文献来源
- Ma, W., et al. (2022). Rural income growth, ethnic differences, and household cooking fuel choice: Evidence from China
- Appendix B. Supplementary data【数据+Stata】
示例代码
- 注：参数
b(1)表示Base = Quintile 1
use "C:\Download\1-s2.0-S014098832200038X-mmc1\data.dta"
keep if utype == 1
mlogit energy q_2 q_3 q_4 q_5 f_minzudummy minzuq_2 minzuq_3 minzuq_4 minzuq_5 hhage hhgender hhedustage hhsize f_dependencyratio percept_air asset_car distance_county east middle, b(1)
margins, dydx(*)
得到结果
Iteration 0: log likelihood = -6949.9061
Iteration 1: log likelihood = -6146.9848
Iteration 2: log likelihood = -6121.4034
Iteration 3: log likelihood = -6121.2541
Iteration 4: log likelihood = -6121.254
Multinomial logistic regression Number of obs = 6,461
LR chi2(38) = 1657.30
Prob > chi2 = 0.0000
Log likelihood = -6121.254 Pseudo R2 = 0.1192
-----------------------------------------------------------------------------------
energy | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------+----------------------------------------------------------------
dirtyonly |
q_2 | -.2599817 .1017211 -2.56 0.011 -.4593513 -.0606121
q_3 | -.6241438 .1221168 -5.11 0.000 -.8634883 -.3847993
q_4 | -.6980468 .1179335 -5.92 0.000 -.9291921 -.4669014
q_5 | -.8656507 .1424867 -6.08 0.000 -1.144919 -.5863819
f_minzudummy | -.2634868 .156156 -1.69 0.092 -.5695469 .0425733
minzuq_2 | .0828505 .2134944 0.39 0.698 -.3355907 .5012918
minzuq_3 | .082414 .2871114 0.29 0.774 -.4803139 .6451419
minzuq_4 | .5413588 .2844655 1.90 0.057 -.0161834 1.098901
minzuq_5 | -.2096055 .4112672 -0.51 0.610 -1.015674 .5964634
hhage | -.0084841 .0032031 -2.65 0.008 -.014762 -.0022061
hhgender | .0976351 .1244575 0.78 0.433 -.1462971 .3415674
hhedustage | -.1447357 .0378025 -3.83 0.000 -.2188273 -.0706441
hhsize | -.1098006 .0175959 -6.24 0.000 -.144288 -.0753132
f_dependencyratio | -.0382551 .0672515 -0.57 0.569 -.1700655 .0935554
percept_air | -.2147999 .0474945 -4.52 0.000 -.3078875 -.1217123
asset_car | .0424943 .1234936 0.34 0.731 -.1995487 .2845373
distance_county | .0020541 .0014479 1.42 0.156 -.0007838 .004892
east | -.6488415 .0928188 -6.99 0.000 -.830763 -.4669199
middle | .1468132 .0846518 1.73 0.083 -.0191013 .3127277
_cons | 1.671943 .277405 6.03 0.000 1.128239 2.215647
------------------+----------------------------------------------------------------
mixenergy | (base outcome)
------------------+----------------------------------------------------------------
cleanonly |
q_2 | .1581553 .108178 1.46 0.144 -.0538697 .3701802
q_3 | .2829591 .1165275 2.43 0.015 .0545695 .5113488
q_4 | .5501665 .1085406 5.07 0.000 .3374309 .7629021
q_5 | .8178368 .1166754 7.01 0.000 .5891573 1.046516
f_minzudummy | -.427302 .2147871 -1.99 0.047 -.848277 -.006327
minzuq_2 | .092752 .2753566 0.34 0.736 -.446937 .632441
minzuq_3 | .0519216 .3217973 0.16 0.872 -.5787895 .6826328
minzuq_4 | .5251888 .3011482 1.74 0.081 -.0650508 1.115428
minzuq_5 | .6556076 .3163719 2.07 0.038 .03553 1.275685
hhage | -.0286874 .0029337 -9.78 0.000 -.0344372 -.0229375
hhgender | -.1810994 .1099555 -1.65 0.100 -.3966083 .0344095
hhedustage | .0940326 .0294905 3.19 0.001 .0362322 .1518329
hhsize | -.0229626 .0159914 -1.44 0.151 -.0543052 .00838
f_dependencyratio | -.1988339 .0676357 -2.94 0.003 -.3313975 -.0662703
percept_air | .2727867 .0381119 7.16 0.000 .1980887 .3474846
asset_car | .6609623 .0888324 7.44 0.000 .4868539 .8350706
distance_county | -.010092 .0015752 -6.41 0.000 -.0131794 -.0070047
east | .5834746 .0800175 7.29 0.000 .4266431 .7403061
middle | .1971282 .0878649 2.24 0.025 .0249162 .3693402
_cons | .7269859 .2544304 2.86 0.004 .2283114 1.22566
-----------------------------------------------------------------------------------
. margins, dydx(*)
Average marginal effects Number of obs = 6,461
Model VCE: OIM
dy/dx wrt: q_2 q_3 q_4 q_5 f_minzudummy minzuq_2 minzuq_3 minzuq_4 minzuq_5 hhage hhgender
hhedustage hhsize f_dependencyratio percept_air asset_car distance_county east
middle
1._predict: Pr(energy==dirtyonly), predict(pr outcome(0))
2._predict: Pr(energy==mixenergy), predict(pr outcome(1))
3._predict: Pr(energy==cleanonly), predict(pr outcome(2))
-----------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
------------------+----------------------------------------------------------------
q_2 |
_predict |
1 | -.051856 .0147885 -3.51 0.000 -.0808408 -.0228711
2 | .0038011 .0193489 0.20 0.844 -.0341221 .0417244
3 | .0480548 .0192442 2.50 0.013 .0103369 .0857728
------------------+----------------------------------------------------------------
q_3 |
_predict |
1 | -.118007 .0176868 -6.67 0.000 -.1526725 -.0833416
2 | .0213712 .0215149 0.99 0.321 -.0207972 .0635397
3 | .0966358 .020669 4.68 0.000 .0561252 .1371463
------------------+----------------------------------------------------------------
q_4 |
_predict |
1 | -.1476476 .0168663 -8.75 0.000 -.1807049 -.1145902
2 | -.0056852 .0202585 -0.28 0.779 -.0453911 .0340208
3 | .1533327 .0189478 8.09 0.000 .1161957 .1904697
------------------+----------------------------------------------------------------
q_5 |
_predict |
1 | -.1921873 .0203257 -9.46 0.000 -.2320249 -.1523496
2 | -.0242138 .0228152 -1.06 0.289 -.0689309 .0205032
3 | .2164011 .0200938 10.77 0.000 .1770179 .2557843
------------------+----------------------------------------------------------------
f_minzudummy |
_predict |
1 | -.0131625 .0241929 -0.54 0.586 -.0605797 .0342546
2 | .0782415 .0346283 2.26 0.024 .0103713 .1461117
3 | -.065079 .0393869 -1.65 0.098 -.142276 .012118
------------------+----------------------------------------------------------------
minzuq_2 |
_predict |
1 | .0069283 .0326446 0.21 0.832 -.057054 .0709106
2 | -.0193345 .0453767 -0.43 0.670 -.1082712 .0696021
3 | .0124062 .0503209 0.25 0.805 -.0862209 .1110334
------------------+----------------------------------------------------------------
minzuq_3 |
_predict |
1 | .0095963 .0436439 0.22 0.826 -.0759441 .0951368
2 | -.0141254 .0551897 -0.26 0.798 -.1222953 .0940445
3 | .0045291 .0589683 0.08 0.939 -.1110466 .1201047
------------------+----------------------------------------------------------------
minzuq_4 |
_predict |
1 | .050692 .0414248 1.22 0.221 -.0304992 .1318831
2 | -.1160971 .0540506 -2.15 0.032 -.2220344 -.0101597
3 | .0654051 .0536277 1.22 0.223 -.0397033 .1705135
------------------+----------------------------------------------------------------
minzuq_5 |
_predict |
1 | -.0772121 .0613803 -1.26 0.208 -.1975152 .0430911
2 | -.0637928 .0635908 -1.00 0.316 -.1884285 .0608428
3 | .1410049 .0579733 2.43 0.015 .0273794 .2546304
------------------+----------------------------------------------------------------
hhage |
_predict |
1 | .000577 .0004654 1.24 0.215 -.0003351 .0014891
2 | .0044093 .0005419 8.14 0.000 .0033471 .0054714
3 | -.0049863 .0005124 -9.73 0.000 -.0059906 -.0039819
------------------+----------------------------------------------------------------
hhgender |
_predict |
1 | .0276335 .0182285 1.52 0.130 -.0080937 .0633607
2 | .0139804 .0210965 0.66 0.508 -.027368 .0553288
3 | -.0416139 .0196439 -2.12 0.034 -.0801153 -.0031125
------------------+----------------------------------------------------------------
hhedustage |
_predict |
1 | -.0292702 .0055637 -5.26 0.000 -.040175 -.0183655
2 | .0013584 .0059056 0.23 0.818 -.0102163 .0129331
3 | .0279118 .005311 5.26 0.000 .0175025 .0383211
------------------+----------------------------------------------------------------
hhsize |
_predict |
1 | -.0158834 .0026075 -6.09 0.000 -.0209941 -.0107727
2 | .0129688 .0029514 4.39 0.000 .0071842 .0187533
3 | .0029146 .0029104 1.00 0.317 -.0027897 .008619
------------------+----------------------------------------------------------------
f_dependencyratio |
_predict |
1 | .0072598 .0100843 0.72 0.472 -.012505 .0270247
2 | .028678 .012095 2.37 0.018 .0049723 .0523837
3 | -.0359378 .0123069 -2.92 0.003 -.0600589 -.0118167
------------------+----------------------------------------------------------------
percept_air |
_predict |
1 | -.0523716 .0068692 -7.62 0.000 -.0658349 -.0389084
2 | -.0148515 .0075147 -1.98 0.048 -.02958 -.0001231
3 | .0672232 .0066579 10.10 0.000 .0541739 .0802724
------------------+----------------------------------------------------------------
asset_car |
_predict |
1 | -.0375687 .0177926 -2.11 0.035 -.0724416 -.0026957
2 | -.0875721 .0188577 -4.64 0.000 -.1245326 -.0506117
3 | .1251408 .0153141 8.17 0.000 .0951256 .155156
------------------+----------------------------------------------------------------
distance_county |
_predict |
1 | .0010025 .0002128 4.71 0.000 .0005855 .0014195
2 | .0010894 .0002746 3.97 0.000 .0005512 .0016277
3 | -.0020919 .0002802 -7.47 0.000 -.0026411 -.0015428
------------------+----------------------------------------------------------------
east |
_predict |
1 | -.1420728 .0132295 -10.74 0.000 -.1680022 -.1161435
2 | -.014411 .015209 -0.95 0.343 -.0442201 .0153982
3 | .1564838 .0137883 11.35 0.000 .1294592 .1835083
------------------+----------------------------------------------------------------
middle |
_predict |
1 | .0100802 .0122624 0.82 0.411 -.0139536 .034114
2 | -.0384099 .0159192 -2.41 0.016 -.0696109 -.0072088
3 | .0283297 .015573 1.82 0.069 -.0021928 .0588521
-----------------------------------------------------------------------------------
（完）
