Machines Secondary analysis

Oct 2012

2

ContentsOverview of approachPart 1: key findingsPart 2: key findingsDiscussionNext steps

3

Overview of project design

Aim to fully explore BGPS data in relation to machine

players

Iterative and exploratory analysis – meeting in August to

discuss key findings

Some further changes due to limitations of what is possible

Findings presented in two parts:

Part 1 – changes over time

Part 2 – In-depth profile of machine player sub-types

using 2010 data

4

Part 1: Changes over time

0

5

10

15

20

25

1999 2007 2010

Perc

en

t

Male Female

Past year prevalence of playing slots, by survey year and sex

5

Part 1: Changes over time

Past year prevalence of playing machines in bookmaker’s, by survey year and sex

4

6

11

2007 2010

Males FemalesBase: All aged 16+

6

Part 1: Changes over time

Past year prevalence of playing machines in bookmaker’s, by survey year, age and sex

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

16-34 35-54 55+ 16-34 35-54 55+

2007 2010

Per

cen

t

Male FemaleBase: All respondents

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

16-34 35-54 55+ 16-34 35-54 55+

2007 2010

Per

cen

t

Male FemaleBase: All respondents

7

Part 1: other profile changes [slots]

Slot machine gamblers:

Age and sex profile different Decreases among those aged 16-35; increases among

those aged 35+

Some variations by marital status (likely age related)

Changes in profile by educational attainment (reflecting age changes and changing profile of education status in past decade?)

Changes in profile by income – greater % of slot machine gamblers from lower income groups. But reflective of broader changes?

Need to question how all of the above are related to policy change

8

Part 1: other profile changes [slots]

Slot machine gamblers:

Slot machine gamblers in 2010 more engaged in gambling than in 1999 and 2007

Take part in more activities per yr and per wk 9% 7+ activities 1999; 21% 7+ activities

2010 Increased frequency of gambling (25% vs.

30%) Increased frequency of playing machines

(15% vs. 20%

Mean DSM-IV scores higher in 2010 than 1999

Potentially reflection of changing profile of slot machine players

9

Part 1: other profile changes [Fobts]

Fobt machine gamblers:

Increasingly male, increasingly younger

Higher levels of educational attainment

Lower income groups

BUT

Unlike slots, no evidence of any change in levels of engagement in gambling

Heavily engaged in other activities – majority take part in 7+ activities in past year & over two thirds gambler once a week or more

10

Part 2: Machine gambler sub-types

Methods Used Latent Class Analysis to identify machine gamblers sub-types Number of approaches tried to determine which method was most

robust Past year participation & Frequency Venue of play

Used standard criteria to identify the best approach Key part of the criteria is also how useful and how interpretable

resultant groups are.

11

Machine gambler sub-groups

Based on venue of participation in the past year:

Class 1 Class 2 Class 3 Class 4 Class 5 Total

% % % % % %

Where played slot machines:

In a pub 100 0 0 0 100 56

In an amusement arcade 10 100 9 1 76 30

In a bingo hall 1 0 0 17 17 4

In a bookmakers 19 0 100 0 71 31

In a sports/social club 3 0 1 27 41 9

In a casino 1 1 7 34 30 8

In a motorway service station 1 0 0 5 25 3

Somewhere else 1 0 0 0 0 0

12

Machine gambler sub-groups: categorisation Class 1: Mainly Pubs (46.7%; n=483)

Class 2: Only AGCs (17.1%; n=194)

Class 3: Mainly bookmaker’s (15.2%; n=147)

Class 4: Other venues (11.3%; n=130)

Class 5: Multi-venues (9.7%; n=94)

13

Machine gambler sub-groups: categorisation Class 1: Mainly Pubs (46.7%; n=483)

Class 2: Only AGCs (17.1%; n=194)

Class 3: Mainly bookmaker’s (15.2%; n=147)

Class 4: Other venues (11.3%; n=130)

Class 5: Multi-venues (9.7%; n=94)

14

Machine gambler sub-groups: context

Mainly used regression to model membership of belonging to

each group

Need context of understanding what differentiates machines

gamblers from other gamblers first:Variables entered into model:AgeSexEthnicityMarital statusEducational qualificationsGeneral healthSmoking statusAlcohol consumption Personal IncomeNS-SEC of household reference personEconomic activity

Significant variables in model:Age (younger)Sex (men)

Educational qualifications (lower quals)

Smoking status (smokers)Alcohol consumption (heavier drinkers)

15

Machine gamblers: gambling characteristics

Variables entered into model:AgeSex

No of gambling activities in past weekPGSI categorisationAge first gambledParental gambling status

Significant variables in model:Age (younger)Sex (men)

No of gambling activities in past week (more activities)PGSI categorisation (low, mod, PGs)Age first gambled (younger)

16

Machine gamblers: summary

Compared with other gamblers (excl NL only):

Machine gamblers more likely to be younger,

male, to engage in other risk behaviours, to be

highly engaged in gambling generally, to have

started younger and to experience some

problems with behaviour

17

Machine gamblers sub-groups

How does this vary among our machine gambler sub-groups?

Are all machine players alike?

18

High time/high spend machine players

12

27

18

25

17

Mainly pubs Bookmaker's Other venues Multi venue All

Base: All regular machine players

19

% gamble 2+ days per week

29

18

45

26

43

31

Mainly pubs AGCs Bookmaker's Other venues Multi venue All

Base: All machine players

20

Regression 1: socio-demographics

Pubs AGCs Bookies Other Multi

Age Sex Ethnicity Marital status

Educational qualifications

General health

Smoking Alcohol Income Economic activity NS-SEC of household reference person

21

Regression 2: Gambling characteristics

Pubs AGCs Bookies Other Multi

No of activities in past week

PGSI categorisation Parental gambling status

Age first gambled

Pubs – odds lower among PGs

AGCs – odds lower among more engaged gamblers

Bookies – odds higher among PGs

Multi – odds higher among PGs and more engaged gamblers

22

Discussion

What are most salient findings from GC perspective?

How do we refer to B2/Fobt players?

What implications for stakeholders – how do we manage this?

Need to fully quality assure statistics (double verification and checking process)

23

Next steps

• Agree timescale for production of final report

• Agree publication strategy

• Agree stakeholder management and impact strategy