Table 2.
Logistic regressions with aid as share of federal spending treatment and controls
Not enough aid | Too much aid | |
---|---|---|
Treated | 1.16 | 0.98 |
(0.53) | (0.88) | |
Male | 0.66* | 0.95 |
(0.08) | (0.74) | |
Over 35 | 1.95** | 2.04*** |
(0.02) | (0.00) | |
Urban | 1.02 | 0.89 |
(0.93) | (0.50) | |
Income (ln) | 0.99 | 0.90 |
(0.95) | (0.39) | |
Academic Education | 3.20*** | 0.52*** |
(0.00) | (0.00) | |
Party | ||
Labor | 3.31*** | 0.91 |
(0.00) | (0.60) | |
Greens | 17.74*** | 0.29*** |
(0.00) | (0.00) | |
Other | 2.20* | 1.53 |
(0.08) | (0.12) | |
Don’t Know | 2.34* | 0.56** |
(0.06) | (0.03) | |
Constant | 0.02*** | 0.99 |
(0.00) | (0.98) | |
n | 815 | 815 |
Notes: Odds ratios and p-values shown; regressions run with survey weights and robust SEs. *p < 0.1, **p < 0.05, ***p < 0.01; in all regressions the omitted party is the (centre-right) Coalition; academic education is a binary variable, coded one if the respondent has completed a degree from an academic tertiary institution (vocational tertiary education along with having no tertiary education is coded as zero).