Each dataset provides monthly data at the national level of Social Security Retirement Insurance applications filed via the Internet, and Social Security Retirement Insurance applications submitted via telephone, in person through a local SSA field office, or by mail that could be filed via the Internet. Percentage of online applications is derived by dividing the number of retirement insurance applications filed via the Internet by the total number of retirement insurance applications that could be filed via the Internet.

Abstract This study aims to verify the validity of the retirement satisfaction inventory (RSI) for Brazilians and its invariance with regard to gender, age, education, marital status, income and region of the country, and to investigate whether the reasons for retirement influence a person's retirement satisfaction. At total of 1,002 retirees participated in the study, including both men and women ranging in age from 44 to 88. The analyses indicated RSI being subdivided into two scales: i) the scale of satisfaction with retirement, and (ii) the reasons for retirement with good psychometric characteristics. The latter was found to be a predictor of the former. The instruments were structured differently than in other countries, but they were shown to be applicable in the Brazilian context, especially with regard to assessing interventions such as retirement preparation programs.

AbstractWe study the effect of inconsistent time preferences on actual and planned retirement timing decisions in two independent datasets. Theory predicts that hyperbolic time preferences can lead to dynamically inconsistent retirement timing. In an online experiment with more than 2,000 participants, we find that time-inconsistent participants retire on average 1.75 years earlier than time-consistent participants do. The planned retirement age of non-retired participants decreases with age. This negative age effect is about twice as strong among time-inconsistent participants. The temptation of early retirement seems to rise in the final years of approaching retirement. Consequently, time-inconsistent participants have a higher probability of regretting their retirement decision. We find similar results for a representative household survey (German SAVE panel). Using smoking behavior and overdraft usage as time preference proxies, we confirm that time-inconsistent participants retire earlier and that non-retirees reduce their planned retirement age within the panel.MethodsWe conduct an online experiment in cooperation with a large and well-circulated German newspaper, the Frankfurter Allgemeine Zeitung (FAZ). Participants are recruited via a link on the newspaper's website and two announcements in the print edition. In total, 3,077 participants complete the experiment, which takes them on average 11 minutes. Participants answer questions about retirement planning, time preferences, risk preferences, financial literacy, and demographics. The initial sample for this study consists of 256 retired participants and 2,173 non-retired participants.Usage NotesOur dataset: STATA Do File is attached Additional Datasets: In addition, a German Household Panle is used in this paper. The data cannot be uploaded by us but is available via the Max Planck Institute (https://www.mpisoc.mpg.de/en/social-policy-mea/research/save-2001-2013/). We upload the Do-Files used in the analysis and the results in an excel format (xlsx).

Retirement Age Men in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

24 sheets of data, each sheet representing a table from the database that stores the information. The order of the sheets is based on the sequence of reporting forms for the Financial Transactions Report. See attachment for explanation of codes by scrolling down to the Attachments Section.

CSI: Savings: Adequate Retirement Income Probability: 1-24% data was reported at 24.000 % in Aug 2018. This stayed constant from the previous number of 24.000 % for Jul 2018. CSI: Savings: Adequate Retirement Income Probability: 1-24% data is updated monthly, averaging 27.000 % from Dec 1997 (Median) to Aug 2018, with 249 observations. The data reached an all-time high of 33.000 % in Oct 2004 and a record low of 21.000 % in Jun 2017. CSI: Savings: Adequate Retirement Income Probability: 1-24% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H029: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

Find out about retirement trends in PBGC's data tables. The tables include statistics on the people and pensions that PBGC protects, including how many Americans are in PBGC-insured pension plans, how many get PBGC benefits, and where they live. This data set will be updated periodically. (Updated annually)

The annual substitute service servicemen shall retire early according to the provisions of Article 2, paragraph 1, of the Substitute Service Servicemen's Early Retirement Act, and the statistics of the number of servicemen who retire early shall be complied with if they meet the requirements.

United States CSI: Savings: Adequate Retirement Income Probability: 50% data was reported at 11.000 % in May 2018. This records an increase from the previous number of 10.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data is updated monthly, averaging 15.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 21.000 % in Jan 2004 and a record low of 10.000 % in Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

Despite concern about the viability of public retirement programs and potential undersaving for retirement, we still know little about the impact of government provided information on individual behavior. We exploit plausibly exogenous variation in exposure to the world's largest personalized retirement benefits statement from the US Social Security Administration to evaluate the effects of information and encouragement on individual retirement savings decisions. Using three natural experiments between 2011 and 2014 and administrative data, we find no impact of the statements on individual retirement savings in their employer provided retirement accounts.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

An analysis of Census data has been undertaken to examine households who may be at risk of homelessness once those who are employed retire from the workforce. The cohort needed to meet all of the following criteria: • Reference person aged 50+ years • Employed reference person or partner • Current tenure is renting • Income is considered moderate (defined as $600-1249 per week) Show full description

Dataset of assets that have been retired or that are currently being retired from data.ct.gov. Datasets may be retired for a number of reasons, including age, factual inaccuracies, low usage, and/or replacement by another asset.

To read the Open Data Portal's data retirement policy in full, please visit the following link: https://ctopendata.github.io/open-data-handbook/open_data_resources/data_retirement

This dataset sets forth the Police Retirement System holdings (both equity and fixed income) of the identified pension/retirement system as of the close of the fiscal year.

United States CSI: Savings: Adequate Retirement Income Probability: 0% data was reported at 24.000 % in Aug 2018. This records an increase from the previous number of 20.000 % for Jul 2018. United States CSI: Savings: Adequate Retirement Income Probability: 0% data is updated monthly, averaging 19.000 % from Dec 1997 (Median) to Aug 2018, with 249 observations. The data reached an all-time high of 28.000 % in Dec 1997 and a record low of 13.000 % in Oct 2002. United States CSI: Savings: Adequate Retirement Income Probability: 0% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H029: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data was reported at 0.000 % in May 2018. This records a decrease from the previous number of 1.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data is updated monthly, averaging 2.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 7.000 % in Feb 2001 and a record low of 0.000 % in May 2018. United States CSI: Savings: Adequate Retirement Income Probability: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

United States CSI: Savings: Adequate Retirement Income Probability: 100% data was reported at 8.000 % in Aug 2018. This records an increase from the previous number of 6.000 % for Jul 2018. United States CSI: Savings: Adequate Retirement Income Probability: 100% data is updated monthly, averaging 4.000 % from Dec 1997 (Median) to Aug 2018, with 249 observations. The data reached an all-time high of 8.000 % in Aug 2018 and a record low of 1.000 % in Jul 1999. United States CSI: Savings: Adequate Retirement Income Probability: 100% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H029: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

Structured Data Archiving And Application Retirement Market size was valued at USD 6.43 Billion in 2024 and is projected to reach USD 14.413 Billion by 2032, growing at a CAGR of 9.5% from 2026 to 2032.

Structured Data Archiving And Application Retirement Market Drivers

Regulatory Compliance Requirements: Organizations in a variety of sectors must adhere to legal requirements pertaining to data archiving and preservation. Structured data must be kept on file for legal, auditing, and compliance reasons, according to regulations. Data from defunct or decommissioned applications must be archived by organizations in order to comply with laws like Sarbanes-Oxley (SOX), GDPR, HIPAA, and others. The demand for application retirement and structured data archiving solutions is driven by the necessity to comply with regulations.

Cost Optimization and Efficiency: By retiring old programs that are no longer in active use, businesses aim to reduce IT expenses and streamline processes. Updating out-of-date apps requires resources for infrastructure, upkeep, and license. Organizations can enhance operational efficiency, save storage costs, and decommission outdated applications by using structured data archiving and application retirement solutions. These services also free up resources for more strategic projects.

Data Governance and Risk Management: Organizations must manage data at every stage of its lifespan, including the archiving and retirement procedures, in order to implement effective data governance standards. Solutions for structured data archiving make it easier to manage structured data assets by offering features like data classification, audit trails, retention policies, and access controls. Through the implementation of application retirement and organized data archiving methods, organizations can reduce the risks associated with data loss, security breaches, and unauthorized access.

This dataset sets forth the Fire Department Retirement System holdings (both equity and fixed income) of the identified pension/retirement system as of the close of the fiscal year.