---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- name: log: C:\Users\chouse\Dropbox\basu-house\analysis\code\../temp/fixed_effects_ipd_cumtight.log log type: text opened on: 18 Jan 2017, 15:00:18 . /****************************************************************************/ > /*** REGRESSION SPECIFICATIONS ***/ > /****************************************************************************/ > > /*** wage used in analysis: ***/ > local mywage ln_hrly_wage_ipd; . 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 cumtight; . local tableA1_nounion `mywage' ur hgc potexperience potexperience2 tenurey tenurey2 trend cumtight; . local cycregs_base `mywage' potexperience potexperience2 cumtight; . local cycregs_cntrl `mywage' potexperience potexperience2 hgc tenurey tenurey2 `industries' cumtight; . local uc_regs `mywage' hgc potexperience potexperience2 tenurey tenurey2 trend `industries' cumtight; . /****************************************************************************/ > /*** 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 | hrly_wage_cpi79 > 100; /\* BLS censoring *\/ */ > /* drop if non_nlsy==1; /\* drop non-NLSY years *\/ */ > 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) . /* 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 potexperience2 = potexperience*potexperience; (1,913 missing values generated) . /*** END GENERATE VARIABLES ***/ > > /********************************************************************************************/ > /*** TABLE A1 ***/ > /********************************************************************************************/ > /*** 1978--2013 ***/ > /* Column (1): no industry fixed effects */ > areg `tableA1_nounion' [pweight = csampweight], > absorb(id); (sum of wgt is 3.9716e+08) Linear regression, absorbing indicators Number of obs = 62,914 F( 8, 60197) = 4638.65 Prob > F = 0.0000 R-squared = 0.6383 Adj R-squared = 0.6220 Root MSE = 0.3976 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0078925 .0014184 -5.56 0.000 -.0106725 -.0051124 hgc | .0509599 .0075577 6.74 0.000 .0361468 .0657729 potexperience | .0048477 .0071741 0.68 0.499 -.0092135 .0189089 potexperience2 | -.0010567 .0000274 -38.51 0.000 -.0011105 -.0010029 tenurey | .0347253 .0010874 31.94 0.000 .0325941 .0368566 tenurey2 | -.0005939 .0000458 -12.98 0.000 -.0006836 -.0005042 trend | .0482093 .0072245 6.67 0.000 .0340492 .0623694 cumtight | .0005537 .0000242 22.85 0.000 .0005062 .0006012 _cons | 1.367178 .0810181 16.87 0.000 1.208382 1.525974 ---------------+---------------------------------------------------------------- id | absorbed (2709 categories) . estadd local indcontr "No"; added macro: e(indcontr) : "No" . est sto m1, title("1978--2013"); . /* Column (2): new hires */ > areg `tableA1_nounion' [pweight = csampweight] if tenurey <= 1, > absorb(id); (sum of wgt is 1.4597e+08) Linear regression, absorbing indicators Number of obs = 24,262 F( 8, 21582) = 762.67 Prob > F = 0.0000 R-squared = 0.5353 Adj R-squared = 0.4776 Root MSE = 0.4000 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0155537 .0024267 -6.41 0.000 -.0203103 -.0107972 hgc | .0620475 .0111265 5.58 0.000 .0402386 .0838563 potexperience | .0148128 .0107535 1.38 0.168 -.0062649 .0358904 potexperience2 | -.0008443 .0000559 -15.11 0.000 -.0009538 -.0007347 tenurey | .0047197 .0419385 0.11 0.910 -.0774828 .0869222 tenurey2 | .0597745 .0413047 1.45 0.148 -.0211858 .1407347 trend | .0282386 .0107497 2.63 0.009 .0071684 .0493089 cumtight | .0007467 .0000471 15.86 0.000 .0006544 .000839 _cons | 1.331908 .1163278 11.45 0.000 1.103897 1.55992 ---------------+---------------------------------------------------------------- id | absorbed (2672 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_nounion' `industries' [pweight = csampweight], > absorb(id); (sum of wgt is 3.7145e+08) Linear regression, absorbing indicators Number of obs = 58,940 F( 16, 56217) = 2473.89 Prob > F = 0.0000 R-squared = 0.6585 Adj R-squared = 0.6419 Root MSE = 0.3841 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0074138 .0014261 -5.20 0.000 -.010209 -.0046187 hgc | .0458507 .007567 6.06 0.000 .0310192 .0606821 potexperience | -.0008953 .0071723 -0.12 0.901 -.014953 .0131624 potexperience2 | -.000994 .0000276 -36.04 0.000 -.0010481 -.00094 tenurey | .0340852 .0010998 30.99 0.000 .0319295 .0362409 tenurey2 | -.000557 .0000465 -11.99 0.000 -.0006481 -.000466 trend | .0510778 .0072223 7.07 0.000 .0369221 .0652335 cumtight | .0005749 .0000247 23.27 0.000 .0005265 .0006233 ind_ag | -.0711688 .0124625 -5.71 0.000 -.0955954 -.0467423 ind_mining | .2437391 .0202985 12.01 0.000 .2039539 .2835244 ind_constr | .1544036 .0077834 19.84 0.000 .139148 .1696592 ind_manufd | .1285451 .0068999 18.63 0.000 .1150213 .1420689 ind_manufn | .1073399 .0080502 13.33 0.000 .0915614 .1231184 ind_trans | .1069171 .0089595 11.93 0.000 .0893565 .1244777 ind_trade | -.0569563 .0059632 -9.55 0.000 -.0686442 -.0452685 ind_fire | .1222487 .0130281 9.38 0.000 .0967136 .1477838 _cons | 1.4011 .0814322 17.21 0.000 1.241492 1.560707 ---------------+---------------------------------------------------------------- id | absorbed (2707 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_nounion' `industries' if tenurey <= 1 [pweight = csampweight], > absorb(id); (sum of wgt is 1.3338e+08) Linear regression, absorbing indicators Number of obs = 22,238 F( 16, 19553) = 455.92 Prob > F = 0.0000 R-squared = 0.5668 Adj R-squared = 0.5073 Root MSE = 0.3827 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ur | -.0156243 .0023976 -6.52 0.000 -.0203237 -.0109249 hgc | .0453052 .0111301 4.07 0.000 .0234894 .0671211 potexperience | -.0010948 .0107222 -0.10 0.919 -.0221113 .0199216 potexperience2 | -.0007687 .0000566 -13.58 0.000 -.0008796 -.0006578 tenurey | .0007393 .0430089 0.02 0.986 -.0835617 .0850403 tenurey2 | .0521911 .0418785 1.25 0.213 -.0298943 .1342764 trend | .0405867 .0107021 3.79 0.000 .0196097 .0615637 cumtight | .0007902 .0000494 16.01 0.000 .0006934 .0008869 ind_ag | .0032408 .0172103 0.19 0.851 -.0304928 .0369744 ind_mining | .2636131 .0313012 8.42 0.000 .20226 .3249661 ind_constr | .2131205 .0110132 19.35 0.000 .1915336 .2347073 ind_manufd | .1315868 .0111062 11.85 0.000 .1098178 .1533558 ind_manufn | .1055711 .0124953 8.45 0.000 .0810793 .1300629 ind_trans | .1362584 .0146402 9.31 0.000 .1075623 .1649545 ind_trade | -.0646354 .0085231 -7.58 0.000 -.0813414 -.0479295 ind_fire | .1212209 .020781 5.83 0.000 .0804883 .1619534 _cons | 1.487649 .116612 12.76 0.000 1.25908 1.716219 ---------------+---------------------------------------------------------------- id | absorbed (2669 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" "Does not include union"); (note: file ../../slides/tab_fixed_effects_ipd_cumtight_tableA1_1978_2013.tex not found) (output written to ../../slides/tab_fixed_effects_ipd_cumtight_tableA1_1978_2013.tex) . /********************************************************************************************/ > /*** Cyclicality of Wages ***/ > /********************************************************************************************/ > /*** BASE ***/ > reg `cycregs_base' i.datey [pweight = csampweight]; (sum of wgt is 3.9716e+08) Linear regression Number of obs = 62,914 F(28, 62885) = 819.61 Prob > F = 0.0000 R-squared = 0.2896 Root MSE = .54513 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .0197992 .0014733 13.44 0.000 .0169115 .022687 potexperience2 | -.001546 .0000519 -29.79 0.000 -.0016477 -.0014443 cumtight | .0005012 .000026 19.27 0.000 .0004502 .0005522 | datey | 1979 | .0663602 .0200683 3.31 0.001 .0270262 .1056941 1980 | .0677342 .0193657 3.50 0.000 .0297775 .105691 1981 | .0767301 .0190587 4.03 0.000 .039375 .1140852 1982 | .1210588 .0193876 6.24 0.000 .083059 .1590586 1983 | .1193147 .0194486 6.13 0.000 .0811954 .157434 1984 | .1797065 .0196553 9.14 0.000 .1411822 .2182308 1985 | .2322028 .0199341 11.65 0.000 .193132 .2712737 1986 | .2997161 .020292 14.77 0.000 .2599438 .3394884 1987 | .38229 .020792 18.39 0.000 .3415375 .4230424 1988 | .4304453 .0210188 20.48 0.000 .3892485 .4716421 1989 | .463888 .0214262 21.65 0.000 .4218927 .5058834 1990 | .5172342 .0218328 23.69 0.000 .474442 .5600265 1991 | .53475 .0221083 24.19 0.000 .4914176 .5780824 1992 | .5785154 .0224287 25.79 0.000 .5345551 .6224756 1993 | .6382215 .0231009 27.63 0.000 .5929437 .6834994 1994 | .7082052 .0237153 29.86 0.000 .6617233 .7546872 1996 | .8194748 .024106 33.99 0.000 .772227 .8667225 1998 | .9779008 .0248496 39.35 0.000 .9291956 1.026606 2000 | 1.10303 .0259394 42.52 0.000 1.052189 1.153872 2002 | 1.245502 .0275392 45.23 0.000 1.191525 1.299478 2004 | 1.39366 .0288508 48.31 0.000 1.337112 1.450207 2006 | 1.492951 .0302958 49.28 0.000 1.433572 1.552331 2008 | 1.638146 .0316876 51.70 0.000 1.576038 1.700254 2010 | 1.791453 .03396 52.75 0.000 1.724891 1.858014 2012 | 1.972442 .0382817 51.52 0.000 1.89741 2.047474 | _cons | 2.086419 .0171904 121.37 0.000 2.052725 2.120112 -------------------------------------------------------------------------------- . regsave using `myinput'`prg'_data, addlabel(scenario, base) replace; file ../input/fixed_effects_ipd_cumtight_data.dta saved . /*** CNTRL ***/ > reg `cycregs_cntrl' i.datey [pweight = csampweight]; (sum of wgt is 3.7145e+08) Linear regression Number of obs = 58,940 F(39, 58900) = 1121.21 Prob > F = 0.0000 R-squared = 0.4504 Root MSE = .47599 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .0550238 .0015379 35.78 0.000 .0520095 .0580381 potexperience2 | -.001298 .0000496 -26.16 0.000 -.0013952 -.0012007 hgc | .0963316 .0013513 71.29 0.000 .0936831 .0989801 tenurey | .0432694 .0012513 34.58 0.000 .0408167 .045722 tenurey2 | -.0007659 .0000603 -12.70 0.000 -.0008842 -.0006477 ind_ag | -.1633137 .0119969 -13.61 0.000 -.1868277 -.1397998 ind_mining | .2498759 .0175147 14.27 0.000 .215547 .2842048 ind_constr | .247773 .0073033 33.93 0.000 .2334586 .2620874 ind_manufd | .1747501 .0062889 27.79 0.000 .1624238 .1870764 ind_manufn | .1317765 .0076852 17.15 0.000 .1167134 .1468396 ind_trans | .1662519 .0084332 19.71 0.000 .1497228 .1827811 ind_trade | -.0595543 .0060316 -9.87 0.000 -.0713763 -.0477324 ind_fire | .2320798 .0124878 18.58 0.000 .2076037 .2565559 cumtight | .000638 .0000253 25.25 0.000 .0005885 .0006875 | datey | 1979 | .046253 .0192232 2.41 0.016 .0085756 .0839305 1980 | .012947 .0186797 0.69 0.488 -.0236653 .0495593 1981 | -.0323124 .0184267 -1.75 0.080 -.0684288 .003804 1982 | -.0477583 .0187572 -2.55 0.011 -.0845226 -.0109941 1983 | -.1081876 .0189211 -5.72 0.000 -.145273 -.0711021 1984 | -.11721 .0191528 -6.12 0.000 -.1547497 -.0796703 1985 | -.1232573 .0196189 -6.28 0.000 -.1617105 -.0848042 1986 | -.1140678 .0200263 -5.70 0.000 -.1533194 -.0748162 1987 | -.0729379 .0209436 -3.48 0.000 -.1139875 -.0318883 1988 | -.0840047 .0211707 -3.97 0.000 -.1254994 -.04251 1989 | -.1114283 .0216836 -5.14 0.000 -.1539282 -.0689285 1990 | -.1116813 .022411 -4.98 0.000 -.1556069 -.0677556 1991 | -.154681 .022703 -6.81 0.000 -.1991789 -.1101831 1992 | -.1637243 .0231888 -7.06 0.000 -.2091744 -.1182743 1993 | -.1575267 .0237798 -6.62 0.000 -.2041351 -.1109183 1994 | -.1511265 .0245522 -6.16 0.000 -.199249 -.103004 1996 | -.1332331 .0256162 -5.20 0.000 -.183441 -.0830253 1998 | -.1010174 .0266481 -3.79 0.000 -.1532477 -.0487871 2000 | -.0824075 .0282138 -2.92 0.003 -.1377066 -.0271083 2002 | -.0722136 .0298226 -2.42 0.015 -.1306661 -.0137612 2004 | -.0390055 .0317085 -1.23 0.219 -.1011544 .0231433 2006 | -.0554517 .0337531 -1.64 0.100 -.121608 .0107047 2008 | -.0085305 .035486 -0.24 0.810 -.0780832 .0610223 2010 | .0124084 .0379915 0.33 0.744 -.062055 .0868719 2012 | .0413932 .0423011 0.98 0.328 -.0415171 .1243035 | _cons | .8676637 .0236741 36.65 0.000 .8212624 .9140649 -------------------------------------------------------------------------------- . regsave using `myinput'`prg'_data, addlabel(scenario, cntrl) append; file ../input/fixed_effects_ipd_cumtight_data.dta saved . /*** CNTRLFE ***/ > areg `cycregs_cntrl' i.datey [pweight = csampweight], absorb(id); (sum of wgt is 3.7145e+08) Linear regression, absorbing indicators Number of obs = 58,940 F( 39, 56194) = 1046.41 Prob > F = 0.0000 R-squared = 0.6624 Adj R-squared = 0.6459 Root MSE = 0.3819 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- potexperience | .0107703 .0081839 1.32 0.188 -.0052702 .0268109 potexperience2 | -.0015142 .0000428 -35.41 0.000 -.001598 -.0014304 hgc | .0471033 .0083957 5.61 0.000 .0306476 .0635589 tenurey | .0347879 .0011025 31.55 0.000 .0326269 .0369488 tenurey2 | -.0006453 .000047 -13.72 0.000 -.0007375 -.0005531 ind_ag | -.0701451 .0124665 -5.63 0.000 -.0945796 -.0457106 ind_mining | .2447621 .0200741 12.19 0.000 .2054167 .2841075 ind_constr | .1587674 .0077664 20.44 0.000 .1435451 .1739896 ind_manufd | .1298355 .0068464 18.96 0.000 .1164164 .1432546 ind_manufn | .1096708 .007985 13.73 0.000 .0940203 .1253214 ind_trans | .1070745 .0089514 11.96 0.000 .0895298 .1246193 ind_trade | -.0556068 .0059185 -9.40 0.000 -.0672072 -.0440065 ind_fire | .1217022 .013004 9.36 0.000 .0962144 .1471901 cumtight | .0005227 .0000249 20.99 0.000 .0004739 .0005715 | datey | 1979 | .0717987 .0233688 3.07 0.002 .0259958 .1176016 1980 | .0802205 .0256949 3.12 0.002 .0298583 .1305828 1981 | .0934563 .0302863 3.09 0.002 .0340949 .1528177 1982 | .1240301 .0364713 3.40 0.001 .0525461 .1955141 1983 | .119532 .0430131 2.78 0.005 .035226 .203838 1984 | .1681511 .0500398 3.36 0.001 .0700728 .2662294 1985 | .2140843 .0573622 3.73 0.000 .1016541 .3265145 1986 | .2817877 .065057 4.33 0.000 .1542756 .4092998 1987 | .3797041 .073703 5.15 0.000 .2352458 .5241624 1988 | .4356012 .0829152 5.25 0.000 .2730869 .5981155 1989 | .4580487 .0903867 5.07 0.000 .2808902 .6352071 1990 | .5109818 .0986573 5.18 0.000 .3176129 .7043508 1991 | .5207183 .1060933 4.91 0.000 .3127748 .7286618 1992 | .5616479 .1139781 4.93 0.000 .33825 .7850457 1993 | .6108607 .1216638 5.02 0.000 .3723989 .8493226 1994 | .6752779 .1300553 5.19 0.000 .4203687 .930187 1996 | .7986098 .1450851 5.50 0.000 .514242 1.082978 1998 | .9401228 .1607051 5.85 0.000 .6251397 1.255106 2000 | 1.084076 .1776451 6.10 0.000 .7358904 1.432262 2002 | 1.197398 .1927878 6.21 0.000 .8195328 1.575263 2004 | 1.331441 .2083399 6.39 0.000 .923094 1.739789 2006 | 1.428754 .2240821 6.38 0.000 .9895522 1.867957 2008 | 1.586124 .2395594 6.62 0.000 1.116586 2.055662 2010 | 1.718099 .2549073 6.74 0.000 1.218479 2.217719 2012 | 1.899735 .2759683 6.88 0.000 1.358835 2.440634 | _cons | 1.443069 .0997721 14.46 0.000 1.247515 1.638623 ---------------+---------------------------------------------------------------- id | absorbed (2707 categories) . regsave using `myinput'`prg'_data, addlabel(scenario, cntrlfes) append; file ../input/fixed_effects_ipd_cumtight_data.dta saved . /********************************************************************************************/ > /*** User Cost ***/ > /********************************************************************************************/ > > /****************************************************/ > /* Prepare dataset to predict for ENTRY-LEVEL WAGES */ > /****************************************************/ > preserve; . collapse (mean) hgc potexperience; . expand 2012; (2,011 observations created) . gen employer_starty = _n; . 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. }; . egen not_ok = anymatch(employer_starty), values(1995 1997 1999 2001 2003 2005 2007 2009 2011 2013); . drop if not_ok; (9 observations deleted) . /*** merge in cumulative tightness ***/ > gen year = employer_starty; . merge m:1 year using `myinput'cum_tight_experiment_data, keep(match) nogen; Result # of obs. ----------------------------------------- not matched 0 matched 26 ----------------------------------------- . drop year; . 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; . 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; . 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 ***/ > foreach ii of var employer_starty employer_currenty {; 2. egen not_ok = anymatch(`ii'), values(1995 1997 1999 2001 2003 2005 2007 2009 2011 2013); 3. drop if not_ok==1; 4. drop not_ok; 5. }; (42 observations deleted) (30 observations deleted) . /*** merge in cumulative tightness ***/ > gen year = employer_starty; . merge m:1 year using `myinput'cum_tight_experiment_data, keep(match) nogen; Result # of obs. ----------------------------------------- not matched 0 matched 131 ----------------------------------------- . drop year; . save `tempdata'topredict, replace; (note: file ../temp/topredict.dta not found) file ../temp/topredict.dta saved . restore; . /*** END prediction datasets ***/ > > /*** fixed effects for ***/ > 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) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 missing values generated) (2,120 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 3.7145e+08) note: y_2010_2010 omitted because of collinearity note: y_2012_2012 omitted because of collinearity Linear regression, absorbing indicators Number of obs = 58,940 F( 409, 55824) = 110.35 Prob > F = 0.0000 R-squared = 0.6672 Adj R-squared = 0.6486 Root MSE = 0.3805 -------------------------------------------------------------------------------- | Robust ln_hrly_wage~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- hgc | .0382705 .0110232 3.47 0.001 .016665 .059876 potexperience | .0033025 .010905 0.30 0.762 -.0180714 .0246764 potexperience2 | -.0015074 .0000436 -34.59 0.000 -.0015928 -.001422 tenurey | .0754585 .0086078 8.77 0.000 .0585872 .0923299 tenurey2 | -.0027117 .0005862 -4.63 0.000 -.0038607 -.0015628 trend | .0119722 .0442713 0.27 0.787 -.0747997 .0987442 ind_ag | -.0684193 .012493 -5.48 0.000 -.0929057 -.0439329 ind_mining | .2416306 .0202331 11.94 0.000 .2019736 .2812877 ind_constr | .1597093 .0077583 20.59 0.000 .144503 .1749155 ind_manufd | .127512 .0068824 18.53 0.000 .1140224 .1410016 ind_manufn | .1061027 .0079846 13.29 0.000 .0904529 .1217525 ind_trans | .1061635 .008937 11.88 0.000 .0886469 .1236801 ind_trade | -.0545455 .0059039 -9.24 0.000 -.0661171 -.0429739 ind_fire | .1185922 .0130216 9.11 0.000 .0930697 .1441146 cumtight | .0005319 .0000254 20.98 0.000 .0004822 .0005816 y_1978_1978 | -1.720388 1.420727 -1.21 0.226 -4.505022 1.064246 y_1978_1979 | -1.687318 1.377895 -1.22 0.221 -4.388001 1.013364 y_1979_1979 | -1.634166 1.378033 -1.19 0.236 -4.335119 1.066787 y_1978_1980 | -1.670065 1.335382 -1.25 0.211 -4.287423 .9472936 y_1979_1980 | -1.659101 1.335278 -1.24 0.214 -4.276254 .9580519 y_1980_1980 | -1.670705 1.335403 -1.25 0.211 -4.288104 .9466946 y_1978_1981 | -1.719114 1.29289 -1.33 0.184 -4.253187 .8149581 y_1979_1981 | -1.71625 1.292551 -1.33 0.184 -4.249658 .8171583 y_1980_1981 | -1.644687 1.292598 -1.27 0.203 -4.178187 .8888138 y_1981_1981 | -1.641366 1.292727 -1.27 0.204 -4.17512 .8923881 y_1978_1982 | -1.691013 1.250342 -1.35 0.176 -4.141692 .7596667 y_1979_1982 | -1.715162 1.250038 -1.37 0.170 -4.165244 .7349205 y_1980_1982 | -1.638588 1.250059 -1.31 0.190 -4.088712 .8115357 y_1981_1982 | -1.618609 1.24993 -1.29 0.195 -4.06848 .8312621 y_1982_1982 | -1.644397 1.250085 -1.32 0.188 -4.094573 .8057784 y_1978_1983 | -1.685907 1.208038 -1.40 0.163 -4.053669 .6818551 y_1979_1983 | -1.718106 1.20747 -1.42 0.155 -4.084756 .6485439 y_1980_1983 | -1.630884 1.207512 -1.35 0.177 -3.997615 .7358475 y_1981_1983 | -1.670091 1.207282 -1.38 0.167 -4.036371 .6961885 y_1982_1983 | -1.665025 1.207399 -1.38 0.168 -4.031535 .7014863 y_1983_1983 | -1.634324 1.207613 -1.35 0.176 -4.001252 .7326051 y_1978_1984 | -1.702273 1.165492 -1.46 0.144 -3.986644 .5820981 y_1979_1984 | -1.714154 1.165154 -1.47 0.141 -3.997864 .5695559 y_1980_1984 | -1.616312 1.164928 -1.39 0.165 -3.899579 .6669556 y_1981_1984 | -1.646386 1.164755 -1.41 0.158 -3.929313 .6365401 y_1982_1984 | -1.643608 1.16485 -1.41 0.158 -3.926721 .6395058 y_1983_1984 | -1.609668 1.164728 -1.38 0.167 -3.892542 .6732067 y_1984_1984 | -1.577797 1.164829 -1.35 0.176 -3.86087 .7052757 y_1978_1985 | -1.667919 1.123181 -1.48 0.138 -3.869361 .5335228 y_1979_1985 | -1.691606 1.12271 -1.51 0.132 -3.892124 .5089131 y_1980_1985 | -1.60343 1.12249 -1.43 0.153 -3.803517 .5966579 y_1981_1985 | -1.620819 1.122157 -1.44 0.149 -3.820253 .5786149 y_1982_1985 | -1.613959 1.122362 -1.44 0.150 -3.813796 .5858771 y_1983_1985 | -1.577112 1.122164 -1.41 0.160 -3.776561 .622336 y_1984_1985 | -1.558969 1.122101 -1.39 0.165 -3.758294 .6403561 y_1985_1985 | -1.568678 1.122194 -1.40 0.162 -3.768186 .6308303 y_1978_1986 | -1.663438 1.081023 -1.54 0.124 -3.782251 .455375 y_1979_1986 | -1.71399 1.080437 -1.59 0.113 -3.831653 .4036731 y_1980_1986 | -1.534506 1.080106 -1.42 0.155 -3.65152 .5825078 y_1981_1986 | -1.54825 1.079717 -1.43 0.152 -3.664502 .5680022 y_1982_1986 | -1.588963 1.079973 -1.47 0.141 -3.705717 .5277917 y_1983_1986 | -1.558407 1.079606 -1.44 0.149 -3.674442 .5576273 y_1984_1986 | -1.508059 1.079548 -1.40 0.162 -3.62398 .6078629 y_1985_1986 | -1.512768 1.07948 -1.40 0.161 -3.628556 .6030202 y_1986_1986 | -1.465326 1.07955 -1.36 0.175 -3.581252 .6506 y_1978_1987 | -1.663488 1.039216 -1.60 0.109 -3.700357 .3733811 y_1979_1987 | -1.657665 1.038162 -1.60 0.110 -3.69247 .3771398 y_1980_1987 | -1.436509 1.038481 -1.38 0.167 -3.471939 .5989213 y_1981_1987 | -1.538802 1.037645 -1.48 0.138 -3.572593 .4949887 y_1982_1987 | -1.553897 1.03789 -1.50 0.134 -3.588167 .4803736 y_1983_1987 | -1.49447 1.037154 -1.44 0.150 -3.527298 .5383579 y_1984_1987 | -1.423903 1.037089 -1.37 0.170 -3.456605 .6087985 y_1985_1987 | -1.399488 1.036962 -1.35 0.177 -3.43194 .632965 y_1986_1987 | -1.410911 1.036862 -1.36 0.174 -3.443168 .6213459 y_1987_1987 | -1.398213 1.037045 -1.35 0.178 -3.430829 .6344025 y_1978_1988 | -1.65332 .9968578 -1.66 0.097 -3.607168 .3005274 y_1979_1988 | -1.571628 .9969275 -1.58 0.115 -3.525612 .3823567 y_1980_1988 | -1.508083 .9964281 -1.51 0.130 -3.461089 .4449223 y_1981_1988 | -1.52358 .9951838 -1.53 0.126 -3.474147 .4269863 y_1982_1988 | -1.547432 .9960898 -1.55 0.120 -3.499774 .4049106 y_1983_1988 | -1.42588 .9948691 -1.43 0.152 -3.37583 .5240697 y_1984_1988 | -1.409946 .9946483 -1.42 0.156 -3.359463 .5395706 y_1985_1988 | -1.403196 .994427 -1.41 0.158 -3.35228 .5458871 y_1986_1988 | -1.350173 .9945261 -1.36 0.175 -3.29945 .5991048 y_1987_1988 | -1.373251 .9942485 -1.38 0.167 -3.321984 .5754828 y_1988_1988 | -1.329599 .9944322 -1.34 0.181 -3.278692 .6194949 y_1978_1989 | -1.636675 .9550776 -1.71 0.087 -3.508633 .2352836 y_1979_1989 | -1.600643 .9545537 -1.68 0.094 -3.471574 .2702888 y_1980_1989 | -1.501369 .9541239 -1.57 0.116 -3.371458 .3687203 y_1981_1989 | -1.518771 .9531811 -1.59 0.111 -3.387012 .3494703 y_1982_1989 | -1.546245 .9534593 -1.62 0.105 -3.415032 .322541 y_1983_1989 | -1.450641 .952907 -1.52 0.128 -3.318344 .4170634 y_1984_1989 | -1.456596 .9525095 -1.53 0.126 -3.323521 .4103287 y_1985_1989 | -1.388816 .952104 -1.46 0.145 -3.254946 .4773136 y_1986_1989 | -1.330553 .9518643 -1.40 0.162 -3.196213 .5351075 y_1987_1989 | -1.333781 .9517321 -1.40 0.161 -3.199182 .5316203 y_1988_1989 | -1.335971 .9517608 -1.40 0.160 -3.201428 .5294864 y_1989_1989 | -1.343573 .9518354 -1.41 0.158 -3.209176 .522031 y_1978_1990 | -1.608552 .9135353 -1.76 0.078 -3.399087 .1819833 y_1979_1990 | -1.575305 .9126688 -1.73 0.084 -3.364141 .2135321 y_1980_1990 | -1.458659 .9137377 -1.60 0.110 -3.249591 .3322727 y_1981_1990 | -1.522921 .910869 -1.67 0.095 -3.30823 .2623881 y_1982_1990 | -1.51866 .9115607 -1.67 0.096 -3.305325 .2680052 y_1983_1990 | -1.506112 .9105839 -1.65 0.098 -3.290863 .2786381 y_1984_1990 | -1.392343 .9099114 -1.53 0.126 -3.175775 .3910889 y_1985_1990 | -1.347626 .9097149 -1.48 0.139 -3.130673 .4354207 y_1986_1990 | -1.326742 .9093155 -1.46 0.145 -3.109006 .4555222 y_1987_1990 | -1.338238 .9091895 -1.47 0.141 -3.120256 .4437789 y_1988_1990 | -1.298593 .9092198 -1.43 0.153 -3.08067 .4834836 y_1989_1990 | -1.287541 .9091372 -1.42 0.157 -3.069456 .4943737 y_1990_1990 | -1.274153 .909211 -1.40 0.161 -3.056212 .5079065 y_1978_1991 | -1.591922 .8727504 -1.82 0.068 -3.302519 .1186742 y_1979_1991 | -1.560152 .8717843 -1.79 0.074 -3.268855 .1485505 y_1980_1991 | -1.57167 .8717374 -1.80 0.071 -3.280281 .1369409 y_1981_1991 | -1.461876 .869506 -1.68 0.093 -3.166113 .2423613 y_1982_1991 | -1.525362 .8699413 -1.75 0.080 -3.230452 .1797287 y_1983_1991 | -1.426666 .8681195 -1.64 0.100 -3.128186 .2748543 y_1984_1991 | -1.423545 .8679274 -1.64 0.101 -3.124689 .2775979 y_1985_1991 | -1.322159 .8674298 -1.52 0.127 -3.022327 .3780089 y_1986_1991 | -1.323464 .8672534 -1.53 0.127 -3.023286 .3763585 y_1987_1991 | -1.301415 .8668418 -1.50 0.133 -3.000431 .3976001 y_1988_1991 | -1.348828 .8667064 -1.56 0.120 -3.047578 .3499224 y_1989_1991 | -1.31007 .8666388 -1.51 0.131 -3.008688 .3885474 y_1990_1991 | -1.281877 .8665139 -1.48 0.139 -2.98025 .4164961 y_1991_1991 | -1.302715 .8666865 -1.50 0.133 -3.001426 .3959961 y_1978_1992 | -1.55748 .8318732 -1.87 0.061 -3.187957 .0729968 y_1979_1992 | -1.487974 .8302886 -1.79 0.073 -3.115345 .1393966 y_1980_1992 | -1.517309 .8315257 -1.82 0.068 -3.147104 .1124871 y_1981_1992 | -1.443502 .827587 -1.74 0.081 -3.065578 .1785737 y_1982_1992 | -1.492833 .8281778 -1.80 0.071 -3.116067 .1304008 y_1983_1992 | -1.39991 .8264887 -1.69 0.090 -3.019833 .2200133 y_1984_1992 | -1.430017 .8258103 -1.73 0.083 -3.04861 .1885768 y_1985_1992 | -1.333531 .8251288 -1.62 0.106 -2.950789 .2837267 y_1986_1992 | -1.307542 .8248115 -1.59 0.113 -2.924177 .3090943 y_1987_1992 | -1.310025 .8245766 -1.59 0.112 -2.926201 .3061502 y_1988_1992 | -1.2973 .8243405 -1.57 0.116 -2.913013 .3184124 y_1989_1992 | -1.226266 .8241269 -1.49 0.137 -2.84156 .3890283 y_1990_1992 | -1.269921 .8240424 -1.54 0.123 -2.88505 .3452072 y_1991_1992 | -1.283795 .8240116 -1.56 0.119 -2.898863 .3312731 y_1992_1992 | -1.258705 .8241447 -1.53 0.127 -2.874034 .3566235 y_1978_1993 | -1.509806 .7914133 -1.91 0.056 -3.060982 .0413689 y_1979_1993 | -1.468699 .7917383 -1.86 0.064 -3.020511 .0831133 y_1980_1993 | -1.402355 .7880597 -1.78 0.075 -2.946957 .1422472 y_1981_1993 | -1.36306 .7867238 -1.73 0.083 -2.905044 .1789234 y_1982_1993 | -1.482491 .7875087 -1.88 0.060 -3.026013 .0610312 y_1983_1993 | -1.334951 .7845269 -1.70 0.089 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0.685 -.7291166 1.110085 y_1979_2008 | .3723372 .4536529 0.82 0.412 -.5168254 1.2615 y_1980_2008 | .1571445 .4243952 0.37 0.711 -.6746728 .9889619 y_1981_2008 | .0506699 .3993545 0.13 0.899 -.7320675 .8334073 y_1982_2008 | .0537138 .40219 0.13 0.894 -.7345813 .8420089 y_1983_2008 | .0939608 .3510183 0.27 0.789 -.5940374 .7819589 y_1984_2008 | -.0062333 .3287873 -0.02 0.985 -.6506586 .6381919 y_1985_2008 | .0420912 .3142088 0.13 0.893 -.57376 .6579425 y_1986_2008 | -.1595608 .2906692 -0.55 0.583 -.7292743 .4101528 y_1987_2008 | -.177866 .2747456 -0.65 0.517 -.7163692 .3606372 y_1988_2008 | -.1490101 .2629458 -0.57 0.571 -.6643855 .3663654 y_1989_2008 | -.1622809 .2459748 -0.66 0.509 -.6443932 .3198313 y_1990_2008 | -.3240673 .2434162 -1.33 0.183 -.8011645 .15303 y_1991_2008 | -.5648705 .2259814 -2.50 0.012 -1.007795 -.1219454 y_1992_2008 | -.5083708 .2177097 -2.34 0.020 -.9350832 -.0816585 y_1993_2008 | -.3788871 .2053209 -1.85 0.065 -.7813175 .0235433 y_1994_2008 | -.3963835 .1911925 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y_1979_2010 | .6831782 .4977214 1.37 0.170 -.292359 1.658715 y_1980_2010 | .5040687 .4699928 1.07 0.283 -.4171202 1.425258 y_1981_2010 | .4618197 .4298227 1.07 0.283 -.3806356 1.304275 y_1982_2010 | .4542134 .4086133 1.11 0.266 -.3466714 1.255098 y_1983_2010 | .3967183 .3783737 1.05 0.294 -.3448966 1.138333 y_1984_2010 | .2014487 .3438967 0.59 0.558 -.472591 .8754883 y_1985_2010 | .176214 .3283939 0.54 0.592 -.4674401 .8198681 y_1986_2010 | .1252317 .3034528 0.41 0.680 -.4695377 .7200012 y_1987_2010 | .0963347 .2810987 0.34 0.732 -.4546206 .64729 y_1988_2010 | -.0761626 .2581502 -0.30 0.768 -.5821386 .4298134 y_1989_2010 | -.0294406 .2468525 -0.12 0.905 -.5132732 .454392 y_1990_2010 | -.0932846 .2314193 -0.40 0.687 -.5468678 .3602987 y_1991_2010 | -.3077938 .2147496 -1.43 0.152 -.7287044 .1131167 y_1992_2010 | -.1991042 .2008026 -0.99 0.321 -.5926787 .1944702 y_1993_2010 | -.3528751 .1905202 -1.85 0.064 -.7262959 .0205457 y_1994_2010 | -.2194357 .1647014 -1.33 0.183 -.5422515 .1033802 y_1995_2010 | -.4137902 .1593174 -2.60 0.009 -.7260534 -.1015271 y_1996_2010 | -.4168318 .1565067 -2.66 0.008 -.7235859 -.1100777 y_1997_2010 | -.4184326 .1365604 -3.06 0.002 -.6860918 -.1507734 y_1998_2010 | -.3359585 .1318023 -2.55 0.011 -.5942919 -.0776251 y_1999_2010 | -.4100222 .1222248 -3.35 0.001 -.6495837 -.1704607 y_2000_2010 | -.4619371 .1147622 -4.03 0.000 -.6868718 -.2370025 y_2001_2010 | -.4098266 .1170681 -3.50 0.000 -.6392808 -.1803724 y_2002_2010 | -.2675417 .1075185 -2.49 0.013 -.4782786 -.0568048 y_2003_2010 | -.2626629 .1218606 -2.16 0.031 -.5015105 -.0238153 y_2004_2010 | -.3662392 .0975613 -3.75 0.000 -.55746 -.1750184 y_2005_2010 | -.2562465 .0916666 -2.80 0.005 -.4359136 -.0765793 y_2006_2010 | -.2505327 .091118 -2.75 0.006 -.4291245 -.0719409 y_2007_2010 | -.2381588 .0876841 -2.72 0.007 -.4100203 -.0662974 y_2008_2010 | -.1325257 .0843996 -1.57 0.116 -.2979495 .0328981 y_2009_2010 | -.2050886 .0793006 -2.59 0.010 -.3605184 -.0496589 y_2010_2010 | 0 (omitted) y_1978_2012 | 1.016228 .5981054 1.70 0.089 -.1560621 2.188519 y_1979_2012 | 1.289792 .6201143 2.08 0.038 .0743637 2.50522 y_1980_2012 | .9690647 .537899 1.80 0.072 -.0852209 2.02335 y_1981_2012 | .9253984 .5045625 1.83 0.067 -.0635474 1.914344 y_1982_2012 | .9750929 .5007596 1.95 0.052 -.0063992 1.956585 y_1983_2012 | .8316128 .4488435 1.85 0.064 -.0481233 1.711349 y_1984_2012 | .5776839 .421315 1.37 0.170 -.2480962 1.403464 y_1985_2012 | .5690792 .3857114 1.48 0.140 -.1869176 1.325076 y_1986_2012 | .5526923 .3610599 1.53 0.126 -.1549874 1.260372 y_1987_2012 | .4810335 .3334576 1.44 0.149 -.1725455 1.134613 y_1988_2012 | .2778243 .3112726 0.89 0.372 -.332272 .8879206 y_1989_2012 | .323376 .2953647 1.09 0.274 -.2555407 .9022928 y_1990_2012 | .1074751 .276586 0.39 0.698 -.4346353 .6495855 y_1991_2012 | .0458151 .2518167 0.18 0.856 -.4477474 .5393775 y_1992_2012 | .0877056 .240484 0.36 0.715 -.3836447 .5590558 y_1993_2012 | -.254371 .230465 -1.10 0.270 -.706084 .1973419 y_1994_2012 | .0118182 .1979338 0.06 0.952 -.3761334 .3997699 y_1995_2012 | -.1911388 .1870057 -1.02 0.307 -.5576711 .1753935 y_1996_2012 | -.2156763 .2027869 -1.06 0.288 -.6131399 .1817873 y_1997_2012 | -.1680973 .1522778 -1.10 0.270 -.4665627 .1303681 y_1998_2012 | -.2331439 .1493009 -1.56 0.118 -.5257747 .059487 y_1999_2012 | -.2860699 .1505952 -1.90 0.057 -.5812375 .0090978 y_2000_2012 | -.2711188 .1245519 -2.18 0.030 -.5152414 -.0269962 y_2001_2012 | -.1290677 .1222834 -1.06 0.291 -.368744 .1106086 y_2002_2012 | -.203382 .1239247 -1.64 0.101 -.4462753 .0395113 y_2003_2012 | -.0646012 .131409 -0.49 0.623 -.3221637 .1929613 y_2004_2012 | -.3646482 .1247975 -2.92 0.003 -.609252 -.1200443 y_2005_2012 | -.1710325 .091621 -1.87 0.062 -.3506103 .0085453 y_2006_2012 | -.0636115 .0938896 -0.68 0.498 -.2476358 .1204127 y_2007_2012 | -.171641 .092572 -1.85 0.064 -.3530826 .0098006 y_2008_2012 | -.090229 .0924927 -0.98 0.329 -.2715153 .0910574 y_2009_2012 | -.1045595 .088275 -1.18 0.236 -.277579 .0684601 y_2010_2012 | .0445096 .0756505 0.59 0.556 -.1037659 .1927851 y_2011_2012 | .055726 .0667281 0.84 0.404 -.0750616 .1865136 y_2012_2012 | 0 (omitted) _cons | 3.241055 1.464695 2.21 0.027 .3702435 6.111867 ---------------+---------------------------------------------------------------- id | absorbed (2707 categories) . 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_cumtight_entry_wage.dta saved . save `myoutput'`prg'_entry_wage, replace; (note: file ../output/fixed_effects_ipd_cumtight_entry_wage.dta not found) file ../output/fixed_effects_ipd_cumtight_entry_wage.dta saved . export excel using `myoutput'`prg'_entry_wage, replace; file ../output/fixed_effects_ipd_cumtight_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); (131 real changes made) . /* /\*** save data to plot (long format) ***\/ */ > /* preserve; */ > /* keep what employer_currenty employer_starty; */ > /* save `myoutput'`prg'_data_long, replace; */ > /* export excel using `myoutput'`prg'_data_long.xlsx, replace firstrow(variables); */ > /* restore; */ > > /* 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. 131 -> 23 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, but with gaps delta: 1 unit . /* /\*** save data in wide format ***\/ */ > /* save `myoutput'`prg'_data_wide, replace; */ > /* export excel `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) (1 real change made) (8 real changes made) (2 real changes made) (9 real changes made) (3 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, but with gaps 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); . tw > (tsline PDVholes, lc(black) lw(0.2) cmissing(no)) > (tsline PDV, lc(black) mc(black) recast(scatter)), > xtitle("") xlabel(1978(4)2006) xmtick(1978(1)2006) > ytitle("Logged Real Dollars") ylabel(, angle(h) grid) > plotregion(lstyle(none)) yscale(noline axis(1)) > legend(off); . 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) . /*** plot UC ***/ > /*** in 2006 we can add up 7 to get to 2012 but UC involves taking the > difference, substracting one > ***/ > tsline lUC, lc(black) lw(1.1) xtitle("") xlabel(1978(4)2006) xmtick(1978(1)2006) > plotregion(lstyle(none)) yscale(noline axis(1)) > ytitle("Logged Real Dollars") ylabel(, grid angle(h)); . keep year UC lUC; . save `myinput'`prg'_user_cost, replace; file ../input/fixed_effects_ipd_cumtight_user_cost.dta saved . /*** output for MATLAB ***/ > outsheet using `myinput'`prg'_user_cost.csv, replace comma; . end of do-file . do cycregs_ipd.do; . /********************************************************************** > Program: cycregs_ipd.do > Purpose: determine how cyclical different measures of wages are > > Date Started: 26 August 2015 > Date Revised: 12 July 2016 > **********************************************************************/ . . #delimit ; delimiter now ; . clear all; . capture log close;