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Session Overview
Parallel Session 6.3: Special Session on Job Quality
Tuesday, 11/July/2023:
2:00pm - 3:30pm

Location: Room A (R1 temporary building)

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Job Quality: Metrics, Disparities and Implications for Improving Worker Outcomes

Chair(s): Lonnie Golden (Penn State University, United States of America), Florence Bonnet (International Labour Organization)

The ILO’s “decent work” has been a longstanding conceptualization for over 20 years. Job quality has come even more to the fore recently, in academic and gray (think tanks and policy) literature circles, as part of the “future of work” discussion, the recognition of adverse or risky working conditions many workers endured during the pandemic, and then the selected labor shortages and retention problems now in the post pandemic labor market, particularly in developed countries. The non-US research, particularly across or within European countries, is expansive, so this session is intended to add some new US research. The scope will include identifying the various elements of job quality, their metrics, determinants, disparities across key sectors, jobs and their incumbents and the associated well-being outcomes. All recognize the importance of extending beyond the traditional focus on only earnings levels, a binary “good” or “lousy/bad” jobs dichotomy, and need for rigorous measurement for both individual and national employment quality.

Core components of job quality—wages and benefits—are quite well documented, using national data sets. Yet, there is more to job quality than just workers’ compensation. Working conditions (e.g. hours/schedules/shifts stability and predictability, health/safety, mistreatment/nondiscrimination; interpersonal relations, employer/organizational culture, work processes, autonomy/control, job design, opportunities for advancement, job contract type and worker voice. These all may have consequences for workers’ productivity and their own and their families’ well-being. Data on the multidimensional components of job quality--and their associated economic and health outcomes--are too scattered across surveys, and often lack emerging features of jobs, such as precarity, gig work and employer surveillance practices. Designing policy initiatives to improve jobs needs more comprehensive data that fully captures job quality and its associated outcomes.

US federal and state level initiatives, including through the U.S. Department of Labor and the Department of Health & Human Services, are now trying to advance the understanding of existing data sources and empirical approaches to measure job quality.To inform these data-driven efforts, this panel brings 5 papers together to review job quality metrics in data sets; identify what key metrics are including or missing; present new innovative measures and data collection strategies available to fill in data gaps; and show how new measures of job quality affect a wide array of worker outcomes.

The first paper by Pamela Joshi at Brandeis University and co-authors provide a review of job quality metrics in national surveys and assesses how well those metrics map on to recent job quality frameworks advanced across disciplines. The authors assess the validity of existing measures and pinpoint missing metrics, particularly for low-wage workers and workers in precarious arrangements. The paper illustrates how specific job quality metrics should be measured at the family level, for example, family-sustaining wages and children’s exposure to parents’ and other family members’ varying work schedules.

Using novel data collection strategies, the second paper is an empirical analysis from the city of Pittsburgh’s Wage Study. Drawing on a university-community partnership charged to understand mass resignation among hospital workers Bellantine from Wayne State University and co-authors analyze how job quality factors such as staffing, safety, discrimination and harassment are associated with burnout, secondary traumatic stress and compassion satisfaction. The study documents differences in job quality and outcomes across wage groups.

The third paper explores the degree to which worker voice is an important but too often neglected dimension in assessments of job quality. Its novelty is in its validated measure differentiating between voice efforts that address worker and employer interests, and to capture whether actions to exert voice are effective in achieving their intended results, i.e., a measure of “voice efficacy.” It measures whether workers’ perception that exercising voice at work is instrumental in getting their desired result. Tested with a US representative sample, they find that workers differentiate between their interests and their employers’ interests, indicating that voice as a dimension of job quality must include both sets of interests.

The final two papers analyze unique, large, representative survey data collected at the US state level--the Employment Quality Index of Illinois (EQIL)(Bellisle, Dickson, Fugiel and Golden) and for Pennsylvania (EQiPA)(Golden). These both ask workers to rate their own overall employment quality and a wide range of their working conditions --- 7 different dimensions of job quality--and empirically decompose the relative contribution of each working condition metric. It also links working conditions with other outcomes, such as job satisfaction, life satisfaction, daily happiness at work, schedule satisfaction, work-life balance and health. They both reveal that while objective indicators of job quality, such as wages and benefit coverage, are significantly contributing components, they are dwarfed as predictors of self-reported quality by subjective indicators, such as job security, autonomy, meaningful work and advancement opportunities.


Presentations of the Special Session


Job Quality Metrics to Inform Good Jobs Policies: What’s Available, What’s Missing and How to Fill Data Gaps

Pamela Joshi, Elizabeth Wong, Abigail N. Walters, Dolores Acevedo-Garcia
Brandeis University

The upheaval in the labor market caused by the COVID-19 pandemic renewed policy interest in job quality. To inform these efforts, this paper reviews U.S. large scale surveys and assesses how measures compare to job quality frameworks. We assess measurement validity, pinpoint missing variables, identify whether metrics are available by race/ethnicity, immigrant status, LGBTQ+, and disabilities, and at the family level. Our initial review finds that measures of wages, benefits, work schedules, and some working conditions are scattered across 9 data sets, but there are few metrics of worker voice, work processes, and opportunities for advancement. Questions about access to employer benefits are not differentiated from utilization. Only 3 surveys ask about benefit quality and 4 surveys collect data for all workers in a family unit. The paper concludes with recommendations to improve the data infrastructure needed to monitor job quality for workers and working families.


The Missing Worker Voice in Job Quality: Developing a Conceptual Framework and Survey Instrument for Worker Voice

Yaminette Díaz-Linhart1, Arrow Minster2, Dongwoo Park1, Duanyi Yang1, Thomas Kochan2
1Cornell, 2MIT

While researchers and policy makers agree that worker voice is an important dimension of job quality, to date there is no accepted or validated measure of worker voice suitable for assessing the quality of workers’ jobs. Existing measures of voice fall short by not including both individual and collective voice efforts or differentiating between voice efforts that address worker and employer interests; nor do they capture whether actions to exert voice are effective in achieving their intended results. To overcome these problems, we developed a measure of “voice efficacy,” which is a worker’s perception that exercising voice at work is instrumental in getting their desired result. We test voice efficacy as a potential dimension of job quality through a national (US) representative sample. We find that workers differentiate between their interests and their employers’ interests, indicating that voice as a dimension of job quality must include both sets of interests.


The Quality of Employment: Remote Work, More/Less Work and Reconfigured Workweeks—Mismatches with Preferences in the US and PA Labor Market

Lonnie Golden
Penn State University

Data are analyzed from a survey of workers employed in the US state of Pennsylvania (PA) (705N). It asks workers to evaluate their overall employment quality -- where 10 is the top of the ladder and 0 is the worst. It considers both its objective and subjective working conditions as evaluated by workers themselves. The vast array of working conditions include wage/salary level, benefits coverage, work schedules; and “subjective” conditions such as job and pay satisfaction, hours mismatch, perceived workplace mistreatment and support, voice/control/power/autonomy, meaningfulness/purpose of work and job security. It includes two emerging conditions, remote/telework and reconfigured work, i.e., 4-day workweeks. Descriptive and OLS findings find that workers’ assessments vary by total compensation, occupation/industry, hourly/salaried or employee/non-employee (i.e., contractor/self-employment/on-demand), hours/schedule volatility, part-time/full-time status? There is evidence that some workers receive a compensating wage differential and for particular working conditions or industries, but most do not.


Measuring Job Quality: Using a Multi-dimensional Employment Quality Index to Improve Jobs for Marginalized Workers

Alison Dickson, Peter Fugiel, Dylan Bellisle, Larissa Petrucci, Lonnie Golden
University of Illinois Urbana-Champaign

Using data from over 3,000 workers across Illinois, we create an overall measure of job quality (EQ-IL) with seven dimensions. These include not just objective measures such as pay and benefits, but also subjective components such as job security, working conditions, promotion opportunities, and work schedules. Data generated from a Qualtrics survey of 3539 workers in Illinois in fall 2021. OLS regressions provide evidence that multiple low-quality job dimensions are concentrated among low-wage jobs and high-quality job dimensions are concentrated among high-wage jobs. Moreover, job security and promotion opportunities are stronger predictors of self-reported employment quality than wages and benefits. Disparate access to high-quality jobs by race and gender is partially driven by industry segregation – women and ethno-racial minoritized groups are over-represented in the restaurant, entertainment, and retail industries -with the highest proportion of workers earning less than $15/hour, higher levels of underemployment and volatile work schedules, and less access to employer-provided benefits, job security, and promotion opportunities.

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