Showing papers by "Peter K. Schott published in 2020"

Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time

Abstract: We show that unexpected changes in the trajectory of COVID-19 infections predict US stock returns, in real time. Parameter estimates indicate that an unanticipated doubling (halving) of projected infections forecasts next-day decreases (increases) in aggregate US market value of 4 to 11 percent, indicating that equity markets may begin to rebound even as infections continue to rise, if the trajectory of the disease becomes less severe than initially anticipated. Using the same variation in unanticipated projected cases, we find that COVID-19-related losses in market value at the firm level rise with capital intensity and leverage, and are deeper in industries more conducive to disease transmission. These relationships provide important insight into current record job losses. Measuring US states' drops in market value as the employment weighted average declines of the industries they produce, we find that states with milder drops in market value exhibit larger initial jobless claims per worker. This initially counter-intuitive result suggests that investors value the relative ease with which labor versus capital costs can be shed as revenues decline.

Imputing Missing Values in the US Census Bureau's County Business Patterns

Abstract: The County Business Patterns data published by the US Census Bureau track employment by county and industry from 1946 to the present. Two features of the data limit their usefulness to researchers: (1) employment for the majority of county-industry cells is suppressed to protect confidentiality, and (2) industry classifications change over time. We address both issues. First, we develop a linear programming method that exploits the large set of adding-up constraints implicit in the hierarchical arrangement of the data to impute missing employment. Second, we provide concordances to map all data to a consistent set of industry codes. Finally, we construct a user-friendly, 1975 to 2016 county-level panel that classifies industries according to a consistent set of 2012 NAICS codes in all years. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time

Abstract: We show that unexpected changes in the trajectory of COVID-19 infections predict US stock returns, in real time. Parameter estimates indicate that an unanticipated doubling (halving) of projected infections forecasts next-day decreases (increases) in aggregate US market value of 4 to 11 percent, indicating that equity markets may begin to rebound even as infections continue to rise, if the trajectory of the disease becomes less severe than initially anticipated. Using the same variation in unanticipated projected cases, we find that COVID-19-related losses in market value at the firm level rise with capital intensity and leverage, and are deeper in industries more conducive to disease transmission. These relationships provide important insight into current record job losses. Measuring US states' drops in market value as the employment weighted average declines of the industries they produce, we find that states with milder drops in market value exhibit larger initial jobless claims per worker. This initially counter-intuitive result suggests that investors value the relative ease with which labor versus capital costs can be shed as revenues decline.

Using Equity Market Reactions to Infer Exposure to Trade Liberalization

Abstract: We develop a new method for identifying firm exposure to changes in policy using asset prices that has several advantages over standard measures: it is natively firm level, it encompasses the net impact of all avenues of exposure, it yields estimates for firms in all sectors of the economy, and it captures aspects of policy change that can be difficult to quantify using standard approaches. We provide guidelines on how our method can be used in a wide range of settings and, applying it to two well-studied US trade liberalizations, find that it offers new insight into those policies’ distributional implications. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Imputing Missing Values in the US Census Bureau's County Business Patterns

Abstract: The County Business Patterns data published by the US Census Bureau track employment by county and industry from 1946 to the present Two features of the data limit their usefulness to researchers: (1) employment for the majority of county-industry cells is suppressed to protect confidentiality, and (2) industry classifications change over time We address both issues First, we develop a linear programming method that exploits the large set of adding-up constraints implicit in the hierarchical arrangement of the data to impute missing employment Second, we provide concordances to map all data to a consistent set of industry codes Finally, we construct a user-friendly, 1975 to 2016 county-level panel that classifies industries according to a consistent set of 2012 NAICS codes in all years