Research Database

We develop and calibrate a search-theoretic model of the labor market in order to forecast the evolution of the aggregate US labor market during and after the coronavirus pandemic. The model is designed to capture the heterogeneity of the transitions of individual workers across states of unemployment and employment and across different employers. The model is designed also to capture the trade-offs in the choice between temporary and permanent layoffs. Under reasonable parametrizations of the model, the lockdown instituted to prevent the spread of the novel coronavirus is shown to have long-lasting negative effects on unemployment. This is because the lockdown disproportionately disrupts the employment of workers who need years to find stable jobs.

U.S. government concerns about great disparities in housing conditions are at least 100 years old. For the first 50 years of this period, U.S. housing crises were widely considered to stem from the failure of the construction industry to adopt new technology -- in particular, factory production methods. The introduction of these methods in many industries had already greatly narrowed the quality of goods consumed by low- and high-income Americans. It was widely known why the industry failed to adopt these methods: Monopolies in traditional construction blocked and sabotaged them. Very little has changed in the last 50 years. The industry still fails to adopt factory methods, with monopolies, like HUD and NAHB, blocking attempts to adopt them. As a result, the productivity record of the construction industry has been horrendous. One thing has changed. Today there is very little discussion of factory-built housing; of the very few that recognize the industry's failure to adopt factory methods, there is no realization that monopolies are blocking the methods. That these monopolies, in particular, HUD and NAHB, can cause so much hardship in our country, and through misinformation and deceit cover it up, seems almost beyond belief. But, unfortunately, it's a history that is not uncommon. There are many other industries where monopolies have inflicted great harm on Americans, like the tobacco industry, yet through misinformation and deceit cover up the great harm.

The Covid-19 crisis has exposed the vast inequalities that exist within the US economy. As the virus has spread silently, it has laid bare other crises that face our nation---especially the economic vulnerabilities of the country's poor and marginalized. Many of these vulnerabilities can, in fact, be traced back to a single cause that itself has spread silently, but over the last several decades, not months: Monopolies. That monopolies are "silent spreaders of poverty and economic inequality" was well known to economic and legal scholars of the 1930s and 1940s. Wendell Berge, who was Assistant Attorney General for Antitrust in the 1940s, wrote: "Monopoly conditions have often grown up almost unnoticed by the public until one day it is suddenly realized that an industry is no longer competitive but is governed by an economic oligarchy." The harm caused by these monopolies that have mostly avoided detection often exist in markets with small firms, low concentration levels, and small price-cost margins, as in residential construction, or wreak their harm in public institutions, where prices and concentration have no meaning. While there has been a very welcome resurgence in the concern about monopolies in the last decade or so, this has primarily involved vast corporations, and often about their threat to democratic institutions. Though greatly welcomed, we should not let apprehension with these larger companies distract us from the many hidden monopolies that have silently spread harm to the poor for the last 100 years -- not just the last 10 or so. We should stand on the shoulders of giants that taught us this about monopolies, not only Berge, but Thurman Arnold, Henry Simons, and others.

In this paper, we review the relationship between inflation rates, nominal interest rates, and rates of growth of monetary aggregates for a large group of OECD countries. We conclude that the low-frequency behavior of these series maintains a close relationship, as predicted by standard quantity theory models. In an estimated model, we show those relationships to be relatively invariant to alternative frictions that can deliver very different high-frequency dynamics. We argue that these relationships are useful for policy design aimed at controlling inflation.

We modify a standard SIR epidemiological model to allow for testing and asymptomatic agents. We explore cross country variation's ability to allow for identification of key parameters of the model: the fatality rate and the evolution over time of the normalized transmission rate. We first show that as long as tests are applied only to agents who exhibit symptoms, those parameters cannot be identified. We briefly discuss which additional information may allow for identification. Finally, we also describe conditions under which the normalized transmission rate can be computed with very high accuracy, and how cross country evidence can be used to evaluate the effect of lockdowns on evolution of the effective transmission rate over time.

From introduction: This paper is intended to introduce economists to a simple SIR model of the progression of COVID-19 to aid understanding of how such a model might be incorporated into more standard macroeconomic models. An SIR model is a Markov model of the spread of an epidemic in which the total population is divided into categories of being susceptible to the disease (S); actively infected with the disease (I); and resistant (R), meaning those that have recovered, died from the disease, or have been vaccinated. The initial distribution of the population across these states and the transition rates at which agents move between these three states determine how an epidemic plays out over time. These transition rates are determined by characteristics of the underlying disease and by the extent of mitigation and social distancing measures. 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.

This note develops a framework for thinking about the following question: What is the maximum amount of consumption that a utilitarian welfare function would be willing to trade off to avoid the deaths associated with COVID-19? The answer depends crucially on the mortality rate associated with the coronavirus. If the mortality rate averages 0.81%, as projected in one prominent study, our answer is 41% of one year’s consumption. If the mortality rate instead averages 0.44% across age groups, as suggested by a recent seroprevalence study, our answer is 28%.