Experiencing states of unhappiness is normal and part of human existence. Yet, if these states occur often or for longer periods of time, this can become a large burden and greatly reduce a person’s overall quality of life. I refer to these states as cumulative unhappiness, and I empirically investigated which factors and variables are correlated with them. Using large-scale German panel data (N = 8,646; mean age = 51.7 years, SD = 10.7 years), I attempted to model the correlates of cumulative unhappiness over a period of nine years and included factors such as sociodemographic-background variables, social origin, education, income, household situation, social capital, personality traits, unemployment, and health. Bivariate analyses indicated that health and household income are the two most relevant predictors of unhappiness. In multivariate modeling using dominance analysis, I demonstrated that about 26% of the total variation of cumulative unhappiness can be explained by all independent variables together. In these analyses, the most relevant influential factors were health (14.8%), social status and income (4.0%), and social capital (3.1%). These results indicate that cumulative unhappiness can be explained to some extent.