How do subjective life expectancies compare with mortality tables? Similarities and differences in three national samples

Bell, David; Comerford, David; Douglas, Elaine

doi:10.1016/j.jeoa.2020.100241

Cited by 10 publications

(10 citation statements)

References 24 publications

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“…Specifically, it shows a non-linearity in the mapping from beliefs to reported percentages. In the English data that is the object of the current analysis, the available data suggest a reluctance to report responses of 0% and 100% (Oswald, 2008;Bell et al, 2020). On average in England, women live longer than men.…”

Section: Are Gender Differences In Survival Beliefs Robust?mentioning

confidence: 88%

“…If so, it might explain an anomaly in reported probabilities across English and Irish samples. When asked for SSPs regarding surviving 11-15 years into the future, just 6 percent of English respondents to ELSA answered 100% compared to over 30 percent of Irish respondents (Bell et al, 2020). The authors suggest cultural differences as a potential mechanism driving this divergence.…”

mentioning

confidence: 88%

“…The results of Oswald ( 2008) demonstrate respondents' reports can be systematically higher or lower than their beliefs and that this is especially true in the tails of the distribution. Various results suggest that this insight applies to reported probabilities (e.g., Merkle et al, 2013;Bell et al, 2020) with the result that the bias term is larger if the respondent's belief is especially high or especially low. To the extent that reporting bias varies systematically with likelihood beliefs, it is overly restrictive to interpret reported percentages as a linear translation of respondents' likelihood beliefs.…”

mentioning

confidence: 96%

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Apparent age and gender differences in survival optimism: To what extent are they a bias in the translation of beliefs onto a percentage scale?

Comerford

2021

Judgm. decis. mak.

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A standard way to elicit expectations asks for the percentage chance an event will occur. Previous research demonstrates noise in reported percentages. The current research models a bias; a five percentage point change in reported probabilities implies a larger change in beliefs at certain points in the probability distribution. One contribution of my model is that it can parse bias in beliefs from biases in reports. I reconsider age and gender differences in Subjective Survival Probabilities (SSPs). These are generally interpreted as differences in survival beliefs, e.g., that males are more optimistic than females and older respondents are more optimistic than younger respondents. These demographic differences (in the English Longitudinal Study of Ageing) can be entirely explained by reporting bias. Older respondents are no more optimistic than younger respondents and males are no more optimistic than females. Similarly, in forecasting, information is obscured by taking reported percentages at face value. Accounting for reporting bias thus better exploits the private information contained in reports. Relative to a face-value specification, a specification that does this delivers improved forecasts of mortality events, raising the pseudo R-squared from less than 3 percent to over 6 percent.

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Section: Are Gender Differences In Survival Beliefs Robust?mentioning

confidence: 88%

mentioning

confidence: 88%

mentioning

confidence: 96%

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Apparent age and gender differences in survival optimism: To what extent are they a bias in the translation of beliefs onto a percentage scale?

Comerford

2021

Judgm. decis. mak.

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“…Obviously, how age demographics and life expectancy influence age-related mechanisms depend on the individuals' preciseness and ability to estimate such societal demographic characteristics. Extant research has investigated the extent to which subjective life expectancy correlates with actual estimates (e.g., Mirowsky 1999) and life tables (e.g., Bell and Douglas 2020).…”

Section: Lifespan Theory In Broader Institutional Contextsmentioning

confidence: 99%

Age effect on entry to entrepreneurship: embedded in life expectancy

2020

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Major improvements in life expectancies associate with interesting societal transformations.By changing individuals' preferences for engaging in both productive and non-productive activities across their lifespan, changes in life expectancy paradoxically affect both the financing and the costs of social welfare systems. This raises the importance of knowledge on how life expectancy affects the relationship between age and entry to entrepreneurship. Using lifespan theory, we propose a conceptual model for understanding how age-related psychological effects (i.e., risk-willingness and self-efficacy) on individuals' entry to entrepreneurship are embedded in life expectancy at the societal level. Based on data from 34 Organisation for Economic Co-operation and Development countries, our analysis shows that increased life expectancy at the societal level moves the peak for entrepreneurial entry toward older ages and creates an evener entry distribution across working ages.The results suggest that this effect is driven partly by a cross-level moderation from life expectancy on the relationship between age and individuals' risk-willingness and self-efficacy, which in turn promotes entry to entrepreneurship. These findings highlight the importance of considering entrepreneurial activity in the context of age and of future-time perspective in terms of life expectancy.

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“…When comparing the elderly stratum with the lifestyle significant differences were found ( P = .000). Contrary to this finding, Bell et al 36 report that elderly with previous savings, asset allocation, and insurance decisions have better subjective longevity (Irish case), which they attribute to cultural differences that influence their lifestyle.…”

Section: Discussionmentioning

confidence: 85%

Healthy Lifestyles Associated With Socioeconomic Determinants in the Older Adult Population

González

Castaño

Zemanate

et al. 2022

J Prim Care Community Health

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Introduction/Objective: The evaluation of lifestyle in older adults and the study of socioeconomic determinants becomes an essential indicator of the health conditions of older adults. The purpose of this study was to establish the relationship between socioeconomic factors and healthy lifestyles in older adults. Methods: Study with a quantitative approach, descriptive type, non-experimental design, cross-sectional in a sample of 407 elderlies who have applied a self-designed instrument for socioeconomic characterization and the FANTASTIC test to assess lifestyle. For data analysis, a bivariate analysis was applied using chi2 and multivariate analysis using ordinal logistic regression. Results: 53% of elderlies aged between 60 and 70 years reported their lifestyle as excellent and very good. Age, average household income, and perceived health status are associated with healthy lifestyles in older adults. Conclusions: This study found that in addition to socioeconomic determinants, self-perceived health is a factor that influences the lifestyles of this population.

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How do subjective life expectancies compare with mortality tables? Similarities and differences in three national samples

Cited by 10 publications

References 24 publications

Apparent age and gender differences in survival optimism: To what extent are they a bias in the translation of beliefs onto a percentage scale?

Apparent age and gender differences in survival optimism: To what extent are they a bias in the translation of beliefs onto a percentage scale?

Age effect on entry to entrepreneurship: embedded in life expectancy

Healthy Lifestyles Associated With Socioeconomic Determinants in the Older Adult Population

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