This paper investigates the dynamic impact of social distancing policy on coronavirus disease (COVID-19) infection control, mobility of people, and consumption expenditures in the Republic of Korea. We employ structural and threshold vector autoregressive (VAR) models using big-data-driven mobility data, credit card expenditure, and a social distancing index. We find that the social distancing policy significantly reduces the spread of COVID-19, but there exists a significant, growing trade-off between infection control and economic activity over time. When the level of stringency in social distancing is already high, its marginal effect on mobility is estimated to be smaller than when social distancing stringency is low. The effect of social distancing also becomes secondary after vaccination. Increased vaccination is found to significantly reduce the critical cases while it increases visitors and consumption expenditures. The results also show that the effect of social distancing policy on mobility reduction is strongest among the population of age under 20 and the weakest among the population of age over 60.

We derive a model in which firms operate in an epidemic environment and internalize infections among their employees in the workplace. The model is calibrated to fit the moments of the Covid-19 epidemic. We show that firms have incentives to fight against infections and can do so very effectively by increasing teleworking and rotating employees between on-site work, teleworking, and leave. The fight against infections in firms flattens the aggregate infections curve. Subsidies to teleworking reduce infections and save lives. Subsidies to sick leave reduce the cost of sick workers and raise workplace infections. Firms delay and weaken the fight against infections during economic downturns. We also consider the problem of a government that values output and lives. We show that the government prefers to severely restrict the epidemic by tolerating short-term output losses when it has a high valuation of life.

We study how the clarity of COVID-19 risk communications affects COVID-19 insurance demand using proprietary prefecture-level insurance data from China. We find that when local disclosures of COVID-19 risk contain case origin information, local purchases of COVID-19 insurance and local Internet searches for COVID-19 information increase, even after controlling for newly confirmed local cases and new deaths. Our results are robust to using the disclosure clarity of a major neighboring city. The findings suggest that providing improved knowledge about risk to individuals lead them to engage in more risk management. Our evidence contributes to the debate over how risk communication affects individuals' risk-related behaviors.

We adopt a time series approach to investigate the historical relation between unemployment, life expectancy, and mortality rates. We fit Vector-autoregressions for the overall US population and for groups identified based on gender and race. We use our results to assess the long-run effects of the COVID-19 economic recession on mortality and life expectancy. We estimate the size of the COVID-19-related unemployment shock to be between 2 and 5 times larger than the typical unemployment shock, depending on race and gender, resulting in a significant increase in mortality rates and drop in life expectancy. We also predict that the shock will disproportionately affect African-Americans and women, over a short horizon, while the effects for white men will unfold over longer horizons. These figures translate in more than 0.8 million additional deaths over the next 15 years.

We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.

This paper explores the robustness of laboratory expectation formation and public signal credibility to external uncertainty shocks and online experimentation. We exploit the recent pandemic as a source of exogenous background uncertainty in a New Keynesian learning-to-forecast experiment (LtFE) where participants receive projections of varying precision about future inflation. We compare results from identical LtFE completed immediately before the onset of the pandemic, soon after (online), and well after (online and in-person). Baseline LtFEs with no communication are robust to both factors. However, both background uncertainty and online experimentation impact how subjects use public signals. The pandemic led to a decreased appetite for and tolerance of overly precise communication while increasing the efficacy of projections that also convey uncertainty. Subjects became more averse to central bank forecast errors after the onset of the pandemic if the central bank conveyed a precise outlook but not if it conveyed forecast uncertainty.

This paper augments the European Commission's open-economy DSGE model (GM) with COVID-specific shocks ('forced savings', labour hoarding) and financially-constrained investors to account for the extreme volatility of private domestic demand and hours worked during COVID-19, and it estimates the model on euro area data for the period 1998q4-2021q4. It takes a pragmatic approach of adapting the workhorse model of a policy institution to COVID-19 data. 'Forced savings' are central to explain quarterly real GDP growth during the pandemic, complemented by contributions from foreign demand and trade, and the negative impact of persistently higher savings after the first wave. We provide extensive model validation, including a comparison to off-model evidence for COVID-related restrictions, and a comparison of different model specifications.

With this paper, Eichenbaum, Rebelo, and Trabandt have made another insightful and influential contribution to the growing literature on the macroeconomics of epidemics. Their papers are paving the way to a new fascinating research program whose objective is to develop empirically plausible macroeconomic models of epidemics. I argued that estimating synthetic COVID shocks in familiar DSGE models provides a good benchmark to evaluate progress toward this goal (Ferroni et al., 2021). Furthermore, evaluating alternative containment measures and how these measures should be deployed (e.g., should containment measures be targeted to the workplaces or somewhere else?) are important matters this research agenda should address. My work with Rottner (Melosi and Rottner, 2020) contributes to developing methods allowing researchers to study contact tracing and testing in macro-epidemiological models of the type studied in Eichenbaum, Rebelo, and Trabandt's influential works.

Using an integrated epi-econ model, we compute the value of vaccines for Covid-19, both under a planner's solution and in competitive equilibrium. The specific model, developed in Boppart, Harmenberg, Hassler, Krusell, and Olsson (2020), factors in not just value-of-life aspects along with standard economic variables but also the value of leisure activities that rely on a social component. We find that the societal value of vaccination is large; we estimate that, translated into monetary terms, the value of vaccinating one young individual in the competitive equilibrium is $17,800. Externalities are large: less than half the societal value is internalized by individuals (assuming that they act purely in their self-interest). Finally, behavioral responses are important, with a substantial share of the value of vaccines being attributed to people enjoying more socially-oriented leisure when more people are vaccinated.

This comment briefly summarizes Hevia et al. (2022), who utilize a unique dataset to shed light on the inequality of the epidemiological burden for low- and high-income individuals in Bogotá. Some suggestions and ideas for future research are provided.

We investigate how politico-economic factors shaped government responses to the spread of COVID-19. Our simple framework uses epidemiological, economic and politico-economic arguments. Confronting the theory with US state level data we find strong evidence for partisanship even when we control for fundamentals including the electorate's political views. Moreover, we detect an important role for the proximity of elections which we interpret as indicative of career concerns. Finally, we find suggestive evidence for complementarities between voluntary activity reductions and government imposed restrictions.

We document the heterogeneous effect of Covid-19 on health and economic outcomes across socioeconomic strata in Bogotá. We assess its distributional impact and evaluate policy counterfactuals in a heterogeneous agent quantitative dynamic general equilibrium model intertwined with a behavioral epidemiological model.

In this paper we ask how to best allocate a given time-varying supply of vaccines during the second phase of the Covid-19 pandemic across individuals of different ages. Building on our previous heterogeneous household model of optimal economic mitigation and redistribution (Glover et al., 2021) we contrast the actual vaccine deployment path that prioritized older, retired individuals with one that first vaccinates younger workers. Vaccinating the old first saves more lives but slows the economic recovery, relative to inoculating the young first. Vaccines deliver large welfare benefits in both scenarios (relative to a world without vaccines), but the old-first policy is optimal under a utilitarian social welfare function. The welfare gains from having vaccinated the old first are especially significant once the economy is hit by a more infectious Delta variant in the summer of 2021.

This note discusses age-specific vaccination programs designed to curb the Covid-19 pandemic. We first provide some comments on the analysis by Glover et al. (2021b) and point directions where further research can be carried out. Additionally, we adapt the framework from Brotherhood et al. (2021) to assess the effects of different vaccination schemes when more infectious variants can emerge when more infections take place. We find that policy prescriptions crucially depend on taking individual behavioral responses into account and on whether variants can appear.

We use data on deaths in New York City, Madrid, Stockholm, and other world cities as well as in various U.S. states and other regions and countries to estimate, quickly and with limited data, a standard epidemiological model of COVID-19. We allow for a time-varying contact rate in order to capture behavioral and policy-induced changes associated with social distancing. We simulate the model forward to consider possible scenarios for various countries, states, and cities, including the potential impact of herd immunity on re-opening.

This paper models the macroeconomic and distributional consequences of lockdown shocks during the COVID-19 pandemic. The model features heterogeneous life-cycle households, labor market search and matching frictions, and multiple industries of employment. We calibrate the model to data from New Zealand, where the health effects of the pandemic were especially mild. In this context, we model lockdowns as supply shocks, ignoring the demand shocks associated with health concerns about the virus. We then study the impact of a large-scale wage subsidy scheme implemented during the lockdown. The policy prevents job losses equivalent to 6.5% of steady state employment. Moreover, we find significant heterogeneity in its impact. The subsidy saves 17.2% of jobs for workers under the age of 30, but just 2.6% of jobs for those over 50. Nevertheless, our welfare analysis of fiscal alternatives shows that the young prefer increases in unemployment transfers as this enables greater consumption smoothing across employment states.

We assess the impact of demographic changes on human capital accumulation and aggregate output using an overlapping generations model with endogenous savings and human capital investment decisions. We focus on China as it has experienced rapid changes in demographics as well as human capital levels between 1970 and 2010. Additionally, further variations in demographics are expected due to the recently introduced two-child policy. Model simulations indicate that education shares and income per capita will be lower with a fertility rebound as compared to status quo fertility. We find education policy to be effective in mitigating these adverse outcomes associated with higher fertility. While long-run declines in output per capita can be offset by a 4.7% increase in the government education budget, it requires a 28% increase to achieve the same outcome in the short-run.

I study the effects of the 2020 coronavirus outbreak in the United States and subsequent fiscal policy response in a nonlinear DSGE model. The pandemic is a shock to the utility of contact-intensive services that propagates to other sectors via general equilibrium, triggering a deep recession. I use a calibrated version of the model that matches the path of the US unemployment rate in 2020 to analyze different types of fiscal policies. I find that the pandemic shock changes the ranking of policy multipliers. Unemployment benefits are the most effective tool to stabilize income for borrowers, who are the hardest hit during a pandemic, while liquidity assistance programs are the most effective if the policy objective is to stabilize employment in the affected sector. I also study the effects of the $2.2 trillion CARES Act of 2020.

We report empirical evidence indicating that US net business formation has recently turned more volatile, procyclical and persistent. To study these stylized facts, we estimate a DSGE model with endogenous entry and exit. Business units feature heterogeneous productivity and they shut down if the present value of expected future dividends falls below the current liquidation value. The model provides a better fit than a constant exit rate model with the fluctuations of US business formation. The introduction of the extensive margin amplifies the effects of technology and risk-premium shocks, and reduces the procyclicality of firm-level production. The main sources of variability of the US aggregate fluctuations during the Great Recession are countercyclical technology shocks, persistent adverse risk-premium shocks, and expansionary monetary policy shocks.