<p>Argentina to china ------log: K:\Argentian_to_China.log opened on: 25 Jul 2006, 21:51:25</p><p>After Argentina: 1-20 After Bolivia 21-40 After Brazil 41-60 After china 61-81</p><p>. 2</p><p>. *Dropping Argentina . drop if ccode==330320 (10 observations deleted)</p><p>. . . *Descriptive stats after dropping . tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remote dist op > en english gdpdfrationew white whitemg, stat(n mean sd median min max) col(sta > t)</p><p> variable | N mean sd p50 min max ------+------rimp | 1000 490606.7 1561357 7348 0 1.39e+07 immig | 1000 33352.59 117064.7 2790 0 1137050 gdp | 1000 2.76e+11 9.70e+11 1.63e+10 1.92e+08 8.99e+12 gdpau | 1000 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 1000 105.7001 16.95095 102.7439 60.87417 207.3465 gdpdfau | 1000 100.7173 4.633489 100.8223 94.42464 109.9797 pop | 1000 4.93e+07 1.54e+08 1.02e+07 41000 1.26e+09 popau | 1000 1.82e+07 612793.8 1.82e+07 1.73e+07 1.92e+07 xrate1 | 1000 1043.108 10901.71 16.2185 .0068 270182.6 remote | 1000 6703.117 4131.335 6764 1293 39620 dist | 1000 13385.54 3528.833 14305 2409 17972 open | 1000 .7133269 .3866026 .63975 .0671 3.2192 english | 1000 .38 .4856293 0 0 1 gdpdfratio~w | 1000 1.052493 .1814267 1.017907 .5844526 2.009028 white | 1000 .13 .3364717 0 0 1 whitemg | 1000 20501.65 116468.1 0 0 1137050 ------3</p><p>. sort white</p><p>. *IIIB. Descriptives Original Unites(sub sample for "white" and "non-white" cou > ntries)--RHS Variables: . by white: tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remo > te dist phone open english gdpdfrationew white whitemg, stat(n mean sd median > min max) col(stat) </p><p>------> white = 0</p><p> variable | N mean sd p50 min max ------+------rimp | 870 404267.6 1597736 2944.5 0 1.39e+07 immig | 870 14771.19 27168.46 1403.5 0 158613 gdp | 870 2.34e+11 1.00e+12 1.00e+10 1.92e+08 8.99e+12 gdpau | 870 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 870 104.8278 17.31375 101.6682 60.87417 207.3465 gdpdfau | 870 100.7173 4.633836 100.8223 94.42464 109.9797 pop | 870 5.31e+07 1.65e+08 1.03e+07 41000 1.26e+09 popau | 870 1.82e+07 612839.6 1.82e+07 1.73e+07 1.92e+07 xrate1 | 870 1182.981 11681.74 22.9426 .0068 270182.6 remote | 870 7124.075 4103.561 6927 1293 39620 dist | 870 13218.32 3418.617 14051 2410 17972 phone | 870 157.0011 242.1507 47.025 .54 1449.75 open | 870 .7215039 .4077011 .63975 .0671 3.2192 english | 870 .3908046 .4882114 0 0 1 gdpdfratio~w | 870 1.044054 .1863521 1 .5844526 2.009028 white | 870 0 0 0 0 0 whitemg | 870 0 0 0 0 0 ------</p><p>------> white = 1</p><p> variable | N mean sd p50 min max ------+------rimp | 130 1068414 1138347 522863.5 40432 3843839 immig | 130 157705 288518.8 21174 2612 1137050 gdp | 130 5.56e+11 6.58e+11 2.54e+11 5.15e+10 2.69e+12 gdpau | 130 3.83e+11 4.46e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 130 111.5379 12.89742 112.2247 85.45601 143.9587 gdpdfau | 130 100.7173 4.649088 100.8223 94.42464 109.9797 pop | 130 2.34e+07 2.55e+07 8837000 3477200 8.22e+07 popau | 130 1.82e+07 614856.7 1.82e+07 1.73e+07 1.92e+07 xrate1 | 130 107.0323 293.6987 4.4722 .3774 1261.556 remote | 130 3885.938 3089.96 2888 1530 12501 dist | 130 14504.62 4031.974 15931 2409 17493 phone | 130 734.716 269.9717 652.085 343.99 1487.08 open | 130 .6586038 .1850905 .64065 .3517 1.2967 english | 130 .3076923 .4633239 0 0 1 gdpdfratio~w | 130 1.108974 .1314272 1.116445 .8254872 1.441221 white | 130 1 0 1 1 1 whitemg | 130 157705 288518.8 21174 2612 1137050 ------</p><p>. . 4</p><p>. **Regression of Imports . tsset ccode year panel variable: ccode, 117100 to 725980 time variable: year, 1991 to 2000</p><p>. . **I. Aggregate Imports . xtgls lrimp limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8299.66 Log likelihood = -540.6866 Prob > chi2 = 0.0000</p><p>------lrimp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .338514 .0307509 11.01 0.000 .2782434 .3987846 lgdp | 1.168648 .0334382 34.95 0.000 1.10311 1.234186 lgdpau | .1384731 .5989043 0.23 0.817 -1.035358 1.312304 lgdpdfrati~w | -.9267992 .2451255 -3.78 0.000 -1.407236 -.446362 lpopau | -2.572674 2.066631 -1.24 0.213 -6.623197 1.477849 lpop | .0370743 .0408698 0.91 0.364 -.043029 .1171777 lxrate1 | -.1349864 .0164338 -8.21 0.000 -.167196 -.1027769 lremote | -.3984378 .0760015 -5.24 0.000 -.547398 -.2494777 ldist | -2.066601 .1571122 -13.15 0.000 -2.374535 -1.758666 lopen | .2688013 .0556107 4.83 0.000 .1598063 .3777963 english | .8035028 .129222 6.22 0.000 .5502322 1.056773 white | 4.496072 .5725474 7.85 0.000 3.3739 5.618244 lwhitemg | -.42037 .0508472 -8.27 0.000 -.5200288 -.3207113 _cons | 41.05261 19.04506 2.16 0.031 3.724989 78.38023 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000946 lgdp | -.000303 .001118 lgdpau | -.000933 .001412 .358686 lgdpdfrati~w | -.00078 .000162 -.009192 .060087 lpopau | .001874 -.008298 -1.21225 .048169 4.27096 lpop | -.000141 -.000746 .000127 .000227 -.000301 .00167 lxrate1 | .000084 .000167 .000386 .000244 -.002787 -.000091 .00027 lremote | .000504 .000098 -.000459 -.001885 -.003407 -.000293 .000059 ldist | .001669 .001008 .000565 -.001211 -.015741 -.000091 .001572 lopen | -.000182 .00063 .002507 .0008 -.016946 .000344 .000183 english | -.001162 .002811 .003001 .002368 -.011489 -.001251 .000639 white | .00458 -.001846 .003302 -.009482 -.027747 .005097 .000469 lwhitemg | -.00056 .00017 -.00029 .000889 .002922 -.000401 -.000062 _cons | -.023991 .075904 10.6784 -.596227 -38.7409 -.002348 .016431</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .005776 ldist | .005231 .024684 lopen | .001344 .001106 .003093 english | -.003291 .003063 -.000614 .016698 white | -.000592 .000319 -.000136 .001262 .327811 lwhitemg | .000139 -.000484 -.00006 .000065 -.028682 .002585 _cons | -.027672 -.070299 .175027 .059838 .298171 -.030607 362.714 5</p><p>. . **II. Conservative Estimates . *2.1. Aggregate reference priced Imports (conservative) . xtgls lrrefp_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 63519.45 Log likelihood = -974.5601 Prob > chi2 = 0.0000</p><p>------lrrefp_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0532069 .0141344 3.76 0.000 .025504 .0809098 lgdp | 1.61302 .051051 31.60 0.000 1.512962 1.713078 lgdpau | .8721251 .361443 2.41 0.016 .1637097 1.58054 lgdpdfrati~w | -.4511029 .1504743 -3.00 0.003 -.7460272 -.1561786 lpopau | -6.307587 1.221196 -5.17 0.000 -8.701088 -3.914086 lpop | -.3581126 .0836639 -4.28 0.000 -.5220908 -.1941345 lxrate1 | -.122523 .0205693 -5.96 0.000 -.1628381 -.082208 lremote | -.9560443 .0975771 -9.80 0.000 -1.147292 -.7647966 ldist | -3.228652 .1593626 -20.26 0.000 -3.540997 -2.916307 lopen | -.0533902 .0270733 -1.97 0.049 -.1064529 -.0003275 english | .9857242 .0865166 11.39 0.000 .8161548 1.155294 white | 4.679344 .76076 6.15 0.000 3.188282 6.170406 lwhitemg | -.4659537 .0724452 -6.43 0.000 -.6079436 -.3239639 _cons | 94.66219 11.47936 8.25 0.000 72.16305 117.1613 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .0002 lgdp | -.000263 .002606 lgdpau | -.001251 .001792 .130641 lgdpdfrati~w | -.000606 .000527 -.012104 .022643 lpopau | .004959 -.007491 -.432172 .024398 1.49132 lpop | .000152 -.004012 -.001466 .00045 .004245 .007 lxrate1 | -.000028 .000284 -.000042 .000427 -.003258 -.000306 .000423 lremote | .000414 .000319 -.006688 -.003436 .02359 -.001071 .000054 ldist | .000604 -.001298 -.006464 -.001629 .016117 .002153 .000566 lopen | .000044 -.000102 -.000984 .000937 .003624 .000269 -.000074 english | -.000439 .000286 .003185 .002675 -.020927 .000309 .001148 white | .001112 -.012407 -.010561 -.00422 .044329 .018372 -.001128 lwhitemg | -.000094 .000629 .000431 .00023 -.002911 -.000828 .000151 _cons | -.055537 .090705 3.86221 -.080032 -13.698 -.062081 .045872</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .009521 ldist | .00463 .025396 lopen | .000645 .000244 .000733 english | -.002672 .002302 -.000405 .007485 white | .007392 -.027208 .00179 -.003047 .578756 lwhitemg | 9.2e-06 .00369 -.00013 .000507 -.053548 .005248 _cons | -.333086 -.392557 -.044881 .24836 -.262544 -.000635 131.776</p><p>. 6</p><p>. . *2.2. Aggregate Differentiated Imports (conservative) . xtgls lrdiff_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 7627.17 Log likelihood = -936.7348 Prob > chi2 = 0.0000</p><p>------lrdiff_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0773631 .0189838 4.08 0.000 .0401555 .1145707 lgdp | 1.69107 .0684305 24.71 0.000 1.556949 1.825191 lgdpau | .3077696 .4777676 0.64 0.519 -.6286376 1.244177 lgdpdfrati~w | -.5382237 .1943257 -2.77 0.006 -.9190951 -.1573523 lpopau | -3.957813 1.655334 -2.39 0.017 -7.202208 -.7134185 lpop | -.5268908 .0807841 -6.52 0.000 -.6852248 -.3685568 lxrate1 | -.1561201 .0261984 -5.96 0.000 -.207468 -.1047721 lremote | -.4022688 .1220836 -3.30 0.001 -.6415482 -.1629894 ldist | -4.391715 .3316741 -13.24 0.000 -5.041785 -3.741646 lopen | .0345804 .0377026 0.92 0.359 -.0393155 .1084762 english | 1.335583 .1454115 9.18 0.000 1.050582 1.620584 white | 10.64587 1.886094 5.64 0.000 6.949191 14.34254 lwhitemg | -.8771812 .1879148 -4.67 0.000 -1.245487 -.508875 _cons | 77.45135 15.73515 4.92 0.000 46.61102 108.2917 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00036 lgdp | -.00057 .004683 lgdpau | -.001565 .00354 .228262 lgdpdfrati~w | -.000674 .001816 -.01682 .037762 lpopau | .004845 -.01893 -.765733 .049831 2.74013 lpop | .000412 -.004028 -.001624 -.001392 .005481 .006526 lxrate1 | -2.6e-06 .000429 -.000099 .000905 -.004782 -.000598 .000686 lremote | -.000072 -.001022 -.006534 -.001832 .022654 -.000849 .000101 ldist | .001547 .008835 -.008599 -.00195 -.014922 -.002102 .001187 lopen | .000084 -.000561 -.000552 .001645 .002677 .000858 -.000178 english | -.000741 .001641 .005764 .002015 -.04338 .003596 .00192 white | .000633 -.031652 .000576 -.001205 -.002467 .028427 .004414 lwhitemg | -.000069 .00157 -.00119 -.00033 .006259 -.001765 -.000435 _cons | -.047892 .105743 6.82462 -.406294 -25.153 -.033848 .065702</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .014904 ldist | .004936 .110008 lopen | .00057 -.000641 .001421 english | -.004853 .006126 -.001252 .021145 white | .013555 -.140031 .002375 .028165 3.55735 lwhitemg | .000748 .010382 -.000098 -.0031 -.346834 .035312 _cons | -.346075 -.794035 -.03029 .448351 1.46256 -.182559 247.595</p><p>. . *2.3. Aggregate Homogenous Imports (conservative) . xtgls lrhomo_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog 7</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 2251.68 Log likelihood = -1114.211 Prob > chi2 = 0.0000</p><p>------lrhomo_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0970674 .0381287 2.55 0.011 .0223364 .1717984 lgdp | .9069852 .0788128 11.51 0.000 .752515 1.061455 lgdpau | -.5706697 .8845912 -0.65 0.519 -2.304437 1.163097 lgdpdfrati~w | -.2915543 .3582313 -0.81 0.416 -.9936747 .4105662 lpopau | .5302299 3.021501 0.18 0.861 -5.391803 6.452262 lpop | .048334 .0960327 0.50 0.615 -.1398866 .2365546 lxrate1 | -.1197343 .0331282 -3.61 0.000 -.1846643 -.0548043 lremote | .4065538 .2443268 1.66 0.096 -.0723179 .8854255 ldist | -1.575912 .3317722 -4.75 0.000 -2.226174 -.9256507 lopen | .3868819 .0794016 4.87 0.000 .2312577 .5425061 english | 1.75146 .2374831 7.38 0.000 1.286002 2.216919 white | 2.044587 2.24951 0.91 0.363 -2.364372 6.453545 lwhitemg | .1556904 .1973381 0.79 0.430 -.2310851 .5424659 _cons | -.9757449 28.58945 -0.03 0.973 -57.01005 55.05856 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001454 lgdp | -.001366 .006211 lgdpau | -.001922 .000713 .782502 lgdpdfrati~w | -.000366 .001439 -.019034 .12833 lpopau | .003209 -.007137 -2.61503 .051932 9.12947 lpop | .00037 -.00545 .000174 -.001111 -.00018 .009222 lxrate1 | .00011 .00086 .000291 .002363 -.007335 -.001169 .001097 lremote | .001345 .001146 -.031226 -.016923 .099268 .000923 -.000966 ldist | .003838 -.006394 -.020747 -.007878 .063138 .010709 -.000312 lopen | .000253 -.001172 .004258 .003137 -.023133 .003083 -.000203 english | -.003188 .000522 .009449 .007002 -.020471 -.001601 .001621 white | .002914 -.022535 .091077 .003124 -.360429 .017507 .000499 lwhitemg | -.000407 .000748 -.009608 -.001698 .039682 7.1e-06 -.000188 _cons | -.032027 .101285 23.3323 -.28772 -84.2508 -.131856 .117796</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .059696 ldist | .022814 .110073 lopen | .002122 -.000909 .006305 english | -.015052 .013877 -.002297 .056398 white | .043371 -.03116 .007116 .036936 5.0603 lwhitemg | -.000958 .006655 -.0004 -.002505 -.432887 .038942 _cons | -1.5906 -1.79583 .240797 .092209 3.7102 -.475057 817.357</p><p>. . **III. Liberal Estimates . *3.1. Aggregate reference priced Imports (liberal) . xtgls lrrefp_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression 8</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 49747.28 Log likelihood = -904.7707 Prob > chi2 = 0.0000</p><p>------lrrefp_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0247763 .0136404 1.82 0.069 -.0019583 .051511 lgdp | 1.524983 .0553389 27.56 0.000 1.416521 1.633446 lgdpau | .6807611 .3590922 1.90 0.058 -.0230466 1.384569 lgdpdfrati~w | -.248376 .138675 -1.79 0.073 -.520174 .0234219 lpopau | -6.674125 1.217093 -5.48 0.000 -9.059583 -4.288667 lpop | -.11781 .0947004 -1.24 0.213 -.3034194 .0677993 lxrate1 | -.0448301 .0190099 -2.36 0.018 -.0820889 -.0075713 lremote | -.6297863 .1502675 -4.19 0.000 -.9243052 -.3352675 ldist | -2.385144 .1746081 -13.66 0.000 -2.72737 -2.042919 lopen | -.0428036 .0272664 -1.57 0.116 -.0962448 .0106376 english | 1.20701 .1133638 10.65 0.000 .9848211 1.429199 white | 3.810157 .8823719 4.32 0.000 2.08074 5.539574 lwhitemg | -.2786357 .0810255 -3.44 0.001 -.4374427 -.1198287 _cons | 92.61579 11.52454 8.04 0.000 70.0281 115.2035 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000186 lgdp | -.000201 .003062 lgdpau | -.000884 .00206 .128947 lgdpdfrati~w | -.000352 .000709 -.007476 .019231 lpopau | .003174 -.006947 -.42794 .011419 1.48132 lpop | .000124 -.004943 -.001814 .000032 .003245 .008968 lxrate1 | 8.1e-07 .000205 6.6e-06 .000498 -.002892 -.000211 .000361 lremote | -4.7e-06 -.000821 -.01329 -.005093 .049279 -.000016 -.000288 ldist | .000845 -.006011 -.00416 .001447 .001765 .010879 .000738 lopen | .000019 -.000116 -.000962 .000847 .003493 .0003 -.000069 english | -.000314 -.000535 .003684 .003001 -.020287 .000811 .001043 white | .000555 -.017458 -.015772 -.001284 .024794 .030536 .002177 lwhitemg | -.000091 .000812 .000041 -.000381 .002602 -.001617 -.000238 _cons | -.0354 .134679 3.86566 .002821 -13.7091 -.140034 .040166</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .02258 ldist | -.003324 .030488 lopen | .001117 .000313 .000743 english | -.004897 .007714 -.00045 .012851 white | .015372 .03697 .00117 .009136 .77858 lwhitemg | .001024 -.002342 4.2e-06 -.000664 -.068247 .006565 _cons | -.610479 -.228561 -.048108 .20046 -.583856 -.022194 132.815</p><p>. . *3.2. Aggregate Differentiated Imports (liberal) . xtgls lrdiff_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1) 9</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8770.44 Log likelihood = -968.9937 Prob > chi2 = 0.0000</p><p>------lrdiff_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .2146451 .0310052 6.92 0.000 .153876 .2754141 lgdp | 1.612228 .0717951 22.46 0.000 1.471512 1.752943 lgdpau | -.0896754 .6154667 -0.15 0.884 -1.295968 1.116617 lgdpdfrati~w | -.1463901 .2592893 -0.56 0.572 -.6545878 .3618076 lpopau | -1.533464 2.15483 -0.71 0.477 -5.756852 2.689925 lpop | -.5235453 .0813233 -6.44 0.000 -.682936 -.3641546 lxrate1 | -.1282733 .0282722 -4.54 0.000 -.1836857 -.0728608 lremote | -.3446117 .1203695 -2.86 0.004 -.5805315 -.1086919 ldist | -3.16112 .3320202 -9.52 0.000 -3.811867 -2.510372 lopen | .1164332 .0492957 2.36 0.018 .0198154 .213051 english | .9256633 .1411117 6.56 0.000 .6490895 1.202237 white | 10.26602 1.845337 5.56 0.000 6.649228 13.88282 lwhitemg | -.9020659 .1840195 -4.90 0.000 -1.262737 -.5413944 _cons | 36.06817 20.44121 1.76 0.078 -3.995864 76.1322 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000961 lgdp | -.001428 .005155 lgdpau | -.001845 .00381 .378799 lgdpdfrati~w | -.001196 .002655 -.024475 .067231 lpopau | .004688 -.017983 -1.28894 .095636 4.64329 lpop | .000856 -.00436 -.001793 -.0017 .007565 .006613 lxrate1 | .00008 .000318 .000013 .001149 -.00525 -.000599 .000799 lremote | .000024 -.000724 -.003982 -.000813 .00779 -.001159 .00022 ldist | .004325 .001852 -.010437 -.002808 -.014716 .000598 .001487 lopen | .000139 -.000752 .001454 .001563 -.007163 .0014 -.000247 english | -.001611 .003376 .003393 .004466 -.022093 .000589 .002008 white | .000603 -.027274 .01181 .001784 -.004941 .021291 .00483 lwhitemg | -.000019 .001556 -.002037 -.000586 .00455 -.00139 -.000445 _cons | -.055618 .143792 11.5589 -1.0115 -42.9943 -.080545 .067955</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .014489 ldist | .008321 .110237 lopen | .000365 -.000958 .00243 english | -.004864 -.001932 -.001618 .019913 white | .011435 -.153387 .003553 .019326 3.40527 lwhitemg | .000791 .013711 -.000238 -.002374 -.333529 .033863 _cons | -.198283 -.68061 .081981 .243729 1.37177 -.169869 417.843</p><p>. . *3.3. Aggregate Homogenous Imports (liberal) . xtgls lrhomo_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 10</p><p>Wald chi2(13) = 8492.21 Log likelihood = -1112.538 Prob > chi2 = 0.0000</p><p>------lrhomo_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0162451 .0154015 1.05 0.292 -.0139413 .0464314 lgdp | 1.382452 .0514318 26.88 0.000 1.281647 1.483256 lgdpau | .6014429 .5141061 1.17 0.242 -.4061866 1.609072 lgdpdfrati~w | .0109744 .1695993 0.06 0.948 -.321434 .3433829 lpopau | -5.653197 1.747016 -3.24 0.001 -9.077285 -2.229109 lpop | .0506689 .0720608 0.70 0.482 -.0905676 .1919054 lxrate1 | -.0603234 .0202273 -2.98 0.003 -.0999682 -.0206785 lremote | -1.056879 .1633455 -6.47 0.000 -1.377031 -.7367281 ldist | -3.433123 .2099507 -16.35 0.000 -3.844619 -3.021627 lopen | .0833814 .0426805 1.95 0.051 -.0002708 .1670336 english | 2.450852 .1368374 17.91 0.000 2.182656 2.719048 white | 3.798655 1.143881 3.32 0.001 1.556688 6.040621 lwhitemg | -.1953167 .0957082 -2.04 0.041 -.3829013 -.0077321 _cons | 90.85325 16.65969 5.45 0.000 58.20086 123.5056 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000237 lgdp | -.000389 .002645 lgdpau | -.000096 -.00022 .264305 lgdpdfrati~w | -.000509 .000629 .001925 .028764 lpopau | -.001336 .001955 -.877858 -.004789 3.05206 lpop | .000194 -.002876 -.000227 .00006 -.0017 .005193 lxrate1 | -.000019 .000236 .000325 .000641 -.002452 -.000425 .000409 lremote | .000256 .001103 -.017675 -.00197 .054139 -.001186 -.000183 ldist | .000612 .000828 -.011281 -.001839 .024238 -.000019 .000271 lopen | .000074 -.000356 -.000302 .002021 .001397 .000752 -.000128 english | -.000746 .000329 .002398 .001759 -.009265 .001753 .001055 white | .000364 -.014108 -.002971 -.001611 .005877 .024266 -.000311 lwhitemg | -5.6e-06 .000726 -.000898 .000199 .002929 -.001572 .000089</p><p>_cons | .022173 -.057042 7.89726 .019769 -28.3182 .026523 .030041</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .026682 ldist | .01737 .044079 lopen | .000916 .000121 .001822 english | -.004044 .002509 -.001384 .018724 white | .002832 -.00823 .000923 .032668 1.30846 lwhitemg | .001311 .002585 2.1e-06 -.002835 -.105911 .00916 _cons | -.838427 -.701935 -.028979 .059036 -.047804 -.052536 277.545</p><p>. . *IV. Aggregate NON-Manufacturing Imports (Sum of Sitc0,1,2,3,4) . xtgls lrmnmf limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1176.29 Log likelihood = -569.4546 Prob > chi2 = 0.0000 11</p><p>------lrmnmf | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1846138 .0230724 8.00 0.000 .1393927 .2298348 lgdp | -.1120138 .0273215 -4.10 0.000 -.1655629 -.0584647 lgdpau | .4792267 .1337772 3.58 0.000 .2170282 .7414252 lgdpdfrati~w | -.0821706 .0494154 -1.66 0.096 -.1790231 .0146819 lpopau | -2.380507 .5334076 -4.46 0.000 -3.425967 -1.335047 lpop | .6013053 .0661679 9.09 0.000 .4716186 .730992 lxrate1 | -.0076798 .008685 -0.88 0.377 -.024702 .0093424 lremote | .0063762 .0688453 0.09 0.926 -.128558 .1413105 ldist | -1.988487 .3728848 -5.33 0.000 -2.719327 -1.257646 lopen | .0668596 .0076416 8.75 0.000 .0518823 .081837 english | .9848173 .2096714 4.70 0.000 .5738689 1.395766 white | 11.67829 .6515851 17.92 0.000 10.4012 12.95537 lwhitemg | -1.000845 .0646473 -15.48 0.000 -1.127552 -.8741386 _cons | 44.27108 6.184305 7.16 0.000 32.15007 56.3921 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000532 lgdp | .000129 .000746</p><p> lgdpau | .001085 -.000438 .017896 lgdpdfrati~w | -.000365 -.000543 -.000488 .002442 lpopau | -.006046 .002774 -.065526 .000682 .284524 lpop | -.00033 -.000636 -.000434 .000478 -.006516 .004378 lxrate1 | -9.6e-06 -.000035 .000139 .000188 -.000973 -6.4e-06 .000075 lremote | .00046 .000768 -.002706 -.001163 .009152 -.001112 -.000065 ldist | .001263 .000148 .000537 -.000496 -.036603 .010483 .000105 lopen | .000104 -.000035 .00016 -.000107 -.001134 -1.7e-06 -.000024 english | -.00079 .000271 -.001759 .000693 -.006838 .005089 .000149 white | .000792 -.000725 .001943 .000844 -.042378 .009867 .000569 lwhitemg | -.000251 -.00008 -.000646 .000105 .005534 -.000776 -.00004 _cons | .05541 -.050737 .647301 .020746 -2.65141 -.024759 .012581</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .00474 ldist | .001744 .139043 lopen | .000044 .000196 .000058 english | -.001355 .015595 -.000295 .043962 white | .000129 -.067357 8.2e-06 .041742 .424563 lwhitemg | -9.9e-06 .006936 -.000033 -.001921 -.040066 .004179 _cons | -.140425 -.926662 .012941 -.085104 1.11328 -.124003 38.2456</p><p>. . *V. Aggregate Manufacturing Imports (Sum of Sitc5,6,7,8,9) . xtgls lrmmfn limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremot > e ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 4381.88 Log likelihood = -593.8769 Prob > chi2 = 0.0000</p><p>------lrmmfn | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------12</p><p> limmig | .0972493 .022489 4.32 0.000 .0531716 .1413269 lgdp | .1644259 .0401597 4.09 0.000 .0857143 .2431375 lgdpau | -.0669899 .0390466 -1.72 0.086 -.1435198 .0095399 lgdpdfrati~w | -.0947424 .0266873 -3.55 0.000 -.1470485 -.0424363 lpopau | 2.361342 .1777164 13.29 0.000 2.013025 2.70966 lpop | .8383358 .0982928 8.53 0.000 .6456854 1.030986 lxrate1 | -.0101346 .0051911 -1.95 0.051 -.020309 .0000398 lremote | .1188065 .0219481 5.41 0.000 .075789 .1618239 ldist | -3.204647 .3363132 -9.53 0.000 -3.863809 -2.545485 lopen | .0142535 .0206075 0.69 0.489 -.0261365 .0546436 english | 1.187138 .2873131 4.13 0.000 .6240146 1.750261 white | 23.86449 2.986887 7.99 0.000 18.0103 29.71868 lwhitemg | -2.185924 .2760863 -7.92 0.000 -2.727043 -1.644804 _cons | -19.58185 4.525773 -4.33 0.000 -28.45221 -10.7115 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000506 lgdp | -.000188 .001613 lgdpau | -.000369 -.000424 .001525 lgdpdfrati~w | .000167 -.000638 .000332 .000712 lpopau | .00268 .000905 -.005608 .000138 .031583 lpop | -.000281 -.001435 .001094 .000317 -.002955 .009661 lxrate1 | .000016 .00005 -.000033 .000015 .000345 -.00015 .000027 lremote | -.00015 .000684 -.0003 -.000464 .000168 -.000438 .000013 ldist | .001788 .000046 -.001587 .000077 .013655 -.002019 4.7e-07 lopen | -.000232 -.000181 .000211 -.000165 -.002267 .000749 -.000078 english | -.000567 -.000694 .000646 .000336 -.007203 -.000731 .000011 white | .000986 -.003582 .001573 .001299 -.004475 .026564 -.000297 lwhitemg | -.00026 .000042 .000041 -.000027 -.00144 -.00303 .000024 _cons | -.044921 -.023074 .065163 -.00008 -.494989 -.074806 -.004011</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .000482 ldist | -1.7e-06 .113107 lopen | .000062 -.000593 .000425 english | -.00039 .058627 .000133 .082549 white | -.001575 .014278 .000924 .040247 8.92149 lwhitemg | .000035 -.003245 -3.7e-07 -.002622 -.812552 .076224 _cons | -.006319 -1.25032 .03166 -.445454 -.458776 .102881 20.4826</p><p>. . . **VI. SITC-1 Digit Level Disaggregate Imports . xtgls lrmsitc0 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 497.85 Log likelihood = -552.5183 Prob > chi2 = 0.0000</p><p>------lrmsitc0 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .511787 .0503265 10.17 0.000 .4131489 .6104251 lgdp | -.0082559 .0820274 -0.10 0.920 -.1690267 .1525149 lgdpau | 2.047825 .7888814 2.60 0.009 .5016457 3.594004 lgdpdfrati~w | -.587426 .2806609 -2.09 0.036 -1.137511 -.0373406 13</p><p> lpopau | -9.815061 2.709088 -3.62 0.000 -15.12478 -4.505346 lpop | .3459539 .1014968 3.41 0.001 .1470239 .5448839 lxrate1 | -.0964723 .0261672 -3.69 0.000 -.147759 -.0451855 lremote | -.1342027 .2622122 -0.51 0.609 -.6481292 .3797238 ldist | -1.630433 .3251368 -5.01 0.000 -2.26769 -.9931767 lopen | .0609694 .0534703 1.14 0.254 -.0438305 .1657693 english | .0732928 .2324622 0.32 0.753 -.3823247 .5289103 white | 9.418267 1.110492 8.48 0.000 7.241742 11.59479 lwhitemg | -.8488783 .0979154 -8.67 0.000 -1.040789 -.6569676 _cons | 123.4599 25.74104 4.80 0.000 73.00844 173.9115 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002533 lgdp | -.001528 .006729 lgdpau | .000544 -.000829 .622334 lgdpdfrati~w | -.0013 .001358 -.018037 .078771 lpopau | -.006264 -.003855 -2.07723 .068581 7.33916 lpop | -.000283 -.004855 -.000427 -.000609 -.000786 .010302 lxrate1 | .000153 -.000217 .000522 .001003 -.004709 -.000365 .000685 lremote | .001725 .00523 -.024104 -.013196 .067893 -.003603 .000101 ldist | .005124 .001464 -.010137 -.004074 -.00892 .003999 .000652 lopen | -.000244 .00066 .000467 -.000244 -.00993 .000574 6.2e-06 english | -.00215 .001317 .005359 .005452 -.025872 .000361 .001175 white | .013151 -.028987 .018425 -.003845 -.088859 .033825 .006175 lwhitemg | -.001324 .001448 -.003051 -.000384 .01514 -.001923 -.000502 _cons | .048323 -.038372 18.4732 -.609184 -67.6956 -.035098 .063673</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .068755 ldist | .032787 .105714 lopen | .000996 .001962 .002859 english | -.013342 .018711 .000178 .054039 white | .010821 1.5e-06 -.00157 .028103 1.23319 lwhitemg | .00118 .000237 .000072 -.001663 -.104038 .009587 _cons | -1.45801 -1.00455 .10492 .17444 .856045 -.171545 662.601</p><p>. xtgls lrmsitc1 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1383.98 Log likelihood = -316.3832 Prob > chi2 = 0.0000</p><p>------lrmsitc1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0398224 .0175509 2.27 0.023 .0054232 .0742215 lgdp | .1840792 .0538204 3.42 0.001 .0785932 .2895652 lgdpau | -.2209656 .2750134 -0.80 0.422 -.759982 .3180508 lgdpdfrati~w | -.3738215 .1892098 -1.98 0.048 -.744666 -.002977 lpopau | 2.372464 .9838943 2.41 0.016 .4440669 4.300862 lpop | .1342727 .0675905 1.99 0.047 .0017976 .2667477 lxrate1 | -.0746543 .0176963 -4.22 0.000 -.1093384 -.0399701 lremote | -.1452021 .1011605 -1.44 0.151 -.3434731 .0530688 ldist | -1.390984 .2130651 -6.53 0.000 -1.808584 -.9733838 lopen | .0070515 .0234153 0.30 0.763 -.0388416 .0529446 english | 1.976966 .2086705 9.47 0.000 1.56798 2.385953 14</p><p> white | 9.986312 1.756516 5.69 0.000 6.543604 13.42902 lwhitemg | -.745627 .1807838 -4.12 0.000 -1.099957 -.3912972 _cons | -23.87571 10.07776 -2.37 0.018 -43.62775 -4.123668 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000308 lgdp | -.000331 .002897 lgdpau | .00026 -.001545 .075632 lgdpdfrati~w | -.000563 -.000158 -.001237 .0358 lpopau | -.001083 .006222 -.259624 .021011 .968048 lpop | .000118 -.002715 .001005 .000787 -.007224 .004568 lxrate1 | -.000017 .000148 .001108 .000728 -.005185 -.000133 .000313 lremote | .000151 .000852 -.005848 -.002964 .020904 -.000505 -.000296 ldist | .000856 .000728 -.001709 -.001582 -.001366 .002431 -.000296 lopen | .00002 -.000256 -.000174 .001031 -.00048 .000306 -9.0e-06 english | -.000207 -.002602 .003605 .003435 -.012626 .00448 .000278 white | .001672 -.022047 .014928 .002906 -.072596 .023812 .001103 lwhitemg | -.000172 .001297 -.00117 1.8e-06 .005359 -.001632 -.000081 _cons | .00628 -.096674 2.40369 -.323117 -9.44227 .063029 .059363</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .010233 ldist | .000952 .045397 lopen | -.00016 -.000108 .000548 english | -.003384 .015753 -.000018 .043543 white | .008161 -.027619 .00128 .066684 3.08535 lwhitemg | -.000346 .001498 -.000062 -.006242 -.313248 .032683 _cons | -.298712 -.429198 .015077 -.026269 1.09395 -.071059 101.561</p><p>. xtgls lrmsitc2 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 825.43 Log likelihood = -842.224 Prob > chi2 = 0.0000</p><p>------lrmsitc2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1345911 .0645674 2.08 0.037 .0080413 .2611408 lgdp | .4692669 .116048 4.04 0.000 .2418169 .6967169 lgdpau | -2.105175 1.093709 -1.92 0.054 -4.248805 .0384562 lgdpdfrati~w | -.9305827 .4385169 -2.12 0.034 -1.79006 -.0711054 lpopau | 4.30252 3.745178 1.15 0.251 -3.037895 11.64293 lpop | .2510543 .1270483 1.98 0.048 .0020442 .5000643 lxrate1 | -.1334429 .040498 -3.30 0.001 -.2128175 -.0540684 lremote | 1.450071 .310872 4.66 0.000 .840773 2.059369 ldist | -1.716694 .4948899 -3.47 0.001 -2.686661 -.7467281 lopen | .0203839 .0778175 0.26 0.793 -.1321356 .1729035 english | .468886 .2832642 1.66 0.098 -.0863016 1.024074 white | 11.21101 1.493082 7.51 0.000 8.284621 14.13739 lwhitemg | -.8586002 .1422561 -6.04 0.000 -1.137417 -.5797834 _cons | -23.36563 35.37632 -0.66 0.509 -92.70196 45.97069 ------</p><p>. vce 15</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .004169 lgdp | -.003951 .013467 lgdpau | -.002848 .002241 1.1962 lgdpdfrati~w | -.002218 .002213 .005315 .192297 lpopau | .005436 -.016297 -3.99302 .029084 14.0264 lpop | .001275 -.01118 -.000326 -.001138 .004602 .016141 lxrate1 | 2.2e-06 .001152 .001619 .002022 -.011961 -.001201 .00164 lremote | .002615 .003522 -.045727 -.028262 .12367 -.001628 -.000521 ldist | .012395 -.00858 -.013791 -.012232 .004171 .014096 -.00261 lopen | .000195 -.00135 .007246 .001621 -.03593 .002472 -.000223 english | -.006453 .005966 .016795 .007733 -.057742 -.005338 .000593 white | .023915 -.061869 -.020085 -.022782 -.004402 .056561 .007768 lwhitemg | -.002806 .003691 -.001176 .000182 .014468 -.002929 -.000937 _cons | -.10608 .152212 35.3476 -.484758 -128.858 -.191591 .170373</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .096641 ldist | .027064 .244916 lopen | .000771 .003725 .006056 english | -.020283 .022725 .000943 .080239 white | .077374 .121311 .00592 .06488 2.22929 lwhitemg | -.002247 -.016522 -.000299 -.00719 -.201826 .020237 _cons | -1.98678 -2.34931 .35761 .428573 -.917148 -.048182 1251.48</p><p>. xtgls lrmsitc3 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 544.66 Log likelihood = -88.70038 Prob > chi2 = 0.0000</p><p>------lrmsitc3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .039675 .0318828 1.24 0.213 -.0228142 .1021642 lgdp | .1624306 .061158 2.66 0.008 .0425631 .2822982 lgdpau | .4037818 .4192476 0.96 0.335 -.4179285 1.225492 lgdpdfrati~w | .2944551 .1811843 1.63 0.104 -.0606596 .6495698 lpopau | 2.103155 1.479407 1.42 0.155 -.7964303 5.00274 lpop | .2852261 .0800574 3.56 0.000 .1283165 .4421358 lxrate1 | -.0261835 .0167028 -1.57 0.117 -.0589204 .0065534 lremote | .0201974 .1367297 0.15 0.883 -.247788 .2881827 ldist | -2.247343 .446629 -5.03 0.000 -3.12272 -1.371966 lopen | .0390521 .0357491 1.09 0.275 -.0310148 .1091189 english | .8614665 .193968 4.44 0.000 .4812962 1.241637 white | 15.44603 1.241377 12.44 0.000 13.01298 17.87909 lwhitemg | -1.299636 .117273 -11.08 0.000 -1.529486 -1.069785 _cons | -32.25415 15.22946 -2.12 0.034 -62.10333 -2.404967 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001017 lgdp | -.000683 .00374 lgdpau | -.000592 -.000191 .175769 lgdpdfrati~w | -.000762 .000961 -.000689 .032828 lpopau | .001336 .001628 -.592028 .013729 2.18865 16</p><p> lpop | -.000237 -.00262 .0006 .000453 -.005984 .006409 lxrate1 | .000056 .000093 .000237 .000563 -.002972 -.000117 .000279 lremote | .000981 .002139 -.008717 -.003237 .029782 -.001626 6.3e-06 ldist | .002891 -.004361 -.010342 -.007348 .00037 .009873 -6.0e-06 lopen | -.000028 -.000089 .000425 .00027 -.004738 .000506 -.000038 english | -.001732 -.000917 .003608 .002699 -.014711 .005097 .000112 white | .004406 -.018441 .035947 -.003551 -.288233 .026494 .003414 lwhitemg | -.000566 .00102 -.003757 .000144 .029496 -.002011 -.000375 _cons | -.028375 -.03859 5.38144 -.171866 -20.9917 -.03824 .041185</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .018695 ldist | .017574 .199477 lopen | 1.3e-06 .000408 .001278 english | -.005545 .006771 .000183 .037624 white | -.008255 -.004924 -8.8e-06 .050278 1.54102 lwhitemg | .001 .000859 .000042 -.004095 -.141977 .013753 _cons | -.6181 -1.86315 .058353 .069423 3.90538 -.393972 231.936</p><p>. xtgls lrmsitc4 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 235.17 Log likelihood = 180.9602 Prob > chi2 = 0.0000</p><p>------lrmsitc4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0151204 .0186459 0.81 0.417 -.0214249 .0516656 lgdp | .3209387 .0483423 6.64 0.000 .2261895 .4156879 lgdpau | -.173805 .3485854 -0.50 0.618 -.8570198 .5094098 lgdpdfrati~w | -.0114794 .1398854 -0.08 0.935 -.2856497 .2626909 lpopau | 3.579083 1.261434 2.84 0.005 1.106717 6.051448 lpop | -.1348569 .0563596 -2.39 0.017 -.2453197 -.0243941 lxrate1 | -.0825497 .0153516 -5.38 0.000 -.1126383 -.052461 lremote | .2655511 .1278111 2.08 0.038 .0150459 .5160563 ldist | -1.047237 .2387727 -4.39 0.000 -1.515223 -.5792507 lopen | .0608183 .0274479 2.22 0.027 .0070214 .1146152 english | .2189757 .1235605 1.77 0.076 -.0231983 .4611498 white | 9.352014 1.980881 4.72 0.000 5.469559 13.23447 lwhitemg | -.8174043 .1863908 -4.39 0.000 -1.182724 -.4520852 _cons | -51.47672 13.04809 -3.95 0.000 -77.0505 -25.90294 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000348 lgdp | -.000331 .002337 lgdpau | .000337 -.000318 .121512 lgdpdfrati~w | -.000431 .000647 -.006262 .019568 lpopau | -.002974 .00558 -.416363 .026883 1.59122 lpop | .00006 -.001803 -.000425 -.000058 -.001047 .003176 lxrate1 | .00001 .000013 .000463 .00043 -.004506 -.000137 .000236 lremote | .00019 .001313 -.006435 -.001189 .024262 -.00116 -.000087 ldist | .001219 -.001102 -.001036 -.001298 -.011989 .003693 -.000111 lopen | -6.4e-06 -.000117 -.000264 .000332 .000584 .000351 -.000034 english | -.000623 .000095 -.000097 .000476 .011175 .001174 -.000031 white | .002172 -.013085 .01569 -.005896 -.143545 .010012 .001719 17</p><p> lwhitemg | -.000273 .00067 -.001984 .000453 .014427 -.000701 -.00013 _cons | .032691 -.107107 3.8018 -.292057 -15.6775 -.006372 .064771</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .016336 ldist | .004514 .057012 lopen | -.000021 -.000463 .000753 english | -.001213 .002488 .000319 .015267 white | .001463 -.002495 .000141 .014075 3.92389 lwhitemg | .000657 -.000745 6.7e-06 -.001311 -.362451 .034742 _cons | -.429225 -.394147 -.0007 -.217295 2.09417 -.188318 170.253</p><p>. xtgls lrmsitc5 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 458.26 Log likelihood = -677.9144 Prob > chi2 = 0.0000</p><p>------lrmsitc5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1735456 .0535736 3.24 0.001 .0685432 .278548 lgdp | .705941 .1074582 6.57 0.000 .4953268 .9165552 lgdpau | -.6265281 .6844125 -0.92 0.360 -1.967952 .7148957 lgdpdfrati~w | -.2598566 .2846807 -0.91 0.361 -.8178206 .2981073 lpopau | 3.773803 2.359868 1.60 0.110 -.8514533 8.399059 lpop | -.2971846 .1486523 -2.00 0.046 -.5885378 -.0058314 lxrate1 | -.1323262 .0318702 -4.15 0.000 -.1947906 -.0698618 lremote | .035029 .2406657 0.15 0.884 -.4366671 .5067251 ldist | -3.299867 .6875681 -4.80 0.000 -4.647476 -1.952258 lopen | .0601088 .0676139 0.89 0.374 -.072412 .1926296 english | -.3283619 .4359132 -0.75 0.451 -1.182736 .5260123 white | 21.44721 4.1654 5.15 0.000 13.28318 29.61124 lwhitemg | -1.907003 .3839546 -4.97 0.000 -2.65954 -1.154466 _cons | -23.57195 23.21185 -1.02 0.310 -69.06633 21.92244 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00287 lgdp | -.002403 .011547 lgdpau | .001238 -.001788 .46842 lgdpdfrati~w | -.0019 .003183 -.011504 .081043 lpopau | -.008539 -.000467 -1.55995 .065084 5.56898 lpop | -.00004 -.008269 -.001421 -.001622 -.006036 .022098 lxrate1 | .000129 .000436 .002141 .001304 -.011 -.000792 .001016 lremote | .001021 .005356 -.021373 -.012872 .041602 -.003196 .000344 ldist | .011118 .013276 -.005743 -.004538 -.059221 .003194 .000345 lopen | .000433 -.001144 -.000614 .000157 -.012222 .002018 -.000159 english | -.006571 .002493 .002796 .006187 -.021466 .017395 .000863 white | .00725 -.038224 .126626 .012294 -.40143 .077274 .005987 lwhitemg | -.001282 .001623 -.01322 -.002429 .044693 -.005872 -.00064 _cons | .037919 -.241343 13.8894 -.747251 -51.1578 -.026539 .116621</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .05792 ldist | .032778 .47275 18</p><p> lopen | .000523 .002993 .004572 english | -.005751 .128073 -.00147 .19002 white | .027064 .247443 .004365 .308785 17.3506 lwhitemg | -.000111 -.038604 -.000259 -.032776 -1.57274 .147421 _cons | -1.01263 -4.08456 .181546 -1.23879 .2129 .049785 538.79</p><p>. xtgls lrmsitc6 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1132.68 Log likelihood = -571.577 Prob > chi2 = 0.0000</p><p>------lrmsitc6 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1920115 .0461401 4.16 0.000 .1015785 .2824445 lgdp | .5938788 .1027903 5.78 0.000 .3924135 .795344 lgdpau | .8041999 .6520733 1.23 0.217 -.4738403 2.08224 lgdpdfrati~w | .0008576 .2829278 0.00 0.998 -.5536707 .5553858 lpopau | -3.537551 2.257374 -1.57 0.117 -7.961922 .8868202 lpop | .5101734 .1306041 3.91 0.000 .254194 .7661528 lxrate1 | -.0858654 .0299192 -2.87 0.004 -.1445059 -.0272249 lremote | -.2492362 .2355304 -1.06 0.290 -.7108673 .2123948 ldist | -2.322274 .441103 -5.26 0.000 -3.18682 -1.457728 lopen | -.0627382 .0698884 -0.90 0.369 -.199717 .0742406 english | 1.192898 .3275796 3.64 0.000 .5508542 1.834943 white | 23.86623 3.127337 7.63 0.000 17.73676 29.99569 lwhitemg | -2.276009 .2774887 -8.20 0.000 -2.819877 -1.732141 _cons | 43.40252 21.71512 2.00 0.046 .8416751 85.96337 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002129 lgdp | -.001864 .010566 lgdpau | -.002276 .005607 .4252 lgdpdfrati~w | -.001929 .003408 -.013183 .080048 lpopau | .00673 -.037593 -1.42332 .044078 5.09574 lpop | .000338 -.00896 -.004031 -.002857 .019602 .017057 lxrate1 | .000054 .00067 .000537 .001421 -.006231 -.000858 .000895 lremote | .000556 .004295 -.014568 -.018229 .030033 -.003058 .000335 ldist | .007518 -.003443 -.009723 -.003166 .000834 .016603 .000254 lopen | .00021 -.000033 .000524 .002438 -.012036 .001223 -.000044 english | -.003403 .001936 .005928 .006769 -.024576 -.000552 .000715 white | .006242 -.036875 .02183 -.014939 -.03072 .06086 .003959 lwhitemg | -.000899 .001245 -.003603 -.000296 .012871 -.004111 -.000418 _cons | -.100383 .38085 12.6357 -.300238 -47.0059 -.415753 .077904</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .055475 ldist | .007104 .194572 lopen | .001177 .001667 .004884 english | -.010414 .058156 -.001422 .107308 white | .025663 .047367 .004428 .010148 9.78023 lwhitemg | .000326 -.008098 -.000482 .000738 -.852255 .077 _cons | -.700622 -1.90383 .140805 -.264362 -.928547 -.001073 471.546 19</p><p>. xtgls lrmsitc7 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1645.55 Log likelihood = -750.0744 Prob > chi2 = 0.0000</p><p>------lrmsitc7 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0638416 .0314262 2.03 0.042 .0022474 .1254358 lgdp | .9924335 .0955229 10.39 0.000 .8052121 1.179655 lgdpau | .9332851 .6752196 1.38 0.167 -.3901209 2.256691 lgdpdfrati~w | -.0764988 .2622384 -0.29 0.771 -.5904766 .4374791 lpopau | 3.257037 2.360896 1.38 0.168 -1.370235 7.884308 lpop | -.4670377 .137272 -3.40 0.001 -.7360858 -.1979895 lxrate1 | -.1368609 .0231409 -5.91 0.000 -.1822162 -.0915056 lremote | -.474844 .1922184 -2.47 0.013 -.8515851 -.098103 ldist | -1.905602 .7226692 -2.64 0.008 -3.322008 -.4891964 lopen | -.0211241 .0694803 -0.30 0.761 -.1573029 .1150548 english | .1655184 .3376879 0.49 0.624 -.4963378 .8273746 white | 28.82227 2.638941 10.92 0.000 23.65004 33.9945 lwhitemg | -2.67603 .2558904 -10.46 0.000 -3.177566 -2.174494 _cons | -68.63984 23.88449 -2.87 0.004 -115.4526 -21.8271 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000988 lgdp | -.001697 .009125 lgdpau | .000039 -.000011 .455921 lgdpdfrati~w | -.001622 .003132 -.013598 .068769 lpopau | -.006847 -.003417 -1.52484 .04704 5.57383 lpop | .001587 -.010855 -.00319 -.003068 .024025 .018844 lxrate1 | .000063 .000207 .000753 .000919 -.010369 -.000624 .000535 lremote | .001013 .004829 -.019202 -.004009 .027679 -.003555 .00016 ldist | .005697 .013233 -.024042 -.001448 -.05109 .008581 .001499 lopen | .000273 -.00078 -.000872 .004734 -.00577 .000959 -.000219 english | -.000894 .001907 .003023 .00374 -.016135 .000689 .001103 white | .016277 -.058623 .032855 -.019931 -.418511 .06724 .010332 lwhitemg | -.001465 .004025 -.004429 .001436 .046007 -.004867 -.000981 _cons | .059259 -.141413 13.7914 -.46003 -52.5096 -.419651 .138053</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .036948 ldist | .061485 .522251 lopen | .000754 .001868 .004828 english | .000091 .070297 -.00089 .114033 white | .026775 .097587 -.005124 .224883 6.96401 lwhitemg | -.001833 -.016551 .00071 -.024773 -.665272 .06548 _cons | -.919417 -4.52685 .094788 -.552466 5.05259 -.475731 570.469</p><p>. xtgls lrmsitc8 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic 20</p><p>Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 508.27 Log likelihood = -579.107 Prob > chi2 = 0.0000</p><p>------lrmsitc8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .4076176 .0587569 6.94 0.000 .2924562 .522779 lgdp | .2678914 .0990281 2.71 0.007 .0737999 .4619829 lgdpau | 1.14394 .7455715 1.53 0.125 -.3173531 2.605233 lgdpdfrati~w | -.5557374 .317146 -1.75 0.080 -1.177332 .0658574 lpopau | -4.813697 2.575597 -1.87 0.062 -9.861775 .2343818 lpop | .3271842 .104812 3.12 0.002 .1217565 .5326119 lxrate1 | -.077144 .0347013 -2.22 0.026 -.1451574 -.0091307 lremote | .0145153 .2298887 0.06 0.950 -.4360582 .4650888 ldist | -1.24992 .5802791 -2.15 0.031 -2.387246 -.1125941 lopen | .0940633 .0722913 1.30 0.193 -.0476251 .2357516 english | -.0567149 .2834604 -0.20 0.841 -.6122871 .4988572 white | 19.4662 2.069661 9.41 0.000 15.40974 23.52266 lwhitemg | -1.837652 .1960193 -9.37 0.000 -2.221843 -1.453461 _cons | 53.19728 24.36479 2.18 0.029 5.443169 100.9514 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .003452 lgdp | -.003149 .009807 lgdpau | -.002355 .006458 .555877 lgdpdfrati~w | -.002566 .005831 -.009845 .100582 lpopau | .005229 -.035546 -1.85926 .050496 6.6337 lpop | .00103 -.006236 -.004103 -.00392 .011153 .010986 lxrate1 | .000336 .000468 -.000228 .000864 -.004036 -.00064 .001204 lremote | .000262 .003476 -.008888 -.016988 -.00477 .000517 .001496 ldist | .011066 .00171 -.004426 -.010698 -.074925 .006284 .000555 lopen | -.000323 .00081 .00222 .000052 -.02332 .001687 -.000138 english | -.00391 .006753 .006067 .002472 -.02619 .000037 -.00157 white | .022605 -.039788 -.003895 -.069753 .093701 .015572 -.000445 lwhitemg | -.002598 .002277 -.001381 .004229 .002083 -.000434 -.000283 _cons | -.09715 .2621 16.3237 -.486689 -60.0048 -.176015 .049048</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .052849 ldist | .037483 .336724 lopen | .002485 .003691 .005226 english | .006675 .09156 -.000084 .08035 white | .109405 .210865 -.007837 .143817 4.2835 lwhitemg | -.007743 -.03059 .000732 -.016929 -.396787 .038424 _cons | -.586722 -2.37685 .233272 -.798194 -3.89669 .333839 593.643</p><p>. xtgls lrmsitc9 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1484.73 Log likelihood = -257.1413 Prob > chi2 = 0.0000 21</p><p>------lrmsitc9 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0003907 .0244479 0.02 0.987 -.0475263 .0483077 lgdp | .4252807 .0840039 5.06 0.000 .260636 .5899253 lgdpau | -.3677946 .5008063 -0.73 0.463 -1.349357 .6137677 lgdpdfrati~w | .1726432 .2287232 0.75 0.450 -.2756461 .6209325 lpopau | 1.278287 1.725879 0.74 0.459 -2.104375 4.660948 lpop | -.2142471 .1202029 -1.78 0.075 -.4498405 .0213463 lxrate1 | -.0393842 .0229213 -1.72 0.086 -.0843091 .0055407 lremote | -.0215932 .1729928 -0.12 0.901 -.3606528 .3174664 ldist | -3.913304 .5205561 -7.52 0.000 -4.933576 -2.893033 lopen | .0089625 .050361 0.18 0.859 -.0897433 .1076684 english | -.0029618 .2961515 -0.01 0.992 -.5834081 .5774845 white | 16.28025 2.664749 6.11 0.000 11.05744 21.50306 lwhitemg | -1.178902 .2867591 -4.11 0.000 -1.740939 -.616864 _cons | 21.0442 17.2728 1.22 0.223 -12.80986 54.89825 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000598 lgdp | -.000748 .007057 lgdpau | .000143 -.002203 .250807 lgdpdfrati~w | -.000986 .002671 -.009699 .052314 lpopau | -.001353 .000534 -.836369 .051599 2.97866 lpop | .000216 -.006576 .000027 -.001145 -.003143 .014449 lxrate1 | -.000026 .000183 .001372 .00105 -.006521 -.00054 .000525 lremote | .000158 .002451 -.017983 -.004097 .05229 -.002806 -.000139 ldist | .002518 .003334 -.011345 -.001756 -.012255 .018532 -.000386 lopen | .000055 -.000175 -.002148 .001265 .002918 .000887 -.000038 english | -.001213 .000911 .001545 .002011 -.003273 .003975 .000601 white | .004498 -.037757 .046252 -.013286 -.289903 .064586 .002061 lwhitemg | -.000481 .001575 -.005598 .000481 .035543 -.005092 -.000032 _cons | .005002 -.056507 7.6109 -.644262 -27.8077 -.179095 .078134</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .029926 ldist | .014257 .270979 lopen | .00012 .001052 .002536 english | .000111 .058062 .00018 .087706 white | -.003985 .150962 -.001298 .101451 7.10089 lwhitemg | .002974 -.019562 .000197 -.01032 -.753993 .082231 _cons | -.801409 -2.60447 -.013298 -.636777 1.98481 -.235338 298.349 22</p><p>. . clear</p><p>. insheet using k:\book1.txt (104 vars, 1010 obs)</p><p>. *Dropping Bolivia . drop if ccode==330680 (10 observations deleted)</p><p>.. . . *Descriptive stats after dropping . tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remote dist op > en english gdpdfrationew white whitemg, stat(n mean sd median min max) col(sta > t) </p><p> variable | N mean sd p50 min max ------+------rimp | 1000 491185.4 1561186 8397.5 0 1.39e+07 immig | 1000 33456.37 117040.1 2986.5 0 1137050 gdp | 1000 2.79e+11 9.69e+11 1.74e+10 1.92e+08 8.99e+12 gdpau | 1000 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 1000 105.7409 16.93165 102.8924 60.87417 207.3465 gdpdfau | 1000 100.7173 4.633489 100.8223 94.42464 109.9797 pop | 1000 4.95e+07 1.54e+08 1.04e+07 41000 1.26e+09 popau | 1000 1.82e+07 612793.8 1.82e+07 1.73e+07 1.92e+07 xrate1 | 1000 1043.081 10901.71 16.2185 .0068 270182.6 remote | 1000 6716.166 4140.378 6764 1293 39620 dist | 1000 13372.2 3532.384 14305 2409 17972 open | 1000 .7103861 .3893649 .63975 .0671 3.2192 english | 1000 .38 .4856293 0 0 1 gdpdfratio~w | 1000 1.052882 .1811695 1.017907 .5844526 2.009028 white | 1000 .13 .3364717 0 0 1 whitemg | 1000 20501.65 116468.1 0 0 1137050 ------</p><p>. 23</p><p>. sort white</p><p>. *IIIB. Descriptives Original Unites(sub sample for "white" and "non-white" cou > ntries)--RHS Variables: . by white: tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remo > te dist phone open english gdpdfrationew white whitemg, stat(n mean sd median > min max) col(stat) </p><p>------> white = 0</p><p> variable | N mean sd p50 min max ------+------rimp | 870 404932.9 1597580 3326 0 1.39e+07 immig | 870 14890.48 27128.58 1606 0 158613 gdp | 870 2.37e+11 1.00e+12 1.09e+10 1.92e+08 8.99e+12 gdpau | 870 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 870 104.8747 17.29439 101.7385 60.87417 207.3465 gdpdfau | 870 100.7173 4.633836 100.8223 94.42464 109.9797 pop | 870 5.34e+07 1.65e+08 1.07e+07 41000 1.26e+09 popau | 870 1.82e+07 612839.6 1.82e+07 1.73e+07 1.92e+07 xrate1 | 870 1182.951 11681.74 22.9426 .0068 270182.6 remote | 870 7139.074 4112.485 6927 1293 39620 dist | 870 13202.99 3422.075 14051 2410 17972 phone | 870 158.6633 242.1575 49.45 .54 1449.75 open | 870 .7181237 .4107774 .63975 .0671 3.2192 english | 870 .3908046 .4882114 0 0 1 gdpdfratio~w | 870 1.044501 .1860843 1 .5844526 2.009028 white | 870 0 0 0 0 0 whitemg | 870 0 0 0 0 0 ------</p><p>------> white = 1</p><p> variable | N mean sd p50 min max ------+------rimp | 130 1068414 1138347 522863.5 40432 3843839 immig | 130 157705 288518.8 21174 2612 1137050 gdp | 130 5.56e+11 6.58e+11 2.54e+11 5.15e+10 2.69e+12 gdpau | 130 3.83e+11 4.46e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 130 111.5379 12.89742 112.2247 85.45601 143.9587 gdpdfau | 130 100.7173 4.649088 100.8223 94.42464 109.9797 pop | 130 2.34e+07 2.55e+07 8837000 3477200 8.22e+07 popau | 130 1.82e+07 614856.7 1.82e+07 1.73e+07 1.92e+07 xrate1 | 130 107.0323 293.6987 4.4722 .3774 1261.556 remote | 130 3885.938 3089.96 2888 1530 12501 dist | 130 14504.62 4031.974 15931 2409 17493 phone | 130 734.716 269.9717 652.085 343.99 1487.08 open | 130 .6586038 .1850905 .64065 .3517 1.2967 english | 130 .3076923 .4633239 0 0 1 gdpdfratio~w | 130 1.108974 .1314272 1.116445 .8254872 1.441221 white | 130 1 0 1 1 1 whitemg | 130 157705 288518.8 21174 2612 1137050 ------24</p><p>. . . . **Regression of Imports . tsset ccode year panel variable: ccode, 117100 to 725980 time variable: year, 1991 to 2000</p><p>. . . **I. Aggregate Imports . xtgls lrimp limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8941.38 Log likelihood = -525.8864 Prob > chi2 = 0.0000</p><p>------lrimp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3296604 .0307149 10.73 0.000 .2694603 .3898605 lgdp | 1.203869 .0335511 35.88 0.000 1.13811 1.269628 lgdpau | .081836 .6100588 0.13 0.893 -1.113857 1.277529 lgdpdfrati~w | -.864801 .2469888 -3.50 0.000 -1.34889 -.3807118 lpopau | -2.636948 2.104918 -1.25 0.210 -6.762512 1.488615 lpop | .0280567 .0412204 0.68 0.496 -.0527338 .1088472 lxrate1 | -.1239667 .0166097 -7.46 0.000 -.1565212 -.0914122 lremote | -.4472963 .0743425 -6.02 0.000 -.5930049 -.3015878 ldist | -2.065089 .1602401 -12.89 0.000 -2.379154 -1.751024 lopen | .2880444 .0567726 5.07 0.000 .1767721 .3993166 english | .9578183 .1294459 7.40 0.000 .704109 1.211528 white | 4.640046 .5840784 7.94 0.000 3.495274 5.784819 lwhitemg | -.4311582 .0520272 -8.29 0.000 -.5331296 -.3291868 _cons | 43.19581 19.38302 2.23 0.026 5.205792 81.18583 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000943 lgdp | -.00031 .001126 lgdpau | -.000869 .001322 .372172 lgdpdfrati~w | -.000826 .000209 -.009797 .061003 lpopau | .001732 -.007926 -1.25879 .050021 4.43068 lpop | -.000119 -.000783 .000093 .000086 -.000208 .001699 lxrate1 | .000089 .000157 .00034 .000189 -.002552 -.000109 .000276 lremote | .000459 .000047 -.000311 -.001526 -.00403 -.000152 .000099 ldist | .001563 .000974 .000729 -.001141 -.01587 .000085 .001728 lopen | -.000157 .000584 .002381 .000824 -.016847 .000338 .000128 english | -.001145 .002913 .002535 .002018 -.009255 -.001627 .000527 white | .004923 -.002672 .003276 -.010919 -.027429 .005532 .000245 lwhitemg | -.000593 .000246 -.000301 .001019 .002976 -.000446 -.000041 _cons | -.022057 .073246 11.0972 -.613719 -40.1769 -.005251 .012474</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .005527 ldist | .004863 .025677 lopen | .001487 .000729 .003223 25</p><p> english | -.003305 .003048 -.001091 .016756 white | .000293 -.000425 .000077 -.004028 .341148 lwhitemg | .000076 -.000397 -.000094 .000529 -.029943 .002707 _cons | -.016711 -.080501 .180604 .039121 .307895 -.032793 375.701</p><p>. . **II. Conservative Estimates . *2.1. Aggregate reference priced Imports (conservative) . xtgls lrrefp_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 67602.80 Log likelihood = -953.1911 Prob > chi2 = 0.0000</p><p>------lrrefp_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0516757 .0137985 3.75 0.000 .0246312 .0787203 lgdp | 1.612168 .0505333 31.90 0.000 1.513125 1.711212 lgdpau | .9303925 .3540296 2.63 0.009 .2365071 1.624278 lgdpdfrati~w | -.4690614 .1464071 -3.20 0.001 -.7560139 -.1821088 lpopau | -6.457324 1.19471 -5.40 0.000 -8.798914 -4.115735 lpop | -.3600165 .0835404 -4.31 0.000 -.5237526 -.1962804 lxrate1 | -.1277465 .0202192 -6.32 0.000 -.1673753 -.0881176 lremote | -.9387314 .099227 -9.46 0.000 -1.133213 -.7442501 ldist | -3.173494 .1544958 -20.54 0.000 -3.476301 -2.870688 lopen | -.0543478 .026387 -2.06 0.039 -.1060653 -.0026304 english | .9686182 .085799 11.29 0.000 .8004552 1.136781 white | 4.488264 .7700904 5.83 0.000 2.978915 5.997614 lwhitemg | -.4443603 .0732114 -6.07 0.000 -.5878519 -.3008686 _cons | 95.01211 11.24677 8.45 0.000 72.96886 117.0554 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00019 lgdp | -.000239 .002554 lgdpau | -.001208 .001895 .125337 lgdpdfrati~w | -.000567 .000527 -.011502 .021435 lpopau | .004755 -.007463 -.414001 .023128 1.42733 lpop | .000128 -.003983 -.00168 .000404 .004533 .006979 lxrate1 | -.000024 .000259 -.000077 .000409 -.003019 -.000269 .000409 lremote | .000376 .000032 -.006727 -.003436 .023728 -.000632 .000045 ldist | .000557 -.001447 -.006546 -.001606 .016639 .002303 .00054 lopen | .000041 -.000096 -.000929 .000865 .003446 .000252 -.000072 english | -.00041 .000202 .002942 .002604 -.019639 .000361 .001104 white | .000927 -.01162 -.011299 -.005291 .046939 .017055 -.001076 lwhitemg | -.000082 .000515 .000428 .000311 -.002974 -.000627 .000146 _cons | -.052653 .092203 3.70148 -.073172 -13.1233 -.066884 .043192</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .009846 ldist | .004929 .023869 lopen | .000664 .000234 .000696 english | -.002641 .002149 -.000397 .007361 white | .011105 -.02983 .001755 -.003301 .593039 lwhitemg | -.0002 .004066 -.000123 .000538 -.054641 .00536 26</p><p>_cons | -.340465 -.385684 -.043211 .235684 -.289129 -.001976 126.49</p><p>. . *2.2. Aggregate Differentiated Imports (conservative) . xtgls lrdiff_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 7332.89 Log likelihood = -918.5758 Prob > chi2 = 0.0000</p><p>------lrdiff_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0998726 .0217109 4.60 0.000 .0573199 .1424252 lgdp | 1.758343 .0692549 25.39 0.000 1.622606 1.89408 lgdpau | .2193579 .5382292 0.41 0.684 -.835552 1.274268 lgdpdfrati~w | -.5166171 .2126787 -2.43 0.015 -.9334597 -.0997746 lpopau | -3.545012 1.868345 -1.90 0.058 -7.206901 .1168756 lpop | -.6270366 .0809715 -7.74 0.000 -.7857379 -.4683353 lxrate1 | -.1641048 .02673 -6.14 0.000 -.2164945 -.111715 lremote | -.4331675 .1366783 -3.17 0.002 -.701052 -.1652829 ldist | -4.082617 .339113 -12.04 0.000 -4.747266 -3.417968 lopen | .0518637 .0417086 1.24 0.214 -.0298836 .1336111 english | 1.107911 .1427071 7.76 0.000 .8282103 1.387612 white | 9.788132 1.877769 5.21 0.000 6.107771 13.46849 lwhitemg | -.8448008 .1878309 -4.50 0.000 -1.212943 -.476659 _cons | 70.37748 17.81278 3.95 0.000 35.46508 105.2899 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000471 lgdp | -.000779 .004796 lgdpau | -.001827 .003732 .289691 lgdpdfrati~w | -.000778 .002176 -.018242 .045232 lpopau | .005418 -.019597 -.975035 .05917 3.49071 lpop | .000582 -.00399 -.001668 -.00215 .00469 .006556 lxrate1 | .000014 .00054 -2.1e-06 .000963 -.004936 -.000811 .000714 lremote | -.000079 -.000076 -.008357 -.001955 .025734 -.001192 .000071 ldist | .001997 .008322 -.011832 -.002363 -.004969 -.001949 .001294 lopen | .000095 -.000541 -.000258 .001936 .001542 .000954 -.0002 english | -.000826 .002715 .006598 .000832 -.046751 .002428 .001708 white | .001289 -.028866 .006225 -.005272 -.023756 .025126 .003237 lwhitemg | -.000108 .001528 -.001813 -.000035 .0084 -.001679 -.000365 _cons | -.053222 .104656 8.73097 -.522074 -32.2252 -.017334 .065714</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .018681 ldist | .00971 .114998 lopen | .00062 -.000601 .00174 english | -.004728 .005171 -.001415 .020365 white | .010399 -.143673 .002251 .019981 3.52602 lwhitemg | .000972 .011372 -.000118 -.002665 -.345784 .03528 _cons | -.442854 -.957281 -.021873 .488394 1.73423 -.21445 317.295</p><p>. 27</p><p>. *2.3. Aggregate Homogenous Imports (conservative) . xtgls lrhomo_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 2262.72 Log likelihood = -1105.278 Prob > chi2 = 0.0000</p><p>------lrhomo_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0975552 .0386671 2.52 0.012 .021769 .1733414 lgdp | .9197125 .0777565 11.83 0.000 .7673125 1.072113 lgdpau | -.6501132 .8548457 -0.76 0.447 -2.32558 1.025354 lgdpdfrati~w | -.4157705 .3531413 -1.18 0.239 -1.107915 .2763737 lpopau | .6884364 2.921371 0.24 0.814 -5.037345 6.414218 lpop | .0140361 .096096 0.15 0.884 -.1743087 .2023808 lxrate1 | -.1452723 .0335994 -4.32 0.000 -.2111259 -.0794186 lremote | .5051519 .2320821 2.18 0.030 .0502794 .9600244 ldist | -1.358994 .326708 -4.16 0.000 -1.99933 -.718658 lopen | .3629126 .0764861 4.74 0.000 .2130026 .5128227 english | 1.732392 .2396771 7.23 0.000 1.262634 2.202151 white | .8912906 2.197608 0.41 0.685 -3.415942 5.198523 lwhitemg | .2375222 .1950566 1.22 0.223 -.1447818 .6198261 _cons | -3.894558 27.55129 -0.14 0.888 -57.89409 50.10498 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001495 lgdp | -.001401 .006046 lgdpau | -.002039 .001369 .730761 lgdpdfrati~w | -.000432 .001695 -.019273 .124709 lpopau | .004545 -.010906 -2.44762 .042669 8.53441 lpop | .000368 -.00547 .000228 -.001128 .001491 .009234 lxrate1 | .000121 .00092 .000398 .002277 -.00828 -.001277 .001129 lremote | .001219 .00034 -.028918 -.015998 .094079 .000848 -.000904 ldist | .004091 -.007875 -.02031 -.007543 .05994 .012805 -.00007 lopen | .000217 -.00111 .00412 .00308 -.021412 .003001 -.000172 english | -.003167 .000372 .010521 .007336 -.029523 -.000603 .001698 white | .003117 -.021843 .10953 .010298 -.423765 .017717 .000532 lwhitemg | -.000446 .000738 -.011532 -.002484 .046287 -.000109 -.000213 _cons | -.051963 .171525 21.8737 -.13898 -78.625 -.179572 .128169</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .053862 ldist | .018525 .106738 lopen | .00179 -.001419 .00585 english | -.015314 .01153 -.001969 .057445 white | .038902 -.007702 .007066 .044076 4.82948 lwhitemg | -.000354 .004263 -.000418 -.003328 -.417618 .038047 _cons | -1.45552 -1.68838 .223111 .227372 4.06778 -.513671 759.074</p><p>. . **III. Liberal Estimates . *3.1. Aggregate reference priced Imports (liberal) . xtgls lrrefp_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog 28</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 53790.89 Log likelihood = -918.9735 Prob > chi2 = 0.0000</p><p>------lrrefp_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0241371 .0133383 1.81 0.070 -.0020054 .0502797 lgdp | 1.573185 .054189 29.03 0.000 1.466976 1.679393 lgdpau | .711032 .3520977 2.02 0.043 .0209332 1.401131 lgdpdfrati~w | -.2898556 .1388316 -2.09 0.037 -.5619606 -.0177505 lpopau | -6.656072 1.191499 -5.59 0.000 -8.991367 -4.320778 lpop | -.2094904 .0937993 -2.23 0.026 -.3933338 -.0256471 lxrate1 | -.0600908 .0190584 -3.15 0.002 -.0974445 -.0227371 lremote | -.6112231 .148039 -4.13 0.000 -.9013742 -.321072 ldist | -2.589178 .16477 -15.71 0.000 -2.912121 -2.266235 lopen | -.0511602 .0267408 -1.91 0.056 -.1035712 .0012507 english | 1.110476 .1118133 9.93 0.000 .8913261 1.329626 white | 3.358489 .9081755 3.70 0.000 1.578498 5.13848 lwhitemg | -.252626 .083387 -3.03 0.002 -.4160614 -.0891906 _cons | 93.7931 11.28386 8.31 0.000 71.67714 115.9091 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000178 lgdp | -.000177 .002936 lgdpau | -.000901 .002157 .123973 lgdpdfrati~w | -.000367 .000798 -.008048 .019274 lpopau | .003363 -.007215 -.410787 .012618 1.41967 lpop | .000087 -.004801 -.002125 -.00012 .004032 .008798 lxrate1 | -3.7e-06 .000202 -.000067 .000492 -.002762 -.000199 .000363 lremote | .00002 -.001214 -.012422 -.005419 .045607 .000819 -.000228 ldist | .000737 -.005365 -.004869 .001156 .005008 .009722 .000696 lopen | .000019 -.000107 -.000965 .000779 .003535 .000281 -.000068 english | -.000331 -.000144 .003515 .003103 -.019724 .000097 .001034 white | .000383 -.01675 -.016955 -.003004 .032472 .029174 .001688 lwhitemg | -.000076 .000732 .000209 -.000266 .001438 -.001418 -.000175 _cons | -.037173 .134096 3.71435 .004133 -13.1436 -.141142 .039738</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .021916 ldist | -.0018 .027149 lopen | .001137 .000266 .000715 english | -.004707 .006299 -.000484 .012502 white | .019827 .030267 .001283 .005691 .824783 lwhitemg | .000648 -.001655 -9.1e-06 -.00042 -.072383 .006953 _cons | -.585379 -.240331 -.048272 .210041 -.644468 -.012318 127.326</p><p>. . *3.2. Aggregate Differentiated Imports (liberal) . xtgls lrdiff_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares 29</p><p>Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 7875.13 Log likelihood = -946.8322 Prob > chi2 = 0.0000</p><p>------lrdiff_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .2083725 .0304076 6.85 0.000 .1487746 .2679703 lgdp | 1.620122 .0715295 22.65 0.000 1.479927 1.760317 lgdpau | -.1622169 .6089295 -0.27 0.790 -1.355697 1.031263 lgdpdfrati~w | -.2158456 .2549943 -0.85 0.397 -.7156253 .2839341 lpopau | -1.290109 2.130316 -0.61 0.545 -5.465451 2.885233 lpop | -.5111695 .0808836 -6.32 0.000 -.6696984 -.3526406 lxrate1 | -.1423948 .027769 -5.13 0.000 -.1968212 -.0879685 lremote | -.3528825 .1310003 -2.69 0.007 -.6096384 -.0961267 ldist | -3.219778 .3335455 -9.65 0.000 -3.873515 -2.566041 lopen | .1039948 .04859 2.14 0.032 .0087602 .1992294 english | .922975 .1418094 6.51 0.000 .6450338 1.200916 white | 10.43812 1.854309 5.63 0.000 6.803739 14.0725 lwhitemg | -.9242772 .1850743 -4.99 0.000 -1.287016 -.5615382 _cons | 34.37695 20.26184 1.70 0.090 -5.335526 74.08942 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000925 lgdp | -.001383 .005116 lgdpau | -.001951 .003802 .370795 lgdpdfrati~w | -.001153 .002641 -.022712 .065022 lpopau | .005237 -.01922 -1.25978 .088159 4.53825 lpop | .000838 -.004238 -.001921 -.001792 .00883 .006542 lxrate1 | .000074 .000385 .000042 .0011 -.005178 -.000698 .000771 lremote | -6.7e-06 -.00009 -.005074 -.001106 .00963 -.001083 .000106 ldist | .004139 .002658 -.011895 -.003191 -.009256 .00021 .001347 lopen | .00014 -.000689 .001288 .001637 -.006325 .001316 -.000265 english | -.001554 .003645 .003685 .003915 -.023158 .000518 .001826 white | .000449 -.026205 .012323 .000763 -.002336 .02089 .003913 lwhitemg | 1.1e-06 .001516 -.002106 -.000541 .004523 -.001391 -.000387 _cons | -.060472 .149704 11.309 -.922969 -42.0715 -.096105 .068687</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .017161 ldist | .010633 .111253 lopen | .000446 -.000839 .002361 english | -.004516 -.001699 -.001719 .02011 white | .010241 -.158591 .002876 .018144 3.43846 lwhitemg | .000815 .014343 -.000196 -.002347 -.337274 .034252 _cons | -.259452 -.772858 .070512 .244581 1.36488 -.173402 410.542</p><p>. . *3.3. Aggregate Homogenous Imports (liberal) . xtgls lrhomo_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 30</p><p>Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8261.13 Log likelihood = -1095.858 Prob > chi2 = 0.0000</p><p>------lrhomo_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0167552 .0143443 1.17 0.243 -.0113592 .0448695 lgdp | 1.357355 .0475193 28.56 0.000 1.264219 1.450491 lgdpau | .7210373 .4628158 1.56 0.119 -.1860651 1.62814 lgdpdfrati~w | -.0460182 .1487632 -0.31 0.757 -.3375886 .2455523 lpopau | -6.43196 1.575639 -4.08 0.000 -9.520156 -3.343764 lpop | .0615968 .0678965 0.91 0.364 -.0714779 .1946716 lxrate1 | -.0509399 .0183262 -2.78 0.005 -.0868587 -.0150211 lremote | -.8316394 .154405 -5.39 0.000 -1.134268 -.5290111 ldist | -3.434425 .2274905 -15.10 0.000 -3.880298 -2.988552 lopen | .0517122 .0386546 1.34 0.181 -.0240494 .1274738 english | 2.456877 .1284634 19.13 0.000 2.205093 2.70866 white | 3.961717 1.153195 3.44 0.001 1.701495 6.221938 lwhitemg | -.177148 .0970437 -1.83 0.068 -.3673502 .0130541 _cons | 99.04228 15.11592 6.55 0.000 69.41563 128.6689 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000206 lgdp | -.000343 .002258 lgdpau | .000054 .000019 .214199 lgdpdfrati~w | -.000372 .000432 .002742 .02213 lpopau | -.001688 .001124 -.712822 -.007675 2.48264 lpop | .000175 -.002478 -.000373 .000217 -.001128 .00461 lxrate1 | -.000015 .000186 .000235 .000434 -.001952 -.000332 .000336 lremote | .000164 .000534 -.01626 -.000796 .051373 -.000692 -.000144 ldist | .000563 .000591 -.009463 -.001047 .017413 .000936 .000319 lopen | .000056 -.000274 .000243 .001908 -.000311 .000623 -.00012 english | -.000644 .00028 .001252 .001066 -.006246 .002022 .000931 white | .000372 -.012646 -.005778 -.000776 .009237 .0234 .000028 lwhitemg | -.000015 .000649 -.00081 .000158 .003445 -.001586 .000052 _cons | .024554 -.039848 6.44143 .03729 -23.1015 .007301 .023447</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .023841 ldist | .014389 .051752 lopen | .00082 .000124 .001494 english | -.003431 .003098 -.001146 .016503 white | .004113 .000468 .000702 .032136 1.32986 lwhitemg | .001329 .0015 4.4e-06 -.00286 -.108168 .009417 _cons | -.77315 -.695556 -.01399 .026314 -.144315 -.051082 228.491</p><p>. . *IV. Aggregate NON-Manufacturing Imports (Sum of Sitc0,1,2,3,4) . xtgls lrmnmf limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 563.82 Log likelihood = -567.9489 Prob > chi2 = 0.0000 31</p><p>------lrmnmf | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3530836 .0403831 8.74 0.000 .2739341 .432233 lgdp | .0881648 .052424 1.68 0.093 -.0145845 .190914 lgdpau | .4474832 .3300783 1.36 0.175 -.1994583 1.094425 lgdpdfrati~w | -.59248 .1252841 -4.73 0.000 -.8380323 -.3469278 lpopau | -3.190708 1.251765 -2.55 0.011 -5.644122 -.7372941 lpop | .5019608 .0790703 6.35 0.000 .3469858 .6569358 lxrate1 | -.1305852 .0228351 -5.72 0.000 -.1753412 -.0858291 lremote | .2495812 .1460481 1.71 0.087 -.0366677 .5358302 ldist | -1.336929 .3888455 -3.44 0.001 -2.099052 -.5748058 lopen | .1229301 .0186631 6.59 0.000 .0863511 .159509 english | .6274749 .2174493 2.89 0.004 .2012822 1.053668 white | 10.60006 .7064178 15.01 0.000 9.215509 11.98462 lwhitemg | -1.003895 .0702914 -14.28 0.000 -1.141664 -.8661267 _cons | 47.2497 13.19297 3.58 0.000 21.39195 73.10744 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001631 lgdp | -.00042 .002748 lgdpau | .003545 -.003371 .108952 lgdpdfrati~w | -.001313 -.00081 -.003397 .015696 lpopau | -.021424 .021467 -.391585 .009066 1.56692 lpop | -.000683 -.002041 -.000565 .001118 -.007114 .006252 lxrate1 | .000054 .000078 .001447 .000805 -.008022 -.000305 .000521 lremote | .00199 .001956 -.011553 -.00541 .034087 -.00257 -.000325 ldist | .003994 -.003969 .001248 -.000828 -.072603 .011925 .000979 lopen | .000399 -.000387 .000219 -.000711 -.003924 .00017 -.000165 english | -.001925 .000872 -.001281 .003769 -.002426 .00557 .001212 white | .006009 -.002802 .024198 .000805 -.163238 .009822 .003912 lwhitemg | -.000714 -.000195 -.002999 .000175 .016548 -.000383 -.000373 _cons | .218469 -.277099 3.78908 -.015389 -15.5602 -.006743 .088346</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .02133 ldist | .014995 .151201 lopen | .000407 .001064 .000348 english | -.003564 .012169 -.000982 .047284 white | .004532 -.030454 .00033 .054168 .499026 lwhitemg | .000064 .005187 -.000019 -.002894 -.046981 .004941 _cons | -.601801 -.527998 .051257 -.144103 2.13956 -.227453 174.054</p><p>. . *V. Aggregate Manufacturing Imports (Sum of Sitc5,6,7,8,9) . xtgls lrmmfn limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremot > e ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 3214.87 Log likelihood = -586.0307 Prob > chi2 = 0.0000</p><p>------lrmmfn | Coef. Std. Err. z P>|z| [95% Conf. Interval] 32</p><p>------+------limmig | .1510212 .0322566 4.68 0.000 .0877994 .2142429 lgdp | .2508267 .0544986 4.60 0.000 .1440115 .357642 lgdpau | -.1217539 .0680508 -1.79 0.074 -.255131 .0116232 lgdpdfrati~w | -.1232767 .0443028 -2.78 0.005 -.2101086 -.0364448 lpopau | 2.717633 .2750038 9.88 0.000 2.178636 3.256631 lpop | .8100947 .102931 7.87 0.000 .6083535 1.011836 lxrate1 | -.0144324 .0086939 -1.66 0.097 -.0314721 .0026073 lremote | .1402415 .033778 4.15 0.000 .0740378 .2064451 ldist | -3.433874 .34476 -9.96 0.000 -4.109591 -2.758157 lopen | -.0092624 .0311895 -0.30 0.766 -.0703927 .0518678 english | .406043 .2783117 1.46 0.145 -.1394379 .9515239 white | 23.29539 2.989728 7.79 0.000 17.43563 29.15515 lwhitemg | -2.200056 .2776116 -7.92 0.000 -2.744165 -1.655947 _cons | -23.58013 5.187604 -4.55 0.000 -33.74765 -13.41261 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00104 lgdp | -.000628 .00297 lgdpau | -.00073 -.000398 .004631 lgdpdfrati~w | .000345 -.001153 .000915 .001963 lpopau | .004968 -.00064 -.015808 .000314 .075627 lpop | -.000247 -.0025 .001326 .000608 -.00499 .010595 lxrate1 | 4.6e-06 .000081 -.00005 .00003 .000704 -.000236 .000076 lremote | -.000346 .001226 -.000716 -.001084 -.00007 -.000825 .00004 ldist | .003746 -.000329 -.003309 .000045 .022454 -.001643 -.000124 lopen | -.000347 -.000229 .00024 -.000465 -.004426 .001163 -.000178 english | -.000732 .000183 .000701 .000146 -.005547 .000917 .000036 white | .003271 -.00634 .000971 .002333 -.000979 .029138 -.000598 lwhitemg | -.000584 .000248 .000182 -.000135 -.001999 -.002966 .000056 _cons | -.084302 -.011585 .170465 -.007581 -.989789 -.038256 -.008081</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .001141 ldist | -.000089 .118859 lopen | .000227 -.000835 .000973 english | -.000115 .056339 -.000054 .077457 white | -.002767 .023613 .00153 .040104 8.93848 lwhitemg | .00017 -.005102 -.000014 -.003342 -.817617 .077068 _cons | -.001166 -1.4131 .063868 -.497393 -.57155 .121847 26.9112</p><p>. . . **VI. SITC-1 Digit Level Disaggregate Imports . xtgls lrmsitc0 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 534.36 Log likelihood = -541.7396 Prob > chi2 = 0.0000</p><p>------lrmsitc0 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .531839 .050399 10.55 0.000 .4330588 .6306191 lgdp | -.0043676 .0788178 -0.06 0.956 -.1588477 .1501126 lgdpau | 1.975581 .8052843 2.45 0.014 .397253 3.553909 33 lgdpdfrati~w | -.6418171 .2897674 -2.21 0.027 -1.209751 -.0738833 lpopau | -10.03899 2.767672 -3.63 0.000 -15.46352 -4.614448 lpop | .353779 .0980323 3.61 0.000 .1616393 .5459187 lxrate1 | -.1089129 .0258885 -4.21 0.000 -.1596535 -.0581723 lremote | -.0711244 .2614848 -0.27 0.786 -.5836253 .4413764 ldist | -1.491232 .3197512 -4.66 0.000 -2.117933 -.8645307 lopen | .0522087 .0539087 0.97 0.333 -.0534503 .1578678 english | .0314133 .2319926 0.14 0.892 -.4232839 .4861106 white | 9.368772 1.137529 8.24 0.000 7.139257 11.59829 lwhitemg | -.8475055 .1012837 -8.37 0.000 -1.046018 -.6489931 _cons | 126.9617 26.26554 4.83 0.000 75.48215 178.4412 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00254 lgdp | -.001545 .006212 lgdpau | .000493 -.00058 .648483 lgdpdfrati~w | -.001432 .001541 -.019299 .083965 lpopau | -.006169 -.003805 -2.1668 .076602 7.66001 lpop | -.000276 -.004381 -.000378 -.000615 -.001043 .00961 lxrate1 | .00016 -.000189 .000496 .000872 -.004743 -.000395 .00067 lremote | .001844 .004573 -.022663 -.013358 .064758 -.003441 .000271 ldist | .004973 .00158 -.009036 -.00377 -.01266 .004102 .000721 lopen | -.000264 .000642 .000543 -.000413 -.010529 .00065 2.1e-06 english | -.002236 .001487 .005361 .005708 -.027243 .000759 .001081 white | .013462 -.027101 .020154 -.005454 -.096508 .031147 .005819 lwhitemg | -.001349 .001406 -.003186 -.000238 .015607 -.001773 -.000464 _cons | .048824 -.037311 19.2454 -.719345 -70.6123 -.034319 .062786</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .068374 ldist | .032039 .102241 lopen | .001112 .002174 .002906 english | -.014013 .020767 .000151 .053821 white | .01229 .008921 -.001528 .030825 1.29397 lwhitemg | .001185 -.000664 .000088 -.002045 -.11059 .010258 _cons | -1.4219 -.936851 .10941 .173455 .839463 -.168551 689.879</p><p>. xtgls lrmsitc1 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1337.96 Log likelihood = -379.1516 Prob > chi2 = 0.0000</p><p>------lrmsitc1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0392904 .0186352 2.11 0.035 .002766 .0758148 lgdp | .2263024 .0569796 3.97 0.000 .1146245 .3379803 lgdpau | -.2623685 .3271173 -0.80 0.423 -.9035067 .3787696 lgdpdfrati~w | -.4148412 .205335 -2.02 0.043 -.8172904 -.0123919 lpopau | 2.717031 1.159513 2.34 0.019 .4444284 4.989634 lpop | .1218427 .0714392 1.71 0.088 -.0181755 .261861 lxrate1 | -.0782518 .0191146 -4.09 0.000 -.1157157 -.0407879 lremote | -.1524501 .1162422 -1.31 0.190 -.3802807 .0753805 ldist | -1.340912 .2121364 -6.32 0.000 -1.756692 -.925132 lopen | .0009132 .0263523 0.03 0.972 -.0507363 .0525627 34</p><p> english | 1.991091 .2152992 9.25 0.000 1.569113 2.41307 white | 9.554786 1.782526 5.36 0.000 6.061099 13.04847 lwhitemg | -.7228078 .1831726 -3.95 0.000 -1.08182 -.3637961 _cons | -29.60026 11.69123 -2.53 0.011 -52.51466 -6.685866 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000347 lgdp | -.000369 .003247 lgdpau | .000263 -.001622 .107006 lgdpdfrati~w | -.000676 -.000036 -.000733 .042162 lpopau | -.001221 .006873 -.364155 .022681 1.34447 lpop | .000117 -.003022 .000967 .000861 -.008454 .005104 lxrate1 | -.000022 .000189 .001328 .000822 -.006118 -.000176 .000365 lremote | .000199 .001021 -.007373 -.003907 .024862 -.000609 -.000395 ldist | .000918 .000782 -.00188 -.001812 -.000013 .002416 -.000353 lopen | .000028 -.000263 -.000261 .001035 -.0008 .000336 -7.9e-06 english | -.000329 -.002889 .004592 .004119 -.01589 .00514 .000374 white | .00179 -.024233 .015851 .001914 -.089451 .027197 .001122 lwhitemg | -.000183 .001441 -.001332 .00004 .006964 -.001891 -.000089 _cons | .008279 -.110665 3.33041 -.364344 -12.9867 .084111 .069992</p><p>| lremote ldist lopen english white lwhitemg _cons</p><p>------+------lremote | .013512 ldist | .001007 .045002 lopen | -.000033 -.000099 .000694 english | -.004759 .015564 -.000199 .046354 white | .011193 -.027397 .001327 .072766 3.1774 lwhitemg | -.000446 .001372 -.000058 -.006781 -.321975 .033552 _cons | -.354097 -.444461 .021327 .010612 1.31661 -.09042 136.685</p><p>. xtgls lrmsitc2 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1024.99 Log likelihood = -831.0683 Prob > chi2 = 0.0000</p><p>------lrmsitc2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1387979 .064022 2.17 0.030 .013317 .2642788 lgdp | .5368966 .1089111 4.93 0.000 .3234347 .7503585 lgdpau | -2.128205 1.067819 -1.99 0.046 -4.221092 -.0353172 lgdpdfrati~w | -1.026817 .4243351 -2.42 0.016 -1.858499 -.1951356 lpopau | 4.272985 3.659822 1.17 0.243 -2.900134 11.4461 lpop | .2124929 .1239528 1.71 0.086 -.0304501 .4554358 lxrate1 | -.1479946 .0390998 -3.79 0.000 -.2246289 -.0713603 lremote | 1.697969 .2699161 6.29 0.000 1.168943 2.226994 ldist | -1.696918 .4895356 -3.47 0.001 -2.65639 -.7374454 lopen | -.010243 .0771621 -0.13 0.894 -.1614779 .1409919 english | .3234176 .2601568 1.24 0.214 -.1864803 .8333155 white | 10.88508 1.466107 7.42 0.000 8.011561 13.7586 lwhitemg | -.8363452 .1419931 -5.89 0.000 -1.114647 -.5580438 _cons | -25.34248 34.43188 -0.74 0.462 -92.82772 42.14275 ------35</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .004099 lgdp | -.00432 .011862 lgdpau | -.002308 .005576 1.14024 lgdpdfrati~w | -.001643 .004032 .001447 .18006 lpopau | .004376 -.02716 -3.80964 .038005 13.3943 lpop | .001526 -.010044 -.002441 -.002341 .01107 .015364 lxrate1 | .00006 .001566 .000708 .001379 -.008654 -.001473 .001529 lremote | .00152 -.002352 -.031579 -.019856 .080827 .002243 .000872 ldist | .012133 -.010521 -.011264 -.010018 -.001216 .0151 -.002314 lopen | .000344 -.00082 .006051 .001039 -.032548 .002151 -.000382 english | -.005576 .011014 .007568 .001626 -.030481 -.008737 -.000424 white | .025995 -.049034 -.039035 -.035089 .058284 .04813 .004738 lwhitemg | -.002911 .002951 .000039 .000923 .010289 -.002471 -.000739 _cons | -.087135 .328163 33.5939 -.632316 -122.643 -.29697 .120655</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .072855 ldist | .022368 .239645 lopen | .002897 .004229 .005954 english | -.003953 .025791 -.000809 .067682 white | .111282 .130729 .00213 .030922 2.14947 lwhitemg | -.004279 -.016804 -.000086 -.005141 -.198463 .020162 _cons | -1.34054 -2.21118 .304134 -.000926 -1.96746 .017015 1185.55</p><p>. xtgls lrmsitc3 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 568.34 Log likelihood = -128.7319 Prob > chi2 = 0.0000</p><p>------lrmsitc3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0358428 .0287154 1.25 0.212 -.0204384 .0921239 lgdp | .1471111 .0586495 2.51 0.012 .0321603 .2620619 lgdpau | .3781364 .4075728 0.93 0.354 -.4206916 1.176964 lgdpdfrati~w | .3387665 .1825399 1.86 0.063 -.0190052 .6965381 lpopau | 2.613721 1.439424 1.82 0.069 -.2074979 5.43494 lpop | .2913011 .078941 3.69 0.000 .1365796 .4460226 lxrate1 | -.0327854 .0166537 -1.97 0.049 -.0654261 -.0001447 lremote | -.0108014 .1270729 -0.09 0.932 -.2598598 .238257 ldist | -2.250527 .4188052 -5.37 0.000 -3.07137 -1.429684 lopen | .055512 .0376227 1.48 0.140 -.0182272 .1292512 english | .833979 .1872768 4.45 0.000 .4669232 1.201035 white | 15.76888 1.202743 13.11 0.000 13.41154 18.12621 lwhitemg | -1.332742 .1119232 -11.91 0.000 -1.552108 -1.113377 _cons | -39.52253 14.80217 -2.67 0.008 -68.53425 -10.51082 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000825 lgdp | -.000625 .00344 lgdpau | -.000559 -.000107 .166116 36</p><p> lgdpdfrati~w | -.000705 .000718 -.000222 .033321 lpopau | .001397 .001594 -.557032 .015269 2.07194 lpop | -.000131 -.002519 .000508 .000643 -.005162 .006232 lxrate1 | .000053 .000091 .000214 .000548 -.002724 -.000113 .000277 lremote | .000858 .002127 -.007201 -.003412 .020333 -.001741 .000095 ldist | .002275 -.003714 -.009467 -.007275 -.001081 .008839 .000083 lopen | -.000017 -.000048 .000165 .000264 -.004258 .00051 -.000047 english | -.001262 -.000755 .003324 .002877 -.015328 .004858 .000185 white | .003994 -.016418 .030461 -.003283 -.275056 .024857 .003718 lwhitemg | -.00047 .000905 -.003093 .000196 .026915 -.001893 -.000391 _cons | -.025329 -.041401 5.03126 -.207483 -19.8895 -.039041 .035991</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .016148 ldist | .01541 .175398 lopen | .000084 .00028 .001415 english | -.005144 .008085 .000113 .035073 white | -.005788 -.020538 -.00036 .0452 1.44659 lwhitemg | .000587 .002898 .000059 -.00384 -.131609 .012527 _cons | -.455417 -1.61052 .056896 .069064 3.94157 -.384213 219.104</p><p>. xtgls lrmsitc4 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 265.34 Log likelihood = 124.579 Prob > chi2 = 0.0000</p><p>------lrmsitc4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | -.0019244 .018669 -0.10 0.918 -.0385149 .034666 lgdp | .2641678 .0494399 5.34 0.000 .1672673 .3610683 lgdpau | -.3366129 .3512339 -0.96 0.338 -1.025019 .3517929 lgdpdfrati~w | -.1125331 .1433355 -0.79 0.432 -.3934655 .1683992 lpopau | 4.541124 1.278785 3.55 0.000 2.034752 7.047496 lpop | -.0727873 .0596916 -1.22 0.223 -.1897807 .0442061 lxrate1 | -.1034402 .0152353 -6.79 0.000 -.1333008 -.0735796 lremote | .372213 .1287654 2.89 0.004 .1198374 .6245885 ldist | -1.342023 .276949 -4.85 0.000 -1.884833 -.7992128 lopen | .0234167 .0283062 0.83 0.408 -.0320625 .078896 english | .1496322 .1586976 0.94 0.346 -.1614093 .4606738 white | 9.217225 2.010751 4.58 0.000 5.276225 13.15822 lwhitemg | -.7682341 .1915088 -4.01 0.000 -1.143584 -.3928838 _cons | -60.74661 13.54797 -4.48 0.000 -87.30014 -34.19308 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000349 lgdp | -.000337 .002444 lgdpau | .000278 -.000742 .123365 lgdpdfrati~w | -.000455 .000427 -.005722 .020545 lpopau | -.002902 .005191 -.423476 .026591 1.63529 lpop | .000048 -.002191 -.000108 -.000069 .000734 .003563 lxrate1 | 4.0e-07 1.6e-06 .00049 .000472 -.004408 -.000119 .000232 lremote | .000293 .001892 -.007422 -.001205 .02501 -.001532 -.000052 ldist | .001509 -.001256 -.002032 -.001617 .000072 .004285 .000032 37</p><p> lopen | 2.8e-06 -.000016 .000041 .00021 -.00124 .000154 -.000029 english | -.000435 -.001761 -.000028 .000659 .019911 .004348 .000205 white | .002678 -.015224 .017151 -.005169 -.142631 .014973 .001873 lwhitemg | -.000303 .001009 -.002009 .000527 .014035 -.001169 -.000135 _cons | .029654 -.089394 3.89334 -.294738 -16.3658 -.044311 .06074</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .016581 ldist | .006946 .076701 lopen | .000144 -.000398 .000801 english | -.001311 .014969 -.000358 .025185 white | -.000869 .013531 -.000832 .031418 4.04312 lwhitemg | .000659 -.002305 .000112 -.002881 -.378843 .036676 _cons | -.448634 -.789784 .020383 -.495064 1.87503 -.16615 183.547</p><p>. xtgls lrmsitc5 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 476.50 Log likelihood = -662.8887 Prob > chi2 = 0.0000</p><p>------lrmsitc5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .207551 .0561382 3.70 0.000 .097522 .3175799 lgdp | .7713611 .1075816 7.17 0.000 .5605051 .9822171 lgdpau | -.8215292 .6992795 -1.17 0.240 -2.192092 .5490336 lgdpdfrati~w | -.3276602 .2991492 -1.10 0.273 -.913982 .2586615 lpopau | 3.581739 2.412787 1.48 0.138 -1.147236 8.310714 lpop | -.3437838 .147252 -2.33 0.020 -.6323924 -.0551752 lxrate1 | -.1588031 .0325271 -4.88 0.000 -.2225549 -.0950512 lremote | .2015504 .241032 0.84 0.403 -.2708636 .6739644 ldist | -2.994239 .6796182 -4.41 0.000 -4.326266 -1.662211 lopen | .041709 .0706586 0.59 0.555 -.0967793 .1801974 english | -.6254806 .4269973 -1.46 0.143 -1.46238 .2114186 white | 19.71043 4.19578 4.70 0.000 11.48685 27.93401 lwhitemg | -1.795107 .3834079 -4.68 0.000 -2.546573 -1.043641 _cons | -20.18484 23.63031 -0.85 0.393 -66.4994 26.12971 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .003152 lgdp | -.002779 .011574 lgdpau | .001294 -.001123 .488992 lgdpdfrati~w | -.002033 .004219 -.011615 .08949 lpopau | -.008723 -.003119 -1.62958 .070359 5.82154 lpop | .00004 -.008456 -.001235 -.001932 -.005698 .021683 lxrate1 | .000178 .000602 .002264 .001386 -.011701 -.000812 .001058 lremote | .00078 .004041 -.020979 -.013508 .041949 -.003017 .00049 ldist | .012467 .008934 -.004969 -.003635 -.058379 .003783 .000689 lopen | .000454 -.001048 -.000894 .000178 -.012901 .002136 -.000182 english | -.006706 .003245 .002019 .006291 -.014773 .017404 .000417 white | .008868 -.03856 .133394 .023754 -.359139 .077535 .004863 lwhitemg | -.001458 .001788 -.013974 -.003634 .042371 -.005865 -.000611 _cons | .034167 -.159007 14.4761 -.8619 -53.4882 -.033956 .116797</p><p>| lremote ldist lopen english white lwhitemg _cons 38</p><p>------+------lremote | .058096 ldist | .028967 .461881 lopen | .000784 .00338 .004993 english | -.004724 .117872 -.001777 .182327 white | .027715 .239792 .005486 .282341 17.6046 lwhitemg | .000373 -.037383 -.000371 -.030828 -1.5814 .147002 _cons | -.966937 -3.89876 .190657 -1.25191 -.609089 .090192 558.392</p><p>. xtgls lrmsitc6 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1031.57 Log likelihood = -552.0824 Prob > chi2 = 0.0000</p><p>------lrmsitc6 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .2323486 .0484969 4.79 0.000 .1372964 .3274007 lgdp | .6044931 .100046 6.04 0.000 .4084064 .8005797 lgdpau | .4173084 .5884669 0.71 0.478 -.7360655 1.570682 lgdpdfrati~w | -.0467191 .2646579 -0.18 0.860 -.565439 .4720009 lpopau | -1.963648 2.03184 -0.97 0.334 -5.94598 2.018684 lpop | .4700728 .1253728 3.75 0.000 .2243466 .715799 lxrate1 | -.1025077 .027845 -3.68 0.000 -.157083 -.0479325 lremote | -.1507604 .2037709 -0.74 0.459 -.550144 .2486233 ldist | -2.232936 .4538649 -4.92 0.000 -3.122495 -1.343377 lopen | -.1029724 .0737505 -1.40 0.163 -.2475207 .0415759 english | .8276879 .3230809 2.56 0.010 .194461 1.460915 white | 23.057 3.164095 7.29 0.000 16.85549 29.25851 lwhitemg | -2.246904 .27925 -8.05 0.000 -2.794224 -1.699584 _cons | 26.2191 19.69166 1.33 0.183 -12.37584 64.81404 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002352 lgdp | -.00211 .010009 lgdpau | -.002373 .004466 .346293 lgdpdfrati~w | -.00153 .001974 -.00015 .070044 lpopau | .007605 -.031231 -1.15523 .008124 4.12837 lpop | .000463 -.00805 -.00298 -.001744 .015024 .015718 lxrate1 | .000058 .000577 .000621 .000995 -.005336 -.000745 .000775 lremote | .000455 .003562 -.016948 -.015792 .034444 -.002449 .000629 ldist | .008673 -.002876 -.010281 -.003255 .007062 .015048 .000454 lopen | .000274 -.000148 .000684 .001326 -.015136 .00142 -.000089 english | -.003389 .003739 .005756 .005191 -.023683 -.00167 .000613 white | .00718 -.033543 .017272 -.005988 -.005666 .054294 .003664 lwhitemg | -.000996 .001221 -.003017 -.00064 .008851 -.003818 -.000373 _cons | -.120725 .306048 10.2814 -.043455 -38.1311 -.35837 .057423</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .041523 ldist | .007879 .205993 lopen | .00146 .001663 .005439 english | -.005282 .059085 -.002182 .104381 white | .017386 .032859 .00378 .003508 10.0115 39</p><p> lwhitemg | .000373 -.007253 -.000427 .000941 -.868922 .077981 _cons | -.593603 -2.1039 .186837 -.347685 -.995841 .038755 387.761</p><p>. xtgls lrmsitc7 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1914.37 Log likelihood = -745.0421 Prob > chi2 = 0.0000</p><p>------lrmsitc7 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0706538 .0353299 2.00 0.046 .0014085 .139899 lgdp | 1.020578 .1013136 10.07 0.000 .8220067 1.219149 lgdpau | .4971219 .7076627 0.70 0.482 -.8898714 1.884115 lgdpdfrati~w | -.1426582 .2894428 -0.49 0.622 -.7099555 .4246392 lpopau | 4.883862 2.461795 1.98 0.047 .0588328 9.708891 lpop | -.4624511 .1477941 -3.13 0.002 -.7521221 -.17278 lxrate1 | -.1538592 .0257107 -5.98 0.000 -.2042512 -.1034672 lremote | -.2710804 .1964616 -1.38 0.168 -.656138 .1139772 ldist | -1.430823 .7705546 -1.86 0.063 -2.941082 .0794366 lopen | -.0113747 .0732048 -0.16 0.877 -.1548535 .132104 english | .0336668 .3405476 0.10 0.921 -.6337942 .7011278 white | 28.37503 2.622664 10.82 0.000 23.2347 33.51536 lwhitemg | -2.637692 .2534914 -10.41 0.000 -3.134526 -2.140858 _cons | -91.06037 24.68889 -3.69 0.000 -139.4497 -42.67103 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001248 lgdp | -.002084 .010264 lgdpau | -.000236 .000609 .500786 lgdpdfrati~w | -.002195 .003614 -.014341 .083777 lpopau | -.006599 -.005652 -1.67463 .066611 6.06043 lpop | .001983 -.012758 -.004131 -.0031 .025496 .021843 lxrate1 | 6.1e-06 .000515 .001299 .001035 -.011492 -.000932 .000661 lremote | .001386 .004776 -.018222 -.005648 .015214 -.003477 .000414 ldist | .007332 .011583 -.030126 -.00819 -.047523 .015227 .001619 lopen | .000231 -.00041 -.002509 .004865 -.000791 .00056 -.000217 english | -.001499 .004121 .004795 .00488 -.014131 -.002618 .001228 white | .018837 -.061637 .048015 -.028733 -.445488 .072764 .009475 lwhitemg | -.00169 .004269 -.00574 .002273 .048004 -.005177 -.000917 _cons | .045306 -.100259 15.1484 -.710151 -56.5645 -.487962 .136501</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .038597 ldist | .074023 .593754 lopen | .001619 .002049 .005359 english | .000264 .057766 -.000877 .115973 white | .033125 .106935 -.008195 .214109 6.87837 lwhitemg | -.002419 -.01689 .000972 -.024139 -.654955 .064258 _cons | -.874115 -5.29072 .044562 -.509342 4.94078 -.466792 609.541</p><p>. xtgls lrmsitc8 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog 40</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 469.81 Log likelihood = -576.3594 Prob > chi2 = 0.0000</p><p>------lrmsitc8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .4020182 .0584349 6.88 0.000 .2874879 .5165485 lgdp | .2572493 .1004956 2.56 0.010 .0602817 .454217 lgdpau | 1.04971 .7351346 1.43 0.153 -.391127 2.490548 lgdpdfrati~w | -.5317048 .3168677 -1.68 0.093 -1.152754 .0893445 lpopau | -4.347096 2.544399 -1.71 0.088 -9.334027 .639835 lpop | .3309592 .1052131 3.15 0.002 .1247453 .5371731 lxrate1 | -.085766 .0343972 -2.49 0.013 -.1531833 -.0183486 lremote | .0169579 .2276187 0.07 0.941 -.4291666 .4630824 ldist | -1.371435 .5959734 -2.30 0.021 -2.539521 -.2033486 lopen | .0936772 .0716478 1.31 0.191 -.0467499 .2341043 english | -.1287559 .300756 -0.43 0.669 -.7182268 .460715 white | 19.32596 2.065805 9.36 0.000 15.27705 23.37486 lwhitemg | -1.816684 .1974954 -9.20 0.000 -2.203768 -1.4296 _cons | 49.31758 24.05102 2.05 0.040 2.178448 96.45671 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .003415 lgdp | -.00309 .010099 lgdpau | -.002289 .006924 .540423 lgdpdfrati~w | -.00244 .006764 -.008228 .100405 lpopau | .004821 -.038872 -1.8093 .040084 6.47397 lpop | .001065 -.006059 -.00404 -.00366 .010002 .01107 lxrate1 | .000329 .000556 -.000167 .000975 -.004446 -.000585 .001183 lremote | .000116 .002462 -.009495 -.017888 .001469 .000506 .001382 ldist | .011479 .003953 -.002258 -.005663 -.094279 .007574 .001019 lopen | -.000293 .000876 .002241 .00017 -.023615 .001678 -.000131 english | -.00336 .009315 .008171 .006526 -.045518 .000632 -.001161 white | .023264 -.036439 -.003678 -.067484 .071497 .015223 -.000647 lwhitemg | -.002675 .001751 -.001661 .003552 .005969 -.000488 -.0003 _cons | -.096807 .279964 15.8699 -.425186 -58.4144 -.177361 .047672</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .05181 ldist | .033429 .355184 lopen | .002315 .004012 .005133 english | .004922 .106398 .000293 .090454 white | .108151 .225628 -.007675 .1511 4.26755 lwhitemg | -.007269 -.033361 .000664 -.018906 -.398528 .039004 _cons | -.60018 -2.34238 .234025 -.741115 -3.74287 .314 578.451</p><p>. xtgls lrmsitc9 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1) 41</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1448.30 Log likelihood = -252.1083 Prob > chi2 = 0.0000</p><p>------lrmsitc9 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .00334 .0237078 0.14 0.888 -.0431265 .0498065 lgdp | .4388091 .0823144 5.33 0.000 .2774759 .6001423 lgdpau | -.3725147 .4879191 -0.76 0.445 -1.328819 .5837892 lgdpdfrati~w | .1579858 .2252095 0.70 0.483 -.2834166 .5993883 lpopau | 1.441363 1.683383 0.86 0.392 -1.858007 4.740732 lpop | -.2293311 .1192428 -1.92 0.054 -.4630427 .0043805 lxrate1 | -.0435224 .0229245 -1.90 0.058 -.0884536 .0014087 lremote | -.0004721 .1689138 -0.00 0.998 -.3315371 .330593 ldist | -3.915923 .5183804 -7.55 0.000 -4.93193 -2.899916 lopen | -.0022764 .0493871 -0.05 0.963 -.0990733 .0945204 english | -.0437274 .2870981 -0.15 0.879 -.6064294 .5189746 white | 16.55575 2.588683 6.40 0.000 11.48202 21.62947 lwhitemg | -1.217411 .2774657 -4.39 0.000 -1.761234 -.6735886 _cons | 18.25097 16.90846 1.08 0.280 -14.889 51.39093 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000562 lgdp | -.000698 .006776 lgdpau | .000127 -.00223 .238065 lgdpdfrati~w | -.000933 .002488 -.009675 .050719 lpopau | -.00124 .001822 -.793565 .052543 2.83378 lpop | .000204 -.006357 .000051 -.001036 -.005047 .014219 lxrate1 | -.000034 .000171 .001329 .001028 -.006385 -.0005 .000526 lremote | .000108 .002356 -.016853 -.00344 .04866 -.002492 -.000089 ldist | .002357 .003075 -.011099 -.001151 -.012701 .018601 -.00034 lopen | .000054 -.000216 -.002007 .001392 .001797 .000858 -.000014 english | -.001183 .000676 .001469 .001969 -.004156 .004589 .000586 white | .003963 -.037498 .039049 -.014713 -.267579 .066869 .002062 lwhitemg | -.000439 .001628 -.004651 .000772 .031795 -.005333 -.000015 _cons | .00473 -.071149 7.22322 -.668435 -26.4899 -.153218 .075764</p><p>| lremote ldist lopen english white lwhitemg _cons</p><p>------+------lremote | .028532 ldist | .013795 .268718 lopen | .000042 .000893 .002439 english | .00008 .054476 .000183 .082425 white | .000085 .153239 .000064 .107719 6.70128 lwhitemg | .002428 -.019677 .000073 -.010887 -.70828 .076987 _cons | -.757551 -2.57281 .004945 -.589669 1.7049 -.189702 285.896 42</p><p>. . . clear</p><p>. insheet using k:\book1.txt (104 vars, 1010 obs)</p><p>. *Dropping Brazil . drop if ccode==330760 (10 observations deleted)</p><p>. </p><p>. . . *Descriptive stats after dropping . tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remote dist op > en english gdpdfrationew white whitemg, stat(n mean sd median min max) col(sta > t) </p><p> variable | N mean sd p50 min max ------+------rimp | 1000 488487 1561789 7348 0 1.39e+07 immig | 1000 33427.69 117047.8 2790 0 1137050 gdp | 1000 2.72e+11 9.69e+11 1.63e+10 1.92e+08 8.99e+12 gdpau | 1000 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 1000 105.5044 16.69005 102.7439 60.87417 207.3465 gdpdfau | 1000 100.7173 4.633489 100.8223 94.42464 109.9797 pop | 1000 4.80e+07 1.54e+08 1.02e+07 41000 1.26e+09</p><p> popau | 1000 1.82e+07 612793.8 1.82e+07 1.73e+07 1.92e+07 xrate1 | 1000 1043.11 10901.71 16.2185 .0068 270182.6 remote | 1000 6699.616 4128.267 6764 1293 39620 dist | 1000 13362.32 3531.729 14305 2409 17972 open | 1000 .7136704 .3861407 .63975 .0671 3.2192 english | 1000 .38 .4856293 0 0 1 gdpdfratio~w | 1000 1.050508 .1786471 1.016437 .5844526 2.009028 white | 1000 .13 .3364717 0 0 1 whitemg | 1000 20501.65 116468.1 0 0 1137050 ------43</p><p>. . sort white</p><p>. *IIIB. Descriptives Original Unites(sub sample for "white" and "non-white" cou > ntries)--RHS Variables: . by white: tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remo > te dist phone open english gdpdfrationew white whitemg, stat(n mean sd median > min max) col(stat) </p><p>------> white = 0</p><p> variable | N mean sd p50 min max ------+------rimp | 870 401831.3 1598090 2944.5 0 1.39e+07 immig | 870 14857.51 27144.21 1403.5 0 158613 gdp | 870 2.29e+11 1.00e+12 1.00e+10 1.92e+08 8.99e+12 gdpau | 870 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 870 104.6028 17.00813 101.6928 60.87417 207.3465 gdpdfau | 870 100.7173 4.633836 100.8223 94.42464 109.9797 pop | 870 5.17e+07 1.64e+08 1.03e+07 41000 1.26e+09 popau | 870 1.82e+07 612839.6 1.82e+07 1.73e+07 1.92e+07 xrate1 | 870 1182.983 11681.74 22.9426 .0068 270182.6 remote | 870 7120.051 4100.423 6927 1293 39620 dist | 870 13191.63 3420.734 14040 2410 17972 phone | 870 157.8029 242.2526 47.025 .54 1449.75 open | 870 .7218987 .4071896 .63975 .0671 3.2192 english | 870 .3908046 .4882114 0 0 1 gdpdfratio~w | 870 1.041772 .1831318 1 .5844526 2.009028 white | 870 0 0 0 0 0 whitemg | 870 0 0 0 0 0 ------</p><p>------> white = 1</p><p> variable | N mean sd p50 min max ------+------rimp | 130 1068414 1138347 522863.5 40432 3843839 immig | 130 157705 288518.8 21174 2612 1137050 gdp | 130 5.56e+11 6.58e+11 2.54e+11 5.15e+10 2.69e+12 gdpau | 130 3.83e+11 4.46e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 130 111.5379 12.89742 112.2247 85.45601 143.9587 gdpdfau | 130 100.7173 4.649088 100.8223 94.42464 109.9797 pop | 130 2.34e+07 2.55e+07 8837000 3477200 8.22e+07 popau | 130 1.82e+07 614856.7 1.82e+07 1.73e+07 1.92e+07 xrate1 | 130 107.0323 293.6987 4.4722 .3774 1261.556 remote | 130 3885.938 3089.96 2888 1530 12501 dist | 130 14504.62 4031.974 15931 2409 17493 phone | 130 734.716 269.9717 652.085 343.99 1487.08 open | 130 .6586038 .1850905 .64065 .3517 1.2967 english | 130 .3076923 .4633239 0 0 1 gdpdfratio~w | 130 1.108974 .1314272 1.116445 .8254872 1.441221 white | 130 1 0 1 1 1 whitemg | 130 157705 288518.8 21174 2612 1137050 ------</p><p>. 44</p><p>. . **Regression of Imports . tsset ccode year panel variable: ccode, 117100 to 725980 time variable: year, 1991 to 2000</p><p>. . **I. Aggregate Imports . xtgls lrimp limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 10453.98 Log likelihood = -547.6266 Prob > chi2 = 0.0000</p><p>------lrimp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3221989 .0303017 10.63 0.000 .2628088 .3815891 lgdp | 1.199812 .0328465 36.53 0.000 1.135434 1.26419 lgdpau | .1660327 .6066228 0.27 0.784 -1.022926 1.354992 lgdpdfrati~w | -.8910203 .2459937 -3.62 0.000 -1.373159 -.4088814 lpopau | -2.682483 2.093429 -1.28 0.200 -6.785529 1.420563 lpop | .0200435 .041031 0.49 0.625 -.0603758 .1004628 lxrate1 | -.1340721 .0169401 -7.91 0.000 -.1672742 -.1008701 lremote | -.4469176 .0757392 -5.90 0.000 -.5953636 -.2984715 ldist | -2.163849 .16602 -13.03 0.000 -2.489242 -1.838456 lopen | .277687 .0563841 4.92 0.000 .1671762 .3881977 english | .948382 .128343 7.39 0.000 .6968344 1.19993 white | 4.54096 .5828932 7.79 0.000 3.39851 5.68341 lwhitemg | -.419032 .0516372 -8.11 0.000 -.520239 -.317825 _cons | 42.98905 19.25377 2.23 0.026 5.252359 80.72575 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000918 lgdp | -.00029 .001079 lgdpau | -.000883 .001348 .367991 lgdpdfrati~w | -.000853 .000131 -.009555 .060513 lpopau | .001934 -.00787 -1.24634 .050439 4.38245 lpop | -.000125 -.000741 .000152 .000158 -.000558 .001684 lxrate1 | .00009 .000164 .000392 .000116 -.002609 -.000102 .000287 lremote | .000409 .000023 -.000434 -.001737 -.002839 -.000194 .000077 ldist | .001532 .001183 .001375 -.001709 -.01723 .000056 .001872 lopen | -.000147 .000593 .002471 .000706 -.016941 .000331 .000134 english | -.001145 .002853 .002895 .001995 -.010358 -.001534 .000542 white | .004629 -.002555 .002759 -.011154 -.022885 .005227 .000213 lwhitemg | -.000569 .000227 -.000273 .001036 .002687 -.000425 -.000048 _cons | -.024454 .070276 10.9936 -.618279 -39.6981 -.001181 .010661</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .005736 ldist | .004324 .027563 lopen | .001397 .00074 .003179 english | -.003447 .003034 -.000986 .016472 white | .000261 -.003384 -.00025 -.002807 .339765 lwhitemg | .00014 -.000288 -.000075 .000376 -.029587 .002666 45</p><p>_cons | -.028329 -.09186 .180304 .049737 .277718 -.030272 370.708</p><p>. . **II. Conservative Estimates . *2.1. Aggregate reference priced Imports (conservative) . xtgls lrrefp_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 72470.03 Log likelihood = -971.6456 Prob > chi2 = 0.0000</p><p>------lrrefp_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0497779 .0139602 3.57 0.000 .0224164 .0771394 lgdp | 1.635324 .0508471 32.16 0.000 1.535665 1.734982 lgdpau | .8873081 .3611032 2.46 0.014 .1795588 1.595057 lgdpdfrati~w | -.4229691 .1487882 -2.84 0.004 -.7145885 -.1313496 lpopau | -6.43209 1.220048 -5.27 0.000 -8.82334 -4.04084 lpop | -.398691 .0840446 -4.74 0.000 -.5634154 -.2339666 lxrate1 | -.1106484 .0211669 -5.23 0.000 -.1521348 -.0691621 lremote | -.9656801 .0910697 -10.60 0.000 -1.144173 -.7871868 ldist | -3.190204 .1583738 -20.14 0.000 -3.500611 -2.879797 lopen | -.0546493 .0269337 -2.03 0.042 -.1074383 -.0018602 english | 1.031093 .0867598 11.88 0.000 .8610472 1.201139 white | 4.484711 .7561797 5.93 0.000 3.002626 5.966796 lwhitemg | -.4480635 .0721937 -6.21 0.000 -.5895606 -.3065664 _cons | 96.10324 11.44105 8.40 0.000 73.6792 118.5273 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000195 lgdp | -.000278 .002585 lgdpau | -.001242 .00178 .130396 lgdpdfrati~w | -.000596 .000581 -.011872 .022138 lpopau | .00487 -.007602 -.431369 .024177 1.48852 lpop | .000189 -.004041 -.001482 .000361 .004856 .007063 lxrate1 | -.000029 .000325 -.000037 .00041 -.003365 -.000384 .000448 lremote | .000449 .00041 -.0064 -.002962 .022409 -.001335 .000105 ldist | .000646 -.001374 -.006229 -.001208 .015 .002159 .000625 lopen | .000045 -.000097 -.00096 .000958 .003524 .000262 -.000075 english | -.00043 .000431 .003184 .002596 -.020998 -8.2e-06 .001217 white | .001232 -.012377 -.010171 -.003772 .042554 .018836 -.001243 lwhitemg | -.000095 .000675 .000431 .000212 -.002865 -.000948 .000165 _cons | -.055231 .093487 3.85074 -.090041 -13.6571 -.069606 .046694</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .008294 ldist | .003519 .025082 lopen | .000582 .000202 .000725 english | -.002592 .002349 -.000408 .007527 white | .005195 -.023743 .001709 -.003065 .571808 lwhitemg | .000054 .00324 -.000132 .000496 -.053239 .005212 _cons | -.298239 -.366649 -.042895 .249775 -.265581 .003394 130.898 46</p><p>. . *2.2. Aggregate Differentiated Imports (conservative) . xtgls lrdiff_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8565.73 Log likelihood = -933.1487 Prob > chi2 = 0.0000</p><p>------lrdiff_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0922318 .0214827 4.29 0.000 .0501264 .1343372 lgdp | 1.647111 .0690919 23.84 0.000 1.511693 1.782528 lgdpau | .1909031 .4977579 0.38 0.701 -.7846844 1.166491 lgdpdfrati~w | -.4581472 .2121884 -2.16 0.031 -.8740288 -.0422655 lpopau | -3.220877 1.736563 -1.85 0.064 -6.624478 .1827238 lpop | -.5431275 .0812242 -6.69 0.000 -.7023239 -.3839311 lxrate1 | -.1613751 .0268418 -6.01 0.000 -.2139841 -.1087661 lremote | -.2816059 .1126217 -2.50 0.012 -.5023404 -.0608714 ldist | -4.358191 .3372323 -12.92 0.000 -5.019154 -3.697228 lopen | .0706411 .0415152 1.70 0.089 -.0107273 .1520094 english | 1.138888 .1462681 7.79 0.000 .8522076 1.425568 white | 10.53739 1.949025 5.41 0.000 6.717373 14.35741 lwhitemg | -.8458002 .1944462 -4.35 0.000 -1.226908 -.4646926 _cons | 68.02983 16.53934 4.11 0.000 35.61332 100.4463 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000462 lgdp | -.000687 .004774 lgdpau | -.001316 .002637 .247763 lgdpdfrati~w | -.000833 .002586 -.015897 .045024 lpopau | .00349 -.015564 -.835266 .05314 3.01565 lpop | .000478 -.004165 -.000551 -.002216 .00201 .006597 lxrate1 | .000026 .000403 -.000254 .001001 -.004779 -.000579 .00072 lremote | -.000023 -.001535 -.004995 -.000331 .017511 -.00087 .000195 ldist | .002186 .007849 -.008432 -.000256 -.021479 -.0017 .001347 lopen | .000102 -.000703 .000383 .001824 -.001259 .00111 -.000223 english | -.000867 .001711 .004439 .001597 -.037057 .003223 .00198 white | .00089 -.031599 .008488 -.004765 -.029362 .027883 .005079 lwhitemg | -.000099 .001554 -.001686 -.000056 .008036 -.001704 -.000501 _cons | -.037228 .08738 7.4542 -.52638 -27.816 -.005514 .067285</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .012684 ldist | .002549 .113726 lopen | .000458 -.001007 .001724 english | -.005285 .005238 -.001434 .021394 white | .014558 -.137588 .00308 .028073 3.7987 lwhitemg | .000661 .009905 -.000153 -.003064 -.371102 .037809 _cons | -.24921 -.693166 .014381 .394969 1.67629 -.194187 273.55</p><p>. . *2.3. Aggregate Homogenous Imports (conservative) . xtgls lrhomo_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog 47</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 2328.19 Log likelihood = -1113.511 Prob > chi2 = 0.0000</p><p>------lrhomo_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1021156 .0335705 3.04 0.002 .0363187 .1679125 lgdp | .920797 .0751801 12.25 0.000 .7734467 1.068147 lgdpau | -.4461326 .853224 -0.52 0.601 -2.118421 1.226156 lgdpdfrati~w | -.3397956 .3489667 -0.97 0.330 -1.023758 .3441666 lpopau | .1968337 2.916265 0.07 0.946 -5.518942 5.912609 lpop | -.0441361 .0934995 -0.47 0.637 -.2273918 .1391196 lxrate1 | -.1394802 .0332415 -4.20 0.000 -.2046324 -.0743281 lremote | .3932255 .2338323 1.68 0.093 -.0650774 .8515285 ldist | -1.469456 .3258212 -4.51 0.000 -2.108054 -.8308584 lopen | .3683613 .0756889 4.87 0.000 .2200137 .5167089 english | 1.794696 .2281092 7.87 0.000 1.34761 2.241782 white | 1.066016 2.286539 0.47 0.641 -3.415519 5.547551 lwhitemg | .2156932 .2051721 1.05 0.293 -.1864367 .617823 _cons | 1.658797 27.46345 0.06 0.952 -52.16857 55.48617 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001127 lgdp | -.001049 .005652 lgdpau | -.001557 .000891 .727991 lgdpdfrati~w | 9.0e-06 .000806 -.02067 .121778 lpopau | .002272 -.008284 -2.4394 .046962 8.5046 lpop | .000371 -.005171 .000424 -.000388 -.000233 .008742 lxrate1 | .00011 .00089 .000394 .002194 -.008014 -.001253 .001105 lremote | .001067 .00061 -.028031 -.016569 .093448 .000775 -.001108 ldist | .003354 -.007045 -.017302 -.00568 .047122 .012963 -.000236 lopen | .000203 -.001115 .004304 .003395 -.021777 .003064 -.000182 english | -.002441 -2.7e-06 .007345 .005586 -.01694 -.001942 .001783 white | .003193 -.021852 .108603 .012607 -.425634 .017439 .000627 lwhitemg | -.000423 .000716 -.011556 -.00273 .04735 -.000216 -.000227 _cons | -.025218 .134046 21.7806 -.17662 -78.2454 -.156777 .127709</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .054678 ldist | .016072 .106159 lopen | .001783 -.001374 .005729 english | -.013429 .01429 -.002524 .052034 white | .035745 .009477 .00616 .039093 5.22826 lwhitemg | -.000013 .001896 -.000314 -.002813 -.457174 .042096 _cons | -1.45474 -1.54612 .222865 .08602 3.98537 -.508474 754.241</p><p>. . **III. Liberal Estimates . *3.1. Aggregate reference priced Imports (liberal) . xtgls lrrefp_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression 48</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 68278.78 Log likelihood = -906.6679 Prob > chi2 = 0.0000</p><p>------</p><p> lrrefp_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0254022 .0141927 1.79 0.073 -.0024151 .0532195 lgdp | 1.59689 .0525662 30.38 0.000 1.493862 1.699918 lgdpau | .6520706 .3902323 1.67 0.095 -.1127707 1.416912 lgdpdfrati~w | -.1978852 .1408348 -1.41 0.160 -.4739163 .0781458 lpopau | -6.57492 1.320931 -4.98 0.000 -9.163898 -3.985942 lpop | -.2367292 .0907562 -2.61 0.009 -.4146081 -.0588504 lxrate1 | -.0317558 .018847 -1.68 0.092 -.0686953 .0051837 lremote | -.6092037 .1517003 -4.02 0.000 -.9065308 -.3118766 ldist | -2.533549 .1626507 -15.58 0.000 -2.852338 -2.214759 lopen | -.0347603 .0293845 -1.18 0.237 -.0923528 .0228323 english | 1.173341 .1143198 10.26 0.000 .9492788 1.397404 white | 3.611596 .8510207 4.24 0.000 1.943627 5.279566 lwhitemg | -.2775641 .0780208 -3.56 0.000 -.4304821 -.1246461 _cons | 93.12775 12.42432 7.50 0.000 68.77652 117.479</p><p>------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000201 lgdp | -.000218 .002763 lgdpau | -.000805 .001936 .152281 lgdpdfrati~w | -.000346 .000864 -.007371 .019834 lpopau | .002664 -.006814 -.505772 .012092 1.74486 lpop | .00014 -.004481 -.001561 -.000274 .003496 .008237 lxrate1 | .000013 .000235 -.000043 .000456 -.002528 -.000291 .000355 lremote | .000032 -.00087 -.015573 -.003473 .055506 -.000049 -.000184 ldist | .000937 -.005239 -.003391 .000921 .000835 .009142 .000672 lopen | .000015 -.000066 -.00095 .001122 .003253 .000265 -.000066 english | -.000331 .000017 .003943 .00246 -.01913 -.000481 .000978 white | .000715 -.015414 -.017362 -.001831 .033359 .027274 .002011 lwhitemg | -.000105 .000729 -.000055 -.000228 .002702 -.001492 -.000227 _cons | -.03021 .127838 4.55583 -.019408 -16.0924 -.131965 .035731</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .023013 ldist | -.002893 .026455 lopen | .001147 .000247 .000863 english | -.004621 .005583 -.000522 .013069 white | .01742 .030834 .001034 .004048 .724236 lwhitemg | .0009 -.001997 4.4e-06 -.000274 -.063358 .006087 _cons | -.662383 -.187876 -.044824 .20094 -.636387 -.023842 154.364</p><p>. . *3.2. Aggregate Differentiated Imports (liberal) . xtgls lrdiff_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares 49</p><p>Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 9658.75 Log likelihood = -963.6933 Prob > chi2 = 0.0000</p><p>------</p><p> lrdiff_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .189206 .0295921 6.39 0.000 .1312066 .2472053 lgdp | 1.600593 .0696457 22.98 0.000 1.46409 1.737096 lgdpau | -.0585307 .5530397 -0.11 0.916 -1.142469 1.025407 lgdpdfrati~w | -.1844107 .2472434 -0.75 0.456 -.6689988 .3001774 lpopau | -1.364298 1.943511 -0.70 0.483 -5.173509 2.444913 lpop | -.5264478 .0800042 -6.58 0.000 -.6832532 -.3696425 lxrate1 | -.1414232 .0280161 -5.05 0.000 -.1963338 -.0865126 lremote | -.2799386 .10954 -2.56 0.011 -.4946331 -.065244 ldist | -3.393783 .3332941 -10.18 0.000 -4.047027 -2.740539 lopen | .1097714 .0473161 2.32 0.020 .0170336 .2025092 english | .9179382 .1438201 6.38 0.000 .6360561 1.19982 white | 10.46261 1.921512 5.44 0.000 6.696512 14.2287 lwhitemg | -.8934413 .1918132 -4.66 0.000 -1.269388 -.5174944 _cons | 34.53754 18.52528 1.86 0.062 -1.771341 70.84643</p><p>------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000876 lgdp | -.001238 .004851 lgdpau | -.001604 .002935 .305853 lgdpdfrati~w | -.001172 .002662 -.020263 .061129 lpopau | .003974 -.015787 -1.04068 .080631 3.77723 lpop | .000724 -.004162 -.000958 -.001696 .00549 .006401 lxrate1 | .000085 .000289 -.000291 .001146 -.004516 -.00053 .000785 lremote | .000055 -.001219 -.003042 -.00047 .006196 -.000985 .000215 ldist | .004183 .00249 -.00996 -.002268 -.019778 .000408 .001429 lopen | .000138 -.00081 .001626 .001513 -.007365 .001388 -.000285 english | -.0015 .002896 .002246 .004462 -.019727 .001207 .002048 white | .000428 -.027482 .011894 .001904 -.005201 .021903 .00545 lwhitemg | -.000035 .001524 -.001827 -.00057 .004282 -.001391 -.000516 _cons | -.050793 .131749 9.3434 -.875337 -35.0749 -.069063 .064007</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .011999 ldist | .005332 .111085 lopen | .000365 -.00112 .002239 english | -.005157 -.001471 -.00174 .020684 white | .012318 -.149492 .003449 .023337 3.69221 lwhitemg | .000649 .012644 -.000207 -.002667 -.362083 .036792 _cons | -.138224 -.601105 .083957 .232473 1.31913 -.158223 343.186</p><p>. . *3.3. Aggregate Homogenous Imports (liberal) . xtgls lrhomo_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1) 50</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8203.08 Log likelihood = -1124.035 Prob > chi2 = 0.0000</p><p>------lrhomo_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0263254 .0163716 1.61 0.108 -.0057623 .0584132 lgdp | 1.395924 .0515678 27.07 0.000 1.294853 1.496995 lgdpau | .6708442 .5569601 1.20 0.228 -.4207775 1.762466 lgdpdfrati~w | -.0527853 .1889307 -0.28 0.780 -.4230826 .317512 lpopau | -5.978946 1.892656 -3.16 0.002 -9.688485 -2.269408 lpop | .0141241 .0696363 0.20 0.839 -.1223605 .1506087 lxrate1 | -.0743806 .0222541 -3.34 0.001 -.1179978 -.0307633 lremote | -1.205969 .1626665 -7.41 0.000 -1.52479 -.8871488 ldist | -3.66639 .231587 -15.83 0.000 -4.120292 -3.212488 lopen | .0863571 .0459177 1.88 0.060 -.00364 .1763543 english | 2.366468 .1392743 16.99 0.000 2.093496 2.639441 white | 3.350233 1.161603 2.88 0.004 1.073533 5.626932 lwhitemg | -.1853271 .0982163 -1.89 0.059 -.3778276 .0071733 _cons | 98.42704 17.85286 5.51 0.000 63.43608 133.418 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000268 lgdp | -.000455 .002659 lgdpau | -.000312 .000124 .310205 lgdpdfrati~w | -.000633 .000667 .002985 .035695 lpopau | -.001046 .001017 -1.03234 -.007655 3.58215 lpop | .000248 -.00271 -.000348 .000324 -.001054 .004849 lxrate1 | -.000033 .000295 .000447 .000668 -.00319 -.000494 .000495 lremote | .000385 .001148 -.016621 -.002277 .049832 -.001356 -.000286 ldist | .000805 .001182 -.01049 -.002298 .017075 .000199 .000303 lopen | .000094 -.000416 -.000106 .002524 .000867 .000877 -.000149 english | -.000888 .000961 .00335 .00215 -.012138 .001074 .001358 white | .000551 -.012795 -.000597 -.000527 -.004067 .022356 -.000085 lwhitemg | -.000013 .000662 -.001031 .000132 .003659 -.001552 .000078 _cons | .020816 -.057563 9.23227 .034968 -32.9374 .02032 .03906</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .02646 ldist | .016678 .053633 lopen | .001113 .000274 .002108 english | -.004972 .003247 -.001602 .019397 white | -.000322 -.003555 .001123 .03031 1.34932 lwhitemg | .001523 .001654 3.5e-06 -.002781 -.110389 .009646 _cons | -.783458 -.700885 -.029507 .078254 .038081 -.052909 318.725</p><p>. . *IV. Aggregate NON-Manufacturing Imports (Sum of Sitc0,1,2,3,4) . xtgls lrmnmf limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 51</p><p>Wald chi2(13) = 686.44 Log likelihood = -577.7911 Prob > chi2 = 0.0000</p><p>------lrmnmf | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .2947635 .0397758 7.41 0.000 .2168044 .3727227 lgdp | .1884155 .0736343 2.56 0.011 .0440949 .3327361 lgdpau | -1.565925 .2996087 -5.23 0.000 -2.153147 -.9787028 lgdpdfrati~w | -.5505116 .1822316 -3.02 0.003 -.907679 -.1933441 lpopau | 2.716679 1.010003 2.69 0.007 .7371084 4.696249 lpop | .5040758 .0870795 5.79 0.000 .3334031 .6747484 lxrate1 | -.208019 .0255026 -8.16 0.000 -.2580031 -.1580349 lremote | -.0466478 .0862935 -0.54 0.589 -.21578 .1224844 ldist | -1.704546 .3785474 -4.50 0.000 -2.446486 -.9626071 lopen | .0771781 .0337473 2.29 0.022 .0110346 .1433216 english | .6086579 .2090259 2.91 0.004 .1989748 1.018341 white | 9.643816 .7492735 12.87 0.000 8.175267 11.11236 lwhitemg | -.9524957 .0751496 -12.67 0.000 -1.099786 -.8052051 _cons | 6.526554 9.822983 0.66 0.506 -12.72614 25.77925 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------</p><p> limmig | .001582 lgdp | -.001665 .005422 lgdpau | .000278 .001308 .089765 lgdpdfrati~w | -.000306 .002032 .009475 .033208 lpopau | .00051 -.008845 -.28855 -.008377 1.02011 lpop | .000172 -.003748 -.003513 -.00186 .005251 .007583 lxrate1 | .000014 .000482 .00396 .001879 -.012685 -.000681 .00065 lremote | .00043 .000496 -.004137 -.002436 .008818 -.000356 -.00024 ldist | .002724 -.009841 .00119 -.003424 -.025874 .017254 .000282 lopen | -.000128 -.000275 -.003069 -.000115 .006751 .000668 -.000297 english | -.002139 .002262 .009497 .005039 -.041521 .004384 .001578 white | .007829 -.008416 .028523 .012309 -.107406 .013279 .004294 lwhitemg | -.000684 -.000105 -.00304 -.001629 .011661 -.000383 -.000491 _cons | -.020258 .142081 2.44947 -.116892 -8.99338 -.187012 .100325</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .007447 ldist | .007227 .143298 lopen | .000187 .000188 .001139 english | -.000504 .009595 -.000821 .043692 white | -.000636 -.024747 -.002249 .055697 .561411 lwhitemg | .000351 .005838 .000292 -.00328 -.05336 .005647 _cons | -.176166 -1.09704 -.035697 .206661 1.13463 -.153428 96.491</p><p>. . *V. Aggregate Manufacturing Imports (Sum of Sitc5,6,7,8,9) . xtgls lrmmfn limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremot > e ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 4006.61 Log likelihood = -591.9787 Prob > chi2 = 0.0000 52</p><p>------lrmmfn | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0968355 .0223026 4.34 0.000 .0531232 .1405478 lgdp | .1441149 .0382858 3.76 0.000 .0690761 .2191538 lgdpau | -.0560287 .0384821 -1.46 0.145 -.1314523 .0193948 lgdpdfrati~w | -.0789483 .0260692 -3.03 0.002 -.130043 -.0278536 lpopau | 2.157802 .17082 12.63 0.000 1.823001 2.492603 lpop | .866884 .0966456 8.97 0.000 .677462 1.056306 lxrate1 | -.0087579 .0049299 -1.78 0.076 -.0184203 .0009046 lremote | .0994862 .0210305 4.73 0.000 .0582673 .1407051 ldist | -3.273745 .3324093 -9.85 0.000 -3.925255 -2.622235 lopen | .0060072 .0194949 0.31 0.758 -.0322021 .0442165 english | 1.18584 .2866586 4.14 0.000 .6239998 1.747681 white | 23.82918 2.980861 7.99 0.000 17.9868 29.67156 lwhitemg | -2.183569 .2752865 -7.93 0.000 -2.723121 -1.644017 _cons | -15.66007 4.389389 -3.57 0.000 -24.26311 -7.057024 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000497 lgdp | -.000204 .001466 lgdpau | -.000365 -.00034 .001481 lgdpdfrati~w | .000159 -.000596 .000307 .00068 lpopau | .002527 .000416 -.005369 .000174 .029179 lpop | -.000258 -.001422 .001029 .000347 -.002973 .00934 lxrate1 | .000012 .000037 -.000027 .000012 .000288 -.000138 .000024 lremote | -.000148 .000616 -.000261 -.000432 .000028 -.000445 .000012 ldist | .001755 -.000056 -.001485 .000093 .013279 -.001228 -.000011 lopen | -.000213 -.000135 .00019 -.000151 -.002009 .00072 -.000067 english | -.000411 -.000438 .000408 .000247 -.005722 -.000921 .00002 white | .001138 -.003741 .001295 .001339 -.005142 .024829 -.000314 lwhitemg | -.000258 .000102 .000036 -.00004 -.0011 -.002896 .00003 _cons | -.042231 -.012207 .060147 -.00181 -.444112 -.075504 -.002928</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .000442 ldist | -.000031 .110496 lopen | .000066 -.000547 .00038 english | -.000282 .055314 .000044 .082173 white | -.001619 .013434 .001005 .039864 8.88553 lwhitemg | .000057 -.003256 -.000031 -.002677 -.808998 .075783 _cons | -.002688 -1.22995 .02662 -.436482 -.401515 .09361 19.2667</p><p>. . **VI. SITC-1 Digit Level Disaggregate Imports . xtgls lrmsitc0 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 527.72 Log likelihood = -558.5398 Prob > chi2 = 0.0000</p><p>------lrmsitc0 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .5214381 .0518647 10.05 0.000 .4197851 .6230911 lgdp | .0322439 .0864032 0.37 0.709 -.1371032 .201591 53</p><p> lgdpau | 2.30862 .8208576 2.81 0.005 .6997684 3.917471 lgdpdfrati~w | -.7304942 .3070547 -2.38 0.017 -1.33231 -.1286781 lpopau | -11.43099 2.822191 -4.05 0.000 -16.96238 -5.899593 lpop | .3228242 .1107236 2.92 0.004 .1058099 .5398384 lxrate1 | -.0929292 .027753 -3.35 0.001 -.1473242 -.0385343 lremote | -.0476003 .261086 -0.18 0.855 -.5593194 .4641188 ldist | -1.779939 .3347786 -5.32 0.000 -2.436093 -1.123785 lopen | .071421 .0547033 1.31 0.192 -.0357955 .1786374 english | .0925529 .2336526 0.40 0.692 -.3653979 .5505036 white | 9.510747 1.184586 8.03 0.000 7.189001 11.83249 lwhitemg | -.8674905 .1037603 -8.36 0.000 -1.070857 -.664124 _cons | 143.6483 26.77422 5.37 0.000 91.17179 196.1248 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00269 lgdp | -.0017 .007466 lgdpau | .000524 -.000302 .673807 lgdpdfrati~w | -.001711 .002144 -.021083 .094283 lpopau | -.005588 -.006547 -2.25118 .089967 7.96476 lpop | -.000354 -.005827 -.001254 -.000892 .002523 .01226 lxrate1 | .000138 -.000138 .000696 .000833 -.005636 -.000474 .00077 lremote | .001641 .004702 -.021366 -.01436 .054728 -.00253 .000327 ldist | .00572 .000379 -.010118 -.006552 -.01062 .004434 .000282 lopen | -.000282 .000801 .000685 -.000585 -.011758 .000393 .000028 english | -.002356 .003138 .005383 .00678 -.028567 -.000961 .001526 white | .012488 -.030152 .022602 -.004178 -.089703 .039348 .007178 lwhitemg | -.001247 .001408 -.003395 -.000488 .015168 -.002133 -.000575 _cons | .036932 .005898 19.9889 -.87802 -73.401 -.089514 .075762</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .068166 ldist | .033857 .112077 lopen | .001125 .001718 .002992 english | -.011866 .016799 .000394 .054594 white | .016033 -.002743 -.001709 .032757 1.40324 lwhitemg | .00095 .000961 .000064 -.002387 -.117938 .010766 _cons | -1.32199 -1.03098 .131168 .201571 .675241 -.163194 716.859</p><p>. xtgls lrmsitc1 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1488.09 Log likelihood = -396.161 Prob > chi2 = 0.0000</p><p>------lrmsitc1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0451795 .0196805 2.30 0.022 .0066063 .0837527 lgdp | .1705712 .0529194 3.22 0.001 .066851 .2742913 lgdpau | -.2917645 .3233423 -0.90 0.367 -.9255039 .3419748 lgdpdfrati~w | -.4782278 .2206239 -2.17 0.030 -.9106427 -.0458129 lpopau | 3.132248 1.157989 2.70 0.007 .8626319 5.401864 lpop | .0566452 .0666636 0.85 0.395 -.0740132 .1873035 lxrate1 | -.0949207 .0188967 -5.02 0.000 -.1319574 -.0578839 lremote | -.1703986 .1158766 -1.47 0.141 -.3975125 .0567152 ldist | -1.462528 .2281544 -6.41 0.000 -1.909702 -1.015354 54</p><p> lopen | .0280982 .0292877 0.96 0.337 -.0293047 .0855011 english | 1.851555 .2032767 9.11 0.000 1.45314 2.24997 white | 9.789214 1.764437 5.55 0.000 6.330981 13.24745 lwhitemg | -.7225267 .1820798 -3.97 0.000 -1.079397 -.3656569 _cons | -32.14702 11.81289 -2.72 0.007 -55.29986 -8.994182 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000387 lgdp | -.00041 .0028 lgdpau | .000405 -.001557 .10455 lgdpdfrati~w | -.000626 -.000316 -.00317 .048675 lpopau | -.001882 .007676 -.359359 .034949 1.34094 lpop | .000185 -.002761 .0009 .000833 -.007707 .004444 lxrate1 | -8.7e-06 .000172 .001305 .000649 -.00644 -.000181 .000357 lremote | .000249 .000847 -.007416 -.003718 .02632 -.000272 -.000425 ldist | .001116 .000367 -.002263 -.001206 -.002538 .002641 -.000316 lopen | .000033 -.000248 -.000594 .001176 -.000153 .000338 -2.7e-06 english | -.000403 -.001963 .002934 .003473 -.009624 .003661 .000216 white | .002001 -.020761 .014062 -.000821 -.077061 .022374 .000906 lwhitemg | -.0002 .001252 -.001208 .000356 .005661 -.001484 -.000069 _cons | .012806 -.113883 3.32087 -.511114 -13.0819 .072736 .076449</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .013427 ldist | .002808 .052054 lopen | -.000087 4.4e-06 .000858 english | -.003192 .016163 -7.7e-06 .041321 white | .013746 -.031962 .001267 .05553 3.11324 lwhitemg | -.000687 .002246 -.000072 -.005283 -.317097 .033153 _cons | -.395956 -.472638 .018431 -.063789 1.18456 -.081143 139.544</p><p>. xtgls lrmsitc2 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1030.09 Log likelihood = -854.9093 Prob > chi2 = 0.0000</p><p>------lrmsitc2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1569718 .0647439 2.42 0.015 .030076 .2838675 lgdp | .4804542 .1096566 4.38 0.000 .2655312 .6953772 lgdpau | -2.452851 1.131665 -2.17 0.030 -4.670872 -.2348288 lgdpdfrati~w | -1.138654 .4486308 -2.54 0.011 -2.017954 -.2593535 lpopau | 5.170641 3.880492 1.33 0.183 -2.434984 12.77627 lpop | .2366786 .1232333 1.92 0.055 -.0048544 .4782115 lxrate1 | -.1658214 .0417319 -3.97 0.000 -.2476145 -.0840282 lremote | 1.821793 .2738816 6.65 0.000 1.284995 2.358591 ldist | -1.679792 .4897643 -3.43 0.001 -2.639712 -.7198717 lopen | -.002079 .0776952 -0.03 0.979 -.1543588 .1502007 english | .2945389 .2602547 1.13 0.258 -.2155509 .8046287 white | 11.08193 1.448979 7.65 0.000 8.241985 13.92188 lwhitemg | -.8335773 .1398237 -5.96 0.000 -1.107627 -.5595278 _cons | -31.98039 36.43603 -0.88 0.380 -103.3937 39.43291 ------55</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .004192 lgdp | -.004389 .012025 lgdpau | -.00208 .005133 1.28066 lgdpdfrati~w | -.001867 .004017 -.006257 .20127 lpopau | .00283 -.025833 -4.28199 .071922 15.0582 lpop | .001601 -.010117 -.002194 -.002304 .010212 .015186 lxrate1 | .000103 .001679 .000863 .001377 -.010559 -.001592 .001742 lremote | .001559 -.001762 -.030395 -.01925 .074098 .002149 .000934 ldist | .012331 -.010881 -.010735 -.011314 -.006963 .01549 -.002687 lopen | .000332 -.000864 .006708 .001096 -.034519 .002219 -.000386 english | -.005797 .011026 .007638 .001812 -.029111 -.008962 -.000596 white | .026942 -.048489 -.034508 -.03507 .030909 .046348 .005944 lwhitemg | -.003018 .002901 -.000221 .001093 .012604 -.00228 -.000888 _cons | -.069687 .313862 37.7426 -1.00742 -137.786 -.287845 .149772</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .075011 ldist | .024534 .239869 lopen | .002969 .004427 .006037 english | -.00427 .026544 -.00067 .067733 white | .114348 .137177 .002396 .030139 2.09954 lwhitemg | -.004698 -.017299 -.000086 -.005054 -.193067 .019551 _cons | -1.31174 -2.14703 .316959 -.025076 -1.71409 -.007166 1327.58</p><p>. xtgls lrmsitc3 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100</p><p>Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 551.06 Log likelihood = -159.6335 Prob > chi2 = 0.0000</p><p>------lrmsitc3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .03089 .0308152 1.00 0.316 -.0295067 .0912868 lgdp | .1710803 .0653523 2.62 0.009 .0429921 .2991685 lgdpau | .5183105 .4629533 1.12 0.263 -.3890613 1.425682 lgdpdfrati~w | .3685359 .2005396 1.84 0.066 -.0245145 .7615864 lpopau | 2.754279 1.648476 1.67 0.095 -.4766748 5.985233 lpop | .2983737 .0856321 3.48 0.000 .1305378 .4662096 lxrate1 | -.0312634 .0197864 -1.58 0.114 -.070044 .0075173 lremote | -.0287943 .1448256 -0.20 0.842 -.3126472 .2550587 ldist | -2.169167 .4248357 -5.11 0.000 -3.00183 -1.336504 lopen | .0618375 .0432379 1.43 0.153 -.0229072 .1465822 english | .9142913 .1952453 4.68 0.000 .5316175 1.296965 white | 15.70965 1.203847 13.05 0.000 13.35015 18.06915 lwhitemg | -1.339147 .1106917 -12.10 0.000 -1.556099 -1.122196 _cons | -46.86431 17.04653 -2.75 0.006 -80.2749 -13.45373 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00095 56</p><p> lgdp | -.000781 .004271 lgdpau | -.000553 -.000588 .214326 lgdpdfrati~w | -.000823 .00062 -.000448 .040216 lpopau | .000874 .003475 -.723534 .017477 2.71747 lpop | -.000112 -.003017 .000959 .00078 -.006384 .007333 lxrate1 | .000075 .000111 .000379 .000765 -.003847 -.000134 .000392 lremote | .000909 .003015 -.009777 -.005021 .030307 -.002068 .000051 ldist | .002765 -.003267 -.011427 -.009137 .004961 .00931 .000305 lopen | -6.9e-06 -.000105 .000468 .000073 -.006612 .000623 -.000054 english | -.001601 -.000614 .0044 .003653 -.018691 .005068 .000394 white | .004917 -.020082 .044085 .000224 -.372295 .028552 .005231 lwhitemg | -.000569 .001134 -.004362 -.000161 .036936 -.002198 -.000555 _cons | -.01919 -.082043 6.573 -.21377 -26.3986 -.038305 .04772</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .020974 ldist | .01932 .180485 lopen | .000166 .000366 .00187 english | -.005588 .004179 .000038 .038121 white | -.008455 -.024148 -.000353 .052228 1.44925 lwhitemg | .000875 .002845 .000092 -.004505 -.130078 .012253 _cons | -.645758 -1.75737 .086397 .13137 5.26754 -.518376 290.584</p><p>. xtgls lrmsitc4 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 237.28 Log likelihood = 189.1243 Prob > chi2 = 0.0000</p><p>------lrmsitc4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0072205 .0186602 0.39 0.699 -.0293528 .0437937 lgdp | .2404367 .0503815 4.77 0.000 .1416907 .3391827 lgdpau | -.2421561 .3390402 -0.71 0.475 -.9066627 .4223506 lgdpdfrati~w | -.1026149 .1382634 -0.74 0.458 -.3736062 .1683763 lpopau | 3.988339 1.244519 3.20 0.001 1.549126 6.427551 lpop | -.0585405 .0603023 -0.97 0.332 -.1767309 .0596499 lxrate1 | -.0845236 .0148497 -5.69 0.000 -.1136284 -.0554188 lremote | .2590063 .125488 2.06 0.039 .0130543 .5049583 ldist | -1.324052 .2773294 -4.77 0.000 -1.867608 -.7804961 lopen | .0320977 .028096 1.14 0.253 -.0229695 .0871648 english | .2651403 .165037 1.61 0.108 -.0583262 .5886068 white | 9.563504 2.049991 4.67 0.000 5.545596 13.58141 lwhitemg | -.801855 .1955694 -4.10 0.000 -1.185164 -.4185461 _cons | -53.09676 13.34342 -3.98 0.000 -79.24939 -26.94413 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000348 lgdp | -.000331 .002538 lgdpau | .000271 -.00057 .114948 lgdpdfrati~w | -.000394 .000303 -.005524 .019117 lpopau | -.002471 .004159 -.39609 .025125 1.54883 lpop | .00003 -.002145 -.000156 -5.1e-06 .000566 .003636 lxrate1 | 2.5e-06 -3.3e-06 .000492 .000471 -.004485 -.000104 .000221 lremote | .000243 .001663 -.007539 -.001278 .02684 -.001425 -.000088 57</p><p> ldist | .001548 -.001597 -.001835 -.001324 .002019 .004566 .000048 lopen | -7.1e-06 .000019 .000014 .00013 -.001241 .000163 -.000029 english | -.00047 -.0017 -.00036 .000874 .020888 .004794 .000136 white | .002275 -.015249 .015582 -.003768 -.137221 .015903 .001758 lwhitemg | -.000277 .000953 -.001949 .0004 .013749 -.00128 -.000129 _cons | .022876 -.074278 3.65599 -.27493 -15.6585 -.046062 .06206</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .015747 ldist | .005976 .076912 lopen | .000093 -.000537 .000789 english | -.001067 .016194 -.00028 .027237 white | -.00076 .017119 -.000775 .034663 4.20246 lwhitemg | .000705 -.002761 .000094 -.003328 -.394226 .038247 _cons | -.455754 -.817706 .022061 -.524104 1.77784 -.156082 178.047</p><p>. xtgls lrmsitc5 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 474.85 Log likelihood = -674.1886 Prob > chi2 = 0.0000</p><p>------lrmsitc5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1783945 .0535215 3.33 0.001 .0734943 .2832947 lgdp | .7215629 .1049987 6.87 0.000 .5157691 .9273567 lgdpau | -.6495234 .6711086 -0.97 0.333 -1.964872 .6658253 lgdpdfrati~w | -.3550858 .2811822 -1.26 0.207 -.9061928 .1960211 lpopau | 3.57805 2.314394 1.55 0.122 -.958079 8.114179 lpop | -.2862868 .1489346 -1.92 0.055 -.5781933 .0056197 lxrate1 | -.1473387 .0330567 -4.46 0.000 -.2121287 -.0825488 lremote | .0764217 .2357478 0.32 0.746 -.3856355 .5384788 ldist | -3.234183 .6949335 -4.65 0.000 -4.596227 -1.872138 lopen | .0495064 .066293 0.75 0.455 -.0804254 .1794382 english | -.3521774 .4406803 -0.80 0.424 -1.215895 .5115401 white | 20.32036 4.151958 4.89 0.000 12.18268 28.45805 lwhitemg | -1.829631 .3815685 -4.80 0.000 -2.577492 -1.081771 _cons | -21.04283 22.79743 -0.92 0.356 -65.72498 23.63931 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002865 lgdp | -.002346 .011025 lgdpau | .001267 -.001409 .450387 lgdpdfrati~w | -.0019 .003399 -.010703 .079063 lpopau | -.008621 -.001431 -1.49974 .062286 5.35642 lpop | -.000076 -.008077 -.001718 -.001778 -.005525 .022182 lxrate1 | .00015 .000533 .002285 .001283 -.011751 -.000905 .001093 lremote | .001002 .004647 -.020666 -.012562 .04046 -.002953 .000455 ldist | .011681 .012067 -.005679 -.004568 -.061324 .004919 .000465 lopen | .000444 -.001069 -.000842 .000013 -.011092 .001975 -.000182 english | -.006286 .00233 .001989 .005829 -.021402 .018982 .000759 white | .007275 -.035456 .125009 .021565 -.354763 .076035 .005503 lwhitemg | -.001306 .001498 -.013083 -.003152 .041385 -.005896 -.000625 _cons | .032408 -.209686 13.3518 -.724856 -49.1615 -.051059 .122449 58</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .055577 ldist | .032146 .482933 lopen | .000635 .003446 .004395 english | -.004057 .136972 -.001401 .194199 white | .026014 .262304 .005654 .304937 17.2388 lwhitemg | -.000053 -.040333 -.000394 -.032986 -1.5569 .145594 _cons | -.974764 -4.14761 .162528 -1.34028 -.701835 .121154 519.723</p><p>. xtgls lrmsitc6 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1127.63 Log likelihood = -565.2828 Prob > chi2 = 0.0000</p><p>------lrmsitc6 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1900617 .0451619 4.21 0.000 .1015461 .2785773 lgdp | .5555698 .1009714 5.50 0.000 .3576694 .7534701 lgdpau | .7913349 .6457329 1.23 0.220 -.4742783 2.056948 lgdpdfrati~w | .0014067 .2773915 0.01 0.996 -.5422707 .5450841 lpopau | -3.47761 2.236274 -1.56 0.120 -7.860627 .9054069 lpop | .5116208 .1304963 3.92 0.000 .2558528 .7673888 lxrate1 | -.0807213 .0296621 -2.72 0.007 -.138858 -.0225847 lremote | -.3249329 .2359001 -1.38 0.168 -.7872885 .1374227 ldist | -2.397304 .4427249 -5.41 0.000 -3.265029 -1.52958 lopen | -.0551216 .0674799 -0.82 0.414 -.1873797 .0771365 english | 1.212358 .3262706 3.72 0.000 .572879 1.851836 white | 22.84366 3.226074 7.08 0.000 16.52067 29.16665 lwhitemg | -2.177105 .2855925 -7.62 0.000 -2.736856 -1.617354 _cons | 44.9609 21.5084 2.09 0.037 2.805212 87.11659 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00204 lgdp | -.001775 .010195 lgdpau | -.002178 .005538 .416971 lgdpdfrati~w | -.001812 .003413 -.017577 .076946 lpopau | .006318 -.036726 -1.39604 .056129 5.00092 lpop | .000325 -.00857 -.004129 -.002859 .018263 .017029 lxrate1 | .000051 .000673 .000597 .001421 -.006893 -.000883 .00088 lremote | .000377 .00432 -.013742 -.017207 .02958 -.003076 .000371 ldist | .007282 -.002683 -.009466 -.002527 -.004483 .017448 .000157 lopen | .00021 -.000018 .000024 .002471 -.00908 .001138 -.000024 english | -.003253 .002014 .005868 .006276 -.025793 -.000749 .000687 white | .006407 -.035332 .027609 -.013659 -.06158 .060903 .003307 lwhitemg | -.000919 .001187 -.004008 -.000369 .01541 -.004337 -.000342 _cons | -.093738 .362332 12.3959 -.39705 -46.0969 -.406675 .088346</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .055649 ldist | .006149 .196005 lopen | .00116 .001717 .004554 english | -.009986 .058786 -.001535 .106453 59</p><p> white | .021912 .063385 .004698 .015779 10.4076 lwhitemg | .000761 -.010277 -.000512 .00008 -.905429 .081563 _cons | -.708236 -1.85559 .105278 -.251196 -.723394 -.010805 462.611</p><p>. xtgls lrmsitc7 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100</p><p>Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1653.87 Log likelihood = -753.6071 Prob > chi2 = 0.0000</p><p>------lrmsitc7 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0506985 .0310801 1.63 0.103 -.0102174 .1116143 lgdp | 1.02293 .0940213 10.88 0.000 .838652 1.207209 lgdpau | .9987403 .6731527 1.48 0.138 -.3206148 2.318095 lgdpdfrati~w | -.0382828 .2606955 -0.15 0.883 -.5492365 .472671 lpopau | 3.051735 2.346451 1.30 0.193 -1.547225 7.650694 lpop | -.4766871 .1375531 -3.47 0.001 -.7462863 -.2070879 lxrate1 | -.1418511 .0223176 -6.36 0.000 -.1855928 -.0981094 lremote | -.4941997 .187456 -2.64 0.008 -.8616066 -.1267927 ldist | -2.194643 .7065325 -3.11 0.002 -3.579421 -.8098645 lopen | -.031975 .0692615 -0.46 0.644 -.1677251 .103775 english | .2174131 .3389429 0.64 0.521 -.4469028 .8817289 white | 28.17106 2.741298 10.28 0.000 22.79822 33.5439 lwhitemg | -2.623387 .2664181 -9.85 0.000 -3.145557 -2.101217 _cons | -64.45412 23.54532 -2.74 0.006 -110.6021 -18.30613 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000966 lgdp | -.001636 .00884 lgdpau | -.000024 .000704 .453135 lgdpdfrati~w | -.00167 .00363 -.012718 .067962 lpopau | -.005997 -.008924 -1.51155 .044577 5.50583 lpop | .001475 -.01067 -.00325 -.003197 .025577 .018921 lxrate1 | .000052 .000289 .00049 .000763 -.008815 -.000639 .000498 lremote | .001047 .004042 -.017673 -.003611 .020074 -.003576 .000286 ldist | .005227 .015014 -.020786 -.000801 -.055743 .004393 .001199 lopen | .000278 -.00074 -.00066 .004609 -.005765 .001145 -.000301 english | -.000824 .002535 .001181 .002479 -.008558 .001238 .000938 white | .014236 -.052976 .021817 -.025197 -.300805 .066452 .007587 lwhitemg | -.001272 .003536 -.003538 .001794 .035655 -.00487 -.000733 _cons | .051606 -.077103 13.5856 -.458857 -51.5321 -.407998 .119769</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .03514 ldist | .058738 .499188 lopen | .000923 .002052 .004797 english | .002379 .076688 -.001546 .114882 white | .030152 .061107 -.005606 .204671 7.51471 lwhitemg | -.001909 -.013495 .000715 -.02281 -.719351 .070979 _cons | -.775237 -4.26058 .08276 -.731808 3.62495 -.347519 554.382</p><p>. xtgls lrmsitc8 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr 60</p><p>> emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 526.05 Log likelihood = -576.9765 Prob > chi2 = 0.0000</p><p>------lrmsitc8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3900469 .0580403 6.72 0.000 .27629 .5038039 lgdp | .2104455 .0968577 2.17 0.030 .020608 .400283 lgdpau | 1.112981 .7336377 1.52 0.129 -.3249223 2.550885 lgdpdfrati~w | -.4207132 .3119715 -1.35 0.177 -1.032166 .1907397 lpopau | -4.536055 2.53173 -1.79 0.073 -9.498154 .4260448 lpop | .3057661 .1050498 2.91 0.004 .0998722 .5116599 lxrate1 | -.0791068 .0358469 -2.21 0.027 -.1493653 -.0088482 lremote | -.2086243 .2325389 -0.90 0.370 -.6643922 .2471437 ldist | -1.57188 .5912799 -2.66 0.008 -2.730768 -.4129932 lopen | .1068875 .0673858 1.59 0.113 -.0251862 .2389613 english | .0316311 .2875001 0.11 0.912 -.5318588 .595121 white | 20.16305 2.062426 9.78 0.000 16.12077 24.20533 lwhitemg | -1.887005 .1984065 -9.51 0.000 -2.275874 -1.498135 _cons | 56.02295 24.01485 2.33 0.020 8.954711 103.0912 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .003369 lgdp | -.00278 .009381 lgdpau | -.002031 .00662 .538224 lgdpdfrati~w | -.002562 .006091 -.006703 .097326 lpopau | .003462 -.035701 -1.79832 .03715 6.40966 lpop | .001012 -.006093 -.004773 -.003993 .013612 .011035 lxrate1 | .000253 .000414 -.000105 .000994 -.004438 -.000695 .001285 lremote | .000559 .003071 -.010721 -.017027 .004112 .00102 .001243 ldist | .012126 .000937 -.005282 -.01008 -.072213 .008181 .000039 lopen | -.000443 .001267 .001888 -.000011 -.020659 .001144 -.000067 english | -.003778 .00564 .0058 .003099 -.022943 -.000794 -.001204 white | .019213 -.038382 -.005402 -.069052 .09512 .011521 -.00018 lwhitemg | -.002379 .002086 -.001698 .00399 .00401 -.00013 -.000266 _cons | -.096128 .277301 15.8017 -.354999 -58.0005 -.225548 .061744</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .054074 ldist | .03709 .349612 lopen | .002079 .002963 .004541 english | .004317 .088205 .000351 .082656 white | .104153 .18642 -.009017 .154854 4.2536 lwhitemg | -.007065 -.028956 .000697 -.017588 -.399717 .039365 _cons | -.692388 -2.53349 .206598 -.757153 -3.55732 .287144 576.713</p><p>. xtgls lrmsitc9 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1) 61</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1466.73 Log likelihood = -238.9903 Prob > chi2 = 0.0000</p><p>------lrmsitc9 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0053588 .0235757 0.23 0.820 -.0408487 .0515663 lgdp | .4341813 .0832435 5.22 0.000 .271027 .5973355 lgdpau | -.3420987 .487735 -0.70 0.483 -1.298042 .6138442 lgdpdfrati~w | .1575776 .2199147 0.72 0.474 -.2734473 .5886024 lpopau | 1.318546 1.681895 0.78 0.433 -1.977909 4.615 lpop | -.2446215 .121147 -2.02 0.043 -.4820652 -.0071778 lxrate1 | -.0374014 .0226653 -1.65 0.099 -.0818245 .0070217 lremote | -.0205358 .1699572 -0.12 0.904 -.3536457 .3125742 ldist | -4.072042 .5256251 -7.75 0.000 -5.102249 -3.041836 lopen | .0087833 .0486493 0.18 0.857 -.0865675 .1041342 english | -.0190324 .3014296 -0.06 0.950 -.6098236 .5717588 white | 16.34935 2.569678 6.36 0.000 11.31287 21.38583 lwhitemg | -1.195081 .2753303 -4.34 0.000 -1.734719 -.6554438 _cons | 21.50396 16.88691 1.27 0.203 -11.59378 54.6017 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000556 lgdp | -.000679 .006929 lgdpau | .000165 -.002295 .237885 lgdpdfrati~w | -.000877 .00251 -.009485 .048362 lpopau | -.001305 .001568 -.793465 .048689 2.82877 lpop | .000193 -.006601 .000396 -.001082 -.005197 .014677 lxrate1 | -.000028 .000174 .001223 .001005 -.006105 -.000499 .000514 lremote | .000118 .002289 -.017125 -.003602 .050145 -.002706 -.000073 ldist | .002277 .003557 -.010779 -.000713 -.014734 .019253 -.000368 lopen | .000053 -.000136 -.001837 .001268 .001714 .000778 -.000022 english | -.001166 .001097 .001362 .002019 -.003559 .003594 .00058 white | .003949 -.038492 .042587 -.012579 -.279285 .067317 .002267 lwhitemg | -.000438 .001655 -.005136 .000487 .033802 -.005354 -.000048 _cons | .00518 -.068954 7.22204 -.609624 -26.3936 -.165527 .074057</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .028885 ldist | .01335 .276282 lopen | .000063 .000802 .002367 english | .000254 .060985 .00007 .09086 white | -.000688 .161519 -.001086 .111822 6.60325 lwhitemg | .002612 -.020825 .000157 -.011562 -.697299 .075807 _cons | -.769165 -2.63795 .002201 -.65504 1.74754 -.200897 285.168 62</p><p>. . . clear</p><p>. insheet using k:\book1.txt (104 vars, 1010 obs)</p><p>. *Dropping China . drop if ccode==331520 (10 observations deleted)</p><p>. . . *Descriptive stats after dropping . tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remote dist op > en english gdpdfrationew white whitemg, stat(n mean sd median min max) col(sta > t) </p><p> variable | N mean sd p50 min max ------+------rimp | 1000 458823.6 1535558 7348 0 1.39e+07 immig | 1000 32411.46 116845.9 2790 0 1137050 gdp | 1000 2.71e+11 9.68e+11 1.63e+10 1.92e+08 8.99e+12 gdpau | 1000 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 1000 105.6778 16.90859 102.8924 60.87417 207.3465 gdpdfau | 1000 100.7173 4.633489 100.8223 94.42464 109.9797 pop | 1000 3.75e+07 1.01e+08 1.02e+07 41000 1.02e+09 popau | 1000 1.82e+07 612793.8 1.82e+07 1.73e+07 1.92e+07 xrate1 | 1000 1043.063 10901.71 16.2185 .0068 270182.6 remote | 1000 6735.145 4145.995 6764 1293 39620 dist | 1000 13413.53 3504.725 14305 2409 17972 open | 1000 .7101113 .3894175 .63885 .0671 3.2192 english | 1000 .38 .4856293 0 0 1 gdpdfratio~w | 1000 1.052233 .1808002 1.017907 .5844526 2.009028 white | 1000 .13 .3364717 0 0 1</p><p> whitemg | 1000 20501.65 116468.1 0 0 1137050 ------63</p><p>. . sort white</p><p>. *IIIB. Descriptives Original Unites(sub sample for "white" and "non-white" cou</p><p>> ntries)--RHS Variables: . by white: tabstat rimp immig gdp gdpau gdpdefnew gdpdfau pop popau xrate1 remo > te dist phone open english gdpdfrationew white whitemg, stat(n mean sd median > min max) col(stat) </p><p>------> white = 0</p><p> variable | N mean sd p50 min max ------+------rimp | 870 367735.3 1566659 2944.5 0 1.39e+07 immig | 870 13689.43 25276.66 1403.5 0 158613 gdp | 870 2.29e+11 1.00e+12 1.00e+10 1.92e+08 8.99e+12 gdpau | 870 3.83e+11 4.44e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 870 104.8021 17.26477 101.7385 60.87417 207.3465 gdpdfau | 870 100.7173 4.633836 100.8223 94.42464 109.9797 pop | 870 3.96e+07 1.07e+08 1.03e+07 41000 1.02e+09 popau | 870 1.82e+07 612839.6 1.82e+07 1.73e+07 1.92e+07 xrate1 | 870 1182.929 11681.74 22.9426 .0068 270182.6 remote | 870 7160.889 4116.735 6935.5 1293 39620 dist | 870 13250.49 3391.555 14051 2410 17972 phone | 870 158.6855 242.1138 49.13 .54 1449.75 open | 870 .7178078 .4108407 .63875 .0671 3.2192 english | 870 .3908046 .4882114 0 0 1 gdpdfratio~w | 870 1.043755 .1856371 1 .5844526 2.009028 white | 870 0 0 0 0 0 whitemg | 870 0 0 0 0 0 ------</p><p>------> white = 1</p><p> variable | N mean sd p50 min max ------+------rimp | 130 1068414 1138347 522863.5 40432 3843839 immig | 130 157705 288518.8 21174 2612 1137050 gdp | 130 5.56e+11 6.58e+11 2.54e+11 5.15e+10 2.69e+12 gdpau | 130 3.83e+11 4.46e+10 3.80e+11 3.19e+11 4.52e+11 gdpdefnew | 130 111.5379 12.89742 112.2247 85.45601 143.9587 gdpdfau | 130 100.7173 4.649088 100.8223 94.42464 109.9797 pop | 130 2.34e+07 2.55e+07 8837000 3477200 8.22e+07 popau | 130 1.82e+07 614856.7 1.82e+07 1.73e+07 1.92e+07 xrate1 | 130 107.0323 293.6987 4.4722 .3774 1261.556 remote | 130 3885.938 3089.96 2888 1530 12501 dist | 130 14504.62 4031.974 15931 2409 17493 phone | 130 734.716 269.9717 652.085 343.99 1487.08 open | 130 .6586038 .1850905 .64065 .3517 1.2967 english | 130 .3076923 .4633239 0 0 1 gdpdfratio~w | 130 1.108974 .1314272 1.116445 .8254872 1.441221 white | 130 1 0 1 1 1 whitemg | 130 157705 288518.8 21174 2612 1137050 ------64</p><p>. . . **Regression of Imports . tsset ccode year panel variable: ccode, 117100 to 725980 time variable: year, 1991 to 2000</p><p>. . **I. Aggregate Imports . xtgls lrimp limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8503.02 Log likelihood = -551.5371 Prob > chi2 = 0.0000</p><p>------lrimp | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3009236 .0304778 9.87 0.000 .2411881 .3606591 lgdp | 1.258485 .0362056 34.76 0.000 1.187524 1.329447 lgdpau | .3129244 .5961125 0.52 0.600 -.8554347 1.481283 lgdpdfrati~w | -.899496 .2433124 -3.70 0.000 -1.37638 -.4226125 lpopau | -2.954412 2.053723 -1.44 0.150 -6.979635 1.070812 lpop | -.0663154 .0465894 -1.42 0.155 -.157629 .0249982 lxrate1 | -.1208819 .0163549 -7.39 0.000 -.152937 -.0888268 lremote | -.4530894 .0765452 -5.92 0.000 -.6031153 -.3030635 ldist | -2.089104 .154211 -13.55 0.000 -2.391352 -1.786856 lopen | .2440567 .0548695 4.45 0.000 .1365144 .3515991 english | 1.11635 .1412637 7.90 0.000 .8394786 1.393222 white | 4.158475 .5843665 7.12 0.000 3.013138 5.303812 lwhitemg | -.3825582 .0518142 -7.38 0.000 -.4841122 -.2810041 _cons | 43.00773 18.90574 2.27 0.023 5.953166 80.06229 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000929 lgdp | -.00038 .001311 lgdpau | -.001069 .001507 .35535 lgdpdfrati~w | -.000889 .000232 -.008574 .059201 lpopau | .00222 -.008244 -1.19929 .045989 4.21778 lpop | -.000015 -.001053 -.000083 .000123 .000096 .002171 lxrate1 | .000077 .000195 .000376 .000227 -.002589 -.000166 .000267 lremote | .000481 -.000029 -.000844 -.001858 -.001762 -.000124 .000039 ldist | .00156 .00123 .000591 -.001269 -.015381 -.000493 .001492 lopen | -.000102 .000411 .002002 .000745 -.014601 .00064 .000162 english | -.001547 .003666 .003841 .002627 -.012854 -.002583 .000768 white | .004692 -.003368 .0018 -.010728 -.02323 .007386 .000092 lwhitemg | -.000574 .000317 -.000134 .00101 .002478 -.000628 -.000033 _cons | -.024608 .071827 10.5549 -.57401 -38.2225 -.001821 .014945</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .005859 ldist | .004776 .023781 lopen | .001383 .001327 .003011 english | -.003677 .003341 -.001342 .019955 white | .000297 -.003865 .001013 -.005584 .341484 65</p><p> lwhitemg | .000095 -.00022 -.000176 .000722 -.029793 .002685 _cons | -.040744 -.061445 .147123 .062934 .298283 -.029724 357.427</p><p>. . **II. Conservative Estimates . *2.1. Aggregate reference priced Imports (conservative) . xtgls lrrefp_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 62286.86 Log likelihood = -975.1741 Prob > chi2 = 0.0000</p><p>------lrrefp_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0444149 .0134334 3.31 0.001 .0180859 .0707438 lgdp | 1.710634 .0482333 35.47 0.000 1.616098 1.805169 lgdpau | .89427 .3489646 2.56 0.010 .2103119 1.578228 lgdpdfrati~w | -.4601134 .1441119 -3.19 0.001 -.7425675 -.1776592 lpopau | -6.520976 1.178136 -5.53 0.000 -8.83008 -4.211871 lpop | -.5277791 .0782672 -6.74 0.000 -.68118 -.3743782 lxrate1 | -.0995151 .0203183 -4.90 0.000 -.1393384 -.0596919 lremote | -.8903187 .0986475 -9.03 0.000 -1.083664 -.6969731 ldist | -2.901653 .1381609 -21.00 0.000 -3.172444 -2.630863 lopen | -.0724382 .026775 -2.71 0.007 -.1249162 -.0199602 english | 1.047941 .0796937 13.15 0.000 .8917438 1.204137 white | 3.353383 .760627 4.41 0.000 1.862582 4.844185 lwhitemg | -.3402286 .0726238 -4.68 0.000 -.4825685 -.1978886 _cons | 94.24926 11.07188 8.51 0.000 72.54878 115.9498 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00018 lgdp | -.00023 .002326 lgdpau | -.001116 .001811 .121776 lgdpdfrati~w | -.000537 .0007 -.011046 .020768 lpopau | .004398 -.007806 -.402591 .022258 1.38801 lpop | .000127 -.003533 -.001688 -6.8e-06 .005768 .006126 lxrate1 | -.000023 .000297 -.000069 .000407 -.003034 -.000352 .000413 lremote | .000359 -.000278 -.006828 -.003194 .023677 .000045 .000031 ldist | .000634 -.002605 -.00628 -.001211 .015411 .004618 .000419 lopen | .000039 -.000098 -.000997 .000903 .003722 .00026 -.000081 english | -.000398 .000569 .002763 .002346 -.019234 -.000152 .001121 white | .000603 -.007538 -.01189 -.006445 .048433 .009648 -.000578 lwhitemg | -.000046 .000125 .000462 .000398 -.002857 .000065 .000082 _cons | -.049924 .11196 3.60511 -.073758 -12.7668 -.111897 .044966</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .009731 ldist | .003476 .019088 lopen | .000712 .000354 .000717 english | -.002188 .002122 -.00042 .006351 white | .016833 -.015312 .001454 -.00428 .578553 lwhitemg | -.000725 .002652 -.000086 .000548 -.053506 .005274 _cons | -.325342 -.322697 -.047708 .230045 -.466978 .011662 122.587 66</p><p>. . *2.2. Aggregate Differentiated Imports (conservative) . xtgls lrdiff_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 8134.57 Log likelihood = -934.681 Prob > chi2 = 0.0000</p><p>------lrdiff_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0927471 .0217776 4.26 0.000 .0500637 .1354304 lgdp | 1.754094 .069305 25.31 0.000 1.618259 1.889929 lgdpau | .2442193 .5412866 0.45 0.652 -.8166831 1.305122 lgdpdfrati~w | -.4590086 .2113536 -2.17 0.030 -.8732541 -.0447631 lpopau | -3.152578 1.876069 -1.68 0.093 -6.829606 .5244509 lpop | -.6528599 .0794337 -8.22 0.000 -.808547 -.4971727 lxrate1 | -.1529159 .0271723 -5.63 0.000 -.2061726 -.0996592 lremote | -.4088683 .1273173 -3.21 0.001 -.6584056 -.159331 ldist | -4.177883 .34224 -12.21 0.000 -4.848661 -3.507105 lopen | .0493332 .04133 1.19 0.233 -.0316721 .1303385 english | 1.114558 .1438239 7.75 0.000 .8326681 1.396447 white | 9.971391 1.922736 5.19 0.000 6.202897 13.73988 lwhitemg | -.8499996 .1918582 -4.43 0.000 -1.226035 -.4739644 _cons | 64.26126 17.71156 3.63 0.000 29.54723 98.97528 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000474 lgdp | -.000766 .004803 lgdpau | -.001982 .005096 .292991 lgdpdfrati~w | -.000779 .001747 -.018512 .04467 lpopau | .005775 -.024019 -.987804 .058519 3.51964 lpop | .000545 -.004004 -.002923 -.000835 .011504 .00631 lxrate1 | 7.1e-06 .000521 .000262 .000867 -.005694 -.000733 .000738 lremote | -.000061 -.000986 -.007364 -.001909 .024512 -.001455 .00018 ldist | .002052 .008267 -.009037 -.002985 -.01743 -.002176 .001394 lopen | .000098 -.000575 -.00037 .001694 .001843 .000938 -.000189 english | -.000926 .00276 .00706 .003199 -.040972 .002105 .00196 white | .000936 -.030145 .00096 .002241 .016723 .024037 .004172 lwhitemg | -.000082 .001557 -.001627 -.000633 .005811 -.001556 -.000436 _cons | -.055418 .151251 8.81067 -.509719 -32.2491 -.090492 .068505</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .01621 ldist | .006393 .117128 lopen | .000564 -.000622 .001708 english | -.006083 .004486 -.001425 .020685 white | .01231 -.15185 .002302 .019983 3.69692 lwhitemg | .001026 .011853 -.000106 -.002727 -.361465 .03681 _cons | -.371983 -.810016 -.02212 .39889 1.29387 -.182962 313.699</p><p>. . *2.3. Aggregate Homogenous Imports (conservative) . xtgls lrhomo_cm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l 67</p><p>> remote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 2100.24 Log likelihood = -1096.358 Prob > chi2 = 0.0000</p><p>------lrhomo_cm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0896354 .0408567 2.19 0.028 .0095578 .169713 lgdp | .9385221 .0809313 11.60 0.000 .7798996 1.097145 lgdpau | -.4898539 .8527417 -0.57 0.566 -2.161197 1.181489 lgdpdfrati~w | -.3353722 .350313 -0.96 0.338 -1.021973 .3512286 lpopau | .254947 2.922043 0.09 0.930 -5.472151 5.982045 lpop | -.0119147 .1003021 -0.12 0.905 -.2085033 .1846738 lxrate1 | -.1214161 .0333385 -3.64 0.000 -.1867583 -.0560739 lremote | .5025283 .2359443 2.13 0.033 .040086 .9649706 ldist | -1.594668 .3432501 -4.65 0.000 -2.267426 -.9219105 lopen | .3128152 .0798548 3.92 0.000 .1563026 .4693278 english | 1.800445 .245244 7.34 0.000 1.319776 2.281114 white | 1.586647 2.231569 0.71 0.477 -2.787148 5.960441 lwhitemg | .1813989 .1969353 0.92 0.357 -.2045873 .567385 _cons | 1.131385 27.74669 0.04 0.967 -53.25113 55.5139 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001669 lgdp | -.001582 .00655 lgdpau | -.001442 .001057 .727168 lgdpdfrati~w | -.00027 .001665 -.012597 .122719 lpopau | .001637 -.007666 -2.43396 .042712 8.53833 lpop | .000365 -.005793 -.00004 -.001243 -.001793 .010061 lxrate1 | .000124 .000869 .000584 .00216 -.007801 -.001162 .001111 lremote | .001398 .000729 -.02902 -.014051 .088879 .000605 -.000743 ldist | .004268 -.007197 -.01912 -.005939 .056511 .011426 -.000177 lopen | .000278 -.00112 .004547 .002203 -.025915 .003019 -.0002 english | -.003513 .001057 .008797 .005695 -.013441 -.001626 .001512 white | .00386 -.024197 .097383 .002802 -.398584 .020347 .000873 lwhitemg | -.000524 .000885 -.010486 -.001615 .043826 -.000195 -.000216 _cons | -.019271 .110654 21.7324 -.346057 -78.9905 -.108536 .11432</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .05567 ldist | .020774 .117821 lopen | .002035 -.000896 .006377 english | -.014644 .014565 -.002233 .060145 white | .042815 -.030415 .006724 .036915 4.9799 lwhitemg | -.000921 .006585 -.000405 -.002542 -.428345 .038784 _cons | -1.41109 -1.78309 .280893 -.026733 4.16514 -.519954 769.879</p><p>. . **III. Liberal Estimates . *3.1. Aggregate reference priced Imports (liberal) . xtgls lrrefp_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog 68</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 58599.31 Log likelihood = -902.8966 Prob > chi2 = 0.0000</p><p>------lrrefp_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0234512 .0136014 1.72 0.085 -.0032071 .0501095 lgdp | 1.61367 .0527911 30.57 0.000 1.510202 1.717139 lgdpau | .7103971 .3704967 1.92 0.055 -.0157631 1.436557 lgdpdfrati~w | -.2475358 .1378728 -1.80 0.073 -.5177614 .0226899 lpopau | -6.698916 1.252251 -5.35 0.000 -9.153284 -4.244549 lpop | -.2781317 .0914068 -3.04 0.002 -.4572857 -.0989776 lxrate1 | -.0401154 .0185587 -2.16 0.031 -.0764898 -.0037409 lremote | -.6414414 .1502885 -4.27 0.000 -.9360014 -.3468814 ldist | -2.603725 .1682161 -15.48 0.000 -2.933422 -2.274028 lopen | -.0477102 .0281023 -1.70 0.090 -.1027897 .0073693 english | 1.195772 .1026757 11.65 0.000 .9945317 1.397013 white | 3.170141 .9199891 3.45 0.001 1.366995 4.973287</p><p> lwhitemg | -.2408803 .0860646 -2.80 0.005 -.4095637 -.0721968 _cons | 94.96341 11.80479 8.04 0.000 71.82645 118.1004 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000185 lgdp | -.000206 .002787 lgdpau | -.000808 .001874 .137268 lgdpdfrati~w | -.000337 .000773 -.007474 .019009 lpopau | .002819 -.00644 -.454935 .01227 1.56813 lpop | .000137 -.00456 -.00155 -.00011 .003029 .008355 lxrate1 | 4.7e-06 .000223 -.000116 .000427 -.002283 -.000263 .000344 lremote | -7.2e-06 -.000727 -.01438 -.004076 .051203 -.000215 -.000099 ldist | .000849 -.00515 -.003873 .001023 .002171 .009333 .000611 lopen | .000017 -.000076 -.000947 .00098 .003369 .000254 -.000068 english | -.0003 -3.5e-07 .003507 .002401 -.018321 -.00006 .000916 white | .000656 -.015993 -.016568 -.001367 .028646 .028095 .002174 lwhitemg | -.000099 .000788 .000015 -.000316 .002518 -.00155 -.000232 _cons | -.031641 .121904 4.10214 -.014933 -14.4716 -.125239 .033373</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .022587 ldist | -.002759 .028297 lopen | .00109 .000213 .00079 english | -.004227 .004908 -.000482 .010542 white | .016012 .031515 .000924 .007226 .84638 lwhitemg | .000962 -.002045 .000013 -.000658 -.076064 .007407 _cons | -.620081 -.220786 -.045495 .196666 -.574135 -.023005 139.353</p><p>. . *3.2. Aggregate Differentiated Imports (liberal) . xtgls lrdiff_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares 69</p><p>Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 9183.91 Log likelihood = -965.4305 Prob > chi2 = 0.0000</p><p>------lrdiff_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .2026021 .0302513 6.70 0.000 .1433105 .2618936 lgdp | 1.65904 .071875 23.08 0.000 1.518168 1.799913 lgdpau | -.0820392 .6287854 -0.13 0.896 -1.314436 1.150358 lgdpdfrati~w | -.2167693 .2568132 -0.84 0.399 -.7201139 .2865754 lpopau | -1.592251 2.195489 -0.73 0.468 -5.89533 2.710828 lpop | -.5997309 .0804951 -7.45 0.000 -.7574984 -.4419635 lxrate1 | -.130131 .0282252 -4.61 0.000 -.1854514 -.0748106 lremote | -.3353942 .1207713 -2.78 0.005 -.5721015 -.0986869 ldist | -3.169824 .3340453 -9.49 0.000 -3.824541 -2.515107 lopen | .106769 .0490349 2.18 0.029 .0106623 .2028756 english | .8452789 .1321576 6.40 0.000 .5862549 1.104303 white | 10.10365 1.891989 5.34 0.000 6.395423 13.81188</p><p> lwhitemg | -.8960496 .1886029 -4.75 0.000 -1.265704 -.5263947 _cons | 37.17543 20.7402 1.79 0.073 -3.474609 77.82546 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000915 lgdp | -.001415 .005166 lgdpau | -.002046 .004574 .395371 lgdpdfrati~w | -.001133 .002522 -.024454 .065953 lpopau | .005354 -.020432 -1.34464 .092829 4.82017 lpop | .000911 -.004368 -.002517 -.001521 .010154 .006479 lxrate1 | .000067 .000395 .000182 .001052 -.005604 -.000693 .000797 lremote | -5.5e-07 -.000757 -.004468 -.001031 .01036 -.001316 .000256 ldist | .004044 .002429 -.01051 -.003047 -.013948 .000454 .001535 lopen | .000135 -.000738 .001175 .001643 -.005505 .001409 -.000251 english | -.001467 .003771 .004265 .004205 -.024925 -.000345 .001964 white | .000784 -.027319 .011546 .001728 .001855 .01879 .004254 lwhitemg | -.000019 .001584 -.002205 -.000606 .004559 -.001221 -.000404 _cons | -.059494 .158515 12.0477 -.959273 -44.4805 -.098936 .068557</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .014586 ldist | .008262 .111586 lopen | .000286 -.001057 .002404 english | -.004737 -.000053 -.001487 .017466 white | .012489 -.162249 .003635 .009995 3.57962 lwhitemg | .000765 .014724 -.000257 -.001695 -.35068 .035571 _cons | -.225586 -.714492 .062784 .255804 1.38805 -.178831 430.156</p><p>. . *3.3. Aggregate Homogenous Imports (liberal) . xtgls lrhomo_lm limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 l > remote ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1) 70</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 7887.34 Log likelihood = -1101.117 Prob > chi2 = 0.0000</p><p>------lrhomo_lm | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0187487 .015558 1.21 0.228 -.0117443 .0492417 lgdp | 1.366442 .0504274 27.10 0.000 1.267606 1.465278 lgdpau | .4992118 .4946897 1.01 0.313 -.4703622 1.468786 lgdpdfrati~w | -.0378588 .1602273 -0.24 0.813 -.3518984 .2761809 lpopau | -5.306318 1.681914 -3.15 0.002 -8.602809 -2.009826 lpop | .0464244 .0742739 0.63 0.532 -.0991497 .1919986 lxrate1 | -.0611328 .0195863 -3.12 0.002 -.0995212 -.0227445 lremote | -.8063548 .1609511 -5.01 0.000 -1.121813 -.4908964 ldist | -3.270022 .2179495 -15.00 0.000 -3.697195 -2.842849 lopen | .0759124 .0417408 1.82 0.069 -.0058981 .1577229 english | 2.40162 .1418155 16.93 0.000 2.123666 2.679573 white | 3.820727 1.165457 3.28 0.001 1.536474 6.10498 lwhitemg | -.1759238 .0978431 -1.80 0.072 -.3676928 .0158451 _cons | 84.4765 16.12806 5.24 0.000 52.86608 116.0869 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000242 lgdp | -.000395 .002543 lgdpau | -.000073 -.000025 .244718 lgdpdfrati~w | -.000439 .000614 .001947 .025673 lpopau | -.001287 .001812 -.812189 -.004951 2.82884 lpop | .000187 -.002896 -.000148 -.000017 -.00307 .005517 lxrate1 | -.000017 .00022 .000289 .000616 -.002153 -.000412 .000384 lremote | .000187 .000743 -.017791 -.001324 .05591 -.001146 -.000143 ldist | .000588 .000312 -.011539 -.001351 .025895 .00031 .000297 lopen | .000065 -.000307 -4.3e-06 .001835 .000287 .0007 -.000131 english | -.000747 .000308 .002296 .001541 -.009005 .001948 .000953 white | .000349 -.01412 -.00372 -.001392 .003205 .02573 -.00028 lwhitemg | -8.4e-06 .000729 -.000963 .000196 .003474 -.001673 .000088 _cons | .021716 -.049057 7.3201 .016022 -26.346 .038992 .025722</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .025905 ldist | .016804 .047502 lopen | .000878 .000054 .001742 english | -.003903 .003442 -.001299 .020112 white | .003623 -.007464 .000809 .035733 1.35829 lwhitemg | .001382 .002844 -7.0e-06 -.003127 -.110235 .009573 _cons | -.846169 -.74561 -.017425 .044979 -.021962 -.061388 260.114</p><p>. . *IV. Aggregate NON-Manufacturing Imports (Sum of Sitc0,1,2,3,4) . xtgls lrmnmf limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremo > te ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 647.47 71</p><p>Log likelihood = -596.1406 Prob > chi2 = 0.0000</p><p>------lrmnmf | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3701501 .0451682 8.19 0.000 .281622 .4586782 lgdp | .2198115 .0709504 3.10 0.002 .0807512 .3588717 lgdpau | .0754711 .5957142 0.13 0.899 -1.092107 1.24305 lgdpdfrati~w | -.5087659 .209498 -2.43 0.015 -.9193745 -.0981573 lpopau | -2.371609 2.032149 -1.17 0.243 -6.354548 1.611329 lpop | .3934901 .0901672 4.36 0.000 .2167658 .5702145 lxrate1 | -.1350211 .0241 -5.60 0.000 -.1822563 -.087786 lremote | .1364151 .1996343 0.68 0.494 -.2548609 .527691 ldist | -1.372845 .4065008 -3.38 0.001 -2.169572 -.576118 lopen | .1083192 .038455 2.82 0.005 .0329487 .1836897 english | .6776742 .2062805 3.29 0.001 .2733718 1.081977 white | 10.36831 .7137487 14.53 0.000 8.969383 11.76723 lwhitemg | -1.018885 .0708284 -14.39 0.000 -1.157706 -.8800634 _cons | 43.12892 19.37836 2.23 0.026 5.148044 81.1098 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00204 lgdp | -.001126 .005034 lgdpau | .000638 -.002881 .354875 lgdpdfrati~w | -.001058 .00072 -.009684 .043889 lpopau | -.007994 .010604 -1.17388 .034036 4.12963 lpop | -.000383 -.003839 .001042 -.000459 -.007893 .00813 lxrate1 | .000132 .00025 .000648 .001073 -.005067 -.000534 .000581 lremote | .003484 .003413 -.017054 -.0067 .037494 -.004366 .000135 ldist | .005956 -.006352 -.012356 -.002658 -.027385 .013337 .001223 lopen | .000236 -.000282 -.001627 -.000917 .000262 .000472 -.000144 english | -.002172 .001076 .000052 .003732 -.006252 .005097 .001089 white | .009965 -.008793 .015393 -.000547 -.102812 .013245 .004098 lwhitemg | -.000897 .000081 -.002136 -.000231 .011316 -.000512 -.00043 _cons | .048091 -.120639 10.4801 -.278799 -37.8715 -.021297 .052605</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .039854 ldist | .033635 .165243 lopen | .000793 .001082 .001479 english | -.002333 .010087 -.000997 .042552 white | .013298 -.005823 .000442 .047752 .509437 lwhitemg | -.000076 .004193 4.0e-06 -.00244 -.047531 .005017 _cons | -.868073 -1.19577 .021441 -.098337 1.11705 -.155113 375.521</p><p>. . *V. Aggregate Manufacturing Imports (Sum of Sitc5,6,7,8,9) . xtgls lrmmfn limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lremot > e ldist lopen english white lwhitemg, igls panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 2869.31 Log likelihood = -614.6592 Prob > chi2 = 0.0000</p><p>------72</p><p> lrmmfn | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1163283 .0322254 3.61 0.000 .0531678 .1794889 lgdp | .2581178 .0574143 4.50 0.000 .1455878 .3706478 lgdpau | -.1200933 .0666258 -1.80 0.071 -.2506774 .0104908 lgdpdfrati~w | -.1223602 .0443988 -2.76 0.006 -.2093802 -.0353402 lpopau | 2.177206 .2712064 8.03 0.000 1.645651 2.708761 lpop | .7083675 .1059801 6.68 0.000 .5006504 .9160847 lxrate1 | -.007332 .0090099 -0.81 0.416 -.0249911 .010327 lremote | .1376469 .0347624 3.96 0.000 .0695138 .20578 ldist | -3.149097 .3510575 -8.97 0.000 -3.837157 -2.461037 lopen | -.0208108 .0327777 -0.63 0.525 -.0850539 .0434322 english | 1.036803 .2832616 3.66 0.000 .4816201 1.591985 white | 23.45514 2.910262 8.06 0.000 17.75113 29.15915 lwhitemg | -2.1822 .2700738 -8.08 0.000 -2.711535 -1.652865 _cons | -15.97756 5.223766 -3.06 0.002 -26.21595 -5.739163 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001038 lgdp | -.000611 .003296 lgdpau | -.000708 -.000609 .004439 lgdpdfrati~w | .000381 -.001253 .000934 .001971 lpopau | .004929 .000322 -.015089 .000475 .073553 lpop | -.000297 -.002368 .001405 .000496 -.005501 .011232 lxrate1 | .000015 .000115 -.000056 .000046 .000847 -.000256 .000081 lremote | -.000376 .001387 -.000775 -.001138 .000055 -.00077 .000038 ldist | .003506 .000076 -.00333 -.000074 .020847 -.003008 -.000083 lopen | -.00039 -.000349 .000273 -.000507 -.004916 .001173 -.000209 english | -.000921 -.001265 .001334 .000673 -.007704 .001543 .00002 white | .002865 -.006173 .001316 .002195 -.00276 .031429 -.000575 lwhitemg | -.000532 .000058 .000199 -.000068 -.001981 -.003357 .000044 _cons | -.081428 -.036635 .167761 -.005599 -.975407 -.031798 -.01127</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .001208 ldist | .000026 .123241 lopen | .00022 -.000889 .001074 english | -.000659 .058966 .000182 .080237 white | -.002714 .012293 .001367 .037387 8.46963 lwhitemg | .000097 -.003844 .000046 -.002195 -.774155 .07294 _cons | -.007541 -1.41481 .074879 -.476959 -.48312 .119832 27.2877</p><p>. . **VI. SITC-1 Digit Level Disaggregate Imports . xtgls lrmsitc0 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 504.78</p><p>Log likelihood = -571.8361 Prob > chi2 = 0.0000</p><p>------lrmsitc0 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .5320964 .0503269 10.57 0.000 .4334574 .6307354 lgdp | .0042832 .0781931 0.05 0.956 -.1489724 .1575388 73</p><p> lgdpau | 2.170211 .806741 2.69 0.007 .5890279 3.751395 lgdpdfrati~w | -.685599 .2836498 -2.42 0.016 -1.241542 -.1296557 lpopau | -10.36304 2.765664 -3.75 0.000 -15.78364 -4.942437 lpop | .320605 .0966643 3.32 0.001 .1311465 .5100635 lxrate1 | -.1117859 .026319 -4.25 0.000 -.1633702 -.0602015 lremote | .0088012 .2557274 0.03 0.973 -.4924153 .5100177 ldist | -1.515418 .3145398 -4.82 0.000 -2.131904 -.8989311 lopen | .0335465 .0542731 0.62 0.537 -.0728268 .1399199 english | -.0737541 .2334579 -0.32 0.752 -.5313231 .3838149 white | 9.240847 1.074031 8.60 0.000 7.135785 11.34591 lwhitemg | -.8328864 .0956994 -8.70 0.000 -1.020454 -.645319 _cons | 127.1649 26.18766 4.86 0.000 75.838 178.4917 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002533 lgdp | -.001532 .006114 lgdpau | .00006 -.000258 .650831 lgdpdfrati~w | -.001412 .001604 -.01759 .080457 lpopau | -.004946 -.004688 -2.16917 .067743 7.6489 lpop | -.000155 -.004258 -.000667 -.000646 -.001333 .009344 lxrate1 | .000158 -.000122 .000442 .000844 -.004472 -.000533 .000693 lremote | .001853 .004487 -.024113 -.012711 .067507 -.003528 .00037 ldist | .004706 .001905 -.010672 -.002884 -.006973 .002988 .001105 lopen | -.000267 .000694 -.000469 -.000441 -.007059 .000695 -.000015 english | -.002485 .001718 .005149 .005422 -.022273 -.000413 .001271 white | .01383 -.025359 .014133 -.006908 -.079274 .028616 .00527 lwhitemg | -.001384 .001332 -.002741 -.000135 .014359 -.001726 -.000435 _cons | .040141 -.034371 19.2497 -.627765 -70.4192 -.008037 .055972</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .065397 ldist | .030749 .098935 lopen | .00132 .00216 .002946 english | -.011641 .022183 -.000186 .054503 white | .01563 .002804 -.001499 .02049 1.15354 lwhitemg | .000876 -.000085 .000085 -.000863 -.098464 .009158 _cons | -1.39101 -.935073 .074903 .078234 .750279 -.162013 685.793</p><p>. xtgls lrmsitc1 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1440.15 Log likelihood = -341.8415 Prob > chi2 = 0.0000</p><p>------lrmsitc1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0431455 .0186342 2.32 0.021 .0066232 .0796678 lgdp | .2421201 .0566671 4.27 0.000 .1310546 .3531857 lgdpau | -.2038179 .3197992 -0.64 0.524 -.8306129 .4229771 lgdpdfrati~w | -.3738305 .2006081 -1.86 0.062 -.7670151 .0193541 lpopau | 2.178835 1.13585 1.92 0.055 -.0473904 4.405061 lpop | .0391753 .0752013 0.52 0.602 -.1082165 .1865672 lxrate1 | -.0656738 .0182763 -3.59 0.000 -.1014946 -.029853 lremote | -.1896313 .1094551 -1.73 0.083 -.4041593 .0248967 ldist | -1.455753 .1766631 -8.24 0.000 -1.802006 -1.1095 74</p><p> lopen | -.0097694 .0261121 -0.37 0.708 -.0609482 .0414095 english | 1.980704 .2101035 9.43 0.000 1.568909 2.3925 white | 9.69222 1.772436 5.47 0.000 6.218309 13.16613 lwhitemg | -.7397517 .1811666 -4.08 0.000 -1.094832 -.3846716 _cons | -19.97117 11.42062 -1.75 0.080 -42.35517 2.412824 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000347 lgdp | -.00036 .003211 lgdpau | .000275 -.001834 .102272 lgdpdfrati~w | -.000618 .000125 -.001576 .040244 lpopau | -.001136 .007227 -.349468 .024703 1.29016 lpop | .000089 -.003119 .001187 .000568 -.009185 .005655 lxrate1 | -.000015 .00017 .001262 .000744 -.00583 -.00017 .000334 lremote | .000176 .001041 -.007606 -.003502 .02741 -.00069 -.00038 ldist | .000776 .00099 -.001291 -.001977 .000163 .001933 -.000333 lopen | .000023 -.000277 -.00024 .001114 -.00049 .000351 -.000015 english | -.000405 -.003818 .006224 .00295 -.024092 .00546 .000398 white | .001685 -.026044 .017964 -.000864 -.096097 .029407 .001105 lwhitemg | -.000175 .001657 -.001522 .000245 .007973 -.002109 -.000095 _cons | .008287 -.110973 3.2097 -.374324 -12.4944 .08994 .06712</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .01198 ldist | -.000394 .03121 lopen | -.00014 -.000123 .000682 english | -.0056 .008092 -.000316 .044143 white | .008524 -.029537 .000946 .081397 3.14153 lwhitemg | -.000336 .001498 -.00002 -.007208 -.316705 .032821 _cons | -.363065 -.313835 .016764 .199599 1.42286 -.10576 130.43</p><p>. xtgls lrmsitc2 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 961.42 Log likelihood = -859.3556 Prob > chi2 = 0.0000</p><p>------lrmsitc2 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1314308 .0652084 2.02 0.044 .0036247 .259237 lgdp | .5746729 .1131853 5.08 0.000 .3528337 .7965121 lgdpau | -2.215686 1.128766 -1.96 0.050 -4.428025 -.0033457 lgdpdfrati~w | -1.061831 .446206 -2.38 0.017 -1.936378 -.1872831 lpopau | 4.555544 3.868621 1.18 0.239 -3.026814 12.1379 lpop | .1286072 .1311975 0.98 0.327 -.1285351 .3857496 lxrate1 | -.1491693 .0398467 -3.74 0.000 -.2272674 -.0710712 lremote | 1.758053 .2739409 6.42 0.000 1.221138 2.294967 ldist | -1.598336 .5018526 -3.18 0.001 -2.581949 -.6147224 lopen | -.0391906 .0780765 -0.50 0.616 -.1922177 .1138364 english | .4941011 .2686274 1.84 0.066 -.0323989 1.020601 white | 10.99527 1.494663 7.36 0.000 8.065782 13.92475 lwhitemg | -.8487058 .1428954 -5.94 0.000 -1.128776 -.5686359 _cons | -28.72077 36.33852 -0.79 0.429 -99.94296 42.50143 ------75</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .004252 lgdp | -.004643 .012811 lgdpau | -.002516 .006285 1.27411 lgdpdfrati~w | -.001817 .004367 -.002552 .1991 lpopau | .004595 -.029207 -4.25843 .057404 14.9662 lpop | .00191 -.011225 -.00295 -.002518 .011993 .017213 lxrate1 | .000062 .001643 .00084 .001458 -.009701 -.001534 .001588 lremote | .001683 -.002868 -.031592 -.019609 .079137 .002891 .000862 ldist | .012798 -.011446 -.012002 -.010317 -.000666 .016106 -.002392 lopen | .000361 -.000905 .006738 .00122 -.034518 .002382 -.00039 english | -.005961 .012221 .00849 .002029 -.031694 -.010584 -.000579 white | .028221 -.053554 -.04248 -.03773 .06501 .053752 .005148 lwhitemg | -.003091 .00321 .000287 .001143 .009856 -.002765 -.000808 _cons | -.092389 .354864 37.5308 -.872353 -136.931 -.317823 .134478</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .075044 ldist | .023553 .251856 lopen | .003046 .004054 .006096 english | -.005398 .02758 -.001172 .072161 white | .122652 .144109 .002081 .031593 2.23402 lwhitemg | -.005139 -.018279 -.000039 -.005734 -.203686 .020419 _cons | -1.34219 -2.3258 .317249 -.006026 -2.21419 .03927 1320.49</p><p>. xtgls lrmsitc3 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 551.52 Log likelihood = -80.48529 Prob > chi2 = 0.0000</p><p>------lrmsitc3 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0206897 .0274681 0.75 0.451 -.0331468 .0745262 lgdp | .1550936 .0578407 2.68 0.007 .0417279 .2684593 lgdpau | .4123087 .3938321 1.05 0.295 -.359588 1.184205 lgdpdfrati~w | .3928156 .1798607 2.18 0.029 .0402952 .7453361 lpopau | 2.06711 1.394247 1.48 0.138 -.6655642 4.799784 lpop | .2691138 .0815298 3.30 0.001 .1093184 .4289092 lxrate1 | -.017098 .0153005 -1.12 0.264 -.0470864 .0128904 lremote | -.0706034 .1272472 -0.55 0.579 -.3200032 .1787965 ldist | -1.956855 .4277135 -4.58 0.000 -2.795158 -1.118552 lopen | .0234547 .032819 0.71 0.475 -.0408693 .0877788 english | 1.184134 .1897784 6.24 0.000 .8121755 1.556093 white | 15.80639 1.211831 13.04 0.000 13.43125 18.18154 lwhitemg | -1.322878 .112863 -11.72 0.000 -1.544086 -1.101671 _cons | -33.64056 14.42154 -2.33 0.020 -61.90626 -5.374865 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000754 76</p><p> lgdp | -.000551 .003346 lgdpau | -.000478 -8.7e-06 .155104 lgdpdfrati~w | -.000639 .000701 -.000198 .03235 lpopau | .001048 .000163 -.522109 .016216 1.94393 lpop | -.0001 -.002649 .000414 .000589 -.004463 .006647 lxrate1 | .000049 .000077 .000216 .000521 -.002492 -.000083 .000234 lremote | .000745 .001782 -.007891 -.003437 .023643 -.001372 .000027 ldist | .002832 -.004041 -.011001 -.008746 .002436 .008626 .000249 lopen | -.00006 3.0e-06 .000614 .000224 -.00479 .000426 -.000041 english | -.00106 -.000883 .00259 .002682 -.00277 .003576 .000122 white | .003638 -.016746 .029074 -.003927 -.253582 .027048 .003018 lwhitemg | -.000447 .000934 -.003118 .000222 .026042 -.002094 -.000335 _cons | -.027908 -.010349 4.76115 -.207457 -18.729 -.052583 .031215</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .016192 ldist | .01789 .182939 lopen | .000083 .000038 .001077 english | -.004057 .004243 -.000098 .036016 white | -.004895 -.013388 -.000503 .040572 1.46853 lwhitemg | .000766 .001848 .000091 -.003475 -.133606 .012738 _cons | -.514533 -1.70973 .056331 -.068309 3.52818 -.358835 207.981</p><p>. xtgls lrmsitc4 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 236.05</p><p>Log likelihood = 161.4715 Prob > chi2 = 0.0000</p><p>------lrmsitc4 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .002144 .0181251 0.12 0.906 -.0333805 .0376685 lgdp | .2539975 .0490269 5.18 0.000 .1579066 .3500885 lgdpau | -.2935432 .3539342 -0.83 0.407 -.9872415 .4001551 lgdpdfrati~w | -.097179 .1377093 -0.71 0.480 -.3670843 .1727264 lpopau | 4.136554 1.281792 3.23 0.001 1.624287 6.648821 lpop | -.1450449 .0622541 -2.33 0.020 -.2670606 -.0230292 lxrate1 | -.0859645 .0148016 -5.81 0.000 -.114975 -.0569539 lremote | .2971044 .1297307 2.29 0.022 .0428369 .5513719 ldist | -1.334522 .2708482 -4.93 0.000 -1.865375 -.8036695 lopen | .0041506 .0283135 0.15 0.883 -.0513427 .059644 english | .3022001 .1692592 1.79 0.074 -.0295419 .6339421 white | 9.60008 2.194585 4.37 0.000 5.298773 13.90139 lwhitemg | -.7919 .2129487 -3.72 0.000 -1.209272 -.3745281 _cons | -53.39735 13.50412 -3.95 0.000 -79.86494 -26.92975 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000329 lgdp | -.00029 .002404 lgdpau | .000181 -.000888 .125269 lgdpdfrati~w | -.000391 .000457 -.006077 .018964 lpopau | -.002293 .007065 -.429219 .02623 1.64299 lpop | .000028 -.002282 -.00003 -.000217 -.001878 .003876 lxrate1 | -8.7e-07 -.000022 .000457 .000446 -.004366 -.000081 .000219 77</p><p> lremote | .000292 .001807 -.00857 -.001099 .029001 -.001191 -.000066 ldist | .001607 -.001236 -.002756 -.001322 .000898 .004472 .000087 lopen | 3.8e-06 -.000018 .00018 .000202 -.001736 .000129 -.000022 english | -.000461 -.001579 .000378 .000624 .019873 .00362 .000194 white | .002249 -.015812 .019534 -.004431 -.160928 .016094 .002175 lwhitemg | -.000266 .001028 -.002264 .000439 .01549 -.001195 -.000158 _cons | .020408 -.113791 3.95866 -.28011 -16.3891 -.010676 .060536</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .01683 ldist | .007245 .073359 lopen | .000088 -.000269 .000802 english | -.001736 .012769 -.000561 .028649 white | -.001046 .011559 -.0012 .030426 4.8162 lwhitemg | .000722 -.002063 .000147 -.002791 -.460444 .045347 _cons | -.493436 -.759263 .024627 -.472813 2.13203 -.186505 182.361</p><p>. xtgls lrmsitc5 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 443.52 Log likelihood = -691.6555 Prob > chi2 = 0.0000</p><p>------lrmsitc5 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1567877 .0540348 2.90 0.004 .0508815 .2626939 lgdp | .8935452 .1104243 8.09 0.000 .6771176 1.109973 lgdpau | -.1634072 .7087413 -0.23 0.818 -1.552515 1.2257 lgdpdfrati~w | -.261134 .292706 -0.89 0.372 -.8348273 .3125592 lpopau | 1.795953 2.432942 0.74 0.460 -2.972525 6.564432 lpop | -.4174763 .152964 -2.73 0.006 -.7172803 -.1176723 lxrate1 | -.1267762 .0318088 -3.99 0.000 -.1891204 -.064432 lremote | .1314299 .2383792 0.55 0.581 -.3357848 .5986446 ldist | -2.524553 .6485265 -3.89 0.000 -3.795642 -1.253465 lopen | .0044613 .0773271 0.06 0.954 -.1470971 .1560196 english | -.0357812 .4311383 -0.08 0.934 -.8807967 .8092344 white | 20.08104 4.190711 4.79 0.000 11.86739 28.29468 lwhitemg | -1.85644 .3842633 -4.83 0.000 -2.609583 -1.103298 _cons | -13.52107 23.77497 -0.57 0.570 -60.11915 33.07702 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .00292 lgdp | -.0028 .012194 lgdpau | .00024 .000855 .502314 lgdpdfrati~w | -.002091 .004106 -.009524 .085677 lpopau | -.004415 -.006222 -1.66708 .058605 5.91921 lpop | .000206 -.00942 -.003004 -.001877 -.002839 .023398 lxrate1 | .000141 .000523 .002248 .001212 -.01132 -.000833 .001012 lremote | .001055 .003448 -.022936 -.01346 .046822 -.002436 .000379 ldist | .010514 .00586 -.006384 -.002901 -.029442 .006075 .000434 lopen | .000349 -.000948 -.002109 .001142 -.009828 .002378 -.000174 english | -.006744 .000955 .00585 .007191 -.021828 .017299 .000728 white | .007707 -.043614 .132782 .027551 -.338765 .081333 .004905 lwhitemg | -.001218 .00207 -.014224 -.003918 .039807 -.005982 -.000578 _cons | .005926 -.121891 14.7639 -.721878 -54.4233 -.068242 .116799 78</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .056825 ldist | .024121 .420587 lopen | .00095 .002883 .005979 english | -.008946 .107131 -.001836 .18588 white | .017425 .220725 .007649 .280084 17.5621 lwhitemg | .001313 -.033633 -.000505 -.030301 -1.58146 .147658 _cons | -.933751 -3.85869 .168719 -1.04349 -.598485 .089005 565.249</p><p>. xtgls lrmsitc6 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 949.50 Log likelihood = -596.3461 Prob > chi2 = 0.0000</p><p>------lrmsitc6 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .1945751 .0468861 4.15 0.000 .10268 .2864702 lgdp | .5522242 .0962124 5.74 0.000 .3636513 .7407971 lgdpau | .9788009 .680708 1.44 0.150 -.3553623 2.312964 lgdpdfrati~w | -.0877012 .2901319 -0.30 0.762 -.6563492 .4809468 lpopau | -3.991961 2.349609 -1.70 0.089 -8.59711 .6131888 lpop | .5675594 .1229778 4.62 0.000 .3265273 .8085915 lxrate1 | -.0808365 .029741 -2.72 0.007 -.1391277 -.0225453 lremote | -.2672937 .2400018 -1.11 0.265 -.7376885 .2031012 ldist | -2.394391 .4417941 -5.42 0.000 -3.260292 -1.528491 lopen | -.1267485 .0751734 -1.69 0.092 -.2740856 .0205886 english | 1.152388 .3248099 3.55 0.000 .5157726 1.789004 white | 23.16495 3.266911 7.09 0.000 16.76192 29.56798 lwhitemg | -2.213617 .2877011 -7.69 0.000 -2.777501 -1.649733 _cons | 47.22496 22.48111 2.10 0.036 3.162799 91.28712 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .002198 lgdp | -.001979 .009257 lgdpau | -.002393 .005327 .463363 lgdpdfrati~w | -.001922 .002865 -.019264 .084177 lpopau | .007086 -.03348 -1.5479 .064694 5.52066 lpop | .000437 -.007196 -.003588 -.001793 .013561 .015124 lxrate1 | .000046 .000667 .000638 .001363 -.006848 -.000842 .000885 lremote | .000571 .003917 -.014977 -.01854 .03158 -.003095 .000409 ldist | .007823 .000168 -.008521 -.000995 -.004129 .009545 .000331 lopen | .000287 -.000538 -.000989 .002635 -.007489 .002183 -.000086 english | -.003087 -.00034 .004831 .004487 -.016691 .003029 .00054 white | .007053 -.031459 .036255 -.012014 -.081653 .054246 .004084 lwhitemg | -.00097 .001206 -.0047 -.000465 .016012 -.004235 -.000415 _cons | -.105512 .290393 13.6977 -.509051 -50.7674 -.268411 .084027</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .057601 ldist | .00648 .195182 lopen | .001411 .002094 .005651 79</p><p> english | -.009862 .054593 -.002354 .105501 white | .02908 .012245 .008327 .024056 10.6727 lwhitemg | .000384 -.005974 -.000755 -.000236 -.924775 .082772 _cons | -.719729 -1.82545 .095243 -.340396 -.174966 -.042173 505.4</p><p>. xtgls lrmsitc7 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1741.92 Log likelihood = -755.8081 Prob > chi2 = 0.0000</p><p>------lrmsitc7 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .0761027 .0351822 2.16 0.031 .0071468 .1450586 lgdp | 1.050285 .0977194 10.75 0.000 .8587585 1.241811 lgdpau | 1.096502 .6937875 1.58 0.114 -.2632967 2.4563 lgdpdfrati~w | -.098043 .2773678 -0.35 0.724 -.6416739 .4455879 lpopau | 2.348774 2.412666 0.97 0.330 -2.379965 7.077513 lpop | -.5459289 .1434497 -3.81 0.000 -.8270852 -.2647727 lxrate1 | -.1439734 .0220276 -6.54 0.000 -.1871467 -.1008 lremote | -.3531701 .1870561 -1.89 0.059 -.7197932 .013453 ldist | -1.365118 .7292576 -1.87 0.061 -2.794436 .0642008 lopen | -.0858798 .0738272 -1.16 0.245 -.2305784 .0588189 english | .1399102 .3455959 0.40 0.686 -.5374453 .8172657 white | 27.5809 2.726449 10.12 0.000 22.23716 32.92464 lwhitemg | -2.573226 .264302 -9.74 0.000 -3.091248 -2.055203 _cons | -64.14936 24.08465 -2.66 0.008 -111.3544 -16.94431 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .001238 lgdp | -.002033 .009549 lgdpau | -.000872 .002796 .481341 lgdpdfrati~w | -.002145 .004483 -.014898 .076933 lpopau | -.004312 -.012393 -1.60369 .057163 5.82096 lpop | .001898 -.011856 -.005838 -.004377 .029237 .020578 lxrate1 | .000049 .000302 .000387 .000728 -.008443 -.000648 .000485 lremote | .00151 .003282 -.016377 -.00486 .014064 -.002433 .000282 ldist | .007339 .010241 -.025293 -.005002 -.059535 .011733 .001371 lopen | .000327 -.000928 -.000465 .004467 -.007808 .00129 -.000342 english | -.001476 .004699 .003699 .003412 -.006842 -.002737 .000746 white | .017404 -.055841 .016427 -.032743 -.306239 .068294 .00731 lwhitemg | -.00154 .00373 -.003107 .002364 .036348 -.004824 -.000716 _cons | .022935 -.018526 14.4065 -.566647 -54.2599 -.480535 .114654</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .03499 ldist | .064507 .531817 lopen | .001119 .001409 .00545 english | .001036 .067016 -.001613 .119437 white | .034461 .103144 -.005481 .1899 7.43353 lwhitemg | -.002367 -.016791 .00076 -.021847 -.709614 .069856 _cons | -.765275 -4.45818 .118632 -.705261 3.44775 -.33935 580.07 80</p><p>. xtgls lrmsitc8 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 453.23</p><p>Log likelihood = -599.7668 Prob > chi2 = 0.0000</p><p>------lrmsitc8 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | .3439262 .0566712 6.07 0.000 .2328526 .4549997 lgdp | .2432789 .0944545 2.58 0.010 .0581515 .4284064 lgdpau | 1.285355 .7725292 1.66 0.096 -.2287741 2.799485 lgdpdfrati~w | -.6761342 .3207121 -2.11 0.035 -1.304718 -.0475501 lpopau | -5.105458 2.66463 -1.92 0.055 -10.32804 .1171206 lpop | .2480497 .1012742 2.45 0.014 .049556 .4465434 lxrate1 | -.0783973 .0337842 -2.32 0.020 -.1446131 -.0121814 lremote | -.0198089 .2326684 -0.09 0.932 -.4758305 .4362128 ldist | -.867798 .5463329 -1.59 0.112 -1.938591 .2029948 lopen | .0342647 .0747095 0.46 0.646 -.1121633 .1806927 english | .4964351 .2521541 1.97 0.049 .0022221 .990648 white | 21.45588 2.265634 9.47 0.000 17.01532 25.89645 lwhitemg | -2.003462 .2190765 -9.15 0.000 -2.432844 -1.57408 _cons | 53.09238 25.05679 2.12 0.034 3.98197 102.2028 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .003212 lgdp | -.002902 .008922 lgdpau | -.002358 .006181 .596801 lgdpdfrati~w | -.002565 .005105 -.012926 .102856 lpopau | .005276 -.033259 -1.99397 .060138 7.10025 lpop | .00095 -.004913 -.003477 -.002761 .005178 .010256 lxrate1 | .000157 .000263 -.000136 .000872 -.003292 -.000578 .001141 lremote | .000186 .003654 -.009279 -.017256 -.001255 .000879 .000762 ldist | .010588 .003819 -.004433 -.007266 -.086109 .009367 .000593 lopen | -.00032 .000706 .001586 .000593 -.021697 .002041 -.000203 english | -.003744 .005244 .004337 .003272 -.023223 .001971 -.000068 white | .018383 -.036751 .016129 -.05733 .02749 .019814 .000767 lwhitemg | -.002144 .002069 -.003527 .003026 .009745 -.001444 -.000272 _cons | -.094954 .20864 17.4869 -.600197 -64.104 -.143777 .044555</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .054135 ldist | .036008 .29848 lopen | .002501 .005611 .005582 english | .005428 .061763 .000076 .063582 white | .091923 .107036 -.002956 .095938 5.1331 lwhitemg | -.005694 -.020097 .000205 -.010895 -.486219 .047994 _cons | -.638926 -1.90492 .201056 -.509817 -2.30777 .16392 627.843</p><p>. xtgls lrmsitc9 limmig lgdp lgdpau lgdpdfrationew lpopau lpop lxrate1 lr > emote ldist lopen english white lwhitemg, panels(hetero)corr(psar1)nolog</p><p>Cross-sectional time-series FGLS regression</p><p>Coefficients: generalized least squares 81</p><p>Panels: heteroskedastic Correlation: panel-specific AR(1)</p><p>Estimated covariances = 100 Number of obs = 1000 Estimated autocorrelations = 100 Number of groups = 100 Estimated coefficients = 14 Time periods = 10 Wald chi2(13) = 1475.85 Log likelihood = -261.5178 Prob > chi2 = 0.0000</p><p>------lrmsitc9 | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------limmig | -.0002448 .0232973 -0.01 0.992 -.0459067 .045417 lgdp | .4552445 .0820751 5.55 0.000 .2943802 .6161088 lgdpau | -.3031349 .4988006 -0.61 0.543 -1.280766 .6744962 lgdpdfrati~w | .1090158 .229233 0.48 0.634 -.3402726 .5583041 lpopau | 1.172501 1.720264 0.68 0.496 -2.199154 4.544156 lpop | -.2661756 .1135901 -2.34 0.019 -.488808 -.0435431 lxrate1 | -.0407659 .0229251 -1.78 0.075 -.0856982 .0041663 lremote | -.0177213 .1678211 -0.11 0.916 -.3466447 .3112021 ldist | -3.566922 .5140525 -6.94 0.000 -4.574447 -2.559398 lopen | .0074912 .05166 0.15 0.885 -.0937606 .1087429 english | -.1110954 .3289126 -0.34 0.736 -.7557523 .5335615 white | 15.64878 2.603621 6.01 0.000 10.54577 20.75178 lwhitemg | -1.121965 .2784393 -4.03 0.000 -1.667696 -.5762336 _cons | 17.90001 17.16763 1.04 0.297 -15.74792 51.54794 ------</p><p>. vce</p><p>| limmig lgdp lgdpau lgdpdf~w lpopau lpop lxrate1 ------+------limmig | .000543 lgdp | -.000724 .006736 lgdpau | .000028 -.001747 .248802 lgdpdfrati~w | -.000995 .002273 -.007416 .052548 lpopau | -.001016 -.000522 -.830368 .042624 2.95931 lpop | .000259 -.006103 .000154 -.000898 -.001817 .012903 lxrate1 | -.00002 .000152 .001403 .001126 -.006722 -.000535 .000526 lremote | .000189 .002243 -.016574 -.004776 .047625 -.002975 -.000194 ldist | .002476 .001442 -.007637 -.002459 -.01682 .01902 -.000115 lopen | .000052 -.000138 -.001928 .001029 .001599 .00081 -.000063 english | -.000974 -.001573 .00496 .002414 -.007211 .006707 .000934 white | .004888 -.040474 .056753 -.008633 -.332402 .062878 .003388 lwhitemg | -.000498 .001936 -.006715 .000134 .039742 -.005107 -.000171 _cons | .001549 -.03043 7.50184 -.538307 -27.5481 -.195105 .07897</p><p>| lremote ldist lopen english white lwhitemg _cons ------+------lremote | .028164 ldist | .011362 .26425 lopen | .000229 .0001 .002669 english | -.00171 .066956 -.00087 .108184 white | -.00469 .172959 -.00353 .143599 6.77884 lwhitemg | .002926 -.021203 .000433 -.014104 -.715033 .077528 _cons | -.70962 -2.50393 .011964 -.722699 2.27951 -.265841 294.727</p><p>. end of do-file</p><p>. log close log: K:\Argentian_to_China.log log type: text closed on: 25 Jul 2006, 22:05:07 ------</p>