Working Papers
Political Competition and Climate Policy: A Dynamic Game of Pollution Control (February 2025) [Abstract]
In this paper, I analyze a dynamic game of political pollution control where two policy-motivated parties compete for votes through tax policies. Voters evaluate the direct effects of the tax policies and vote probabilistically. Depending on the voters’ time horizon, the ambition of the green party’s climate policy is drastically affected.
Learning dynamics in a network of Cournot economies (July 2023) [Abstract]
In this paper, firms in a network of Cournot economies learn about the intercept of the demand curve using past sales history. I decompose the learning process into economically interpretable components and find that learning aggregate quantities happens at a faster rate than individual quantities both within markets and within firms. This speed depends on the network topology and the slope of the demand function. The slowest learning component is the distribution of a correct aggregate amount between markets, which drives the slow convergence of individual quantities. The convergence rate of individual quantities is the same for all sufficiently connected networks and is independent of the slope of the demand function. Increasing the density of a random network has a non-monotonic effect on the convergence speed of aggregate quantities. Convergence speeds first decrease relative to isolated market-firm pairs, increasing again after the graph becomes sufficiently connected.