https://orcid.org/0000-0003-2453-1407
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Abstract
During 2021 and 2022, many news media outlets have reported that millions of workers in the United States have been quitting their jobs in record numbers. In a global economy rebounding from the economic downturn caused by the Covid-19 outbreak and demanding more workers, a high rate of resignations has exacerbated labor shortages and may be aggravating unemployment and underemployment rates if many workers are not participating at all in the labor force or only working part time. Many reasons have been offered to explain this ‘Great Resignation’ including high day care costs for working parents that may in turn be causing the trend of lower female labor force participation; the supposed ‘liberating’ experience of not working at all or to work from home instead of having to work from one’s usual work place during the Covid-19 quarantine/lockdown periods; stagnant/low wages and greater job tenure uncertainty which make working less attractive and more stressful; and the feeling by many of not wanting to work further for bad bosses or management who create bad work environments so that resignation becomes a means of escape from such conditions. This article analyses data of US labor trends since 2003 and demonstrates that resignations have been trending upward in the US aggregate economy and that quit rates mostly have been trending higher within many US industries. These phenomena can be explained by the concept of labor market segmentation, high unemployment, and underemployment rates that exist even in good economic times in some industries, minority group composition within industries, wage stagnation, and type of managerial supervision. Some of these same factors help to explain labor under-utilization greater than national/aggregate rates within these industries as well.
Notes
1 According to the US BLS, ‘The quits rate is the number of quits during the entire month as a percent of total employment. Quits are generally voluntary separations initiated by the employee’. https://www.bls.gov/news.release/jolts.nr0.htm.
2 There is a high correlation among the different measurements that the BLS uses to measure unemployment and underemployment (or labor underutilization) outside of the main unemployment number published by it on a monthly basis (US BLS Citation2022e). See the appendix for more discussion and elaboration on this. The appendix for this paper can be found at https://data.mendeley.com/datasets/h3dwz5mvpg/3 on the Mendeley site. The data for this article is also on the Mendeley site at https://data.mendeley.com/datasets/tpcjs62wyk.
3 Dalton and Groen (Citation2022) find that as of 2021, most employees in major industries do not have the option to telework or telecommute. See the appendix for more discussion and elaboration on this.
4 Unfortunately, data for major industry categories are available for some industries or sectors but not for others. See the appendix for more discussion and elaboration on this.
5 See the appendix for more information on defining lost work and absences.
6 Lambert also finds that the organizations with the greatest number of managers with the highest pay rates are those typically in markets with little or no competition, are capital intensive, and often are oligopolistic. See the appendix for more details on this.
7 Job opening and quit rates correlate with business cycle fluctuations, and this article accounts for unemployment rates in modeling quits and job opening rates.
8 Another model shows that quit rates and job openings rates can be used as statistically significant predictors of the ratio of industry to national unemployment rates when quit rates and job opening rates are specified on a one year lagged basis (see Table 3, Model 2 in the appendix for a lagged version).
9 The Beveridge Curve shows the relationship between national level job openings rates and the unemployment rate (US BLS 2000–2022). An industry level version of this can be drawn, and some type of measurement (industry unemployment rate to industry job openings rate ratio) can be constructed, yet this ratio is highly correlated with the ratio of industry unemployment to national unemployment percentage, and as an ANOVA test demonstrates, there is not much of a difference between a competitive sector ‘Beveridge ratio’ and that of a core sector ratio. See the appendix under footnote 9 for the ANOVA test results. The competitive sector has high unemployment rates but also high job openings rates whereas the core sector has low unemployment and low job openings rates. Hence, there is not much difference between the two averages of ratios for each sector. Yet there are statistically significant differences between the two sectors when it comes to quit rates and industry to national unemployment rates. See the appendix under footnote 9.
10 From the Bureau of Labor Statistics Annual Occupational Employment and Wage Statistics (OEWS), the average hourly pay for employees within an industry is adjusted for inflation using a Consumer Price Index where 1982–1984 years is the base (US BLS 2003–2021h).
11 Individually these variables do not correlate with quit rates or ratios of industry unemployment to national unemployment rates. When used together in an interaction term, the resulting variable correlates well with quit rates and the industry to national unemployment rates as dependent variables.
12 To examine whether high day care costs are causing some workers, especially females, to quit their jobs, the percentage of the workforce that is female for each industry is multiplied by a cost-of-living index (base year = 1982–84) which accounts for tuition, school fees, and childcare expenses and inflation from 2003 to 2021 (US BLS 2003–2021h) in order to see if the interaction of these two factors influence quit rates. See the appendix for more discussion and background on this variable.
13 See the discussion related to this footnote in the appendix that discusses the variable more.
14 The BLS defines a job opening as ‘A job is open only if it meets all three of certain conditions.’ See the appendix for more on this.
15 This is calculated by taking the total number of employees classified as managers and dividing this by total employment in the industry and then multiplying by 100. See the appendix for more on this variable.
16 The BLS gives some data on people who work 1 to 34 h per week or part time, but the range of industries is not as wide as the ones examined in this paper. Therefore, these data are not used.
17 If the panel data are used in an ordinary least squares model similar to random effects regression, no variance inflation factor (VIF) greater than 2.09 is found among the independent variables, and the average VIF for them is 1.85. An r of 0.61 would have a VIF score of 1/(1 0.61) = 2.56. The cutoff for VIFs for multicollinearity is usually 5, and sometimes a VIF of 10.0 is permissible (Berenson et al., Citation2014).
18 Recall that ‘1’ was used for competitive industries and ‘0’ for monopolistic ones, so therefore higher values correspond to the competitive sector.
19 In looking at Table 1, the government or public administration sector also has high levels of work time lost and absences too. Although stable employment, many governmental jobs entail risks to health and/or injury such as police, fire-fighters, EMS workers, and public health frontline workers.
20 Going back to the definition of absences, using ‘personal days’ is not counted by the BLS. Therefore, it is still possible that although the statistical results of this article show higher absences correlated with lower quit rates and lower industry to national unemployment rates, workers in the competitive sector could be calling in for personal days at a higher rate than those in the core sector.
21 See the appendix for a table which shows health insurance coverage according to industry as listed by the BLS.
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