Estimating worklife expectancy: an econometric approach

Millimet, Daniel L.; Nieswiadomy, Michael; Ryu, Hang K.; Slottje, Daniel J.

doi:10.1016/s0304-4076(02)00168-9

Cited by 38 publications

(47 citation statements)

References 16 publications

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“…Smith (1986) and Hayward and Grady (1990) found that WLE was higher among whites than among non-whites, and that the differences in WLE were greater among males than among females. Millimet et al (2003) documents that the gap between whites and non-whites decreases with age and education. More recent studies have confirmed previous findings of WLE gaps between individuals of different genders (Warner et al 2010), different races, and different educational levels (Dudel and Myrskylä 2017).…”

Section: Working Life Expectancymentioning

confidence: 99%

The Legacy of the Great Recession in Italy: A Wider Geographical, Gender, and Generational Gap in Working Life Expectancy

2018

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Under the pressure of population aging the Italian pension system has undergone reforms to increase labor force participation and retirement age, and, thus, the length of working life. However, how the duration of working life has developed in recent years is not well understood. This paper is the first to analyze trends in working life expectancy in Italy. We use data from a nationally representative longitudinal sample of 880,000 individuals from 2003 to 2013 and estimate working life expectancy by gender, occupational category, and region of residence using a Markov chain approach. We document large and increasing heterogeneity in the length of working life. From 2003From -2004From to 2012From -2013, working life expectancy for men declined from 35.2 to 27.2 years and for women from 34.7 to 23.7 years, increasing the gender gap to 3.5 years. Both young and old were hit, as roughly half of the decline was attributable to ages below 40, half above 40. Working life expectancy declined for all occupational groups, but those in manual occupations lost most, 8.5 years (men) and 10.5 years (women). The North-South economic gradient widened such that men living in the North were expected to work 8 years longer than women living in the South. The fraction of working life of total life expectancy at age 15 declined to record lows at 40% for men and 34% for women in 2012-2013. Policies aiming Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1120 5-018-1910-7) contains supplementary material, which is available to authorized users. 3at increasing total population working life expectancy need to take into consideration the socio-demographic disparities highlighted by our results.

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Section: Working Life Expectancymentioning

confidence: 99%

The Legacy of the Great Recession in Italy: A Wider Geographical, Gender, and Generational Gap in Working Life Expectancy

2018

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“…However, it hinders the comparison of the Finnish estimates of worklife expectancy, for example, with the earlier published US figures (7). In principle, the Finnish worklife expectancy estimates could be compared with the more recent US estimates of worklife expectancy for a population with three states, "employed", "unemployed", and "inactive" (8). However, the demographic characteristics of the two countries differ, for example, with respect to educational level and racial composition so that direct comparisons would be misleading or uninformative.…”

Section: Construction Of the Worklife Table And Estimation Of Worklifmentioning

confidence: 99%

“…This was followed by a study in the United States (US) for 1972 (21). Later US studies were conducted for the years 1992-1993 (34), 1997-1998 (7), and 1992-2000 (8). In the US studies, the applied methods assumed an increment-decrement model or a Markov process model of labor force activity.…”

Section: Multistate Models For Worklife Tablesmentioning

confidence: 99%

“…A life table can be constructed, for example, for a fixed group of people who share a membership-defining event (the cohort), such as entry into the workforce. For a stratified analysis, the cohort can be divided into particular subgroups specified by additional characteristics, for example, level of education (6), race (7), marital status, number of children, and occupation (8). Because of their many uses and their direct implications in various aspects of social planning, ordinary life tables and worklife tables have been constructed, and related expectancies have been calculated for several national population health surveys, for example, in the United States (7,8), The Netherlands (9), Australia (10), and Finland (11)(12)(13).…”

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confidence: 99%

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Multistate worklife expectancies

Nurminen¹,

Nurminen²

2005

Scand J Work Environ Health

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This communication reviews the demographic concept of worklife expectancy and draws the epidemiologists' attention to its usefulness in occupational health research and pension policy making. The distinctions between different analytic approaches to the quantification of expected workforce status and mobility are pointed out. A recently developed multivariate large-sample regression method for the analysis of worklife tables is placed into the general context of life tables. Given aggregated data from multiple cross-sectional or longitudinal population surveys, a multistate regression model can be used to estimate consistently marginal probabilities that a person is in a given work-health state or transition probabilities between the states and, thereby, worklife expectancies. The methodology is illustrated through the application to data from Finnish population statistics on employment, disability, retirement, and mortality. The paper closes with a discussion of the methodological issues and empirical findings on pension policy in Finland.

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“…Summary measures for workyears can provide useful indicators for evaluating labor-force potential, as well as for evaluating the need for employer and pension plan accommodation (31). Although the calculation of worklife expectancy tables is complex (32)(33)(34), their wide use demonstrates that they are comprehensible. In the approach adopted by Nurminen et al (35), worklife expectancies were estimated using a logistic regression model for multinomial probabilities of the health states of excellent, good, fair, and poor work ability.…”

Section: Worklife Expectancymentioning

confidence: 99%

Working population health metrics

Nurminen¹

2004

Scand J Work Environ Health

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Estimating worklife expectancy: an econometric approach

Cited by 38 publications

References 16 publications

The Legacy of the Great Recession in Italy: A Wider Geographical, Gender, and Generational Gap in Working Life Expectancy

The Legacy of the Great Recession in Italy: A Wider Geographical, Gender, and Generational Gap in Working Life Expectancy

Multistate worklife expectancies

Working population health metrics

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