30 datasets found
  1. k

    Data from: What Has Driven the Recent Increase in Retirements?

    • kansascityfed.org
    pdf
    Updated Jun 21, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2024). What Has Driven the Recent Increase in Retirements? [Dataset]. https://www.kansascityfed.org/research/economic-bulletin/what-has-driven-the-recent-increase-in-retirements/
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jun 21, 2024
    Description

    During the pandemic, the share of retirees in the U.S. population rose much faster than its normal pace. Typically, an increase in this share is driven by more people transitioning from employment to retirement. However, we show that the recent increase was instead driven by fewer people transitioning from retirement back into employment, likely due to pandemic-related health risks. More retirees may rejoin the workforce as these health risks fade, but the retirement share is unlikely to return to a normal level for some time.

  2. COVID-19 impact on retirement savings/plans in the U.S. 2020, by generation

    • statista.com
    Updated Jul 16, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). COVID-19 impact on retirement savings/plans in the U.S. 2020, by generation [Dataset]. https://www.statista.com/statistics/1221507/covid-19-impact-on-retirement-savings-us/
    Explore at:
    Dataset updated
    Jul 16, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 24, 2020 - May 4, 2020
    Area covered
    United States
    Description

    The impact of the coronavirus (COVID-19) pandemic on the retirement plans of households in the United States varied greatly across the generations considered. As of 2020, approximately one-quarter of the millennial surveyed (24 to 38 years of age) declared to have withdrawn from their emergency fund or savings account because of the coronavirus pandemic. On the other hand, this impact was reported by only *** percent of the respondents aged between 55 to 73 years (boomers).

  3. Reasons workers aged 50 years and over left work during the coronavirus...

    • cy.ons.gov.uk
    • ons.gov.uk
    xlsx
    Updated Mar 14, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Office for National Statistics (2022). Reasons workers aged 50 years and over left work during the coronavirus (COVID-19) pandemic, by retirement status [Dataset]. https://cy.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/datasets/reasonsworkersaged50yearsandoverleftworkduringthecoronaviruscovid19pandemicbyretirementstatus
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Reasons for leaving work and those that have returned to work during the coronavirus (COVID-19) pandemic, broken down by retirement status. Includes information on whether those looking for paid work or have looked for paid work experienced age discrimination. Data from the Over 50s Lifestyle Study, Great Britain.

  4. Retirement confidence after the coronavirus pandemic in the U.S. 2020

    • statista.com
    • thefarmdosupply.com
    Updated Jul 10, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Retirement confidence after the coronavirus pandemic in the U.S. 2020 [Dataset]. https://www.statista.com/statistics/1189267/retirement-confidence-after-the-coronavirus-pandemic/
    Explore at:
    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    When surveyed in **********, ** percent of Americans felt declined confidence in retiring comfortable, due to the coronavirus pandemic. When surveyed again in *************, a lower share, only ** percent, felt declined confidence to retire. Meanwhile, a higher share (** percent) felt increased confidence in **********. This share was lower in December that year, when only ** percent had improved confidence to retire comfortably.

  5. Retirees' thoughts on their retirement age after COVID-19 2021, by region

    • statista.com
    Updated Jul 7, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Retirees' thoughts on their retirement age after COVID-19 2021, by region [Dataset]. https://www.statista.com/statistics/1263221/opinion-retirement-age-after-covid-19-region/
    Explore at:
    Dataset updated
    Jul 7, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 16, 2021 - May 7, 2021
    Area covered
    Worldwide
    Description

    Most retired people would have retired the same age as they did, even if they would have known in advance about the outbreak of the coronavirus pandemic in 2020 and its impact on the world economy. The share was slightly lower among Asian retirees though, where one fifth stated they would have retired later if they would have known, and ** percent would have retired earlier.

  6. Prospects for the recovery of retirement savings in France and worldwide...

    • statista.com
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department, Prospects for the recovery of retirement savings in France and worldwide 2021 [Dataset]. https://www.statista.com/study/117791/retirement-in-france/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    France
    Description

    When surveyed in early 2021, more than a year after the start of the COVID-19 pandemic, the French were more pessimistic than the rest of the countries in which the survey was conducted regarding the time it would take for their retirement savings balance to return to its pre-pandemic level. Just over half (55 percent) thought it would take them one year or less, compared to nearly three-quarters (73 percent) of people in the rest of the countries surveyed.

  7. Share of nurses to retire due to COVID-19 U.S. 2023, by likelihood

    • statista.com
    Updated Jul 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Share of nurses to retire due to COVID-19 U.S. 2023, by likelihood [Dataset]. https://www.statista.com/statistics/1389420/probability-of-nurses-to-retire-due-to-covid-19-pandemic-united-states/
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2023
    Area covered
    United States
    Description

    In 2023, nearly *** in **** nurses said that they would retire from nursing due to the COVID-19 pandemic. According to this 2023 survey, ** percent of surveyed nurses stated they would likely retire from nursing as a consequence of the pandemic, of which ***** percent said it to be extremely likely. Nonetheless, most responding nurses stated it was unlikely for them to retire due to COVID-19.

  8. d

    Presidential address: retirement and the emergence of the metaverse and...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Sun Huh (2023). Presidential address: retirement and the emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic [Dataset]. http://doi.org/10.7910/DVN/LBB7QS
    Explore at:
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Sun Huh
    Description

    Suppl. 1. Manuscript before English proofreading by ChatGPT. Suppl. 2. Manuscript after English proofreading by ChatGPT. Suppl. 3. Fifty-eight terms on digital standards of journal publishing (queried via ChatGPT) and the acceptability of its answers. Suppl. 4. Answer of ChatGPT (2023 Jan 9 ver.) to 58 topics on digital standards of journal publishing (cited January 19, 2023, 8:30 PM–January 21, 2023, 21:00 PM [Seoul time]).

  9. Reasons workers aged 50 years and over left employment during the...

    • cy.ons.gov.uk
    • ons.gov.uk
    xlsx
    Updated Mar 14, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Office for National Statistics (2022). Reasons workers aged 50 years and over left employment during the coronavirus (COVID-19) pandemic, by occupation [Dataset]. https://cy.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/datasets/reasonsworkersaged50yearsandoverleftemploymentduringthecoronaviruscovid19pandemicbyoccupation
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Reasons for leaving work during the (COVID-19) pandemic and savings and sources of retirement funding, broken down by occupation group. Data from the Over 50s Lifestyle Study, Great Britain.

  10. Retirement Homes in the UK - Market Research Report (2015-2030)

    • ibisworld.com
    Updated Sep 11, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IBISWorld (2025). Retirement Homes in the UK - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-kingdom/market-research-reports/retirement-homes-industry/
    Explore at:
    Dataset updated
    Sep 11, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    United Kingdom
    Description

    Retirement homes depend on self-funders or local council funding that covers the retirement needs of people who satisfy financial assessment means tests. Tightening government budgets have meant publicly funded fees have failed to cover providers’ operating costs, forcing retirement homes to cross-subsidise local authority beds with fees from self-funded residents. Revenue is anticipated to climb at a compound annual rate of 3.2% over the five years through 2025-26 to £12.0 billion, and it’s set to rise by 0.8% in 2025-26. Much of this is down to care homes' fees mounting to cover costs and being paid for by self-funders, who are saw their disposable income tick upwards in 2024-25, lifting industry revenue. Although the ageing population supports revenue growth, constrained government spending, delayed reform changes and rising costs (particularly for labour) have put pressure on profit. Demand for beds far outstrips the supply, which is driving investment into the industry. Mounting demand from residents who had delayed joining a retirement home during the pandemic contributed to strong growth in revenue in 2021-22. Care homes' fees then edged up in the three years through 2024-25 to cope with enhanced staffing costs, mounting mortgage payments and heightened energy costs – these were all the result of high inflation. This has been to the dismay of many retirees whose purse strings have tightened thanks to the cost-of-living crisis, making hit harder for them to afford to move into retirement homes. Higher fees have therefore dampened some of demand for beds, but they’ve also increased the sales value of care homes, supporting revenue. Retirement home revenue is expected to rise at a compound annual rate of 1.5% over the five years through 2030-31 to £12.9 billion, driven by an ageing population. By 2036, the number of people aged 85 and over will hit 2.6 million, representing 3.5% of the UK population, according to the Office for National Statistics. However, medical advances will make an older population healthier, allowing people to live independently for longer, dampening growth. Sustainable initiatives will be incorporated into the designs of new homes, helping reduce operational costs for retirement homes and supporting profitability. As real disposable income rises, there will be greater demand for luxury retirement homes, driving sales value and supporting industry revenue growth.

  11. f

    Comparison meta-regression.

    • figshare.com
    xls
    Updated Apr 4, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Lawrence Ejike Ugwu; Wojujutari Kenni Ajele; Erhabor Sunday Idemudia (2024). Comparison meta-regression. [Dataset]. http://doi.org/10.1371/journal.pgph.0003074.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Apr 4, 2024
    Dataset provided by
    PLOS Global Public Health
    Authors
    Lawrence Ejike Ugwu; Wojujutari Kenni Ajele; Erhabor Sunday Idemudia
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Retirement is a pivotal life transition that often changes routines, identity, and objectives. With increasing life expectancies and evolving societal norms, examining the interplay between retirement anxiety and life satisfaction is vital. This study delves into this relationship, recognising the complexities of retirement. A systematic review and meta-analysis followed PRISMA guidelines. Research from 2003 to 2023 was sourced from databases like CINAHL, PubMed/Medline, PsycINFO, ERIC, and Google Scholar, focusing on diverse methodologies and outcomes related to retirement registered in Prospero database (CRD42023427949). The quality assessment used an eight-criterion risk of bias scale, and analyses included qualitative and quantitative approaches, such as random-effects meta-analysis and moderator analyses. After reviewing 19 studies with varied geographical and demographic scopes, a mixed relationship between retirement and life satisfaction emerged: 32% of studies reported a positive relationship, 47% were negative, and 21% found no significant correlation. Meta-analysis indicated high heterogeneity and non-significant mean effect size, suggesting no consistent impact of retirement on life satisfaction. Moderator analyses highlighted the influence of measurement tools on outcomes. The findings reveal a complex interplay between retirement anxiety and life satisfaction, stressing the need for holistic retirement policies that encompass mental health, social integration, and adaptability, focusing on cultural sensitivity. Challenges include potential biases in data sources, methodological diversity, the scarcity of longitudinal studies, and difficulties in addressing recent societal shifts, like the COVID-19 pandemic. Variability in measurement tools and possible publication bias may have also influenced results. This study contributes to understanding retirement, emphasising the relationship between retirement anxiety and life satisfaction. It advocates for ongoing, detailed, culturally informed research to grasp retirement’s multifaceted aspects fully.

  12. Share of U.S. teachers who are planning to leave or retire early 2022

    • thefarmdosupply.com
    • statista.com
    Updated Jul 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Share of U.S. teachers who are planning to leave or retire early 2022 [Dataset]. https://www.thefarmdosupply.com/?_=%2Fstatistics%2F1316289%2Fshare-teachers-leave-retire-early-us%2F%23RslIny40YoL1bbEgyeyUHEfOSI5zbSLA
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 14, 2022 - Jan 24, 2022
    Area covered
    United States
    Description

    In January 2022, a survey of teachers in the United States found that ** percent of educators were considering leaving or retiring from teaching earlier than planned due to the effects of the COVID-19 pandemic. Only *** percent of teachers said that the COVID-19 pandemic would cause them to teach longer than they had planned.

  13. c

    The Impact of the COVID19 Pandemic on Income by Percent Change 2019 to 2020...

    • communityprosperityhub.com
    • decent-work-and-economic-growth-fredericton.hub.arcgis.com
    Updated Aug 10, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Fredericton - Ville de Fredericton (2022). The Impact of the COVID19 Pandemic on Income by Percent Change 2019 to 2020 Fredericton [Dataset]. https://www.communityprosperityhub.com/datasets/the-impact-of-the-covid19-pandemic-on-income-by-percent-change-2019-to-2020-fredericton
    Explore at:
    Dataset updated
    Aug 10, 2022
    Dataset authored and provided by
    City of Fredericton - Ville de Fredericton
    Description

    Footnotes:1Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.2Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.3Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).4The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).5Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).6Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.7The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.8The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.9The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.10All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.11The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.12Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.13The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.14Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.15The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.16All cash benefits received from federal, provincial, territorial or municipal governments during the reference period. It includes: * Old Age Security pension, Guaranteed Income Supplement, Allowance or Allowance for the Survivor; * retirement, disability and survivor benefits from Canada Pension Plan and Québec Pension Plan; * benefits from Employment Insurance and Québec parental insurance plan; * child benefits from federal and provincial programs; * social assistance benefits; * workers' compensation benefits; * Canada workers benefit (CWB); * Goods and services tax credit and harmonized sales tax credit; * other income from government sources. For the 2021 Census, this includes various benefits from new and existing federal, provincial and territorial government income programs intended to provide financial support to individuals affected by the COVID-19 pandemic and the public health measures implemented to minimize the spread of the virus.17The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.18Refers to the sum of payments received from COVID-19 - Emergency and recovery benefits and Employment Insurance (EI) benefits.19The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars. In 2019, earning replacement benefits is equal to Employment Insurance (EI) benefits.20All Employment Insurance (EI) benefits received during the reference period, before income tax deductions. It includes benefits for unemployment, sickness, maternity, paternity, adoption, compassionate care, work sharing, retraining, and benefits to self-employed fishers

  14. a

    The Impact of the Covid19 Pandemic on Women Income by 2019 Income Rank...

    • decent-work-and-economic-growth-fredericton.hub.arcgis.com
    • no-poverty-hub-fredericton.hub.arcgis.com
    • +1more
    Updated Aug 10, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Fredericton - Ville de Fredericton (2022). The Impact of the Covid19 Pandemic on Women Income by 2019 Income Rank Fredericton [Dataset]. https://decent-work-and-economic-growth-fredericton.hub.arcgis.com/datasets/the-impact-of-the-covid19-pandemic-on-women-income-by-2019-income-rank-fredericton
    Explore at:
    Dataset updated
    Aug 10, 2022
    Dataset authored and provided by
    City of Fredericton - Ville de Fredericton
    Description

    Footnotes:1Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.2Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.3Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).4This category includes women and girls, as well as some non-binary persons.5The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).6Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).7Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.8The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.9The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.10The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.11All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.12The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.13Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.14The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.15Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.16The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.17All cash benefits received from federal, provincial, territorial or municipal governments during the reference period. It includes: * Old Age Security pension, Guaranteed Income Supplement, Allowance or Allowance for the Survivor; * retirement, disability and survivor benefits from Canada Pension Plan and Québec Pension Plan; * benefits from Employment Insurance and Québec parental insurance plan; * child benefits from federal and provincial programs; * social assistance benefits; * workers' compensation benefits; * Canada workers benefit (CWB); * Goods and services tax credit and harmonized sales tax credit; * other income from government sources. For the 2021 Census, this includes various benefits from new and existing federal, provincial and territorial government income programs intended to provide financial support to individuals affected by the COVID-19 pandemic and the public health measures implemented to minimize the spread of the virus.18The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.19Refers to the sum of payments received from COVID-19 - Emergency and recovery benefits and Employment Insurance (EI) benefits.20The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars. In 2019, earning replacement benefits is equal to Employment Insurance (EI) benefits.21All Employment Insurance (EI) benefits received during the reference period, before income tax deductions. It includes benefits for unemployment, sickness, maternity, paternity, adoption, compassionate

  15. f

    Data_Sheet_1_Occupational outcomes of people with multiple sclerosis during...

    • frontiersin.figshare.com
    docx
    Updated Nov 27, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bruno Kusznir Vitturi; Alborz Rahmani; Alfredo Montecucco; Guglielmo Dini; Paolo Durando (2023). Data_Sheet_1_Occupational outcomes of people with multiple sclerosis during the COVID-19 pandemic: a systematic review with meta-analysis.docx [Dataset]. http://doi.org/10.3389/fpubh.2023.1217843.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    Nov 27, 2023
    Dataset provided by
    Frontiers
    Authors
    Bruno Kusznir Vitturi; Alborz Rahmani; Alfredo Montecucco; Guglielmo Dini; Paolo Durando
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundPeople with Multiple Sclerosis (PwMS) are vulnerable to unfavorable occupational outcomes and the COVID-19 pandemic brought major consequences on people’s professional lives. In this view, we decided to investigate the occupational outcomes of PwMS during the COVID-19 pandemic.MethodsWe performed a systematic review with meta-analysis searching key terms in four databases. We initially included any peer-reviewed original article that enrolled adult patients with the diagnosis of MS and assessed any occupational variable during the COVID-19 pandemic. There were no time limits and no language restrictions. The primary outcomes were the prevalence of unemployment, retirement and employment status change among people with MS during the COVID-19 pandemic. Other outcomes included the modality and characteristics of work: type of work, full-time work, part-time work and remote work. We also searched for data from studies that addressed any change in the work status due to the COVID-19 outbreak.ResultsWe identified 49 eligible articles comprising a total sample size of 17,364 individuals with MS. The pooled prevalence of unemployment and retirement was 0.47 (95% CI = 0.42–0.53). The pooled prevalence of PwMS who were unemployed or retired was positively associated with the progressive phenotype of the disease (p = 0.017) and the use of glatiramer acetate (p = 0.004), but negatively associated with hospitalization due to COVID-19 (p = 0.008) and the use of immunosuppressants (p = 0.032), siponimod (p 

  16. Public Health Official Departures

    • data.world
    csv, zip
    Updated Jun 7, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    The Associated Press (2022). Public Health Official Departures [Dataset]. https://data.world/associatedpress/public-health-official-departures
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Jun 7, 2022
    Dataset provided by
    data.world, Inc.
    Authors
    The Associated Press
    Description

    Changelog:

    Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

    Overview

    Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

    In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

    According to experts, that is the largest exodus of public health leaders in American history.

    Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

    The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

    Findings

    The AP and KHN found that:

    • One in five Americans live in a community that has lost its local public health department leader during the pandemic
    • Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

    To get total numbers of exits by state, broken down by state and local departments, use this query

    Methodology

    KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

    The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

    Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

    The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

    Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

    Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

    KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

    Attribution

    Findings and the data should be cited as: "According to a KHN and Associated Press report."

    Is Data Missing?

    If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

  17. f

    Data_Sheet_1_Dynamics of Loneliness Among Older Adults During the COVID-19...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Varsha D. Badal; Ellen E. Lee; Rebecca Daly; Emma M. Parrish; Ho-Cheol Kim; Dilip V. Jeste; Colin A. Depp (2023). Data_Sheet_1_Dynamics of Loneliness Among Older Adults During the COVID-19 Pandemic: Pilot Study of Ecological Momentary Assessment With Network Analysis.PDF [Dataset]. http://doi.org/10.3389/fdgth.2022.814179.s001
    Explore at:
    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Varsha D. Badal; Ellen E. Lee; Rebecca Daly; Emma M. Parrish; Ho-Cheol Kim; Dilip V. Jeste; Colin A. Depp
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectiveThe COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect.MethodsWe used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.2; range 68–93 years).ResultsParticipants completed mean 83.9% of EMA surveys (SD = 16.1%). EMA ratings of loneliness were moderately correlated with UCLA loneliness scale scores. Network models showed that loneliness was contemporaneously associated with negative affect (worried, anxious, restless, irritable). Negative (but not happy or positive) mood tended to be followed by loneliness and then by exercise or outdoor physical activity. Negative affect had significant and high inertia (stability).ConclusionsThe data suggest that EMA is feasible and acceptable to older adults. EMA-assessed loneliness was moderately associated with scale-assessed loneliness. Network models in these independent living older adults indicated strong links between negative affect and loneliness, but feelings of loneliness were followed by outdoor activity, suggesting adaptive behavior among relatively healthy adults.

  18. c

    Retirement Home Services Market is Growing at Compound Annual Growth Rate...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Cognitive Market Research, Retirement Home Services Market is Growing at Compound Annual Growth Rate (CAGR) of 3.90% from 2023 to 2030. [Dataset]. https://www.cognitivemarketresearch.com/retirement-home-services-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global Retirement Home Services market is growing at a compound annual growth rate (CAGR) of 3.90% from 2023 to 2030. Rising Global Life Expectancy Is Driving The Growth of the Market

    People are living longer lives than they were a few decades ago. This is due to low rates of cardiovascular and infectious disease mortality. The majority of deaths in the world were caused by three primary health conditions: ischemic heart disease, chronic obstructive pulmonary disease (COPD), and stroke.

    Since the 1990s, the average number of fatalities has grown. The number of people dying from illnesses such as heart disease has increased as the world population has grown.

    The decrease in age-specific mortality rates for various illnesses is evidence of the healthcare industry's success.Life expectancy increases as a result of breakthroughs in public healthcare facilities and significant developments in the healthcare business, as well as higher living standards, increased nutrition, better education, and lifestyle changes. An individual's global average age is mostly determined by living conditions and place of residence. These factors will boost market growth during the forecast period.

    Technological Developments Will Boost Market Expansion
    

    During the forecast period, technological advancements in long-term healthcare are anticipated to propel market expansion. This is brought on by the increase in Internet usage, which has sparked the development of online marketplaces, mobile apps, and mHealth. There is a rising need for support services including smartphone apps, trackers, wearables, communication tools, and smart alarms. These tools allow nurses and caregivers to monitor, document, and observe patients as well as connect with medical specialists.The use of computer and mobile phone-based patient data management among these technologies is spreading throughout long-term care.

    Apps that create electronic health records (EHRs) and mobile health records (MHRs) are now available, making it simpler for consumers and healthcare professionals to access and exchange health information.

    (Source:health-e.in/blog/phr-apps-india/)

    The main technological advancements are mHealth and mobile-based healthcare applications that produce electronic health records (EHRs) and mobile health records (MHRs). When there are medical emergencies, other technologies, like alarm integration methods, are employed to notify service providers and caregivers. As they lessen the dependency on carers, smart houses are becoming more popular in industrialized nations. Thus, the market's expansion over the course of the forecast period will be fueled by the rising acceptance of such cutting-edge technical solutions.

    The Aspects of the Retirement Home Services Market are Limitingits Growth

    Negative Reputation Of Retirement Homes Is A Significant Barrier To Market Growth
    

    Though living in the comfort of one's own home is always preferable, living in an old age home has its advantages. However, just a few old age facilities provide the bare minimum of quality for a comfortable stay. The cost of services supplied by old age homes is heavily influenced by the quality of those services. Many individuals enroll in retirement homes that lack basic infrastructure and services because they cannot afford the hefty service fees. Residents at nursing facilities are rarely given privacy. The environment in certain nursing facilities frequently results in despair, boredom, neglect, and, in some cases, abuse.

    Impact of COVID-19 on The Retirement Home Services Market

    Due to the risk of getting the virus in communal living arrangements, the pandemic has reduced demand for retirement homes. However, the epidemic has increased demand for retirement homes that provide specialized nursing care services. Retirement homes that provide specialized services for nursing care are growing more popular as individuals seek a safe and comfortable place to live. Introduction of Retirement Home Services

    A retirement home is a multi-residence living complex designed for the elderly, sometimes known as an old people's home or old age home. Everyone or a couple resides in a room or suite of rooms that is akin to an apartment. There are more facilities in the building. This will include places for gathering, eating, playing, and receiving some kind of healt...

  19. G

    Robo-Advisory Retirement Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Aug 29, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Growth Market Reports (2025). Robo-Advisory Retirement Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/robo-advisory-retirement-market
    Explore at:
    pptx, csv, pdfAvailable download formats
    Dataset updated
    Aug 29, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Robo-Advisory Retirement Market Outlook



    According to our latest research, the global Robo-Advisory Retirement market size reached USD 1.42 billion in 2024, highlighting its rapidly expanding influence in the financial services sector. Driven by technological innovation and evolving investor preferences, the market is poised for robust expansion, with a projected CAGR of 23.5% from 2025 to 2033. By the end of the forecast period, the Robo-Advisory Retirement market is anticipated to attain a value of USD 11.3 billion. This surge is primarily attributed to increasing demand for cost-effective, transparent, and personalized retirement planning solutions globally, as per our comprehensive analysis.




    The remarkable growth of the Robo-Advisory Retirement market is underpinned by a confluence of technological advancements and shifting demographic trends. The proliferation of artificial intelligence (AI) and machine learning (ML) algorithms has enabled robo-advisors to deliver increasingly sophisticated and tailored retirement planning services. These technologies empower platforms to analyze vast datasets, predict market trends, and optimize investment portfolios, thereby enhancing user outcomes. Additionally, the growing comfort among consumers with digital financial tools, especially among millennials and Generation Z, is fueling adoption rates. As these younger cohorts begin to prioritize retirement planning earlier in their careers, they are more likely to leverage digital-first solutions that offer convenience, lower fees, and automated portfolio management.




    Another significant growth driver for the Robo-Advisory Retirement market is the rising need for financial inclusion and accessibility. Traditional retirement planning services have often been associated with high advisory fees and minimum investment thresholds, which can exclude a substantial segment of the population. Robo-advisors, by contrast, democratize access to professional financial advice by leveraging automation to reduce costs. This has enabled individuals with modest investable assets to access high-quality retirement planning, portfolio management, and tax optimization services. The ongoing shift towards remote and digital financial interactions, accelerated by the COVID-19 pandemic, has further entrenched the role of robo-advisors in retirement planning, making them an integral part of the financial ecosystem.




    Regulatory evolution and increasing collaboration between traditional financial institutions and fintech firms are further propelling the Robo-Advisory Retirement market. Governments and regulatory bodies across major economies are updating guidelines to accommodate digital advisory services, creating a more conducive environment for innovation. Meanwhile, established banks and asset managers are either launching their proprietary robo-advisory platforms or forming strategic alliances with fintech startups to expand their digital offerings. This trend is fostering healthy competition, driving continuous improvement in service delivery, and expanding the overall market reach. As regulatory clarity improves and consumer trust in digital platforms strengthens, the market is expected to witness sustained momentum throughout the forecast period.




    From a regional perspective, North America currently dominates the Robo-Advisory Retirement market, accounting for the largest revenue share in 2024. The regionÂ’s leadership is driven by high digital adoption rates, a mature financial services landscape, and supportive regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing market, propelled by rapid urbanization, rising disposable incomes, and increasing awareness of retirement planning. Europe also represents a significant market, characterized by a strong emphasis on investor protection and a growing population of tech-savvy retirees. Latin America and the Middle East & Africa, while still nascent, are expected to exhibit steady growth as digital infrastructure continues to improve and financial literacy initiatives gain traction.





    <h2 id='service-type-analysis' &

  20. a

    The Impact of the Covid19 Pandemic on Male Income by 2019 Income Rank...

    • community-prosperity-hub-fredericton.hub.arcgis.com
    • peace-justice-and-strong-institutions-fredericton.hub.arcgis.com
    • +2more
    Updated Aug 10, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Fredericton - Ville de Fredericton (2022). The Impact of the Covid19 Pandemic on Male Income by 2019 Income Rank Fredericton [Dataset]. https://community-prosperity-hub-fredericton.hub.arcgis.com/datasets/the-impact-of-the-covid19-pandemic-on-male-income-by-2019-income-rank-fredericton/explore
    Explore at:
    Dataset updated
    Aug 10, 2022
    Dataset authored and provided by
    City of Fredericton - Ville de Fredericton
    Description

    Footnotes:1Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.2Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol.3Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).4This category includes men and boys, as well as some non-binary persons.5The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).6Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).7Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.8The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.9The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.10The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.11All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.12The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.13Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.14The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.15Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.16The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.17All cash benefits received from federal, provincial, territorial or municipal governments during the reference period. It includes: * Old Age Security pension, Guaranteed Income Supplement, Allowance or Allowance for the Survivor; * retirement, disability and survivor benefits from Canada Pension Plan and Québec Pension Plan; * benefits from Employment Insurance and Québec parental insurance plan; * child benefits from federal and provincial programs; * social assistance benefits; * workers' compensation benefits; * Canada workers benefit (CWB); * Goods and services tax credit and harmonized sales tax credit; * other income from government sources. For the 2021 Census, this includes various benefits from new and existing federal, provincial and territorial government income programs intended to provide financial support to individuals affected by the COVID-19 pandemic and the public health measures implemented to minimize the spread of the virus.18The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars.19Refers to the sum of payments received from COVID-19 - Emergency and recovery benefits and Employment Insurance (EI) benefits.20The reference period for this variable is calendar year 2019. The variable is intended for comparison with its 2020 equivalent and other 2019 income variables. Income for 2019 is presented in 2020 constant dollars. In 2019, earning replacement benefits is equal to Employment Insurance (EI) benefits.21All Employment Insurance (EI) benefits received during the reference period, before income tax deductions. It includes benefits for unemployment, sickness, maternity, paternity, adoption, compassionate

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
(2024). What Has Driven the Recent Increase in Retirements? [Dataset]. https://www.kansascityfed.org/research/economic-bulletin/what-has-driven-the-recent-increase-in-retirements/

Data from: What Has Driven the Recent Increase in Retirements?

Related Article
Explore at:
pdfAvailable download formats
Dataset updated
Jun 21, 2024
Description

During the pandemic, the share of retirees in the U.S. population rose much faster than its normal pace. Typically, an increase in this share is driven by more people transitioning from employment to retirement. However, we show that the recent increase was instead driven by fewer people transitioning from retirement back into employment, likely due to pandemic-related health risks. More retirees may rejoin the workforce as these health risks fade, but the retirement share is unlikely to return to a normal level for some time.

Search
Clear search
Close search
Google apps
Main menu