We develop an economic theory of mental health. The theory is grounded in classic and modern psychiatric literature, is disciplined with micro data, and is formalized in a life-cycle heterogeneous agent framework. In our model, individuals experiencing mental illness have pessimistic expectations and lose time due to rumination. As a result, they work less, consume less, invest less in risky assets, and forego treatment which in turn reinforces mental illness. We quantify the societal burden of mental illness and evaluate the efficacy of prominent policy proposals. We show that expanding the availability of treatment services and improving treatment of mental illness in late adolescence substantially improve mental health and welfare.
We propose a new sorting framework: composite sorting. Composite sorting comprises of (1) distinct worker types assigned to the same occupation, and (2) a given worker type simultaneously being part of both positive and negative sorting. Composite sorting arises when fixed investments mitigate variable costs of mismatch. We completely characterize optimal sorting and additionally show it is more positive when mismatch costs are less concave. We then characterize equilibrium wages. Wages have a regional hierarchical structure − relative wages depend solely on sorting within skill groups. Quantitatively, composite sorting can generate a sizable portion of within-occupations wage dispersion in the US.
This paper studies stochastic hysteresis − general dependence on the path of past decisions and shocks. We develop a new methodology for deriving the explicit dynamics of optimal policy with path-dependence and show that stochastic hysteresis changes optimal policy both qualitatively and quantitatively. We showcase our methodology by deriving new results for optimal policy with stochastic habits, tipping points, robustness concerns, limited commitment, and dynamic private information.
This paper studies stochastic hysteresis − general dependence on the path of past decisions and shocks. We develop a new methodology for deriving the explicit dynamics of optimal policy with path-dependence and show that stochastic hysteresis changes optimal policy both qualitatively and quantitatively. We showcase our methodology by deriving new results for optimal policy with stochastic habits, tipping points, robustness concerns, limited commitment, and dynamic private information.
We characterize optimal policies in a multidimensional nonlinear taxation model with bunching. We develop an empirically relevant model with cognitive and manual skills, firm heterogeneity, and labor market sorting. The analysis of optimal policy is based on two main results. We first derive an optimality condition − a general ABC formula − that states that the entire schedule of benefits of taxes second order stochastically dominates the entire schedule of tax distortions. Second, we use Legendre transforms to represent our problem as a linear program. This linearization allows us to solve the model quantitatively and to precisely characterize the regions and patterns of bunching. At an optimum, 9.8 percent of workers is bunched both locally and nonlocally. We introduce two notions of bunching – blunt bunching and targeted bunching. Blunt bunching constitutes 30 percent of all bunching, occurs at the lowest regions of cognitive and manual skills, and lumps the allocations of these workers resulting in a significant distortion. Targeted bunching constitutes 70 percent of all bunching and recognizes the workers’ comparative advantage. The planner separates workers on their dominant skill and bunches them on their weaker skill, thus mitigating distortions along the dominant skill dimension. Tax wedges are particularly high for low skilled workers who are bluntly bunched and are also high along the dimension of comparative disadvantage for somewhat more skilled workers who are targetedly bunched.