We study an R&D race between an established firm and a startup under asymmetric information. R&D investment brings success stochastically, but only if the innovation is feasible. The only asymmetry is that the established firm has better information about the feasibility of the innovation. We show that there is an equilibrium in which the poorly informed startup wins more often, and has higher expected profits, than the better-informed incumbent. When the informational asymmetry is large, this is the unique equilibrium outcome. The channel by which better information becomes a competitive disadvantage appears to be new and stems from the fact that better information dulls the incentive to learn from one’s rival.

We offer a propensity score perspective to interpret and analyze the marginal treatment effect (MTE). Specifically, we redefine MTE as the expected treatment effect conditional on the propensity score and a latent variable representing unobserved resistance to treatment. As with the original MTE, the redefined MTE can be used as a building block for constructing standard causal estimands. The weights associated with the new MTE, however, are simpler, more intuitive, and easier to compute. Moreover, the redefined MTE immediately reveals treatment effect heterogeneity among individuals at the margin of treatment, enabling us to evaluate a wide range of policy effects.

This paper studies economies where firms acquire capital in primary markets and then, after idiosyncratic productivity shocks, retrade it in secondary markets that incorporate bilateral trade with search, bargaining, and liquidity frictions. We distinguish between full or partial sales (one firm gets all or some of the other’s capital) and document several long- and short-run empirical patterns between these variables and the cost of liquidity, as measured by inflation. Quantitatively, the model can match these patterns plus the standard business cycle facts. We also investigate the impact of search frictions, monetary and fiscal policy, persistence in shocks, and returns to scale.

This paper develops a micro-founded general equilibrium model of payments to study the impact of a central bank digital currency (CBDC) on intermediation of private banks. If banks have market power in the deposit market, a CBDC can enhance competition, raising the deposit rate, expanding intermediation, and increasing output. A calibration to the US economy suggests that a CBDC can raise bank lending by 1.57% and output by 0.19%. These crowding-in effects remain robust, albeit with smaller magnitudes, after taking into account endogenous bank entry. We also assess the role of a non-interest-bearing CBDC as the use of cash declines.