Get the Lowdown: The International Side of the Fall in the U.S. Natural Rate of Interest
Abstract: Much consideration has been given among scholars and policymakers to the decline in the U.S. natural rate of interest since the 2007 – 09 global financial crisis. In this paper, I investigate its determinants and drivers through the lens of the workhorse two-country New Keynesian model that captures the trade and technological interconnectedness of the U.S. with the rest of the world economy. Using Bayesian techniques, I bring the set of binding log-linearized equilibrium conditions from this model to the data, but augmented with survey-based forecasts in order to align the solution with observed expectations incorporating the macro effects of the zero-lower bound constraint. With this structural framework, I recover a novel open-economy estimate of the U.S. natural rate. The paper’s main results are: (a) the decline in the U.S. natural rate largely follows the slide of the long-run real interest rate in the forecast data, but is partly cushioned in the short run by the contribution of domestic and to a significant extent also foreign productivity shocks; (b) the fall of U.S. measured labor productivity during this time contributed to a concomitant fall in U.S. output potential; (c) the past decade is also characterized by the compression of markups (negative cost-push shocks) which accounts for much of the cyclical upswing in U.S. output in spite of the fall in its potential; and (d) monetary policy has shown its efficacy boosting U.S. output and sustaining U.S. inflation close to its 2 percent target against the drag on inflation from the negative cost-push shocks during this time. Finally, I also argue that ignoring the international linkages may result in biased estimates and can distort the empirical inferences on U.S. monetary policy in important ways.
Appendix DOI: https://doi.org/10.24149/gwp403app
The Geography of Jobs and the Gender Wage Gap
Sitian Liu and Yichen Su
Abstract: Prior studies have shown that women are more willing to trade off wages for short commutes than men. Given the gender difference in commuting preferences, we show that the wage return to commuting (i.e., the wage penalty for reducing commute time) that stems from the spatial distribution of jobs contributes to the gender wage gap. We propose a simple job choice model, which predicts that differential commuting preferences would lead to a larger gender wage gap for workers who face greater wage returns to commuting based on their locations of residence and occupations. We then show empirical evidence that validates the model's prediction. Moreover, we estimate the model components: (i) the indifference curves between wages and commutes by gender, and (ii) the wage return to commuting faced by each worker. Our model shows that differential commuting choices account for about 16-21% of the gender wage gap on average, but the contribution varies widely across residential locations. The model also shows that policies that increase commute speed or density in the central city neighborhoods could moderately lower the gender wage gap.
A Counterfactual Economic Analysis of COVID-19 Using a Threshold Augmented Multi-Country Model
Alexander Chudik, Kamiar Mohaddes, M. Hashem Pesaran, Mehdi Raissi and Alessandro Rebucci
Abstract: This paper develops a threshold-augmented dynamic multi-country model (TG-VAR) to quantify the macroeconomic effects of COVID-19. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and in the case of several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long-term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses. We distinguish common global factors from trade-related spillovers, and identify the COVID-19 shock using GDP growth forecast revisions of the IMF in 2020Q1. We account for sample uncertainty by bootstrapping the multi-country model estimated over four decades of quarterly observations. Our results show that the COVID-19 pandemic will lead to a significant fall in world output that is most likely long-lasting, with outcomes that are quite heterogenous across countries and regions. While the impact on China and other emerging Asian economies is estimated to be less severe, the United States, the United Kingdom and several other advanced economies may experience deeper and longer-lasting effects. Non-Asian emerging markets stand out for their vulnerability. We show that no country is immune to the economic fallout of the pandemic because of global interconnections as evidenced by the case of Sweden. We also find that long-term interest rates could fall significantly below their recent lows in core advanced economies, but this does not seem to be the case in emerging markets.
Imperfect Substitutability in Real Estate Markets and the Effect of Housing Demand on the Macroeconomy
J. Scott Davis, Kevin X.D. Huang and Ayse Sapci
Abstract: Changes in housing demand can have a macroeconomic effect through the collateral channel, where the change in residential real estate prices is associated with a change in commercial real estate prices, affecting firm collateral and thus firm investment. We argue that this channel is weaker when residential and commercial real estate are poor substitutes. Using cross-state heterogeneity in the strength of zoning regulations as a proxy for heterogeneity in the substitutability of residential and commercial real estate, we first show with firm level data that the strength of local zoning regulations has a negative effect on the estimated increase in firm investment following an increase in local residential real estate prices. We then construct a DSGE model where land has both residential and commercial uses and estimate it using Bayesian techniques and U.S. macroeconomic data. We find the average elasticity of substitution between commercial and residential real estate in the U.S. to be around 0.35, but in states with strong zoning restrictions it can be as low as 0.16 and in states with weak zoning restrictions it can be as high as 0.66. Simulations of the model show how these differences in zoning restrictions can affect the transmission of a housing demand shock to the macroeconomy.
Understanding the Estimation of Oil Demand and Oil Supply Elasticities
Abstract: This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models. I not only summarize the state of the literature, but also draw attention to a number of econometric problems that have been overlooked in this literature. Once these problems are recognized, seemingly conflicting conclusions in the recent literature can be resolved. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the dominant driver of the real price of oil. The focus of this paper is not only on correcting some misunderstandings in the recent literature, but on the substantive and methodological insights generated by this exchange, which are of broader interest to applied researchers.
The Distributional Effects of COVID-19 and Optimal Mitigation Policies (Revised October 2020, new title)
Abstract: This paper develops a quantitative heterogeneous agent–life cycle model with a fully integrated epidemiological model in which economic decisions affect the spread of COVID-19 and, conversely, the virus affects economic decisions. The calibrated model is used to study the distributional consequences and effectiveness of two mitigation policies: a stay-at-home subsidy that subsidizes reduced hours worked and a stay-at-home order that limits outside hours. First, the stay-at-home subsidy is preferred because it reduces deaths by more and output by less, leading to a larger average welfare gain that benefits all individuals. Second, optimal mitigation policies involve a stay-at-home subsidy of $450–$900 per week for 16–18 months, depending on the welfare criterion. Finally, it is possible to simultaneously improve public health and economic outcomes, suggesting that debates regarding a supposed tradeoff between economic and health objectives may be misguided.
Monetary Policy and Economic Performance Since the Financial Crisis
Dario Caldara, Etienne Gagnon, Enrique Martínez-García and Christopher J. Neely
Abstract: We review the macroeconomic performance over the period since the Global Financial Crisis and the challenges in the pursuit of the Federal Reserve’s dual mandate. We characterize the use of forward guidance and balance sheet policies after the federal funds rate reached the effective lower bound. We also review the evidence on the efficacy of these tools and consider whether policymakers might have used them more forcefully. Finally, we examine the post-crisis experience of other major central banks with these policy tools.
The Business Cycle Mechanics of Search and Matching Models
Joshua Bernstein, Alexander W. Richter and Nathaniel A. Throckmorton
Abstract: This paper estimates a real business cycle model with unemployment driven by shocks to labor productivity and the job separation rate. We make two contributions. First, we develop a new identification scheme based on the matching elasticity that allows the model to perfectly match a range of labor market moments, including the volatilities of unemployment and vacancies. Second, we use our model to revisit the importance of shocks to the job separation rate and highlight how their correlation with labor productivity affects their transmission mechanism.
A Generalized Time Iteration Method for Solving Dynamic Optimization Problems with Occasionally Binding Constraints
Ayşe Kabukçuoğlu and Enrique Martínez-García
Abstract: We study a generalized version of Coleman (1990)’s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)’s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI.
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
Alexander Chudik, M. Hashem Pesaran and Mahrad Sharifvaghefi
Abstract: This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both the variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at the variable selection stage in the presence of structural breaks, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches we focus on the recently developed One Covariate at a time Multiple Testing (OCMT) method that allows a natural distinction between the selection and forecasting stages, and provide theoretical justification for using the full (not down-weighted) sample in the selection stage of OCMT and down-weighting of observations only at the forecasting stage (if needed). The benefits of the proposed method are illustrated by empirical applications to forecasting output growths and stock market returns.
Supplement DOI: https://doi.org/10.24149/gwp394supp
Oil Prices, Gasoline Prices and Inflation Expectations: A New Model and New Facts
Lutz Kilian and Xiaoqing Zhou
Abstract: The conventional wisdom that inflation expectations respond to the level of the price of oil (or the price of gasoline) is based on testing the null hypothesis of a zero slope coefficient in a static single-equation regression model fit to aggregate data. Given that the regressor in this model is not stationary, the null distribution of the t-test statistic is nonstandard, invalidating the use of the normal approximation. Once the critical values are adjusted, these regressions provide no support for the conventional wisdom. Using a new structural vector regression model, however, we demonstrate that gasoline price shocks may indeed drive one-year household inflation expectations. The model shows that there have been several such episodes since 1990. In particular, the rise in household inflation expectations between 2009 and 2013 is almost entirely explained by a large increase in gasoline prices. However, on average, gasoline price shocks account for only 39% of the variation in household inflation expectations since 1981.
The Impact of the COVID-19 Pandemic on the Demand for Density: Evidence from the U.S. Housing Market (Revised October 2020)
Sitian Liu and Yichen Su
Abstract: Cities are shaped by the strength of agglomeration and dispersion forces. We show that the COVID-19 pandemic has re-introduced disease transmission as a dispersion force in modern cities. We use detailed housing data to study the impact of the COVID-19 pandemic on the location demand for housing. We find that the pandemic has led to a reduced demand for housing in neighborhoods with high population density. The reduced demand for density is driven partially by the diminished need of living close to jobs that are telework-compatible and the declining value of access to consumption amenities. Neighborhoods with high pre-COVID-19 home prices also see a greater drop in housing demand. While the national housing market recovered after June, we show that the pandemic's negative effect on the demand for density persisted and strengthened, indicating that the change in the demand for density has lasted beyond an aggregate recovery of housing demand.
Haste Makes Waste: Banking Organization Growth and Operational Risk
W. Scott Frame, Ping McLemore and Atanas Mihov
Abstract: This study shows that banking organization growth is associated with higher operational losses per dollar of total assets and incidence of tail risks. Event studies using M&A activity and instrumental variable regressions provide consistent evidence. The relationship between banking organization growth and operational risk varies by loss event types and balance sheet categories. We demonstrate that higher growth predicts worse operational risk realizations during the global financial crisis. These findings have implications for bank performance, risk management and supervision in a continually consolidating banking industry.
Joint Bayesian Inference about Impulse Responses in VAR Models
Atsushi Inoue and Lutz Kilian
Abstract: Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in empirically relevant settings there need not exist a posterior draw for the impulse response function that matches the posterior median or mean response function, even as the number of posterior draws approaches infinity. As a result, the use of these summary statistics may distort the shape of the impulse response function which is of foremost interest in applied work. The same concern applies to error bands based on the upper and lower quantiles of the marginal posterior distributions of the impulse responses. In addition, these error bands fail to capture the full uncertainty about the estimates of the structural impulse responses. In response to these concerns, we propose new estimators of impulse response functions under quadratic loss, under absolute loss and under Dirac delta loss that are consistent with Bayesian statistical decision theory, that are optimal in the relevant sense, that respect the dynamics of the impulse response functions and that are easy to implement. We also propose joint credible sets for these estimators derived under the same loss function. Our analysis covers a much wider range of structural VAR models than previous proposals in the literature including models that combine short-run and long-run exclusion restrictions and models that combine zero restrictions, sign restrictions and narrative restrictions.
The Shale Revolution and the Dynamics of the Oil Market
Nathan S. Balke, Xin Jin and Mine Yücel
Abstract: We build and estimate a dynamic, structural model of the world oil market in order to quantify the impact of the shale revolution. We model the shale revolution as a dramatic decrease in shale production costs and explore how the resultant increase in shale production affects the level and volatility of oil prices over our sample. We find that oil prices in 2018 would have been roughly 36% higher had the shale revolution not occurred and that the shale revolution implies a reduction in current oil price volatility around 25% and a decline in long-run volatility of over 50%.
Quantitative Easing and Financial Risk Taking: Evidence from Agency Mortgage REITs
W. Scott Frame and Eva Steiner
Abstract: An emerging literature documents a link between central bank quantitative easing (QE) and financial institution credit risk-taking. This paper tests the complementary hypothesis that QE may also affect financial risk-taking. We study Agency MREITs – levered shadow banks that invest in guaranteed U.S. Agency mortgage-backed securities (MBS) principally funded with repo debt. We show that Agency MREIT growth is inversely related to the Federal Reserve’s Agency MBS purchases, reflecting investor portfolio rebalancing. We also find that these institutions increased leverage during the later stages of QE, consistent with “reaching for yield” behavior. Agency MREITs seem to concurrently adjust their liquidity and interest rate risk profiles.
Impulse Response Analysis for Structural Dynamic Models with Nonlinear Regressors
Sílvia Gonçalves, Ana María Herrera, Lutz Kilian and Elena Pesavento
Abstract: We study the construction of nonlinear impulse responses in structural dynamic models that include nonlinearly transformed regressors. Such models have played an important role in recent years in capturing asymmetries, thresholds and other nonlinearities in the responses of macroeconomic variables to exogenous shocks. The conventional approach to estimating nonlinear responses is by Monte Carlo integration. We show that the population impulse responses in this class of models may instead be derived analytically from the structural model. We use this insight to study under what conditions linear projection (LP) estimators may be used to recover the population impulse responses. We find that, unlike in vector autoregressive models, the asymptotic equivalence between estimators based on the structural model and LP estimators breaks down. Only in one important special case can the population impulse response be consistently estimated by LP methods. The construction of this LP estimator, however, differs from the LP approach currently used in the literature. Simulation evidence suggests that the modified LP estimator is less accurate in finite samples than estimators based on the structural model, when both are valid.
Mind the Gap!—A Monetarist View of the Open-Economy Phillips Curve
Ayşe Dur and Enrique Martínez-García
Abstract: In many countries, inflation has become less responsive to domestic factors and more responsive to global factors over the past decades. We introduce money and credit into the workhorse open-economy New Keynesian model. With this framework, we show that: (i) an efficient forecast of domestic inflation is based solely on domestic and foreign slack, and (ii) global liquidity (global money as well as global credit) is tied to global slack in equilibrium. Then, motivated by the theory, we evaluate empirically the performance of open-economy Phillips-curve-based forecasts constructed using global liquidity measures (such as G7 credit growth and G7 money supply growth) instead of global slack as predictive regressors. Using 50 years of quarterly U.S. data, we document that these global liquidity variables perform significantly better than their domestic counterparts and outperform in practice the poorly-measured indicators of global slack that global liquidity proxies for.
Appendix DOI: https://doi.org/10.24149/gwp392app
Entry and Exit, Unemployment, and Macroeconomic Tail Risk
Joshua Bernstein, Alexander W. Richter and Nathaniel A. Throckmorton
Abstract: This paper builds a nonlinear business cycle model with endogenous firm entry and exit and equilibrium unemployment. The entry and exit mechanism generates asymmetry and amplifies the transmission of productivity shocks, exposing the economy to significant tail risk. When calibrating the rates of entry and exit to match their shares of job creation and destruction, our quantitative model generates higher-order moments consistent with U.S. data. Firm exit particularly amplifies the severity and persistence of deep recessions such as the COVID-19 crisis. In the absence of entry and exit, the model generates almost no asymmetry or tail risk.
Work from Home After the COVID-19 Outbreak (Revised July 2020)
Alexander Bick, Adam Blandin and Karel Mertens
Abstract: Based on rich novel survey data, we document that 35.2 percent of the U.S. workforce worked entirely from home in May 2020, up from 8.2 percent in February. Highly educated, high-income and white workers were more likely to shift to working from home and maintain employment following the pandemic. Individuals working from home daily before the pandemic lost employment at similar rates as daily commuters. This suggests that, apart from the potential for home-based work, demand conditions also mattered for job losses. We find that 71.7 percent of workers that could work from home effectively did so in May.
Are the Largest Banking Organizations Operationally More Risky?
Filippo Curti, W. Scott Frame and Atanas Mihov
Abstract: This study demonstrates that, among large U.S. bank holding companies (BHCs), the largest ones are exposed to more operational risk. Specifically, they have higher operational losses per dollar of total assets, a result largely driven by the BHCs' failure to meet professional obligations to clients and/or faulty product design. Operational risk at the largest U.S. institutions is also found to: (i) be particularly persistent, (ii) have a counter-cyclical component (higher losses occur during economic downturns) and (iii) materialize through more frequent tail-risk events. We illustrate two plausible channels of BHC size that contribute to operational risk – institutional complexity and moral hazard incentives arising from “too-big-to-fail." Our findings have important implications for large banking organization performance, risk and supervision.
A Matter of Perspective: Mapping Linear Rational Expectations Models into Finite-Order VAR Form
Abstract: This paper considers the characterization of the reduced-form solution of a large class of linear rational expectations models. I show that under certain conditions, if a solution exists and is unique, it can be cast in finite-order VAR form. I also investigate the conditions for the VAR form to be stationary with a well-defined residual variance-covariance matrix in equilibrium, for the shocks to be recoverable, and for local identification of the structural parameters for estimation from the sample likelihood. An application to the workhorse New Keynesian model with accompanying Matlab codes illustrates the practical use of the finite-order VAR representation. In particular, I argue that the identification of monetary policy shocks based on structural VARs can be more closely aligned with theory using the finite-order VAR form of the model solution characterized in this paper.
A Quantitative Model of the Oil Tanker Market in the Arabian Gulf
Lutz Kilian, Nikos Nomikos and Xiaoqing Zhou
Abstract: Using a novel dataset, we develop a structural model of the Very Large Crude Carrier (VLCC) market between the Arabian Gulf and the Far East. We study how fluctuations in oil tanker rates, oil exports, shipowner profits, and bunker fuel prices are determined by shocks to the supply and demand for oil tankers, to the utilization of tankers, and to bunker fuel costs. Our analysis shows that time charter rates respond only slightly to fuel cost shocks. In response to higher fuel costs, voyage profits decline, as cost shocks are only partially passed on to round-trip voyage rates. Oil exports from the Arabian Gulf also decline, reflecting lower demand for VLCCs. Positive utilization shocks are associated with higher profits, a slight increase in time charter rates and slightly lower fuel prices and oil export volumes. Tanker supply and tanker demand shocks have persistent effects on time charter rates, round-trip voyage rates, the volume of oil exports, fuel prices, and profits with the expected sign.
Mobility and Engagement Following the SARS-Cov-2 Outbreak (Revised June 2020, new title)
Tyler Atkinson, Jim Dolmas, Christoffer Koch, Evan Koenig, Karel Mertens, Anthony Murphy and Kei-Mu Yi
Abstract: We develop a Mobility and Engagement Index (MEI) based on a range of mobility metrics from Safegraph geolocation data, and validate the index with mobility data from Google and Unacast. We construct MEIs at the county, MSA, state and nationwide level, and link these measures to indicators of economic activity. According to our measures, the bulk of sheltering-in-place and social disengagement occurred during the week of March 15 and simultaneously across the U.S. At the national peak of the decline in mobility in early April, localities that engaged in a 10% larger decrease in mobility than average saw an additional 0.6% of their populations claiming unemployment insurance, an additional 2.8 percentage point reduction in small businesses employment, an additional 2.6 percentage point increase in small business closures, and an additional 3.2 percentage point reduction in new-business applications. A gradual and broad-based resumption of mobility and engagement started in the third week of April.
Villains or Scapegoats? The Role of Subprime Borrowers in Driving the U.S. Housing Boom
James Conklin, W. Scott Frame, Kristopher Gerardi and Haoyang Liu
Abstract: An expansion in mortgage credit to subprime borrowers is widely believed to have been a principal driver of the 2002–2006 U.S. house price boom. By contrast, this paper documents a robust, negative correlation between the growth in the share of purchase mortgages to subprime borrowers and house price appreciation at the county-level during this time. Using two different instrumental variables approaches, we also establish causal evidence that house price appreciation lowered the share of purchase loans to subprime borrowers. Further analysis using micro-level credit bureau data shows that higher house price appreciation lowered the transition rate into first-time homeownership for subprime individuals. Finally, the paper documents that subprime borrowers did not play a significant role in the increased speculative activity and underwriting fraud that the literature has linked directly to the housing boom. Taken together, these results are more consistent with subprime borrowers being priced out of housing boom markets rather than inflating prices in those markets.
A Novel MIMIC-Style Model of European Bank Technical Efficiency and Productivity Growth
Marwan Izzeldin, Emmanuel Mamatzakis, Anthony Murphy and Mike Tsionas
Abstract: Using Bayesian Monte Carlo methods, we augment a stochastic distance function measure of bank efficiency and productivity growth with indicators of capitalization, return and risk. Our novel Multiple Indicator-Multiple Cause (MIMIC) style model generates more precise estimates of policy relevant parameters such as returns to scale, technical inefficiency and productivity growth. We find considerable variation in the performance of EU-15 banks over the period 2008 to 2015. For the vast majority of banks, productivity growth – the sum of efficiency and technical changes – is negative, implying that the industry would benefit from innovation. We show that greater technical efficiency is associated with higher profitability, higher capital, a lower probability of default and lower return volatility.
Checking the Path Towards Recovery from the COVID-19 Isolation Response
Finn E. Kydland and Enrique Martínez-García
Abstract: This paper examines the impact of the behavioral changes and governments' responses to the spread of the COVID-19 pandemic using a unique dataset of daily private forecasters' expectations on a sample of 32 emerging and advanced economies from January 1 till April 13, 2020. We document three important lessons from the data: First, there is evidence of a relation between the stringency of the policy interventions and the health outcomes consistent with slowing down the spread of the pandemic. Second, we find robust evidence that private forecasters have come to anticipate a sizeable contraction in economic activity followed by a check mark recovery as a result of the governments' increasingly stringent response. The evidence suggests also that workplace restrictions have further contributed to the downturn and to the subsequent sluggish recovery—opening up the question about the costs of tighter work restrictions. Finally, we argue inflation expectations have not changed significantly so far. Through the lens of the neoclassical growth model, these changes in macro expectations can result from the resulting work disruptions and the potential productivity slowdown from the gradual de-escalation of the confinement.
exuber: Recursive Right-Tailed Unit Root Testing with R
Kostas Vasilopoulos, Efthymios Pavlidis and Enrique Martínez-García
Abstract: This paper introduces the R package exuber for testing and date-stamping periods of mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum ADF test (SADF) of Phillips, Wu and Yu (2011), the generalized SADF (GSADF) of Phillips, Shi and Yu (2015a,b), and the panel GSADF proposed by Pavlidis, Yusupova, Paya, Peel, Martínez-García, Mack and Grossman (2016); generates finite-sample critical values based on Monte Carlo and bootstrap methods; and implements the corresponding date-stamping procedures. The recursive least-squares algorithm that we introduce in our implementation of these techniques utilizes the matrix inversion lemma and in that way achieves significant speed improvements. We illustrate the speed gains in a simulation experiment, and provide illustrations of the package using artificial series and a panel on international house prices.
U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak
Daniel Lewis, Karel Mertens and James Stock
Abstract: This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the response to the novel Coronavirus in the United States. The WEI shows a strong and sudden decline in economic activity starting in the week ending March 21, 2020. In the most recent week ending April 4, the WEI indicates economic activity has fallen further to -8.89% scaled to 4-quarter growth in GDP.
COVID-19: A View from the Labor Market
Joshua Bernstein, Alexander W. Richter and Nathaniel A. Throckmorton
Abstract: This paper examines the response of the U.S. labor market to a large and persistent job separation rate shock, motivated by the ongoing economic effects of the COVID-19 pandemic. We use nonlinear methods to analytically and numerically characterize the responses of vacancy creation and unemployment. Vacancies decline in response to the shock when firms expect persistent job destruction and the number of unemployed searching for work is low. Quantitatively, under our baseline forecast the unemployment rate peaks at 19.7%, 2 months after the shock, and takes 1 year to return to 5%. Relative to a scenario without the shock, unemployment uncertainty rises by a factor of 11. Nonlinear methods are crucial. In the linear economy, the unemployment rate “only” rises to 9.2%, vacancies increase, and uncertainty is unaffected. In both cases, the severity of the COVID-19 shock depends on the separation rate persistence.
Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries
Alexander Chudik, M. Hashem Pesaran and Alessandro Rebucci
Abstract: This paper considers a modification of the standard Susceptible-Infected-Recovered (SIR) model of epidemics that allows for different degrees of compulsory as well as voluntary social distancing. It is shown that the fraction of the population that self-isolates varies with the perceived probability of contracting the disease. Implications of social distancing both on the epidemic and recession curves are investigated and their trade off is simulated under a number of different social distancing and economic participation scenarios. We show that mandating social distancing is very effective at flattening the epidemic curve, but is costly in terms of employment loss. However, if targeted towards individuals most likely to spread the infection, the employment loss can be somewhat reduced. We also show that voluntary self-isolation driven by individuals’ perceived risk of becoming infected kicks in only towards the peak of the epidemic and has little or no impact on flattening the epidemic curve. Using available statistics and correcting for measurement errors, we estimate the rate of exposure to COVID-19 for 21 Chinese provinces and a selected number of countries. The exposure rates are generally small, but vary considerably between Hubei and other Chinese provinces as well as across countries. Strikingly, the exposure rate in Hubei province is around 40 times larger than the rates for other Chinese provinces, with the exposure rates for some European countries being 3-5 times larger than Hubei (the epicenter of the epidemic). The paper also provides country-specific estimates of the recovery rate, showing it to be about 21 days (a week longer than the 14 days typically assumed), and relatively homogeneous across Chinese provinces and for a selected number of countries.
Complementarity and Macroeconomic Uncertainty
Tyler Atkinson, Michael Plante, Alexander W. Richter and Nathaniel A. Throckmorton
Abstract: Macroeconomic uncertainty—the conditional volatility of the unforecastable component of a future value of a time series—shows considerable variation in the data. A typical assumption in business cycle models is that production is Cobb-Douglas. Under that assumption, this paper shows there is usually little, if any, endogenous variation in output uncertainty, and first moment shocks have similar effects in all states of the economy. When the model departs from Cobb-Douglas production and assumes capital and labor are gross complements, first-moment shocks have state-dependent effects and can cause meaningful variation in uncertainty compared to the data. Estimating several variants of a nonlinear real business cycle model reveals the data strongly prefers a model with high complementarity between capital and labor inputs.
A Quantitative Evaluation of the Housing Provident Fund Program in China
Abstract: The Housing Provident Fund (HPF) is the largest public housing program in China. It was created in 1999 to enhance homeownership. This program involves a mandatory saving scheme based on labor income. Past deposits are refunded when the worker purchases a house or retires. Moreover, the program provides mortgages at subsidized rates to facilitate these home purchases. I calibrate a heterogeneous-agent life-cycle model to quantify the effects of these policies. My analysis shows that a housing program with these features is expected to raise the rate of homeownership by 8.7 percentage points and to increase the average home size by 20%. I discuss the economic mechanisms by which these outcomes are achieved and which features of the HPF program are most effective. I also consider several extensions of the model such as requiring employers to contribute to the program and allowing renters to withdraw funds from the HPF.
Understanding the Exposure at Default Risk of Commercial Real Estate Construction and Land Development Loans
Shan Luo and Anthony Murphy
Abstract: We study and model the determinants of exposure at default (EAD) for large U.S. construction and land development loans from 2010 to 2017. EAD is an important component of credit risk, and commercial real estate (CRE) construction loans are more risky than income producing loans. This is the first study modeling the EAD of construction loans. The underlying EAD data come from a large, confidential supervisory dataset used in the U.S. Federal Reserve’s annual Comprehensive Capital Assessment Review (CCAR) stress tests. EAD reflects the relative bargaining ability and information sets of banks and obligors. We construct OLS and Tobit regression models, as well as several other machine-learning models, of EAD conversion measures, using a four-quarter horizon. The popular LEQ and CCF conversion measure is unstable, so we focus on EADF and AUF measures. Property type, the lagged utilization rate and loan size are important drivers of EAD. Changing local and national economic conditions also matter, so EAD is sensitive to macro-economic conditions. Even though default and EAD risk are negatively correlated, a conservative assumption is that all undrawn construction commitments will be fully drawn in default.
The Econometrics of Oil Market VAR Models
Lutz Kilian and Xiaoqing Zhou
Abstract: Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact in the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference. We not only explain how the workhorse models in this literature have evolved, but also examine alternative oil market VAR models. We help the reader understand why the latter models sometimes generated unconventional, puzzling or erroneous conclusions. Finally, we discuss the construction of extraneous measures of oil demand and oil supply shocks that have been used as external or internal instruments for VAR models.
How Does Immigration Fit into the Future of the U.S. Labor Market?
Pia M. Orrenius, Madeline Zavodny and Stephanie Gullo
Abstract: U.S. GDP growth is anticipated to remain sluggish over the next decade, and slow labor force growth is a key underlying reason. Admitting more immigrants is one way U.S. policymakers can bolster growth in the workforce and the economy. A larger role for immigrant workers also can help mitigate other symptoms of the economy’s long-run malaise, such as low productivity growth, declining domestic geographic mobility, and falling entrepreneurship, as well as help address the looming mismatch between the skills U.S. employers want and the skills U.S. workers have. While some might argue that technological change and globalization mean there is less need to admit immigrant workers, such arguments fail to account for both recent data and historical experience. Of course, immigration—like anything else—is not without costs, which are disproportionately borne by the least educated. A plan to increase employment-based immigration as a way to spur economic growth could be paired with new programs to help low-skilled U.S. natives and earlier immigrants so that the benefits of immigration are shared more equitably.
The Effect of Immigration on Business Dynamics and Employment
Pia M. Orrenius, Madeline Zavodny and Alexander Abraham
Abstract: Immigration, like any positive labor supply shock, should increase the return to capital and spur business investment. These changes should have a positive impact on business creation and expansion, particularly in areas that receive large immigrant inflows. Despite this clear prediction, there is sparse empirical evidence on the effect of immigration on business dynamics. One reason may be data unavailability since public-access firm-level data are rare. This study examines the impact of immigration on business dynamics and employment by combining U.S. data on immigrant inflows from the Current Population Survey with data on business formation and survival and job creation and destruction from the National Establishment Time Series (NETS) database for the period 1997 to 2013. The results indicate that immigration increases the business growth rate by boosting business survival and raises employment by reducing job destruction. The effects are largely driven by less-educated immigrants.
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.