Industry-specific Job Loss and Opioid Death

Dr. Lauren Jones, Department of Human Sciences
Rank at time of award: Assistant Professor
Dr. Michael Betz, Department of Human Sciences
Rank at time of award: Assistant Professor


The United States is experiencing an unprecedented rise in fatal and nonfatal drug overdoses stemming mainly from prescription opioids and heroin. Twelve and a half million Americans reported misusing prescription opioids and 78 people died per day from opioid overdoses in 2016 (USDHHS 2017). Overdose deaths are concentrated most heavily among those with a high school degree or less (Case and Deaton 2015). Concurrently, labor market opportunities have declined sharply for this population in the past two decades. One theory, widely cited in the popular press, posits that large-scale job losses in specific, low-skill industries are responsible for creating the conditions – such as low economic opportunity and mental health issues – necessary for drug abuse among this population (Quinones 2015; Hari 2017).
We intend to investigate this theory empirically by examining the connection between the decline in low-skill industries and the opioid epidemic. We will link restricted overdose death data from the National Centers for Health Statistics and emergency room admission (ED) data from the State Emergency Department Database (SEDD) with county-level proprietary industry employment data; we will use the resulting database to test for the connection between industry-specific job loss and opioid misuse and death. Using a Bartik (1991) instrument, we will exploit exogenous industry-specific employment demand shocks that affect some counties and groups more severely than others. This approach will provide causal evidence to the ongoing debate about the causes of the opioid crisis.
While many have studied opioid use and overdose deaths at an individual level, questions remain unanswered at the community level. Developing a county-level database and conducting a study investigating the connection between industry change and opioid misuse and overdose rates will position our research team to answer other questions about how geographic, economic, and social factors influence community-level opioid overdose rates. The county-level nature of our database is key to our proposal. Most regional approaches examine overdoes at a state-level or are limited to metro areas because most rural data is censored to avoid disclosure. Nonmetro areas are also relatively sparsely populated and as such, any analyses comparing rates across metro and nonmetro areas must account for the lower population density in such areas. Yet, nonmetro areas are experiencing even higher levels of opioid overdose rates and differ greatly from metro areas across key economic, demographic, and social factors (Snyder 2017). Thus, it is important to create separate nonmetro models that can help reveal processes unique to more remote, rural areas in order to create effective metro and nonmetro policies. Additionally, many approaches to help reduce opioid use and overdose have been implemented as community-level policy, necessitating county-level data in order to examine effectiveness.


Opioid use and overdose deaths have become the preeminent public health concern in the United States. Our project seeks to identify community-level economic factors that influence opioid overdose deaths.