2017 American Society of Criminology Annual Meeting, Philadelphia 15th-17th Nov.

Jakob Demant, Department of Sociologyjd@soc.ku.dk

How Long Can You DelayGratification? Using Self-Control to ExplainOnline and Offline Drug PurchaseBehaviors among Danish Students aged 12-25

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RESEARCH QUESTION:

IS THERE ANY DIFFERENCES BETWEEN BUYERS OF ILLICIT DRUGS ONLINE AND OFFLINE? WILL THE LESS IMMEDIATE BUYING IN ONLINE MARKETS CATER TOWARDS SPECIFIC PEOPLE?

SELF-CONTROL HAS NOT BEEN TESTED ON THE DIFFERENCES IN PURCHASES OF DRUGS:

HYPOTHESIS:

USERS OF CANNABIS THAT PURCHASES THEIR DRUG OVER THE INTERNET TREND TO HAVE HIGHER SELF-CONTROL THAN USERS OF CANNABIS THAT PURCHASES OFFLINE.

Background (Online buyers)

• Based on a large convenience survey (GDS): Cryptomarkets are associated with substantially less threats and violence than alternative market types used by cryptomarket customers, even though a large majority of these alternatives were closed networks where violence should be relatively less common. (Barrett 2015)

• Subjective availability is perceived as higher on cryptomarketsthan on offine markets among cryptobuyers. Further, with the availability of drugs that the cryptomakrts represents it demands a high level of self-control. Barrett et al. 2016.

• Absent literature on clear-web drug dealing and buying.

• Donner (2016): Self-control has a large explanatory factor in describing cyberoffending. Low self-control => more digital piracy. This finding is established in relation to non-offenders and not towards offline offending.

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Self-control is widely acknowledged within the field of criminology as being of crucial importance in the explanation of criminal activity (Gottfredson1990; Hirschi and Gottfredson2000; Tittle, Ward, and Grasmick2003; Lagrange and Silverman 1999; Hay and Meldrum 2016; Meldrum and Hay 2012:691; Buker 2011:273).

Underlying motivator of criminal activity

Self-control

• Criminal behaviour - especially among young people -tend to offer immediate gratifications thus complying with a focus on the immediate present, typically at the expense of long-term goals (Gottfredson 1990:96).

• This means that an individual with low self-control will tend to pursue immediate gratifications, whereas an individual with a high level of self-control will tend to overcome the temptations of immediate gratifications in the pursuit of long-term goals (Gottfredson 1990:96).

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Assumptions of markets

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1) Open street markets: immediate purchase of drugs

2) Closed social networks: Close proximity to seller and in some instances drug consumption together

3) Social media drug buying: Accessed by internet, delivery by post or currier. Familiarity with ex Wicker, Signal or other encryption apps.

4) Cryptomarket drug dealing: National, regional or global orders, postal delivery. Technology knowledge of bitcoins, PGP and a general high OPSEC.

Data: Youthprofilesurvey 2016 [Ungeprofilundersøgelsen]

n=47.332, collected in fall 2016.

Danish Young people in 7th, 8th, 9th grade, youth education (vocational, high school).

Age: 12-25

Survey rolled out in 48 (of 98) municipalities around Denmark. Delivered electrical in school classes.

Not representative: Copenhagen is not included, no data from young adults outside the educational system.

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Buying drugs questions(Dependent) • Questions of drug purchase

is only asked among those that have smoked cannabis

• ”Where do you normally buy your cannabis?”

• Social media purchase and cryptomarket purchase has been grouped together: 323 persons (0,6% af all, 3,5% of those that have smoked cannabis) has bought cannabis online.

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I do not but, but get it offeredI grow it myself I buy from friends I but locally, from others than my friends I buy in a open streetmarket (eg. Christiania) I buy by Facebook or other social media I buy on darknet (Silkroad, AlphaBay) Other

Brief Self-Control Scale; BSCS(independent)

• Danish version: Pedersen & Lindstand

• 13-item measure of self-control that avoids criterion contamination and maintains content validity.

• The BSCS focuses on processes that directly involve self-control (e.g., breaking a habit, working toward long-term goals), rather than distal behavioral outcomes of self-control.

• BSCS has shown good reliability and validity among college students (Tangney, Baumeister & Boone, 2004; de Ridder et al., 2012) and relates to a variety of behaviors

• Scale 1-4, resulting in a score between 13-52 points

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4485378/#R63https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4485378/#R19

Buys online: Logistic regression model(1) (2) (3) (4)

Buys cannabis online Buys cannabis online Buys cannabis online Buys cannabis online

Self-Control full 0.907*** 0.920*** 0.949*** 0.954***

Gender 2.131*** 1.625*** 1.629***

Age 1.221*** 1.155** 1.153**

7th/8th grade 1 1 1

9th grade 0.634 0.718 0.761

HHX/HG- Business high school 0.236*** 0.293*** 0.346**

Vocational/Carework 0.339** 0.405* 0.476*

High School 0.235*** 0.248*** 0.296**

Cannabis last 12month, 0 1 1

Cannabis last 12month, 1-2 0.716 0.636

Cannabis last 12month, 3-5 0.946 0.811

Cannabis last 12month, 6-9 1.340 1.129

Cannabis last 12month, 10-19 2.398* 2.063

Cannabis last 12month, 5-39 2.593* 2.086

Cannabis last 12month, 40+ 8.763*** 6.662***

Drunk last 30days, 0 1

Drunk last 30days, 1 0.755

Drunk last 30days, 2 0.708

Drunk last 30days, 3-5 0.581*

Drunk last 30days, 5-9 1.104

Drunk last 30days, 10+ 2.285*

Been offered cannabis 1.716*

Constant 1.040 0.0380*** 0.0201*** 0.0133***

Observations 5998 5998 5998 5998

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Multi-nominal regression model

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Do not buy Home grower Offline Online More options

Self-Control full 1 0.630* 0.801*** 0.633** 0.788***

Gender 1 1.205 1.897*** 2.622* 1.333***

Age 1 0.837 1.059* 1.241 1.029

7th/8th grade 1 1 1 1 1

9th grade 1 0.990 1.257 0.442 1.111

HHX/HG- Business high school 1 0.624 0.858 0.250 0.618*

Vocational/Carework 1 1.362 1.228 1.200 0.842

High School 1 0.468 0.891 0.467 0.688

Cannabis last 12month, 0 1 1 1 1 1

Cannabis last 12month, 1-2 1 0.618 0.741** 0.891 0.668*

Cannabis last 12month, 3-5 1 0.879 0.972 0.976 1.758***

Cannabis last 12month, 6-9 1 5.363 1.853*** 0.884 3.547***

Cannabis last 12month, 10-19 1 4.419 3.588*** 1.339 5.571***

Cannabis last 12month, 5-39 1 4.434 11.04*** 4.123 12.61***

Cannabis last 12month, 40+ 1 85.49*** 33.04*** 27.77*** 38.90***

Drunk last 30days, 0 1 1 1 1 1

Drunk last 30days, 1 1 0.734 0.981 0.565 0.932

Drunk last 30days, 2 1 0.310 1.148 0.340 1.077

Drunk last 30days, 3-5 1 0.938 1.103 0.543 0.995

Drunk last 30days, 5-9 1 0.00000212*** 0.919 2.472 1.270

Drunk last 30days, 10+ 1 2.759 1.030 1.128 2.014*

Been offered cannabis 1 0.257* 0.777* 1.062 1.794***

Constant 1 0.502 0.0949*** 0.000111*** 0.0537***

Observations 5998

(zero) Findings

• Online purchases do not cater strongly towards a specific personality type

• Higher self-control do not explain online purchase of illicit drugs

• The multi-nominal regression model shows some indication of larger self-control explaining online buys.

• Intensive cannabis consumption explains online purchases, but to a lover degree than within off-line purchase forms.

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Discussion

• Is the assumption of delayed gratification within digital buying problematic because delivery time will be as fast here as in offline?

• Self-control is a very broad personality trait (Hoyle)

• This leads into the question of situational factors.• What is the interaction orders of online drug dealing space?

• More qualitative studies need to be done.

• Questions of internet behavior will be included in the 2018 survey as well as further questions of digital crimes and risk behavior.

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Limitations

• Low statistical power (Low n of online byers).

• Recoding crypto and social media drug dealing into online drug purchases can theoretically be contested. • The actual knowledge of social media drug dealing is absent

(NDDSM study from University of Copenhagen is addressing this).

• Copenhagen with its open cannabis markets are not included in the survey

• In further surveys we will include CPR numbers so survey can be related to register data.

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