Econometrica最新文献

Emissions abatement alone cannot address the consequences of global warming for weather disasters. We model how society adapts to manage disaster risks to capital stock. Optimal adaptation—a mix of firm-level efforts and public spending—varies as society learns about the adverse consequences of global warming for disaster arrivals. Taxes on capital are needed alongside those on carbon to achieve the first best. We apply our model to country-level control of flooding from tropical cyclones. Learning rationalizes empirical findings, including the responses of Tobin's q, equity risk premium, and risk-free rate to disaster arrivals. Adaptation is more valuable under learning than a counterfactual no-learning environment. Learning alters social-cost-of-carbon projections due to the interaction of uncertainty resolution and endogenous adaptive response.

This paper shows that there is more scope for a borrower to engage in a sustainable infinite debt rollover (a “Ponzi scheme”) when interest/growth rates are stochastic. In this context, I prove that the relevant “r vs. g” comparison uses the yield r_long to an infinite-maturity zero-coupon bond. I show that r_long is lower than the risk-neutral expectation of the short-term yield when it is variable, and that r_long is close to the minimal realization of the short-term yield when it is highly persistent. The paper applies these results to illustrative heterogeneous agent dynamic stochastic general equilibrium models to obtain similarly weakened sufficient conditions for the existence of public debt bubbles.

We investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and increase its own payoff. We consider demand-steering rules that reward firms that cut prices with additional exposure to consumers. We examine the impact of these rules both in theory and by using simulations with artificial intelligence pricing algorithms (specifically Q-learning algorithms, which are commonly used in computer science). Our theoretical results indicate that these policies (which require little information to implement) can have strongly beneficial effects, even when sellers are infinitely patient and seek to collude. Similarly, our simulations suggest that platform design can benefit consumers and the platform, but that achieving these gains may require policies that condition on past behavior and treat sellers in a nonneutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to low prices.

R&D investment reduces current profits, so short-term pressure to hit profit targets may distort R&D. In the data, firms just meeting Wall Street forecasts have lower R&D growth and subsequent innovation, while managers just missing receive lower pay. But short-termist distortions might not quantitatively matter if aggregation or equilibrium dampen their impact. So I build and estimate a quantitative endogenous growth model in which short-termism arises naturally as discipline on conflicted managers and boosts firm value by about 1%. But short-termism reduces R&D, and the social return to R&D is higher than the private return due to standard channels including knowledge spillovers and imperfect competition. So at the macro level, short-termist distortions slow growth by 5 basis points yearly and lower social welfare by about 1%.

We study how discounting and monitoring jointly determine whether cooperation is possible in repeated games with imperfect (public or private) monitoring. Our main result provides a simple bound on the strength of players' incentives as a function of discounting, monitoring precision, and on-path payoff variance. We show that the bound is tight in the low-discounting/low-monitoring double limit, by establishing a public-monitoring folk theorem where the discount factor and the monitoring structure can vary simultaneously.