Early Retirement in the Three Types of Welfare States

Kim, Jeungkun

doi:10.1177/0164027509337192

Cited by 20 publications

(18 citation statements)

References 29 publications

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“…Different types of welfare state regime might have different labor and retirement policies, so the policy interventions might affect incentives of the population. So it is important to find that if the type of welfare state regime has interacted effect on the relationship between health and income on early retirement or not 24 , 25 ) . For this purpose, data of total social expenditures (% of GDP) was used as the indicator of social welfare type of countries.…”

Section: Methodsmentioning

confidence: 99%

Comparison the effects of poor health and low income on early retirement: a systematic review and meta-analysis

et al. 2017

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The main aim of this study was to estimate the effects of poor health and low income on early retirement. For this purpose systematic review and meta-analysis were conducted. Web of Science, PUBMED and Scopus databases were searched systematically. Finally 17 surveys were added in meta-analysis. These studies were conducted in 13 countries. At the end a Meta regression was done to show the effects of welfare system type on effect sizes of poor health and low income. The results of this study showed that poor health had effect on the risk of early retirement. (Poor health pooled effect sizes: 1.279 CI: (1.15 1.41), low income pooled effect sizes: 1.042 CI: (0.92 1.17), (poor health pooled marginal effects: 0.046 CI: (−0.03 0.12), low income pooled marginal effects: −0.002 CI: (−0.003 0.000). The results of this study showed that association between poor health and early retirement was stronger in comparison with low income and early retirement.

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Section: Methodsmentioning

confidence: 99%

Comparison the effects of poor health and low income on early retirement: a systematic review and meta-analysis

et al. 2017

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“…Population aging is a global phenomenon in Europe, North America, and the Asia-Pacific Region, and is often accompanied by relatively low employment rates for people aged 55 – 64 years, ranging from 73.6% in Japan, 65.0% in G7 countries, 64.1% in USA, and 59.1% in EU 28 to 53.4% in Italy and, for people 65 years and over, ranging from 31.5% in Korea, 19.3% in USA, 14.9% in G7 countries, and 5.7% in EU 28 to 2.0 in Spain (OECD, 2017 , data from 2016). The context of high unemployment, industrial restructuring, and negative stereotypes of aged workers have contributed, especially in the last two decades of the twentieth century, to the institutionalization of early retirement (hereafter, ER) as a specific employment policy to make room for younger employees (Von Nordheim, 2004 ; Kim, 2009 ; Engelhardt, 2012 ). However, empirical data do not support the hypothesis that employment of the young and old are interchangeable, and suggest that encouraging later retirement will have no adverse effect on youth employment (Kalwij et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Early Retirement: A Meta-Analysis of Its Antecedent and Subsequent Correlates

2018

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Early or voluntary retirement (ER) can be defined as the full exit from an organizational job or career path of long duration, decided by individuals of a certain age at the mid or late career before mandatory retirement age, with the aim of reducing their attachment to work and closing a process of gradual psychological disengagement from working life. Given the swinging movements that characterize employment policies, the potential effects of ER—both for individuals and society—are still controversial. This meta-analysis examined the relationships between ER and its antecedent and subsequent correlates. Our review of the literature was generated with 151 empirical studies, containing a total number of 706,937 participants, with a wide range of sample sizes (from N = 27 to N = 127,384 participants) and 380 independent effect sizes (ESs), which included 171 independent samples. A negligible ES value for antecedent correlates of early retirement (family pull, job stress, job satisfaction, and income) was obtained (which ranged from r = −0.13 to 0.19), while a fair ES was obtained for workplace timing for retirement, organizational pressures, financial security, and poor physical and mental health, (ranging from r = 0.28 to 0.25). Regarding ER subsequent correlates, poor ESs were obtained, ranging from r = 0.08 to 0.18 for the relationships with subsequent correlates, and fair ESs only for social engagement (r = −0.25). Examination of the potential moderator variables has been conducted. Only a reduced percentage of variability of primary studies has been explained by moderators. Although potential moderator factors were examined, there are several unknown or not measurable factors which contribute to ER and about which there are very little data available. The discussion is aimed to offer theoretical and empirical implications suggestion in order to improve employee's well-being.

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“…5 Early retirement depends not only on the individual's health status [6][7][8][9][10][11] but also on nonmedical factors, such as working conditions, [12][13][14] socioeconomic status, 9,12,[15][16][17] and sex. 18 At the macro level, the society's social security system [18][19][20][21] as well as unemployment rates 22 influence the likelihood of returning to work.…”

Section: Introductionmentioning

confidence: 99%

Early retirement in cancer patients with or without comorbid mental health conditions: A prospective cohort study

Schnittger

Meyer

Wienholz

et al. 2014

Cancer

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BACKGROUND: The authors investigated whether cancer patients who have comorbid mental health disorders (MD) are at greater risk of early retirement compared with those who do not have MD. METHODS: Individuals ages 18 to 55 years from a consecutive sample of patients who were admitted for inpatient oncologic treatment were interviewed using structured clinical interviews to ascertain MD. The patients were followed for 15 months, and the date of early retirement was documented. Rates of early retirement per 100 person-years (py) in patients with and without MD were compared using multivariate Poisson regression models. RESULTS: At baseline, 491 patients were interviewed, and 150 of those patients (30.6%) were diagnosed with MD. Forty-one patients began full early retirement during follow-up. In patients with MD, the incidence of early retirement was 9.3 per 100 py compared with 6.1 per 100 py in mentally healthy patients. The crude rate ratio (RR) was 1.5 (95% confidence interval [CI], 0.8-2.8). The effect of MD on early retirement was modified in part by income: in patients with low income, the adjusted RR was 11.7, whereas no effect was observed in higher income groups. Patients with depression were at greater risk of retirement when they had higher income (RR, 3.4; P 5.05). The effects of anxiety (RR, 2.4; P 5.05), adjustment disorders (RR, 1.7; P 5.21), and alcohol dependence (RR, 1.8; P 5.40) on early retirement were equal across income groups. CONCLUSIONS: Mental health conditions are risk factors for early retirement in cancer patients, although this effect differs according to the type of disorder and the patient's income level. Cancer 2014;120:2199-206.

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Early Retirement in the Three Types of Welfare States

Cited by 20 publications

References 29 publications

Comparison the effects of poor health and low income on early retirement: a systematic review and meta-analysis

Comparison the effects of poor health and low income on early retirement: a systematic review and meta-analysis

Early Retirement: A Meta-Analysis of Its Antecedent and Subsequent Correlates

Early retirement in cancer patients with or without comorbid mental health conditions: A prospective cohort study

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