Panel: The Consequences of Occupational Injuries and Illnesses, and the Effectiveness of Policies Aimed to Prevent Them
(Employment and Training Programs)

Saturday, November 4, 2017: 8:30 AM-10:00 AM
Soldier Field (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Panel Organizers:  Matthew S. Johnson, Duke University
Panel Chairs:  Frank Neuhauser, University of California, Berkeley
Discussants:  Perry Singleton, Syracuse University and Frank Neuhauser, University of California, Berkeley

Disability Status and Long-Term Employment Outcomes: The Case of Italian Injured Workers
Monica Galizzi1, Roberto Leombruni2 and Lia Pacelli2, (1)University of Massachusetts, Lowell, (2)University of Torino

The Impact of State Policies to Reduce Back Injuries Among Nursing Staff in Hospitals and Nursing Homes
Melissa McInerney, Tufts University and Shihang Wang, North Carolina State University

Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA
Matthew S. Johnson, Duke University, David I. Levine, University of California, Berkeley and Michael Toffel, Harvard University

Occupational injuries and illnesses in the U.S. have been estimated to cost society over $250 billion per year. Despite this sizable burden, data limitations and a lack of credible research designs have hampered researchers’ ability to characterize the consequences of workplace injuries for injured workers, and to evaluate the effectiveness of existing policies intended to improve workplace safety and health. This panel presents papers on the determinants of economic losses arising from workplace injury and the effectiveness of public policies designed to improve occupational safety and health, each of which combines rigorous research designs with novel or underutilized data sources for tracking workplace injuries.

The first two papers use matching methods with large administrative databases from the US and Italy to estimate how earnings losses due to workplace injury vary by employer, worker, and injury characteristics. The first paper, "Exploring the Socioeconomic Gradient in Disability Risk Following Workplace Injury," uses administrative earnings data linked to workers' compensation claims to study how the economic consequences of occupational injury vary over the income distribution, and across employer characteristics, for workers in California who were injured on the job between 2005-2012. This paper is one of the first rigorous examinations of how these characteristics affect the economic losses resulting from workplace injury.

The second paper, “Disability Status and Long-term Employment Outcomes: The Case of Italian Injured Workers,” also uses earnings records linked to workers' compensation claims to obtain rigorous estimates of the long-term economic costs resulting from workplace injury, and how these costs vary by injury type. Furthermore, by contrasting patterns of post-injury earnings losses between Italy and the US, this paper sheds light on how the institutional environment (e.g. generosity of disability systems) moderates the process leading from workplace injury to long-term disability and earnings losses.

The third and fourth papers in the panel provide new evidence on the effectiveness of public policy interventions intended to prevent injuries and highlight the important of high-quality data for shaping policy recommendations. “The Impact of State Policies to Reduce Back Injuries Among Nursing Staff in Hospitals and Nursing Homes” exploits a series of quasi-experiments created by the staggered adoption of state-level laws adopted in response to high injury rates among the rapidly growing health care workforce. This study analyzes the effects of this policy using two independent datasets on injuries –one reported by employers, another reported by individual workers—allowing the researchers to distinguish changes in injury occurrence from changes in workers' compensation claim filing behavior.

The fourth paper, “Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA,” studies a longstanding policy tool used by the U.S. Occupational Safety and Health Administration (OSHA) to improve safety: workplace inspections. Using establishment-level administrative data, this study first estimates the extent to which a large inspection program by OSHA reduced workplace injuries. It then uses a machine learning algorithm, combined with data on establishment characteristics from multiple sources, to examine how OSHA could improve effectiveness by targeting its inspections where they will most effectively reduce injuries.

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