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The multinomial logit (MNL) model is a popular method for modeling categorical outcomes that have no natural ordering—outcomes such as occupation, political party, or restaurant choice.
In longitudinal/panel data, we observe a sequence of outcomes over time. Say that we observe restaurant choices made by individuals each week. Do you think that restaurant choices are independent from week to week? Probably not. Someone who likes Italian food is likely to choose an Italian restaurant multiple times. These choices are driven by underlying personal preferences and characteristics, some of which are not observed.
Stata's new xtmlogit command fits random-effects and conditional fixed-effects MNL models for categorical outcomes observed over time.
To fit a random-effects multinomial logit model, we can type
. xtset subject . xtmlogit restaurant age
and estimate the standard multinomial logit coefficients accounting for time-invariant subject-specific characteristics by including random effects specific to each outcome level.
With the command above, the random effects are assumed to be normally distributed and independent across outcome levels (restaurant choices), but several variance–covariance structures are supported, including a completely unrestricted covariance:
. xtmlogit restaurant age, covariance(unstructured)
If you suspect that subject-specific effects might be correlated with age, you can use a conditional fixed-effects estimator to account for this:
. xtmlogit restaurant age, fe
We wish to find out whether individuals are more likely to be out of the labor force if they have children under the age of five in their household. We will use a (fictitious) dataset of men and women who were asked about their employment status every two years.
. use https://www.stata-press.com/data/r17/estatus (Fictitious employment status data)
Here is an excerpt of the dataset, showing the employment history for three individuals:
. list id year estatus hhchild age in 22/41, sepby(id) noobs
| id year estatus hhchild age | ||
| 5 2002 Employed Yes 38 | ||
| 5 2004 Employed No 40 | ||
| 5 2006 Employed No 42 | ||
| 5 2008 Employed No 44 | ||
| 5 2010 Out of labor force No 46 | ||
| 5 2012 Out of labor force No 48 | ||
| 5 2014 Unemployed No 50 | ||
| 6 2002 Unemployed Yes 31 | ||
| 6 2004 Employed Yes 33 | ||
| 6 2006 Out of labor force Yes 35 | ||
| 6 2008 Unemployed Yes 37 | ||
| 6 2010 Out of labor force Yes 39 | ||
| 6 2012 Unemployed No 41 | ||
| 7 2002 Out of labor force Yes 33 | ||
| 7 2004 Employed Yes 35 | ||
| 7 2006 Employed Yes 37 | ||
| 7 2008 Out of labor force Yes 39 | ||
| 7 2010 Employed No 41 | ||
| 7 2012 Employed No 43 | ||
| 7 2014 Employed No 45 | ||
The outcome of interest is employment status (estatus), which has three levels: Employed, Unemployed (but seeking employment), and Out of labor force (not seeking employment). Our predictor of interest, hhchild, indicates whether they have children under the age of five in their household at the time of the interview.
Before we can fit our model, we need to specify our panel identifier variable, id, by using xtset.
. xtset id Panel variable: id (unbalanced)
Now we can use xtmlogit to model the probability of each employment type by hhchild while controlling for the effects of age, annual household income (hhincome), and whether a significant other was also living in the household (hhsigno). We will start with a random-effects model (the default) and use the rrr option to get exponentiated coefficients that can be interpreted as relative-risk ratios.
. xtmlogit estatus i.hhchild age hhincome i.hhsigno, rrr
Fitting comparison model ...
Refining starting values:
Grid node 0: log likelihood = -4504.5591
Grid node 1: log likelihood = -4538.6352
Fitting full model:
Iteration 0: log likelihood = -4504.5591
Iteration 1: log likelihood = -4495.871
Iteration 2: log likelihood = -4490.5098
Iteration 3: log likelihood = -4490.4197
Iteration 4: log likelihood = -4490.4196
Random-effects multinomial logistic regression Number of obs = 4,761
Group variable: id Number of groups = 800
Random effects u_i ~ Gaussian Obs per group:
min = 5
avg = 6.0
max = 7
Integration method: mvaghermite Integration pts. = 7
Wald chi2(8) = 199.25
Log likelihood = -4490.4196 Prob > chi2 = 0.0000
| estatus | RRR Std. err. z P>|z| [95% conf. interval] | |||||
| Out_of_labor_force | ||||||
| hhchild | ||||||
| Yes | 1.579937 .1513905 4.77 0.000 1.309414 1.906349 | |||||
| age | .9947946 .0065832 -0.79 0.430 .981975 1.007781 | |||||
| hhincome | .9954927 .0018251 -2.46 0.014 .9919221 .9990762 | |||||
| hhsigno | ||||||
| Yes | 1.642859 .1550291 5.26 0.000 1.365452 1.976625 | |||||
| _cons | .4949307 .1392991 -2.50 0.012 .2850836 .859244 | |||||
| Unemployed | ||||||
| hhchild | ||||||
| Yes | .9607243 .1148148 -0.34 0.737 .7601038 1.214296 | |||||
| age | 1.004257 .008211 0.52 0.603 .9882918 1.02048 | |||||
| hhincome | .9696874 .0025722 -11.60 0.000 .964659 .9747421 | |||||
| hhsigno | ||||||
| Yes | 1.099323 .1310654 0.79 0.427 .8702452 1.388701 | |||||
| _cons | .8078165 .280628 -0.61 0.539 .4088963 1.595924 | |||||
| Employed | (base outcome) | |||||
| var(u1) | .8573133 .1083915 .6691459 1.098394 | |||||
| var(u2) | .7378532 .1388652 .5102376 1.067008 | |||||
The first two sections in the output show the relative-risk ratio estimates of our predictors with respect to the base category Employed. The last section shows the estimated variances of the random effects. By default, the random effects are uncorrelated, but their covariance structure can be changed using the covariance() option. For example, correlations between random effects can be estimated using covariance(unstructured), or each category can share a common random effect using covariance(shared).
Adjusting for age, household income, and having a significant other at home, the relative risk of being out of the labor force for individuals having at least one child under the age of 5 in the household versus having no children under 5 in the household is 1.6 times as large as the relative risk of employment (95% CI [1.3, 1.9]). To understand these effects in terms of probabilities, we can use the margins command.
. margins hhchild Predictive margins Number of obs = 4,761 Model VCE: OIM 1._predict: Pr(estatus==Out_of_labor_force), predict(pr outcome(1)) 2._predict: Pr(estatus==Unemployed), predict(pr outcome(2)) 3._predict: Pr(estatus==Employed), predict(pr outcome(3))
| Delta-method | ||||||
| Margin std. err. z P>|z| [95% conf. interval] | ||||||
| _predict#hhchild | ||||||
| 1#No | .3025675 .0131546 23.00 0.000 .276785 .32835 | |||||
| 1#Yes | .3912476 .0120405 32.49 0.000 .3676486 .4148466 | |||||
| 2#No | .1628713 .0101131 16.11 0.000 .1430501 .1826925 | |||||
| 2#Yes | .1398537 .0079462 17.60 0.000 .1242794 .1554279 | |||||
| 3#No | .5345612 .0136994 39.02 0.000 .5077108 .5614116 | |||||
| 3#Yes | .4688987 .0116594 40.22 0.000 .4460468 .4917507 | |||||
For an individual without children, the expected probability of being out of the labor force (labeled 1#No) is 0.30, the expected probability of being unemployed (2#No) is 0.16, and the expected probability of being employed is 0.53 (3#No). We also find that individuals with children in the household increase their probability of being out of the labor force by 9 percentage points. We could see how these probabilities change by household income using an additional margins command and visualize the results using marginsplot.
. quietly margins hhchild, at(hhincome=(20(20)100))
. marginsplot, by(_predict, label("Out of labor force" "Unemployed" "Employed"))
byopts(rows(1) title("Marginal probabilities of employment status"))
legend(order(4 "Child under 5 at home" 3 "No child under 5 at home"))
To get separate graphs for each outcome, we used the by(_predict) option in marginsplot. The rest of the options add titles and labels.
Comparing the lines within each employment category, we see that having a child at home does not have much impact on the probability of being unemployed but does influence the decision to work or to be out of the labor force.
In the model we just fit, we used random effects to account for unobserved characteristics of the individuals in our dataset. Random-effects models require that the random effects be uncorrelated with the predictors, and the random-effects MNL model is no exception. A widely used alternative is the fixed-effects estimator. To fit our model with conditional fixed effects, we simply add the fe option.
. xtmlogit estatus i.hhchild age hhincome i.hhsigno, fe rrr
note: 80 groups (451 obs) omitted because of no variation in the outcome variable over time.
Computing initial values ...
Setting up 26,168 permutations:
....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%
Fitting full model:
Iteration 0: log likelihood = -2154.4175
Iteration 1: log likelihood = -2154.2058
Iteration 2: log likelihood = -2154.2057
Fixed-effects multinomial logistic regression Number of obs = 4,310
Group variable: id Number of groups = 720
Obs per group:
min = 5
avg = 6.0
max = 7
LR chi2(8) = 67.42
Log likelihood = -2154.2057 Prob > chi2 = 0.0000
| estatus | RRR Std. err. z P>|z| [95% conf. interval] | |||||
| Out_of_labor_force | ||||||
| hhchild | ||||||
| Yes | 1.784236 .2237128 4.62 0.000 1.395488 2.28128 | |||||
| age | .9977834 .0146507 -0.15 0.880 .9694778 1.026915 | |||||
| hhincome | .9895225 .0086923 -1.20 0.231 .9726318 1.006707 | |||||
| hhsigno | ||||||
| Yes | 1.658753 .1654425 5.07 0.000 1.364217 2.016878 | |||||
| Unemployed | ||||||
| hhchild | ||||||
| Yes | 1.181866 .1933766 1.02 0.307 .8576197 1.628702 | |||||
| age | 1.004991 .0194887 0.26 0.797 .967511 1.043924 | |||||
| hhincome | .9717411 .0116616 -2.39 0.017 .9491514 .9948684 | |||||
| hhsigno | ||||||
| Yes | 1.11936 .1454154 0.87 0.385 .8677426 1.443939 | |||||
| Employed | (base outcome) | |||||
The results are similar to those of the random-effects estimator. And they can be interpreted in the same way.
Learn more in the Stata Longitudinal-Data/Panel-Data Reference Manual.
Fit Bayesian fixed-effects and random-effects MNL models using the bayes prefix.
