Let us consider Example 16.1 in Wooldridge (2010), concerning school and employment decisions for young men. The data contain information on employment and schooling for young men over several years. We will work with the data for 1987.
The outcome is status, coded 1=in school, 2=at home (meaning not in school and not working), and 3=working. The predictors are education, a quadratic on work experience, and an indicator for black.
We read the data from the Stata website, keep the year 1987, drop missing values, label the outcome, and fit the model.
. use https://www.stata.com/data/jwooldridge/eacsap/keane, clear
. keep if year == 87
(10,985 observations deleted)
. drop if missing(status)
(21 observations deleted)
. label define status 1 "school" 2 "home" 3 "work"
. label values status status
. mlogit status educ exper expersq black, base(1)
Iteration 0: log likelihood = -1199.7182
Iteration 1: log likelihood = -960.26272
Iteration 2: log likelihood = -908.7673
Iteration 3: log likelihood = -907.85992
Iteration 4: log likelihood = -907.85723
Iteration 5: log likelihood = -907.85723
Multinomial logistic regression Number of obs = 1,717
LR chi2(8) = 583.72
Prob > chi2 = 0.0000
Log likelihood = -907.85723 Pseudo R2 = 0.2433
─────────────┬────────────────────────────────────────────────────────────────
status │ Coefficient Std. err. z P>|z| [95% conf. interval]
─────────────┼────────────────────────────────────────────────────────────────
school │ (base outcome)
─────────────┼────────────────────────────────────────────────────────────────
home │
educ │ -.6736313 .0698999 -9.64 0.000 -.8106325 -.53663
exper │ -.1062149 .173282 -0.61 0.540 -.4458414 .2334116
expersq │ -.0125152 .0252291 -0.50 0.620 -.0619633 .036933
black │ .8130166 .3027231 2.69 0.007 .2196902 1.406343
_cons │ 10.27787 1.133336 9.07 0.000 8.056578 12.49917
─────────────┼────────────────────────────────────────────────────────────────
work │
educ │ -.3146573 .0651096 -4.83 0.000 -.4422699 -.1870448
exper │ .8487367 .1569856 5.41 0.000 .5410507 1.156423
expersq │ -.0773003 .0229217 -3.37 0.001 -.1222261 -.0323746
black │ .3113612 .2815339 1.11 0.269 -.240435 .8631574
_cons │ 5.543798 1.086409 5.10 0.000 3.414475 7.673121
─────────────┴────────────────────────────────────────────────────────────────
The results agree exactly with Table 16.1 in Wooldridge (2010, page 645).
Let us focus on the coefficient of black in the work equation, which is 0.311. Exponentiating we obtain
. di exp( _b[work:black] ) 1.3652822
Thus, the relative probability of working rather than being in school is 37% higher for blacks than for non-blacks with the same education and work experience. (Relative probabilities are also called relative odds.)
A common mistake is to interpret this coefficient as meaning that the probability of working is higher for blacks. It is only the relative probability of work over school that is higher. To obtain a fuller picture we need to consider the second equation as well. The coefficient of black in the home equation is 0.813. Exponentiating, we obtain
. di exp(_b[home:black] ) 2.2546993
Thus, the relative probability of being at home rather than in school for blacks is more than double the corresponding relative probability for non blacks with the same education and work experience.
In short, black is associated with an increase in the relative probability of work over school, but also a much large increase in the relative probability of home over school. What happens with the actual probability of working depends on how these two effects balance out.
To determine the effect of black in the probability scale we need to compute marginal effects, which can be done using continuous or discrete calculations.
The continuous calculation is based on the derivative of the probability of working with respect to a predictor. Let \(\pi_{ij}=\Pr\{Y_i=j\}\) denote the probability that the i-th observation follows on the j-th category, which is given by \[ \pi_{ij} = \frac{e^{x_i'\beta_j}}{\sum_r e^{x_i'\beta_r}} \] where \(\beta_j = 0\) when j is the baseline or reference outcome, in this case school.
Taking derivatives w.r.t. the k-th predictor we obtain, after some simplification \[ \frac{\partial\pi\_{ij}}{\partial x_{ik}} = \pi_{ij} ( \beta_{jk} - \sum_r \pi_{ir} \beta_{rk} ) \] noting again that the coefficient is zero for the baseline outcome.
To compute these we predict the probabilities and then apply the formula.
. predict p1 p2 p3, pr
. gen me1 = p1*( -(p2*_b[2:black] + p3*_b[3:black]))
. gen me2 = p2*(_b[2:black] -(p2*_b[2:black] + p3*_b[3:black]))
. gen me3 = p3*(_b[3:black] -(p2*_b[2:black] + p3*_b[3:black]))
. sum me*
Variable │ Obs Mean Std. dev. Min Max
─────────────┼─────────────────────────────────────────────────────────
me1 │ 1,717 -.0183811 .0241438 -.1011232 -.0007906
me2 │ 1,717 .058979 .0355181 .0073935 .1402041
me3 │ 1,717 -.0405979 .0404273 -.1246674 .0587828
We find that the average marginal effect of black on work is actually negative: -0.0406. This means that the probability of working is on average about four percentage points lower for blacks than for non-blacks with the same education and experience.
Stata can do this calculation using the dydx() option of
the margins command. Here’s the marginal effect for
work:
. margins, dydx(black) pr(out(3))
Average marginal effects Number of obs = 1,717
Model VCE: OIM
Expression: Pr(status==work), predict(out(3))
dy/dx wrt: black
─────────────┬────────────────────────────────────────────────────────────────
│ Delta-method
│ dy/dx std. err. z P>|z| [95% conf. interval]
─────────────┼────────────────────────────────────────────────────────────────
black │ -.0405979 .0197356 -2.06 0.040 -.079279 -.0019168
─────────────┴────────────────────────────────────────────────────────────────
This agrees exactly with our hand calculation. Note that Stata uses the derivative for continuous variables, and a discrete difference for factor variables, which we consider next.
For the discrete calculation we compute predicted probabilities by setting ethnicity to black and then to non-black and averaging:
. gen keep_black = black
. quietly replace black = 1
. predict p11 p12 p13, pr
. sum p1?
Variable │ Obs Mean Std. dev. Min Max
─────────────┼─────────────────────────────────────────────────────────
p11 │ 1,717 .0450738 .0712937 .0015405 .4842191
p12 │ 1,717 .2274114 .2114531 .0237205 .9368684
p13 │ 1,717 .7275148 .2156368 .0615363 .9393418
. scalar w1 = r(mean)
. quietly replace black = 0
. predict p01 p02 p03, pr
. sum p0?
Variable │ Obs Mean Std. dev. Min Max
─────────────┼─────────────────────────────────────────────────────────
p01 │ 1,717 .0630648 .0941436 .0025128 .5787593
p02 │ 1,717 .1659493 .1861749 .014167 .8990285
p03 │ 1,717 .7709859 .1986715 .0975198 .9462531
. di w1 - r(mean)
-.04347105
. quietly replace black = keep_black
We find that the average probability of working is 0.7275 if black and 0.7710 if not black, a difference of -0.0435, so the probability of working is on average just over four percentage points lower for blacks.
Stata can calculate the predictive margins if you specify black as a
factor variable when you fit the model, and then issue the command
margins black. This only works for factor variables.
. quietly mlogit status educ exper expersq i.black, base(1)
. margins black, pr(out(3))
Predictive margins Number of obs = 1,717
Model VCE: OIM
Expression: Pr(status==work), predict(out(3))
─────────────┬────────────────────────────────────────────────────────────────
│ Delta-method
│ Margin std. err. z P>|z| [95% conf. interval]
─────────────┼────────────────────────────────────────────────────────────────
black │
0 │ .7709859 .0119827 64.34 0.000 .7475001 .7944716
1 │ .7275148 .0154176 47.19 0.000 .6972968 .7577328
─────────────┴────────────────────────────────────────────────────────────────
The marginal effect can then be obtained as a discrete difference.
. margins, dydx(black) pr(out(3))
Average marginal effects Number of obs = 1,717
Model VCE: OIM
Expression: Pr(status==work), predict(out(3))
dy/dx wrt: 1.black
─────────────┬────────────────────────────────────────────────────────────────
│ Delta-method
│ dy/dx std. err. z P>|z| [95% conf. interval]
─────────────┼────────────────────────────────────────────────────────────────
1.black │ -.043471 .0199503 -2.18 0.029 -.082573 -.0043691
─────────────┴────────────────────────────────────────────────────────────────
Note: dy/dx for factor levels is the discrete change from the base level.
These results agree exactly with our hand calculations.
The take away conclusion here is that multinomial logit coefficients can only be interpreted in terms of relative probabilities. To reach conclusions about actual probabilities we need to calculate continuous or discrete marginal effects.
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. Second Edition. Cambridge, Massachussets: The MIT Press.
Updated fall 2022
