The Impact of COVID 19 on Essential Workers
in Selected Industries
Summary of Findings
The purpose of the summary is to discuss the results of Hurst’s multivariate regression modes to determine which industries deemed essential workers were adversely affected with high mortality rates due to the initial COVID 19 outbreak. The industries include nursing facilities, agricultural, mining, construction, food manufacturing, transit, warehousing, ambulance service, hospital, food, and drink places.
It is important to note the International Association of Machinist and Aerospace Workers (IAM) represents workers in industries considered essential workers. Recently, the IAM has had an increasing presence in the health care industry including hospitals and nursing facilities. Therefore, it is in the IAM’s interest to see which industries were disproportionately impacted by COVID.
Chen et al (2022) shows that industries with the highest mortality rates include accommodation and food services (45.4 per 100,000); transportation and warehousing (43.4); agriculture, forestry, fishing, and hunting (42.3); mining (39.6); and construction (38.7)”. Transit and medical variable were also included based on the research by Heinzerling et al (2022. They found, “public transportation industries in California experienced cumulative COVID-19 outbreak incidence and mortality rates 1.5 times as high as that for all industries; outbreak incidence was 5.2 times as high, and mortality was 1.8 times as high in bus and urban transit industries as in all industries” (p.1055). Additionally, Heinzerling et al (2022) suggest an extension of their research should occupational risks (or industries) with race, ethnicity, and socioeconomic factor as it relates to COVID mortality rates.
Yu (2021) provides compelling evidence on the variation of COVID on disparate groups across the United States. Empirically, Yu used a straightforward linear regression cross-sectional model to find the predictors for the COVID-19 accumulated case and death rates. Yu ran seven multivariate regression models and used models 1-3 as the benchmark models to explain the regression findings.
Hurst (2022) extends the Yu’s model by using average annual pay (instead of employment) for selected NAICS code three-digit industries to test the premise of essential workers in the respective industries had high mortality rates due to COVID. Additionally, Hurst includes race and ethnicity, median income, poverty, unemployment, and state variables. The assumption is that these variables coupled with industry/occupation variables can help explain the variation and disparity in COVID cases and deaths across the county. Multiple regression models were performed using the variables mentioned above and like Yu, Hurst’s results (data and script available upon request) show how essential workers were overly exposed to COVID which helps explain positively correlated industry variables with mortality rates despite differences in average annual pay (proxies for compensation and wellbeing).
Further, workers in nursing facilities in the various models were statistically significant compared to the other industry variables. The results are aligned with prior research and reports showing nursing homes were the epicenters for coronavirus outbreaks in states such as Washington and New York
References
Amy Heinzerling, Alyssa Nguyen, Matt Frederick, Elena Chan, Kathryn Gibb, Andrea Rodriguez, Jessie Wong, Erin Epson, James Watt, Barbara Materna, and Seema Jain, 2022:Workplaces Most Affected by COVID-19 Outbreaks in California, January 2020–August 2021, American Journal of Public Health 112, 1180_1190
Yea-Hung Chen, Ruijia Chen, Marie-Laure Charpignon, Mathew V Kiang, Alicia R Riley, M Maria Glymour, Kirsten Bibbins-Domingo, Andrew C Stokes. COVID-19 mortality among working-age Americans in 46 states, by industry and occupation. medRxiv. 2022
Tazewell Hurst. (2022), The Impact of COVID 19 on Essential Workers in Selected Industries., Upper Marlboro MD: IAMAW Strategic Resources Department
William Yu, Health in America: What Explains the Variation in COVID-19 Mortality Rate Across the United States. The UCLA Anderson Forecast, March 2021.
