1 . import Data 2 . gologit2 CatHFCS season Gender Age Loc Land Quality Maize Sorghum Cassava Rice HHS Income Livestock Education Cash CropD, autofit lrforce gamma ------------------------------------------------------------------------------ Testing parallel lines assumption using the .05 level of significance... Step 1: Constraints for parallel lines imposed for Age (P Value = 0.9999) Step 2: Constraints for parallel lines imposed for Livestock (P Value = 0.9999) Step 3: Constraints for parallel lines imposed for Rice (P Value = 0.9997) Step 4: Constraints for parallel lines imposed for HHS (P Value = 0.9998) Step 5: Constraints for parallel lines imposed for Quality (P Value = 0.9995) Step 6: Constraints for parallel lines imposed for Land (P Value = 0.9986) Step 7: Constraints for parallel lines imposed for Maize (P Value = 0.9981) Step 8: Constraints for parallel lines imposed for Education (P Value = 0.9977) Step 9: Constraints for parallel lines imposed for Loc (P Value = 0.9981) Step 10: Constraints for parallel lines imposed for Sorghum (P Value = 0.9946) Step 11: Constraints for parallel lines imposed for Gender (P Value = 0.9924) Step 12: Constraints for parallel lines imposed for season (P Value = 0.9952) Step 13: Constraints for parallel lines imposed for Income (P Value = 0.9857) Step 14: Constraints for parallel lines imposed for Cash (P Value = 0.9821) Step 15: Constraints for parallel lines imposed for Cassava (P Value = 0.2607) Step 16: Constraints for parallel lines imposed for CropD (P Value = 0.2387) Step 17: All explanatory variables meet the pl assumption Wald test of parallel lines assumption for the final model: ( 1) [0]Age - [1]Age = 0 ( 2) [0]Livestock - [1]Livestock = 0 ( 3) [0]Rice - [1]Rice = 0 ( 4) [0]HHS - [1]HHS = 0 ( 5) [0]Quality - [1]Quality = 0 ( 6) [0]Land - [1]Land = 0 ( 7) [0]Maize - [1]Maize = 0 ( 8) [0]Education - [1]Education = 0 ( 9) [0]Loc - [1]Loc = 0 (10) [0]Sorghum - [1]Sorghum = 0 (11) [0]Gender - [1]Gender = 0 (12) [0]season - [1]season = 0 (13) [0]Income - [1]Income = 0 (14) [0]Cash - [1]Cash = 0 (15) [0]Cassava - [1]Cassava = 0 (16) [0]CropD - [1]CropD = 0 chi2( 16) = 0.00 Prob > chi2 = 1.0000 An insignificant test statistic indicates that the final model does not violate the proportional odds/ parallel lines assumption If you re-estimate this exact same model with gologit2, instead of autofit you can save time by using the parameter pl(Age Livestock Rice HHS Quality Land Maize Education Loc Sorghum Gender season Income Cash Cassava CropD) ------------------------------------------------------------------------------ Generalized Ordered Logit Estimates Number of obs = 592 LR chi2(16) = 330.68 Prob > chi2 = 0.0000 Log likelihood = -234.95056 Pseudo R2 = 0.4131 ( 1) [0]Age - [1]Age = 0 ( 2) [0]Livestock - [1]Livestock = 0 ( 3) [0]Rice - [1]Rice = 0 ( 4) [0]HHS - [1]HHS = 0 ( 5) [0]Quality - [1]Quality = 0 ( 6) [0]Land - [1]Land = 0 ( 7) [0]Maize - [1]Maize = 0 ( 8) [0]Education - [1]Education = 0 ( 9) [0]Loc - [1]Loc = 0 (10) [0]Sorghum - [1]Sorghum = 0 (11) [0]Gender - [1]Gender = 0 (12) [0]season - [1]season = 0 (13) [0]Income - [1]Income = 0 (14) [0]Cash - [1]Cash = 0 (15) [0]Cassava - [1]Cassava = 0 (16) [0]CropD - [1]CropD = 0 ------------------------------------------------------------------------------ CatHFCS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | 3.432762 .3099344 11.08 0.000 2.825302 4.040223 Gender | -.6591834 .2862753 -2.30 0.021 -1.220273 -.0980941 Age | -.0075583 .0101544 -0.74 0.457 -.0274605 .0123439 Loc | -.0152025 .5984753 -0.03 0.980 -1.188193 1.157788 Land | -.1342014 .136924 -0.98 0.327 -.4025676 .1341648 Quality | -.2346319 .3970703 -0.59 0.555 -1.012875 .5436116 Maize | .9065238 .3675679 2.47 0.014 .1861038 1.626944 Sorghum | .3142695 .3847169 0.82 0.414 -.4397618 1.068301 Cassava | 1.149883 .4309163 2.67 0.008 .3053027 1.994463 Rice | .3272758 .3050526 1.07 0.283 -.2706162 .9251679 HHS | -.0790059 .0604875 -1.31 0.192 -.1975592 .0395474 Income | .6025216 .3237477 1.86 0.063 -.0320121 1.237055 Livestock | -.0025973 .0182827 -0.14 0.887 -.0384308 .0332361 Education | .0406453 .0518808 0.78 0.433 -.0610393 .1423298 Cash | .9798514 .350187 2.80 0.005 .2934975 1.666205 CropD | .854281 .1221258 7.00 0.000 .6149187 1.093643 _cons | -6.589622 .8873224 -7.43 0.000 -8.328742 -4.850502 -------------+---------------------------------------------------------------- 1 | season | 3.432762 .3099344 11.08 0.000 2.825302 4.040223 Gender | -.6591834 .2862753 -2.30 0.021 -1.220273 -.0980941 Age | -.0075583 .0101544 -0.74 0.457 -.0274605 .0123439 Loc | -.0152025 .5984753 -0.03 0.980 -1.188193 1.157788 Land | -.1342014 .136924 -0.98 0.327 -.4025676 .1341648 Quality | -.2346319 .3970703 -0.59 0.555 -1.012875 .5436116 Maize | .9065238 .3675679 2.47 0.014 .1861038 1.626944 Sorghum | .3142695 .3847169 0.82 0.414 -.4397618 1.068301 Cassava | 1.149883 .4309163 2.67 0.008 .3053027 1.994463 Rice | .3272758 .3050526 1.07 0.283 -.2706162 .9251679 HHS | -.0790059 .0604875 -1.31 0.192 -.1975592 .0395474 Income | .6025216 .3237477 1.86 0.063 -.0320121 1.237055 Livestock | -.0025973 .0182827 -0.14 0.887 -.0384308 .0332361 Education | .0406453 .0518808 0.78 0.433 -.0610393 .1423298 Cash | .9798514 .350187 2.80 0.005 .2934975 1.666205 CropD | .854281 .1221258 7.00 0.000 .6149187 1.093643 _cons | -13.08218 1.209979 -10.81 0.000 -15.4537 -10.71067 ------------------------------------------------------------------------------ Alternative parameterization: Gammas are deviations from proportionality ------------------------------------------------------------------------------ CatHFCS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Beta | season | 3.432762 .3099344 11.08 0.000 2.825302 4.040223 Gender | -.6591834 .2862753 -2.30 0.021 -1.220273 -.0980941 Age | -.0075583 .0101544 -0.74 0.457 -.0274605 .0123439 Loc | -.0152025 .5984753 -0.03 0.980 -1.188193 1.157788 Land | -.1342014 .136924 -0.98 0.327 -.4025676 .1341648 Quality | -.2346319 .3970703 -0.59 0.555 -1.012875 .5436116 Maize | .9065238 .3675679 2.47 0.014 .1861038 1.626944 Sorghum | .3142695 .3847169 0.82 0.414 -.4397618 1.068301 Cassava | 1.149883 .4309163 2.67 0.008 .3053027 1.994463 Rice | .3272758 .3050526 1.07 0.283 -.2706162 .9251679 HHS | -.0790059 .0604875 -1.31 0.192 -.1975592 .0395474 Income | .6025216 .3237477 1.86 0.063 -.0320121 1.237055 Livestock | -.0025973 .0182827 -0.14 0.887 -.0384308 .0332361 Education | .0406453 .0518808 0.78 0.433 -.0610393 .1423298 Cash | .9798514 .350187 2.80 0.005 .2934975 1.666205 CropD | .854281 .1221258 7.00 0.000 .6149187 1.093643 -------------+---------------------------------------------------------------- Alpha | _cons_1 | -6.589622 .8873224 -7.43 0.000 -8.328742 -4.850502 _cons_2 | -13.08218 1.209979 -10.81 0.000 -15.4537 -10.71067 ------------------------------------------------------------------------------ 3 . mfx2 Frequencies for CatHFCS... CatHFCS | Freq. Percent Cum. ------------+----------------------------------- 0 | 389 65.71 65.71 1 | 199 33.61 99.32 2 | 4 0.68 100.00 ------------+----------------------------------- Total | 592 100.00 Computing marginal effects after gologit2 for CatHFCS == 0... Marginal effects after gologit2 y = Pr(CatHFCS==0) (predict, o(0)) = .7731792 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| -.5700325 .03766 -15.14 0.000 -.643843 -.496222 .5 Gender*| .1256723 .05867 2.14 0.032 .010686 .240658 .753378 Age | .0013255 .00178 0.74 0.457 -.002167 .004819 35.6892 Loc*| .0026654 .1049 0.03 0.980 -.202942 .208273 .469595 Land | .0235353 .02401 0.98 0.327 -.023518 .070588 1.35272 Quality*| .0405737 .06768 0.60 0.549 -.09207 .173217 .391892 Maize*| -.1362561 .04654 -2.93 0.003 -.227467 -.045045 .793919 Sorghum*| -.0551661 .06759 -0.82 0.414 -.187644 .077312 .493243 Cassava*| -.2073835 .07897 -2.63 0.009 -.362161 -.052606 .445946 Rice*| -.0595116 .05735 -1.04 0.299 -.171915 .052891 .290541 HHS | .0138555 .0106 1.31 0.191 -.006926 .034637 5.08784 Income*| -.1169109 .06821 -1.71 0.087 -.250597 .016775 .162162 Livest~k | .0004555 .00321 0.14 0.887 -.005829 .00674 2.88851 Educat~n | -.0071281 .00909 -0.78 0.433 -.02494 .010684 5.22973 Cash*| -.1905785 .07361 -2.59 0.010 -.334857 -.046301 .277027 CropD | -.1498179 .02186 -6.85 0.000 -.19267 -.106966 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatHFCS == 1... Marginal effects after gologit2 y = Pr(CatHFCS==1) (predict, o(1)) = .22637665 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| .567646 .03786 14.99 0.000 .493435 .641857 .5 Gender*| -.1253203 .05851 -2.14 0.032 -.240005 -.010635 .753378 Age | -.0013222 .00178 -0.74 0.457 -.004807 .002162 35.6892 Loc*| -.0026587 .10464 -0.03 0.980 -.207745 .202428 .469595 Land | -.0234757 .02395 -0.98 0.327 -.070411 .023459 1.35272 Quality*| -.0404719 .0675 -0.60 0.549 -.172761 .091817 .391892 Maize*| .1359371 .04643 2.93 0.003 .044934 .22694 .793919 Sorghum*| .0550257 .06744 0.82 0.415 -.077151 .187202 .493243 Cassava*| .20681 .07883 2.62 0.009 .052306 .361314 .445946 Rice*| .0593553 .0572 1.04 0.299 -.052764 .171475 .290541 HHS | -.0138204 .01058 -1.31 0.191 -.034553 .006912 5.08784 Income*| .1165781 .068 1.71 0.086 -.016701 .249857 .162162 Livest~k | -.0004543 .0032 -0.14 0.887 -.006723 .005814 2.88851 Educat~n | .00711 .00906 0.78 0.433 -.010656 .024876 5.22973 Cash*| .1900156 .07338 2.59 0.010 .046194 .333837 .277027 CropD | .1494387 .02184 6.84 0.000 .106629 .192249 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatHFCS == 2... Marginal effects after gologit2 y = Pr(CatHFCS==2) (predict, o(2)) = .00044415 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| .0023865 .00147 1.63 0.104 -.000489 .005262 .5 Gender*| -.0003521 .00028 -1.27 0.205 -.000896 .000192 .753378 Age | -3.36e-06 .00000 -0.68 0.496 -.000013 6.3e-06 35.6892 Loc*| -6.75e-06 .00027 -0.03 0.980 -.000527 .000514 .469595 Land | -.0000596 .00007 -0.84 0.403 -.000199 .00008 1.35272 Quality*| -.0001018 .00018 -0.57 0.571 -.000454 .000251 .391892 Maize*| .000319 .00023 1.37 0.170 -.000137 .000775 .793919 Sorghum*| .0001404 .00019 0.72 0.469 -.000239 .00052 .493243 Cassava*| .0005735 .00043 1.33 0.183 -.00027 .001417 .445946 Rice*| .0001563 .00019 0.84 0.399 -.000207 .00052 .290541 HHS | -.0000351 .00003 -1.01 0.311 -.000103 .000033 5.08784 Income*| .0003328 .0003 1.11 0.267 -.000255 .000921 .162162 Livest~k | -1.15e-06 .00001 -0.14 0.887 -.000017 .000015 2.88851 Educat~n | .000018 .00003 0.70 0.485 -.000033 .000069 5.22973 Cash*| .0005629 .00042 1.33 0.183 -.000265 .001391 .277027 CropD | .0003793 .00024 1.59 0.113 -.000089 .000848 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Preparing final results... Original results are now active. mfx results are stored as gologit2_mfx. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model gologit2_mfx (Marginal effects after gologit2) ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------ CatHFCS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | -.5700325 .0376591 -15.14 0.000 -.643843 -.4962219 Gender | .1256723 .0586674 2.14 0.032 .0106863 .2406583 Age | .0013255 .0017822 0.74 0.457 -.0021675 .0048185 Loc | .0026654 .1049035 0.03 0.980 -.2029417 .2082726 Land | .0235353 .0240071 0.98 0.327 -.0235177 .0705883 Quality | .0405737 .0676766 0.60 0.549 -.09207 .1732174 Maize | -.1362561 .0465373 -2.93 0.003 -.2274675 -.0450448 Sorghum | -.0551661 .0675919 -0.82 0.414 -.1876438 .0773116 Cassava | -.2073835 .0789693 -2.63 0.009 -.3621606 -.0526065 Rice | -.0595116 .0573495 -1.04 0.299 -.1719146 .0528914 HHS | .0138555 .010603 1.31 0.191 -.006926 .034637 Income | -.1169109 .0682086 -1.71 0.087 -.2505972 .0167755 Livestock | .0004555 .0032065 0.14 0.887 -.0058291 .0067401 Education | -.0071281 .009088 -0.78 0.433 -.0249402 .010684 Cash | -.1905785 .0736126 -2.59 0.010 -.3348565 -.0463005 CropD | -.1498179 .0218637 -6.85 0.000 -.19267 -.1069658 -------------+---------------------------------------------------------------- 1 | season | .567646 .0378636 14.99 0.000 .4934347 .6418572 Gender | -.1253203 .0585138 -2.14 0.032 -.2400052 -.0106353 Age | -.0013222 .0017778 -0.74 0.457 -.0048067 .0021623 Loc | -.0026587 .104638 -0.03 0.980 -.2077455 .2024281 Land | -.0234757 .0239468 -0.98 0.327 -.0704107 .0234592 Quality | -.0404719 .0674955 -0.60 0.549 -.1727606 .0918168 Maize | .1359371 .0464308 2.93 0.003 .0449345 .2269397 Sorghum | .0550257 .0674383 0.82 0.415 -.077151 .1872023 Cassava | .20681 .0788301 2.62 0.009 .052306 .3613141 Rice | .0593553 .0572049 1.04 0.299 -.0527643 .171475 HHS | -.0138204 .0105778 -1.31 0.191 -.0345526 .0069118 Income | .1165781 .0680008 1.71 0.086 -.016701 .2498572 Livestock | -.0004543 .0031984 -0.14 0.887 -.006723 .0058143 Education | .00711 .0090644 0.78 0.433 -.0106558 .0248759 Cash | .1900156 .0733797 2.59 0.010 .0461939 .3338372 CropD | .1494387 .0218423 6.84 0.000 .1066286 .1922487 -------------+---------------------------------------------------------------- 2 | season | .0023865 .0014671 1.63 0.104 -.000489 .0052621 Gender | -.0003521 .0002776 -1.27 0.205 -.0008962 .0001921 Age | -3.36e-06 4.92e-06 -0.68 0.496 -.000013 6.30e-06 Loc | -6.75e-06 .0002655 -0.03 0.980 -.0005272 .0005137 Land | -.0000596 .0000713 -0.84 0.403 -.0001993 .0000801 Quality | -.0001018 .0001798 -0.57 0.571 -.0004542 .0002506 Maize | .000319 .0002327 1.37 0.170 -.0001371 .0007752 Sorghum | .0001404 .0001938 0.72 0.469 -.0002395 .0005202 Cassava | .0005735 .0004303 1.33 0.183 -.0002699 .001417 Rice | .0001563 .0001854 0.84 0.399 -.0002071 .0005197 HHS | -.0000351 .0000347 -1.01 0.311 -.000103 .0000328 Income | .0003328 .0003001 1.11 0.267 -.0002554 .0009209 Livestock | -1.15e-06 8.14e-06 -0.14 0.887 -.0000171 .0000148 Education | .000018 .0000259 0.70 0.485 -.0000327 .0000687 Cash | .0005629 .0004225 1.33 0.183 -.0002652 .0013911 CropD | .0003793 .000239 1.59 0.113 -.0000892 .0008477 ------------------------------------------------------------------------------ 4 . gologit2 CatHDDS season Gender Age Loc Land Quality Maize Sorghum Cassava Rice HHS Income Livestock Education Cash CropD, autofit lrforce gamma ------------------------------------------------------------------------------ Testing parallel lines assumption using the .05 level of significance... Step 1: Constraints for parallel lines imposed for CropD (P Value = 0.9442) Step 2: Constraints for parallel lines imposed for Quality (P Value = 0.8566) Step 3: Constraints for parallel lines imposed for Land (P Value = 0.7993) Step 4: Constraints for parallel lines imposed for Cash (P Value = 0.7268) Step 5: Constraints for parallel lines imposed for Cassava (P Value = 0.7176) Step 6: Constraints for parallel lines imposed for Maize (P Value = 0.4850) Step 7: Constraints for parallel lines imposed for HHS (P Value = 0.2069) Step 8: Constraints for parallel lines imposed for Education (P Value = 0.2404) Step 9: Constraints for parallel lines imposed for Income (P Value = 0.2266) Step 10: Constraints for parallel lines imposed for Sorghum (P Value = 0.1920) Step 11: Constraints for parallel lines imposed for Livestock (P Value = 0.1360) Step 12: Constraints for parallel lines imposed for Rice (P Value = 0.1004) Step 13: Constraints for parallel lines imposed for Loc (P Value = 0.1239) Step 14: Constraints for parallel lines imposed for Gender (P Value = 0.0558) Step 15: Constraints for parallel lines are not imposed for season (P Value = 0.00002) Age (P Value = 0.04409) Wald test of parallel lines assumption for the final model: ( 1) [0]CropD - [1]CropD = 0 ( 2) [0]Quality - [1]Quality = 0 ( 3) [0]Land - [1]Land = 0 ( 4) [0]Cash - [1]Cash = 0 ( 5) [0]Cassava - [1]Cassava = 0 ( 6) [0]Maize - [1]Maize = 0 ( 7) [0]HHS - [1]HHS = 0 ( 8) [0]Education - [1]Education = 0 ( 9) [0]Income - [1]Income = 0 (10) [0]Sorghum - [1]Sorghum = 0 (11) [0]Livestock - [1]Livestock = 0 (12) [0]Rice - [1]Rice = 0 (13) [0]Loc - [1]Loc = 0 (14) [0]Gender - [1]Gender = 0 chi2( 14) = 18.26 Prob > chi2 = 0.1950 An insignificant test statistic indicates that the final model does not violate the proportional odds/ parallel lines assumption If you re-estimate this exact same model with gologit2, instead of autofit you can save time by using the parameter pl(CropD Quality Land Cash Cassava Maize HHS Education Income Sorghum Livestock Rice Loc Gender) ------------------------------------------------------------------------------ Generalized Ordered Logit Estimates Number of obs = 592 LR chi2(18) = 321.49 Prob > chi2 = 0.0000 Log likelihood = -449.77134 Pseudo R2 = 0.2633 ( 1) [0]CropD - [1]CropD = 0 ( 2) [0]Quality - [1]Quality = 0 ( 3) [0]Land - [1]Land = 0 ( 4) [0]Cash - [1]Cash = 0 ( 5) [0]Cassava - [1]Cassava = 0 ( 6) [0]Maize - [1]Maize = 0 ( 7) [0]HHS - [1]HHS = 0 ( 8) [0]Education - [1]Education = 0 ( 9) [0]Income - [1]Income = 0 (10) [0]Sorghum - [1]Sorghum = 0 (11) [0]Livestock - [1]Livestock = 0 (12) [0]Rice - [1]Rice = 0 (13) [0]Loc - [1]Loc = 0 (14) [0]Gender - [1]Gender = 0 ------------------------------------------------------------------------------ CatHDDS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | 1.976132 .2382446 8.29 0.000 1.509181 2.443083 Gender | -.1737952 .2104536 -0.83 0.409 -.5862767 .2386862 Age | -.0259418 .0085983 -3.02 0.003 -.0427943 -.0090894 Loc | 2.163764 .432962 5.00 0.000 1.315174 3.012354 Land | -.1143371 .101889 -1.12 0.262 -.3140357 .0853616 Quality | .7883879 .2833884 2.78 0.005 .232957 1.343819 Maize | .390575 .2530988 1.54 0.123 -.1054896 .8866396 Sorghum | .0410559 .2775423 0.15 0.882 -.502917 .5850287 Cassava | 1.076194 .3139823 3.43 0.001 .4608004 1.691588 Rice | .7259645 .2352523 3.09 0.002 .2648785 1.187051 HHS | -.0769309 .0441346 -1.74 0.081 -.1634332 .0095713 Income | .0892954 .2417329 0.37 0.712 -.3844924 .5630831 Livestock | .0533888 .0160779 3.32 0.001 .0218766 .0849009 Education | .0658351 .0381043 1.73 0.084 -.0088481 .1405182 Cash | .1162672 .2540332 0.46 0.647 -.3816288 .6141631 CropD | .1522758 .0845127 1.80 0.072 -.013366 .3179175 _cons | -1.469541 .5936677 -2.48 0.013 -2.633108 -.3059737 -------------+---------------------------------------------------------------- 1 | season | 4.450727 .5459789 8.15 0.000 3.380628 5.520826 Gender | -.1737952 .2104536 -0.83 0.409 -.5862767 .2386862 Age | -.0034848 .0101664 -0.34 0.732 -.0234107 .0164411 Loc | 2.163764 .432962 5.00 0.000 1.315174 3.012354 Land | -.1143371 .101889 -1.12 0.262 -.3140357 .0853616 Quality | .7883879 .2833884 2.78 0.005 .232957 1.343819 Maize | .390575 .2530988 1.54 0.123 -.1054896 .8866396 Sorghum | .0410559 .2775423 0.15 0.882 -.502917 .5850287 Cassava | 1.076194 .3139823 3.43 0.001 .4608004 1.691588 Rice | .7259645 .2352523 3.09 0.002 .2648785 1.187051 HHS | -.0769309 .0441346 -1.74 0.081 -.1634332 .0095713 Income | .0892954 .2417329 0.37 0.712 -.3844924 .5630831 Livestock | .0533888 .0160779 3.32 0.001 .0218766 .0849009 Education | .0658351 .0381043 1.73 0.084 -.0088481 .1405182 Cash | .1162672 .2540332 0.46 0.647 -.3816288 .6141631 CropD | .1522758 .0845127 1.80 0.072 -.013366 .3179175 _cons | -7.429785 .8285697 -8.97 0.000 -9.053751 -5.805818 ------------------------------------------------------------------------------ Alternative parameterization: Gammas are deviations from proportionality ------------------------------------------------------------------------------ CatHDDS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Beta | season | 1.976132 .2382446 8.29 0.000 1.509181 2.443083 Gender | -.1737952 .2104536 -0.83 0.409 -.5862767 .2386862 Age | -.0259418 .0085983 -3.02 0.003 -.0427943 -.0090894 Loc | 2.163764 .432962 5.00 0.000 1.315174 3.012354 Land | -.1143371 .101889 -1.12 0.262 -.3140357 .0853616 Quality | .7883879 .2833884 2.78 0.005 .232957 1.343819 Maize | .390575 .2530988 1.54 0.123 -.1054896 .8866396 Sorghum | .0410559 .2775423 0.15 0.882 -.502917 .5850287 Cassava | 1.076194 .3139823 3.43 0.001 .4608004 1.691588 Rice | .7259645 .2352523 3.09 0.002 .2648785 1.187051 HHS | -.0769309 .0441346 -1.74 0.081 -.1634332 .0095713 Income | .0892954 .2417329 0.37 0.712 -.3844924 .5630831 Livestock | .0533888 .0160779 3.32 0.001 .0218766 .0849009 Education | .0658351 .0381043 1.73 0.084 -.0088481 .1405182 Cash | .1162672 .2540332 0.46 0.647 -.3816288 .6141631 CropD | .1522758 .0845127 1.80 0.072 -.013366 .3179175 -------------+---------------------------------------------------------------- Gamma_2 | season | 2.474595 .572906 4.32 0.000 1.35172 3.597471 Age | .022457 .0111546 2.01 0.044 .0005944 .0443197 -------------+---------------------------------------------------------------- Alpha | _cons_1 | -1.469541 .5936677 -2.48 0.013 -2.633108 -.3059737 _cons_2 | -7.429785 .8285697 -8.97 0.000 -9.053751 -5.805818 ------------------------------------------------------------------------------ 5 . mfx2 Frequencies for CatHDDS... CatHDDS | Freq. Percent Cum. ------------+----------------------------------- 0 | 160 27.03 27.03 1 | 301 50.84 77.87 2 | 131 22.13 100.00 ------------+----------------------------------- Total | 592 100.00 Computing marginal effects after gologit2 for CatHDDS == 0... Marginal effects after gologit2 y = Pr(CatHDDS==0) (predict, o(0)) = .20606332 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| -.3226632 .0353 -9.14 0.000 -.391852 -.253475 .5 Gender*| .0276993 .03267 0.85 0.397 -.036335 .091734 .753378 Age | .0042441 .0014 3.03 0.002 .001497 .006991 35.6892 Loc*| -.3414725 .06653 -5.13 0.000 -.471866 -.211079 .469595 Land | .0187057 .01668 1.12 0.262 -.013992 .051403 1.35272 Quality*| -.1227301 .04233 -2.90 0.004 -.205704 -.039757 .391892 Maize*| -.0681879 .047 -1.45 0.147 -.160308 .023932 .793919 Sorghum*| -.0067157 .0454 -0.15 0.882 -.095696 .082264 .493243 Cassava*| -.1703944 .04853 -3.51 0.000 -.265516 -.075272 .445946 Rice*| -.108453 .03241 -3.35 0.001 -.171983 -.044923 .290541 HHS | .012586 .00724 1.74 0.082 -.001603 .026775 5.08784 Income*| -.0143501 .03817 -0.38 0.707 -.089157 .060456 .162162 Livest~k | -.0087345 .00265 -3.30 0.001 -.013922 -.003547 2.88851 Educat~n | -.0107707 .00624 -1.73 0.084 -.023002 .001461 5.22973 Cash*| -.0187321 .04029 -0.46 0.642 -.097699 .060235 .277027 CropD | -.0249125 .01384 -1.80 0.072 -.052034 .002209 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatHDDS == 1... Marginal effects after gologit2 y = Pr(CatHDDS==1) (predict, o(1)) = .72301731 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| -.0831983 .04445 -1.87 0.061 -.170313 .003917 .5 Gender*| -.0157997 .01805 -0.88 0.381 -.051181 .019581 .753378 Age | -.0040145 .00135 -2.97 0.003 -.006664 -.001365 35.6892 Loc*| .1744804 .04864 3.59 0.000 .079142 .269818 .469595 Land | -.011172 .01021 -1.09 0.274 -.031184 .00884 1.35272 Quality*| .0660431 .02498 2.64 0.008 .017084 .115002 .391892 Maize*| .044792 .03388 1.32 0.186 -.021604 .111188 .793919 Sorghum*| .0040098 .02712 0.15 0.882 -.049137 .057156 .493243 Cassava*| .0937957 .03177 2.95 0.003 .031532 .15606 .445946 Rice*| .0533856 .01912 2.79 0.005 .015913 .090858 .290541 HHS | -.007517 .00458 -1.64 0.101 -.016494 .00146 5.08784 Income*| .0083113 .02145 0.39 0.698 -.033727 .050349 .162162 Livest~k | .0052167 .00195 2.68 0.007 .001395 .009038 2.88851 Educat~n | .0064328 .00391 1.64 0.100 -.001233 .014098 5.22973 Cash*| .0108966 .0229 0.48 0.634 -.033983 .055776 .277027 CropD | .0148791 .00875 1.70 0.089 -.002277 .032036 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatHDDS == 2... Marginal effects after gologit2 y = Pr(CatHDDS==2) (predict, o(2)) = .07091937 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| .4058615 .03097 13.11 0.000 .345166 .466557 .5 Gender*| -.0118996 .01515 -0.79 0.432 -.041591 .017791 .753378 Age | -.0002296 .00067 -0.34 0.732 -.001543 .001084 35.6892 Loc*| .1669921 .0521 3.21 0.001 .064879 .269105 .469595 Land | -.0075336 .0069 -1.09 0.275 -.021053 .005986 1.35272 Quality*| .056687 .02566 2.21 0.027 .00639 .106984 .391892 Maize*| .0233959 .01478 1.58 0.113 -.005576 .052368 .793919 Sorghum*| .0027059 .0183 0.15 0.882 -.033168 .038579 .493243 Cassava*| .0765987 .02931 2.61 0.009 .019161 .134036 .445946 Rice*| .0550675 .02323 2.37 0.018 .009537 .100597 .290541 HHS | -.005069 .0031 -1.64 0.102 -.011136 .000998 5.08784 Income*| .0060388 .01684 0.36 0.720 -.026961 .039039 .162162 Livest~k | .0035178 .00123 2.85 0.004 .001101 .005935 2.88851 Educat~n | .0043379 .0027 1.61 0.108 -.000954 .00963 5.22973 Cash*| .0078354 .01758 0.45 0.656 -.026622 .042293 .277027 CropD | .0100334 .00598 1.68 0.093 -.001681 .021748 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Preparing final results... Original results are now active. mfx results are stored as gologit2_mfx. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model gologit2_mfx (Marginal effects after gologit2) ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------ CatHDDS | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | -.3226632 .0353009 -9.14 0.000 -.3918517 -.2534747 Gender | .0276993 .0326712 0.85 0.397 -.0363351 .0917337 Age | .0042441 .0014017 3.03 0.002 .0014968 .0069914 Loc | -.3414725 .0665284 -5.13 0.000 -.4718657 -.2110792 Land | .0187057 .0166828 1.12 0.262 -.013992 .0514033 Quality | -.1227301 .0423342 -2.90 0.004 -.2057036 -.0397566 Maize | -.0681879 .0470007 -1.45 0.147 -.1603075 .0239317 Sorghum | -.0067157 .0453989 -0.15 0.882 -.0956958 .0822644 Cassava | -.1703944 .0485326 -3.51 0.000 -.2655165 -.0752723 Rice | -.108453 .032414 -3.35 0.001 -.1719833 -.0449227 HHS | .012586 .0072394 1.74 0.082 -.001603 .026775 Income | -.0143501 .0381673 -0.38 0.707 -.0891565 .0604564 Livestock | -.0087345 .0026467 -3.30 0.001 -.013922 -.003547 Education | -.0107707 .0062406 -1.73 0.084 -.0230021 .0014607 Cash | -.0187321 .0402898 -0.46 0.642 -.0976986 .0602345 CropD | -.0249125 .0138378 -1.80 0.072 -.052034 .002209 -------------+---------------------------------------------------------------- 1 | season | -.0831983 .0444472 -1.87 0.061 -.1703132 .0039165 Gender | -.0157997 .0180519 -0.88 0.381 -.0511807 .0195814 Age | -.0040145 .0013518 -2.97 0.003 -.006664 -.001365 Loc | .1744804 .0486428 3.59 0.000 .0791423 .2698184 Land | -.011172 .0102105 -1.09 0.274 -.0311843 .0088402 Quality | .0660431 .0249794 2.64 0.008 .0170844 .1150018 Maize | .044792 .0338763 1.32 0.186 -.0216044 .1111884 Sorghum | .0040098 .0271161 0.15 0.882 -.0491368 .0571564 Cassava | .0937957 .031768 2.95 0.003 .0315316 .1560599 Rice | .0533856 .0191189 2.79 0.005 .0159132 .0908579 HHS | -.007517 .0045803 -1.64 0.101 -.0164943 .0014602 Income | .0083113 .0214485 0.39 0.698 -.0337269 .0503495 Livestock | .0052167 .0019497 2.68 0.007 .0013953 .0090381 Education | .0064328 .0039111 1.64 0.100 -.0012327 .0140984 Cash | .0108966 .0228981 0.48 0.634 -.0339829 .0557761 CropD | .0148791 .0087535 1.70 0.089 -.0022774 .0320355 -------------+---------------------------------------------------------------- 2 | season | .4058615 .0309675 13.11 0.000 .3451664 .4665566 Gender | -.0118996 .0151488 -0.79 0.432 -.0415906 .0177914 Age | -.0002296 .0006703 -0.34 0.732 -.0015434 .0010842 Loc | .1669921 .0520996 3.21 0.001 .0648788 .2691055 Land | -.0075336 .006898 -1.09 0.275 -.0210534 .0059861 Quality | .056687 .0256624 2.21 0.027 .0063896 .1069843 Maize | .0233959 .0147819 1.58 0.113 -.005576 .0523679 Sorghum | .0027059 .0183032 0.15 0.882 -.0331676 .0385795 Cassava | .0765987 .0293055 2.61 0.009 .0191609 .1340364 Rice | .0550675 .02323 2.37 0.018 .0095374 .1005975 HHS | -.005069 .0030957 -1.64 0.102 -.0111363 .0009984 Income | .0060388 .0168371 0.36 0.720 -.0269614 .039039 Livestock | .0035178 .0012332 2.85 0.004 .0011007 .0059349 Education | .0043379 .0027001 1.61 0.108 -.0009543 .00963 Cash | .0078354 .0175806 0.45 0.656 -.026622 .0422928 CropD | .0100334 .005977 1.68 0.093 -.0016812 .021748 ------------------------------------------------------------------------------ 6 . gologit2 CatMAHFP season Gender Age Loc Land Quality Maize Sorghum Cassava Rice HHS Income Livestock Education Cash CropD, autofit lrforce gamma ------------------------------------------------------------------------------ Testing parallel lines assumption using the .05 level of significance... Step 1: Constraints for parallel lines imposed for Cash (P Value = 0.9075) Step 2: Constraints for parallel lines imposed for Maize (P Value = 0.8380) Step 3: Constraints for parallel lines imposed for Loc (P Value = 0.7532) Step 4: Constraints for parallel lines imposed for Education (P Value = 0.7421) Step 5: Constraints for parallel lines imposed for CropD (P Value = 0.6476) Step 6: Constraints for parallel lines imposed for Cassava (P Value = 0.6360) Step 7: Constraints for parallel lines imposed for Land (P Value = 0.3533) Step 8: Constraints for parallel lines imposed for Rice (P Value = 0.3325) Step 9: Constraints for parallel lines imposed for Sorghum (P Value = 0.2803) Step 10: Constraints for parallel lines imposed for Gender (P Value = 0.2349) Step 11: Constraints for parallel lines imposed for Quality (P Value = 0.1626) Step 12: Constraints for parallel lines imposed for season (P Value = 0.1811) Step 13: Constraints for parallel lines imposed for Income (P Value = 0.0949) Step 14: Constraints for parallel lines imposed for Livestock (P Value = 0.1105) Step 15: Constraints for parallel lines are not imposed for Age (P Value = 0.04553) HHS (P Value = 0.00723) Wald test of parallel lines assumption for the final model: ( 1) [0]Cash - [1]Cash = 0 ( 2) [0]Maize - [1]Maize = 0 ( 3) [0]Loc - [1]Loc = 0 ( 4) [0]Education - [1]Education = 0 ( 5) [0]CropD - [1]CropD = 0 ( 6) [0]Cassava - [1]Cassava = 0 ( 7) [0]Land - [1]Land = 0 ( 8) [0]Rice - [1]Rice = 0 ( 9) [0]Sorghum - [1]Sorghum = 0 (10) [0]Gender - [1]Gender = 0 (11) [0]Quality - [1]Quality = 0 (12) [0]season - [1]season = 0 (13) [0]Income - [1]Income = 0 (14) [0]Livestock - [1]Livestock = 0 chi2( 14) = 13.87 Prob > chi2 = 0.4592 An insignificant test statistic indicates that the final model does not violate the proportional odds/ parallel lines assumption If you re-estimate this exact same model with gologit2, instead of autofit you can save time by using the parameter pl(Cash Maize Loc Education CropD Cassava Land Rice Sorghum Gender Quality season Income Livestock) ------------------------------------------------------------------------------ Generalized Ordered Logit Estimates Number of obs = 592 LR chi2(18) = 123.68 Prob > chi2 = 0.0000 Log likelihood = -509.76845 Pseudo R2 = 0.1082 ( 1) [0]Cash - [1]Cash = 0 ( 2) [0]Maize - [1]Maize = 0 ( 3) [0]Loc - [1]Loc = 0 ( 4) [0]Education - [1]Education = 0 ( 5) [0]CropD - [1]CropD = 0 ( 6) [0]Cassava - [1]Cassava = 0 ( 7) [0]Land - [1]Land = 0 ( 8) [0]Rice - [1]Rice = 0 ( 9) [0]Sorghum - [1]Sorghum = 0 (10) [0]Gender - [1]Gender = 0 (11) [0]Quality - [1]Quality = 0 (12) [0]season - [1]season = 0 (13) [0]Income - [1]Income = 0 (14) [0]Livestock - [1]Livestock = 0 ------------------------------------------------------------------------------ CatMAHFP | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | 1.291177 .1750847 7.37 0.000 .9480178 1.634337 Gender | -.0781734 .2039357 -0.38 0.701 -.47788 .3215332 Age | -.0102351 .0077457 -1.32 0.186 -.0254163 .0049462 Loc | 1.077718 .4291403 2.51 0.012 .2366182 1.918817 Land | .2362304 .0991981 2.38 0.017 .0418058 .430655 Quality | 1.089953 .2979941 3.66 0.000 .5058957 1.674011 Maize | .3149795 .2550127 1.24 0.217 -.1848362 .8147952 Sorghum | -.0235917 .2756034 -0.09 0.932 -.5637643 .516581 Cassava | .6888866 .3031495 2.27 0.023 .0947245 1.283049 Rice | .5507831 .2275523 2.42 0.016 .1047889 .9967774 HHS | -.044972 .0458791 -0.98 0.327 -.1348933 .0449494 Income | -.0704924 .2418531 -0.29 0.771 -.5445158 .403531 Livestock | .0432838 .0143094 3.02 0.002 .0152378 .0713297 Education | .0430849 .0368977 1.17 0.243 -.0292333 .1154032 Cash | .348581 .2589582 1.35 0.178 -.1589677 .8561297 CropD | -.2043996 .0840843 -2.43 0.015 -.3692019 -.0395974 _cons | -1.882697 .5768035 -3.26 0.001 -3.013211 -.7521827 -------------+---------------------------------------------------------------- 1 | season | 1.291177 .1750847 7.37 0.000 .9480178 1.634337 Gender | -.0781734 .2039357 -0.38 0.701 -.47788 .3215332 Age | .0094452 .0107223 0.88 0.378 -.01157 .0304605 Loc | 1.077718 .4291403 2.51 0.012 .2366182 1.918817 Land | .2362304 .0991981 2.38 0.017 .0418058 .430655 Quality | 1.089953 .2979941 3.66 0.000 .5058957 1.674011 Maize | .3149795 .2550127 1.24 0.217 -.1848362 .8147952 Sorghum | -.0235917 .2756034 -0.09 0.932 -.5637643 .516581 Cassava | .6888866 .3031495 2.27 0.023 .0947245 1.283049 Rice | .5507831 .2275523 2.42 0.016 .1047889 .9967774 HHS | .0842864 .0543315 1.55 0.121 -.0222015 .1907742 Income | -.0704924 .2418531 -0.29 0.771 -.5445158 .403531 Livestock | .0432838 .0143094 3.02 0.002 .0152378 .0713297 Education | .0430849 .0368977 1.17 0.243 -.0292333 .1154032 Cash | .348581 .2589582 1.35 0.178 -.1589677 .8561297 CropD | -.2043996 .0840843 -2.43 0.015 -.3692019 -.0395974 _cons | -5.148012 .6884761 -7.48 0.000 -6.4974 -3.798624 ------------------------------------------------------------------------------ Alternative parameterization: Gammas are deviations from proportionality ------------------------------------------------------------------------------ CatMAHFP | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Beta | season | 1.291177 .1750847 7.37 0.000 .9480178 1.634337 Gender | -.0781734 .2039357 -0.38 0.701 -.47788 .3215332 Age | -.0102351 .0077457 -1.32 0.186 -.0254163 .0049462 Loc | 1.077718 .4291403 2.51 0.012 .2366182 1.918817 Land | .2362304 .0991981 2.38 0.017 .0418058 .430655 Quality | 1.089953 .2979941 3.66 0.000 .5058957 1.674011 Maize | .3149795 .2550127 1.24 0.217 -.1848362 .8147952 Sorghum | -.0235917 .2756034 -0.09 0.932 -.5637643 .516581 Cassava | .6888866 .3031495 2.27 0.023 .0947245 1.283049 Rice | .5507831 .2275523 2.42 0.016 .1047889 .9967774 HHS | -.044972 .0458791 -0.98 0.327 -.1348933 .0449494 Income | -.0704924 .2418531 -0.29 0.771 -.5445158 .403531 Livestock | .0432838 .0143094 3.02 0.002 .0152378 .0713297 Education | .0430849 .0368977 1.17 0.243 -.0292333 .1154032 Cash | .348581 .2589582 1.35 0.178 -.1589677 .8561297 CropD | -.2043996 .0840843 -2.43 0.015 -.3692019 -.0395974 -------------+---------------------------------------------------------------- Gamma_2 | Age | .0196803 .0098414 2.00 0.046 .0003915 .0389692 HHS | .1292583 .0481232 2.69 0.007 .0349385 .2235781 -------------+---------------------------------------------------------------- Alpha | _cons_1 | -1.882697 .5768035 -3.26 0.001 -3.013211 -.7521827 _cons_2 | -5.148012 .6884761 -7.48 0.000 -6.4974 -3.798624 ------------------------------------------------------------------------------ 7 . mfx2 Frequencies for CatMAHFP... CatMAHFP | Freq. Percent Cum. ------------+----------------------------------- 0 | 326 55.07 55.07 1 | 184 31.08 86.15 2 | 82 13.85 100.00 ------------+----------------------------------- Total | 592 100.00 Computing marginal effects after gologit2 for CatMAHFP == 0... Marginal effects after gologit2 y = Pr(CatMAHFP==0) (predict, o(0)) = .55559379 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| -.3085453 .03907 -7.90 0.000 -.385121 -.231969 .5 Gender*| .01934 .05054 0.38 0.702 -.079714 .118394 .753378 Age | .0025271 .00191 1.32 0.186 -.001221 .006275 35.6892 Loc*| -.2608834 .09938 -2.63 0.009 -.455658 -.066109 .469595 Land | -.0583275 .0245 -2.38 0.017 -.106337 -.010318 1.35272 Quality*| -.2652526 .06943 -3.82 0.000 -.401338 -.129167 .391892 Maize*| -.0766661 .06091 -1.26 0.208 -.196053 .042721 .793919 Sorghum*| .0058248 .06804 0.09 0.932 -.12754 .139189 .493243 Cassava*| -.1691137 .07322 -2.31 0.021 -.312632 -.025596 .445946 Rice*| -.1364418 .05601 -2.44 0.015 -.246222 -.026661 .290541 HHS | .011104 .01133 0.98 0.327 -.011098 .033306 5.08784 Income*| .0173551 .05935 0.29 0.770 -.098977 .133687 .162162 Livest~k | -.0106872 .00353 -3.02 0.002 -.017614 -.00376 2.88851 Educat~n | -.0106381 .00911 -1.17 0.243 -.028493 .007217 5.22973 Cash*| -.0864699 .06427 -1.35 0.178 -.212429 .039489 .277027 CropD | .0504682 .02075 2.43 0.015 .009794 .091142 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatMAHFP == 1... Marginal effects after gologit2 y = Pr(CatMAHFP==1) (predict, o(1)) = .33805757 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| .1823066 .02664 6.84 0.000 .130086 .234528 .5 Gender*| -.0117932 .03057 -0.39 0.700 -.071708 .048122 .753378 Age | -.0034248 .00169 -2.03 0.042 -.006729 -.000121 35.6892 Loc*| .1537403 .05593 2.75 0.006 .044124 .263356 .469595 Land | .0358765 .0154 2.33 0.020 .005697 .066056 1.35272 Quality*| .1497103 .03712 4.03 0.000 .076958 .222462 .391892 Maize*| .0488033 .04016 1.22 0.224 -.029907 .127513 .793919 Sorghum*| -.003583 .04186 -0.09 0.932 -.085622 .078456 .493243 Cassava*| .1011599 .04301 2.35 0.019 .016861 .185459 .445946 Rice*| .0789236 .03088 2.56 0.011 .018399 .139448 .290541 HHS | -.0191145 .00946 -2.02 0.043 -.037649 -.00058 5.08784 Income*| -.0107798 .03723 -0.29 0.772 -.083755 .062195 .162162 Livest~k | .0065735 .00225 2.92 0.003 .002168 .010979 2.88851 Educat~n | .0065433 .00563 1.16 0.245 -.004499 .017586 5.22973 Cash*| .0512013 .03672 1.39 0.163 -.020771 .123173 .277027 CropD | -.0310423 .01306 -2.38 0.017 -.056641 -.005444 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Computing marginal effects after gologit2 for CatMAHFP == 2... Marginal effects after gologit2 y = Pr(CatMAHFP==2) (predict, o(2)) = .10634865 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- season*| .1262387 .01955 6.46 0.000 .087929 .164549 .5 Gender*| -.0075468 .01999 -0.38 0.706 -.046734 .031641 .753378 Age | .0008977 .00102 0.88 0.377 -.001093 .002888 35.6892 Loc*| .1071432 .0457 2.34 0.019 .017577 .19671 .469595 Land | .022451 .00955 2.35 0.019 .003735 .041167 1.35272 Quality*| .1155423 .03584 3.22 0.001 .045296 .185789 .391892 Maize*| .0278628 .02106 1.32 0.186 -.013407 .069132 .793919 Sorghum*| -.0022419 .02619 -0.09 0.932 -.053571 .049087 .493243 Cassava*| .0679538 .03151 2.16 0.031 .0062 .129707 .445946 Rice*| .0575182 .0263 2.19 0.029 .005965 .109071 .290541 HHS | .0080105 .00513 1.56 0.118 -.002036 .018057 5.08784 Income*| -.0065753 .02214 -0.30 0.766 -.049965 .036814 .162162 Livest~k | .0041136 .00139 2.96 0.003 .001387 .00684 2.88851 Educat~n | .0040947 .00352 1.16 0.244 -.002798 .010987 5.22973 Cash*| .0352686 .02795 1.26 0.207 -.019503 .09004 .277027 CropD | -.0194259 .00809 -2.40 0.016 -.035283 -.003568 3.55743 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 Preparing final results... Original results are now active. mfx results are stored as gologit2_mfx. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Model gologit2_mfx (Marginal effects after gologit2) ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------ CatMAHFP | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 0 | season | -.3085453 .0390701 -7.90 0.000 -.3851213 -.2319692 Gender | .01934 .0505389 0.38 0.702 -.0797144 .1183945 Age | .0025271 .0019121 1.32 0.186 -.0012205 .0062748 Loc | -.2608834 .0993764 -2.63 0.009 -.4556576 -.0661093 Land | -.0583275 .0244952 -2.38 0.017 -.1063371 -.0103179 Quality | -.2652526 .0694326 -3.82 0.000 -.4013379 -.1291673 Maize | -.0766661 .0609131 -1.26 0.208 -.1960535 .0427214 Sorghum | .0058248 .0680443 0.09 0.932 -.1275395 .1391892 Cassava | -.1691137 .0732247 -2.31 0.021 -.3126315 -.0255959 Rice | -.1364418 .0560114 -2.44 0.015 -.2462221 -.0266615 HHS | .011104 .0113279 0.98 0.327 -.0110982 .0333062 Income | .0173551 .0593542 0.29 0.770 -.098977 .1336871 Livestock | -.0106872 .0035341 -3.02 0.002 -.0176139 -.0037605 Education | -.0106381 .0091098 -1.17 0.243 -.0284929 .0072168 Cash | -.0864699 .0642658 -1.35 0.178 -.2124285 .0394888 CropD | .0504682 .0207524 2.43 0.015 .0097942 .0911421 -------------+---------------------------------------------------------------- 1 | season | .1823066 .0266439 6.84 0.000 .1300855 .2345276 Gender | -.0117932 .0305694 -0.39 0.700 -.0717082 .0481217 Age | -.0034248 .0016857 -2.03 0.042 -.0067287 -.0001209 Loc | .1537403 .0559275 2.75 0.006 .0441243 .2633562 Land | .0358765 .0153978 2.33 0.020 .0056974 .0660556 Quality | .1497103 .037119 4.03 0.000 .0769584 .2224622 Maize | .0488033 .0401589 1.22 0.224 -.0299066 .1275132 Sorghum | -.003583 .0418573 -0.09 0.932 -.0856217 .0784557 Cassava | .1011599 .0430107 2.35 0.019 .0168605 .1854593 Rice | .0789236 .0308806 2.56 0.011 .0183988 .1394485 HHS | -.0191145 .0094565 -2.02 0.043 -.0376489 -.00058 Income | -.0107798 .0372327 -0.29 0.772 -.0837545 .062195 Livestock | .0065735 .0022476 2.92 0.003 .0021682 .0109788 Education | .0065433 .0056339 1.16 0.245 -.0044989 .0175856 Cash | .0512013 .0367212 1.39 0.163 -.0207708 .1231734 CropD | -.0310423 .0130606 -2.38 0.017 -.0566407 -.0054439 -------------+---------------------------------------------------------------- 2 | season | .1262387 .0195464 6.46 0.000 .0879285 .1645488 Gender | -.0075468 .0199941 -0.38 0.706 -.0467344 .0316408 Age | .0008977 .0010156 0.88 0.377 -.0010929 .0028882 Loc | .1071432 .045698 2.34 0.019 .0175767 .1967097 Land | .022451 .009549 2.35 0.019 .0037354 .0411666 Quality | .1155423 .0358407 3.22 0.001 .0452958 .1857888 Maize | .0278628 .0210563 1.32 0.186 -.0134068 .0691324 Sorghum | -.0022419 .0261887 -0.09 0.932 -.0535707 .049087 Cassava | .0679538 .0315076 2.16 0.031 .0062001 .1297075 Rice | .0575182 .0263032 2.19 0.029 .0059648 .1090715 HHS | .0080105 .0051259 1.56 0.118 -.0020362 .0180571 Income | -.0065753 .0221379 -0.30 0.766 -.0499649 .0368142 Livestock | .0041136 .0013909 2.96 0.003 .0013874 .0068398 Education | .0040947 .0035166 1.16 0.244 -.0027976 .0109871 Cash | .0352686 .0279453 1.26 0.207 -.0195033 .0900404 CropD | -.0194259 .0080908 -2.40 0.016 -.0352835 -.0035682 ------------------------------------------------------------------------------