Working papers
Heterogeneity in Sectoral Production and the Macro Effect of Sectoral Shocks
Abstract
The effect of a negative sectoral shock on GDP depends on how important the shocked sector is as a direct and indirect supplier and how easily sectors can substitute inputs. Past estimates of the parameters that determine these qualities in the US have been restrictive: they have not been allowed to vary across industries or across time. This paper uses a novel empirical strategy to relax those restrictions, by exploiting variation in input expenditure share shifts within industries rather than across industries. The resulting estimates exhibit significant sectoral and temporal heterogeneity, and are dynamically correlated with weighted patents. In a calibrated GE model of multi-sector production, this heterogeneity (1) raises[lowers] the GDP effect of negative shocks to sectors whose customers are less[more] able to substitute inputs (e.g. the GDP effect of "Chemical products" shocks rises), (2) raises[lowers] the GDP effect of negative sectoral shocks in years where sectors are less[more] able to substitute inputs, and (3) raises[lowers] the GDP effect of negative shocks to sectors as they become more[less] central input suppliers (e.g. between 1997 and 2023 the GDP effect of "Paper products" shocks fell and the GDP effect of "Computer and electronic products" shocks rose due to changes in their importance as input suppliers).
Pre-PhD
Insurer Competition in the Age of Provider Consolidation: The relationship between hospital and insurance competition in the ACA individual market
Abstract
This paper investigates the impact of hospital competition (or lack thereof) on insurer participation in the ACA's individual market. Using public data from CMS, and private data from the American Hospital Association (AHA), I construct the Herfindahl–Hirschman Index (HHI) for hospital and insurer markets at the county-level in 34 of the 36 states using federally facilitated marketplaces, across 2015 and 2016 (hospital HHI is lagged by one year). I fit a linear model on 2063 counties across two years in these states, controlling for county-level covariates and fixed effects for year-"rating area" (a geographic designation created by the ACA, which typically amounts to a collection of counties). I estimate my parameters using OLS. I find higher hospital HHI levels are associated with higher insurer HHI levels at a coefficient of .033, log linearized. I lay the groundwork for further analysis once more years of data are available, contributing to the existing literature by focusing on insurer competition rather than premium price as my primary outcome, leveraging "rating areas" for better model specification, and outlining a novel approach to hospital market HHI construction using hospital "radii" rather than pre-existing geographic bounds.