Note: This chapter uses a suite of commands, called spost, written by J. Scott Long and Jeremy Freese. The commands must be downloaded prior to their use, and this can be done by typing search spost in the Stata command line (see How can I use the search command to search for programs and get additional help? for more information about using search).
Table 3.1, page 37.
use https://stats.idre.ucla.edu/stat/stata/examples/long/binlfp2, clear
describe
Contains data from https://stats.idre.ucla.edu/stat/stata/examples/long/binlfp2.dta
obs: 753 Data from 1976 PSID-T Mroz
vars: 8 30 Apr 2001 16:17
size: 13,554 (99.9% of memory free) (_dta has notes)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
lfp byte %9.0g lfplbl Paid Labor Force: 1=yes 0=no
k5 byte %9.0g # kids < 6
k618 byte %9.0g # kids 6-18
age byte %9.0g Wife's age in years
wc byte %9.0g collbl Wife College: 1=yes 0=no
hc byte %9.0g collbl Husband College: 1=yes 0=no
lwg float %9.0g Log of wife's estimated wages
inc float %9.0g Family income excluding wife's
-------------------------------------------------------------------------------
Sorted by: lfp
sum
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
lfp | 753 .5683931 .4956295 0 1
k5 | 753 .2377158 .523959 0 3
k618 | 753 1.353254 1.319874 0 8
age | 753 42.53785 8.072574 30 60
wc | 753 .2815405 .4500494 0 1
-------------+--------------------------------------------------------
hc | 753 .3917663 .4884694 0 1
lwg | 753 1.097115 .5875564 -2.054124 3.218876
inc | 753 20.12897 11.6348 -.0290001 96
Table 3.2, page 38.
NOTE: The x-standardized coefficient for K618 is -0.115 in the text, but is corrected in the errata for the book to be -0.015.
reg lfp k5 k618 age wc hc lwg inc
Source | SS df MS Number of obs = 753
-------------+------------------------------ F( 7, 745) = 18.83
Model | 27.7657494 7 3.96653564 Prob > F = 0.0000
Residual | 156.962006 745 .210687257 R-squared = 0.1503
-------------+------------------------------ Adj R-squared = 0.1423
Total | 184.727756 752 .245648611 Root MSE = .45901
------------------------------------------------------------------------------
lfp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.294836 .0359027 -8.21 0.000 -.3653185 -.2243534
k618 | -.011215 .0139627 -0.80 0.422 -.038626 .016196
age | -.0127411 .0025377 -5.02 0.000 -.017723 -.0077591
wc | .163679 .0458284 3.57 0.000 .0737109 .2536471
hc | .018951 .042533 0.45 0.656 -.0645477 .1024498
lwg | .1227402 .0301915 4.07 0.000 .0634697 .1820107
inc | -.0067603 .0015708 -4.30 0.000 -.009844 -.0036767
_cons | 1.143548 .1270527 9.00 0.000 .894124 1.392972
------------------------------------------------------------------------------
listcoef /*listcoef part of spostado*/
regress (N=753): Unstandardized and Standardized Estimates
Observed SD: .49562951
SD of Error: .45900682
-------------------------------------------------------------------------------
lfp | b t P>|t| bStdX bStdY bStdXY SDofX
-------------+-----------------------------------------------------------------
k5 | -0.29484 -8.212 0.000 -0.1545 -0.5949 -0.3117 0.5240
k618 | -0.01122 -0.803 0.422 -0.0148 -0.0226 -0.0299 1.3199
age | -0.01274 -5.021 0.000 -0.1029 -0.0257 -0.2075 8.0726
wc | 0.16368 3.572 0.000 0.0737 0.3302 0.1486 0.4500
hc | 0.01895 0.446 0.656 0.0093 0.0382 0.0187 0.4885
lwg | 0.12274 4.065 0.000 0.0721 0.2476 0.1455 0.5876
inc | -0.00676 -4.304 0.000 -0.0787 -0.0136 -0.1587 11.6348
-------------------------------------------------------------------------------
Table 3.3, page 49.
logit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -454.32339
Iteration 2: log likelihood = -452.64187
Iteration 3: log likelihood = -452.63296
Iteration 4: log likelihood = -452.63296
Logit estimates Number of obs = 753
LR chi2(7) = 124.48
Prob > chi2 = 0.0000
Log likelihood = -452.63296 Pseudo R2 = 0.1209
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799
k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085
age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162
wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026
hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564
lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901
inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583
_cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092
------------------------------------------------------------------------------
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
Calculation of the Ratio column was done using the display command.
*constant display -1.463/-0.875 1.672 display 7.43/7.70 .96493506 *k5 display -0.065/-0.039 1.6666667 display 0.95/0.95 1 *k618 display -0.063/-0.038 1.6578947 display 4.92/4.97 .98993964 *age display 0.807/0.488 1.6536885 display 3.51/3.60 .975 *wc display 0.112/0.057 1.9649123 display 0.54/0.46 1.173913 *hc display 0.605/0.366 1.6530055 display 4.01/4.17 .9616307 *lwg display -0.034/-0.021 1.6190476 display 4.20/4.30 .97674419 *inc display 3.182/1.918 1.6590198 display 4.94/5.04 .98015873
Table 3.4, page 66.
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
prchange, fromto
probit: Changes in Predicted Probabilities for lfp
from: to: dif: from: to: dif: from: to: dif:
x=min x=max min->max x=0 x=1 0->1 x-1/2 x+1/2 -+1/2
k5 0.6573 0.0132 -0.6441 0.6573 0.3193 -0.3380 0.7371 0.4051 -0.3320
k618 0.5985 0.4763 -0.1221 0.5985 0.5835 -0.0150 0.5857 0.5706 -0.0151
age 0.7490 0.3216 -0.4274 0.9646 0.9615 -0.0031 0.5855 0.5708 -0.0148
wc 0.5238 0.7082 0.1844 0.5238 0.7082 0.1844 0.4813 0.6705 0.1892
hc 0.5694 0.5917 0.0223 0.5694 0.5917 0.0223 0.5669 0.5893 0.0224
lwg 0.1698 0.8347 0.6649 0.4192 0.5642 0.1450 0.5057 0.6480 0.1423
inc 0.7294 0.0869 -0.6425 0.7292 0.7223 -0.0068 0.5822 0.5741 -0.0080
from: to: dif:
x-1/2sd x+1/2sd -+sd/2 MargEfct
k5 0.6651 0.4873 -0.1778 -0.3422
k618 0.5881 0.5682 -0.0199 -0.0151
age 0.6368 0.5178 -0.1190 -0.0148
wc 0.5348 0.6206 0.0858 0.1911
hc 0.5727 0.5836 0.0109 0.0224
lwg 0.5358 0.6197 0.0839 0.1431
inc 0.6242 0.5310 -0.0932 -0.0080
NotInLF inLF
Pr(y|x) 0.4218 0.5782
k5 k618 age wc hc lwg inc
x= .237716 1.35325 42.5378 .281541 .391766 1.09711 20.129
sd(x)= .523959 1.31987 8.07257 .450049 .488469 .587556 11.6348
Figure 3.10, page 67. Probability Labor Force Participation by Age and Wife’s Education
logit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -454.32339
Iteration 2: log likelihood = -452.64187
Iteration 3: log likelihood = -452.63296
Iteration 4: log likelihood = -452.63296
Logit estimates Number of obs = 753
LR chi2(7) = 124.48
Prob > chi2 = 0.0000
Log likelihood = -452.63296 Pseudo R2 = 0.1209
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799
k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085
age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162
wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026
hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564
lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901
inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583
_cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092
------------------------------------------------------------------------------
prgen age, from(30) to (60) gen(wc_yes) x(wc=1) rest(mean) n(7)
logit: Predicted values as age varies from 30 to 60.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 42.537849 1 .39176627 1.0971148 20.128965
label var wc_yesp1 "Attended College"
prgen age, from(30) to (60) gen(wc_no) x(wc=0) rest(mean) n(7)
logit: Predicted values as age varies from 30 to 60.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 42.537849 0 .39176627 1.0971148 20.128965
label var wc_nop1 "Did Not Attended College"
graph twoway (scatter wc_yesp1 wc_nop1 wc_nox, msymbol(Oh Sh) c(l l) xtitle("Age") ///
ytitle("Pr(ln Labor Force)") xlabel(30 35 40 45 50 55 60) ylabel(0 .25 .50 .75 1) )
Figure 3.11, page 68.
logit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -454.32339
Iteration 2: log likelihood = -452.64187
Iteration 3: log likelihood = -452.63296
Iteration 4: log likelihood = -452.63296
Logit estimates Number of obs = 753
LR chi2(7) = 124.48
Prob > chi2 = 0.0000
Log likelihood = -452.63296 Pseudo R2 = 0.1209
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799
k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085
age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162
wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026
hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564
lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901
inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583
_cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092
------------------------------------------------------------------------------
prgen inc, from(0) to (100) gen(p30) x(age=30) rest(mean) n(6)
logit: Predicted values as inc varies from 0 to 100.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 30 .2815405 .39176627 1.0971148 20.128965
label var p30p1 "Age 30"
prgen inc, from(0) to (100) gen(p40) x(age=40) rest(mean) n(6)
logit: Predicted values as inc varies from 0 to 100.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 40 .2815405 .39176627 1.0971148 20.128965
label var p40p1 "Age 40"
prgen inc, from(0) to (100) gen(p50) x(age=50) rest(mean) n(6)
logit: Predicted values as inc varies from 0 to 100.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 50 .2815405 .39176627 1.0971148 20.128965
label var p50p1 "Age 50"
prgen inc, from(0) to (100) gen(p60) x(age=60) rest(mean) n(6)
logit: Predicted values as inc varies from 0 to 100.
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 60 .2815405 .39176627 1.0971148 20.128965
label var p60p1 "Age 60"
graph twoway (scatter p30p1 p40p1 p50p1 p60p1 p60x, msymbol(Oh Dh Sh Th) c(l l l l) xtitle("Family Income") ///
ytitle("Pr(ln Labor Force)") xlabel(0(20)100) ylabel(0 .25 .50 .75 1) )
Table 3.5, page 69. Probability of Employment by College Attendance and the Number of Young Children in the Probit Model
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
prtab k5 wc
probit: Predicted probabilities of positive outcome for lfp
----------------------------
| Wife College:
# kids < | 1=yes 0=no
6 | NoCol College
----------+-----------------
0 | 0.6055 0.7752
1 | 0.2719 0.4527
2 | 0.0692 0.1602
3 | 0.0092 0.0309
----------------------------
k5 k618 age wc hc lwg inc
x= .2377158 1.3532537 42.537849 .2815405 .39176627 1.0971148 20.128965
NOTE: The difference column was generated using the display command.
display 0.6055 - 0.7752 -.1697 display 0.2719 - 0.4527 -.1808 display 0.0692 - 0.1602 -.091 display 0.0092 - 0.0309 -.0217
Table 3.6, page 71.
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
listcoef, std
probit (N=753): Unstandardized and Standardized Estimates
Observed SD: .49562951
Latent SD: 1.1524248
-------------------------------------------------------------------------------
lfp | b z P>|z| bStdX bStdY bStdXY SDofX
-------------+-----------------------------------------------------------------
k5 | -0.87471 -7.703 0.000 -0.4583 -0.7590 -0.3977 0.5240
k618 | -0.03859 -0.953 0.340 -0.0509 -0.0335 -0.0442 1.3199
age | -0.03782 -4.971 0.000 -0.3053 -0.0328 -0.2649 8.0726
wc | 0.48831 3.604 0.000 0.2198 0.4237 0.1907 0.4500
hc | 0.05717 0.461 0.645 0.0279 0.0496 0.0242 0.4885
lwg | 0.36563 4.165 0.000 0.2148 0.3173 0.1864 0.5876
inc | -0.02053 -4.297 0.000 -0.2388 -0.0178 -0.2072 11.6348
-------------------------------------------------------------------------------
Table 3.7, page 74.
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
prchange
probit: Changes in Predicted Probabilities for lfp
min->max 0->1 -+1/2 -+sd/2 MargEfct
k5 -0.6441 -0.3380 -0.3320 -0.1778 -0.3422
k618 -0.1221 -0.0150 -0.0151 -0.0199 -0.0151
age -0.4274 -0.0031 -0.0148 -0.1190 -0.0148
wc 0.1844 0.1844 0.1892 0.0858 0.1911
hc 0.0223 0.0223 0.0224 0.0109 0.0224
lwg 0.6649 0.1450 0.1423 0.0839 0.1431
inc -0.6425 -0.0068 -0.0080 -0.0932 -0.0080
NotInLF inLF
Pr(y|x) 0.4218 0.5782
k5 k618 age wc hc lwg inc
x= .237716 1.35325 42.5378 .281541 .391766 1.09711 20.129
sd(x)= .523959 1.31987 8.07257 .450049 .488469 .587556 11.6348
NOTE: The ‘average’ column is the marginal effects averaged over all observations to illustrate the fact that they are close to the marginals computed when all variables are held at their mean. Therefore, we didn’t calculate the ‘average’ column.
Table 3.8, page 78.
probit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -453.92167
Iteration 2: log likelihood = -452.69643
Iteration 3: log likelihood = -452.69496
Probit estimates Number of obs = 753
LR chi2(7) = 124.36
Prob > chi2 = 0.0000
Log likelihood = -452.69496 Pseudo R2 = 0.1208
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -.8747112 .1135583 -7.70 0.000 -1.097281 -.6521411
k618 | -.0385945 .0404893 -0.95 0.340 -.117952 .0407631
age | -.0378235 .0076093 -4.97 0.000 -.0527375 -.0229095
wc | .4883144 .1354873 3.60 0.000 .2227642 .7538645
hc | .0571704 .1240052 0.46 0.645 -.1858754 .3002161
lwg | .3656287 .0877792 4.17 0.000 .1935847 .5376727
inc | -.020525 .0047769 -4.30 0.000 -.0298875 -.0111626
_cons | 1.918422 .3806536 5.04 0.000 1.172355 2.66449
------------------------------------------------------------------------------
prchange, help
probit: Changes in Predicted Probabilities for lfp
min->max 0->1 -+1/2 -+sd/2 MargEfct
k5 -0.6441 -0.3380 -0.3320 -0.1778 -0.3422
k618 -0.1221 -0.0150 -0.0151 -0.0199 -0.0151
age -0.4274 -0.0031 -0.0148 -0.1190 -0.0148
wc 0.1844 0.1844 0.1892 0.0858 0.1911
hc 0.0223 0.0223 0.0224 0.0109 0.0224
lwg 0.6649 0.1450 0.1423 0.0839 0.1431
inc -0.6425 -0.0068 -0.0080 -0.0932 -0.0080
NotInLF inLF
Pr(y|x) 0.4218 0.5782
k5 k618 age wc hc lwg inc
x= .237716 1.35325 42.5378 .281541 .391766 1.09711 20.129
sd(x)= .523959 1.31987 8.07257 .450049 .488469 .587556 11.6348
Pr(y|x): probability of observing each y for specified x values
Avg|Chg|: average of absolute value of the change across categories
Min->Max: change in predicted probability as x changes from its minimum to
its maximum
0->1: change in predicted probability as x changes from 0 to 1
-+1/2: change in predicted probability as x changes from 1/2 unit below
base value to 1/2 unit above
-+sd/2: change in predicted probability as x changes from 1/2 standard
dev below base to 1/2 standard dev above
MargEfct: the partial derivative of the predicted probability/rate with
respect to a given independent variable
Table 3.9, page 81.
logit lfp k5 k618 age wc hc lwg inc
Iteration 0: log likelihood = -514.8732
Iteration 1: log likelihood = -454.32339
Iteration 2: log likelihood = -452.64187
Iteration 3: log likelihood = -452.63296
Iteration 4: log likelihood = -452.63296
Logit estimates Number of obs = 753
LR chi2(7) = 124.48
Prob > chi2 = 0.0000
Log likelihood = -452.63296 Pseudo R2 = 0.1209
------------------------------------------------------------------------------
lfp | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
k5 | -1.462913 .1970006 -7.43 0.000 -1.849027 -1.076799
k618 | -.0645707 .0680008 -0.95 0.342 -.1978499 .0687085
age | -.0628706 .0127831 -4.92 0.000 -.0879249 -.0378162
wc | .8072738 .2299799 3.51 0.000 .3565215 1.258026
hc | .1117336 .2060397 0.54 0.588 -.2920969 .515564
lwg | .6046931 .1508176 4.01 0.000 .3090961 .9002901
inc | -.0344464 .0082084 -4.20 0.000 -.0505346 -.0183583
_cons | 3.18214 .6443751 4.94 0.000 1.919188 4.445092
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listcoef
logit (N=753): Factor Change in Odds
Odds of: inLF vs NotInLF
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lfp | b z P>|z| e^b e^bStdX SDofX
-------------+--------------------------------------------------------
k5 | -1.46291 -7.426 0.000 0.2316 0.4646 0.5240
k618 | -0.06457 -0.950 0.342 0.9375 0.9183 1.3199
age | -0.06287 -4.918 0.000 0.9391 0.6020 8.0726
wc | 0.80727 3.510 0.000 2.2418 1.4381 0.4500
hc | 0.11173 0.542 0.588 1.1182 1.0561 0.4885
lwg | 0.60469 4.009 0.000 1.8307 1.4266 0.5876
inc | -0.03445 -4.196 0.000 0.9661 0.6698 11.6348
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