Measuring the Unemployment Gap
Paper Session
Sunday, Jan. 3, 2021 3:45 PM - 5:45 PM (EST)
- Chair: Emmanuel Saez, University of California-Berkeley
Dynamic Beveridge Curve Accounting
Abstract
We develop a dynamic decomposition of the empirical Beveridge curve, i.e., the level of vacancies conditional on unemployment. Using a standard model, we show that three factors can shift the Beveridge curve: reduced-form matching efficiency, changes in the job separation rate, and out-of-steady-state dynamics. We find that the shift in the Beveridge curve during and after the Great Recession was due to all three factors, and each factor taken separately had a large effect. Comparing the pre-2010 period to the post-2010 period, a fall in matching efficiency and out-of-steady-state dynamics both pushed the curve upward, while the changes in the separation rate pushed the curve downward. The net effect was the observed upward shift in vacancies given unemployment. In previous recessions changes in matching efficiency were relatively unimportant, while dynamics and the separation rate had more impact. Thus, the unusual feature of the Great Recession was the deterioration in matching efficiency, while separations and dynamics have played significant, partially offsetting roles in most downturns. The importance of these latter two margins contrasts with much of the literature, which abstracts from one or both of them. We show that these factors affect the slope of the empirical Beveridge curve, an important quantity in recent welfare analyses estimating the natural rate of unemployment.A Unified Approach to Measuring u*: An update in COVID Times
Abstract
This paper bridges the gap between two popular approaches to estimating the natural rate of unemployment, u∗ and proposes an updated application to the Covid times when unemployment is increasing dramatically. The first approach uses detailed labor market indicators such as labor market flows, cross-sectional data on unemployment and vacancies or various measures of demographic changes. The second approach which comprises reduced form models and DSGE models relies mainly on price and wage Phillips curve relationships, together with model-specific assumptions on aggregate demand. We combine the key features of these two approaches to estimate the natural rate of unemployment in the United States using both data on labor market flows and a forward-looking Phillips curve linking inflation to current and expected deviations of unemployment from its unobserved natural rate. We estimate that the natural rate of unemployment stood at 4.1% as of the third quarter of 2018 and that the unemployment gap is roughly closed. Identification of a secular downward trend in the inflow rate from detailed unemployment flows facilitates the estimation of u∗t. We identify the increase in labor force attachment of women, decline in job destruction and reallocation intensity, and dual aging in the labor market of workers and firms as the main drivers of the secular downward trend in the inflow rate.Duration Structure of Unemployment Hazards and the Trend Unemployment Rate
Abstract
This paper develops a new model of the trend unemployment rate in which three time-varying factors—level, slope, and curvature—characterize the duration profile of unemployment hazards. This model allows us to decompose the trend unemployment rate by the duration of unemployment. The estimated trend unemployment rate exhibited a secular decline until 2000, a slow uptrend until 2011, and a decline until 2019. The short-term component trends downward while the long-term component trends upward, suggesting falling frictional unemployment but rising structural unemployment. The short-term unemployment rate gap has a strong Phillips correlation with PCE inflation.Discussant(s)
Regis Barnichon
,
Federal Reserve Bank of San Francisco
JEL Classifications
- E3 - Prices, Business Fluctuations, and Cycles
- E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy