Table 3.
Bivariate models for conducting overservice enforcement efforts: State agencies (n=44)
| Bivariate analyses | Multivariate model |
||
|---|---|---|---|
| Independent variables | Percent of agencies1 |
Χ2 (p) | Odds ratio (95% CI) |
| Population of jurisdiction | |||
| < 5 million | 48% | 4.36 (.037)* |
1.06 (0.06, 19.01) |
| ≥ 5 million | 79% | Ref. | |
| Percent black | |||
| < 7% | 64% | 0.096 (.757) |
-- |
| ≥ 7% | 59% | ||
| Percent Hispanic | |||
| < 7% | 57% | 0.302 (.583) |
-- |
| ≥ 7% | 65% | ||
| Percent poverty | |||
| < 13% | 64% | 0.096 (.757) |
-- |
| ≥ 13% | 59% | ||
| Region | |||
| Dry (South) | 55% | 2.58 (.276) | -- |
| Moderate (Mid-Atlantic, Pacific, South Coast) |
79% | ||
| Wet (North Central/New England) |
53% | ||
|
Number of fulltime agents per 1,000,000 population |
|||
| < 6 | 56% | 0.703 (.402) |
-- |
| ≥ 6 | 68% | ||
|
How common is overservice in jurisdiction? |
|||
| Not common | 75% | 0.114 (.736) |
-- |
| Somewhat/very common | 67% | ||
|
Who is primarily responsible for enforcement of alcohol retail laws? |
|||
| State | 65% | 6.03 (.049)* |
0.54 (0.11, 2.62) |
| Local | 17% | 0.07 (0.04, 1.02) |
|
| Both | 71% | Ref. | |
|
Number of on-premise establishments |
|||
| < 5000 | 48% | 5.08 (.024)* |
0.18 (0.10, 3.03) |
| ≥ 5000 | 81% | Ref. | |
|
Number of off-premise establishments |
|||
| < 3000 | 60% | 0.107 (.744) |
-- |
| ≥ 3000 | 65% | ||
1
percentages reported are the percent of agencies doing overservice enforcement at each level of the independent variable
Ref. = referent group
*
significant at p < .05