The name is absent



Stata Technical Bulletin

31


WhiteCol I

white I

ed I
exper I

4.813311

1.423556

1.035201

4.34508
.1669526
.0194922

1.741

3.011

1.837

0.082

0.003

0.066

.8204383

1.131219
.9976936

28.23852

1.791439

1.074119

Prof      I

white I

5.896191

4.451944

2.350

0.019

1.342357

—

25.89852

ed I

2.178969

.2497737

6.795

0.000

1.740518

2.72787

exper I

1.036294

.0186916

1.977

0.048

1.000299

1.073584

(Outcome Occ==Menial is the comparison group)

The standard Stata output allows you to immediately determine the factor change in the odds of each outcome category relative
to the comparison group Menial. The output does not include comparisons among other outcomes, such as BlueCol versus
Craft. Iistcoef provides all possible comparisons. Since this can generate extensive output, we illustrate two options that limit
which coefficients are listed. By specifying the variable ed, only coefficients for ed are listed. The option pv(.05) excludes
from printing any contrast that is not significant at the .05 level. Note that the coefficients below correspond to those listed
above (for example, the effect of ed on the odds of Prof versus Menial is 2.179 in both sets of output):

. Iistcoef ed, pv(.0δ)

mlogit (N=337): Factor Change in the Odds of occ when P>∣z∣ < 0.05

Variable: ed (sd= 2.94643)

Odds comparing    I

Group 1 - Group 2 I

b

z

P>∣z∣

e^b

e^bStdX

—

BlueCol -Craft    I

-0.19324

-2.494

0.013

0.8243

0.5659

BlueCol -WhiteCol I

-0.45258

-4.425

0.000

0.6360

0.2636

BlueCol -Prof     I

-0.87828

-8.735

0.000

0.4155

0.0752

Craft -BlueCol I

0.19324

2.494

0.013

1.2132

1.7671

Craft -WhiteCol I

-0.25934

-2.773

0.006

0.7716

0.4657

Craft -Prof     I

-0.68504

-7.671

0.000

0.5041

0.1329

Whitecol-BlueCol I

0.45258

4.425

0.000

1.5724

3.7943

WhiteCol-Craft    I

0.25934

2.773

0.006

1.2961

2.1471

WhiteCol-Prof     I

-0.42569

-4.616

0.000

0.6533

0.2853

WhiteCol-Menial I

0.35316

3.011

0.003

1.4236

2.8308

Prof    -BlueCol I

0.87828

8.735

0.000

2.4067

13.3002

Prof    -Craft    I

0.68504

7.671

0.000

1.9838

7.5264

Prof -WhiteCol I

0.42569

4.616

0.000

1.5307

3.5053

Prof    -Menial I

0.77885

6.795

0.000

2.1790

9.9228

Menial -WhiteCol I

-0.35316

-3.011

0.003

0.7025

0.3533

Menial -Prof     I

-0.77885

-6.795

0.000

0.4589

0.1008

Example with zip and zinb

For the zip and zinb models, the output of Iistcoef makes it much simpler to be sure about the proper interpretation.
In the standard output from zip, which follows, the direction of effects can be difficult to determine.

. zip art fem mar kidδ phd ment, inf(fem mar kidδ phd ment) nolog

Zero-inflated poisson regression                  Number of obs =        91δ

Nonzero obs =        640

Inflation model = logit

Log likelihood = -1604.773

Zero obs        =

LR chi2(5)

Prob > chi2     =

275

78.56

0.0000

—

art I

Coef.

Std. Err.

z

P>∣z∣

[957. Conf.

—

Interval]

—

—

art

I
fem I

-.2091446

.0634047

-3.299

0.001

-.3334155

-.0848737

mar I

.103751

.071111

1.459

0.145

-.035624

.243126

kid5 I

-.1433196

.0474293

-3.022

0.003

-.2362793

-.0503599

phd I

-.0061662

.0310086

-0.199

0.842

-.066942

.0546096

ment I

.0180977

.0022948

7.886

0.000

.0135999

.0225955

.cons I

.6408391

.1213072

5.283

0.000

.4030814

.8785967

∙+∙



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