Working life expectancy in municipal and wellbeing services county occupations

Themes
Occupational health
High risk occupations
Occupational wellbeing and work ability

Working life expectancy in municipal and wellbeing services county occupations

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Working life expectancy by occupation

11.311.912.112.312.312.412.412.412.512.512.712.712.812.812.912.913.113.113.113.113.113.213.213.413.413.513.613.713.713.713.81414141414.114.114.114.314.515516 firefighter, security guard921 933 assistant worker(industry, construction,agriculture/forestry)831 driver6 park worker7 construction/repair worker91323 kitchen assistant9 other worker91321 hospital/ward assistant2331 primary school teacher914 janitor223 head nurse, ward manager51311 child care worker234 special education teacher512 kitchen supervisor, cook51321, 51324, 51326 hoitaja,lähihoitaja5 other service worker12291 education manager323 nurse, public health nurse51312 family day care provider232 lecturer, part-time teacher4 office employee51331 home care worker31 technical expert346 social/youth worker3431 ward secretary2332 kindergarten teacher12292 manager in social andhealth care3 other expert3226 physiotherapist,occupational therapist2446 social worker241 financial secretary, publicrelations officer51332 teachers' aide51325 dental nurse213 technical specialist1 other manager247 municipal administrationspecialist243 librarian2 other specialist2445 psychologist, therapist245 artist, journalist222 physicianWorking life expectancy in years0246810121416

Working life expectancy in employees over fifty in municipal and wellbeing services county occupations

Description

A decline in birth rates, increased life expectancy, and subsequent ageing population have significantly raised the old-age dependency ratio over the past two decades. This creates pressure to increase work participation and lengthen the working careers. However, different occupations have different work disability risks related to both individual and work characteristics, resulting in differences in working life expectancy. We have modelled the working life expectancy in different professions for people over the age of fifty using data collected in the Finnish Public Sector study (FPS) conducted by the Finnish Institute of Occupational Health. These data allow estimating the probabilities of labour market exit. The working life expectancy modelling was carried out in collaboration with the University of Turku and funded by the Finnish Work Environment Fund (project no. 230161).

What do the indicators show?

The data are based on FPS study cohorts from 2000-02, 2004, 2008 and 2011-12 (n = 47,688). Work participation during the follow-up was ascertained utilising linked data from the Earnings and Accrual Register, maintained by the Finnish Centre for Pensions. The register contains data on all Finnish residents’ earnings (e.g., wages and salaries from employment, self-employment, and social security benefits that accrue pension benefits) The data also covers the start and end dates of all employments/job contracts. The working life expectancy calculation was based on three-month periods. A person was defined as working if the three-month period included at least one working day and as not working if the three-month period did not include any work at all. Mortality was obtained from Statistics Finland’s register. Participants were followed from age 50 until the first of the following occurred: age 68 years, death, or the end of the register follow-up 31st December 2018. All participants were working at baseline.

We used the occupational classification of Statistics Finland (occupational classification 2001). There are more than 150 people in all occupational groups, but the exact group sizes are not provided to protect the anonymity of the participants. Each occupation is provided with the occupation code and occupation title according to the classification. Occupational groups with one digit accuracy are compilation groups containing the occupations of the category in question that are not specified with more detailed codes. For example, '1 other manager', which includes all other managers except the separately listed '12291 manager in the educational sector' and '12292 manager in the social and health care sector'.

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Contact information

Jenni Ervasti

+358 30 474 2806

Creative Commons License

Creative Commons License

The publication is licensed under Creative Commons 4.0 International -license.