Recently Added
CLEAR searches the existing literature for research relevant to this topic area's focus. Browse the most recently reviewed research below.
Displaying 621 - 630 of 642
Study Type: Descriptive Analysis
This study used data from the New Beneficiary Survey and claims folders to describe the experiences of recipients’ experiences under Social Security Disability Insurance (DI) work incentive…Study Type: Causal Impact Analysis
The study examined the effect of the introduction of Medicare Part D on self-employment. The authors used a nonexperimental approach and data from the American Community Survey to estimate the…Study Type: Causal Impact Analysis
The study examined the impact of the Affordable Care Act (ACA) on the employment outcomes of older workers. The author used data from the Current Population Survey for 2011–2016 and regression…Study Type: Causal Impact Analysis
The study’s objective was to examine the impact of a work flexibility intervention called STAR (Support. Transform. Achieve. Results) on workers’ expectations of retirement age and bridge jobs. The…Study Type: Causal Impact Analysis
The study examined the impact of the potential wage replacement rate through Unemployment Insurance (UI) on the probability of transitioning to non-employment. The study used a nonexperimental…Study Type: Causal Impact Analysis
The study’s objective was to examine the impact of the expansion of Medicaid to low-income adults in 2014 on retirement rates of workers ages 55 to 64. The authors used a nonexperimental analysis to…Study Type: Causal Impact Analysis
The study examined the impact of state-level reforms of health insurance on early retirement and Social Security retirement benefit claims. The authors used 1996–2010 data from the Health and…Study Type: Causal Impact Analysis
The study examined the impact of state mandates that health insurance plans cover prostate cancer screenings on the earnings and employment of men older than age 50 The study used nonexperimental…Study Type: Causal Impact Analysis
The study examined the impact of removing the Social Security earnings test on Social Security claims, earnings, and labor force participation for female beneficiaries. The study used a statistical…Study Type: Causal Impact Analysis
The study’s objective was to examine the impact of firm-specific training and general training on older men’s retirement decisions The study used a nonexperimental design and data from the U.S.…
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.