This paper proposes a new method to estimate the welfare impact of conflicts and remedy common data constraints in conflict-affected environments. The method first estimates how agents regard spatial welfare differentials by voting with their feet, using pre-conflict data. Then, it infers a lower-bound estimate for the conflict-driven welfare shock from partially observed post-conflict migration patterns. Results for the conflict in Eastern Ukraine between 2014 and 2019 show a large lower-bound welfare loss for Donetsk residents equivalent to 8–32 percent of lifetime income depending on agents’ time preference and risk aversion parameters.

In 2003, the Federal Reserve introduced primary credit as its main discount window lending program. This program replaced the adjustment credit program, which, subject to a number of restrictions, had generated a stigma associated with borrowing from the Federal Reserve. Lessening the stigma of borrowing was viewed as essential for reducing the reluctance to borrow from the Federal Reserve. We develop a structural model of daily borrowing. Using this model, we estimate the implicit cost associated with borrowing. Our results suggest that the stigma of borrowing is significantly reduced.

The welfare effects of trade shocks turn on the nature and magnitude of the costs workers face in moving between sectors. Using an Euler-type equilibrium condition derived from a rational expectations model of dynamic labor adjustment, we estimate the mean and variance of workers' switching costs from the US CPS. We estimate high values of both parameters, implying slow adjustment of the economy and sharp movements in wages in response to trade shocks. However, import-competing workers can still benefit from tariff removal; liberalization lowers their wages in the short and long run but raises their option value.