Shock-Dependent Exchange Rate Pass-Through: Evidence Based on a Narrative Sign Approach
Lian An, Mark A. Wynne and Ren Zhang
Abstract: This paper studies shock-dependent exchange rate pass-through for Japan with a Bayesian structural vector autoregression model. We identify the shocks by complementing the traditional sign and zero restrictions with narrative sign restrictions related to the Plaza Accord. We find that the narrative sign restrictions are highly informative, and substantially sharpen and even change the inferences of the structural vector autoregression model originally identified with only the traditional sign and zero restrictions. We show that there is a significant variation in the exchange rate pass-through across different shocks. Nevertheless, the exogenous exchange rate shock remains the most important driver of exchange rate fluctuations. Finally, we apply our model to “forecast” the dynamics of the exchange rate and prices conditional on certain foreign exchange interventions in 2018, which provides important policy implications for our shock-identification exercise.
Distant Lending, Specialization, and Access to Credit
Wenhua Di and Nathaniel Pattison
Abstract: Small business lending has historically been very local, but distances between small businesses and their lenders have steadily increased over the last forty years. This paper investigates a new lending strategy made possible by distant small business lending: industry specialization. Using data on all Small Business Administration 7(a) loans from 2001-2017, we document a substantial increase in remote, specialized small business lenders, i.e., lenders that originate many distant loans and concentrate these loans within a small number of industries. These lenders target low-risk industries and, consistent with expertise, experience better loan performance within these industries. We then examine whether this industry-specialized lending serves as a substitute or complement to traditional, geographically specialized lending. We exploit the staggered entry of a remote, specialized lender to estimate the impact of specialized lending on credit access. Entry significantly increases total lending, with no evidence of substitution away from other lenders. The results indicate that specialized lending can deepen credit markets by providing new loans to low-risk but underfinanced small businesses.
Who Signs up for E-Verify? Insights from DHS Enrollment Records
Pia Orrenius, Madeline Zavodny and Sarah Greer
Abstract: E-Verify is a federal electronic verification system that allows employers to check whether their newly hired workers are authorized to work in the United States. To use E-Verify, firms first must enroll with the Department of Homeland Security (DHS). Participation is voluntary for most private-sector employers in the United States, but eight states currently require all or most employers to use E-Verify. This article uses confidential data from DHS to examine patterns of employer enrollment in E-Verify. The results indicate that employers are much more likely to sign up in mandatory E-Verify states than in states without such mandates, but enrollment is still below 50 percent in states that require its use. Large employers are far more likely to sign up than small employers. In addition, employers are more likely to newly enroll in E-Verify when a state’s unemployment rate or population share of likely unauthorized immigrants rises. However, enrollment rates are lower in industries with higher shares of unauthorized workers. Taken as a whole, the results suggest that enrolling in the program is costly for employers in terms of both compliance and difficulty in hiring workers. A strictly enforced nationwide mandate that all employers use an employment eligibility program like E-Verify would be incompatible with the current reliance on a large unauthorized workforce. Allowing more workers to enter legally or legalizing existing workers might be necessary before implementing E-Verify nationally.
Did Tax Cuts and Jobs Act Create Jobs and Stimulate Growth? Early Evidence Using State-Level Variation in Tax Changes
Abstract: The Tax Cuts and Jobs Act (TCJA) of 2017 is the most extensive overhaul of the U.S. income tax code since the Tax Reform Act of 1986. Existing estimates of TCJA’s economic impact are based on economic projections using pre-TCJA estimates of tax effects. Following recent pioneering work of Zidar (2019), I exploit plausibly exogenous state-level variation in tax changes and find that an income tax cut equaling 1 percent of GDP led to a 1 percentage point higher nominal GDP growth and about 0.3 percentage point faster job growth in 2018.