Essays in Monetary Policy and Growth

Abstract

This thesis is composed of three essays studying both monetary policy as well as economic growth. The first two chapters study optimal monetary (and fiscal) policy. The third chapter studies the relationship between transformative artificial intelligence, economic growth, and asset pricing.

The first chapter (joint with Daniele Caratelli) studies optimal monetary policy in a world with menu costs. We analytically characterize optimal monetary policy in a multisector economy with menu costs and show that inflation and output should move inversely following sectoral shocks. That is, after negative productivity shocks, inflation should be allowed to rise, and vice versa. In a baseline parameterization, optimal policy stabilizes nominal wages. This nominal wage targeting contrasts with inflation targeting, the optimal policy prescribed by the textbook New Keynesian model in which firms are permitted to adjust their prices only randomly and exogenously. The key intuition is that stabilizing inflation causes shocks to spill over across sectors, needlessly increasing the number of firms that must pay the fixed menu cost of price adjustment compared to optimal policy. Finally, we show in a quantitative model that, following a sectoral shock, nominal wage targeting reduces the welfare loss arising from menu costs by 81% compared to inflation targeting.

The second chapter offers a reexamination of optimal monetary and fiscal policy at the zero lower bound. I make five conceptual points about optimal monetary and fiscal policy at the zero lower bound (ZLB) in representative agent New Keynesian models, using the simplest possible version of such a model. (1) Monetary policy is typically described as facing a time consistency problem at the zero lower bound; but if ZLB episodes are a repeated game rather than a one-shot game – as is empirically realistic – then the time consistency problem can be easily overcome by reputational effects. (2) The ZLB is not special, in terms of the constraint it creates for monetary policy: an intratemporal rigidity, such as the minimum wage or rent control, creates exactly the same kind of constraint on monetary policy as the intertemporal rigidity of the ZLB. (3) Austerity is stimulus: in the representative agent New Keynesian model, fiscal stimulus works through the change in government spending. Promising to cut future spending – committing to austerity – has precisely the same effect on inflation and the output gap as a decision to raise spending today. (4) Fiscal stimulus can be contractionary, when targeted heterogeneously: if fiscal spending is targeted at certain sectors, this can in fact lower inflation and deepen the output gap. (5) Fiscal policy faces a time consistency problem at the ZLB, just as monetary policy does. Overall, I suggest that – in this class of models – the power of monetary policy at the ZLB has been underrated, and the power of fiscal policy has been overrated.

The third chapter (joint with Trevor Chow and J. Zachary Mazlish) studies how asset prices can be used to forecast the pace of development of AI technology. We study the implications of transformative artificial intelligence for asset prices, and in particular, how financial market prices can be used to forecast the arrival of such technology. We take into account the double-edged nature of transformative AI: while advanced AI could lead to a rapid acceleration in economic growth, some researchers are concerned that building a superintelligence misaligned with human values could create an existential risk for humanity. We show that under standard asset pricing theory, either possibility -- aligned AI accelerating growth or unaligned AI risking extinction -- would predict a large increase in real interest rates, due to consumption smoothing. The simple logic is that, under expectations of either rapid future growth or future extinction, agents will save less, increasing real interest rates. We contribute a variety of new empirical evidence confirming that, contrary to some recent work, higher growth expectations cause higher long-term real interest rates, as measured using inflation-linked bonds and rich cross-country survey data on inflation expectations. We conclude that monitoring real interest rates is a promising framework for forecasting AI timelines.