How the Opioid Crisis Has Impacted Unemployment Rates in California

One thing you may not have guessed about me is that I am an Economics freak. I studied it for the past four years, learned how to code, and wrote a fifteen page paper based on my findings on how the opioid crisis has impacted unemployment in the state of California looking at county-level data. In my first blog, I mentioned how I wanted to write about other topics besides navigating post-grad life. So now my fellow reader, I am here to educate you about the opioid crisis' impact on California's unemployment– but as a synopsis.

I used county-level panel data, sourced from the CDC and the State of California. I examine how unemployment rates are impacted using opioid deaths (per county in California) as my treatment. My control variables are county population and county crime rates—taking into account violent and non-violent crimes. I also have an interaction between urban=1 and nonurban=0 to see if there is any significance in urban versus nonurban counties.

UNEMPLOYit= B0+B1(DEATHSit)+B2(CRIMEit)+B3(POPit)+B4(urban)+ai+uit

The opioid crisis has three waves: Wave 1 (1990-1999) with prescription opioids, Wave 2 (2010) with increased heroin use, and Wave 3 (2013-present) with fentanyl. Crime and unemployment affect the economy, as opioid use can lead to higher unemployment and increased crime. County population, crime rates, and opioid overdose deaths may impact the correlation. My analysis considers county unemployment rates, crime rates, and population, reflecting opioid demand and overdose deaths.

On average, over 130 people die daily from opioid-related overdoses (HRSA, 2023). In California alone, nearly 7,000 individuals died from such overdoses in 2022. Nationally, there are over 83,000 opioid-related overdose deaths annually, with fentanyl involved in over 90% of these cases. Opioids remain a leading cause of drug overdose deaths, prompting the launch of a user-friendly platform offering resources to prevent and address opioid overdoses and substance use disorders (Office of the Governor, 2023). The opioid crisis has also increased crime and unemployment in California from 1999-2022, with violent crimes reported at 207,874 in 1999 and 193,019 in 2022 (California DOJ). There is some variation in the crime rates, but this does not indicate a slowdown in the opioid crisis. Unemployment rates have remained consistent, with spikes in 2008-09 and 2020-21 due to the financial crisis and the COVID-19 pandemic. Examining whether these trends are linked to the opioid crisis is essential to see the true impact of these drugs.

I ran two tests to see if there was any variance in my data and compared the results. First, I ran the Random Effects (RE) & Fixed Effects (FE), then Hausman Taylor. The data I collect was suppose to help me understand the relationship between county-level unemployment rates and my control variables such as county deaths, county crime rates, county population, and an indicator as to whether the city is urban or not.

First for RE & FE (If you don't like numbers I would skip this), the chi-square, yielded a value of 8.52. The result is marginally non-significant with a p-value of 0.0744, indicating weak evidence that the predictors collectively influence county unemployment rates. The coefficient for county deaths is 0.005, with a standard error of 0.0024184. The z-value is 2.30, and the p-value is 0.022. This positive coefficient suggests that higher death rates are associated with higher unemployment rates, and this effect is statistically significant at the 0.05 level. County crimes have a p-value of 0.497. This positive coefficient is not statistically significant, suggesting no clear evidence that crime rates affect unemployment rates in this model. Additionally, county population and urban indicators were not statistically significant. The analysis indicates county deaths have a statistically significant positive association with county unemployment rates. The other variables, including crime rates, population size, and urban status, do not show significant relationships with unemployment in this model.

From the Hausman Taylor, I found the following: there are more opioid-related deaths in urban counties, creating higher unemployment rates in urban counties. While urban deaths and urban "status" do not have significant effects, county crime rates have a marginally significant positive association with unemployment rates. When it came to my attention that opioid deaths and unemployment rates are higher in urban counties, I was shocked. I assumed that unemployment would be higher in the middle of California, where very few residents work, and opioids are a pastime. But the discovery makes sense. People with jobs move to urban counties; they have more money to afford the drugs, and more drug use makes a person more likely to overdose. So higher opioid usage in large urban cities causes more deaths, increasing unemployment rates. One would think non-urban counties would have higher opioid usage, making them more susceptible to overdose, causing increased unemployment rates, but no.

In conclusion, through an examination of county-level data spanning over two decades, it becomes evident that the opioid epidemic has not only taken away lives but has also significantly impacted California's unemployment. The findings reveal a complex relationship between opioid-related deaths, crime rates, population size, and urbanization. Despite variations in the tests I ran, from OLS regression to the Hausman-Taylor, there is a consistent pattern: the correlation between opioid-related deaths and unemployment rates.

If you want to see the actual data please do no hesitate to reach out.

Hope you enjoyed the change in content.

California Employment Development Department. "California County-Level Unemployment Rates." California Employment Development Department| Alameda, Alpine, Amador, Butte, Calaveras, Colusa, Contra Costa, Del Norte, El Dorado, Fresno, Glenn, Humboldt, Imperial, Inyo, Kern, Kings, Lake, Lassen, Los Angeles, Madera, Marin, Mariposa, Mendocino, Merced, Modoc, Mono, Monterey, Napa, Nevada, Orange, Placer, Plumas, Riverside, Sacramento, San Benito, San Bernardino, San Diego, San Francisco, San Joaquin, San Luis Obispo, San Mateo, Santa Barbara, Santa Clara, Santa Cruz, Shasta, Sierra, Siskiyou, Solano, Sonoma, Stanislaus, Sutter, Tehama, Trinity, Tulare, Tuolumne, Ventura, Yolo, Yuba | All Offenses | All Races | Adult and Juvenile, 1980 - 2020." Sage Data, Sage Publishing

Centers for Disease Control and Prevention (CDC). "Understanding the Epidemic." Centers for Disease Control and Prevention, [insert publication date if available], www.cdc.gov/opioids/basics/epidemic.html.