关键词:
Business cycles
Labor supply
Macroeconomics
摘要:
In this dissertation, I study the role of labor supply in macroeconomic fluctations and the movement of employment in response to these fluctuations. The first chapter is a theoretical and empirical study of the role of firm-specific labor supply in amplifying business cycles. The second chapter focuses on measuring the aggregate labor supply elasticity at the extensive margin, using a novel survey approach. Finally, in the third chapter I measure the effects of government policies in the early stages of the COVID-19 pandemic on employment, using decentralized implementation of these policies. In the first chapter, I assess the role of labor market monopsony---finitely-elastic firm-specific labor supply---in the context of a New Keynesian model. First, I modify a basic New Keynesian model to include firm-specific labor and calibrate the labor supply elasticities to micro-empirical estimates. Consistent with this mechanism serving as a source of real rigidity, firm-specific labor substantially reduces the slope of the Phillips curve relative to the perfectly competitive labor market benchmark. However, this depends strongly on the elasticity chosen, and requires distinguishing the firm-specific and aggregate labor supply elasticities, which previous work often fails to do. Second, I provide a cross-sectional empirical test for this mechanism. I estimate the firm-specific labor supply elasticity by industry in the Survey of Income and Program Participation using a dynamic monopsony model. I then estimate industry responses to monetary policy shocks. Contrary to the New Keynesian model, I find no evidence that industry differences in firm-specific labor supply elasticities lead to different industry price responses to monetary policy shocks. My results do not support the theory that firm-specific labor is a source of real rigidity. The second chapter is an innovative investigation into measuring the aggregate labor supply curve using survey methods. I measure extensive