youth gambling and the internet: the good, the bad and the ugly jeffrey l. derevensky, ph.d....
TRANSCRIPT
Youth gambling and the Internet: The good, the bad and the ugly
Jeffrey L. Derevensky, Ph.D.
Professor, School/Applied Child Psychology
Professor, Psychiatry
McGill UniversityInternational Centre for Youth Gambling Problems and High-Risk
Behaviors
www.youthgambling.com
Alberta Gambling Research Institute Annual ConferenceMarch, 2009
Is gambling dangerous??
Internet gambling on anything
The appeal of the Internet…
Celebrity endorsements….
Prevalence Findings of Internet Wagering
• Vary considerably
• Dependent upon method & date of data collection
• Dependent upon population studied
• Difficulties collecting data have been articulated by Wood & Williams (2009)
Adolescent Internet use…
Internet Use Media Awareness Network, 2009
• In Canada, 99% of youth age 9-17 reported use of Internet, 94% have Internet access at home; 61% report having high speed access
• 37% report having their own Internet connections
• 89% of grade 4 school students play games on the Internet
• Where no household rules exist for Internet use, 74% report an adult was never present when the child was on the Internet
• 94% of students’ top 50 Internet sites include marketing material
• Large percentage of adolescents report observing Internet gambling pop-up messages & believe they were the target (Derevensky et al., 2008)
What we know about the Internet
• Access is widespread
• Access is inexpensive
• Internet is anonymous
• Internet is convenient
• Internet is entertaining
• Internet is used for many purposes
Two Internet gambling studies2004-2006
Gambling Severity by Age Group
N = 2292
Gambling Groups1
NonGambler(n = 745)
SocialGamblera
(n = 1333)
At-RiskGamblerb
(n = 139)
ProbablePathological
Gamblerc
(n = 75)
Age Group***
Under 18 years
n = 1242
35.5 51.0 8.9 4.6
18 and overn =
105029.0 66.5 2.8 1.7
Total 32.4 58.2 6.1 3.3
1Percentage. Gambling Groups are based on DSM-IV and DSM-IV-MR scores.aDSM-IV score (0-2); DSM-IV-MR-J score (0-1). bDSM-IV score (3-4); DSM-IV-MR-J score (2-3). cDSM-IV score ( ≥5); DSM-IV-MR-J score (≥4). ***p<.001.
Participation in Gambling in the Past 12 Months
0
10
20
30
40
50
lotteryscratch
cards /draws
sportsbetting
slotmachines
electronicgaming
machines
card games internetgambling
Type of Gambling
Per
cen
tag
e
male
female
Frequency of Play on Internet Gambling Games Without Money by Gender and Age
N = 2679
Frequency of Play
Never(%)
Occasionallya
(%)Regularlyb
(%)Dailyc
(%)
Gender**
Male n = 1162 59.0 33.6 4.9 2.5
Female n = 1517 62.0 32.0 4.9 1.1
Age***
12-13 years n = 166 53.6 34.4 7.8 4.2
14-15 years n = 475 56.0 36.4 5.5 2.1
16-17 years n = 786 56.6 36.0 5.5 1.9
18-20 years n = 973 62.9 31.7 4.6 0.8
21-24 years n = 235 74.9 21.7 2.1 1.3
> 25 years n = 44 88.7 6.8 0 4.5
Total 60.7 32.7 4.9 1.71Percentage. aLess than once a week. bOnce a week or more. cOnce a day or more. ***p<.001. **p<.05.
Frequency of Play on Internet Gambling Sites Without Moneyin the Past 12 Months by Gambling Severity
N = 2205
Internet Gambling Without Money1
Yes(n = 1082)
No(n = 1123)
Gambling Groups***
Non Gambler n = 726 33.9 66.1
Social Gambler n = 1278 56.6 43.4
At-Risk Gambler n = 129 74.4 25.6
Probable Pathological Gambler
n = 72 80.6 19.4
Total 49.1 50.9
1Percentage. ***p<.001.
Frequency of Play on Internet Gambling Sites With Moneyin the Past 12 Months by Gambling Severity
N = 2292
Internet Gambling With Money1
Yes(n = 183)
No(n = 2109)
Gambling Groups***
Non Gambler n = 745 0 100
Social Gambler n = 1333 9.5 90.5
At-Risk Gambler n = 139 21.6 78.4
Probable Pathological Gambler
n = 75 34.7 65.3
Total 8.0* 92.0
1Percentage *13.1% males; 4.6% females are gambling on Internet
Average Amount of Money Spent on Internet Gambling in the Last 12 Months by Gambling Severity
N=2291Amount of Money Spent
< $50 $50-$100 $100-$500 > $500
Gambling***Groups
Social Gambler
98.9 0.6 0.4 0.1
At-Risk Gambler
96.5 0 2.2 1.4
Probable Pathological Gambler
86.6 1.3 1.3 10.7
1Percentage.
Most Money Wagered in One Internet Gambling Session by Gender and Age
Amount of Money Wagered
< $50 $50-$100 $100-$500 > $500
Gender***
Males 96.8 0.9 1.2 1.0
Females 99.6 0.1 0.3 0
Age
12-13 years 98.1 0.6 1.2 0
14-15 years 97.2 0.8 0.6 1.2
16-17 years 98.3 0.3 1.0 0.4
18-20 years 98.5 0.5 0.6 0.3
21-24 years 100.0 0 0 0
> 25 years 100.0 0 0 0
1Percentage.
Most Money Wagered in One Internet Gambling Sessionby Gambling Severity
Amount of Money Wagered
< $50 $50-$100 $100-$500 > $500
Gambling***Groups
Social Gambler
99.0 0.4 0.5 0.2
At-Risk Gambler
93.5 2.2 2.2 2.2
Probable Pathological Gambler
85.3 2.7 4.0 8.0
1Percentage.
Most Money Won in One Internet Gambling Sessionby Gender and Age
Amount of Money Won
< $50 $50-$100 $100-$500 > $500
Gender***
Males 96.1 0.9 0.9 2.2
Females 98.9 0.5 0.3 0.3
Age
12-13 years 96.9 1.2 0 1.8
14-15 years 96.7 1.1 0.4 1.9
16-17 years 97.6 0.5 0.5 1.4
18-20 years 97.7 0.7 0.9 0.6
21-24 years 100.0 0 0 0
> 25 years 100.0 0 0 0
1Percentage.
Most Money Won in One Internet Gambling Session by Gambling Severity
Amount of Money Won
< $50$50-$100
$100-$500
> $500
Gambling***Groups
Social Gambler
98.4 0.2 0.8 0.6
At-Risk Gambler
92.1 4.3 0 3.6
Probable Pathological Gambler
77.3 5.3 4.0 13.3
1Percentage.
Most Money Lost in One Internet Gambling Session by Gender and Age
Amount of Money Lost
< $50 $50-$100 $100-$500 > $500
Gender***
Males 96.8 0.9 1.0 1.2
Females 99.5 0.3 0.2 0.1
Age
12-13 years 98.7 0 0 1.2
14-15 years 96.7 1.1 0.4 1.9
16-17 years 98.7 0.4 0.5 0.4
18-20 years 98.3 0.6 0.9 0.1
21-24 years 100.0 0 0 0
> 25 years 99.0 1.0 0 0
1Percentage.
Most Money Lost in One Internet Gambling Session by Gambling Group
Amount of Money Lost
< $50 $50-$100 $100-$500 > $500
Gambling***Groups
Social Gambler
99.0 0.6 0.5 0.1
At-Risk Gambler
95.1 0 2.9 2.1
Probable Pathological Gambler
81.3 5.3 4.0 9.3
1Percentage.
Age of Onset for Internet Gambling Without Moneyby Gambling Severity
1Percentage. Gambling Groups are based on DSM-IV and DSM-IV-MR scores.aDSM-IV score (0-2); DSM-IV-MR-J score (0-1). bDSM-IV score (3-4); DSM-IV-MR-J score (2-3). cDSM-IV score ( ≥5); DSM-IV-MR-J score (≥4). ***p<.001.
Gambling Groups1
SocialGamblera
(n = 1333)
At-RiskGamblerb
(n = 138)
ProbablePathological
Gamblerc
(n = 72)
Age***
Under 10 years 4.4 13.1 16.9
10-11 years 8.8 10.9 16.9
12-13 years 15.2 19.0 25.4
14-15 years 17.2 15.3 15.5
16-17 years 9.1 7.3 2.8
Over 18 years 2.8 0.7 2.8
Age of Onset for Internet Gambling With Moneyby Gambling Severity
Gambling Groups1
SocialGamblera
(n = 1333)
At-RiskGamblerb
(n = 138)
ProbablePathological
Gamblerc
(n = 72)
Age***
Under 10 years n = 13 0.5 3.6 1.4
10-11 years n = 8 0.5 0.7 7.2
12-13 years n = 30 1.0 7.2 5.6
14-15 years n = 52 2.6 5.8 11.1
16-17 years n = 29 1.7 2.9 4.2
Over 18 years n = 26 1.9 0.7 0
1Percentage. Gambling Groups are based on DSM-IV and DSM-IV-MR scores.aDSM-IV score (0-2); DSM-IV-MR-J score (0-1). bDSM-IV score (3-4); DSM-IV-MR-J score (2-3). cDSM-IV score ( ≥5); DSM-IV-MR-J score (≥4). ***p<.001.
Types of Internet Gambling Activities by Gambling Group
0
2
4
6
8
10
12
14
Roulette Blackjack SportsBetting
Slotmachines
Cards Maj Jong StockMarket
Games
Fre
qu
ency
%
Social Gambler
At-Risk Gambler
Probable Pathological
Follow-up study (McBride & Derevensky, 2007)
• Montreal high-school students: N = 1113
• Canadian and U.S. college and university students: N = 1273
• On-line gaming newsletter link: N = 546
Past year gambling
0
10
20
30
40
50
60
70
80
90
100
Fre
qu
en
cy
%
Male Female
High School
College/University
Internet Sample 18-24
Internet Sample 25+
Past-Year Gambling Frequencies
0
10
20
30
40
50
60
70
80
Fre
qu
en
cy
%
Never Lessthan
once amonth
Monthly Weekly
High School
College/University
Internet Sample 18-24
Internet Sample 25+
Use of “Demo/Practice” Sites
0
10
20
30
40
50
60
70
80
90
Fre
qu
en
cy %
Male Female
High School
College/University
Internet Sample
“Demo/Practice” Site Frequency
0
10
20
30
40
50
60
Fre
qu
en
cy
%
Never Less thanonce amonth
Monthly Weekly Daily
High School
College/University
Internet Sample
“Demo/Practice” Activities
0
10
20
30
40
50
60
70
80
90
Fre
qu
en
cy %
Roulette Blackjack Dice Sportsbetting
Slotmachines
Cards Maj Jong Keno
High School
College/University
Internet Sample
Gambling for Money on Internet
0
10
20
30
40
50
60
Fre
qu
en
cy
%
Male Female
High School
College/University
Internet Sample
Gambling for Money on Internet Frequency
010
2030
40
5060
70
8090
100
Fre
qu
en
cy
%
never less thanonce amonth
monthly weekly
High School
College/University
Internet Sample
Internet Sample Online Gambling Activities
0
10
20
30
40
50
60
70
80
Fre
qu
en
cy
%
Roulette Blackjack Baccarat Dice Keno Sportsbetting
HorseRacing
Slots Cards StockMarket
Gambling Severity by Sample
0
10
20
30
40
50
60
70
80
Fre
qu
en
cy
%
Non-gambler Social gambler Problem gambler
High School
College/University
Internet Sample
Internet Sample of Problem Gamblers
0
10
20
30
40
50
60
70
80
90
Fre
qu
en
cy
%
Male Female 18-24 25+
Have gambled formoney
Have used"demo/practice" sites
Reasons Youth Gamble on Internet
0
10
20
30
40
50
60
70
80
90
100
Gamediversity
High speedplay
Bonuses Competition Convenience Privacy Anonymity Don't need toleave house
Good odds Fair/reliablepayouts
24-houraccessibility
Internet sample 18-24
Reasons Youth Don’t Gamble on Internet
0
5
10
15
20
25
30
35
40
45
50
Fre
qu
en
cy
%
Need a creditcard
Worried aboutfraud
Bets might berigged
Lack ofambience
Unsure ofcollectingwinnings
Easier to hideproblems
Don't want togive personal
info
How Youth Are Paying
0
5
10
15
20
25
30
35
40
45
Fre
qu
ency
%
Personalcredit card
Family creditcard (with
permission)
Family creditcard (withoutpermission)
Debitcard/ATM
Personalcheque
Wire/banktransfer
Facts & Concerns
Internet Gambling• Provides a form of entertainment• Enhances levels of excitement and arousal• Provides an opportunity to win money• Younger generation of teens very attracted to Internet
gambling sights• Practice sights are exceedingly popular amongst
problem gamblers-age of onset is before 13• Adolescents are gambling on the net, occasionally• Over 10% of problem gamblers spent over $500 on
Internet gambling• Sites are widely advertised
Internet Gambling
• Offers free games and trial (practice) sites• Incorporates video-game technology• Reward and loyalty programs• Initial deposit bonuses• Bettor’s Insurance• Graphics add to the excitement of the game• Perceived elements of skill• Convenience and ease of access• Allows individuals to lie about their age• Allows underage youth to gamble on prohibited
activities• Reinforcement schedules are quick
Gambling for money on the Internet
• 9% of high school students have gambled for money on the Internet– 13% of males, 6% of females
• 6% of College and University students have gambled for money on the Internet– 11% of males, 3% of females
• 42% of the Internet sample have gambled for money on the Internet– 53% of males, 20% of females– 70% at least weekly
Conclusions
• Playing on Internet gambling sites without money is a common practice amongst adolescents and young adults
• At-Risk and PPGs play on Internet with and without money more often than non-gamblers and social gamblers
• Internet wagers for money increases with severity of gambling problems
• Most money won and lost increases by gambling severity
• Gambling sites permitting playing without money have been shown to– have differential payout rates
– represent breeding ground for future players
• Much Internet wagering incorporates videogame technology
• Recent studies suggest young male adults are more likely to engage in Internet gambling
….. a fourth wave of gambling
• significant expansion of Internet gambling
• greater government regulation, ownership
& taxation
• likely see new innovative harm minimizationefforts including technological advances (e.g. Techlink & biometric devices)
• more gambling problems emerging
• more research and hopefully collaborativeresearch efforts
• potential for future problems amongst youth remains high
• use of gambling blocking software may be advisable
• regulated, grey market, dark grey/black market sites
• young adults highest risk group
• we will see other forms of Internet wagering including mobile gambling, interactive t.v.
• gambling has become normalized
• sensitizing parents