Mental Health Treatment, Human Capital and the Labor Market
In this paper, we examine the relationships between mental health, treatment choices and labor market outcomes. Our analysis features a dynamic decision-making model that explains how forward-looking individuals make optimal mental health treatment decisions over the life cycle and that can be used to evaluate policies affecting mental health and mental health treatment.
The model captures a key tradeoff that individuals face when evaluating treatment options for mental health illnesses. Therapy (i.e., “the couch”) is both expensive and time consuming, but may be more effective than pharmaceuticals (i.e., “pills”) at resolving underlying causes of anxiety or depression. Pills, on the other hand, are relatively low-cost (in terms of time and money) palliative remedies, but may have undesirable side effects and are perhaps less effective at resolving the underlying sources of mental health problems. These differences suggest that optimal treatment choices are determined by a set of dynamic tradeoffs related to the labor market and may vary over the life-cycle.
To fix idea, for high versus low earners, there are countervailing dynamics that could influence treatment choices and mental health outcomes. For example, forward-looking high earners may face relatively stronger incentives to make long-term health investments, which means that the couch may be the best option. However, high earners also face a high opportunity cost of time, potentially making pills the more attractive option. An individual’s optimal treatment strategy will, therefore, be a function of several factors, including: limitations caused by the mental illness, current earnings and employment, prevailing labor market conditions, along with the individual’s stage in her lifecycle, which influences the dynamic returns to on-the-job training.
This research will occur in 3 phases. First, using data from the Medical Expenditure Panel Survey, we will provide evidence on the characteristics of various mental health treatments, including their effectiveness at improving mental health and their side effects and/or time cost. Second, we will specify and estimate a structural dynamic model of treatment choices (in light of variation in treatment qualities) and employment decisions. The model will be designed to capture the dynamic tradeoffs that determine mental health treatment decisions. Third, the estimated structural model will be used to assess policies that affect the labor market or mental health treatment decisions.