Table 5 of Actuarial Note 159 presents death rates experienced by the general population in the Social Security coverage area consistent with estimates in the 2017 Trustees Reports.

Table 2 of Actuarial Note 159 contains data about the number of Old Age, Survivors, and Disability Insurance (OASDI) and Supplemental Security Income (SSI) claimants whose cases are pending an Administrative Law Judge's (ALJ) determination at the end of each fiscal year. There is a subset of under age 18 and age 18+ data.

This data collection hold machine readable files for each of the tables in Actuarial Note 159 - Probability of Death While Pending an Administrative Law Judge Determination.

Table 7 of Actuarial Note 159 provides a comparison of death rates for claimants with cases pending an Administrative Law Judge's (ALJ) determination, to the death rates for Disability Insurance (DI) disabled worker beneficiaries who are in their first two years of entitlement.

Abstract It has been shown that under the social security factor rule current contribution rates are insufficient to cover social security benefits, since the actuarially fair rates are 30.69% and 35.27% for men and women, respectively. However, if the social security reform were approved as submitted, the fair rates would be reduced to 22.25% and 21.60%, respectively. Besides the minimum age, part of this reduction is due to the proposed rules allowing pension values lower than the minimum wage. These results served the objective of this work, which was to compare the actuarially fair social security rates for the General Social Welfare Policy (GSWP), based on the social security factor rules and the minimum age proposal present in Proposed Constitutional Amendment n. 287/2016. The demographic changes that have taken place in Brazil in recent years raise questions about the sustainability of the national social security system and approving social security reform has been a government priority. Therefore, there is an undisputed need for an actuarial study that calculates actuarially fair rates and compares the current scenario with the reform proposals. Multiple decrement actuarial models were used to calculate the fair rates considering a standard family (25-year-old worker, spouse, and two children), in which the man is three years older than the woman. The IBGE 2015 Extrapolated (mortality) and Álvaro Vindas (disability) tables were adopted as biometric assumptions, and a real wage growth rate of 2% p.a. and real interest rate of 3% p.a. were used.

Table 4 of Actuarial Note 159 contains data about the death rate among claimants while an Administrative Law Judge (ALJ) determination is pending.

Table 6 of Actuarial Note 159 presents the ratio of the adjusted death rates for claimants with cases pending an Administrative Law Judge's (ALJ) determination to the similarly adjusted death rates for the general population.

Table 3 of Actuarial Note 159 contains data about annualized exposure among claimants awaiting a decision on their Social Security disability claim by an Administrative Law Judge (ALJ). Following standard actuarial practice, this exposure is approximated as the average number of living claimants pending at the beginning and the end of each year, plus one-half of the deaths occurring during the year.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Examples of time and age coordinates of the event as a function of the length of the year utilised to calculate the exact age at event.

Description of the functions in qlifetable for building quarterly life tables.

Examples of exact ages at events as a function of the length of the year utilised to calculate them when births and events happen at exactly the same moment in two different time years.

Range of 10-year other cause mortality risk predictions.

Final adjusted model for the 10-year competing risk of other cause mortality.