The older workers topic area examines a broad range of employment and training programs funded by the U.S. Department of Labor, Employment and Training Administration and other organizations and broad federal or state policies that support and/or improve the employment prospects and financial security of workers age 40 and older. CLEAR assessed the strength of causal evidence provided in each study and summarized each study’s design, methods, findings, and the intervention examined.
Older Workers
Status: Literature reviewed in this topic area currently covers 2005 – 2017.
Synthesis Reports
Synthesis reports look at the research evidence across studies within a topic area. They also highlight gaps in the literature, and suggest areas in which further research is needed.
Recently Added
CLEAR searches the existing literature for research relevant to this topic area's focus. Browse the most recently reviewed research below.
Displaying 41 - 43 of 43
Study Type: Causal Impact Analysis
The study’s objective is to examine the impact of 1983 and 2000 policy changes that removed the Social Security earnings test for certain age groups on the earnings and employment outcomes of…Study Type: Causal Impact Analysis
The study’s objective was to examine the impact of Medicare as a Secondary Payer (MSP) on older workers’ labor force participation and full-time employment The study used a nonexperimental…Study Type: Causal Impact Analysis
The study examined the impact of the generosity of unemployment insurance (UI) benefits on retirement decisions. The study used a statistical model and data from the March Current Population Survey…
CLEAR Icon Key
Below is a key for icons used to indicate important details about a study, such as its type, evidence rating, and outcome findings.
High Causal Evidence
Strong evidence the effects are caused by the examined intervention.
Moderate Causal Evidence
Evidence that the effects are caused to some degree by the examined intervention.
Low Causal Evidence
Little evidence that the effects are caused by the examined intervention.
Causal Impact Analysis
Uses quantitative methods to assess the effectiveness of a program, policy, or intervention.
Descriptive Analysis
Describes a program, policy, or intervention using qualitative or quantitative methods.
Implementation Analysis
Examines the implementation of a program, policy, or intervention.
Favorable
The study found at least one favorable impact in the outcome domain, and no unfavorable impacts.
Mixed
The study found some favorable and some unfavorable impacts in the outcome domain.
None
The study found no statistically significant impacts in the outcome domain.
Unfavorable
The study found at least one unfavorable impact in the outcome domain, and no favorable impacts.
Not applicable
Not applicable because no outcomes were examined in the outcome domain.
Favorable - low evidence
The study found at least one favorable impact in the outcome domain, and no unfavorable impacts. The study received a low causal evidence ratings so these findings should be interpreted with caution.
Mixed - low evidence
The study found some favorable and some unfavorable impacts in the outcome domain. The study received a low causal evidence ratings so these findings should be interpreted with caution.
None - low evidence
The study found no statistically significant impacts in the outcome domain. The study received a low causal evidence ratings so these findings should be interpreted with caution.
Unfavorable - low evidence
The study found at least one unfavorable impact in the outcome domain, and no favorable impacts. The study received a low causal evidence ratings so these findings should be interpreted with caution.