With ``2020 hindsight,'' the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound at both the national level and across cities. We argue this pattern reflects a larger role for fundamentally-rooted explanations than previously thought. We construct a city-level long-run fundamental using a spatial equilibrium regression framework in which house prices are determined by local income, amenities, and supply. The fundamental predicts not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound and foreclosures. This evidence motivates our neo-Kindlebergerian model, in which an improvement in fundamentals triggers a boom-bust-rebound. Agents learn about the fundamentals by observing ``dividends'' but become over-optimistic due to diagnostic expectations. A bust ensues when over-optimistic beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single fundamental shock and accounts quantitatively for cross-city patterns in the dynamics of prices and foreclosures.
The Greek economy experienced a boom until 2007, followed by a decade-long collapse with magnitude and persistence that have no precedent among modern developed economies. We assess quantitatively the sources of the boom and bust and the role of policies in an estimated dynamic general equilibrium model with heterogeneous households and multiple production, banking, government, and external sectors. Demand from the rest of the world and the government fueled the boom in production, whereas realized and anticipated transfers fueled the boom in consumption. Contractionary tax policies, amplified by a decline in factor utilization and financial frictions, account for the largest fraction of the bust in production, whereas the rise of uninsurable idiosyncratic risk accounts for the largest fraction of the bust in consumption, decline in prices, and the sudden stop of capital flows. Fiscal policy amplified the depression by concentrating the burden of adjustment on taxes instead of spending and by raising the fraction of taxes that firms prepay before revenues are realized. By contrast, equity injections to banks mitigated the depression by lowering the cost of borrowing.
We use supervisory loan-level data to document that small firms (SMEs) obtain shorter maturity credit lines than large firms; have less active maturity management; post more collateral; have higher utilization rates; and pay higher spreads. We rationalize these facts as the equilibrium outcome of a trade-off between lender commitment and discretion. Using the COVID recession, we test the prediction that SMEs are subject to greater lender discretion by examining credit line utilization. We show that SMEs do not drawdown in contrast to large firms despite SME demand, but that PPP loans helped alleviate the shortfall.
We document the importance of covenant violations in transmitting bank health to nonfinancial firms using a new supervisory data set of bank loans. Roughly one-third of loans in our data breach a covenant during the 2008-09 period, providing lenders the opportunity to force a renegotiation of loan terms or to accelerate repayment of otherwise long-term credit. Lenders in worse health are more likely to force a reduction in the loan commitment following a violation. The reduction in credit to borrowers who violate a covenant can account for the majority of the cross-sectional variation in credit supply during the 2008-09 crisis.
We propose a three-step factor-flows simulation-based approach to forecast the duration distribution of unemployment. Step 1: estimate individual transition hazards across employment, temporary layoff, permanent layoff, quitter, entrant, and out of the labor force, with each hazard depending on an aggregate component as well as an individual's labor force history. Step 2: relate the aggregate components to the overall unemployment rate using a factor model. Step 3: combine the individual duration dependence, factor structure, and an auxiliary forecast of the unemployment rate to simulate a panel of individual labor force histories. Applying our approach to the November Blue Chip forecast of the COVID-19 recession, we project that 750,000 workers laid off in April 2020 remain unemployed eight months later. Total long-term unemployment rises thereafter and eventually reaches 4.2 million individuals unemployed for more than 26 weeks and 1.4 million individuals unemployed for more than 46 weeks. Long-term unemployment rises even more in a more pessimistic recovery scenario, but remains below the level in the Great Recession due to a high amount of labor market churn.
We construct a new dataset tracking the daily value of life insurers' assets at the security level. Outside of the 2008-09 crisis, a $1 drop in the market value of assets reduces an insurer's market equity by \$0.10. During the financial crisis, this pass-through rises to 1. We explain this pattern by viewing insurance companies as asset insulators, institutions with stable, long-term liabilities that can ride out transitory dislocations in market prices. Illustrating the macroeconomic importance of insulation, insurers' market equity declined by $50 billion less than the duration-adjusted value of their securities during the crisis.
Cross-sectional or panel studies have joined time series techniques as an important element in empirical macroeconomists' toolkit. The econometric best practices for these studies and their aggregate implications remain active topics of research. In this paper, I offer several pieces of advice for practitioners in this literature. I begin by casting regional analysis in a Rubin (1978) potential outcomes framework. Using this framework, I discuss the importance of contamination of other areas through spillovers and how to make inference about aggregate responses based on regional variation. I then turn to econometric issues including the choice of endogenous variable in a regional regression and whether or not to weight by population.
We analyze a unique episode in the history of monetary economics, the 2016 Indian ``demonetization.'' This policy made 86\% of cash in circulation illegal tender overnight, with new notes gradually introduced over the next several months. We present a model of demonetization where agents hold cash both to satisfy a cash-in-advance constraint and for tax evasion purposes. We test the predictions of the model in the cross-section of Indian districts using several novel data sets including: the geographic distribution of demonetized and new notes for causal inference; nightlight activity and employment surveys to measure economic activity including in the informal sector; debit/credit cards and e-wallet transactions data; and banking data on deposit and credit growth. Districts experiencing more severe demonetization had relative reductions in economic activity, faster adoption of alternative payment technologies, and lower bank credit growth. The cross-sectional responses cumulate to a contraction in employment and nightlights-based output due to demonetization of 2 p.p. and of bank credit of 2 p.p. in 2016Q4 relative to their counterfactual paths, effects which dissipate over the next few months. These cumulated effects provide a lower bound for the aggregate effects of demonetization. Our analysis rejects money non-neutrality using a large scale natural experiment, something that is yet rare in the vast literature on the effects of monetary policy.
We revisit an old question: does industry labor reallocation affect the business cycle? Our empirical methodology exploits variation in a local labor market's exposure to industry reallocation based on the area's initial industry composition and national industry employment trends for identification. Applied to confidential employment data over 1980-2014, we find sharp evidence of reallocation contributing to higher local area unemployment if it occurs during a national recession, but little difference in outcomes during an expansion. A multi-area, multi-sector search and matching model with imperfect mobility across industries and downward nominal wage rigidity can reproduce these cross-sectional patterns.
A geographic cross-sectional fiscal spending multiplier measures the effect of an increase in spending in one region in a monetary union. Empirical studies of such multipliers have proliferated in recent years. I review this research and what the evidence implies for national multipliers. Based on an updated analysis of the American Recovery and Reinvestment Act and a survey of empirical studies, my preferred point estimate for a cross-sectional output multiplier is 1.8. Drawing on a complementary theoretical literature, the paper discusses conditions under which the cross-sectional multiplier provides a rough lower bound for a particular national multiplier, the closed economy, no-monetary-policy-response fiscal spending multiplier. Putting these elements together, the cross-sectional evidence suggests a national no-monetary-policy-response multiplier of about 1.7 or above. The paper concludes by offering suggestions for future research on cross-sectional multipliers.
By how much does an extension of unemployment benefits affect macroeconomic outcomes such as unemployment? Answering this question is challenging because U.S. law extends benefits for states experiencing high unemployment. We use data revisions to decompose the variation in the duration of benefits into the part coming from actual differences in economic conditions and the part coming from measurement error in the real-time data used to determine benefit extensions. Using only the variation coming from measurement error, we find that benefit extensions have a limited influence on state-level macroeconomic outcomes. We use our estimates to quantify the effects of the increase in the duration of benefits during the Great Recession and find that they increased the unemployment rate by at most 0.3 percentage point.
The flow opportunity cost of moving from unemployment to employment consists of foregone public benefits and the foregone value of non-working time in units of consumption. We construct a time series of the opportunity cost of employment using detailed microdata and administrative or national accounts data to estimate benefit levels, eligibility and take-up of benefits, consumption by labor force status, hours per worker, taxes, and preference parameters. Our estimated opportunity cost is procyclical and volatile over the business cycle. The estimated cyclicality implies far less unemployment volatility in many leading models of the labor market than that observed in the data, irrespective of the level of the opportunity cost.
Monetary policy affects the real economy in part through its effects on financial institutions. High frequency event studies show the introduction of unconventional monetary policy in the winter of 2008-09 had a strong, beneficial impact on banks and especially on life insurance companies. I interpret the positive effects on life insurers as expansionary policy recapitalizing the sector by raising the value of legacy assets. Expansionary policy had small positive or neutral effects on banks and life insurers through 2013. The interaction of low nominal interest rates and administrative costs forced money market funds to waive fees, producing a possible incentive to reach for yield to reduce waivers. I show money market funds with higher costs reached for higher returns in 2009-11, but not thereafter. Some private defined benefit pension funds increased their risk taking beginning in 2009, but again such behavior largely dissipated by 2012. In sum, unconventional monetary policy helped to stabilize some sectors and provoked modest additional risk taking in others. I do not find evidence that the riskiness of the financial institutions studied fomented a tradeoff between expansionary policy and financial stability at the end of 2013.
Abstract This paper investigates the effect of bank lending frictions on employment outcomes. I construct a new dataset that combines information on banking relationships and employment at two thousand nonfinancial firms during the 2008-09 crisis. The paper first verifies empirically the importance of banking relationships, which imply a cost to borrowers that switch lenders. I then use the dispersion in lender health following the Lehman crisis as a source of exogenous variation in the availability of credit to borrowers. I find that credit matters. Firms that had pre-crisis relationships with less healthy lenders had a lower likelihood of obtaining a loan following the Lehman bankruptcy, paid a higher interest rate if they did borrow, and reduced employment by more compared to pre-crisis clients of healthier lenders. Consistent with frictions deriving from asymmetric information, the effects vary by firm type. Lender health has an economically and statistically significant effect on employment at small and medium firms, but the data cannot reject the hypothesis of no effect at the largest or most transparent firms. Abstracting from general equilibrium effects, I find that the withdrawal of credit accounts for between one-third and one-half of the employment decline at small and medium firms in the sample in the year following the Lehman bankruptcy.
The American Recovery and Reinvestment Act (ARRA) of 2009 included $88 billion of aid to state governments administered through the Medicaid reimbursement process. We examine the effect of these transfers on states’ employment. Because state fiscal relief outlays are endogenous to a state’s economic environment, OLS results are biased downward. We address this problem by using a state’s prerecession Medicaid pending level to instrument for ARRA state fiscal relief. In our preferred specification, a state’s receipt of a marginal $100,000 in Medicaid outlays results in an additional 3.8 job-years, 3.2 of which are outside the government, health, and education sectors
Unemployment insurance (UI) provides an important cushion for workers who lose their jobs. In addition, UI may act as a macroeconomic stabilizer during recessions. This chapter examines UI’s macroeconomic stabilization role, considering both the regular UI program which provides benefits to short-term unemployed workers as well as automatic and emergency extensions of benefits that cover long-term unemployed workers. We make a number of analytic points concerning the macroeconomic stabilization role of UI. First, recipiency rates in the regular UI program are quite low. Second, the automatic component of benefit extensions, Extended Benefits (EB), has played almost no role historically in providing timely, countercyclical stimulus while emergency programs are subject to implementation lags. Additionally, except during an exceptionally high and sustained period of unemployment, large UI extensions have limited scope to act as macroeconomic stabilizers even if they were made automatic because relatively few individuals reach long-term unemployment. Finally, the output effects from increasing the benefit amount for short-term unemployed are constrained by estimated consumption responses of below 1. We propose five changes to the UI system that would increase UI benefits during recessions and improve the macroeconomic stabilization role: (I) Expand eligibility and encourage take-up of regular UI benefits. (II) Make EB fully federally financed. (III) Remove look-back provisions from EB triggers that make automatic extensions turn off during periods of prolonged unemployment. (IV) Add additional automatic extensions to increase benefits during periods of extremely high unemployment. (V) Add an automatic federally financed increase in the weekly UI benefit amount during recessions. We caution that these reforms may not by themselves have a large macroeconomic impact. Still, they would help to better align the UI system with its microeconomic objective. Together with other policy reforms to automatic stabilizers, these proposed changes to the UI system could help to mitigate future recessions.