Association for Public Policy Analysis & Management

Panel: Workers with Disabling Injury and Illness: Data and Research to Inform Policy

(Employment and Training Programs)

Millions of American workers leave the labor force every year, at least temporarily, because of injury or illness. Without steady earnings, these workers and their families often end up in public programs such as Social Security Disability Insurance, Supplemental Security Income, Medicare, and Medicaid. The four papers included in this panel shed light on data available for research on this topic, provide examples of innovative research using such data, and address the important role of the employer in improving outcomes for workers.

The first paper in this panel, “Improving Access to Data on Disability-Related Topics”, describes a U.S. Department of Labor project aimed at improving researcher access to state data on workers claiming Workers’ Compensation (WC), State Disability Insurance (SDI), or Paid Family and Medical Leave (PFML) benefits. Access to these data is essential to evaluating and identifying effective policies to improve outcomes for workers who experience medical conditions that challenge their ability to work.

The second paper in this panel, “Rhode Island’s Temporary Disability Insurance Program: An Exploratory Outcomes Analysis”, provides an example of the type of research that can be conducted with state administrative data on workers who experience off-the-job medical conditions and claim SDI benefits. Specifically, the authors examine the extent to which the characteristics of claimants in Rhode Island’s Temporary Disability Insurance program are associated with participation in its partial return-to-work (PRTW) benefit and with certain measures of benefit receipt duration.

The third paper in this panel, “Using Predictive Analytics for Early Identification of Short-Term Disability Claimants Who Exhaust Their Benefits”, provides another example for research on workers who experience off-the-job medical conditions, albeit using data on claims under private short-term disability insurance. Such claims can be an early identification point of workers with medical conditions who could, with appropriate intervention, remain in the workforce. The authors compare the performance of alternative models that use the claims data to predict exhaustion of STDI benefits, including both logistic regression and machine learning models.

The fourth paper in this panel, “Organizational Work Disability Management: Examining the Role of Workplace and Supervisor Interventions in Facilitating Return-to-Work”, addresses the important role of the employer in improving outcomes for workers, presenting qualitative and quantitative data collected within several large employers. Specific objectives are to: (1) examine the role of organizations in work disability management; (2) highlight several organizational return-to-work interventions that have successfully improved work disability management outcomes; and (3) examine the importance of enhancing supportive relationships and optimizing communication between frontline supervisors and injured workers.