I want to share a somewhat oversimplified explanation first. For all variables in the model that are dichotomous indicators, which is all variables except income and age, a positive coefficient means that a score of “yes” on that indicator increases the likelihood of higher categorical levels of the SWB outcome. A negative coefficient means that a score of “yes” increases the likelihood of the present or lower categorical levels of the SWB outcome. Of course, because this is not a regular ordered logit model, these coefficients sometimes vary depending on the outcome level we’re looking at. Coefficients are the same at all levels only for those terms for which the proportional odds, or the parallel lines assumptions, held. (If that assumption was upheld for all terms, I would have used an ordered logit though, so it’s not that simple to interpret, unfortunately.) A PPO/gologit model is interpreted by considering the coefficients at each level of the ordered outcome. So in this case, we have three levels of an ordered outcome, from low to high they are suffering, struggling, and thriving, numerically identified by 1, 2, and 3. The regression coefficients reflect the effects of each of the 11 variables in the model, the key predictors and the controls, and the coefficient represents the odds of a respondent being either above the lowest outcome category (of suffering), or of being in the highest outcome category (which is thriving). Revisit the equation for the model too, noting the role of this probability of being "> j".