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Sean Wu (Harvard) "Weighing the House for Inflation Targeting"
Abstract: (How) should the monetary authority take the housing price into account, acknowledging that housing dominates household balance sheets and has played a crucial role in recent crises? I develop a dynamic model with nominal rigidity and financial frictions in which home-owners borrow against their housing value, creating a “housing financial accelerator” which amplifies shocks through the feedback between aggregate demand and housing price. I analytically derive the optimal targeting rule of monetary policy, and show that it features the housing price, in addition to non-durable inflation and output. The optimal nominal interest rate implemented depends on whether borrowing is constrained, which is in turn affected by the size and direction of shocks. I present a few conjectures and sketch directions of future work.
Sophia Mo (Harvard) "Information Frictions and Intersectoral Labor Flows" joint with Xinyue Lin
Abstract:
Sectoral reallocation is important for both economic growth and individual welfare. What drive labor allocation across sectors? In this project, we focus on one force that has not been studied intensively--information frictions--and compare its quantitative importance with other reasons that are relatively better understood, in particular skill differentials. We first use a coworker design to provide reduced-form evidence that workers do not possess perfect information on the wage distribution across sectors. Further, we build a dynamic discrete choice model with across-firm information frictions to understand the relative importance of information frictions vs. skill differentials in labor reallocation across sectors and to evaluate policies that aim at reducing barriers to job transitions, such as wage disclosure policies and job retraining programs.