Longevity expectations in the pension fund, insurance, and employee benefits industries

Figures & data

Table 1 Nations of birth and current residence

Nation	Nation of birth		Nation of residence
	N	Percent	N	Percent
Austria	1	1	1	1
Belgium	11	11	16	16
Brazil	1	1	1	1
Canada	1	1	1	1
France	4	4	4	4
Germany	11	11	11	11
Greece	1	1
Ireland (Republic of)	1	1	2	2
Israel	1	1	1	1
Luxembourg			2	2
Italy	2	2
Netherlands	12	12	9	9
Nigeria	1	1
Norway	1	1	1	1
Romania	1	1
Singapore	1	1
South Africa	3	3
Spain	1	1	1	1
Sweden	9	9	8	8
Switzerland	7	7	16	16
United Arab Emirates			1	1
United Kingdom	27	27	22	22
United States	3	3	3	3
Sum	100	100	100	100

Table 2 Belief in an afterlife

Response	N	Percent of responses
There is no life after death	44	48
I believe in reincarnation	9	10
I believe in heaven and hell	20	22
Prefer not to say	11	12
Do not know	7	8
No response	9	NA

Abbreviation: NA, not applicable.

Figure 1 Expected age of death.

Table 3 Pearson product-moment correlations among attitudinal variables

Variable	Disease	Expect live	Productive	Retire by	No retire	Technology
Disease	1.00
Expect live	0.13*	1.00
Productive	0.22*	0.19*	1.00
Retire by	0.10*	0.34**	0.05	1.00
No retire	0.00	0.00	0.00	0.00	1.00
Technology	0.15*	0.21*	0.16*	0.05	0.00	1.00

Notes:

* Small effect size (r≥0.10);

** medium effect size (r≥0.30). Variables: disease = aging is a disease (1= yes); expect live = age to which respondent expects to live; productive = number of productive years remaining; retire by = age by which respondent expects to retire; no retire = respondent does not expect to retire (1= does not expect to retire; few respondents chose this option); technology = “If you had access to medical technology to maintain your good health and youthful state for as long as you wanted, up to what age would you like to live?”

Table 4 Sex-related differences in attitudes toward the future

Attitudinal variable	Mean score		F	Significant at P<0.05
	Female	Male
Disease	0.12	0.11	0.01	No
Expect live	89.20	86.8	1.04	No
Productive	29.17	20.69	8.71	Yes
Retire by	64.35	65.83	1.29	No
No retire	0.08	0.01	2.89	No
Technology	99.38	100.75	0.02	No

Notes: Variables: disease = aging is a disease (1= yes); expect live = age to which respondent expects to live; productive = number of productive years remaining; retire by = age by which respondent expects to retire; no retire = respondent does not expect to retire (1= does not expect to retire; few respondents chose this option); technology = “If you had access to medical technology to maintain your good health and youthful state for as long as you wanted, up to what age would you like to live?”

Table 5 Degree to which companies rely on different variables in developing theories of longevity

Variable	Count	Mean percentage of reliance	SD
Actuarial tables	82	80.29	26.41
Analytical reports	39	29.21	20.04
Simulations biomedical	22	8.59	12.07

Notes: Variables: analytical reports = the company’s own analytical reports; simulations biomedical = simulations involving recent biomedical discoveries.

Abbreviation: SD, standard deviation.

Table 6 How companies adjust their theories of longevity

Variable	N	Percent of participants
Peer review	28	28
Analytical report industry association	32	32
Government reports	37	37
Internal reports	18	18
Not biomedical	29	29

Notes: The question asked of respondents was: “My company would adjust its internal life expectancy estimates if the following reports on biomedical discoveries were available:”. Variables: peer review = peer-reviewed publications showing how biomedical discoveries may influence mortality and productivity; analytical report industry association = analytical reports by industry associations showing how biomedical discoveries may influence mortality and productivity; government reports = government-issued reports; internal reports = internal analytical reports; not biomedical = the respondent’s company will not incorporate the possible effects of biomedical discoveries into their estimates.

Table 7 How respondents influence their companies’ theories of longevity

Variable	Count	Percent of participants
Retirement policy	52	52
Health care policy	42	42
Government-related lobbying	18	18
Industrial relations	22	22
Investment strategy	19	19
Investment decisions	14	14
Other factors	11	11

Note: The question asked of respondents was: “At my organization I can influence the company’s (click all that apply):”.

Table 8 How respondents develop personal theories of longevity

Variables	N	Mean	SD
Actuarial tables	76	29.32	24.22
Family history	90	43.90	28.42
Corporate analytical reports	36	6.84	12.06
Gerontologists	46	11.50	10.34
Biomedical	64	20.12	21.73
Other	30	19.17	25.05

Note: The question asked of respondents was: “I form my own expectations about my life expectancy using (enter percentage as a whole number, eg, 10):”.

Abbreviation: SD, standard deviation.

Figure 2 Expected retirement age (women).

Table 9 Respondent willingness to engage in therapies to reach 150 years of age

Treatment option	N	Percent of participants
Stem cell therapy	28	28
Organ replacement	15	15
Gene therapy	19	19
Blood filtration and enrichment	22	22
Injections of drugs that slow down aging	28	28
Noninvasive	56	56

Note: The question asked of respondents was: “Would you consider undergoing the following preventative and therapeutic procedures to allow you extend your healthy life span beyond 150 years (check everything that applies):”.

Figure 3 Age which respondents would desire to reach, given supporting technology.

Table S1 Survey questions and response options

Original survey		Rephrasing or recoding used in analyses and reporting
Question	Response options
In what year were you born? (enter 4-digit birth year)	(Open-ended response)	BirthYear
What is your gender?	(“Male” or “Female”)	Sex (within data, coded as 1= male, 0= female, or missing)
In what country do you currently reside?	(Open-ended response)	CountryResidence
In what country were you born?	(Open-ended response)	CountryBirth
Which of the following best describes your current relationship status?	(Open-ended response)	RelationshipStatus
How many children do you have?	(Open-ended response)	NumberChildren
What is the highest level of education you have completed?	(Open-ended response)	HighestEdLevel
My educational background is in (choose everything that applies)	Arts	EducArtsSocialSciences
	Economics	EducEconomics
	Law	EducLaw
	Actuarial science	EducActuarialScience EducMatha EducLanguagesLinga EducFinanceBankinga Insurancea BusinesOther
	Science/technics Other (please specify)	EducScience
Do you identify with any of the following religions? (Please select all that apply)	Protestantism	RelProtestantism
	Catholicism	RelCatholicism
	Christianity	RelChristianity
	Judaism	RelJudaism
	Islam	RelIslam
	Buddhism	RelBuddhism
	Hinduism	RelHinduism
	Native American	RelNativeAmerican
	Inter/Non-denominational	RelInterNonDenom
	No religion	RelNoReligion
	Prefer not to say	RelPreferNotToSay Afterlife
	Other (please specify)	AfterLifeOther
Do you consider aging to be a disease?	(“Yes” or “No”)	AgingIsDisease
	Please explain	AgingIsDiseaseExplain
I expect to live to	(Open-ended response)	ExpectToLive
The number of productive years ahead of me is at least	(Open-ended response)	NumberProductiveYears
I plan to retire from paid work at or before	(Open-ended response)	IWillRetireBy (for integer response)
	(Open-ended response)	IWillNeverRetire (there were some text responses to this effect)
If you had access to medical technology to maintain your good health and youthful state for as long as you wanted, up to what age would you like to live?	Over 65 Over 100 Over 150 200 Over 200	If you had access to medical technology to maintain your good health and youthful state for as long as you wanted, up to what age would you like to live?
What is the top end of the range for life expectancy estimates (number of years remaining) within your company for today’s 65 year old females (please enter the number)?	(Open-ended response)	What is the top end of the range for life expectancy estimates (number of years remaining) within your company for today’s 65 year old females (please enter the number)?
What best describes your company?	Pension fund Insurance company Consulting company Non-profit Other (please specify)	CompType
At my organization I can influence the company’s (click all that apply)	Retirement policy	InfluenceRetirementPolicy
	Healthcare policy	InfluenceHealthcarePolicy
	Government relations and lobbying	InfluenceGovRelLobbying
	Industry relations	InfluenceIndustryRel
	Investment strategy	InfluenceInvestStrategy
	Investment decisions	InfluenceInvestDecisions
	Other (please specify)	InfluenceOther
I form my own expectations about my life expectancy using (enter percentage as a whole number, eg, 10)	Actuarial tables	MyExpectActuarialTables
	Family history	MyExpectFamilyHistory
	My company’s analytical reports	MyExpectCorpAnalyticalRep
	Predictions of leading gerontologists	MyExpectGerontologists
	My own opinion considering biomedical discoveries in the press	MyExpectMyOpinionBiomedial
	Other	MyExpectOther
My company uses the following to predict life expectancy (enter percentage as a whole number, eg, 10)	Actuarial tables	CompExpectActuarialTables
	Our own analytical reports	CompExpectAnalyticalReports
	Simulations involving recent biomedical discoveries	CompExpectSimulationsBiomedical
	Other	CompExpectOther
My company would adjust its internal life expectancy estimates if the following reports on biomedical discoveries were available	Peer-reviewed publications showing how biomedical discoveries may influence mortality and productivity	CompAdjustIfPeer-Review
	Analytical reports by industry associations showing how biomedical discoveries may influence mortality and productivity	CompAdjustExpectIfAnalyticalReportIndustAssoc
	Government-issued reports	CompAdjustExpectIfGovernmentReports
	Internal analytical reports	CompAdjustExpectIfInternalReports
	We will not incorporate the possible effects of biomedical discoveries into our estimates	CompAdjustExpectIfNOTBiomedical
	Other (please specify)	CompAdjustExpectIfOther
How strongly do news about biomedical discoveries (eg, stem cells, gene therapy, cancer treatment) affect your perception of longevity	Weakly. These news do not affect my perception of longevity	HowStronglyNewsAffectsExpectLongevity
	Strongly. These news make me feel that I will live significantly longer
	I am reconsidering my longevity
	Other (please specify)	StronglyNewsAffectsExpectLongevityOther
Would you consider undergoing the following preventative and therapeutic procedures to allow you to extend your healthy life span beyond 150 years (check everything that applies)	Stem cell therapy	Consider150StemCell
	Organ replacement	Consider150OrganReplace
	Gene therapy	Consider150GeneTherapy
	Blood filtration and enrichment	Consider150BloodFilt
	Injections of drugs that slow down aging	Consider150Injections
	I would not undergo any invasive procedures	Consider150NoInvasive
	Other (please specify)	Consider150Other
In your opinion, what should be the retirement age for today’s 40-year old female?	55
	65
	75
	80
	(Where the subject’s response is either 55, 65, 75 or 80, assign that value to this variable)	RetireAge40Female
	Whatever the government sets it to be	RetireAge40FemGovtSets
	Must be adjusted with the possible impact of biomedical advances in mind	RetireAge40FemBiomed
	Other (please specify)	RetireAge40FemOther
In your opinion, your organization will be able to remain solvent if the average life expectancy of today’s 65-year old females increases to	(Open-ended response)	CompSolventIf65FebReaches
	My company is able to sustain any increases in life expectancy	CompCanSustainAnyLifeExpect
	My company will benefit from increases in life expectancy	CompBenefitIncreaseLifeExp
	Other (please specify)	SolventOther
Would you like to live to 200 years of age if you had the ability to maintain your health at the same level as in your healthiest and most productive age?	(“Yes”, “No”, or no response)	LikeLive200
	Other (please specify)	Live200Other
Did this survey affect your perception of your own life expectancy?	(“Yes”, “No”, or no response)	DidSurveyChange Your ExpectLife

Notes:

a Additional response categories were created based on classification of open-ended responses. Subject responses for these categories were then treated as if they were an affirmative response for a preset category or multiple-choice response option.