Hello,
I am struggling a bit with my regression. My basic regression looks like this:
So, I am regressing log wages on immigrants arrival where arrival=9999 is coded as being native. I am looking for cohorts effects here, how is the immigrant log wage of these immigrant arrivals relative to native in the year 2004.
However, in a second step, I would like to have the age-adjusted wage differential of immigrant to native. So, I am using a third-order polynomial in workers age (bb03a, bb03a2=bb03a^2 and bb03a3=bb03a^3) additionally. Regression looks like:
However, I would like to have estimates on the cohorts predicted at the mean of age of the population. So, the above regression at mean of bb03a. I was thinking on using the code:
But the output says, last estimates not found...
Output on the first regression is:
Output on second regression:
I am struggling a bit with my regression. My basic regression looks like this:
Code:
svy: regress lnhourlyw_w i.ib9999.arrival if year==2004
However, in a second step, I would like to have the age-adjusted wage differential of immigrant to native. So, I am using a third-order polynomial in workers age (bb03a, bb03a2=bb03a^2 and bb03a3=bb03a^3) additionally. Regression looks like:
Code:
svy: regress lnhourlyw_w bb03a bb03a2 bb03a3 c.bb03a##i.ib9999.arrival c.bb03a2##i.ib9999.arrival c.bb03a3##i.ib9999.arrival i.ib9999.arrival if year==2004
Code:
svy: margins, at(bb03a=41 arrival)
Output on the first regression is:
Code:
svy: regress lnhourlyw_w i.ib9999.arrival if year==2004
(running regress on estimation sample)
Survey: Linear regression
Number of strata = 1 Number of obs = 10,726
Number of PSUs = 10,726 Population size = 1,317,293
Design df = 10,725
F( 6, 10720) = 99.52
Prob > F = 0.0000
R-squared = 0.0279
Linearized
lnhourlyw_w Coef. Std. Err. t P>t [95% Conf. Interval]
arrival
0 -.1686351 .0151419 -11.14 0.000 -.198316 -.1389543
1980 -.1678049 .0202635 -8.28 0.000 -.207525 -.1280847
1985 -.2158353 .0165672 -13.03 0.000 -.2483101 -.1833604
1990 -.2542113 .0122076 -20.82 0.000 -.2781405 -.2302822
1995 -.1508089 .0222109 -6.79 0.000 -.1943463 -.1072715
2000 -.0889885 .0228124 -3.90 0.000 -.133705 -.044272
_cons 3.689774 .0057737 639.07 0.000 3.678457 3.701092
Code:
. svy: regress lnhourlyw_w bb03a bb03a2 bb03a3 c.bb03a##i.ib9999.arrival c.bb03a2##i.ib9999.ar > rival c.bb03a3##i.ib9999.arrival i.ib9999.arrival if year==2004 (running regress on estimation sample) Survey: Linear regression Number of strata = 1 Number of obs = 10,726 Number of PSUs = 10,726 Population size = 1,317,293 Design df = 10,725 F( 27, 10699) = 114.85 Prob > F = 0.0000 R-squared = 0.2216 Linearized lnhourlyw_w Coef. Std. Err. t P>t [95% Conf. Interval] bb03a .1339664 .0152074 8.81 0.000 .1041572 .1637757 bb03a2 -.0022769 .0004 -5.69 0.000 -.0030609 -.001493 bb03a3 .0000126 3.33e-06 3.79 0.000 6.09e-06 .0000191 bb03a 0 (omitted) arrival 0 26.78865 12.59468 2.13 0.033 2.100745 51.47656 1980 14.41574 8.178109 1.76 0.078 -1.614872 30.44634 1985 6.221622 4.590746 1.36 0.175 -2.777089 15.22033 1990 .4615053 1.790314 0.26 0.797 -3.047841 3.970851 1995 4.245991 1.883982 2.25 0.024 .5530385 7.938944 2000 -1.446823 .9976211 -1.45 0.147 -3.402345 .5086989 arrival#c.bb03a 0 -1.455179 .6982813 -2.08 0.037 -2.823939 -.0864182 1980 -.8610379 .4995845 -1.72 0.085 -1.840316 .1182402 1985 -.3955509 .3064418 -1.29 0.197 -.9962336 .2051318 1990 -.0196936 .126786 -0.16 0.877 -.2682177 .2288306 1995 -.3470192 .1477938 -2.35 0.019 -.6367225 -.057316 2000 .085056 .0853952 1.00 0.319 -.0823344 .2524463 bb03a2 0 (omitted) arrival#c.bb03a2 0 .0258944 .0128356 2.02 0.044 .0007342 .0510546 1980 .0163834 .0100669 1.63 0.104 -.0033496 .0361163 1985 .007667 .0067382 1.14 0.255 -.0055412 .0208751 1990 -.0002907 .0029442 -0.10 0.921 -.0060619 .0054805 1995 .0087511 .0037584 2.33 0.020 .001384 .0161182 2000 -.0015658 .0023226 -0.67 0.500 -.0061184 .0029869 bb03a3 0 (omitted) arrival#c.bb03a3 0 -.0001527 .0000782 -1.95 0.051 -.0003061 6.05e-07 1980 -.0001011 .0000669 -1.51 0.130 -.0002322 .0000299 1985 -.0000473 .0000488 -0.97 0.332 -.0001429 .0000483 1990 6.95e-06 .0000224 0.31 0.756 -.000037 .0000509 1995 -.0000703 .0000309 -2.28 0.023 -.0001308 -9.75e-06 2000 9.36e-06 .00002 0.47 0.640 -.0000299 .0000486 _cons 1.260013 .1825066 6.90 0.000 .9022663 1.61776
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int bb03a float(bb03a2 bb03a3 arrival) 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 18 324 5832 9999 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441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 2010 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 2000 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 21 441 9261 9999 end label values bb03a BB03AD
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