With nominal/ordinal outcomes, we are often interested in the overall effect on the outcome rather than effects on each category. For example, how age or marital status affect self-rated health as a holistic construct. We develop a total marginal effect (ME) measure as a summary measure.

Does the nominal independent variable (IV) in the left or right panel have a larger effect? Intuitively, it is the one on the right because it patterns more unequal outcomes. We develop a marginal effect (ME) inequality statistic which summarize the holistic effect of a nominal or ordinal IV.

For more intensive obligatory roles, role-accumulation is beneficial only for the young and middle-aged. But there is a silver lining here: older adulthood is associated with obligatory role loss, and the null findings for them suggest resilience and strong mental health in the face of role loss.

As sociological theories of identity and role-accumulation predict, voluntary roles are beneficial for mental health at all ages. In contrast to some theories of aging, they are even more beneficial for the oldest adults who tend to hold fewer social roles overall.

Love a good hot take. In my experience the coefficients across the two almost never differ meaningfully. But those are effects on the rate/count. You often do see quite different predictions for the observed count (e.g. Pr(y=0)) and Poisson is usually very wrong and NB much closer in these cases.

I made this to teach R^2. Pretty proud of myself.