The first signs of a recession can lead to a negative feedback loop, as workers’ worries about unemployment dampen demand and thus deepen the recession. This column uses a heterogeneous agent model to quantify the importance of the “unemployment risk” channel for fluctuations in the economic cycle of the US economy. It shows that the channel explains about a third of the observed fluctuations in unemployment. As the amplification of demand through precautionary savings is inefficient, this result provides additional justification for stabilization policies by policymakers.
“Fear of unemployment could well lead to further increases in the savings rate that would dampen consumption growth in the short term.. “1 This statement, taken from the minutes of a Federal Open Market Committee (FOMC) meeting in the aftermath of the Great Recession, embodies the common view that the first signs of a recession may be amplified because workers who start to worry about unemployment increase their savings. The resulting reduction in demand can then trigger a feedback loop that further increases unemployment and deepens the recession. Although often mentioned in policy-making and the public press, we have little idea about the quantitative importance of this’unemployment risk channel‘macroeconomic transmission. This is problematic because the design of macroeconomic stabilization policies depends on the extent of this inefficient amplification.
Quantifying the unemployment risk channel is difficult because it requires a general equilibrium analysis where the time varying risks faced by individual workers play a role for aggregate demand through individual saving decisions. Traditional macroeconomic models, in which the representative household implicitly insures its members against unemployment, are therefore not suitable. However, recent advances in heterogeneous agent macroeconomics have enabled us to study the role of idiosyncratic uninsured risks, such as unemployment, for macroeconomic dynamics and policy effects.2 In a recent working paper (Broer et al. 2021), we draw on this literature to quantify the importance of the unemployment risk channel for fluctuations in the economic cycle of the US economy.
Figure 1 Estimated response of unemployment to monetary policy and total factor productivity shocks
Remarks: This figure presents the estimated responses of unemployment and transitions from employment to unemployment (EU) and vice versa (EU) in response to the shocks identified on the federal funds rate (left) and total factor productivity (PTF) ) (right-hand), taken from Broer et al. (2021). Share of unemployment response explained by EU / EU in brackets.
To study the implications of the risk of time-varying unemployment for aggregate demand, it is first necessary to measure how this risk fluctuates over the business cycle. In particular, since workers’ savings respond to fluctuations in the prospects for job loss as well as to changes in the likely duration of unemployment, we need to have a good idea of how severance rates and job search contribute to overall fluctuations in unemployment. Figure 1 shows how changes in monthly inflows and outflows contribute to aggregate movements in unemployment in the United States in response to two common macroeconomic shocks (monetary policy and total factor productivity, respectively).3 Two stylized facts emerge. First, fluctuations in the separation rate explain a large part (between one-third and two-thirds depending on the shock) of fluctuations in unemployment. And second, the maximum response in the job search is six to 16 months behind the terminations.
To study the implications of these stylized facts, and fluctuations in the risk of unemployment in general, on the dynamics of aggregate demand, we rely on a recent model (Ravn and Sterck 2021) from the literature on heterogeneous agents. To account for stylized facts, two additional ingredients are crucial: first, terminations (which are often considered constant in standard models) must be sensitive to changes in economic conditions. And secondly, companies’ vacancies must react slowly, and therefore with a lag in relation to departures, to changes in the expected profitability of jobs (as in Coles and Kelishomi 2018).
The resulting framework allows a quantification of the unemployment risk channel by comparing its predictions to those that result when households are insured against idiosyncratic unemployment risk (and therefore only the average unemployment rate matters for their savings decisions) . Such a comparison shows that the unemployment risk channel accounts for about a third of the observed fluctuations in unemployment. It is important to note that taking into account the two stylized facts is crucial for this result: when the separations are exogenous and the vacant positions of the companies freely adjust to changes in economic conditions (and other parameters are adjusted for match the observed volatility of unemployment in the United States), the size of the unemployment risk channel is only half as large as in the benchmark economy.
These results convey two important messages for decision makers. First, because boosting demand through precautionary savings is ineffective, they provide additional justification for stabilization policies. And second, they illustrate, once again, a common theme of recent research studying the interplay between inequalities and macroeconomic dynamics: by affecting the risk of unemployment and its consequences, structural labor market policies can have a negative component. significant global demand.
Broer, T, J Druedahl, K Harmenberg and E berg (2021), “The unemployment risk channel in business cycle fluctuations”, CEPR Working Document 16639.
Coles, MG and A Moghaddasi Kelishomi (2018), “Do Job Destruction Shocks Matter in Unemployment Theory? “, American Economic Journal: Macroeconomics 10 (3): 118–36.
Fernald, JG (2014), “Productivity and potential output before, during and after the Great Recession”, NBER Macroeconomics Annual 29 (1): 1-51.
Jordà, Ò (2005), “Estimation and inference of impulse responses by local projections”, American Economic Review 95 (1): 161–182.
Kaplan, G and GL Violante (2018), “Microeconomic heterogeneity and macroeconomic shocks”, Economic Outlook Journal 32 (3): 167-94.
Krueger, D, K Mitman and F Perri (2016), “Macroeconomics and household heterogeneity”, Macroeconomics Manual Flight. 2, Elsevier, 843-921.
Miranda-Agrippino, S and G Ricco (2021), “The transmission of monetary policy shocks”, American Economic Journal: Macroeconomics 13 (3): 74-107.
Ravn, MO and V Sterk (2021), “Macroeconomic fluctuations with HANK & SAM: an analytical approach”, Journal of the European Economic Association 19 (2): 1162-1202.
2 See Kaplan and Violante (2018) and Krueger et al. (2016) for recent surveys.
3 Impulse responses for labor market transitions are calculated using a smoothed version of the local projection method of Jordà (2005). We take the monetary policy shocks of Miranda-Agrippino and Ricco (2021), and the first difference of the quarterly total factor productivity (TFP) series of Fernald (2014) for the technological shocks.