Abstract: Measuring Job Quality: Using a Multi-Dimensional Employment Quality Index to Improve Jobs for Marginalized Workers in the COVID-19 Era (Society for Social Work and Research 27th Annual Conference - Social Work Science and Complex Problems: Battling Inequities + Building Solutions)

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Measuring Job Quality: Using a Multi-Dimensional Employment Quality Index to Improve Jobs for Marginalized Workers in the COVID-19 Era

Thursday, January 12, 2023
Encanto B, 2nd Level (Sheraton Phoenix Downtown)
* noted as presenting author
Dylan Bellisle, PhD, Postdoctoral Research Associate, University of Illinois Urbana-Champaign
The COVID-19 pandemic caused major disruptions in the U.S. labor market, and many employers state they cannot find workers. Yet, the growth of low-wage jobs during and before the pandemic brings attention to the quality of jobs. Some scholars point to this growth as evidence of job market bifurcation - many low- and high-quality jobs yet limited “mid-quality jobs” - and recent surveys show many unemployed workers are offered jobs that pay less than their previous job. Yet, beyond wages what defines a “good job” and who has these jobs? While researchers and policymakers are interested in measuring indicators of job quality, no standard definition of job quality exists. Using data from 3,000 workers across Illinois, we examine the utility of an index of job quality (EQ-IL) with seven core dimensions and consider what job characteristics matter most to workers. We ask the following questions:

Beyond wages, what job characteristics matter most to workers and does EQ-IL lend evidence to job market bifurcation?

  1. What industries illustrate job market bifurcation through the concentration of high and low job quality dimensions?
  2. To what extent does industry segregation by race and gender help explain disparate experiences of low-quality jobs?


Data are from a Qualtrics administered survey of 3086 workers in Illinois in fall of 2021. Recruitment quotas were set, and survey weights created to adjust the sample to reflect the geography, race and ethnicity, gender, and industry of workers in Illinois. We use descriptive chi-square, logistic, and OLS regression analyses to examine how various dimensions of job quality are related to self-reported employment quality and job satisfaction. We use OLS regressions to examine job quality by industry, ethno-racial group, and gender.


Findings illustrate job market bifurcation. Multiple low-quality job dimensions are concentrated among low-wage jobs and high-quality job dimensions are concentrated among high-wage jobs. Interestingly, regression analyses suggest that job security and promotion opportunities are stronger predictors of self-reported employment quality than common measures of job quality (e.g., wages and benefits), indicating workers in the era of COVID desire security and upward mobility. Analyses suggest that individuals with more gender and racial privilege are more likely to have “high-quality jobs”. Disparate access to high-quality jobs is partially driven by industry segregation – with women and ethno-racial minoritized groups over-represented in industries with low-quality job characteristics. For example, women and ethno-racial minoritized groups are over-represented in the restaurant, entertainment, and retail industries - industries with the highest proportion of workers earning less than $15/hour, less access to employer-provided benefits, job security and promotion opportunities, and unpredictable work schedules which help drive work-family conflict.


Our study illustrates continued job market bifurcation during a time when employers state they cannot find workers. As policymakers consider measuring job quality, it is imperative to include dimensions like work schedules and paid time-off as these are potential policy levers, and matter for work-life balance. Employers should consider improving job security and promotion opportunities as paths towards improving employment quality and attracting workers.