group life demographic trends and mortality improvement€¦ · 11/15/2013 munich re 1 group life...

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GROUP LIFE DEMOGRAPHIC TRENDS

AND MORTALITY IMPROVEMENT

Amy Whinnett, FSA, MAAA

Date: 21 November 2013

Purpose

To assess the impact of recent

trends in aging and mortality

improvement on Group Life

experience and to provide a

framework for developing future

best estimate assumptions

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Agenda

1. Demographic Trends

Historical Changes

Future Projections

Cost of Aging

2. Mortality Improvement

Historical Population Trends

Segmentation

3. Mortality Improvement and Aging Projections

Current Industry Practices

Considerations for Future Projections

DEMOGRAPHIC TRENDS

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Recent Aging Trends in the US Workforce

Population as driver of

work force

Labor force

participation rate

impact

Aging of Baby Boomer

population

Impact of 2007-2009

recession

Group Life insured

trends

Stable growth in average age over the past decade

40.0

40.5

41.0

41.5

42.0

42.5

43.0

Average Age of US Workforce

Source: US Bureau of Labor Statistics

Male Female

40.0

40.5

41.0

41.5

42.0

42.5

43.0

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Average Age of US Workforce Source: US Bureau of Labor Statistics

Male Female


Future Demographic Trends in the US Workforce

Aging projected to

continue through 2020

Aging rate expected to

decline in post-recession

period

Females expected to

continue to represent

47% of US workforce

Future drivers include

fertility, mortality,

immigration, education

Older work force driven

by financial conditions

and access to healthcare

Stable growth in average age over the past decade

30.0

32.0

34.0

36.0

38.0

40.0

42.0

44.0

1980 1990 2000 2010 2020

Median Age of US Workforce Source: US Bureau of Labor Statistics

Male Female


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The Cost of Aging

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Cost of one year of aging varies based on age of the insured population

-15%

-10%

-5%

0%

5%

10%

15%

17-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90

Annual Average Cost of Aging

Male

Female

MORTALITY IMPROVEMENT

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Population Mortality Improvement


1.3%

-2% -1% -1% 0% 1% 1% 2% 2% 3% 3%

20-35 35-65 65-85 85-100 Total 20-65

Male Avg Mortality Improvement

Compressed Mortality File

2000-2007 2007-2010 2000-2010

0.9%

-2%

-2%

-1%

-1%

0%

1%

1%

2%

2%

3%

20-35 35-65 65-85 85-100 Total 20-65

Female Avg Mortality Improvement

Compressed Mortality File

2000-2007 2007-2010 2000-2010

Population mortality improvement steady at 1% per year for working ages

Lifestyle Drivers of Mortality Improvement – Obesity

Levels


Rates of obesity drive mortality improvement rates and vary by geographical location

Source: CDC Behavioral Risk Factor Surveillance System

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Lifestyle Drivers of Mortality Improvement - Smoking


Smokers experienced little or no mortality improvement over past decade

Source: CDC Behavioral Risk Factor Surveillance System

Mortality Improvement by Region

Up to +/- 2% variation

in mortality

improvement by

geographical region

Lifestyle variances

including obesity and

smoking

Access to health care

as driver – potential for

narrower gap with

health care reform

Mortality improvement variation reflected by geographical region

Region States

1 CT, ME, MA, NH, RI, VT

2 NJ, NY

3 DE, DC, MD, PA, VA, WV

4 AL, FL, GA, KY, MS, NC, SC, TN

5 IL, IN, MI, MN, OH, WI

6

AR, LA, NM, OK,

TX

7 IA, KS, MO, NE

8 CO, MT, ND, SD, UT, WY

9 AZ, CA, HI, NV

10 AK, ID, OR, WA

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

1 2 3 4 5 6 7 8 9 10

Male Female


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Mortality Improvement by Education Level

Indicator of MI by

socioeconomic status

Highest education

levels reflect MI

approximately double

population levels

Mortality deterioration

at lowest education

levels

1999-2011 NCHS MI of

counties by per capita

income reflect

consistent patterns

Mortality improvement varies significantly by education level

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

Male Female

Mortality Improvement Rates by Highest Education Level 1993-2001 US 25-64 Year Old Adults

Some HS

HS Graduate

Some College

College Graduate

All


Mortality Improvement by Education Level and Cause


Significant spread in mortality improvement by education regardless of cause of death

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

Accidental Deaths

Diabetes COPD

Mortality Improvement Rates by Education and Cause

1993-2001 US 25-64 Year Old Adults

Some HS

HS Graduate

Some College

College Graduate

All

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

Cancer Stroke Heart Disease

Mortality Improvement Rates by Education and Cause

1993-2001 US 25-64 Year Old Adults

Some HS

HS Graduate

Some College

College Graduate

All

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Industries with Lowest Education Level Attained


Agriculture

Forestry, Fishing and Hunting

Accommodation and Food Services

Construction

Administrative and Support and

Waste Management Services

Transportation and Warehousing

Mining

Quarrying, and Oil and Gas

Extraction Manufacturing

Potential Reduction in Mortality

Improvement of 0.5-1% Below

Standard MI Assumption

No HS Diploma

18%

High School

Only 38%

Some College

29%

Bachelors Degree+

15%

Education Level of US Workforce by NAICS Segment

Source: Bureau of Labor Statistics, 2009

Industries with Average Education Level Attained


Retail Trade

Health Care and Social Assistance

Real Estate and Rental and Leasing

Arts, Entertainment, and

Recreation

Wholesale Trade

Utilities

Assume Standard MI Assumption

No HS Diploma

10%

High School

Only 31%

Some College

35%

Bachelors Degree+

24%



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Industries with Highest Education Level Attained


Federal, State and Local

Government

Educational Services

Information

Finance and Insurance

Management of Companies and

Enterprises

Professional, Scientific, and

Technical Services

Potential Increase in Mortality

Improvement of 0.5-1% Above

Standard MI Assumption

No HS Diploma

4%

High School

Only 20%

Some College

30%

Bachelors Degree+

46%



MORTALITY IMPROVEMENT AND AGING

PROJECTIONS

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Current Industry Practice

2013 Munich Re survey of 27 Group Life carriers

45% of carriers incorporate mortality improvement assumptions in pricing

active lives (includes most large carriers)

17% apply MI assumption to retirees and spouses

11% apply MI assumption to dependents

Review of 10 carriers’ income statements for 2008-2012

Claims as % of volume has been stable, indicating MI offset by aging

Higher rate of aging during recession 2007-2009 indicates potentially higher

mortality improvement during that period

Industry rule of thumb assumptions of 1-1.5% per year MI for males, 0.5-0.75%

for females


Monitoring Historical Experience

SOA 2013 Group Life Mortality Study

Comparison with 2006 study to assess trends by segment (age, gender,

traditional/voluntary, industry)

Consider data limitations of study comparison such as company mix,

changes in risk control

Carrier level historical experience to set manual rate MI assumptions

Monitor trends in A/E incidence

Regression model fitting study year as variable

Case level MI trends for experience rating

Monitoring system for census changes at case level is key to understanding

Isolate impact of aging against mortality improvement


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Setting Future Aging and Mortality Improvement

Assumptions

Consider timeframe between experience period and rate effective period

Years of experience used in setting manual/experience rate

Gap between experience period and rate effective date

Rate Guarantee period, renewal practices, manual rate filing frequency

Baseline aging/mortality improvements derived based on historical population

and industry trends

Segmentation of MI assumptions

Age/gender

Socioeconomic drivers – industry, salary, average face amount

Geographical location

Active/Spouse/Retiree

Traditional vs. Voluntary – consider impact of anti-selection


Considerations for using Past Experience to set Future

Aging and Mortality Improvement Assumptions

Medical advances

Potential for new diseases/pandemics

Impact of health care reform, lower overall spend but smaller gap by income

Lifestyle trends such as obesity, smoking, alcohol consumption and driving

habits

Consideration of potential human lifespan – will expected lifetime converge to

certain limit?

Shifts in makeup of insured population

Changes in employers offering Group Life and mix of employees covered

Changes in Medical UW practices including simplified/automated UW over GI

limit

New distribution channels such as health care exchanges


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THANK YOU FOR YOUR ATTENTION

QUESTIONS?

group life demographic trends and mortality improvement€¦ · 11/15/2013 munich re 1 group life...

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