We study electric vehicle (EV) tax credits in the US Inflation Reduction Act (IRA), the largest climate policy in US history, with three goals. First, we provide the first ex-post microeconomic welfare analysis of this central component of the IRA. Event studies around changes in eligibility for EV tax credits find that short-run economic incidence falls largely on consumers. Additionally, domestic content restrictions on tax credits for purchased vehicles have driven enormous shifts to leasing. Our equilibrium model shows that compared to pre-IRA policy, IRA EV credits generated $1.87 of US benefits per dollar spent in 2023, at taxpayer cost of $32,000 per additional EV sold. Compared to scenarios with no EV credits, however, the IRA EV credits created only $1.02 of benefits per dollar of government spending. Second, we characterize the gains from policies targeting heterogeneity in externalities across vehicles. We find that relative to uniform credits, differentiating credits across EVs according to their heterogeneous externalities would substantially increase policy benefits. Third, we quantify tradeoffs in the IRA EV credits between foreign and domestic welfare and between trade and the environment. We find that the IRA EV credits benefit the environment but undermine trade, since they decrease global carbon emissions but use profit shifting to decrease foreign producer surplus. A controversial IRA loophole that removes domestic content restrictions on tax credits for EV leases has negative domestic benefits.

We quantify the firm responses and real wage effects of foreign demand shocks. We use Belgian micro data to construct firm-specific measures of demand shocks which capture that firms pass on foreign demand shocks to domestic suppliers. Our estimates of firm responses to these shocks suggest that firms face upward-sloping labor supply curves and have sizable fixed labor costs. We specify a general equilibrium model with these features to quantify the aggregate effects of simulated tariff shocks on wages. We find that ignoring fixed labor costs substantially underestimates aggregate effects on wages, whereas incorporating upward-sloping labor supply appears less consequential.

In granular settings in which people choose from a large set of potential residence-workplace pairs, observed outcomes in part reflect idiosyncratic choices. Using both Monte Carlo simulations and event studies of neighborhood employment booms, we demonstrate that calibration procedures that equate observed shares and modeled probabilities perform very poorly in these high-dimensional settings. Parsimonious specifications of spatial linkages deliver better counterfactual predictions. To quantify the uncertainty about counterfactual outcomes induced by the idiosyncratic component of individuals' decisions, we introduce a quantitative spatial model with a finite number of individuals. Applying this model to Amazon's proposed second headquarters in New York City reveals that its predicted consequences for most neighborhoods vary substantially across realizations of the individual idiosyncrasies.