Bringing Back the Jobs Lost to Covid-19: The Role of Fiscal Policy

Covid-19 induced job losses occurred predominantly in industries with intensive worker–client interaction as well as in pink-collar and blue-collar occupations. We study the ability of fiscal policy to stabilize employment by occupation and industry during the Covid-19 crisis. We use a multisector, multioccupation macro-economic model and investigate different fiscal-policy instruments that help the economy recover faster. We show that fiscal stimuli foster job growth for hard-hit pink-collar workers, whereas stimulating blue-collar job creation is more challenging. Only a cut in labor income taxes generates a substantial number of blue-collar jobs.     

Uncertainty and the Cost of Bank versus Bond Finance

How does uncertainty affect the costs of raising finance in the bond market and via bank loans? Empirically, this paper finds that heightened uncertainty is accompanied by an increase in corporate bond spreads, whereas spreads on bank loans remain unchanged. This finding can be explained with a model that includes costly state verification and in which banks maintain long-term relationships with borrowers and acquire information beyond what is publicly available. After an unexpected increase in uncertainty, the probability of borrower default increases. Banks leave the loan spread unchanged to maintain the relationship. In contrast, bond spreads increase because investors demand compensation for the increased default risk.     

Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a (potentially misspecified) stand-alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification—if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample.     

Lower Bound Uncertainty and Long-Term Interest Rates

Nominal interest rates are constrained by an effective lower bound, but the level of the lower bound is uncertain. This paper uses a simple shadow rate term structure model to study how lower bound uncertainty affects long-term interest rates. A decline in lower bound uncertainty, in the sense of a mean-preserving contraction, is associated with a drop in expected short rates. The effect on the variance of short rates, and hence the term premium, is ambiguous. A calibration to Canadian data suggests that a decline in lower bound uncertainty is associated with a modest drop in interest rates.