Two key components of the upcoming health reform in the U.S. are a new regulation of the individual health insurance market and an increase in income redistribution in the economy. Which component contributes more to the welfare outcome of the reform? We address this question by constructing a general equilibrium life cycle model that incorporates both medical expenses and labor income risks. We replicate the key features of the current health insurance system in the U.S. and calibrate the model using the Medical Expenditures Panel Survey dataset. We find that the reform decreases the number of uninsured more than twice and generates substantial welfare gains. However, these welfare gains mostly come from the redistributive measures embedded in the reform. If the reform only reorganizes the individual market, introduces individual mandates but does not include any income-based transfers, the welfare gains are much smaller. This result is mostly driven by the fact that most uninsured people have low income.

Health shocks are an important source of risk. People in bad health work less, earn less, face higher medical expenses, die earlier, and accumulate much less wealth compared to those in good health. Importantly, the dynamics of health are much richer than those implied by a low-order Markov process. We first show that these dynamics can be parsimoniously captured by a combination of some lag-dependence and ex-ante heterogeneity, or health types. We then study the effects of health shocks in a structural life-cycle model with incomplete markets. Our estimated model reproduces the observed inequality in economic outcomes by health status, including the income-health and wealth-health gradients. Our model has several implications concerning the pecuniary and non-pecuniary effects of health shocks over the life-cycle. The (monetary) lifetime costs of bad health are very concentrated and highly unequally distributed across health types, with the largest component of these costs being the loss in labor earnings. The non-pecuniary effects of health are very important along two dimensions. First, individuals value good health mostly because it extends life expectancy. Second, health uncertainty substantially increases lifetime inequality by affecting the variation in lifespans.

The Covid-19 pandemic has disrupted working life in many ways, the negative consequences of which may be distributed unevenly under lockdown regulations. In this paper, we construct a new set of pandemic-related indices from the Occupational Information Network (O*NET) using factor analysis. The indices capture two key dimensions of job task requirements: (i) the extent to which jobs can be adaptable to work from home; and (ii) the degree of infection risk at workplace. The interaction of these two dimensions help identify which groups of workers are more vulnerable to income losses, and which groups of occupations pose more risk to public health. This information is crucial for both designing appropriate supporting programs and finding a strategy to reopen the economy while controlling the spread of the virus. In our application, we map the indices to the labor force survey of a developing country, Thailand, to analyze these new labor market risks. We document differences in job characteristics across income groups, at both individual and household levels. First, low income individuals tend to work in occupations that require less physical interaction (lower risk of infection) but are less adaptable to work from home (higher risk of income/job loss) than high income people. Second, the positive occupational sorting among low-income couples makes them less able to partially insure themselves. Consequently, low-income families tend to face a disproportionately larger risk of income/job loss from lockdown measures. In addition, the different exposure to infection and income risks between income groups can play an important role in shaping up the timing and optimal strategies to unlock the economy.

Having low income is one of the requirements for Medicaid eligibility. Given that earning ability is unobservable, once an individual with high labor income stops working it is impossible to distinguish him from those whose potential labor income is low. This can affect the ability of Medicaid to target the most disadvantaged people given that a large fraction of its beneficiaries do not work. In this paper we ask two questions: 1) Does Medicaid significantly distort work incentives? 2) Can the insurance-incentives trade-off of Medicaid be improved without changing the size of the redistribution in the economy? Our tool is a general equilibrium model with heterogeneous agents calibrated using the Medical Expenditure Panel Survey Dataset to match the life-cycle patterns of employment and insurance take-up behavior as well as the key aggregate statistics. We find that around 20% of Medicaid enrollees do not work in order to be eligible. These distortions are costly for the economy: if Medicaid eligibility could be linked to (unobservable) productivity the resulting ex-ante welfare gains are equivalent to 1.5% of the annual consumption. We show that asset testing can achieve a similar outcome but only if asset limits are allowed to be different for workers and non-workers.

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