used to construct the Peer Influence scale were formulated to measure the central ideas in Sutherland's

(Sutherland and Cressey 1974) theory of differential association and

the subcultural theories of Cohen (1955) and Johnson (1973). As

youth seek independence from their parents, they become more

120
dependent on the norms of their friends and acquaintances for guidance

as to how they should behave. Schcools and neighborhoods undoubtedly

differ in the proportions of youth who are law-abiding or law-violating.

Nevertheless, most youth are at least acquainted with peers who do

and do not use alcohol, marijuana, and other illicit drugs, who are

or are not sexually active, and who vary in the extent of their

involvement in other kinds of delinquent behavior. The persons whom

youth select as friends are influential with respect to the degree

of their conformity with societal norms. According to the theory of

differential association, one should predict delinquent behavior if

in a youth's reference groups, the balance of definitions favorable

to violation of the law outweighs the definitions favorable to conformity. Delinquent behavior is a function not only of priority but

also of the frequency, duration, and intensity of involvement with

groups holding definitions favorable or unfavorable to violation

of the law.

Use of illicit drugs was sufficiently extensive in New York City in

the late 1950s that Cloward and Ohlin (1960) identified one of their

three types of subcultures as a retreatist, or drug-using subculture.

Researchers have shown that friendship with persons who use drugs

increases the probability that an individual will spend more and more

tine with drug-using friends, and a youth's drug use will receive

positive reinforcement. This is likely to increase the youth's involvement in the drug subculture. As this occurs, the probability

that a person will begin to sell drugs increases, and involvement

in the drug distribution network provides a person with opportunities

to try other drugs.

The Peer Influence scale consists of five items. The first dealt

with whether many of the adults in the neighborhood thought of the

respondent's friends as delinquents. Twenty-six percent said yes.

The second question asked whether the respondent's friends or the

people he ran around with at age 13 to 15 got into trouble with the

police, and how often this occurred. Of the total sample, 63 percent

denied their friends ever got into such trouble. Ten percent of the

sample said their friends got into trouble with the police very often

or fairly often, 22 percent said this occurred only a few times, and

4 percent said it occurred only once. The third item dealt with the

frequency of stealing or boosting among the respondent's friends

when he was 13 to 15 years old. Fifty-eight percent claimed their

friends never went out stealing or boosting. A similar question was

asked about the frequency with which the respondent and his friends

engaged in gang fights. Seventy-one percent of the men said this

never occurred. The last item in the Peer Influence scale asked

whether at least some of the respondent's friends were drinking beer,

wine, or liquor (24 percent), smoking marijuana (24 percent), or

using other drugs (9 percent), when he was 13 years old.

In constructing the Peer Influence scale a positive answer regarding

whether adults in the neighborhood thought of the respondent's friends

as delinquents received a score of 5, whereas a negative answer was

121
coded 1. The responses to the items on friends in trouble with the

police, friends stealing or boosting, and friends involved in gang

fights were coded: Yes, very often = 5; Yes, fairly often = 4; Yes,

but only a few times = 3; Yes, but only once = 2; and No = 1. On

the peer drug use item each respondent was given a score of 1. If

he indicated that his friends were using alcohol when he was 13, 1

point was added; for use of marijuana, 2 points were added; and 4

points were added if his friends had used other illicit drugs. The

range of possible scores for the Peer Influence scale is 5-28; the

men for the sample is 9.84 with a standard deviation of 5.22. The

data in table 7.2 show the inter-item correlations for the Peer

Influence scale. The average is .31 and the scale is reasonably

reliable with an alpha of .663.

Perception of Drug Availability

A unique feature of the Manhattan study is that it is based on a

sample of young men from the normal population, but these men grew

up in areas in which drug use is known to have been extensive.

While the presence of large numbers of drug users is not sufficient

to produce new users, it was anticipated that the respondents would be

knowledgeable about persons who sold drugs or places where a wide

range of drugs, including heroin, could be obtained. Perception of

drug availability was initially measured according to four dimensions: (1) the number of persons in the neighborhood who were smoking

marijuana; (2) the number of heroin users in the neighborhood;

(3) knowledge of a particular person in the neighborhood who sold

drugs; and (4) physical proximity to a heroin copping area.

More than 55 percent of the respondents said that at least a few

people in their neighborhood were smoking marijuana, and 29 percent

of the men knew at least a few people who were heroin users. Thirtynine percent of the respondents knew someone in their neighborhood

who sold drugs, and 37 percent of the men knew the location of a

heroin copping area. For 14 percent of the men, the nearest copping

area was on their block. In other words, when the men in this study

were 13 to 15 years old, many of them were aware of the places in

their neighborhood where drugs could be obtained.

The concept of perception of drug availability is derived from the

theory of deviant subcultures in which emphasis is placed on the process whereby an individual is socialized into the drug subculture.

A key part of the socialization process involves learning where and

how to enter the group. It is also important to learn the rituals

and proper language to signify to others that one is part of the ingroup and is not an outsider. The items in the Perception of Drug

Availability scale tap these components of the drug subculture indirectly. In testing the Perception of Drug Availability scale for

internal consistency or reliability, it was necessary to eliminate

the item on proximity to a heroin copping area in order to increase

scale reliability. The resulting three item Drug Availability scale

has a moderately strong alpha value of .762. The range of scores on

this scale is 3-15 with an overall mean of 9.66 and a standard

deviation of 5.18.

122
TABLE 7.2 Inter-Item Cm-relations (Pearson's r) for the

Peer Influence Scale

1.

2.

3.

4.

5.

1 2 3 4 5

Adults think of friends

as delinquents --

Frequency friends in

trouble with police .294 --

Frequency friends stealing or boosting .290 .391 --

Frequency friends in

gang fights .399 .342 .216 --

Drug use by friends .307 .353 .224 .280 --

Alpha = .663

Standardized Item Alpha = .692

123
Labeling

In recent years, sociologists have developed the labeling perspective

to emphasize the importance of the processes of making and applying

rules. In contrast with other theories regarding crime and deviance

in which attention is focused on acts that constitute violations of

laws or social norms or on the characteristics of persons who violate

such rules, the labeling perspective suggests that initial deviant

acts are relatively unimportant and inconsequential unless there is

some societal reaction to them. The central proposition in the

labeling perspective is that deviance always involves social definition. Becker (1963:9) suggests: "Social groups create deviance by

making the rules whose infraction constitutes deviance, and by

applying these rules to particular people and labeling them as outsiders. From this point of view, deviance is not a quality of the act

the person commits, but rather a consequence of the application by

others of rules and sanctions to an 'offender.' The deviant is one

to whom that label has successfully been applied; deviant behavior

is behavior that people so label." While one's significant others

can be parents, peers, or teachers, there is an emphasis in the labeling perspective on the importance of officials in the criminal justice

system. One of the serious limitations of the labeling perspective

is that it is difficult to define operationally its key concepts.

In the Manhattan study, three items were included in the interview

schedule to measure indirectly the process to which the labeling

perspective has called attention. The format and response alternatives for these items were similar. "When you were 13 to 15 years

old, suppose we had asked your (teachers/friends/parents-motherfather) if you were going to get into trouble with the law. Would

they have said: definitely yes, probably, not sure, probably not,

definitely not?" The respective percentages for "definitely yes" and

"probably" are: teachers (2 and 6 percent), friends (4 and 10 percent), and parents (3 and 5 percent).

The three items in the Labeling scale were strongly related (teachersfriends, r = .61; teachers-parents, r = .51; friends-parents, r =

.58). An alpha value of .794 indicates the scale is reliable. The

range of scores on the Labeling scale is 3-15 with a mean of 5.36

and a standard deviation of 2.77. Higher scores on the Labeling

scale indicate that teachers, friends, or parents thought the respondent was headed for trouble with the law when he was 13 to 15

years old.

Differentiating Power of the Six Scales

The six scales described in this chapter were developed to represent

salient concepts from various theories of deviance that might shed

light on the etiology of illicit drug use. In chapter 8 of this

report, data will be presented to assess the power of these scales

as well as the index of drug sales to predict scores on the Lifetime

Drug Use Index. However, at this point attention is focused on

differences in the men scores on these scales according to race

and age group and race and educational attainment.

124
The results of a two-way analysis of variance in which race and age

group are the independent variables and the six scales are the dependent variables are shown in table 7.3. The values of F indicate

statistically significant differences by race on all of the scales

except Family Influence. There are no significant differences by

age group, and none of the F values for a two-way interaction effect

are significant. Comparison of the mean scores by race shows that

blacks have the second highest score on School Adjustment and the

highest score on all of the other scales. Whites ranked second on

the Family Influence, Peer Influence, and Labeling scales and third

on the Early Deviance, School Adjustment, and Drug Availability scales.

These findings demonstrate the importance of using race as a control

variable in further analysis.

The data presented in table 7.4 show the mean scores on the six

scales by race and educational attatinment. As expected, the values of

F indicate statistically significant differences by race on all of

the scales except Family Influence. There are also significant

differences according to educational attainment on all of the scales

except Drug Availability. None of the two-way interaction effects

are statistically significant. Comparison of the relative size of the

mean scores for whites and blacks in terms of educational attainment

reveals that the men with less than a high school education score the

highest on the Early Deviance, Peer Influence, Drug Availability, and

Labeling scales, regardless of race. For both blacks and whites, those

with less than a high school education have the lowest scores on the

Family Influence scale and are quite low on the School Adjustment

scale. Overall, there is a substantial degree of concordance between

the ranks of blacks and whites on these six scales vis-a-vis educational attainment. Unfortunately, the sample is too small to permit

simultaneous controls for race and educational attainment. One

alternative would be to include education in the causal model, but

its location is problematic. The correlation (gamma = -.67) between

race and educational attainment is sufficiently high to justify a

control for race and not for educational level. That is, a control

for race is, to a considerable extent, a control for educational

attainment as well.

Conclusion

In this chapter, the focus is shifted from comparison of the national

and Manhattan samples to the construction of interval-level indices

to measure several etiological factors--family influence, early

deviance, school adjustment, peer influence, perception of drug

availability, and labeling. Each of these indices is designed to

measure concepts central to various sociological theories of deviance.

The indices reflect an effort to measure the subjective perception

the respondents had of their sociocultural environment when they

were 13 to 15 years old. Two other dimensions of the sociocultural

environment are also measured by use of race as a control in all of

the analysis and by the sampling design in which it was specified

that the respondents had to have resided in areas of Manhattan in

125
TABLE 7.3 Analysis of Variance: Mean Scores on the Six Scales by Race and Age Group
TABLE 7.4 Analysis of Variance: Mean Scores on the Six Scales by Race and Educational Attainment
which the rates of opiate use were high. Ideally, to explain drug

use, one would use a longitudinal research design. Then measures of

key variables could be obtained prior to use of illicit drugs. This

option was not available to the researchers. Consequently, a retrospective life history approach was selected. The utility of this

approach for the measurement of the predictors of illicit drug use

will be assessed in the following chapter.

128
Chapter 8

Causal Models to Explain

Illicit Drug Use

In the previous chapters, a number of comparisons were made between

the results obtained in the national survey and in the parallel

Manhattan study. While the former study was nationwide in scope, it

was almost exclusively epidemiological in focus. As noted in chapter

7, the interview schedule for the Manhattan sample was developed on

the basis of major concepts in extant theories of deviance. The

purpose of this chapter is to explain the use of illicit drugs by the

use of causal modeling. This chapter includes: (1) a brief discussion

of path analysis, a technique used in causal modeling; (2) the rationale underlying the models in which illicit drug use is predicted;

(3) a presentation of the results of those tests; and (4) an interpretation of the results from a substantive perspective--what they

mean for understanding illicit drug use--and from a theoretical

perspective--what they mean for understanding deviance. For the

reader who is unfamiliar with path analysis, a brief description of

this technique is provided, and the strengths and limitations of the

data with regard to this technique are noted.

Path Analysis: An Introduction

Path analysis is a statistical technique that is widely used in causal

modeling. It allows a researcher to assess how well empirical relationships among measured variables fit the relationships presumed

to exist among concepts in a causal model. The causal model is derived from one or more theories. If the theory on which the model

is based is logically sound and is rigorous from a conceptual perspective, the researcher would diagram the causal relationships that

the theory suggests should exist among the various concepts. These

relationships are called direct paths. In a specified model, some

paths between what may be called predictor and effect concepts would

not appear in the original model. Unfortunately, few theories in the

social sciences are sufficiently rigorous to justify use of a specified model. Consequently, most researchers begin with a saturated

model. In a saturated model every predictor concept has a direct

path to every effect concept to the right of it in the model. In

such a case, the most important decision a researcher must make concerns the appropriate temporal order of occurrence of the concepts

in the model. Initially, measured variables or scales representing

each of the concepts are constructed and, by means of path analysis,

the fit between the saturated model and the data is examined.

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354-188 0 - 81 - 10
In path analysis, the Pearsonian correlation coefficient, r, is used

to measure the strength of association among all variables in the model.

Because Pearson's r is a parametric statistic, its use implies that

all variables in the model are measured at the interval level.

However, in practice, the minimum requirement is that all of the

variables are measured at the ordinal level.

A matrix of the correlation coefficients of all of the variables in

the model is developed. This matrix is then used in a stepwise

multiple regression analysis to generate estimates of the direct

effects of every predictor variable in the model on every variable

to the right of it. In a stepwise procedure, two types of regression

coefficients are produced. The first, the B values, are unstandardized, and reflect the number of unit changes in the dependent variable

produced by a one unit change in each independent or predictor variable

in the regression equation. Because they are unstandardized, the B

values can be larger than 1.0. The second type of regression coefficient that is produced is a beta weight, b. It is standardized, and

its value is always between .000 and 1.000. The beta weight is called

a path coefficient; it represents the slope produced when a dependent

variable is regressed against a predictor variable, while simultaneously controlling all of the other variables in the model. Because

the effects of the other variables are statistically controlled, a

path coefficient is most accurately described as a standardized

partial regression coefficient.

The stepwise procedure is used to identify the predictor variables

that have statistically significant direct effects on the dependent

variables in the model. The predictor variables are entered, one by

one, into the regression equation. The first variable to enter the

regression is the one accounting for the largest amount of variance

in the dependent variable. The second variable that enters the

equation is the one accounting for the largest amount of variance

in the dependent variable that is left unexplained by the first variable, and so on, until all of the predictor variables have entered

the equation.

Determination of which predictor variables have a statistically

significant direct effect on a dependent variable is made in two

ways. First, stepwise multiple regression yields an F score for each

independent variable. This score can be assessed for significance

by the use of standard F tables. A second test for significance

involves application of a rule-of-thumb. If the B value is at least

two times the size of the standard error of B, the direct effect of

that predictor variable is considered to be statistically significant.

At this point, the model is revised, and only the direct paths that

are statistically significant are retained. However, the researcher

must determine whether there is justification for deletion of some

direct paths from the original model. This is accomplished by the

use of partial correlation techniques. For each direct path to be

deleted, the researcher computes two partial correlation coefficients.

The first is a partial r in which all variables intervening between

130
the predictor and effect variables in the model are controlled simultaneously. To delete the path, the partial r value should be considerably lower than the simple r value between the predictor and

effect variables, and it should not be statistically significant.

The second partial r value for the path involves simultaneous controls for all variables that occur prior to the predictor variable

in the model. This partial r value should also be substantially

lower than the simple r and should not be statistically significant.

If both of the partial r values meet these criteria, then the researcher is justified in deleting the direct path from the model.

Through examination of the partial correlations, a number of direct

paths can be deleted.

After the model has been revised by the elimination of some direct

paths, the dependent variables are regressed against the remaining

predictor variables that have a direct and significant effect. A

simple multiple regression, not a stepwise regression, is used.

The resulting coefficients provide a means to test the fit between

the data and the model.

Assumptions Underlying Path Analysis

There are a number of assumptions that should be met before path

analysis is used to test causal models. One of these is related to

measurement. The variables in the model should be measured at the

interval level. As mentioned previously, this assumption is often

violated in practice by the use of variables measured at the ordinal

level. There are at least three other assumptions underlying path

analysis that deserve mention before this technique is applied to the

data obtained from the Manhattan sample.

Causal Model Derived From Theory. A key assumption in path analysis

is that the model is derived from an existing theory or theories.

Sociology as a discipline and deviance as a specialty within the

discipline can best be described as having only a few theoretical

perspectives that are sufficiently developed to be tested (see

Elliott et al. 1979). As noted in chapter 7, the items used in

constructing the scales were developed to represent major concepts

in the following theories of deviance: (a) Sutherland's theory of

differential association (Sutherland and Cressey 1978); (b) Hirschi's

(1969) theory of social control; (c) Merton's (1957) theory of anomie;

(d) Reckless' (1956) theory of containment; (e) Cloward and Ohlin's

(1960) opportunity theory, and (f) the labeling perspective exemplified by the work of Lemert (1951) and Becker (1963). None of these

theories dominates the field of deviance, yet all share some of the

same concepts. In this sense, the models developed in this chapter

do not constitute either a fully adequate test of any one theory

or a test of all of the theories. Rather, the central concepts of

these theories have been operationalized specifically for this study

and its central purpose, to explain illicit drug use among young

men who grew up in areas of Manhattan in which the rate of opiate use

was high.

131
Specification of Temporal Order. Another important assumption in

path analysis is that the temporal order of the variables in the model

is either known or the relative time-order of occurrence can be

established on logical grounds. For this reason, path analysis is

most appropriate for data obtained in a longitudinal study. However,

the data in the Manhattan study are cross-sectional in that the

respondents were interviewed at only one point in time: 1975.

Therefore, the time-order of occurrence among the variables in the

models will, of necessity, be justified by assumption. Further,

the data used to construct the independent and dependent variables

are based on retrospective recall. It is impossible to assess fully

the degree to which the variables reflect faulty memory or a retrospective reconstruction of events in which each respondent tried

to achieve cognitive consistency and to reduce cognitive dissonance.

However, thorough analysis of the retrospective life history data

obtained in the national and Manhattan samples suggests that dissumulation by individual respondents was minimal and does not affect the

major findings. The data are internally consistent and consistent

with comparable studies. Studies of the validity of retrospective

self-report data on drug use and other forms of deviance indicate

that such data can generally be trusted (see Ball 1967; Bonito et

al. 1976). In addition, the interviewers were well trained. Independent follow-up checks with the respondents on key variables indicated that virtually all of the respondents were acting in good faith

when they were interviewed.

The Concept of Cause. The final assumption in path analysis is that

the model reflects causal relationships among the variables. In this

chapter, the concept of cause is defined in probabilistic and statistical terms. In sociology there is a long and distinguished tradition behind the use of this kind of definition in searching for causal

relationships among variables in models (Stouffer 1950; Lazarsfeld

1955; Hymn 1955; Coleman 1964; Rosenberg 1968; Davis 1971; Duncan

1975; Heise 1975). The analysis reported in this chapter rests upon

this tradition.

The conception of causality utilized in this chapter has been clearly

articulated by Hirschi and Selvin (1967). They listed three criteria

of causality: (1) the independent and dependent variables must be

statistically correlated; (2) the independent variables must be temporally antecedent to the dependent variables; and (3) the relationships between the independent (X) and dependent (Y) variables must

not be spurious; that is, the relationship must not be the result of

both X and Y being caused by a factor of factors antecedent to them.

It is relatively easy to meet the first criterion. The statistical

correlation among the variables in a model is obtained by the calculation of simple r values. If one's data are cross-sectional, the

second criterion, time-order of occurrence among the variables, may

be established by: (1) examination of the way in which the variables

are operationalized and a determination of what each variable means

conceptually; (2) reliance on the theoretical-epistemic meanings of

132
each variable vis-a-vis all other variables (see the section in this

chapter on rationale for the model and placement of the variables in

the model); and (3) examination of the strength of the path coefficients (i.e., direct paths) among the variables if the temporal order

is not apparent. For example, if there is some question about the

ordering of two variables in a model, A and B, and little doubt that

three other variables (X, Y, Z) are antecedent to the two variables

in question, one can regress B against X, Y, Z and A. This procedure would then be repeated after exchanging the positions of A and

B. Then, by examining the relative strengths of the path coefficients

one can often obtain relatively unambiguous clues as to the proper

relative location of the two variables in question.

The testing of relationships for spuriousness is difficult, but not

impossible. It requires comparison of the correlation between an

independent (X) and a dependent variable (Y) with the partial correlation between X and Y when statistical controls are introduced for

all variables antecedent to both X and Y. If the partial r value is

not substantially lower or remains statistically different from zero,

it can be assumed that the original XY relationship is not spurious.

Path analysis provides another way to test for spuriousness. This

involves an examination of the direct paths in the model that are not

significant. The partial correlations for the nonsignificant paths

should be zero; at a minimum, they must not be significantly different

from zero. In this chapter, the partial correlation coefficients are

computed while controlling for all of the antecedent variables.

Illicit Drug Use: Model I

Because the model is not derived from a single, rigorously specified

theory and the data are cross-sectional, the location of the variables in the model must be established. Development of the rationale

for the placement of the eight variables in model I, the saturated

model of illicit drug use, was facilitated by an earlier analysis of

the data from the Manhattan study in which path analysis was used.

O'Donnell and Clayton (1979) tested a model in which age at onset of

marijuana use was the principal dependent variable. Three of the

predictor variables in model I are exactly the same as those used

in the earlier analysis--School Adjustment, Peer Influence, and

Labeling; further, only slight modifications have been made in the

Family Influence, Early Deviance, and Drug Availability scales for

this analysis.

An important finding in the earlier analysis was that different

models had to be used for blacks and whites (O'Donnell and Clayton

1979). A few examples of the differences between whites and blacks

will show the need for separate models. First, two variables--

School Adjustment and Labeling--were deleted from the model for whites

because they did not have significant effects on variables subsequent

to them. In contrast, only School Adjustment could be deleted from the

model for blacks. Second, three variables--Drug Availability, Peer

Influence, and Labeling--had a significant influence on age at onset

133
MODEL I: The Saturated Model
of marijuana use among blacks. For whites, only Drug Availability

and Family Influence had a significant effect on the age at which

marijuana use began. Third, among whites there was a direct path

from Peer Influence to Drug Availability, while this path was not

present for the blacks.

In addition to the minor modifications in the measures of Family

Influence, Drug Availability, and Early Deviance, there are three

other differences between the model tested by O'Donnell and Clayton

(1979) and model I. First, in model I, age at onset of marijuana

use is deleted because marijuana use is part of the Index of Lifetime

Use of Illicit Drugs. Second, Drug Sales has been added as a predictor variable. Third, the new dependent variable is the Illicit

Drug Use Index. Thus, the eight variables included in model I are:

Family Influence (FI); Early Deviance (SD); School Adjustment (SA);

Peer Influence (PI); Drug Availability (DA); Labeling (IA); Drug

Sales (SELL); and the Index of Lifetime Use of Illicit Drugs (LIFDEX).

Variable Location

Family Influence was measured retrospectively in terms of relationships when the respondent was 13 to 15 years old. It is assumed that

this measure reflects parent-child relationships that evolved over

an extended period of time, beginning at birth. While school adjustment and involvement in deviant activities may influence the relationship between parents and a child, it is assumed that, in general,

Family Influence precedes Early Deviance, School Adjustment, and Peer

Influence. Therefore, Family Influence is located at the far left

sife of the model.

School Adjustment was included in model I primarily because of the

role it plays in Hirschi's theory of social control. Like Family

Influence, it represents a factor that may serve to insulate a youth

from involvement in drug use. School Adjustment is placed after

Early Deviance because of the relative predictive power of these

measures. It is placed prior to Peer Influence because School Adjustment should be less predictive of illicit drug use than the behavior

exhibited by peers when the respondent was 13 to 15 years old.

Cohen (1955) suggests that status frustration resulting from competition in school leads to involvement in deliquent subcultures.

Similarly, Hirschi (1969) proposes that lack of attachment to school

is related to involvement in delinquency. However, in model I,

Early Deviance is placed after Family Influence, but prior to School

Adjustment and Peer Influence. This location is contrary to the

theorists' suggestions, but it is appropriate on the basis of the

relative predictive power of these variables.

The measure of Peer Influence is based on items concerning the involvement of the respondent's friends and acquaintances in delinquency

and drug use when he was 13 to 15 years old. The literature suggests

that there is a high correlation between one's involvement in deviance, poor adjustment in school, and association with peers who are

135
delinquent. Peer Influence occupies the fourth position in model I

because it is assumed that scores on this measure would be predicted

by Family Influence, Early Deviance, and School Adjustment.

Drug Availability and Labeling occupy the fifth and sixth positions,

respectively, in model I. Their relative location is based on the

fact that the items in the Drug Availability scale referred specifically to the neighborhood in which the respondent lived when he was

13 to 15 years old, whereas the items in the Labeling scale may be

affected by the respondent's knowledge as to whether his parents,

teachers, and friends had been accurate in predicting his future

trouble with the law.

The Drug Sales scale occupies the seventh position in model I. As

noted in chapter 5, marijuana is the drug that men in Manhattan sold

most frequently. Drug Sales should, therefore, be a predictor

of involvement in the use of illicit drugs, the terminal variable

in model.

Correlations Among the Eight Variables in the Model

Path analysis builds upon the correlations (Pearson's r) that exist

among the variables in the model to be tested. Thus, one initially

examines these correlational values. With 8 variables in the model,

there are 28 r values for each racial group. The values presented

above the diagonal in table 8.1 are for the 125 blacks in the sample,

while those below the diagonal are for the 98 whites. There are three

observations about table 8.1 that deserve mention. First, the sign

of the r value (positive or negative) is in the predicted direction

for each of the 28 correlations for both racial groups. Second, it

is apparent that the strength of the association among the variables

is considerably higher among the whites than among the blacks. In

fact, the average correlation for whites is .434, while for blacks

it is only .245. In only one instance was a correlational value higher

for blacks than for whites; this was the correlation between Drug

Sales and Labeling, with a value of .464 for blacks and .436 for

whites. Third., for whites, the range of correlations is from -.167

(LIFDEX with School Adjustment) to .847 (LIFDEX with Drug Sales); 9

of the 28 r values are above .5. In contrast, there is only one

correlation above .5 for blacks (Labeling with Peer Influence = .543),

while the lowest correlation, -.007, occurs for Early Deviance and

Family Influence. In fact, 6 of the 28 correlations for blacks are

below .10. Therefore, these data suggest that there are marked racial

differences in the strength of the interrelationships among these

variables. While this is true, it is possible that the structure of

these correlations is similar in the two groups. To check the validity

of this observation, the 28 correlations were ranked from largest to

smallest for each race. A Spearman's rho computed for the two sets

of ranks was .726; this indicates a statistically significant consistency among them. While the strength of the associations among the

variables is lower among blacks, the structure of the interrelationships among these 8 variables is quite similar for blacks and whites.

136
TABLE 8.1 Correlations (Pearson's r) among the 8 variables in the Model: Blacks

Above the Diagonal and Whites Below the Diagonal
Test of Model I

Shown in tables 8.2 and 8.3 for blacks and whites, respectively, are

(a) the predictor variables and the order in which they entered the

regression equation at each stage of model I, as one moves from right

to left; (b) the unstandardized B values and the standardized partial

betas, or path coefficients; (c) the standard error of B and the F

value for each variable in the equation; (d) the incremental amount

of variance explained by each variable; and (e) the simple correlation (r) between each dependent variable and the predictor variables.

While the information in tables 8.2 and 8.3 is presented separately

for blacks and whites, the two tables my be described at the same

time because the same procedures were used to test model I for blacks

and whites.

Lifetime Use of Illicit Drugs

Among the blacks, only one variable, Drug Sales, has a direct and

statistically significant effect on use of illicit drugs. Evidence

of this is found in the amount of variance--23 percent--in LIFDEX

attributable to Drug Sales, the size of the F value for Drug Sales,

and the fact that the standard error of the B value is approximately

one-fourth the size of the B value. None of the other variables meet

any of these criteria for the blacks. Among the whites, two of the

variables have direct and statistically signficant effects on illicit

drug use, but the predictive value of Drug Sales is overwhelming. It

accounts for 72 percent of the variance in the Lifetime Use of Illicit

Drugs Index. The B value indicates that a one unit change in the sales

index produces a 25 unit change in LIFDEX for whites. In view of

the statistical and substantive significance of involvement in selling

drugs for whites, it is impressive that the Early Deviance scale also

has a direct effect on illicit drug use. Drug Sales accounts for most

of the variance, but Early Deviance explains an additional 3 percent.

These findings provide strong support for the idea that selling drugs

may be a key to understanding involvement in the drug subculture.

In fact, knowledge that a white in this sample has sold drugs allows

one to predict with a high degree of accuracy that he has also used

illicit drugs. However, one might question whether Drug Sales and

LIFDEX are measuring the same phenomenon, especially among whites,

as the correlation between the two scales is .847. In addition, use

of marijuana is included in the LIFDEX measure and marijuana dominates

the Drug Sales scale. To check this, the LIFDEX measure was recomputed,

with use of marijuana deleted, and then regressed against Drug Sales

and the other predictor variables in the model. The results were

essentially identical to those reported above. Involvement in the

sale of drugs is a key predictor of use of illicit drugs for blacks

as well as whites.

The direct effect of Early Deviance on LIFDEX for whites was unexpected, in view of its location in model I. The value of B indicates

that a one unit change in the Early Deviance scale produces a change

of almost 63 units in the Index of Lifetime Use of Illicit Drugs.

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TABLE 8.2 Stepwise Multiple Regression: Model I for Blacks

Value Standard Value IncreDependent/Independent of Beta Error of mental Simple

Variable /Variable B Weight of B F r

2

 r

LIFDEX-Sell 9.97 .424 2.16 21.368 .228 .478

LIFDEX-Labeling 62.90 .166 41.24 2.327 .015 .331

LIFDEX-Drug Avail. 31.05 .133 23.09 1.808 .005 .211

LIFDEX-Early Dev. -41.01 -.096 43.45 .891 .009 .120

LIFDEX-Peer Influ. -11.84 -.053 23.37 .257 .002 .202

LIFDEX-School Adj. 21.75 .035 55.85 .152 .001 -.045

LIFDEX-Fam. Influ. 3.47 .017 16.54 .044 .000 -.036

SELL-Labeling 6.47 .403 1.66 15.276 .216 .464

SELL-Peer Influ. 1.80 .191 .98 3.345 .022 .377

SELL-School Adj. 2.62 .098 2.37 1.222 .006 -.113

SELL-Drug Avail. -0.67 -.068 .98 .470 .002 .207

SELL-Early Dev. 1.11 .061 1.85 .361 .003 .266

SELL-Fam. Influ. 0.21 .024 .71 .084 .001 -.055

LABELING-Peer Influ. .19 .388 .05 14.982 .295 .543

LABELING-School Adj. -.38 -.232 .13 9.284 .062 -.379

LABELING-Drug Avail. .10 .163 .05 3.521 .034 .416

LABELING-Early Dev. .14 .122 .10 1.838 .008 .442

LABELING-Fam. Influ. -.02 -.047 .04 .407 .002 -.154

DRUG AVAIL.-Early Dev. .70 .385 .16 19.108 .248 .498

DRUG AVAIL.-Peer Influ. .29 .300 .08 11.738 .072 .476

DRUG AVAIL.-School Adj. .27 .101 .21 1.619 .008 -.106

DRUG AVAIL.-Fam. Influ. -.07 -.085 .07 1.244 .007 -.133

PEER INFLU.-Early Dev. .87 .454 .16 30.892 .235 .485

PEER INFLU.-Fam. Influ. -.16 -.172 .07 4.896 .033 -.184

PEER INFLU.-School Adj. -.27 -.094 .23 1.311 .008 -.255

SCHOOL ADJ.-Early Dev. -.22 -.318 .06 13.883 .102 -.319

SCHOOL ADJ.-Fam. Influ. .03 .094 .03 1.224 .009 .097

EARLY DEV.-Fam. Influ. -.003 -.007 .04 .005 .000 -.007

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TABLE 8.3 Stepwise Multiple Regression: Model I for Whites

Value Standard Value IncreDependent/Independent of Beta Error of

Variable/Variable

mental Simple

B Weight of B F r

2

 r

LIFDEX-Sell 25.27 .729 2.12 141.713 .718 .847

LIFDEX-Early Dev. 62.70 .167 27.90 5.050 .033 .558

LIFDEX-Peer Influ. 14.59 .097 15.00 .947 .004 .513

LIFDEX-School Adj. 29.27 .069 28.25 1.074 .003 -.167

LIFDEX-Labeling 27.43 .082 27.00 1.032 .002 .481

LIFDEX-Fam. Influ. 10.74 .050 13.16 .666 .001 -.267

LIFDEX-Drug Avail. -6.68 -.038 13.85 .233 .OOl .334

SELL-Early Dev. 3.16 .129 1.34 5.581 .221 .470

SELL-Labeling 2.28 .236 1.31 3.016 .041 .436

SELL-Fam. Influ. -.80 -.129 .64 1.552 .012 -.309

SELL-School Adj. 1.60 .130 1.39 1.332 .016 -.179

SELL-Peer Influ. .60 .140 .74 .671 .003 .439

SELL-Drug Avail. -.40 -.080 .68 .350 .003 .281

LABELING-Peer Influ. .18 .439 .04 22.582 .416

LABELING-School Adj. -.45 -.354 .10 21.250 .152

LABELING-Early Dev. .15 .132 .10 2.020 .009

LABELING-Fam. Influ. -.04 -.062 .05 .646 .003

LABELING-Drug Avail. DID NOT ENTER THE EQUATION

.645

-.548

.578

-.420

DRUG AVAIL.-Peer Influ. .66 .772 .08 69.140 .555 .745

DRUG AVAIL.-Fam Influ. .16 .132 .10 2.881 .007 -.221

DRUG AVAIL.-School Adj. -.31 -.126 .19 2.668 .013 -.276

DRUG AVAIL.-Early Dev. .03 .013 .20 .019 .000 .499

PEER INFLU.-Early Dev. 1.51 .604 .21 52.603 .429 .655

PEER INFLU.-Fam. Influ. -.32 -.224 .12 7.226 .039 -.398

PEER INFLU.-School Adj. .16 .056 .24 .420 .002 -.268

SCHOOL ADJ.-Fam. Influ. .15 .299 .05 9.896 .155 .394

SCHOOL ADJ.-Early Dev. -.16 -.293 .08 9.477 .077 -.390

EARLY DEV.-Fam. Influ. -.19 -.325 .06 11.308 .105 -.325

140
This suggests that a white from a high drug use area in Manhattan

who becomes involved in deviant activities at an early age is moving

toward involvement in the drug subculture. This finding lends support

to Jessor and Jessor's (1977) problem behavior theory, at least for

the whites in the sample. Yet, this is not the case for blacks.

In fact, the beta and B values for the LIFDEX-Early Deviance path are

not statistically significant, and the negative sign for this path

is contrary to predictions (see table 8.2). This suggests that

whatever impact early involvement in deviant activities has on illicit

drug use among blacks must be indirect.

Drug Sales

Johnson (1973) identified two variables that facilitate a person's

transition from involvement in the cannabis subculture to involvement in the heroin subculture. These variables are having heroinusing friends and selling drugs. In technical terms, these are

intervening variables. Single and Kandel (1978) confirmed the role

of drug selling as an intervening variable in the marijuana-heroin

relationship. The data from the Manhattan study underscore the predictive value of involvement in drug sales for use of the entire range

of illicit drugs, not simply use of heroin. However, what predicts

involvement in drug sales? In model I, 6 variables could have a

direct effect on the measure of drug sales, and the results are

somewhat different for blacks and whites. The best predictor of

Drug Sales for blacks is Labeling, which accounts for 22 percent of

the variance. A change of one unit in Labeling produces a change

of more than 6 units in the Drug Sales index. Peer Influence accounts

for an additional 2 percent of the variance, but the beta weight is

significant at only the .05 level. Among the whites the strongest

predictor of involvement in Drug Sales is Early Deviance. It accounts

for 22 percent of the variance. Early Deviance did not have a direct

effect on Drug Sales for blacks. While Labeling did have a direct

effect on Drug Sales for whites, as it did for blacks, the beta is

significant at only the .05 level and accounts for an additional 4

percent of the variance in Drug Sales.

Labeling

According to the labeling perspective, the proper focus in the study

of deviance is not the individuals who commit acts in violation of

norms or the law, or the acts themselves, but rather the reaction of

society and its sanctioning agents to such individuals and their

acts. In other words, society creates deviance by defining certain

acts as deviant and by assigning labels to persons who do them.

operationalization of the process of labeling is difficult, especially

in survey research. In this study the Labeling variable reflects

responses to questions about whether, when he was 13 to 15 years old,

the respondent's parents, teachers, and friends thought he was going

to get into trouble with the law.

141
Among the whites, two variables have a direct effect on Labeling.

The measure of Peer Influence, which is based on questions about the

deviant activities and drug use of the respondent's friends when he

was 13 to 15 years old, accounts for 42 percent of the variance in

the Labeling variable, and School Adjustment accounts for an additional 15 percent of the variance. The sign of the School AdjustmentLabeling path coefficient is negative; thus indicates that the poorer

his adjustment in school, the more likely a white respondent is to have

said his parents, teachers, and friends thought he was headed for

trouble with the law. The same two variables, Peer Influence and

School Adjustment, have significant direct effects on Labeling among

blacks. These variables account for 30 percent and 6 percent, respectively, of the variance explained. A third variable, perception

of Drug Availability, accounts for an additional 3 percent of the

variance in the Labeling variable for blacks.

Drug Availability

The sample was selected from areas of Manhattan that had the highest

rates of opiate use in New York City, and possibly the highest in the

entire United States. In terms of the availability of drugs in this

setting, any respondent who wished to try an illicit drug would seem

to be guaranteed the opportunity to do so. However, widespread use

of drugs in an area does not mean that every respondent was aware of

their existence and knew how to obtain them. The respondents' perception of the availability of drugs was measured in terms of knowledge

about copping areas and acquaintance with known drug users or sellers

in his neighborhood when he was 13 to 15 years old.

In an earlier paper, O'Donnell and Clayton (1979) found that perception of Drug Availability was a strong predictor of age at onset of

marijuana use for blacks and especially for whites. However, the

data for whites in table 8.3 indicate that Drug Availability does not

have a significant effect on any of the variables subsequent to it in

model I. In addition, only one variable, Peer Influence, has a direct

path to Drug Availability, and it accounts for 56 percent of the

variance in Drug Availability. This means that Drug Availability can

and should be deleted from the model for whites because it is a

terminal variable that is located in the middle of the model; it does

not predict anything subsequent to it, and it is predicted by only

one factor. On the other hand, Drug Availability does seem to be

an important variable for blacks. Not only does it have a direct

impact on Labeling; two variables have a direct effect on it. Early

Deviance accounts for 25 percent of the variance in Drug Availability

and Peer Influence accounts for an additional 7 percent.

Peer Influence

A substantial body of literature exists concerning the impact of the

attitudes and behavior of one's peers on a person's own attitudes

and behavior. As noted in chapter 7, the influence of the peer group

is a central element in various theories of deviance, and research on

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adolescent drug use also reflects the importance of peers. As Kandel

notes:

The data derived from longitudinal studies indicate that

a socialization process takes place. Extent of perceived drug use in the peer group, self-reported drug

use by peers, and perceived peer tolerance for drug use

are all very strong predictors of a youth's subsequent

initiation into various forms of use. These peer

influences appear to be more important at certain

pints of the process of involvement than at others.

(Kandel 1978, p. 24)

In this study, the Peer Influence scale did not have a direct effect

on the illicit drug use index for either blacks or whites. It did

have a significant impact on Drug Availability, Labeling, and Drug

Sales among blacks and on Drug Availability and Labeling among whites.

The Peer Influence scale consists of items assessing how involved

in various forms of deviance (e.g., boosting, gang fights, using

alcohol, marijuana, and other drugs) the respondent's friends were

when he was 13 to 15 years old. Early Deviance and Family Influence

have significant direct effects on the Peer Influence measure for

both races. Among the whites, Early Deviance accounts for 43 percent

of the variance in Peer Influence. Family Influence, with a predicted

negative sign, accounts for an additional 4 percent. Early Deviance

accounts for only 24 percent of the variance in Peer Influence in

blacks, and Family Influence accounts for another 3 percent. It is

important to note that Early Deviance is antecedent to Peer Influence

in model I. The relative location of these variables makes explicit

the concept of deviance proneness articulated by Jessor and Jessor

(1977). The data discussed above suggest that early involvement in

deviance (i.e., deviance proneness) coupled with low family influence are predictive of having friends who are oriented toward deviance.

School Adjustment

Much of the research on adolescent drug use has focused on the effects

of low grades and truancy on initiation into and involvement with

marijuana use (Jessor and Jessor 1977; Kandel et al. 1978; Smith

and Fogg 1978). According to Hirschi's (1969) social control theory,

attachment to school and parents should operate as buffers against

use of illicit drugs. In this study, School Adjustment has a direct

effect on only one variable in the model, Labeling. However, it

intervenes between Early Deviance and Labeling for blacks and Early

Deviance, Family Influence, and Labeling for whites. Early Deviance

accounts for 10 percent of the variance in School Adjustment for blacks

Among whites, Family Influence accounts for 16 percent of the variance

in School Adjustment, while Early Deviance accounts for an additional

8 percent.

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Early Deviance

The only variable antecedent to Early Deviance in model I is Family

Influence. Family Influence accounts for almost 11 percent of the

variance in Early Deviance for whites. The story is different for

blacks. Family Influence accounts for so little of the variance in

Early Deviance for blacks that it will be necessary to place these

two variables in the same relative location in the revised model.

Trimming Model II

The significant direct paths suggested by the data are presented

separately for blacks and whites in model II (see tables 8.2 and 8.3).

Visual inspection of the diagrams indicates that different variables

explain illicit drug use for blacks and whites. Therefore, further

steps taken to test these models will be discussed, first for blacks

and then for whites. These steps include: (1) use of partial correlation techniques to justify deletion of direct paths in model II

and (2) recomputation of the coefficients for the remaining direct

paths by means of simple multiple regression rather than stepwise

multiple regression. This two-step process is called trimming the

model.

Model II: Blacks

In model I, the saturated model, there were 8 variables and 28 possible direct paths. To this point, the analysis suggests that only

11 direct paths are empirically justified, and these are found in

model II for blacks. The next step is to determine whether deletion

of 17 direct paths from the saturated model is appropriate. The partial correlation coefficients pertinent to model II for blacks are

found in table 8.4. For 13 paths the prediction of a nonsignificant

or zero partial r was correct. The four exceptions are marked by an

asterisk and are:

1b. LIFDEX-Labeling: DA, PI, SA, ED, FI

3b. LIFDEX-Peer Influence: SA, ED, FI

9b. SELL-Early Deviance: FI

10b. LABELING-Early Deviance: FI

It should be noted that these four equations are tests for spuriousness in that the variables that are statistically controlled occur

prior to the predictor variable in model II. In each case, the

parallel test for the presence of an intervening variable (equations

1a, 3a, 9a, 10a) yielded a partial r value that is: (a) considerably

lower than the simple r for that path and (b) statistically nonsignificant. If the partial r values for both the test for spuriousness and for intervening variables were statistically significant,

this would suggest that the direct path should be retained in the

model. Since only 4 of the partial r values used in testing for

spuriousness are still significant, and there is evidence that the

predictor variables in these relationships operate indirectly,

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MODEL II for Blacks in the Manhattan Study
TABLE 8.4 Partial Correlation Equations: Paths to be Deleted from Model I for Blacks
rather than directly, deletion of these direct paths from model II

for blacks is appropriate.

Model II: Whites

The first step required to trim model II for whites is the elimination of Drug Availability. It is a terminal variable in that it

appears in the middle section of the model and does not have a direct

effect on any variables subsequent to it. In addition, only one

variable, Peer Influence, has a direct and significant effect on

Drug Availability. With the elimination of Drug Availability, model

II for whites has 7 variables and 21 potential direct paths. Because

11 direct paths merged in the test of model I, the saturated model,

it is necessary to justify the deletion of the other 9 paths from

model II. The partial r values pertinent to these paths for whites

are found in table 8.5. For 5 of the paths there is clear evidence

to support their deletion. The pairs of equations that require additional comment are:

1a. LIFDEX-Labeling: SELL

1b. LIFDEX-Labeling: PI, SA, ED, FI

2a. LIFDEX-Peer Influence: LA, SELL

2b. LIFDEX-Peer Influence: SA, ED, FI

5a. SELL-Peer Influence: LA

5b. SELL-Peer Influence: SA, ED, FI

8a. LABELING-Early Deviance: SA, PI

8b. LABELING-Early Deviance: FI

The simple r among whites for the relationship between lifetime illicit drug use and Labeling is .481. As the data in table 8.5 indicate, a control for Drug Sales as an intervening variable produces

a partial r of .233, which is still significant, but it is less than

one-half the size of the original relationship. This means that Drug

Sales intervenes between illicit drug use and Labeling and that

there are also other unmeasured variables that could affect the relationship. A test of the relationship between LIFDEX and Labeling for

spuriousness reveals a substantial reduction (i.e., over half) with

4 control variables in the equation (partial r of .205). These

reductions, as well as the fact that the beta (.082, F = 1.032) for

the LIFDEX-Labeling path is so low, provide support for the decision

to eliminate this path from model II for whites (see table 8.5).

A similar argument can be made for deletion of the LIFDEX-Peer Influence path from model II for whites. In testing for the presence

of intervening variables (Labeling and Drug Sales), the partial r

value (.203) is statistically significant, but is only 40 percent the

size of the simple r value of .513. There is also a substantial spurious element in the LIFDEX-Peer Influence relationship: this is revealed by the partial r value of .209 when School Adjustment, Early

Deviance, and Family Influence are statistically controlled. The

magnitude of these reductions and the F value of only .947 and beta

= .097 for the LIFDEX-Peer Influence suggest that deletion of this

path from model II is appropriate (table 8.4).

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MODEL II for Whites in the Manhattan Study
TABLE 8.5 Partial Correlation Equations: Paths to be Deleted from Model I for Whites
The third pair of partial r values in table 8.5 that deserves comment

concerns the path between Drug Sales and Peer Influence. This path

in model II is significant for blacks. With a control for Labeling

as an intervening variable, the partial r (.229) is still statistically significant even though it is substantially lower than the

simple r of .439. However, it appears that the relationship between

Drug Sales and Peer Influence is spurious. Simultaneous controls on

School Adjustment, Early Deviance, and Family Influence yield a partial r of only .153. This is sufficient evidence to permit deletion

of the Drug Sales-Peer Influence path fran model II for whites.

There is also evidence to support deletion of the Early Deviance

path from model II for whites. The simple r (.578) was reduced

only slightly to .515 when Family Influence was statistically controlled. However, a simultaneous control on School Adjustment and

Peer Influence yields a partial r (.145) that is not statistically

significant. Therefore, the influence of Early Deviance on Labeling

among whites is essentially indirect instead of direct.

Model III: Blacks and Whites

At this point, the models for blacks and whites have been thoroughly

trimmed. Therefore, model III represents the best fit between the

data and the interrelationships among the variables suggested by

the relevant theories. The separate diagrams in model III for blacks

and whites contain beta weights or path coefficients for each of

the direct paths that remain in the model; the unstandardized B

values are shown in parentheses. These values are derived from simple

multiple regression analyses in which a dependent variable is regressed against only those variables having a direct effect on it.

The diagrams also contain the adjusted r-square values and the residual or error terms at each stage of the model from right to left.

The error term is the square root of one minus the adjusted r-square

value.

When one examines different models that include the same variables,

it is appropriate to compare only the unstandardized B values for

specific paths. This approach to model III produces several conclusions that deserve mention. First, involvement in drug sales is

a much more powerful predictor of illicit drug use for whites than

blacks. For whites, a one unit change in the Drug Sales index produces a 26 point change in the illicit drug use index. For blacks

a one unit change in this variable produces only an eleven point

change in the illicit drug use index. In addition, Early Deviance

has a significant impact on illicit drug use for whites, in spite

of the fact that Drug Sales accounts for such a large proportion of

the variance in illicit drug use. Early Deviance does not have a

direct path to illicit drug use for blacks.

The impact of Labeling on drug sales is twice as strong for blacks

as for whites. A one unit change in Labeling produces 5.9 units of

change in Drug Sales for blacks in comparison with only 2.39 units for

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MODEL III for Blacks in the Manhattan Study
MODEL III for Whites in the Manhattan Study
whites. Peer Influence has a significant effect on Drug Sales for

blacks, but not for whites, and Early Deviance predicts involvement

in drug sales for whites, but not blacks. Peer Influence and School

Adjustment have a significant effect on Labeling for both blacks and

whites. The B values for these variables are relatively small and

of approximately equal magnitude.

Family Influence and Early Deviance have direct paths to Peer Influence for both blacks and whites. While the differences between the

races are not substantial, the impact of these variables on Peer

Influence is greater among whites than blacks. School Adjustment is

directly affected by Early Deviance at about the same level for both

races. Among whites, Family Influence also has an impact on School

Adjustment. Finally, there is a direct effect of Family Influence

on Early Deviance for whites, but not for blacks. In fact, the simple

correlation of Family Influence and Early Deviance among blacks is

only -.01.

An examination of the adjusted r-square values at each stage of

model III reveals that the predictor variables account for a larger

percent of explained variance for whites than blacks. This suggests

a better fit between the data for whites and the variables in the

model than is true for blacks. Although not presented in tabular

form, the data for the blacks were fitted to model III for whites and

the data for the whites were fitted to model III for blacks. The

results indicate that the models are unique. Wither model is sufficiently flexible to fit data fran the other racial group.

Model III: General Observations

It is relatively easy to describe the findings that emerge when causal

models are tested by means of path analysis. It is considerably

more difficult to describe the implications of these models for an

understanding of the causes of illicit drug use. Nevertheless, three

general observations about the use of illicit drugs can be derived

from Model III. First, it is apparent that different combinations

of factors explain illicit drug use among blacks and whites. The

sample was designed to include men who grew up in health areas in

Manhattan that had high rates of opiate use. It was assumed that

the experience of living in such a sociocultural context would permit

use of a single model to explain illicit drug use. However, high

areas may exist more in the eyes of researchers and public officials

than in the eyes of the young men who lived in these areas. Neighborhoods with high rates of use simply are not homogeneous on other

factors. They undoubtedly differ in ways that are salient for an

explanation of illicit drug use. While virtually all of the men in

the Manhattan study, black and white, grew up in neighborhoods where

opiate use was high by objective standards, it is clear from model

III that the impact of this sociocultural context is mediated by

other factors, including ecological variables which were not measured.

Researchers should assess various facets of the sociocultural context

at the neighborhood level. An objective macro-level index, such as

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the rate of opiate use, must be supplemented by micro-level measures

that assess the richness and diversity, as well as the order, that

permeates urban neighborhoods (see Suttles 1968; Liebow 1967).

Neighborhoods differ, for example, in the extent to which residents

know each other and in the stability or transiency of the population.

Another possible reason the models differ for blacks and whites is

that race may influence the number of supportive resources that are

available to young men. Race may also affect the range of opportunities available for achieving success in society. Although it is

speculative, the blacks may have been assigned to tracks other than

the college preparatory one. In our society, the ticket required

for admission into higher status occupations and the higher incomes

associated with them must be stamped: College Graduate! For a youth

who grows up in a high drug use area in Manhattan, such a ticket may

be the only route out of the ghetto. The whites in the Manhattan

sample were obviously in the express lane for education tickets in

comparison with the blacks. Forty-six percent of the whites were

college graduates in contrast with only 8 percent of the blacks. On

the other hand, only 9 percent of the whites did not finish high

school, but 32 percent of the blacks were dropouts. Consequently,

it is not surprising that the final models for blacks and whites

differ in many respects. Given the apparent disparity between the

races in access to educational success, perhaps the degree of similarity between the models, not the differences, should generate

surprise.

A second general observation derived from model III concerns the

importance of Drug Sales as a predictor of use of illicit drugs.

The data presented in chapter 5 confirm that prior use of marijuana

is almost a necessary condition for involvement in the marketing of

illicit drugs, and marijuana is the common medium of exchange in

that marketing system. However, the process of moving from using

marijuana to selling drugs to using other illicit drugs has been

virtually ignored by researchers (for an exception, see Sutter 1969).

Most observers view initial use of marijuana as the key transition

or rite-de-passage into the marijuana subculture (Kandel 1975; Jessor

and Jessor 1977; O'Donnell and Clayton 1979) just as initial use of

heroin or first intravenous injection has been viewed as evidence

of entry into the heroin subculture (O'Donnell and Jones 1968).

The data in model III suggest that the events that occur subsequent

to one's first use of marijuana but prior to use of other drugs

deserve special attention. Sutter (1969) refers to young persons who

purchase their own supply and deal in marijuana on a small-time

basis as "pot heads." Some pot heads, attracted to the money that

can be made by dealing, become "players" or the intermediaries for

the flow of drugs and stolen merchandise from adult thieves to adolescents. Sutter suggests that a player on his way to a career in

hustling will come in contact with heroin. While Sutter does not

identify any specific stages, it is quite possible that there are

clear-cut stages of involvement in selling drugs. Such stages may

parallel the transition fran use of marijuana to use of other illicit

154
drugs. Identification of stages in the sale of drugs might specify

Kandel's (1975) four stage model of drug use.

The difference by race in the predictive power of Drug Sales for

illicit drug use is also intriguing. With knowledge that a white

in this sample has sold drugs, one can predict with a high degree

of accuracy that he has used illicit drugs. For blacks the accuracy

of one's prediction is considerably lower. This may mean that selling

drugs and using illicit drugs other than marijuana are roughly coequal indicators of involvement in the drug subculture for whites.

Among blacks, involvement in the drug subculture may be more differentiated; that is, there may be more stages in the process of

becoming involved in and committed to illicit drug use and its

attendant lifestyle.

Although some of the comments are speculative, model III provides a

persuasive basis for the argument that more research should focus

on the sale of drugs as an important key to understanding the etiology

of illicit drug use. Three questions deserve immediate attention:

(1) What is the average length of time between onset of marijuana

use and the sale of drugs? (2) Is there a level of usage that, once

attained, seems to thrust a person into the drug distribution system?

(3) What are the factors that differentiate between those who use

and do not sell, those who use and sell only marijuana, and those who

branch out into using and selling a variety of drugs?

A third general observation derived from model III concerns the

differential impact of Early Deviance on Drug Sales and illicit drug

use for blacks and whites. Early involvement in deviant activities

seems to propel whites toward involvment in more serious forms of

deviance. This can be seen in the direct paths that exist between

Early Deviance and both Drug Sales and the Index of Illicit Drug

Use in model III. The only impact Early Deviance has on Drug Sales

for blacks is indirect and operates through Peer Influence. There

are at least two variables that intervene between Early Deviance and

illicit drug use for blacks, regardless of which paths are followed.

These findings from model III suggest that deviance proneness and

transition proneness, concepts central to the problem behavior theory

of Jessor and Jessor (1977), may operate differently for blacks and

whites. For example, the items used in constructing the Early

Deviance, Drug Sales, and LIFDEX measures reflect behavior that requires individual initiative. It is possible that the whites in the

study who scored high on these measures were virtually all devianceprone. On the other hand, for blacks involvement in the activities

that make up the Early Deviance scale may be inconsequential in

predicting later involvement in selling or using illicit drugs because a larger proportion of all black youth are involved in those

activities. Therefore, these activities do not tend to single out

the deviance-prone black youth because the activities are not viewed

as deviant by black youth.

In this section of chapter 8 three general observations have been

derived from model III. This has been accomplished without straying

155
too far from either the model that has been tested or the substantive

focus of the Manhattan study, illicit drug use. In the next chapter

the findings from the Manhattan study are linked with findings from

other studies of drug use and with the broader field of deviance.

This will be accomplished by relating the findings from this study to

broader concepts such as subculture, socialization, developmental

transitions, and differential association.

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Chapter 9

Toward Theoretical Synthesis

The purpose of this chapter is to assess the implications of the

findings, and this can best be accomplished by placing the national

and Manhattan studies in perspective. First, several of the historical and social conditions that led to initiation of the national

survey are reviewed. Second, the findings in the Manhattan study, as

reported in chapter 8, are linked to contemporary sociological theories.

Historical Context

In conducting research, investigators can become so involved in the

search for answers to specific questions that they lose sight of the

broader historical, political, and social conditions that led to the

initial emphasis on those questions. Such a narrow focus can readily

occur in research on social problems such as illicit drug use.

In the late 1960s and early 1970s use of illicit drugs became so widespread in the United States that some knowledgeable individuals suggested an epidemic had occured (Jaffe 1973; DuPont and Greene 1973).

At the same time, high levels of opiate use on the part of soldiers

in Vietnam were reported, and it was feared that existing treatment

facilities would be inadequate to cope with addicted veterans upon

their return to this country. This fear, it may be noted, was based

to a considerable extent on the idea that a high proportion of individuals addicted to opiates would, after treatment, relapse.

The existence of these conditions led to recognition of the need for

reliable data to answer basic epidemiological questions. A fundamental question was: Who was using what drugs? Related questions

pertained to where, when, and how these drugs were being used. The

Marihuana Commission (NCMDA, see, e.g., 1972) funded national surveys

to obtain information relevant to these questions and the Special

Action Office for Drug Abuse Prevention (SAODAP) commissioned two

major studies to provide epidemiological data in subpopulations in

which drug use was thought to be most extensive. The first was Robins'

(1973) study of enlisted men who returned from Vietnam in September

1971. The second was the national study of youug men and drugs

(O'Donnell et al. 1976). After SAODAP was phased out, the National

Institute on Drug Abuse (NIDA) continued to chart the epidemiology of

drug use by means of its national household surveys (Abelson et al.

1977; Fishburne et al. 1980) and the annual surveys (1975-80) of high

school seniors conducted by Johnston and his associates (1980).

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It is now only ten years from the time Robins began her study of drug

use among Vietnam veterans. In this decade, the primary emphasis

in the drug field has shifted from description to explanation. The

two studies described in this report reflect this important recent

change. While greater emphasis is now placed on explanation than was

the case a decade ago, interest in etiological issues is apparent in

the writings of Lindesmith (1938, 1947, 1968); O'Donnell and Jones

(1968); Ausubel (1958); Chein et al. (1964); Martin and Fraser (1961);

Robins and Murphy (1967); Wikler (1965, 1968), and others. These

scholars focused almost exclusively on the opiates, and many of them

were more concerned with the psychopharmcological than the psychosocial determinants of addiction. In contrast, a number of recent

etiological efforts have been directed toward explanation of initiation

into marijuana use (Jessor and Jessor 1977; Kandel 1975; Smith and

Fogg 1978; O'Donnell and Clayton 1979).

Toward a Theoretical Synthesis

The major theoretical perspectives on drug use have been summarized

(Lettieri et al. 1980; Kandel 1980). Lettieri's volume is comprehensive and both multidisciplinary and multidimensional in focus. In 43

chapters diverse theories are discussed in which the principal causative forces in drug abuse are identified as: (a) one's relationship

to others; (b) one's relationship to society; and (c) one's relationship to nature. Stated differently, there are 12 theories dealing

with psychological factors, 15 theories focusing on interactive factors,

8 theories concerning sociological factors, and 8 theories dealing

with the biological factors that explain drug abuse. Then, what each

theory has to say about the issues of (a) initiation, (b) continuation,

(c) the transition fran use to abuse, (d) cessation, and (e) relapse

is presented in separate sections. On the other hand, Kandel organized her review of the empirical literature in terms of four theoretical

perspectives: (1) Jessor and Jessor's (1977) problem behavior-proneness theory: (2) Akers' (1977) social learning theory, which is a

modification of Sutherland's (Sutherland and Cressey, 1974) theory of

differential association in terms of Skinner's operant conditioning

theory; (3) Kaplan's (1975, 1977, 1978) theory in which the key factor

is self-derogation; and (4) Kandel's (1978) theory of socialization in

which elements of reference group theory, subcultural theory, and symbolic interactionism are combined.

While Letteiri's volume is more comprehensive, the theories covered

by Kandel closely parallel the ones relied upon to construct the interview schedule for the Manhattan study. It should be noted, however,

that no attempt was made to test a specific theory in the field of

deviance. Rather, questions were designed to measure some of the

central concepts in the existing theories.

Drug Sales: An Indicator of Subcultural Involvement

Elaborating on the theories of differential association (Sutherland

1947) and anomie (Merton 1938), Cohen (1955) and Cloward and Ohlin

158
(1960) focused on deviant subcultures, and Yinger (1960) drew a useful

distinction between subcultures and contracultures. The existence and

importance of a drug subculture is also emphasized in the literature

on addiction. O'Donnell observes:

The concept of a drug subculture implies that addicts

are in contact with each other. In this contact, learning

takes place. The learning can be of facts and techniques.

For example, the neophyte can learn from more experienced

addicts that his withdrawal symptoms are the result of not

having had his usual dose of narcotics, and will be relieved

by a dose; that the intravenous route enhances the drug

effect; how to obtain narcotics, or money for narcotics;

new sources of narcotics; how to prepare narcotics for

administration, and other knowledge of this kind. He will

usually learn new attitudes too. He may learn to define

himself as an addict, learn new justifications for his

drug use, and new and negative attitudes toward the laws

which try to prevent drug abuse. In his contact with other

addicts he learns their argot, with the implicit value

orientations built into it. As with other forms of deviant

behavior, he is likely to feel more and more at home with

other addicts, spend more time and invest more interest

in those new contacts, and gradually withdraw from ties to

family, friends, and old associations. This in turn means

that his old values are no longer reinforced by the old

ties, so that it becomes still easier to adopt the values

of the addict subculture. Finally, he is perceived by himself and other addicts, by his family and other older

associates, and often by police and health agencies as a

member of a deviant subgroup, whose values and orientations

are shared with them but are opposed to those of the wider

culture. (O'Donnell 1969, pp. 84-85)

In O'Donnell's (1969:85) study of narcotic addicts in Kentucky, the

sale of narcotics was employed as the most important indicator of

involvement in the drug subculture. Similarly, in his study of

Marijuana Users and Drug Subcultures, Johnson (1973) identified three

subcultural variables--peer group marijuana use, buying and selling

drugs, and having heroin-using friends--that helped propel marijuana

users into a lifestyle dominated by the use of heroin and the intravenous injection of heroin (see Johnson 1980). Johnson suggested

the existence of a causal chain in which extent of marijuana use is

the independent variable, heroin use is the dependent variable,and

the three subculture factors are intervening variables. Johnson's

(1973:66, 78-79, 86, 108, 111, 116) hypotheses are:

1. "The respondent's own use of cannabis is a more important determinant of having heroin-using friends than vice versa...

2. College students engage in cannabis selling for one main, very

rational reason: they need to obtain a supply of the drug that they

159
use regularly. In the process of gaining this supply, students most

frequently buy a larger quantity than they can reasonably use and are

likely to sell it to friends who want some...

3. Involvement in selling cannabis, not hard drug use, is the crucial factor in explaining hard drug sales...

4. In the sample population, cannabis use appears to be a prerequisite

for the use, purchase and sale of hard drugs...

5. A student who becomes involved in selling drugs is likely to gain

friends who use dangerous drugs and hence to be turned on by such

friends...

6. Involvement in selling drugs is the crucial factor in understanding

why students gain intimate heroin-using friends...

7. The most critical assumption made...is that the influence of an

intimate friend who uses a particular drug is frequently the precipitating (immediately causal) factor in the respondent's use of that

drug."

These seven hypotheses outline a simple causal chain in which (a) marijuana use leads to (b) selling marijuana which leads to (c) sales

of hard drugs which leads to (d) having heroin-using friends which

leads to (e) heroin use. Stated differently, this model depicts the

three subcultural factors (i.e., selling marijuana, selling hard

drugs, and having heroin-using friends) as intervening variables in the

relationship between use of marijuana and heroin. In testing such a

causal chain statistical controls on one or more of the intervening

variables will cause the zero-order relationship between the first

and last variables, marijuana and heroin, to vanish or be reduced

substantially (Blalock 1964:68; Rosenberg 1968:54-83). Johnson's

data confirmed the existence of the causal chain. When the intervening subcultural variables were statistically controlled, the relationship between marijuana use and heroin use was, as expected,reduced

almost to zero. This signifies the importance of drug sales as a

barometer of one's involvement in a drug subculture. Such a reduction

should also strengthen the argument that marijuana use is causally

related to heroin use via involvement in the subculture. Unfortunately, Johnson ignored this point and Goode, in the foreword to

Johnson's book, completely misinterpreted this finding to mean that

the relationship between use of marijuana and use of heroin is spurious (see O'Donnell and Clayton 1981).

The hypotheses developed by Johnson have recently been tested using

data from Kandel's large-scale survey of adolescents.

Marihuana users whose friends use illicit drugs, particularly

drugs other than marihuana, are more likely to use these

drugs themselves than adolescents whose friends do not use

drugs. Marihuana users who are involved in drug "dealing"

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are the most likely to have friends who report other illicit

drug use, and they themselves are also the most likely

to use drugs other than marihuana. Controlling for both

friends' use and illicit marketing reduces the relationship

between one's marihuana use and the use of other illicit

drugs. These factors, therefore, are two important reasons

why marihuana users are more likely to use other drugs.

(Single and Kandel 1978, p. 127)

Unlike Johnson, Single and Kandel found that the frequency of one's

own marijuana use has a direct and independent effect on use of other

illicit drugs, even when friends' use and drug sales are simultaneously

controlled.

In the present study, it was assumed that growing up in areas of

Manhattan known to have high rates of drug use would facilitate the use

of illicit drugs. It was also assumed that a drug subculture existed

in these areas. Involvement in the sale of illicit drugs was used

as an indicator of involvement in a drug subculture.

It is clear from Model III in chapter 8 that involvement in drug

sales is an important predictor of illicit drug use for both racial

groups, but particularly for whites. Among the blacks, the standardized partial beta (i.e., path coefficient with all other variables

controlled) for the relationship between drug sales and illicit

drug use is .478. The comparable figure for whites is .751. The

unstandardized betas for the relationship between drug sales and other

illicit drug use are 11.24 for blacks and 26.03 for whites. This

indicates that drug sales is at least twice as strong a predictor of

illicit drug use for whites as it is for blacks.

The findings in the studies of Johnson (1973) and Single and Kandel

(1978) are consistent with the data from the Manhattan study. They

confirm the significance of drug selling as a predictor of other

illicit drug use. In addition, these three studies provide empirical

support for the theory of deviant subcultures. However, these studies

also point to the need for further work. As Single and Kandel (1978:

127) indicate: "The subcultural model of drug escalation must be

expanded to consider situational, social and psychological factors

other than friends' use and involvement in marketing activities."

Deviance Proneness

The appearance of problem-behavior theory (Jessor and Jessor 1977)

constitutes a significant development in the drug field. While the

theory is a synthetic blend of assumptions, concepts, and variables

from diverse sources, its roots are firmly anchored in the broader

fields of developmental and social psychology. The key predictor

variables used by Jessor and Jessor (1977) are organized within two

social psychological categories. The first, the personality system,

consists of: (1) the motivational-instigation structure (e.g.,

values placed on academic achievement-independence-affection;

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354-188 0 - 81 - 12
expectations for academic achievement-independence-affection; and

independence-achievement value discrepancy); (2) the personal belief

structure (e.g., social criticism, alienation, self-esteem, and

internal-external locus of control); and (3) the personal control

structure (e.g., attitudinal tolerance of deviance, religiosity, and

what is called positive-negative functions discrepancy). The second

broad category, the perceived environment system consists of: (1)

distal structure (e.g., parental supports and controls; friends' supports and controls; parent-friends compatability, parent-friends

influence) and (2) proximal structure (e.g., parental approval of

problem behavior, friends' approval of problem behavior, and friends'

models for problem behavior).

The variables in the personality system and the perceived environment

system are conceptualized as having both direct and joint influences

on the behavior system which consists of: (1) the problem behavior

structure (e.g., marijuana use: sexual intercourse; activist protest;

drinking: problem drinking; general deviant behavior such as stealing,

lying, property destruction, disruptive behavior, and aggression:

and what the Jessors label a multiple problem-behavior index) and

(2) the conventional behavior structure (e.g., church attendance and

academic achievement).

Problem-behavior theory is particularly interesting because it attempts

to explain a number of substantively different kinds of behavior,

both conventional and unconventional, and because it judiciously

blends predictor variables that are psychological, sociological, and

interactive in nature. Equally interesting are three of the assumptions on which the theory is based:

1. "The most obvious dynamic in the behavior system is the relation

between the two structures that comprise the system, the problembehavior and the conventional behavior structures, with engagement

in either serving as a constraint upon or an alternative to engaging

in the other" (Jessor and Jessor 1977:36). This is an important point

often ignored in the deviance field. Individuals are seldom either

all good or all bad. Delinquents often engage extensively in conventional activities while those who are generally conventional or

"straight" are seldom completely free of involvement in delinquent

behavior. As the Jessors (1977:36) note: "The extent of problem

behavior, then, depends in part on its balance with involvement in

conventional behavior."

2. "There is another kind of linkage between behavior that is likely

to be of dynamic significance, the degree to which behaviors are

linked in the social ecology so that the likelihood of occurrence of

one is enhanced by the occurrence of the other" (Jessor and Jessor

1977: 36). An excellent example of this assumption was discussed

earlier in terms of interrelationship of marijuana use, involvement

in drug sales, and use of other illicit drugs.

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3. "The theory has been organized to account for proneness to engage

in behavior that departs from regulatory norms; insofar as the regulatory nom are age norms, and insofar as problem behaviors can serve

to mark transitions in age-graded status, the theory yields an account

of transition proneness" (Jessor and Jessor 1977:41).

Evidence supporting the Jessors' first two assumptions can be found in

a study of crime-days among addicts in Baltimore in which it was found

that there was a six-fold diminution of crime when addicts were not

using drugs daily compared to when they were "on a run" (Ball et al.

1980, 1981). During abstinent periods, these addicts were occupied

with conventional activities. However, when they were heavily involved

in the use of heroin, they were also heavily involved in criminal

behavior.

The focus on transition or deviance proneness highlights three general

theses of developmental psychology. First, each individual has agegraded developmental tasks to perform and norms to follow, both

prescriptive (boys your age should do this) and proscriptive (boys

your age should not be doing that). Second, the ability to perform

successfully developmental tasks occurring later in the life span is

contingent upon successful completion of tasks at earlier stages of

development. Third, precocity, or involvement in behavior before one's

time, creates a situation in which a person is evaluated, by himself

and by significant others, according to the disparity between what he

is doing and what he should be doing at that age. Precocity is defined

as deviant whether one is viewed positively as a child prodigy or

negatively as a problem child. Therefore, involvement in an activity

that is not appropriate for one's age produces developmental problems

for that individual and often leads to negative evaluations of the

person.

These assumptions yield three simple hypotheses: (1) To the extent

that individuals are involved in conventional activities they will

not be involved in deviant activities, and vice versa; (2) Involvement

in one form of deviant behavior is predictive of and associated with

involvement in other forms of deviance; and (3) The earlier the age

at which one is involved in deviant behavior the greater is the

likelihood of later, more extensive and more serious involvement in

deviance. Stated differently, persons who make the transition into

deviant behavior earlier than their peers are more likely to be involved in multiple forms of deviance and are more likely to be committed to a deviant lifestyle. While these are not new ideas, they

are clearly expressed within a comprehensive model of problem behavior.

The idea of deviance- or transition-proneness is particularly intriguing.

The Manhattan study was conducted prior to publication of the Jessors'

theory. However, the early deviance variable in Model III in chapter

8 clearly reflects the idea of deviance proneness and transition

into deviance. Among whites, early deviance has a direct effect on

both illicit drug use (beta = .205, B = 77.266) and drug sales (beta =

163
.751, B = 26.026). Early deviance does not have a direct effect on these

two variables among blacks. Instead, its effect on drug sales was

indirect through peer influence and labeling.

While these findings are clear from a purely statistical viewpoint, at

least two observations should be made from a theoretical perspective.

First, an early involvement in general types of deviance (i.e., deviance

or transition proneness) seems to thrust whites in the sample toward

participation in the illicit drugs subculture. Second, although early

deviance did not have a direct statistical effect on either drug sales

or illicit drug use among blacks, they were more likely than whites

to be involved in the drug subculture. For example, blacks were more

likely to have grown up in the areas of Manhattan having the highest

rates of opiate use and to have used heroin and cocaine. In addition,

O'Donnell and Clayton (1979) found that the average age at onset of

marijuana use was lower among blacks. For those who have used both

drugs, the hiatus between onset of marijuana use and initiation into

heroin use is shorter for blacks than whites.

problem-behavior theory was developed to explain relatively minor

forms of deviance among whites in a western college town. Transition

into deviance and proneness to deviance may have different meanings

and reflect different processes according to the prevalence of deviance,

homogeneity, or the type of social order in a community (see Suttles

1968; Liebow 1967). The whites in Manhattan who smoked marijuana at

an early age or who engaged extensively in unconventional activities

when they were 13 to 15 were less likely than blacks to make the transition to use of other illicit drugs. As a consequence, they were

probably less involved in other kinds of deviance. Thus, for whites

early involvement in deviance did not block their later opportunities

for success in such areas as education and employment. The blacks

who became involved in deviance at an early age were not as likely to

shift back into conventional pursuits, in part because they quickly

become involved in more serious kinds of deviant activities. The

differential impact of early involvemeant in deviance on blacks and

whites is an issue that deserves special attention in future research.

It also must be considered in any attempt to develop a general theory

of deviance applicable to youth from middle-class suburban communities

as well as youth from lower-class ghetto communities.

The Relative Impact of Socialization Agents

Sociological theories of deviance differ with regard to the influences posited as having the greatest impact on behavior, but there

is general agreement regarding the important role played by parents,

peers, and experiences in school in determnining the paths, conventional

or deviant, that an individual will take. Consensus exists because

these theories share a common heritage, symbolic interactionism.

Cooley (1918), Mead (1934), and Thomas (1927) emphasized that: (a) behavior is learned; (b) it is learned through a lifelong process of

socialization: (c) socialization operates at each stage of development

through taking the role of significant others and the generalized

164
other; (d) decisions about behavior are made through definition of

the situation; and (e) enactment of a specific behavior entails

planning or anticipating the act and its consequences, engaging in the

act, and reviewing the act after it occurs, or what Mead calls the

social act.

Over time a number of schools of thought have emerged from symbolic

interactionism as scholars focused attention on one particular concept, process, or phenomenon. For example, distinctive schools developed around the concept of reference groups (Sherif and Sherif 1964)

and the social comparison process (Thibaut and Kelley 1959). With

regard to the concept of reference groups, Kemper (1968) identified

two types; the comparison, consisting of others with whom an individual

shares general characteristics (e.g., age, race, sex); and the normative, consisting of significant others with whom an individual shares

beliefs, values, norms, and lifespaces.

Another line of thought that grew out of the symbolic interactionist

tradition followed the lead of W. I. Thomas in focusing on how

individuals rationalize and explain their involvement in deviant

behavior. For example, Scott and Lyman (1968) suggested that parsons

use excuses and justifications when they are called upon to account

for untoward or deviant actions. Sykes and Matza (1957) said that

techniques of neutralization, including denial of responsibility,

denial of injury, denial of a victim, condemnation of the condemners,

and appeal to high loyalties, permit an individual to rationalize

norm violations.

Symbolic interactionism and the schools of thought emergent from it

have had a significant impact on the study of drug use. This is

evident in what Kandel (1980) calls socialization theory, and in studies

of deviance that build upon Sutherland's theory of differential association (Sutherland and Cressey 1978). Kandel (1980) gives the social

learning-reinforcement theory developed by Burgess and Akers (1966;

see also Akers 1977; Akers et al. 1979) independent status, but it is

best viewed as a restatement of the theory of differential association

in behavioristic terms. Ideas found in symbolic interactionism also

permeate the work of Jessor and Jessor (1977).

A key question in research on drug use, particularly among adolescents,

concerns the relative impact of various socialization agents. With

regard to the impact of peers, Kandel notes:

The data derived from longitudinal studies indicate that a

socialization process takes place. Extent of perceived

drug use in the peer group, self-reported drug use by

peers, and perceived tolerance for drug use are all very

strong predictors of a youth's subsequent initiation into

various forms of use. These peer influences appear to be

more important at certain points of the process of involvement than at others. (Kandel 1978, p. 24)

165
With regard to the influence of parents on adolescent drug use,

Kandel concludes:

Parent models, in the form of use of hard liquor,

predict adolescent initiation both to hard liquor and to

other illicit drugs, although they do not predict marijuana

use. Parental use of psychoactive drugs predicts initiation to other illicit drugs. Parents' specific rules

against the use of drugs are ineffective, but parents'

tolerance of marijuana use by their children or their

belief in the harmlessness of various drugs favor subsequent

drug use by their children. Lack of closeness between

parents and children predicts subsequent initiation to

marijuana and, especially, to other illicit drugs. Analyses of changes over time indicate that although parents

appear to be able to shield their children from initial

involvement in heavy drug use, they do not have the ability

to help their children give up a habit of heavy use once

it is formed. In this high school sample, parental influences were, however, of greatest importance in the third

stage of drug involvement--the use of other illicit drugs.

(Kandel 1978, p. 25)

These conclusions about the relative impact of parents and peers

are based on an analysis of data obtained in a longitudinal study of

adolescents in the State of New York (Kandel et al. 1978). The

investigators predicted use of hard liquor, marijuana, and other

illicit drugs at Time 2 from measurements taken six months earlier

at Time 1. A broader implication of the study conducted by Kandel

and her colleagues is that persons are socialized into drug use by

means of modeling and reinforcement. These are processes directly

linked to theoretical developments in symbolic interactionism.

A direct test of the relative impact of the modeling and reinforcement

processes on alcohol and marijuana use was conducted in a study of

adolescents in three midwestern states (Akers et al. 1979). Their

analysis included fifteen types of variables that were grouped according to the following concepts: (1) imitation, or the number of admired

models that the respondent reported having observed using the substance; (2) definitions favorable or unfavorable to use, consisting

of a technique of neutralization scale, a scale of law-abiding or

law-violating definitions, and the respondent's positive or negative

definitions of use; (3) differential association, which focuses on

significant adults' normative qualities (perception of approvingdisapproving attitudes toward use), significant peers' normative

qualities, and a differential peer association scale (three items

that dealt with types of friends and whether they used the substance);

(4) differential reinforcement-social, including praise for not using,

friends' rewarding or punishing reactions, parents' rewarding or

punishing reactions, informal parental deterrence, formal deterrence,

and interference with other important activities; and (5) differential

reinforcement combining social and nonsocial factors.

166
Akers and his co-workers found that the variables measuring differential association ranked first in relative effectiveness in explaining

variance in both alcohol and marijuana use. The single most important

variable was differential peer association, "a scale of three items

measuring how many of respondents' best friends, friends with whom

they associate most often, and friends whom they have known for the

longest time use the substance" (Akers et al. 1979:655). It was

followed by definitions, combined social and nonsocial differential

reinforcement, differential reinforcement-social, and imitation,

respectively.

While Akers' research team (1979) included explicit measures of the

processes of modeling and reinforcement, which figure prominently in

social learning theory, it is important to note that the study also

reaffirmed the predictive value of the major concepts in Sutherland's

theory of differential association. Specifically, the measures of

definitions and differential association with drug-using friends were

also significant predictors. This study reaffirms the value of the

general concepts derived from symbolic interactionism such as significant others, taking the role of the other, and reference group.

In summary, recent studies clearly demonstrate that the best predictor

of marijuana use among adolescents is peer influence, particularly

the perceived or actual use of marijuana by friends. Akers and others

(1979) claim that differential peer association is the most effective,

but not the only, significant predictor of both alcohol and marijuana

use. Kandel and her co--workers (1978) have concluded that: (1) the

relative impact of parents and peers varies by stage of drug involvement (parents being particularly influential for use of other illicit

drugs by adolescents) and (2) different factors are predictive of use

at each stage of involvement.

Data relevant to these conclusions were obtained in the national and

Manhattan studies of young man. Using data from the national study,

Clayton and Lacy (1982) found that friends' use, wife or partner's

use, and siblings' use were consistently ranked first, second, and third

in predicting the extent of use of alcohol, marijuana, and psychedelics, stimulants, sedatives, heroin, other opiates, and cocaine. In

other words, Kandel's conclusion that different factors are predictive

of use at different stages is challenged by these findings. O'Donnell

and Clayton (1979) employed path analysis to examine the determinants

of age at first use of marijuana (AF). Among whites, family influence

had a direct, as well as an indirect, effect on AF. While family

influence had only an indirect effect on AF for blacks, the total

effect of family influence on age at first use of marijuana was not

appreciably different for the two racial groups. For peer influence

on AF there was both a direct and indirect effect for blacks, while

among whites the peer influence effect was only indirect. However,

for both racial groups part of the effect of peer influence on age at

first use of marijuana was spurious--that is, it was not causal. In

other words, O'Donnell and Clayton (1979) suggest that the relative

impact of family and peer influences on marijuana use appears to vary

167
according to racial background and hence cannot be summarized by any

broad conclusion. In the analyses presented in chapter 8, neither

family influence nor peer influence had a direct effect on

use of illicit drugs. Peer influence did have a direct effect on drug

sales for blacks. This finding points to the importance of focusing

more attention on variables that may intervene between family and peer

influence and use of other illicit drugs. Possible candidates are

behavioral variables such as use of marijuana and involvement in drug

sales, psychological changes including the effects of official or

informal labeling as a drug user on one's self concept, changes in

reference groups and significant others that make an individual more

likely to receive positive reinforcement for drug use, and changes

in personal definitions about the appropriateness and harmlessness

of using drugs.

Drug use is a learned behavior. It is learned through a variety of

processes and is affected by a wide range of significant and particular others. Use of different kinds of drugs seems to occur through

a series of stages, but further attention must be devoted to relative

impact of parents and peers on various kinds of drug use and to identification of relevant intervening variables. While there is a solid

theoretical and empirical base from which to address these issues of

socialization into drug use, future research must include indices of

change over time that are more sensitive than the ones employed thus

far.

Labeling or Social Reaction

Among the various conceptual frameworks in the field of deviance,

none has eluded rigorous operationalization and quantitative testing

as effectively as the societal reaction or labeling perspective.

This perspective became popular in recent decades, though its intellectual roots can be traced to early symbolic interactionism and to phenomenology. Some of the ideas in the labeling perspective were suggested

by Tannenbaum in 1938, but the importance of societal reaction in the

formation of a deviant identity was most clearly enunciated in Lemert's

(1951) Social Pathology. A link to phenomenology and ethnomethodology

was effected in 1956 by means of Garfinkle's discussion of status

degradation ceremonies. In the 1960s the labeling perspective received a great deal of attention through the writings of Becker (1963),

Erikson (1962), Goffman (1961), Kitsuse (1962), and Kitsuse and

Cicourel (1972). Since then there has been an explosion of articles

attempting to expand, refine, explain, and modify the perspective

itself or its component parts and underlying assumptions (Davis

1972; Gibbons and Jones 1971; Gibbs 1966; Kitsuse 1962; Lemert 1974;

Matza 1969; Orcutt 1973; Prus 1975; Sagarin 1975; Schur 1969, 1971;

Ward 1971; Warren and Johnson 1972). Gove (1975) has edited

two volumes evaluating the labeling perspective. The societal reaction

perspective has been used in studies dealing with crime and criminal

careers (Downes and Rock 1971), disability (Friedson 1965; Haber and

Smith 1971), mental illness (Gibbs 1962; Gove 1970; Scheff 1966, 1974)

and alcoholism (Trice and Roman 1970). While the perspective has not

168
been applied to drug use or users in general, it has been applied to

heroin addicts (see McAuliffe 1975).

The labeling perspective is readily distinguished from other theories

of deviance in terms of its focus. The labeling perspective is neither

concerned with explanation of initial deviant acts nor with the attributes of parsons who are likely to engage in these acts. As

Erickson notes:

Deviance is not a property inherent in certain forms of

behavior; it is a property conferred upon these forms by

the audiences which directly or indirectly witness them.

The critical variable in the study of deviance, then, is

the social audience rather than the individual actor, since

it is the audience which eventually determines whether or

not an episode of behavior or any class of episodes is

labelled deviant. (Erickson 1962, p. 11)

The focus, then, is not on explaining why individuals do or do not

commit rule-breaking acts or what Lemert calls primary deviation, but

rather on the process leading to secondary deviation. Lemert (1967:

17) defines secondary deviation as "deviant behavior or social roles

based upon it, which becomes a means of defense, attack or adaptation

to the overt and covert problems created by the societal reaction to

primary deviation."

Gove (1975:5) places in perspective the relevance of an individual's

social characteristics in the labeling process: "What concern the

societal reaction theorists have with an individual's personal and

social attributes is focused on how these attributes affect the way

others respond to an act of primary deviance. Thus, they are not

concerned with whether a particular social attribute is related to the

likelihood that an individual will commit a deviant act but with whether

that societal attribute facilitates or impedes that individual's

ability to avoid the imposition of a deviant label."

It is generally assumed that persons with minimal resources or power

are most vulnerable to societal labeling, whereas individuals whose

social attributes are similar to those exhibited by the audiences

responsible for applying the labels are least likely to be labeled.

Labeling, either formally through status degradation ceremonies or

informally through consensus among members of a relevant audience,

may leave the individual with no option other than acceptance of a

deviant role and lifestyle. The deviant label becomes a master status.

As Gove (1975:12) indicates: "The labeling perspective is primarily

concerned with the transformation of the person's identity, role, and

behavior. In contrast to the traditional formulation, the labeling

theorists argue that reacting to persons as if they were deviants is

the major cause of deviant identities and lifestyles."

In the Manhattan study, an effort was made to develop a scale measuring

the labeling process. It was to serve as an independent variable to

169
predict the eventual adoption of a deviant identity or lifestyle involving use of illicit drugs other than marijuana. Since it was impossible to observe directly the reactions of salient audiences to the

behavior of the men in the sample, the respondents were, in essence,

asked to take the role of significant others in their formative years

and to indicate if they believed these others had labeled them. Because a number of years had elapsed, no attempt was made to identify

specific acts or incidents. Rather, the respondents were asked if they

thought their parents, teachers, and friends thought they were "heading

for trouble with the law" when they were 13 to 15 years old. By this

age, significant others have a fairly good idea regarding the chances

a young male has of succeeding in the realm of education. Thus, one

social attribute that might impede or facilitate imposition of a deviant

label is educational ability. Another social attribute that affects

one's chances of success in society is race because race is correlated

with educational attainment.

The importance of these social attributes in the labeling process

can be seen in the findings reported earlier in table 7.4. The mean

score on the labeling scale for blacks (6.22) was significantly higher

than that for whites (4.86). Within both racial groups there was a

strong, linear relationship between labeling and eventual educational

attainment. These data seem to confirm the importance of certain

social attributes in the imposition of deviant labels.

A related question is whether awareness of being labeled as a deviant

is predictive of adoption of a deviant identity or lifestyle. In a

study of the determinants of early marijuana use in the Manhattan sample,

O'Donnell and Clayton (1979:93) found that "labeling has no effect

on age at first use for whites; all of the correlations for that

relationship are found to be spurious. For blacks, on the other

hand, while it is true that more than half of the labeling-age at first

use of marijuana correlation is spurious, there is a sizable direct

effect from labeling to age at first use of marijuana."

In chapter 8, labeling was used as an independent variable in relation

to drug sales and use of other illicit drugs. If the labeling perspective is correct, early labeling as a person who is "headed for trouble

with the law" should be predictive of involvement in activities that

are illegal, such as selling and using illicit drugs. However, labeling

did not exhibit a direct and significant statistical effect on use of

illicit drugs for either blacks or whites. Its direct and substantial

effect on involvement in drug sales in both groups deserves comment.

First, the measure of drug sales reflects, to a considerable extent,

the sale of marijuana (see chapter 6). Thus, it is likely that the

measure of labeling, keyed to when the respondents were 13 to 15 years

old, and the drug sales index, are tapping events that are more closely

linked temporally than is the case for the measures of labeling and

use of illicit drugs. Another possibility is that drug selling may be

a better indicator of adoption of a deviant lifestyle than the measure

of use of illicit drugs. Labeling may be an important predictor of

subsequent deviant behavior, but it is apparent that considerable work

170
is needed to identify appropriate ways to measure the labeling process

and to link it temporally and causally to involvement in various forms

of deviance.

Labeling can also be viewed as a dependent variable that may be explained by forces occurring antecedent to it in time and in the

testing of models. As noted earlier, Erickson (1962:11) claims that

deviance is a property conferred upon form of behavior by audiences,

and the critical variable is the audience, not the individual actor.

If this were the case, one would expect the measure of labeling to be

directly and significantly affected by the audiences whose influence

is assessed in the measures of the school adjustment, family influence,

and peer influence. This was not the case in the Manhattan study.

Only peer influence and school adjustment had a direct effect on labeling. Family influence had an indirect effect on labeling for both

races but no direct effect. However, an examination of Model III in

chapter 8 suggests that labeling may serve a crucial mediating

function between the influence of family, peers, and school and

involvement in deviance. This would appear to be particularly true for

involvement in the drug subculture by means of drug sales. The

strength, consistency, and logic of the relationships require further

investigation before more is said about this finding.

To this point the measure of labeling has been discussed in terms of

the assumption that it reflects societal reaction. However, the

content and format of the three items used to construct the measure

suggest an alternative interpretation that approximates Kaplan's

(1975) notion of self-derogation. As noted in chapter 8, the respondents answered the questions about others' views when they were 13 to

15 years old, but they knew whether or not their parents, peers, and

teachers had correctly identified them as headed for trouble with the

law. If their answers were unduly influenced by knowledge of the

accuracy of the perceptions of parents, peers, and teachers, the labeling variable may be a measure of negatively evaluated self-concept.

According to Kaplan (1975), a person's sense of self-worth may be

damaged by self-devaluating experiences in various membership groups.

As a consequence, the person engages in deviant behavior to enhance

his or her self-esteem through demonstration of competence in deviant

activities or membership in the deviant subculture. If this line of

reasoning is correct, the measure of labeling my, in fact, reflect

a negatively evaluated self-concept and it would be predictive of drug

sales, which reflects subcultural involvement. This line of reasoning

is speculative at best. It is mentioned to underscore the possibility

of contamination in the measurement of labeling and to indicate that

from the individual's perspective, labeling is integrally linked to

one's self-concept.

171

Appendix A: Sampling Procedures

To select the sample, the boundaries of the 17 Selective Service

Boards in Manhattan were superimposed on a street map of the 347

health areas in the borough. Unfortunately, the geographical boundariesforthe Selective Service Boards did not coincide with the health

area boundaries. While the primary consideration in setting boundariesforthe health areas was the creation of administrative units

approximately equal in population size, the boundaries for the Selective Service Boards were defined according to cultural characteristics. Nevertheless, it was possible to exclude six of the Selective

Service Boards because they covered health areas that had relatively

low rates of opiate use. It was also apparent that five of the

boards should be included because they served health areas with the

highest rates of opiate use in Manhattan. Any and all names drawn

from the files in these boards would be included in the sample.

Listed below are pertinent data about these five Selective Service

Boards.

1.

2.

3.

Selective Service Boards 11 and 12:

Location: Central Harlem, 10 Health Areas

Total Rate: 145.7 regular opiate users per 1,000 population, 15-44 years old

Sex-Specific Rate: 231 regular opiate users per 1,000 males,

15-44 years old

Rank in City: First

Selective Service Boards 9 and 10:

Location: East Harlem, 9 Health Areas

Total Rate: 72.5 regular opiate users per 1,000 population, 15-44 years old

Sex-Specific Rate: 126.7 regular opiate users per 1,000 population, 15-44 years old

Rank in City: Second

Selective Service Board 14:

Location: Riverside, 11 Health Areas

Total Rate: 62.3 regular opiate users per 1,000 population, 15-44 years old

Sex-Specific Rate: 78.8 regular opiate users per 1,000 population, 15-44 years old

Rank in City: Third

173
Because the health districts and the areas served by the Selective

Service Boards were not identical, the six remaining boards were problematic. Each covered a number of health areas, some of which were

moderately high in opiate use and some of which were relatively law.

Therefore, a decision was made to include in the Manhattan sample

only those persons drawn from these boards whose street address,

according to Selective Service files, was in one of the health areas

with a moderately high rate of opiate use. This meant that blocks

within the board's territory had to be identified as being in health

areas with moderately high rates or in ones with relatively low rates.

Twice the number of respondents needed to represent these six boards

were chosen, and anyone whose street address was not in an area with

a moderately high rate was excluded from the sample The rates of

opiate use for the areas in which these six boards (numbers 3, 5, 8,

13, and 16) were located ranged from a low of 27 per 1,000 population

15-44 years old to a high of 60. The sex-specific rates were all

above 50 per 1,000 males 15-44 years old.

The names of the men in the Manhattan sample were chosen randomly to

be representative of the men born in the years 1944 through 1954, or

eleven birth cohorts. All 273 men drawn from the five boards in the

highest opiate use areas were included in the sample. Of 646 names

drawn from the other six boards, 267 had street addresses that were

located in areas with moderately high rates of opiate use. Thus,

the final sample included the following number of potential respondents:

n n

Board 9 55 Board 3 20/106

Board 10 55 Board 5 45/106

Board 11 54 Board 8 46/109

Board 12 54 Board 13 69/107

Board 14 55 Board 15 53/110

Total 273 Board 16 34/108

Total 267/646

Technically, the sample is a purposive one. Selective Service

Boards in Manhattan were chosen according to rates of opiate use

in the health areas in which the boards were located, and in five of

the boards all of the men selected randomly were included in the

sample. In the other six boards, an additional criterion for selection was residence in a block located in a health area with a moderately

high rate of opiate use. Thus, the target sample included 540 men

who registered with Selective Service Boards in Manhattan serving

areas with moderate or high rates of opiate use.

In an initial analysis of the data, it became apparent that the

sample could not even be said to represent residents of moderate or

high drug use areas in Manhattan. In the interview, the respondent

was asked to list each address at which he had lived from birth to

his eighteenth birthday and the length of time he resided at each

174
place. It was found that some of the men had never lived in Manhattan, and 7 men had never lived in New York City before the age

of 18. This was a surprise because Selective Service regulations

specified that within a month of his eighteenth birthday, a male

was to register with the board in the area where he lived. The

actual distribution was as follows:

Blacks Whites Others Total

Lived in Manhattan at age 18 114 70 59 243

Living elsewhere, with previous

residence in Manhattan 4 8 5 17

Living elsewhere, but in New

York City at 18 or earlier 7 18 2 27

Living elsewhere, never in New

York City before age 18 0 2 5 7

125 98 7 1 294

At first glance, it would appear that as many as 51 of the respondents may have violated Selective Service regulations. However, the

number is considerably smaller. Of the 17 men who had lived in Manhattan and were living elsewhere at the age of 18, it may be that

most or all of them had maintained legal residence in New York and

had properly registered at a Selective Service Board in the city.

For example, a college student would register at home, not where

he attended college. The 27 men who had previously lived in New York

City seem, for the most part, to be life-long residents of Brooklyn,

Queens, or the Bronx. In some cases, there may simply be an error

in the response given to the interviewer. In other cases, changes

of address when the young men first left home may have been forgotten.

On the other hand, there may have been advantages, real or imagined,

in registering at a board in another borough.

Among the 7 men who listed no address in New York City before the age

of 18, several were residents of Puerto Rico. It was a common practice during the 1950s for Puerto Rican families to live in New York

for periods of time and then return to Puerto Rico. It is conceivable that these young men were in Manhattan when they reached the age

of 18 and perceived their residence in New York as only a visit, not

a period of residence. If this speculative interpretation is correct,

they would report themselves to be continuous residents of Puerto

Rico. Also instructive is the man, born in South America, who moved

to New York soon after his eighteenth birthday and then registered

with his birth cohort, even if registration occurred when the man

was in his twenties. In contrast, one respondent appears to be a

professional criminal from Baltimore, who used a fictitious address

to register in New York to comply with the law, but had little

chance of receiving a draft notice.

Nevertheless, the sample does consist of men who were living in or

near high drug use areas in Manhattan at the age of 18, when Selective Service registration usually occurred. Most of the respondents

175
were raised in or near high drug use areas, and almost all of the men

lived in large metropolitan areas during most of their childhood.

An accurate description of the original target sample would be that

it was a sample of men who registered with Selective Service Boards

in moderate or high drug use areas in Manhattan.

176
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Service, 1976.

VOSS, H.L. Drugs, Crime, and Occupational Prestige. Journal of Psychoactive Drugs. Vol. 13, No. 3, July-September, 1981, forthcoming.

Ward, R.H. The labeling theory: A Critical Analysis. Criminology,

9(August-November):206-290, 1971.

Warren, C. and Johnson, J. A critique of labeling theory from the

phenomenological perspective. In: Scott, R. and Douglas, J., eds.

Theoretical Perspectives on Deviance. New York: Basic Books,

1972. pp. 62-92.

Wikler, A. Conditioning factors in opiate addiction and relapse.

In: Wilner, P.M. and Kassebaum, G.G., eds. Narcotics. New York:

McGraw-Hill, 1965. pp. 85-100.

Wikler, A. Ed., The Addictive States. Baltimore: Williams and

Wilkins, 1968.

Wikler, A. Dynamics of drug dependence: Implications of a conditioning theory for research and treatment. Archives of General Psychiatry, 27:611-16, 1973.

Wolfgang, M. The culture of youth. In: President's Commission

on Law Enforcement. Task Force Report: Juvenile Delinquency and

Youth Crime. Washington, D.C.: U.S. Government Printing Office,

1967.

Yinger, J.M. Contraculture and subculture. American Sociological

Review, 25:625-35, 1960.

187
While limited supplies last, single copies of the monographs may

be obtained free of charge from the National Clearinghouse for

Drug Abuse Information (NCDAI). Please contact NCDAI also for

information about availability of coming issues and other

publications of the National Institute on Drug Abuse relevant to

drug abuse research.

Additional copies may be purchased from the U.S. Government

Printing Office (GPO) and/or the National Technical Information

Service (NTIS) as indicated. NTIS prices are for paper copy.

Microfiche copies, at $3.50, are also available from NTIS.

Prices from either source are subject to change.

Addresses are:

NCDAI

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Room 10A5600 Fishers Lane

Rockville, Maryland 20857

GPO NTIS

Superintendent of Documents National Technical Information

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1 FINDINGS OF DRUG ABUSE RESEARCH. Not available from NCDAI.

Vol. 1: GPO out of stock NTIS PB #272 867/AS $27.50

Vol. 2: GPO out of stock NTIS PB #272 868/AS $27.50

2 OPERATIONAL DEFINITIONS IN SOCIO-BEHAVIORAL DRUG USE RESEARCH

1975. Jack Elinson, Ph.D., and David Nurco, Ph.D., eds. Not

available from NCDAI.

GPO out of stock NTIS PB #246 338/AS $14

3 AMINERGIC HYPOTHESES OF BEHAVIOR: REALITY OR CLICHE? Bruce J.

Bernard, Ph.D., ed.

GPO Stock #017-024-00486-3 NTIS PB #246 687/AS $14

188
4 NARCOTIC ANTAGONISTS: THE SEARCH FOR LONG-ACTING PREPARATIONS.

Robert Willette, Ph.D., ed.

GPO Stock #017-024-00488-O NTIS PB #247 096/AS $6.50

5 YOUNG MEN AND DRUGS: A NATIONWIDE SURVEY. John A. O'Donnell,

Ph.D., et al. Not available from NCDAI.

GPO Stock #017-024-00511-8 NTIS PB #247 446/AS $14

6 EFFECTS OF LABELING THE "DRUG ABUSER": AN INQUIRY. Jay R.

Williams, Ph.D.

GPO Stock #017-024-00512-6 NTIS PB #249 092/AS $6.50

7 CANNABINOID ASSAYS IN HUMANS. Robert Willette, Ph.D., ed.

GPO Stock #017-024-00510-O NTIS PB #251 905/AS $12.50

8 Rx: 3x/WEEK LAAM - ALTERNATIVE TO METHADONE. Jack Blaine, M.D.,

and Pierre Renault, M.D., eds.

Not available from GPO NTIS PB #253 763/AS $12.50

9 NARCOTIC ANTAGONISTS: NALTREXONE PROGRESS REPORT. Demetrios

Julius, M.D., and Pierre Renault, M.D., eds.

GPO Stock #017-024-00521-5 NTIS PB #255 833/AS $15.50

10 EPIDEMIOLOGY OF DRUG ABUSE: CURRENT ISSUES. Louise G. Richards,

Ph.D., and Louise B. Blevens, eds. Examines methodological issues

in surveys and data collection. Not available from NCDAI.

GPO Stock #017-024-00571-1 NTIS PB #266 691/AS $20

11 DRUGS AND DRIVING. Robert Willette, Ph.D., ed. Reviews

research on effects of drugs on psychomotor performance, focusing

on measures of impairment by different drugs at various levels.

Not available from NCDAI.

GPO Stock #017-024-00576-2 NTIS PB #269 602/AS $14

12 PSYCHODYNAMICS OF DRUG DEPENDENCE. Jack D. Blaine, M.D., and

Demetrios A. Julius, M.D., eds. Theoretical and clinical papers

concerned with the intrapsychic determinants of drug addiction.

GPO Stock #017-024-00642-4 NTIS PB #276 084/AS $15.50

13 COCAINE: 1977. Robert C. Petersen, Ph.D., and Richard C.

Stillman, M.D., eds. Reports the extent and limits of current

knowledge about cocaine, its use and misuse.

GPO Stock #017-024-00592-4 NTIS PB #269 175/AS $17

14 MARIHUANA RESEARCH FINDINGS: 1976. Robert C. Petersen, Ph.D.,

ed. Technical papers on which the 6th Marihuana and Health report

to Congress was based.

GPO Stock #017-024-00622-O NTIS PB #271 279/AS $20

15 REVIEW OF INHALANTS: EUPHORIA TO DYSFUNCTION. Charles Wm.

Sharp, Ph.D., and Mary Lee Brehm, Ph.D., eds. Review of inhalant

abuse, including an extensive bibliography.

GPO Stock #017-024-00650-5 NTIS PB #275 798/AS $26

189
16 THE EPIDEMIOLOGY OF HEROIN AND OTHER NARCOTICS. Joan Dunne

Rittenhouse, Ph.D., ed. Task Force report on research

technologies and implications for studying heroin-narcotic use.

GPO Stock #017-024-00690-4 NTIS PB #276 357/AS $18.50

17 RESEARCH ON SMOKING BEHAVIOR. Murray E. Jarvik, M.D., Ph.D.,

et al., eds. Includes epidemiology, etiology, consequences of

use, and approaches to behavioral change. From a NIDA-supported

UCLA conference.

GPO Stock #017-024-00694-7 NTIS PB #276 353/AS $27.50

18 BEHAVIORAL TOLERANCE: RESEARCH AND TREATMENT IMPLICATIONS.

Norman A. Krasnegor, Ph.D., ed. Theoretical and empirical studies

of nonpharmacologic factors in development of drug tolerance.

GPO Stock #017-024-00699-8 NTIS PB #276 337/AS $14

19 THE INTERNATIONAL CHALLENGE OF DRUG ABUSE. Robert C. Petersen,

Ph.D., ed. Papers from the VI World Congress of Psychiatry which

deal with drug issues of particular interest worldwide.

GPO Stock #017-024-00822-2 NTIS PB #293 807/AS $26

20 SELF-ADMINISTRATION OF ABUSED SUBSTANCES: METHODS FOR STUDY.

Norman A. Krasnegor, Ph.D., ed. Techniques used to study basic

processes underlying abuse of drugs, ethanol, food, and tobacco.

GPO Stock #017-024-00794-3 NTIS PB #288 471/AS $20

21 PHENCYCLIDINE (PCP) ABUSE: AN APPRAISAL. Robert C. Petersen,

Ph.D., and Richard C. Stillman, M.D., eds. Pioneering volume for

clinicians and researchers assessing what is known about the

problem of PCP abuse.

GPO Stock #017-024-00785-4 NTIS PB #288 472/AS $23

22 QUASAR: QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS OF

ANALGESICS, NARCOTIC ANTAGONISTS, AND HALLUCINOGENS. Gene

Barnett, Ph.D.; Milan Trsic, Ph.D.; and Robert Willette, Ph.D.;

eds. Reports from an interdisciplinary conference on the

molecular nature of drug-receptor interactions. Not available

from NCDAI.

GPO Stock #017-024-00786-2 NTIS PB #292 265/AS $33.50

23 CIGARETTE SMOKING AS A DEPENDENCE PROCESS. Norman A.

Krasnegor, Ph.D., ed. Discusses factors involved in the onset,

maintenance, and cessation of the cigarette smoking habit.

Includes an agenda for future research.

GPO Stock #017-024-00895-8 NTIS PB #297 721/AS $17

24 SYNTHETIC ESTIMATES FOR SMALL AREAS: STATISTICAL WORKSHOP

PAPERS AND DISCUSSION. Joseph Steinberg, ed. Papers from a workshop cosponsored by NIDA and the National Center for Health Statistics on a class of statistical approaches that yield needed

estimates of data for States and local areas. Not available from

NCDAI.

GPO Stock #017-024-00911-3 NTIS PB #299 009/AS $21.50

190
25 BEHAVIORAL ANALYSIS AND TREATMENT OF SUBSTANCE ABUSE. Norman

A. Krasnegor, Ph.D., ed. Papers present commonalities and

implications for treatment of dependency on drugs, ethanol, food,

and tobacco.

GPO Stock #017-024-00939-3 NTIS PB #80-112428 $20

26 THE BEHAVIORAL ASPECTS OF SMOKING. Norman A. Krasnegor, Ph.D.,

ed. Reprint of the behavioral section of the 1979 Report of the

Surgeon General on Smoking and Health; introduction by editor.

GPO Stock #017-024-00947-4 NTIS PB #80-118755 $15.50

27 PROBLEMS OF DRUG DEPENDENCE, 1979: PROCEEDINGS OF THE 41ST

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. Not available from

NCDAI.

GPO Stock #017-024-00981-4 NTIS PB #80-175482 $35

28 NARCOTIC ANTAGONISTS: NALTREXONE PHARMACOCHEMISTRY AND

SUSTAINED-RELEASE PREPARATIONS. Robert Willette, Ph.D., and

Gene Barnett, Ph.D., eds. Papers report research on sustainedrelease and long-acting devices for use with the narcotic antagonist naltrexone.

GPO Stock #017-024-01081-2 NTIS PB #81-238875 $21.50

29 DRUG ABUSE DEATHS IN NINE CITIES: A SURVEY REPORT. Louis A.

Gottschalk, M.D., et al. Epidemiologic study providing data on

drug-involved deaths and procedures for their investigations.

Not available from NCDAI.

GPO Stock #017-024-00982-2 NTIS PB #80-178882 $15.50

30 THEORIES ON DRUG ABUSE: SELECTED CONTEMPORARY PERSPECTIVES.

Dan J. Lettieri, Ph.D.; Mollie Sayers; and Helen Wallenstein

Pearson, eds. Volume presents summaries of the major contemporary theories of drug abuse by each of 43 leading theorists.

GPO Stock #017-024-00997-1 Not available from NTIS

31 MARIJUANA RESEARCH FINDINGS: 1980. Robert C. Petersen, Ph.D.,

ed. The text of the 8th Marijuana and Health report to Congress

and the background scientific papers on which this summary report

was based.

GPO Stock #017-024-01010-3 NTIS PB #80-215171 $18.50

32 GC/MS ASSAYS FOR ABUSED DRUGS IN BODY FLUIDS. Rodger L. Foltz,

Ph.D.; Allison F. Fentiman, Jr., Ph.D.; and Ruth B. Foltz. A

collection of methods for the quantitative analysis of several

important drugs of abuse by the technique of gas chromatographymass spectrometry.

GPO Stock #017-024-01015-4 NTIS PB #81-133246 $17

34 PROBLEMS OF DRUG DEPENDENCE, 1980: PROCEEDINGS 0F THE 42ND

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. Comprehensive

assemblage of ongoing research on drug abuse, addiction, and new

compounds.

GPO Stock #017-024-01061-8 NTIS PB #81-194847 $32

191
35 DEMOGRAPHIC TRENDS AND DRUG ABUSE, 1980-1995. Louise G.

Richards, Ph.D., ed. Estimates of probable extent and nature of

nonmedical drug use, 1980-1995, based on age structure and other

characteristics of U.S. population.

GPO Stock #017-024-01087-1

36 NEW APPROACHES TO TREATMENT OF CHRONIC PAIN: A REVIEW OF MULTIDISCIPLINARY PAIN CLINICS AND PAIN CENTERS. Lorenz K.Y. Ng, M.D.,

ed. A sharing of ideas among active practitioners in the treatment of pain.

GPO Stock #017-024-01082-1 NTIS PB #81-240913 $17

U.S. GOVERNMENT PRINTING OFFICE: 1981 0 - 354-188

192