UC Berkeley

In this dissertation, I study labor market dynamics and positive and normative analysis of monetary policy over the business cycle. The first chapter studies how monetary policy can be more inclusive to benefit people who are particularly vulnerable over the business cycle by developing a tractable New Keynesian model with two types of labor. The second chapter focuses on labor market dynamics during the early COVID-19 period. This chapter studies the effect of the Stay-at-Home (SAH) orders on the labor market outcomes. The third chapter focuses on the effects of monetary policy. This chapter tries to estimate the extent of the information channel of monetary policy.

In Chapter 1, “Good Jobs and Bad Jobs over the Business Cycle: Implications for Inclusive Monetary Policy,” I study how monetary policy can be more inclusive and benefit people who are more vulnerable to economic fluctuations. To shed light on this question, I study heterogeneity (“types”) in labor market arrangements and implications of this heterogeneity for welfare and optimal monetary policy. I document that the experiences of regular and irregular workers over the business cycle differ considerably. For example, the share of irregular workers in employment rises during recessions, suggesting that firms actively adjust labor composition over the business cycle. I develop a tractable New Keynesian model with regular and irregular labor types that reflect the cyclical nature of labor composition. I find that workers, who are marginally attached to either the regular or the irregular labor market, face larger volatilities in their consumption and disutility from labor supply and hence suffer larger welfare losses over the business cycle. I find that optimal monetary policy rule should react to employment dynamics in specific segments of the labor market than the overall stance of the labor market. When a central bank follows that rule, it benefits not only people who are more vulnerable to economic fluctuations but generate higher economy-wide welfare.

Chapter 2, “Unemployment Effects of Stay-at-Home Orders: Evidence from High Frequency Claims Data,” is based on the joint work with Peter McCrory, Todd Messer, and Preston Mui, which is forthcoming in \emph{Review of Economics and Statistics}. We use the high-frequency, decentralized implementation of Stay-at-Home orders in the United States to disentangle the labor market effects of Stay-At-Home orders from the general economic disruption wrought by the COVID-19 pandemic. We find that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9\% of its employment level relative to other states. A back-of-the-envelope calculation implies that, of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. We present a currency union model to provide conditions for mapping this estimate to aggregate employment losses.

Chapter 3, "Estimating the Effects of Central Bank Communications," is based on the joint work with Nicholas Sander. We estimate the extent to which expectations changes depend on explicit information given by central banks. We compare impulse responses to high-frequency monetary surprises during announcements when the Bank of England also releases a detailed inflation report to those where a simple press statement is released. We find that when a simple press statement is released policy has conventional signs: unemployment and inflation fall following a surprise tightening. However, when a detailed inflation report is released, surprise tightening raise unemployment and inflation suggesting the information effect can be controlled by central banks.