| Literature DB >> 34276444 |
Jonny Engebø1,2, Torbjørn Torsheim1,3, Ståle Pallesen1,3,4.
Abstract
The purpose of gambling regulation can be to ensure revenue for the public, to prevent crime and gambling problems. One regulatory measure involves restriction of what games can be offered in a market. In this study, the effects of two regulatory market changes are investigated: First, a restriction of availability when slot machines were banned from the Norwegian market in 2007, and second the introduction of regulated online interactive games to the same market in 2014. Data collected from the general population in the period from 2005 through 2018, comprising 2,000 respondents every year, are used to investigate how participation in gambling changed over time. The respondents were asked if they took part in various games or lotteries. Logistic regression analyses were used to predict the proportion participating in five groups of games and if changes in participation coincided with major market changes. The first change was associated with a reduction in gambling on slot machines as well as a reduction in gambling participation overall. Following the slot machine ban, results show an increase in women participating in games offered in land-based bingo premises. A general increase in gambling on foreign websites was also seen, albeit much smaller than the reduction in slot machine gambling. The increases can partly be explained as substitution of one type of gambling with another. New regulated online interactive games were introduced in 2014. Despite the relatively large growth of such games internationally, Norway included, increased online gambling in general and an increased marketing of foreign gambling websites, the participation on foreign websites seemed stable. However, the overall participation in online interactive games increased. The introduction of the regulated alternative seems to have had a channelizing effect. Overall, the changes in gambling participation coinciding with two major regulatory changes can be explained by transformations of physical and social availability, and in terms of mechanisms outlined by the model of total consumption.Entities:
Keywords: channelization of gambling; gambling problems; gambling reforms; gambling regulation; prevention of gambling problems; substitution
Year: 2021 PMID: 34276444 PMCID: PMC8278013 DOI: 10.3389/fpsyt.2021.672471
Source DB: PubMed Journal: Front Psychiatry ISSN: 1664-0640 Impact factor: 4.157
Descriptive statistics of study variables (N = 28,251).
| Epochs | ||
| 1st (2005–2007) | 22.1 | 6,243 |
| 2nd (2008–2013) | 42.5 | 12,008 |
| 3rd (2014–2018) | 35.4 | 10,000 |
| Gender | ||
| Female | 50.3 | 14,217 |
| Male | 49.7 | 14,034 |
| Age | ||
| 15–17 yrs. | 4.0 | 1,140 |
| 18–24 yrs. | 11.2 | 3,164 |
| 25–39 yrs. | 24.7 | 6,984 |
| 40–59 yrs. | 33.4 | 9,444 |
| 60 yrs. and older | 26.6 | 7,519 |
| Gambled on one or more game types | ||
| No | 23.7 | 6,700 |
| Yes | 76.3 | 21,551 |
| Gambled on slot machines (to 2007) / IVT Multix (from 2009) | ||
| No | 94.7 | 26,742 |
| Yes | 5.3 | 1,509 |
| Gambled in land-based bingo premises | ||
| No | 98.0 | 27,689 |
| Yes | 2.0 | 562 |
| Gambled on foreign web sites | ||
| No | 95.8 | 27,066 |
| Yes | 4.2 | 1,185 |
| Gambled on online interactive games, not poker | ||
| No | 97.2 | 27,455 |
| Yes | 2.8 | 796 |
Data from every year from 2005 through 2018. n = 2,000–2,168 for each year. Mean/standard deviation (SD) for age: 45.94/(18.36).
Predicted probabilities (0–1), mean and standard deviation (SD) of participation in gambling per time epoch (N = 28,251, n = 6,243–12,008).
| Mean | 1 (2005 | 0.821 | 0.187 | 0.017 | 0.036 | 0.007 |
| 2 (2008–2013) | 0.763 | 0.015 | 0.023 | 0.042 | 0.016 | |
| 3 (2014–2018) | 0.727 | 0.016 | 0.018 | 0.045 | 0.056 | |
| Total | 0.763 | 0.053 | 0.020 | 0.042 | 0.028 | |
| 1 (2005–2007) | 0.025 | 0.140 | 0.007 | 0.046 | 0.006 | |
| 2 (2008–2013) | 0.012 | 0.013 | 0.008 | 0.054 | 0.017 | |
| 3 (2014–2018) | 0.018 | 0.012 | 0.006 | 0.061 | 0.045 | |
| Total | 0.039 | 0.097 | 0.008 | 0.055 | 0.036 |
Figure 1Predicted probability to participate in gambling, one or more games, overall and by gender (N = 28,251).
Figure 2(A–D) Predicted participation in specific groups of games (N = 28,251). Panel (A) shows predicted participation on slot machines and IVTs (Multix), total and by gender, in three epochs (Epoch 1 from 2005 through 2007, Epoch 2 from 2008 through 2013 and Epoch 3 from 2014 through 2018). Panel (B) shows predicted participation on games in bingo premises, land-based, total and by gender, in three epochs (Epoch 1 from 2005 through 2007, Epoch 2 from 2008 through 2013 and Epoch 3 from 2014 through 2018). Panel (C) shows predicted participation on foreign websites, total and by gender, in three epochs (Epoch 1 from 2005 through 2007, Epoch 2 from 2008 through 2013 and Epoch 3 from 2014 through 2018). Panel (D) shows predicted participation in online interactive games, not poker, total and by gender, in three epochs (Epoch 1 from 2005 through 2007, Epoch 2 from 2008 through 2013 and Epoch 3 from 2014 through 2018).
Accumulated explained variation (Nagelkerke R Square) and significance per block.
| Nagelkerke/ | Nagelkerke/ | Nagelkerke/ | Nagelkerke/ | Nagelkerke/ | |
| Block 1 | 0.011/0.000 | 0.307/0.000 | 0.013/0.000 | 0.196/0.000 | 0.153/0.000 |
| Block 2 | 0.012/0.000 | 0.310/0.000 | 0.014/0.075 | 0.196/0.056 | 0.154/0.086 |
| Block 3 | 0.013/0.131 | 0.315/0.000 | 0.017/0.041 | 0.198/0.013 | 0.155/0.101 |
| Total | 0.013/0.000 | 0.315/0.000 | 0.017/0.000 | 0.198/0.000 | 0.155/0.000 |
Logistic regression analyses of five gambling variables in the Norwegian gambling market year 2005–2018.
| Block 1 | Year | −0.028 | 0.010 | −0.084 | 0.027 | 0.020 | 0.029 | 0.022 | 0.021 | 0.092 | 0.025 |
| Epoch 1 | 0.229 | 0.059 | 2.380 | 0.142 | −0.459 | 0.173 | −0.182 | 0.129 | −0.522 | 0.208 | |
| Epoch 3 | −0.037 | 0.061 | 0.461 | 0.184 | −0.173 | 0.185 | −0.089 | 0.134 | 0.772 | 0.160 | |
| Age | 0.002 | 0.001 | −0.044 | 0.002 | −0.017 | 0.002 | −0.059 | 0.002 | −0.044 | 0.002 | |
| Gender (f = 0. m = 1) | 0.076 | 0.028 | 0.911 | 0.061 | −0.194 | 0.086 | 1.960 | 0.087 | 0.910 | 0.080 | |
| Block 3 | Year | −0.013 | 0.029 | −0.072 | 0.090 | −0.218 | 0.082 | −0.024 | 0.073 | 0.067 | 0.079 |
| Epoch 1 | 0.723 | 0.183 | 3.933 | 0.532 | −1.171 | 0.497 | −1.324 | 0.462 | −1,119 | 0.659 | |
| Epoch 3 | −0.251 | 0.250 | 3.378 | 0.808 | −0.595 | 0.788 | −0.901 | 0.635 | −0.761 | 0.656 | |
| Age | 0.003 | 0.003 | −0.041 | 0.011 | −0.024 | 0.010 | −0.074 | 0.010 | −0.057 | 0.011 | |
| Gender (f = 0. m = 1) | 0.038 | 0.114 | 0.532 | 0.386 | −1.240 | 0.336 | 1.964 | 0.350 | 1,364 | 0.354 | |
| Epoch 1 by Year | −0.180 | 0.042 | −0.359 | 0.062 | 0.139 | 0.126 | 0.218 | 0.091 | 0.286 | 0.195 | |
| Epoch3 by Year | 0.004 | 0.020 | −0.246 | 0.073 | 0.135 | 0.065 | 0.037 | 0.045 | 0.101 | 0.053 | |
| Year by Age | 0.000 | 0.001 | 0.002 | 0.002 | 0.002 | 0.002 | 0.002 | 0.002 | 0.000 | 0.002 | |
| Year by Gender | 0.008 | 0.019 | 0.138 | 0.060 | 0.185 | 0.058 | −0.053 | 0.058 | −0.071 | 0.055 | |
| Epoch 1 by Age | −0.006 | 0.003 | −0.012 | 0.010 | −0.001 | 0.010 | 0.019 | 0.009 | 0.012 | 0.014 | |
| Epoch3 by Age | 0.003 | 0.003 | −0.015 | 0.010 | −0.009 | 0.011 | −0.007 | 0.010 | 0.016 | 0.011 | |
| Epoch 1 by Gender | −0.029 | 0.116 | 0.202 | 0.348 | 0.875 | 0.345 | 0.212 | 0.359 | −0.654 | 0.438 | |
| Epoch 3 by Gender | −0.031 | 0.122 | −1.035 | 0.393 | −0.952 | 0.390 | 0.914 | 0.378 | 0.351 | 0.361 | |
Results for the first and third block, before and after interactions between variables were included. Epoch 2 is the contrast to Epoch 1 and 3. (N = 28,251).
p < 0.05,
p < 0.01,
p < 0.001.