"The Anatomy of Monthly Earnings Volatility: Evidence From Paycheck Microdata" - Peter Ganong, University of Chicago

Apr 1, 1:20 pm2:35 pm


Event Description

Peter Ganong is an Associate Professor of Public Policy, University of Chicago Harris School. His research area studies the effect of public policies on people facing difficult financial circumstances. 


We analyze monthly earnings volatility using administrative payroll data. While it is well-documented that wages are largely stable, we find that this wage stability does not translate into earnings stability for most U.S. workers. Even within stable employment relationships, and even when wages are constant, many workers nevertheless face substantial monthly earnings volatility. The standard deviation of monthly earnings changes is 28%, while the standard deviation of base wage changes is only 2%. There is substantial heterogeneity in this volatility, with much higher volatility for hourly workers than for salaried workers. This degree of volatility is far higher than what is implied by benchmark models of earnings processes which are calibrated to previously-available annual data and used as inputs for leading macro models. To understand the welfare consequences of pay volatility, we estimate the amenity value of volatility using worker quits in a model of a frictional labor market. We find that workers have a high willingness to pay to reduce earnings volatility. Overall, this analysis shows that high-frequency labor market shocks are an important source of risk and fragility which has been masked by past studies of annual earnings.