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table_4_table_c3_FINAL.txt
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table_4_table_c3_FINAL.txt
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---------------------------------------------------------------------------------------------------
name: <unnamed>
log: XXX/jhr_output/table_4_ta
> ble_c3.txt
log type: text
opened on: 17 Mar 2021, 20:58:14
. global outsheet XXX/jhr_outp
> ut
.
.
. use $datadir/jhr_samples_created
.
.
.
. **** taking out combat
. local demos_everdissolve female ged hsd asc_smc college_pl afqsc black hispanic o
> ther_race sum_combat age age_sq
. local demos_everkids female ged hsd asc_smc college_pl afqsc black hispanic other
> _race sum_combat age age_sq
. local demos_nbr_kids female ged hsd asc_smc college_pl afqsc black hispanic other
> _race sum_combat age age_sq
. local demos_age_marr female ged hsd asc_smc college_pl afqsc black hispanic other
> _race sum_combat
. local demos_evermarr female ged hsd asc_smc college_pl afqsc black hispanic other
> _race sum_combat age age_sq
. local demos_marr_nm_ent female ged hsd asc_smc college_pl afqsc black hispanic ot
> her_race sum_combat age age_sq
. local demos_married female ged hsd asc_smc college_pl afqsc black hispanic other_
> race sum_combat age age_sq
. local demos_re_enlist female ged hsd asc_smc college_pl afqsc black hispanic othe
> r_race sum_combat age age_sq
.
.
. **** taking out combat
. local demos_everdissolve2 female ged hsd asc_smc college_pl afqsc black hispanic
> other_race age age_sq
. local demos_everkids2 female ged hsd asc_smc college_pl afqsc black hispanic othe
> r_race age age_sq
. local demos_nbr_kids2 female ged hsd asc_smc college_pl afqsc black hispanic othe
> r_race age age_sq
. local demos_age_marr2 female ged hsd asc_smc college_pl afqsc black hispanic othe
> r_race age age_sq
. local demos_evermarr2 female ged hsd asc_smc college_pl afqsc black hispanic othe
> r_race age age_sq
. local demos_married2 female ged hsd asc_smc college_pl afqsc black hispanic other
> _race age age_sq
. local demos_marr_nm_ent2 female ged hsd asc_smc college_pl afqsc black hispanic o
> ther_race age age_sq
. local demos_re_enlist2 female ged hsd asc_smc college_pl afqsc black hispanic oth
> er_race age age_sq
.
. gen pre2002 = mind < td(01oct2001) if initial_year ~= .
(7,501 missing values generated)
. gen post2001 = mind >= td(01oct2001) if initial_year ~= .
(7,501 missing values generated)
.
. *****
. ** TABLE 4 & App Table C3
. *****
. *** Table 4, Panel A
. foreach var in evermarr {
2. foreach var2 in totmoves_not {
3. foreach cond in all {
4.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(
> exper_br_year_sex_fe)
5. qui summ `var' if e(sample) == 1, de
6. local mean = r(mean)
7. qui summ `var2' if e(sample) == 1, de
8. local mean2 = r(mean)
9. outreg2 using $outsheet/app_c3_panel_a.xls, replace label ctitle ("`c
> ond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
10.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
11. qui summ `var' if e(sample) == 1, de
12. local mean = r(mean)
13. qui summ `var2' if e(sample) == 1, de
14. local mean2 = r(mean)
15. outreg2 using $outsheet/table_4_panel_a.xls, replace label ctitle ("`
> cond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
16. outreg2 using $outsheet/app_c3_panel_a.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
>
17. }
18. foreach cond in male pre2002 term6 {
19.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(e
> xper_br_year_sex_fe)
20.
. qui summ `var' if e(sample) == 1, de
21. local mean = r(mean)
22. qui summ `var2' if e(sample) == 1, de
23. local mean2 = r(mean)
24. outreg2 using $outsheet/app_c3_panel_a.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
25.
.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
26.
. qui summ `var' if e(sample) == 1, de
27. local mean = r(mean)
28. qui summ `var2' if e(sample) == 1, de
29. local mean2 = r(mean)
30. outreg2 using $outsheet/table_4_panel_a.xls, label ctitle ("`cond'")
> title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
31. outreg2 using $outsheet/app_c3_panel_a.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
32. }
33. }
34.
. }
Linear regression, absorbing indicators Number of obs = 144,254
Absorbed variable: exper_br_year_sex_fe No. of categories = 12,165
F( 1, 132088) = 1394.54
Prob > F = 0.0000
R-squared = 0.1183
Adj R-squared = 0.0371
Root MSE = 0.4877
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0871604 .002334 37.34 0.000 .0825858 .091735
_cons | .5046079 .0018636 270.77 0.000 .5009552 .5082605
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 144,254
Absorbed variable: exper_br_year_sex_fe No. of categories = 12,165
F( 12, 132077) = 256.25
Prob > F = 0.0000
R-squared = 0.1292
Adj R-squared = 0.0489
Root MSE = 0.4848
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0788909 .0023509 33.56 0.000 .0742831 .0834986
female | 0 (omitted)
ged | .0598285 .0047552 12.58 0.000 .0505084 .0691485
hsd | .0305838 .0156728 1.95 0.051 -.0001346 .0613022
asc_smc | .0146766 .0054566 2.69 0.007 .0039817 .0253715
college_pl | -.051759 .0115687 -4.47 0.000 -.0744335 -.0290845
afqsc | -.0015393 .0000875 -17.59 0.000 -.0017109 -.0013677
black | -.0356851 .0039433 -9.05 0.000 -.0434139 -.0279563
hispanic | .0312789 .0045562 6.87 0.000 .0223487 .040209
other_race | -.0546971 .0060157 -9.09 0.000 -.0664879 -.0429064
sum_combat | -.0036843 .0002522 -14.61 0.000 -.0041785 -.00319
age | .101739 .0047121 21.59 0.000 .0925033 .1109748
age_sq | -.0016474 .0000823 -20.02 0.000 -.0018087 -.0014861
_cons | -.8615763 .0664888 -12.96 0.000 -.9918931 -.7312595
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_a.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 125,395
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,319
F( 1, 117075) = 1441.66
Prob > F = 0.0000
R-squared = 0.1030
Adj R-squared = 0.0393
Root MSE = 0.4877
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0935252 .0024632 37.97 0.000 .0886974 .098353
_cons | .4964209 .0019711 251.85 0.000 .4925576 .5002843
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 125,395
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,319
F( 12, 117064) = 264.46
Prob > F = 0.0000
R-squared = 0.1156
Adj R-squared = 0.0526
Root MSE = 0.4843
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0854183 .0024809 34.43 0.000 .0805557 .0902809
female | 0 (omitted)
ged | .0603327 .0048549 12.43 0.000 .0508171 .0698483
hsd | .033163 .0161778 2.05 0.040 .0014547 .0648713
asc_smc | .0154042 .0059553 2.59 0.010 .0037319 .0270766
college_pl | -.0512281 .0124251 -4.12 0.000 -.075581 -.0268751
afqsc | -.0016472 .0000906 -18.18 0.000 -.0018248 -.0014696
black | -.0172181 .0042744 -4.03 0.000 -.0255958 -.0088403
hispanic | .0324579 .0048405 6.71 0.000 .0229707 .0419451
other_race | -.0690318 .0064755 -10.66 0.000 -.0817236 -.05634
sum_combat | -.0031403 .0002683 -11.71 0.000 -.0036661 -.0026144
age | .1086027 .0051277 21.18 0.000 .0985526 .1186529
age_sq | -.0017292 .0000899 -19.22 0.000 -.0019055 -.0015529
_cons | -.9918455 .0720944 -13.76 0.000 -1.133149 -.8505416
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_a.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 64,311
Absorbed variable: exper_br_year_sex_fe No. of categories = 7,553
F( 1, 56757) = 763.13
Prob > F = 0.0000
R-squared = 0.1525
Adj R-squared = 0.0397
Root MSE = 0.4895
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0981892 .0035544 27.62 0.000 .0912226 .1051558
_cons | .4567482 .003037 150.39 0.000 .4507955 .4627008
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 64,311
Absorbed variable: exper_br_year_sex_fe No. of categories = 7,553
F( 12, 56746) = 119.69
Prob > F = 0.0000
R-squared = 0.1622
Adj R-squared = 0.0506
Root MSE = 0.4867
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0916073 .0035632 25.71 0.000 .0846233 .0985912
female | 0 (omitted)
ged | .0497646 .0094385 5.27 0.000 .0312652 .0682641
hsd | .0428038 .0250661 1.71 0.088 -.0063259 .0919335
asc_smc | .0106357 .0092474 1.15 0.250 -.0074893 .0287606
college_pl | -.0546488 .0177323 -3.08 0.002 -.0894041 -.0198934
afqsc | -.0009968 .0001384 -7.20 0.000 -.0012679 -.0007256
black | -.0293539 .0056564 -5.19 0.000 -.0404404 -.0182673
hispanic | .0475051 .0075704 6.28 0.000 .0326672 .0623431
other_race | -.0334381 .0090073 -3.71 0.000 -.0510924 -.0157837
sum_combat | -.006565 .0005429 -12.09 0.000 -.007629 -.005501
age | .1206657 .0089881 13.43 0.000 .103049 .1382824
age_sq | -.0020024 .0001607 -12.46 0.000 -.0023173 -.0016875
_cons | -1.199437 .1241715 -9.66 0.000 -1.442813 -.9560598
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_a.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 17,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 3,743
F( 1, 14030) = 72.67
Prob > F = 0.0000
R-squared = 0.2393
Adj R-squared = 0.0364
Root MSE = 0.4908
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0705896 .0082808 8.52 0.000 .0543582 .086821
_cons | .4752634 .0049894 95.25 0.000 .4654835 .4850433
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 17,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 3,743
F( 12, 14019) = 28.17
Prob > F = 0.0000
R-squared = 0.2548
Adj R-squared = 0.0553
Root MSE = 0.4860
------------------------------------------------------------------------------
| Robust
evermarr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0610741 .0082771 7.38 0.000 .0448499 .0772983
female | 0 (omitted)
ged | .0744532 .0177227 4.20 0.000 .0397144 .1091921
hsd | -.0573396 .0490567 -1.17 0.242 -.1534973 .0388181
asc_smc | .0074591 .0165056 0.45 0.651 -.0248941 .0398124
college_pl | -.0655365 .0359306 -1.82 0.068 -.1359652 .0048921
afqsc | -.0021958 .0003008 -7.30 0.000 -.0027854 -.0016063
black | -.043073 .0135631 -3.18 0.001 -.0696584 -.0164876
hispanic | .0299856 .0154232 1.94 0.052 -.000246 .0602172
other_race | -.0743141 .0188731 -3.94 0.000 -.111308 -.0373202
sum_combat | -.0053091 .0008183 -6.49 0.000 -.0069131 -.0037052
age | .1303016 .0154725 8.42 0.000 .0999735 .1606297
age_sq | -.0020852 .0002714 -7.68 0.000 -.0026172 -.0015532
_cons | -1.249978 .2169478 -5.76 0.000 -1.675224 -.8247312
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_a.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_a.xls
dir : seeout
. *** Table 4, Panel B
. foreach var in marr_nm_ent {
2. foreach var2 in totmoves_not {
3. foreach cond in all {
4.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(e
> xper_br_year_sex_fe)
5. qui summ `var' if e(sample) == 1, de
6. local mean = r(mean)
7. qui summ `var2' if e(sample) == 1, de
8. local mean2 = r(mean)
9. outreg2 using $outsheet/app_c3_panel_b.xls, replace label ctitle ("`c
> ond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
10.
.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
11. qui summ `var' if e(sample) == 1, de
12. local mean = r(mean)
13. qui summ `var2' if e(sample) == 1, de
14. local mean2 = r(mean)
15. outreg2 using $outsheet/table_4_panel_b.xls, replace label ctitle ("`
> cond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
16. outreg2 using $outsheet/app_c3_panel_b.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
17.
. }
18. foreach cond in male pre2002 term6 {
19.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(e
> xper_br_year_sex_fe)
20.
. qui summ `var' if e(sample) == 1, de
21. local mean = r(mean)
22. qui summ `var2' if e(sample) == 1, de
23. local mean2 = r(mean)
24. outreg2 using $outsheet/app_c3_panel_b.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
25.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
26.
. qui summ `var' if e(sample) == 1, de
27. local mean = r(mean)
28. qui summ `var2' if e(sample) == 1, de
29. local mean2 = r(mean)
30. outreg2 using $outsheet/table_4_panel_b.xls, label ctitle ("`cond'")
> title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
31. outreg2 using $outsheet/app_c3_panel_b.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
32. }
33. }
34.
. }
Linear regression, absorbing indicators Number of obs = 144,254
Absorbed variable: exper_br_year_sex_fe No. of categories = 12,165
F( 1, 132088) = 1240.45
Prob > F = 0.0000
R-squared = 0.1136
Adj R-squared = 0.0320
Root MSE = 0.4912
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0830723 .0023587 35.22 0.000 .0784494 .0876952
_cons | .4796488 .0018705 256.43 0.000 .4759827 .483315
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 144,254
Absorbed variable: exper_br_year_sex_fe No. of categories = 12,165
F( 12, 132077) = 231.61
Prob > F = 0.0000
R-squared = 0.1236
Adj R-squared = 0.0429
Root MSE = 0.4885
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0749671 .0023771 31.54 0.000 .070308 .0796262
female | 0 (omitted)
ged | .0576988 .0048199 11.97 0.000 .0482519 .0671457
hsd | .0231079 .0158452 1.46 0.145 -.0079484 .0541643
asc_smc | .0131613 .0055292 2.38 0.017 .0023241 .0239984
college_pl | -.0445182 .0116862 -3.81 0.000 -.067423 -.0216135
afqsc | -.0014895 .0000881 -16.91 0.000 -.0016621 -.0013168
black | -.0340194 .003977 -8.55 0.000 -.0418142 -.0262246
hispanic | .0308211 .0046098 6.69 0.000 .021786 .0398562
other_race | -.0493461 .0060342 -8.18 0.000 -.0611729 -.0375193
sum_combat | -.0036688 .0002546 -14.41 0.000 -.0041677 -.0031699
age | .098024 .0046778 20.96 0.000 .0888557 .1071924
age_sq | -.0015848 .0000816 -19.43 0.000 -.0017446 -.0014249
_cons | -.8369308 .0660862 -12.66 0.000 -.9664585 -.7074031
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_b.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 125,395
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,319
F( 1, 117075) = 1305.75
Prob > F = 0.0000
R-squared = 0.0989
Adj R-squared = 0.0349
Root MSE = 0.4904
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0897847 .0024847 36.14 0.000 .0849147 .0946546
_cons | .4777113 .0019756 241.80 0.000 .473839 .4815835
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 125,395
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,319
F( 12, 117064) = 240.38
Prob > F = 0.0000
R-squared = 0.1105
Adj R-squared = 0.0472
Root MSE = 0.4873
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0818956 .0025042 32.70 0.000 .0769874 .0868037
female | 0 (omitted)
ged | .0592035 .0049128 12.05 0.000 .0495744 .0688326
hsd | .0290208 .0163133 1.78 0.075 -.0029531 .0609947
asc_smc | .015832 .006017 2.63 0.009 .0040388 .0276252
college_pl | -.0447994 .0125264 -3.58 0.000 -.0693508 -.0202479
afqsc | -.0015639 .0000911 -17.17 0.000 -.0017425 -.0013854
black | -.0185274 .0043077 -4.30 0.000 -.0269704 -.0100844
hispanic | .0318906 .004885 6.53 0.000 .0223161 .0414652
other_race | -.0639932 .0064722 -9.89 0.000 -.0766786 -.0513079
sum_combat | -.0031237 .0002704 -11.55 0.000 -.0036536 -.0025937
age | .1054497 .0050884 20.72 0.000 .0954766 .1154228
age_sq | -.001682 .0000892 -18.87 0.000 -.0018568 -.0015073
_cons | -.9667979 .0716192 -13.50 0.000 -1.10717 -.8264255
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_b.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 64,311
Absorbed variable: exper_br_year_sex_fe No. of categories = 7,553
F( 1, 56757) = 683.26
Prob > F = 0.0000
R-squared = 0.1472
Adj R-squared = 0.0337
Root MSE = 0.4915
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0934717 .0035759 26.14 0.000 .0864629 .1004805
_cons | .4375781 .003039 143.99 0.000 .4316216 .4435347
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 64,311
Absorbed variable: exper_br_year_sex_fe No. of categories = 7,553
F( 12, 56746) = 107.60
Prob > F = 0.0000
R-squared = 0.1561
Adj R-squared = 0.0436
Root MSE = 0.4890
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .08703 .0035871 24.26 0.000 .0799994 .0940607
female | 0 (omitted)
ged | .0472967 .0094835 4.99 0.000 .028709 .0658843
hsd | .0322721 .0251462 1.28 0.199 -.0170145 .0815587
asc_smc | .0078508 .0093815 0.84 0.403 -.010537 .0262386
college_pl | -.0440149 .0178285 -2.47 0.014 -.0789589 -.0090709
afqsc | -.0009835 .0001387 -7.09 0.000 -.0012554 -.0007115
black | -.0276569 .0056842 -4.87 0.000 -.0387981 -.0165158
hispanic | .047062 .0076216 6.17 0.000 .0321236 .0620003
other_race | -.0295735 .0090252 -3.28 0.001 -.0472629 -.0118842
sum_combat | -.006477 .000543 -11.93 0.000 -.0075413 -.0054128
age | .1136921 .0090023 12.63 0.000 .0960476 .1313366
age_sq | -.0018864 .0001609 -11.73 0.000 -.0022017 -.0015711
_cons | -1.119495 .1244027 -9.00 0.000 -1.363325 -.8756651
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_b.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 17,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 3,743
F( 1, 14030) = 64.40
Prob > F = 0.0000
R-squared = 0.2351
Adj R-squared = 0.0310
Root MSE = 0.4917
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0667578 .008319 8.02 0.000 .0504515 .0830641
_cons | .4495861 .0049818 90.25 0.000 .4398211 .4593511
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 17,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 3,743
F( 12, 14019) = 24.31
Prob > F = 0.0000
R-squared = 0.2485
Adj R-squared = 0.0473
Root MSE = 0.4875
------------------------------------------------------------------------------
| Robust
marr_nm_ent | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0575577 .0083238 6.91 0.000 .041242 .0738734
female | 0 (omitted)
ged | .0725369 .0177784 4.08 0.000 .0376889 .1073848
hsd | -.0555236 .0482443 -1.15 0.250 -.1500889 .0390416
asc_smc | .0133634 .0165968 0.81 0.421 -.0191686 .0458954
college_pl | -.0495367 .0360754 -1.37 0.170 -.1202493 .021176
afqsc | -.0020552 .0003019 -6.81 0.000 -.002647 -.0014635
black | -.0356776 .0135784 -2.63 0.009 -.0622931 -.0090621
hispanic | .0275871 .0155116 1.78 0.075 -.0028178 .057992
other_race | -.0784961 .0189523 -4.14 0.000 -.1156451 -.0413471
sum_combat | -.0052729 .0008223 -6.41 0.000 -.0068847 -.0036611
age | .1096254 .0154545 7.09 0.000 .0793326 .1399183
age_sq | -.0017353 .0002709 -6.40 0.000 -.0022664 -.0012042
_cons | -.9921961 .2167503 -4.58 0.000 -1.417056 -.5673366
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_b.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_b.xls
dir : seeout
. *** Table 4, Panel C
. foreach var in married {
2. foreach var2 in totmoves_not {
3. foreach cond in all {
4.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(e
> xper_br_year_sex_fe)
5. qui summ `var' if e(sample) == 1, de
6. local mean = r(mean)
7. qui summ `var2' if e(sample) == 1, de
8. local mean2 = r(mean)
9. outreg2 using $outsheet/app_c3_panel_c.xls, replace label ctitle ("`c
> ond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
10.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
11. qui summ `var' if e(sample) == 1, de
12. local mean = r(mean)
13. qui summ `var2' if e(sample) == 1, de
14. local mean2 = r(mean)
15. outreg2 using $outsheet/table_4_panel_c.xls, replace label ctitle ("`
> cond'") title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
16. outreg2 using $outsheet/app_c3_panel_c.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
17. }
18. foreach cond in male pre2002 term6 {
19.
. areg `var' `var2' if `cond' == 1 & sample_`var'_us == 1, robust absorb(e
> xper_br_year_sex_fe)
20.
. qui summ `var' if e(sample) == 1, de
21. local mean = r(mean)
22. qui summ `var2' if e(sample) == 1, de
23. local mean2 = r(mean)
24. outreg2 using $outsheet/app_c3_panel_c.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
25.
. areg `var' `var2' `demos_`var'' if `cond' == 1 & sample_`var'_us == 1, r
> obust absorb(exper_br_year_sex_fe)
26.
. qui summ `var' if e(sample) == 1, de
27. local mean = r(mean)
28. qui summ `var2' if e(sample) == 1, de
29. local mean2 = r(mean)
30. outreg2 using $outsheet/table_4_panel_c.xls, label ctitle ("`cond'")
> title("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
31. outreg2 using $outsheet/app_c3_panel_c.xls, label ctitle ("`cond'") t
> itle("`var'") dec(3) addtex(Mean, `mean', Indep Mean, `mean2') keep(`var2')
32. }
33. }
34.
. }
Linear regression, absorbing indicators Number of obs = 182,694
Absorbed variable: exper_br_year_sex_fe No. of categories = 13,335
F( 1, 169358) = 1342.05
Prob > F = 0.0000
R-squared = 0.1066
Adj R-squared = 0.0362
Root MSE = 0.4786
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0736988 .0020118 36.63 0.000 .0697558 .0776418
_cons | .5682225 .0016452 345.38 0.000 .5649979 .5714471
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 182,694
Absorbed variable: exper_br_year_sex_fe No. of categories = 13,335
F( 12, 169347) = 734.36
Prob > F = 0.0000
R-squared = 0.1414
Adj R-squared = 0.0737
Root MSE = 0.4692
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0642332 .0020008 32.10 0.000 .0603116 .0681548
female | 0 (omitted)
ged | .069391 .0038264 18.13 0.000 .0618913 .0768907
hsd | .0347319 .0134439 2.58 0.010 .0083821 .0610817
asc_smc | .0036528 .0042845 0.85 0.394 -.0047447 .0120504
college_pl | -.0759224 .0082928 -9.16 0.000 -.0921762 -.0596686
afqsc | -.0014186 .0000738 -19.21 0.000 -.0015634 -.0012739
black | -.051839 .0033858 -15.31 0.000 -.0584751 -.045203
hispanic | .0392667 .0037046 10.60 0.000 .0320058 .0465277
other_race | -.0463474 .0051118 -9.07 0.000 -.0563664 -.0363283
sum_combat | -.0037191 .0002129 -17.47 0.000 -.0041364 -.0033018
age | .1233415 .0028977 42.56 0.000 .117662 .129021
age_sq | -.001728 .0000483 -35.80 0.000 -.0018226 -.0016334
_cons | -1.315776 .0426508 -30.85 0.000 -1.399371 -1.232181
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_c.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 158,592
Absorbed variable: exper_br_year_sex_fe No. of categories = 9,082
F( 1, 149509) = 1443.23
Prob > F = 0.0000
R-squared = 0.0941
Adj R-squared = 0.0391
Root MSE = 0.4766
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0805919 .0021214 37.99 0.000 .076434 .0847498
_cons | .5703356 .0017409 327.62 0.000 .5669236 .5737477
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 158,592
Absorbed variable: exper_br_year_sex_fe No. of categories = 9,082
F( 12, 149498) = 758.96
Prob > F = 0.0000
R-squared = 0.1338
Adj R-squared = 0.0811
Root MSE = 0.4660
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0711111 .002107 33.75 0.000 .0669814 .0752409
female | 0 (omitted)
ged | .0716378 .0039069 18.34 0.000 .0639804 .0792952
hsd | .0421973 .0138095 3.06 0.002 .0151311 .0692636
asc_smc | .0069347 .0046229 1.50 0.134 -.0021262 .0159955
college_pl | -.0766052 .008838 -8.67 0.000 -.0939275 -.059283
afqsc | -.0015007 .0000764 -19.65 0.000 -.0016504 -.001351
black | -.0396481 .0036681 -10.81 0.000 -.0468376 -.0324586
hispanic | .0420577 .0038978 10.79 0.000 .0344181 .0496973
other_race | -.0588062 .0054875 -10.72 0.000 -.0695616 -.0480507
sum_combat | -.0031457 .0002255 -13.95 0.000 -.0035876 -.0027038
age | .1299803 .0030269 42.94 0.000 .1240475 .135913
age_sq | -.0018073 .0000503 -35.90 0.000 -.0019059 -.0017086
_cons | -1.435066 .0446371 -32.15 0.000 -1.522554 -1.347578
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_c.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 80,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,345
F( 1, 72428) = 727.44
Prob > F = 0.0000
R-squared = 0.1339
Adj R-squared = 0.0341
Root MSE = 0.4836
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0839873 .003114 26.97 0.000 .0778839 .0900907
_cons | .5326205 .0027193 195.86 0.000 .5272907 .5379504
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 80,774
Absorbed variable: exper_br_year_sex_fe No. of categories = 8,345
F( 12, 72417) = 362.11
Prob > F = 0.0000
R-squared = 0.1721
Adj R-squared = 0.0765
Root MSE = 0.4728
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0760882 .00307 24.78 0.000 .0700709 .0821054
female | 0 (omitted)
ged | .068181 .0073381 9.29 0.000 .0537984 .0825636
hsd | .0444747 .0213614 2.08 0.037 .0026065 .0863429
asc_smc | -.0059162 .0071163 -0.83 0.406 -.0198641 .0080317
college_pl | -.0703786 .0126075 -5.58 0.000 -.0950892 -.045668
afqsc | -.0009234 .0001177 -7.85 0.000 -.0011541 -.0006928
black | -.0505702 .0048972 -10.33 0.000 -.0601688 -.0409717
hispanic | .0574874 .0061536 9.34 0.000 .0454264 .0695483
other_race | -.031964 .0076594 -4.17 0.000 -.0469765 -.0169516
sum_combat | -.0064984 .0004722 -13.76 0.000 -.007424 -.0055728
age | .1562328 .0058767 26.58 0.000 .1447144 .1677512
age_sq | -.0022692 .0001013 -22.40 0.000 -.0024678 -.0020707
_cons | -1.86527 .083744 -22.27 0.000 -2.029408 -1.701132
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_c.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
Linear regression, absorbing indicators Number of obs = 22,267
Absorbed variable: exper_br_year_sex_fe No. of categories = 4,258
F( 1, 18008) = 66.95
Prob > F = 0.0000
R-squared = 0.2117
Adj R-squared = 0.0254
Root MSE = 0.4891
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0581486 .0071066 8.18 0.000 .0442189 .0720782
_cons | .5439695 .0044121 123.29 0.000 .5353214 .5526176
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
note: female omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 22,267
Absorbed variable: exper_br_year_sex_fe No. of categories = 4,258
F( 12, 17997) = 90.51
Prob > F = 0.0000
R-squared = 0.2548
Adj R-squared = 0.0780
Root MSE = 0.4757
------------------------------------------------------------------------------
| Robust
married | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
totmoves_not | .0472298 .0069885 6.76 0.000 .0335317 .0609279
female | 0 (omitted)
ged | .0830324 .0145023 5.73 0.000 .0546066 .1114582
hsd | -.0364164 .0419761 -0.87 0.386 -.1186936 .0458607
asc_smc | .0054425 .01314 0.41 0.679 -.0203132 .0311982
college_pl | -.0632999 .0259207 -2.44 0.015 -.114107 -.0124928
afqsc | -.0019256 .0002558 -7.53 0.000 -.0024269 -.0014242
black | -.0523027 .0117604 -4.45 0.000 -.0753542 -.0292512
hispanic | .036289 .0126359 2.87 0.004 .0115214 .0610566
other_race | -.069473 .0164032 -4.24 0.000 -.1016249 -.0373212
sum_combat | -.0051537 .0006983 -7.38 0.000 -.0065224 -.0037849
age | .1432645 .0094889 15.10 0.000 .1246654 .1618636
age_sq | -.0019847 .0001589 -12.49 0.000 -.0022961 -.0016733
_cons | -1.617657 .1389475 -11.64 0.000 -1.890007 -1.345306
------------------------------------------------------------------------------
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/table_4_panel_c.xls
dir : seeout
C:\Program Files\Stata16\ado\plus/o/outreg2.ado
XXX/jhr_output/app_c3_panel_c.xls
dir : seeout
.
end of do-file