Temporary Layoffs, Loss-of-Recall, and Cyclical Unemployment Dynamics

This paper measures and models the role of temporary layoffs (TL) in cyclical unemployment dynamics, motivating the analysis by the extraordinary surge in temporary layoffs at the onset of the pandemic recession — roughly 15% of employed workers moved to temporary-layoff status from March to April 2020. The paper documents two opposing effects of temporary layoffs on total unemployment: a stabilizing direct effect (workers on TL return to employment rapidly via recall) and a destabilizing indirect effect through “loss-of-recall” — workers initially on temporary layoff who fail to be recalled and instead transition to jobless unemployment (JL), inheriting that state’s far lower reemployment probability. A new recursive accumulation method is used to construct a time series of the stock of workers in jobless unemployment whose most recent exit from employment was to temporary-layoff status (JL-from-TL); this stock has a standard deviation 16 times that of GDP and 2 times that of total unemployment, and is a high-correlation indicator of labor market slack. A search-and-matching model with staggered Nash wage bargaining, endogenous layoff thresholds, and separate recall and new-hire channels replicates the pre-pandemic cyclical behavior of TL and JL flows. Applying the model to the pandemic recession, the paper finds that the Paycheck Protection Program (PPP) reduced employment shortfalls by roughly 2 percentage points at peak, primarily by dampening loss-of-recall — the program’s forgivable loan structure reduced firms’ incentive to permanently separate workers who had been placed on temporary layoff.

Antonella Trigari | Macro Paper Warehouse

Temporary Layoffs, Loss-of-Recall, and Cyclical Unemployment Dynamics