Table 3.
Regression coefficients for models predicting gambling problems conditional on frequency of participation on each form
| DV: log(PGSI+1) | ||
| Model 3 | Model 4 | |
| Jurisdiction: WA (0), Rest of Australia (1) | 0.043*** (0.025, 0.060) | |
| Scratchcards | 0.062*** (0.047, 0.076) | 0.049* (0.004, 0.094) |
| Sports betting | 0.067*** (0.049, 0.085) | 0.064* (0.007, 0.120) |
| Race betting | 0.050*** (0.035, 0.065) | 0.071** (0.024, 0.118) |
| Novelty betting | 0.161*** (0.101, 0.221) | 0.071 (−0.075, 0.217) |
| Bingo | 0.004 (−0.030, 0.038) | −0.165 (−0.357, 0.027) |
| Keno | 0.056*** (0.036, 0.076) | 0.068 (−0.093, 0.229) |
| Poker | 0.102*** (0.073, 0.131) | 0.085 (−0.010, 0.179) |
| Casino games | 0.149*** (0.117, 0.180) | 0.163*** (0.075, 0.251) |
| EGMs | 0.168*** (0.154, 0.182) | 0.129*** (0.055, 0.204) |
| Scratchcards × Jurisdiction | 0.003 (−0.045, 0.050) | |
| Sports betting × Jurisdiction | −0.001 (−0.061, 0.059) | |
| Race betting × Jurisdiction | −0.030 (−0.080, 0.020) | |
| Novelty betting × Jurisdiction | 0.107 (−0.054, 0.267) | |
| Bingo × Jurisdiction | 0.167 (−0.028, 0.363) | |
| Keno × Jurisdiction | −0.015 (−0.177, 0.148) | |
| Poker × Jurisdiction | 0.017 (−0.082, 0.116) | |
| Casino games × Jurisdiction | −0.017 (−0.111, 0.077) | |
| EGMs × Jurisdiction | 0.032 (−0.044, 0.109) | |
| Observations | 5,221 | 5,221 |
| R2 | 0.379 | 0.383 |
| Adjusted R2 | 0.378 | 0.381 |
| Residual Std. Error | 0.478 (df = 5,212) | 0.477 (df = 5,202) |
| F Statistic | 353.766*** (df = 9; 5,212) | 170.071*** (df = 19; 5,202) |
Notes: All gambling form independent variables and PGSI dependent variable transformed via log(+1). Lotteries were excluded on a theoretical basis because they were found to reduce harm. Esports betting, fantasy sports betting and skin gambling were excluded due to their low prevalence. *p < 0.05, **p < 0.01, ***p < 0.001.