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An SEIR Infectious Disease Model with Testing and Conditional Quarantine
We extend the baseline Susceptible-Exposed-Infectious-Recovered (SEIR) infectious disease epidemiology model to understand the role of testing and case-dependent quarantine. Our model nests the SEIR model. During a period of asymptomatic infection, testing can reveal infection that otherwise would only be revealed later when symptoms develop. Along with those displaying symptoms, such individuals are deemed known positive cases. Quarantine policy is case-dependent in that it can depend on whether a case is unknown, known positive, known negative, or recovered. Testing therefore makes possible the identification and quarantine of infected individuals and release of non-infected individuals. We fix a quarantine technology—a parameter determining the differential rate of transmission in quarantine—and compare simple testing and quarantine policies. We start with a baseline quarantine-only policy that replicates the rate at which individuals are entering quarantine in the US in March, 2020. We show that the total deaths that occur under this policy can be achieved under looser quarantine measures and a substantial increase in random testing of asymptomatic individuals. Testing at a higher rate in conjunction with targeted quarantine policies can (i) dampen the economic impact of the coronavirus and (ii) reduce peak symptomatic infections—relevant for hospital capacity constraints. Our model can be plugged into richer quantitative extensions of the SEIR model of the kind currently being used to forecast the effects of public health and economic policies.
AUTHORS: Berger, David W.; Herkenhoff, Kyle F.; Mongey, Simon
DATE: 2020-03-25

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What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios
This note is intended to introduce economists to a simple SIR model of the progression of COVID-19 in the United States over the next 12-18 months. An SIR model is a Markov model of the spread of an epidemic in a population in which the total population is divided into categories of being susceptible to the disease (S), actively infected with the disease (I), and recovered (or dead) and no longer contagious (R). How an epidemic plays out over time is determined by the transition rates between these three states. This model allows for quantitative statements regarding the tradeoff between the severity and timing of suppression of the disease through social distancing and the progression of the disease in the population. Example applications of the model are provided. Special attention is given to the question of if and when the fraction of active infections in the population exceeds 1% (at which point the health system is forecast to be severely challenged) and 10% (which may result in severe staffing shortages for key financial and economic infrastructure) as well as the cumulative burden of the disease over an 18 month horizon.
AUTHORS: Atkeson, Andrew
DATE: 2020-03-18

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How Deadly is COVID-19? Understanding the Difficulties with Estimation of its Fatality Rate
To understand how best to combat COVID-19, we must understand how deadly is the disease. There is a substantial debate in the epidemiological literature as to whether the fatality rate is 1% or 0.1% or somewhere in between. In this note, I use an SIR model to examine why it is difficult to estimate the fatality rate from the disease and how long we might have to wait to resolve this question absent a large-scale randomized testing program. I focus on uncertainty over the joint distribution of the fatality rate and the initial number of active cases at the start of the epidemic around January 15, 2020. I show how the model with a high initial number of active cases and a low fatality rate gives the same predictions for the evolution of the number of deaths in the early stages of the pandemic as the same model with a low initial number of active cases and a high fatality rate. The problem of distinguishing these two parameterizations of the model becomes more severe in the presence of effective mitigation measures. As discussed by many, this uncertainty could be resolved now with large-scale randomized testing.
AUTHORS: Atkeson, Andrew
DATE: 2020-03-31

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Asset Prices and Unemployment Fluctuations
Recent critiques have demonstrated that existing attempts to account for the unemployment volatility puzzle of search models are inconsistent with the procylicality of the opportunity cost of employment, the cyclicality of wages, and the volatility of risk-free rates. We propose a model that is immune to these critiques and solves this puzzle by allowing for preferences that generate time-varying risk over the cycle, and so account for observed asset pricing fluctuations, and for human capital accumulation on the job, consistent with existing estimates of returns to labor market experience. Our model reproduces the observed fluctuations in unemployment because hiring a worker is a risky investment with long-duration surplus flows. Intuitively, since the price of risk in our model sharply increases in recessions as observed in the data, the benefit from creating new matches greatly drops, leading to a large decline in job vacancies and an increase in unemployment of the same magnitude as in the data.
AUTHORS: Kehoe, Patrick J.; Lopez, Pierlauro; Midrigan, Virgiliu; Pastorino, Elena
DATE: 2020-01-08

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Monetary Policy and Sovereign Risk in Emerging Economies (NK-Default)
This paper develops a New Keynesian model with sovereign default risk (NK-Default). We focus on the interaction between monetary policy, conducted according to an interest rate rule that targets inflation, and external defaultable debt issued by the government. Monetary policy and default risk interact since both affect domestic consumption, production, and inflation. We find that default risk amplifies monetary frictions and generates a tension for monetary policy, which increases the volatility of inflation and nominal rates. These monetary frictions in turn discipline sovereign borrowing, slowing down debt accumulation and lowering sovereign spreads. Our framework replicates the positive comovements of spreads with nominal domestic rates and inflation, a salient feature of emerging markets data, and can rationalize the experience of Brazil during the 2015 downturn, with high inflation, nominal rates, and spreads.
AUTHORS: Arellano, Cristina; Bai, Yan; Mihalache, Gabriel
DATE: 2020-01-10

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Housing Wealth Effects: The Long View
We provide new time-varying estimates of the housing wealth effect back to the 1980s. We use three identification strategies: OLS with a rich set of controls, the Saiz housing supply elasticity instrument, and a new instrument that exploits systematic differences in city-level exposure to regional house price cycles. All three identification strategies indicate that housing wealth elasticities were if anything slightly smaller in the 2000s than in earlier time periods. This implies that the important role housing played in the boom and bust of the 2000s was due to larger price movements rather than an increase in the sensitivity of consumption to house prices. Full-sample estimates based on our new instrument are smaller than recent estimates, though they remain economically important. We ﬁnd no significant evidence of a boom-bust asymmetry in the housing wealth elasticity. We show that these empirical results are consistent with the behavior of the housing wealth elasticity in a standard life-cycle model with borrowing constraints, uninsurable income risk, illiquid housing, and long-term mortgages. In our model, the housing wealth elasticity is relatively insensitive to changes in the distribution of LTV for two reasons: First, low-leverage homeowners account for a substantial and stable part of the aggregate housing wealth elasticity; Second, a rightward shift in the LTV distribution increases not only the number of highly sensitive constrained agents but also the number of underwater agents whose consumption is insensitive to house prices.
AUTHORS: Guren, Adam M.; McKay, Alisdair; Nakamura, Emi; Steinsson, Jon
DATE: 2020-01-31

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A Welfare Analysis of Occupational Licensing in U.S. States
We assess the welfare consequences of occupational licensing for workers and consumers. We estimate a model of labor market equilibrium in which licensing restricts labor supply but also affects labor demand via worker quality and selection. On the margin of occupations licensed differently between U.S. states, we find that licensing raises wages and hours but reduces employment. We estimate an average welfare loss of 12 percent of occupational surplus. Workers and consumers respectively bear 70 and 30 percent of the incidence. Higher willingness to pay offsets 80 percent of higher prices for consumers, and higher wages compensate workers for 60 percent of the cost of mandated investment in occupation-specific human capital.
AUTHORS: Kleiner, Morris M.; Soltas, Evan J.
DATE: 2019-10-15

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Rising Bank Concentration
Concentration of insured deposit funding among the top four commercial banks in the U.S. has risen from 15% in 1984 to 44% in 2018, a roughly three-fold increase. Regulation has often been attributed as a factor in that increase. The Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994 removed many of the restrictions on opening bank branches across state lines. We interpret the Riegle-Neal act as lowering the cost of expanding a bank's funding base. In this paper, we build an industry equilibrium model in which banks endogenously climb a funding base ladder. Rising concentration occurs along a transition path between two steady states after branching costs decline.
AUTHORS: Corbae, Dean; D'Erasmo, Pablo
DATE: 2020-03-02

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Labor Market Dynamics and Development
We build a dataset of harmonized rotating panel labor force surveys covering 42 countries across a wide range of development and document three new empirical findings on labor market dynamics. First, labor market flows (job-finding rates, employment-exit rates, and job-to-job transition rates) are two to three times higher in the poorest as compared with the richest countries. Second, employment hazards in poorer countries decline more sharply with tenure; much of their high turnover can be attributed to high separation rates among workers with low tenure. Third, wage-tenure profiles are much steeper in poorer countries, despite the fact that wage-experience profiles are flatter. We show that these facts are consistent with theories with endogenous separation, particularly job ladder and learning models. We disaggregate our results and investigate possible driving forces that may explain why separation operates differently in rich and poor countries.
AUTHORS: Lu, Will Jianyu; Donovan, Kevin; Schoellman, Todd
DATE: 2020-03-19

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Aggregate implications of innovation policy
In this paper we present a tractable model of innovating firms and the aggregate economy that we use to assess quantitatively the link between the responses of firms to changes in innovation policy and the impact of those policy changes on aggregate output and welfare. We show that, to a first-order approximation, a wide range of policy changes have a long-run impact in direct proportion to the fiscal expenditures on those policies, and that to evaluate the aggregate impact of a policy change, there is no need to calculate changes in firms' decisions in response to these policy changes. ; We use these results to compare the relative magnitudes of the impact on aggregates in the long run of three innovation policies in the United States: the Research and Experimentation Tax Credit, federal expenditure on R&D, and the corporate profits tax. We argue that the corporate profits tax is a relatively important policy through its negative effects on innovation and physical capital accumulation. We also use a calibrated version of our model to examine the absolute magnitude of the impact of these policies on aggregates. We show that, depending on the magnitude of spillovers, it is possible for changes in innovation policies to have very large impact on aggregates in the long run. However, over a 15-year horizon, the impact of changes in innovation policies on aggregate output is not very sensitive to the magnitude of spillovers. ; On the basis of these results we conclude that, while it is possible to make comparisons about the relative importance of different policies and sharp predictions about their aggregate impact in the medium term, it is very difficult to shed much light on the implications of innovation policies for long-run aggregate outcomes and welfare in the absence of direct quantitative evidence on the magnitude of spillovers.
AUTHORS: Atkeson, Andrew; Burstein, Ariel
DATE: 2011

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