We develop a dynamic regulation game for a stock externality under asymmetric information and future market uncertainty. Within this framework, regulation is characterized as the implementation of a welfare-maximization program conditional on informational constraints. We identify the most general executable such programs and find these yield simple and intuitive policy rules. We apply our theory to carbon dioxide emissions trading schemes and find substantial welfare gains are possible, compared to current practices.
Submitted,
2020
The Market Stability Reserve (MSR), implemented in 2018 to complement the EU emission trading system (EU ETS), is designed such that the supply of allowances responds endogenously to demand. We show that an endogenous cap such as the MSR produces a Green Paradox. Abatement policies announced early but realized in the future are counter-effective because of the MSR: they increase cumulative emissions. We present the mechanisms in a two-period model, and then provide quantitative evidence of our result for an annual model disciplined on the price rise in the EU ETS that followed the introduction of the MSR. Our results point to the need for better coordination between different policies, such as the European Green Deal. We conclude with suggestions to improve the workings of an endogenous cap, ahead of the MSR review scheduled for 2021.
R&R at Economic Policy,
2020
We study disease control in a game of imperfect information. While disease control games of perfect information tend to have multiple equilibria, we show that even a small amount of uncertainty leads to equilibrium uniqueness. In equilibrium, an epidemic may occur even though it is inefficient and could have been avoided. Moreover, less harmful diseases may cause more deaths. We extend the game to study cooperation and let a subset of players commit to control the disease whenever the expected benefit of doing so is sufficiently high. The equilibrium is again unique. Selection of a more favorable equilibrium is facilitated by this type of cooperation.
2020
The question in which we are interested is how a market inhabited by multiple agents, about whom we are differentially uncertain, and who trade goods the use of which imposes a negative effect on others, is to be ideally regulated. We show that a priori asymmetric uncertainty, when combined with a posteriori observed outcomes, is a rich source of information that can be used to reduce aggregate uncertainty. The observation implies that whereas asymmetric information usually entails a cost on welfare, it can help achieve greater efficiency in regulation.
2019
