University of Connecticut, Janet & Mark L. Goldenson
Center for Actuarial Research
Annual Advisory Board Meeting, September 20, 2013
University of Connecticut
Department of Mathematics
College of
Liberal Arts and Sciences
Table of Content





Children’s Museum Enterprise Risk Management Analysis
Long-Term Care Predictive Modeling
Simulation Study of Modified Credibility Weighting for Group LongTerm Disability Termination Rates
Affordable Healthcare Act
National Retirement Satisfaction Index
2
Children’s Museum ERM Analysis
Children’s Museum ERM
Analysis
4
AAMIR ADDONNA
KATHLEEN SNAJDER
SUZANNE XIE
ADVISOR: JAY VADIVELOO
UNIVERSITY OF CONNECTICUT
Purpose of Report
5
 Provide Museum with an outside perspective on
their risks and help determine the steps they can
take to handle the risks.
 Risks deemed most pressing are addressed.
 Help Museum realize their strategic plan, not change
it.
Types of Risks
6
 Legal
 Employee litigation, risk of harm while on premises
 Operational
 Staff/personnel, loss of buildings and contents, IT security
 Strategic
 Reputation, failure to stay competitive in market
 Financial
 Support from donations and government, insufficient financial
controls
Suggestions for Legal Risks
7
 Inevitable part of running a business
 Should be discussed with Museum’s legal
department or lawyers
 One suggestion: reevaluate current insurance
coverage for cost effectiveness and completeness
Suggestions for Operational Risks
8
 Need to constantly improve to “keep it fresh”
 Gauging and improving reputation
 Administer quick survey or have a suggestion box
 Offer incentives for visitors to provide feedback
 Attracting and maintaining quality staff
 Host staff appreciation events
 Provide some sort of incentive compensation plan
 Attract new talent by upholding reputation (discussed later)
Suggestions for Strategic Risks
9
 Need to stabilize revenues and manage reputation
 Offer additional programs to counteract seasonality
 Attendance plummets when school year begins
 Local partnership
 Create partnership with some local businesses, such as zoos, to
offer combined discounts
 Mutually beneficial scholarship
 Host a competition where high school students propose new
exhibit ideas for Museum
Suggestions for Financial Risks
10
 Need to build reserves to absorb revenue fluctuations
 In 2011-2012, 50% or more of each month’s revenue was
obtained from outside support.
 Gifts, grants and donations comprise the largest percentage of
revenue
 Build endowment over the next 3-5 years
 Endowment of $30 million will generate a perpetuity of
$122,000 each month at an annual interest rate of 5%
 Amount covers current development efforts (on average each
month)
 Museum can only focus on other endeavors when it
becomes financially independent from donations
Game Plan to Build Endowment
11
 Create 3-5 year plan
 Sell Museum to corporations and the community,
like in 1995

Show Museum’s impact on the community
 Distinguish from competitors
 Relationships are crucial
 Develop endowment campaign in partnership with local
schools and government
 Full visibility given to donors whose endowment donation is
geared towards specific programs
 Embrace technology
 Market smart and efficiently; promote “lifelong learning”
Conclusion
12
 Conflicting challenges
 Remain affordable to community while remaining fresh and
dynamic
 Plan needs to be long-term, since short-term
suggestions will not solve main problems
 Should gain financial independence

Achieved through high level of commitment, planning, and
support from all constituents
Long-Term Care Predictive Modeling
Long-Term Care Predictive Modeling Project
Project Advisors: Jay Vadiveloo, Brian Hartman
Research Team: Peter Camacho, Wanqing Ma, and Gao Niu
14
Initial Goals and Data Description
 Our Client came to us seeking a better understanding of the factors that drive
their future economic risk. Mortality, Disability, and Lapse decrements for their
Individual and Group long-term care (LTC) active policies (A total of 6 Models)
 Historical claims data on in-force policies (2000 – 2012).
 32 unique independent variables; 3 decrements (dependent variables)
 Over 10M records were submitted
 Less than 17% of the raw data were removed for missing and inconsistent
entries.
 Altogether, 8 predictive models developed
 Poisson GLM Modeling (Industry standard).
 Modeling done in SAS and TW proprietary software Emblem.
15
Basic Model
 Modelling based on a Poisson GLM with log monthly exposure as offset
 Predictor variables developed based on client input and use of Stepwise Logistic
Regression techniques
Monthly Rate of Claims = (Monthly Claims /Monthly Exposure)
Monthly Rate of Claims≈EXP(β0+β1*X1+...+βn *
Xn )
Client
Call
Internal
Meeting
Modeling
Work
16
EVOLUTION OF RESEARCH
 Additional Variables.
 Special interest in Alzheimer’s related Claims.
 Creation of conditional Alzheimer’s model gave the
client a powerful new way to anticipate these high
liability claims.
 Challenging to model due to low frequency of Alzheimer's
incidence relative to high total months of exposure.
 As such, we needed an alternative to classic modeling
procedures
17
CONDITIONAL MODEL
Conditional Monthly Incidence of Alzheimer’s
Of all those who went on claim, what proportion
when on claim due to Alzheimer’s?
Model 3:
Overall Monthly Rate
of Alzheimer’s Claims
Monthly Rate of Alzheimer’s Related Claims ≈
(Monthly Claims )* (Monthly Conditional Rate of Alzheimer’s)
Model 1:
Original Model
Model 2:
Conditional Model
18
SECTION II: RESULTS
19
CONTEXT EXAMPLE : GROUP LONG TERM CARE MORTALITY MODEL
 Note: Have beta estimates to 2 decimal places
 Model Interpretation
 E[Monthly Mortality Rate] = EXPONETIAL(β0 + β1* X1 + β2 * X2 + β3 * X3)
 X1= AttAge
 X2=1 if Gender=Female, -1 if Gender=Male
 X3= Duration
 Example: For a 40 years old female whose contract is in duration 10,
X1 = 40, X2 = 1, X3 = 10
 E[Monthly Mortality Rate per 1000] = 0.01

20
POWER OF OUR MODELS
21
22
23
24
25
26
27
DATA TRANSFER AND FINAL DELIVERABLES
 Written report of findings and PowerPoint presentation to client
 SAS codes for all 8 predictive models
 8 Predictive models
 3x ILTC ( Mortality, Lapse, Incidence)
 3x GLTC ( Mortality, Lapse, Incidence)
 2x Conditional Alzheimer's Incidence (ILCT, GLTC)
 Rate Calculator
 Single user interface collection of all of all 8 models.
 Manual variable input.
 Audit check
28
RATE CALCULATOR : DEMO
Note: [Here I think we should rap things up with Demo of the rate calculator
that Gao was able to put together. This allows us to leave the audience with a
tangible final product of the work we did]**
29
Simulation Study of Modified Credibility Weighting for Group
Long-Term Disability Termination Rates
Simulation Study of Modified Credibility
Weighting for Group Long-Term
Disability Termination Rates
31
HUITZE RUAN
SUZANNE XIE
ADVISOR: JAY VADIVELOO
UNIVERSITY OF CONNECTICUT
Abstract
32
 Work Group’s model
 Limited Fluctuation Credibility(LFC) Theory
 Selected Variance Factor (A)
 Our model
 Positive and negative dependence of termination rates
 Monte Carlo simulation process
 Results
 Reduction in credibility weights is significantly smaller than the
reduction in credibility weights proposed by Work Group
LFC Model
33
 Suggested formula for blended termination rates:
Z ´ q +(1- Z)´ q
A
E
qA: Company’s actual termination rates with 85% margin
qE: Experience terminations rates based on 2012 GLTD
Valuation Tables
Z: Credibility weighting factor using the LFC model
LFC Model
34
 Independence Model
Use confidence level of 85% and error tolerance of 5%
2
 K= æ 1.44 ö
ç
÷
è 0.05 ø
 Z= min( N ,100%)
K
K=number of expected terminations needed for full credibility
N=number of expected terminations for the same period using company’s
actual exposure
Dependence Model
35
 Work group suggests the use of a Selected Variance Factor
(A), an arbitrarily defined number, to reflect the
dependence characteristic
 Our model suggests the use of a Selected Variance Factor
(A*) is defined as:
æ std(q̂ D ) ö
A* = ç
I ÷
è std(q̂ ) ø
2
Where std(q̂ I )and std (qˆ D ) are standard deviation of actual
termination rates based on the independence and dependence model,
respectively
Dependence Model
36
 Work Group’s model
æ 1.44 ö
ç
÷ ´A
è 0.05 ø
2
 K=
 Z= min(
N
,100%)
K
 Our model
æ 1.44 ö æ std(q̂ D ) ö
 K*= ç
÷ ´ç
I ÷
è 0.05 ø è std(q̂ ) ø
2
 Z= min(
2
N
,100%)
*
K
Our Selected Variance Factor (A*)
std(q̂ D ) 2
)
=(
I
std(q̂ )
Methodology (Independent Model)
37
Monte Carlo simulation
 Use 1000 lives and 1000 simulations per set of 1000 lives
 Use termination rates from a 2008 LTD table for a male
aged 42 with an elimination period (EP) of 90 days
Methodology(Independent Model)
38
 Choose two stage termination rates by different durations as
suggested in the Guideline
Group
Months after EP
1
<=3
2
>3 and <=24
3
>=24 and <=60
4
>=60 and <=120
5
>120
 For each simulation, capture initial exposure counts at the
beginning of stage 2 (B) and actual claim terminations in
stage 2 (A)
 Average claim termination rate in stage 2 is A/B
Methodology (Dependent Model)
39
 Same methodology as independent model for first stage
 q1A=(actual terminations in stage 1)/1000
 The new expected termination rate in stage 2 is adjusted
under two extreme situations:


Positive relationship: q2*E = (q1A/ q1E)*( q2E)
Negative relationship:q2*E = (q1E/ q1A)*( q2E)
 Calculate A/B, the average claim termination rate in stage 2
Methodology (Cont.)
40
 Repeat simulation process 1000 times. Calculate
mean (µ) and standard deviation (σ) of the actual
termination rates in stage 2 (A/B)
 Take ratio of both models:


Ratio1 = µdependence/µindependence ≈ 1
std(q̂ D )
Ratio2 = σdependence/σindependence =
std(q̂ I )
Methodology (Cont.)
41
 Test termination rates at beginning, middle, and end of
each duration group
 Adjust q2*E to account for less direct dependency between
termination rates in later durations





Group 1(<4 months)
: Z = 1; disregarded
Group 2 (4 to 24 months) : no adjustment to q2*E
Group 3 (25 to 60 months) : 75% of original q2*E
Group 4 (61-120 months) : 50% of original q2*E
Group 5 (> 120 months) : 25% of original q2*E
Results (Cont.)
42
Positive Dependency Relationship
Duration Group
(Months)
Selected Variance
Factors (A)
Selected Variance
Factors (A*)
Guideline
Simulation
4 to 24
4.0
1.8
25 to 60
3.0
1.2
61 to120
2.5
1
> 120
2.0
1
Floor of 1 set for A*
Results
43
Positive Dependency Relationship
Duration
Group
(Months)
100% Credibility
Value assuming
independence
100%
Credibility
Value (K)
Guideline
Selected Values
for 100%
Credibility (K*)
Simulation
4 to 24
830
3,300
1,500
25 to 60
830
2,500
1,000
61 to 120
830
2,100
830
> 120
830
1,700
830
Approximations made to raw calculated values to determine K*
Results (Cont.)
44
Negative Dependency Relationship
Duration Group
(Months)
Selected Variance
Factors (A)
Selected Variance
Factors (A*)
Guideline
Simulation
4 to 24
4.0
1.8
25 to 60
3.0
1.6
61 to120
2.5
1.2
> 120
2.0
1
Floor of 1 set for A*
Results (Cont.)
45
Negative Dependency Relationship
Duration
Group
(Months)
100% Credibility
Value assuming
Indepedence
100%
Credibility
Value (K)
Guideline
Selected Values
for 100%
Credibility (K*)
Simulation
4 to 24
830
3,300
1,500
25 to 60
830
2,500
1,300
61 to 120
830
2,100
1000
> 120
830
1,700
830
Approximations made to raw calculated values to determine K*
Consolidated Table
46
•
By taking an average of the 2 extreme dependency
relationships, we obtain the following results:
Duration
Group
(Months)
100%
Credibility
Value (K)
Guideline
Selected
100%
Variance
Credibility
Factors (A) Factors
(K*)
Guideline
Simulation
Selected
Variance
Factors
(A*)
Simulation
4 to 24
3,300
4.0
1,500
1.8
25 to 60
2,500
3.0
1,150
1.4
61 to 120
2,100
2.5
915
1.1
> 120
1,700
2.0
830
1
Conclusion
47
Shown two extreme dependency relationships
 Positive dependence

Higher than expected terminations in stage 1 imply higher
than expected terminations in stage
 Negative dependence

Lower than expected terminations in stage 1 imply lower than
expected terminations in stage 2
Conclusion
48
 The adjusted credibility factors to recognized dependency
are slightly different for both dependence relationships
 The consolidated dependency factors can be used to adjust
the credibility weighted factors for GLTD terminations
 For either type of dependency, reduction in credibility
weights is significantly smaller than the reduction in
credibility weights proposed by the Actuarial Guideline
Affordable Healthcare Act
Pocketbook guide to the
Affordable Care Act (“ACA”)
An ongoing collaborative project
with
UCONN Goldenson Center for
Actuarial Research
• Jay Vadiveloo
• Pete Camacho
• Nehal Sapre
• Mark Spong
American Institute for Economic
Research (AIER)
• Stephen Adams
• Jules Clark
• Natalia Smirnova
Understanding
ACA
Creating a
Plan
Long Term
Projections
• Legal requirements
• Demographic context
• Political climate
• Compare apples to apples
• Identify key groups
• Project costs
• Determine implications
50
What is the AIER/Goldenson Center Study?
First-level analysis aimed at helping individuals
anticipate the personal financial impact of the ACA
next year and in five years
• Narrow – direct costs to individuals – premiums
and out-of-pocket expenses
• Does not include:
•
•
•
•
•
Tax impact (state and federal fiscal impact)
Potential changes in employer-based health insurance
Potential changes in the delivery system that affect
costs
Follow-on work will address these important issues
51
Why the AIER/Goldenson Center Study?
ACA is arguably the most significant public
personal finance policy act since Social Security
was established in 1935
• Also a deeply politicized issue
•
•
The public is getting misleading and conflicting
information from all directions
52
Why: People Confused About ACA
53
Why: Few Trusted Sources
54
Why Goldenson Center & AIER?
•
The mission of the Goldenson Center for Actuarial
Research is to provide high-quality applied actuarial
research services to serve the needs of the insurance
and financial services industry in the region
•
The mission of the American Institute for Economic
Research is to provide factual, unbiased research
and critical analysis of the economic issues affecting
the everyday life of people in America
55
Summary of the ACA
• Individual and employer mandate
• Creation of state-based health benefit exchanges
- Four benefit categories of plans along with a catastrophic
plan
- Provision of premium credits and cost sharing subsidies
• Expansion of Medicaid to 133% of Federal Poverty Line
• Guaranteed availability of coverage; i.e., restrictions on
excluding individuals with pre-existing conditions
• Single risk pool for individuals and small groups per state
56
Change of Coverage Landscape
Health insurance coverage
of the total population preACA
employer
individual
16%
1%
13%
16%
medicaid
49%
5%
medicare
other public
?
uninsured
57
Apples to Apples Comparison Issues
• Typical annual increase in
healthcare premiums
regardless of ACA
• Level of coverage
discrepancies
• What does being uncovered
pre-ACA actually mean?
58
Cost Drives Premiums
• Initial premiums will be available
in October 2013
• Estimate longer term premiums
by estimating costs and assuming
prior loss ratios are a reasonable
proxy
• Included in long term cost
estimates
• Wellness Programs
• Morbidity estimates
• Cost controlling by insurers
• Pent-up demand
59
Next Steps
Decomposition and
estimation of healthcare
costs over the next five
years considering
parameters such as severity,
frequency, changes in
morbidity, pent up demand
etc.
Calculate total
premiums collected
using historic loss
ratio and the
estimated costs
• Compare expected costs for a large group with
the expected costs of a “pooled” individual to see
the effect of the pooling principle
• Survey of insurance carriers and large employers
60
National Retirement Satisfaction Index
National Retirement Satisfaction Index
2013 Review
A presentation to the 2013 Goldenson Center Board Meeting
by Gary Rohrig and Gao Niu
September 20, 2013
Agenda

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
Project Overview
Project Background
Index Definition
Conceptual Model
Financial Projections
Non-Financial Improvements
Analysis
Scenarios
Next Steps
Acknowledgements
63
Project Overview

Problem


Philosophy


Due to limitations in the current retirement preparedness indices, there is a need to
reliably account for satisfied living in retirement
In order to give a full and positive picture of someone's level of satisfied living after
leaving the work force, we will research, develop and implement a national index to
accurately account for both financial and non-financial drivers related to retirement
Scope & Value

Determine partnerships
– Idea was initiated by two members of the Advisory Board
– Current project is supported by the Goldenson Center, UConn’s Roper Center and Towers
Watson
– UConn project team consists of five faculty members and a number of graduate students under
the direction of Jay Vadiveloo

Develop a National Retirement Satisfaction Index (NRSI) which will be updated
annually

The findings of the NRSI will be publicly available and actively marketed
64
Project Background

Existing national indices




National Retirement Risk Index
(Boston College)
International Retirement Security
Survey (AARP)
Retirement Confidence Survey
(Employee Benefit Research
Institute)
Others focused on specific areas



Across Generations Retirement
Income Survey (New York Life)
Retirement Preparedness Survey
(Merrill Lynch)
Fidelity Retirement Index (Fidelity)
65
Project Background
Comparison with other Indices
Other Indices
NRSI
Do not rely on national U.S. data
Relies on U.S. Census data and other
nationally recognized data sources
Capture only purely economic data
Captures both economic and noneconomic factors
Infrequent updates
Will be updated annually
Use arbitrary constraints that put
people in an “at risk” category
Takes a more holistic view of retirement
preparedness and uses a financial
metric that evaluates retirement
satisfaction on a scale of 0 to 100
66
Index Definition
Benchmark


Retirement Satisfaction Equity (RSE): A measure of retirement satisfaction. The actuarial PV of equity in
financial and non-financial factors at retirement
RSE = Accumulated Assets + Social Security + Potential Income – [Health & Living Expenses]

For working population, RSE is measured at an anticipated retirement age

For the current retiree population, RSE is measured as of today

Overall RSE is a weighted combination of both RSE measures

RSE is an extension of the accounting principal of Equity = Assets – Liabilities

NRSI calibrated to range between 0 and 100

When RSE is negative, NRSI is set to 0

Maximum NRSI of 100 calculates RSE assuming individuals do not retire and continue to work
indefinitely
–
Ex: If Maximum RSE = $100,000 and Projected RSE = $50,000, then NRSI = 50
67
Index Definition

Financial Factors


Assets from savings, social welfare, investments
Liabilities from living, out-of-pocket health costs and other expenses
–

Our liabilities will represent the minimum needs for survival in retirement
Non-Financial Factors

Health Status: As health status declines due to aging and disease incidence, an increase
in out-of-pocket medical expenses can result, increasing liabilities
–

We have measured improvements in health status that cause a decrease in health liabilities
Adaptability: Captures post retirement part-time income that could be earned by all
retirees, thus increasing future part-time retirement income
– For the working population, the effect of improvement in educational levels also increases the
potential for part-time income

Financial Planning: Applied only to the working population, improvements in the level of
financial planning helps to increase asset growth rates and thus increase future assets
for retirement

Job Satisfaction: Applied only to the working population, the delaying of retirement is
measured in terms of national job ranks and captures the impact of accumulating more
assets before retirement and decreasing lifetime liabilities after retirement
68
Financial Projections
• Data sources
Economic Factors
Retirees
Workers
Accumulated Assets
HRS
U.S. Census
Projected Income & Expense
U.S. Census
U.S. Census
Social Security Income
HRS
HRS
Interest Rates
U.S. Federal Reserve
US Federal Reserve
Non-Economic Factors
Retirees
Workers
Future Health Improvements
HRS
HRS
Adaptability
HRS
HRS
Financial Planning
N/A
HRS
Job Satisfaction
N/A
U..S Census, U.S. Bureau
of Labor Statistics, Wall
Street Journal, HRS
69
Actuarial Projections
•
Working population
Assets at retirement accumulated by interest only
•
•
Future liabilities at retirement (living expenses and health care expenses)
discounted by interest and survivorship
Future social security benefits and potential post-retirement part-time income
discounted by interest and survivorship
•
•
•
Retiree population
All components of RSE discounted by interest and survivorship
•
•
Projected up to retirement age 63
Inflation rates used in projections for wages, living expenses, and
health expenses
Actuarial Present Value
of Expenditure for Retirees
Total Actuarial Present Value
of Expenditures for Workers
$2,500,000
$1,500,000
$2,000,000
$1,000,000
$1,500,000
$500,000
$1,000,000
$500,000
$0
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
84
$0
Total Maximum Expenditures
Total Actual Expenditures
Total Maximum Expenditures
70
Total Actual Expenditures
70
Health Expense Projections
•
We use multi-variable regression for annual health expenses
HRS Data contains:
•
Health Score (State of Health)
•
•
Attained Age
Out-of-Pocket (“OOP”) Health Expense
•
•
OOP increases by attained age and higher health score
•
•
Best health status: score = 1
Retiree OOP Expenses are modeled using average HRS health scores
Annual Out-Of-Pocket Medical Expenses for Health Conditions
$10,000
$8,000
$6,000
$4,000
$2,000
$0
55
57
59
61
63
65
67
69
71
73
75
77
79
81
83
85
87
89
91
93
95
97
99
101
Attained Age
Medical Expenses: Health Score 1
Medical Expenses: Health Score 2
Medical Expenses: Health Score 4
Medical Expenses: Health Score 5
Medical Expenses: Health Score 3
71
Health Expense Projections
•
Annual out-of-pocket expense projection
Predict improvements in health score over time
•
Using a five-year “calendar-year effect” on the 2012 HRS average health score by age
Cumulative improvements over multiple HRS waves in health score  improvement factor by age
•
•
Project health expenses
•
Baseline expenses
•
•
Expense by age using average reported HRS health score: 3.027
Improved expenses
•
•
•
Expense by age using improved health score
Improved health score  apply the improvement factor to the overall average HRS reported health score
Average Projected Health Score for Workers
2.7
2.7
2.6
2.6
2.5
2.5
2.4
2.4
30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84
Improved Health Score
72
Health Expense Projections
•
We accumulate annual expenses to
get the total actuarial present value
•
Current retiree health expenses
•
•
Baseline - using average HRS
reported health score
Actual - using actual current HRS
reported health score
Working population health expenses
•
•
•
Baseline - using average HRS
reported health score with medical
inflation
Actual - using improved HRS reported
health score with medical inflation
73
Adaptability Improvement
•
We use multi-variable regression for annual part-time income by
level of education
HRS data contains:
•
Age of retirement
Level of attained education (years)
Part-time income earned
•
•
•
Higher education level  higher part-time income
Higher attained age  lower part-time income
•
•
Part-Time Income
Part-Time Income by Attained Level of Education, All HRS respondents
$3,500
40%
$3,000
35%
$2,500
30%
25%
$2,000
20%
$1,500
15%
$1,000
10%
$500
5%
$0
0%
Less than High
School
Some High School
HS Graduate
HRS Population Percentage
Some College
4-year degree
Graduate Degree
Average Part Time Income
74
Adaptability Improvement
•
Part-time income increases income in retirement
Retirees
•
•
•
Potential part-time income calculated for each retiree using a regression equation based on attained age and level of
education
Workers
•
HRS data selected for regression – people over 55 reporting any part-time income amount (about
1:16)
•
•
Baseline income amount using US census average education level
Improved income amount using projected gain from educational improvement
Improvements in level of education
•
•
Forecasted using a 10-year “calendar year effect” on US Census data
The additional annual part-time income is accumulated
•
•
Inflation and discount factors are applied the same way as in the baseline financial projection
Average Annual Part Time Retirement Income
$60,000
$50,000
$40,000
$30,000
$20,000
$10,000
$0
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
Average HRS Part-Time Income, positive responses only
Projected Part-Time Income using Logarithmic Regression
of Average HRS Part-Time Income, positive responses only
75
Financial Planning Improvement
•
We use HRS reported assets by financial planning level to
model gains in projected assets
•
HRS Data contains:
•
•
•
•
Attained age
Level of financial planning (score of 1 to 5, 5 being the most planning)
Total assets
Higher level of financial planning  higher accumulated
assets
Average HRS Assets by Financial Planning Score
$1,000,000
$900,000
$800,000
$700,000
$600,000
$500,000
$400,000
$300,000
$200,000
$100,000
$Avg. Total Assets in
2000
Avg. Total Assets in
2002
Avg. Total Assets in
2004
1
2
3
4
Avg. Total Assets in
2006
Avg. Total Assets in
2008
5
76
Financial Planning Improvement
•
Gains from financial planning are forecasted for current workers
•
HRS data  baseline of level of financial planning for retirees
•
Average current levels of financial planning are determined
•
Improvements in financial planning are computed
•
5-year “calendar year effect” using HRS average financial planning score
Accumulated assets are recalculated
•
•
•
•
Improvement factor = ratio of improved financial planning assets to baseline financial assets
The improvement factor is multiplied by the investment return rate of the baseline calculation of
financial assets  financial-planning adjusted return rate
Accumulated assets are recalculated using the financial-planning adjusted return rate
Average Total Assets - All HRS Waves - Weighted v. Financial Planning Score
$800,000
$700,000
$600,000
$500,000
$400,000
$300,000
$200,000
$100,000
$-
0
1
2
Avg. Total Assets - All Waves - Weighted
3
4
5
6
Linear (Avg. Total Assets - All Waves - Weighted)
77
Job Satisfaction Improvement
•
We determine the number of years beyond retirement workers will be willing to work
Delaying retirement affects RSE
•
•
•
•
•
•
More satisfied working environment  more years in employment (delay retirement)
Use Wall Street Journal job ranks to model level of job satisfaction
Top 200 jobs ranked – lowest rank = best job (2013, actuary = 1)
Jobs matched to corresponding HRS (Retirees) and US Bureau of Labor Statistics (Workers) job groups
Retirement Age & Job Rank
Retirement Age
•
Increase accumulated assets before retirement – more accumulation years
Decrease liabilities after retirement – less expenditures in retirement years
64.5
78.0
64.0
77.5
63.5
77.0
Retirement Age
63.0
76.5
62.5
76.0
Job Rank (Population Shifted
Method)
62.0
75.5
Retirees
Workers
78
Job Satisfaction Improvement
•
Overall job rank  level of job satisfaction  age of retirement
From HRS data
•
Age of retirement
Occupation group of retiree
•
•
From US BLS and WSJ
•
Job rankings
Occupations and job groups
•
•
Determine the revised retirement age for working population
•
•
𝐴𝑔𝑒𝐽𝑜𝑏 = 𝐴𝑔𝑒𝑅𝑒𝑡 + 𝐴𝑔𝑒𝑅𝑒𝑡 ∗
𝑅𝑎𝑛𝑘𝑊𝑜𝑟𝑘𝑖𝑛𝑔 −𝑅𝑎𝑛𝑘𝑅𝑒𝑡𝑖𝑟𝑒𝑒
𝑅𝑎𝑛𝑘𝑅𝑒𝑡𝑖𝑟𝑒𝑒
Job-Satisfied Retirement Age, by Job Ranking
65.5
35%
65.0
Retirement Age
40%
30%
64.5
25%
64.0
20%
63.5
15%
63.0
10%
62.5
5%
62.0
0%
24
35
37
58
63
64
76
78
Job Ranking
81
115
127
131
HRS Population
Percentage
HRS Average
Retirement Age
Regressed
Retirement Age, by
job rank
144
79
Analysis
•
Preliminary Results
Increase to Baseline
Factor
Retiree
Population
Working
Population
Overall
Baseline
54
48
49
Health Care
1
6
5
Adaptability
10
9
9
Financial Planning
0
5
3
Job Satisfaction
0
11
8
TOTAL
65
79
74
Note: These values are preliminary and subject to change
80
Next Steps
• Put together final report after models are validated by UConn and
TW and report is reviewed by select -industry representatives
• Models should be set up so that the NRSI can be updated
annually
• Disseminate and publicize report
• Explore with industry leaders how NRSI findings can be used to
better design and market retirement products and services
• Explore how an Individual Retirement Satisfaction Index that is
company specific can be designed to be used in parallel with the
NRSI
81
Thank you, your questions are welcome
82
Appendix
83
Scenarios
Using the model we
generate multiple
scenarios
•
•
•
Econometric factors
changed  shift of
interest rates
Non-Financial factors
changed  shift of
cumulative improvement
factors
Scenarios create a guide
for improvements
workers and retirees
•
•
Interventions can be
taken to improve one’s
satisfaction in retirement
Summary of Analysis Results by Scenario
By Group
Scenario
Description
Changes
Investment Returns : 5.53%
Discount Rate : 3.66%
Medical Inflation : 5.81%
Expense Inflation : 3.06%
Wage Growth : 3.12%
1
Baseline
US Population, All Ages
2
Mild Economic Stress
US Population, All Ages
Investment Returns Decrease by .74%
Expense Inflation Increase by .13%
3
Moderate Loss of Assets
US Population, All Ages
Investment Returns Decrease by 1.49%
Expense Inflation Increase by .27%
Savings Ratio Decrease by 25%
4
Mild Economic & Medical
Hardship
US Population, All Ages
Investment Returns Decrease by .74%
Medical Inflation Increase by .28%
Expense Inflation Increase by .13%
Wage Growth Decrease by .17%
Savings Ratio Decrease by 10%
5
Extreme Decrease of
Savings
US Population, All Ages
Savings Ratio Decrease by 50%
6
Moderate Educational
Improvement
US Population, All Ages
Educational Improvement Increase by 4x
7
Moderate Financial
Planning Improvement
US Population, All Ages
Financial Planning Improvement Increase by 2x
8
Moderate Health
Improvement
US Population, All Ages
Health Improvement Increase by 4x
9
Multiple Facet
Improvement
US Population, All Ages
Health Improvement Increase by 4x
Financial Planning Improvement Increase by 2x
Educational Improvement Increase by 4x
84
Scenarios
With economic stressors / non-financial interventions, the NRSI shifts
•
Summary of Analysis Results by Scenario
By Group
NRSI Index by Grouping
Retirees
Scenario
Description
Base
Health Care Adaptability
Working Population
Financial
Planning
Job
Satisfaction
Base
Financial
Job
Health Care Adaptability Planning Satisfaction
1
Baseline
US Population, All Ages
54
55
65
65
65
48
54
63
68
79
2
Mild Economic Stress
US Population, All Ages
54
55
65
65
65
39
46
58
63
74
3
Moderate Loss of Assets
US Population, All Ages
54
55
65
65
65
0
10
25
31
41
4
Mild Economic & Medical
Hardship
US Population, All Ages
54
55
65
65
65
18
29
44
51
64
5
Extreme Decrease of Savings
US Population, All Ages
54
55
65
65
65
7
13
22
27
35
54
55
65
65
65
48
54
64
68
79
54
55
65
65
65
48
54
63
78
90
6
7
Moderate Educational
Improvement
US Population, All Ages
Moderate Financial Planning
Improvement
US Population, All Ages
8
Moderate Health Improvement
US Population, All Ages
54
58
67
67
67
48
71
81
83
94
9
Multiple Facet Improvement
US Population, All Ages
54
58
67
67
67
48
71
81
89
100
85
Summary
NRSI Summary for Working Population
Base
Health Care
Adaptability
Financial Planning
Job Satisfaction
90
79
11
5
9
6
48
79
74
11
5
12
7
39
64
41
10
6
15
10
0
13
7
15
11
18
35
8
5
9
6
7
12
Total
94
100
11
2
10
11
8
10
11
4
10
6
15
9
6
23
23
48
48
48
48
86
Summary
NRSI Summary for Retirees
Base
Health Care
Adaptability
Financial Planning
Job Satisfaction
Total
65
0
10
1
65
0
10
1
65
0
10
1
65
0
10
1
65
0
10
1
65
0
10
1
65
0
10
1
67
0
9
4
67
0
9
4
54
54
54
54
54
54
54
54
54
87