2015 fellows symposium retirement benefits for a …...proportion of respondents with rhi by...

2015 Fellows Symposium

Retirement Benefits for a 21st Century Workforce

Offering vs. Choice in Retirement Plans:

A Cross Sectional Analysis of Investment

Menus with Traditional and Life-Cycle

Mutual Funds

Tai Kam, Robert L. McDonald,

David P. Richardson and Thomas A. Rietz

October 2, 2015

The General Question:

How do people

allocate their

retirement

assets?

UBS Tactical

Allocation Diagram

Our study

• TIAA-CREF Data:

– Cross section of holdings and allocations

– 654,197 active participants

– 1,073 plans and 2,361 plan combinations

– 98 of the largest institutional clients in 2012

• Questions:

– How do plan characteristics influence

participant decisions?

– How have life-cycle funds affected decisions?

What Should People Do?

• Campbell and Viceira (2002) Figure 7.3: A stylized

lifecycle model of HOLDINGS over time:

Relative allocations correspond

to the relative slopes

CRRA, b=0.96, g=5

rf = 2% RP = 4%

Probabilistic death (max 100)

Deterministic retirement (65)

Mandatory Soc. Sec. savings

10%, invested at 2%

Correlation between stock returns

and labor income 0.3-0.5

What is a Life-Cycle Fund?

https://www.tiaa-cref.org/public/products-services/mutual-funds/lifecycle

Offerings versus Choices

-

100

200

300

400

500

600

1 5 9 131721252933374145495357616569737781

Nu

mb

er o

f P

lans

and T

housa

nds

of

Par

tici

pan

ts

Number of Fund Choices Offered by Plan or Plan

Combination

Panel A: Choices at Plan Level

# of Plans # of Plan Participants (x100)

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 > 10Per

centa

ge

of

Par

tici

pan

ts

Number of Funds Chosen

All Participants No Life-Cycle Funds

Some Life-Cycle Funds Only Life-Cycle Funds

• Participants have lots of choices

• Range: 1 to 84

• Median: 36

• Participants only choose a few

• Median: 2 to 3

Life-Cycle investors are different

Table 2

No Life

Cycle Funds

Some Life

Cycle Funds

Only Life

Cycle Funds

Number of funds 3 4 >> 1

% Equity 60% 64% << 80%

Age 52.13 48.31 >> 40.50

Tenure 15.94 12.86 >> 5.45

Match Rate 187% 193% >> 170%

% Female 52% 54% << 58%

Ann. Contribution $8,880 $9.310 >> $3,630

Compensation $63,400 $61,000 >> $38,800

Plan Assets $115,200 $86,000 >> $13,000

Zip+4 Wealth $156,700 $130,000 >> $57,000

Do Participants Naïvely Diversify? NO

• “1/N” rule:

– Put 1/Nth of allocation into all N funds available

– Result: NO. Same as Huberman and Jiang, JF, 2006

• “Conditional 1/n” rule:

– Put 1/nth of allocation into n<N funds chosen

– Result: NO. Differs from Huberman and Jiang

Are there Menu Effects? YES

• “Number Available Effect”

– Does number of funds available affect number chosen?

– Result: YES (small). Differs from Huberman and Jiang.

• “Equity Exposure Effect” – Does fraction of equity funds offered affect equity allocation?

– Result: YES (economically meaningful). Differs from

Huberman and Jiang.

Participants to use life-cycle funds only

have higher equity allocations…but they

drop faster

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

25-3030-3535-4040-4545-5050-5555-6060-6565-70 70+

% A

llo

cate

d t

o E

qu

ity

Age Range

Female, No Life-Cycle Funds

Male, No Life-Cycle Funds

Female, Some Life-Cycle Funds

Male, Some Life-Cycle Funds

Female, Only Life-Cycle Funds

Male, Only Life-Cycle Funds

Figure 10

Equity exposure and life cycle fund use

affect equity allocations

Variable Overall

Constant 0.5534**

Life-Cycle Only (1/0) 0.4954**

% Equity Offered 0.4755**

Life-Cycle Only x % Equity Offered -0.3770**

Female (1/0) -0.0554**

Life-Cycle Only x Female 0.0486**

Age -0.0056**

Life-Cycle Only x Age -0.0017**

Age x Female 0.0001

Life-Cycle Only x Age x Female 0.0000

Final sample size 645,082

Pseudo R2 20.12%

Table 6

The Problem with Life-Cycle

Funds? • Is the “glide path” optimal for anyone?

• Does one size fit all?

“Households who expect high future labor

income…should have the strongest desire to hold stocks.”

Campbell and Viceira

• What about expenses?

– Retail investors indirectly get institutional fees on

acquired funds, but pay up for the life-cycle fund. Is this

cheaper net than buying funds directly?

2030 Life Cycle Fund Direct and Indirect Expenses

Retail Ret. Premier Inst.

Direct Fund Expenses 0.43% 0.45% 0.30% 0.15%

Acquired Fund Fees and Expenses 0.36% 0.36% 0.36% 0.36%

Reimbursement (runs out 9/30/16) (0.18%) (0.20%) (0.15%) (0.15%)

Total: 0.61% 0.61% 0.51% 0.36%

RHI In Higher Education:

Impact on Retirement

Decisions Robert Clark

TIAA-CREF Fellows Symposium

October 2, 2015

What is Retiree Health

Insurance? Employee benefit that allows retirees to

remain in the employer provided health

plan

Retirees usually required to enroll in

Medicare at age 65 and Medicare

become primary payer

Employer usually subsidizes retirees by

part or all of the premium for the retiree

and in some cases, spouses

Retiree Health Insurance Policies

Retirees still pay premiums, deductibles, and co-pays

Over time, employers alter deductibles and co-pays but tend to retain the percentage subsidy of the premium

Percent of premium paid by employer often related to years of service

A Dying Benefit in Private Sector

Private sector employers have been rapidly terminating RHI plans

Employer has limited control over costs: health care inflation, changes in Medicare, increase in ratio of retirees to workers

Change in accounting rules on reporting in 1990 required reporting unfunded liabilities associated with these plans

Public Employers and

Higher Education

RHI plans still a very common benefit for public employees and for employees of colleges and universities

Trend toward reduced subsidies especially for shorter term employees

Impact of RHI Coverage on

Retirement Decisions

RHI coverage has value

Value is much greater for those

expecting to retire prior to age

65 and Medicare coverage

Employers trying to encourage

workers to retire at younger ages

can couple RHI with generous

pension plans

Retirement Decisions

Affected by RHI

RHI subsidized by the employer implies health expenditures in retirement will lower and individuals can retire at younger ages

RHI subsidized by the employer suggests that individuals need to save less while working to attain the same level of income in retirement

Annual Cost of RHI and

Unfunded Liabilities

With rapidly rising annual

expenditures for RHI, employers

have become increasing

concerned about this benefit

Employers typically do not pre-

fund this promise

Annual Cost of RHI and

Unfunded Liabilities

Thus, many employers now

have large unfunded liabilities

associated with these

programs

State and local governments

now must report unfunded

liabilities of RHI plans

RHI and Higher Education

RHI common in higher education

Academic leaders worried about

the cost of this benefit and

consider options to reduce or

eliminate the cost

Academic leaders worried that the

elimination of RHI could result in

faculty delaying retirement until

even later ages.

Retirement Plans of University

Faculty: Role of RHI

The TIAA-CREF Institute awarded a grant

to fund a survey of older faculty and to

examine how RHI coverage might affect retirement plans

Survey conducted by Mathew

Greenwald & Associates

Survey of approximately 900 full-time

faculty and administrators age 50 and

older employed at U.S. universities

Objectives of Project

Are older faculty covered by RHI planning to retire earlier than those that do not have the ability to remain in the institution’s health plan?

Are older faculty covered by RHI saving less because of the promise of health insurance when they retire?

Economic Literature

There is a short literature examining the

role of RHI on retirement age and

retirement saving but little focus on

public employees and on university

faculty

Two observations:

Public employees tend to retire from

their career jobs at relatively young ages

University faculty tend to retire from their

institutions are relatively older ages

Survey Means: In Percent

Sample With RHI Coverage

Total Sample 48.8

Full Professor 48.4 49.8

Associate Professor 25.7 51.1

Assistant Professor 9.0 42.5

Instructor/Lecturer 9.5 37.6

Administrator 5.4 56.2

Survey Means: In Percent

Sample With RHI Coverage

Public Institution 66.1 57.1

Private Institution 32.5 32.1

Doctoral 57.0 53.7

Masters 25.0 44.4

Baccalaureate 16.5 39.5

Expected Retirement Age

Expected Retirement Age

Without RHI 68.0

Age 50-59 66.1

Age 60 + 70.0

With RHI 68.5

Age 50-59 66.9

Age 60+ 69.7

Expected Retirement Age:

Premium Subsidy

Percent Premium Paid by Faculty Expected Retirement

Age

Zero 67.9

1.0-25.0 68.9

26.0-50.0 67.9

51.0-75.0 67.5

76.0-99.0 70.4

100.0 69.3

RHI and Pension Plans

Type of Pension Percent with RHI Percent without RHI

Covered by DB 62.9 37.1

Covered by DC 41.9 58.1

Regression Results: Expected

Retirement Age

1. Faculty age 50-59 who report that they are in poor health expect to retire almost two years earlier than those in excellent or good health

2. Faculty at doctoral institutions expect to retire at older ages

3. Administrators expect to retire about one year earlier than faculty

4. Faculty at public institutions expect to retire almost one year earlier than those at private institutions


Retirement Age

5. Males expect to retire about 1.5 years later than females, result is larger among the younger sample

7. Faculty with more years of tenure expect to retire at younger ages

8. Faculty age 50-59 covered by a DB plan expect to retire 2.4 years sooner than those with other types of plans

9. Faculty age 50 to 59 who have developed a retirement plan are expecting to retire 2.5 years sooner than those that have not


Retirement Age

Key finding for this project:

Coverage by RHI has no effect on

expected retirement age of faculty 60

and over

Coverage of one’s spouse has no effect

on expected retirement age of faculty

Retirement Saving

Most institutions allow faculty to enroll in voluntary supplemental retirement saving plans in addition to their primary pension plan.

These plans can be 401(k), 403(b), or 457 plans

Workers without RHI coverage might be expected to be more likely to enroll in these plans and to have higher contribution rates

Proportion of Respondents with RHI Employed at

Institutions with Voluntary Retirement Savings Plans

Institution Has Supplemental Retirement Plan Sample Mean Has RHI No RHI

Does Not Participate in Plan

Participate in Plan

27.4%

72.6%

54.1%

49.5%

45.9%

50.5%

Proportion of Respondents with RHI by Supplemental Account Balance

Supplemental Account Balance Sample Mean Has RHI No RHI Total

Less than $50,000

12.1% 52.2%

47.8%

100%

$50,000 to less than $100,000 12.3% 58.8%

41.2%

100%

$100,000 to less than $250,000

16.8% 52.7%

47.3%

100%

$250,000 to less than $500,000

14.6% 50.6%

49.4%

100%

$500,000 to less than $750,000

7.2% 57.5%

42.5%

100%

$750,000 to less than $1 million

2.7% 26.7%

73.3%

100%

$1 million or more

3.8% 28.6%

71.4%

100%

Do Not Know 15.7%

49.4%

50.6%

100%

Refused 15.0% 47.0% 53.0% 100%

Participation in

Supplemental Saving Plans

1. Individuals in excellent health are 8.1 percentage

points more likely to participate in a saving plan

relative to those in good health

2. Individuals in bad health are 8.7 percentage point

more likely to participate in a saving plan relative to

those in good health

3. Faculty at public institutions are 7.3 percentage points

less likely to participate in a retirement saving plan

than those at private universities

4. Faculty whose primary plan is a DC plan are less likely

to enroll in a supplemental saving plan

Participation in

Supplemental Saving Plans

Most important for this project

The proportion of Faculty covered

by RHI are not significantly less

likely to enroll in a retirement

saving plan than those that are

not covered by RHI

Key Findings from Project

1. Faculty who report that they anticipate

that their institution will provide them

with health insurance in retirement do

not report that they expect to retire

earlier than those not covered by RHI

2. Faculty who report they anticipate that

their institution will provide them with

health insurance in retirement do not

seem to be saving any less than those

not covered by RHI

LATE-CAREER RESEARCH PRODUCTIVITY AND THE TIMING OF

RETIREMENT

A report on research in progress October 2, 2015

David Blau (Ohio State) John Ham (National U. of Singapore)

Bruce Weinberg (Ohio State)

Supported by a grant from the National Institute on Aging

Overview

• Scientific researchers are typically viewed as being most innovative early in their careers

• The U.S. scientific workforce is aging rapidly

• We don’t know how productivity evolves at the end of the career / near retirement

• The answer to this question is important for understanding the implications of an aging scientific workforce, and possible policy responses

Project Goals 1. Document trends in innovative scientific research output

over the late career

2. Analyze causes of the aging of the scientific workforce, and possible policy responses

3. Estimate effect of retirement on scientific productivity, using variation across universities and within universities over time in discontinuities in retirement incentives induced by Defined Benefit pension plans

4. Simulate impact of the aging of the scientific workforce on the amount of innovative scientific research, and project impact of future trends in aging

Outline of Presentation

• Document aging of the scientific workforce

• Describe pensions and pension incentives

• Next steps

Aging of the U.S. Scientific Workforce

• Use data from the NSF’s Survey of Doctorate Recipients

• Longitudinal survey of representative sample of individuals who received doctorate in U.S.

• Nine waves, 1993-2010

• Restricted-access version of SDR provides extensive career details, and IPEDS code of employer

• Use Census data to compare to aggregate population aging trends

Aging of the U.S. Scientific Workforce

• We have generated full career histories, including years of service with current and former employers, and salary history

• Scientists: – life sciences (biology, medical science, etc.) – physical sciences (chemistry, physics, astronomy, geology) – Engineering – computer science and mathematics – Social science (economics, psychology, etc.). – Not restricted by sector (industry). Educational institutions,

government, private; but pensions available only for public institutions and federal government

The scientific workforce is aging (figure 1); so is the workforce as a whole (figure 2)

0

.01

.02

.03

.04

20 40 60 80Age

scientist_sdr_1993 scientist_sdr_2010

Figure 1: Age Distribution of Scientists 1993 and 2010

0

.01

.02

.03

.04

20 40 60 80Age

workforce_1993 workforce_2010

Figure 2: Age Distribution of Workforce 1993 and 2010

The scientific workforce is older than the workforce as a whole (figure 6), and has been aging more rapidly than the workforce as a

whole (figure 5), especially at older ages (figure 7)

0

.00

2.0

04

.00

6.0

08

20 40 60 80Age

scientist_share_sdr_1993 scientist_share_sdr_2010

Figure 5: Age Distribution of Scientists Share of Workforce 1993 and 2010

42

44

46

48

50

1980 1990 2000 2010 2020Census year

(mean) scientist_mean_age (mean) workforce50_mean_age

Figure 6: Mean Age of Scientists and Workforce

.15

.2.2

5.3

.35

1980 1990 2000 2010 2020Census year

(mean) scientist_sharege55 (mean) workforce50_sharege55

Figure 7: Share of Scientists and Workforce aged 55+

Possible causes of rapid aging of scientific workforce: increase in age at PhD completion

(figure 13), slower rate of retirement (figure 17)

29

30

31

32

33

age

_a

t_ph

d_

sdr

1960 1970 1980 1990 2000 2010Census year

Figure 13: Trend in Age at Science PhD completion

0.1

.2.3

.4

50 55 60 65 70 75Age

1993 rethazard_ 2008 rethazard_

Figure 17: Hazard Rate of Exit to Non-Employment

Other trends of interest: decline in rate of exit from scientific research to other occupations (figure 18), large increase in share of foreign-born among U.S. doctorates

(figure 12)

0

.02

.04

.06

.08

.1

20 30 40 50 60 70Age

1993 sciencehazard 2008 sciencehazard

Figure 18: Hazard Rate of Exit to Non-scientific Employment

.1.2

.3.4

.5

fore

ignb

orn

_sd

r_n

ew

ph

d

1960 1970 1980 1990 2000 2010Census year

Figure 12: Trend in Share Foreign-born in new US Science PhDs

Policy issues • Immigration: government controls who is allowed to enter the US

to work as a scientist, or stay in the US after training

• Subsidies for education and training, to increase number of new scientists

• Science funding policy: increases in NSF and NIH funding could improve career prospects of young scientists by supporting post-doctoral fellowships and increasing success in grant applications from young investigators

• Retirement policy: – Retrospective – impact of elimination of mandatory retirement, and

shift from Defined Benefit to Defined Contribution pension plans

– Prospective – decline in Social Security benefits, financial problems in state pension systems

Defined Benefit Pensions

• We have collected, coded, and are checking DB plans at the top 50 or so research universities with DB plans

– Many of these plans cover multiple state universities

– Current and, to the extent possible, past plan parameters;

changes in plan parameters provide a very useful source of identification

– Buyout windows

– From web sites and plan documents provided by universities

Example of changes in plan: Ohio State. Benefit = b*(Years of Service)*(Final Average Salary)

year Value of b in %

Benefit as % of FAS for retirement with

Gain from 35th year

34 YoS 35 YoS

1965 1.75 59.5 61.25 1.75

1968 1.9 64.6 66.5 1.9

1971 2.0 68 70 2.0

1989 2.1% + 2.5% for each YoS > 30 73 75.5 2.5

1997 2.1% + 2.5% for year 31, 2.6% for year 32,… 73.6 76.5 2.9

1999 2.2% + 2.5% for year 31, 2.6% for year 32,…; + 2.5% instead of 2.2% for each of the first 30 YoS, if YoS >= 35

76.6 88.5 11.9

2015 2.2% 74.8 77 2.2

Illustrate DB pension incentives

• Simulated careers for people turning 65 in 2005 with varying start dates

• Compute “benefit factor” (bf) for alternative retirement ages:

• Benefit = b*YoS*FAS = bf*FAS

• Illustrate increase in benefit factor resulting from delaying retirement by one year

• Use State University of New York (SUNY) DB plan as an example

Illustration of sharp pension incentives

Defined Contribution Pension Plans

• Obtained the 16,175 DC pension plans administered by TIAA-CREF

• We coded them using plan rules in TIAA-CREF data base

• Checked carefully; some ambiguities

• Thanks to Dave and Tai for helping us resolve many of these

• Matched Institution name to IPEDS Institution code, in order to merge with SDR

Match rate to IPEDS

Organization Type # Matched # Unmatched Total Share College (2-Year) 454 132 586 0.775 College (4-Year) 817 243 1060 0.771 University 523 161 684 0.765 Postgraduate 21 11 32 0.656 Teaching Hospital 15 186 201 0.075 Other 256 13,356 13,612 0.016 Total 2086 14,089 16,175 0.129

Illustrate pension Incentives in DC plans

• For each plan, calculate the account balance at each possible age of exit from the employer, using an artificial salary history

• Assume 2% annual real rate of return on balances held in account; no loans or withdrawals before exit

• Hired at age 27; can leave at any age from 28-67

• Converted balance at exit date to a single life annuity using standard life table and 2% interest rate

• Calculated increase in annuity resulting from delaying claiming by one year

• Did this for 91 plans from research universities with “simple” formulas – no discretion over contribution rates

Illustrate for four randomly chosen plans

500

00

100

00

01

50

00

02

00

00

0sa

lary

30 40 50 60 70age

Assumed salary profile

0

200

00

400

00

600

00

800

00

30 40 50 60 70age

1 annuity 2 annuity

3 annuity 4 annuity

Annuity as a function of age at exit from employer

0

200

04

00

06

00

0

30 40 50 60 70age

1 gain 2 gain

3 gain 4 gain

Gain in annuity from delaying exit by one year

0.5

11

.5

30 40 50 60 70age

1 propgain 2 propgain

3 propgain 4 propgain

Proportional gain in annuity from delaying exit by one year

Distribution of proportional gain at ages 50 and 60 across all 91 plans

0.2

.4.6

.8

Fra

ctio

n

.09 .1 .11 .12 .13 .14propgain

Distribution of proportional gain at age 50

0.2

.4.6

.8

Fra

ctio

n

.085 .09 .095 .1 .105propgain

Distribution of proportional gain at age 60

DC Pension Incentives

• Very smooth; no abrupt changes by age or years of service

• There is variation across plans, and some variation within plans over time

• But not as helpful to us in generating exogenous variation in retirement incentives, compared to DB plans

• DB plans have idiosyncratic rules, differ a lot across employers, and sometimes change very abrupt

• Nevertheless, it is important to account for DC pension incentives as control variables

Ongoing work • Quantitative analysis of causes of aging of the scientific workforce

• Use data on publications and patents from SDR to characterize the

age profile of scientific output; currently have self reported counts covering a few years – Awaiting rich data on publication records from NSF

• Project the impact of aging of the scientific workforce on the rate of

scientific output, assuming no changes in behavior – an accounting exercise

• Estimate a model of retirement from the scientific workforce, with pension incentives as key explanatory variables

• Simulate impact of retirement age on scientific output

Age profile of publications per two year period, self-reported

23

45

6

(me

an

) a

rtic

le

30 40 50 60 70 80U_DEM_AGE

TIAA-CREF Institute

2015 Fellows Symposium: Retirement Benefits for a 21st Century Workforce

The Performance of TIAA’s Traditional Retirement Annuity 1970-

2013 (David F. Babbel, Mark Meyer, and Miguel Herce)

New York City, October 2, 2015

Privileged and Confidential

Prepared at the Request of Counsel

Draft: Subject to Revision

Preliminary Work Product

TIAA’s Traditional Retirement Annuity

Data & Summary Statistics

Results

Mean–Variance Analysis

Sharpe & Sortino Ratios

Stochastic Dominance Analysis

Outline

65







Results




Outline

66





TIAA’s Traditional Retirement Annuity (“TIAA RA”) is a deferred fixed annuity, first

introduced in 1918

Flexible Premiums: contributions can be made at regular intervals during the accumulation phase

All premiums contributed during a given period (whose length depends on market conditions)

constitute an investment “vintage”

For each vintage the Company declares annual crediting rates, applying on March 1 of each

year

As time goes by, older vintages tend to merge and receive the same crediting rate

During the decumulation phase, payout options are linked to the participant’s persistence and the

Company’s ability to distribute surplus contingency reserves

We do not study the decumulation phase in this paper


67





Contributions received during the period covered by a given investment vintage received

the rates declared for that vintage on March 1 of each year

Crediting rates do not necessarily change every year

As vintages mature they tend to get similar rates

TIAA’s Traditional Retirement Annuity Hypothetical Illustration of the Vintage System of Investment and Rate Crediting

68

Vintage 6 4.50% 4.50% 5.00% 5.00% 3.75% 3.75% 4.00% 4.00% 4.25% 4.25%

Vintage 5 5.00% 5.25% 5.25% 5.00% 5.50% 5.00% 4.75% 4.50% 4.00% 4.25% 4.25%

Vintage 4 6.25% 5.75% 5.00% 5.00% 5.25% 5.25% 4.50% 4.50% 4.00% 4.25% 4.25% 4.25%

Vintage 3 6.75% 6.25% 6.25% 5.75% 5.75% 5.75% 6.00% 5.50% 5.00% 5.00% 4.50% 4.25% 4.25%

Vintage 2 7.25% 6.75% 6.75% 6.25% 6.00% 5.75% 6.00% 6.00% 5.50%% 5.25% 4.75% 4.50% 4.25% 4.25%

Vintage 1 7.75% 8.00% 8.00% 7.25% 6.75% 6.25% 6.25% 6.50% 6.25% 4.25% 4.25% 4.50% 4.25% 4.25% 4.25%

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 Year 12 Year 13 Year 14 Year 15

Note: The crediting rates in this illustration are hypothetical. A participant contributing to all these vintages would

have received six different crediting rates for Year 11





TIAA’s Traditional Retirement Annuity – Example Actual Crediting Rates in Effect for the Period 3/1/2014 – 2/28/2015, for Existing Vintages

69

3.50%

3.75%

3.25%

3.75%

4.00%

3.75%

4.00%

4.50%

5.00%

4.25%

4.75%

5.00%

0% 1% 2% 3% 4% 5% 6%

6/1/2014 - 7/31/2014

7/1/2013 - 5/31/2014

1/1/2012 - 6/30/2013

10/1/2011 - 12/31/2011

3/1/2011 - 9/30/2011

9/1/2010 - 2/28/2011

1/1/2010 - 8/31/2010

1/1/2009 - 12/31/2009

1/1/2008 - 12/31/2008

1/1/2000 - 12/31/2007

1/1/1992 - 12/31/1999

Pre-1992

Vin

tage /

Gro

up o

f V

inta

ges

Source: TIAA-CREF, TIAA Retirement Annuity Accounts, TIAA Traditional Annuity, Group Retirement Annuity (GRA), as of 6/30/2014.

By 2014, older

vintages receive

the same

crediting rate

(note vintages

grouped here)

Recent

individual

vintages receive

their own

crediting rates





TIAA’s Traditional Retirement Annuity Scope of Our Analysis

70

As we have illustrated, a participant who contributes regular premiums to the TIAA RA will, over time,

participate in a potentially large number of investment vintages

Each vintage will receive its own series of annual crediting rates, applying from March first to

February 28th of each year

The overall annualized return obtained by the participant over a given period of time will be a

blended rate. This is a weighted rate that takes into account the various vintages into which the

participant’s premiums have been pooled

Contrast this with making the same regular contributions into a stock mutual fund, where the full

balance earns a single rate of return over a given year, independently of when the contributions

have been made

In order to compare the performance of the TIAA RA with that of asset classes like stock or bond

mutual funds, we then focus on investment cohorts, where a single investment is made at a point in

time for a given cohort. This is the analysis we conduct in this paper

This approach is the familiar one behind questions like “How much would $1 be worth today if

invested in Berkshire Hathaway on March 1, 1970? And how risky would that be?”

Alternatively, and perhaps more interestingly, the analysis could be: “How would a series of regular

contributions in the TIAA RA have done, compared to the same series invested in an alternative asset?

We do not conduct this analysis in this paper







Results




Outline

71





Data and Summary Statistics

72

The TIAA data we use are crediting rates for investment cohorts starting on March 1 of

1970, 1975, 1980, 1985, 1990, 1995, 2000, and 2005

We follow these eight cohorts through December 31, 2013

And compare their separate performance with that of:

Large and small US stocks (“LS” and “SS,” respectively)

Long-term US corporate and government bonds (“LTCB” and “LTGB,” respectively)

Intermediate-term US government bonds (“ITGB”)

3-month U.S. Treasury Bills, a proxy for money market instruments (“MM”)

These data are from Morningstar’s Ibbotson SBBI 2014 Classic Yearbook. Returns are net of

estimated fund fees from ICI

All return series are calculated at the monthly frequency





Data and Summary Statistics

73

3% Min. Guaranteed

0%

2%

4%

6%

8%

10%

12%

14%

16%

Annualiz

ed M

onth

ly R

etu

rn /

Yie

ld

Annualized Monthly Returns for TIAA RA and 20Y T-Bond Yields

1970 Cohort 1975 Cohort




20Y T-Bond

Sources: TIAA, Ibbotson SBBI 2014 Classic Yearbook





Cohort Statistic Large Stocks

Small Stocks

Long- Term Corp. Bonds

Long- Term Gov’t Bonds

Interm.- Term Gov’t Bonds

Money Market

TIAA RA

1970 (526)

Mean: 0.79% 1.23% 0.61% 0.59% 0.50% 0.38% 0.58%

STDEV: 4.46% 6.26% 2.80% 3.12% 1.59% 0.26% 0.13%

1975 (466)

Mean: 0.88% 1.40% 0.63% 0.62% 0.51% 0.37% 0.58%

STDEV: 4.36% 5.98% 2.83% 3.19% 1.61% 0.27% 0.14%

1980 (406)

Mean: 0.89% 1.21% 0.72% 0.72% 0.56% 0.35% 0.61%

STDEV: 4.43% 5.79% 2.88% 3.28% 1.61% 0.28% 0.18%

1985 (346)

Mean: 0.85% 1.09% 0.67% 0.69% 0.49% 0.27% 0.58%

STDEV: 4.44% 5.82% 2.56% 3.01% 1.34% 0.20% 0.18%

1990 (286)

Mean: 0.77% 1.18% 0.60% 0.61% 0.44% 0.23% 0.52%

STDEV: 4.28% 5.90% 2.58% 2.92% 1.30% 0.18% 0.12%

1995 (226)

Mean: 0.75% 1.17% 0.58% 0.58% 0.41% 0.20% 0.49%

STDEV: 4.47% 6.24% 2.76% 3.07% 1.28% 0.18% 0.11%

2000 (166)

Mean: 0.36% 0.87% 0.61% 0.56% 0.41% 0.13% 0.49%

STDEV: 4.52% 6.27% 3.00% 3.28% 1.34% 0.16% 0.11%

2005 (106)

Mean: 0.60% 0.90% 0.49% 0.46% 0.35% 0.11% 0.35%

STDEV: 4.43% 6.07% 3.28% 3.48% 1.26% 0.16% 0.04%

Notes: Each cohort’s data span the period from March of the indicated year through December of 2013. The figure in parenthesis under each cohort year is the number of monthly observations in the cohort.

Data and Summary Statistics Sample Means and Standard Deviations for Net Monthly Returns

74

TIAA RA

Average returns

are generally

higher that both

money market

and intermediate-

term government

bond returns, and

their volatility is

much smaller







Results




Outline

75





Mean-Variance Analysis Efficient Frontiers – 1970 Cohort (Mar-1970 – Dec-2013)

76

0.00%

0.25%

0.50%

0.75%

1.00%

1.25%

1.50%

1.75%

0% 1% 2% 3% 4% 5% 6% 7%

Ave

rag

e N

et

Mo

nth

ly R

etu

rn

Monthly Standard Deviation

Excluding TIAA RA

Including TIAA RA

Money

Market

TIAA

Intermediate-

Term Gov't Bonds

Long-Term

Government Bonds

Large Stocks

Small Stocks

Long Term

Corporate Bonds

Note the potentially large scope for improvement that inclusion of TIAA RA

investments brings to an optimal mean-variance portfolio for more than two-thirds

of the expected return range.





Mean Variance Analysis Optimal Portfolio Weights – 1970 Cohort

77

0%

20%

40%

60%

80%

100%

Op

tim

al p

ort

folio

Weig

hts


TIAA RA

Small Stocks

0%

20%

40%

60%

80%

100%

Op

tim

al P

ort

folio

We

igh

ts


Money

Market

Intermediate

Gov't Bonds

Small Stocks

Long Term

Gov't Bonds

Long term

Corporate Bonds

Excluding TIAA

RA Returns

Including TIAA

RA Returns






78

In the absence of the TIAA RA asset, money market instruments, intermediate-term bonds,

and long-term corporate bonds enter the optimal portfolios at various levels of portfolio

risk, along with small stocks and long-term government bonds

Large stocks never enter mean-variance-efficient portfolios model when calibrations are

based on the past 43 years of historical returns and correlations

Including the TIAA RA returns, we observe that no optimal mean-variance portfolio along

the efficient frontier for this cohort includes money market instruments, intermediate-term

bonds or long-term corporate or government bonds. Not even large US stocks are included





Mean Variance Analysis Optimal Portfolio Weights – 1975 and Subsequent Cohorts

79

In general, our findings for these cohorts are similar to those for the 1970 cohort:

Small stocks are in the portfolios both with and without the TIAA RA investment

Also, the TIAA RA replaces every other asset class except small stocks when it is allowed as an

investment

There are, however, some differences in these cohorts worth noting

When TIAA RA is excluded: long-term corporate bonds now show up in efficient

portfolios for intermediate levels of volatility for the 1980, 1985, 1990, and 2000 cohorts.

The absence of large stocks in any significant level across all cohorts is also quite

remarkable

When TIAA RA is included: the ability of the TIAA RA to replace asset classes other

than small stocks in optimal portfolios is reduced by the significant presence of long-term

government bonds in every cohort. In addition, we observe the appearance of long-term

corporate bonds in the 2000 cohort, and of money market investments for low volatility

levels in the 1985 cohort

These patterns are illustrated with the 1985 cohort:






80

0.00%

0.25%

0.50%

0.75%

1.00%

1.25%

1.50%

1.75%

0% 1% 2% 3% 4% 5% 6% 7%

Avera

ge N

et M

onth

ly R

etu

rn


Excluding TIAA RA

Including TIAA RA

Money

Market

TIAA

Intermediate

Gov't Bonds

Long Term Gov't Bonds

Large Stocks

Small

Stocks

Long Term

Corporate Bonds

0%

20%

40%

60%

80%

100%

Optim

al P

ort

folio

Weig

hts


Money

Market

Small Stocks

Long Term Gov't

Bonds

Intermediate

Gov't Bonds

Long Term

Corporate Bonds

Large Stocks

0%

20%

40%

60%

80%

100%

Optim

al p

ort

folio

Weig

hts


TIAA RA

Small Stocks

Money

Market

Long Term Gov't

Bonds

With no TIAA RA, Long-Term Corporate Bonds now

are in the optimal portfolios

And including TIAA RA, there are also Money Market

and Long-Term Government Bond investments in

some optimal portfolios







Results




Outline

81





R is the asset return, Rf is the risk-free rate of return, E[R – Rf] is the expected value of the

excess of the asset return over the risk-free rate, and Var[R – Rf] is the variance of the

excess return

The Sharpe ratio is used as a measure of how well an investor is compensated per unit of

risk taken. Higher ratios denote greater return for the same level of risk. In this analysis

we take the risk-free rate to be the money market monthly return

The Sortino ratio is based on the Sharpe ratio, but penalizes for only those returns that fall

below the target return, which in our case will be the average riskless rate of return over

the period of analysis

The denominator in the Sortino ratio is the variance formula of the excess returns calculated over

the range of return values where the asset returns of interest are below the risk-free rate

Sharpe and Sortino Ratios

82





Sharpe and Sortino Ratios Monthly and Annual Returns – 1970 to 1985 Cohorts

83

Large Stocks

Small Stocks

Long-Term Corporate

Bonds Long-Term Gov’t Bonds

Intermediate-Term Gov’t

Bonds TIAA RA

1970 Cohort Monthly Returns

Sharpe Ratio 0.092 0.135 0.080 0.068 0.078 1.075

Sortino Ratio 0.133 0.203 0.125 0.105 0.121 11.737

Annual Returns

Sharpe Ratio 0.281 0.397 0.274 0.259 0.264 1.133

Sortino Ratio 0.467 0.915 0.641 0.656 0.587 9.851


Sharpe Ratio 0.117 0.172 0.090 0.078 0.086 1.080

Sortino Ratio 0.169 0.260 0.140 0.121 0.134 12.808

Annual Returns

Sharpe Ratio 0.353 0.494 0.301 0.293 0.286 1.127

Sortino Ratio 0.600 1.190 0.710 0.763 0.642 10.361


Sharpe Ratio 0.121 0.148 0.129 0.113 0.133 1.554

Sortino Ratio 0.174 0.217 0.209 0.180 0.221 21.761

Annual Returns

Sharpe Ratio 0.358 0.405 0.470 0.457 0.485 1.716

Sortino Ratio 0.613 0.929 1.715 1.792 1.316 16.593


Sharpe Ratio 0.131 0.140 0.155 0.139 0.164 2.461

Sortino Ratio 0.186 0.203 0.251 0.221 0.256 N/A

Annual Returns

Sharpe Ratio 0.382 0.367 0.592 0.582 0.567 2.569

Sortino Ratio 0.654 0.845 2.043 2.765 1.677 N/A





Sharpe and Sortino Ratios Monthly and Annual Returns – 1990 to 2005 Cohorts

84

Large Stocks

Small Stocks

Long-Term Corporate

Bonds Long-Term Gov’t Bonds

Intermediate-Term Gov’t

Bonds TIAA RA


Sharpe Ratio 0.126 0.161 0.145 0.133 0.167 2.257

Sortino Ratio 0.181 0.243 0.231 0.205 0.260 N/A

Annual Returns

Sharpe Ratio 0.339 0.409 0.645 0.705 0.632 2.326

Sortino Ratio 0.578 0.970 1.909 3.129 1.913 N/A


Sharpe Ratio 0.125 0.155 0.139 0.127 0.165 2.347

Sortino Ratio 0.177 0.234 0.224 0.196 0.263 N/A

Annual Returns

Sharpe Ratio 0.318 0.384 0.630 0.699 0.658 2.407

Sortino Ratio 0.551 0.902 1.945 3.950 2.366 N/A


Sharpe Ratio 0.050 0.118 0.157 0.130 0.210 2.779

Sortino Ratio 0.068 0.173 0.257 0.203 0.342 N/A

Annual Returns

Sharpe Ratio 0.110 0.273 0.787 0.796 0.900 2.778

Sortino Ratio 0.184 0.602 3.285 6.905 4.483 N/A


Sharpe Ratio 0.112 0.130 0.115 0.102 0.193 1.812

Sortino Ratio 0.154 0.188 0.196 0.165 0.333 N/A

Annual Returns

Sharpe Ratio 0.221 0.269 0.550 0.656 0.874 1.708

Sortino Ratio 0.374 0.527 2.338 5.603 16.735 N/A





The TIAA RA portfolio has higher Sharpe and Sortino ratios across all the cohorts we

study

For the 1985 through the 2005 cohorts the Sortino ratio is not defined. The reason for this

unusual result is that not a single TIAA RA excess return in these cohorts happens to be

below the corresponding money market return

What we can say from this ratio analysis is that the structure of TIAA RA returns appears

to be very different from that of other asset classes, and that this structure does not lend

itself well to traditional mean-variance metrics for comparison

Moreover, these mean-variance findings are derived from return distributions that, for most

investment classes, are decidedly not normal

Accordingly, we now turn to present alternative and more powerful analyses that buttress

the implications our mean-variance analyses

Sharpe and Sortino Ratios

Conclusions

85







Results




Outline

86





Stochastic dominance methods provide no guidance into the construction of a portfolio

from various individual securities, and

Stochastic dominance methods do not yield an equilibrium price for securities.

Stochastic dominance provides evaluative criteria under very general conditions with

respect to an investor’s attitudes toward risk and considers higher moments of the asset

return distributions

The various degrees of stochastic dominance we describe below refer to different aspects

of investors’ attitudes towards risk.

Stochastic Dominance Results

87






First Degree Stochastic Dominance

88





In addition, second degree dominance

(SSD) requires investors to be risk

averse. This implies that a return

distribution that stochastically

dominates another in the second

degree will be preferred by any risk-

averse investor

Graphically, the return distribution F

dominates the return distribution G in

the second degree if:

The distribution F is above the

distribution G for part of the range of

returns,

G starts at a lower return than F, and

The area where the CDF of F is above

the CDF of G is smaller than the area

where the CDF of G is above the CDF

of F


Second Degree Stochastic Dominance

89

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-20% -10% 0% 10% 20%

Cum

ula

tive P

robabili

ty

Return

Cumulative Distribution G

Cumulative Distribution F





In addition, second degree dominance

(SSD) requires investors to be risk

averse. This implies that a return

distribution that stochastically

dominates another in the second

degree will be preferred by any risk-

averse investor

Graphically, the return distribution F

dominates the return distribution G in

the second degree if:

The distribution F is above the

distribution G for part of the range of

returns,

G starts at a lower return than F, and

The area where the CDF of F is above

the CDF of G is smaller than the area

where the CDF of G is above the CDF

of F


Absence of Second Degree Stochastic Dominance

90

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

-20% -10% 0% 10% 20%

Cum

ula

tive P

robabili

ty

Return

Cumulative Distribution G

Cumulative Distribution F





The development of third degree stochastic dominance (TSD) was motivated by a long observed

preference among some investors for positively skewed (i.e. asymmetric) returns

A subset of the class of investors who prefer returns exhibiting third degree stochastic dominance is

the important group whose preferences are characterized by decreasing absolute risk aversion

(DARA)

Such investors are willing to pay less for insuring against a given sized risk, on average, as they

accumulate greater wealth, which appears to accord with observed behavior toward risk.

Fourth degree stochastic dominance (4SD) was developed to capture investors’ aversion toward

kurtosis, where returns are characterized by peaked distributions and fat tails, such that losses can be

extreme

Of course kurtosis can favor investors who have asymmetric claims toward returns, such as

investors in call options, but for investors who have equal claims to both tails of a distribution,

such as investors in stocks and bonds, the fatter tails cause a disproportionate loss in utility


Higher Degrees of Stochastic Dominance

91






1970 Cohort

92

The results shown here are, in most cases, what is to be expected in the sense that it is not common

for empirical return distributions to stochastically dominate other distributions

Remarkably, though, the table shows the TIAA RA return distribution is the dominating one, by the

second degree, over intermediate-term government bonds and money market returns






1970 Cohort – TIAA RA Dominates Money Market in Second Degree

93

The figure illustrates this result

comparing the TIAA RA return

distribution to the Money Market

return distribution

During the early 1980s, money

market returns were higher than those

provided by the TIAA RA

This happened in 32 cases out of 526

months in the 1970 cohort sample

In the rest of the months, the TIAA

RA returns exceeded those posted by

money market funds

0.0

0.2

0.4

0.6

0.8

1.0

Cum

ula

tive p

robabili

ty

Return

Money Market

TIAA RA






Subsequent Cohorts

94

The results we obtain for

the 1970 cohort are

generally the same for

subsequent cohorts

The table to the left shows

the changes with respect to

what is observed for the

1970 cohort

In particular, in addition to

TIAA RA return

distributions dominating

intermediate-term

government bonds in the

second degree,

We observe that they

dominate money market

returns in the first degree

for the 1985 – 2005

cohorts

1975 Cohort Unchanged with respect to the 1970 Cohort

1980 Cohort

Long-term corporate bond returns dominate long-term government bonds in the 2

nd degree

1985 Cohort

Long-term corporate bonds do not dominate long-term government bonds

TIAA RA returns dominates money market returns in the 1

st degree

1990 Cohort



st degree

1995 Cohort



st degree

2000 Cohort

Long-term corporate bond returns dominate long-term government bonds in the 2

nd degree


st degree

LT corporate and government bonds IT government bonds and the TIAA RA asset class dominate large stocks in the 2

nd

degree

2005 Cohort


st degree





End of Presentation

95

Trends in Retirement Income Distributions by

TIAA-CREF Participants

Jeffrey Brown, Illinois and NBER

James Poterba, MIT and NBER

David P. Richardson, TIAA-CREF Institute

Research Motivation

• Need to understand how retirees draw down assets in a

primary DC environment

• Demographic Pressures

• Aging population

• Increased longevity risk

• Labor Market Shifts

• Majority of workers now covered by DC plans

• Increased labor force by women

• Government Policy Changes

• Social Security and Medicare Reforms

• Qualified plan design reforms

2015 Fall Fellows Symposium: October 2, 2015 97

Research Questions

• What trends do we see from retirees taking distributions from

retirement assets?

• What are the factors that influence the timing, size, and types

of retirement distribution?

• Does plan design play a key role in distribution decisions?


Prior Research

• King (1996): Trends in the Selection of TC Life Annuity Income

Options, 1978 -1994

• Annuitization required over most of period

• Relatively simple investment menu

• High nominal interest rates

• Women 2x or more likely to choose single-life option

• Women less likely to opt for a guaranteed period

• Trend of average older first annuity age


Prior Research

• Ameriks (2002): Recent trends in the Selection of Retirement

Income Streams by TIAA-CREF Participants

• Trends over the 1995 – 2001 period

• Full menu of income options

• Simple investment menu

• Falling nominal interest rates

• Continuation of trend of older first annuity age

• Strong proportional take-up of non-annuity options

• Women 2x or more likely to choose single-life option

• Women more likely to opt for a guaranteed period


Distribution Options

• Income:

• Annuity (1918): fixed or variable, single or joint, guarantee period

option

• Interest Payout Only Option (1989): TIAA only, limited duration

(IPRO)

• Minimum Distribution Option (1991): required minimum distributions

for retirees age 70.5 and older (MDO)

• Systematic Withdrawals and Transfers (1996): participant sets the

schedule (SWAT)

• Other Cash Withdrawals:

• Transfer Payout Annuity (1991): TIAA only, count only cash outs.

(TPA)

• Other lump-sums: periodic or single, cash distributions


System Snapshot: Distribution Choices of TIAA-CREF

Participants - Age 59.5 or older and non-contributing


Payment Type% with

income

% with

distribution

% with

assets

Annuity Certain 4% 3% 2%

Joint Life 5% 4% 2%

Joint Life with Guarantee 31% 23% 13%

Single Life 9% 6% 4%

Single Life with Guarantee 17% 13% 8%

Unique Participants with IA Payments 64% 47% 28%

TPA1 13% 8%

IPRO 4% 3% 2%

MDO 34% 25% 15%

SWAT 6% 4% 3%

Cash Withdrawals 21% 12%

Unique Participants with DA Payments2 47% 28%

Unique Participants with Income Payments 74% 44%

Unique Participants with Payments 59%

Unique Participants with No Payments 41%

Total Participants 446,551 604,561 1,019,076

2 Rollover to IRA and plan to plan asset transfers are not included.

1 Include periodic cash disbursements. Rollover to IRA and asset transfers to other CREF accounts are

not included.

Initial Income Selections

Source: author calculations of TIAA-CREF administrative records


Who Annuitizes and How?

• The extensive margin: Do I annuitize?

• A “working life”, as opposed to “at retirement”, decision

for many TC participants.

• The intensive margin: How do I annuitize?

• A “during retirement” decision for most participants.

• Financial considerations

• Pricing/interest rate

• Estate planning

• Psychological considerations

• Inertia/Uncertainty

• Mental Accounting


Asset Allocations, by age cohort

Source: Richardson, David P. and Benjamin Bissette (2014) “Trends in Premium and Asset

Allocations by TIAA-CREF Participants: 2005 – 2011.”TIAA-CREF Institute Research Dialogue

20 20

28

35

42

1117

24

34

44

6770

65

59

52

39

51

55

51

47

77

5 5 5

3

5

4

4

32

2 1 1 1

4

5

4

3

34 2 1 1 0

43

23

138

4

0

10

20

30

40

50

60

70

80

90

100

Under 35 35 - 44 45 - 54 55 - 64 65 + Under 35 35 - 44 45 - 54 55 - 64 65 +

Average Asset Allocations by AgeDecember 2006 and December 2011

TIAA Traditional CREF TIAA Real Estate Retirement Mutual Funds Lifecycle Funds

December 2006 December 2011


New Settlements: TIAA vs. CREF vs. REA

Source: author calculations of TIAA actuarial data


Single v. Joint Life, by Gender

Source: author calculations of TIAA-CREF administrative data


Guarantee period usage, by Gender and annuity type

Source: author calculations of TIAA-CREF data


First Life Annuity Issue Ages

Source: author calculations of TIAA-CREF administrative data


Final Thoughts

• Growing popularity of single life annuities and guarantee

periods

• Possible interest rate effect?

• Continuing trend of delaying first annuity draw.

• Counter to optimal Social Security strategy?

• Having annuities in the investment menu during the

accumulation phase seems important.

• “Working life” instead of “at retirement” decision.

• Can help participants overcome behavioral biases.


How Retirees Manage Retirement

Savings for Retirement Income

Paul J. Yakoboski, Ph.D.

Senior Economist, TIAA-CREF Institute

TIAA-CREF Institute Fellows Symposium


October 2, 2015

113

Savings to Income Survey

What factors influence how retirees with significant assets in tax-qualified

retirement accounts convert their savings to income during retirement?

• In particular, what are the similarities and differences between those

who annuitize some of their savings and those who do not?

Surveyed 1,000 retired TIAA-CREF participants

• Age 60 or older

• Retired with at least $400,000 in DC and/or IRA assets

• No DB pension income

• 500 receiving annuitized payments (annuitants)

• 500 not receiving annuitized payments (non-annuitants)

114

Annuitant and non-annuitant demographics

42%

61%

47%

30%

9%

4% 2% 5%

0%

20%

40%

60%

Annuitants Non-annuitants

Current age

60-74 75-84 85 or older DNA

18%

31%

23%

31% 30%

26%

29%

13%

0%

10%

20%

30%

40%


Years retired

2-4 years 5-9 years

10-15 years over 15 years

115

Retirement outcomes

28%

18%

61%

74%

11% 8%

0%

20%

40%

60%

80%


How did your standard of living change with retirement?

Increased Did not changeDecreased

31%

19%

64%

78%

5% 2%

0%

20%

40%

60%

80%


How has your lifestyle in retirement compared with your pre-retirement expectations?

Exceeded Met Fallen short

116

Retirement outcomes

Extremely

confident

Very

confident

Somewhat

confident

Not too/not at

all confident

…be able to maintain a comfortable standard of living throughout

retirement?

Annuitants 19% 54% 26% 2%

Non-annuitants 22 52 23 2

…not outlive your savings and financial assets?

Annuitants 22 49 25 3


…have the best strategy to manage and draw income from your

retirement savings during retirement?



How confident are you that you (and your spouse) will…

117

Top financial priorities in retirement

Very high High Moderate Low/not

Ensuring the financial security of your spouse

if you die first



Not outliving savings and financial assets



Having a guaranteed income stream sufficient to cover basic expenses



How much of a priority is [this issue] when it comes to

managing your personal finances during retirement?

118

Low financial priorities in retirement


Having the flexibility to adjust your income as needed over time

Annuitants 15% 49% 27% 10%


Earning a high rate of return on your financial assets



Leaving an inheritance



Having professionals manage your financial assets





119

Mid-level financial priorities in retirement


Maintaining direct control of your financial assets



Preserving your financial assets



Maintaining the same standard of living throughout retirement





120

Annuitants’ motivations

Extremely

important

Very

important

Somewhat

important

Not too/

not at all

important

Cannot outlive the

income stream 50% 34% 10% 7%

Providing income for

spouse if annuitant dies

first

49 32 8 11

The certainty of a

constant level of

income

39 39 16 7

Helping to cover basic

living expenses 37 43 14 5

Maintaining standard of

living over time 28 44 22 6

Reasons for annuitizing retirement savings

121

Non-annuitants’ motivations

Extremely

important

Very

important

Somewhat

important

Not too/

not at all

important

Wanting to keep direct

control of the money 26% 41% 25% 8%

Being unable to access

the money if needed 18 37 26 18

Having Social Security

or other income sources 18 34 30 18

Think it is a poor

investment 14 25 35 26

The possibility of losing

money if you die

prematurely

19 26 24 32

The expense of fees and

charges 13 24 33 31

Reasons for not annuitizing retirement savings

122

More on non-annuitants

3% 7%

16%

40%

35%

0%

10%

20%

30%

40%

Likelihood of annuitizing in the future

28% 25%

48%

0%

10%

20%

30%

40%

50%

A pretty goodidea

Somewhat ofan idea

No idea

Would you have an idea of how much income your savings would provide if annuitized?

123

So what’s going on?

Annuitants 54%

Non-annuitants 58%

Amount of advice typically followed?

All Most Some Little/none



Advice received about annuitizing retirement savings?

Do Don’t No advice

Annuitants 60% 9% 30%

Non-annuitants 21 37 42

Worked with a financial advisor in deciding how to

manage and draw income from retirement savings?

124

So what’s going on?

Yes 25%

No 48%

Don't know/ Not sure

27%

Did you participate in a plan while working that had a deferred annuity as an investment option?

Non-annuitants

125

Key takeaways

Annuitants are more likely to have experienced an increased standard of living in

retirement and a lifestyle that has exceeded preretirement expectations.

Annuitants and non-annuitants share the same top financial priorities for their

personal finances in retirement, each of which is consistent with annuitization.

Furthermore, the most important reasons for retirees deciding to annuitize are

consistent with these top financial priorities.

So why do some retirees annuitize, while others do not? It is possible that non-

annuitants do not understand that annuitization would address their top financial

priorities.

• 80% of non-annuitants were advised to not annuitize or did not receive

advice regarding annuitization; 60% of annuitants were advised to do so.

• In-plan deferred annuities present an opportunity for participants to

become socialized to annuities and annuitization. For 75% of non-

annuitants, either their plan(s) did not have a deferred annuity or they did

not realize it, the latter likely being the case for many.

2015 fellows symposium retirement benefits for a …...proportion of respondents with rhi by...

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