Example 17.1: Married Woman's Labor Force Participation
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz, clear
regress inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Source | SS df MS Number of obs = 753
-------------+------------------------------ F( 7, 745) = 38.22
Model | 48.8080578 7 6.97257968 Prob > F = 0.0000
Residual | 135.919698 745 .182442547 R-squared = 0.2642
-------------+------------------------------ Adj R-squared = 0.2573
Total | 184.727756 752 .245648611 Root MSE = .42713
------------------------------------------------------------------------------
inlf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0034052 .0014485 -2.35 0.019 -.0062488 -.0005616
educ | .0379953 .007376 5.15 0.000 .023515 .0524756
exper | .0394924 .0056727 6.96 0.000 .0283561 .0506287
expersq | -.0005963 .0001848 -3.23 0.001 -.0009591 -.0002335
age | -.0160908 .0024847 -6.48 0.000 -.0209686 -.011213
kidslt6 | -.2618105 .0335058 -7.81 0.000 -.3275875 -.1960335
kidsge6 | .0130122 .013196 0.99 0.324 -.0128935 .0389179
_cons | .5855192 .154178 3.80 0.000 .2828442 .8881943
------------------------------------------------------------------------------
logit inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -406.94123
Iteration 2: log likelihood = -401.85151
Iteration 3: log likelihood = -401.76519
Iteration 4: log likelihood = -401.76515
Logit estimates Number of obs = 753
LR chi2(7) = 226.22
Prob > chi2 = 0.0000
Log likelihood = -401.76515 Pseudo R2 = 0.2197
------------------------------------------------------------------------------
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0213452 .0084214 -2.53 0.011 -.0378509 -.0048394
educ | .2211704 .0434396 5.09 0.000 .1360303 .3063105
exper | .2058695 .0320569 6.42 0.000 .1430391 .2686999
expersq | -.0031541 .0010161 -3.10 0.002 -.0051456 -.0011626
age | -.0880244 .014573 -6.04 0.000 -.116587 -.0594618
kidslt6 | -1.443354 .2035849 -7.09 0.000 -1.842373 -1.044335
kidsge6 | .0601122 .0747897 0.80 0.422 -.086473 .2066974
_cons | .4254524 .8603696 0.49 0.621 -1.260841 2.111746
------------------------------------------------------------------------------
probit inlf nwifeinc educ exper expersq age kidslt6 kidsge6
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -405.78215
Iteration 2: log likelihood = -401.32924
Iteration 3: log likelihood = -401.30219
Iteration 4: log likelihood = -401.30219
Probit estimates Number of obs = 753
LR chi2(7) = 227.14
Prob > chi2 = 0.0000
Log likelihood = -401.30219 Pseudo R2 = 0.2206
------------------------------------------------------------------------------
inlf | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901
------------------------------------------------------------------------------
Changes in probability if kidslt6 changes
mfx compute, at(mean kidslt6=1)
Marginal effects after probit
y = Pr(inlf) (predict)
= .32416867
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
nwifeinc | -.004323 .00175 -2.48 0.013 -.007744 -.000902 20.1290
educ | .047065 .00912 5.16 0.000 .029187 .064943 12.2869
exper | .0443479 .00704 6.30 0.000 .03055 .058146 10.6308
expersq | -.0006785 .00022 -3.11 0.002 -.001106 -.000251 178.039
age | -.0190025 .00284 -6.69 0.000 -.024568 -.013437 42.5378
kidslt6 | -.3121957 .03077 -10.15 0.000 -.372509 -.251882 1.00000
kidsge6 | .0129451 .0157 0.82 0.410 -.017829 .04372 1.35325
------------------------------------------------------------------------------
mfx compute, at(mean kidslt6=1.5)
Marginal effects after probit
y = Pr(inlf) (predict)
= .1866692
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
nwifeinc | -.0032274 .00136 -2.37 0.018 -.005892 -.000563 20.1290
educ | .0351375 .00789 4.46 0.000 .019683 .050592 12.2869
exper | .033109 .00683 4.85 0.000 .019731 .046487 10.6308
expersq | -.0005065 .00018 -2.88 0.004 -.000851 -.000162 178.039
age | -.0141867 .00232 -6.12 0.000 -.018733 -.00964 42.5378
kidslt6 | -.2330773 .01067 -21.84 0.000 -.253993 -.212162 1.50000
kidsge6 | .0096645 .01189 0.81 0.416 -.013647 .032976 1.35325
------------------------------------------------------------------------------
Example 17.2: Married Women's Annual Labor Supply
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz, clear
regress hours nwifeinc educ exper expersq age kidslt6 kidsge6
Source | SS df MS Number of obs = 753
-------------+------------------------------ F( 7, 745) = 38.50
Model | 151647606 7 21663943.7 Prob > F = 0.0000
Residual | 419262118 745 562767.944 R-squared = 0.2656
-------------+------------------------------ Adj R-squared = 0.2587
Total | 570909724 752 759188.463 Root MSE = 750.18
------------------------------------------------------------------------------
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -3.446636 2.544 -1.35 0.176 -8.440898 1.547626
educ | 28.76112 12.95459 2.22 0.027 3.329284 54.19297
exper | 65.67251 9.962983 6.59 0.000 46.11365 85.23138
expersq | -.7004939 .3245501 -2.16 0.031 -1.337635 -.0633524
age | -30.51163 4.363868 -6.99 0.000 -39.07858 -21.94469
kidslt6 | -442.0899 58.8466 -7.51 0.000 -557.6148 -326.565
kidsge6 | -32.77923 23.17622 -1.41 0.158 -78.2777 12.71924
_cons | 1330.482 270.7846 4.91 0.000 798.8906 1862.074
------------------------------------------------------------------------------
tobit hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0)
Tobit estimates Number of obs = 753
LR chi2(7) = 271.59
Prob > chi2 = 0.0000
Log likelihood = -3819.0946 Pseudo R2 = 0.0343
------------------------------------------------------------------------------
hours | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
nwifeinc | -8.814243 4.459096 -1.98 0.048 -17.56811 -.0603725
educ | 80.64561 21.58322 3.74 0.000 38.27453 123.0167
exper | 131.5643 17.27938 7.61 0.000 97.64231 165.4863
expersq | -1.864158 .5376615 -3.47 0.001 -2.919667 -.8086479
age | -54.40501 7.418496 -7.33 0.000 -68.96862 -39.8414
kidslt6 | -894.0217 111.8779 -7.99 0.000 -1113.655 -674.3887
kidsge6 | -16.218 38.64136 -0.42 0.675 -92.07675 59.64075
_cons | 965.3053 446.4358 2.16 0.031 88.88531 1841.725
-------------+----------------------------------------------------------------
_se | 1122.022 41.57903 (Ancillary parameter)
------------------------------------------------------------------------------
Obs. summary: 325 left-censored observations at hours<=0
428 uncensored observations
Changes in probability
* fixup for expersq : take square of mean rather than mean of square per JMW
summ exper,meanonly
local exp2=r(mean)^2
mfx compute, at(mean expersq=`exp2') predict(ystar(0,.))
Marginal effects after tobit
y = E(hours*|hours>0) (predict, ystar(0,.))
= 687.31745
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
nwifeinc | -5.687381 2.87788 -1.98 0.048 -11.3279 -.046836 20.1290
educ | 52.03649 13.82 3.77 0.000 24.9495 79.1234 12.2869
exper | 84.89173 12.398 6.85 0.000 60.593 109.19 10.6308
expersq | -1.202846 .36661 -3.28 0.001 -1.92139 -.484297 113.014
age | -35.10478 4.66947 -7.52 0.000 -44.2568 -25.9528 42.5378
kidslt6 | -576.8666 70.93 -8.13 0.000 -715.887 -437.847 .237716
kidsge6 | -10.46465 24.94 -0.42 0.675 -59.3456 38.4163 1.35325
------------------------------------------------------------------------------
* marginal effects conditional on positive hours
mfx compute, at(mean expersq=`exp2') predict(e(0,.))
Marginal effects after tobit
y = E(hours|hours>0) (predict, e(0,.))
= 1065.1973
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
nwifeinc | -3.987413 2.01764 -1.98 0.048 -7.94192 -.032909 20.1290
educ | 36.48269 9.68927 3.77 0.000 17.4921 55.4733 12.2869
exper | 59.51744 8.68378 6.85 0.000 42.4975 76.5373 10.6308
expersq | -.843313 .25692 -3.28 0.001 -1.34686 -.339765 113.014
age | -24.6119 3.27362 -7.52 0.000 -31.0281 -18.1957 42.5378
kidslt6 | -404.4402 49.722 -8.13 0.000 -501.893 -306.987 .237716
kidsge6 | -7.336744 17.485 -0.42 0.675 -41.607 26.9335 1.35325
------------------------------------------------------------------------------
Example 17.3: Poisson Regression for Number of Arrests
use http://fmwww.bc.edu/ec-p/data/wooldridge/crime1, clear
reg narr86 pcnv avgsen tottime ptime86 qemp86 inc86 black hispan born60
Source | SS df MS Number of obs = 2725
-------------+------------------------------ F( 9, 2715) = 23.57
Model | 145.702778 9 16.1891976 Prob > F = 0.0000
Residual | 1864.64438 2715 .686793509 R-squared = 0.0725
-------------+------------------------------ Adj R-squared = 0.0694
Total | 2010.34716 2724 .738012906 Root MSE = .82873
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.131886 .0404037 -3.26 0.001 -.2111112 -.0526609
avgsen | -.0113316 .0122413 -0.93 0.355 -.0353348 .0126717
tottime | .0120693 .0094364 1.28 0.201 -.006434 .0305725
ptime86 | -.0408735 .008813 -4.64 0.000 -.0581544 -.0235925
qemp86 | -.0513099 .0144862 -3.54 0.000 -.079715 -.0229047
inc86 | -.0014617 .000343 -4.26 0.000 -.0021343 -.0007891
black | .3270097 .0454264 7.20 0.000 .2379359 .4160835
hispan | .1938094 .0397156 4.88 0.000 .1159335 .2716853
born60 | -.022465 .0332945 -0.67 0.500 -.0877502 .0428202
_cons | .576566 .0378945 15.22 0.000 .502261 .6508711
------------------------------------------------------------------------------
poisson narr86 pcnv avgsen tottime ptime86 qemp86 inc86 black hispan born60
Iteration 0: log likelihood = -2249.0104
Iteration 1: log likelihood = -2248.7614
Iteration 2: log likelihood = -2248.7611
Iteration 3: log likelihood = -2248.7611
Poisson regression Number of obs = 2725
LR chi2(9) = 386.32
Prob > chi2 = 0.0000
Log likelihood = -2248.7611 Pseudo R2 = 0.0791
------------------------------------------------------------------------------
narr86 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
pcnv | -.4015713 .0849712 -4.73 0.000 -.5681117 -.2350308
avgsen | -.0237723 .019946 -1.19 0.233 -.0628658 .0153212
tottime | .0244904 .0147504 1.66 0.097 -.0044199 .0534006
ptime86 | -.0985584 .0206946 -4.76 0.000 -.1391192 -.0579977
qemp86 | -.0380187 .0290242 -1.31 0.190 -.0949051 .0188677
inc86 | -.0080807 .001041 -7.76 0.000 -.010121 -.0060404
black | .6608376 .0738342 8.95 0.000 .5161252 .80555
hispan | .4998133 .0739267 6.76 0.000 .3549196 .644707
born60 | -.0510286 .0640518 -0.80 0.426 -.1765678 .0745106
_cons | -.5995888 .0672501 -8.92 0.000 -.7313966 -.467781
------------------------------------------------------------------------------
Change in expected arrests if pcnv changes by .10
display "Change in expected arrests if pcnv changes by .10 is " _b[pcnv]*.10
Change in expected arrests if pcnv changes by .10 is -.04015713
Example 17.4: Duration of Recidivism
use http://fmwww.bc.edu/ec-p/data/wooldridge/recid, clear
cnreg ldurat workprg priors tserved felon alcohol drugs black married educ age, censored(cens)
Censored normal regression Number of obs = 1445
LR chi2(10) = 166.74
Prob > chi2 = 0.0000
Log likelihood = -1597.059 Pseudo R2 = 0.0496
------------------------------------------------------------------------------
ldurat | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
workprg | -.0625715 .1200369 -0.52 0.602 -.2980382 .1728951
priors | -.1372529 .0214587 -6.40 0.000 -.1793466 -.0951592
tserved | -.0193305 .0029779 -6.49 0.000 -.0251721 -.013489
felon | .4439947 .1450865 3.06 0.002 .1593903 .7285991
alcohol | -.6349093 .1442166 -4.40 0.000 -.9178072 -.3520113
drugs | -.2981602 .1327356 -2.25 0.025 -.5585367 -.0377836
black | -.5427179 .1174428 -4.62 0.000 -.7730958 -.31234
married | .3406837 .1398431 2.44 0.015 .066365 .6150024
educ | .0229196 .0253974 0.90 0.367 -.0269004 .0727395
age | .0039103 .0006062 6.45 0.000 .0027211 .0050994
_cons | 4.099386 .3475351 11.80 0.000 3.417655 4.781117
-------------+----------------------------------------------------------------
_se | 1.81047 .0623022 (Ancillary parameter)
------------------------------------------------------------------------------
Obs. summary: 552 uncensored observations
893 right-censored observations
Change in durat if a man serves for a felony
mfx compute, nose
Marginal effects after cnreg
y = Fitted values (predict)
= 4.8341597
-------------------------------------------------------------------------------
variable | dy/dx X
---------------------------------+---------------------------------------------
workprg*| -.0625715 .465052
priors | -.1372529 1.43183
tserved | -.0193305 19.1820
felon*| .4439947 .314187
alcohol*| -.6349093 .209689
drugs*| -.2981602 .241522
black*| -.5427179 .485121
married*| .3406837 .255363
educ | .0229196 9.70242
age | .0039103 345.436
-------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1
mat pct=e(Xmfx_dydx)
matmap pct pct, m(100*(exp(@)-1))
mat list pct
pct[1,10]
workprg priors tserved felon alcohol drugs
r1 -6.0654125 -12.825026 -1.9144899 55.892217 -47.001643 -25.781754
black married educ age
r1 -41.883343 40.590851 2.3184231 .39179407
Example 17.5: Wage Offer Equation for Married Women
use http://fmwww.bc.edu/ec-p/data/wooldridge/mroz, clear
reg lwage educ exper expersq
Source | SS df MS Number of obs = 428
-------------+------------------------------ F( 3, 424) = 26.29
Model | 35.0223023 3 11.6741008 Prob > F = 0.0000
Residual | 188.305149 424 .444115917 R-squared = 0.1568
-------------+------------------------------ Adj R-squared = 0.1509
Total | 223.327451 427 .523015108 Root MSE = .66642
------------------------------------------------------------------------------
lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
educ | .1074896 .0141465 7.60 0.000 .0796837 .1352956
exper | .0415665 .0131752 3.15 0.002 .0156697 .0674633
expersq | -.0008112 .0003932 -2.06 0.040 -.0015841 -.0000382
_cons | -.5220407 .1986321 -2.63 0.009 -.9124668 -.1316145
------------------------------------------------------------------------------
heckman lwage educ exper expersq, sel(inlf = nwifeinc educ exper expersq age kidslt6 kidsge6) twostep
Heckman selection model -- two-step estimates Number of obs = 753
(regression model with sample selection) Censored obs = 325
Uncensored obs = 428
Wald chi2(6) = 180.10
Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lwage |
educ | .1090655 .015523 7.03 0.000 .0786411 .13949
exper | .0438873 .0162611 2.70 0.007 .0120163 .0757584
expersq | -.0008591 .0004389 -1.96 0.050 -.0017194 1.15e-06
_cons | -.5781033 .3050062 -1.90 0.058 -1.175904 .0196979
-------------+----------------------------------------------------------------
inlf |
nwifeinc | -.0120237 .0048398 -2.48 0.013 -.0215096 -.0025378
educ | .1309047 .0252542 5.18 0.000 .0814074 .180402
exper | .1233476 .0187164 6.59 0.000 .0866641 .1600311
expersq | -.0018871 .0006 -3.15 0.002 -.003063 -.0007111
age | -.0528527 .0084772 -6.23 0.000 -.0694678 -.0362376
kidslt6 | -.8683285 .1185223 -7.33 0.000 -1.100628 -.636029
kidsge6 | .036005 .0434768 0.83 0.408 -.049208 .1212179
_cons | .2700768 .508593 0.53 0.595 -.7267472 1.266901
-------------+----------------------------------------------------------------
mills |
lambda | .0322619 .1336246 0.24 0.809 -.2296376 .2941613
-------------+----------------------------------------------------------------
rho | 0.04861
sigma | .66362876
lambda | .03226186 .1336246
------------------------------------------------------------------------------
This page prepared by Oleksandr Talavera (revised 9 Nov 2002)
Send your questions/comments/suggestions to Kit Baum
at baum@bc.edu
These pages are maintained by the Faculty Micro
Resource Center's GSA Program,
a unit of Boston College Academic Technology Services
|