Related Experiment Video
Updated: May 26, 2026

06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
What can we learn from analyzing historical data on social security entitlements?
1Congressional Budget Office, Social Security Administration, USA.
Social Security Bulletin
|December 24, 2011
Summary
Social Security retirement and Disability Insurance (DI) benefit entitlement ages closely follow program rule changes. More recent cohorts increasingly use DI benefits in their late 30s and 40s, especially during economic downturns.
Area of Science:
- Social Sciences
- Economics
- Public Policy
Background:
- Social Security administrative records provide crucial data for understanding retirement and disability benefit trends.
- Examining lifetime entitlement patterns across birth cohorts is essential for assessing program sustainability and impact.
Purpose of the Study:
- To analyze lifetime patterns of initial entitlement to retired-worker and Disability Insurance (DI) benefits.
- To investigate how entitlement ages for these benefits have evolved across different birth cohorts.
- To identify factors influencing DI benefit uptake, particularly during economic recessions and specific age milestones.
Main Methods:
- Utilized Social Security administrative records.
- Analyzed data based on birth-year cohorts.
- Examined age-at-entitlement patterns for retired-worker and DI benefits.
Main Results:
- Entitlement ages demonstrated close adherence to changes in program rules, such as the increasing full retirement age.
- The proportion of a cohort newly entitled to DI benefits increased during economic recessions and at ages 50 and 55.
- More recent cohorts show a rising trend in cumulative DI benefit entitlement rates in their late 30s and 40s.
Conclusions:
- Social Security benefit entitlement ages are responsive to legislative and policy changes.
- Economic conditions, particularly recessions, significantly impact DI benefit claims.
- There is a discernible shift towards greater reliance on DI benefits at younger ages in recent birth cohorts.
Related Concept Videos
Applications of Life Tables
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Longitudinal Research
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Cross-Sectional Research
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Assumptions of Survival Analysis
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
