Measuring Job Quality

• Published in: Policy File 2022
• Main Authors: Katz, Batia, Congdon, William J, Shakesprere, Jessica
• Format: Report
• Language: English
• Subjects: Big Data; Urban Institute

In this report, we review the current state of data on and measurement of job quality in the United States and what the literature suggests a more complete set of job quality measures might include, and we outline potential approaches to improving measurement. We discuss current, relevant large-scale data sources that capture elements of job quality, including household surveys, employer surveys, and administrative data sources, to explain what elements are present or missing. For elements not included in existing national sources, we also scan relevant literature for smaller surveys or other sources that have developed or used relevant metrics and concepts. We find that standard survey and administrative measures characterizing jobs currently focus on measures of wages, hours, and certain benefits, with only occasional supplements to capture other job elements and some elements rarely being measured at all. Clear gaps exist in the data available, and detailed metrics are typically missing on scheduling, other nonwage benefits, workplace culture, job design, forward prospects, and other nonmonetary job elements. Additionally, while the sources we review collectively cover many elements of job quality, we find that this is not true of any single source or easily linked set of sources. These gaps hamper progress in understanding job quality and how it relates to worker outcomes. We identify prospects for gathering more complete data on job quality, primarily exploring the possibility of developing household survey measures that capture a wider range of job quality elements. And we discuss how this would be helpful for researchers and policymakers trying to better understand, and ultimately improve, worker well-being, mobility, and equity. With more complete and robust data, researchers could measure and monitor job quality levels, trends, and distributions across the workforce and conduct causal analysis to better understand which elements of job quality relate to worker outcomes in both the short and long term. Moreover, these data and this research could help inform new policies to improve job quality.