Related Experiment Video
Updated: Jun 30, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
"Deaths of despair" over the business cycle: New estimates from a shift-share instrumental variables approach
1University of California, Berkeley School of Public Health, Division of Health Policy and Management, 2121 Berkeley Way, Room 5302, Berkeley, CA 94720, United States.
Abstract:
This study presents new evidence of the effects of short-term economic fluctuations on suicide, fatal drug overdose, and alcohol-related mortality among working-age adults in the United States from 2003-2017. Using a shift-share instrumental variables approach, I find that a one percentage point increase in the aggregate employment rate decreases current-year non-drug suicides by 1.7 percent. These protective effects are concentrated among working-age men and likely reflect a combination of individual labor market experiences as well as the indirect effects of local economic growth. I find no consistent evidence that short-term business cycle changes affect drug or alcohol-related mortality. While the estimated protective effects are small relative to secular increases in suicide in recent decades, these findings are suggestive of important, short-term economic factors affecting specific causes of death and should be considered alongside the longer-term and multifaceted social, economic, and cultural determinants of America's "despair" epidemic.
Related Concept Videos
Econometric Views (EViews)
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Friedman Two-way Analysis of Variance by Ranks
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Causality in Epidemiology

