---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- name: log: C:\Users\chouse\Dropbox\basu-house\analysis\code\../temp/fixed_effects_ipd.log log type: text opened on: 18 Jan 2017, 14:59:45 . /****************************************************************************/ > /*** REGRESSION SPECIFICATIONS ***/ > /****************************************************************************/ > > /*** wage used in analysis: ***/ > local mywage ln_hrly_wage_ipd; . /*** separation rate used in calculation ***/ > /* MK uses delta = 0.295 for annual */ > /* local delta = 0.3873; /\* consistent with 4 percent monthly *\/ */ > local delta = 0.295; . /* annual separation rate from monthly data */ > /* Annual sep rate = 1 - Pr(don't separate) */ > /* = 1 - Pr(stay for a year) */ > /* = 1 - Pr(don't separate for each month)^12 */ > /* = 1 - (1- month sep)^12 */ > > local industries > ind_ag > ind_mining > ind_constr > ind_manufd > ind_manufn > ind_trans > ind_trade > ind_fire > /* ind_other */; . /* Make "OTHER" the base case */ > > local tableA1 `mywage' ur hgc potexperience potexperience2 tenurey tenurey2 trend union; . local tableA1_nounion `mywage' ur hgc potexperience potexperience2 tenurey tenurey2 trend /* trend2 */; . local cycregs_base `mywage' potexperience potexperience2; . local cycregs_cntrl `mywage' potexperience potexperience2 hgc tenurey tenurey2 `industries'; . local uc_regs `mywage' hgc potexperience potexperience2 tenurey tenurey2 trend `industries'; . /****************************************************************************/ > /*** NLSY DATA ***/ > /****************************************************************************/ > use `myinput'nlsy_data, clear; . /*** BEGIN RESTRICTIONS ***/ > /* drop if yofd(startd) < 1978; /\* jobs that start too early *\/ */ > drop if hrly_wage_cpi79 < 1 & !missing(hrly_wage_cpi79); (2,543 observations deleted) . /* BLS censoring */ > drop if hrly_wage_cpi79 > 100 & !missing(hrly_wage_cpi79); (182 observations deleted) . /* BLS censoring */ > /* drop if hrly_wage_cpi79 < 1 | hrly_wage_cpi79 > 100; /\* BLS censoring *\/ */ > drop if non_nlsy==1; (7,561 observations deleted) . /* drop non-NLSY years */ > drop if employer_starty < 1978; (4,028 observations deleted) . /* added 11 Jan 2016 */ > /* drop if */ > /* employer_starty == 1995 | */ > /* employer_starty == 1997 | */ > /* employer_starty == 1999 | */ > /* employer_starty == 2001 | */ > /* employer_starty == 2003 | */ > /* employer_starty == 2005 | */ > /* employer_starty == 2007 | */ > /* employer_starty == 2009 | */ > /* employer_starty == 2011; */ > > preserve; . collapse (max)maxage=age (min) minage=age (sd) stdage=age /* [pw=csampweight] */, by(datey); . tsset datey, yearly; time variable: datey, 1978 to 2012, but with gaps delta: 1 year . tsline minage maxage stdage, lc(black black red) lw(0.8 0.8 0.8) xtitle("") ytitle("Years") ylabel(,angle(h) grid) legend(c(3) region(lstyle(none))); . restore; . preserve; . collapse hrly_wage_cpi79 tenurey potexperience [pw=csampweight], by(datey); . tsset datey, yearly; time variable: datey, 1978 to 2012, but with gaps delta: 1 year . tsline hrly_wage_cpi79, lc(black) lw(0.8) xtitle("") ytitle("Hourly Wage (CPI, Index=100 in 1979m1)") ylabel(,angle(h) grid); . tsline tenurey, lc(black) lw(0.8) xtitle("") ytitle("Average tenure (years)") ylabel(,angle(h) grid); . tsline potexperience, lc(black) lw(0.8) xtitle("") ytitle("Average potential experience (years)") ylabel(,angle(h) grid); . restore; . /* END RESTRICTIONS */ > > /*** GENERATE VARIABLES ***/ > gen ln_hrly_wage_ipd = log(hrly_wage_ipd); (213 missing values generated) . gen age2 = age*age; (213 missing values generated) . gen tenurey2 = tenurey*tenurey; (2,061 missing values generated) . egen trend = min(datey); . replace trend = datey-trend+1; (126,824 real changes made, 213 to missing) . gen trend2 = trend*trend; (213 missing values generated) . gen potexperience2 = potexperience*potexperience; (1,913 missing values generated) . gen mksamp = .; (126,824 missing values generated) . replace mksamp = 1 if employer_starty >= 1978 & datey <= 2004; (114,100 real changes made) . /*** END GENERATE VARIABLES ***/ > > /********************************************************************************************/ > /*** TABLE A1 ***/ > /********************************************************************************************/ > /*** 1978--2004 ***/ > /* Column (1): no industry fixed effects */ > areg /* `tableA1' */ `tableA1_nounion' [pweight = csampweight] if mksamp==1, > absorb(id); (sum of wgt is 3.5103e+08) Linear regression, absorbing indicators Number of obs = 57,023 F( 7, 54310) = 4228.03 Prob > F = 0.0000 R-squared = 0.6097 Adj R-squared = 0.5902 Root MSE = 0.3886 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0200785 .0016053 -12.51 0.000 -.0232249 -.0169321 hgc | .0365129 .0080426 4.54 0.000 .0207493 .0522766 potexperience | -.0006997 .0075266 -0.09 0.926 -.0154519 .0140525 potexperience2 | -.0012175 .0000417 -29.22 0.000 -.0012992 -.0011359 tenurey | .0350345 .0013638 25.69 0.000 .0323613 .0377076 tenurey2 | -.001013 .0000785 -12.90 0.000 -.0011669 -.0008591 trend | .0601188 .0076408 7.87 0.000 .0451428 .0750949 _cons | 1.601259 .0862198 18.57 0.000 1.432267 1.77025 ---------------+---------------------------------------------------------------- id | absorbed (2706 categories) . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m1, title("1978--2004"); . /* Column (2): new hires */ > areg /* `tableA1' */ `tableA1_nounion' [pweight = csampweight] if tenurey <= 1 & mksamp==1, > absorb(id); (sum of wgt is 1.4209e+08) Linear regression, absorbing indicators Number of obs = 23,852 F( 7, 21173) = 728.89 Prob > F = 0.0000 R-squared = 0.5106 Adj R-squared = 0.4487 Root MSE = 0.3979 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0261956 .0024367 -10.75 0.000 -.0309718 -.0214194 hgc | .0649447 .0111089 5.85 0.000 .0431703 .086719 potexperience | .0275396 .01075 2.56 0.010 .0064687 .0486105 potexperience2 | -.0010813 .0000805 -13.43 0.000 -.0012391 -.0009235 tenurey | -.0197017 .0416686 -0.47 0.636 -.1013754 .0619719 tenurey2 | .0839541 .0410954 2.04 0.041 .0034041 .1645042 trend | .0230571 .0106834 2.16 0.031 .0021169 .0439973 _cons | 1.382986 .1163806 11.88 0.000 1.154871 1.611101 ---------------+---------------------------------------------------------------- id | absorbed (2672 categories) . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m2, title("1978--2004, New Hires"); . /* Columns (3): industry fixed effects */ > areg /* `tableA1' */ `tableA1_nounion' `industries' [pweight = csampweight] if mksamp==1, > absorb(id); (sum of wgt is 3.2962e+08) Linear regression, absorbing indicators Number of obs = 53,599 F( 15, 50880) = 2185.74 Prob > F = 0.0000 R-squared = 0.6358 Adj R-squared = 0.6164 Root MSE = 0.3732 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0199786 .0015814 -12.63 0.000 -.0230782 -.0168791 hgc | .0286395 .0079391 3.61 0.000 .0130788 .0442002 potexperience | -.0088911 .0074268 -1.20 0.231 -.0234477 .0056654 potexperience2 | -.0011309 .0000413 -27.38 0.000 -.0012119 -.00105 tenurey | .0348557 .0013691 25.46 0.000 .0321722 .0375393 tenurey2 | -.0009794 .0000786 -12.46 0.000 -.0011334 -.0008254 trend | .0648531 .0075314 8.61 0.000 .0500915 .0796147 ind_ag | -.0660927 .0126546 -5.22 0.000 -.0908958 -.0412896 ind_mining | .2390907 .0202564 11.80 0.000 .199388 .2787933 ind_constr | .157426 .0079263 19.86 0.000 .1418904 .1729616 ind_manufd | .1239923 .007078 17.52 0.000 .1101194 .1378651 ind_manufn | .0972164 .008212 11.84 0.000 .0811208 .1133121 ind_trans | .1202531 .0093548 12.85 0.000 .1019175 .1385886 ind_trade | -.064132 .0060429 -10.61 0.000 -.0759762 -.0522878 ind_fire | .1208142 .0130537 9.26 0.000 .0952288 .1463995 _cons | 1.673886 .0853969 19.60 0.000 1.506507 1.841265 ---------------+---------------------------------------------------------------- id | absorbed (2704 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m3, title("1978--2004"); . /* Column (4): industry fixed effects, new hires */ > areg /* `tableA1' */ `tableA1_nounion' `industries' [pweight = csampweight] if tenurey <= 1 & mksamp == 1, > absorb(id); (sum of wgt is 1.2973e+08) Linear regression, absorbing indicators Number of obs = 21,851 F( 15, 19167) = 412.99 Prob > F = 0.0000 R-squared = 0.5413 Adj R-squared = 0.4770 Root MSE = 0.3800 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0266071 .0023922 -11.12 0.000 -.031296 -.0219181 hgc | .0483295 .0110431 4.38 0.000 .0266841 .0699749 potexperience | .0112803 .0106853 1.06 0.291 -.0096639 .0322245 potexperience2 | -.0009629 .0000826 -11.66 0.000 -.0011247 -.000801 tenurey | -.005781 .0425713 -0.14 0.892 -.0892246 .0776625 tenurey2 | .0609285 .0415351 1.47 0.142 -.0204839 .1423409 trend | .034976 .0105906 3.30 0.001 .0142176 .0557345 ind_ag | -.0056296 .0175698 -0.32 0.749 -.040068 .0288088 ind_mining | .2638997 .0313627 8.41 0.000 .202426 .3253733 ind_constr | .2063776 .0109618 18.83 0.000 .1848915 .2278636 ind_manufd | .1266114 .0111157 11.39 0.000 .1048236 .1483992 ind_manufn | .0943076 .0125033 7.54 0.000 .0698 .1188153 ind_trans | .1321695 .0151143 8.74 0.000 .1025442 .1617948 ind_trade | -.0710206 .0084982 -8.36 0.000 -.0876779 -.0543633 ind_fire | .1059511 .0207362 5.11 0.000 .0653063 .1465959 _cons | 1.544971 .1158738 13.33 0.000 1.317848 1.772094 ---------------+---------------------------------------------------------------- id | absorbed (2669 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m4, title("1978--2004, New Hires"); . la var ur "UR"; . la var hgc "Grade"; . la var potexperience "Experience"; . la var potexperience2 "Experience$^2$"; . la var tenurey "Tenure"; . la var tenurey2 "Tenure$^2$"; . la var trend "Trend"; . la var union "Union"; . esttab m1 m2 m3 m4 using `slides'tab_`prg'_tableA1_1978_2004.tex, replace > drop(ind_*) scalars("indcontr Indstry Controls") > booktabs se r2 label mtitles nogap addnotes("All regressions include individual fixed effects" "Only men"); (note: file ../../slides/tab_fixed_effects_ipd_tableA1_1978_2004.tex not found) (output written to ../../slides/tab_fixed_effects_ipd_tableA1_1978_2004.tex) . /*** 1978--2013 ***/ > /* Column (1): no industry fixed effects */ > areg /* `tableA1' */ `tableA1_nounion' [pweight = csampweight], > absorb(id); (sum of wgt is 4.0432e+08) Linear regression, absorbing indicators Number of obs = 64,062 F( 7, 61343) = 5306.38 Prob > F = 0.0000 R-squared = 0.6316 Adj R-squared = 0.6152 Root MSE = 0.4013 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0078022 .0014125 -5.52 0.000 -.0105706 -.0050338 hgc | .0472378 .0075676 6.24 0.000 .0324054 .0620702 potexperience | .0077852 .0071712 1.09 0.278 -.0062704 .0218407 potexperience2 | -.0010784 .0000273 -39.49 0.000 -.0011319 -.0010249 tenurey | .0292427 .0010558 27.70 0.000 .0271734 .031312 tenurey2 | -.0005825 .0000453 -12.85 0.000 -.0006714 -.0004936 trend | .0511122 .0072211 7.08 0.000 .0369588 .0652656 _cons | 1.401612 .080919 17.32 0.000 1.24301 1.560213 ---------------+---------------------------------------------------------------- id | absorbed (2712 categories) . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m1, title("1978--2013"); . /* Column (2): new hires */ > areg /* `tableA1' */ `tableA1_nounion' [pweight = csampweight] if tenurey <= 1, > absorb(id); (sum of wgt is 1.4876e+08) Linear regression, absorbing indicators Number of obs = 24,744 F( 7, 22062) = 822.47 Prob > F = 0.0000 R-squared = 0.5224 Adj R-squared = 0.4643 Root MSE = 0.4051 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.019675 .0024134 -8.15 0.000 -.0244055 -.0149445 hgc | .0630871 .0112102 5.63 0.000 .0411142 .08506 potexperience | .0234239 .0107314 2.18 0.029 .0023896 .0444582 potexperience2 | -.0009497 .000056 -16.95 0.000 -.0010595 -.0008399 tenurey | -.0202448 .0419683 -0.48 0.630 -.1025055 .062016 tenurey2 | .0781037 .0413722 1.89 0.059 -.0029888 .1591962 trend | .0267263 .0107426 2.49 0.013 .0056701 .0477826 _cons | 1.348124 .1172599 11.50 0.000 1.118286 1.577962 ---------------+---------------------------------------------------------------- id | absorbed (2675 categories) . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m2, title("1978--2013, New Hires"); . /* Columns (3): industry fixed effects */ > areg /* `tableA1' */ `tableA1_nounion' `industries' [pweight = csampweight], > absorb(id); (sum of wgt is 3.7816e+08) Linear regression, absorbing indicators Number of obs = 60,022 F( 15, 57296) = 2636.56 Prob > F = 0.0000 R-squared = 0.6516 Adj R-squared = 0.6350 Root MSE = 0.3879 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0072268 .0014207 -5.09 0.000 -.0100114 -.0044422 hgc | .0423044 .00759 5.57 0.000 .027428 .0571807 potexperience | .0025184 .0071764 0.35 0.726 -.0115474 .0165843 potexperience2 | -.0010163 .0000275 -36.97 0.000 -.0010702 -.0009624 tenurey | .0286954 .0010665 26.91 0.000 .026605 .0307857 tenurey2 | -.0005485 .000046 -11.92 0.000 -.0006387 -.0004583 trend | .0536149 .0072278 7.42 0.000 .0394484 .0677813 ind_ag | -.070693 .0126547 -5.59 0.000 -.0954963 -.0458896 ind_mining | .2455901 .020357 12.06 0.000 .2056902 .28549 ind_constr | .1519688 .0077302 19.66 0.000 .1368176 .16712 ind_manufd | .1293021 .0069427 18.62 0.000 .1156944 .1429097 ind_manufn | .1082958 .0080051 13.53 0.000 .0926058 .1239858 ind_trans | .1085523 .0089431 12.14 0.000 .0910237 .1260808 ind_trade | -.0559044 .005932 -9.42 0.000 -.0675312 -.0442777 ind_fire | .1248195 .0129933 9.61 0.000 .0993526 .1502864 _cons | 1.433305 .081474 17.59 0.000 1.273615 1.592994 ---------------+---------------------------------------------------------------- id | absorbed (2711 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m3, title("1978--2013"); . /* Column (4): industry fixed effects, new hires */ > areg /* `tableA1' */ `tableA1_nounion' `industries' if tenurey <= 1 [pweight = csampweight], > absorb(id); (sum of wgt is 1.3597e+08) Linear regression, absorbing indicators Number of obs = 22,687 F( 15, 19999) = 461.60 Prob > F = 0.0000 R-squared = 0.5531 Adj R-squared = 0.4931 Root MSE = 0.3881 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.019865 .0023886 -8.32 0.000 -.0245469 -.0151831 hgc | .047018 .0112 4.20 0.000 .0250651 .0689709 potexperience | .0087221 .0107017 0.82 0.415 -.0122542 .0296983 potexperience2 | -.0008739 .0000569 -15.37 0.000 -.0009853 -.0007624 tenurey | -.0145814 .043067 -0.34 0.735 -.0989963 .0698335 tenurey2 | .0621502 .0419661 1.48 0.139 -.0201068 .1444072 trend | .038024 .0106936 3.56 0.000 .0170636 .0589844 ind_ag | .0000908 .0176461 0.01 0.996 -.0344969 .0346785 ind_mining | .2616367 .0310474 8.43 0.000 .2007812 .3224922 ind_constr | .2087667 .0109422 19.08 0.000 .1873192 .2302143 ind_manufd | .1307972 .0111104 11.77 0.000 .10902 .1525744 ind_manufn | .1045939 .0125284 8.35 0.000 .0800373 .1291505 ind_trans | .1377333 .0146519 9.40 0.000 .1090143 .1664523 ind_trade | -.0654281 .0084961 -7.70 0.000 -.0820812 -.0487751 ind_fire | .1220521 .0206761 5.90 0.000 .0815252 .1625789 _cons | 1.498562 .1173692 12.77 0.000 1.268509 1.728616 ---------------+---------------------------------------------------------------- id | absorbed (2673 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m4, title("1978--2013, New Hires"); . la var ur "UR"; . la var hgc "Grade"; . la var potexperience "Experience"; . la var potexperience2 "Experience$^2$"; . la var tenurey "Tenure"; . la var tenurey2 "Tenure$^2$"; . la var trend "Trend"; . la var union "Union"; . esttab m1 m2 m3 m4 using `slides'tab_`prg'_tableA1_1978_2013.tex, replace > drop(ind_*) scalars("indcontr Indstry Controls") > booktabs se r2 label mtitles nogap addnotes("All regressions include individual fixed effects" "Only men"); (note: file ../../slides/tab_fixed_effects_ipd_tableA1_1978_2013.tex not found) (output written to ../../slides/tab_fixed_effects_ipd_tableA1_1978_2013.tex) . /********************************************************************************************/ > /*** Cyclicality of Wages ***/ > /********************************************************************************************/ > /*** BASE ***/ > reg `cycregs_base' i.datey [pweight = csampweight]; (sum of wgt is 4.1084e+08) Linear regression Number of obs = 65,069 F(27, 65041) = 870.78 Prob > F = 0.0000 R-squared = 0.2833 Root MSE = .54887 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .022076 .0014538 15.18 0.000 .0192265 .0249254 potexperience2 | -.0016085 .000051 -31.51 0.000 -.0017086 -.0015085 | datey | 1979 | .0693891 .0188618 3.68 0.000 .0324199 .1063583 1980 | .0773378 .0182901 4.23 0.000 .0414891 .1131864 1981 | .0888348 .0180887 4.91 0.000 .053381 .1242886 1982 | .1354651 .0184377 7.35 0.000 .0993273 .171603 1983 | .1353769 .018527 7.31 0.000 .0990639 .1716898 1984 | .1938406 .0187448 10.34 0.000 .1571008 .2305804 1985 | .2478635 .0190174 13.03 0.000 .2105893 .2851377 1986 | .3182096 .0194087 16.40 0.000 .2801686 .3562506 1987 | .4008881 .0199036 20.14 0.000 .3618771 .4398991 1988 | .4526554 .0200884 22.53 0.000 .4132822 .4920287 1989 | .4900937 .020519 23.88 0.000 .4498764 .530311 1990 | .5472558 .0209387 26.14 0.000 .506216 .5882956 1991 | .5650553 .0212112 26.64 0.000 .5234813 .6066294 1992 | .6108589 .0215597 28.33 0.000 .5686019 .6531158 1993 | .6732645 .0222716 30.23 0.000 .6296121 .7169169 1994 | .7397125 .0228607 32.36 0.000 .6949055 .7845194 1996 | .8555568 .0232265 36.84 0.000 .8100328 .9010808 1998 | 1.02435 .0239872 42.70 0.000 .977335 1.071365 2000 | 1.164011 .0250821 46.41 0.000 1.11485 1.213172 2002 | 1.309205 .0264969 49.41 0.000 1.257271 1.361139 2004 | 1.456486 .0277399 52.51 0.000 1.402116 1.510857 2006 | 1.567985 .0290918 53.90 0.000 1.510966 1.625005 2008 | 1.728561 .0305074 56.66 0.000 1.668767 1.788356 2010 | 1.885114 .0327607 57.54 0.000 1.820903 1.949325 2012 | 2.074174 .0369222 56.18 0.000 2.001806 2.146541 | _cons | 2.073265 .0161456 128.41 0.000 2.04162 2.104911 -------------------------------------------------------------------------------- . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m_base; . regsave using `myinput'`prg'_data, addlabel(scenario, base) replace; file ../input/fixed_effects_ipd_data.dta saved . /*** CNTRL ***/ > reg `cycregs_cntrl' i.datey [pweight = csampweight]; (sum of wgt is 3.7816e+08) Linear regression Number of obs = 60,022 F(38, 59983) = 1154.25 Prob > F = 0.0000 R-squared = 0.4408 Root MSE = .48029 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .0590592 .0015166 38.94 0.000 .0560867 .0620317 potexperience2 | -.0013345 .0000492 -27.14 0.000 -.0014309 -.0012381 hgc | .1004971 .0013494 74.47 0.000 .0978522 .103142 tenurey | .0420151 .0012526 33.54 0.000 .03956 .0444702 tenurey2 | -.0009083 .0000601 -15.12 0.000 -.0010261 -.0007906 ind_ag | -.1564225 .0120118 -13.02 0.000 -.1799658 -.1328793 ind_mining | .2450956 .0175806 13.94 0.000 .2106375 .2795536 ind_constr | .2436311 .0072998 33.37 0.000 .2293235 .2579388 ind_manufd | .1759492 .0063454 27.73 0.000 .1635121 .1883862 ind_manufn | .1370056 .0077378 17.71 0.000 .1218394 .1521718 ind_trans | .1694453 .0083675 20.25 0.000 .153045 .1858457 ind_trade | -.0530025 .0060345 -8.78 0.000 -.0648301 -.0411748 ind_fire | .2337204 .0124871 18.72 0.000 .2092456 .2581951 | datey | 1979 | .0516021 .0180105 2.87 0.004 .0163016 .0869027 1980 | .0121505 .0175274 0.69 0.488 -.0222033 .0465044 1981 | -.0380976 .0173598 -2.19 0.028 -.072123 -.0040723 1982 | -.0560455 .0177332 -3.16 0.002 -.0908026 -.0212883 1983 | -.1179617 .0179461 -6.57 0.000 -.1531361 -.0827874 1984 | -.1291093 .018188 -7.10 0.000 -.1647578 -.0934609 1985 | -.1350038 .0186939 -7.22 0.000 -.1716439 -.0983638 1986 | -.1258282 .0191269 -6.58 0.000 -.163317 -.0883394 1987 | -.0831615 .0200814 -4.14 0.000 -.122521 -.0438019 1988 | -.0905446 .0203114 -4.46 0.000 -.130355 -.0507342 1989 | -.115995 .0208446 -5.56 0.000 -.1568506 -.0751395 1990 | -.1162957 .0215833 -5.39 0.000 -.158599 -.0739924 1991 | -.1558721 .0219315 -7.11 0.000 -.1988579 -.1128863 1992 | -.1636836 .0224405 -7.29 0.000 -.2076672 -.1197001 1993 | -.1579328 .0230443 -6.85 0.000 -.2030996 -.1127659 1994 | -.1513419 .0238589 -6.34 0.000 -.1981054 -.1045783 1996 | -.1292067 .0249598 -5.18 0.000 -.1781279 -.0802854 1998 | -.0905897 .0260175 -3.48 0.000 -.1415841 -.0395954 2000 | -.0605922 .0276455 -2.19 0.028 -.1147775 -.0064068 2002 | -.043889 .029234 -1.50 0.133 -.1011877 .0134096 2004 | -.0079155 .0310683 -0.25 0.799 -.0688094 .0529784 2006 | -.0125751 .0331986 -0.38 0.705 -.0776446 .0524943 2008 | .0410662 .0348364 1.18 0.238 -.0272133 .1093458 2010 | .0672194 .0372587 1.80 0.071 -.0058078 .1402466 2012 | .1045813 .0416748 2.51 0.012 .0228986 .1862641 | _cons | .8210326 .0225925 36.34 0.000 .7767512 .8653139 -------------------------------------------------------------------------------- . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m_cntrl; . regsave using `myinput'`prg'_data, addlabel(scenario, cntrl) append; file ../input/fixed_effects_ipd_data.dta saved . /*** CNTRLFE ***/ > areg `cycregs_cntrl' i.datey [pweight = csampweight], absorb(id); (sum of wgt is 3.7816e+08) Linear regression, absorbing indicators Number of obs = 60,022 F( 38, 57273) = 1078.16 Prob > F = 0.0000 R-squared = 0.6567 Adj R-squared = 0.6402 Root MSE = 0.3851 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .0183043 .0081819 2.24 0.025 .0022677 .034341 potexperience2 | -.0016135 .0000423 -38.11 0.000 -.0016965 -.0015306 hgc | .0475972 .0084137 5.66 0.000 .0311062 .0640881 tenurey | .0299708 .0010706 28.00 0.000 .0278725 .0320692 tenurey2 | -.0006454 .0000467 -13.82 0.000 -.000737 -.0005539 ind_ag | -.0691965 .0126379 -5.48 0.000 -.0939669 -.0444262 ind_mining | .2470296 .0201093 12.28 0.000 .2076152 .2864441 ind_constr | .1572112 .0077119 20.39 0.000 .1420959 .1723266 ind_manufd | .1309667 .006876 19.05 0.000 .1174897 .1444436 ind_manufn | .1112528 .0079287 14.03 0.000 .0957124 .1267931 ind_trans | .108406 .0089257 12.15 0.000 .0909115 .1259005 ind_trade | -.0541384 .0058757 -9.21 0.000 -.0656548 -.0426221 ind_fire | .1238115 .0129574 9.56 0.000 .0984149 .1492081 | datey | 1979 | .0707698 .022083 3.20 0.001 .027487 .1140525 1980 | .0679111 .0246443 2.76 0.006 .019608 .1162141 1981 | .076993 .0294821 2.61 0.009 .019208 .1347781 1982 | .1044795 .0358544 2.91 0.004 .0342047 .1747542 1983 | .0980895 .0425094 2.31 0.021 .0147709 .1814081 1984 | .1428442 .0496353 2.88 0.004 .0455587 .2401297 1985 | .1864325 .0570094 3.27 0.001 .0746938 .2981713 1986 | .2522192 .0647703 3.89 0.000 .1252691 .3791694 1987 | .3489313 .0734557 4.75 0.000 .2049577 .4929048 1988 | .4049737 .0827099 4.90 0.000 .2428618 .5670856 1989 | .4275551 .0902207 4.74 0.000 .250722 .6043881 1990 | .4802044 .0984949 4.88 0.000 .2871539 .6732548 1991 | .489031 .105965 4.62 0.000 .281339 .696723 1992 | .5292165 .113885 4.65 0.000 .3060012 .7524317 1993 | .5775615 .1215924 4.75 0.000 .3392396 .8158833 1994 | .6407825 .1299944 4.93 0.000 .3859927 .8955722 1996 | .7643476 .1450506 5.27 0.000 .4800477 1.048647 1998 | .9079267 .1607221 5.65 0.000 .5929106 1.222943 2000 | 1.05907 .1776968 5.96 0.000 .7107832 1.407357 2002 | 1.176804 .19285 6.10 0.000 .7988167 1.554791 2004 | 1.310447 .2084734 6.29 0.000 .9018375 1.719056 2006 | 1.413067 .224151 6.30 0.000 .9737297 1.852404 2008 | 1.578227 .2397065 6.58 0.000 1.108401 2.048053 2010 | 1.713087 .2550983 6.72 0.000 1.213093 2.213081 2012 | 1.898879 .2761934 6.88 0.000 1.357539 2.44022 | _cons | 1.446677 .0994691 14.54 0.000 1.251717 1.641637 ---------------+---------------------------------------------------------------- id | absorbed (2711 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m_cntrlfes; . regsave using `myinput'`prg'_data, addlabel(scenario, cntrlfes) append; file ../input/fixed_effects_ipd_data.dta saved . /********************************************************************************************/ > /*** User Cost ***/ > /********************************************************************************************/ > > /****************************************************/ > /* Prepare dataset to predict for ENTRY-LEVEL WAGES */ > /****************************************************/ > preserve; . collapse (mean) hgc potexperience; . /* collapse (mean) hgc potexperience, by(datey); */ > > expand 2012; (2,011 observations created) . gen employer_starty = _n; . /* gen employer_starty = datey; */ > drop if employer_starty < 1978; (1,977 observations deleted) . gen employer_currenty = employer_starty; . gen trend = employer_currenty - 1978 + 1; . /* trend = 1 in 1978 */ > > gen potexperience2 = potexperience * potexperience; . gen tenurey = 0.5; . gen tenurey2 = tenure*tenure; . gen ind_ag = 0; . gen ind_mining = 0; . gen ind_constr = 0; . gen ind_manufd = 0; . gen ind_manufn = 0; . gen ind_trans = 0; . gen ind_trade = 0; . gen ind_fire = 0; . local years = 1978; . foreach ii of num 1979/1994 1996(2)2012 {; 2. local junk = `ii'; 3. local years : list years | junk; 4. }; . foreach b of loc years {; 2. forval a=1978/`b' {; 3. gen y_`a'_`b' = (employer_starty==`a') & (employer_currenty==`b') if !missing(employer_starty) & !missing(employer_currenty); 4. }; 5. }; . foreach ii of num 1978/1994 1996(2)2012 {; 2. assert y_`ii'_`ii' == 1 if employer_currenty == `ii'; 3. }; . egen not_ok = anymatch(employer_currenty), values(1995 1997 1999 2001 2003 2005 2007 2009 2011 2013); . drop if not_ok; (9 observations deleted) . save `tempdata'topredict_entry, replace; (note: file ../temp/topredict_entry.dta not found) file ../temp/topredict_entry.dta saved . restore; . /********************************************/ > /* Prepare dataset to predict for USER COST */ > /********************************************/ > preserve; . collapse (mean) hgc potexperience; . /* collapse (mean) hgc potexperience, by(datey); */ > gen tenurey = 0.5; . gen ind_ag = 0; . gen ind_mining = 0; . gen ind_constr = 0; . gen ind_manufd = 0; . gen ind_manufn = 0; . gen ind_trans = 0; . gen ind_trade = 0; . gen ind_fire = 0; . expand 2012; (2,011 observations created) . gen employer_starty = _n; . /* gen employer_starty = datey; */ > drop if employer_starty < 1978; (1,977 observations deleted) . /*** > add 7: > ------ > 1 2012 > 2 2011 > 3 2010 > 4 2009 > 5 2008 > 6 2007 > 7 2006 > ***/ > drop if employer_starty > 2006; (6 observations deleted) . expand 7; (174 observations created) . bysort employer_starty: gen employer_currenty = _n; . replace employer_currenty = employer_currenty - 1; (203 real changes made) . replace employer_currenty = employer_currenty + employer_starty; (203 real changes made) . /* allow the person to age through time */ > bysort employer_starty: gen toadd = _n; . replace toadd = toadd - 1; (203 real changes made) . replace potexperience = potexperience + toadd; (174 real changes made) . replace tenurey = tenurey + toadd; (174 real changes made) . gen potexperience2 = potexperience*potexperience; . gen tenurey2 = tenurey*tenurey; . gen trend = employer_currenty - 1978 + 1; . /* trend = 1 in 1978 */ > > local years = 1978; . foreach ii of num 1979/1994 1996(2)2012 {; 2. local junk = `ii'; 3. local years : list years | junk; 4. }; . foreach b of loc years {; 2. forval a=1978/`b' {; 3. gen y_`a'_`b' = (employer_starty==`a') & (employer_currenty==`b') if !missing(employer_starty) & !missing(employer_currenty); 4. }; 5. }; . /*** get rid of non-NLSY years ***/ > egen not_ok = anymatch(employer_currenty), values(1995 1997 1999 2001 2003 2005 2007 2009 2011 2013); . drop if not_ok==1; (54 observations deleted) . drop not_ok; . save `tempdata'topredict, replace; (note: file ../temp/topredict.dta not found) file ../temp/topredict.dta saved . restore; . /*** END prediction datasets ***/ > > /*** fixed effects ***/ > foreach b of loc years {; 2. forval a=1978/`b' {; 3. gen y_`a'_`b' = (employer_starty==`a') & (datey==`b') if !missing(employer_starty) & !missing(datey); 4. }; 5. }; (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) 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missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) . /***************************************************************/ > /*** USER COST REGRESSION ***/ > /***************************************************************/ > set matsize 800; . areg `uc_regs' y_1978_1978-y_2012_2012 [pweight=csampweight], absorb(id); (sum of wgt is 3.7816e+08) note: y_2010_2010 omitted because of collinearity note: y_2012_2012 omitted because of collinearity Linear regression, absorbing indicators Number of obs = 60,022 F( 408, 56903) = 111.01 Prob > F = 0.0000 R-squared = 0.6616 Adj R-squared = 0.6430 Root MSE = 0.3836 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- hgc | .0374993 .0110226 3.40 0.001 .0158951 .0591036 potexperience | .0100131 .010857 0.92 0.356 -.0112668 .031293 potexperience2 | -.0016147 .0000432 -37.34 0.000 -.0016994 -.0015299 tenurey | .0695122 .0085728 8.11 0.000 .0527095 .0863149 tenurey2 | -.0024892 .0005799 -4.29 0.000 -.0036258 -.0013526 trend | .0285717 .0432556 0.66 0.509 -.0562096 .113353 ind_ag | -.0680813 .0126514 -5.38 0.000 -.0928781 -.0432845 ind_mining | .2448813 .0202119 12.12 0.000 .205266 .2844967 ind_constr | .1576796 .0077002 20.48 0.000 .1425871 .172772 ind_manufd | .128684 .0069069 18.63 0.000 .1151465 .1422215 ind_manufn | .1079647 .0079366 13.60 0.000 .092409 .1235204 ind_trans | .1067812 .0089211 11.97 0.000 .0892958 .1242665 ind_trade | -.053214 .0058552 -9.09 0.000 -.0646903 -.0417377 ind_fire | .1210865 .0129579 9.34 0.000 .095689 .1464841 y_1978_1978 | -1.129433 1.381682 -0.82 0.414 -3.837537 1.578672 y_1978_1979 | -1.119293 1.339867 -0.84 0.404 -3.74544 1.506855 y_1979_1979 | -1.054687 1.33998 -0.79 0.431 -3.681055 1.571681 y_1978_1980 | -1.136459 1.298407 -0.88 0.381 -3.681345 1.408426 y_1979_1980 | -1.111756 1.298358 -0.86 0.392 -3.656545 1.433032 y_1980_1980 | -1.118332 1.298512 -0.86 0.389 -3.663422 1.426758 y_1978_1981 | -1.195114 1.256968 -0.95 0.342 -3.658779 1.268551 y_1979_1981 | -1.192731 1.256676 -0.95 0.343 -3.655824 1.270361 y_1980_1981 | -1.118643 1.256772 -0.89 0.373 -3.581924 1.344638 y_1981_1981 | -1.10756 1.256921 -0.88 0.378 -3.571134 1.356013 y_1978_1982 | -1.197941 1.215457 -0.99 0.324 -3.580244 1.184362 y_1979_1982 | -1.218159 1.215222 -1.00 0.316 -3.600001 1.163682 y_1980_1982 | -1.130437 1.215282 -0.93 0.352 -3.512397 1.251524 y_1981_1982 | -1.10792 1.215226 -0.91 0.362 -3.489771 1.27393 y_1982_1982 | -1.129181 1.215374 -0.93 0.353 -3.51132 1.252959 y_1978_1983 | -1.219851 1.174211 -1.04 0.299 -3.521311 1.081609 y_1979_1983 | -1.239421 1.173725 -1.06 0.291 -3.539929 1.061086 y_1980_1983 | -1.146188 1.173792 -0.98 0.329 -3.446827 1.15445 y_1981_1983 | -1.178101 1.173603 -1.00 0.315 -3.47837 1.122169 y_1982_1983 | -1.17044 1.173751 -1.00 0.319 -3.470997 1.130118 y_1983_1983 | -1.136663 1.173989 -0.97 0.333 -3.437687 1.164361 y_1978_1984 | -1.256286 1.13274 -1.11 0.267 -3.476462 .9638899 y_1979_1984 | -1.256961 1.132433 -1.11 0.267 -3.476536 .9626129 y_1980_1984 | -1.15267 1.132281 -1.02 0.309 -3.371947 1.066607 y_1981_1984 | -1.176019 1.132132 -1.04 0.299 -3.395005 1.042967 y_1982_1984 | -1.165898 1.132233 -1.03 0.303 -3.385081 1.053284 y_1983_1984 | -1.13705 1.132176 -1.00 0.315 -3.356122 1.082021 y_1984_1984 | -1.099339 1.132304 -0.97 0.332 -3.318662 1.119984 y_1978_1985 | -1.241991 1.09148 -1.14 0.255 -3.381299 .8973167 y_1979_1985 | -1.255138 1.091095 -1.15 0.250 -3.39369 .8834148 y_1980_1985 | -1.160158 1.090899 -1.06 0.288 -3.298325 .97801 y_1981_1985 | -1.168267 1.090594 -1.07 0.284 -3.305837 .9693032 y_1982_1985 | -1.157065 1.090777 -1.06 0.289 -3.294994 .9808627 y_1983_1985 | -1.12374 1.09066 -1.03 0.303 -3.261439 1.013959 y_1984_1985 | -1.102357 1.090632 -1.01 0.312 -3.240001 1.035287 y_1985_1985 | -1.108402 1.090756 -1.02 0.310 -3.24629 1.029486 y_1978_1986 | -1.260586 1.050416 -1.20 0.230 -3.319407 .7982353 y_1979_1986 | -1.285117 1.049906 -1.22 0.221 -3.34294 .7727052 y_1980_1986 | -1.119151 1.049628 -1.07 0.286 -3.176429 .9381265 y_1981_1986 | -1.119559 1.049205 -1.07 0.286 -3.176006 .9368889 y_1982_1986 | -1.145504 1.049427 -1.09 0.275 -3.202387 .9113789 y_1983_1986 | -1.125696 1.049158 -1.07 0.283 -3.182051 .9306595 y_1984_1986 | -1.070875 1.049155 -1.02 0.307 -3.127226 .9854757 y_1985_1986 | -1.073278 1.049106 -1.02 0.306 -3.129532 .9829769 y_1986_1986 | -1.020652 1.049209 -0.97 0.331 -3.077108 1.035804 y_1978_1987 | -1.276137 1.009574 -1.26 0.206 -3.254908 .7026347 y_1979_1987 | -1.263695 1.008684 -1.25 0.210 -3.240723 .713332 y_1980_1987 | -1.030635 1.009031 -1.02 0.307 -3.008342 .9470728 y_1981_1987 | -1.130492 1.00821 -1.12 0.262 -3.10659 .8456062 y_1982_1987 | -1.133785 1.008387 -1.12 0.261 -3.11023 .8426601 y_1983_1987 | -1.077629 1.007776 -1.07 0.285 -3.052876 .8976183 y_1984_1987 | -1.006088 1.007754 -1.00 0.318 -2.981291 .9691155 y_1985_1987 | -.9816271 1.007647 -0.97 0.330 -2.956622 .9933674 y_1986_1987 | -.9863145 1.007582 -0.98 0.328 -2.961181 .9885519 y_1987_1987 | -.968892 1.007791 -0.96 0.336 -2.944169 1.006385 y_1978_1988 | -1.278712 .9684464 -1.32 0.187 -3.176872 .6194486 y_1979_1988 | -1.204491 .968461 -1.24 0.214 -3.10268 .693698 y_1980_1988 | -1.119541 .9681627 -1.16 0.248 -3.017145 .7780633 y_1981_1988 | -1.133864 .9668133 -1.17 0.241 -3.028823 .7610956 y_1982_1988 | -1.147716 .9676573 -1.19 0.236 -3.04433 .748898 y_1983_1988 | -1.031522 .9665639 -1.07 0.286 -2.925993 .862949 y_1984_1988 | -1.009804 .966374 -1.04 0.296 -2.903903 .8842941 y_1985_1988 | -1.005087 .9661581 -1.04 0.298 -2.898762 .8885886 y_1986_1988 | -.9447713 .9663264 -0.98 0.328 -2.838777 .949234 y_1987_1988 | -.9652123 .9660661 -1.00 0.318 -2.858707 .9282828 y_1988_1988 | -.9122068 .9662589 -0.94 0.345 -2.80608 .9816661 y_1978_1989 | -1.283946 .9277097 -1.38 0.166 -3.102263 .5343699 y_1979_1989 | -1.245602 .9275061 -1.34 0.179 -3.06352 .5723148 y_1980_1989 | -1.138404 .9269185 -1.23 0.219 -2.955169 .6783619 y_1981_1989 | -1.149768 .925908 -1.24 0.214 -2.964553 .6650169 y_1982_1989 | -1.169188 .9260046 -1.26 0.207 -2.984162 .6457861 y_1983_1989 | -1.074116 .9256949 -1.16 0.246 -2.888483 .7402516 y_1984_1989 | -1.074763 .9252938 -1.16 0.245 -2.888344 .7388183 y_1985_1989 | -1.007639 .9249 -1.09 0.276 -2.820449 .8051698 y_1986_1989 | -.9439387 .9247127 -1.02 0.307 -2.756381 .8685035 y_1987_1989 | -.9469881 .9246106 -1.02 0.306 -2.75923 .8652539 y_1988_1989 | -.9368001 .924639 -1.01 0.311 -2.749098 .8754976 y_1989_1989 | -.9417879 .9247526 -1.02 0.308 -2.754308 .8707324 y_1978_1990 | -1.284778 .8872644 -1.45 0.148 -3.023822 .4542648 y_1979_1990 | -1.24967 .8863896 -1.41 0.159 -2.986999 .4876588 y_1980_1990 | -1.115055 .8875261 -1.26 0.209 -2.854611 .6245015 y_1981_1990 | -1.16743 .8846059 -1.32 0.187 -2.901262 .5664029 y_1982_1990 | -1.164069 .8852109 -1.32 0.189 -2.899088 .5709489 y_1983_1990 | -1.149559 .884426 -1.30 0.194 -2.883039 .5839206 y_1984_1990 | -1.024935 .8837703 -1.16 0.246 -2.75713 .7072595 y_1985_1990 | -.9855561 .8835719 -1.12 0.265 -2.717362 .7462498 y_1986_1990 | -.9585292 .8832397 -1.09 0.278 -2.689684 .7726257 y_1987_1990 | -.9673909 .8831401 -1.10 0.273 -2.69835 .7635686 y_1988_1990 | -.9253655 .8831335 -1.05 0.295 -2.656312 .8055812 y_1989_1990 | -.9016182 .8831151 -1.02 0.307 -2.632529 .8292925 y_1990_1990 | -.8907576 .8832146 -1.01 0.313 -2.621863 .8403481 y_1978_1991 | -1.272689 .8474927 -1.50 0.133 -2.93378 .3884011 y_1979_1991 | -1.257001 .8466535 -1.48 0.138 -2.916447 .4024443 y_1980_1991 | -1.2463 .8465993 -1.47 0.141 -2.905639 .4130393 y_1981_1991 | -1.125925 .8443356 -1.33 0.182 -2.780828 .5289775 y_1982_1991 | -1.193248 .8446264 -1.41 0.158 -2.84872 .4622246 y_1983_1991 | -1.088955 .8430224 -1.29 0.196 -2.741283 .5633742 y_1984_1991 | -1.075647 .8428204 -1.28 0.202 -2.72758 .5762861 y_1985_1991 | -.9795833 .8423447 -1.16 0.245 -2.630584 .6714171 y_1986_1991 | -.97408 .8422431 -1.16 0.247 -2.624881 .6767212 y_1987_1991 | -.9484278 .841854 -1.13 0.260 -2.598467 .7016109 y_1988_1991 | -.9859877 .8417264 -1.17 0.241 -2.635776 .6638008 y_1989_1991 | -.9414165 .8416798 -1.12 0.263 -2.591114 .7082807 y_1990_1991 | -.9154385 .8415993 -1.09 0.277 -2.564978 .7341009 y_1991_1991 | -.9456477 .8417947 -1.12 0.261 -2.59557 .7042747 y_1978_1992 | -1.2566 .8077889 -1.56 0.120 -2.839871 .3266709 y_1979_1992 | -1.208442 .8062826 -1.50 0.134 -2.78876 .3718766 y_1980_1992 | -1.216863 .8074835 -1.51 0.132 -2.799535 .3658092 y_1981_1992 | -1.129847 .8034912 -1.41 0.160 -2.704694 .4450005 y_1982_1992 | -1.180163 .8039269 -1.47 0.142 -2.755864 .3955384 y_1983_1992 | -1.080462 .8024734 -1.35 0.178 -2.653314 .4923903 y_1984_1992 | -1.101741 .8017813 -1.37 0.169 -2.673237 .4697546 y_1985_1992 | -1.008711 .8011336 -1.26 0.208 -2.578938 .5615153 y_1986_1992 | -.972957 .8008548 -1.21 0.224 -2.542637 .596723 y_1987_1992 | -.9754573 .8006439 -1.22 0.223 -2.544724 .5938092 y_1988_1992 | -.949076 .8004314 -1.19 0.236 -2.517926 .6197741 y_1989_1992 | -.8767077 .8002353 -1.10 0.273 -2.445173 .6917581 y_1990_1992 | -.916827 .8001668 -1.15 0.252 -2.485158 .6515044 y_1991_1992 | -.9397869 .8001997 -1.17 0.240 -2.508183 .628609 y_1992_1992 | -.9175271 .8003706 -1.15 0.252 -2.486258 .6512038 y_1978_1993 | -1.228129 .7684599 -1.60 0.110 -2.734315 .2780571 y_1979_1993 | -1.202035 .768635 -1.56 0.118 -2.708564 .3044937 y_1980_1993 | -1.125025 .7651112 -1.47 0.141 -2.624647 .3745973 y_1981_1993 | -1.066239 .7636568 -1.40 0.163 -2.56301 .4305327 y_1982_1993 | -1.182751 .764355 -1.55 0.122 -2.680892 .3153886 y_1983_1993 | -1.035349 .7615827 -1.36 0.174 -2.528055 .4573575 y_1984_1993 | -1.092525 .7611346 -1.44 0.151 -2.584353 .3993029 y_1985_1993 | -.9552104 .7604254 -1.26 0.209 -2.445649 .5352277 y_1986_1993 | -.9028866 .7598416 -1.19 0.235 -2.392181 .5864073 y_1987_1993 | -.9807106 .7595716 -1.29 0.197 -2.469475 .5080542 y_1988_1993 | -.9405279 .7595358 -1.24 0.216 -2.429222 .5481666 y_1989_1993 | -.8827635 .758874 -1.16 0.245 -2.370161 .6046339 y_1990_1993 | -.9314453 .7588801 -1.23 0.220 -2.418855 .5559641 y_1991_1993 | -.931872 .758812 -1.23 0.219 -2.419148 .5554038 y_1992_1993 | -.9133276 .7586487 -1.20 0.229 -2.400283 .5736282 y_1993_1993 | -.8599953 .7590175 -1.13 0.257 -2.347674 .6276832 y_1978_1994 | -1.170246 .7304265 -1.60 0.109 -2.601887 .2613936 y_1979_1994 | -1.068468 .7287858 -1.47 0.143 -2.496893 .3599559 y_1980_1994 | -1.108855 .7266567 -1.53 0.127 -2.533106 .3153963 y_1981_1994 | -1.071772 .7242369 -1.48 0.139 -2.491281 .3477363 y_1982_1994 | -1.207937 .7239551 -1.67 0.095 -2.626893 .2110194 y_1983_1994 | -1.001778 .7217212 -1.39 0.165 -2.416355 .4127999 y_1984_1994 | -1.026833 .7212035 -1.42 0.155 -2.440396 .3867301 y_1985_1994 | -.9825397 .7196472 -1.37 0.172 -2.393052 .427973 y_1986_1994 | -.8934843 .7190306 -1.24 0.214 -2.302788 .5158197 y_1987_1994 | -.9502027 .718841 -1.32 0.186 -2.359135 .4587297 y_1988_1994 | -.9440945 .718306 -1.31 0.189 -2.351978 .4637894 y_1989_1994 | -.8489591 .7177298 -1.18 0.237 -2.255714 .5577953 y_1990_1994 | -.8920527 .7177398 -1.24 0.214 -2.298827 .5147215 y_1991_1994 | -.8561746 .7176857 -1.19 0.233 -2.262843 .5504936 y_1992_1994 | -.8908677 .7175283 -1.24 0.214 -2.297227 .5154918 y_1993_1994 | -.8590645 .7173184 -1.20 0.231 -2.265013 .5468836 y_1994_1994 | -.8210975 .7175197 -1.14 0.252 -2.22744 .5852452 y_1978_1996 | -1.078967 .6565267 -1.64 0.100 -2.365763 .2078291 y_1979_1996 | -1.027191 .6545522 -1.57 0.117 -2.310117 .2557347 y_1980_1996 | -1.018016 .6514387 -1.56 0.118 -2.29484 .2588072 y_1981_1996 | -.9233704 .6465828 -1.43 0.153 -2.190676 .3439355 y_1982_1996 | -1.10335 .647451 -1.70 0.088 -2.372358 .1656577 y_1983_1996 | -.950337 .6422288 -1.48 0.139 -2.209109 .3084352 y_1984_1996 | -.9493375 .6416893 -1.48 0.139 -2.207052 .3083771 y_1985_1996 | -.896602 .6396272 -1.40 0.161 -2.150275 .357071 y_1986_1996 | -.8214607 .6388284 -1.29 0.198 -2.073568 .4306465 y_1987_1996 | -.9517718 .6379538 -1.49 0.136 -2.202165 .2986213 y_1988_1996 | -.8882126 .6371525 -1.39 0.163 -2.137035 .36061 y_1989_1996 | -.8312729 .6360374 -1.31 0.191 -2.07791 .415364 y_1990_1996 | -.8680985 .635946 -1.37 0.172 -2.114556 .3783593 y_1991_1996 | -.8552595 .6353497 -1.35 0.178 -2.100549 .3900296 y_1992_1996 | -.8122682 .6351482 -1.28 0.201 -2.057162 .4326258 y_1993_1996 | -.789691 .6346563 -1.24 0.213 -2.033621 .4542389 y_1994_1996 | -.7840625 .63461 -1.24 0.217 -2.027902 .4597768 y_1995_1996 | -.7216644 .6344424 -1.14 0.255 -1.965175 .5218464 y_1996_1996 | -.774335 .6345855 -1.22 0.222 -2.018126 .4694561 y_1978_1998 | -.9273105 .5880436 -1.58 0.115 -2.079879 .2252584 y_1979_1998 | -.8868041 .5857065 -1.51 0.130 -2.034792 .2611839 y_1980_1998 | -.9080639 .5782674 -1.57 0.116 -2.041471 .2253436 y_1981_1998 | -.8716967 .5726428 -1.52 0.128 -1.99408 .2506865 y_1982_1998 | -.9074443 .572166 -1.59 0.113 -2.028893 .2140043 y_1983_1998 | -.857817 .5665447 -1.51 0.130 -1.968248 .2526138 y_1984_1998 | -.8325691 .5633095 -1.48 0.139 -1.936659 .2715207 y_1985_1998 | -.7410166 .5615849 -1.32 0.187 -1.841726 .359693 y_1986_1998 | -.7953653 .558623 -1.42 0.155 -1.89027 .2995389 y_1987_1998 | -.8298942 .5570846 -1.49 0.136 -1.921783 .2619949 y_1988_1998 | -.8407211 .5558642 -1.51 0.130 -1.930218 .2487758 y_1989_1998 | -.6998849 .5557852 -1.26 0.208 -1.789227 .3894573 y_1990_1998 | -.7928461 .5547855 -1.43 0.153 -1.880229 .2945365 y_1991_1998 | -.8057801 .5534149 -1.46 0.145 -1.890476 .2789163 y_1992_1998 | -.8238232 .5533259 -1.49 0.137 -1.908345 .2606986 y_1993_1998 | -.6636569 .552799 -1.20 0.230 -1.747146 .4198324 y_1994_1998 | -.6869512 .5523033 -1.24 0.214 -1.769469 .3955664 y_1995_1998 | -.6923615 .5517899 -1.25 0.210 -1.773873 .3891498 y_1996_1998 | -.6822104 .5516835 -1.24 0.216 -1.763513 .3990923 y_1997_1998 | -.6748781 .5516257 -1.22 0.221 -1.756068 .4063115 y_1998_1998 | -.6135627 .5520615 -1.11 0.266 -1.695606 .4684809 y_1978_2000 | -.7038878 .5317413 -1.32 0.186 -1.746104 .3383282 y_1979_2000 | -.740613 .5245819 -1.41 0.158 -1.768796 .2875706 y_1980_2000 | -.7154006 .5150065 -1.39 0.165 -1.724816 .2940152 y_1981_2000 | -.676512 .5071894 -1.33 0.182 -1.670606 .317582 y_1982_2000 | -.8654759 .5153073 -1.68 0.093 -1.875481 .1445292 y_1983_2000 | -.7259805 .4968782 -1.46 0.144 -1.699865 .2479037 y_1984_2000 | -.7058135 .490471 -1.44 0.150 -1.667139 .2555124 y_1985_2000 | -.6061741 .4892557 -1.24 0.215 -1.565118 .3527699 y_1986_2000 | -.6898342 .4833322 -1.43 0.154 -1.637168 .2574997 y_1987_2000 | -.7456466 .4806429 -1.55 0.121 -1.687709 .1964161 y_1988_2000 | -.7611418 .4774608 -1.59 0.111 -1.696968 .1746842 y_1989_2000 | -.6700067 .476284 -1.41 0.160 -1.603526 .2635127 y_1990_2000 | -.7053433 .4755682 -1.48 0.138 -1.63746 .226773 y_1991_2000 | -.679768 .4739406 -1.43 0.151 -1.608694 .2491582 y_1992_2000 | -.6693214 .4728818 -1.42 0.157 -1.596172 .2575296 y_1993_2000 | -.6719737 .4723325 -1.42 0.155 -1.597748 .2538006 y_1994_2000 | -.6781161 .4718542 -1.44 0.151 -1.602953 .2467208 y_1995_2000 | -.6284383 .4705049 -1.34 0.182 -1.550631 .293754 y_1996_2000 | -.6276489 .4699892 -1.34 0.182 -1.54883 .2935326 y_1997_2000 | -.6223275 .469665 -1.33 0.185 -1.542874 .2982186 y_1998_2000 | -.5552173 .4692394 -1.18 0.237 -1.474929 .3644947 y_1999_2000 | -.4854668 .4688888 -1.04 0.301 -1.404492 .433558 y_2000_2000 | -.5091268 .4694439 -1.08 0.278 -1.429239 .4109858 y_1978_2002 | -.3362595 .4914481 -0.68 0.494 -1.2995 .6269815 y_1979_2002 | -.4123139 .4741214 -0.87 0.385 -1.341595 .5169667 y_1980_2002 | -.5779673 .4585229 -1.26 0.207 -1.476675 .3207402 y_1981_2002 | -.5206253 .4488204 -1.16 0.246 -1.400316 .3590652 y_1982_2002 | -.5268392 .4463671 -1.18 0.238 -1.401721 .348043 y_1983_2002 | -.5699857 .4337049 -1.31 0.189 -1.42005 .2800784 y_1984_2002 | -.4806808 .4253472 -1.13 0.258 -1.314364 .3530021 y_1985_2002 | -.5765549 .4184428 -1.38 0.168 -1.396705 .2435953 y_1986_2002 | -.4213901 .4157021 -1.01 0.311 -1.236169 .3933883 y_1987_2002 | -.5562371 .4118116 -1.35 0.177 -1.36339 .250916 y_1988_2002 | -.701515 .4039159 -1.74 0.082 -1.493193 .0901625 y_1989_2002 | -.5340618 .4007444 -1.33 0.183 -1.319523 .2513995 y_1990_2002 | -.7139583 .3988792 -1.79 0.073 -1.495764 .0678472 y_1991_2002 | -.713436 .3982133 -1.79 0.073 -1.493936 .0670643 y_1992_2002 | -.6930546 .3957664 -1.75 0.080 -1.468759 .0826498 y_1993_2002 | -.6532946 .3923203 -1.67 0.096 -1.422245 .1156554 y_1994_2002 | -.5777554 .3915739 -1.48 0.140 -1.345243 .1897317 y_1995_2002 | -.5614385 .3913432 -1.43 0.151 -1.328474 .2055964 y_1996_2002 | -.6393772 .3893167 -1.64 0.101 -1.40244 .1236858 y_1997_2002 | -.5199942 .3889627 -1.34 0.181 -1.282363 .2423748 y_1998_2002 | -.5237322 .3889832 -1.35 0.178 -1.286142 .2386771 y_1999_2002 | -.4698305 .3878148 -1.21 0.226 -1.22995 .2902887 y_2000_2002 | -.4785567 .3871446 -1.24 0.216 -1.237362 .2802489 y_2001_2002 | -.4982342 .3868129 -1.29 0.198 -1.25639 .2599213 y_2002_2002 | -.4693128 .3866197 -1.21 0.225 -1.22709 .288464 y_1978_2004 | -.3274569 .4599871 -0.71 0.477 -1.229034 .5741204 y_1979_2004 | -.294762 .4438594 -0.66 0.507 -1.164729 .5752049 y_1980_2004 | -.2713662 .4262979 -0.64 0.524 -1.106913 .5641801 y_1981_2004 | -.3460389 .4072629 -0.85 0.396 -1.144276 .4521986 y_1982_2004 | -.3196747 .4008703 -0.80 0.425 -1.105383 .4660334 y_1983_2004 | -.3557075 .3796974 -0.94 0.349 -1.099916 .3885015 y_1984_2004 | -.3224663 .374769 -0.86 0.390 -1.057016 .4120831 y_1985_2004 | -.4113932 .3588841 -1.15 0.252 -1.114808 .2920216 y_1986_2004 | -.4269701 .3506115 -1.22 0.223 -1.114171 .2602304 y_1987_2004 | -.4126164 .3445701 -1.20 0.231 -1.087976 .2627429 y_1988_2004 | -.4835782 .3363832 -1.44 0.151 -1.142891 .1757347 y_1989_2004 | -.4218528 .3309854 -1.27 0.202 -1.070586 .2268805 y_1990_2004 | -.5293983 .3293773 -1.61 0.108 -1.17498 .1161831 y_1991_2004 | -.5943515 .3237759 -1.84 0.066 -1.228954 .0402511 y_1992_2004 | -.6384866 .3217911 -1.98 0.047 -1.269199 -.0077742 y_1993_2004 | -.491867 .3178063 -1.55 0.122 -1.114769 .1310352 y_1994_2004 | -.5263393 .3159399 -1.67 0.096 -1.145583 .0929046 y_1995_2004 | -.5468314 .3126632 -1.75 0.080 -1.159653 .0659903 y_1996_2004 | -.4589105 .3127994 -1.47 0.142 -1.071999 .1541781 y_1997_2004 | -.5360104 .3097213 -1.73 0.084 -1.143066 .071045 y_1998_2004 | -.4566717 .3096313 -1.47 0.140 -1.063551 .1502074 y_1999_2004 | -.4418135 .3079748 -1.43 0.151 -1.045446 .1618189 y_2000_2004 | -.4235916 .3065429 -1.38 0.167 -1.024417 .1772342 y_2001_2004 | -.448374 .3063285 -1.46 0.143 -1.04878 .1520316 y_2002_2004 | -.4713077 .305792 -1.54 0.123 -1.070662 .1280463 y_2003_2004 | -.3820746 .3055055 -1.25 0.211 -.9808672 .2167179 y_2004_2004 | -.3246172 .30641 -1.06 0.289 -.9251825 .275948 y_1978_2006 | -.0794175 .4464829 -0.18 0.859 -.9545265 .7956915 y_1979_2006 | .0553817 .4342319 0.13 0.899 -.7957154 .9064788 y_1980_2006 | -.1020538 .4133081 -0.25 0.805 -.91214 .7080325 y_1981_2006 | -.1318884 .3857577 -0.34 0.732 -.8879757 .6241989 y_1982_2006 | -.1297265 .3860039 -0.34 0.737 -.8862963 .6268432 y_1983_2006 | -.1990401 .3494935 -0.57 0.569 -.8840494 .4859691 y_1984_2006 | -.2637804 .3361266 -0.78 0.433 -.9225905 .3950296 y_1985_2006 | -.1929711 .3210027 -0.60 0.548 -.8221383 .4361961 y_1986_2006 | -.341564 .305686 -1.12 0.264 -.9407103 .2575822 y_1987_2006 | -.3643532 .2950856 -1.23 0.217 -.9427225 .2140162 y_1988_2006 | -.338627 .2884727 -1.17 0.240 -.9040351 .226781 y_1989_2006 | -.3081867 .276861 -1.11 0.266 -.8508358 .2344624 y_1990_2006 | -.3711373 .2705147 -1.37 0.170 -.9013477 .1590731 y_1991_2006 | -.5768719 .2628864 -2.19 0.028 -1.092131 -.0616131 y_1992_2006 | -.5644324 .2556026 -2.21 0.027 -1.065415 -.0634498 y_1993_2006 | -.4631477 .2478006 -1.87 0.062 -.9488382 .0225429 y_1994_2006 | -.39469 .2454738 -1.61 0.108 -.8758199 .08644 y_1995_2006 | -.4017204 .2377387 -1.69 0.091 -.8676896 .0642488 y_1996_2006 | -.4823634 .2384994 -2.02 0.043 -.9498235 -.0149032 y_1997_2006 | -.5518833 .2403487 -2.30 0.022 -1.022968 -.0807985 y_1998_2006 | -.5277803 .2439838 -2.16 0.031 -1.00599 -.0495706 y_1999_2006 | -.3719233 .2318571 -1.60 0.109 -.8263646 .0825179 y_2000_2006 | -.4120305 .2301756 -1.79 0.073 -.863176 .0391151 y_2001_2006 | -.382326 .2285509 -1.67 0.094 -.8302871 .0656351 y_2002_2006 | -.3402987 .227098 -1.50 0.134 -.785412 .1048146 y_2003_2006 | -.2775419 .2276809 -1.22 0.223 -.7237977 .1687138 y_2004_2006 | -.3058362 .2248305 -1.36 0.174 -.7465053 .134833 y_2005_2006 | -.3758799 .2233715 -1.68 0.092 -.8136893 .0619296 y_2006_2006 | -.3662817 .2249861 -1.63 0.104 -.8072558 .0746924 y_1978_2008 | .1871813 .4645629 0.40 0.687 -.7233646 1.097727 y_1979_2008 | .3053944 .4471335 0.68 0.495 -.5709899 1.181779 y_1980_2008 | .1156622 .4190241 0.28 0.783 -.7056275 .9369519 y_1981_2008 | .0312972 .3937084 0.08 0.937 -.7403734 .8029678 y_1982_2008 | .0342108 .3966848 0.09 0.931 -.7432937 .8117153 y_1983_2008 | .0915771 .3453591 0.27 0.791 -.5853287 .7684829 y_1984_2008 | .0009036 .3233364 0.00 0.998 -.6328376 .6346448 y_1985_2008 | .0547655 .3088616 0.18 0.859 -.5506049 .660136 y_1986_2008 | -.1351819 .2854721 -0.47 0.636 -.6947088 .424345 y_1987_2008 | -.1421569 .269392 -0.53 0.598 -.6701668 .3858529 y_1988_2008 | -.0917107 .2576261 -0.36 0.722 -.5966593 .4132378 y_1989_2008 | -.0989537 .2395274 -0.41 0.680 -.5684287 .3705213 y_1990_2008 | -.2437279 .2384675 -1.02 0.307 -.7111256 .2236697 y_1991_2008 | -.4952278 .2209744 -2.24 0.025 -.9283389 -.0621167 y_1992_2008 | -.4305112 .2127072 -2.02 0.043 -.8474184 -.0136039 y_1993_2008 | -.278674 .2018077 -1.38 0.167 -.6742182 .1168703 y_1994_2008 | -.2904876 .1858922 -1.56 0.118 -.6548374 .0738623 y_1995_2008 | -.3414499 .1790027 -1.91 0.056 -.6922963 .0093964 y_1996_2008 | -.4329904 .1818576 -2.38 0.017 -.7894323 -.0765485 y_1997_2008 | -.3699464 .167291 -2.21 0.027 -.6978376 -.0420552 y_1998_2008 | -.3078117 .1651299 -1.86 0.062 -.6314672 .0158438 y_1999_2008 | -.3067866 .1621177 -1.89 0.058 -.6245382 .0109649 y_2000_2008 | -.4019557 .166387 -2.42 0.016 -.7280751 -.0758364 y_2001_2008 | -.2466669 .1581148 -1.56 0.119 -.5565729 .0632391 y_2002_2008 | -.2117268 .1537646 -1.38 0.169 -.5131063 .0896526 y_2003_2008 | -.2719024 .155592 -1.75 0.081 -.5768636 .0330587 y_2004_2008 | -.2668269 .1509919 -1.77 0.077 -.562772 .0291182 y_2005_2008 | -.1414233 .1466167 -0.96 0.335 -.428793 .1459463 y_2006_2008 | -.3057934 .1466635 -2.08 0.037 -.5932547 -.0183321 y_2007_2008 | -.2123729 .1441159 -1.47 0.141 -.4948408 .070095 y_2008_2008 | -.2481951 .1485362 -1.67 0.095 -.5393268 .0429367 y_1978_2010 | .4447817 .5107158 0.87 0.384 -.5562241 1.445787 y_1979_2010 | .5689645 .4920665 1.16 0.248 -.3954886 1.533418 y_1980_2010 | .4149193 .4662563 0.89 0.374 -.4989458 1.328784 y_1981_2010 | .3935342 .4250218 0.93 0.354 -.4395109 1.226579 y_1982_2010 | .3871165 .4041033 0.96 0.338 -.4049283 1.179161 y_1983_2010 | .3527216 .3740615 0.94 0.346 -.380441 1.085884 y_1984_2010 | .1616166 .3401289 0.48 0.635 -.505038 .8282712 y_1985_2010 | .1459149 .3247775 0.45 0.653 -.4906508 .7824805 y_1986_2010 | .1078513 .2997527 0.36 0.719 -.4796657 .6953684 y_1987_2010 | .0902556 .277573 0.33 0.745 -.453789 .6343002 y_1988_2010 | -.0580673 .255053 -0.23 0.820 -.5579725 .441838 y_1989_2010 | .0110953 .2420267 0.05 0.963 -.4632785 .4854691 y_1990_2010 | -.0587716 .2284338 -0.26 0.797 -.5065032 .3889599 y_1991_2010 | -.2737761 .212521 -1.29 0.198 -.6903184 .1427662 y_1992_2010 | -.1524901 .1979386 -0.77 0.441 -.5404509 .2354706 y_1993_2010 | -.2939946 .190144 -1.55 0.122 -.666678 .0786887 y_1994_2010 | -.1531083 .1626863 -0.94 0.347 -.4719744 .1657579 y_1995_2010 | -.337204 .1562222 -2.16 0.031 -.6434003 -.0310076 y_1996_2010 | -.3506774 .1551341 -2.26 0.024 -.6547411 -.0466137 y_1997_2010 | -.3390464 .1345202 -2.52 0.012 -.6027067 -.0753862 y_1998_2010 | -.2470045 .129827 -1.90 0.057 -.5014661 .0074572 y_1999_2010 | -.3102404 .1181162 -2.63 0.009 -.5417488 -.0787321 y_2000_2010 | -.3358423 .1123847 -2.99 0.003 -.556117 -.1155676 y_2001_2010 | -.3306949 .1147088 -2.88 0.004 -.5555248 -.1058651 y_2002_2010 | -.1384938 .1045298 -1.32 0.185 -.3433728 .0663851 y_2003_2010 | -.2470199 .1160715 -2.13 0.033 -.4745208 -.019519 y_2004_2010 | -.221343 .0918958 -2.41 0.016 -.4014594 -.0412266 y_2005_2010 | -.1612645 .0883481 -1.83 0.068 -.3344273 .0118983 y_2006_2010 | -.1630789 .0873033 -1.87 0.062 -.3341938 .0080361 y_2007_2010 | -.1258418 .0838281 -1.50 0.133 -.2901455 .0384618 y_2008_2010 | -.0577309 .0830083 -0.70 0.487 -.2204275 .1049658 y_2009_2010 | -.1625492 .0765269 -2.12 0.034 -.3125423 -.012556 y_2010_2010 | 0 (omitted) y_1978_2012 | .9360867 .5946191 1.57 0.115 -.2293701 2.101544 y_1979_2012 | 1.120039 .6139682 1.82 0.068 -.0833418 2.32342 y_1980_2012 | .8257439 .5324983 1.55 0.121 -.2179558 1.869444 y_1981_2012 | .8039856 .4995368 1.61 0.108 -.1751094 1.783081 y_1982_2012 | .8558573 .4963236 1.72 0.085 -.1169397 1.828654 y_1983_2012 | .737074 .4444368 1.66 0.097 -.1340246 1.608173 y_1984_2012 | .4909864 .41749 1.18 0.240 -.3272964 1.309269 y_1985_2012 | .489677 .3821243 1.28 0.200 -.2592888 1.238643 y_1986_2012 | .4884909 .3581228 1.36 0.173 -.2134318 1.190414 y_1987_2012 | .4315468 .3303485 1.31 0.191 -.2159383 1.079032 y_1988_2012 | .2485615 .30866 0.81 0.421 -.356414 .8535369 y_1989_2012 | .3258535 .2909198 1.12 0.263 -.244351 .896058 y_1990_2012 | .0970204 .2740792 0.35 0.723 -.4401764 .6342172 y_1991_2012 | .0342427 .2504107 0.14 0.891 -.4565637 .525049 y_1992_2012 | .0879813 .2393849 0.37 0.713 -.3812146 .5571772 y_1993_2012 | -.2265921 .2329447 -0.97 0.331 -.683165 .2299808 y_1994_2012 | .0402031 .1974767 0.20 0.839 -.3468523 .4272586 y_1995_2012 | -.1495863 .1855068 -0.81 0.420 -.5131806 .214008 y_1996_2012 | -.1822293 .2036231 -0.89 0.371 -.5813317 .2168732 y_1997_2012 | -.1235871 .1523391 -0.81 0.417 -.4221727 .1749984 y_1998_2012 | -.1842937 .1490538 -1.24 0.216 -.4764401 .1078527 y_1999_2012 | -.2409483 .1508494 -1.60 0.110 -.536614 .0547173 y_2000_2012 | -.1593763 .1251969 -1.27 0.203 -.4047629 .0860104 y_2001_2012 | -.0844473 .1253796 -0.67 0.501 -.330192 .1612975 y_2002_2012 | -.0669107 .1227366 -0.55 0.586 -.3074752 .1736537 y_2003_2012 | -.0216576 .1247911 -0.17 0.862 -.2662489 .2229337 y_2004_2012 | -.2304876 .1145258 -2.01 0.044 -.4549589 -.0060163 y_2005_2012 | -.1047609 .0925772 -1.13 0.258 -.2862128 .0766909 y_2006_2012 | -.0200183 .0938949 -0.21 0.831 -.2040527 .1640162 y_2007_2012 | -.0682475 .0904026 -0.75 0.450 -.2454372 .1089421 y_2008_2012 | .0101576 .0933284 0.11 0.913 -.1727666 .1930817 y_2009_2012 | -.0751625 .0871034 -0.86 0.388 -.2458857 .0955607 y_2010_2012 | .0537334 .0784445 0.68 0.493 -.1000183 .2074851 y_2011_2012 | .0896828 .0674635 1.33 0.184 -.042546 .2219116 y_2012_2012 | 0 (omitted) _cons | 2.65181 1.424228 1.86 0.063 -.1396857 5.443305 ---------------+---------------------------------------------------------------- id | absorbed (2711 categories) . estadd local indcontr "Yes"; added macro: e(indcontr) : "Yes" . est sto m_chi; . /* regsave using `tempdata'coefs_`prg', addlabel(scenario, areg) replace; */ > esttab m_base m_cntrl m_cntrlfes m_chi using `slides'tab_`prg'_chi_1978_2013.tex, replace > /* drop(ind_* y_* o*) */ keep(potexperience* hgc tenure* ind*) scalars("indcontr Indstry Controls") > booktabs se r2 label mtitles nogap addnotes("Include individual fixed effects" "Only men"); (note: file ../../slides/tab_fixed_effects_ipd_chi_1978_2013.tex not found) (output written to ../../slides/tab_fixed_effects_ipd_chi_1978_2013.tex) . clear; . /* Entry-level wages */ > use `tempdata'topredict_entry, clear; . predict what, xb; . rename employer_currenty year; . rename what lentry_wage; . la var lentry_wage "log(entry wage)"; . keep lentry_wage year; . save `myinput'`prg'_entry_wage, replace; file ../input/fixed_effects_ipd_entry_wage.dta saved . export excel using `myoutput'`prg'_entry_wage, replace firstrow(variables); file ../output/fixed_effects_ipd_entry_wage.xls saved . outsheet using `myinput'`prg'_entry_wage.csv, replace comma; . /* Calculating wage strips for the user cost */ > use `tempdata'topredict, clear; . predict what, xb; . replace what = exp(what); (149 real changes made) . /* Calculate USER COST OF LABOR */ > keep employer_starty what toadd; . reshape wide what, i(employer_starty) j(toadd); (note: j = 0 1 2 3 4 5 6) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 149 -> 29 Number of variables 3 -> 8 j variable (7 values) toadd -> (dropped) xij variables: what -> what0 what1 ... what6 ----------------------------------------------------------------------------- . tsset employer_starty; time variable: employer_st~y, 1978 to 2006 delta: 1 unit . /* /\*** save data in wide format ***\/ */ > /* save `myoutput'`prg'_data_wide, replace; */ > /* export excel using `myoutput'`prg'_data_wide.xlsx, replace firstrow(variables); */ > > /*** Fill in missing wages with an earlier wage ***/ > foreach ii of num 1/6 {; 2. local jj = `ii' - 1; 3. replace what`ii' = what`jj' if missing(what`ii'); 4. }; (7 real changes made) (7 real changes made) (8 real changes made) (8 real changes made) (9 real changes made) (9 real changes made) . /* /\*** save "filled-in" dataset in wide format ***\/ */ > /* export excel `myoutput'`prg'_data_wide_filledin, replace; */ > > /*** Get ready to interpolate the measure of PDV ***/ > rename employer_starty year; . tsset year; time variable: year, 1978 to 2006 delta: 1 unit . tsfill; . /* See MK (JMCB, 2014) for these values */ > gen beta = 0.9569; . /* gen delta = 0.295; */ > > gen PDV = what0 > + ((beta*(1-`delta'))^1)*what1 > + ((beta*(1-`delta'))^2)*what2 > + ((beta*(1-`delta'))^3)*what3 > + ((beta*(1-`delta'))^4)*what4 > + ((beta*(1-`delta'))^5)*what5 > + ((beta*(1-`delta'))^6)*what6; (6 missing values generated) . rename PDV PDVholes; . ipolate PDVholes year, gen(PDV); . gen UC = PDV - beta*(1-`delta')*F.PDV; (1 missing value generated) . /* see equation (1), p. 55 of Kudlyak, JME 2014 */ > /* save for graphing */ > gen lUC = log(UC); (1 missing value generated) . keep year UC lUC; . save `myinput'`prg'_user_cost, replace; file ../input/fixed_effects_ipd_user_cost.dta saved . /*** output for MATLAB ***/ > outsheet using `myinput'`prg'_user_cost.csv, replace comma; . /*** Erase temporary datasets ***/ > erase `tempdata'topredict_entry.dta; . erase `tempdata'topredict.dta; . log close; name: log: C:\Users\chouse\Dropbox\basu-house\analysis\code\../temp/fixed_effects_ipd.log log type: text closed on: 18 Jan 2017, 15:00:18 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