Do prescription opioids cause unemployment or does unemployment cause prescription opioid abuse?

U.S. Employment and Opioids: Is There a Connection?

Janet Currie, Jonas Y. Jin, Molly Schnell. NBER Working Paper 24440. March 2018

UW CHOICE student Samantha Clark

Opioid abuse is one of the main public health challenges facing the US today. In 2016, the CDC reported that opioid-related overdose deaths had tripled from 1999 to 2014 and that drug overdoses, largely driven by this increase in opioid-related deaths, are now the leading cause of death among Americans under 50. A key component in developing effective policies to combat the opioid epidemic is understanding the mechanisms, both environmental and physiological, through which adherent opioid use transitions into abuse. A new National Bureau of Economic Research (NBER) working paper offers insight into one of these potential mechanisms: the link between employment and legitimate opioid prescription rates in working age adults (18-64). This study is unique in that it addresses temporality concerns in the relationship between these two variables (endogeneity due to reverse causality, in this case) by modeling the association in both directions and including instrumental variables (IV). My colleague Kangho provides a great primer on IV methods and interpretation here. Additionally, the use of detailed panel data from 2006-2014 and the inclusion of county-level fixed effects allowed the researchers to perform a more robust and detailed analysis than what had been done previously.

The authors first provide a thorough summary of the existing literature regarding the association between employment and legal opioid use. They note that while some researchers have found a link between unemployment and the use of opioids (see also here), others conclude that changes in economic conditions explain little of the variation in opioid overdoses and deaths. This conflicting evidence and the limitations of previous studies (use of cross-sectional data, etc.) make this analysis a timely addition to the literature.

The data that the authors used came primarily from two sources: employment information pulled from the government Quarterly Workforce Indicators (QWI) database and opioid prescription rates extracted from the QuintilesIMS dataset. Both sources included data disaggregated by county, gender, age group, and quarter, with industry also included in the QWI data. Prior to any analyses, the employment and opioid prescription variables were transformed into per capita measurements using population data from the 2010 U.S. Census. The data were then split into “low education” and “high education” groups using education level measurements from the 2000 U.S. Census. The outcome/predictor variables were also logged, presumably to account for non-linearity since OLS regression was used.

The authors ran numerous models, including standard OLS, OLS with IV, and OLS with IV and county-level fixed effects (all models contained fixed effects for year and quarter).  Each of these models was run bidirectionally, with opioid prescriptions per capita and employment-to-population ratio alternately serving as the dependent and independent variables. Acting under the assumption that there was a delay in the effect of the predictor on the outcome, the independent variable was lagged in each regression. The incorporation of county-level fixed effects allowed the authors to reduce omitted variable bias by controlling for heterogeneity across counties and observed and unobserved time-invariant factors.

In the analysis with employment-to-population ratio as the independent or predictor variable, a Bartik-style shift-share instrument was incorporated. Bartik-style shift-share instruments are commonly used in labor economics to generate an estimate of local labor demand that accounts for local industry composition but is based on national-level changes in industry. Use of this instrument enabled the authors to better isolate employment changes due to shifts in labor supply (which is what the authors hypothesize would be affected by increasing opioid prescriptions) from any demand-side driven fluctuations. This shift-share variable is an ideal IV for this analysis because it’s both highly correlated with employment and there is no direct link to opioid prescription rates.

Similarly, an IV for opioid prescriptions per capita in people aged 65 and older of the same gender was used in the model with opioid prescription rates as the independent or predictor variable. This allowed the authors to isolate the effect of opioid prescription rates on employment from local prescriber behavior, which likely has a large impact on the number of opioid prescriptions by county. Underlying the choice of this IV was the assumption that the location where elderly and working-age individuals get their opioid prescriptions is highly correlated. Additionally, it’s doubtful that there is any direct relationship between opioid prescriptions in the elderly and employment in working age adults.

The authors present results from each stage of the model-building process (OLS, OLS with just county-level fixed effects or IV, OLS with county-level fixed effects and IV), as well as descriptive statistics for the two outcome variables of interest. The detailed nature of the data enabled the authors to analyze the relationship between opioid prescriptions per capita and employment to population ratio by gender, age group, and education level of counties. Because both the dependent and independent variables were logged, the results correspond to elasticities (1% change in independent variable associated with β1% change in dependent variable). Additional analyses controlling for the potential confounder of percent insured in both models did not affect the main findings.

Results from the primary models of interest (those incorporating both IV and county-level fixed effects) diverged in terms of significance of findings. For the regression assessing the legal impact of opioid prescriptions on employment-to-population ratios, results for women indicate that a 10% increase in opioid prescriptions per capita would lead to an increase in employment of .38% in high-education counties and .52% in low-education counties, with no corresponding relationship in men. The authors interpret this positive relationship as suggesting that legally-prescribed opioids may be allowing women suffering from chronic pain to remain in the workforce longer. In the analysis examining employment-to-population ratios on opioid prescriptions per capita, the evidence was less consistent across model specifications and suggests that there isn’t a clear causal link between employment level and opioid prescriptions within counties.

The authors come to the overall conclusion that there isn’t a definitive relationship between opioid prescription and employment, and any causal relationship may not be bidirectional. They also note that opioid abuse in specific geographic areas could be more related to factors like longer-term economic disruptions and prescribing behavior. Despite the lack of strong results, this study has important implications from a policy perspective since the observed association between legal opioid use and employment indicates that policy interventions focused on the workplace might be effective.

The inconclusive study findings may also be a product of the opioid-use variable being restricted to legal prescriptions. Focusing instead on illegal opioid use is an interesting (although difficult) area for future research, as illicit use is largely confined to abusers, whose ability to maintain employment is more affected by opioid use.  This relationship would likely be stronger than that using legal opioid prescriptions since that measure captures functional users as well as abusers.