Research Database

In this appendix, we provide details on the data sources and construction of variables for our analysis in "What Do Survey Data Tell Us about U.S. Businesses?" We also include the auxiliary tables and figures omitted from the main text.

We develop and calibrate an overlapping generations general equilibrium model of the U.S. economy with heterogeneous consumers who face idiosyncratic earnings and health risk to study the implications of exogenous trends in increasing college attainment, decreasing fertility, and increasing longevity between 2005 and 2100. While all three trends contribute to a higher old age dependency ratio, increasing college attainment has different macroeconomic implications because it increases labor productivity. Decreasing fertility and increasing longevity require the government to increase the average labor tax rate from 32.0 to 44.4 percent. Increasing college attainment lowers the required tax increase by 10.1 percentage points. The required tax increase is higher under general equilibrium than in a small open economy with a constant interest rate because the reduction in the interest rate lowers capital income tax revenues.

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.

We use a simple random growth model to study the role of changing dynamics of family firms in shaping the evolution of top wealth shares in the United States over the course of the past century. Our model generates a time path for top wealth shares. The path is remarkably similar to those found by Saez and Zucman (2016) and Gomez (2019) when the volatility of idiosyncratic shocks to the value of family firms is similar to that found for public firms by Herskovic, Kelly, Lustig, and Van Nieuwerburgh (2016). We also show that consideration of family firms contributes not only to overall wealth inequality but also to considerable upward and downward mobility of families within the distribution of wealth. We interpret our results as indicating that improving our understanding of how families found new firms and eventually diversify their wealth is central to improving our understanding of the distribution of great wealth and its evolution over time.

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.

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.

This online appendix accompanies Staff Report 620: Star Wars at Central Banks.

Supplemental data and code for replication.

This appendix contains the pre-registered analysis for our comment on “Star Wars: The Empirics Strike Back” by Brodeur et al (2016). To structure the analysis, we reproduce the pre-registration; our results appear in red under each of the relevant parts. The time-stamped version of the pre-registration is available from the Open Science Framework website at the address https://doi.org/10.17605/OSF.IO/58MNJ.

To understand this appendix deeply, we recommend carefully reading Brodeur et al (2016). The body of our comment paper outlines only the intuition of their method. In some of the figures presented in this appendix, we use labels that differ from those in Brodeur et al. (2016), and we do so to more clearly connect to the intuition we offer.

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.

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.