Table 5.
Models of logistic regression for variable “hard discrimination”
| Variables | Categories | Model 1: soft discrimination | Model 2: hard discrimination | Model 3: hard discrimination (+ soft discr. as predictor) |
|---|---|---|---|---|
| Gender | (R = men) | 1.00 | 1.00 | 1.00 |
| Women | 1.19 | 1.63* | 1.64* | |
| Age | (R = 45–49) | 1.00 | 1.00 | 1.00 |
| 50–54 | 1.12 | 1.28 | 1.19 | |
| 55–59 | 0.97 | 1.09 | 0.99 | |
| 60–64 | 0.81 | 0.80 | 0.76 | |
| Education level | (R = primary) | 1.00 | 1.00 | |
| Vocational | 1.07 | 0.93 | 0.79 | |
| Secondary | 1.48 | 1.27 | 0.98 | |
| Tertiary | 1.36 | 1.34 | 1.15 | |
| Place of residence | (R = rural area) | 1.00 | 1.00 | 1.00 |
| Big city, suburbs | 1.25 | 1.86* | 2.24** | |
| Small and medium sized city | 1.24 | 1.61 | 1.65 | |
| Occupational status | (R = working) | 1.00 | 1.00 | 1.00 |
| Unemployed | 3.30*** | 14.32*** | 13.86*** | |
| Retired but still working | 1.19 | 1.35 | 1.31 | |
| Non-active | 2.46*** | 7.29*** | 6.90*** | |
| Occupational group | (R = professionals, managers) | 1.00 | 1.00 | 1.00 |
| Services, technicians, office clerks | 1.21 | 1.57 | 1.65 | |
| Manual and agriculture workers | 1.34 | 1.14 | 1.06 | |
| Type of sector | (R = non-profit or mixed) | 1.00 | 1.00 | 1.00 |
| Public | 0.59* | 0.58 | 0.64 | |
| Private | 0.65 | 0.55 | 0.60 | |
| Sector of economy | (R = production) | 1.00 | 1.00 | 1.00 |
| Services | 1.06 | 0.64 | 0.56* | |
| Public services | 1.36 | 0.80 | 0.63 | |
| Soft discrimination | (0 = no discrimination) | – | – | 1.00 |
| Occurs (1) | – | – | 11.53*** | |
| Constant | 0.22*** | 0.06*** | 0.02*** | |
| Model fit | Nagelkerke R 2 | 0.074 | 0.274 | 0.455 |
| Cox & Snell R 2 | 0.052 | 0.159 | 0.264 |
*** p < 0.001; ** p < 0.01; * p < 0.05; N = 1000