Table 6 Multi-logit model estimation of change in the frequency and severity of drought 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 | 41 (16.3%) | 109 (43.4%) | 40 (15.9%) | 35(13.9%) | 130 (51.8%) | 40 (15.9%) | |
intercept | −4.470 (0.201) | 2.173 (0.251) | −1.264 (0.571) | −0.138 (0.910) | 0.753 (0.458) | −1.514 (0.209) | |
distance | −0.017 (0.139) | −0.023 (0.026*) | −0.005 (0.624) | −0.013 (0.217) | −0.019 (0.036*) | 0.000 (0.971) | |
age | −0.011 (0.553) | 0.035 (0.018*) | 0.018 (0.291) | 0.001 (0.949) | 0.031 (0.028*) | 0.027 (0.102) | |
rateCultivated | −0.531 (0.506) | 0.643 (0.356) | −1.018 (0.164) | ||||
rateIrrigable | 4.284 (0.182) | −3.833 (0.015*) | 0.410 (0.819) | 0.002 (0.998) | −0.834 (0.123) | −0.788 (0.225) | |
rateRainfed | 5.035 (0.120) | −2.238 (0.159) | 1.609 (0.385) | ||||
incomeAgri | 1.523 (0.106) | 1.210 (0.126) | 1.663 (0.066) | ||||
incomeLS | 1.554 (0.212) | −0.020 (0.985) | 0.296 (0.792) | ||||
incomeFR | 3.768 (0.137) | −11.772 (0.130) | 2.496 (0.319) | 1.235 (0.546) | −14.384 (0.083) | 1.668 (0.409) | |
incomeNMH | 4.910 (0.165) | −3.306 (0.559) | −11.823 (0.356) | ||||
incomeMH | 1.957 (0.104) | −1.405 (0.218) | −1.092 (0.378) | ||||
incomeMjob | 2.076 (0.036*) | 0.687 (0.380) | −0.456 (0.641) | ||||
incomeSalaried | 0.396 (0.791) | 1.441 (0.206) | 1.592 (0.192) | 1.208 (0.435) | 2.430 (0.065) | 3.075 (0.029*) | |
incomeTourism | −3.624 (0.212) | −2.065 (0.105) | −1.019 (0.473) | ||||
incomeObusiness | −0.927 (0.477) | −0.660 (0.493) | −2.020 (0.121) | ||||
Gender | female | 0.685 (0.153) | −0.045 (0.911) | 0.264 (0.567) | |||
male (base) | 0 | 0 | 0 | ||||
Ethnicity | Bai | −0.170 (0.908) | 0.722 (0.584) | 0.973 (0.500) | 15.293 (0.994) | 16.048 (0.993) | 16.363 (0.993) |
Dulong | −1.790 (1.000) | 13.742 (0.998) | −1.205 (1.000) | −0.096 (0.673) | 16.284 (0.998) | 0.236 (1.000) | |
Han | 0.674 (0.499) | 0.986 (0.230) | 0.531 (0.594) | 0.195 (0.824) | 0.311 (0.648) | 0.233 (0.783) | |
Lisu | −1.905 (0.026*) | −0.322 (0.658) | −0.761 (0.359) | −0.488 (0.543) | −0.198 (0.758) | 0.163 (0.831) | |
Naxi | −0.161 (0.830) | 0.192 (0.786) | 0.251 (0.717) | 0.356 (0.613) | −0.120 (0.847) | 0.441 (0.514) | |
Nu | −17.698 (0.994) | −2.586 (0.060) | −1.511 (0.301) | −17.871 (0.997) | −2.365 (0.042*) | −1.048 (0.459) | |
Yi | −35.760 (0.994) | −33.712 (0.996) | −34.935 (0.889) | −0.517 (0.973) | 15.708 (0.998) | −0.733 (1.000) | |
Tibetan (base) | 0 | 0 | 0 | 0 | 0 | 0 | |
Education | junior school | 0.926 (0.090) | 0.896 (0.041*) | 0.949 (0.072) | |||
above college | 0.649 (1.000) | 14.743 (0.997) | 0.067 (1.000) | ||||
high school | 16.444 (0.989) | 15.427 (0.989) | 15.599 (0.989) | ||||
illiteracy | 0.825 (0.303) | 1.110 (0.111) | 1.041 (0.169) | ||||
primary school (base) | 0 | 0 | 0 | ||||
Likelihood ratio test: Chi-Square = 142.129; df = 75; p = 0.000. Pseudo R2: Cox & Snell = 0.435; Nagelkerke = 0.470; McFadden = 0.221. | Likelihood ratio test: Chi-Square = 67.181; df = 39; p = 0.003. Pseudo R2: Cox & Snell = 0.236; Nagelkerke = 0.259; McFadden = 0.111. | ||||||
