Table 8 Multi-logit model estimation of change in the frequency and severity of erratic rainfall in the last 10 years and 10 years ago.
From: Ethnic diversity and divergent perceptions of climate change: a case study in Southwest China
Variables | Change in frequency | Change in severity | |||||
|---|---|---|---|---|---|---|---|
Less frequent | More frequent | New occurrence | Less severe | More severe | New occurrence | ||
frequency | 15 (18.5%) | 31 (38.3%) | 14 (17.3%) | 14 (17.3%) | 31 (38.3%) | 14 (17.3%) | |
intercept | 1.285 (0.868) | 8.407 (0.013**) | 6.744 (0.086) | 3.173 (0.496) | 6.872 (0.081) | 0.680 (0.883) | |
distance | −0.031 (0.280) | −0.047 (0.020*) | −0.024 (0.328) | ||||
age | −0.095 (0.060) | −0.023 (0.469) | −0.075 (0.068) | −0.005 (0.874) | −0.021 (0.550) | −0.055 (0.232) | |
rateCultivated | 16.415 (0.015*) | −1.074 (0.512) | −3.321 (0.152) | −3.668 (0.180) | −7.162 (0.008**) | −7.683 (0.016*) | |
rateIrrigable | −9.760 (0.145) | −2.927 (0.306) | 0.308 (0.930) | 1.480 (0.747) | 2.789 (0.485) | 5.560 (0.236) | |
rateRainfed | −7.737 (0.254) | −3.185 (0.240) | 0.957 (0.780) | 2.044 (0.656) | 2.525 (0.519) | 6.136 (0.180) | |
incomeAgri | 1.438 (0.657) | −0.950 (0.697) | 1.643 (0.515) | 0.394 (0.888) | −3.527 (0.176) | 4.171 (0.220) | |
incomeLS | −2.731 (0.385) | −0.383 (0.870) | −7.551 (0.082) | −5.203 (0.162) | −0.437 (0.869) | −8.999 (0.116) | |
incomeFR | −14.176 (0.272) | −0.630 (0.874) | −0.505 (0.900) | −6.347 (0.360) | −1.262 (0.743) | −3.577 (0.492) | |
incomeMH | −743.009 (0.557) | −1299.192 (0.877) | 5.922 (0.172) | ||||
incomeMjob | −0.847 (0.755) | −2.241 (0.266) | −2.217 (0.303) | −2.351 (0.338) | −2.872 (0.183) | 0.749 (0.790) | |
incomeSalaried | −108.686 (0.978) | −1.945 (0.414) | −5.638 (0.114) | −4.286 (0.157) | −9.194 (0.041*) | −9.255 (0.071) | |
incomeObusiness | 4.290 (0.241) | −0.006 (0.998) | −4.427 (0.146) | −0.949 (0.713) | −2.510 (0.307) | −4.412 (0.241) | |
Ethnicity | Bai | −0.895 (0.778) | 0.126 (0.949) | 0.190 (0.935) | −0.629 (0.768) | −0.940 (0.669) | −0.643 (0.775) |
Dulong | 3.965 (0.928) | −2.356 (1.000) | 14.447 (0.999) | −4.546 (0.948) | −3.179 (1.000) | 13.775 (0.999) | |
Han | −17.719 (0.993) | 0.668 (0.733) | 5.427 (0.039*) | −14.382 (0.994) | 0.765 (0.694) | 6.937 (0.026*) | |
Lisu | −3.338 (0.111) | −2.012 (0.140) | −0.829 (0.621) | −0.876 (0.537) | −0.097 (0.932) | 1.241 (0.465) | |
Naxi | −1.061 (0.618) | 0.172 (0.902) | 1.054 (0.519) | 1.207 (0.439) | 0.661 (0.619) | 0.286 (0.850) | |
Yi | −4.219 (1.000) | 15.267 (0.998) | 0.881 (1.000) | 2.100 (1.000) | 16.899 (0.998) | 3.152 (1.000) | |
Tibetan(base) | 0 | 0 | 0 | 0 | 0 | 0 | |
Education | Junior school | −0.915 (0.502) | −0.112 (0.911) | 1.645 (0.211) | −0.089 (0.935) | 0.277 (0.793) | 3.022 (0.053*) |
Above college | −21.754 (0.969) | −16.820 (0.998) | −17.331 (0.999) | −16.619 (0.999) | −16.298 (0.998) | −17.204 (0.998) | |
High school | −16.748 (0.994) | 0.116 (0.944) | 2.132 (0.306) | −15.762 (0.994) | 0.209 (0.894) | 3.139 (0.175) | |
Illiteracy | −4.123 (0.114) | 0.192 (0.877) | −0.507 (0.771) | 1.530 (0.366) | 2.358 (0.150) | −0.078 (0.973) | |
Primary school(base) | 0 | 0 | 0 | 0 | 0 | 0 | |
Likelihood ratio test: Chi-Square = 81.653; df = 63; p = 0.057. Pseudo R2: Cox & Snell = 0.640; Nagelkerke = 0.687; McFadden = 0.380. | Likelihood ratio test: Chi-Square = 84.597; df = 63; p = 0.036. Pseudo R2: Cox & Snell = 0.653; Nagelkerke = 0.701; McFadden = 0.396. | ||||||
