---------------------------------------------------------------------------------------------------------------------------------- name: log: C:\users\s352u532\documents\stata\wealtheffect\CLM_Wealth_IVRegressions.v4.txt log type: text opened on: 17 Apr 2013, 14:26:19 . . ** Model 1 - Constant Elasticities . ivregress 2sls d.lncons s2-s51 /// > (d.lnh d.lnst d.lninc = dl(2/4).lnh dl(2/4).lnst dl(2/4).lninc ) , cluster(state) first First-stage regressions ----------------------- Number of obs = 1275 N. of clusters = 51 F( 9, 1215) = 39.02 Prob > F = 0.0000 R-squared = 0.4252 Adj R-squared = 0.3973 Root MSE = 0.0470 ------------------------------------------------------------------------------ | Robust D.lnh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0012166 .0025094 0.48 0.628 -.0037067 .0061398 s3 | -.0003208 .0023856 -0.13 0.893 -.0050011 .0043595 s4 | -.006506 .0019583 -3.32 0.001 -.010348 -.002664 s5 | .0050869 .0018729 2.72 0.007 .0014124 .0087614 s6 | -.0011472 .0022308 -0.51 0.607 -.0055239 .0032296 s7 | -.0057588 .0029641 -1.94 0.052 -.0115742 .0000565 s8 | .0168119 .0040312 4.17 0.000 .0089029 .0247208 s9 | .01628 .0023398 6.96 0.000 .0116896 .0208704 s10 | -.0041763 .0023219 -1.80 0.072 -.0087316 .000379 s11 | -.0047596 .0024086 -1.98 0.048 -.009485 -.0000342 s12 | .020393 .0021319 9.57 0.000 .0162103 .0245757 s13 | .0012587 .0019935 0.63 0.528 -.0026524 .0051697 s14 | .0067306 .0023224 2.90 0.004 .0021742 .0112869 s15 | .0039918 .0020973 1.90 0.057 -.0001229 .0081065 s16 | .0044349 .0018863 2.35 0.019 .000734 .0081357 s17 | -.0039185 .00212 -1.85 0.065 -.0080779 .0002408 s18 | .0036892 .002092 1.76 0.078 -.0004151 .0077935 s19 | -.0054097 .0021842 -2.48 0.013 -.0096948 -.0011245 s20 | -.0038239 .0031171 -1.23 0.220 -.0099394 .0022916 s21 | .0073856 .0026271 2.81 0.005 .0022315 .0125397 s22 | .0039799 .0026066 1.53 0.127 -.0011341 .0090939 s23 | .0027945 .0015962 1.75 0.080 -.0003371 .0059261 s24 | -.0064928 .0027135 -2.39 0.017 -.0118165 -.0011691 s25 | -.0013657 .0021421 -0.64 0.524 -.0055682 .0028369 s26 | -.00102 .0024909 -0.41 0.682 -.0059069 .0038669 s27 | .0101729 .0021108 4.82 0.000 .0060318 .0143141 s28 | .0028318 .0026657 1.06 0.288 -.002398 .0080616 s29 | -.0042008 .0027201 -1.54 0.123 -.0095374 .0011357 s30 | -.0026709 .002407 -1.11 0.267 -.0073932 .0020513 s31 | -.0070775 .0026573 -2.66 0.008 -.0122909 -.001864 s32 | .0020329 .0028107 0.72 0.470 -.0034815 .0075472 s33 | .0028458 .0019849 1.43 0.152 -.0010483 .0067399 s34 | -.0102757 .0016691 -6.16 0.000 -.0135504 -.0070009 s35 | .0019324 .0026272 0.74 0.462 -.0032219 .0070867 s36 | -.0012282 .0017653 -0.70 0.487 -.0046914 .0022351 s37 | -.0065924 .0015661 -4.21 0.000 -.009665 -.0035198 s38 | .0157749 .0021481 7.34 0.000 .0115605 .0199893 s39 | .0074528 .0022969 3.24 0.001 .0029465 .011959 s40 | .0070792 .0024733 2.86 0.004 .0022268 .0119316 s41 | .0060769 .0024701 2.46 0.014 .0012306 .0109231 s42 | .0061106 .0028654 2.13 0.033 .0004889 .0117323 s43 | .001595 .0025079 0.64 0.525 -.0033253 .0065154 s44 | -.0087441 .0018711 -4.67 0.000 -.0124151 -.005073 s45 | .0012233 .0020879 0.59 0.558 -.0028731 .0053196 s46 | .0008863 .002649 0.33 0.738 -.0043108 .0060834 s47 | .007587 .0028067 2.70 0.007 .0020805 .0130935 s48 | .0079675 .0023181 3.44 0.001 .0034196 .0125154 s49 | .0060094 .0020029 3.00 0.003 .0020798 .009939 s50 | .0026497 .001977 1.34 0.180 -.001229 .0065283 s51 | .0033737 .0024995 1.35 0.177 -.0015302 .0082775 | lnh | L2D. | .8787676 .0786875 11.17 0.000 .7243892 1.033146 L3D. | -.582924 .1078022 -5.41 0.000 -.7944232 -.3714249 L4D. | -.2003207 .0538359 -3.72 0.000 -.3059425 -.094699 | lnst | L2D. | -.0304848 .0100143 -3.04 0.002 -.050132 -.0108376 L3D. | -.0254145 .014157 -1.80 0.073 -.0531893 .0023604 L4D. | -.0203989 .0086888 -2.35 0.019 -.0374455 -.0033522 | lninc | L2D. | .1811462 .0952539 1.90 0.057 -.0057341 .3680266 L3D. | .3483824 .078084 4.46 0.000 .1951878 .5015769 L4D. | .3599533 .1197109 3.01 0.003 .1250903 .5948163 | _cons | .0089948 .0018421 4.88 0.000 .0053808 .0126088 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 9, 1215) = 30.26 Prob > F = 0.0000 R-squared = 0.1252 Adj R-squared = 0.0827 Root MSE = 0.1458 ------------------------------------------------------------------------------ | Robust D.lnst | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0528762 .0073861 7.16 0.000 .0383852 .0673672 s3 | .0354374 .0072071 4.92 0.000 .0212976 .0495772 s4 | -.0050625 .0062708 -0.81 0.420 -.0173653 .0072403 s5 | .0053121 .0059455 0.89 0.372 -.0063525 .0169767 s6 | .000184 .007164 0.03 0.980 -.0138711 .0142391 s7 | -.0055424 .0095196 -0.58 0.561 -.0242191 .0131343 s8 | -.0124571 .0119944 -1.04 0.299 -.0359891 .0110749 s9 | -.0304179 .0070756 -4.30 0.000 -.0442996 -.0165361 s10 | -.0078151 .0078291 -1.00 0.318 -.0231751 .007545 s11 | .0319776 .0073512 4.35 0.000 .0175552 .0464 s12 | .0189393 .0059981 3.16 0.002 .0071717 .030707 s13 | .0056293 .0062068 0.91 0.365 -.0065478 .0178065 s14 | .0144181 .0066865 2.16 0.031 .0012998 .0275363 s15 | .0021839 .0062219 0.35 0.726 -.0100229 .0143907 s16 | .0191649 .0055571 3.45 0.001 .0082623 .0300674 s17 | -.0208272 .0065308 -3.19 0.001 -.03364 -.0080143 s18 | .0300894 .0060782 4.95 0.000 .0181644 .0420144 s19 | .0311962 .0066126 4.72 0.000 .0182228 .0441696 s20 | -.0062105 .0100628 -0.62 0.537 -.0259528 .0135318 s21 | .0001143 .0082208 0.01 0.989 -.0160143 .0162428 s22 | .0178718 .0079423 2.25 0.025 .0022896 .0334541 s23 | -.0007303 .0048086 -0.15 0.879 -.0101644 .0087037 s24 | -.0317657 .0088402 -3.59 0.000 -.0491095 -.0144219 s25 | -.0160117 .0067162 -2.38 0.017 -.0291884 -.0028351 s26 | .0537919 .0075783 7.10 0.000 .0389239 .0686598 s27 | .0049448 .0059154 0.84 0.403 -.0066609 .0165504 s28 | .0292648 .0079718 3.67 0.000 .0136247 .0449048 s29 | .010706 .0078167 1.37 0.171 -.0046296 .0260417 s30 | -.0035754 .0078364 -0.46 0.648 -.0189497 .0117988 s31 | .0108648 .0084567 1.28 0.199 -.0057265 .0274561 s32 | -.0207351 .0089172 -2.33 0.020 -.0382299 -.0032404 s33 | .0173198 .0062051 2.79 0.005 .005146 .0294937 s34 | .0192015 .0054048 3.55 0.000 .0085978 .0298052 s35 | -.0096855 .0082541 -1.17 0.241 -.0258794 .0065084 s36 | .0044384 .0054104 0.82 0.412 -.0061763 .0150532 s37 | .0248183 .0049329 5.03 0.000 .0151404 .0344961 s38 | .00333 .0059885 0.56 0.578 -.0084188 .0150789 s39 | -.0105058 .0068811 -1.53 0.127 -.024006 .0029944 s40 | .0165846 .0079112 2.10 0.036 .0010635 .0321058 s41 | .0458324 .0071367 6.42 0.000 .0318307 .0598341 s42 | .0052475 .009383 0.56 0.576 -.0131611 .0236562 s43 | .0323124 .0074004 4.37 0.000 .0177933 .0468314 s44 | .0123071 .0058837 2.09 0.037 .0007638 .0238504 s45 | .02211 .0058399 3.79 0.000 .0106526 .0335675 s46 | .017421 .0082611 2.11 0.035 .0012134 .0336287 s47 | .0053966 .0088749 0.61 0.543 -.0120152 .0228085 s48 | .0083797 .0067384 1.24 0.214 -.0048404 .0215998 s49 | .0085008 .0059597 1.43 0.154 -.0031917 .0201933 s50 | .0347331 .0053767 6.46 0.000 .0241845 .0452817 s51 | -.0035844 .0073006 -0.49 0.624 -.0179076 .0107389 | lnh | L2D. | .2158113 .1543074 1.40 0.162 -.0869272 .5185498 L3D. | -.3801105 .2349324 -1.62 0.106 -.8410286 .0808076 L4D. | -.2986929 .1264744 -2.36 0.018 -.5468255 -.0505604 | lnst | L2D. | -.1052478 .030176 -3.49 0.001 -.1644506 -.0460449 L3D. | -.1118926 .0345439 -3.24 0.001 -.179665 -.0441203 L4D. | -.2037722 .0279713 -7.29 0.000 -.2586495 -.1488948 | lninc | L2D. | -.6059178 .3205376 -1.89 0.059 -1.234786 .0229507 L3D. | 1.213439 .2959706 4.10 0.000 .6327685 1.794109 L4D. | -.9694708 .2346321 -4.13 0.000 -1.4298 -.5091418 | _cons | .0993698 .0035549 27.95 0.000 .0923952 .1063443 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 9, 1215) = 47.62 Prob > F = 0.0000 R-squared = 0.1907 Adj R-squared = 0.1514 Root MSE = 0.0202 ------------------------------------------------------------------------------ | Robust D.lninc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0112243 .0005965 18.82 0.000 .0100541 .0123945 s3 | .011085 .0005521 20.08 0.000 .0100018 .0121682 s4 | .0056532 .0004769 11.85 0.000 .0047175 .0065888 s5 | .0069318 .0005159 13.44 0.000 .0059197 .007944 s6 | .0099358 .0005532 17.96 0.000 .0088505 .011021 s7 | .0142478 .0007444 19.14 0.000 .0127873 .0157083 s8 | .0227418 .000947 24.01 0.000 .0208838 .0245998 s9 | .0068991 .0005901 11.69 0.000 .0057413 .0080569 s10 | .0102929 .0006144 16.75 0.000 .0090875 .0114984 s11 | .0080628 .0005912 13.64 0.000 .0069028 .0092228 s12 | .0070995 .0004979 14.26 0.000 .0061226 .0080764 s13 | .0098288 .0004507 21.81 0.000 .0089445 .0107131 s14 | .0083845 .0004923 17.03 0.000 .0074187 .0093503 s15 | .0085502 .0004796 17.83 0.000 .0076093 .0094911 s16 | .0068956 .0004278 16.12 0.000 .0060563 .0077349 s17 | .0101352 .0005145 19.70 0.000 .0091258 .0111447 s18 | .0090212 .0004774 18.90 0.000 .0080846 .0099577 s19 | .0133657 .0005181 25.80 0.000 .0123492 .0143821 s20 | .0150499 .0008347 18.03 0.000 .0134123 .0166875 s21 | .0124497 .0006673 18.66 0.000 .0111406 .0137588 s22 | .0130776 .0006696 19.53 0.000 .0117639 .0143914 s23 | .0042125 .0003684 11.43 0.000 .0034897 .0049353 s24 | .0119959 .0006848 17.52 0.000 .0106524 .0133395 s25 | .008547 .0004984 17.15 0.000 .0075692 .0095249 s26 | .0130765 .0005876 22.25 0.000 .0119236 .0142294 s27 | .0099199 .0004448 22.30 0.000 .0090471 .0107926 s28 | .0098242 .0006303 15.59 0.000 .0085875 .0110608 s29 | .0143914 .00062 23.21 0.000 .0131751 .0156077 s30 | .0119654 .0005907 20.26 0.000 .0108065 .0131243 s31 | .0112641 .0006811 16.54 0.000 .0099279 .0126004 s32 | .012681 .0007286 17.40 0.000 .0112515 .0141104 s33 | .010506 .0004787 21.95 0.000 .0095667 .0114452 s34 | .005654 .0004205 13.45 0.000 .004829 .006479 s35 | .0114129 .0006784 16.82 0.000 .010082 .0127438 s36 | .0058278 .0004019 14.50 0.000 .0050393 .0066163 s37 | .009111 .0004212 21.63 0.000 .0082846 .0099373 s38 | .0072124 .0004466 16.15 0.000 .0063362 .0080886 s39 | .01079 .0005262 20.51 0.000 .0097577 .0118223 s40 | .0120486 .000659 18.28 0.000 .0107557 .0133416 s41 | .0095826 .0006061 15.81 0.000 .0083936 .0107717 s42 | .014802 .0006715 22.04 0.000 .0134847 .0161193 s43 | .0100921 .0005891 17.13 0.000 .0089364 .0112478 s44 | .0097412 .0004728 20.60 0.000 .0088136 .0106688 s45 | .0062573 .0004248 14.73 0.000 .005424 .0070907 s46 | .0119037 .000629 18.92 0.000 .0106696 .0131377 s47 | .0147748 .000686 21.54 0.000 .0134288 .0161207 s48 | .0109745 .0005111 21.47 0.000 .0099718 .0119773 s49 | .0083782 .0004324 19.38 0.000 .0075299 .0092265 s50 | .0106039 .0004294 24.69 0.000 .0097614 .0114464 s51 | .0182737 .0005609 32.58 0.000 .0171733 .0193741 | lnh | L2D. | .121742 .0142535 8.54 0.000 .0937778 .1497061 L3D. | .0134677 .0229951 0.59 0.558 -.0316468 .0585822 L4D. | -.1079924 .0221232 -4.88 0.000 -.1513963 -.0645885 | lnst | L2D. | .0147788 .003986 3.71 0.000 .0069586 .0225991 L3D. | .0354781 .0039939 8.88 0.000 .0276425 .0433138 L4D. | -.0092794 .0036088 -2.57 0.010 -.0163596 -.0021992 | lninc | L2D. | .0178413 .0358693 0.50 0.619 -.0525312 .0882139 L3D. | -.0753776 .0213927 -3.52 0.000 -.1173484 -.0334068 L4D. | .0064432 .0291952 0.22 0.825 -.0508355 .0637218 | _cons | .0061555 .0005646 10.90 0.000 .0050478 .0072631 ------------------------------------------------------------------------------ Instrumental variables (2SLS) regression Number of obs = 1275 Wald chi2(53) = 388.74 Prob > chi2 = 0.0000 R-squared = 0.3373 Root MSE = .02663 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust D.lncons | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnh | D1. | .1825271 .0255004 7.16 0.000 .1325472 .2325069 | lnst | D1. | .0583996 .0173922 3.36 0.001 .0243116 .0924876 | lninc | D1. | .8781843 .0769288 11.42 0.000 .7274066 1.028962 | s2 | -.0005613 .0010787 -0.52 0.603 -.0026755 .0015529 s3 | -.0033306 .0009331 -3.57 0.000 -.0051594 -.0015017 s4 | .001688 .0003988 4.23 0.000 .0009063 .0024697 s5 | -.0006521 .0004755 -1.37 0.170 -.0015841 .0002799 s6 | -.0032898 .0006041 -5.45 0.000 -.0044739 -.0021057 s7 | -.0061877 .0008582 -7.21 0.000 -.0078696 -.0045057 s8 | -.0159488 .0015224 -10.48 0.000 -.0189326 -.0129649 s9 | .0003189 .0010396 0.31 0.759 -.0017188 .0023566 s10 | -.0019752 .0006148 -3.21 0.001 -.0031802 -.0007702 s11 | -.0033117 .0006648 -4.98 0.000 -.0046146 -.0020088 s12 | -.0049076 .0008454 -5.80 0.000 -.0065646 -.0032505 s13 | -.0022442 .0006851 -3.28 0.001 -.003587 -.0009014 s14 | -.0022793 .0007359 -3.10 0.002 -.0037217 -.0008369 s15 | .000888 .000624 1.42 0.155 -.0003349 .0021109 s16 | -.0001874 .0005845 -0.32 0.748 -.001333 .0009582 s17 | -.0030981 .0006379 -4.86 0.000 -.0043484 -.0018478 s18 | .0006481 .0007861 0.82 0.410 -.0008926 .0021888 s19 | -.0073703 .0011591 -6.36 0.000 -.009642 -.0050985 s20 | -.0077355 .0008656 -8.94 0.000 -.009432 -.0060389 s21 | -.0080141 .000819 -9.79 0.000 -.0096193 -.006409 s22 | .0009728 .000874 1.11 0.266 -.0007402 .0026858 s23 | .0035155 .0004036 8.71 0.000 .0027245 .0043065 s24 | .0023492 .000752 3.12 0.002 .0008753 .0038231 s25 | .0027576 .0005873 4.70 0.000 .0016064 .0039087 s26 | -.0031584 .0012218 -2.59 0.010 -.005553 -.0007637 s27 | -.0014069 .0008112 -1.73 0.083 -.0029968 .000183 s28 | -.003528 .0008013 -4.40 0.000 -.0050985 -.0019574 s29 | -.0037774 .0010124 -3.73 0.000 -.0057617 -.0017932 s30 | .0052027 .0007408 7.02 0.000 .0037509 .0066546 s31 | .0032241 .0007081 4.55 0.000 .0018362 .004612 s32 | -.003047 .0008198 -3.72 0.000 -.0046537 -.0014403 s33 | -.0025059 .0007322 -3.42 0.001 -.003941 -.0010708 s34 | .0058187 .000479 12.15 0.000 .00488 .0067575 s35 | -.0011582 .0007264 -1.59 0.111 -.0025819 .0002655 s36 | .0040155 .000417 9.63 0.000 .0031982 .0048327 s37 | -.0055051 .0008379 -6.57 0.000 -.0071474 -.0038627 s38 | -.0031544 .0008248 -3.82 0.000 -.004771 -.0015378 s39 | -.0023991 .0007749 -3.10 0.002 -.0039178 -.0008804 s40 | -.0042162 .000778 -5.42 0.000 -.0057411 -.0026913 s41 | -.0036098 .0009039 -3.99 0.000 -.0053814 -.0018382 s42 | -.0043862 .0009719 -4.51 0.000 -.0062912 -.0024812 s43 | -.0014684 .0008332 -1.76 0.078 -.0031013 .0001646 s44 | -.0060122 .0007562 -7.95 0.000 -.0074944 -.00453 s45 | .003015 .0006457 4.67 0.000 .0017496 .0042805 s46 | -.0042056 .0008206 -5.13 0.000 -.0058138 -.0025973 s47 | -.0053414 .0009533 -5.60 0.000 -.0072098 -.0034729 s48 | -.0077145 .0008312 -9.28 0.000 -.0093436 -.0060855 s49 | -.0007054 .0006461 -1.09 0.275 -.0019718 .000561 s50 | -.0002239 .0009961 -0.22 0.822 -.0021761 .0017284 s51 | -.0109917 .0011978 -9.18 0.000 -.0133395 -.008644 _cons | -.0111425 .0013731 -8.11 0.000 -.0138337 -.0084512 ------------------------------------------------------------------------------ Instrumented: D.lnh D.lnst D.lninc Instruments: s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39 s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 L2D.lnh L3D.lnh L4D.lnh L2D.lnst L3D.lnst L4D.lnst L2D.lninc L3D.lninc L4D.lninc . sum chratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- chratio | 1275 .3010725 .0938772 .0721587 .7651958 . local ch = r(mean) . sum csratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- csratio | 1275 .2663826 .139835 .0746406 .9381273 . local cs = r(mean) . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . local hw = r(mean) . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . local sw = r(mean) . estadd scalar hwe = _b[D.lnh] * `ch' . estadd scalar swe = _b[D.lnst] * `cs' . estadd scalar we_diff = e(hwe) - e(swe) . estadd scalar hwelas = e(hwe) * 1 / `ch' . estadd scalar swelas = e(swe) * 1 / `cs' . estadd scalar elas_diff = e(hwelas) - e(swelas) . test D.lnh * `ch' = 0 ( 1) .3010725*D.lnh = 0 chi2( 1) = 51.23 Prob > chi2 = 0.0000 . estadd scalar hwe_p = r(p) . test D.lns * `cs' = 0 ( 1) .2663826*D.lnst = 0 chi2( 1) = 11.27 Prob > chi2 = 0.0008 . estadd scalar swe_p = r(p) . test D.lnh * `ch' = D.lns * `cs' ( 1) .3010725*D.lnh - .2663826*D.lnst = 0 chi2( 1) = 17.30 Prob > chi2 = 0.0000 . estadd scalar we_diff_p = r(p) . test D.lnh = 0 ( 1) D.lnh = 0 chi2( 1) = 51.23 Prob > chi2 = 0.0000 . estadd scalar hwelas_p = r(p) . test D.lnst = 0 ( 1) D.lnst = 0 chi2( 1) = 11.27 Prob > chi2 = 0.0008 . estadd scalar swelas_p = r(p) . test D.lnh = D.lns ( 1) D.lnh - D.lnst = 0 chi2( 1) = 14.42 Prob > chi2 = 0.0001 . estadd scalar elas_diff_p = r(p) . est store iv1 . . gen hwe1 = ( _b[D.lnh] ) * cons_real / h_real if e(sample) (2907 missing values generated) . gen swe1 = ( _b[D.lnst] ) * cons_real / st_real if e(sample) (2907 missing values generated) . . ** Model 2 - Includes wealth ratios . ivregress 2sls d.lncons s2-s51 /// > (d.lnh d.lnst d.lnw d.lninc = dl(2/4).lnh dl(2/4).lnst dl(2/4).lnw dl(2/4).lninc ) , cluster(state) first First-stage regressions ----------------------- Number of obs = 1275 N. of clusters = 51 F( 12, 1212) = 39.40 Prob > F = 0.0000 R-squared = 0.4356 Adj R-squared = 0.4067 Root MSE = 0.0466 ------------------------------------------------------------------------------ | Robust D.lnh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0024706 .0030285 0.82 0.415 -.0034711 .0084124 s3 | -.0001257 .0027206 -0.05 0.963 -.0054634 .005212 s4 | -.0075155 .0020135 -3.73 0.000 -.0114658 -.0035652 s5 | .0052769 .0020628 2.56 0.011 .0012299 .009324 s6 | -.0022157 .0023241 -0.95 0.341 -.0067755 .002344 s7 | -.007264 .0030543 -2.38 0.018 -.0132563 -.0012717 s8 | .0145711 .0041288 3.53 0.000 .0064707 .0226715 s9 | .0143579 .0023732 6.05 0.000 .0097017 .019014 s10 | -.0054717 .0023289 -2.35 0.019 -.0100407 -.0009026 s11 | -.004826 .0026735 -1.81 0.071 -.0100712 .0004192 s12 | .0206073 .002406 8.56 0.000 .0158869 .0253277 s13 | -.0017098 .0019762 -0.87 0.387 -.0055869 .0021673 s14 | .0064276 .0025309 2.54 0.011 .0014623 .011393 s15 | .0026071 .002186 1.19 0.233 -.0016816 .0068958 s16 | .0033885 .0019971 1.70 0.090 -.0005298 .0073067 s17 | -.0073871 .002169 -3.41 0.001 -.0116426 -.0031317 s18 | .0037539 .0023834 1.58 0.116 -.0009221 .0084299 s19 | -.0029482 .002814 -1.05 0.295 -.0084691 .0025728 s20 | -.0061045 .0031618 -1.93 0.054 -.0123078 .0000987 s21 | .0058542 .0026999 2.17 0.030 .0005572 .0111513 s22 | .0026625 .0027329 0.97 0.330 -.0026991 .0080242 s23 | .0006001 .0016169 0.37 0.711 -.0025722 .0037723 s24 | -.0097609 .0027295 -3.58 0.000 -.015116 -.0044059 s25 | -.0040308 .00213 -1.89 0.059 -.0082098 .0001481 s26 | .0010611 .0031347 0.34 0.735 -.0050888 .0072111 s27 | .0089907 .0022034 4.08 0.000 .0046677 .0133136 s28 | .0028175 .002949 0.96 0.340 -.0029683 .0086033 s29 | -.0072953 .0028285 -2.58 0.010 -.0128445 -.001746 s30 | -.0060753 .0024213 -2.51 0.012 -.0108257 -.0013248 s31 | -.0091459 .0027171 -3.37 0.001 -.0144766 -.0038152 s32 | -.0003134 .0028301 -0.11 0.912 -.0058659 .005239 s33 | .0036235 .0022947 1.58 0.115 -.0008786 .0081256 s34 | -.0083437 .0022105 -3.77 0.000 -.0126805 -.0040068 s35 | -.0002309 .0026658 -0.09 0.931 -.0054611 .0049993 s36 | -.0032719 .0017917 -1.83 0.068 -.006787 .0002432 s37 | -.0063303 .0017856 -3.55 0.000 -.0098334 -.0028272 s38 | .0148288 .0022568 6.57 0.000 .0104012 .0192565 s39 | .0054477 .0023206 2.35 0.019 .0008948 .0100005 s40 | .0058917 .002556 2.31 0.021 .000877 .0109064 s41 | .0074705 .0029981 2.49 0.013 .0015885 .0133525 s42 | .0034668 .0028494 1.22 0.224 -.0021235 .009057 s43 | .002016 .0028639 0.70 0.482 -.0036029 .0076348 s44 | -.0092967 .0019908 -4.67 0.000 -.0132025 -.0053909 s45 | .0023136 .0024816 0.93 0.351 -.0025551 .0071824 s46 | .0001113 .0028025 0.04 0.968 -.0053871 .0056096 s47 | .0057596 .0028482 2.02 0.043 .0001716 .0113476 s48 | .0072456 .0024692 2.93 0.003 .0024013 .01209 s49 | .004309 .0020296 2.12 0.034 .0003271 .008291 s50 | .0034529 .0024442 1.41 0.158 -.0013424 .0082483 s51 | .0023042 .0025901 0.89 0.374 -.0027774 .0073858 | lnh | L2D. | .6869032 .076657 8.96 0.000 .5365081 .8372983 L3D. | -.6893928 .1249984 -5.52 0.000 -.93463 -.4441556 L4D. | -.1073128 .0654416 -1.64 0.101 -.2357042 .0210785 | lnst | L2D. | -.2606026 .0520688 -5.00 0.000 -.3627577 -.1584476 L3D. | -.1106903 .0490295 -2.26 0.024 -.2068824 -.0144982 L4D. | .0623195 .0316897 1.97 0.049 .0001467 .1244922 | lnw | L2D. | .4068679 .0850196 4.79 0.000 .2400658 .5736699 L3D. | .1432397 .0790795 1.81 0.070 -.0119083 .2983877 L4D. | -.1623892 .0573778 -2.83 0.005 -.27496 -.0498183 | lninc | L2D. | .1933251 .0965234 2.00 0.045 .0039536 .3826966 L3D. | .355392 .077734 4.57 0.000 .2028839 .5079001 L4D. | .3912187 .125601 3.11 0.002 .1447991 .6376383 | _cons | .0123847 .0024243 5.11 0.000 .0076284 .0171409 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 12, 1212) = 70.89 Prob > F = 0.0000 R-squared = 0.1758 Adj R-squared = 0.1336 Root MSE = 0.1417 ------------------------------------------------------------------------------ | Robust D.lnst | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0319425 .0080821 3.95 0.000 .0160861 .047799 s3 | .0212021 .0073147 2.90 0.004 .0068512 .035553 s4 | -.0059293 .0056858 -1.04 0.297 -.0170844 .0052257 s5 | -.002874 .00569 -0.51 0.614 -.0140373 .0082893 s6 | -.0017552 .0066171 -0.27 0.791 -.0147374 .0112269 s7 | -.0066664 .0086797 -0.77 0.443 -.0236954 .0103626 s8 | -.0088005 .0107156 -0.82 0.412 -.0298237 .0122227 s9 | -.0244838 .0062557 -3.91 0.000 -.0367571 -.0122106 s10 | -.0059073 .007094 -0.83 0.405 -.0198251 .0080106 s11 | .0207776 .0072187 2.88 0.004 .0066151 .0349402 s12 | .0093362 .0059487 1.57 0.117 -.0023347 .021007 s13 | .0167163 .0055226 3.03 0.003 .0058814 .0275512 s14 | .0054183 .0064105 0.85 0.398 -.0071586 .0179952 s15 | .0007694 .0055706 0.14 0.890 -.0101597 .0116986 s16 | .0157858 .0051012 3.09 0.002 .0057776 .025794 s17 | -.0051379 .0059627 -0.86 0.389 -.0168363 .0065604 s18 | .0178396 .006165 2.89 0.004 .0057444 .0299347 s19 | .005379 .0080169 0.67 0.502 -.0103496 .0211075 s20 | -.0027367 .0089545 -0.31 0.760 -.0203048 .0148314 s21 | .000055 .0073624 0.01 0.994 -.0143895 .0144995 s22 | .0149958 .0071668 2.09 0.037 .000935 .0290566 s23 | .0029399 .0041253 0.71 0.476 -.0051536 .0110333 s24 | -.020037 .0079462 -2.52 0.012 -.0356269 -.0044471 s25 | -.0062023 .0059798 -1.04 0.300 -.0179343 .0055297 s26 | .0282451 .0087485 3.23 0.001 .0110812 .045409 s27 | .0038175 .0052902 0.72 0.471 -.0065615 .0141964 s28 | .0175411 .0077987 2.25 0.025 .0022408 .0328415 s29 | .0214063 .0069927 3.06 0.002 .0076871 .0351255 s30 | .0093019 .0070581 1.32 0.188 -.0045457 .0231494 s31 | .0123007 .0075572 1.63 0.104 -.0025259 .0271273 s32 | -.0145139 .0078585 -1.85 0.065 -.0299316 .0009039 s33 | .0037952 .0064205 0.59 0.555 -.0088014 .0163917 s34 | -.0025879 .0063613 -0.41 0.684 -.0150683 .0098925 s35 | -.0054317 .0072601 -0.75 0.455 -.0196754 .008812 s36 | .0074084 .0048077 1.54 0.124 -.0020238 .0168407 s37 | .0155778 .0051188 3.04 0.002 .0055352 .0256205 s38 | .0001668 .0054536 0.03 0.976 -.0105328 .0108664 s39 | -.005561 .0060587 -0.92 0.359 -.0174478 .0063258 s40 | .0144121 .0071772 2.01 0.045 .0003309 .0284933 s41 | .0242635 .0078038 3.11 0.002 .0089532 .0395739 s42 | .0137962 .0084937 1.62 0.105 -.0028678 .0304602 s43 | .0173592 .0075424 2.30 0.022 .0025616 .0321569 s44 | .0084952 .0056125 1.51 0.130 -.0025162 .0195065 s45 | .0047969 .0063968 0.75 0.453 -.0077532 .017347 s46 | .0117872 .0077363 1.52 0.128 -.0033907 .0269652 s47 | .007393 .0079677 0.93 0.354 -.0082389 .023025 s48 | .0027088 .0062279 0.43 0.664 -.0095098 .0149274 s49 | .0104317 .0053277 1.96 0.050 -.0000209 .0208842 s50 | .0154481 .0061701 2.50 0.012 .0033428 .0275534 s51 | -.0053496 .0066689 -0.80 0.423 -.0184335 .0077344 | lnh | L2D. | .2459675 .2028494 1.21 0.226 -.1520075 .6439425 L3D. | .9271831 .2525874 3.67 0.000 .431626 1.42274 L4D. | -.1160122 .1623581 -0.71 0.475 -.4345463 .2025219 | lnst | L2D. | -.0882808 .1677505 -0.53 0.599 -.4173944 .2408329 L3D. | 1.405092 .1404772 10.00 0.000 1.129486 1.680697 L4D. | -.0642537 .1007745 -0.64 0.524 -.2619655 .1334581 | lnw | L2D. | .0450002 .2784571 0.16 0.872 -.5013112 .5913116 L3D. | -2.732756 .2375585 -11.50 0.000 -3.198828 -2.266684 L4D. | -.1301581 .1647035 -0.79 0.430 -.4532937 .1929774 | lninc | L2D. | -.4874237 .2956983 -1.65 0.100 -1.067561 .0927138 L3D. | 1.118261 .2840838 3.94 0.000 .5609104 1.675612 L4D. | -.9303873 .2348058 -3.96 0.000 -1.391058 -.4697165 | _cons | .0753522 .0053699 14.03 0.000 .064817 .0858875 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 12, 1212) = 51.77 Prob > F = 0.0000 R-squared = 0.1967 Adj R-squared = 0.1556 Root MSE = 0.0864 ------------------------------------------------------------------------------ | Robust D.lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .017015 .0047843 3.56 0.000 .0076287 .0264014 s3 | .0119182 .0042411 2.81 0.005 .0035974 .020239 s4 | -.0038426 .0034317 -1.12 0.263 -.0105754 .0028902 s5 | .0019696 .0035329 0.56 0.577 -.0049618 .0089009 s6 | .0017226 .0038344 0.45 0.653 -.0058002 .0092454 s7 | -.0032517 .0051392 -0.63 0.527 -.0133345 .0068311 s8 | .0078234 .006472 1.21 0.227 -.0048742 .020521 s9 | -.0012377 .0038753 -0.32 0.749 -.0088408 .0063654 s10 | -.0018951 .0042571 -0.45 0.656 -.0102472 .006457 s11 | .010476 .004166 2.51 0.012 .0023026 .0186493 s12 | .0160535 .0038085 4.22 0.000 .0085815 .0235255 s13 | .0159082 .0031042 5.12 0.000 .0098181 .0219983 s14 | .0089912 .0038879 2.31 0.021 .0013635 .0166189 s15 | .0062436 .0033037 1.89 0.059 -.0002381 .0127253 s16 | .0143454 .0029358 4.89 0.000 .0085856 .0201052 s17 | .0013313 .0034506 0.39 0.700 -.0054386 .0081012 s18 | .0131621 .0035868 3.67 0.000 .0061251 .020199 s19 | -.0019749 .0048152 -0.41 0.682 -.0114219 .0074721 s20 | .0007133 .0053183 0.13 0.893 -.0097208 .0111474 s21 | .0076443 .0044085 1.73 0.083 -.0010047 .0162934 s22 | .0131673 .0042359 3.11 0.002 .0048568 .0214778 s23 | .0081688 .0024354 3.35 0.001 .0033906 .0129469 s24 | -.008642 .0046839 -1.85 0.065 -.0178315 .0005476 s25 | .001072 .003506 0.31 0.760 -.0058065 .0079505 s26 | .0132108 .0051755 2.55 0.011 .0030568 .0233648 s27 | .0104313 .0031619 3.30 0.001 .004228 .0166347 s28 | .0123024 .0045632 2.70 0.007 .0033497 .021255 s29 | .0156721 .003677 4.26 0.000 .0084581 .022886 s30 | .0102447 .0039523 2.59 0.010 .0024906 .0179988 s31 | .0072709 .0043828 1.66 0.097 -.0013279 .0158697 s32 | -.0023698 .0047432 -0.50 0.617 -.0116756 .0069361 s33 | .0043621 .0038185 1.14 0.254 -.0031294 .0118536 s34 | -.0062111 .0039293 -1.58 0.114 -.0139201 .0014979 s35 | .0021839 .0043508 0.50 0.616 -.006352 .0107199 s36 | .0082372 .0027631 2.98 0.003 .0028163 .0136581 s37 | .0070274 .0028398 2.47 0.013 .001456 .0125988 s38 | .0113654 .0034019 3.34 0.001 .0046911 .0180397 s39 | .004887 .0036338 1.34 0.179 -.0022423 .0120163 s40 | .0146312 .0042892 3.41 0.001 .0062162 .0230463 s41 | .0151838 .0046853 3.24 0.001 .0059916 .0243759 s42 | .0160107 .004804 3.33 0.001 .0065856 .0254357 s43 | .0109929 .0044206 2.49 0.013 .00232 .0196657 s44 | .0028573 .0030936 0.92 0.356 -.0032121 .0089268 s45 | .0034978 .0038783 0.90 0.367 -.004111 .0111067 s46 | .0093217 .0045862 2.03 0.042 .000324 .0183195 s47 | .011828 .0047455 2.49 0.013 .0025177 .0211384 s48 | .0083329 .0038015 2.19 0.029 .0008747 .0157912 s49 | .0128465 .0031449 4.08 0.000 .0066764 .0190165 s50 | .0098436 .0036887 2.67 0.008 .0026065 .0170806 s51 | .0025755 .0040252 0.64 0.522 -.0053217 .0104727 | lnh | L2D. | .4885179 .1231465 3.97 0.000 .246914 .7301218 L3D. | .1491584 .1731472 0.86 0.389 -.1905431 .4888599 L4D. | -.0263731 .1088536 -0.24 0.809 -.2399355 .1871893 | lnst | L2D. | -.1443382 .1098674 -1.31 0.189 -.3598896 .0712131 L3D. | .664054 .0848659 7.82 0.000 .4975536 .8305545 L4D. | .0707924 .0606551 1.17 0.243 -.0482083 .1897931 | lnw | L2D. | .1795934 .1814717 0.99 0.323 -.1764402 .535627 L3D. | -1.358839 .1420354 -9.57 0.000 -1.637501 -1.080176 L4D. | -.2759665 .1005328 -2.75 0.006 -.473204 -.078729 | lninc | L2D. | -.1712625 .1768801 -0.97 0.333 -.5182877 .1757627 L3D. | .7567938 .1529996 4.95 0.000 .4566203 1.056967 L4D. | -.4006358 .087396 -4.58 0.000 -.5721001 -.2291715 | _cons | .0415051 .0035008 11.86 0.000 .0346369 .0483733 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 12, 1212) = 36.79 Prob > F = 0.0000 R-squared = 0.2072 Adj R-squared = 0.1666 Root MSE = 0.0200 ------------------------------------------------------------------------------ | Robust D.lninc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s2 | .0099454 .000803 12.38 0.000 .0083699 .0115209 s3 | .0099627 .0006672 14.93 0.000 .0086537 .0112718 s4 | .0051952 .0004499 11.55 0.000 .0043125 .0060779 s5 | .0063402 .0005199 12.19 0.000 .0053202 .0073602 s6 | .0093602 .0005559 16.84 0.000 .0082696 .0104508 s7 | .0135792 .0007204 18.85 0.000 .0121659 .0149926 s8 | .0221761 .0009188 24.14 0.000 .0203735 .0239786 s9 | .0066367 .0005786 11.47 0.000 .0055015 .0077719 s10 | .0099636 .0005804 17.17 0.000 .0088248 .0111023 s11 | .0071108 .0006669 10.66 0.000 .0058024 .0084191 s12 | .0063501 .0005319 11.94 0.000 .0053065 .0073937 s13 | .009621 .0005191 18.53 0.000 .0086025 .0106395 s14 | .007515 .0005337 14.08 0.000 .006468 .0085621 s15 | .0079003 .0004762 16.59 0.000 .006966 .0088347 s16 | .0062122 .000445 13.96 0.000 .0053392 .0070852 s17 | .0101052 .0006002 16.84 0.000 .0089276 .0112827 s18 | .0080165 .000582 13.77 0.000 .0068746 .0091584 s19 | .0121332 .0008646 14.03 0.000 .0104369 .0138296 s20 | .014465 .0008156 17.73 0.000 .0128648 .0160653 s21 | .0118638 .0006549 18.12 0.000 .010579 .0131486 s22 | .0123387 .0006773 18.22 0.000 .0110098 .0136676 s23 | .003678 .0003878 9.48 0.000 .0029172 .0044388 s24 | .0116997 .0006922 16.90 0.000 .0103416 .0130579 s25 | .0083342 .0005279 15.79 0.000 .0072985 .0093699 s26 | .0117198 .000884 13.26 0.000 .0099855 .0134541 s27 | .0093659 .0004405 21.26 0.000 .0085017 .0102301 s28 | .0088395 .0007097 12.46 0.000 .0074471 .0102319 s29 | .0141078 .0007085 19.91 0.000 .0127177 .0154979 s30 | .0117357 .0006645 17.66 0.000 .010432 .0130393 s31 | .0105947 .000691 15.33 0.000 .0092391 .0119504 s32 | .0122975 .0007002 17.56 0.000 .0109238 .0136711 s33 | .0096797 .0005866 16.50 0.000 .0085288 .0108306 s34 | .0045833 .000665 6.89 0.000 .0032786 .005888 s35 | .0109308 .0006564 16.65 0.000 .009643 .0122185 s36 | .005285 .0004156 12.72 0.000 .0044696 .0061005 s37 | .0084326 .0005043 16.72 0.000 .0074433 .0094219 s38 | .0065725 .0004356 15.09 0.000 .005718 .0074271 s39 | .0104244 .0005159 20.21 0.000 .0094123 .0114365 s40 | .0114188 .0006462 17.67 0.000 .0101511 .0126866 s41 | .0083256 .0008046 10.35 0.000 .0067469 .0099042 s42 | .0145036 .0006859 21.14 0.000 .0131578 .0158494 s43 | .0090025 .0007117 12.65 0.000 .0076062 .0103987 s44 | .009217 .0004935 18.68 0.000 .0082489 .0101851 s45 | .0052136 .0006172 8.45 0.000 .0040027 .0064245 s46 | .0111341 .0006438 17.29 0.000 .009871 .0123971 s47 | .0142366 .0006685 21.30 0.000 .012925 .0155481 s48 | .0102236 .0005134 19.92 0.000 .0092165 .0112308 s49 | .0078709 .0004345 18.12 0.000 .0070186 .0087233 s50 | .0092748 .0006603 14.05 0.000 .0079793 .0105702 s51 | .0177202 .0005374 32.98 0.000 .0166659 .0187744 | lnh | L2D. | .0598827 .0283371 2.11 0.035 .0042874 .115478 L3D. | .0634357 .0264996 2.39 0.017 .0114455 .1154259 L4D. | -.0447442 .0286526 -1.56 0.119 -.1009583 .01147 | lnst | L2D. | -.0628218 .0270492 -2.32 0.020 -.1158903 -.0097533 L3D. | .1099859 .0218227 5.04 0.000 .0671714 .1528004 L4D. | .0476137 .0145019 3.28 0.001 .019162 .0760653 | lnw | L2D. | .1420635 .0530213 2.68 0.007 .0380397 .2460873 L3D. | -.1367283 .0379095 -3.61 0.000 -.2111038 -.0623528 L4D. | -.0992668 .0255286 -3.89 0.000 -.1493519 -.0491817 | lninc | L2D. | .0306658 .0319123 0.96 0.337 -.0319437 .0932753 L3D. | -.0781321 .0217791 -3.59 0.000 -.120861 -.0354032 L4D. | .0208178 .0327387 0.64 0.525 -.043413 .0850485 | _cons | .0055068 .0007228 7.62 0.000 .0040887 .006925 ------------------------------------------------------------------------------ Instrumental variables (2SLS) regression Number of obs = 1275 Wald chi2(54) = 345.77 Prob > chi2 = 0.0000 R-squared = 0.2989 Root MSE = .02738 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust D.lncons | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnh | D1. | -.0192933 .0869473 -0.22 0.824 -.1897069 .1511203 | lnst | D1. | -.1501072 .0949063 -1.58 0.114 -.3361201 .0359056 | lnw | D1. | .3983146 .1748623 2.28 0.023 .0555909 .7410384 | lninc | D1. | .9542449 .0741999 12.86 0.000 .8088158 1.099674 | s2 | -.0006494 .0009258 -0.70 0.483 -.002464 .0011652 s3 | -.0040429 .0009168 -4.41 0.000 -.0058399 -.002246 s4 | .0004636 .0007396 0.63 0.531 -.000986 .0019131 s5 | -.0012771 .000665 -1.92 0.055 -.0025804 .0000262 s6 | -.0048485 .0009946 -4.87 0.000 -.0067978 -.0028991 s7 | -.007976 .001235 -6.46 0.000 -.0103965 -.0055555 s8 | -.0186863 .0022859 -8.17 0.000 -.0231665 -.0142061 s9 | -.0014846 .001545 -0.96 0.337 -.0045127 .0015435 s10 | -.0036899 .0010435 -3.54 0.000 -.0057352 -.0016446 s11 | -.0041263 .0007173 -5.75 0.000 -.0055321 -.0027205 s12 | -.0058127 .0011828 -4.91 0.000 -.008131 -.0034945 s13 | -.005334 .0016082 -3.32 0.001 -.008486 -.002182 s14 | -.0037209 .001141 -3.26 0.001 -.0059573 -.0014846 s15 | -.0010479 .0012128 -0.86 0.388 -.003425 .0013292 s16 | -.0019412 .0010676 -1.82 0.069 -.0040337 .0001513 s17 | -.0059854 .0015129 -3.96 0.000 -.0089507 -.0030202 s18 | -.0003777 .0009221 -0.41 0.682 -.0021849 .0014295 s19 | -.0068458 .0010079 -6.79 0.000 -.0088213 -.0048703 s20 | -.0100068 .0014288 -7.00 0.000 -.0128072 -.0072064 s21 | -.010134 .0014393 -7.04 0.000 -.012955 -.007313 s22 | -.0009117 .001309 -0.70 0.486 -.0034774 .0016539 s23 | .0011479 .0012484 0.92 0.358 -.001299 .0035948 s24 | -.0002541 .0014713 -0.17 0.863 -.0031378 .0026295 s25 | .0003629 .0013341 0.27 0.786 -.0022519 .0029776 s26 | -.0029812 .0010115 -2.95 0.003 -.0049637 -.0009986 s27 | -.0032811 .0013664 -2.40 0.016 -.0059593 -.000603 s28 | -.0043459 .0009446 -4.60 0.000 -.0061972 -.0024946 s29 | -.0069376 .0017768 -3.90 0.000 -.0104201 -.0034551 s30 | .0020352 .001656 1.23 0.219 -.0012104 .0052809 s31 | .0010772 .0012094 0.89 0.373 -.0012932 .0034476 s32 | -.0053772 .0014939 -3.60 0.000 -.0083052 -.0024493 s33 | -.0031541 .0008049 -3.92 0.000 -.0047317 -.0015764 s34 | .0056883 .0004182 13.60 0.000 .0048687 .0065079 s35 | -.003354 .0013778 -2.43 0.015 -.0060545 -.0006535 s36 | .0017718 .001148 1.54 0.123 -.0004783 .0040218 s37 | -.0065447 .0007796 -8.40 0.000 -.0080726 -.0050168 s38 | -.0049624 .0014222 -3.49 0.000 -.0077499 -.002175 s39 | -.0046045 .0014513 -3.17 0.002 -.0074489 -.0017601 s40 | -.0061697 .0012891 -4.79 0.000 -.0086964 -.0036431 s41 | -.0034982 .0008781 -3.98 0.000 -.0052192 -.0017772 s42 | -.0072154 .0017381 -4.15 0.000 -.0106221 -.0038088 s43 | -.00208 .000883 -2.36 0.018 -.0038108 -.0003493 s44 | -.0073299 .0008684 -8.44 0.000 -.009032 -.0056278 s45 | .002781 .0007037 3.95 0.000 .0014019 .0041602 s46 | -.0057024 .0011271 -5.06 0.000 -.0079115 -.0034934 s47 | -.0077095 .0015846 -4.87 0.000 -.0108153 -.0046037 s48 | -.009479 .0013136 -7.22 0.000 -.0120535 -.0069044 s49 | -.0028689 .0012942 -2.22 0.027 -.0054054 -.0003324 s50 | -.0008674 .0009507 -0.91 0.362 -.0027308 .0009959 s51 | -.0134592 .0016847 -7.99 0.000 -.0167612 -.0101572 _cons | -.0100152 .0011215 -8.93 0.000 -.0122132 -.0078172 ------------------------------------------------------------------------------ Instrumented: D.lnh D.lnst D.lnw D.lninc Instruments: s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39 s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 L2D.lnh L3D.lnh L4D.lnh L2D.lnst L3D.lnst L4D.lnst L2D.lnw L3D.lnw L4D.lnw L2D.lninc L3D.lninc L4D.lninc . sum chratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- chratio | 1275 .3010725 .0938772 .0721587 .7651958 . local ch = r(mean) . sum csratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- csratio | 1275 .2663826 .139835 .0746406 .9381273 . local cs = r(mean) . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . local hw = r(mean) . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . local sw = r(mean) . estadd scalar hwe = ( _b[D.lnh] + _b[D.lnw] * `hw' ) * `ch' . estadd scalar swe = ( _b[D.lnst] + _b[D.lnw] * `sw' ) * `cs' . estadd scalar we_diff = e(hwe) - e(swe) . estadd scalar hwelas = e(hwe) * 1 / `ch' . estadd scalar swelas = e(swe) * 1 / `cs' . estadd scalar elas_diff = e(hwelas) - e(swelas) . test ( D.lnh + D.lnw * `hw' ) * `ch' = 0 ( 1) .3010725*D.lnh + .1375805*D.lnw = 0 chi2( 1) = 32.60 Prob > chi2 = 0.0000 . estadd scalar hwe_p = r(p) . test ( D.lns + D.lnw * `sw' ) * `cs' = 0 ( 1) .2663826*D.lnst + .1446543*D.lnw = 0 chi2( 1) = 33.85 Prob > chi2 = 0.0000 . estadd scalar swe_p = r(p) . test ( D.lnh + D.lnw * `hw' ) * `ch' = ( D.lns + D.lnw * `sw' ) * `cs' ( 1) .3010725*D.lnh - .2663826*D.lnst - .0070738*D.lnw = 0 chi2( 1) = 12.16 Prob > chi2 = 0.0005 . estadd scalar we_diff_p = r(p) . test ( D.lnh + D.lnw * `hw' ) = 0 ( 1) D.lnh + .456968*D.lnw = 0 chi2( 1) = 32.60 Prob > chi2 = 0.0000 . estadd scalar hwelas_p = r(p) . test ( D.lnst + D.lnw * `sw' ) = 0 ( 1) D.lnst + .543032*D.lnw = 0 chi2( 1) = 33.85 Prob > chi2 = 0.0000 . estadd scalar swelas_p = r(p) . test _b[D.lnh] + _b[D.lnw] * `hw' = _b[D.lnst] + _b[D.lnw] * `sw' ( 1) D.lnh - D.lnst - .086064*D.lnw = 0 chi2( 1) = 10.15 Prob > chi2 = 0.0014 . estadd scalar elas_diff_p = r(p) . est store iv2 . . gen hwe2 = ( _b[D.lnh] + _b[D.lnw] * h_real / w_real ) * cons_real / h_real if e(sample) (2907 missing values generated) . gen swe2 = ( _b[D.lnst] + _b[D.lnw] * st_real / w_real ) * cons_real / st_real if e(sample) (2907 missing values generated) . . ** Model 3 - Includes all demographics (age and poverty) but no wealth ratios . ivregress 2sls d.lncons age1r age3r poverty s2-s51 /// urate > (d.lnh d.lnst d.lninc age1h age3h age1s age3s /// > povertyh povertys /// > = dl(2/4).lnh dl(2/4).lnst dl(2/4).lninc /// > l(2/4).age1h l(2/4).age3h /// > l(2/4).age1s l(2/4).age3s /// > l(2/4).povertyh l(2/4).povertys) , cluster(state) first First-stage regressions ----------------------- Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 133.50 Prob > F = 0.0000 R-squared = 0.5165 Adj R-squared = 0.4841 Root MSE = 0.0435 ------------------------------------------------------------------------------ | Robust D.lnh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.275383 .0893016 -3.08 0.002 -.4505886 -.1001775 age3ratio | -.2814385 .1644856 -1.71 0.087 -.6041515 .0412745 poverty | -.380966 .0660106 -5.77 0.000 -.5104758 -.2514563 s2 | .0591565 .0203754 2.90 0.004 .019181 .099132 s3 | .0688694 .0231811 2.97 0.003 .0233891 .1143496 s4 | .0514701 .0186364 2.76 0.006 .0149063 .088034 s5 | .0491066 .0123062 3.99 0.000 .0249624 .0732509 s6 | .0188541 .0105346 1.79 0.074 -.0018144 .0395225 s7 | .0220385 .0186304 1.18 0.237 -.0145136 .0585905 s8 | .0897475 .0179068 5.01 0.000 .0546152 .1248799 s9 | .0499153 .017352 2.88 0.004 .0158716 .0839591 s10 | .0597993 .0270437 2.21 0.027 .0067407 .1128578 s11 | .0306953 .0120168 2.55 0.011 .0071189 .0542716 s12 | .0593452 .0164145 3.62 0.000 .0271408 .0915497 s13 | .0460432 .0217445 2.12 0.034 .0033816 .0887048 s14 | .0498939 .0167838 2.97 0.003 .0169648 .082823 s15 | .0467791 .0163428 2.86 0.004 .0147153 .078843 s16 | .0447898 .0167742 2.67 0.008 .0118796 .0777001 s17 | .0364927 .0184291 1.98 0.048 .0003356 .0726497 s18 | .0590126 .0183304 3.22 0.001 .0230493 .0949759 s19 | .0514028 .0180742 2.84 0.005 .015942 .0868636 s20 | .0328501 .0180541 1.82 0.069 -.0025711 .0682714 s21 | .0321023 .0131204 2.45 0.015 .0063607 .057844 s22 | .0460963 .019834 2.32 0.020 .0071829 .0850097 s23 | .0446734 .016047 2.78 0.005 .0131899 .0761569 s24 | .0207218 .0157981 1.31 0.190 -.0102733 .0517168 s25 | .0448145 .0196632 2.28 0.023 .0062362 .0833928 s26 | .0666219 .0207945 3.20 0.001 .0258241 .1074196 s27 | .0600361 .0201503 2.98 0.003 .0205021 .0995702 s28 | .0491325 .0171693 2.86 0.004 .0154471 .082818 s29 | .0455237 .021004 2.17 0.030 .0043148 .0867325 s30 | .0365812 .0195295 1.87 0.061 -.0017348 .0748972 s31 | .0088774 .0145198 0.61 0.541 -.0196098 .0373645 s32 | .0313375 .0179103 1.75 0.080 -.0038017 .0664768 s33 | .0600539 .0180127 3.33 0.001 .0247139 .0953938 s34 | .0232108 .0141753 1.64 0.102 -.0046003 .051022 s35 | .054373 .018633 2.92 0.004 .0178159 .0909301 s36 | .0420093 .0182349 2.30 0.021 .0062332 .0777854 s37 | .0472795 .0196517 2.41 0.016 .0087238 .0858351 s38 | .0575045 .0178329 3.22 0.001 .0225172 .0924917 s39 | .0533997 .0228891 2.33 0.020 .0084925 .098307 s40 | .053103 .0203801 2.61 0.009 .0131182 .0930879 s41 | .0556342 .0174268 3.19 0.001 .0214436 .0898249 s42 | .0563525 .0220851 2.55 0.011 .0130226 .0996824 s43 | .0534292 .0185645 2.88 0.004 .0170065 .089852 s44 | .036032 .0132222 2.73 0.007 .0100907 .0619734 s45 | .0337531 .0109629 3.08 0.002 .0122445 .0552618 s46 | .0284367 .0133962 2.12 0.034 .0021539 .0547195 s47 | .0340396 .0165502 2.06 0.040 .0015689 .0665104 s48 | .0397584 .0138734 2.87 0.004 .0125395 .0669773 s49 | .0411514 .0174 2.37 0.018 .0070135 .0752893 s50 | .0691729 .024209 2.86 0.004 .021676 .1166697 s51 | .0323171 .0154378 2.09 0.037 .0020288 .0626054 | lnh | L2D. | 2.754116 1.05937 2.60 0.009 .6756823 4.83255 L3D. | -2.43392 1.194802 -2.04 0.042 -4.778065 -.0897747 L4D. | .2071921 .6562042 0.32 0.752 -1.08025 1.494634 | lnst | L2D. | .4453854 .2068369 2.15 0.031 .0395812 .8511896 L3D. | -.1033024 .1418621 -0.73 0.467 -.3816292 .1750244 L4D. | -.4411583 .1409333 -3.13 0.002 -.7176628 -.1646538 | lninc | L2D. | .2491198 .0992927 2.51 0.012 .0543122 .4439274 L3D. | .4284118 .0807984 5.30 0.000 .2698892 .5869344 L4D. | .4234031 .0987627 4.29 0.000 .2296354 .6171708 | age1h | L2. | -4.255543 1.790588 -2.38 0.018 -7.768592 -.7424948 L3. | 4.494451 2.276746 1.97 0.049 .0275824 8.96132 L4. | -.5401881 1.078401 -0.50 0.617 -2.655959 1.575583 | age3h | L2. | -.4009306 1.704564 -0.24 0.814 -3.745205 2.943344 L3. | .3061055 1.84804 0.17 0.868 -3.319661 3.931872 L4. | -1.18686 1.339262 -0.89 0.376 -3.814428 1.440708 | age1s | L2. | -.6708812 .3687334 -1.82 0.069 -1.394319 .0525565 L3. | .3742811 .2359226 1.59 0.113 -.0885879 .8371501 L4. | .7737695 .2420196 3.20 0.001 .2989384 1.248601 | age3s | L2. | -1.037464 .4137286 -2.51 0.012 -1.84918 -.2257479 L3. | -.5541391 .3540761 -1.57 0.118 -1.24882 .1405415 L4. | .3760478 .4103324 0.92 0.360 -.4290049 1.181101 | povertyh | L2. | -3.209009 1.259428 -2.55 0.011 -5.679947 -.7380714 L3. | 1.334603 .9054611 1.47 0.141 -.441869 3.111075 L4. | 1.208059 .5383902 2.24 0.025 .1517633 2.264355 | povertys | L2. | .3423565 .2163474 1.58 0.114 -.0821069 .7668199 L3. | .9300228 .2088394 4.45 0.000 .5202897 1.339756 L4. | .3015038 .2393654 1.26 0.208 -.1681198 .7711274 | _cons | .1874678 .0582088 3.22 0.001 .0732649 .3016708 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 77.10 Prob > F = 0.0000 R-squared = 0.2470 Adj R-squared = 0.1965 Root MSE = 0.1365 ------------------------------------------------------------------------------ | Robust D.lnst | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .3847534 .2206152 1.74 0.081 -.0480832 .81759 age3ratio | -1.617069 .3740936 -4.32 0.000 -2.351023 -.8831152 poverty | .0402783 .2336799 0.17 0.863 -.4181906 .4987473 s2 | .2899475 .0596818 4.86 0.000 .1728545 .4070404 s3 | .3307764 .0693584 4.77 0.000 .1946984 .4668544 s4 | .2224647 .053535 4.16 0.000 .1174316 .3274978 s5 | .1249347 .0368953 3.39 0.001 .0525479 .1973216 s6 | .1441242 .0310935 4.64 0.000 .0831203 .2051281 s7 | .2784394 .0508397 5.48 0.000 .1786942 .3781845 s8 | .1202523 .0504321 2.38 0.017 .021307 .2191976 s9 | .1993913 .0463317 4.30 0.000 .1084908 .2902918 s10 | .3781952 .0754793 5.01 0.000 .2301083 .5262821 s11 | .1460096 .0350486 4.17 0.000 .077246 .2147733 s12 | .2292392 .0443067 5.17 0.000 .1423114 .3161669 s13 | .3311742 .0614792 5.39 0.000 .2105549 .4517936 s14 | .2338534 .0480703 4.86 0.000 .1395417 .3281651 s15 | .2150694 .0468432 4.59 0.000 .1231653 .3069734 s16 | .2593821 .048177 5.38 0.000 .1648611 .3539031 s17 | .2405883 .0525625 4.58 0.000 .1374632 .3437133 s18 | .255783 .0553988 4.62 0.000 .1470933 .3644728 s19 | .2104027 .056685 3.71 0.000 .0991893 .3216161 s20 | .2346342 .0497735 4.71 0.000 .136981 .3322875 s21 | .1913088 .0368992 5.18 0.000 .1189144 .2637032 s22 | .3102781 .0565563 5.49 0.000 .1993173 .4212388 s23 | .217388 .0474886 4.58 0.000 .1242177 .3105584 s24 | .1863871 .0441999 4.22 0.000 .099669 .2731052 s25 | .2563769 .0562784 4.56 0.000 .1459614 .3667925 s26 | .2531391 .064156 3.95 0.000 .127268 .3790102 s27 | .2864739 .0601213 4.76 0.000 .1685188 .404429 s28 | .2408464 .0492367 4.89 0.000 .1442464 .3374464 s29 | .2928752 .0582209 5.03 0.000 .1786485 .4071018 s30 | .2744315 .0544195 5.04 0.000 .1676631 .3811999 s31 | .2340146 .0390898 5.99 0.000 .1573223 .3107068 s32 | .2453159 .0493349 4.97 0.000 .1485231 .3421086 s33 | .1948623 .0561922 3.47 0.001 .084616 .3051087 s34 | .2188797 .0389798 5.62 0.000 .1424033 .2953562 s35 | .220758 .054366 4.06 0.000 .1140944 .3274216 s36 | .2649116 .0530545 4.99 0.000 .1608212 .3690021 s37 | .2939822 .0601861 4.88 0.000 .1759 .4120644 s38 | .2598463 .0511862 5.08 0.000 .1594215 .3602712 s39 | .3208693 .0632358 5.07 0.000 .1968035 .444935 s40 | .2996724 .0552217 5.43 0.000 .1913299 .4080148 s41 | .2481806 .0497948 4.98 0.000 .1504857 .3458756 s42 | .306029 .0620601 4.93 0.000 .18427 .4277881 s43 | .258066 .0545879 4.73 0.000 .1509672 .3651649 s44 | .1422047 .0411843 3.45 0.001 .061403 .2230064 s45 | .1056472 .0258053 4.09 0.000 .0550186 .1562759 s46 | .1922216 .0366155 5.25 0.000 .1203838 .2640594 s47 | .2395222 .0457235 5.24 0.000 .1498149 .3292295 s48 | .2035093 .0397266 5.12 0.000 .1255677 .281451 s49 | .2666429 .0492348 5.42 0.000 .1700465 .3632393 s50 | .3552587 .0733722 4.84 0.000 .211306 .4992114 s51 | .2218965 .0436009 5.09 0.000 .1363535 .3074394 | lnh | L2D. | -.1035151 1.650449 -0.06 0.950 -3.341618 3.134588 L3D. | -4.35648 2.30596 -1.89 0.059 -8.880664 .1677052 L4D. | 5.175969 1.419876 3.65 0.000 2.390239 7.961698 | lnst | L2D. | 2.753968 .6719764 4.10 0.000 1.435582 4.072354 L3D. | -3.272773 .6143202 -5.33 0.000 -4.47804 -2.067505 L4D. | .624171 .4616976 1.35 0.177 -.2816578 1.53 | lninc | L2D. | -.6822157 .3508007 -1.94 0.052 -1.37047 .0060387 L3D. | 1.184675 .2634134 4.50 0.000 .6678707 1.70148 L4D. | -1.475857 .2680121 -5.51 0.000 -2.001684 -.9500301 | age1h | L2. | .4996086 3.013484 0.17 0.868 -5.412704 6.411922 L3. | 12.53303 4.245556 2.95 0.003 4.203447 20.86261 L4. | -10.61522 2.76646 -3.84 0.000 -16.04288 -5.187555 | age3h | L2. | 1.195963 2.529583 0.47 0.636 -3.766959 6.158886 L3. | -1.768233 3.618975 -0.49 0.625 -8.868492 5.332025 L4. | -5.234184 2.040443 -2.57 0.010 -9.237437 -1.230932 | age1s | L2. | -4.261445 1.013713 -4.20 0.000 -6.250303 -2.272587 L3. | 5.160948 .9769561 5.28 0.000 3.244206 7.07769 L4. | -.5489372 .7261921 -0.76 0.450 -1.973692 .8758175 | age3s | L2. | -6.595778 1.646876 -4.01 0.000 -9.826872 -3.364685 L3. | 4.53661 1.378546 3.29 0.001 1.831967 7.241253 L4. | -2.046434 1.044817 -1.96 0.050 -4.096315 .0034478 | povertyh | L2. | .8736819 1.711494 0.51 0.610 -2.484189 4.231553 L3. | -1.634731 3.066114 -0.53 0.594 -7.650303 4.380841 L4. | 2.905435 2.358829 1.23 0.218 -1.722476 7.533345 | povertys | L2. | 3.267209 .8167916 4.00 0.000 1.664703 4.869716 L3. | 1.222896 .6678394 1.83 0.067 -.0873729 2.533166 L4. | -.3559412 .6253314 -0.57 0.569 -1.582812 .8709294 | _cons | .2382111 .1220536 1.95 0.051 -.0012523 .4776745 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 41.62 Prob > F = 0.0000 R-squared = 0.2752 Adj R-squared = 0.2266 Root MSE = 0.0193 ------------------------------------------------------------------------------ | Robust D.lninc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0472165 .0295081 -1.60 0.110 -.10511 .0106769 age3ratio | -.1588466 .088699 -1.79 0.074 -.3328699 .0151766 poverty | -.0972704 .0269945 -3.60 0.000 -.1502324 -.0443084 s2 | .0338364 .0101178 3.34 0.001 .0139858 .053687 s3 | .0376879 .011483 3.28 0.001 .0151588 .060217 s4 | .027525 .0091298 3.01 0.003 .0096128 .0454372 s5 | .0232978 .00612 3.81 0.000 .0112907 .0353049 s6 | .0222246 .0056694 3.92 0.000 .0111016 .0333476 s7 | .0312256 .0099157 3.15 0.002 .0117715 .0506796 s8 | .0439177 .0077382 5.68 0.000 .0287357 .0590997 s9 | .0237812 .0089722 2.65 0.008 .0061781 .0413843 s10 | .0388647 .0137259 2.83 0.005 .0119351 .0657943 s11 | .0219405 .0061283 3.58 0.000 .009917 .0339639 s12 | .025308 .0084795 2.98 0.003 .0086717 .0419443 s13 | .032853 .0115473 2.85 0.005 .0101978 .0555083 s14 | .0277231 .0085717 3.23 0.001 .0109058 .0445403 s15 | .0267721 .0083707 3.20 0.001 .0103491 .0431951 s16 | .0260493 .0089471 2.91 0.004 .0084954 .0436031 s17 | .0300071 .0097016 3.09 0.002 .010973 .0490412 s18 | .0309833 .0093228 3.32 0.001 .0126924 .0492743 s19 | .0348569 .0087862 3.97 0.000 .0176188 .0520949 s20 | .0320803 .0091977 3.49 0.001 .0140348 .0501257 s21 | .0268751 .0072121 3.73 0.000 .0127254 .0410248 s22 | .0337902 .010529 3.21 0.001 .0131329 .0544476 s23 | .0233341 .0086299 2.70 0.007 .0064025 .0402656 s24 | .0277851 .0083653 3.32 0.001 .0113727 .0441975 s25 | .029628 .0101957 2.91 0.004 .0096246 .0496314 s26 | .0371733 .009966 3.73 0.000 .0176206 .056726 s27 | .0330106 .0103971 3.17 0.002 .012612 .0534092 s28 | .0287871 .0086794 3.32 0.001 .0117586 .0458156 s29 | .0364337 .0106856 3.41 0.001 .0154691 .0573983 s30 | .0319327 .0102524 3.11 0.002 .0118181 .0520473 s31 | .024164 .0081786 2.95 0.003 .008118 .04021 s32 | .0295003 .0094175 3.13 0.002 .0110237 .0479769 s33 | .0313854 .0088225 3.56 0.000 .014076 .0486947 s34 | .0219328 .0074561 2.94 0.003 .0073042 .0365613 s35 | .0315041 .0091779 3.43 0.001 .0134975 .0495108 s36 | .0261397 .0097234 2.69 0.007 .0070629 .0452166 s37 | .0329392 .0103173 3.19 0.001 .0126972 .0531813 s38 | .0280334 .0093293 3.00 0.003 .0097297 .0463371 s39 | .0327908 .0117996 2.78 0.006 .0096406 .055941 s40 | .0322202 .0102597 3.14 0.002 .0120912 .0523493 s41 | .0293731 .0088616 3.31 0.001 .0119869 .0467592 s42 | .0380471 .011241 3.38 0.001 .0159928 .0601014 s43 | .0308869 .0093941 3.29 0.001 .0124562 .0493175 s44 | .026188 .0063688 4.11 0.000 .0136928 .0386832 s45 | .0188412 .0050404 3.74 0.000 .0089522 .0287302 s46 | .0261088 .0071526 3.65 0.000 .0120757 .0401419 s47 | .0304181 .008884 3.42 0.001 .012988 .0478481 s48 | .0271887 .0074332 3.66 0.000 .0126052 .0417722 s49 | .0274168 .0093355 2.94 0.003 .009101 .0457326 s50 | .038277 .0123835 3.09 0.002 .0139812 .0625728 s51 | .0345015 .0081103 4.25 0.000 .0185893 .0504136 | lnh | L2D. | -.3787448 .2461998 -1.54 0.124 -.8617771 .1042875 L3D. | .1959162 .2986271 0.66 0.512 -.3899762 .7818085 L4D. | -.4405226 .2245943 -1.96 0.050 -.881166 .0001209 | lnst | L2D. | .4703859 .0609771 7.71 0.000 .3507517 .5900201 L3D. | -.110365 .0587866 -1.88 0.061 -.2257016 .0049715 L4D. | -.316716 .0784424 -4.04 0.000 -.4706163 -.1628158 | lninc | L2D. | .0399521 .0370697 1.08 0.281 -.0327769 .112681 L3D. | -.0404554 .0217617 -1.86 0.063 -.0831508 .0022399 L4D. | .0086593 .03126 0.28 0.782 -.0526712 .0699899 | age1h | L2. | .5846238 .43548 1.34 0.180 -.2697673 1.439015 L3. | -.6536765 .5807655 -1.13 0.261 -1.793111 .485758 L4. | .9354875 .3703739 2.53 0.012 .2088314 1.662144 | age3h | L2. | 1.330591 .4692305 2.84 0.005 .409983 2.251199 L3. | -.3078061 .4213806 -0.73 0.465 -1.134535 .5189229 L4. | -.0043014 .3532308 -0.01 0.990 -.6973235 .6887207 | age1s | L2. | -.780063 .134186 -5.81 0.000 -1.04333 -.5167964 L3. | .2259321 .1108512 2.04 0.042 .0084473 .4434169 L4. | .514352 .09887 5.20 0.000 .3203737 .7083304 | age3s | L2. | -.8887929 .1594683 -5.57 0.000 -1.201662 -.5759237 L3. | .1951898 .1240532 1.57 0.116 -.0481968 .4385764 L4. | .461853 .2005626 2.30 0.021 .0683586 .8553474 | povertyh | L2. | -.7431833 .262764 -2.83 0.005 -1.258714 -.2276527 L3. | .8786245 .4800291 1.83 0.067 -.0631699 1.820419 L4. | .2221036 .3506079 0.63 0.527 -.4657727 .9099798 | povertys | L2. | .3908171 .1195936 3.27 0.001 .1561802 .625454 L3. | .0866109 .1044414 0.83 0.407 -.1182982 .29152 L4. | -.017806 .0838455 -0.21 0.832 -.182307 .146695 | _cons | .063467 .0226975 2.80 0.005 .0189356 .1079985 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 117.02 Prob > F = 0.0000 R-squared = 0.4848 Adj R-squared = 0.4503 Root MSE = 0.0142 ------------------------------------------------------------------------------ | Robust age1h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0554406 .0320443 -1.73 0.084 -.11831 .0074287 age3ratio | -.0556511 .0519875 -1.07 0.285 -.1576482 .046346 poverty | -.1297246 .0235813 -5.50 0.000 -.17599 -.0834592 s2 | .0166428 .0064054 2.60 0.009 .0040757 .0292099 s3 | .0188906 .0072498 2.61 0.009 .0046668 .0331143 s4 | .0139805 .005871 2.38 0.017 .0024618 .0254991 s5 | .0153701 .003942 3.90 0.000 .0076361 .0231041 s6 | .0051606 .0033793 1.53 0.127 -.0014693 .0117905 s7 | .0048274 .0059589 0.81 0.418 -.0068637 .0165186 s8 | .0283402 .0057104 4.96 0.000 .0171366 .0395437 s9 | .0135721 .0055179 2.46 0.014 .0027463 .0243978 s10 | .0148052 .0084987 1.74 0.082 -.0018689 .0314793 s11 | .0098952 .003855 2.57 0.010 .0023318 .0174585 s12 | .0173941 .0052678 3.30 0.001 .0070589 .0277293 s13 | .0111618 .0068787 1.62 0.105 -.0023339 .0246574 s14 | .013937 .0053088 2.63 0.009 .0035214 .0243527 s15 | .0134818 .0051932 2.60 0.010 .0032929 .0236706 s16 | .0123927 .0053317 2.32 0.020 .0019321 .0228533 s17 | .0091444 .0058434 1.56 0.118 -.0023201 .020609 s18 | .017 .005778 2.94 0.003 .0056638 .0283361 s19 | .01475 .0056632 2.60 0.009 .003639 .025861 s20 | .0086072 .0057508 1.50 0.135 -.0026755 .01989 s21 | .0091722 .0042093 2.18 0.030 .0009137 .0174307 s22 | .0121548 .0063009 1.93 0.054 -.0002072 .0245168 s23 | .0132182 .0051257 2.58 0.010 .0031618 .0232746 s24 | .0048476 .0050459 0.96 0.337 -.0050523 .0147474 s25 | .0116756 .0062161 1.88 0.061 -.0005201 .0238713 s26 | .0195377 .0065059 3.00 0.003 .0067735 .0323019 s27 | .0158644 .0063359 2.50 0.012 .0034336 .0282953 s28 | .0142209 .0054399 2.61 0.009 .003548 .0248937 s29 | .0107206 .0066463 1.61 0.107 -.0023191 .0237603 s30 | .0089443 .0061952 1.44 0.149 -.0032105 .021099 s31 | .0017063 .0046933 0.36 0.716 -.0075018 .0109143 s32 | .0079233 .0057159 1.39 0.166 -.0032911 .0191377 s33 | .0179475 .0056481 3.18 0.002 .0068661 .0290289 s34 | .0059216 .0045192 1.31 0.190 -.0029448 .014788 s35 | .0155303 .0058904 2.64 0.008 .0039737 .0270869 s36 | .0114713 .0057992 1.98 0.048 .0000936 .022849 s37 | .0124296 .0061738 2.01 0.044 .0003169 .0245424 s38 | .0155875 .0056334 2.77 0.006 .0045351 .0266399 s39 | .0136219 .007231 1.88 0.060 -.000565 .0278088 s40 | .0143793 .0064466 2.23 0.026 .0017313 .0270272 s41 | .016151 .00552 2.93 0.003 .0053209 .026981 s42 | .0145682 .0069424 2.10 0.036 .0009474 .0281889 s43 | .0153897 .0058575 2.63 0.009 .0038976 .0268819 s44 | .0105103 .0041682 2.52 0.012 .0023324 .0186881 s45 | .0099958 .0036149 2.77 0.006 .0029036 .017088 s46 | .0080978 .0043 1.88 0.060 -.0003385 .0165341 s47 | .0094398 .0053026 1.78 0.075 -.0009637 .0198433 s48 | .0114541 .0044329 2.58 0.010 .0027569 .0201513 s49 | .010837 .0055448 1.95 0.051 -.0000417 .0217157 s50 | .0186427 .0075722 2.46 0.014 .0037863 .033499 s51 | .0081279 .0049053 1.66 0.098 -.001496 .0177519 | lnh | L2D. | .5928916 .3708316 1.60 0.110 -.1346625 1.320446 L3D. | -.5913126 .4262222 -1.39 0.166 -1.427541 .2449153 L4D. | .0580079 .2244275 0.26 0.796 -.3823083 .4983241 | lnst | L2D. | .1576003 .0706652 2.23 0.026 .0189585 .2962422 L3D. | -.0373118 .0439884 -0.85 0.396 -.1236149 .0489913 L4D. | -.1506248 .0443404 -3.40 0.001 -.2376185 -.0636311 | lninc | L2D. | .0798757 .0323375 2.47 0.014 .016431 .1433204 L3D. | .1331789 .0257479 5.17 0.000 .0826626 .1836951 L4D. | .1245841 .0340197 3.66 0.000 .057839 .1913292 | age1h | L2. | -.5593529 .6395616 -0.87 0.382 -1.814143 .6954367 L3. | .9405254 .7945299 1.18 0.237 -.6183047 2.499356 L4. | -.2587845 .3668227 -0.71 0.481 -.9784732 .4609043 | age3h | L2. | -.0500273 .597226 -0.08 0.933 -1.221757 1.121702 L3. | -.0116714 .6809641 -0.02 0.986 -1.347691 1.324348 L4. | -.241975 .496905 -0.49 0.626 -1.216879 .7329292 | age1s | L2. | -.2587033 .127352 -2.03 0.042 -.508562 -.0088447 L3. | .1194017 .0782664 1.53 0.127 -.0341534 .2729567 L4. | .2424613 .0788909 3.07 0.002 .0876811 .3972415 | age3s | L2. | -.3336809 .1405403 -2.37 0.018 -.6094143 -.0579475 L3. | -.1551073 .1063098 -1.46 0.145 -.363682 .0534675 L4. | .1560451 .1238609 1.26 0.208 -.0869641 .3990542 | povertyh | L2. | -1.064205 .4003888 -2.66 0.008 -1.849749 -.2786611 L3. | .5745526 .3019412 1.90 0.057 -.0178418 1.166947 L4. | .3484534 .1799563 1.94 0.053 -.0046125 .7015192 | povertys | L2. | .1092584 .068906 1.59 0.113 -.0259319 .2444487 L3. | .2863327 .065538 4.37 0.000 .1577503 .4149152 L4. | .1105248 .0777023 1.42 0.155 -.0419235 .262973 | _cons | .042141 .0194082 2.17 0.030 .0040631 .080219 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 150.40 Prob > F = 0.0000 R-squared = 0.5279 Adj R-squared = 0.4962 Root MSE = 0.0132 ------------------------------------------------------------------------------ | Robust age3h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0789497 .0259881 -3.04 0.002 -.129937 -.0279623 age3ratio | -.0861765 .0514194 -1.68 0.094 -.1870589 .0147059 poverty | -.1161306 .0198288 -5.86 0.000 -.1550338 -.0772273 s2 | .019891 .0062501 3.18 0.001 .0076287 .0321534 s3 | .0235308 .0071298 3.30 0.001 .0095425 .0375191 s4 | .0169711 .0057308 2.96 0.003 .0057275 .0282148 s5 | .0148295 .0037639 3.94 0.000 .007445 .0222141 s6 | .0057967 .0031783 1.82 0.068 -.000439 .0120324 s7 | .00867 .0055958 1.55 0.122 -.0023086 .0196487 s8 | .0274447 .0055416 4.95 0.000 .0165723 .0383171 s9 | .016562 .0052713 3.14 0.002 .00622 .0269039 s10 | .0211982 .0082695 2.56 0.010 .0049738 .0374226 s11 | .0094114 .0036492 2.58 0.010 .002252 .0165709 s12 | .0192085 .0049539 3.88 0.000 .0094891 .0289279 s13 | .0164589 .0066195 2.49 0.013 .0034718 .0294461 s14 | .0163472 .0051156 3.20 0.001 .0063106 .0263838 s15 | .0153438 .0049738 3.08 0.002 .0055854 .0251021 s16 | .0149822 .005086 2.95 0.003 .0050036 .0249607 s17 | .0126776 .0056107 2.26 0.024 .0016697 .0236855 s18 | .0195283 .0055987 3.49 0.001 .008544 .0305126 s19 | .0169891 .0055507 3.06 0.002 .0060988 .0278794 s20 | .0115356 .0054547 2.11 0.035 .0008337 .0222375 s21 | .0102038 .0039468 2.59 0.010 .0024603 .0179472 s22 | .0162933 .0059759 2.73 0.006 .0045688 .0280178 s23 | .014441 .0048437 2.98 0.003 .0049378 .0239442 s24 | .0072301 .0047804 1.51 0.131 -.0021489 .016609 s25 | .015439 .0059875 2.58 0.010 .0036918 .0271862 s26 | .0218826 .0064162 3.41 0.001 .0092943 .034471 s27 | .0207197 .006136 3.38 0.001 .0086812 .0327583 s28 | .0161877 .0052349 3.09 0.002 .0059171 .0264583 s29 | .0161104 .0064183 2.51 0.012 .003518 .0287027 s30 | .0130067 .0059427 2.19 0.029 .0013474 .0246659 s31 | .0037736 .0043118 0.88 0.382 -.0046859 .0122332 s32 | .0113181 .0053901 2.10 0.036 .000743 .0218932 s33 | .0193616 .0055407 3.49 0.000 .008491 .0302321 s34 | .0079441 .0043122 1.84 0.066 -.0005162 .0164044 s35 | .0182352 .0056846 3.21 0.001 .0070823 .0293882 s36 | .0143838 .0055191 2.61 0.009 .0035557 .025212 s37 | .016291 .0060012 2.71 0.007 .0045169 .0280651 s38 | .0190651 .0054335 3.51 0.000 .0084049 .0297253 s39 | .0192456 .00695 2.77 0.006 .00561 .0328812 s40 | .0185019 .0062091 2.98 0.003 .0063199 .0306839 s41 | .0180728 .0053249 3.39 0.001 .0076256 .02852 s42 | .0196887 .0067666 2.91 0.004 .006413 .0329644 s43 | .0178116 .0056665 3.14 0.002 .0066942 .0289291 s44 | .0116822 .0040541 2.88 0.004 .0037282 .0196362 s45 | .009754 .0033395 2.92 0.004 .0032022 .0163059 s46 | .0091432 .0040421 2.26 0.024 .0012128 .0170737 s47 | .0118148 .0049585 2.38 0.017 .0020864 .0215431 s48 | .0125947 .0041898 3.01 0.003 .0043745 .020815 s49 | .0140515 .0052742 2.66 0.008 .0037038 .0243992 s50 | .0240939 .0074055 3.25 0.001 .0095647 .0386231 s51 | .0111006 .0046747 2.37 0.018 .0019291 .0202721 | lnh | L2D. | .7125762 .3126427 2.28 0.023 .0991859 1.325966 L3D. | -.7289065 .351852 -2.07 0.039 -1.419224 -.0385896 L4D. | .1513137 .1907299 0.79 0.428 -.2228894 .5255168 | lnst | L2D. | .1289911 .0638473 2.02 0.044 .0037257 .2542565 L3D. | -.0249888 .0437672 -0.57 0.568 -.1108581 .0608804 L4D. | -.128644 .0442644 -2.91 0.004 -.2154886 -.0417993 | lninc | L2D. | .0761677 .0295272 2.58 0.010 .0182367 .1340987 L3D. | .1285908 .0242472 5.30 0.000 .081019 .1761626 L4D. | .122292 .0282518 4.33 0.000 .0668632 .1777208 | age1h | L2. | -1.462647 .5324493 -2.75 0.006 -2.507287 -.4180064 L3. | 1.504727 .6890008 2.18 0.029 .1529398 2.856514 L4. | -.1458323 .3194911 -0.46 0.648 -.7726587 .4809941 | age3h | L2. | .4889161 .497372 0.98 0.326 -.4869042 1.464736 L3. | -.1016663 .5304175 -0.19 0.848 -1.14232 .9389879 L4. | -.6774956 .3552173 -1.91 0.057 -1.374415 .019424 | age1s | L2. | -.1854926 .1125014 -1.65 0.099 -.4062149 .0352298 L3. | .1028769 .0729218 1.41 0.159 -.0401923 .2459461 L4. | .2429422 .0739448 3.29 0.001 .0978661 .3880184 | age3s | L2. | -.3156568 .1274025 -2.48 0.013 -.5656146 -.0656991 L3. | -.1843404 .1146966 -1.61 0.108 -.4093696 .0406889 L4. | .0865317 .1353636 0.64 0.523 -.1790452 .3521086 | povertyh | L2. | -.9766029 .3836522 -2.55 0.011 -1.72931 -.2238953 L3. | .3894126 .281186 1.38 0.166 -.162261 .9410862 L4. | .3820795 .1603672 2.38 0.017 .0674466 .6967124 | povertys | L2. | .0987158 .0661547 1.49 0.136 -.0310766 .2285081 L3. | .2880388 .0660842 4.36 0.000 .1583847 .4176929 L4. | .0876336 .0724415 1.21 0.227 -.0544931 .2297604 | _cons | .0545602 .0177057 3.08 0.002 .0198224 .0892981 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 87.24 Prob > F = 0.0000 R-squared = 0.2607 Adj R-squared = 0.2111 Root MSE = 0.0413 ------------------------------------------------------------------------------ | Robust age1s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .2465416 .0683126 3.61 0.000 .1125155 .3805678 age3ratio | -.4486629 .1054259 -4.26 0.000 -.6555034 -.2418223 poverty | .0173298 .0695892 0.25 0.803 -.119201 .1538606 s2 | .0870674 .017117 5.09 0.000 .0534846 .1206502 s3 | .0983759 .0198965 4.94 0.000 .05934 .1374118 s4 | .0646715 .0152981 4.23 0.000 .0346573 .0946857 s5 | .0369493 .0105729 3.49 0.000 .0162058 .0576928 s6 | .0442867 .0089519 4.95 0.000 .0267234 .0618499 s7 | .0851509 .0145341 5.86 0.000 .0566358 .1136661 s8 | .0320367 .0145675 2.20 0.028 .0034561 .0606174 s9 | .0588537 .0132199 4.45 0.000 .0329169 .0847906 s10 | .1135771 .0215281 5.28 0.000 .07134 .1558141 s11 | .0446934 .0100632 4.44 0.000 .02495 .0644369 s12 | .0692834 .0125833 5.51 0.000 .0445954 .0939713 s13 | .098758 .0175287 5.63 0.000 .0643676 .1331484 s14 | .0691194 .0137622 5.02 0.000 .0421186 .0961202 s15 | .064403 .0133812 4.81 0.000 .0381497 .0906563 s16 | .0775297 .0137398 5.64 0.000 .0505728 .1044865 s17 | .0713299 .0150015 4.75 0.000 .0418976 .1007621 s18 | .0764981 .0158816 4.82 0.000 .0453391 .1076571 s19 | .0638044 .0163488 3.90 0.000 .0317288 .0958801 s20 | .0725398 .0142587 5.09 0.000 .044565 .1005146 s21 | .0568069 .0105809 5.37 0.000 .0360477 .0775662 s22 | .093779 .0161756 5.80 0.000 .0620432 .1255147 s23 | .0659534 .0135316 4.87 0.000 .039405 .0925018 s24 | .0554223 .0126631 4.38 0.000 .0305778 .0802668 s25 | .0768657 .016072 4.78 0.000 .0453332 .1083983 s26 | .0761848 .0184859 4.12 0.000 .0399163 .1124532 s27 | .0863287 .0172193 5.01 0.000 .0525454 .1201121 s28 | .0729301 .0140997 5.17 0.000 .0452672 .100593 s29 | .0865342 .0165995 5.21 0.000 .0539668 .1191016 s30 | .0819223 .0155286 5.28 0.000 .0514559 .1123888 s31 | .0717205 .0111656 6.42 0.000 .0498141 .093627 s32 | .0750065 .0141161 5.31 0.000 .0473114 .1027016 s33 | .0584345 .0161849 3.61 0.000 .0266804 .0901886 s34 | .0649629 .0110879 5.86 0.000 .043209 .0867167 s35 | .0668293 .0155884 4.29 0.000 .0362457 .097413 s36 | .0798738 .0151323 5.28 0.000 .0501849 .1095627 s37 | .0871129 .0172336 5.05 0.000 .0533015 .1209244 s38 | .0767333 .0146344 5.24 0.000 .0480213 .1054453 s39 | .0964757 .0180482 5.35 0.000 .0610659 .1318855 s40 | .0900072 .0157675 5.71 0.000 .0590721 .1209423 s41 | .0750485 .014235 5.27 0.000 .0471201 .102977 s42 | .0909378 .0177575 5.12 0.000 .0560983 .1257772 s43 | .0777216 .0156548 4.96 0.000 .0470076 .1084357 s44 | .0420633 .011863 3.55 0.000 .0187887 .0653379 s45 | .0304978 .0073097 4.17 0.000 .0161564 .0448391 s46 | .0576553 .0104716 5.51 0.000 .0371104 .0782001 s47 | .0740063 .0130776 5.66 0.000 .0483486 .0996639 s48 | .0604388 .0113661 5.32 0.000 .038139 .0827386 s49 | .0809152 .0140356 5.76 0.000 .053378 .1084524 s50 | .1051954 .0210463 5.00 0.000 .0639036 .1464872 s51 | .0668692 .0124855 5.36 0.000 .0423733 .0913651 | lnh | L2D. | -.1714493 .4838916 -0.35 0.723 -1.120822 .7779232 L3D. | -1.041703 .678923 -1.53 0.125 -2.373718 .290312 L4D. | 1.59253 .4268907 3.73 0.000 .7549908 2.43007 | lnst | L2D. | .9392257 .2166046 4.34 0.000 .5142578 1.364194 L3D. | -.9740962 .1919787 -5.07 0.000 -1.350749 -.5974431 L4D. | .2941921 .1437555 2.05 0.041 .0121506 .5762336 | lninc | L2D. | -.1898682 .1033838 -1.84 0.067 -.3927024 .0129659 L3D. | .319166 .0747754 4.27 0.000 .1724602 .4658717 L4D. | -.4296685 .0777528 -5.53 0.000 -.5822157 -.2771213 | age1h | L2. | .3469948 .8727209 0.40 0.691 -1.365242 2.059232 L3. | 3.317835 1.206811 2.75 0.006 .9501285 5.685541 L4. | -3.305164 .8186782 -4.04 0.000 -4.911372 -1.698956 | age3h | L2. | .6238225 .786801 0.79 0.428 -.919844 2.167489 L3. | -.8898492 1.150199 -0.77 0.439 -3.146484 1.366786 L4. | -1.580623 .6561731 -2.41 0.016 -2.868004 -.2932426 | age1s | L2. | -1.548785 .3319158 -4.67 0.000 -2.199988 -.8975819 L3. | 1.541704 .3253954 4.74 0.000 .9032937 2.180114 L4. | -.4413444 .2233207 -1.98 0.048 -.8794891 -.0031996 | age3s | L2. | -2.093059 .5266721 -3.97 0.000 -3.126365 -1.059754 L3. | 1.360646 .4070495 3.34 0.001 .562034 2.159258 L4. | -.6814437 .3255036 -2.09 0.037 -1.320066 -.042821 | povertyh | L2. | .1935863 .5367537 0.36 0.718 -.8594991 1.246672 L3. | -.4731117 .9673622 -0.49 0.625 -2.371031 1.424807 L4. | .7745134 .7454562 1.04 0.299 -.6880365 2.237063 | povertys | L2. | 1.009121 .2530512 3.99 0.000 .5126466 1.505596 L3. | .327112 .2043184 1.60 0.110 -.0737511 .7279751 L4. | -.0970999 .187235 -0.52 0.604 -.4644461 .2702464 | _cons | .0213283 .0365083 0.58 0.559 -.0502991 .0929558 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 58.97 Prob > F = 0.0000 R-squared = 0.2396 Adj R-squared = 0.1887 Root MSE = 0.0427 ------------------------------------------------------------------------------ | Robust age3s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .1305667 .0678676 1.92 0.055 -.0025864 .2637198 age3ratio | -.41603 .1247627 -3.33 0.001 -.6608085 -.1712514 poverty | .0184056 .0723406 0.25 0.799 -.1235233 .1603344 s2 | .0909344 .0197722 4.60 0.000 .0521423 .1297265 s3 | .1052369 .0229569 4.58 0.000 .0601966 .1502773 s4 | .0707599 .0177581 3.98 0.000 .0359194 .1056004 s5 | .040063 .0121056 3.31 0.001 .0163124 .0638136 s6 | .0463751 .0102431 4.53 0.000 .0262786 .0664715 s7 | .0886449 .0169964 5.22 0.000 .0552987 .121991 s8 | .0391629 .0164581 2.38 0.017 .0068728 .071453 s9 | .0634831 .0154427 4.11 0.000 .0331852 .0937809 s10 | .1191389 .0252402 4.72 0.000 .0696189 .1686589 s11 | .0446662 .0115558 3.87 0.000 .0219943 .067338 s12 | .072152 .0148733 4.85 0.000 .0429713 .1013327 s13 | .105448 .0205177 5.14 0.000 .0651932 .1457028 s14 | .0741192 .0159436 4.65 0.000 .0428387 .1053998 s15 | .0688117 .0155739 4.42 0.000 .0382564 .0993671 s16 | .0820973 .0160594 5.11 0.000 .0505896 .1136051 s17 | .0767059 .0174946 4.38 0.000 .0423824 .1110295 s18 | .080329 .0183292 4.38 0.000 .044368 .1162899 s19 | .0649851 .0185891 3.50 0.000 .0285142 .101456 s20 | .0750528 .0165871 4.52 0.000 .0425098 .1075958 s21 | .0611431 .0121993 5.01 0.000 .0372087 .0850775 s22 | .0975692 .0188763 5.17 0.000 .0605347 .1346036 s23 | .0689425 .01581 4.36 0.000 .037924 .099961 s24 | .0602145 .0146545 4.11 0.000 .0314632 .0889659 s25 | .0813614 .0187366 4.34 0.000 .0446011 .1181216 s26 | .0790483 .0210676 3.75 0.000 .0377147 .1203819 s27 | .0905184 .0199644 4.53 0.000 .0513492 .1296877 s28 | .0757483 .0163481 4.63 0.000 .0436741 .1078225 s29 | .092903 .0194147 4.79 0.000 .0548122 .1309938 s30 | .0874298 .0181561 4.82 0.000 .0518084 .1230512 s31 | .0739437 .0130503 5.67 0.000 .0483397 .0995477 s32 | .0788497 .0164572 4.79 0.000 .0465614 .111138 s33 | .0600626 .018444 3.26 0.001 .0238763 .0962489 s34 | .0692884 .0130459 5.31 0.000 .0436929 .0948838 s35 | .0704714 .0180324 3.91 0.000 .0350927 .1058502 s36 | .0839909 .0176886 4.75 0.000 .0492868 .118695 s37 | .0933171 .019937 4.68 0.000 .0542018 .1324325 s38 | .0825781 .0170136 4.85 0.000 .0491984 .1159579 s39 | .1015323 .021133 4.80 0.000 .0600703 .1429943 s40 | .0957434 .0184906 5.18 0.000 .0594658 .132021 s41 | .0764386 .0165347 4.62 0.000 .0439983 .1088789 s42 | .0968234 .0206401 4.69 0.000 .0563285 .1373184 s43 | .0806807 .0180865 4.46 0.000 .0451958 .1161655 s44 | .0445556 .0135262 3.29 0.001 .0180178 .0710934 s45 | .0331472 .0086146 3.85 0.000 .0162457 .0500488 s46 | .060403 .0121612 4.97 0.000 .0365434 .0842626 s47 | .0754727 .0152645 4.94 0.000 .0455244 .105421 s48 | .0647071 .0131672 4.91 0.000 .0388737 .0905405 s49 | .084907 .0164473 5.16 0.000 .0526382 .1171759 s50 | .1127238 .0242761 4.64 0.000 .0650951 .1603524 s51 | .0701779 .0145005 4.84 0.000 .0417286 .0986272 | lnh | L2D. | .2068584 .5322497 0.39 0.698 -.8373904 1.251107 L3D. | -1.608883 .7008572 -2.30 0.022 -2.983932 -.2338345 L4D. | 1.709519 .4600751 3.72 0.000 .806873 2.612164 | lnst | L2D. | .8443234 .2155197 3.92 0.000 .4214838 1.267163 L3D. | -.8831499 .2003075 -4.41 0.000 -1.276144 -.4901561 L4D. | .2221898 .1347515 1.65 0.099 -.0421862 .4865659 | lninc | L2D. | -.2265801 .1132482 -2.00 0.046 -.4487678 -.0043925 L3D. | .3985877 .0833865 4.78 0.000 .2349874 .5621881 L4D. | -.4468043 .0853642 -5.23 0.000 -.6142847 -.2793238 | age1h | L2. | -.1396679 .9910647 -0.14 0.888 -2.08409 1.804754 L3. | 4.254701 1.390478 3.06 0.002 1.52665 6.982752 L4. | -3.369289 .8910278 -3.78 0.000 -5.117443 -1.621134 | age3h | L2. | -.0955562 .7777919 -0.12 0.902 -1.621547 1.430435 L3. | -.0922104 .9752992 -0.09 0.925 -2.005701 1.821281 L4. | -1.904994 .6856076 -2.78 0.006 -3.250123 -.559864 | age1s | L2. | -1.263516 .3157883 -4.00 0.000 -1.883078 -.6439543 L3. | 1.421335 .2960202 4.80 0.000 .8405578 2.002113 L4. | -.1203934 .2214837 -0.54 0.587 -.554934 .3141472 | age3s | L2. | -2.084358 .5228429 -3.99 0.000 -3.110151 -1.058565 L3. | 1.157371 .4615699 2.51 0.012 .2517925 2.062949 L4. | -.7998249 .3037981 -2.63 0.009 -1.395863 -.2037873 | povertyh | L2. | .3072826 .5322475 0.58 0.564 -.736962 1.351527 L3. | -.5731227 .9408177 -0.61 0.543 -2.418963 1.272717 L4. | .9357589 .7250852 1.29 0.197 -.486824 2.358342 | povertys | L2. | 1.017336 .249027 4.09 0.000 .5287569 1.505915 L3. | .4083065 .2033408 2.01 0.045 .0093614 .8072516 L4. | -.0909158 .1925925 -0.47 0.637 -.4687732 .2869417 | _cons | .040903 .0386626 1.06 0.290 -.0349511 .1167571 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 54.04 Prob > F = 0.0000 R-squared = 0.4765 Adj R-squared = 0.4415 Root MSE = 0.0055 ------------------------------------------------------------------------------ | Robust povertyh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0361434 .0103914 -3.48 0.001 -.0565309 -.0157559 age3ratio | -.0470646 .0189734 -2.48 0.013 -.0842895 -.0098397 poverty | -.0294477 .0106297 -2.77 0.006 -.0503027 -.0085928 s2 | .0093672 .0024749 3.78 0.000 .0045116 .0142229 s3 | .0105851 .0028286 3.74 0.000 .0050355 .0161348 s4 | .0069313 .0022542 3.07 0.002 .0025087 .0113539 s5 | .0065856 .0014951 4.40 0.000 .0036524 .0095189 s6 | .0029368 .0012591 2.33 0.020 .0004666 .0054071 s7 | .003917 .0023034 1.70 0.089 -.0006022 .0084363 s8 | .013934 .0021906 6.36 0.000 .0096361 .0182319 s9 | .0067297 .0020964 3.21 0.001 .0026167 .0108427 s10 | .0090217 .003297 2.74 0.006 .0025531 .0154902 s11 | .0047016 .0014412 3.26 0.001 .001874 .0075291 s12 | .0080204 .0020221 3.97 0.000 .0040532 .0119877 s13 | .0070688 .0026546 2.66 0.008 .0018606 .0122769 s14 | .0073166 .0020137 3.63 0.000 .0033658 .0112675 s15 | .0071801 .0019798 3.63 0.000 .0032958 .0110644 s16 | .0065576 .0020508 3.20 0.001 .002534 .0105811 s17 | .0059495 .0022241 2.68 0.008 .001586 .0103131 s18 | .0091167 .0022373 4.07 0.000 .0047271 .0135063 s19 | .0077155 .0022026 3.50 0.000 .0033941 .012037 s20 | .0051344 .0022145 2.32 0.021 .0007896 .0094792 s21 | .0044851 .0015856 2.83 0.005 .0013743 .0075959 s22 | .0069995 .0024314 2.88 0.004 .0022293 .0117697 s23 | .0066188 .0019725 3.36 0.001 .0027487 .0104888 s24 | .003498 .0018917 1.85 0.065 -.0002135 .0072095 s25 | .0068817 .0023807 2.89 0.004 .002211 .0115525 s26 | .0100782 .0025588 3.94 0.000 .0050579 .0150985 s27 | .0092351 .0024534 3.76 0.000 .0044217 .0140484 s28 | .0076737 .0020724 3.70 0.000 .0036078 .0117396 s29 | .0072331 .0025209 2.87 0.004 .0022871 .012179 s30 | .0060189 .0023621 2.55 0.011 .0013846 .0106532 s31 | .0017235 .0018402 0.94 0.349 -.0018868 .0053338 s32 | .0045745 .0022058 2.07 0.038 .0002467 .0089022 s33 | .0094035 .0022186 4.24 0.000 .0050507 .0137564 s34 | .0039052 .0017243 2.26 0.024 .0005223 .0072881 s35 | .0083246 .0022721 3.66 0.000 .003867 .0127823 s36 | .0065952 .0022338 2.95 0.003 .0022126 .0109779 s37 | .0071549 .0023808 3.01 0.003 .0024838 .011826 s38 | .0083808 .0021551 3.89 0.000 .0041526 .0126091 s39 | .0079104 .0027907 2.83 0.005 .0024352 .0133856 s40 | .0074822 .0025004 2.99 0.003 .0025766 .0123879 s41 | .0086435 .0021093 4.10 0.000 .0045052 .0127818 s42 | .0087003 .002642 3.29 0.001 .0035168 .0138838 s43 | .0084591 .0022507 3.76 0.000 .0040434 .0128749 s44 | .0053541 .0016 3.35 0.001 .002215 .0084932 s45 | .0049366 .0013024 3.79 0.000 .0023815 .0074918 s46 | .0043338 .0016059 2.70 0.007 .001183 .0074846 s47 | .0049268 .002026 2.43 0.015 .0009519 .0089017 s48 | .0056712 .0016724 3.39 0.001 .0023901 .0089524 s49 | .0061474 .0021396 2.87 0.004 .0019496 .0103452 s50 | .0108218 .00294 3.68 0.000 .0050536 .0165899 s51 | .0051283 .0018537 2.77 0.006 .0014914 .0087651 | lnh | L2D. | .2769304 .1375964 2.01 0.044 .0069727 .5468881 L3D. | -.2669798 .1550511 -1.72 0.085 -.5711827 .0372231 L4D. | .0322769 .0775489 0.42 0.677 -.1198704 .1844241 | lnst | L2D. | .0514846 .025352 2.03 0.042 .0017451 .1012241 L3D. | -.0096015 .0181267 -0.53 0.596 -.0451652 .0259622 L4D. | -.0546018 .017371 -3.14 0.002 -.0886829 -.0205208 | lninc | L2D. | .0237293 .0112175 2.12 0.035 .001721 .0457376 L3D. | .041856 .009191 4.55 0.000 .0238236 .0598884 L4D. | .0478308 .0133764 3.58 0.000 .021587 .0740746 | age1h | L2. | -.5305181 .2305143 -2.30 0.022 -.9827763 -.0782598 L3. | .5436764 .3042462 1.79 0.074 -.0532402 1.140593 L4. | -.0276633 .1224971 -0.23 0.821 -.2679969 .2126703 | age3h | L2. | -.0461388 .2264283 -0.20 0.839 -.4903804 .3981028 L3. | .0382063 .2339877 0.16 0.870 -.4208665 .4972791 L4. | -.1270487 .160471 -0.79 0.429 -.4418852 .1877879 | age1s | L2. | -.0805951 .0446138 -1.81 0.071 -.1681254 .0069351 L3. | .0332362 .0320891 1.04 0.301 -.0297211 .0961934 L4. | .0896706 .0269693 3.32 0.001 .0367581 .1425832 | age3s | L2. | -.1020939 .0508754 -2.01 0.045 -.2019089 -.0022788 L3. | -.0660038 .0426281 -1.55 0.122 -.1496382 .0176306 L4. | .0528319 .0491205 1.08 0.282 -.0435401 .1492039 | povertyh | L2. | .0999295 .1733862 0.58 0.564 -.240246 .440105 L3. | -.0342793 .1250116 -0.27 0.784 -.2795461 .2109875 L4. | -.0603432 .0752406 -0.80 0.423 -.2079616 .0872753 | povertys | L2. | -.0012568 .027277 -0.05 0.963 -.0547729 .0522593 L3. | .1226154 .0259987 4.72 0.000 .0716072 .1736236 L4. | .0434767 .0312303 1.39 0.164 -.0177957 .1047491 | _cons | .0242846 .0063284 3.84 0.000 .0118685 .0367007 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 30, 1194) = 57.88 Prob > F = 0.0000 R-squared = 0.2480 Adj R-squared = 0.1976 Root MSE = 0.0180 ------------------------------------------------------------------------------ | Robust povertys | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .0526191 .0253918 2.07 0.038 .0028015 .1024366 age3ratio | -.2050144 .0549233 -3.73 0.000 -.3127713 -.0972575 poverty | .098117 .0295269 3.32 0.001 .0401867 .1560473 s2 | .0374209 .0083909 4.46 0.000 .0209584 .0538835 s3 | .0414148 .0097951 4.23 0.000 .0221973 .0606323 s4 | .0272426 .007589 3.59 0.000 .0123533 .042132 s5 | .0154672 .0051067 3.03 0.003 .0054481 .0254863 s6 | .0185243 .0044722 4.14 0.000 .00975 .0272985 s7 | .0364805 .0076091 4.79 0.000 .0215518 .0514093 s8 | .0122058 .0068003 1.79 0.073 -.0011361 .0255476 s9 | .0277261 .0068374 4.06 0.000 .0143115 .0411407 s10 | .0487053 .0110158 4.42 0.000 .0270929 .0703178 s11 | .0177415 .0048691 3.64 0.000 .0081885 .0272945 s12 | .0296072 .00651 4.55 0.000 .016835 .0423795 s13 | .0428013 .0090768 4.72 0.000 .024993 .0606095 s14 | .0297634 .0069026 4.31 0.000 .0162208 .043306 s15 | .0273807 .0067478 4.06 0.000 .0141418 .0406196 s16 | .0317559 .0070502 4.50 0.000 .0179237 .045588 s17 | .0308687 .007668 4.03 0.000 .0158244 .0459129 s18 | .031215 .0077948 4.00 0.000 .015922 .046508 s19 | .0264341 .0077346 3.42 0.001 .0112592 .041609 s20 | .0304396 .0073304 4.15 0.000 .0160577 .0448214 s21 | .0244913 .0054319 4.51 0.000 .0138341 .0351485 s22 | .0385409 .0082959 4.65 0.000 .0222646 .0548171 s23 | .0275386 .0068389 4.03 0.000 .014121 .0409562 s24 | .0258975 .0064614 4.01 0.000 .0132205 .0385745 s25 | .0328129 .0081606 4.02 0.000 .0168021 .0488238 s26 | .0332343 .0087444 3.80 0.000 .0160782 .0503904 s27 | .0362201 .008588 4.22 0.000 .0193708 .0530695 s28 | .0300449 .0070341 4.27 0.000 .0162444 .0438454 s29 | .0376638 .0084934 4.43 0.000 .0210002 .0543274 s30 | .035752 .0080002 4.47 0.000 .0200559 .051448 s31 | .030666 .0059181 5.18 0.000 .0190549 .0422771 s32 | .0323997 .0073377 4.42 0.000 .0180034 .046796 s33 | .0244564 .0076428 3.20 0.001 .0094617 .0394511 s34 | .0282813 .0057344 4.93 0.000 .0170308 .0395319 s35 | .0274711 .0077137 3.56 0.000 .0123371 .042605 s36 | .0334197 .0077112 4.33 0.000 .0182906 .0485487 s37 | .0367144 .0085998 4.27 0.000 .019842 .0535868 s38 | .0333308 .0074226 4.49 0.000 .018768 .0478937 s39 | .0418148 .0093266 4.48 0.000 .0235165 .0601131 s40 | .0385082 .0081675 4.71 0.000 .0224839 .0545324 s41 | .0303825 .0070628 4.30 0.000 .0165256 .0442394 s42 | .0392936 .0090052 4.36 0.000 .0216259 .0569614 s43 | .0320919 .0077124 4.16 0.000 .0169606 .0472232 s44 | .0173775 .0056494 3.08 0.002 .0062937 .0284613 s45 | .0132251 .0036859 3.59 0.000 .0059937 .0204566 s46 | .0244553 .0053662 4.56 0.000 .013927 .0349836 s47 | .0313787 .0067468 4.65 0.000 .0181419 .0446156 s48 | .0254109 .0057745 4.40 0.000 .0140817 .0367401 s49 | .0343235 .0072716 4.72 0.000 .020057 .04859 s50 | .0458815 .0103924 4.41 0.000 .0254922 .0662708 s51 | .0284117 .0063527 4.47 0.000 .0159479 .0408755 | lnh | L2D. | .0506895 .2051253 0.25 0.805 -.3517567 .4531358 L3D. | -.4866341 .297618 -1.64 0.102 -1.070547 .0972784 L4D. | .6762038 .1842756 3.67 0.000 .3146638 1.037744 | lnst | L2D. | .3328351 .0795606 4.18 0.000 .1767409 .4889293 L3D. | -.3610635 .0870857 -4.15 0.000 -.5319214 -.1902056 L4D. | .1010108 .052734 1.92 0.056 -.0024509 .2044724 | lninc | L2D. | -.0741575 .0443657 -1.67 0.095 -.1612008 .0128859 L3D. | .1389351 .0329864 4.21 0.000 .0742173 .2036528 L4D. | -.1668478 .0335544 -4.97 0.000 -.23268 -.1010155 | age1h | L2. | -.035284 .3855768 -0.09 0.927 -.7917675 .7211995 L3. | 1.504054 .5926861 2.54 0.011 .3412319 2.666876 L4. | -1.388094 .381122 -3.64 0.000 -2.135837 -.6403503 | age3h | L2. | -.0403571 .316537 -0.13 0.899 -.6613877 .5806735 L3. | -.2366338 .4331284 -0.55 0.585 -1.086411 .6131436 L4. | -.6408961 .255475 -2.51 0.012 -1.142126 -.1396662 | age1s | L2. | -.5156902 .1124473 -4.59 0.000 -.7363065 -.2950738 L3. | .5958083 .137945 4.32 0.000 .3251668 .8664499 L4. | -.0884015 .0848464 -1.04 0.298 -.2548661 .0780631 | age3s | L2. | -.7803707 .1981471 -3.94 0.000 -1.169126 -.3916155 L3. | .46967 .1868132 2.51 0.012 .1031513 .8361886 L4. | -.2641308 .1195518 -2.21 0.027 -.4986858 -.0295758 | povertyh | L2. | .333174 .2259165 1.47 0.141 -.1100635 .7764115 L3. | -.4716819 .4311571 -1.09 0.274 -1.317592 .3742279 L4. | .2341155 .3385064 0.69 0.489 -.430018 .898249 | povertys | L2. | .3632693 .1055633 3.44 0.001 .156159 .5703796 L3. | .1573277 .0966018 1.63 0.104 -.0322005 .3468559 L4. | -.1537462 .0798179 -1.93 0.054 -.3103451 .0028527 | _cons | .0167257 .0154529 1.08 0.279 -.0135921 .0470435 ------------------------------------------------------------------------------ Instrumental variables (2SLS) regression Number of obs = 1275 Wald chi2(62) = 752.62 Prob > chi2 = 0.0000 R-squared = 0.3689 Root MSE = .02598 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust D.lncons | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnh | D1. | -.3452801 .4952919 -0.70 0.486 -1.316034 .6254742 | lnst | D1. | .9486691 .2757699 3.44 0.001 .4081701 1.489168 | lninc | D1. | .6358108 .0802059 7.93 0.000 .47861 .7930115 | age1h | .6335717 .7656566 0.83 0.408 -.8670877 2.134231 age3h | 1.044401 1.050339 0.99 0.320 -1.014226 3.103028 age1s | -1.038556 .6070903 -1.71 0.087 -2.228431 .1513197 age3s | -2.278537 .6321969 -3.60 0.000 -3.51762 -1.039453 povertyh | .6312158 1.128196 0.56 0.576 -1.580008 2.842439 povertys | .7664668 .8638223 0.89 0.375 -.9265938 2.459527 age1ratio | .0174814 .078076 0.22 0.823 -.1355448 .1705076 age3ratio | -.2706184 .0840071 -3.22 0.001 -.4352693 -.1059674 poverty | .0958487 .0886672 1.08 0.280 -.0779358 .2696333 s2 | .0400995 .0112243 3.57 0.000 .0181002 .0620988 s3 | .0460656 .0128234 3.59 0.000 .0209323 .071199 s4 | .0436413 .0096398 4.53 0.000 .0247477 .0625349 s5 | .0266605 .0077099 3.46 0.001 .0115494 .0417716 s6 | .0285173 .0053513 5.33 0.000 .0180289 .0390057 s7 | .0489143 .007087 6.90 0.000 .035024 .0628046 s8 | .0116609 .0152539 0.76 0.445 -.0182362 .041558 s9 | .0446093 .0074818 5.96 0.000 .0299454 .0592733 s10 | .0643891 .0114921 5.60 0.000 .041865 .0869131 s11 | .0203804 .0069364 2.94 0.003 .0067854 .0339754 s12 | .0379578 .0071524 5.31 0.000 .0239392 .0519763 s13 | .0581366 .0093049 6.25 0.000 .0398994 .0763738 s14 | .0378903 .0085374 4.44 0.000 .0211572 .0546233 s15 | .0421169 .0083116 5.07 0.000 .0258265 .0584072 s16 | .045423 .0077337 5.87 0.000 .0302652 .0605809 s17 | .0463051 .0086085 5.38 0.000 .0294327 .0631774 s18 | .0389228 .0106083 3.67 0.000 .0181309 .0597147 s19 | .0220145 .0111365 1.98 0.048 .0001873 .0438416 s20 | .0425175 .0074615 5.70 0.000 .0278933 .0571417 s21 | .0316708 .0062462 5.07 0.000 .0194284 .0439133 s22 | .0526849 .0086571 6.09 0.000 .0357172 .0696525 s23 | .0446336 .007819 5.71 0.000 .0293087 .0599585 s24 | .0466209 .0073146 6.37 0.000 .0322847 .0609572 s25 | .0522321 .0090316 5.78 0.000 .0345306 .0699336 s26 | .0291336 .0129793 2.24 0.025 .0036946 .0545725 s27 | .0446893 .0103175 4.33 0.000 .0244674 .0649112 s28 | .0358388 .0089254 4.02 0.000 .0183454 .0533322 s29 | .050309 .0099463 5.06 0.000 .0308147 .0698032 s30 | .0586116 .0086724 6.76 0.000 .041614 .0756091 s31 | .04924 .0054087 9.10 0.000 .0386391 .059841 s32 | .049318 .0069797 7.07 0.000 .0356381 .062998 s33 | .0241295 .011744 2.05 0.040 .0011116 .0471473 s34 | .045112 .0063443 7.11 0.000 .0326774 .0575467 s35 | .0404783 .0099109 4.08 0.000 .0210534 .0599033 s36 | .0517619 .008315 6.23 0.000 .0354647 .068059 s37 | .0426115 .0101701 4.19 0.000 .0226784 .0625446 s38 | .0418395 .0087133 4.80 0.000 .0247619 .0589172 s39 | .0555664 .0092242 6.02 0.000 .0374873 .0736455 s40 | .0504633 .0084172 6.00 0.000 .0339659 .0669606 s41 | .0325829 .0095298 3.42 0.001 .0139048 .051261 s42 | .0496128 .0105166 4.72 0.000 .0290007 .0702248 s43 | .0370117 .0101591 3.64 0.000 .0171002 .0569232 s44 | .0174239 .0083708 2.08 0.037 .0010174 .0338305 s45 | .0313419 .0061976 5.06 0.000 .0191948 .0434889 s46 | .0331607 .0062238 5.33 0.000 .0209623 .0453592 s47 | .0407472 .0066244 6.15 0.000 .0277636 .0537309 s48 | .03135 .0069744 4.49 0.000 .0176803 .0450196 s49 | .0501127 .0073677 6.80 0.000 .0356723 .0645532 s50 | .0508484 .0130992 3.88 0.000 .0251745 .0765224 s51 | .0319491 .0073853 4.33 0.000 .0174742 .046424 _cons | .0151794 .0441594 0.34 0.731 -.0713715 .1017303 ------------------------------------------------------------------------------ Instrumented: D.lnh D.lnst D.lninc age1h age3h age1s age3s povertyh povertys Instruments: age1ratio age3ratio poverty s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39 s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 L2D.lnh L3D.lnh L4D.lnh L2D.lnst L3D.lnst L4D.lnst L2D.lninc L3D.lninc L4D.lninc L2.age1h L3.age1h L4.age1h L2.age3h L3.age3h L4.age3h L2.age1s L3.age1s L4.age1s L2.age3s L3.age3s L4.age3s L2.povertyh L3.povertyh L4.povertyh L2.povertys L3.povertys L4.povertys . sum chratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- chratio | 1275 .3010725 .0938772 .0721587 .7651958 . local ch = r(mean) . sum csratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- csratio | 1275 .2663826 .139835 .0746406 .9381273 . local cs = r(mean) . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . local hw = r(mean) . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . local sw = r(mean) . sum age1r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age1ratio | 1275 .312477 .0413183 .2286251 .4784656 . local age1r = r(mean) . sum age3r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age3ratio | 1275 .3036991 .0331339 .1347572 .3859731 . local age3r = r(mean) . sum poverty if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- poverty | 1275 .1272549 .0382546 .029 .272 . local pov = r(mean) . estadd scalar hwe = ( _b[D.lnh] + _b[age1h] * `age1r' /// > + _b[age3h] * `age3r' + _b[povertyh] * `pov' ) * `ch' . estadd scalar swe = ( _b[D.lnst] + _b[age1s] * `age1r' /// > + _b[age3s] * `age3r' + _b[povertys] * `pov' ) * `cs' . estadd scalar we_diff = e(hwe) - e(swe) . estadd scalar hwelas = e(hwe) * 1 / `ch' . estadd scalar swelas = e(swe) * 1 / `cs' . estadd scalar elas_diff = e(hwelas) - e(swelas) . test ( D.lnh + age1h * `age1r' /// > + age3h * `age3r' + povertyh * `pov') * `ch' = 0 ( 1) .3010725*D.lnh + .0940782*age1h + .0914355*age3h + .038313*povertyh = 0 chi2( 1) = 51.76 Prob > chi2 = 0.0000 . estadd scalar hwe_p = r(p) . test ( D.lnst + age1s * `age1r' /// > + age3s * `age3r' + povertys * `pov') * `cs' = 0 ( 1) .2663826*D.lnst + .0832384*age1s + .0809002*age3s + .0338985*povertys = 0 chi2( 1) = 3.19 Prob > chi2 = 0.0739 . estadd scalar swe_p = r(p) . test ( D.lnh + age1h * `age1r' /// > + age3h * `age3r' + povertyh * `pov') * `ch' = /// > ( D.lnst + age1s * `age1r' /// > + age3s * `age3r' + povertys * `pov') * `cs' ( 1) .3010725*D.lnh - .2663826*D.lnst + .0940782*age1h + .0914355*age3h - .0832384*age1s - .0809002*age3s + .038313*povertyh - .0338985*povertys = 0 chi2( 1) = 28.41 Prob > chi2 = 0.0000 . estadd scalar we_diff_p = r(p) . test ( D.lnh + age1h * `age1r' /// > + age3h * `age3r' + povertyh * `pov') = 0 ( 1) D.lnh + .312477*age1h + .3036991*age3h + .1272549*povertyh = 0 chi2( 1) = 51.76 Prob > chi2 = 0.0000 . estadd scalar hwelas_p = r(p) . test ( D.lnst + age1s * `age1r' /// > + age3s * `age3r' + povertys * `pov') = 0 ( 1) D.lnst + .312477*age1s + .3036991*age3s + .1272549*povertys = 0 chi2( 1) = 3.19 Prob > chi2 = 0.0739 . estadd scalar swelas_p = r(p) . test ( D.lnh + age1h * `age1r' /// > + age3h * `age3r' + povertyh * `pov') = /// > ( D.lnst + age1s * `age1r' /// > + age3s * `age3r' + povertys * `pov') ( 1) D.lnh - D.lnst + .312477*age1h + .3036991*age3h - .312477*age1s - .3036991*age3s + .1272549*povertyh - .1272549*povertys = 0 chi2( 1) = 26.02 Prob > chi2 = 0.0000 . estadd scalar elas_diff_p = r(p) . estadd scalar deriv_hy = `ch' * ( _b[age1h] ) . estadd scalar deriv_ho = `ch' * ( _b[age3h] ) . estadd scalar deriv_hp = `ch' * ( _b[povertyh] ) . estadd scalar deriv_sy = `cs' * ( _b[age1s] ) . estadd scalar deriv_so = `cs' * ( _b[age3s] ) . estadd scalar deriv_sp = `cs' * ( _b[povertys] ) . est store iv3 . . gen hwe3 = ( _b[D.lnh] + _b[age1h] * age1r /// > + _b[age3h] * age3r +_b[povertyh] * poverty) * cons_real / h_real if e(sample) (2907 missing values generated) . gen swe3 = ( _b[D.lnst] + _b[age1s] * age1r /// > + _b[age3s] * age3r +_b[povertys] * poverty ) * cons_real / st_real if e(sample) (2907 missing values generated) . . ** Model 4 - Age demographics and wealth ratios but no poverty rate . ivregress 2sls d.lncons age1r age3r s2-s51 /// urate > (d.lnh d.lnst d.lnw d.lninc age1h age3h age1w age3w age1s age3s /// > = dl(2/4).lnh dl(2/4).lnst dl(2/4).lnw dl(2/4).lninc /// > l(2/4).age1h l(2/4).age3h l(2/4).age1w l(2/4).age3w /// > l(2/4).age1s l(2/4).age3s ) , cluster(state) first First-stage regressions ----------------------- Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 146.31 Prob > F = 0.0000 R-squared = 0.4982 Adj R-squared = 0.4637 Root MSE = 0.0443 ------------------------------------------------------------------------------ | Robust D.lnh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.3184232 .0846877 -3.76 0.000 -.4845766 -.1522697 age3ratio | -.3700626 .1635418 -2.26 0.024 -.6909246 -.0492007 s2 | .0441555 .0190266 2.32 0.020 .0068262 .0814849 s3 | .0496956 .0219965 2.26 0.024 .0065395 .0928518 s4 | .0384085 .017665 2.17 0.030 .0037506 .0730665 s5 | .0383452 .0115198 3.33 0.001 .0157439 .0609466 s6 | .0167144 .0104409 1.60 0.110 -.0037703 .0371991 s7 | .0291136 .018664 1.56 0.119 -.0075045 .0657316 s8 | .0602104 .0158775 3.79 0.000 .0290594 .0913614 s9 | .0554583 .0168867 3.28 0.001 .0223272 .0885893 s10 | .0537135 .0261036 2.06 0.040 .0024994 .1049277 s11 | .0199808 .0112798 1.77 0.077 -.0021497 .0421113 s12 | .0607507 .0164633 3.69 0.000 .0284503 .093051 s13 | .0463589 .0214594 2.16 0.031 .0042566 .0884612 s14 | .0433616 .0162613 2.67 0.008 .0114576 .0752657 s15 | .040624 .0159677 2.54 0.011 .009296 .071952 s16 | .0417998 .0167357 2.50 0.013 .008965 .0746345 s17 | .0331111 .0179581 1.84 0.065 -.002122 .0683441 s18 | .0429358 .0173959 2.47 0.014 .0088058 .0770658 s19 | .0314091 .016372 1.92 0.055 -.0007121 .0635303 s20 | .0324258 .017836 1.82 0.069 -.0025676 .0674193 s21 | .0343047 .0130343 2.63 0.009 .0087319 .0598774 s22 | .0415115 .019593 2.12 0.034 .0030709 .0799522 s23 | .0382797 .0156492 2.45 0.015 .0075767 .0689828 s24 | .0225575 .0151706 1.49 0.137 -.0072066 .0523217 s25 | .038725 .0190782 2.03 0.043 .0012944 .0761555 s26 | .0424079 .0183233 2.31 0.021 .0064584 .0783573 s27 | .0463285 .0193211 2.40 0.017 .0084213 .0842358 s28 | .0392258 .0166884 2.35 0.019 .0064838 .0719677 s29 | .0407672 .0202385 2.01 0.044 .0010601 .0804742 s30 | .0362647 .0190541 1.90 0.057 -.0011186 .0736481 s31 | .018858 .0144166 1.31 0.191 -.0094266 .0471427 s32 | .0362134 .0178315 2.03 0.042 .0012288 .071198 s33 | .0377952 .0157109 2.41 0.016 .0069711 .0686193 s34 | .0257085 .014203 1.81 0.071 -.0021573 .0535742 s35 | .0394847 .017814 2.22 0.027 .0045344 .0744351 s36 | .0378736 .0180503 2.10 0.036 .0024597 .0732874 s37 | .0377032 .0192271 1.96 0.050 -.0000195 .0754259 s38 | .0526799 .0173894 3.03 0.003 .0185625 .0867972 s39 | .0537091 .0225115 2.39 0.017 .0095425 .0978758 s40 | .0531005 .020053 2.65 0.008 .0137573 .0924437 s41 | .0438828 .016607 2.64 0.008 .0113006 .076465 s42 | .0490123 .0212177 2.31 0.021 .0073842 .0906404 s43 | .0397284 .0176187 2.25 0.024 .0051612 .0742956 s44 | .0195734 .0119954 1.63 0.103 -.0039609 .0431078 s45 | .0382683 .0113401 3.37 0.001 .0160195 .060517 s46 | .0294378 .0132644 2.22 0.027 .0034137 .0554618 s47 | .0364208 .0164052 2.22 0.027 .0042346 .0686071 s48 | .0377554 .0138442 2.73 0.006 .0105936 .0649172 s49 | .0424268 .0173856 2.44 0.015 .008317 .0765366 s50 | .0530472 .0230343 2.30 0.021 .0078548 .0982396 s51 | .030301 .0152208 1.99 0.047 .0004384 .0601635 | lnh | L2D. | 3.927245 1.39543 2.81 0.005 1.189473 6.665017 L3D. | -3.400698 1.541574 -2.21 0.028 -6.425198 -.3761971 L4D. | 1.390301 .897913 1.55 0.122 -.371365 3.151967 | lnst | L2D. | 1.280101 1.141714 1.12 0.262 -.9598905 3.520093 L3D. | -1.384855 .8218853 -1.68 0.092 -2.997358 .2276478 L4D. | .8327173 .6118112 1.36 0.174 -.3676294 2.033064 | lnw | L2D. | -1.953187 1.911524 -1.02 0.307 -5.703513 1.797138 L3D. | 2.137131 1.454574 1.47 0.142 -.7166804 4.990942 L4D. | -2.299623 1.06818 -2.15 0.032 -4.395346 -.2038999 | lninc | L2D. | .289779 .1008724 2.87 0.004 .0918717 .4876863 L3D. | .4440165 .0873424 5.08 0.000 .2726546 .6153784 L4D. | .3858325 .1036759 3.72 0.000 .1824249 .5892401 | age1h | L2. | -8.299242 2.138445 -3.88 0.000 -12.49478 -4.103708 L3. | 6.713167 2.671202 2.51 0.012 1.472386 11.95395 L4. | -2.039757 1.446694 -1.41 0.159 -4.878107 .7985941 | age3h | L2. | -1.509696 2.692338 -0.56 0.575 -6.791946 3.772554 L3. | 1.291532 2.813491 0.46 0.646 -4.228414 6.811478 L4. | -2.737487 1.701531 -1.61 0.108 -6.075817 .6008425 | age1w | L2. | 6.045048 2.891673 2.09 0.037 .3717116 11.71838 L3. | -4.437538 2.179063 -2.04 0.042 -8.712765 -.1623116 L4. | 3.327658 1.710749 1.95 0.052 -.0287558 6.684073 | age3w | L2. | .8876716 3.597514 0.25 0.805 -6.170494 7.945837 L3. | -1.673451 2.882491 -0.58 0.562 -7.328772 3.981871 L4. | 3.606527 1.848337 1.95 0.051 -.0198296 7.232884 | age1s | L2. | -3.29721 1.709762 -1.93 0.054 -6.651689 .057269 L3. | 3.119572 1.270184 2.46 0.014 .6275267 5.611618 L4. | -1.11068 .9581802 -1.16 0.247 -2.990587 .7692276 | age3s | L2. | -1.276341 2.141445 -0.60 0.551 -5.477763 2.925081 L3. | .7932026 1.640835 0.48 0.629 -2.426044 4.012449 L4. | -1.419072 1.14644 -1.24 0.216 -3.668337 .8301925 | _cons | .1831789 .0568388 3.22 0.001 .0716636 .2946942 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 94.23 Prob > F = 0.0000 R-squared = 0.2669 Adj R-squared = 0.2164 Root MSE = 0.1348 ------------------------------------------------------------------------------ | Robust D.lnst | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .5549695 .2249703 2.47 0.014 .1135877 .9963513 age3ratio | -1.634837 .3521688 -4.64 0.000 -2.325777 -.9438975 s2 | .3055685 .0461534 6.62 0.000 .2150175 .3961194 s3 | .3428169 .0539626 6.35 0.000 .2369446 .4486893 s4 | .2360232 .0431768 5.47 0.000 .1513122 .3207343 s5 | .1444738 .0294764 4.90 0.000 .0866423 .2023052 s6 | .1452686 .0276158 5.26 0.000 .0910877 .1994495 s7 | .2659114 .0468908 5.67 0.000 .1739137 .3579091 s8 | .1479575 .0353604 4.18 0.000 .0785819 .217333 s9 | .2007856 .0414121 4.85 0.000 .1195369 .2820344 s10 | .3862343 .0644091 6.00 0.000 .2598665 .5126021 s11 | .1592736 .0272332 5.85 0.000 .1058433 .2127039 s12 | .2277675 .0401226 5.68 0.000 .1490487 .3064864 s13 | .3320433 .0549304 6.04 0.000 .2242723 .4398143 s14 | .2389689 .0402492 5.94 0.000 .1600016 .3179361 s15 | .220744 .0400119 5.52 0.000 .1422423 .2992456 s16 | .2557225 .0421753 6.06 0.000 .1729764 .3384686 s17 | .2460546 .0459471 5.36 0.000 .1559085 .3362007 s18 | .2701333 .0432351 6.25 0.000 .1853079 .3549586 s19 | .246763 .0405604 6.08 0.000 .1671852 .3263407 s20 | .231446 .0436282 5.30 0.000 .1458493 .3170426 s21 | .183252 .0331651 5.53 0.000 .1181836 .2483204 s22 | .3041657 .0485504 6.26 0.000 .2089119 .3994195 s23 | .2272744 .0404946 5.61 0.000 .1478257 .3067231 s24 | .1868889 .0386343 4.84 0.000 .1110901 .2626876 s25 | .2634202 .0479141 5.50 0.000 .1694148 .3574256 s26 | .2893207 .0444454 6.51 0.000 .2021207 .3765206 s27 | .3006575 .0495053 6.07 0.000 .2035303 .3977847 s28 | .245828 .0406807 6.04 0.000 .1660142 .3256418 s29 | .2983633 .0500437 5.96 0.000 .2001798 .3965468 s30 | .2783976 .0483942 5.75 0.000 .1834502 .3733449 s31 | .2157863 .0362652 5.95 0.000 .1446356 .2869371 s32 | .2427471 .0452722 5.36 0.000 .1539251 .3315692 s33 | .2313311 .0388981 5.95 0.000 .1550147 .3076475 s34 | .2102351 .0351831 5.98 0.000 .1412075 .2792628 s35 | .236272 .0438181 5.39 0.000 .1503028 .3222411 s36 | .2709127 .0461924 5.86 0.000 .1802852 .3615401 s37 | .3041788 .0489682 6.21 0.000 .2081054 .4002522 s38 | .2632755 .0441957 5.96 0.000 .1765655 .3499854 s39 | .3214129 .0555267 5.79 0.000 .212472 .4303538 s40 | .2914609 .0480534 6.07 0.000 .1971822 .3857396 s41 | .2526897 .0397991 6.35 0.000 .1746057 .3307738 s42 | .3125906 .052407 5.96 0.000 .2097703 .4154109 s43 | .2682444 .0430029 6.24 0.000 .1838747 .3526142 s44 | .169054 .0294828 5.73 0.000 .11121 .2268979 s45 | .1063475 .0251674 4.23 0.000 .0569702 .1557248 s46 | .1847848 .0323502 5.71 0.000 .1213151 .2482544 s47 | .2390403 .041245 5.80 0.000 .1581194 .3199613 s48 | .1990372 .0349458 5.70 0.000 .130475 .2675993 s49 | .2616274 .0443971 5.89 0.000 .1745223 .3487325 s50 | .3666891 .0573497 6.39 0.000 .2541715 .4792068 s51 | .225255 .0386906 5.82 0.000 .1493457 .3011644 | lnh | L2D. | 2.875105 3.121213 0.92 0.357 -3.248578 8.998787 L3D. | 1.815437 3.024478 0.60 0.548 -4.118455 7.749329 L4D. | 6.502044 2.49649 2.60 0.009 1.604039 11.40005 | lnst | L2D. | 5.378821 2.68991 2.00 0.046 .1013358 10.65631 L3D. | 5.083651 2.666497 1.91 0.057 -.1478994 10.3152 L4D. | 1.960243 2.008569 0.98 0.329 -1.980482 5.900967 | lnw | L2D. | -5.365478 4.902414 -1.09 0.274 -14.9838 4.252844 L3D. | -13.10673 4.610795 -2.84 0.005 -22.15291 -4.060556 L4D. | -3.046079 3.793056 -0.80 0.422 -10.48789 4.395731 | lninc | L2D. | -.6931364 .3406673 -2.03 0.042 -1.361511 -.024762 L3D. | .9619396 .2717738 3.54 0.000 .4287313 1.495148 L4D. | -1.325531 .2751371 -4.82 0.000 -1.865338 -.7857242 | age1h | L2. | -6.352748 5.047979 -1.26 0.208 -16.25666 3.551166 L3. | 2.304417 5.426537 0.42 0.671 -8.34221 12.95104 L4. | -14.2346 5.036176 -2.83 0.005 -24.11536 -4.353848 | age3h | L2. | -1.062253 5.530115 -0.19 0.848 -11.9121 9.787589 L3. | -7.907917 4.94425 -1.60 0.110 -17.60832 1.792485 L4. | -5.157902 3.588152 -1.44 0.151 -12.1977 1.881895 | age1w | L2. | 13.11389 8.371269 1.57 0.117 -3.31017 29.53796 L3. | 20.64588 7.670862 2.69 0.007 5.595986 35.69577 L4. | 8.552748 7.090001 1.21 0.228 -5.357523 22.46302 | age3w | L2. | 3.548544 8.453414 0.42 0.675 -13.03668 20.13377 L3. | 13.73632 8.10585 1.69 0.090 -2.166997 29.63965 L4. | 1.270324 6.491471 0.20 0.845 -11.46566 14.00631 | age1s | L2. | -10.38007 4.682582 -2.22 0.027 -19.56709 -1.193055 L3. | -9.083093 4.381658 -2.07 0.038 -17.67971 -.4864727 L4. | -4.495982 3.732198 -1.20 0.229 -11.81839 2.826427 | age3s | L2. | -7.053351 4.553049 -1.55 0.122 -15.98623 1.879532 L3. | -3.163415 4.626782 -0.68 0.494 -12.24096 5.914129 L4. | -2.372595 3.317777 -0.72 0.475 -8.881927 4.136738 | _cons | .1826853 .1132637 1.61 0.107 -.0395332 .4049038 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 71.21 Prob > F = 0.0000 R-squared = 0.2816 Adj R-squared = 0.2322 Root MSE = 0.0824 ------------------------------------------------------------------------------ | Robust D.lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .1083062 .1505626 0.72 0.472 -.187091 .4037034 age3ratio | -1.012029 .2575855 -3.93 0.000 -1.5174 -.5066571 s2 | .1784591 .0319521 5.59 0.000 .1157704 .2411478 s3 | .2022698 .0371337 5.45 0.000 .1294151 .2751244 s4 | .1439464 .0300914 4.78 0.000 .0849085 .2029844 s5 | .0955097 .0205606 4.65 0.000 .0551706 .1358488 s6 | .0865633 .0190974 4.53 0.000 .049095 .1240316 s7 | .1535629 .0324579 4.73 0.000 .089882 .2172438 s8 | .1102434 .0247217 4.46 0.000 .0617405 .1587462 s9 | .1343488 .028959 4.64 0.000 .0775326 .1911651 s10 | .229144 .0448957 5.10 0.000 .1410606 .3172274 s11 | .0928103 .0189904 4.89 0.000 .0555521 .1300685 s12 | .1478672 .0282684 5.23 0.000 .0924058 .2033286 s13 | .2011018 .0380814 5.28 0.000 .1263877 .275816 s14 | .1478629 .0280057 5.28 0.000 .0929169 .2028089 s15 | .1377599 .0277853 4.96 0.000 .0832464 .1922735 s16 | .1567448 .02929 5.35 0.000 .0992791 .2142106 s17 | .149104 .0318454 4.68 0.000 .0866247 .2115833 s18 | .1625918 .0299897 5.42 0.000 .1037534 .2214302 s19 | .1424155 .028443 5.01 0.000 .0866115 .1982195 s20 | .1379784 .0303528 4.55 0.000 .0784275 .1975292 s21 | .1150803 .0228859 5.03 0.000 .0701793 .1599814 s22 | .1790491 .033605 5.33 0.000 .1131175 .2449806 s23 | .1409758 .028155 5.01 0.000 .0857369 .1962148 s24 | .1110818 .0265699 4.18 0.000 .0589529 .1632107 s25 | .1591081 .0332512 4.79 0.000 .0938707 .2243455 s26 | .1696268 .0308698 5.49 0.000 .1090617 .230192 s27 | .1813683 .0343678 5.28 0.000 .1139403 .2487964 s28 | .1472162 .0282995 5.20 0.000 .0916937 .2027386 s29 | .1799795 .0347781 5.18 0.000 .1117463 .2482126 s30 | .1681581 .0335464 5.01 0.000 .1023416 .2339746 s31 | .1230422 .0250171 4.92 0.000 .0739596 .1721247 s32 | .1471893 .0314576 4.68 0.000 .0854708 .2089078 s33 | .1386517 .0270898 5.12 0.000 .0855027 .1918006 s34 | .1212174 .0245042 4.95 0.000 .0731412 .1692936 s35 | .1446439 .0304438 4.75 0.000 .0849146 .2043733 s36 | .1635604 .0320616 5.10 0.000 .1006569 .2264639 s37 | .1791129 .033896 5.28 0.000 .1126105 .2456152 s38 | .1660238 .0306724 5.41 0.000 .1058458 .2262018 s39 | .1956069 .0386047 5.07 0.000 .1198661 .2713476 s40 | .1789664 .0333495 5.37 0.000 .1135361 .2443967 s41 | .1498197 .0277389 5.40 0.000 .0953972 .2042421 s42 | .1906058 .0363479 5.24 0.000 .1192928 .2619188 s43 | .1579362 .029775 5.30 0.000 .099519 .2163534 s44 | .1012697 .0207139 4.89 0.000 .0606301 .1419094 s45 | .0770483 .0182413 4.22 0.000 .0412597 .1128369 s46 | .1112466 .0224677 4.95 0.000 .067166 .1553272 s47 | .1441633 .0286508 5.03 0.000 .0879516 .2003751 s48 | .1241931 .0242527 5.12 0.000 .0766104 .1717758 s49 | .1603187 .0308223 5.20 0.000 .0998467 .2207907 s50 | .2152132 .039305 5.48 0.000 .1380985 .2923278 s51 | .1353881 .0269939 5.02 0.000 .0824273 .1883488 | lnh | L2D. | 3.808011 1.90386 2.00 0.046 .0727208 7.543301 L3D. | 1.596389 1.807823 0.88 0.377 -1.950481 5.143259 L4D. | 3.83502 1.668807 2.30 0.022 .5608933 7.109146 | lnst | L2D. | 4.453946 1.937721 2.30 0.022 .6522214 8.25567 L3D. | 4.435279 1.673282 2.65 0.008 1.152373 7.718184 L4D. | 1.214679 1.293196 0.94 0.348 -1.322515 3.751873 | lnw | L2D. | -5.556433 3.427999 -1.62 0.105 -12.28202 1.169151 L3D. | -10.08191 2.895397 -3.48 0.001 -15.76255 -4.401267 L4D. | -2.447209 2.457557 -1.00 0.320 -7.268828 2.374409 | lninc | L2D. | -.2444701 .1989125 -1.23 0.219 -.6347278 .1457876 L3D. | .7035664 .1490198 4.72 0.000 .4111962 .9959367 L4D. | -.5805333 .1188239 -4.89 0.000 -.8136606 -.347406 | age1h | L2. | -8.101157 2.969188 -2.73 0.006 -13.92657 -2.275742 L3. | .4620805 3.397988 0.14 0.892 -6.204623 7.128784 L4. | -8.094149 3.245523 -2.49 0.013 -14.46172 -1.726576 | age3h | L2. | -1.701032 3.456914 -0.49 0.623 -8.483347 5.081282 L3. | -6.650978 3.083163 -2.16 0.031 -12.70001 -.601948 L4. | -3.494004 2.250238 -1.55 0.121 -7.908872 .920865 | age1w | L2. | 13.6405 5.72603 2.38 0.017 2.406276 24.87471 L3. | 15.86325 4.993824 3.18 0.002 6.065589 25.66092 L4. | 5.739635 4.511388 1.27 0.204 -3.111511 14.59078 | age3w | L2. | 3.84751 5.78848 0.66 0.506 -7.509233 15.20425 L3. | 12.49614 4.750816 2.63 0.009 3.175245 21.81703 L4. | 1.652779 4.098957 0.40 0.687 -6.389196 9.694753 | age1s | L2. | -9.117317 3.276068 -2.78 0.005 -15.54482 -2.689815 L3. | -7.343434 2.80385 -2.62 0.009 -12.84446 -1.842403 L4. | -2.51754 2.385872 -1.06 0.292 -7.198516 2.163437 | age3s | L2. | -5.245563 3.210655 -1.63 0.103 -11.54473 1.053602 L3. | -4.86089 2.804598 -1.73 0.083 -10.36339 .6416076 L4. | -1.465534 2.096658 -0.70 0.485 -5.579084 2.648017 | _cons | .1853997 .0808496 2.29 0.022 .0267764 .3440231 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 145.78 Prob > F = 0.0000 R-squared = 0.2841 Adj R-squared = 0.2348 Root MSE = 0.0192 ------------------------------------------------------------------------------ | Robust D.lninc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.1033858 .028463 -3.63 0.000 -.1592289 -.0475426 age3ratio | -.2201758 .0866353 -2.54 0.011 -.3901504 -.0502012 s2 | .0257215 .0095524 2.69 0.007 .0069802 .0444628 s3 | .027569 .0108647 2.54 0.011 .0062529 .0488851 s4 | .0217496 .0086593 2.51 0.012 .0047604 .0387388 s5 | .0187459 .0058311 3.21 0.001 .0073055 .0301863 s6 | .0197671 .0052892 3.74 0.000 .0093899 .0301443 s7 | .0281397 .0093095 3.02 0.003 .0098749 .0464045 s8 | .0376826 .0067837 5.55 0.000 .0243733 .0509919 s9 | .0219376 .0082397 2.66 0.008 .0057718 .0381035 s10 | .0327477 .0131241 2.50 0.013 .0069988 .0584966 s11 | .0180671 .0057371 3.15 0.002 .0068111 .029323 s12 | .0221035 .0082172 2.69 0.007 .0059817 .0382252 s13 | .03004 .0110137 2.73 0.006 .0084316 .0516483 s14 | .0224758 .0081197 2.77 0.006 .0065452 .0384064 s15 | .0222548 .0079247 2.81 0.005 .0067068 .0378028 s16 | .0212555 .0085328 2.49 0.013 .0045145 .0379966 s17 | .0278123 .0091127 3.05 0.002 .0099337 .045691 s18 | .0231214 .0088585 2.61 0.009 .0057414 .0405014 s19 | .0255653 .0083431 3.06 0.002 .0091965 .0419342 s20 | .0289412 .0085662 3.38 0.001 .0121347 .0457477 s21 | .0242262 .0066034 3.67 0.000 .0112706 .0371819 s22 | .0285124 .0100037 2.85 0.004 .0088856 .0481393 s23 | .0189635 .0081226 2.33 0.020 .0030274 .0348996 s24 | .0268902 .0075569 3.56 0.000 .0120639 .0417166 s25 | .0259495 .0096261 2.70 0.007 .0070636 .0448354 s26 | .0265188 .0090992 2.91 0.004 .0086666 .044371 s27 | .0260296 .0099147 2.63 0.009 .0065774 .0454817 s28 | .0231521 .0083005 2.79 0.005 .0068669 .0394373 s29 | .0340452 .0100023 3.40 0.001 .0144211 .0536693 s30 | .0299419 .0096361 3.11 0.002 .0110363 .0488475 s31 | .0227059 .0073716 3.08 0.002 .0082431 .0371687 s32 | .0264283 .0087927 3.01 0.003 .0091774 .0436792 s33 | .0237539 .0081379 2.92 0.004 .0077876 .0397202 s34 | .0186749 .0073704 2.53 0.011 .0042146 .0331352 s35 | .0257493 .0086553 2.97 0.003 .0087679 .0427306 s36 | .0213865 .0092879 2.30 0.021 .0031639 .039609 s37 | .0250796 .0099446 2.52 0.012 .0055688 .0445905 s38 | .0229096 .0088482 2.59 0.010 .0055498 .0402695 s39 | .0286451 .0112218 2.55 0.011 .0066284 .0506617 s40 | .028271 .0096871 2.92 0.004 .0092654 .0472766 s41 | .0238107 .0085412 2.79 0.005 .0070532 .0405682 s42 | .0345068 .0105708 3.26 0.001 .0137673 .0552462 s43 | .023793 .0089082 2.67 0.008 .0063154 .0412706 s44 | .019953 .0058071 3.44 0.001 .0085597 .0313462 s45 | .018333 .0050371 3.64 0.000 .0084505 .0282155 s46 | .0238188 .00664 3.59 0.000 .0107913 .0368463 s47 | .0271617 .008269 3.28 0.001 .0109382 .0433852 s48 | .023321 .0070301 3.32 0.001 .0095281 .0371138 s49 | .0240409 .008819 2.73 0.007 .0067385 .0413433 s50 | .0274161 .01171 2.34 0.019 .0044416 .0503905 s51 | .0310244 .0077166 4.02 0.000 .0158849 .046164 | lnh | L2D. | -2.424539 .4734458 -5.12 0.000 -3.353419 -1.495659 L3D. | 1.027376 .4479637 2.29 0.022 .1484907 1.906261 L4D. | -1.234535 .4762332 -2.59 0.010 -2.168884 -.3001863 | lnst | L2D. | -1.864704 .406526 -4.59 0.000 -2.66229 -1.067117 L3D. | .8193205 .4288175 1.91 0.056 -.0220007 1.660642 L4D. | -1.138359 .3987127 -2.86 0.004 -1.920616 -.3561022 | lnw | L2D. | 4.096892 .7479061 5.48 0.000 2.629533 5.564251 L3D. | -1.668336 .7337861 -2.27 0.023 -3.107992 -.2286798 L4D. | 1.486163 .745261 1.99 0.046 .0239937 2.948333 | lninc | L2D. | .050218 .0345157 1.45 0.146 -.0175003 .1179363 L3D. | -.0742483 .0222495 -3.34 0.001 -.1179009 -.0305957 L4D. | .0314753 .0317373 0.99 0.322 -.030792 .0937425 | age1h | L2. | 2.781056 .6486678 4.29 0.000 1.508398 4.053714 L3. | -1.692109 .8144492 -2.08 0.038 -3.290023 -.0941956 L4. | 2.24284 .8206968 2.73 0.006 .6326689 3.853011 | age3h | L2. | 5.354467 1.080492 4.96 0.000 3.234588 7.474345 L3. | -1.213283 .7409948 -1.64 0.102 -2.667082 .2405166 L4. | 1.248774 .7668054 1.63 0.104 -.2556647 2.753212 | age1w | L2. | -4.91551 1.014827 -4.84 0.000 -6.906556 -2.924464 L3. | 2.399791 1.251899 1.92 0.055 -.0563803 4.855962 L4. | -2.357266 1.228735 -1.92 0.055 -4.76799 .0534587 | age3w | L2. | -8.069386 1.680345 -4.80 0.000 -11.36615 -4.772623 L3. | 2.30517 1.335863 1.73 0.085 -.3157353 4.926076 L4. | -2.349832 1.269806 -1.85 0.064 -4.841136 .1414724 | age1s | L2. | 2.154358 .5251537 4.10 0.000 1.124029 3.184686 L3. | -1.238191 .7444008 -1.66 0.097 -2.698673 .2222907 L4. | 1.776501 .6252596 2.84 0.005 .5497693 3.003233 | age3s | L2. | 3.745128 .8982311 4.17 0.000 1.982838 5.507418 L3. | -.9075896 .7628004 -1.19 0.234 -2.40417 .5889913 L4. | 1.803792 .7454369 2.42 0.016 .341278 3.266307 | _cons | .0906939 .0218958 4.14 0.000 .0477353 .1336524 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 123.48 Prob > F = 0.0000 R-squared = 0.4659 Adj R-squared = 0.4291 Root MSE = 0.0145 ------------------------------------------------------------------------------ | Robust age1h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0654593 .0310776 -2.11 0.035 -.1264322 -.0044864 age3ratio | -.0870109 .0510706 -1.70 0.089 -.1872092 .0131873 s2 | .0112013 .0061043 1.83 0.067 -.0007752 .0231777 s3 | .0118947 .0070166 1.70 0.090 -.0018716 .0256609 s4 | .0092669 .005623 1.65 0.100 -.0017651 .0202989 s5 | .011656 .0037548 3.10 0.002 .0042893 .0190227 s6 | .0042675 .003389 1.26 0.208 -.0023815 .0109166 s7 | .0068715 .006 1.15 0.252 -.0049002 .0186432 s8 | .0182051 .0050997 3.57 0.000 .0081998 .0282104 s9 | .015043 .005379 2.80 0.005 .0044895 .0255964 s10 | .0122496 .008287 1.48 0.140 -.0040092 .0285084 s11 | .0061728 .0036557 1.69 0.092 -.0009995 .013345 s12 | .0177688 .0053168 3.34 0.001 .0073376 .0282001 s13 | .0109998 .0068012 1.62 0.106 -.0023438 .0243434 s14 | .0114241 .0052235 2.19 0.029 .0011759 .0216724 s15 | .0111302 .0051205 2.17 0.030 .0010839 .0211764 s16 | .0111186 .0053717 2.07 0.039 .0005797 .0216576 s17 | .0076798 .0056984 1.35 0.178 -.0035002 .0188598 s18 | .0112855 .0055904 2.02 0.044 .0003173 .0222536 s19 | .0078436 .0053073 1.48 0.140 -.0025691 .0182563 s20 | .0080707 .0056998 1.42 0.157 -.003112 .0192533 s21 | .0096124 .0042044 2.29 0.022 .0013636 .0178611 s22 | .0102284 .006281 1.63 0.104 -.0020947 .0225515 s23 | .0108075 .0050447 2.14 0.032 .00091 .0207051 s24 | .0050047 .0048436 1.03 0.302 -.0044983 .0145076 s25 | .0092656 .0060587 1.53 0.126 -.0026214 .0211526 s26 | .0109494 .0058979 1.86 0.064 -.0006221 .0225209 s27 | .0109064 .0061885 1.76 0.078 -.0012352 .023048 s28 | .0106519 .0053514 1.99 0.047 .0001527 .0211512 s29 | .0087319 .0064063 1.36 0.173 -.0038369 .0213007 s30 | .0085319 .0060429 1.41 0.158 -.003324 .0203878 s31 | .0047445 .0046632 1.02 0.309 -.0044045 .0138935 s32 | .0092323 .0057224 1.61 0.107 -.0019948 .0204594 s33 | .010205 .0050358 2.03 0.043 .000325 .020085 s34 | .0067048 .0045821 1.46 0.144 -.002285 .0156946 s35 | .0101864 .0056835 1.79 0.073 -.0009644 .0213371 s36 | .0098339 .0057919 1.70 0.090 -.0015295 .0211973 s37 | .0088683 .006158 1.44 0.150 -.0032134 .02095 s38 | .0136296 .0055737 2.45 0.015 .0026942 .024565 s39 | .0133093 .0071552 1.86 0.063 -.0007288 .0273474 s40 | .0139242 .0063703 2.19 0.029 .0014259 .0264225 s41 | .0120021 .0053263 2.25 0.024 .0015522 .022452 s42 | .0116914 .0066994 1.75 0.081 -.0014525 .0248353 s43 | .01045 .005653 1.85 0.065 -.0006409 .021541 s44 | .0046948 .0038673 1.21 0.225 -.0028927 .0122824 s45 | .0117128 .0037542 3.12 0.002 .0043471 .0190785 s46 | .0081762 .0042722 1.91 0.056 -.0002057 .0165581 s47 | .0099236 .0052942 1.87 0.061 -.0004635 .0203107 s48 | .0105492 .0044715 2.36 0.018 .0017763 .0193222 s49 | .010956 .005573 1.97 0.050 .000022 .0218901 s50 | .0125606 .0073793 1.70 0.089 -.0019173 .0270385 s51 | .0072165 .0049015 1.47 0.141 -.0024 .016833 | lnh | L2D. | .9926048 .4921135 2.02 0.044 .0270997 1.95811 L3D. | -.8488628 .5400888 -1.57 0.116 -1.908493 .2107678 L4D. | .4094408 .3198223 1.28 0.201 -.2180364 1.036918 | lnst | L2D. | .4535359 .3952508 1.15 0.251 -.3219288 1.229001 L3D. | -.3760151 .266428 -1.41 0.158 -.8987352 .146705 L4D. | .2243486 .2031739 1.10 0.270 -.1742697 .6229668 | lnw | L2D. | -.6806284 .664192 -1.02 0.306 -1.983744 .6224871 L3D. | .5651814 .4677187 1.21 0.227 -.3524621 1.482825 L4D. | -.6817766 .3590318 -1.90 0.058 -1.386181 .0226281 | lninc | L2D. | .0927331 .0330848 2.80 0.005 .0278222 .157644 L3D. | .1380251 .0280166 4.93 0.000 .0830579 .1929924 L4D. | .1132415 .0357769 3.17 0.002 .0430488 .1834342 | age1h | L2. | -2.093729 .7721667 -2.71 0.007 -3.608687 -.5787721 L3. | 1.570702 .9238426 1.70 0.089 -.2418365 3.383241 L4. | -.800826 .5065774 -1.58 0.114 -1.794709 .1930566 | age3h | L2. | -.2542803 .9401693 -0.27 0.787 -2.098851 1.590291 L3. | .2918341 1.009891 0.29 0.773 -1.689527 2.273195 L4. | -.6027364 .6432428 -0.94 0.349 -1.864751 .6592778 | age1w | L2. | 2.363224 1.002912 2.36 0.019 .3955553 4.330893 L3. | -1.198166 .7369944 -1.63 0.104 -2.644116 .2477852 L4. | 1.151976 .5777873 1.99 0.046 .0183827 2.285569 | age3w | L2. | .0263551 1.282013 0.02 0.984 -2.488898 2.541608 L3. | -.4094221 .9513186 -0.43 0.667 -2.275867 1.457023 L4. | .8866837 .6373287 1.39 0.164 -.3637273 2.137095 | age1s | L2. | -1.324673 .5925793 -2.24 0.026 -2.487288 -.1620585 L3. | .848412 .4270546 1.99 0.047 .0105496 1.686274 L4. | -.3986918 .3244695 -1.23 0.219 -1.035287 .2379031 | age3s | L2. | -.2696704 .7528466 -0.36 0.720 -1.746722 1.207382 L3. | .2113859 .5435197 0.39 0.697 -.8549759 1.277748 L4. | -.2651325 .3859627 -0.69 0.492 -1.022374 .4921093 | _cons | .0399338 .0191818 2.08 0.038 .0023 .0775675 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 154.13 Prob > F = 0.0000 R-squared = 0.5092 Adj R-squared = 0.4754 Root MSE = 0.0135 ------------------------------------------------------------------------------ | Robust age3h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0906182 .0245723 -3.69 0.000 -.1388279 -.0424085 age3ratio | -.105476 .0512739 -2.06 0.040 -.2060732 -.0048788 s2 | .0156732 .005752 2.72 0.007 .0043881 .0269583 s3 | .0182093 .0066787 2.73 0.006 .0051059 .0313126 s4 | .013278 .0054031 2.46 0.014 .0026773 .0238787 s5 | .0116676 .003492 3.34 0.001 .0048165 .0185187 s6 | .0053543 .0031372 1.71 0.088 -.0008007 .0115094 s7 | .0111683 .0056092 1.99 0.047 .0001634 .0221732 s8 | .0185535 .0048898 3.79 0.000 .0089599 .0281471 s9 | .0185381 .005147 3.60 0.000 .0084398 .0286364 s10 | .0197512 .0079528 2.48 0.013 .0041482 .0353542 s11 | .0061928 .0034003 1.82 0.069 -.0004784 .0128641 s12 | .0197287 .004972 3.97 0.000 .0099739 .0294835 s13 | .0167234 .00655 2.55 0.011 .0038725 .0295743 s14 | .0146328 .0049249 2.97 0.003 .0049704 .0242952 s15 | .0137676 .0048395 2.84 0.005 .0042727 .0232626 s16 | .0143755 .0050595 2.84 0.005 .004449 .024302 s17 | .0118644 .0054871 2.16 0.031 .001099 .0226298 s18 | .0149156 .0052525 2.84 0.005 .0046104 .0252207 s19 | .0110096 .0049296 2.23 0.026 .0013379 .0206812 s20 | .0117267 .0054031 2.17 0.030 .001126 .0223273 s21 | .0111449 .003929 2.84 0.005 .0034364 .0188533 s22 | .0152089 .0058902 2.58 0.010 .0036524 .0267653 s23 | .0127315 .0047128 2.70 0.007 .0034853 .0219777 s24 | .0080682 .0046153 1.75 0.081 -.0009868 .0171232 s25 | .0138259 .0058127 2.38 0.018 .0024217 .0252301 s26 | .014871 .0055419 2.68 0.007 .0039981 .0257439 s27 | .0168672 .005828 2.89 0.004 .0054328 .0283015 s28 | .0133957 .0050518 2.65 0.008 .0034843 .0233071 s29 | .0148534 .0062019 2.39 0.017 .0026855 .0270213 s30 | .0131059 .0058164 2.25 0.024 .0016944 .0245175 s31 | .0070852 .0043004 1.65 0.100 -.001352 .0155225 s32 | .0131478 .0053731 2.45 0.015 .0026061 .0236895 s33 | .0126849 .0047572 2.67 0.008 .0033515 .0220182 s34 | .0087765 .0042952 2.04 0.041 .0003496 .0172035 s35 | .014 .0054044 2.59 0.010 .0033969 .0246031 s36 | .0133912 .0054474 2.46 0.014 .0027037 .0240788 s37 | .0137093 .0058232 2.35 0.019 .0022844 .0251341 s38 | .017921 .0052688 3.40 0.001 .0075838 .0282582 s39 | .01966 .0068317 2.88 0.004 .0062564 .0330635 s40 | .0188631 .0061113 3.09 0.002 .0068731 .0308532 s41 | .0146066 .0050295 2.90 0.004 .0047389 .0244743 s42 | .0176562 .0065067 2.71 0.007 .0048903 .0304222 s43 | .0139102 .0053191 2.62 0.009 .0034744 .0243461 s44 | .0068766 .0036364 1.89 0.059 -.0002579 .0140111 s45 | .0110367 .0034368 3.21 0.001 .0042937 .0177796 s46 | .0096276 .0040094 2.40 0.016 .0017614 .0174939 s47 | .0128547 .0049093 2.62 0.009 .0032229 .0224865 s48 | .0122283 .0041704 2.93 0.003 .0040462 .0204105 s49 | .0147741 .0052643 2.81 0.005 .0044458 .0251024 s50 | .0197399 .0069506 2.84 0.005 .0061031 .0333767 s51 | .0107473 .0045834 2.34 0.019 .0017548 .0197397 | lnh | L2D. | 1.243407 .3851733 3.23 0.001 .4877134 1.9991 L3D. | -1.035505 .4538434 -2.28 0.023 -1.925926 -.1450846 L4D. | .5335716 .2510114 2.13 0.034 .0410982 1.026045 | lnst | L2D. | .5722757 .3193796 1.79 0.073 -.0543331 1.198884 L3D. | -.4224862 .2495136 -1.69 0.091 -.912021 .0670486 L4D. | .2832211 .1856268 1.53 0.127 -.0809706 .6474128 | lnw | L2D. | -.9206387 .5345002 -1.72 0.085 -1.969305 .1280272 L3D. | .6735727 .4400795 1.53 0.126 -.1898439 1.536989 L4D. | -.7382669 .3212153 -2.30 0.022 -1.368477 -.1080567 | lninc | L2D. | .0873033 .0299879 2.91 0.004 .0284682 .1461383 L3D. | .1344508 .0260859 5.15 0.000 .0832715 .1856302 L4D. | .1125048 .0298097 3.77 0.000 .0540195 .1709902 | age1h | L2. | -2.736782 .6303135 -4.34 0.000 -3.973429 -1.500135 L3. | 2.196008 .8184998 2.68 0.007 .5901474 3.801869 L4. | -.5723339 .4170893 -1.37 0.170 -1.390645 .245977 | age3h | L2. | -.3683843 .6981278 -0.53 0.598 -1.73808 1.001312 L3. | .2023621 .7865532 0.26 0.797 -1.340821 1.745545 L4. | -1.250065 .4336417 -2.88 0.004 -2.100851 -.3992786 | age1w | L2. | 1.950014 .8524899 2.29 0.022 .2774661 3.622562 L3. | -1.382228 .6636889 -2.08 0.037 -2.684357 -.0800998 L4. | .9618664 .5016194 1.92 0.055 -.022289 1.946022 | age3w | L2. | 1.223568 .9342523 1.31 0.191 -.6093941 3.05653 L3. | -.5330785 .8433928 -0.63 0.527 -2.187778 1.121621 L4. | 1.282793 .5628946 2.28 0.023 .1784186 2.387167 | age1s | L2. | -1.069326 .5000947 -2.14 0.033 -2.05049 -.0881618 L3. | .9641989 .3893496 2.48 0.013 .200312 1.728086 L4. | -.3077971 .281774 -1.09 0.275 -.8606252 .2450311 | age3s | L2. | -.9238412 .5693997 -1.62 0.105 -2.040978 .193296 L3. | .214617 .4879021 0.44 0.660 -.7426255 1.17186 L4. | -.5692069 .358198 -1.59 0.112 -1.271976 .1335619 | _cons | .0501843 .0171673 2.92 0.004 .0165027 .0838658 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 87.26 Prob > F = 0.0000 R-squared = 0.2840 Adj R-squared = 0.2347 Root MSE = 0.0250 ------------------------------------------------------------------------------ | Robust age1w | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .101312 .0497659 2.04 0.042 .0036735 .1989505 age3ratio | -.2667597 .0739507 -3.61 0.000 -.4118478 -.1216716 s2 | .0521387 .0096991 5.38 0.000 .0331096 .0711679 s3 | .058476 .0112485 5.20 0.000 .036407 .0805451 s4 | .0409315 .0090326 4.53 0.000 .0232099 .058653 s5 | .0283903 .006235 4.55 0.000 .0161576 .040623 s6 | .0262056 .0058325 4.49 0.000 .0147626 .0376486 s7 | .0456442 .0098329 4.64 0.000 .0263525 .0649359 s8 | .0313179 .0074892 4.18 0.000 .0166245 .0460112 s9 | .0391238 .0086955 4.50 0.000 .0220636 .0561839 s10 | .0664659 .013433 4.95 0.000 .0401109 .0928209 s11 | .0281943 .0057666 4.89 0.000 .0168804 .0395082 s12 | .0439908 .0085115 5.17 0.000 .0272917 .0606899 s13 | .058206 .0113917 5.11 0.000 .0358561 .080556 s14 | .0429045 .0084714 5.06 0.000 .0262841 .0595249 s15 | .0406807 .0084007 4.84 0.000 .0241989 .0571626 s16 | .0459985 .0088453 5.20 0.000 .0286445 .0633525 s17 | .0431961 .0095399 4.53 0.000 .0244793 .0619129 s18 | .0475708 .0090793 5.24 0.000 .0297576 .0653841 s19 | .041517 .0086135 4.82 0.000 .0246176 .0584164 s20 | .0416959 .0091881 4.54 0.000 .0236692 .0597226 s21 | .0339994 .006959 4.89 0.000 .020346 .0476527 s22 | .0526606 .0101543 5.19 0.000 .0327382 .0725829 s23 | .042193 .0084918 4.97 0.000 .0255324 .0588537 s24 | .0324974 .0080278 4.05 0.000 .0167473 .0482476 s25 | .0464724 .0099733 4.66 0.000 .0269052 .0660395 s26 | .0497388 .0093876 5.30 0.000 .0313207 .0681568 s27 | .0527785 .0103545 5.10 0.000 .0324634 .0730937 s28 | .0436385 .0085731 5.09 0.000 .0268184 .0604585 s29 | .0515833 .0104015 4.96 0.000 .0311759 .0719906 s30 | .0489457 .0100555 4.87 0.000 .0292172 .0686742 s31 | .0371657 .0076123 4.88 0.000 .0222307 .0521008 s32 | .0438925 .0095103 4.62 0.000 .0252338 .0625512 s33 | .0406252 .0081683 4.97 0.000 .0245994 .0566511 s34 | .0350394 .0073884 4.74 0.000 .0205436 .0495353 s35 | .0428311 .0091864 4.66 0.000 .0248078 .0608544 s36 | .0481979 .0096621 4.99 0.000 .0292413 .0671544 s37 | .0515288 .0102234 5.04 0.000 .031471 .0715865 s38 | .0479382 .0092495 5.18 0.000 .0297912 .0660852 s39 | .05706 .0115826 4.93 0.000 .0343355 .0797845 s40 | .0526729 .0100474 5.24 0.000 .0329603 .0723855 s41 | .0441867 .0083848 5.27 0.000 .0277362 .0606373 s42 | .0551523 .0108787 5.07 0.000 .0338086 .0764959 s43 | .0464748 .0090288 5.15 0.000 .0287606 .064189 s44 | .0294635 .0062564 4.71 0.000 .0171888 .0417382 s45 | .0223215 .0054932 4.06 0.000 .0115441 .0330989 s46 | .0330358 .0068216 4.84 0.000 .0196522 .0464194 s47 | .0436833 .0087007 5.02 0.000 .0266129 .0607538 s48 | .0364844 .0073607 4.96 0.000 .022043 .0509258 s49 | .0475546 .0093055 5.11 0.000 .0292977 .0658115 s50 | .0618623 .0119249 5.19 0.000 .0384661 .0852584 s51 | .0396497 .0081611 4.86 0.000 .0236381 .0556613 | lnh | L2D. | 1.046298 .5834149 1.79 0.073 -.0983362 2.190933 L3D. | .5418196 .5684822 0.95 0.341 -.5735176 1.657157 L4D. | 1.206291 .5207696 2.32 0.021 .1845638 2.228018 | lnst | L2D. | 1.50331 .606158 2.48 0.013 .3140544 2.692565 L3D. | 1.178339 .5250904 2.24 0.025 .1481349 2.208544 L4D. | .4495996 .4108998 1.09 0.274 -.3565677 1.255767 | lnw | L2D. | -1.868409 1.057243 -1.77 0.077 -3.942674 .2058554 L3D. | -2.732477 .919074 -2.97 0.003 -4.53566 -.9292937 L4D. | -.8252292 .7801298 -1.06 0.290 -2.35581 .7053513 | lninc | L2D. | -.0597324 .059635 -1.00 0.317 -.1767336 .0572689 L3D. | .1989078 .0430455 4.62 0.000 .1144544 .2833612 L4D. | -.1675483 .0335542 -4.99 0.000 -.2333802 -.1017163 | age1h | L2. | -2.225958 .8990995 -2.48 0.013 -3.989952 -.4619644 L3. | .15438 1.09526 0.14 0.888 -1.994473 2.303233 L4. | -2.638612 .9974409 -2.65 0.008 -4.595547 -.6816763 | age3h | L2. | -.4104788 1.104894 -0.37 0.710 -2.578232 1.757275 L3. | -2.219802 .9899776 -2.24 0.025 -4.162095 -.2775097 L4. | -1.027325 .7359007 -1.40 0.163 -2.471129 .4164802 | age1w | L2. | 4.613977 1.750917 2.64 0.009 1.178755 8.049198 L3. | 3.886342 1.628218 2.39 0.017 .6918485 7.080835 L4. | 1.939531 1.420563 1.37 0.172 -.8475526 4.726614 | age3w | L2. | 1.278643 1.820668 0.70 0.483 -2.293429 4.850714 L3. | 3.760599 1.483616 2.53 0.011 .8498094 6.671389 L4. | .5353769 1.262749 0.42 0.672 -1.942082 3.012836 | age1s | L2. | -3.142177 1.001576 -3.14 0.002 -5.107225 -1.177128 L3. | -1.754418 .8951528 -1.96 0.050 -3.510669 .0018325 L4. | -.9707601 .7430186 -1.31 0.192 -2.42853 .4870098 | age3s | L2. | -1.704891 1.046618 -1.63 0.104 -3.75831 .3485271 L3. | -1.464077 .8728863 -1.68 0.094 -3.176642 .2484877 L4. | -.4724535 .6514774 -0.73 0.468 -1.750624 .8057166 | _cons | .0249175 .0252165 0.99 0.323 -.0245562 .0743913 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 70.34 Prob > F = 0.0000 R-squared = 0.2786 Adj R-squared = 0.2290 Root MSE = 0.0258 ------------------------------------------------------------------------------ | Robust age3w | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .0467953 .0444811 1.05 0.293 -.0404747 .1340652 age3ratio | -.276923 .0852691 -3.25 0.001 -.4442173 -.1096287 s2 | .0569689 .0101171 5.63 0.000 .0371195 .0768182 s3 | .0654089 .0117614 5.56 0.000 .0423336 .0884842 s4 | .0461553 .0095874 4.81 0.000 .0273452 .0649653 s5 | .0302054 .0064346 4.69 0.000 .0175811 .0428298 s6 | .0275409 .0059469 4.63 0.000 .0158732 .0392085 s7 | .0496077 .0102737 4.83 0.000 .0294511 .0697642 s8 | .0351751 .0078157 4.50 0.000 .019841 .0505091 s9 | .0428637 .0092084 4.65 0.000 .0247972 .0609301 s10 | .0735995 .0143988 5.11 0.000 .0453496 .1018494 s11 | .0286351 .0059804 4.79 0.000 .0169019 .0403684 s12 | .046957 .009016 5.21 0.000 .029268 .0646459 s13 | .0650788 .0121573 5.35 0.000 .0412268 .0889308 s14 | .0471887 .0088477 5.33 0.000 .0298298 .0645476 s15 | .0442142 .0087831 5.03 0.000 .0269821 .0614463 s16 | .0499444 .0092709 5.39 0.000 .0317553 .0681335 s17 | .0480112 .0101356 4.74 0.000 .0281257 .0678968 s18 | .051817 .0094889 5.46 0.000 .0332003 .0704338 s19 | .0452331 .0090156 5.02 0.000 .0275449 .0629212 s20 | .0445421 .0096355 4.62 0.000 .0256377 .0634465 s21 | .036493 .0071538 5.10 0.000 .0224575 .0505285 s22 | .0573132 .0106757 5.37 0.000 .0363681 .0782584 s23 | .0448865 .0089026 5.04 0.000 .0274201 .062353 s24 | .0359567 .0083858 4.29 0.000 .0195042 .0524092 s25 | .0512004 .0105966 4.83 0.000 .0304103 .0719905 s26 | .0540065 .0097546 5.54 0.000 .0348684 .0731446 s27 | .0584639 .0109019 5.36 0.000 .037075 .0798529 s28 | .0468595 .008963 5.23 0.000 .0292745 .0644446 s29 | .0580143 .0111267 5.21 0.000 .0361842 .0798445 s30 | .054246 .0106911 5.07 0.000 .0332705 .0752216 s31 | .0391121 .0078728 4.97 0.000 .0236659 .0545583 s32 | .0477432 .0099525 4.80 0.000 .0282169 .0672696 s33 | .0437919 .0086092 5.09 0.000 .0269011 .0606828 s34 | .0388559 .0077953 4.98 0.000 .0235619 .0541498 s35 | .0467394 .0096719 4.83 0.000 .0277635 .0657152 s36 | .0524323 .0101649 5.16 0.000 .0324892 .0723755 s37 | .0576762 .0107468 5.37 0.000 .0365913 .078761 s38 | .0531755 .0096935 5.49 0.000 .0341572 .0721937 s39 | .0631841 .0123356 5.12 0.000 .0389823 .087386 s40 | .0579442 .0106615 5.43 0.000 .0370268 .0788616 s41 | .0469922 .0088149 5.33 0.000 .0296978 .0642866 s42 | .0614474 .0116246 5.29 0.000 .0386405 .0842543 s43 | .0502148 .0094252 5.33 0.000 .0317231 .0687066 s44 | .0320455 .0065669 4.88 0.000 .0191616 .0449294 s45 | .0236498 .005824 4.06 0.000 .0122234 .0350761 s46 | .0349698 .0070715 4.95 0.000 .0210958 .0488439 s47 | .0458486 .0090341 5.08 0.000 .0281241 .063573 s48 | .0393751 .0076082 5.18 0.000 .0244483 .054302 s49 | .0514092 .0097551 5.27 0.000 .0322701 .0705483 s50 | .0696857 .0124131 5.61 0.000 .0453317 .0940397 s51 | .0432059 .0085222 5.07 0.000 .0264857 .059926 | lnh | L2D. | 1.314046 .5995306 2.19 0.029 .1377935 2.490299 L3D. | .1489918 .5428148 0.27 0.784 -.915987 1.213971 L4D. | 1.367206 .4962666 2.75 0.006 .3935527 2.340859 | lnst | L2D. | 1.437781 .5895415 2.44 0.015 .2811262 2.594435 L3D. | 1.225018 .5182451 2.36 0.018 .2082436 2.241792 L4D. | .4711723 .3765577 1.25 0.211 -.2676174 1.209962 | lnw | L2D. | -1.853438 1.056533 -1.75 0.080 -3.926308 .2194329 L3D. | -2.715501 .9030166 -3.01 0.003 -4.48718 -.9438225 L4D. | -.9018994 .7288735 -1.24 0.216 -2.331917 .5281184 | lninc | L2D. | -.0860351 .0649242 -1.33 0.185 -.2134135 .0413434 L3D. | .2341151 .0470488 4.98 0.000 .1418073 .3264229 L4D. | -.1800612 .038902 -4.63 0.000 -.2563851 -.1037372 | age1h | L2. | -2.733908 .9513863 -2.87 0.004 -4.600487 -.8673301 L3. | .4289324 1.041555 0.41 0.681 -1.614552 2.472417 L4. | -2.587371 .973234 -2.66 0.008 -4.496814 -.6779289 | age3h | L2. | -.7228034 1.057557 -0.68 0.494 -2.797684 1.352078 L3. | -1.246361 .8645068 -1.44 0.150 -2.942485 .4497638 L4. | -1.578394 .6741899 -2.34 0.019 -2.901125 -.2556632 | age1w | L2. | 4.163359 1.774105 2.35 0.019 .6826437 7.644075 L3. | 4.850592 1.530151 3.17 0.002 1.848503 7.852681 L4. | 1.86651 1.318052 1.42 0.157 -.7194501 4.45247 | age3w | L2. | 1.697392 1.799673 0.94 0.346 -1.833488 5.228271 L3. | 2.596452 1.489931 1.74 0.082 -.3267273 5.519632 L4. | .8821495 1.32076 0.67 0.504 -1.709124 3.473423 | age1s | L2. | -2.761514 1.016726 -2.72 0.007 -4.756285 -.7667427 L3. | -2.318643 .8604638 -2.69 0.007 -4.006836 -.6304511 L4. | -.8204442 .6942551 -1.18 0.238 -2.182542 .5416538 | age3s | L2. | -1.893878 .9660174 -1.96 0.050 -3.789161 .0014061 L3. | -.9649825 .8642814 -1.12 0.264 -2.660665 .7306997 L4. | -.7261636 .6764825 -1.07 0.283 -2.053393 .6010653 | _cons | .0403029 .0256884 1.57 0.117 -.0100966 .0907024 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 91.78 Prob > F = 0.0000 R-squared = 0.2778 Adj R-squared = 0.2281 Root MSE = 0.0408 ------------------------------------------------------------------------------ | Robust age1s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .2809539 .0700719 4.01 0.000 .1434759 .4184319 age3ratio | -.4532193 .0965709 -4.69 0.000 -.6426872 -.2637514 s2 | .0915046 .0134037 6.83 0.000 .0652072 .117802 s3 | .10194 .0157456 6.47 0.000 .0710478 .1328322 s4 | .0687757 .0124734 5.51 0.000 .0443034 .093248 s5 | .042566 .008478 5.02 0.000 .0259324 .0591995 s6 | .044499 .0080674 5.52 0.000 .0286711 .0603269 s7 | .0811958 .0136155 5.96 0.000 .0544828 .1079087 s8 | .0406336 .0103458 3.93 0.000 .0203357 .0609316 s9 | .0594394 .0119747 4.96 0.000 .0359456 .0829333 s10 | .1159213 .0185599 6.25 0.000 .0795077 .152335 s11 | .0484509 .0078254 6.19 0.000 .0330977 .063804 s12 | .0685527 .0114438 5.99 0.000 .0461005 .0910049 s13 | .0988474 .0158096 6.25 0.000 .0678297 .1298651 s14 | .070543 .0116499 6.06 0.000 .0476865 .0933996 s15 | .0659949 .0115998 5.69 0.000 .0432365 .0887533 s16 | .0762817 .0121901 6.26 0.000 .0523651 .1001982 s17 | .0730414 .0132821 5.50 0.000 .0469826 .0991002 s18 | .0805859 .0125081 6.44 0.000 .0560457 .1051262 s19 | .0741705 .0116434 6.37 0.000 .0513267 .0970143 s20 | .0716006 .0126921 5.64 0.000 .0466992 .0965021 s21 | .0544632 .009666 5.63 0.000 .035499 .0734274 s22 | .0917966 .0140627 6.53 0.000 .0642061 .1193871 s23 | .0687584 .0116628 5.90 0.000 .0458764 .0916404 s24 | .0558428 .0112861 4.95 0.000 .0336999 .0779857 s25 | .0789922 .0138428 5.71 0.000 .0518333 .1061512 s26 | .0868576 .0129124 6.73 0.000 .061524 .1121913 s27 | .0903558 .0143143 6.31 0.000 .0622718 .1184398 s28 | .0741681 .0117744 6.30 0.000 .0510672 .097269 s29 | .0883333 .0144334 6.12 0.000 .0600155 .116651 s30 | .0830815 .0139775 5.94 0.000 .0556582 .1105049 s31 | .0662054 .0105413 6.28 0.000 .0455239 .086887 s32 | .0741157 .0131425 5.64 0.000 .0483307 .0999008 s33 | .0690839 .011183 6.18 0.000 .0471433 .0910246 s34 | .0619411 .0100595 6.16 0.000 .0422049 .0816774 s35 | .071433 .0127235 5.61 0.000 .0464701 .0963958 s36 | .08141 .0133254 6.11 0.000 .0552662 .1075538 s37 | .0899881 .0141533 6.36 0.000 .06222 .1177562 s38 | .0776736 .0127948 6.07 0.000 .0525707 .1027765 s39 | .0965206 .0160365 6.02 0.000 .0650578 .1279834 s40 | .0876182 .0139479 6.28 0.000 .0602531 .1149833 s41 | .076005 .0114431 6.64 0.000 .0535541 .0984558 s42 | .0930173 .0151483 6.14 0.000 .063297 .1227376 s43 | .0805471 .0124655 6.46 0.000 .0560903 .1050039 s44 | .0501404 .0085122 5.89 0.000 .0334398 .066841 s45 | .0302569 .0070954 4.26 0.000 .0163361 .0441777 s46 | .0554242 .0093757 5.91 0.000 .0370296 .0738189 s47 | .0736514 .0119745 6.15 0.000 .0501579 .0971449 s48 | .0589724 .0101258 5.82 0.000 .0391061 .0788387 s49 | .079239 .0128611 6.16 0.000 .0540061 .1044719 s50 | .1084907 .0167343 6.48 0.000 .0756587 .1413227 s51 | .0675801 .011198 6.04 0.000 .0456101 .0895501 | lnh | L2D. | .8565421 .9430651 0.91 0.364 -.9937103 2.706794 L3D. | .3310271 .9122197 0.36 0.717 -1.458708 2.120762 L4D. | 2.147487 .7636131 2.81 0.005 .6493113 3.645662 | lnst | L2D. | 1.867653 .8332275 2.24 0.025 .2328972 3.502409 L3D. | 1.00646 .8236291 1.22 0.222 -.6094646 2.622384 L4D. | .8468973 .640386 1.32 0.186 -.409512 2.103307 | lnw | L2D. | -1.904019 1.493437 -1.27 0.203 -4.834078 1.026039 L3D. | -2.969899 1.438052 -2.07 0.039 -5.791294 -.1485043 L4D. | -1.218889 1.211698 -1.01 0.315 -3.596187 1.158409 | lninc | L2D. | -.1906363 .1004023 -1.90 0.058 -.3876211 .0063485 L3D. | .2599883 .0776907 3.35 0.001 .1075625 .4124141 L4D. | -.3896009 .0814738 -4.78 0.000 -.5494489 -.2297529 | age1h | L2. | -1.933312 1.518165 -1.27 0.203 -4.911885 1.045261 L3. | 1.465562 1.670365 0.88 0.380 -1.81162 4.742744 L4. | -4.75791 1.508243 -3.15 0.002 -7.717017 -1.798804 | age3h | L2. | -.2740049 1.723344 -0.16 0.874 -3.655131 3.107121 L3. | -2.467148 1.477516 -1.67 0.095 -5.365969 .4316729 L4. | -1.694134 1.142881 -1.48 0.139 -3.936417 .5481488 | age1w | L2. | 4.434954 2.52466 1.76 0.079 -.5183182 9.388226 L3. | 3.80157 2.420816 1.57 0.117 -.9479645 8.551105 L4. | 3.287516 2.211929 1.49 0.137 -1.052192 7.627223 | age3w | L2. | 1.486576 2.607476 0.57 0.569 -3.629178 6.60233 L3. | 3.551615 2.527849 1.40 0.160 -1.407914 8.511144 L4. | .5653901 2.019706 0.28 0.780 -3.397185 4.527965 | age1s | L2. | -3.595784 1.415495 -2.54 0.011 -6.372923 -.8186455 L3. | -1.447866 1.348512 -1.07 0.283 -4.093587 1.197855 L4. | -1.964186 1.13965 -1.72 0.085 -4.200128 .2717571 | age3s | L2. | -2.446692 1.468805 -1.67 0.096 -5.328423 .4350393 L3. | -.6411392 1.433783 -0.45 0.655 -3.454158 2.17188 L4. | -.8793311 1.055412 -0.83 0.405 -2.950003 1.19134 | _cons | .010078 .0343583 0.29 0.769 -.0573315 .0774876 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 32, 1192) = 95.56 Prob > F = 0.0000 R-squared = 0.2590 Adj R-squared = 0.2081 Root MSE = 0.0422 ------------------------------------------------------------------------------ | Robust age3s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .1824098 .0703315 2.59 0.010 .0444225 .3203971 age3ratio | -.4358242 .1217747 -3.58 0.000 -.6747408 -.1969075 s2 | .0962919 .0152895 6.30 0.000 .0662946 .1262892 s3 | .1092408 .0177867 6.14 0.000 .074344 .1441376 s4 | .0752053 .0143128 5.25 0.000 .0471243 .1032864 s5 | .0464921 .0096479 4.82 0.000 .0275632 .0654209 s6 | .0466111 .0090049 5.18 0.000 .0289438 .0642784 s7 | .0843607 .0155439 5.43 0.000 .0538642 .1148572 s8 | .0483814 .0115384 4.19 0.000 .0257436 .0710192 s9 | .0637322 .0137099 4.65 0.000 .0368339 .0906305 s10 | .1217212 .0215621 5.65 0.000 .0794173 .164025 s11 | .0493006 .0090257 5.46 0.000 .0315927 .0670086 s12 | .0718298 .0134855 5.33 0.000 .0453719 .0982877 s13 | .1057621 .018287 5.78 0.000 .0698839 .1416403 s14 | .0758464 .0133015 5.70 0.000 .0497495 .1019433 s15 | .0705667 .0131995 5.35 0.000 .0446698 .0964635 s16 | .0808609 .0139585 5.79 0.000 .0534749 .1082468 s17 | .0783961 .015212 5.15 0.000 .0485508 .1082415 s18 | .0852127 .0143205 5.95 0.000 .0571165 .1133089 s19 | .077326 .0135159 5.72 0.000 .0508083 .1038437 s20 | .0738358 .0144626 5.11 0.000 .0454608 .1022107 s21 | .0583598 .0108415 5.38 0.000 .0370894 .0796303 s22 | .0956743 .0161638 5.92 0.000 .0639618 .1273869 s23 | .0721164 .0134175 5.37 0.000 .0457919 .0984409 s24 | .0601804 .0126777 4.75 0.000 .0353073 .0850534 s25 | .0836746 .0159157 5.26 0.000 .0524487 .1149004 s26 | .0911435 .0146859 6.21 0.000 .0623304 .1199565 s27 | .0952332 .0164294 5.80 0.000 .0629994 .1274669 s28 | .0775645 .0134894 5.75 0.000 .0510989 .1040301 s29 | .0947693 .0166554 5.69 0.000 .062092 .1274465 s30 | .0886521 .0160682 5.52 0.000 .0571271 .1201772 s31 | .0678438 .0119914 5.66 0.000 .0443172 .0913704 s32 | .0776978 .014975 5.19 0.000 .0483175 .1070781 s33 | .0724386 .0129656 5.59 0.000 .0470007 .0978765 s34 | .0668059 .0117858 5.67 0.000 .0436827 .0899291 s35 | .0755144 .014517 5.20 0.000 .0470327 .1039962 s36 | .0858966 .015321 5.61 0.000 .0558375 .1159557 s37 | .0967199 .0162065 5.97 0.000 .0649235 .1285163 s38 | .0836293 .0145929 5.73 0.000 .0549987 .1122599 s39 | .1017006 .0185131 5.49 0.000 .0653787 .1380226 s40 | .0929891 .0160152 5.81 0.000 .0615679 .1244102 s41 | .0784462 .0132864 5.90 0.000 .0523788 .1045136 s42 | .0991104 .0174431 5.68 0.000 .0648878 .1333329 s43 | .0842805 .0142568 5.91 0.000 .0563093 .1122516 s44 | .0533653 .0097616 5.47 0.000 .0342135 .0725171 s45 | .0335106 .0084255 3.98 0.000 .0169802 .050041 s46 | .0580411 .0106717 5.44 0.000 .0371037 .0789784 s47 | .0751653 .0136518 5.51 0.000 .0483811 .1019495 s48 | .0632281 .0114879 5.50 0.000 .0406893 .0857668 s49 | .0830957 .0146808 5.66 0.000 .0542927 .1118988 s50 | .1166483 .018879 6.18 0.000 .0796085 .1536882 s51 | .0712367 .0128062 5.56 0.000 .0461115 .0963619 | lnh | L2D. | 1.121586 .9911553 1.13 0.258 -.8230171 3.066189 L3D. | -.0002125 .9484137 -0.00 1.000 -1.860958 1.860534 L4D. | 2.253936 .759444 2.97 0.003 .7639398 3.743932 | lnst | L2D. | 1.632311 .8114003 2.01 0.044 .0403793 3.224243 L3D. | 1.373673 .8218267 1.67 0.095 -.2387154 2.986061 L4D. | .7598681 .5801127 1.31 0.190 -.3782875 1.898024 | lnw | L2D. | -1.646502 1.497649 -1.10 0.272 -4.584824 1.291819 L3D. | -3.472596 1.419219 -2.45 0.015 -6.257042 -.6881508 L4D. | -1.194629 1.118196 -1.07 0.286 -3.388481 .999222 | lninc | L2D. | -.2296086 .1109816 -2.07 0.039 -.4473497 -.0118674 L3D. | .329909 .0869087 3.80 0.000 .1593979 .50042 L4D. | -.4018527 .0877229 -4.58 0.000 -.5739612 -.2297442 | age1h | L2. | -2.180989 1.606625 -1.36 0.175 -5.333116 .9711379 L3. | .9717727 1.699819 0.57 0.568 -2.363198 4.306743 L4. | -4.493422 1.536628 -2.92 0.004 -7.508218 -1.478625 | age3h | L2. | -.8556912 1.750706 -0.49 0.625 -4.290499 2.579116 L3. | -.88995 1.530447 -0.58 0.561 -3.89262 2.11272 L4. | -2.295204 1.080903 -2.12 0.034 -4.415887 -.1745205 | age1w | L2. | 3.873297 2.562764 1.51 0.131 -1.154734 8.901328 L3. | 6.552997 2.354978 2.78 0.005 1.932633 11.17336 L4. | 2.657029 2.081216 1.28 0.202 -1.426225 6.740284 | age3w | L2. | 1.267448 2.668437 0.47 0.635 -3.967909 6.502805 L3. | 2.125276 2.507612 0.85 0.397 -2.794548 7.0451 L4. | 1.206088 2.072144 0.58 0.561 -2.859368 5.271543 | age1s | L2. | -3.047888 1.434036 -2.13 0.034 -5.861404 -.2343724 L3. | -3.033213 1.350447 -2.25 0.025 -5.68273 -.3836959 L4. | -1.362619 1.097598 -1.24 0.215 -3.516058 .7908191 | age3s | L2. | -2.271105 1.394385 -1.63 0.104 -5.006827 .4646167 L3. | -.0569137 1.438948 -0.04 0.968 -2.880067 2.76624 L4. | -1.277713 1.042503 -1.23 0.221 -3.323058 .7676327 | _cons | .0290092 .036912 0.79 0.432 -.0434106 .101429 ------------------------------------------------------------------------------ Instrumental variables (2SLS) regression Number of obs = 1275 Wald chi2(62) = 808.27 Prob > chi2 = 0.0000 R-squared = 0.3729 Root MSE = .0259 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust D.lncons | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnh | D1. | -6.456444 1.635174 -3.95 0.000 -9.661327 -3.251562 | lnst | D1. | -7.381336 1.513023 -4.88 0.000 -10.34681 -4.415865 | lnw | D1. | 13.8718 3.000596 4.62 0.000 7.990738 19.75286 | lninc | D1. | .5481309 .0682173 8.04 0.000 .4144274 .6818343 | age1h | 8.457393 2.984424 2.83 0.005 2.60803 14.30676 age3h | 11.96232 2.653207 4.51 0.000 6.762126 17.16251 age1w | -18.79042 5.488857 -3.42 0.001 -29.54838 -8.032459 age3w | -23.44212 4.800246 -4.88 0.000 -32.85043 -14.03381 age1s | 10.21679 2.511851 4.07 0.000 5.29365 15.13992 age3s | 12.22378 2.559216 4.78 0.000 7.207811 17.23975 age1ratio | -.0159038 .0726316 -0.22 0.827 -.1582591 .1264516 age3ratio | -.5159188 .0733335 -7.04 0.000 -.6596498 -.3721878 s2 | .0658574 .0067377 9.77 0.000 .0526518 .079063 s3 | .0718509 .0079724 9.01 0.000 .0562253 .0874765 s4 | .0594721 .0066953 8.88 0.000 .0463496 .0725946 s5 | .0390998 .0045769 8.54 0.000 .0301293 .0480703 s6 | .0328461 .0047123 6.97 0.000 .0236102 .042082 s7 | .0579625 .0066748 8.68 0.000 .04488 .0710449 s8 | .0342669 .0067439 5.08 0.000 .021049 .0474848 s9 | .0531817 .0065354 8.14 0.000 .0403726 .0659909 s10 | .0877167 .009732 9.01 0.000 .0686424 .106791 s11 | .0351319 .0039697 8.85 0.000 .0273514 .0429125 s12 | .0509054 .0058445 8.71 0.000 .0394504 .0623603 s13 | .0732702 .0083397 8.79 0.000 .0569246 .0896158 s14 | .0527031 .0061647 8.55 0.000 .0406205 .0647857 s15 | .0544042 .0058578 9.29 0.000 .0429232 .0658852 s16 | .0561536 .0061332 9.16 0.000 .0441328 .0681744 s17 | .0591833 .0070507 8.39 0.000 .0453641 .0730024 s18 | .0604343 .0062561 9.66 0.000 .0481725 .072696 s19 | .0474172 .0059474 7.97 0.000 .0357606 .0590739 s20 | .0525625 .0066213 7.94 0.000 .039585 .0655401 s21 | .0373648 .0051905 7.20 0.000 .0271916 .0475381 s22 | .0704899 .0071163 9.91 0.000 .0565422 .0844376 s23 | .0569226 .0057238 9.94 0.000 .0457042 .0681409 s24 | .0568391 .006135 9.26 0.000 .0448147 .0688635 s25 | .0685397 .0072517 9.45 0.000 .0543267 .0827527 s26 | .0594205 .0065837 9.03 0.000 .0465166 .0723243 s27 | .0650632 .0077344 8.41 0.000 .0499041 .0802224 s28 | .0529368 .0058957 8.98 0.000 .0413814 .0644922 s29 | .0670211 .0083141 8.06 0.000 .0507258 .0833165 s30 | .0715915 .0074312 9.63 0.000 .0570267 .0861564 s31 | .0543522 .0051813 10.49 0.000 .0441972 .0645073 s32 | .0562206 .0066074 8.51 0.000 .0432703 .0691709 s33 | .0522021 .0056725 9.20 0.000 .0410843 .0633199 s34 | .0570052 .0049175 11.59 0.000 .047367 .0666434 s35 | .0583752 .0064842 9.00 0.000 .0456664 .0710841 s36 | .0648538 .0064635 10.03 0.000 .0521855 .0775221 s37 | .0599818 .0071743 8.36 0.000 .0459205 .074043 s38 | .056232 .0069249 8.12 0.000 .0426595 .0698045 s39 | .073169 .0081839 8.94 0.000 .0571289 .0892092 s40 | .0649957 .0071367 9.11 0.000 .0510079 .0789834 s41 | .0557041 .0058842 9.47 0.000 .0441713 .0672368 s42 | .0702865 .0084171 8.35 0.000 .0537892 .0867838 s43 | .0596919 .0061255 9.74 0.000 .0476861 .0716976 s44 | .0310468 .0046734 6.64 0.000 .0218871 .0402065 s45 | .032506 .005206 6.24 0.000 .0223025 .0427095 s46 | .042465 .0049795 8.53 0.000 .0327054 .0522247 s47 | .0507933 .0060796 8.35 0.000 .0388774 .0627091 s48 | .0406848 .0053811 7.56 0.000 .0301381 .0512316 s49 | .0595813 .0067745 8.79 0.000 .0463034 .0728591 s50 | .0811388 .0083242 9.75 0.000 .0648236 .0974539 s51 | .0423661 .0063157 6.71 0.000 .0299874 .0547447 _cons | .1023288 .0379881 2.69 0.007 .0278735 .176784 ------------------------------------------------------------------------------ Instrumented: D.lnh D.lnst D.lnw D.lninc age1h age3h age1w age3w age1s age3s Instruments: age1ratio age3ratio s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39 s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 L2D.lnh L3D.lnh L4D.lnh L2D.lnst L3D.lnst L4D.lnst L2D.lnw L3D.lnw L4D.lnw L2D.lninc L3D.lninc L4D.lninc L2.age1h L3.age1h L4.age1h L2.age3h L3.age3h L4.age3h L2.age1w L3.age1w L4.age1w L2.age3w L3.age3w L4.age3w L2.age1s L3.age1s L4.age1s L2.age3s L3.age3s L4.age3s . sum chratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- chratio | 1275 .3010725 .0938772 .0721587 .7651958 . local ch = r(mean) . sum csratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- csratio | 1275 .2663826 .139835 .0746406 .9381273 . local cs = r(mean) . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . local hw = r(mean) . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . local sw = r(mean) . sum age1r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age1ratio | 1275 .312477 .0413183 .2286251 .4784656 . local age1r = r(mean) . sum age3r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age3ratio | 1275 .3036991 .0331339 .1347572 .3859731 . local age3r = r(mean) . estadd scalar hwe = ( _b[D.lnh] + _b[D.lnw] * `hw' + _b[age1h] * `age1r' /// > + _b[age3h] * `age3r' + _b[age1w] * `age1r' * `hw' /// > + _b[age3w] * `age3r' * `hw' ) * `ch' . estadd scalar swe = ( _b[D.lnst] + _b[D.lnw] * `sw' + _b[age1s] * `age1r' /// > + _b[age3s] * `age3r' + _b[age1w] * `age1r' * `sw' /// > + _b[age3w] * `age3r' * `sw' ) * `cs' . estadd scalar we_diff = e(hwe) - e(swe) . estadd scalar hwelas = e(hwe) * 1 / `ch' . estadd scalar swelas = e(swe) * 1 / `cs' . estadd scalar elas_diff = e(hwelas) - e(swelas) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' ) * `ch' = 0 ( 1) .3010725*D.lnh + .1375805*D.lnw + .0940782*age1h + .0914355*age3h + .0429907*age1w + .0417831*age3w = 0 chi2( 1) = 39.11 Prob > chi2 = 0.0000 . estadd scalar hwe_p = r(p) . test ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' ) * `cs' = 0 ( 1) .2663826*D.lnst + .1446543*D.lnw + .0452011*age1w + .0439314*age3w + .0832384*age1s + .0809002*age3s = 0 chi2( 1) = 0.00 Prob > chi2 = 0.9451 . estadd scalar swe_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' ) * `ch' = /// > ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' ) * `cs' ( 1) .3010725*D.lnh - .2663826*D.lnst - .0070738*D.lnw + .0940782*age1h + .0914355*age3h - .0022104*age1w - .0021483*age3w - .0832384*age1s - .0809002*age3s = 0 chi2( 1) = 19.96 Prob > chi2 = 0.0000 . estadd scalar we_diff_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' ) = 0 ( 1) D.lnh + .456968*D.lnw + .312477*age1h + .3036991*age3h + .142792*age1w + .1387808*age3w = 0 chi2( 1) = 39.11 Prob > chi2 = 0.0000 . estadd scalar hwelas_p = r(p) . test ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' ) = 0 ( 1) D.lnst + .543032*D.lnw + .169685*age1w + .1649184*age3w + .312477*age1s + .3036991*age3s = 0 chi2( 1) = 0.00 Prob > chi2 = 0.9451 . estadd scalar swelas_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' ) = /// > ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' ) ( 1) D.lnh - D.lnst - .086064*D.lnw + .312477*age1h + .3036991*age3h - .026893*age1w - .0261376*age3w - .312477*age1s - .3036991*age3s = 0 chi2( 1) = 18.14 Prob > chi2 = 0.0000 . estadd scalar elas_diff_p = r(p) . estadd scalar deriv_hy = `ch' * ( _b[age1h] + _b[age1w] * `hw' ) . estadd scalar deriv_ho = `ch' * ( _b[age3h] + _b[age3w] * `hw' ) . estadd scalar deriv_sy = `cs' * ( _b[age1s] + _b[age1w] * `sw' ) . estadd scalar deriv_so = `cs' * ( _b[age3s] + _b[age3w] * `sw' ) . est store iv4 . . gen hwe4 = ( _b[D.lnh] + _b[D.lnw] * h_real / w_real + _b[age1h] * age1r /// > + _b[age3h] * age3r + _b[age1w] * age1r * h_real / w_real /// > + _b[age3w] * age3r * h_real / w_real ) * cons_real / h_real if e(sample) (2907 missing values generated) . gen swe4 = ( _b[D.lnst] + _b[D.lnw] * st_real / w_real + _b[age1s] * age1r /// > + _b[age3s] * age3r + _b[age1w] * age1r * st_real / w_real /// > + _b[age3w] * age3r * st_real / w_real ) * cons_real / st_real if e(sample) (2907 missing values generated) . . ** Model 5 - Full model specification with age and poverty effects . ** along with wealth ratios. . ivregress 2sls d.lncons age1r age3r s2-s51 poverty /// urate > (d.lnh d.lnst d.lnw d.lninc age1h age3h age1s age3s /// > age1w age3w povertyh povertys povertyw /// > = dl(2/4).lnh dl(2/4).lnst dl(2/4).lnw dl(2/4).lninc /// > l(2/4).age1h l(2/4).age3h l(2/4).age1w l(2/4).age3w /// > l(2/4).age1s l(2/4).age3s l(2/4).povertyh l(2/4).povertys /// > l(2/4).povertyw ) , cluster(state) first First-stage regressions ----------------------- Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 307.88 Prob > F = 0.0000 R-squared = 0.5256 Adj R-squared = 0.4886 Root MSE = 0.0433 ------------------------------------------------------------------------------ | Robust D.lnh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.2455865 .095659 -2.57 0.010 -.4332669 -.0579061 age3ratio | -.3042033 .1743058 -1.75 0.081 -.6461866 .0377799 s2 | .0663752 .0202835 3.27 0.001 .0265795 .1061709 s3 | .077462 .0231251 3.35 0.001 .0320912 .1228328 s4 | .0571102 .0186039 3.07 0.002 .0206098 .0936106 s5 | .0520735 .012374 4.21 0.000 .027796 .076351 s6 | .0216148 .0107583 2.01 0.045 .0005073 .0427223 s7 | .0298792 .0191786 1.56 0.120 -.0077487 .0675071 s8 | .0927066 .0177748 5.22 0.000 .057833 .1275803 s9 | .0550541 .0173635 3.17 0.002 .0209875 .0891208 s10 | .0689668 .0270866 2.55 0.011 .0158235 .12211 s11 | .0338978 .0120851 2.80 0.005 .0101871 .0576085 s12 | .0664921 .0170116 3.91 0.000 .0331159 .0998683 s13 | .0516145 .0218122 2.37 0.018 .0088197 .0944094 s14 | .0559217 .0169034 3.31 0.001 .0227577 .0890857 s15 | .0519113 .0164504 3.16 0.002 .019636 .0841866 s16 | .0512998 .0171112 3.00 0.003 .0177281 .0848715 s17 | .0395828 .0182702 2.17 0.030 .0037371 .0754284 s18 | .0660503 .018438 3.58 0.000 .0298755 .1022251 s19 | .0568097 .0179336 3.17 0.002 .0216245 .0919949 s20 | .0378539 .0182307 2.08 0.038 .0020858 .073622 s21 | .0366534 .0133222 2.75 0.006 .0105156 .0627913 s22 | .0540228 .0200275 2.70 0.007 .0147295 .0933162 s23 | .049272 .0161643 3.05 0.002 .0175582 .0809858 s24 | .0236721 .0156056 1.52 0.130 -.0069457 .0542899 s25 | .0497715 .0195805 2.54 0.011 .011355 .088188 s26 | .071068 .0202389 3.51 0.000 .0313599 .1107762 s27 | .0667432 .0201761 3.31 0.001 .0271583 .1063281 s28 | .0557877 .017406 3.21 0.001 .0216375 .0899379 s29 | .0497144 .0207655 2.39 0.017 .008973 .0904557 s30 | .0405425 .0194506 2.08 0.037 .002381 .0787041 s31 | .0143971 .0149617 0.96 0.336 -.0149574 .0437516 s32 | .0369596 .0182174 2.03 0.043 .0012175 .0727017 s33 | .06416 .017692 3.63 0.000 .0294489 .0988712 s34 | .0320361 .0149599 2.14 0.032 .0026852 .061387 s35 | .0592561 .0185542 3.19 0.001 .0228532 .095659 s36 | .0485371 .018551 2.62 0.009 .0121406 .0849336 s37 | .0557844 .0200065 2.79 0.005 .0165321 .0950366 s38 | .064035 .0179626 3.56 0.000 .0287929 .0992771 s39 | .0612939 .0230245 2.66 0.008 .0161204 .1064674 s40 | .0603869 .0205204 2.94 0.003 .0201265 .1006474 s41 | .062128 .0175627 3.54 0.000 .0276704 .0965855 s42 | .0617263 .0219023 2.82 0.005 .0187545 .104698 s43 | .060117 .0185522 3.24 0.001 .023718 .096516 s44 | .0392022 .0132119 2.97 0.003 .0132809 .0651236 s45 | .0379562 .0118728 3.20 0.001 .0146621 .0612503 s46 | .0333773 .0136376 2.45 0.015 .0066208 .0601339 s47 | .0396796 .0168225 2.36 0.019 .0066742 .0726849 s48 | .0452326 .0141789 3.19 0.001 .017414 .0730512 s49 | .0471268 .0177472 2.66 0.008 .0123072 .0819464 s50 | .0791933 .024147 3.28 0.001 .0318175 .1265691 s51 | .0370996 .0156586 2.37 0.018 .0063778 .0678213 poverty | -.3944465 .0731738 -5.39 0.000 -.5380116 -.2508815 | lnh | L2D. | 3.817623 1.275106 2.99 0.003 1.315899 6.319347 L3D. | -3.687073 1.592435 -2.32 0.021 -6.811386 -.5627589 L4D. | 1.640489 .8725366 1.88 0.060 -.0714043 3.352382 | lnst | L2D. | 1.273567 1.237177 1.03 0.303 -1.153741 3.700875 L3D. | -1.613008 .8562603 -1.88 0.060 -3.292968 .0669512 L4D. | 1.112671 .6200578 1.79 0.073 -.1038653 2.329208 | lnw | L2D. | -1.884568 2.09163 -0.90 0.368 -5.988289 2.219153 L3D. | 2.69592 1.50642 1.79 0.074 -.2596347 5.651475 L4D. | -2.733358 1.059034 -2.58 0.010 -4.811155 -.6555605 | lninc | L2D. | .2747677 .1007929 2.73 0.007 .0770148 .4725205 L3D. | .4473358 .0833434 5.37 0.000 .2838184 .6108533 L4D. | .3785614 .0998925 3.79 0.000 .182575 .5745478 | age1h | L2. | -7.742722 2.049259 -3.78 0.000 -11.76331 -3.722132 L3. | 7.014732 2.928113 2.40 0.017 1.269854 12.75961 L4. | -2.503673 1.478349 -1.69 0.091 -5.404155 .3968083 | age3h | L2. | -1.307067 2.533141 -0.52 0.606 -6.277021 3.662887 L3. | 1.183662 2.930329 0.40 0.686 -4.565565 6.932888 L4. | -4.014602 1.746899 -2.30 0.022 -7.44197 -.5872345 | age1w | L2. | 6.386166 3.480516 1.83 0.067 -.4425127 13.21484 L3. | -5.42295 2.510815 -2.16 0.031 -10.3491 -.4967986 L4. | 3.829114 1.802987 2.12 0.034 .291702 7.366526 | age3w | L2. | 1.63911 3.911912 0.42 0.675 -6.035956 9.314176 L3. | -2.125193 3.273166 -0.65 0.516 -8.547056 4.296671 L4. | 5.342155 1.979979 2.70 0.007 1.45749 9.22682 | age1s | L2. | -3.6013 2.001899 -1.80 0.072 -7.528972 .3263731 L3. | 3.387187 1.424834 2.38 0.018 .5917005 6.182674 L4. | -1.457676 1.021878 -1.43 0.154 -3.462573 .547221 | age3s | L2. | -1.878828 2.325165 -0.81 0.419 -6.440739 2.683083 L3. | .5762102 1.856188 0.31 0.756 -3.065581 4.218002 L4. | -2.575637 1.238614 -2.08 0.038 -5.005766 -.1455093 | povertyh | L2. | -1.789148 2.012189 -0.89 0.374 -5.737008 2.158712 L3. | 1.499196 1.542939 0.97 0.331 -1.528009 4.526401 L4. | 2.166488 1.164722 1.86 0.063 -.1186649 4.451641 | povertys | L2. | 2.018094 1.144676 1.76 0.078 -.2277283 4.263917 L3. | 1.141103 1.212843 0.94 0.347 -1.238463 3.520668 L4. | 1.097311 .9300136 1.18 0.238 -.727351 2.921972 | povertyw | L2. | -2.93824 2.150796 -1.37 0.172 -7.158044 1.281563 L3. | -.1631732 2.248783 -0.07 0.942 -4.575224 4.248878 L4. | -1.560805 1.732445 -0.90 0.368 -4.959814 1.838205 | _cons | .183277 .0625026 2.93 0.003 .0606487 .3059054 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 229.71 Prob > F = 0.0000 R-squared = 0.2751 Adj R-squared = 0.2186 Root MSE = 0.1346 ------------------------------------------------------------------------------ | Robust D.lnst | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .6583932 .2399518 2.74 0.006 .1876143 1.129172 age3ratio | -1.447497 .3422475 -4.23 0.000 -2.118977 -.7760166 s2 | .267336 .0546914 4.89 0.000 .1600328 .3746391 s3 | .3070736 .0632628 4.85 0.000 .1829538 .4311935 s4 | .2108673 .0494434 4.26 0.000 .1138607 .3078739 s5 | .1221745 .0348709 3.50 0.000 .0537588 .1905902 s6 | .1404445 .0296487 4.74 0.000 .0822745 .1986145 s7 | .2693092 .0484348 5.56 0.000 .1742815 .3643369 s8 | .1065614 .0471933 2.26 0.024 .0139695 .1991533 s9 | .1936297 .0433425 4.47 0.000 .108593 .2786665 s10 | .3642126 .0698143 5.22 0.000 .2272388 .5011863 s11 | .1348824 .0331719 4.07 0.000 .0698 .1999649 s12 | .2216736 .0427081 5.19 0.000 .1378814 .3054657 s13 | .322024 .0566978 5.68 0.000 .2107845 .4332636 s14 | .2213995 .0447874 4.94 0.000 .1335278 .3092712 s15 | .2061739 .0435779 4.73 0.000 .1206751 .2916726 s16 | .2493131 .045008 5.54 0.000 .1610086 .3376176 s17 | .2334159 .0481964 4.84 0.000 .1388559 .3279759 s18 | .240553 .0514764 4.67 0.000 .1395578 .3415483 s19 | .1997363 .0532455 3.75 0.000 .09527 .3042026 s20 | .2269166 .0463996 4.89 0.000 .1358818 .3179514 s21 | .1830879 .0349077 5.24 0.000 .1146 .2515758 s22 | .2932959 .0523817 5.60 0.000 .1905245 .3960673 s23 | .2124158 .0444182 4.78 0.000 .1252686 .299563 s24 | .1773025 .0406617 4.36 0.000 .0975255 .2570796 s25 | .2460644 .0518284 4.75 0.000 .1443784 .3477503 s26 | .2325144 .0588761 3.95 0.000 .1170011 .3480277 s27 | .2749318 .0560859 4.90 0.000 .1648928 .3849707 s28 | .2260703 .0459593 4.92 0.000 .1358995 .3162411 s29 | .2798534 .0531138 5.27 0.000 .1756456 .3840613 s30 | .2665175 .0501696 5.31 0.000 .1680861 .3649489 s31 | .2250941 .0372073 6.05 0.000 .1520944 .2980938 s32 | .2424498 .0468534 5.17 0.000 .1505247 .334375 s33 | .1797255 .0525314 3.42 0.001 .0766603 .2827908 s34 | .2044708 .0375731 5.44 0.000 .1307535 .2781881 s35 | .2105703 .0504666 4.17 0.000 .1115563 .3095843 s36 | .2584638 .0497628 5.19 0.000 .1608306 .3560969 s37 | .2822457 .0559863 5.04 0.000 .172402 .3920894 s38 | .2490104 .0478218 5.21 0.000 .1551854 .3428355 s39 | .3088661 .0585425 5.28 0.000 .1940072 .423725 s40 | .2837084 .0507266 5.59 0.000 .1841842 .3832326 s41 | .22559 .0458558 4.92 0.000 .1356222 .3155578 s42 | .2908659 .0570745 5.10 0.000 .1788872 .4028445 s43 | .2380973 .0502897 4.73 0.000 .1394303 .3367642 s44 | .1390665 .0390652 3.56 0.000 .0624217 .2157114 s45 | .1071132 .0263049 4.07 0.000 .0555038 .1587227 s46 | .180152 .0344442 5.23 0.000 .1125733 .2477307 s47 | .2336488 .0434316 5.38 0.000 .1484372 .3188605 s48 | .193733 .0375371 5.16 0.000 .1200862 .2673798 s49 | .2593857 .0461816 5.62 0.000 .1687786 .3499928 s50 | .3283405 .0668059 4.91 0.000 .197269 .4594119 s51 | .2161615 .0411192 5.26 0.000 .1354868 .2968362 poverty | .0938378 .2355602 0.40 0.690 -.3683249 .5560004 | lnh | L2D. | 4.028852 3.42999 1.17 0.240 -2.700697 10.7584 L3D. | 1.701829 3.137331 0.54 0.588 -4.45353 7.857189 L4D. | 6.305885 2.840614 2.22 0.027 .7326774 11.87909 | lnst | L2D. | 7.095042 3.1298 2.27 0.024 .954459 13.23562 L3D. | 5.144373 2.94969 1.74 0.081 -.6428386 10.93158 L4D. | 1.959177 2.456988 0.80 0.425 -2.861367 6.779721 | lnw | L2D. | -7.865947 5.645668 -1.39 0.164 -18.94259 3.2107 L3D. | -13.10393 5.218649 -2.51 0.012 -23.34278 -2.865083 L4D. | -2.853414 4.561104 -0.63 0.532 -11.80218 6.095348 | lninc | L2D. | -.6580832 .3454962 -1.90 0.057 -1.335937 .019771 L3D. | 1.014609 .2729062 3.72 0.000 .4791746 1.550044 L4D. | -1.316069 .2748011 -4.79 0.000 -1.855221 -.7769167 | age1h | L2. | -8.260734 5.947045 -1.39 0.165 -19.92868 3.407207 L3. | 2.795379 6.12702 0.46 0.648 -9.225669 14.81643 L4. | -13.69089 5.734424 -2.39 0.017 -24.94168 -2.440107 | age3h | L2. | -4.50639 6.356076 -0.71 0.478 -16.97684 7.964059 L3. | -7.968554 5.158942 -1.54 0.123 -18.09026 2.153151 L4. | -5.682669 4.595566 -1.24 0.216 -14.69904 3.333708 | age1w | L2. | 16.88202 10.46083 1.61 0.107 -3.641852 37.4059 L3. | 20.27407 9.418658 2.15 0.032 1.794912 38.75322 L4. | 7.328938 8.736036 0.84 0.402 -9.810928 24.4688 | age3w | L2. | 10.51173 9.932882 1.06 0.290 -8.976314 29.99978 L3. | 14.26922 9.22021 1.55 0.122 -3.820585 32.35902 L4. | 1.842117 8.222755 0.22 0.823 -14.29071 17.97494 | age1s | L2. | -13.38729 5.791759 -2.31 0.021 -24.75057 -2.024017 L3. | -8.975681 5.151411 -1.74 0.082 -19.08261 1.131248 L4. | -3.986931 4.635796 -0.86 0.390 -13.08224 5.108374 | age3s | L2. | -12.21527 5.520952 -2.21 0.027 -23.04723 -1.383314 L3. | -3.832215 5.149665 -0.74 0.457 -13.93572 6.271288 L4. | -2.927333 4.428759 -0.66 0.509 -11.61644 5.761772 | povertyh | L2. | 3.562792 3.69391 0.96 0.335 -3.68456 10.81015 L3. | -1.069648 4.274848 -0.25 0.802 -9.456784 7.317488 L4. | 1.661756 3.615741 0.46 0.646 -5.432231 8.755742 | povertys | L2. | 5.395809 2.980928 1.81 0.071 -.4526899 11.24431 L3. | .230489 2.825074 0.08 0.935 -5.31223 5.773208 L4. | -.3426855 2.519786 -0.14 0.892 -5.286437 4.601066 | povertyw | L2. | -4.837602 5.5879 -0.87 0.387 -15.80091 6.125708 L3. | .6331581 5.465048 0.12 0.908 -10.08912 11.35544 L4. | .7670982 4.719263 0.16 0.871 -8.491969 10.02617 | _cons | .0964237 .1182049 0.82 0.415 -.1354912 .3283386 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 347.39 Prob > F = 0.0000 R-squared = 0.2933 Adj R-squared = 0.2383 Root MSE = 0.0821 ------------------------------------------------------------------------------ | Robust D.lnw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .206195 .1607192 1.28 0.200 -.1091318 .5215218 age3ratio | -.8716663 .2565751 -3.40 0.001 -1.37506 -.3682729 s2 | .166731 .0368507 4.52 0.000 .0944308 .2390311 s3 | .1947198 .0424302 4.59 0.000 .1114729 .2779667 s4 | .1377589 .0336646 4.09 0.000 .0717099 .2038079 s5 | .0888917 .0235901 3.77 0.000 .0426085 .1351749 s6 | .0861137 .0203517 4.23 0.000 .0461842 .1260431 s7 | .1564734 .0339886 4.60 0.000 .0897886 .2231581 s8 | .1007786 .0309562 3.26 0.001 .0400434 .1615137 s9 | .1303097 .030518 4.27 0.000 .0704342 .1901853 s10 | .2235781 .0483858 4.62 0.000 .1286466 .3185096 s11 | .084952 .0222928 3.81 0.000 .0412141 .1286898 s12 | .1471874 .0301525 4.88 0.000 .0880291 .2063457 s13 | .1982442 .0395535 5.01 0.000 .1206413 .275847 s14 | .1437667 .0307207 4.68 0.000 .0834934 .2040399 s15 | .1346087 .0299753 4.49 0.000 .0757979 .1934195 s16 | .1577576 .0311529 5.06 0.000 .0966364 .2188788 s17 | .1449255 .0333585 4.34 0.000 .0794772 .2103739 s18 | .1563254 .0346372 4.51 0.000 .0883681 .2242826 s19 | .1273899 .0354332 3.60 0.000 .0578708 .196909 s20 | .1384455 .0323433 4.28 0.000 .0749889 .2019021 s21 | .1165152 .0242228 4.81 0.000 .0689908 .1640396 s22 | .1790415 .0360871 4.96 0.000 .1082395 .2498434 s23 | .1376744 .0305741 4.50 0.000 .0776888 .19766 s24 | .1061855 .0280576 3.78 0.000 .0511373 .1612337 s25 | .1542718 .0357048 4.32 0.000 .0842199 .2243237 s26 | .1505059 .0389273 3.87 0.000 .0741315 .2268802 s27 | .1760568 .0381337 4.62 0.000 .1012394 .2508741 s28 | .1434516 .0313731 4.57 0.000 .0818985 .2050048 s29 | .1734841 .0367941 4.71 0.000 .1012951 .2456732 s30 | .1634913 .0349437 4.68 0.000 .0949327 .2320499 s31 | .1272797 .0262502 4.85 0.000 .0757774 .1787819 s32 | .1479902 .0328919 4.50 0.000 .0834573 .2125232 s33 | .1208103 .034704 3.48 0.001 .0527219 .1888987 s34 | .1208589 .0262434 4.61 0.000 .06937 .1723477 s35 | .1387405 .0341722 4.06 0.000 .0716955 .2057855 s36 | .1614123 .0343508 4.70 0.000 .094017 .2288076 s37 | .1750384 .0379462 4.61 0.000 .1005889 .2494879 s38 | .1632357 .0329368 4.96 0.000 .0986147 .2278568 s39 | .1922403 .0408145 4.71 0.000 .1121633 .2723173 s40 | .1783192 .0353836 5.04 0.000 .1088976 .2477408 s41 | .1425426 .0312184 4.57 0.000 .0812929 .2037923 s42 | .1840763 .0392425 4.69 0.000 .1070836 .2610691 s43 | .150054 .0339377 4.42 0.000 .0834692 .2166388 s44 | .0929044 .0258664 3.59 0.000 .0421552 .1436536 s45 | .0770095 .019041 4.04 0.000 .0396515 .1143675 s46 | .1106354 .0239643 4.62 0.000 .0636182 .1576527 s47 | .1429364 .0303281 4.71 0.000 .0834335 .2024393 s48 | .1248024 .0259119 4.82 0.000 .073964 .1756408 s49 | .1613649 .0321988 5.01 0.000 .0981917 .224538 s50 | .2057824 .0448888 4.58 0.000 .1177118 .2938531 s51 | .1334255 .0285808 4.67 0.000 .0773509 .1895002 poverty | -.1360794 .1348379 -1.01 0.313 -.4006278 .128469 | lnh | L2D. | 4.254049 2.126943 2.00 0.046 .0810448 8.427054 L3D. | 1.412223 1.943129 0.73 0.468 -2.400145 5.22459 L4D. | 3.855577 1.907131 2.02 0.043 .1138388 7.597316 | lnst | L2D. | 5.223622 2.216021 2.36 0.019 .8758487 9.571395 L3D. | 4.424984 1.803851 2.45 0.014 .8858765 7.964091 L4D. | 1.342692 1.554 0.86 0.388 -1.706214 4.391599 | lnw | L2D. | -6.584937 3.926127 -1.68 0.094 -14.28789 1.118018 L3D. | -9.935125 3.19001 -3.11 0.002 -16.19384 -3.676411 L4D. | -2.530882 2.90723 -0.87 0.384 -8.234789 3.173026 | lninc | L2D. | -.2359954 .2006818 -1.18 0.240 -.6297276 .1577369 L3D. | .739038 .1489811 4.96 0.000 .4467411 1.031335 L4D. | -.5785269 .1225809 -4.72 0.000 -.8190272 -.3380266 | age1h | L2. | -8.500858 3.557754 -2.39 0.017 -15.48108 -1.520641 L3. | .784609 3.953677 0.20 0.843 -6.972398 8.541616 L4. | -8.132652 3.660194 -2.22 0.026 -15.31385 -.951451 | age3h | L2. | -3.072211 4.038522 -0.76 0.447 -10.99568 4.85126 L3. | -6.901187 3.261263 -2.12 0.035 -13.2997 -.5026765 L4. | -4.494824 2.983365 -1.51 0.132 -10.34811 1.358458 | age1w | L2. | 15.13168 6.97318 2.17 0.030 1.450484 28.81287 L3. | 15.51134 5.900926 2.63 0.009 3.933883 27.0888 L4. | 5.246243 5.455837 0.96 0.336 -5.457963 15.95045 | age3w | L2. | 7.197102 6.954664 1.03 0.301 -6.447761 20.84196 L3. | 13.03111 5.374423 2.42 0.015 2.486637 23.57558 L4. | 2.695768 5.161075 0.52 0.602 -7.430122 12.82166 | age1s | L2. | -10.50731 3.926809 -2.68 0.008 -18.2116 -2.803015 L3. | -7.327219 3.194906 -2.29 0.022 -13.59554 -1.058899 L4. | -2.383324 2.880423 -0.83 0.408 -8.034637 3.267989 | age3s | L2. | -7.92346 3.904027 -2.03 0.043 -15.58306 -.2638641 L3. | -5.609341 3.092427 -1.81 0.070 -11.6766 .4579178 L4. | -2.30474 2.762643 -0.83 0.404 -7.724971 3.115492 | povertyh | L2. | .1446965 2.248912 0.06 0.949 -4.267609 4.557002 L3. | .6476767 2.653038 0.24 0.807 -4.557512 5.852866 L4. | 2.428091 2.148575 1.13 0.259 -1.787355 6.643537 | povertys | L2. | 3.164906 1.83693 1.72 0.085 -.4391011 6.768913 L3. | 1.212629 1.882105 0.64 0.520 -2.48001 4.905267 L4. | .2359573 1.6037 0.15 0.883 -2.910458 3.382373 | povertyw | L2. | -2.64715 3.514123 -0.75 0.451 -9.541764 4.247465 L3. | -.6357798 3.526425 -0.18 0.857 -7.554531 6.282972 L4. | .0023253 2.985752 0.00 0.999 -5.855639 5.860289 | _cons | .1347552 .0853843 1.58 0.115 -.0327665 .302277 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 215.59 Prob > F = 0.0000 R-squared = 0.3016 Adj R-squared = 0.2472 Root MSE = 0.0190 ------------------------------------------------------------------------------ | Robust D.lninc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0831973 .031459 -2.64 0.008 -.144919 -.0214756 age3ratio | -.2114365 .0899445 -2.35 0.019 -.3879052 -.0349679 s2 | .0326757 .0098447 3.32 0.001 .0133607 .0519908 s3 | .0360621 .0111252 3.24 0.001 .0142348 .0578893 s4 | .0273118 .0088121 3.10 0.002 .0100227 .0446009 s5 | .0229763 .0059496 3.86 0.000 .0113033 .0346492 s6 | .0214631 .0054315 3.95 0.000 .0108066 .0321195 s7 | .0289855 .0097432 2.97 0.003 .0098696 .0481014 s8 | .0464272 .0072206 6.43 0.000 .0322606 .0605938 s9 | .0226259 .0086549 2.61 0.009 .0056452 .0396067 s10 | .0379799 .0134753 2.82 0.005 .0115417 .0644181 s11 | .0224325 .0058845 3.81 0.000 .0108873 .0339778 s12 | .0242324 .0085024 2.85 0.004 .0075509 .0409139 s13 | .032355 .0113696 2.85 0.005 .010048 .0546619 s14 | .0266213 .0083317 3.20 0.001 .0102746 .0429679 s15 | .0257427 .0080924 3.18 0.002 .0098657 .0416197 s16 | .0243281 .0087025 2.80 0.005 .0072541 .0414021 s17 | .0302285 .0093525 3.23 0.001 .0118791 .0485779 s18 | .0301882 .0090397 3.34 0.001 .0124525 .0479239 s19 | .0342412 .0087864 3.90 0.000 .0170025 .0514798 s20 | .0311871 .0088835 3.51 0.000 .0137578 .0486164 s21 | .0254105 .0068715 3.70 0.000 .0119287 .0388922 s22 | .0326758 .0102247 3.20 0.001 .0126153 .0527363 s23 | .0226498 .0083143 2.72 0.007 .0063374 .0389621 s24 | .0279738 .0079074 3.54 0.000 .0124597 .0434878 s25 | .0297495 .0098716 3.01 0.003 .0103816 .0491173 s26 | .035997 .0097155 3.71 0.000 .0169356 .0550585 s27 | .0323898 .0101251 3.20 0.001 .0125247 .052255 s28 | .028173 .008453 3.33 0.001 .0115883 .0447576 s29 | .0371921 .0103065 3.61 0.000 .016971 .0574132 s30 | .0318364 .009958 3.20 0.001 .012299 .0513737 s31 | .0222452 .0078604 2.83 0.005 .0068234 .037667 s32 | .0274002 .0092057 2.98 0.003 .0093388 .0454615 s33 | .0322676 .0086058 3.75 0.000 .0153832 .0491519 s34 | .0206861 .0076217 2.71 0.007 .0057326 .0356396 s35 | .0316139 .0088347 3.58 0.000 .0142803 .0489474 s36 | .0248735 .0094946 2.62 0.009 .0062453 .0435017 s37 | .0311983 .0101492 3.07 0.002 .0112857 .0511108 s38 | .0265962 .0090617 2.94 0.003 .0088175 .044375 s39 | .0316475 .0115992 2.73 0.006 .0088902 .0544048 s40 | .0309885 .0100065 3.10 0.002 .011356 .050621 s41 | .0294204 .0086785 3.39 0.001 .0123934 .0464475 s42 | .0388699 .0108888 3.57 0.000 .0175064 .0602334 s43 | .0301864 .009122 3.31 0.001 .0122894 .0480835 s44 | .0263044 .0060875 4.32 0.000 .014361 .0382479 s45 | .0181184 .0052819 3.43 0.001 .0077554 .0284814 s46 | .0254183 .0068646 3.70 0.000 .0119501 .0388865 s47 | .0287862 .0085828 3.35 0.001 .0119471 .0456253 s48 | .0258207 .007194 3.59 0.000 .0117064 .0399351 s49 | .0257875 .009069 2.84 0.005 .0079943 .0435806 s50 | .0360345 .0120888 2.98 0.003 .0123167 .0597523 s51 | .0334113 .0079264 4.22 0.000 .0178599 .0489627 poverty | -.1186866 .02639 -4.50 0.000 -.1704631 -.0669101 | lnh | L2D. | -2.583663 .5063233 -5.10 0.000 -3.577056 -1.590271 L3D. | 1.014134 .4204113 2.41 0.016 .1892987 1.83897 L4D. | -1.239342 .5192066 -2.39 0.017 -2.258012 -.220673 | lnst | L2D. | -1.965761 .4554233 -4.32 0.000 -2.85929 -1.072233 L3D. | .7418827 .3982668 1.86 0.063 -.0395061 1.523271 L4D. | -1.154932 .4319112 -2.67 0.008 -2.00233 -.3075343 | lnw | L2D. | 4.294094 .838557 5.12 0.000 2.648868 5.93932 L3D. | -1.557874 .6980615 -2.23 0.026 -2.927452 -.1882964 L4D. | 1.530412 .8223056 1.86 0.063 -.0829296 3.143753 | lninc | L2D. | .0544126 .033638 1.62 0.106 -.0115843 .1204094 L3D. | -.0758594 .0231418 -3.28 0.001 -.121263 -.0304557 L4D. | .018617 .0335409 0.56 0.579 -.0471894 .0844233 | age1h | L2. | 3.187542 .7250667 4.40 0.000 1.76498 4.610103 L3. | -1.932369 .8280379 -2.33 0.020 -3.556957 -.3077808 L4. | 2.316332 .9668233 2.40 0.017 .4194506 4.213213 | age3h | L2. | 5.635967 1.16827 4.82 0.000 3.343854 7.92808 L3. | -1.513383 .7144462 -2.12 0.034 -2.915107 -.1116587 L4. | 1.20382 .8836993 1.36 0.173 -.5299741 2.937614 | age1w | L2. | -5.20334 1.253368 -4.15 0.000 -7.662414 -2.744266 L3. | 2.376684 1.246815 1.91 0.057 -.0695327 4.8229 L4. | -2.573209 1.447104 -1.78 0.076 -5.412387 .2659698 | age3w | L2. | -8.191327 1.882996 -4.35 0.000 -11.88571 -4.496939 L3. | 2.268458 1.313557 1.73 0.084 -.3087041 4.845621 L4. | -2.317961 1.512811 -1.53 0.126 -5.286056 .6501341 | age1s | L2. | 2.248712 .6549608 3.43 0.001 .9636962 3.533727 L3. | -1.183929 .7127056 -1.66 0.097 -2.582238 .21438 L4. | 1.881448 .729284 2.58 0.010 .4506121 3.312283 | age3s | L2. | 3.683 1.01065 3.64 0.000 1.700133 5.665868 L3. | -.8800313 .7258278 -1.21 0.226 -2.304086 .5440234 L4. | 1.769486 .843225 2.10 0.036 .1151011 3.42387 | povertyh | L2. | -.6800232 .583602 -1.17 0.244 -1.825034 .4649881 L3. | 1.378013 .7390293 1.86 0.062 -.0719425 2.827969 L4. | .0494945 .6669508 0.07 0.941 -1.259045 1.358034 | povertys | L2. | .5547962 .4991606 1.11 0.267 -.4245435 1.534136 L3. | .3282812 .4600235 0.71 0.476 -.5742724 1.230835 L4. | -.0860175 .4409384 -0.20 0.845 -.9511267 .7790916 | povertyw | L2. | -.3121469 .9531215 -0.33 0.743 -2.182146 1.557852 L3. | -.6360808 .8896682 -0.71 0.475 -2.381586 1.109424 L4. | .1520572 .9022882 0.17 0.866 -1.618208 1.922322 | _cons | .0938574 .022619 4.15 0.000 .0494795 .1382354 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 274.73 Prob > F = 0.0000 R-squared = 0.4951 Adj R-squared = 0.4558 Root MSE = 0.0142 ------------------------------------------------------------------------------ | Robust age1h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.041245 .0357313 -1.15 0.249 -.1113488 .0288589 age3ratio | -.066895 .0547314 -1.22 0.222 -.1742766 .0404866 s2 | .0187492 .0063515 2.95 0.003 .0062878 .0312106 s3 | .0212689 .0072178 2.95 0.003 .0071077 .03543 s4 | .0155426 .005849 2.66 0.008 .0040671 .0270181 s5 | .0163216 .0039616 4.12 0.000 .0085491 .024094 s6 | .0059474 .0034548 1.72 0.085 -.0008309 .0127257 s7 | .0071932 .0061625 1.17 0.243 -.0048975 .0192839 s8 | .0290131 .005638 5.15 0.000 .0179514 .0400747 s9 | .015039 .0055139 2.73 0.006 .0042208 .0258571 s10 | .0174618 .0085152 2.05 0.041 .0007553 .0341683 s11 | .010915 .0038448 2.84 0.005 .0033717 .0184584 s12 | .0197398 .0054764 3.60 0.000 .0089952 .0304844 s13 | .0128756 .006874 1.87 0.061 -.000611 .0263621 s14 | .0157124 .0053493 2.94 0.003 .0052173 .0262075 s15 | .014935 .0052225 2.86 0.004 .0046886 .0251814 s16 | .0143212 .0054498 2.63 0.009 .0036288 .0250136 s17 | .0099378 .0057537 1.73 0.084 -.0013507 .0212264 s18 | .019095 .005801 3.29 0.001 .0077136 .0304763 s19 | .0166418 .00559 2.98 0.003 .0056744 .0276092 s20 | .0099915 .0057964 1.72 0.085 -.001381 .0213639 s21 | .0104665 .0042734 2.45 0.014 .0020821 .0188509 s22 | .0144824 .0063568 2.28 0.023 .0020105 .0269543 s23 | .0145768 .0051489 2.83 0.005 .0044748 .0246789 s24 | .0055027 .0049433 1.11 0.266 -.0041959 .0152012 s25 | .0130551 .0061586 2.12 0.034 .0009721 .025138 s26 | .0208256 .0062704 3.32 0.001 .0085231 .033128 s27 | .0178152 .0063451 2.81 0.005 .0053664 .030264 s28 | .0162219 .0055081 2.95 0.003 .0054151 .0270286 s29 | .0118176 .0065264 1.81 0.070 -.000987 .0246223 s30 | .0100672 .0061334 1.64 0.101 -.0019665 .0221008 s31 | .0033496 .0048386 0.69 0.489 -.0061437 .0128428 s32 | .0095957 .0058329 1.65 0.100 -.0018483 .0210398 s33 | .019247 .0054878 3.51 0.000 .0084801 .0300139 s34 | .0088047 .0048101 1.83 0.067 -.0006327 .018242 s35 | .0168378 .0058395 2.88 0.004 .0053809 .0282948 s36 | .0134542 .0059008 2.28 0.023 .001877 .0250315 s37 | .0150525 .0062986 2.39 0.017 .0026948 .0274102 s38 | .0174862 .0056857 3.08 0.002 .0063309 .0286414 s39 | .0159591 .0072735 2.19 0.028 .0016888 .0302295 s40 | .016429 .0064845 2.53 0.011 .0037067 .0291514 s41 | .018165 .0055338 3.28 0.001 .0073079 .0290221 s42 | .0160521 .0068437 2.35 0.019 .002625 .0294792 s43 | .017367 .0058283 2.98 0.003 .0059321 .028802 s44 | .0114169 .0041529 2.75 0.006 .003269 .0195648 s45 | .0115685 .0039745 2.91 0.004 .0037707 .0193663 s46 | .0095531 .0043674 2.19 0.029 .0009844 .0181217 s47 | .0111168 .0053991 2.06 0.040 .0005239 .0217097 s48 | .0130849 .0045407 2.88 0.004 .0041762 .0219936 s49 | .0125618 .0056673 2.22 0.027 .0014428 .0236809 s50 | .0215071 .0075492 2.85 0.004 .0066959 .0363184 s51 | .0095537 .0049952 1.91 0.056 -.0002468 .0193542 poverty | -.13265 .0255067 -5.20 0.000 -.1826935 -.0826065 | lnh | L2D. | .9400607 .4602253 2.04 0.041 .0371111 1.84301 L3D. | -.932239 .5556517 -1.68 0.094 -2.022413 .1579346 L4D. | .4759138 .3112448 1.53 0.127 -.1347402 1.086568 | lnst | L2D. | .4342228 .4377672 0.99 0.321 -.4246646 1.29311 L3D. | -.4530937 .2833326 -1.60 0.110 -1.008985 .1027972 L4D. | .304499 .2033486 1.50 0.135 -.0944655 .7034634 | lnw | L2D. | -.6343157 .7426371 -0.85 0.393 -2.09135 .8227183 L3D. | .7455975 .4908495 1.52 0.129 -.217436 1.708631 L4D. | -.8020402 .3501999 -2.29 0.022 -1.489123 -.1149576 | lninc | L2D. | .0880404 .0330835 2.66 0.008 .0231315 .1529493 L3D. | .137822 .0264362 5.21 0.000 .0859549 .1896892 L4D. | .1094823 .0346606 3.16 0.002 .0414792 .1774854 | age1h | L2. | -1.889966 .7811039 -2.42 0.016 -3.42247 -.3574608 L3. | 1.614989 1.007312 1.60 0.109 -.3613304 3.591308 L4. | -.9050403 .5160559 -1.75 0.080 -1.917528 .1074473 | age3h | L2. | -.1510094 .8953049 -0.17 0.866 -1.907573 1.605555 L3. | .2218047 1.037047 0.21 0.831 -1.812854 2.256463 L4. | -.9645518 .6584271 -1.46 0.143 -2.256368 .3272645 | age1w | L2. | 2.454551 1.270976 1.93 0.054 -.0390697 4.948172 L3. | -1.498302 .8569915 -1.75 0.081 -3.179696 .1830926 L4. | 1.254824 .6018127 2.09 0.037 .0740839 2.435565 | age3w | L2. | .2164346 1.394029 0.16 0.877 -2.518613 2.951483 L3. | -.5704608 1.059033 -0.54 0.590 -2.648255 1.507334 L4. | 1.381949 .6578027 2.10 0.036 .0913581 2.67254 | age1s | L2. | -1.403495 .730327 -1.92 0.055 -2.836377 .029387 L3. | .9332475 .4919167 1.90 0.058 -.0318798 1.898375 L4. | -.4836555 .3437917 -1.41 0.160 -1.158166 .1908545 | age3s | L2. | -.4286201 .8187898 -0.52 0.601 -2.035064 1.177823 L3. | .1626266 .6075072 0.27 0.789 -1.029286 1.354539 L4. | -.6068255 .4059791 -1.49 0.135 -1.403346 .1896945 | povertyh | L2. | -.5947917 .6790966 -0.88 0.381 -1.927161 .7375774 L3. | .650353 .5033534 1.29 0.197 -.3372128 1.637919 L4. | .5827682 .3823852 1.52 0.128 -.1674613 1.332998 | povertys | L2. | .6552511 .4104238 1.60 0.111 -.1499894 1.460492 L3. | .360694 .380006 0.95 0.343 -.3848674 1.106255 L4. | .2960877 .3086106 0.96 0.338 -.309398 .9015733 | povertyw | L2. | -.9701701 .7681829 -1.26 0.207 -2.477324 .5369839 L3. | -.0834154 .6973403 -0.12 0.905 -1.451578 1.284748 L4. | -.3614727 .57054 -0.63 0.526 -1.480857 .7579114 | _cons | .0405596 .0213293 1.90 0.057 -.0012879 .0824071 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 339.39 Prob > F = 0.0000 R-squared = 0.5362 Adj R-squared = 0.5001 Root MSE = 0.0131 ------------------------------------------------------------------------------ | Robust age3h | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.068585 .0279141 -2.46 0.014 -.1233517 -.0138183 age3ratio | -.0848173 .0550253 -1.54 0.123 -.1927755 .0231408 s2 | .0222804 .0062637 3.56 0.000 .0099912 .0345697 s3 | .0264793 .0071506 3.70 0.000 .0124499 .0405087 s4 | .0188097 .0057616 3.26 0.001 .0075055 .0301138 s5 | .0157304 .0038072 4.13 0.000 .0082608 .0232 s6 | .0067854 .0032548 2.08 0.037 .0003996 .0131713 s7 | .0113296 .0057617 1.97 0.049 .0000253 .0226338 s8 | .0282369 .0055696 5.07 0.000 .0173096 .0391643 s9 | .0183207 .0053063 3.45 0.001 .0079099 .0287315 s10 | .0242075 .0083219 2.91 0.004 .0078802 .0405348 s11 | .0103114 .0037114 2.78 0.006 .0030297 .0175931 s12 | .0213677 .0051543 4.15 0.000 .011255 .0314803 s13 | .0182376 .0066888 2.73 0.006 .0051144 .0313609 s14 | .0183323 .0051753 3.54 0.000 .0081785 .028486 s15 | .0170923 .0050323 3.40 0.001 .0072191 .0269655 s16 | .0171769 .0052011 3.30 0.001 .0069725 .0273813 s17 | .0137466 .0056196 2.45 0.015 .002721 .0247721 s18 | .0217799 .0056674 3.84 0.000 .0106607 .0328992 s19 | .0185596 .0055527 3.34 0.001 .0076653 .029454 s20 | .0132916 .0055345 2.40 0.016 .0024331 .0241501 s21 | .0118046 .0040222 2.93 0.003 .0039131 .019696 s22 | .0189061 .0060658 3.12 0.002 .0070052 .030807 s23 | .0159582 .0049155 3.25 0.001 .0063141 .0256023 s24 | .008317 .0047748 1.74 0.082 -.001051 .0176849 s25 | .0170608 .0060167 2.84 0.005 .0052563 .0288654 s26 | .0234394 .0063107 3.71 0.000 .0110579 .0358208 s27 | .0229146 .0061784 3.71 0.000 .0107927 .0350365 s28 | .018299 .005335 3.43 0.001 .007832 .0287661 s29 | .0174613 .0064152 2.72 0.007 .0048748 .0300477 s30 | .0143208 .0059725 2.40 0.017 .0026028 .0260387 s31 | .0057059 .0044545 1.28 0.200 -.0030337 .0144455 s32 | .0133073 .0054918 2.42 0.016 .0025326 .024082 s33 | .0205164 .0055141 3.72 0.000 .0096979 .0313349 s34 | .0105975 .0045414 2.33 0.020 .0016874 .0195077 s35 | .0198587 .0057083 3.48 0.001 .0086592 .0310582 s36 | .0165129 .0056409 2.93 0.003 .0054456 .0275802 s37 | .0190454 .0061303 3.11 0.002 .007018 .0310728 s38 | .0212604 .0054927 3.87 0.000 .0104839 .032037 s39 | .02184 .0070245 3.11 0.002 .0080582 .0356218 s40 | .0209802 .006278 3.34 0.001 .0086629 .0332975 s41 | .0200342 .0054083 3.70 0.000 .0094233 .0306451 s42 | .0213812 .006777 3.15 0.002 .0080849 .0346776 s43 | .0199703 .0057054 3.50 0.000 .0087764 .0311642 s44 | .0126758 .0040868 3.10 0.002 .0046575 .0206941 s45 | .0108772 .003577 3.04 0.002 .0038592 .0178952 s46 | .0107537 .004138 2.60 0.009 .0026351 .0188723 s47 | .013764 .0050569 2.72 0.007 .0038425 .0236854 s48 | .0144244 .0042937 3.36 0.001 .0060002 .0228486 s49 | .0161266 .0053903 2.99 0.003 .0055509 .0267022 s50 | .0275052 .0074194 3.71 0.000 .0129484 .0420619 s51 | .0127294 .0047489 2.68 0.007 .0034121 .0220466 poverty | -.1187389 .0222566 -5.33 0.000 -.1624058 -.0750719 | lnh | L2D. | 1.199296 .3511128 3.42 0.001 .510422 1.88817 L3D. | -1.122471 .4690803 -2.39 0.017 -2.042794 -.202148 L4D. | .598645 .2422573 2.47 0.014 .1233428 1.073947 | lnst | L2D. | .559741 .3530399 1.59 0.113 -.1329138 1.252396 L3D. | -.4886858 .2596471 -1.88 0.060 -.9981063 .0207347 L4D. | .3570088 .1887903 1.89 0.059 -.0133927 .7274104 | lnw | L2D. | -.878477 .5978876 -1.47 0.142 -2.051516 .2945624 L3D. | .8389883 .4564153 1.84 0.066 -.0564862 1.734463 L4D. | -.8493498 .3205012 -2.65 0.008 -1.478164 -.2205352 | lninc | L2D. | .0823526 .0297596 2.77 0.006 .0239651 .1407402 L3D. | .1359256 .025009 5.44 0.000 .0868586 .1849926 L4D. | .1105038 .0285911 3.86 0.000 .0544088 .1665988 | age1h | L2. | -2.539165 .5852008 -4.34 0.000 -3.687313 -1.391017 L3. | 2.295454 .891917 2.57 0.010 .5455372 4.045371 L4. | -.6948476 .4239702 -1.64 0.101 -1.526666 .1369704 | age3h | L2. | -.2780653 .6742959 -0.41 0.680 -1.601016 1.044885 L3. | .167929 .8324856 0.20 0.840 -1.465385 1.801243 L4. | -1.618303 .4418865 -3.66 0.000 -2.485273 -.7513338 | age1w | L2. | 1.995464 1.001315 1.99 0.047 .0309108 3.960017 L3. | -1.678876 .7601846 -2.21 0.027 -3.170338 -.1874149 L4. | 1.074639 .5296477 2.03 0.043 .0354841 2.113793 | age3w | L2. | 1.392577 1.093301 1.27 0.203 -.7524495 3.537604 L3. | -.6490025 .9858943 -0.66 0.510 -2.583301 1.285296 L4. | 1.763888 .6013996 2.93 0.003 .5839584 2.943818 | age1s | L2. | -1.132978 .5749308 -1.97 0.049 -2.260976 -.0049792 L3. | 1.041552 .4335988 2.40 0.016 .1908425 1.892261 L4. | -.3913635 .3028985 -1.29 0.197 -.9856422 .2029152 | age3s | L2. | -1.076645 .6565017 -1.64 0.101 -2.364684 .2113932 L3. | .132966 .5639481 0.24 0.814 -.9734848 1.239417 L4. | -.8956372 .3834072 -2.34 0.020 -1.647872 -.1434027 | povertyh | L2. | -.596496 .5955475 -1.00 0.317 -1.764944 .5719522 L3. | .4371516 .4770537 0.92 0.360 -.4988149 1.373118 L4. | .6493139 .3508265 1.85 0.064 -.0389982 1.337626 | povertys | L2. | .5537044 .3335676 1.66 0.097 -.1007462 1.208155 L3. | .3673599 .3785415 0.97 0.332 -.3753282 1.110048 L4. | .3061857 .2782015 1.10 0.271 -.2396382 .8520096 | povertyw | L2. | -.7760565 .6235981 -1.24 0.214 -1.999539 .4474263 L3. | -.0694672 .7002796 -0.10 0.921 -1.443397 1.304463 L4. | -.4260598 .5189139 -0.82 0.412 -1.444155 .5920353 | _cons | .0500209 .0189563 2.64 0.008 .0128292 .0872127 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 231.84 Prob > F = 0.0000 R-squared = 0.2859 Adj R-squared = 0.2303 Root MSE = 0.0408 ------------------------------------------------------------------------------ | Robust age1s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .3108753 .0751949 4.13 0.000 .163345 .4584056 age3ratio | -.3977317 .0943496 -4.22 0.000 -.582843 -.2126204 s2 | .0802725 .0159073 5.05 0.000 .0490629 .1114822 s3 | .0914563 .018451 4.96 0.000 .055256 .1276565 s4 | .0614617 .0142943 4.30 0.000 .0334166 .0895068 s5 | .0359899 .0100717 3.57 0.000 .0162295 .0557502 s6 | .0430864 .0086543 4.98 0.000 .0261069 .060066 s7 | .0821757 .0140584 5.85 0.000 .0545934 .109758 s8 | .0286337 .0137902 2.08 0.038 .0015776 .0556898 s9 | .0572664 .0125203 4.57 0.000 .0327019 .0818309 s10 | .1094596 .0201314 5.44 0.000 .0699624 .1489569 s11 | .0412804 .0096027 4.30 0.000 .0224401 .0601207 s12 | .066781 .0122064 5.47 0.000 .0428324 .0907297 s13 | .095887 .0162982 5.88 0.000 .0639104 .1278636 s14 | .0653604 .0129792 5.04 0.000 .0398955 .0908253 s15 | .0617611 .0126221 4.89 0.000 .036997 .0865253 s16 | .0744342 .0130108 5.72 0.000 .0489074 .099961 s17 | .0693352 .0139092 4.98 0.000 .0420458 .0966245 s18 | .0718931 .0149374 4.81 0.000 .0425863 .1011998 s19 | .0601259 .0154833 3.88 0.000 .0297481 .0905037 s20 | .0701961 .0135018 5.20 0.000 .043706 .0966862 s21 | .0543799 .0101691 5.35 0.000 .0344285 .0743313 s22 | .0885998 .0151872 5.83 0.000 .058803 .1183966 s23 | .0643749 .0127974 5.03 0.000 .0392668 .089483 s24 | .0529749 .0118448 4.47 0.000 .0297356 .0762142 s25 | .0738973 .0149687 4.94 0.000 .0445291 .1032656 s26 | .0699756 .0172086 4.07 0.000 .0362128 .1037383 s27 | .0828098 .0162462 5.10 0.000 .0509352 .1146844 s28 | .0684255 .0133233 5.14 0.000 .0422856 .0945654 s29 | .0829162 .0153 5.42 0.000 .0528981 .1129343 s30 | .0795954 .014464 5.50 0.000 .0512174 .1079734 s31 | .0688497 .0108122 6.37 0.000 .0476365 .0900629 s32 | .0739559 .0135935 5.44 0.000 .0472858 .1006261 s33 | .0538141 .0152713 3.52 0.000 .0238522 .083776 s34 | .0603244 .0107717 5.60 0.000 .0391906 .0814582 s35 | .0639385 .0146645 4.36 0.000 .0351672 .0927099 s36 | .0777819 .0143545 5.42 0.000 .0496188 .1059451 s37 | .0834864 .0162202 5.15 0.000 .0516628 .1153099 s38 | .073499 .0138418 5.31 0.000 .0463418 .1006562 s39 | .0928143 .0169007 5.49 0.000 .0596557 .1259729 s40 | .0853401 .0147159 5.80 0.000 .0564679 .1142124 s41 | .0681034 .0132351 5.15 0.000 .0421364 .0940703 s42 | .086623 .0165089 5.25 0.000 .054233 .119013 s43 | .0716994 .0146108 4.91 0.000 .0430334 .1003654 s44 | .0412238 .0113703 3.63 0.000 .0189156 .0635319 s45 | .0305963 .007435 4.12 0.000 .016009 .0451836 s46 | .05406 .0099834 5.41 0.000 .0344728 .0736472 s47 | .0720249 .0126068 5.71 0.000 .0472907 .096759 s48 | .0574325 .01088 5.28 0.000 .0360863 .0787788 s49 | .0786364 .0133557 5.89 0.000 .0524329 .1048399 s50 | .0971284 .019485 4.98 0.000 .0588994 .1353573 s51 | .0649093 .0119026 5.45 0.000 .0415568 .0882619 poverty | .0299152 .0705922 0.42 0.672 -.1085848 .1684151 | lnh | L2D. | 1.204799 1.040187 1.16 0.247 -.8360194 3.245618 L3D. | .2985081 .9545582 0.31 0.755 -1.574309 2.171326 L4D. | 2.10195 .8853178 2.37 0.018 .3649808 3.83892 | lnst | L2D. | 2.389129 .9657934 2.47 0.014 .494268 4.283989 L3D. | 1.025187 .9268934 1.11 0.269 -.7933525 2.843727 L4D. | .8587653 .7885273 1.09 0.276 -.688304 2.405835 | lnw | L2D. | -2.659133 1.721615 -1.54 0.123 -6.036895 .7186283 L3D. | -2.970944 1.647606 -1.80 0.072 -6.203502 .2616138 L4D. | -1.181555 1.470094 -0.80 0.422 -4.06584 1.702731 | lninc | L2D. | -.1796227 .1016312 -1.77 0.077 -.3790204 .019775 L3D. | .2760977 .078433 3.52 0.000 .1222142 .4299812 L4D. | -.3868499 .0808341 -4.79 0.000 -.5454443 -.2282556 | age1h | L2. | -2.492505 1.794369 -1.39 0.165 -6.013009 1.027998 L3. | 1.614125 1.882825 0.86 0.391 -2.079926 5.308176 L4. | -4.611642 1.773742 -2.60 0.009 -8.091676 -1.131607 | age3h | L2. | -1.308026 1.985691 -0.66 0.510 -5.203898 2.587847 L3. | -2.487811 1.568611 -1.59 0.113 -5.565383 .5897614 L4. | -1.876286 1.486355 -1.26 0.207 -4.792475 1.039903 | age1w | L2. | 5.560665 3.157549 1.76 0.078 -.6343604 11.75569 L3. | 3.695293 2.986676 1.24 0.216 -2.164484 9.555071 L4. | 2.965748 2.773606 1.07 0.285 -2.475991 8.407488 | age3w | L2. | 3.600637 3.093178 1.16 0.245 -2.468095 9.669369 L3. | 3.715268 2.966295 1.25 0.211 -2.104523 9.535058 L4. | .8043052 2.633103 0.31 0.760 -4.361773 5.970383 | age1s | L2. | -4.508434 1.755422 -2.57 0.010 -7.952524 -1.064343 L3. | -1.413171 1.6063 -0.88 0.379 -4.564688 1.738346 L4. | -1.838087 1.447553 -1.27 0.204 -4.678146 1.001972 | age3s | L2. | -4.021946 1.77556 -2.27 0.024 -7.505547 -.5383442 L3. | -.836638 1.667033 -0.50 0.616 -4.107311 2.434035 L4. | -1.084344 1.436872 -0.75 0.451 -3.903448 1.73476 | povertyh | L2. | 1.010543 1.102905 0.92 0.360 -1.153328 3.174414 L3. | -.3394739 1.317217 -0.26 0.797 -2.923818 2.24487 L4. | .4868226 1.16542 0.42 0.676 -1.799701 2.773346 | povertys | L2. | 1.648286 .9510793 1.73 0.083 -.2177062 3.514278 L3. | .0463955 .9089664 0.05 0.959 -1.736972 1.829763 L4. | -.0452333 .8053917 -0.06 0.955 -1.62539 1.534924 | povertyw | L2. | -1.444667 1.74748 -0.83 0.409 -4.873176 1.983841 L3. | .1820847 1.717733 0.11 0.916 -3.188061 3.552231 L4. | .1246622 1.492058 0.08 0.933 -2.802715 3.052039 | _cons | -.0155201 .036228 -0.43 0.668 -.0865985 .0555583 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 181.31 Prob > F = 0.0000 R-squared = 0.2675 Adj R-squared = 0.2105 Root MSE = 0.0422 ------------------------------------------------------------------------------ | Robust age3s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .2151765 .0746687 2.88 0.004 .0686785 .3616745 age3ratio | -.3755653 .1186 -3.17 0.002 -.6082553 -.1428753 s2 | .0836913 .0180218 4.64 0.000 .0483331 .1190496 s3 | .0973733 .0207673 4.69 0.000 .0566284 .1381182 s4 | .0668044 .0163292 4.09 0.000 .034767 .0988418 s5 | .0390973 .0113893 3.43 0.001 .0167518 .0614427 s6 | .0449502 .0096515 4.66 0.000 .0260141 .0638863 s7 | .0853504 .0160224 5.33 0.000 .0539149 .1167858 s8 | .034651 .0153615 2.26 0.024 .0045122 .0647899 s9 | .0613212 .0143351 4.28 0.000 .0331961 .0894463 s10 | .114313 .0232778 4.91 0.000 .0686426 .1599835 s11 | .0412425 .0109178 3.78 0.000 .0198221 .0626629 s12 | .0697052 .0143084 4.87 0.000 .0416325 .0977779 s13 | .102411 .0188742 5.43 0.000 .0653803 .1394417 s14 | .069968 .0147607 4.74 0.000 .0410079 .0989282 s15 | .0656792 .0143641 4.57 0.000 .0374972 .0938612 s16 | .0786215 .0148681 5.29 0.000 .0494508 .1077923 s17 | .0741957 .0159546 4.65 0.000 .0428932 .1054981 s18 | .0753549 .016945 4.45 0.000 .0421092 .1086006 s19 | .0618351 .0174533 3.54 0.000 .0275921 .096078 s20 | .0722334 .0153234 4.71 0.000 .0421693 .1022974 s21 | .0582142 .0113827 5.11 0.000 .0358817 .0805467 s22 | .0919604 .0173744 5.29 0.000 .0578724 .1260485 s23 | .0671377 .0146796 4.57 0.000 .0383366 .0959387 s24 | .0569733 .0133423 4.27 0.000 .030796 .0831506 s25 | .0778737 .0171762 4.53 0.000 .0441745 .1115729 s26 | .0725295 .0192048 3.78 0.000 .0348502 .1102089 s27 | .0866418 .018525 4.68 0.000 .0502962 .1229875 s28 | .0709501 .0151839 4.67 0.000 .0411598 .1007405 s29 | .0886338 .0176672 5.02 0.000 .0539713 .1232963 s30 | .0847099 .0166648 5.08 0.000 .052014 .1174057 s31 | .0708148 .0122568 5.78 0.000 .0467674 .0948622 s32 | .0775164 .0154681 5.01 0.000 .0471684 .1078644 s33 | .05546 .0172247 3.22 0.001 .0216657 .0892544 s34 | .064767 .0125533 5.16 0.000 .0401378 .0893962 s35 | .0669453 .0166294 4.03 0.000 .0343188 .0995718 s36 | .0816786 .0164733 4.96 0.000 .0493585 .1139988 s37 | .0893323 .0184374 4.85 0.000 .0531586 .125506 s38 | .0788289 .0157704 5.00 0.000 .0478877 .10977 s39 | .0974647 .0194857 5.00 0.000 .0592343 .1356951 s40 | .0903201 .0168763 5.35 0.000 .0572092 .123431 s41 | .0694829 .0152255 4.56 0.000 .039611 .0993549 s42 | .0918701 .0189357 4.85 0.000 .0547187 .1290215 s43 | .0743032 .0165832 4.48 0.000 .0417673 .106839 s44 | .0433799 .0127654 3.40 0.001 .0183346 .0684253 s45 | .0336903 .0087768 3.84 0.000 .0164704 .0509102 s46 | .0564286 .0113394 4.98 0.000 .0341809 .0786763 s47 | .0733113 .0143441 5.11 0.000 .0451686 .101454 s48 | .0613654 .0123155 4.98 0.000 .0372028 .085528 s49 | .082243 .01526 5.39 0.000 .0523034 .1121827 s50 | .1039581 .0219227 4.74 0.000 .0609464 .1469698 s51 | .0681636 .0135806 5.02 0.000 .0415187 .0948084 poverty | .0351558 .0735217 0.48 0.633 -.1090917 .1794033 | lnh | L2D. | 1.496899 1.088693 1.37 0.169 -.6390878 3.632885 L3D. | -.041206 .9781443 -0.04 0.966 -1.960299 1.877887 L4D. | 2.174027 .8570247 2.54 0.011 .4925673 3.855486 | lnst | L2D. | 2.184262 .9552357 2.29 0.022 .3101155 4.058409 L3D. | 1.39052 .9020714 1.54 0.123 -.3793192 3.16036 L4D. | .747552 .7106604 1.05 0.293 -.6467445 2.141848 | lnw | L2D. | -2.450914 1.735963 -1.41 0.158 -5.856827 .9549988 L3D. | -3.464334 1.596512 -2.17 0.030 -6.596647 -.3320205 L4D. | -1.109653 1.340714 -0.83 0.408 -3.740098 1.520792 | lninc | L2D. | -.218773 .1126183 -1.94 0.052 -.4397271 .0021811 L3D. | .3468146 .0868027 4.00 0.000 .17651 .5171192 L4D. | -.3984368 .0878796 -4.53 0.000 -.5708541 -.2260195 | age1h | L2. | -2.805703 1.891499 -1.48 0.138 -6.516774 .9053675 L3. | 1.147601 1.907349 0.60 0.548 -2.594566 4.889768 L4. | -4.27799 1.724662 -2.48 0.013 -7.66173 -.8942493 | age3h | L2. | -1.961584 1.989313 -0.99 0.324 -5.864563 1.941395 L3. | -.8799804 1.604998 -0.55 0.584 -4.028944 2.268983 L4. | -2.421834 1.374273 -1.76 0.078 -5.11812 .2744523 | age1w | L2. | 5.096282 3.235017 1.58 0.115 -1.250735 11.4433 L3. | 6.405028 2.87023 2.23 0.026 .7737142 12.03634 L4. | 2.202467 2.560441 0.86 0.390 -2.821049 7.225983 | age3w | L2. | 3.493897 3.078534 1.13 0.257 -2.546104 9.533899 L3. | 2.256262 2.803911 0.80 0.421 -3.244936 7.757461 L4. | 1.313235 2.525229 0.52 0.603 -3.641196 6.267666 | age1s | L2. | -4.017188 1.791858 -2.24 0.025 -7.532764 -.5016117 L3. | -2.988171 1.572832 -1.90 0.058 -6.074025 .0976834 L4. | -1.170371 1.359775 -0.86 0.390 -3.838213 1.497471 | age3s | L2. | -3.916063 1.67069 -2.34 0.019 -7.193912 -.6382143 L3. | -.2512269 1.564389 -0.16 0.872 -3.320515 2.818061 L4. | -1.419923 1.332256 -1.07 0.287 -4.033773 1.193927 | povertyh | L2. | 1.145374 1.140365 1.00 0.315 -1.09199 3.382739 L3. | -.412769 1.313978 -0.31 0.753 -2.990759 2.165221 L4. | .4572614 1.087914 0.42 0.674 -1.677197 2.591719 | povertys | L2. | 1.716724 .8996939 1.91 0.057 -.0484513 3.481899 L3. | .026825 .848069 0.03 0.975 -1.637064 1.690714 L4. | -.1742066 .7553455 -0.23 0.818 -1.656174 1.307761 | povertyw | L2. | -1.571203 1.702481 -0.92 0.356 -4.911424 1.769018 L3. | .3016341 1.657988 0.18 0.856 -2.951293 3.554561 L4. | .4031845 1.419733 0.28 0.776 -2.382293 3.188662 | _cons | .0009828 .0385815 0.03 0.980 -.0747131 .0766787 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 824.16 Prob > F = 0.0000 R-squared = 0.2958 Adj R-squared = 0.2410 Root MSE = 0.0249 ------------------------------------------------------------------------------ | Robust age1w | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .1309293 .0534285 2.45 0.014 .026104 .2357547 age3ratio | -.2252894 .0735314 -3.06 0.002 -.369556 -.0810228 s2 | .0492658 .0109964 4.48 0.000 .0276911 .0708405 s3 | .0569221 .0126701 4.49 0.000 .0320637 .0817805 s4 | .0395448 .0100192 3.95 0.000 .0198873 .0592022 s5 | .0267668 .0070604 3.79 0.000 .0129144 .0406192 s6 | .0261856 .0061901 4.23 0.000 .0140408 .0383305 s7 | .0465118 .0103253 4.50 0.000 .0262538 .0667697 s8 | .0292658 .0092136 3.18 0.002 .011189 .0473425 s9 | .0379535 .0091577 4.14 0.000 .0199863 .0559207 s10 | .0651978 .0144169 4.52 0.000 .0369122 .0934835 s11 | .0262337 .0066636 3.94 0.000 .01316 .0393074 s12 | .0439222 .0090784 4.84 0.000 .0261106 .0617337 s13 | .057536 .011798 4.88 0.000 .0343887 .0806834 s14 | .0420013 .0092215 4.55 0.000 .023909 .0600935 s15 | .0400214 .0090074 4.44 0.000 .0223492 .0576936 s16 | .0465092 .0093859 4.96 0.000 .0280944 .064924 s17 | .0421579 .0099463 4.24 0.000 .0226436 .0616722 s18 | .046263 .0103479 4.47 0.000 .0259606 .0665654 s19 | .0377516 .0104948 3.60 0.000 .0171611 .058342 s20 | .0419516 .0097784 4.29 0.000 .0227667 .0611365 s21 | .0344744 .0073665 4.68 0.000 .0200217 .0489272 s22 | .0529466 .0108622 4.87 0.000 .0316353 .0742578 s23 | .0414868 .0091597 4.53 0.000 .0235157 .0594579 s24 | .0311184 .0084433 3.69 0.000 .0145528 .047684 s25 | .0453316 .010648 4.26 0.000 .0244404 .0662227 s26 | .044889 .0115298 3.89 0.000 .0222678 .0675102 s27 | .0516949 .011381 4.54 0.000 .0293658 .0740241 s28 | .0429199 .0094202 4.56 0.000 .0244378 .061402 s29 | .049927 .0109374 4.56 0.000 .0284681 .0713858 s30 | .0477252 .0104348 4.57 0.000 .0272523 .068198 s31 | .0383115 .0080201 4.78 0.000 .0225762 .0540468 s32 | .0441544 .0099519 4.44 0.000 .0246289 .0636798 s33 | .0360612 .0102409 3.52 0.000 .0159689 .0561535 s34 | .0350632 .0079202 4.43 0.000 .0195239 .0506024 s35 | .0415527 .0102112 4.07 0.000 .0215187 .0615867 s36 | .0478152 .0103103 4.64 0.000 .0275866 .0680437 s37 | .0507475 .0113461 4.47 0.000 .0284867 .0730082 s38 | .0473894 .0098825 4.80 0.000 .0280002 .0667786 s39 | .0562707 .0122147 4.61 0.000 .0323057 .0802356 s40 | .05266 .0106439 4.95 0.000 .0317769 .0735431 s41 | .0425143 .0093141 4.56 0.000 .0242404 .0607882 s42 | .0535547 .0116762 4.59 0.000 .0306462 .0764631 s43 | .0446689 .0101429 4.40 0.000 .0247688 .064569 s44 | .027451 .0076813 3.57 0.000 .0123805 .0425214 s45 | .0222966 .0057631 3.87 0.000 .0109896 .0336036 s46 | .0329549 .0072625 4.54 0.000 .018706 .0472038 s47 | .0434138 .0091946 4.72 0.000 .0253742 .0614534 s48 | .0368295 .0078454 4.69 0.000 .0214371 .0522219 s49 | .0479816 .0097178 4.94 0.000 .0289156 .0670476 s50 | .0597446 .0134096 4.46 0.000 .0334353 .086054 s51 | .0392378 .0086097 4.56 0.000 .0223458 .0561298 poverty | -.0471363 .0402036 -1.17 0.241 -.1260146 .031742 | lnh | L2D. | 1.16154 .65451 1.77 0.076 -.1225914 2.445671 L3D. | .4823588 .6185664 0.78 0.436 -.7312517 1.695969 L4D. | 1.215536 .6058757 2.01 0.045 .0268241 2.404248 | lnst | L2D. | 1.716736 .6934378 2.48 0.013 .3562295 3.077242 L3D. | 1.165182 .5796376 2.01 0.045 .0279486 2.302415 L4D. | .4913781 .5000754 0.98 0.326 -.4897563 1.472513 | lnw | L2D. | -2.145819 1.216477 -1.76 0.078 -4.532514 .2408768 L3D. | -2.672899 1.030552 -2.59 0.010 -4.694814 -.6509847 L4D. | -.855288 .936804 -0.91 0.361 -2.693272 .9826961 | lninc | L2D. | -.0571333 .0601165 -0.95 0.342 -.1750803 .0608137 L3D. | .2090673 .0432806 4.83 0.000 .1241519 .2939826 L4D. | -.1678233 .0343148 -4.89 0.000 -.2351481 -.1004986 | age1h | L2. | -2.297652 1.084013 -2.12 0.034 -4.424456 -.1708468 L3. | .2423648 1.266434 0.19 0.848 -2.242344 2.727074 L4. | -2.645849 1.1569 -2.29 0.022 -4.915656 -.3760425 | age3h | L2. | -.7731857 1.28215 -0.60 0.547 -3.28873 1.742359 L3. | -2.301974 1.061057 -2.17 0.030 -4.38374 -.2202088 L4. | -1.331105 .9867598 -1.35 0.178 -3.267101 .6048914 | age1w | L2. | 5.008395 2.140183 2.34 0.019 .8094137 9.207377 L3. | 3.759149 1.932632 1.95 0.052 -.0326236 7.550921 L4. | 1.793491 1.750137 1.02 0.306 -1.64023 5.227211 | age3w | L2. | 2.223405 2.194357 1.01 0.311 -2.081865 6.528675 L3. | 3.886919 1.742841 2.23 0.026 .4675111 7.306327 L4. | .8680702 1.645941 0.53 0.598 -2.361221 4.097362 | age1s | L2. | -3.532165 1.207655 -2.92 0.004 -5.901551 -1.162778 L3. | -1.733259 1.03747 -1.67 0.095 -3.768746 .3022289 L4. | -.9342626 .9147661 -1.02 0.307 -2.729009 .8604837 | age3s | L2. | -2.477891 1.260954 -1.97 0.050 -4.951849 -.0039327 L3. | -1.66422 1.005822 -1.65 0.098 -3.637616 .3091752 L4. | -.7387914 .8889048 -0.83 0.406 -2.482799 1.005216 | povertyh | L2. | -.0608666 .6816611 -0.09 0.929 -1.398267 1.276534 L3. | .2711499 .8105798 0.33 0.738 -1.319186 1.861486 L4. | .7004005 .6819901 1.03 0.305 -.6376457 2.038447 | povertys | L2. | .9437207 .5780627 1.63 0.103 -.1904227 2.077864 L3. | .3456937 .5923467 0.58 0.560 -.8164746 1.507862 L4. | .0852947 .5162664 0.17 0.869 -.9276061 1.098195 | povertyw | L2. | -.7591301 1.08711 -0.70 0.485 -2.892011 1.373751 L3. | -.1832021 1.093719 -0.17 0.867 -2.329049 1.962645 L4. | -.0079595 .9469177 -0.01 0.993 -1.865786 1.849867 | _cons | .0104115 .0268003 0.39 0.698 -.04217 .0629929 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 237.76 Prob > F = 0.0000 R-squared = 0.2904 Adj R-squared = 0.2351 Root MSE = 0.0257 ------------------------------------------------------------------------------ | Robust age3w | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .0772089 .0473577 1.63 0.103 -.0157055 .1701234 age3ratio | -.2321438 .0853187 -2.72 0.007 -.3995368 -.0647509 s2 | .0528428 .0117791 4.49 0.000 .0297325 .075953 s3 | .0625276 .0135403 4.62 0.000 .035962 .0890932 s4 | .0438023 .0107824 4.06 0.000 .0226474 .0649572 s5 | .0278217 .0074534 3.73 0.000 .0131984 .0424451 s6 | .027267 .0063527 4.29 0.000 .0148032 .0397308 s7 | .0504135 .0107297 4.70 0.000 .0293622 .0714648 s8 | .0316157 .0098891 3.20 0.001 .0122135 .0510179 s9 | .0414486 .0097115 4.27 0.000 .022395 .0605023 s10 | .0714331 .0155496 4.59 0.000 .0409252 .101941 s11 | .0258629 .0070946 3.65 0.000 .0119435 .0397824 s12 | .0465383 .0096194 4.84 0.000 .0276653 .0654112 s13 | .0640052 .012656 5.06 0.000 .0391746 .0888359 s14 | .0456032 .0097531 4.68 0.000 .026468 .0647385 s15 | .0429589 .0095208 4.51 0.000 .0242794 .0616385 s16 | .0500503 .0098755 5.07 0.000 .0306748 .0694258 s17 | .0465086 .0106554 4.36 0.000 .025603 .0674142 s18 | .0493896 .0110401 4.47 0.000 .0277291 .07105 s19 | .0399941 .0113184 3.53 0.000 .0177877 .0622006 s20 | .0445149 .0102596 4.34 0.000 .0243859 .0646439 s21 | .0368424 .007562 4.87 0.000 .022006 .0516788 s22 | .0570422 .0114841 4.97 0.000 .0345107 .0795736 s23 | .0435817 .0097094 4.49 0.000 .0245322 .0626311 s24 | .0342573 .0088803 3.86 0.000 .0168343 .0516803 s25 | .0493909 .0114206 4.32 0.000 .0269839 .0717978 s26 | .0474769 .0124243 3.82 0.000 .0231008 .0718531 s27 | .0563724 .0121662 4.63 0.000 .0325026 .0802423 s28 | .0453329 .0099944 4.54 0.000 .025724 .0649417 s29 | .0557152 .0118241 4.71 0.000 .0325166 .0789139 s30 | .0526061 .011173 4.71 0.000 .030685 .0745272 s31 | .0404473 .0082136 4.92 0.000 .0243324 .0565622 s32 | .0478934 .0103918 4.61 0.000 .027505 .0682819 s33 | .0376353 .0111237 3.38 0.001 .0158109 .0594596 s34 | .0385235 .0083534 4.61 0.000 .0221344 .0549126 s35 | .0444772 .0109148 4.07 0.000 .0230626 .0658918 s36 | .0514838 .0109229 4.71 0.000 .0300533 .0729143 s37 | .0559812 .0120818 4.63 0.000 .032277 .0796854 s38 | .0520251 .0104498 4.98 0.000 .0315228 .0725274 s39 | .061866 .0130642 4.74 0.000 .0362345 .0874976 s40 | .0575251 .0113209 5.08 0.000 .0353139 .0797364 s41 | .0443563 .0099999 4.44 0.000 .0247367 .0639759 s42 | .0590578 .0125952 4.69 0.000 .0343463 .0837694 s43 | .0473428 .0108318 4.37 0.000 .0260912 .0685944 s44 | .0289717 .0082503 3.51 0.000 .0127848 .0451586 s45 | .0235532 .0060659 3.88 0.000 .011652 .0354543 s46 | .0346313 .0075493 4.59 0.000 .0198197 .0494428 s47 | .0453123 .0095683 4.74 0.000 .0265395 .064085 s48 | .039383 .008141 4.84 0.000 .0234105 .0553555 s49 | .0515859 .010194 5.06 0.000 .0315856 .0715862 s50 | .0662149 .0142996 4.63 0.000 .0381594 .0942704 s51 | .0423984 .0090434 4.69 0.000 .0246554 .0601414 poverty | -.0376592 .0421276 -0.89 0.372 -.1203124 .0449941 | lnh | L2D. | 1.454931 .6720135 2.17 0.031 .1364589 2.773404 L3D. | .0854855 .5821966 0.15 0.883 -1.056769 1.227739 L4D. | 1.355898 .5638452 2.40 0.016 .249649 2.462147 | lnst | L2D. | 1.679656 .6786642 2.47 0.013 .3481357 3.011177 L3D. | 1.218326 .553196 2.20 0.028 .1329703 2.303681 L4D. | .4970872 .449101 1.11 0.269 -.3840368 1.378211 | lnw | L2D. | -2.173845 1.213306 -1.79 0.073 -4.554319 .2066297 L3D. | -2.661493 .985499 -2.70 0.007 -4.595016 -.7279707 L4D. | -.9011435 .8542423 -1.05 0.292 -2.577144 .7748567 | lninc | L2D. | -.083636 .0654743 -1.28 0.202 -.2120948 .0448227 L3D. | .2455454 .0467418 5.25 0.000 .1538392 .3372517 L4D. | -.1790692 .0402762 -4.45 0.000 -.25809 -.1000484 | age1h | L2. | -2.859497 1.136194 -2.52 0.012 -5.088679 -.6303156 L3. | .554114 1.208327 0.46 0.647 -1.81659 2.924819 L4. | -2.56036 1.085589 -2.36 0.019 -4.690257 -.4304624 | age3h | L2. | -1.149809 1.24643 -0.92 0.356 -3.595272 1.295654 L3. | -1.297789 .9384062 -1.38 0.167 -3.138916 .5433391 L4. | -1.854209 .8791415 -2.11 0.035 -3.579061 -.1293572 | age1w | L2. | 4.618937 2.164417 2.13 0.033 .3724096 8.865465 L3. | 4.709181 1.790726 2.63 0.009 1.195825 8.222538 L4. | 1.647909 1.58904 1.04 0.300 -1.469744 4.765563 | age3w | L2. | 2.730742 2.150233 1.27 0.204 -1.487957 6.94944 L3. | 2.73382 1.666585 1.64 0.101 -.5359745 6.003614 L4. | 1.137341 1.58667 0.72 0.474 -1.975662 4.250345 | age1s | L2. | -3.193504 1.219954 -2.62 0.009 -5.587021 -.7999873 L3. | -2.299806 .9669757 -2.38 0.018 -4.196986 -.4026257 L4. | -.7463004 .8355055 -0.89 0.372 -2.38554 .8929388 | age3s | L2. | -2.725073 1.179917 -2.31 0.021 -5.040037 -.4101085 L3. | -1.184828 .9438274 -1.26 0.210 -3.036592 .6669357 L4. | -.9536293 .8382423 -1.14 0.255 -2.598238 .6909794 | povertyh | L2. | .0397873 .7003313 0.06 0.955 -1.334244 1.413818 L3. | .1275586 .8264163 0.15 0.877 -1.493848 1.748965 L4. | .708355 .6534742 1.08 0.279 -.5737437 1.990454 | povertys | L2. | .9642055 .5610757 1.72 0.086 -.1366098 2.065021 L3. | .341354 .5778172 0.59 0.555 -.7923077 1.475016 L4. | .0194299 .478882 0.04 0.968 -.9201237 .9589835 | povertyw | L2. | -.7856099 1.080642 -0.73 0.467 -2.9058 1.33458 L3. | -.1172135 1.086998 -0.11 0.914 -2.249875 2.015448 L4. | .1217229 .9003541 0.14 0.892 -1.644748 1.888193 | _cons | .0238939 .0271043 0.88 0.378 -.029284 .0770719 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 121.52 Prob > F = 0.0000 R-squared = 0.4864 Adj R-squared = 0.4465 Root MSE = 0.0055 ------------------------------------------------------------------------------ | Robust povertyh | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | -.0263757 .0115683 -2.28 0.023 -.0490724 -.0036791 age3ratio | -.0460289 .0202082 -2.28 0.023 -.0856769 -.0063809 s2 | .0102525 .002501 4.10 0.000 .0053456 .0151593 s3 | .0116007 .002859 4.06 0.000 .0059914 .0172099 s4 | .0074759 .0022644 3.30 0.001 .0030333 .0119186 s5 | .0069457 .0015107 4.60 0.000 .0039817 .0099098 s6 | .0032872 .0013064 2.52 0.012 .0007242 .0058503 s7 | .004959 .0024291 2.04 0.041 .0001932 .0097248 s8 | .0138937 .0021613 6.43 0.000 .0096533 .0181341 s9 | .0073438 .0021305 3.45 0.001 .0031639 .0115237 s10 | .0100695 .0033358 3.02 0.003 .0035248 .0166142 s11 | .0049997 .0014511 3.45 0.001 .0021527 .0078467 s12 | .008872 .0021339 4.16 0.000 .0046853 .0130587 s13 | .0077708 .0026906 2.89 0.004 .0024919 .0130496 s14 | .0080263 .0020626 3.89 0.000 .0039795 .012073 s15 | .0077784 .0020185 3.85 0.000 .0038182 .0117386 s16 | .0073737 .0021343 3.45 0.001 .0031863 .0115611 s17 | .0062532 .0022075 2.83 0.005 .0019221 .0105844 s18 | .0099202 .002278 4.35 0.000 .005451 .0143895 s19 | .0085222 .0022037 3.87 0.000 .0041987 .0128458 s20 | .0057341 .0022552 2.54 0.011 .0013094 .0101588 s21 | .0050501 .0016483 3.06 0.002 .0018161 .0082841 s22 | .0079373 .002495 3.18 0.002 .0030421 .0128325 s23 | .0071758 .0020106 3.57 0.000 .003231 .0111205 s24 | .0037354 .0018693 2.00 0.046 .000068 .0074029 s25 | .0073852 .00238 3.10 0.002 .0027157 .0120547 s26 | .0107716 .0025228 4.27 0.000 .0058219 .0157213 s27 | .0100111 .0024886 4.02 0.000 .0051285 .0148937 s28 | .0084118 .0021246 3.96 0.000 .0042435 .0125801 s29 | .0075839 .0024877 3.05 0.002 .0027031 .0124648 s30 | .0064683 .0023638 2.74 0.006 .0018305 .0111061 s31 | .0024885 .0019439 1.28 0.201 -.0013254 .0063024 s32 | .0053914 .002293 2.35 0.019 .0008927 .0098902 s33 | .0098506 .0021892 4.50 0.000 .0055554 .0141459 s34 | .0049412 .0018684 2.64 0.008 .0012754 .0086069 s35 | .0087926 .0022688 3.88 0.000 .0043413 .0132439 s36 | .0074108 .0023094 3.21 0.001 .0028797 .0119418 s37 | .0082272 .0024654 3.34 0.001 .0033901 .0130642 s38 | .0091743 .0022151 4.14 0.000 .0048283 .0135202 s39 | .0088633 .0028473 3.11 0.002 .003277 .0144497 s40 | .0083184 .0025444 3.27 0.001 .0033264 .0133103 s41 | .0093436 .0021512 4.34 0.000 .005123 .0135642 s42 | .0091772 .0026184 3.50 0.000 .00404 .0143143 s43 | .0092335 .0022789 4.05 0.000 .0047624 .0137045 s44 | .0056636 .0015969 3.55 0.000 .0025305 .0087967 s45 | .0054457 .0014268 3.82 0.000 .0026463 .008245 s46 | .0048687 .0016613 2.93 0.003 .0016092 .0081282 s47 | .0056976 .0021034 2.71 0.007 .0015707 .0098245 s48 | .0063336 .00175 3.62 0.000 .0029001 .009767 s49 | .0069131 .0022223 3.11 0.002 .0025531 .0112731 s50 | .012077 .0029952 4.03 0.000 .0062006 .0179534 s51 | .0057548 .0019177 3.00 0.003 .0019924 .0095171 poverty | -.02808 .0117681 -2.39 0.017 -.0511686 -.0049914 | lnh | L2D. | .525828 .1595198 3.30 0.001 .2128544 .8388016 L3D. | -.3607109 .21169 -1.70 0.089 -.776041 .0546192 L4D. | .2190589 .1137024 1.93 0.054 -.0040222 .44214 | lnst | L2D. | .2810107 .1745476 1.61 0.108 -.0614469 .6234684 L3D. | -.1180396 .1113843 -1.06 0.289 -.3365726 .1004935 L4D. | .1555386 .0814304 1.91 0.056 -.0042257 .3153029 | lnw | L2D. | -.4562308 .29106 -1.57 0.117 -1.027283 .114821 L3D. | .2037942 .1938795 1.05 0.293 -.1765921 .5841805 L4D. | -.3643781 .1424915 -2.56 0.011 -.6439427 -.0848136 | lninc | L2D. | .0254918 .0115018 2.22 0.027 .0029256 .048058 L3D. | .0443035 .0092491 4.79 0.000 .0261571 .0624499 L4D. | .0425172 .0136184 3.12 0.002 .0157984 .0692361 | age1h | L2. | -1.14055 .2536791 -4.50 0.000 -1.638262 -.6428386 L3. | .7445569 .3987756 1.87 0.062 -.03783 1.526944 L4. | -.2801668 .1883961 -1.49 0.137 -.6497949 .0894614 | age3h | L2. | -.3120294 .3226414 -0.97 0.334 -.9450432 .3209843 L3. | .0752083 .3712943 0.20 0.840 -.653261 .8036777 L4. | -.4742416 .2208232 -2.15 0.032 -.9074908 -.0409923 | age1w | L2. | 1.132913 .4692101 2.41 0.016 .2123356 2.053491 L3. | -.4391347 .3170799 -1.38 0.166 -1.061237 .1829676 L4. | .5013932 .2322561 2.16 0.031 .045713 .9570733 | age3w | L2. | .4831462 .5579163 0.87 0.387 -.6114705 1.577763 L3. | -.1012636 .4233056 -0.24 0.811 -.9317777 .7292505 L4. | .6675573 .2724188 2.45 0.014 .1330789 1.202036 | age1s | L2. | -.6396237 .2746883 -2.33 0.020 -1.178555 -.1006926 L3. | .2670904 .1809991 1.48 0.140 -.0880249 .6222058 L4. | -.2087158 .1294006 -1.61 0.107 -.4625962 .0451647 | age3s | L2. | -.3654589 .3333588 -1.10 0.273 -1.0195 .2885822 L3. | -.022707 .2408863 -0.09 0.925 -.4953195 .4499054 L4. | -.3214066 .1626155 -1.98 0.048 -.6404538 -.0023595 | povertyh | L2. | .1818685 .2968044 0.61 0.540 -.4004538 .7641908 L3. | -.0292855 .1861269 -0.16 0.875 -.3944615 .3358905 L4. | .0075445 .1404656 0.05 0.957 -.2680452 .2831341 | povertys | L2. | .0976718 .1620225 0.60 0.547 -.2202121 .4155556 L3. | .1370692 .1545038 0.89 0.375 -.1660631 .4402015 L4. | .0892871 .1183384 0.75 0.451 -.1428897 .3214638 | povertyw | L2. | -.1613435 .30623 -0.53 0.598 -.7621585 .4394715 L3. | -.0090865 .2930961 -0.03 0.975 -.5841331 .5659602 L4. | -.0913776 .2217241 -0.41 0.680 -.5263943 .343639 | _cons | .0203745 .0069561 2.93 0.003 .0067268 .0340223 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 124.73 Prob > F = 0.0000 R-squared = 0.2750 Adj R-squared = 0.2186 Root MSE = 0.0178 ------------------------------------------------------------------------------ | Robust povertys | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .0827794 .0294317 2.81 0.005 .0250351 .1405236 age3ratio | -.1865748 .0507019 -3.68 0.000 -.2860505 -.0870991 s2 | .0338728 .0078195 4.33 0.000 .0185311 .0492145 s3 | .0380744 .0091016 4.18 0.000 .0202173 .0559316 s4 | .0263899 .007133 3.70 0.000 .0123953 .0403846 s5 | .0153073 .0049244 3.11 0.002 .0056457 .0249689 s6 | .018359 .0043242 4.25 0.000 .0098751 .0268429 s7 | .0358243 .0073422 4.88 0.000 .0214191 .0502296 s8 | .0115498 .0064099 1.80 0.072 -.0010262 .0241258 s9 | .0274265 .0064954 4.22 0.000 .0146827 .0401702 s10 | .0475732 .0103717 4.59 0.000 .0272242 .0679222 s11 | .0163568 .0046594 3.51 0.000 .0072152 .0254984 s12 | .0292564 .0063889 4.58 0.000 .0167216 .0417912 s13 | .0417962 .008502 4.92 0.000 .0251155 .0584768 s14 | .0284147 .0065533 4.34 0.000 .0155574 .0412721 s15 | .0266405 .0063935 4.17 0.000 .0140966 .0391845 s16 | .0308899 .0067218 4.60 0.000 .017702 .0440778 s17 | .0302341 .0071221 4.25 0.000 .0162607 .0442075 s18 | .0294799 .0073814 3.99 0.000 .0149979 .043962 s19 | .0240515 .0073973 3.25 0.001 .0095382 .0385647 s20 | .029894 .0069387 4.31 0.000 .0162804 .0435076 s21 | .0237952 .0051965 4.58 0.000 .0135998 .0339906 s22 | .0366646 .0077966 4.70 0.000 .0213678 .0519614 s23 | .0272785 .0065098 4.19 0.000 .0145065 .0400505 s24 | .025191 .0059972 4.20 0.000 .0134247 .0369573 s25 | .031927 .0076165 4.19 0.000 .0169836 .0468703 s26 | .0288267 .0081122 3.55 0.000 .0129108 .0447426 s27 | .0350438 .0081325 4.31 0.000 .0190881 .0509995 s28 | .0284855 .006684 4.26 0.000 .0153717 .0415994 s29 | .0364419 .0078553 4.64 0.000 .02103 .0518537 s30 | .0349891 .007473 4.68 0.000 .0203273 .0496508 s31 | .029745 .0056731 5.24 0.000 .0186145 .0408754 s32 | .0322512 .007068 4.56 0.000 .0183839 .0461185 s33 | .0218872 .0071996 3.04 0.002 .0077617 .0360126 s34 | .0273101 .0056424 4.84 0.000 .0162399 .0383803 s35 | .026684 .0072504 3.68 0.000 .0124589 .0409091 s36 | .0331135 .0073699 4.49 0.000 .018654 .047573 s37 | .035566 .0081894 4.34 0.000 .0194986 .0516334 s38 | .0322764 .0070581 4.57 0.000 .0184287 .0461241 s39 | .0406878 .0087765 4.64 0.000 .0234685 .0579071 s40 | .0368946 .0076401 4.83 0.000 .021905 .0518843 s41 | .0272186 .0066059 4.12 0.000 .0142579 .0401793 s42 | .037912 .0083804 4.52 0.000 .02147 .0543541 s43 | .0291916 .0072129 4.05 0.000 .0150401 .043343 s44 | .0174602 .0054574 3.20 0.001 .0067529 .0281676 s45 | .0143217 .0038876 3.68 0.000 .0066943 .0219492 s46 | .023362 .0051061 4.58 0.000 .0133439 .0333801 s47 | .0308189 .0064877 4.75 0.000 .0180902 .0435475 s48 | .0245779 .0055424 4.43 0.000 .0137039 .035452 s49 | .0338363 .0069428 4.87 0.000 .0202147 .0474579 s50 | .0415744 .0096479 4.31 0.000 .0226455 .0605034 s51 | .0279421 .0060838 4.59 0.000 .0160059 .0398783 poverty | .0931826 .0301781 3.09 0.002 .0339738 .1523913 | lnh | L2D. | .6488714 .4325137 1.50 0.134 -.1997087 1.497452 L3D. | .136611 .4232772 0.32 0.747 -.6938475 .9670694 L4D. | 1.004462 .3476377 2.89 0.004 .3224063 1.686518 | lnst | L2D. | .9957545 .406331 2.45 0.014 .1985442 1.792965 L3D. | .5408488 .3722342 1.45 0.146 -.1894648 1.271162 L4D. | .4579482 .3002753 1.53 0.128 -.1311838 1.04708 | lnw | L2D. | -1.207776 .7203315 -1.68 0.094 -2.621047 .2054948 L3D. | -1.370643 .6650132 -2.06 0.040 -2.675381 -.0659045 L4D. | -.7006038 .5505559 -1.27 0.203 -1.78078 .3795721 | lninc | L2D. | -.0747202 .0451213 -1.66 0.098 -.1632469 .0138066 L3D. | .1267062 .0353319 3.59 0.000 .057386 .1960263 L4D. | -.1504547 .0355811 -4.23 0.000 -.2202639 -.0806454 | age1h | L2. | -1.283874 .7738499 -1.66 0.097 -2.802147 .2343985 L3. | .2631779 .8739025 0.30 0.763 -1.451395 1.977751 L4. | -2.097185 .7164279 -2.93 0.003 -3.502797 -.6915728 | age3h | L2. | -.8001955 .8185883 -0.98 0.329 -2.406244 .8058525 L3. | -1.065044 .6453186 -1.65 0.099 -2.331142 .2010536 L4. | -1.04322 .5423477 -1.92 0.055 -2.107292 .0208513 | age1w | L2. | 2.495243 1.383991 1.80 0.072 -.2201097 5.210595 L3. | 2.546006 1.263924 2.01 0.044 .0662218 5.02579 L4. | 1.506513 1.070005 1.41 0.159 -.5928084 3.605835 | age3w | L2. | 1.508838 1.259934 1.20 0.231 -.9631196 3.980795 L3. | 1.912425 1.135676 1.68 0.092 -.3157415 4.140592 L4. | .8388204 .9628826 0.87 0.384 -1.050329 2.72797 | age1s | L2. | -1.866921 .7750357 -2.41 0.016 -3.38752 -.3463223 L3. | -1.15262 .6621223 -1.74 0.082 -2.451686 .1464464 L4. | -.8572026 .5721423 -1.50 0.134 -1.97973 .2653252 | age3s | L2. | -1.593337 .7106397 -2.24 0.025 -2.987593 -.1990813 L3. | -.6405033 .6539537 -0.98 0.328 -1.923543 .6425361 L4. | -.7150388 .5302137 -1.35 0.178 -1.755304 .3252261 | povertyh | L2. | .5722662 .5063719 1.13 0.259 -.4212219 1.565754 L3. | .7857541 .5984624 1.31 0.189 -.3884131 1.959921 L4. | .2865758 .4994558 0.57 0.566 -.693343 1.266495 | povertys | L2. | .5485099 .4274941 1.28 0.200 -.2902219 1.387242 L3. | 1.234185 .4070297 3.03 0.002 .4356033 2.032766 L4. | .0554652 .3079184 0.18 0.857 -.5486624 .6595928 | povertyw | L2. | -.4043221 .8077739 -0.50 0.617 -1.989153 1.180508 L3. | -2.227931 .7878423 -2.83 0.005 -3.773656 -.6822053 L4. | -.1920074 .5999229 -0.32 0.749 -1.36904 .9850252 | _cons | .0022368 .0153082 0.15 0.884 -.0277974 .0322711 ------------------------------------------------------------------------------ Number of obs = 1275 N. of clusters = 51 F( 42, 1182) = 85.08 Prob > F = 0.0000 R-squared = 0.2767 Adj R-squared = 0.2204 Root MSE = 0.0108 ------------------------------------------------------------------------------ | Robust povertyw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1ratio | .0269127 .0196943 1.37 0.172 -.011727 .0655524 age3ratio | -.1179481 .035845 -3.29 0.001 -.188275 -.0476211 s2 | .0220055 .0050862 4.33 0.000 .0120266 .0319844 s3 | .0251765 .0059023 4.27 0.000 .0135964 .0367566 s4 | .0176819 .0046743 3.78 0.000 .008511 .0268528 s5 | .0114765 .0032088 3.58 0.000 .0051808 .0177722 s6 | .0115354 .0028265 4.08 0.000 .0059899 .0170809 s7 | .0213417 .004915 4.34 0.000 .0116985 .0309848 s8 | .0132872 .0040398 3.29 0.001 .0053613 .0212132 s9 | .0182927 .0043592 4.20 0.000 .0097401 .0268454 s10 | .0300429 .006875 4.37 0.000 .0165544 .0435314 s11 | .0108377 .0030038 3.61 0.000 .0049444 .016731 s12 | .0196162 .0043264 4.53 0.000 .011128 .0281045 s13 | .0262598 .0056508 4.65 0.000 .0151731 .0373465 s14 | .0190219 .0043144 4.41 0.000 .0105572 .0274867 s15 | .0180452 .0042159 4.28 0.000 .0097737 .0263166 s16 | .0201046 .0044677 4.50 0.000 .0113391 .0288701 s17 | .0193712 .0046882 4.13 0.000 .010173 .0285694 s18 | .0202323 .0047993 4.22 0.000 .0108161 .0296485 s19 | .016115 .0047651 3.38 0.001 .0067661 .025464 s20 | .0186918 .0045991 4.06 0.000 .0096684 .0277152 s21 | .0152572 .0034417 4.43 0.000 .0085048 .0220096 s22 | .0230146 .0051416 4.48 0.000 .0129268 .0331023 s23 | .0182518 .0042944 4.25 0.000 .0098264 .0266773 s24 | .0154184 .0039151 3.94 0.000 .0077372 .0230997 s25 | .0206488 .0050066 4.12 0.000 .010826 .0304717 s26 | .019357 .0051991 3.72 0.000 .0091565 .0295575 s27 | .0234078 .0053137 4.41 0.000 .0129825 .0338331 s28 | .018933 .0043806 4.32 0.000 .0103384 .0275276 s29 | .0232725 .0051637 4.51 0.000 .0131415 .0334035 s30 | .0220394 .004943 4.46 0.000 .0123414 .0317374 s31 | .0169943 .0038175 4.45 0.000 .0095046 .0244841 s32 | .0198779 .0047265 4.21 0.000 .0106047 .0291512 s33 | .0158577 .0046018 3.45 0.001 .0068291 .0248864 s34 | .0166723 .0037864 4.40 0.000 .0092435 .0241011 s35 | .0184714 .0047068 3.92 0.000 .0092368 .0277059 s36 | .0213897 .0048818 4.38 0.000 .0118118 .0309676 s37 | .0228095 .0053624 4.25 0.000 .0122887 .0333304 s38 | .0216974 .0046626 4.65 0.000 .0125495 .0308453 s39 | .025848 .0058432 4.42 0.000 .0143839 .0373122 s40 | .0235053 .0050858 4.62 0.000 .0135272 .0334835 s41 | .0181208 .0043259 4.19 0.000 .0096335 .0266081 s42 | .0247849 .0054817 4.52 0.000 .01403 .0355398 s43 | .0193528 .0046917 4.12 0.000 .0101478 .0285577 s44 | .0122958 .0034808 3.53 0.000 .0054667 .019125 s45 | .0106495 .0027134 3.92 0.000 .0053259 .0159732 s46 | .0146838 .0033869 4.34 0.000 .0080388 .0213288 s47 | .0190772 .00432 4.42 0.000 .0106015 .0275529 s48 | .016164 .0036693 4.41 0.000 .0089651 .023363 s49 | .0214476 .0046252 4.64 0.000 .012373 .0305222 s50 | .0271856 .0062806 4.33 0.000 .0148632 .039508 s51 | .0177867 .0040332 4.41 0.000 .0098737 .0256997 poverty | .0320305 .0170574 1.88 0.061 -.0014356 .0654965 | lnh | L2D. | .6671909 .2711041 2.46 0.014 .135292 1.19909 L3D. | .2002384 .2831206 0.71 0.480 -.3552366 .7557135 L4D. | .6022616 .2332589 2.58 0.010 .1446139 1.059909 | lnst | L2D. | .7993374 .2991946 2.67 0.008 .2123256 1.386349 L3D. | .5681349 .2390749 2.38 0.018 .0990764 1.037193 L4D. | .2882973 .1922922 1.50 0.134 -.0889747 .6655694 | lnw | L2D. | -1.103859 .5213488 -2.12 0.034 -2.126731 -.0809865 L3D. | -1.217477 .4281545 -2.84 0.005 -2.057504 -.377449 L4D. | -.5086223 .3567713 -1.43 0.154 -1.208598 .1913535 | lninc | L2D. | -.0299168 .0263435 -1.14 0.256 -.081602 .0217683 L3D. | .0866294 .018519 4.68 0.000 .0502956 .1229631 L4D. | -.0650918 .0155744 -4.18 0.000 -.0956483 -.0345352 | age1h | L2. | -1.335276 .4754156 -2.81 0.005 -2.268029 -.4025235 L3. | -.0379163 .6173668 -0.06 0.951 -1.249173 1.173341 L4. | -1.226286 .4508212 -2.72 0.007 -2.110785 -.341787 | age3h | L2. | -.6339129 .5154888 -1.23 0.219 -1.645288 .3774622 L3. | -.9342211 .4195948 -2.23 0.026 -1.757455 -.1109874 L4. | -.7346802 .3572892 -2.06 0.040 -1.435672 -.0336884 | age1w | L2. | 2.341768 .9665965 2.42 0.016 .4453319 4.238204 L3. | 2.150315 .835601 2.57 0.010 .5108887 3.789742 L4. | 1.004521 .6709491 1.50 0.135 -.3118629 2.320905 | age3w | L2. | 1.221249 .8961118 1.36 0.173 -.5368984 2.979396 L3. | 1.816708 .6916132 2.63 0.009 .4597815 3.173634 L4. | .6716893 .6119599 1.10 0.273 -.5289595 1.872338 | age1s | L2. | -1.555457 .547412 -2.84 0.005 -2.629464 -.481449 L3. | -1.053009 .4406935 -2.39 0.017 -1.917638 -.1883803 L4. | -.5175456 .3557024 -1.45 0.146 -1.215424 .1803328 | age3s | L2. | -1.133329 .5135756 -2.21 0.028 -2.14095 -.1257071 L3. | -.8509729 .4048298 -2.10 0.036 -1.645238 -.0567078 L4. | -.4771317 .3333706 -1.43 0.153 -1.131196 .1769325 | povertyh | L2. | .2567467 .29925 0.86 0.391 -.3303737 .8438671 L3. | .3928078 .365507 1.07 0.283 -.3243072 1.109923 L4. | .3073071 .2857243 1.08 0.282 -.2532762 .8678904 | povertys | L2. | .2362161 .2648796 0.89 0.373 -.2834706 .7559027 L3. | .7050233 .2620433 2.69 0.007 .1909014 1.219145 L4. | .1195413 .1934648 0.62 0.537 -.2600314 .499114 | povertyw | L2. | -.0815419 .4992834 -0.16 0.870 -1.061123 .8980387 L3. | -1.172396 .4997777 -2.35 0.019 -2.152946 -.1918451 L4. | -.2132336 .3626012 -0.59 0.557 -.9246473 .4981801 | _cons | .0118345 .0107578 1.10 0.272 -.0092719 .0329409 ------------------------------------------------------------------------------ Instrumental variables (2SLS) regression Number of obs = 1275 Wald chi2(66) = 1052.50 Prob > chi2 = 0.0000 R-squared = 0.3600 Root MSE = .02616 (Std. Err. adjusted for 51 clusters in state) ------------------------------------------------------------------------------ | Robust D.lncons | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnh | D1. | -8.194106 2.156906 -3.80 0.000 -12.42156 -3.966648 | lnst | D1. | -8.556044 2.109972 -4.06 0.000 -12.69151 -4.420574 | lnw | D1. | 16.50088 4.007005 4.12 0.000 8.647292 24.35446 | lninc | D1. | .5619239 .0699195 8.04 0.000 .4248842 .6989636 | age1h | 12.81982 3.96056 3.24 0.001 5.057268 20.58238 age3h | 15.25985 3.65961 4.17 0.000 8.087147 22.43255 age1s | 13.51128 3.60626 3.75 0.000 6.443144 20.57942 age3s | 15.21548 3.742763 4.07 0.000 7.879803 22.55116 age1w | -26.4314 7.432481 -3.56 0.000 -40.99879 -11.864 age3w | -29.43003 6.68174 -4.40 0.000 -42.526 -16.33406 povertyh | -3.999809 2.682474 -1.49 0.136 -9.257361 1.257743 povertys | -5.506665 2.592939 -2.12 0.034 -10.58873 -.4245984 povertyw | 11.18945 5.558501 2.01 0.044 .2949907 22.08391 age1ratio | .0164917 .0797664 0.21 0.836 -.1398476 .172831 age3ratio | -.4544463 .0916302 -4.96 0.000 -.6340382 -.2748544 s2 | .0477193 .0102999 4.63 0.000 .0275318 .0679068 s3 | .0510226 .0117166 4.35 0.000 .0280585 .0739868 s4 | .0440776 .0087316 5.05 0.000 .0269641 .0611912 s5 | .0281495 .0065622 4.29 0.000 .0152879 .0410111 s6 | .02908 .0046919 6.20 0.000 .019884 .038276 s7 | .0534042 .0067298 7.94 0.000 .0402141 .0665943 s8 | .0061797 .014844 0.42 0.677 -.0229141 .0352734 s9 | .0470814 .0070237 6.70 0.000 .0333151 .0608477 s10 | .0713068 .0111531 6.39 0.000 .0494471 .0931665 s11 | .0242885 .006078 4.00 0.000 .0123759 .0362011 s12 | .0429014 .0069047 6.21 0.000 .0293685 .0564343 s13 | .0656444 .0087658 7.49 0.000 .0484637 .0828251 s14 | .0408473 .0078085 5.23 0.000 .025543 .0561516 s15 | .0434355 .0075437 5.76 0.000 .0286502 .0582207 s16 | .0481559 .0069391 6.94 0.000 .0345555 .0617562 s17 | .050976 .0077418 6.58 0.000 .0358024 .0661495 s18 | .0424106 .0097544 4.35 0.000 .0232924 .0615288 s19 | .028744 .0099629 2.89 0.004 .0092171 .0482709 s20 | .0467524 .0067971 6.88 0.000 .0334305 .0600744 s21 | .0341076 .0053425 6.38 0.000 .0236366 .0445787 s22 | .0605796 .0079711 7.60 0.000 .0449565 .0762027 s23 | .046588 .0070741 6.59 0.000 .032723 .0604531 s24 | .0505411 .0065314 7.74 0.000 .0377399 .0633424 s25 | .0572749 .0083319 6.87 0.000 .0409447 .0736051 s26 | .0393193 .011451 3.43 0.001 .0168757 .0617629 s27 | .0483254 .0099188 4.87 0.000 .028885 .0677659 s28 | .0387975 .0083484 4.65 0.000 .0224349 .0551601 s29 | .0562644 .0092905 6.06 0.000 .0380553 .0744735 s30 | .0642727 .0079721 8.06 0.000 .0486476 .0798978 s31 | .0556441 .0052365 10.63 0.000 .0453808 .0659074 s32 | .0529791 .006427 8.24 0.000 .0403823 .0655759 s33 | .0303167 .010758 2.82 0.005 .0092315 .051402 s34 | .0487093 .0059497 8.19 0.000 .0370481 .0603704 s35 | .0423769 .0090819 4.67 0.000 .0245766 .0601771 s36 | .0542407 .0076394 7.10 0.000 .0392678 .0692136 s37 | .0440776 .0091805 4.80 0.000 .0260841 .0620711 s38 | .04511 .0082297 5.48 0.000 .0289802 .0612398 s39 | .062413 .0091512 6.82 0.000 .044477 .0803489 s40 | .0568111 .0077946 7.29 0.000 .0415339 .0720882 s41 | .0412692 .0088065 4.69 0.000 .0240087 .0585296 s42 | .056236 .010047 5.60 0.000 .0365442 .0759279 s43 | .043612 .0092526 4.71 0.000 .0254773 .0617467 s44 | .0153365 .0076462 2.01 0.045 .0003502 .0303227 s45 | .0274499 .006322 4.34 0.000 .015059 .0398408 s46 | .0370661 .0055469 6.68 0.000 .0261944 .0479379 s47 | .0461285 .0062246 7.41 0.000 .0339286 .0583284 s48 | .0341583 .0060528 5.64 0.000 .0222949 .0460216 s49 | .054248 .006951 7.80 0.000 .0406243 .0678717 s50 | .0587268 .0123434 4.76 0.000 .034534 .0829195 s51 | .0352079 .0066874 5.26 0.000 .0221009 .048315 poverty | .1281002 .0749767 1.71 0.088 -.0188513 .2750518 _cons | .0685798 .0436409 1.57 0.116 -.0169547 .1541144 ------------------------------------------------------------------------------ Instrumented: D.lnh D.lnst D.lnw D.lninc age1h age3h age1s age3s age1w age3w povertyh povertys povertyw Instruments: age1ratio age3ratio s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27 s28 s29 s30 s31 s32 s33 s34 s35 s36 s37 s38 s39 s40 s41 s42 s43 s44 s45 s46 s47 s48 s49 s50 s51 poverty L2D.lnh L3D.lnh L4D.lnh L2D.lnst L3D.lnst L4D.lnst L2D.lnw L3D.lnw L4D.lnw L2D.lninc L3D.lninc L4D.lninc L2.age1h L3.age1h L4.age1h L2.age3h L3.age3h L4.age3h L2.age1w L3.age1w L4.age1w L2.age3w L3.age3w L4.age3w L2.age1s L3.age1s L4.age1s L2.age3s L3.age3s L4.age3s L2.povertyh L3.povertyh L4.povertyh L2.povertys L3.povertys L4.povertys L2.povertyw L3.povertyw L4.povertyw . sum chratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- chratio | 1275 .3010725 .0938772 .0721587 .7651958 . local ch = r(mean) . sum csratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- csratio | 1275 .2663826 .139835 .0746406 .9381273 . local cs = r(mean) . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . local hw = r(mean) . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . local sw = r(mean) . sum age1r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age1ratio | 1275 .312477 .0413183 .2286251 .4784656 . local age1r = r(mean) . sum age3r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age3ratio | 1275 .3036991 .0331339 .1347572 .3859731 . local age3r = r(mean) . sum poverty if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- poverty | 1275 .1272549 .0382546 .029 .272 . local pov = r(mean) . estadd scalar hwe = ( _b[D.lnh] + _b[D.lnw] * `hw' + _b[age1h] * `age1r' /// > + _b[age3h] * `age3r' + _b[age1w] * `age1r' * `hw' /// > + _b[age3w] * `age3r' * `hw' + _b[povertyh] * `pov' /// > + _b[povertyw] * `pov' * `hw' ) * `ch' . estadd scalar swe = ( _b[D.lnst] + _b[D.lnw] * `sw' + _b[age1s] * `age1r' /// > + _b[age3s] * `age3r' + _b[age1w] * `age1r' * `sw' /// > + _b[age3w] * `age3r' * `sw' + _b[povertys] * `pov' /// > + _b[povertyw] * `pov' * `sw' ) * `cs' . estadd scalar we_diff = e(hwe) - e(swe) . estadd scalar hwelas = e(hwe) * 1 / `ch' . estadd scalar swelas = e(swe) * 1 / `cs' . estadd scalar elas_diff = e(hwelas) - e(swelas) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' + povertyh * `pov' /// > + povertyw * `pov' * `hw' ) * `ch' = 0 ( 1) .3010725*D.lnh + .1375805*D.lnw + .0940782*age1h + .0914355*age3h + .0429907*age1w + .0417831*age3w + .038313*povertyh + .0175078*povertyw = 0 chi2( 1) = 32.36 Prob > chi2 = 0.0000 . estadd scalar hwe_p = r(p) . test ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' + povertys * `pov' /// > + povertyw * `pov' * `sw' ) * `cs' = 0 ( 1) .2663826*D.lnst + .1446543*D.lnw + .0832384*age1s + .0809002*age3s + .0452011*age1w + .0439314*age3w + .0338985*povertys + .018408*povertyw = 0 chi2( 1) = 0.40 Prob > chi2 = 0.5264 . estadd scalar swe_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' + povertyh * `pov' /// > + povertyw * `pov' * `hw' ) * `ch' = /// > ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' + povertys * `pov' /// > + povertyw * `pov' * `sw' ) * `cs' ( 1) .3010725*D.lnh - .2663826*D.lnst - .0070738*D.lnw + .0940782*age1h + .0914355*age3h - .0832384*age1s - .0809002*age3s - .0022104*age1w - .0021483*age3w + .038313*povertyh - .0338985*povertys - .0009002*povertyw = 0 chi2( 1) = 21.84 Prob > chi2 = 0.0000 . estadd scalar we_diff_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' + povertyh * `pov' /// > + povertyw * `pov' * `hw') = 0 ( 1) D.lnh + .456968*D.lnw + .312477*age1h + .3036991*age3h + .142792*age1w + .1387808*age3w + .1272549*povertyh + .0581514*povertyw = 0 chi2( 1) = 32.36 Prob > chi2 = 0.0000 . estadd scalar hwelas_p = r(p) . test ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' + povertys * `pov' /// > + povertyw * `pov' * `sw') = 0 ( 1) D.lnst + .543032*D.lnw + .312477*age1s + .3036991*age3s + .169685*age1w + .1649184*age3w + .1272549*povertys + .0691035*povertyw = 0 chi2( 1) = 0.40 Prob > chi2 = 0.5264 . estadd scalar swelas_p = r(p) . test ( D.lnh + D.lnw * `hw' + age1h * `age1r' /// > + age3h * `age3r' + age1w * `age1r' * `hw' /// > + age3w * `age3r' * `hw' + povertyh * `pov' /// > + povertyw * `pov' * `hw' ) = /// > ( D.lnst + D.lnw * `sw' + age1s * `age1r' /// > + age3s * `age3r' + age1w * `age1r' * `sw' /// > + age3w * `age3r' * `sw' + povertys * `pov' /// > + povertyw * `pov' * `sw' ) ( 1) D.lnh - D.lnst - .086064*D.lnw + .312477*age1h + .3036991*age3h - .312477*age1s - .3036991*age3s - .026893*age1w - .0261376*age3w + .1272549*povertyh - .1272549*povertys - .0109521*povertyw = 0 chi2( 1) = 20.55 Prob > chi2 = 0.0000 . estadd scalar elas_diff_p = r(p) . estadd scalar deriv_hy = `ch' * ( _b[age1h] + _b[age1w] * `hw' ) . estadd scalar deriv_ho = `ch' * ( _b[age3h] + _b[age3w] * `hw' ) . estadd scalar deriv_hp = `ch' * ( _b[povertyh] + _b[povertyw] * `hw' ) . estadd scalar deriv_sy = `cs' * ( _b[age1s] + _b[age1w] * `sw' ) . estadd scalar deriv_so = `cs' * ( _b[age3s] + _b[age3w] * `sw' ) . estadd scalar deriv_sp = `cs' * ( _b[povertys] + _b[povertyw] * `sw' ) . . estadd scalar deriv_hw = `ch' * ( _b[D.lnw] + _b[age1w] * `age1r' /// > + _b[age3w] * `age3r' + _b[povertyw] * `pov' ) . estadd scalar deriv_sw = `cs' * ( _b[D.lnw] + _b[age1w] * `age1r' /// > + _b[age3w] * `age3r' + _b[povertyw] * `pov' ) . . est store iv5 . . gen hwe5 = ( _b[D.lnh] + _b[D.lnw] * h_real / w_real + _b[age1h] * age1r /// > + _b[age3h] * age3r + _b[age1w] * age1r * h_real / w_real /// > + _b[age3w] * age3r * h_real / w_real + _b[povertyh] * poverty /// > + _b[povertyw] * poverty * h_real / w_real ) * cons_real / h_real if e(sample) (2907 missing values generated) . gen swe5 = ( _b[D.lnst] + _b[D.lnw] * st_real / w_real + _b[age1s] * age1r /// > + _b[age3s] * age3r + _b[age1w] * age1r * st_real / w_real /// > + _b[age3w] * age3r * st_real / w_real + _b[povertys] * poverty /// > + _b[povertyw] * poverty * st_real / w_real ) * cons_real / st_real if e(sample) (2907 missing values generated) . . preserve . . /* Following graph not used. Run CLM_Wealth_Robustness_Figure5_errorbands.do instead. > > ** Figure 5 > ** Housing Wealth Effects > table year if year >= 1985 , cont(mean hwe1 mean hwe2 mean hwe3 mean hwe4 mean hwe5 ) /// > format(%6.4fc) row replace > > line table1 table2 table3 table4 table5 year , /// > title("Housing Wealth Effects" , size(medium) color(black) ) /// > legend( off ) xtitle( "" ) /// > graphregion(fcolor(white) lcolor(white) lwidth(vvthin)) /// > ylabel( -0.03(0.03)0.12 , format(%9.2fc) glcolor(gs14) gmax gmin) ymtick(##2) /// > clcolor(dknavy red dkorange purple dkgreen ) /// > lpattern(dot dash_dot longdash shortdash solid) /// > clwidth(medthick medthick medthick medthick medthick) /// > xmtick(##5) yline(0, lcolor(black)) name(housing) nodraw xsize(5) > restore , preserve > > ** Stock Wealth Effects > table year if year >= 1985 , cont(mean swe1 mean swe2 mean swe3 mean swe4 mean swe5 ) /// > format(%6.4fc) row replace > > capture graph drop stock > line table1 table2 table3 table4 table5 year , /// > title("Stock Wealth Effects" , size(medium) color(black) ) /// > legend( off ) xtitle("") /// > graphregion(fcolor(white) lcolor(white) lwidth(vvthin)) /// > ylabel( , glcolor(gs14) gmax gmin) /// > clcolor(dknavy red dkorange purple dkgreen ) /// > lpattern(dot dash_dot longdash shortdash solid) /// > clwidth(medthick medthick medthick medthick medthick) /// > ylabel( -0.03(0.03)0.12 , format(%9.2fc) glcolor(gs14) gmax gmin) ymtick(##2) /// > xmtick(##5) yline(0, lcolor(black)) name(stock) nodraw xsize(5) > > ** create a graph containing only the legend > capture graph drop legend > line table1 table2 table3 table4 table5 year if year < 1980, /// > title("" ) /// > legend( label(1 "Model 1") label(2 "Model 2" ) /// > label(3 "Model 3") label(4 "Model 4") label( 5 "Model 5") /// > size(small) rows(2) ) /// > clcolor(dknavy red dkorange purple dkgreen ) /// > lpattern(dot dash_dot longdash shortdash solid) /// > clwidth(medthick medthick medthick medthick medthick) /// > xtitle("") ytitle("") ylabel( , glcolor(gs14)) /// > xlabel( , labcolor(white) tlcolor(white)) /// > ylabel( , nogrid) xsize(5) /// > graphregion(fcolor(white) lcolor(white) lwidth(vthin)) /// > yscale( off) xscale( off) name(legend) nodraw fysize(10) > > graph combine housing stock legend , col(1) xcommon /// > title( "Figure 5: Average Wealth Effects over Time" , /// > size(medium) color(black)) /// > graphregion(fcolor(white) lcolor(black) ) ysize(9) xsize(7) /// > imargin( small) > > graph export ./Paper/v4/CLM_Wealth_WealthEffectsoverTime.v4.emf , as(emf) replace > > */ . . graph drop _all . . restore . . egen hwe_min = min(hwe5) if e(sample) , by(state) (2907 missing values generated) . egen hwe_max = max(hwe5) if e(sample) , by(state) (2907 missing values generated) . egen hwe_p25 = pctile(hwe5) if e(sample) , by(state) p(25) (2907 missing values generated) . egen hwe_p75 = pctile(hwe5) if e(sample) , by(state) p(75) (2907 missing values generated) . egen hwe_p50 = median(hwe5) if e(sample) , by(state) (2907 missing values generated) . egen hwe_mean = mean(hwe5) if e(sample) , by(state) (2907 missing values generated) . egen hwratio_bar = mean(hwratio) if e(sample) , by(state) (2907 missing values generated) . egen chratio_bar = mean(chratio) if e(sample) , by(state) (2907 missing values generated) . . format hwe_min hwe_max hwe_p50 hwe_mean %9.1fc . . ** Figure 7A . graph twoway rcap hwe_min hwe_max state /// > || scatter hwe_mean hwe_p50 hwe_p25 hwe_p75 state , /// > msymbol( x smcircle_hollow dh dh ) msize(large large medium medium) /// > mcolor(maroon dkgreen gs5 gs5) /// > xlabel( 1/51, valuelabel alternate labsize(small)) /// > title("Figure 7A: Range of Housing Wealth Effects over Time by State " /// > , size(medium) color(black) ) /// > legend( label(1 "Range of Values") label(2 "Mean") label(3 "Median") /// > label(4 "25th & 75th Percentiles") order( 1 2 3 4) rows(1) pos(6) ring(0) ) /// > ytitle( "") ylabel(-0.3(0.1)0.3, glcolor(gs14) gmin gmax) ymtick(##2) /// > ysize(5) xsize(8) yline(0 , lcolor(black) ) /// > graphregion(fcolor(white) lcolor(black) lwidth(vthin)) . . graph export ./Paper/v4/CLM_Wealth_HousingWealthbyState.v4.emf , as(emf) replace (file C:\users\s352u532\documents\stata\wealtheffect\Paper/v4/CLM_Wealth_HousingWealthbyState.v4.emf written in Enhanced Metafile > format) . graph export ./Paper/v4/CLM_Wealth_Figure7A.eps , as(eps) replace (file ./Paper/v4/CLM_Wealth_Figure7A.eps written in EPS format) . . egen swe_min = min(swe5) if e(sample) , by(state) (2907 missing values generated) . egen swe_max = max(swe5) if e(sample) , by(state) (2907 missing values generated) . egen swe_p25 = pctile(swe5) if e(sample) , by(state) p(25) (2907 missing values generated) . egen swe_p75 = pctile(swe5) if e(sample) , by(state) p(75) (2907 missing values generated) . egen swe_p50 = median(swe5) if e(sample) , by(state) (2907 missing values generated) . egen swe_mean = mean(swe5) if e(sample) , by(state) (2907 missing values generated) . egen swratio_bar = mean(swratio) if e(sample) , by(state) (2907 missing values generated) . egen csratio_bar = mean(csratio) if e(sample) , by(state) (2907 missing values generated) . . format swe_min swe_max swe_p50 swe_mean %9.1fc . . ** Figure 7B . graph twoway rcap swe_min swe_max state , /// > || scatter swe_mean swe_p50 swe_p25 swe_p75 state , /// > msymbol( x smcircle_hollow dh dh ) msize(large large medium medium) /// > mcolor(maroon dkgreen gs5 gs5) /// > xlabel( 1/51, valuelabel alternate labsize(small)) /// > title("Figure 7B: Range of Stock Wealth Effects over Time by State " /// > , size(medium) color(black) ) /// > legend( label(1 "Range of Values") label(2 "Mean") label(3 "Median") /// > label(4 "25th & 75th Percentiles") order( 1 2 3 4) rows(1) pos(6) ring(0) ) /// > ytitle( "") ylabel(-0.3(0.1)0.3, glcolor(gs14) gmin gmax) ymtick(##2) /// > ysize(5) xsize(8) yline(0 , lcolor(black) ) /// > graphregion(fcolor(white) lcolor(black) lwidth(vthin)) . . graph export ./Paper/v4/CLM_Wealth_StockWealthbyState.v4.emf , as(emf) replace (file C:\users\s352u532\documents\stata\wealtheffect\Paper/v4/CLM_Wealth_StockWealthbyState.v4.emf written in Enhanced Metafile fo > rmat) . graph export ./Paper/v4/CLM_Wealth_Figure7B.eps , as(eps) replace (file ./Paper/v4/CLM_Wealth_Figure7B.eps written in EPS format) . . ** Figure 6 . ** Relationship between total wealth and wealth effects . ** Model 5 . egen wealth_mean = mean(w_real), by(state) . format wealth_mean %9.0fc . format hwe_mean swe_mean %9.2fc . scatter hwe_mean swe_mean wealth_mean if year == 2005 , /// > msymbol(smcircle X) mlab(state state) /// > || lfit hwe_mean wealth_mean if year == 2005 , clcolor(dknavy) /// > || lfit swe_mean wealth_mean if year == 2005 , clcolor(maroon) /// > legend( off ) /// > title("Panel B: Model 5 (full specification)" , size(medium) color(black) ) /// > xtitle( "Average Total Wealth" ) /// > ytitle( "Average Wealth Effect") ylabel( , glcolor(gs14) gmin gmax) ymtick(##2) /// > graphregion(fcolor(white) lcolor(white) lwidth(vvthin)) /// > name(w2) nodraw xsize(5) . . ** Now do it for Model 4 . drop swe_mean hwe_mean . egen hwe_mean = mean(hwe4) if e(sample) , by(state) (2907 missing values generated) . egen swe_mean = mean(swe4) if e(sample) , by(state) (2907 missing values generated) . format hwe_mean swe_mean %9.2fc . . scatter hwe_mean swe_mean wealth_mean if year == 2005 , /// > msymbol(smcircle X) mlab(state state) /// > || lfit hwe_mean wealth_mean if year == 2005 , clcolor(dknavy) /// > || lfit swe_mean wealth_mean if year == 2005 , clcolor(maroon) /// > legend( off ) /// > title( "Panel A: Model 4 (without poverty rates)" , size(medium) color(black) ) /// > xtitle( "" ) /// > ytitle( "Average Wealth Effect") ylabel( , glcolor(gs14) gmin gmax) ymtick(##2) /// > graphregion(fcolor(white) lcolor(white) lwidth(vvthin)) /// > name(w1) nodraw xsize(5) . . ** create a graph containing only the legend . replace hwe_mean = . if year == 1970 (0 real changes made) . replace swe_mean = . if year == 1970 (0 real changes made) . capture graph drop legend . scatter hwe_mean swe_mean wealth_mean if year == 1970 , /// > msymbol(smcircle X) mlab(state state) /// > || lfit hwe_mean wealth_mean if year == 1970 , clcolor(dknavy) /// > || lfit swe_mean wealth_mean if year == 1970 , clcolor(maroon) /// > title("" ) /// > legend( label( 1 "Housing Wealth Effect") label(2 "Stock Wealth Effect") /// > order(1 2) size(small) rows(1) ) /// > xtitle("") ytitle("") ylabel( , glcolor(gs14)) /// > xlabel( , labcolor(white) tlcolor(white)) /// > ylabel( , nogrid) xsize(5) /// > graphregion(fcolor(white) lcolor(white) lwidth(vthin)) /// > yscale( off) xscale( off) name(legend2) fysize(5) nodraw (note: regress could not fit model) (note: regress could not fit model) . . graph combine w1 w2 legend2 , col(1) xcommon /// > title( "Figure 6: Relationship between" "Wealth Effects and Total Wealth" , /// > size(medium) color(black)) /// > graphregion(fcolor(white) lcolor(black) ) ysize(9) xsize(7) /// > imargin( small) . . graph export ./Paper/v4/CLM_Wealth_WealthEffects_vs_Wealth.v4.emf , as(emf) replace (file C:\users\s352u532\documents\stata\wealtheffect\Paper/v4/CLM_Wealth_WealthEffects_vs_Wealth.v4.emf written in Enhanced Metafi > le format) . graph export ./Paper/v4/CLM_Wealth_Figure6.eps , as(eps) replace (file ./Paper/v4/CLM_Wealth_Figure6.eps written in EPS format) . . ** Restore variables based on Model 5 . drop swe_mean hwe_mean . egen hwe_mean = mean(hwe5) if e(sample) , by(state) (2907 missing values generated) . egen swe_mean = mean(swe5) if e(sample) , by(state) (2907 missing values generated) . format hwe_mean swe_mean %9.2fc . . gen dlncons = d.lncons (2703 missing values generated) . gen dlni = d.lninc (2652 missing values generated) . . format cons_real h_real st_real w_real inc_real %9.0fc . format age1ratio age2ratio age3ratio dlncons poverty dlni dlnw dlnst dlnh hwratio swratio %9.3f . . ** Summary Stats - Table 1 . sum cons_real inc_real h_real st_real w_real hwratio swratio /// > age1ratio age2ratio age3ratio poverty /// > dlncons dlni dlnh dlnst dlnw if _est_iv5 , sep(0) format vsquish Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- cons_real | 1275 11,997 2,186 6,887 20,973 inc_real | 1275 29,550 6,544 15,877 63,053 h_real | 1275 45,348 21,778 17,173 170,507 st_real | 1275 56,169 24,989 7,496 120,102 w_real | 1275 101,517 41,570 28,317 260,588 hwratio | 1275 0.457 0.103 0.242 0.735 swratio | 1275 0.543 0.103 0.265 0.758 age1ratio | 1275 0.312 0.041 0.229 0.478 age2ratio | 1275 0.384 0.034 0.292 0.499 age3ratio | 1275 0.304 0.033 0.135 0.386 poverty | 1275 0.127 0.038 0.029 0.272 dlncons | 1275 0.012 0.033 -0.122 0.156 dlni | 1275 0.019 0.022 -0.108 0.096 dlnh | 1275 0.029 0.061 -0.372 0.259 dlnst | 1275 0.056 0.152 -0.423 0.429 dlnw | 1275 0.041 0.094 -0.364 0.265 . . ** Table 2 . table state if state <= 51 & year >= 1985 , cont(mean age1r mean age2r mean age3r mean poverty) -------------------------------------------------------------------------- state | mean(age1ra~o) mean(age2ra~o) mean(age3ra~o) mean(poverty) ----------+--------------------------------------------------------------- AK | 0.361 0.452 0.187 0.095 AL | 0.308 0.375 0.317 0.170 AR | 0.296 0.365 0.339 0.177 AZ | 0.324 0.366 0.310 0.150 CA | 0.347 0.386 0.267 0.144 CO | 0.328 0.412 0.260 0.105 CT | 0.290 0.393 0.317 0.081 DC | 0.362 0.355 0.283 0.188 DE | 0.315 0.379 0.306 0.092 FL | 0.276 0.351 0.373 0.134 GA | 0.341 0.399 0.260 0.143 HI | 0.320 0.380 0.300 0.102 IA | 0.291 0.368 0.341 0.104 ID | 0.314 0.386 0.300 0.129 IL | 0.319 0.383 0.298 0.124 IN | 0.312 0.383 0.305 0.112 KS | 0.310 0.375 0.315 0.113 KY | 0.310 0.382 0.308 0.164 LA | 0.326 0.380 0.293 0.198 MA | 0.313 0.379 0.308 0.102 MD | 0.314 0.405 0.281 0.090 ME | 0.280 0.394 0.326 0.116 MI | 0.308 0.390 0.302 0.124 MN | 0.313 0.391 0.296 0.099 MO | 0.301 0.376 0.323 0.125 MS | 0.322 0.369 0.309 0.212 MT | 0.280 0.394 0.327 0.148 NC | 0.320 0.381 0.299 0.138 ND | 0.313 0.359 0.328 0.120 NE | 0.305 0.373 0.322 0.106 NH | 0.301 0.410 0.289 0.067 NJ | 0.293 0.394 0.313 0.086 NM | 0.317 0.388 0.296 0.198 NV | 0.322 0.391 0.286 0.105 NY | 0.308 0.382 0.310 0.149 OH | 0.301 0.383 0.316 0.120 OK | 0.307 0.371 0.322 0.152 OR | 0.294 0.392 0.314 0.119 PA | 0.282 0.373 0.345 0.109 RI | 0.305 0.369 0.325 0.105 SC | 0.320 0.381 0.300 0.149 SD | 0.300 0.364 0.336 0.130 TN | 0.308 0.384 0.307 0.157 TX | 0.347 0.388 0.265 0.167 UT | 0.394 0.359 0.246 0.093 VA | 0.327 0.395 0.277 0.099 VT | 0.295 0.403 0.302 0.095 WA | 0.315 0.400 0.285 0.106 WI | 0.304 0.384 0.312 0.097 WV | 0.276 0.374 0.350 0.176 WY | 0.303 0.403 0.294 0.108 -------------------------------------------------------------------------- . . ** Tables 3 & 4 . estout iv1 iv2 iv3 iv4 iv5 , /// > cells( b(fmt(%9.3fc) star ) se(fmt(%9.3fc) par) ) /// > drop( s? s?? ) /// > order( D.lninc D.lnh D.lnst D.lnw age1ratio age3ratio poverty /// > age1h age3h povertyh age1s age3s povertys /// > age1w age3w povertyw _cons) /// > stats(N chi2 df_m hwe hwe_p swe swe_p we_diff we_diff_p hwelas hwelas_p swelas swelas_p /// > elas_diff elas_diff_p deriv_hy deriv_ho deriv_hp deriv_sy deriv_so deriv_sp /// > , fmt(%9.0fc %9.3fc ) /// > labels("Obs." "Wald Chi2" " df" "HW Effect" " p-value" "SW Effect" " p-value" /// > "WE Diff." " p-value" "HW Elasticity" " p-value" "SW Elasticity" " p-value" /// > "Elas. Diff." " p-value" "dHWE/dY" "dHWE/dO" "dHWE/dP" "dSWE/dY" "dSWE/dO" /// > "dSWE/dP") /// > layout(@ @ @ @ (@) @ (@) @ (@) @ (@) @ (@) @ (@) @ @ @ @ @ @ ) ) /// > prehead("Elasticity Regressions in First Differences with Interactions" "State Fixed Effects" /// > "Dependent Variable = Log Difference of Consumption") /// > starlevels( * 0.10 ** 0.05 *** 0.01 ) /// > indicate(State Effects = s2 s3 s4) /// > stardetach /// > varwidth(12) modelwidth(8) prefoot(@hline) postfoot(@hline) posthead(@hline) Elasticity Regressions in First Differences with Interactions State Fixed Effects Dependent Variable = Log Difference of Consumption iv1 iv2 iv3 iv4 iv5 > b/se b/se b/se b/se b/se > ----------------------------------------------------------------------------------------------------------- D.lninc 0.878 *** 0.954 *** 0.636 *** 0.548 *** 0.562 ** > * (0.077) (0.074) (0.080) (0.068) (0.070) > D.lnh 0.183 *** -0.019 -0.345 -6.456 *** -8.194 ** > * (0.026) (0.087) (0.495) (1.635) (2.157) > D.lnst 0.058 *** -0.150 0.949 *** -7.381 *** -8.556 ** > * (0.017) (0.095) (0.276) (1.513) (2.110) > D.lnw 0.398 ** 13.872 *** 16.501 ** > * (0.175) (3.001) (4.007) > age1ratio 0.017 -0.016 0.016 > (0.078) (0.073) (0.080) > age3ratio -0.271 *** -0.516 *** -0.454 ** > * (0.084) (0.073) (0.092) > poverty 0.096 0.128 * > (0.089) (0.075) > age1h 0.634 8.457 *** 12.820 ** > * (0.766) (2.984) (3.961) > age3h 1.044 11.962 *** 15.260 ** > * (1.050) (2.653) (3.660) > povertyh 0.631 -4.000 > (1.128) (2.682) > age1s -1.039 * 10.217 *** 13.511 ** > * (0.607) (2.512) (3.606) > age3s -2.279 *** 12.224 *** 15.215 ** > * (0.632) (2.559) (3.743) > povertys 0.766 -5.507 ** > (0.864) (2.593) > age1w -18.790 *** -26.431 ** > * (5.489) (7.432) > age3w -23.442 *** -29.430 ** > * (4.800) (6.682) > povertyw 11.189 ** > (5.559) > _cons -0.011 *** -0.010 *** 0.015 0.102 *** 0.069 > (0.001) (0.001) (0.044) (0.038) (0.044) > State Effects Yes Yes Yes Yes Yes > ----------------------------------------------------------------------------------------------------------- Obs. 1,275 1,275 1,275 1,275 1,275 > Wald Chi2 388.737 345.773 752.619 808.267 1,052.497 > df 53.000 54.000 62.000 62.000 66.000 > HW Effect 0.055 0.049 0.075 0.067 0.081 > p-value (0.000) (0.000) (0.000) (0.000) (0.000) > SW Effect 0.016 0.018 0.008 0.000 -0.005 > p-value (0.001) (0.000) (0.074) (0.945) (0.526) > WE Diff. 0.039 0.031 0.067 0.066 0.086 > p-value (0.000) (0.000) (0.000) (0.000) (0.000) > HW Elasticity 0.183 0.163 0.250 0.222 0.270 > p-value (0.000) (0.000) (0.000) (0.000) (0.000) > SW Elasticity 0.058 0.066 0.030 0.002 -0.019 > p-value (0.001) (0.000) (0.074) (0.945) (0.526) > Elas. Diff. 0.124 0.097 0.221 0.220 0.288 > p-value (0.000) (0.001) (0.000) (0.000) (0.000) > dHWE/dY 0.191 -0.039 0.223 > dHWE/dO 0.314 0.376 0.545 > dHWE/dP 0.190 0.335 > dSWE/dY -0.277 0.003 -0.224 > dSWE/dO -0.607 -0.135 -0.204 > dSWE/dP 0.204 0.152 > ----------------------------------------------------------------------------------------------------------- . . ** Table 5 . tabstat hwe5 swe5 chratio csratio age1r age3r poverty hwratio swratio w_real /// > if _est_iv5 , by(state) format(%12.3fc) Summary statistics: mean by categories of: state state | hwe5 swe5 chratio csratio age1ra~o age3ra~o poverty hwratio swratio w_real -------+---------------------------------------------------------------------------------------------------- AK | -0.001 -0.004 0.329 0.288 0.361 0.187 0.095 0.448 0.552 97,817.929 AL | 0.112 -0.020 0.322 0.383 0.308 0.317 0.170 0.499 0.501 76,693.048 AR | 0.129 0.001 0.371 0.368 0.296 0.339 0.177 0.466 0.534 66,821.892 AZ | 0.078 0.000 0.269 0.272 0.324 0.310 0.150 0.499 0.501 96,209.037 CA | 0.052 -0.022 0.161 0.240 0.347 0.267 0.144 0.589 0.411 132,668.419 CO | 0.029 0.005 0.253 0.198 0.328 0.260 0.105 0.435 0.565 125,779.752 CT | 0.033 0.002 0.171 0.197 0.290 0.317 0.081 0.528 0.472 156,058.724 DC | 0.048 -0.000 0.153 0.105 0.362 0.283 0.188 0.425 0.575 150,173.192 DE | 0.066 -0.011 0.290 0.221 0.315 0.306 0.092 0.441 0.559 124,287.065 FL | 0.094 0.002 0.279 0.253 0.276 0.373 0.134 0.473 0.527 109,229.252 GA | 0.071 -0.017 0.311 0.346 0.341 0.260 0.143 0.490 0.510 86,759.805 HI | 0.039 -0.009 0.165 0.278 0.320 0.300 0.102 0.620 0.380 149,082.043 IA | 0.118 -0.010 0.405 0.215 0.291 0.341 0.104 0.340 0.660 90,098.164 ID | 0.079 -0.006 0.299 0.277 0.314 0.300 0.129 0.474 0.526 88,819.723 IL | 0.059 -0.000 0.257 0.214 0.319 0.298 0.124 0.445 0.555 108,790.134 IN | 0.075 0.001 0.364 0.307 0.312 0.305 0.112 0.436 0.564 83,971.699 KS | 0.082 0.000 0.372 0.188 0.310 0.315 0.113 0.333 0.667 91,325.939 KY | 0.105 -0.008 0.350 0.351 0.310 0.308 0.164 0.470 0.530 73,702.272 LA | 0.108 -0.008 0.346 0.362 0.326 0.293 0.198 0.474 0.526 70,929.894 MA | 0.040 -0.001 0.197 0.180 0.313 0.308 0.102 0.469 0.531 146,917.683 MD | 0.043 -0.013 0.207 0.215 0.314 0.281 0.090 0.506 0.494 129,026.569 ME | 0.073 -0.001 0.295 0.308 0.280 0.326 0.116 0.496 0.504 99,726.718 MI | 0.066 0.006 0.320 0.226 0.308 0.302 0.124 0.407 0.593 97,727.049 MN | 0.045 0.004 0.307 0.169 0.313 0.296 0.099 0.355 0.645 124,178.059 MO | 0.084 0.003 0.348 0.196 0.301 0.323 0.125 0.358 0.642 100,263.942 MS | 0.135 -0.027 0.340 0.404 0.322 0.309 0.212 0.495 0.505 64,274.981 MT | 0.076 0.011 0.308 0.237 0.280 0.327 0.148 0.433 0.567 96,315.125 NC | 0.074 -0.001 0.296 0.325 0.320 0.299 0.138 0.496 0.504 87,872.269 ND | 0.138 -0.008 0.505 0.266 0.313 0.328 0.120 0.339 0.661 81,647.464 NE | 0.104 -0.004 0.432 0.222 0.305 0.322 0.106 0.337 0.663 87,016.360 NH | 0.028 0.011 0.338 0.311 0.301 0.289 0.067 0.471 0.529 115,250.446 NJ | 0.042 -0.007 0.193 0.173 0.293 0.313 0.086 0.472 0.528 148,834.019 NM | 0.105 -0.015 0.278 0.302 0.317 0.296 0.198 0.501 0.499 85,707.602 NV | 0.064 -0.013 0.288 0.349 0.322 0.286 0.105 0.526 0.474 101,637.132 NY | 0.052 0.004 0.216 0.172 0.308 0.310 0.149 0.439 0.561 114,357.537 OH | 0.069 0.005 0.320 0.238 0.301 0.316 0.120 0.415 0.585 92,229.861 OK | 0.098 0.010 0.375 0.310 0.307 0.322 0.152 0.426 0.574 70,441.750 OR | 0.072 -0.006 0.275 0.246 0.294 0.314 0.119 0.474 0.526 110,717.431 PA | 0.073 -0.002 0.276 0.209 0.282 0.345 0.109 0.428 0.572 102,994.984 RI | 0.042 0.014 0.216 0.225 0.305 0.325 0.105 0.498 0.502 109,140.585 SC | 0.096 -0.031 0.292 0.402 0.320 0.300 0.149 0.532 0.468 83,130.137 SD | 0.157 -0.002 0.548 0.288 0.300 0.336 0.130 0.347 0.653 82,818.075 TN | 0.100 -0.013 0.327 0.365 0.308 0.307 0.157 0.494 0.506 80,977.615 TX | 0.061 0.021 0.421 0.336 0.347 0.265 0.167 0.420 0.580 73,065.741 UT | 0.042 -0.004 0.275 0.303 0.394 0.246 0.093 0.505 0.495 88,639.112 VA | 0.046 -0.010 0.232 0.258 0.327 0.277 0.099 0.508 0.492 112,648.569 VT | 0.046 0.001 0.268 0.238 0.295 0.302 0.095 0.464 0.536 114,617.768 WA | 0.053 -0.013 0.220 0.236 0.315 0.285 0.106 0.509 0.491 121,399.603 WI | 0.060 0.002 0.328 0.221 0.304 0.312 0.097 0.393 0.607 101,515.074 WV | 0.126 -0.012 0.337 0.360 0.276 0.350 0.176 0.479 0.521 70,625.972 WY | 0.048 0.007 0.306 0.233 0.303 0.294 0.108 0.428 0.572 102,442.009 -------+---------------------------------------------------------------------------------------------------- Total | 0.073 -0.004 0.301 0.266 0.312 0.304 0.127 0.457 0.543 101,517.161 ------------------------------------------------------------------------------------------------------------ . . ** Table A1 . estout iv5 , /// > cells( b(fmt(%9.3fc) star ) se(fmt(%9.3fc) par) ) /// > stats(N r2_a , fmt(%9.0fc %9.3fc ) /// > labels("Obs." "Adj. R-Square" ) ) /// > prehead("Elasticity Regressions in First Differences with Interactions" /// > "Dependent Variable = Log Difference of Consumption") /// > starlevels( * 0.10 ** 0.05 *** 0.01 ) /// > stardetach /// > varwidth(12) modelwidth(9) prefoot(@hline) postfoot(@hline) posthead(@hline) Elasticity Regressions in First Differences with Interactions Dependent Variable = Log Difference of Consumption iv5 b/se -------------------------------- D.lnh -8.194 *** (2.157) D.lnst -8.556 *** (2.110) D.lnw 16.501 *** (4.007) D.lninc 0.562 *** (0.070) age1h 12.820 *** (3.961) age3h 15.260 *** (3.660) age1s 13.511 *** (3.606) age3s 15.215 *** (3.743) age1w -26.431 *** (7.432) age3w -29.430 *** (6.682) povertyh -4.000 (2.682) povertys -5.507 ** (2.593) povertyw 11.189 ** (5.559) age1ratio 0.016 (0.080) age3ratio -0.454 *** (0.092) s2 0.048 *** (0.010) s3 0.051 *** (0.012) s4 0.044 *** (0.009) s5 0.028 *** (0.007) s6 0.029 *** (0.005) s7 0.053 *** (0.007) s8 0.006 (0.015) s9 0.047 *** (0.007) s10 0.071 *** (0.011) s11 0.024 *** (0.006) s12 0.043 *** (0.007) s13 0.066 *** (0.009) s14 0.041 *** (0.008) s15 0.043 *** (0.008) s16 0.048 *** (0.007) s17 0.051 *** (0.008) s18 0.042 *** (0.010) s19 0.029 *** (0.010) s20 0.047 *** (0.007) s21 0.034 *** (0.005) s22 0.061 *** (0.008) s23 0.047 *** (0.007) s24 0.051 *** (0.007) s25 0.057 *** (0.008) s26 0.039 *** (0.011) s27 0.048 *** (0.010) s28 0.039 *** (0.008) s29 0.056 *** (0.009) s30 0.064 *** (0.008) s31 0.056 *** (0.005) s32 0.053 *** (0.006) s33 0.030 *** (0.011) s34 0.049 *** (0.006) s35 0.042 *** (0.009) s36 0.054 *** (0.008) s37 0.044 *** (0.009) s38 0.045 *** (0.008) s39 0.062 *** (0.009) s40 0.057 *** (0.008) s41 0.041 *** (0.009) s42 0.056 *** (0.010) s43 0.044 *** (0.009) s44 0.015 ** (0.008) s45 0.027 *** (0.006) s46 0.037 *** (0.006) s47 0.046 *** (0.006) s48 0.034 *** (0.006) s49 0.054 *** (0.007) s50 0.059 *** (0.012) s51 0.035 *** (0.007) poverty 0.128 * (0.075) _cons 0.069 (0.044) -------------------------------- Obs. 1,275 Adj. R-Square 0.325 -------------------------------- . . ** Wald Tests for Nested Models . . ** Model 2 vs. Model 1 . est restore iv2 (results iv2 are active now) . test D.lnw ( 1) D.lnw = 0 chi2( 1) = 5.19 Prob > chi2 = 0.0227 . . ** Model 3 vs. Model 1 . est restore iv3 (results iv3 are active now) . test age1r age3r poverty age1h age3h povertyh age1s age3s povertys ( 1) age1ratio = 0 ( 2) age3ratio = 0 ( 3) poverty = 0 ( 4) age1h = 0 ( 5) age3h = 0 ( 6) povertyh = 0 ( 7) age1s = 0 ( 8) age3s = 0 ( 9) povertys = 0 chi2( 9) = 130.01 Prob > chi2 = 0.0000 . . ** Model 4 vs. Model 1 . est restore iv4 (results iv4 are active now) . test D.lnw age1r age3r age1h age3h age1s age3s age1w age3w ( 1) D.lnw = 0 ( 2) age1ratio = 0 ( 3) age3ratio = 0 ( 4) age1h = 0 ( 5) age3h = 0 ( 6) age1s = 0 ( 7) age3s = 0 ( 8) age1w = 0 ( 9) age3w = 0 chi2( 9) = 201.05 Prob > chi2 = 0.0000 . . ** Model 5 vs. Model 1 . est restore iv5 (results iv5 are active now) . test D.lnw age1r age3r poverty age1h age3h povertyh /// > age1s age3s povertys age1w age3w povertyw ( 1) D.lnw = 0 ( 2) age1ratio = 0 ( 3) age3ratio = 0 ( 4) poverty = 0 ( 5) age1h = 0 ( 6) age3h = 0 ( 7) povertyh = 0 ( 8) age1s = 0 ( 9) age3s = 0 (10) povertys = 0 (11) age1w = 0 (12) age3w = 0 (13) povertyw = 0 chi2( 13) = 358.89 Prob > chi2 = 0.0000 . . ** Model 5 vs. Model 3 . test D.lnw age1w age3w povertyw ( 1) D.lnw = 0 ( 2) age1w = 0 ( 3) age3w = 0 ( 4) povertyw = 0 chi2( 4) = 27.16 Prob > chi2 = 0.0000 . . ** Model 5 vs. Model 4 . test poverty povertyh povertys povertyw ( 1) poverty = 0 ( 2) povertyh = 0 ( 3) povertys = 0 ( 4) povertyw = 0 chi2( 4) = 7.88 Prob > chi2 = 0.0959 . . . ** Calculate effects for text in the paper. . sum age1r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age1ratio | 1275 .312477 .0413183 .2286251 .4784656 . display "HWE impact of a 1 Std. Dev. change in Young Percent: " %4.3fc = e(deriv_hy) * `r(sd)' HWE impact of a 1 Std. Dev. change in Young Percent: 0.009 . display "SWE impact of a 1 Std. Dev. change in Young Percent: " %4.3fc = e(deriv_sy) * `r(sd)' SWE impact of a 1 Std. Dev. change in Young Percent: -0.009 . . sum age3r if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- age3ratio | 1275 .3036991 .0331339 .1347572 .3859731 . display "HWE impact of a 1 Std. Dev. change in Old Percent: " %4.3fc = e(deriv_ho) * `r(sd)' HWE impact of a 1 Std. Dev. change in Old Percent: 0.018 . display "SWE impact of a 1 Std. Dev. change in Old Percent: " %4.3fc = e(deriv_so) * `r(sd)' SWE impact of a 1 Std. Dev. change in Old Percent: -0.007 . . sum poverty if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- poverty | 1275 .1272549 .0382546 .029 .272 . display "HWE impact of a 1 Std. Dev. change in Poverty Rate: " %4.3fc = e(deriv_hp) * `r(sd)' HWE impact of a 1 Std. Dev. change in Poverty Rate: 0.013 . display "SWE impact of a 1 Std. Dev. change in Poverty Rate: " %4.3fc = e(deriv_sp) * `r(sd)' SWE impact of a 1 Std. Dev. change in Poverty Rate: 0.006 . . sum hwratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- hwratio | 1275 .456968 .103007 .2415229 .7352813 . display "HWE impact of a 1 Std. Dev. change in Housing Wealth Ratio: " %4.3fc = e(deriv_hw) * `r(sd)' HWE impact of a 1 Std. Dev. change in Housing Wealth Ratio: 0.023 . . sum swratio if e(sample) Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- swratio | 1275 .543032 .103007 .2647187 .7584771 . display "SWE impact of a 1 Std. Dev. change in Stock Wealth Ratio: " %4.3fc = e(deriv_sw) * `r(sd)' SWE impact of a 1 Std. Dev. change in Stock Wealth Ratio: 0.020 . . log close name: log: C:\users\s352u532\documents\stata\wealtheffect\CLM_Wealth_IVRegressions.v4.txt log type: text closed on: 17 Apr 2013, 14:26:34 ----------------------------------------------------------------------------------------------------------------------------------