The association between spending on methamphetamine/amphetamine and cannabis for personal use and...

The association between spending onmethamphetamine/amphetamine and cannabis forpersonal use and earnings from acquisitive crimeamong police detainees in New Zealandadd_3241 789..797

Chris Wilkins & Paul SweetsurSocial and Health Outcomes Research and Evaluation (SHORE), School of Public Health, Massey University, Auckland, New Zealand

ABSTRACT

Aims Few studies have examined the statistical association between methamphetamine/amphetamine use andacquisitive crime. Both methamphetamine/amphetamine and cannabis use have been implicated by New Zealand Policeas factors in acquisitive offending among active criminal populations. The aim of our study was to examine the statisticalassociation between spending on methamphetamine/amphetamine and cannabis and earnings from acquisitive crimeamong police detainees in New Zealand. Setting Four police stations in different regions. Participants A sample of2125 police detainees were interviewed about their drug use and acquisitive crime. Design Statistical models weredeveloped to predict involvement in acquisitive crime using spending on methamphetamine/amphetamine and can-nabis for personal use, and to examine associations between the level of spending on methamphetamine/amphetamineand cannabis for personal use and level of dollar earnings from acquisitive crime. Measurements Self-reportedspending on drug use and self-reported earnings from acquisitive crime in the past 30 days. Findings Spending oncannabis and methamphetamine/amphetamine could predict involvement in acquisitive crime. Level of spending onmethamphetamine/amphetamine and cannabis was associated positively with the level of earnings from propertycrime. Level of spending on methamphetamine/amphetamine was also associated positively with level of earningsfrom drug dealing. There was a largely negative association between level of spending on cannabis and level of earningsfrom drug dealing. Conclusions High spending on methamphetamine/amphetamine is associated statistically withhigher earnings from acquisitive crime among police detainees. Further research into this association, and in particu-lar the causal nature of the association, is required to obtain clear policy recommendations.

Keywords Cannabis, drug dealing, methamphetamine, New Zealand, police detainees, property crime.

Correspondence to: Chris Wilkins, Social and Health Outcomes Research and Evaluation (SHORE), School of Public Health, Massey University, PO Box6137, Wellesley Street, Auckland, New Zealand. E-mail: [email protected] 28 September 2009; initial review completed 18 March 2010; final version accepted 6 October 2010

INTRODUCTION

Methamphetamine use is a serious drug problem in anumber of regions around the world, including NorthAmerica, Central Eastern Europe, Asia and Oceania[1–7]. Studies of frequent methamphetamine users in theUnited States, Australia and New Zealand have foundhigh proportions of users reporting recent involvementin property offending and drug dealing [2,8–10].However, few studies have examined the statistical asso-ciation between methamphetamine or amphetamine use

and acquisitive crime in any detail, particularly outsidethe United States [11,12].

Studies of drug use and acquisitive crime have focusedtraditionally on heroin, and more recently on cocaine[13–18]. The extent to which drug use and crime arecausally related—if at all—also remains unclear (see[13,19–21]). Four models of causality have been pro-posed. The first, known as the ‘drug–crime’ model, is thatfrequent use of expensive drugs leads users to commitacquisitive crime to pay for their drug use. The second,known as the ‘crime–drug’ model, is that the criminal

RESEARCH REPORT doi:10.1111/j.1360-0443.2010.03241.x

© 2010 The Authors, Addiction © 2010 Society for the Study of Addiction Addiction, 106, 789–797

life-style provides the income, peer group and party life-style which encourage drug use. The third model is the‘common cause’ model, which proposes that both druguse and acquisitive crime are related by common psycho-logical or socio-economic factors such as youth delin-quency, unemployment and social exclusion. The fourthmodel, known as the ‘coincidence’ model, argues thatdrug use and crime are not connected causally at all.

Many drug–crime studies have investigated the asso-ciation between the number of days of drug use and thenumber of acquisitive offences [13,15,22–24]. There area number of reasons why this approach may not identifyclearly any economic relationship between drug use andcrime. First, drug users can obtain drugs from a range ofsources without paying cash for them, including receiv-ing them as gifts, bartering for them and receiving themas in-kind payment for illegal and legal work [25,26].Secondly, there are often important differences betweenthe number of acquisitive offences and the level of earn-ings from acquisitive crime. For example, a shoplifter mayoffend on a near-daily basis for fairly modest total earn-ings, while a burglar may offend less frequently but formuch higher total earnings. It is also important to distin-guish spending on drugs for personal consumption fromspending on drugs for the purposes of re-sale to others forprofit [22,25,27].

Heroin and cocaine use has been very low in NewZealand for many decades, due mainly to the country’sgeographical isolation and small population [28,29].Methamphetamine became the dominant stimulant inNew Zealand during the early 2000s, although someamphetamine sulphate continued to be used [5,30]. Asurvey of police detainees in New Zealand in 2005 found24% had used methamphetamine and 6% had usedamphetamine sulphate in the previous 30 days [31]. NewZealand Police identified the increase in methamphet-amine use as a factor in a rise in property offending [32].The New Zealand Police had previously identified can-nabis use as a factor in rates of property offending, par-ticularly among young offenders [33].

The aim of our study was therefore to examinethe statistical association between spending onmethamphetamine/amphetamine and cannabis for per-sonal use and earnings from acquisitive crime amongpolice detainees in New Zealand.

Our specific research questions were:1 Are police detainees who have spent money on

methamphetamine/amphetamine and cannabis forpersonal use in the past 30 days more likely to havebeen involved in property crime and drug dealing inthe past 30 days?

2 To what extent can the level of expenditure onmethamphetamine/amphetamine and cannabis forpersonal use in the past 30 days predict the level of

earnings from property crime and drug dealing in thepast 30 days?

METHOD

The analysis in this paper was conducted using data fromNew Zealand’s Arrestee Drug Abuse Monitoring researchprogramme (NZ-ADAM) [31,34–37]. NZ-ADAM inter-views individuals who have been detained at police sta-tions about their drug use and is conducted at four policestations located in different regions of New Zealand (i.e.Whangarei, Henderson, Hamilton and Dunedin).

Potential participants in the NZ-ADAM study includeall individuals detained at selected police watch housesfor less than 48 hours at the time interviewers arepresent. Interviewing was conducted every week for anentire 12-month period at each site. Interviewers com-pleted four 3-hour shifts per week on a rotating shift basis.The project manager scheduled the shift times to ensurecompleteness of coverage for every day of the week overeach quarter of the year [31]. A number of types of policedetainees were deemed ineligible to participate includingthose under 17 years of age: those deemed unfit for inter-view due to alcohol and drug intoxication; those sufferingfrom mental health issues; those with insufficient Englishlanguage; people exhibiting violent behaviour; and thoseheld in custody for more than 48 hours. The interviewersidentified themselves as civilian researchers, andexplained the purpose of the study and the confidentialityprotections employed. The interviews were conducted ina private room in the police station.

A total of 2163 detainees were interviewed for thestudy between April 2005 and September 2007. Only 38of the detainees interviewed during this time had usedheroin, morphine or methadone on more than 2 daysduring the previous 30 days. The small number of fre-quent opioid users among the detainee sample was insuf-ficient to carry out any meaningful analysis and so theywere removed from the sample. This left a sample of 2125detainees for the analysis.

MEASURES

Expenditure on drugs

Respondents were asked whether they had purchased‘cannabis’, ‘methamphetamine/amphetamines’, ‘heroin/morphine and other opioids’ and ‘ecstasy’ (3′,4-methylenedioxymethamphetamine: MDMA) in the past30 days. If a detainee had purchased a drug type, theywere asked on how many days they had purchased it in thepast 30 days, the dollar amount they spent on a ‘typicaloccasion’ (in New Zealand dollars) (i.e. $NZ1 = $US0.70= €0.48 = £0.38 = $Aust0.82) and what percentage, ifany, they would ‘typically sell on to others’.

790 Chris Wilkins & Paul Sweetsur


Sources of income

Respondents were asked about all their sources of incomeduring the previous 30 days and were read a list of 14legal and illegal sources. If a detainee had earned moneyfrom a source, they were asked to estimate the dollarvalue they earned in the past 30 days.

Demographic variables

Demographic variables were categorized in the followingway for the purposes of analysis: male gender, age (i.e.under 25 years old), Maori ethnicity (i.e. Maori versusEuropean, Pacific Islander and other—Maori are theindigenous people of New Zealand), single marital status,low education (i.e. no high school or did not completecompulsory high school years), unemployed/sicknesssocial welfare beneficiary, temporary accommodation(i.e. living on the street, hostel, caravan, someone else’shouse) and imprisonment in previous 12 months.

ANALYSIS

Dollar expenditure on drugs for personal use

Dollar expenditure on drugs was calculated for each drugtype by multiplying the number of days a drug type waspurchased by the dollar amount typically purchased andthen subtracting the percentage that the respondent saidthey sold on to others. Spending on methamphetamine/amphetamine and cannabis made up 94% of the detain-ees’ total expenditure of drugs in the past 30 days.Expenditure on drug use was highly positively skewed.We categorized spending on drugs in the past 30 daysinto four categories: no spending ($0); low spending ($1–100); medium spending ($101–$1000); and high spend-ing ($1001+). The drug spending categories were chosenwith average incomes among low-income earners in NewZealand in mind, and by examining the distributions ofspending on the different drugs and looking for logicalcut-off points. For example, someone receiving unem-ployment benefit in New Zealand earns approximately$880 net income per month ($NZ). For these low-incomeearners, spending on drugs at the low level (i.e. $1–100)would still be financially manageable using their legalincome. In contrast, low-income earners who spent ondrugs at the middle level ($101–1000) would face aserious financial strain and those who spent on drugs atthe upper level ($1001+) would face a substantial finan-cial burden.

Dollar earnings from acquisitive crime

Two categories of earnings from acquisitive crime werecreated for the analysis: earnings from property crime inthe past 30 days and earnings from drug dealing in the

past 30 days. The property crime category included dollarearnings from ‘shoplifting’, ‘burglary’, ‘car theft’, ‘theft’and ‘robbery’. The drug dealing category included earn-ings from ‘drug dealing’ only. Earnings from crime werehighly positively skewed. Four categories of dollar earn-ings from acquisitive crime were created: no spending($0); low spending ($1–100); medium spending ($101–1000); and high spending ($1001+). These earnings cat-egories were created with an understanding of averageincomes among low-income earners in New Zealand, andby examining the distribution of the dollar earnings fromproperty crime and drug dealing among the detaineesand looking for logical cut-off points.

Bivariate analysis

To provide an initial descriptive picture of the relationshipbetween spending on drugs and dollar earnings fromcrime we conducted two types of bivariate analysis. First,we tested for differences in the demographic characteris-tics of the detainees between those who spent no moneyon drugs with those who spent the other three levels ofspending on drugs (i.e. low, medium and high) using c2

tests. Second, we examined earnings from property crimeand drug dealing by the detainees’ total spending ondrugs. We tested the likelihood of the detainees earningincome from acquisitive crime between those who spentno money on drugs compared to those who spent theother three levels of spending on drugs using c2 tests.We also examined correlations between level of spendingon drugs and level of earnings from crime using non-parametric Spearman’s coefficients.

Regression models

The first type of regression model sought to predictinvolvement in property crime and drug dealing in theprevious 30 days from spending on methamphetamine/amphetamine and cannabis for personal use in the past30 days and demographic variables using odds ratios.These models included all the detainees. The second typeof regression model [a factorial analysis of variance(ANOVA) model] was developed to predict the level ofdollar earnings from property crime and drug dealing inthe previous 30 days from the level of spending onmethamphetamine/amphetamine and cannabis for per-sonal use in the past 30 days and demographic variables.These models included only those detainees who hadearned money from property crime or drug dealingduring the past 30 days. The initial test was whether thedetainees’ expenditure on drugs and demographic cat-egories had any statistically significant influence on theirlevel of dollar earnings from acquisitive crime. If therewas a statistically significant association we testedfurther for differences in the earnings from acquisitive

Spending on methamphetamine/amphetamine and acquisitive crime 791


crime between each pairwise combination of expenditureon drug use category using the Tukey multiple compari-son procedure. Dollar earnings from acquisitive crimewere highly skewed, and so the data were log-transformed for the purposes of analysis. Geometricmeans were produced by back-transforming the logmeans. Tukey–Kramer adjustments were used to controlfor multiple comparisons among the drug use categories.All analysis was conducted using SAS (SAS Institute, Inc.,Cary, NC, USA).

RESULTS

Demographics

The police detainee sample was overwhelmingly male(87%), disproportionately of Maori ethnicity (50%) andhad high levels of unemployment (42%), low educationalachievement (46%) and imprisonment in the past 12months (18%) compared to the general New Zealandpopulation (Table 1). Those police detainees who spent‘high’ total dollar amounts on drug use for personal usewere more likely than those who had spent no money ondrugs to have low educational achievement (57% versus44%, P = 0.0174) and to have been in prison in the past12 months (37% versus 16%, P < 0.0001).

Expenditure on drugs for personal use

Thirty-nine per cent of the police detainees hadpurchased cannabis, 10% had purchasedmethamphetamine/amphetamine, 2% had purchasedecstasy (MDMA) and 0.5% had purchased opioids for per-sonal use in the past 30 days. As a proportion of totaldollar spending on drug use for personal use in the past30 days, the detainees’ expenditure on drugs was domi-nated by spending on methamphetamine/amphetamine

(53%) and cannabis (41%), with low levels of spendingon opioids (5%) and ecstasy (1%). Detainees who hadpurchased methamphetamine/amphetamine had spent amean of $1913 on methamphetamine/amphetamine forpersonal use in the past 30 days [median $400, standarddeviation (SD) $6719] ($NZ). Three per cent had spentmore than $1000 on methamphetamine/amphetaminein the past 30 days. Detainees who had purchased can-nabis had spent a mean of $391 on cannabis for personaluse in the past 30 days (median $160, SD $774). Fourper cent had spent more than $1000 on cannabis in thepast 30 days.

Dollar earnings from property crime and drug dealing

Eleven per cent of the detainees had earned income fromdrug dealing and 10% had earned income from propertycrime in the past 30 days (Table 2). Those detainees whohad earned money from property crime had earned amean of $2338 from property crime in the past 30 days(median $500, SD $5408). Four per cent had earnedmore than $1000 from property crime in the past 30days. Those detainees who had earned money from drugdealing had earned a mean of $4665 from drug dealingin the past 30 days (median $500, SD, $19 384). Threepercent had earned more than $1000 from drug dealingin the past 30 days. Those detainees who spent ‘high’total dollar amounts on drugs were more likely thanthose who spent no money on drugs to have earnedmoney from property crime (37% versus 6%,P < 0.0001) and drug dealing (38% versus 5%,P < 0.0001) in the past 30 days. There were positive cor-relations between total spending on drug use in the pre-vious 30 days and the dollar earnings from propertycrime (r = 0.24, P < 0.0001) and drug dealing in thepast 30 days (r = 0.26, P < 0.0001).

Table 1 Demographic characteristics of the police detainee sample by total spending on drugs for personal use in the past 30days ($NZ).

No spending:$0

Low spending:$1–$100

Medium spending:$101–$1000

High spending:$1001+ All

(n = 1173) (n = 345) (n = 461) (n = 146) (n = 2125)

Male (%) 86 87 90 85 87Under 25 years (%) 50 61** 62** 55 55Maori ethnicity (%) 48 57* 51 47 50Unemployed/sickness benefit (%) 39 47 44 48 42Low educational achievement (%) 44 48 46 57* 46Temporary housing (%) 56 64* 63 65 59Single marital status (%) 58 62 65 61 60Prison in past 12 months 16 18 17 37*** 18

c2 test comparing demographic characteristics of those spending different levels on drug use for personal use to those spending $0 on drug use forpersonal use. *P < 0.05; **P < 0.01; ***P < 0.0001.



Involvement in acquisitive crime

Spending on methamphetamine/amphetamine andspending on cannabis for personal use were both predic-tors of involvement in property crime in the past 30 days(Table 3). Those detainees who had spent money onmethamphetamine/amphetamine were three times morelikely to have been involved in a property crime comparedto those detainees who had spent no money onmethamphetamine/amphetamine. Being under 25 yearsold, of Maori ethnicity, unemployed or receiving sicknessbenefit, low educational achievement, living in tempo-rary housing and having been in prison in the past 12months were also predictors of involvement in propertyoffending.

Purchasing methamphetamine/amphetamine andcannabis for personal use were also both predictors ofinvolvement in drug dealing in the past 30 days. Thosedetainees who had spent money on methamphetamine/amphetamine were six times more likely to have beeninvolved in drug dealing compared to those detainees

who had spent no money on methamphetamine/amphetamine. Being under 25 years old and having beenin prison in the past 12 months were also predictors ofinvolvement in drug dealing.

Earnings from acquisitive crime

Level of spending on cannabis for personal use, level ofspending on methamphetamine/amphetamine for per-sonal use and being of Maori ethnicity were statisticallysignificant predictors of the level of dollar earnings fromproperty crime in the past 30 days (Table 4). Level ofspending on cannabis for personal use, level of spendingon methamphetamine/amphetamine for personal use,being under 25 years old and having been in prison in thepast 12 months were predictors of the level of dollarearnings from drug dealing in the past 30 days.

Those detainees who spent ‘high’ dollar amounts oncannabis reported higher dollar earnings from propertycrime than those who had spent ‘low’ dollar amounts oncannabis ($2213 versus $589, P = 0.0063) (Table 4).

Table 2 Dollar earnings from acquisitive crime in the past 30 days by total spending on drugs for personal use in the past 30 days forpolice detainees ($NZ).

Total spending on drugs for personal use in the past 30 days

No spending:$0

Low spending:$1–$100

Medium spending:$101–$1000

High spending:$1001+ All

(n = 1173) (n = 345) (n = 461) (n = 146) (n = 2125)

% $ % $ % $ % $ % $

Earnings from property crime in past 30 days ($) 6 125 11* 107 18*** 427 37*** 1251 10 250Earnings from drug dealing in past 30 days ($) 5 428 7 82 20*** 156 38*** 3597 11 467

c2 test comparing likelihood of earning money from acquisitive crime of those spending different levels on drug use for personal use to those spendingno money on drug use for personal use. *P < 0.05; ***P < 0.0001.

Table 3 Odds ratios of the likelihood of involvement in property crime and drug dealing in the past 30 days for police detainees.

(n = 2125)

Property crimethe past 30 days

Drug dealingthe past 30 days

Odds ratio P-value Odds ratio P-value

Purchased cannabis for personal use in the past 30 days 2.49 <0.0001 1.98 <0.0001Purchased methamphetamine/amphetamine for personal

use in the past 30 days2.99 <0.0001 5.88 <0.0001

Male 0.65 0.0509 0.87 0.5735Under 25 years 2.00 <0.0001 1.85 0.0003Maori ethnicity 1.65 0.0014 1.28 0.1121Unemployed/sickness benefit 1.67 0.0008 1.19 0.2616Low educational achievement 1.43 0.0184 1.14 0.4025Temporary housing 1.84 0.0006 0.86 0.3880Single marital status 0.95 0.7866 1.09 0.5802Prison past 12 months 1.72 0.0013 1.59 0.0080



Tabl

e4

Th

est

atis

tica

lass

ocia

tion

betw

een

leve

lof

spen

din

gon

can

nab

isan

dm

eth

amph

etam

ine/

amph

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ine

use

and

dem

ogra

phic

vari

able

sth

ele

velo

fdo

llar

earn

ings

from

prop

erty

crim

ean

ddr

ug

deal

ing

inth

epa

st3

0da

ys.

Pro

pert

ycr

ime

Dru

gde

alin

g

Ove

rall

sign

ifica

nce

test

Geo

met

ric

mea

ndo

llars

earn

edin

past

30

days

($N

Z)(n

=2

25

)9

5%

confi

denc

ein

terv

al

Ove

rall

sign

ifica

nce

test

Geo

met

ric

mea

ndo

llars

earn

edin

past

30

days

($N

Z)(n

=2

09

)9

5%

confi

denc

ein

terv

al

Spen

din

gon

can

nab

isfo

rpe

rson

alu

sein

past

30

days

($N

Z)P

=0

.00

98

P=

0.0

00

2

No

spen

d($

0)

11

60

(65

4,2

05

8)

13

73

(73

0,2

58

1)

Low

spen

d($

1–1

00

)5

89

(29

1,1

19

4)

51

2(2

24

,11

70

)

Med

ium

spen

d($

10

1–1

00

0)

91

3(4

89

,17

03

)3

93

(19

8,7

80

)

Hig

hsp

end

($1

00

1+)

22

13

(10

57

,46

39

)1

09

8(4

61

,26

13

)

Spen

din

gon

met

h/a

mph

etam

ine

for

pers

onal

use

inpa

st3

0da

ys($

NZ)

P<

0.0

00

1P

<0

.00

01

No

spen

d($

0)

36

8(2

38

,56

8)

46

6(2

83

,76

6)

Low

spen

d($

1–1

00

)1

45

6(3

65

,57

94

)3

13

(53

,18

10

)

Med

ium

spen

d($

10

1–1

00

0)

94

3(4

75

,18

71

)6

62

(34

3,1

27

7)

Hig

hsp

end

($1

00

1+)

27

35

(11

38

,65

74

)3

14

5(1

52

5,6

48

5)

Gen

der

P=

0.0

70

8P

=0

.79

98

Mal

e1

48

1(8

94

,24

52

)7

79

(45

2,1

34

2)

Fem

ale

79

4(3

90

,16

18

)7

08

(31

3,1

59

6)

Age

P=

0.4

29

6P

=0

.03

63

Un

der

25

year

s9

76

(54

7,1

74

1)

55

3(2

97

,10

29

)

25

and

olde

r1

20

5(6

76

,21

49

)9

96

(51

0,1

94

3)

Eth

nic

ity

P=

0.0

01

5P

=0

.34

35

Mao

ri1

58

5(9

20

,27

29

)8

37

(45

2,1

54

9)

Euro

pean

/Pac

ific/

oth

er7

42

(41

2,1

33

4)

65

8(3

42

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65

)

Empl

oym

ent

stat

us

P=

0.9

06

3P

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.53

81

Un

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ess

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10

99

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87

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80

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29

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94

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Empl

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10

70

(59

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93

4)

68

8(3

63

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05

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Edu

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onal

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79

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45

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Low

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14

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64

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48

7)

Hig

hed

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tion

10

27

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9,1

85

5)

68

9(3

61

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12

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Hou

sin

gst

atu

sP

=0

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48

P=

0.6

44

8

Tem

pora

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ng

98

5(5

84

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60

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98

(38

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26

9)

Ren

tor

own

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ate

hou

se1

19

4(6

28

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69

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89

(40

0,1

55

7)

Mar

ital

stat

us

P=

0.1

23

1P

=0

.84

91

Sin

gle

89

6(5

11

,15

72

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61

(41

3,1

40

3)

Mar

ried

/de

fact

o/di

vorc

ed/s

epar

ated

/wid

owed

13

12

(73

6,2

33

7)

72

4(3

71

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10

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Rec

ent

pris

onh

isto

ryP

=0

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26

P=

0.0

24

9

Pri

son

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st1

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s1

26

5(6

78

,23

59

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02

2(5

09

,20

52

)

No

pris

onin

past

12

mon

ths

93

0(5

52

,15

65

)5

39

(29

8,9

75

)

R2

23

.6%

29

.4%



Those detainees who spent ‘high’ dollar amounts onmethamphetamine/amphetamine reported higher dollarearnings from property crime than those who did notspend any money on methamphetamine/amphetamine($2735 versus $368, P < 0.0001). Those detainees whospent a ‘medium’ dollar amount on methamphetamine/amphetamine also reported higher dollar earnings fromproperty crime than those who spent nothing onmethamphetamine/amphetamine ($943 versus $368,P = 0.0347). Maori detainees reported higher meandollar earnings from property crime than non-Maoridetainees ($1585 versus $742, P = 0.0015).

Those detainees who spent ‘medium’ dollar amountson cannabis reported lower dollar earnings from drugdealing than those who had spent no money on cannabis($393 versus $1373, P = 0.0003). Detainees who spent‘low’ dollar amounts on cannabis also reported lowerdollar earnings from drug dealing than those who hadspent no money on cannabis ($512 versus $1373,P = 0.0449). Those detainees who spent ‘high’ dollaramounts on methamphetamine/amphetamine reportedhigher dollar earnings from drug dealing than those whohad not spent any money on methamphetamine/amphetamine ($3145 versus $466, P < 0.0001). Thosedetainees who spent ‘high’ dollar amounts onmethamphetamine/amphetamine also reported higherdollar earnings from drug dealing than those who hadspent ‘medium’ dollar amounts on methamphetamine/amphetamine ($3145 versus $662, P = 0.0031). Thosedetainees who were aged 25 years or older reportedhigher dollar earnings from drug dealing than youngerdetainees ($996 versus $553, P = 0.0363). Thosedetainees who had been in prison in the past 12 monthsearned higher dollar earnings from drug dealing thanthose who had not been in prison in the past 12 months($1022 versus $539, P = 0.0249).

DISCUSSION

Our paper contributes to the drug–crime literature byproviding a rare study of the association betweenmethamphetamine/amphetamine use and acquisitivecrime. We found there were strong associations betweenthe level of spending on methamphetamine/amphetamine in the past month and the level of earningsfrom acquisitive crime in the past month among policedetainees in New Zealand. Our findings support the casefor further research of methamphetamine use andacquisitive crime, and in particular the causal relation-ships present. Longitudinal studies can identify the tem-poral progression between drug use and crime, and thiscan assist in identifying the causal relationships present[13,38]. Summaries of the drug–crime research litera-ture have concluded that no one causal pathway can

explain the statistical association between drug use andcrime and different causal links can exist for different sub-populations of drug users [13,18,38,39]. Understandingthe causal nature of the associations is important inorder to obtain clear policy implications.

One interpretation of our findings is that if we couldreduce detainees’ methamphetamine use via drug treat-ment we could also reduce their levels of acquisitiveoffending. A number of recent evaluations have foundthat the provision of drug treatment to offenders whohave high levels of drug use prior to their conviction canreduce their subsequent levels of drug use and offending[40–43]. Studies of the effectiveness of drug treatmentand methadone maintenance for heroin users have foundthat while those with high levels of criminal offendingprior to their drug use may continue to offend at somelevel following drug treatment, those with low levels ofcriminality prior to their drug use can report dramaticreductions in acquisitive offending following drug treat-ment [11,38,44]. The effectiveness of treatment foroffenders has been also been found to vary across pro-gramme types, quality of treatment programmes andmode of entry [18].

Our findings do not preclude the possibility of otherexplanations for the drug–crime relationship. It is pos-sible that higher earnings from acquisitive crime providemore income to spend on drugs rather than higher spend-ing on drugs precipitating additional acquisitive crime[45]. Some drug-using offenders have indicated clearlythat they commit acquisitive crime and only later con-sider spending the proceeds on drug use [46]. In thesecircumstances, providing drug treatment to offendersmay have little or no impact on levels of acquisitiveoffending. It is also plausible that developmental factors,such as family dysfunction and youth delinquency, andbroader socio-cultural factors, such as social and eco-nomic exclusion, influence levels of both drug use andcrime. Such an explanation for drug-related crimerequires broader social interventions than merely theprovision of drug treatment.

We acknowledge a number of limitations with ourstudy. First, our police detainee sample is not representa-tive of drug users in the wider population in NewZealand. The detainee sample includes a high proportionof criminally active individuals who tend to come fromdisadvantaged backgrounds. Secondly, our policedetainee sample may not be wholly representative of thepolice detainee population in New Zealand. It was notpractical or ethical to interview police detainees whowere intoxicated, acting violently or suffering frommental health issues. Thirdly, all the data used in theanalysis are self-reported. Although studies have shownself-reporting of illegal drug use to be fairly accuratewhen checked against results from urinalysis, levels of



under-reporting have also been found [38,47]. Someresearch has shown that asking about spending on druguse by individual drug type (as was conducted inNZ-ADAM) can improve the accuracy of reporting com-pared to asking about spending on ‘drug use’ as a genericwhole [48]. As outlined, a number of procedures werefollowed in the NZ-ADAM study to encourage truthfuland accurate self-reporting, including restricting theperiod of questioning to the previous 30 days to facilitateaccurate recall.

Declarations of interest

None.

Acknowledgements

We would like to thank Professor Jonathan Caulkins andan anonymous referee for valuable comments on a draftversion of this paper. The analysis in this paper wasfunded from the New Zealand National Drug Policy Dis-cretionary Fund, which is managed jointly by the NewZealand Inter-Agency Committee on Drugs (IACD) andthe New Zealand Ministerial Committee on Drug Policy(MCDP). The data used in the analysis is from the nationalpilot and 2006 and 2007 waves of the New ZealandArrestee Drug Abuse Monitoring (NZ-ADAM) pro-gramme. NZ-ADAM is funded by the New Zealand Police.We gratefully acknowledge the willingness of the NewZealand Police to share these data with us for the pur-poses of this analysis. NZ-ADAM is conducted by HealthOutcomes International (HOI). We would like to thankHOI for their assistance with the NZ-ADAM database. Wewould like to acknowledge all the interviewers whoworked on the NZ-ADAM study and all the police detain-ees who agreed to be interviewed.

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