Luận án Tác động của thể chế đến nghèo đa chiều ở Việt Nam

0712836 gender | -.2018276 .0395915 -5.10 0.000 -.2794256 -.1242297 Headage2 | -.5235109 .0765253 -6.84 0.000 -.6734977 -.373524 Headage3 | -.2361015 .0815579 -2.89 0.004 -.3959521 -.076251 treem | .1149361 .027925 4.12 0.000 .0602041 .1696681 bcapmax | -.6031384 .0509949 -11.83 0.000 -.7030866 -.5031902 207 region1 | -.4398586 .1309961 -3.36 0.001 -.6966062 -.183111 region2 | .5245471 .1165002 4.50 0.000 .2962108 .7528833 region3 | .1910293 .0996176 1.92 0.055 -.0042177 .3862763 region4 | .555942 .14269 3.90 0.000 .2762747 .8356094 region5 | -.8960285 .1734102 -5.17 0.000 -1.235906 -.5561508 LogTNBQ | -.4601346 .0967708 -4.75 0.000 -.6498019 -.2704673 tt_share | -.5328081 .2669476 -2.00 0.046 -1.056016 -.0096004 thanhthi | -.2700166 .050948 -5.30 0.000 -.3698728 -.1701603 lpapi1 | -.6659111 .3943064 -1.69 0.091 -1.438737 .1069152 lpapi2 | .758269 .4114939 1.84 0.065 -.0482442 1.564782 lpapi3 | .6030146 .2892599 2.08 0.037 .0360756 1.169954 lpapi4 | -.2899056 .3555193 -0.82 0.415 -.9867105 .4068994 lpapi5 | -.9835126 .6686964 -1.47 0.041 -2.294133 .3271082 lpapi6 | -1.739105 .6432019 -2.70 0.007 -2.999757 -.4784521 | year | 2018 | .0492682 .0756827 0.65 0.515 -.0990671 .1976036 | _cons | 7.214432 1.570616 4.59 0.000 4.136081 10.29278 -------------+---------------------------------------------------------------- huyen | var(_cons)| .3787037 .0406544 .306847 .4673876 ------------------------------------------------------------------------------ LR test vs. probit model: chibar2(01) = 518.65 Prob >= chibar2 = 0.0000 . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 18,730 . -4329.991 23 8705.983 8886.254 ----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note. . estat icc Residual intraclass correlation ------------------------------------------------------------------------------ Level | ICC Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ huyen | .274681 .0213878 .2347995 .3185168 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 LogTNBQ tt_share thanhthi lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0060675 .0014626 4.15 0.000 .0032009 .008934 gender | -.0252625 .0049791 -5.07 0.000 -.0350212 -.0155037 Headage2 | -.0655271 .0096705 -6.78 0.000 -.084481 -.0465732 Headage3 | -.0295525 .0102282 -2.89 0.004 -.0495993 -.0095057 treem | .0143864 .0035045 4.11 0.000 .0075176 .0212552 bcapmax | -.075494 .0065572 -11.51 0.000 -.0883459 -.062642 region1 | -.0550565 .0164916 -3.34 0.001 -.0873794 -.0227335 region2 | .0656568 .0145591 4.51 0.000 .0371215 .094192 region3 | .0239109 .0124656 1.92 0.055 -.0005212 .048343 region4 | .0695865 .0178466 3.90 0.000 .0346078 .1045651 region5 | -.1121546 .022008 -5.10 0.000 -.1552895 -.0690197 LogTNBQ | -.0575944 .0121477 -4.74 0.000 -.0814034 -.0337853 tt_share | -.0666908 .0335088 -1.99 0.047 -.1323668 -.0010148 thanhthi | -.0337976 .0064281 -5.26 0.000 -.0463965 -.0211987 lpapi1 | -.0833511 .0493953 -1.69 0.092 -.1801641 .0134618 lpapi2 | .0949114 .0515308 1.84 0.065 -.0060872 .19591 lpapi3 | .0754785 .0362453 2.08 0.037 .0044391 .1465179 lpapi4 | -.0362871 .0445038 -0.82 0.415 -.1235129 .0509387 lpapi5 | -.1231048 .0837391 -1.47 0.042 -.2872304 .0410207 lpapi6 | -.2176812 .080588 -2.70 0.007 -.3756308 -.0597316 | year | 2018 | .0061713 .009489 0.65 0.515 -.0124267 .0247694 208 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. . margins, at((p10) lpapi1) at((p90) lpapi1) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi1 = 1.478123 (p10) 2._at : lpapi1 = 1.765047 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1136973 .0090766 12.53 0.000 .0959074 .1314872 2 | .0895968 .0075276 11.90 0.000 .0748429 .1043506 ------------------------------------------------------------------------------ . margins, at((p10) lpapi2) at((p90) lpapi2) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi2 = 1.603753 (p10) 2._at : lpapi2 = 1.813913 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0914438 .006262 14.60 0.000 .0791704 .1037172 2 | .1113752 .0072954 15.27 0.000 .0970765 .125674 ------------------------------------------------------------------------------ . margins, at((p10) lpapi3) at((p90) lpapi3) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi3 = 1.537757 (p10) 2._at : lpapi3 = 1.800574 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0911602 .0058969 15.46 0.000 .0796025 .1027179 2 | .1109674 .0066454 16.70 0.000 .0979427 .1239921 ------------------------------------------------------------------------------ . margins, at((p10) lpapi4) at((p90) lpapi4) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi4 = 1.637109 (p10) 2._at : lpapi4 = 1.924204 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1064917 .0083783 12.71 0.000 .0900706 .1229127 2 | .0960104 .0069773 13.76 0.000 .0823352 .1096856 ------------------------------------------------------------------------------ . margins, at((p10) lpapi5) at((p90) lpapi5) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi5 = 1.878078 (p10) 2._at : lpapi5 = 2.002023 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1084774 .006778 16.00 0.000 .0951927 .1217621 2 | .0931727 .0065184 14.29 0.000 .080397 .1059485 ------------------------------------------------------------------------------ . margins, at((p10) lpapi6) at((p90) lpapi6) 209 Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi6 = 1.897142 (p10) 2._at : lpapi6 = 2.014868 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1104897 .0057703 19.15 0.000 .09918 .1217993 2 | .0851895 .0066116 12.88 0.000 .0722309 .098148 ------------------------------------------------------------------------------ Mô hình Probit truyền thống . probit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 LogTNBQ tt_share thanhthi lpapi1-lpapi6 i.year if year!=2014 Iteration 0: log likelihood = -5872.8385 Iteration 1: log likelihood = -4738.0045 Iteration 2: log likelihood = -4601.8942 Iteration 3: log likelihood = -4589.6165 Iteration 4: log likelihood = -4589.3185 Iteration 5: log likelihood = -4589.3185 Probit regression Number of obs = 18,730 LR chi2(21) = 2567.04 Prob > chi2 = 0.0000 Log likelihood = -4589.3185 Pseudo R2 = 0.2186 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0680268 .0102992 6.61 0.000 .0478407 .0882129 gender | -.1516925 .0357836 -4.24 0.000 -.2218269 -.081558 Headage2 | -.6328254 .0674902 -9.38 0.000 -.7651037 -.5005471 Headage3 | -.3845199 .0716053 -5.37 0.000 -.5248636 -.2441762 treem | .0895777 .0250833 3.57 0.000 .0404154 .13874 bcapmax | -.561564 .0465742 -12.06 0.000 -.6528478 -.4702802 region1 | -.2203143 .0729335 -3.02 0.003 -.3632615 -.0773672 region2 | .2916329 .0615111 4.74 0.000 .1710734 .4121925 region3 | .0811385 .0521412 1.56 0.120 -.0210565 .1833334 region4 | .4020996 .0757252 5.31 0.000 .253681 .5505182 region5 | -.5980048 .1003842 -5.96 0.000 -.7947542 -.4012554 LogTNBQ | -.8547295 .0652586 -13.10 0.000 -.9826339 -.726825 tt_share | .0074769 .1537281 0.05 0.961 -.2938246 .3087784 thanhthi | -.3911889 .0418866 -9.34 0.000 -.4732851 -.3090926 lpapi1 | -1.017704 .2451896 -4.15 0.000 -1.498267 -.537141 lpapi2 | 1.00371 .2822039 3.56 0.000 .4506006 1.55682 lpapi3 | .5687893 .2096606 2.71 0.007 .157862 .9797165 lpapi4 | -.0631981 .2420762 -0.26 0.794 -.5376587 .4112625 lpapi5 | -1.831248 .4600995 -3.98 0.000 -2.733026 -.9294693 lpapi6 | -1.379689 .3985286 -3.46 0.001 -2.160791 -.5985873 | year | 2018 | .1784491 .0519475 3.44 0.001 .0766339 .2802643 | _cons | 11.10152 .9681585 11.47 0.000 9.203964 12.99908 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 18,730 Model VCE : OIM Expression : Pr(mp), predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 LogTNBQ tt_share thanhthi lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0091613 .0013866 6.61 0.000 .0064437 .0118789 gender | -.0204286 .0048175 -4.24 0.000 -.0298707 -.0109866 Headage2 | -.0852234 .009035 -9.43 0.000 -.1029317 -.0675152 Headage3 | -.0517838 .0096195 -5.38 0.000 -.0706377 -.0329299 treem | .0120635 .0033767 3.57 0.000 .0054453 .0186818 bcapmax | -.0756266 .0062867 -12.03 0.000 -.0879483 -.0633048 region1 | -.02967 .009829 -3.02 0.003 -.0489344 -.0104056 region2 | .0392746 .0082725 4.75 0.000 .0230608 .0554884 region3 | .010927 .0070213 1.56 0.120 -.0028345 .0246885 210 region4 | .0541513 .0101905 5.31 0.000 .0341784 .0741242 region5 | -.0805341 .0135382 -5.95 0.000 -.1070685 -.0539997 LogTNBQ | -.1151075 .0087557 -13.15 0.000 -.1322683 -.0979467 tt_share | .0010069 .0207027 0.05 0.961 -.0395696 .0415835 thanhthi | -.0526819 .0056436 -9.33 0.000 -.0637432 -.0416206 lpapi1 | -.1370555 .0329946 -4.15 0.000 -.2017238 -.0723872 lpapi2 | .135171 .0379968 3.56 0.000 .0606985 .2096434 lpapi3 | .0765996 .0282312 2.71 0.007 .0212675 .1319317 lpapi4 | -.008511 .032601 -0.26 0.794 -.0724077 .0553857 lpapi5 | -.2466165 .0619581 -3.98 0.000 -.3680522 -.1251808 lpapi6 | -.1858045 .0536616 -3.46 0.001 -.2909792 -.0806298 | year | 2018 | .0241588 .0070722 3.42 0.001 .0102975 .0380201 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. Một số kiểm định của mô hình . collin mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 LogTNBQ tt_share thanhthi lpapi1-lpapi6 year if year!=2014 (obs=18,730) Collinearity Diagnostics SQRT R- Variable VIF VIF Tolerance Squared ---------------------------------------------------- mp 1.15 1.07 0.8683 0.1317 tsnguoi 1.44 1.20 0.6940 0.3060 gender 1.10 1.05 0.9059 0.0941 Headage2 7.29 2.70 0.1372 0.8628 Headage3 7.32 2.71 0.1366 0.8634 treem 1.39 1.18 0.7187 0.2813 bcapmax 1.13 1.06 0.8884 0.1116 region1 3.47 1.86 0.2885 0.7115 region2 2.81 1.68 0.3558 0.6442 region3 2.19 1.48 0.4556 0.5444 region4 1.99 1.41 0.5036 0.4964 region5 2.13 1.46 0.4692 0.5308 LogTNBQ 2.76 1.66 0.3620 0.6380 tt_share 2.35 1.53 0.4248 0.5752 thanhthi 1.31 1.14 0.7659 0.2341 lpapi1 3.68 1.92 0.2718 0.7282 lpapi2 2.60 1.61 0.3851 0.6149 lpapi3 2.31 1.52 0.4333 0.5667 lpapi4 2.60 1.61 0.3842 0.6158 lpapi5 2.20 1.48 0.4547 0.5453 lpapi6 1.44 1.20 0.6966 0.3034 year 3.14 1.77 0.3180 0.6820 ---------------------------------------------------- Mean VIF 2.63 . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 18,730 -5872.838 -4589.319 22 9222.637 9395.07 ----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note. . estat classification Probit model for mp -------- True -------- Classified | D ~D | Total -----------+--------------------------+----------- + | 196 77 | 273 - | 1580 16877 | 18457 -----------+--------------------------+----------- Total | 1776 16954 | 18730 Classified + if predicted Pr(D) >= .5 True D defined as mp != 0 -------------------------------------------------- Sensitivity Pr( +| D) 11.04% Specificity Pr( -|~D) 99.55% Positive predictive value Pr( D| +) 71.79% Negative predictive value Pr(~D| -) 91.44% 211 -------------------------------------------------- False + rate for true ~D Pr( +|~D) 0.45% False - rate for true D Pr( -| D) 88.96% False + rate for classified + Pr(~D| +) 28.21% False - rate for classified - Pr( D| -) 8.56% -------------------------------------------------- Correctly classified 91.15% -------------------------------------------------- . linktest Iteration 0: log likelihood = -5872.8385 Iteration 1: log likelihood = -4629.2282 Iteration 2: log likelihood = -4573.1534 Iteration 3: log likelihood = -4568.4368 Iteration 4: log likelihood = -4568.3709 Iteration 5: log likelihood = -4568.3709 Probit regression Number of obs = 18,730 LR chi2(2) = 2608.94 Prob > chi2 = 0.0000 Log likelihood = -4568.3709 Pseudo R2 = 0.2221 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _hat | 1.334092 .0518511 25.73 0.000 1.232466 1.435718 _hatsq | .1408948 .0177386 7.94 0.000 .1061278 .1756618 _cons | .1427866 .0369643 3.86 0.000 .0703379 .2152353 ------------------------------------------------------------------------------ Mô hình 8 Mô hình Probit đa tầng . meprobit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 LogTNBQ tt_share thanhthi##c.lpapi1 thanhthi##c.lpapi2 thanhthi##c.lpapi3 thanhthi##c.lpapi4 thanhthi##c.lpapi5 thanhthi##c.lpapi6 i.year if year!=2014||huyen: Fitting fixed-effects model: Iteration 0: log likelihood = -5118.9391 Iteration 1: log likelihood = -4616.6014 Iteration 2: log likelihood = -4566.0559 Iteration 3: log likelihood = -4563.0238 Iteration 4: log likelihood = -4563.0088 Iteration 5: log likelihood = -4563.0088 Refining starting values: Grid node 0: log likelihood = -4409.5683 Fitting full model: Iteration 0: log likelihood = -4409.5683 (not concave) Iteration 1: log likelihood = -4336.208 Iteration 2: log likelihood = -4306.9672 Iteration 3: log likelihood = -4305.6558 Iteration 4: log likelihood = -4305.6466 Iteration 5: log likelihood = -4305.6466 Mixed-effects probit regression Number of obs = 18,730 Group variable: huyen Number of groups = 704 Obs per group: min = 5 avg = 26.6 max = 114 Integration method: mvaghermite Integration pts. = 7 Wald chi2(27) = 781.33 Log likelihood = -4305.6466 Prob > chi2 = 0.0000 ----------------------------------------------------------------------------------- mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- tsnguoi | .0460464 .0116941 3.94 0.000 .0231264 .0689665 gender | -.1987337 .039738 -5.00 0.000 -.2766188 -.1208486 Headage2 | -.5029426 .0769337 -6.54 0.000 -.6537299 -.3521553 Headage3 | -.2105726 .0820333 -2.57 0.010 -.371355 -.0497902 treem | .117315 .0280248 4.19 0.000 .0623875 .1722426 bcapmax | -.5962481 .0514405 -11.59 0.000 -.6970696 -.4954266 region1 | -.4141335 .1313084 -3.15 0.002 -.6714933 -.1567737 region2 | .5346287 .1167568 4.58 0.000 .3057896 .7634678 region3 | .1993637 .0997303 2.00 0.046 .0038959 .3948314 region4 | .5733244 .1430937 4.01 0.000 .292866 .8537828 region5 | -.8215692 .1748397 -4.70 0.000 -1.164249 -.4788897 LogTNBQ | -.4594921 .0969592 -4.74 0.000 -.6495287 -.2694555 tt_share | -.5579811 .2700316 -2.07 0.039 -1.087233 -.0287288 1.thanhthi | -6.849776 2.651589 -2.58 0.010 -12.0468 -1.652757 212 lpapi1 | -.931017 .4065933 -2.29 0.022 -1.727925 -.1341087 | thanhthi#c.lpapi1 | 1 | 1.409325 .648263 2.17 0.030 .1387528 2.679897 | lpapi2 | 1.177257 .4377015 2.69 0.007 .3193782 2.035137 | thanhthi#c.lpapi2 | 1 | -2.658323 .8368756 -3.18 0.001 -4.298569 -1.018077 | lpapi3 | .3541332 .298592 1.19 0.236 -.2310964 .9393627 | thanhthi#c.lpapi3 | 1 | 2.004492 .467437 4.29 0.000 1.088332 2.920652 | lpapi4 | -.6929274 .3686636 -1.88 0.060 -1.415495 .0296401 | thanhthi#c.lpapi4 | 1 | 2.318061 .5379042 4.31 0.000 1.263788 3.372333 | lpapi5 | -1.096265 .7079471 -1.55 0.121 -2.483815 .2912863 | thanhthi#c.lpapi5 | 1 | .0630681 1.166732 0.05 0.957 -2.223685 2.349821 | lpapi6 | -1.8281 .6720203 -2.72 0.007 -3.145236 -.5109647 | thanhthi#c.lpapi6 | 1 | .6127154 1.226231 0.50 0.617 -1.790653 3.016083 | year | 2018 | .07296 .0760808 0.96 0.338 -.0761557 .2220757 | _cons | 8.420481 1.642413 5.13 0.000 5.201411 11.63955 ------------------+---------------------------------------------------------------- huyen | var(_cons)| .3768246 .040523 .3052128 .4652386 ----------------------------------------------------------------------------------- LR test vs. probit model: chibar2(01) = 514.72 Prob >= chibar2 = 0.0000 . margins, dydx(*) Average marginal effects Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 LogTNBQ tt_share 1.thanhthi lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0057321 .0014608 3.92 0.000 .002869 .0085951 gender | -.0247394 .0049694 -4.98 0.000 -.0344792 -.0149995 Headage2 | -.0626088 .0096606 -6.48 0.000 -.0815433 -.0436743 Headage3 | -.0262131 .0102273 -2.56 0.010 -.0462584 -.0061679 treem | .014604 .0034983 4.17 0.000 .0077475 .0214604 bcapmax | -.0742239 .0065686 -11.30 0.000 -.0870982 -.0613496 region1 | -.0515534 .0164316 -3.14 0.002 -.0837587 -.0193481 region2 | .0665532 .0145118 4.59 0.000 .0381106 .0949959 region3 | .0248178 .0124132 2.00 0.046 .0004885 .0491471 region4 | .0713703 .0177982 4.01 0.000 .0364865 .1062541 region5 | -.102273 .0220296 -4.64 0.000 -.1454502 -.0590958 LogTNBQ | -.0571999 .012096 -4.73 0.000 -.0809075 -.0334922 tt_share | -.0694603 .0337131 -2.06 0.039 -.1355368 -.0033837 1.thanhthi | -.0315706 .005753 -5.49 0.000 -.0428462 -.0202949 lpapi1 | -.0865465 .0493911 -1.75 0.080 -.1833512 .0102582 lpapi2 | .0911876 .0515154 1.77 0.077 -.0097807 .1921559 lpapi3 | .0858306 .0363954 2.36 0.018 .0144969 .1571642 lpapi4 | -.0379822 .0445882 -0.85 0.394 -.1253735 .0494091 lpapi5 | -.135155 .0835969 -1.62 0.106 -.299002 .028692 lpapi6 | -.2148104 .0802536 -2.68 0.007 -.3721046 -.0575162 | year | 2018 | .0090933 .0094963 0.96 0.338 -.0095191 .0277057 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. . margins thanhthi, at((p10) lpapi1) at((p90) lpapi1) Predictive margins Number of obs = 18,730 213 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi1 = 1.478123 (p10) 2._at : lpapi1 = 1.765047 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .1264231 .010158 12.45 0.000 .1065137 .1463325 1 1 | .0685999 .0107759 6.37 0.000 .0474796 .0897203 2 0 | .0909839 .0078526 11.59 0.000 .0755931 .1063746 2 1 | .0828367 .0125559 6.60 0.000 .0582275 .1074458 ------------------------------------------------------------------------------ . margins thanhthi, at((p10) lpapi2) at((p90) lpapi2) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi2 = 1.603753 (p10) 2._at : lpapi2 = 1.813913 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .0926107 .0064795 14.29 0.000 .0799111 .1053102 1 1 | .0942125 .0128047 7.36 0.000 .0691159 .1193092 2 0 | .1250481 .0084207 14.85 0.000 .1085438 .1415525 2 1 | .0616199 .0086239 7.15 0.000 .0447173 .0785225 ------------------------------------------------------------------------------ . margins thanhthi, at((p10) lpapi3) at((p90) lpapi3) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi3 = 1.537757 (p10) 2._at : lpapi3 = 1.800574 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .1013282 .0064932 15.61 0.000 .0886017 .1140547 1 1 | .046923 .0064807 7.24 0.000 .0342211 .0596248 2 0 | .1135331 .0070238 16.16 0.000 .0997667 .1272995 2 1 | .1113908 .0113559 9.81 0.000 .0891336 .133648 ------------------------------------------------------------------------------ . margins thanhthi, at((p10) lpapi4) at((p90) lpapi4) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi4 = 1.637109 (p10) 2._at : lpapi4 = 1.924204 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .1217684 .0095531 12.75 0.000 .1030446 .1404922 1 1 | .0523639 .0083773 6.25 0.000 .0359446 .0687832 2 0 | .0952926 .0072863 13.08 0.000 .0810116 .1095735 2 1 | .099475 .0113192 8.79 0.000 .0772897 .1216602 ------------------------------------------------------------------------------ . margins thanhthi, at((p10) lpapi5) at((p90) lpapi5) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi5 = 1.878078 (p10) 2._at : lpapi5 = 2.002023 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .1162566 .0074987 15.50 0.000 .1015595 .1309537 1 1 | .0825653 .0098617 8.37 0.000 .0632367 .1018938 214 2 0 | .0983576 .007069 13.91 0.000 .0845026 .1122127 2 1 | .0691959 .0090858 7.62 0.000 .051388 .0870038 ------------------------------------------------------------------------------ . margins thanhthi, at((p10) lpapi6) at((p90) lpapi6) Predictive margins Number of obs = 18,730 Model VCE : OIM Expression : Marginal predicted mean, predict() 1._at : lpapi6 = 1.897142 (p10) 2._at : lpapi6 = 2.014868 (p90) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at#thanhthi | 1 0 | .1180338 .0062533 18.88 0.000 .1057776 .1302901 1 1 | .0814443 .0087902 9.27 0.000 .0642159 .0986728 2 0 | .0901239 .0071805 12.55 0.000 .0760503 .1041974 2 1 | .0667292 .0098189 6.80 0.000 .0474846 .0859738 ------------------------------------------------------------------------------ . meprobit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 lpapi1-lpapi6 i.year if year!=2014&thanhthi==0||huyen: Fitting fixed-effects model: Iteration 0: log likelihood = -4199.0111 Iteration 1: log likelihood = -3976.7604 Iteration 2: log likelihood = -3963.1767 Iteration 3: log likelihood = -3962.8135 Iteration 4: log likelihood = -3962.8133 Refining starting values: Grid node 0: log likelihood = -3670.4272 Fitting full model: Iteration 0: log likelihood = -3670.4272 Iteration 1: log likelihood = -3636.9921 Iteration 2: log likelihood = -3610.0747 Iteration 3: log likelihood = -3609.1148 Iteration 4: log likelihood = -3609.1098 Iteration 5: log likelihood = -3609.1098 Mixed-effects probit regression Number of obs = 13,085 Group variable: huyen Number of groups = 621 Obs per group: min = 3 avg = 21.1 max = 60 Integration method: mvaghermite Integration pts. = 7 Wald chi2(18) = 525.72 Log likelihood = -3609.1098 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0515443 .0133051 3.87 0.000 .0254668 .0776218 gender | -.2567867 .0455123 -5.64 0.000 -.3459892 -.1675843 Headage2 | -.5172612 .0815995 -6.34 0.000 -.6771933 -.3573291 Headage3 | -.2141661 .0879812 -2.43 0.015 -.3866062 -.0417261 treem | .1198073 .0310814 3.85 0.000 .0588889 .1807257 bcapmax | -.6762282 .069864 -9.68 0.000 -.8131591 -.5392973 region1 | -.4697602 .1551485 -3.03 0.002 -.7738456 -.1656748 region2 | .8841397 .1328523 6.66 0.000 .6237539 1.144525 region3 | .3571194 .1212369 2.95 0.003 .1194995 .5947392 region4 | .7051102 .1677449 4.20 0.000 .3763362 1.033884 region5 | -.9375096 .2041267 -4.59 0.000 -1.33759 -.5374286 lpapi1 | -1.037124 .4493726 -2.31 0.021 -1.917878 -.1563701 lpapi2 | 1.2233 .4716668 2.59 0.009 .2988498 2.14775 lpapi3 | .4391745 .3236068 1.36 0.175 -.1950832 1.073432 lpapi4 | -.4780359 .4069087 -1.17 0.240 -1.275562 .3194905 lpapi5 | -1.243975 .7717927 -1.61 0.107 -2.756661 .2687106 lpapi6 | -2.253674 .7303436 -3.09 0.002 -3.685121 -.8222268 | year | 2018 | .0491431 .0855121 0.57 0.565 -.1184576 .2167438 | _cons | 5.232591 1.712607 3.06 0.002 1.875944 8.589239 -------------+---------------------------------------------------------------- huyen | var(_cons)| .5498602 .0580207 .4471305 .6761924 ------------------------------------------------------------------------------ LR test vs. probit model: chibar2(01) = 707.41 Prob >= chibar2 = 0.0000 215 . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 13,085 . -3609.11 20 7258.22 7407.804 ----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note. . estat icc Residual intraclass correlation ------------------------------------------------------------------------------ Level | ICC Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ huyen | .3547805 .0241545 .3089773 .4034098 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 13,085 Model VCE : OIM Expression : Marginal predicted mean, predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0074984 .0019444 3.86 0.000 .0036875 .0113093 gender | -.0373561 .0066519 -5.62 0.000 -.0503935 -.0243186 Headage2 | -.0752486 .0120115 -6.26 0.000 -.0987907 -.0517065 Headage3 | -.0311558 .0128331 -2.43 0.015 -.0563082 -.0060035 treem | .017429 .0045332 3.84 0.000 .008544 .0263139 bcapmax | -.0983743 .0103832 -9.47 0.000 -.1187251 -.0780236 region1 | -.0683384 .0226466 -3.02 0.003 -.1127249 -.0239519 region2 | .1286203 .0192126 6.69 0.000 .0909642 .1662763 region3 | .051952 .01762 2.95 0.003 .0174174 .0864865 region4 | .1025759 .0242614 4.23 0.000 .0550244 .1501275 region5 | -.1363842 .0300266 -4.54 0.000 -.1952353 -.0775332 lpapi1 | -.1508757 .0655189 -2.30 0.021 -.2792904 -.022461 lpapi2 | .1779596 .0687631 2.59 0.010 .0431864 .3127328 lpapi3 | .0638889 .0471134 1.36 0.175 -.0284517 .1562295 lpapi4 | -.0695423 .0592208 -1.17 0.240 -.185613 .0465284 lpapi5 | -.1809674 .1123809 -1.61 0.107 -.4012299 .0392951 lpapi6 | -.3278533 .1065179 -3.08 0.002 -.5366245 -.1190821 | year | 2018 | .0071509 .0124509 0.57 0.566 -.0172523 .0315541 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. . meprobit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 lpapi1-lpapi6 i.year if year!=2014&thanhthi==1||huyen: Fitting fixed-effects model: Iteration 0: log likelihood = -921.33542 Iteration 1: log likelihood = -700.18967 Iteration 2: log likelihood = -680.04505 Iteration 3: log likelihood = -678.17899 Iteration 4: log likelihood = -678.14745 Iteration 5: log likelihood = -678.14735 Iteration 6: log likelihood = -678.14735 Refining starting values: Grid node 0: log likelihood = -700.07817 Fitting full model: Iteration 0: log likelihood = -700.07817 (not concave) Iteration 1: log likelihood = -670.7577 Iteration 2: log likelihood = -664.92602 Iteration 3: log likelihood = -664.54253 Iteration 4: log likelihood = -664.53941 Iteration 5: log likelihood = -664.53941 Mixed-effects probit regression Number of obs = 5,645 Group variable: huyen Number of groups = 417 Obs per group: min = 3 avg = 13.5 max = 102 Integration method: mvaghermite Integration pts. = 7 Wald chi2(18) = 119.59 216 Log likelihood = -664.53941 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0095138 .0279883 0.34 0.734 -.0453422 .0643699 gender | -.0185597 .0905322 -0.21 0.838 -.1959995 .1588802 Headage2 | -.4186611 .3069184 -1.36 0.173 -1.02021 .182888 Headage3 | -.1527385 .3104489 -0.49 0.623 -.761207 .4557301 treem | .1198179 .0745908 1.61 0.108 -.0263774 .2660133 bcapmax | -.456026 .0759857 -6.00 0.000 -.6049553 -.3070967 region1 | -.9093002 .2537775 -3.58 0.000 -1.406695 -.4119054 region2 | .0411131 .2100195 0.20 0.845 -.3705175 .4527438 region3 | -.2478276 .1689002 -1.47 0.142 -.5788659 .0832108 region4 | .2323295 .2540784 0.91 0.361 -.265655 .7303141 region5 | -1.689409 .4206445 -4.02 0.000 -2.513857 -.8649612 lpapi1 | .9840599 .8476207 1.16 0.246 -.6772461 2.645366 lpapi2 | -.2615371 .9085673 -0.29 0.773 -2.042296 1.519222 lpapi3 | 1.251604 .7054041 1.77 0.076 -.1309628 2.634171 lpapi4 | .2143082 .8042709 0.27 0.790 -1.362034 1.79065 lpapi5 | -.1892343 1.390844 -0.14 0.892 -2.915238 2.536769 lpapi6 | -3.18219 1.33183 -2.39 0.017 -5.792528 -.5718519 | year | 2018 | -.265092 .1738013 -1.53 0.127 -.6057364 .0755524 | _cons | 1.785875 3.018952 0.59 0.554 -4.131162 7.702911 -------------+---------------------------------------------------------------- huyen | var(_cons)| .2749313 .0838804 .1511911 .4999451 ------------------------------------------------------------------------------ LR test vs. probit model: chibar2(01) = 27.22 Prob >= chibar2 = 0.0000 . estat ic Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 5,645 . -664.5394 20 1369.079 1501.849 ----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note. . estat icc Residual intraclass correlation ------------------------------------------------------------------------------ Level | ICC Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ huyen | .215644 .0516044 .1313345 .3333089 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 5,645 Model VCE : OIM Expression : Marginal predicted mean, predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0005816 .0017114 0.34 0.734 -.0027726 .0039359 gender | -.0011346 .0055358 -0.20 0.838 -.0119847 .0097154 Headage2 | -.0255948 .0188133 -1.36 0.174 -.0624683 .0112786 Headage3 | -.0093377 .0189879 -0.49 0.623 -.0465532 .0278779 treem | .0073251 .0045707 1.60 0.109 -.0016334 .0162835 bcapmax | -.0278791 .0048385 -5.76 0.000 -.0373624 -.0183959 region1 | -.05559 .0158652 -3.50 0.000 -.0866853 -.0244947 region2 | .0025134 .0128363 0.20 0.845 -.0226453 .0276722 region3 | -.0151509 .0103316 -1.47 0.143 -.0354005 .0050987 region4 | .0142035 .0155659 0.91 0.362 -.0163052 .0447121 region5 | -.1032819 .0266349 -3.88 0.000 -.1554854 -.0510785 lpapi1 | .0601605 .0518188 1.16 0.246 -.0414025 .1617234 lpapi2 | -.0159891 .0555493 -0.29 0.773 -.1248637 .0928856 lpapi3 | .0765167 .0432836 1.77 0.077 -.0083176 .1613511 lpapi4 | .0131017 .0491848 0.27 0.790 -.0832986 .1095021 lpapi5 | -.0115688 .0850345 -0.14 0.892 -.1782334 .1550958 lpapi6 | -.194543 .0816582 -2.38 0.017 -.3545901 -.0344959 | year | 217 2018 | -.0159647 .0104176 -1.53 0.125 -.0363829 .0044534 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. Mô hình Probit truyền thống . probit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 LogTNBQ tt_share thanhthi##c.lpapi1 thanhthi##c.lpapi2 thanhthi##c.lpapi3 thanhthi##c.lpapi4 thanhthi##c.lpapi5 thanhthi##c.lpapi6 i.year if year!=2014 Iteration 0: log likelihood = -5872.8385 Iteration 1: log likelihood = -4719.0185 Iteration 2: log likelihood = -4577.3231 Iteration 3: log likelihood = -4563.1005 Iteration 4: log likelihood = -4563.0089 Iteration 5: log likelihood = -4563.0088 Probit regression Number of obs = 18,730 LR chi2(27) = 2619.66 Prob > chi2 = 0.0000 Log likelihood = -4563.0088 Pseudo R2 = 0.2230 ----------------------------------------------------------------------------------- mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- tsnguoi | .06533 .0103496 6.31 0.000 .0450453 .0856148 gender | -.1509258 .0359023 -4.20 0.000 -.2212931 -.0805585 Headage2 | -.621501 .0679651 -9.14 0.000 -.7547102 -.4882918 Headage3 | -.3706192 .0721101 -5.14 0.000 -.5119525 -.229286 treem | .09036 .0251807 3.59 0.000 .0410068 .1397132 bcapmax | -.5571852 .0469272 -11.87 0.000 -.6491608 -.4652096 region1 | -.2092903 .0731812 -2.86 0.004 -.3527228 -.0658578 region2 | .2930253 .0617488 4.75 0.000 .1719998 .4140508 region3 | .0859405 .052279 1.64 0.100 -.0165245 .1884055 region4 | .4151309 .0762533 5.44 0.000 .2656772 .5645847 region5 | -.528796 .1003994 -5.27 0.000 -.7255753 -.3320167 LogTNBQ | -.8545498 .0654757 -13.05 0.000 -.9828799 -.7262197 tt_share | -.0527583 .1541958 -0.34 0.732 -.3549765 .2494599 1.thanhthi | -7.031795 2.265803 -3.10 0.002 -11.47269 -2.590902 lpapi1 | -1.212259 .256498 -4.73 0.000 -1.714986 -.7095322 | thanhthi#c.lpapi1 | 1 | 1.215729 .5486215 2.22 0.027 .1404501 2.291007 | lpapi2 | 1.300164 .3052553 4.26 0.000 .7018742 1.898453 | thanhthi#c.lpapi2 | 1 | -2.18842 .7174576 -3.05 0.002 -3.594611 -.7822287 | lpapi3 | .3545271 .2193907 1.62 0.106 -.0754708 .784525 | thanhthi#c.lpapi3 | 1 | 1.665105 .4146792 4.02 0.000 .8523488 2.477862 | lpapi4 | -.3958241 .2515403 -1.57 0.116 -.888834 .0971858 | thanhthi#c.lpapi4 | 1 | 2.012536 .4597457 4.38 0.000 1.111451 2.913621 | lpapi5 | -1.878314 .4947498 -3.80 0.000 -2.848006 -.9086224 | thanhthi#c.lpapi5 | 1 | .224859 1.047486 0.21 0.830 -1.828175 2.277893 | lpapi6 | -1.469215 .4268055 -3.44 0.001 -2.305738 -.6326917 | thanhthi#c.lpapi6 | 1 | .8087679 1.019796 0.79 0.428 -1.189995 2.80753 | year | 2018 | .191018 .0522835 3.65 0.000 .0885441 .2934918 | _cons | 12.12801 1.043285 11.62 0.000 10.08321 14.17281 ----------------------------------------------------------------------------------- . margins, dydx(*) Average marginal effects Number of obs = 18,730 Model VCE : OIM Expression : Pr(mp), predict() 218 dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 LogTNBQ tt_share 1.thanhthi lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .008736 .0013837 6.31 0.000 .0060241 .011448 gender | -.020182 .0047994 -4.21 0.000 -.0295886 -.0107753 Headage2 | -.0831079 .0090363 -9.20 0.000 -.1008187 -.0653971 Headage3 | -.0495597 .0096198 -5.15 0.000 -.0684142 -.0307052 treem | .0120831 .0033661 3.59 0.000 .0054857 .0186804 bcapmax | -.0745075 .0062913 -11.84 0.000 -.0868382 -.0621768 region1 | -.0279866 .0097927 -2.86 0.004 -.0471799 -.0087932 region2 | .0391837 .0082462 4.75 0.000 .0230214 .055346 region3 | .0114921 .0069901 1.64 0.100 -.0022082 .0251924 region4 | .0555118 .0101879 5.45 0.000 .0355438 .0754798 region5 | -.0707113 .0134423 -5.26 0.000 -.0970577 -.0443648 LogTNBQ | -.1142715 .0087229 -13.10 0.000 -.1313681 -.0971748 tt_share | -.0070549 .0206194 -0.34 0.732 -.0474681 .0333583 1.thanhthi | -.0484872 .0045033 -10.77 0.000 -.0573135 -.0396608 lpapi1 | -.1393839 .032849 -4.24 0.000 -.2037668 -.075001 lpapi2 | .1329599 .037903 3.51 0.000 .0586713 .2072484 lpapi3 | .0785272 .0282822 2.78 0.005 .023095 .1339593 lpapi4 | -.0153176 .032495 -0.47 0.637 -.0790067 .0483715 lpapi5 | -.2469681 .0618331 -3.99 0.000 -.3681588 -.1257774 lpapi6 | -.1813501 .0534065 -3.40 0.001 -.2860249 -.0766754 | year | 2018 | .0256946 .0070774 3.63 0.000 .0118231 .0395661 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. . probit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 lpapi1- lpapi6 i.year if year!=2014&thanhthi==1 Iteration 0: log likelihood = -827.79126 Iteration 1: log likelihood = -707.7177 Iteration 2: log likelihood = -680.44852 Iteration 3: log likelihood = -678.15734 Iteration 4: log likelihood = -678.14736 Iteration 5: log likelihood = -678.14735 Probit regression Number of obs = 5,645 LR chi2(18) = 299.29 Prob > chi2 = 0.0000 Log likelihood = -678.14735 Pseudo R2 = 0.1808 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0129789 .0251691 0.52 0.606 -.0363516 .0623094 gender | .0329586 .0807508 0.41 0.683 -.12531 .1912273 Headage2 | -.438557 .2636926 -1.66 0.096 -.9553849 .0782709 Headage3 | -.2120887 .2666599 -0.80 0.426 -.7347325 .3105551 treem | .1199825 .0670258 1.79 0.073 -.0113856 .2513506 bcapmax | -.4314752 .0678807 -6.36 0.000 -.564519 -.2984315 region1 | -.7329773 .1936513 -3.79 0.000 -1.112527 -.3534277 region2 | .0899088 .156645 0.57 0.566 -.2171098 .3969273 region3 | -.2089886 .1208315 -1.73 0.084 -.445814 .0278368 region4 | .249741 .1862505 1.34 0.180 -.1153032 .6147853 region5 | -1.419362 .3397219 -4.18 0.000 -2.085204 -.7535188 lpapi1 | .6788093 .6551866 1.04 0.300 -.6053328 1.962952 lpapi2 | .1069315 .7309067 0.15 0.884 -1.325619 1.539482 lpapi3 | .9188329 .5712519 1.61 0.108 -.2008002 2.038466 lpapi4 | .4131624 .6546779 0.63 0.528 -.8699827 1.696307 lpapi5 | -.9687798 1.10772 -0.87 0.382 -3.13987 1.202311 lpapi6 | -3.017262 .9866855 -3.06 0.002 -4.95113 -1.083394 | year | 2018 | -.2250962 .1402738 -1.60 0.109 -.5000278 .0498354 | _cons | 3.09989 2.279986 1.36 0.174 -1.368802 7.568581 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 5,645 Model VCE : OIM Expression : Pr(mp), predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year 219 ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0008178 .0015864 0.52 0.606 -.0022914 .003927 gender | .0020768 .005089 0.41 0.683 -.0078974 .012051 Headage2 | -.0276347 .0166416 -1.66 0.097 -.0602516 .0049822 Headage3 | -.0133643 .016808 -0.80 0.427 -.0463073 .0195787 treem | .0075604 .004233 1.79 0.074 -.000736 .0158569 bcapmax | -.0271885 .0044473 -6.11 0.000 -.035905 -.0184719 region1 | -.0461869 .0123697 -3.73 0.000 -.0704311 -.0219427 region2 | .0056654 .0098715 0.57 0.566 -.0136824 .0250132 region3 | -.013169 .0076312 -1.73 0.084 -.0281259 .001788 region4 | .0157369 .0117468 1.34 0.180 -.0072864 .0387601 region5 | -.0894379 .0218484 -4.09 0.000 -.13226 -.0466159 lpapi1 | .0427737 .041314 1.04 0.301 -.0382003 .1237476 lpapi2 | .006738 .0460603 0.15 0.884 -.0835384 .0970145 lpapi3 | .0578982 .0360641 1.61 0.108 -.0127861 .1285826 lpapi4 | .0260345 .0412539 0.63 0.528 -.0548216 .1068906 lpapi5 | -.0610455 .0698505 -0.87 0.382 -.1979499 .0758589 lpapi6 | -.1901261 .0626519 -3.03 0.002 -.3129215 -.0673306 | year | 2018 | -.0139564 .0086336 -1.62 0.106 -.0308778 .0029651 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level. . probit mp tsnguoi gender Headage2 Headage3 treem bcapmax region1-region5 lpapi1- lpapi6 i.year if year!=2014&thanhthi==0 Iteration 0: log likelihood = -4834.5845 Iteration 1: log likelihood = -4024.2094 Iteration 2: log likelihood = -3966.9462 Iteration 3: log likelihood = -3962.8588 Iteration 4: log likelihood = -3962.8133 Iteration 5: log likelihood = -3962.8133 Probit regression Number of obs = 13,085 LR chi2(18) = 1743.54 Prob > chi2 = 0.0000 Log likelihood = -3962.8133 Pseudo R2 = 0.1803 ------------------------------------------------------------------------------ mp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0799275 .0112784 7.09 0.000 .0578222 .1020328 gender | -.183036 .0397413 -4.61 0.000 -.2609274 -.1051445 Headage2 | -.6980548 .0699389 -9.98 0.000 -.8351325 -.560977 Headage3 | -.4586982 .0749033 -6.12 0.000 -.6055059 -.3118905 treem | .0847732 .026893 3.15 0.002 .0320639 .1374826 bcapmax | -.6114422 .0612747 -9.98 0.000 -.7315384 -.491346 region1 | -.3427617 .0788195 -4.35 0.000 -.4972451 -.1882783 region2 | .6613996 .0621405 10.64 0.000 .5396064 .7831927 region3 | .2152697 .0563863 3.82 0.000 .1047545 .3257849 region4 | .5348081 .0811133 6.59 0.000 .3758289 .6937873 region5 | -.7029721 .1018456 -6.90 0.000 -.9025859 -.5033583 lpapi1 | -1.860085 .2555507 -7.28 0.000 -2.360956 -1.359215 lpapi2 | 1.823537 .3048923 5.98 0.000 1.225959 2.421115 lpapi3 | .8355153 .2274825 3.67 0.000 .3896579 1.281373 lpapi4 | -.5886521 .2528324 -2.33 0.020 -1.084195 -.0931097 lpapi5 | -2.858741 .4947075 -5.78 0.000 -3.82835 -1.889132 lpapi6 | -2.289032 .4164297 -5.50 0.000 -3.10522 -1.472845 | year | 2018 | .2769518 .0560768 4.94 0.000 .1670434 .3868602 | _cons | 8.499902 1.015269 8.37 0.000 6.510012 10.48979 ------------------------------------------------------------------------------ . margins, dydx(*) Average marginal effects Number of obs = 13,085 Model VCE : OIM Expression : Pr(mp), predict() dy/dx w.r.t. : tsnguoi gender Headage2 Headage3 treem bcapmax region1 region2 region3 region4 region5 lpapi1 lpapi2 lpapi3 lpapi4 lpapi5 lpapi6 2018.year ------------------------------------------------------------------------------ | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- tsnguoi | .0133777 .0018855 7.10 0.000 .0096823 .0170732 220 gender | -.0306353 .0066445 -4.61 0.000 -.0436584 -.0176123 Headage2 | -.1168357 .0115995 -10.07 0.000 -.1395704 -.094101 Headage3 | -.0767738 .0124889 -6.15 0.000 -.1012517 -.052296 treem | .0141888 .0044985 3.15 0.002 .0053719 .0230056 bcapmax | -.1023391 .0102526 -9.98 0.000 -.1224338 -.0822444 region1 | -.0573692 .0132002 -4.35 0.000 -.083241 -.0314973 region2 | .1107006 .0103282 10.72 0.000 .0904578 .1309435 region3 | .0360304 .0094343 3.82 0.000 .0175395 .0545213 region4 | .0895126 .0135479 6.61 0.000 .0629592 .116066 region5 | -.1176588 .0170505 -6.90 0.000 -.1510771 -.0842405 lpapi1 | -.3113287 .042638 -7.30 0.000 -.3948976 -.2277597 lpapi2 | .3052114 .050942 5.99 0.000 .2053668 .405056 lpapi3 | .139843 .0380505 3.68 0.000 .0652654 .2144205 lpapi4 | -.0985247 .042315 -2.33 0.020 -.1814605 -.0155889 lpapi5 | -.4784769 .0826864 -5.79 0.000 -.6405393 -.3164146 lpapi6 | -.3831229 .0696278 -5.50 0.000 -.5195909 -.2466549 | year | 2018 | .046545 .0094559 4.92 0.000 .0280117 .0650782 ------------------------------------------------------------------------------ Note: dy/dx for factor levels is the discrete change from the base level.