We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that reopening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections.
We assess the economic value of screening testing programs as a policy response to the ongoing COVID-19 pandemic. We find that the fiscal, macroeconomic, and health benefits of rapid SARS-CoV-2 screening testing programs far exceed their costs, with the ratio of economic benefits to costs typically in the range of 2-15 (depending on program details), not counting the monetized value of lives saved. Unless the screening test is highly specific, however, the signal value of the screening test alone is low, leading to concerns about adherence. Confirmatory testing increases the net economic benefits of screening tests by reducing the number of healthy workers in quarantine and by increasing adherence to quarantine measures. The analysis is undertaken using a behavioral SIR model for the United States with 5 age groups, 66 economic sectors, screening and diagnostic testing, and partial adherence to instructions to quarantine or to isolate.
In this paper, I revisit some recent work on the theory of the money supply, using a theoretical framework that closely follows Karl Brunner's work.I argue that had his research proposals been followed by the profession, some of the misunderstandings related to the instability of the money demand relationship could have been avoided.
This paper evaluates the role of rising income inequality in explaining observed growth in college tuition. We develop a competitive model of the college market, in which college quality depends on instructional expenditure and the average ability of admitted students. An innovative feature of our model is that it allows for a continuous distribution of college quality. We find that observed increases in US income inequality can explain more than half of the observed rise in average net tuition since 1990 and that rising income inequality has also depressed college attendance. (JEL D31, I22, I23, I24)
Brazil has had a long period of high inflation.It peaked around 100 percent per year in 1964, decreased until the first oil shock (1973), but accelerated again afterward, reaching levels above 100 percent on average between 1980 and 1994.This last period coincided with severe balance of payments problems and economic stagnation that followed the external debt crisis in the early 1980s.We show that the high-inflation period (1960-1994) was characterized by a combination of fiscal deficits, passive monetary policy, and constraints on debt financing.The transition to the low-inflation period (1995-2016) was characterized by improvements in all of these features, but it did not lead to significant improvements in economic growth.In addition, we document a strong positive correlation between inflation rates and seigniorage revenues, although inflation rates are relatively high for modest levels of seigniorage revenues.Finally, we discuss the role of the weak institutional framework surrounding the fiscal and monetary authorities and the role of monetary passiveness and inflation indexation in accounting for the unique features of inflation dynamics in Brazil.
