UNIVERSITY OF I LLINOIS 

AT U RBANA-C HAMPAIGN 

Department of Finance 

College of Business 

340 Wohlers Hall, MC-706 

1206 South Sixth Street 

Champaign, IL 61820-6980 

Jeffrey R. Brown 

Dean, College of Business (effective 8/16/2015) 

William G. Karnes Professor of Finance 

Telephone 217-333-3322 • E-mail brownjr@illinois.edu 

To:   U.S. Department of Labor

From:   Prof. Jeffrey R. Brown, Ph.D.

Subject: Comment on “Definition of the term "Fiduciary"; Conflict of Interest Rule— 

Retirement Investment Advice.” RIN 1210‐AB32

Date:   July 21, 2015

Ladies and Gentlemen:   

I write today to offer a brief comment on the proposed fiduciary standards referenced

above.  I am the William G. Karnes Professor at the University of Illinois at Urbana‐Champaign, the

Associate Director of the Retirement Research Center at the National Bureau of Economic

Research, a member of the 2015 Social Security Technical Panel on Methods and Assumptions,

and a Trustee for TIAA. However, the opinions I express here are mine alone and should not be

construed as representing any of the organizations with which I am affiliated.  I base this comment

on the knowledge and experience I have accumulated over my career as an academic researcher and 

a participant in various industry and public policy activities related to retirement income security

for all Americans.   

Overall, I am supportive of the effort to impose a best interest standard on a broader set of

players in the financial services and retirement industries. I have long felt that the discrepancy in

treatment between the qualified plan market and the retail market is an issue that warrants 

further attention. Although I am supportive of the spirit of the proposal, however, I am worried that

the details of this proposal may inadvertently harm the retirement income security of individuals

planning for retirement. It is this possibility for unintended adverse consequences in the area of

lifetime income products on which I would like to focus.   

Much of my research has documented the value that guaranteed lifetime income products

can create for individuals by providing a tool for insuring against longevity risk. I have been very

appreciative and supportive of the steps taken by the current Administration to remove barriers to

including lifetime income options as part of qualified plans. My current research agenda is focused

on the difficulties that individuals have in making optimal decisions around retirement planning, 

especially in understanding the role of annuities in guaranteeing lifetime income. This research 
UNIVERSITY OF I LLINOIS 

AT U RBANA-C HAMPAIGN 

Department of Finance 

College of Business 

340 Wohlers Hall, MC-706 

1206 South Sixth Street 

Champaign, IL 61820-6980 

Jeffrey R. Brown 

Dean, College of Business (effective 8/16/2015) 

William G. Karnes Professor of Finance 

Telephone 217-333-3322 • E-mail brownjr@illinois.edu 

suggests that millions of Americans are under-utilizing lifetime income products and thus exposing 

themselves to excessive longevity risk. Overcoming these complex behavioral and psychological 

barriers to annuitization requires a multi‐faceted approach, including changes in the way

retirement options are framed.  An implication of these findings is that the typical consumer is

unlikely to take advantage of guaranteed lifetime income options unless we structure the

retirement planning space in a way to encourage it (via plan architecture, nudges, consumer

education, and advice).  I am attaching a few of my papers in this area that outline some of the

ways in which consumers struggle with this issue.

My concern with the details of the proposed regulations is that the new rules will make it

very expensive and difficult for financial advisors and insurance companies to be able to overcome

these behavioral and psychological biases. The old adage that lifetime income products are “sold,

not bought” may be very true in this context.  Overcoming these barriers does not come easily or 

free – it requires financial services companies, advisers, plan sponsors and others to spend time

working with clients to help them understand the tremendous value that lifetime income products

can create for them.  Raising the cost of this activity and increasing the fiduciary burden of those

involved in this process could hurt these efforts. It would be a shame if a policy intended to help

improve retirement security ended up hurting it by driving advisers and others out of the business

of promoting annuities and other guaranteed lifetime income products.

In summary, I am supportive of the intent of these proposed regulations, but I would urge

extreme caution in designing and implementing these rules.  Policymakers, industry, and

academics have made steady progress in recent years on better understanding consumer needs in

the area of lifetime income and starting to develop communication tools and products to help

guide people onto paths that are likely to lead to better retirement outcomes.  We must be very

careful not to shut down this innovation by well‐intentioned but poorly designed rules.

Thanks for this opportunity to provide input into this process.

         Sincerely,

         Jeffrey R. Brown

         Karnes Profess of Finance

         University of Illinois

Enclosures
304

American Economic Review: Papers & Proceedings 2008, 98:2, 304–309

http://www.aeaweb.org/articles.php?doi=10.1257/aer.98.2.304

According to standard economic models, a 

risk-averse consumer who faces uncertainty 

about length of life should place a high value 

on life annuities that provide guaranteed income 

for life. Yet numerous studies show that few consumers voluntarily annuitize their retirement 

savings. As public and private pension systems 

around the world continue the shift from traditional defined benefit plans, which typically pay 

benefits for life, to defined contribution structures, which rarely require annuitization, retirees find themselves increasingly exposed to 

longevity risk—the risk of being unable to sustain their consumption should they live longer 

than expected.

Numerous papers have attempted to resolve 

the puzzle of why so few individuals purchase 

life annuities despite the large individual welfare gains predicted by economic models (for a 

review of this literature, see Jeffrey R. Brown 

2007). Studies have explored the role of high 

prices and asymmetric information, high fractions of wealth already annuitized by public 

pension plans, bequest motives and other forms 

of risk sharing within families, the option 

value of delayed annuitization, and incomplete 

Why Don’t People Insure Late-Life Consumption? 

A Framing Explanation of the Under-Annuitization Puzzle

By Jeffrey R. Brown, Jeffrey R. Kling, Sendhil Mullainathan, 

and Marian V. Wrobel*

annuity markets, including the absence of inflation protection, the inability to insure against 

medical expenditure or other consumption 

shocks, and the limited ability to access the 

equity premium while annuitizing. By combining a number of these factors, it is possible to 

rationalize very low demand in some specific 

contexts.

As a whole, however, the literature has failed 

to find a sufficiently general explanation of consumer aversion to annuities. Many proposed 

“solutions” simply create new puzzles. For 

example, family risk-sharing implies that annuity demand should increase upon the death of 

one spouse, yet we do not observe this. Nor do 

we observe significantly different annuitization 

propensities between those who self-proclaim 

strong bequest motives and those who do not. 

Several hypotheses suggest that annuitization is 

optimal only at more advanced ages, yet we do 

not observe a substantial fraction of the population annuitizing at older ages. Additionally, 

the industry has created life annuities that

overcome many of the product-based objections 

(e.g., inflation-protected annuities, annuities 

with payout streams linked to equity returns, 

policy riders that provide benefits for long-term 

care expenses), and yet few consumers buy these 

products.

Rather than attempting to rationalize the lack 

of annuity demand, this paper explores the idea 

that aversion to annuities is not a fully rational 

phenomenon. A large literature has documented 

behavioral biases in a wide range of activities 

that are important steps in the process of planning for retirement, including whether to participate in employer sponsored pension plans, 

how much to save, and how to allocate one’s 

portfolio. To the extent that individuals exhibit 

biases in the wealth accumulation aspects of 

planning for retirement, it seems natural that 

similar biases might also extend to the wealth 

decumulation stage of retirement planning.

* Brown: University of Illinois at Urbana-Champaign, 

340 Wohlers Hall, MC-706, 1206 S. Sixth Street, 

Champaign, IL 61820, and NBER (e-mail: brownjr@uiuc.

edu); Kling: Brookings Institution, 1775 Massachusetts 

Avenue, NW, Washington, DC 20036, and NBER (e-mail: 

jkling@brookings.edu); Mullainathan: Department of 

Economics, Harvard University, Littauer 208, Cambridge, 

MA 02138, and NBER (e-mail: mullain@fas.harvard.edu); 

Wrobel: Institute for Quantitative Social Science, Harvard 

University, 1737 Cambridge Street, Cambridge, MA 

02138 (e-mail: mwrobel@iq.harvard.edu). We thank Abby 

Bookman and Garth Wiens for exceptional research assistance. We are grateful to the Pew Charitable Trusts and the 

TIAA-CREF Research Institute for support research. Kling 

also thanks the MacArthur and Mott Foundations for support. The opinions expressed in this report are those of 

the authors and do not necessarily reflect the views of the 

Pew Charitable Trusts, the TIAA-CREF Institute, or the 

MacArthur or Mott Foundations.
VOL. 98 NO. 2 Why Don’t People Insure Late L

I.  The Framing Hypothesis

This paper suggests that a psychologically 

richer model of consumer behavior can explain 

under-annuitization. Since the development of 

prospect theory, economists have increasingly 

understood the importance of framing in economic decisions (Amos Tversky and Daniel 

Kahneman 1981). While loss/gain asymmetry—the differential responses when a choice 

is framed as a loss than when it is framed as a 

gain—is the most commonly discussed example, framing is a more general phenomenon. 

Put simply, experimental findings suggest that 

choices are not based solely on material consequences, but instead are filtered through the 

particular frame that individuals use to interpret 

the choices.

To understand our model of framing, it is 

useful to start with the standard, fully rational model. Suppose an individual planning 

for retirement maximizes the discounted 

sum of each period’s utility of consumption: 

gT

t50 dt

u 1ct2, where u 1ct2 is concave, d , 1 

is the discount factor, and  (time of death)

is a stochastic variable. In this setting, annuities provide valuable insurance by transferring 

resources from states where resources have no 

value (death) to states where resources provide utility through consumption. This is most 

easily seen in the two-period case where the 

individual has probability q of dying. If this 

individual invests wealth W in a simple bond 

with a return R, he can consume W11 1 R2 in 

the second period. If, in contrast, he buys an 

actuarially fair life annuity, he is able to consume W11 1 R2/11 2 q2 if he lives, which, by 

assumption, is all he cares about. To understand 

why the annuity allows for higher consumption, 

imagine all individuals pool their wealth at time 

1 and share it among all survivors at time 2; this 

generates a mortality premium.

The model above implicitly has two components: an investment component, in which 

the individual decides how much to invest in 

each asset, and a consumption component, in 

which she decides how to spend the money 

from that investment. We propose that instead 

of viewing the problem through the consumption frame (focusing on the end result of what 

can be spent over time), many consumers adopt 

an investment frame (focusing on the intermediate results of return and risk features when 

choosing assets and not considering the consequences for consumption). This assumption is 

closely related to the notion of choice bracketing and mental accounting (Richard H. Thaler 

1985; Daniel Read, George Loewenstein, and 

Matthew Rabin 1999). Consumers effectively 

isolate one choice (how to invest) from others (how to consume) and focus on specific 

features of this choice, rather than viewing it 

as part of a broader, integrated set of choices. 

Specifically, suppose that individuals consider 

the rate of return and the variance of payouts, a 

natural frame for someone investigating alternative approaches to investing for retirement. 

In the example above, a bond has return R and 

poses no risk, since it pays the same irrespective of state. On the other hand, the annuity has 

a return 11 1 R2/11 2 q2 with probability 1 2 

q, and return 0 with probability q. If the annuity is actuarially fairly priced and individuals 

have rational expectations about mortality, then 

the annuity has expected return R. Yet despite 

having the same return, the annuity appears 

riskier than the bond. This reversal is key to

our hypothesis: under the consumption frame, 

the annuity is attractive because it serves as a 

form of insurance. In contrast, under the narrow investment frame, the annuity is viewed as 

being riskier than a bond because its return 

depends on a random variable T

Practically, this framework suggests that the 

unattractive feature of the annuity in the investment frame will be the potential for the investment to lose money. This matches the qualitative 

intuition that practitioners provide: people react 

negatively to the possibility that they could lose 

money. Indeed, if an individual dies immediately after annuity purchase, he could lose his 

entire principal. Although not necessary for our 

analysis, loss aversion can strengthen this fear, as 

noted by Wei-Yin Hu and Jason S. Scott (2007). 

The possibility of loss is particularly problematic because the annuity does not offer a higher 

expected return to offset this extra “risk.” This 

insight is also consistent with industry market 

research that has found that many consumers 

think of annuities as a “gamble” rather than as 

insurance. In fact, the annuity is even less attractive: life annuity payouts are typically less than 

actuarially fair due to administrative costs and 

adverse selection, meaning that the higher risk 

is bundled with a lower return. In addition, the 

risk of adverse selection also necessitates that 
306 AEA PAPERS AND PROCEEDINGS MAY 

most annuity contracts are essentially irreversible, resulting in illiquidity.

To summarize, we argue that when choosing 

how to invest their money, individuals focus too 

narrowly on risk and return. The annuity, when 

viewed in this narrow investment frame, looks 

risky and unattractive. In the next section we 

test this hypothesis.

II.  Testing the Framing Hypothesis

In this paper, we provide a preliminary test 

of the framing hypothesis. We created descriptions of potential scenarios to be presented to 

potential consumers, some of which represent 

annuities and some of which represent competing non-annuitized products (such as a savings 

account). The essence of the test is that some of 

the subjects are presented these products in an 

investment frame, which emphasizes the depersonalized return on an account by using words 

such as “invest” and “earnings,” describing periods in terms of years, mentioning the value of 

the initial investment ($100,000 in every case), 

and alluding to the account value at other points 

in the description. The other subjects are presented these products in a consumption frame, 

meaning that they are told how much each 

product would ultimately allow its purchaser to 

consume and for how long, using words such as 

“spend” and “payment,” describing periods in 

terms of the purchaser’s age, and never alluding 

to an account or its value. The key distinction 

is that the consumption frame shifts the frame: 

instead of simply considering the returns on the 

investment, individuals are presented with the 

consumption consequences of the investment. 

The consumption frame implicitly incorporates 

the results of investment decisions, as well as 

the time path of consumption, and, in this sense, 

is broader. The framing hypothesis suggests that 

the life annuity should be attractive in the consumption frame and unattractive in the investment frame.

We collected data to test this hypothesis 

in a four-arm Internet survey conducted in 

December 2007. The Internet survey firm 

Zoomerang hosted the survey and recruited 

respondents over age 50 from a pre-existing 

panel of individuals willing to participate in 

surveys in return for small incentives. A total of 

1,342 individuals, approximately 335 per arm, 

completed the survey. All respondents answered 

seven forced-choice questions. Each question 

described the investment/spending decisions of 

two fictitious people and asked, “Who has made 

the better choice?” In all arms of the survey, 

an introduction stated that both people receive 

$1,000 each month from Social Security, have 

“some savings,” and have already set aside 

money for their children. The presence of savings and money for the children were intended 

to address respondents’ potential concerns 

about insurance from consumption shocks and 

bequests. In a real-life setting, such concerns 

could be addressed via partial annuitization.

Two arms of the survey presented the introduction and the choices using the investment frame, 

and two arms used the consumption frame. 

Brown et al. (2007) provide the exact wording of 

the products and the frames. In all four arms of 

the survey, the choices were described in terms 

of amounts and durations: the specific terms 

“annuity,” “savings account,” and “bond” were 

not used for labels. Several choices were compared in all arms: (a) a life annuity paying $650 

each month until death; (b) a traditional savings 

account bearing 4 percent interest; (c) a consol 

bond paying $400 each month forever; (d) a 35-

year period annuity paying $500 each month; 

and (e) a 20-year period annuity paying $650 

each month. In all four arms of the survey, each 

respondent compared the life annuity separately 

to each of the other products. In addition, in the 

investment frame, each respondent compared a 

principal-protected life annuity (i.e., a life annuity that guaranteed enough payments so that the 

nominal value of the principal would be repaid 

even in the event of an early death) paying $625 

each month to the traditional savings account. 

All choices were designed to be actuarially 

equivalent, and respondents were informed of 

this fact. To avoid spurious effects, the survey 

included several other comparisons that did not 

feature the life annuity, varied the order of the 

comparisons, and varied whether the life annuity was presented first or second within a given 

comparison.

To further address the role of the bequest 

motive in suppressing annuitization, we added 

an additional dimension of variation in the 

survey. Half of the respondents in each frame 

were told that, after death, remaining earnings 

or payments went to charity (the weak bequest 

condition); the other half that they went to children (the strong bequest condition).
VOL. 98 NO. 2 Why Don’t People Insure Late L

We note that while our survey results are 

based on hypothetical scenarios, these scenarios 

are very similar to actual financial decisions that 

respondents and people they know have made. 

This stands in sharp contrast to the frequently 

used (and criticized) contingent valuation situations, which attempt to value the existence of 

things outside of normal experience. Robert B. 

Barsky et al. (1997) showed that data on stated 

preferences regarding risk tolerance and consumption path preferences were related in sensible ways to predicted behaviors. More generally, 

in a review of studies using stated preference 

data versus revealed preference data, Jordan 

J. Louviere, David A. Hensher, and Joffre D. 

Swait (2002) found that estimates of parameters 

based on the two types of data are often quite 

similar. An advantage of this approach is that 

we can obtain stated preferences over alternatives that may not be offered in a market (and 

for which there are no revealed preferences) but 

that are constructed to directly test our hypothesis. While we do not intend these stated preference data to be conclusive, the results are useful 

for guiding future research, possibly including 

experiments using actual product choices.

III.  Results

When questions were presented in the consumption frame, the majority of individuals preferred the consumption stream consistent with a 

life annuity to the consumption streams available from other products of comparable actuarial 

value. Specifically, in this frame, when individuals were told that any payments after death went 

to charity, 72 percent of respondents preferred 

the $650 per month that could be provided by a 

life annuity to the consumption stream from a 

savings account of comparable actuarial value. 

Of respondents, 77 percent preferred the life 

annuity to receiving $650 per month for 20 years 

(age 85); 76 percent preferred the life annuity 

to receiving $500 per month for 35 years (age 

100); and 71 percent preferred the life annuity to 

receiving $400 forever (the consol bond).

In contrast, when individuals faced the same 

choices in the investment frame, the proportions reversed, with the majority of individuals 

not choosing the life annuity. Specifically, only 

21 percent of respondents preferred an account 

earning $650 each month for life (i.e., a life 

annuity) to investing $100,000 at 4 percent. 

Further, only 48 percent preferred the life annuity to an account earning $650 per month for 20 

years; 40 percent preferred the life annuity to an 

account earning $500 per month for 35 years; 

and only 27 percent preferred the life annuity 

to an account earning a 5 percent interest rate 

from which interest but not invested money 

could be withdrawn. Note that in the life and 

period annuity cases, the respondent was explicitly told that, at the end, the investment would be 

worth nothing. In every case, the difference in 

rates between the consumption and investment 

frames was statistically significant.

In order to explore the direct effect of bequest 

motives, as well as any interaction between 

bequests and framing, we also randomized the 

treatment of bequests in our sample. When individuals were told that remaining payments went 

to children, rather than to charity, the percentages of respondents preferring the life annuity 

in the consumption frame declined, although 

it remained above 50 percent in most cases. 

Specifically, 59 percent of respondents preferred the life annuity to a savings account; 65 

percent preferred the life annuity to $650 per 

month to age 85; 53 percent preferred the life 

annuity to $500 per month to age 100; and 49 

percent preferred the life annuity to $400 “forever.” Importantly, however, the percentages of 

respondents preferring the life annuity also fell 

in the investment frame so that the magnitude of 

the between-frame differences remained quite 

similar. Thus, across both a strong and a weak 

framing of bequests, we find a substantially 

larger fraction of the population finds annuities 

attractive when framed in consumption, rather 

than investment, terms.

While the strong effect of the frame on the 

stated preferences for life annuities is the key 

finding of the survey, our research also provides 

insights into how the framing affects various features of the annuity product. Specifically, there 

are at least two distinct features of a life annuity 

that distinguish it from a savings account: (a) the 

conversion from flexible access to money (i.e., 

“liquidity” in the investment frame) to a fixed 

stream of payments; and (b) the application of 

the mortality premium to the annuity payments. 

We are able to isolate the effect of each of these 

factors by comparing alternative products within 

each frame.

In the consumption frame, we find that the 

loss of flexibility did not have much impact on 
308 AEA PAPERS AND PROCEEDINGS MAY 

the respondents’ evaluation of choices. Similar, 

albeit slightly lower, percentages of respondents 

preferred the life annuity to the savings account 

(flexible access) as preferred the life annuity to 

the period-certain annuity (fixed payment). In 

contrast, the loss of flexibility did matter in the 

investment frame: a smaller fraction of respondents chose the life annuity over the savings 

account than chose the life annuity over the 

period-certain annuity.

We also find that the mortality premium, 

which arises from pooling mortality risk, was 

a positive attribute in the consumption frame, 

with respondents consistently favoring life 

annuities relative to period-certain annuities. 

In contrast, the mortality premium was viewed 

neutrally or negatively in the investment frame, 

with respondents split equally on the choice of 

a life or 20 year annuity and a majority disliking the life annuity relative to its 35 year counterpart. These attitudes are consistent with our 

hypothesis: a dislike of illiquidity and loss of 

control are salient in the investment frame, 

but not in the consumption frame. Similarly, a 

desire to insure against longevity risk is salient 

in the consumption frame but not the investment frame.

Our survey also indicates that principal protection is highly valued in the investment frame: 

in the weak bequest condition, 47 percent of 

respondents believe that a principal-protected 

life annuity earning $625 per month is a better choice than a savings account, while only 21 

percent believe that an unprotected life annuity 

dominates. The result is more dramatic in the 

strong bequest arm. Again, this high valuation 

is consistent with our hypothesis, and specifically with an aversion to the loss of wealth with 

a reference point at the amount of the initial 

investment.

IV.  Conclusion

We hypothesize that framing matters for 

annuitization decisions: in a consumption 

frame, annuities are viewed as valuable insurance, whereas in an investment frame, the annuity is a risky asset because the payoff depends 

on an uncertain date of death. Survey evidence 

is consistent with our hypothesis that framing 

matters: the vast majority of individuals prefer an annuity over alternative products when 

presented in a consumption frame, whereas the 

majority of individuals prefer non-­annuitize

products when presented in an investment 

frame. To the extent that the investment frame 

is the dominant frame for consumers making 

financial planning decisions for retirement, this 

finding may help to explain why so few individuals annuitize.

This finding provokes the immediate question: if framing matters, why don’t annuity 

providers use the consumption frame? We conjecture that the investment frame is the dominant frame in the market and in most younger 

customers’ minds, both because it is simpler, 

due to the focus on nearer-term and impersonal 

outcomes, and because little is lost by using this 

frame during the wealth accumulation stage of 

life. We further conjecture that firms tend not to 

“convert” retirement-age customers to the consumption frame for several reasons: resources 

are required to incorporate additional personalized information and thus convert consumers to 

a more complex frame; a given firm may not capture the return from raising a customer’s interest 

in particular products in the consumption frame 

because the converted customer can purchase 

from another lower-cost seller; the compensation of sales staff (e.g., through commissions) 

may be oriented to products most consonant 

with investment frame and the compensation 

system may involve sales people outside the 

direct control of a given firm; and invoking the 

consumption frame may undermine demand for 

the firm’s other non-life-contingent products. 

Exploration of these conjectures would be valuable future work.

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VOL. 98 NO. 2 Why Don’t People Insure Late L

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Cognitive Constraints on Valuing Annuities

February 27, 2015

Jeffrey R. Brown Arie Kapteyn

Department of Finance Center for Economic and Social Research

University of Illinois University of Southern California

515 E. Gregory Drive 635 Downey Way

Champaign, IL 61820 Los Angeles, CA 90089

and NBER kapteyn@usc.edu

brownjr@illinois.edu

Erzo F.P. Luttmer Olivia S. Mitchell

Department of Economics The Wharton School

6106 Rockefeller Center University of Pennsylvania

Dartmouth College 3620 Locust Walk, 3000 SH-DH

Hanover, NH 03755 Philadelphia, PA 19104

and NBER and NBER

Erzo.FP.Luttmer@Dartmouth.edu mitchelo@wharton.upenn.edu

The research reported herein was performed pursuant to a grant from the U.S. Social Security 

Administration (SSA) funded as part of the Financial Literacy Consortium. The authors also acknowledge 

support provided by the Pension Research Council/Boettner Center at the Wharton School of the 

University of Pennsylvania, and the RAND Corporation. The authors thank Jonathan Li, Caroline Tassot, 

Myles Wagner, and Yong Yu for superb research assistance, and Tim Colvin, Tania Gutsche, Bas 

Weerman, and participants of the Netspar 2012 Paris conference and the NBER PE program meetings for 

their invaluable comments and assistance on the project. Brown is a Trustee of TIAA and has served as a 

speaker, author, or consultant for a number of financial services organizations, some of which sell 

annuities and other retirement income products. Mitchell is a Trustee of the Wells Fargo Advantage 

Funds and has received research support from TIAA-CREF. The opinions and conclusions expressed 

herein are solely those of the authors and do not represent the opinions or policy of SSA, any agency of 

the Federal Government, or any other institution with which the authors are affiliated. 
Cognitive Constraints on Valuing Annuities

Jeffrey R. Brown, Arie Kapteyn, Erzo F.P. Luttmer, and Olivia S. Mitchell

Abstract

This paper documents consumers’ difficulty valuing life annuities. We show that the prices at 

which people are willing to buy annuities are substantially below the prices at which they are 

willing to sell them, a finding we show is not attributable to an endowment effect. We also find

that buy values are negatively correlated with sell values and that the sell-buy valuation spread is

negatively correlated with cognition; the spread is larger for those with less education, weaker 

numerical abilities, and lower levels of financial literacy. Our evidence contributes to the 

emerging literature on heterogeneity in financial decision-making abilities. 

Key Words: pension, annuity, retirement income, Social Security, financial literacy, cognition

JEL Codes: D14, D91, G11, H55
Cognitive Constraints on Valuing Annuities

It is difficult for the average person contemplating retirement to determine how to draw 

down his wealth. Choosing a wealth decumulation and consumption strategy that maximizes 

lifetime utility is a highly complex problem that requires the ability to optimize intertemporally 

and under multiple sources of uncertainty, aspects that require substantial cognitive effort. Most

formal models of annuitization, however, ignore the possibility that individuals may differ in 

their decision-making competency. The commonly used life-cycle model assumes a rational and 

fully-informed individual who knows the distribution of mortality rates, market returns, inflation, 

future expenditures, and income, and who can use this knowledge to optimally choose the mix of 

financial products to smooth his marginal utility of consumption across time and states-of-theworld at the least cost. 

Recently, economists have been more actively documenting differences in individual 

decision-making abilities and heterogeneity in the extent to which their behavior can be 

described as economically rational (Choi, Kariv, Müller, and Silverman 2014). A key implication 

of this line of research is that there can be a gap between peoples’ actual decisions and the 

decisions they would have made if they had perfectly developed decision-making abilities. This 

paper explores this idea in the important context of retirement income security, focusing 

specifically on the quality of decision making when valuing an annuitized income stream. Rather 

than assessing quality by comparing actual annuitization behavior to a theoretically optimal level 

of annuitization for each individual – a Herculean task given that existing research provides a 

wide range of results (literally from zero to full annuitization) depending on model assumptions

– we instead focus on whether people are internally consistent in their valuations across different 

elicitation methods. Specifically, we present individuals with scenarios in which they are offered 

an opportunity to exchange an annuity for a lump sum (what we call “selling” the annuity), as 

well as scenarios in which the same individuals are offered an opportunity to exchange a lump 

sum for an annuity (what we call “buying” the annuity). In both cases, we also vary whether the 

trade-off is presented in a manner consistent with compensating or equivalent variation measures 

from welfare economics, allowing us to distinguish variation in valuations from endowment 

effects.
2

Our central hypothesis is that people differ in their ability to meaningfully value a stream 

of life annuity income relative to a lump sum, and that this ability is correlated with measures of 

cognitive ability including education, financial literacy, and numeracy. There are four primary 

reasons that we have chosen to study the lump-sum versus annuity decision rather than other 

financial or economic decisions. First, the annuitization decision is important in its own right as 

an academic research topic. Indeed, there is a vast academic literature, dating back a half 

century, on the role that annuities should and do play in peoples’ portfolios in later life.1 Second, 

as we will discuss in more detail below, it is also important and timely as an issue of retirement 

policy in many developed nations. Third, the annuitization decision is a natural place to look for 

variation in consumers’ decision-making abilities. Valuing an annuity versus a lump sum is 

cognitively challenging because it requires that one wrestle with multiple sources of uncertainty 

(e.g., mortality, returns, inflation) and it also requires that one make a near-term choice with fardistant consequences, two characteristics known to render decision making difficult (Beshears, 

Choi, Laibson, and Madrian 2008). Fourth, because individuals are typically faced with an 

annuitization decision only one or two times in their lives, this is not a transaction that people 

learn about through repeated market interactions (Bernheim 2002). In such settings, individuals 

are known to be less likely to optimize and more likely to adopt rule-of-thumb behaviors (Kling, 

Phaneuf, and Zhao 2012), which can drive a wedge between true versus revealed preferences 

(Beshears, Choi, Laibson, and Madrian 2008).

We provide six pieces of evidence consistent with the hypothesis that individuals have 

difficulty valuing annuities and that the degree of difficulty is correlated with their cognitive 

abilities. First, we show that a non-trivial fraction of the population has implied annuity values 

that are difficult to reconcile with optimizing behavior under any plausible set of parameters. 

Second, we uncover a large divergence between the price at which individuals are willing to buy

versus sell an annuity, a result that cannot be explained by liquidity constraints or endowment 

effects. Third, and even more striking, we find that the buy and sell valuations are negatively

correlated. In other words, people who demand higher sell prices are also more likely to offer 

very low buy prices. Fourth, we show that the size of the buy-sell valuation discrepancy is 

strongly negatively correlated with cognitive ability as measured by education, financial literacy, 

and numeracy. This is consistent with less cognitively capable individuals having much greater 

																																																												 1 Two reviews include Benartzi, Previtero, and Thaler (2011) and Poterba, Venti and Wise (2011).
3

difficulty valuing a stream of annuity payments. Fifth, we use additional experimental variation 

to show that the elicited valuations are sensitive to anchoring effects and thus they violate the 

“invariance” criterion of rational decision making. Finally, we argue that it is difficult to explain 

observed cross-sectional variation in the measured annuity valuation amounts with theoretically 

attractive measures. In other words, the pattern of significant marginal valuation predictors is 

more consistent with individuals using simple heuristics rather than full optimization to value the 

trade-offs.

Our evidence is drawn from a randomized experiment we conducted in the American 

Life Panel (ALP), where we presented respondents with hypothetical choices between a lump 

sum and a Social Security annuity. By varying whether the questions elicited a compensating 

variation (CV) or an equivalent variation (EV) value, whether the individual was buying or 

selling the annuity, the size of the increments, and the order of the questions, we directly 

examine the coherence and stability of subjective valuations placed by respondents on their

Social Security annuities. We collected a number of additional variables to control for potentially 

confounding factors such as heterogeneity in liquidity constraints and beliefs about political risk.

Like most economists, we usually find evidence based on actual choices in natural 

settings more compelling than evidence based on hypothetical choices. We acknowledge 

important drawbacks of using hypothetical choices, such as the possibility that lower stakes 

could lead respondents to exert less effort and seek out fewer resources to assist with their 

decisions. However, although these considerations may make hypothetical choice behavior 

noisy, it would be surprising if they led to systematic patterns in hypothetical choice behavior 

that would be completely absent in actual choices.

Counterbalancing these drawbacks are three important benefits of using a hypothetical 

choice setting. First, the hypothetical setting allows us to observe an individual’s annuitization

decisions for a wide range of annuity prices, from which we obtain individual-specific annuity 

valuations without having to rely on functional form assumptions. In real world settings, 

annuitization decisions are typically made at a single price (and if there is price variation, it is 

generally not exogenous). Second, in a hypothetical setting, it is feasible to observe both the 

price at which an individual is willing to buy and the price at which he is willing to sell the 

annuity. Such within-person variation turns out to be extremely valuable in exploring cognitive 

constraints on individuals’ abilities to value annuities. Third, the hypothetical setting allows us to 
4

elicit annuitization choices for a broadly representative sample of the U.S. population. As 

discussed in the literature overview below, actual annuitization decisions in natural settings are 

typically only observed for rather select populations.

In addition to advancing our academic understanding of consumer behavior in this area, 

our results also have considerable practical policy relevance. In March 2014, for example, the 

UK Chancellor announced the end of a requirement that savers annuitize a portion of their assets 

upon retirement (PricewaterhouseCoopers, 2014), a significant policy change that led to an 

immediate and substantial decline in annuity sales (Gray, 2014). In contrast, the U.S. has been 

taking steps in the direction of encouraging annuities in defined contribution plans (US DOL 

2010), with some analysis going so far as to suggest that people be automatically annuitized 

upon retirement (Gale, Iwry, John, and Walker 2008; Steverman 2012). Numerous other 

countries have also been debating these issues over the past decade.2 These discussions, in part, 

revolve around whether people can make optimal payout decisions using their accumulated 

retirement assets. Moreover, many countries are grappling with fiscally unsustainable pay-asyou-go public pension systems. To the extent that households are poorly equipped to value the 

annuities offered by their public pensions, this has implications for the political feasibility of 

reforms changing the benefit structure, particularly if retirees were to be offered a choice 

between a lump sum and future annuity payments. The same point applies to state and local 

public defined benefit plans (DB) in the U.S., which also face substantial underfunding problems 

(Novy-Marx and Rauh 2011); indeed some reformers have called for a reduction in DB annuities 

in exchange for lump-sum contributions to defined contribution (DC) accounts (e.g., Kilgour 

2006). 

In what follows, we first summarize key prior studies on the demand for annuities from 

both the neoclassical and the behavioral economics literatures. Next, we describe the American 

Life Panel (ALP) internet survey, a broadly representative sample of the U.S. population, and we 

outline how we elicited lump-sum versus annuity preferences in this survey, using a purposebuilt module. We then present our key empirical results, followed by a number of robustness 

checks and further analyses for subgroups that vary according to financial capabilities. We

conclude with a discussion of possible policy implications and future research questions. 

																																																												 2 Similar debates about the role of lifetime income in retirement plans are occurring in many European 

nations, including the Netherlands (Brown and Nijman 2012), Italy (Guazzarotti and Tommasino 2008), 

and many others (Fornero and Luciano 2004). 
5

I. Related Literature

A. Annuity Demand

There is a very large economics literature focused on modeling the optimal level of 

annuitization for life-cycle consumers under various assumptions.

3 That literature began with 

Yaari’s (1965) paper in which he noted a set of conditions under which it would be optimal for 

an individual to annuitize all of his wealth.4 Extensions to the theory went on to show that full 

annuitization would be optimal under more general conditions,

5 a puzzling prediction in light of 

very low annuitization rates in the real world (Mitchell, Piggott, and Takayama 2011). Extended 

life-cycle models have been constructed to measure consumer valuations of life annuities and to 

compute how optimal annuitization will vary with other factors including: pricing (Mitchell, 

Poterba, Warshawsky, and Brown 1999); pre-existing annuitization (Brown 2001; Dushi and

Webb 2006); risk-sharing within families (Kotlikoff and Spivak 1981; Brown and Poterba 2000); 

uncertain health expenses (Turra and Mitchell 2008; Sinclair and Smetters 2004; Peijnenburg, 

Nijman, and Werker 2010a, 2010b); bequests (Brown 2001; Lockwood 2011); inflation (Brown, 

Mitchell, and Poterba 2001, 2002); the option value of learning about mortality (Milevsky and 

Young 2007); stochastic mortality processes (Reichling and Smetters 2012; Maurer, Mitchell, 

Rogalla, and Kartashov 2013); and broader portfolio issuesincluding labor income and the types 

of assets on offer (Inkmann, Lopes, and Michaelides 2011; Koijen, Nijman, and Werker 2011; 

Chai, Horneff, Maurer, and Mitchell 2011; Horneff, Maurer, Mitchell, and Stamos 2009, 2010). 

Opinions differ on the extent to which this theoretical literature has been successful in resolving 

																																																												 3 Rather than providing a comprehensive review here, we instead highlight those studies most germane to 

the research that follows. Readers interested in the broader literature on life annuities may consult 

Benartzi, Previtero, and Thaler (2011); Poterba, Venti, and Wise (2011); Brown (2008); Horneff, Maurer, 

Mitchell, and Dus (2008); and Mitchell, Poterba, Warshawsky, and Brown (1999). Note that we use the 

term “life annuity” because we are interested in products that guarantee income for life, as opposed to 

financial products such as “equity indexed annuities” that are mainly used as tax-advantaged wealth 

accumulation devices (and hence they are rarely converted into life-contingent income).

4 The conditions include no bequest motives, time-separable utility, exponential discounting, and 

actuarially fair annuities (among others).

5 Davidoff, Brown, and Diamond (2005) showed that full annuitization is optimal under complete markets 

with no bequest motives. Peijnenburg, Nijman, and Werker (2010a; 2010b) found that if agents saved

optimally out of annuity income, full annuitization can be optimal even in the presence of liquidity needs 

and precautionary motives. They further found that full annuitization is suboptimal only if agents risk 

substantial liquidity shocks early after annuitization and do not have liquid wealth to cover these 

expenses. This result was robust to the presence of significant loads.
6

the annuity puzzle, even for marginal annuitization decisions (e.g., Shepard 2011). 

There is also a sizable empirical literature on annuities, despite the small size of the 

voluntary life annuity market in most countries. Brown (2001) used the 1992 wave of the U.S.

Health and Retirement Study (HRS) to show that expected annuitization from DC plans was 

correlated with the annuity valuations predicted by a life-cycle model based on demographic 

characteristics, though only for persons with sufficiently long (>1 year) planning horizons. Hurd 

and Panis (2006) explored payouts from DB plans in the HRS and found peoples’ behavior was 

consistent with status quo bias. Bütler and Teppa (2007) used Swiss administrative data to track 

choices made by employees in ten different pension plans and concluded that annuitization was 

higher in plans where an annuity was the default payout option. Chalmers and Reuter (2012) 

exploited exogenous variation in the price of annuities using Oregon public-sector workers; they 

(unexpectedly) found that worker demand for partial lump-sum payouts rose rather than fell as 

the value of the forgone life-annuity payments increased, leading them to conclude that the 

decisions were being made by unsophisticated individuals. Previtero (2014) showed that annuity 

demand was negatively correlated with the prior year’s stock returns, consistent with consumers 

engaging in naïve trend chasing. 

Several experimental papers have also suggested that annuitization decisions may be 

reflective of non-optimizing behavior. Agnew, Anderson, Gerlach, and Szykman (2008) showed

that individuals could be steered toward or away from life annuities in an experimental setting,

depending on whether the products were described using positive or negative frames. Brown, 

Kling, Mullainathan, and Wrobel (2008) used an internet survey to show that perceptions of 

annuity value relative to alternative financial products were heavily influenced by whether the 

products were described using “consumption” or “investment” frames. Beshears, Choi, Laibson, 

Madrian, and Zeldes (2014) also found evidence that framing affects annuity demand. Brown, 

Kapteyn, and Mitchell (2013) showed that Social Security claiming behavior (which is akin to 

making an annuitization decision) was influenced by framing changes. Accordingly, this small 

literature suggests that individuals behave in a manner at odds with optimizing models.

Overall, the difficulty explaining annuitization behavior using optimizing models, 

combined with the empirical and experimental work suggesting behavior is not fully consistent 

with rational decision making, motivates our research into whether individuals are making 

optimization errors in making decisions about annuities and whether the heterogeneity in 
7

decision-making quality is correlated with other observable factors. 

B. Variation in Decision-making Abilities

In contrast to the neoclassical literature, which posits perfectly informed and rational 

individuals optimizing their consumption path over the life cycle, Lusardi and Mitchell (2014) 

provide a comprehensive review of the large and growing literature relating financial literacy to 

behavior, including the robust finding that many households lack basic financial knowledge. 

Research has documented that households make a range of financial mistakes when managing 

financial affairs (e.g., Calvet, Campbell, and Sodini 2007, 2009; Agarwal, Driscoll, Gabaix, and 

Laibson 2009) and that households making mistakes often lack day-to-day financial skills 

(Hilgert, Hogarth, and Beverly 2003). It has also established that financial literacy is correlated 

with the propensity to participate in financial markets (Kimball and Shumway 2006; Christelis, 

Jappelli, and Padula 2010; van Rooij, Lusardi, and Alessie 2011; Almenberg and Dreber 2011; 

and Arrondel, Debbich, and Savignac 2013) and in pensions (Fornero and Monticone 2011). 

Earlier work by Lusardi and Mitchell (2007, 2011) also showed that more financially literate 

individuals were more likely to engage in retirement planning and to accumulate retirement 

wealth.

A related literature has focused on variation in cognitive abilities and financial decision 

making. Fang, Keane, and Silverman (2008) found that cognitive functioning was a stronger 

predictor of Medigap purchase than risk preferences. Agarwal and Mazumder (2013) reported 

that performance on cognitive tests helped explain the quality of financial decisions related to the 

use of credit. A subset of this literature has also focused more specifically on retirementpreparedness among older individuals. For example, McArdle, Smith, and Willis (2011) and 

Banks, O’Dea, and Oldfield (2010) found that cognitive abilities helped explain retirement 

wealth accumulation. 

Taken together, these and many other studies suggest that people differ in their financial 

decision-making abilities and that these differences are important correlates of financial wellbeing late in life. Taking this literature an important step further in establishing causality, Choi, 

Kariv, Müller, and Silverman (2014) conducted a large-scale experiment designed to directly test 

the extent to which individual decisions were consistent with the Generalized Axiom of 

Revealed Preference (GARP); they found substantial heterogeneity. Importantly for our work, 

they found that their measure of decision-making quality was higher among those with more 
8

education and among younger individuals, while they also showed that individuals with better 

decision-making skills accumulated more wealth. Behrman, Mitchell, Soo, and Bravo (2012) 

also reported that the more financially literate saved more in their pensions, controlling for the 

possible endogeneity of financial knowledge.

Our experiments contribute to this literature in two ways. First, we focus on a decision 

important in its own right – annuitization – but where heterogeneity in decision-making quality 

has not been studied. Second, we use an outcome that is a novel measure of decision-making 

ability; we take the spread of peoples’ responses across different approaches to eliciting stated 

valuations for life annuities as a measure of low decision-making quality. We find that this 

spread is strongly inversely related to cognition.

II. Methodology and Data

A. The Social Security Context 

Our experiments, described below in greater detail, use Social Security benefits as the 

context rather than describing an unfamiliar hypothetical annuity product. This approach has 

several advantages. First, most workers have an understanding that Social Security pays benefits 

to retirees that last for as long as they live (Greenwald, Kapteyn, Mitchell, and Schneider 2010; 

Liebman and Luttmer 2014), which means that respondents will understand the nature of our 

“offer” to trade off annuities and lump sums. Second, our context provides a simple way to 

control for possible concerns about the private annuity market that might influence results such 

as the lack of inflation protection (our question makes it clear that Social Security is adjusted for 

inflation), or concerns about counterparty risk of the insurer providing the annuity.

6 Third, given 

the ongoing debate about the U.S. long-term fiscal situation, our setting is highly policy-relevant. 

For example, past discussions of possible pension reforms around the world, as well as at the 

state and local levels in the U.S., have included proposals to partially “buyout” benefits by 

issuing government bonds to workers in exchange for a reduction in their annuitized benefits. 

Several U.S. corporations have also recently offered to buy back defined benefit pension 

annuities from retirees, in exchange for a lump sum (c.f., Wayland 2012).

B. The American Life Panel 

																																																												 6 Below we examine whether concerns about the fiscal sustainability of Social Security influences 

people’s valuation of the Social Security annuity. See Luttmer and Samwick (2012) for a detailed 

analysis of the effects of policy uncertainty on valuations of future Social Security benefits.
9

To test how people value their Social Security annuity streams, we fielded a survey 

between June and August of 2011 using the RAND American Life Panel, a panel of U.S. 

households that regularly take surveys over the Internet. If, at the recruiting stage, households 

lacked Internet access, this was provided by RAND.7 By not requiring Internet access in the 

recruiting stage, the ALP has an advantage over most other Internet panels when it comes to 

generating a representative sample.

8 The American Life Panel included about 4,000 active panel 

members at the time of our experiment. Our survey was conducted over two waves of the ALP to 

keep the length of each questionnaire within manageable bounds, and we invited ALP 

participants age 18 or older to take our survey. For the first wave, we selected 2,954 respondents, 

of whom 2,478 completed the survey for a response rate of 83.9%. Those who completed the 

first wave were invited to participate in a second survey at least two weeks later; of these, 2,355

respondents completed the second wave for a response rate of 95.0%. If a participant indicated 

he did not think he would be eligible to receive Social Security benefits (either on his own 

earnings records or on those of a current, late, or former spouse), he was asked to assume for the 

purposes of the survey that he would receive Social Security benefits equal to the average 

received by people with his age/education/sex characteristics (see Online Appendix B). Our 

sample comprised 2,112 complete responses for both waves 1 and 2.9

Table 1 compares our sample characteristics with those of the same age group in the 

Current Population Survey (CPS).10 Our sample is, on average, five years older, more female, 

more non-Hispanic white, better educated, slightly higher-income, and has a somewhat smaller 

household size than the CPS; the regional distribution is close to that of the CPS. The fact that 

our sample is more highly educated means that, if anything, our respondents should be in a better 

position to provide meaningful responses to complex annuity valuation questions, compared to a 

																																																												 7 Initially these households received a WebTV allowing them to access the Internet. Since 2008, 

households lacking Internet access at the recruiting stage have received a laptop and broadband Internet 

access.

8 A more detailed explanation of the ALP is provided in Online Appendix A, along with a brief 

description of how we estimated Social Security benefits for survey respondents. Our survey instrument is 

included in Online Appendix B. 

9 Of the 2,355 respondents who completed the second wave, we dropped 69 observations from the pilot 

version of wave 2 (where the questionnaire was slightly different). We further dropped 168 observations 

where survey instrument was incorrectly administered due to a technical glitch and we dropped 6 

observations with missing information on basic demographics (age, education, or marital status).

10 Summary statistics of other key variables from our survey (such as annuity valuations, discussed 

below) are listed in Online Appendix Table A.1.
10

national sample. Despite the differences between the ALP and the CPS, our ALP sample does 

include respondents from a wide variety of backgrounds, so in this sense, we think of the ALP as 

broadly representative of the U.S. population. 

Table 1 here

C. Eliciting Lump-Sum versus Annuity Preferences

To elicit preferences over annuitization, respondents were posed several questions of the 

following sort:

In this question, we are going to ask you to make a choice between two money 

amounts. Please click on the option that you would prefer. Suppose Social 

Security gave you a choice between:

(1) Receiving your expected Social Security benefit of $SSB per month. 

or

(2) Receiving a Social Security benefit of $(SSB-X) per month and receiving a 

one-time payment of $LS at age Z. 

The variable SSB is an estimate of the individual’s estimated monthly Social Security benefit; the 

variable LS refers to the lump-sum amount; and Z is the individual’s self-reported expected 

claiming age. For those not currently receiving benefits, the trade-off was posed as a reduction in 

future monthly Social Security benefits in exchange for a lump sum to be received at that 

person’s expected claiming age. For those currently receiving Social Security benefits, the 

questions were modified so as to compare a change in monthly benefits to the receipt of a lump

sum in one year. In both cases, the receipt of the lump sum was in the future rather than 

immediately; we did this to avoid having the answers being affected by possible present-biased 

preferences. Before asking the annuity trade-off question, we instructed all respondents to 

“please assume that all amounts shown are after tax (i.e., you don’t owe any tax on any of the 

amounts we will show you)” and “please think of any dollar amount mentioned in this survey in 

terms of what a dollar buys you today (because Social Security will adjust future dollar amounts 

for inflation).” In the trade-off question, we told married respondents, “Benefits paid to your

spouse will stay the same for either choice.” Thus, individuals were asked to value a single-life 

inflation-indexed annuity. 

In order to probe the reliability of the valuations provided by respondents, we varied the 

question in a systematic way along two dimensions. First, we elicited how large a lump sum

would be required to induce an individual to accept a reduction of (i.e., to sell) a portion of his 
11

Social Security income; below we refer to this version of the question with the shorthand “sell.”

We also elicited how much the individual would be willing to pay in order to increase his Social 

Security annuity (the “buy” condition). The difference in responses to these alternative 

solicitations is a central focus of what follows.

A second dimension along which we varied our questions depended on whether we

elicited a compensating variation (CV) – the annuity/lump-sum trade that would keep people at 

their existing utility level – or an equivalent variation (EV) –the lump-sum amount that would be 

equivalent in utility terms to a given change in the monthly annuity amount. As we discuss in 

greater detail below, an analysis of the CV versus EV distinction should allow us to distinguish 

our findings from a simple status quo bias or endowment effect. This is because in the EV 

version of the questions, each individual had to choose an increment or decrement to his annuity. 

The status quo was not an option in this scenario. 

In total, we elicited all four measures and designate them as CV-Sell (as in the example 

above), CV-Buy, EV-Sell, and EV-Buy. The chart below illustrates the essential differences 

across these four scenarios. We define SSB as the monthly Social Security benefit the individual 

was currently receiving (if the individual was a current recipient), or was expected to receive in 

the future (if the individual was not a recipient); X is the increment (or decrement, if subtracted) 

to that monthly Social Security benefit. Finally, we set LS as the lump-sum amount offered in 

exchange for the change in monthly benefits. In essence, this paper is about how individuals 

tradeoff a monthly benefit of $X for a lump-sum of amount $LS.

Four Variants of the Annuity Valuation Tradeoff Question

“Sell”-version “Buy”-version

Choice A Choice B Choice A Choice B

Compensating 

Variation (CV) [SSB-X] + LS [SSB] [SSB+X] - LS [SSB]

Equivalent 

Variation (EV) [SSB]+ LS [SSB+X] [SSB] - LS [SSB-X]

Note: SSB stands for current/expected monthly Social Security benefits, X is the amount by which 

monthly Social Security benefits would change and LS is a one-time, lump-sum payment. Positive 

amounts are received by the individual while negative amounts indicate a payment by the individuals. 

Amounts between square brackets are paid monthly for as long as the individual lives, whereas LS is a 

one-time payment. The individual is asked to elect Choice A or Choice B.

The CV-Sell scenario presented individuals with a choice between their current or 
12

expected Social Security benefits (SSB), versus a scenario in which their benefits would be

reduced by $X per month in exchange for receiving a lump sum of $LS. The EV-Sell scenario 

provided a choice between receiving a higher monthly benefit (SSB+X) or receiving $SSB plus a 

lump sum of $LS. Note that within the Sell scenario, one can obtain EV simply by adding $X to 

each side of the CV trade-off. Given that X=$100 per month in the baseline versions, the change 

in benefits is modest relative to total monthly income for most individuals. We would therefore 

expect CV and EV to be comparable, barring strong endowment effects that could be present in 

the CV formulation but not in the EV formulation (where the status quo was not an option).

Switching to the Buy scenarios, the CV-Buy question provided a choice between SSB and 

a benefit increased by $X in exchange for paying $LS to Social Security. EV-Buy provided a

choice between receiving a lower monthly benefit (SSB-X) and paying a lump sum to maintain 

the existing benefit. Note that in these Buy scenarios, one can obtain CV simply by adding $X to 

each of the EV scenarios. Again, it is worth noting that no status quo option was available in the 

EV case.

In order to converge on the subjective valuation resulting from any given measure above, 

the survey used a “branching” approach. For example, we started with a $100 increment to the 

monthly annuity versus a $20,000 lump sum. Then, based on the individual’s response, we either 

increased or decreased the amount of the lump-sum payment. By walking each respondent 

through a multi-stage branching process, we converged on a small range of lump-sum values that 

approximated the respondent’s implied subjective valuation of the change in the annuity stream. 

As part of our experimental design, we chose one of our four approaches as a benchmark, 

permitting us to do additional sensitivity tests along other dimensions. While there is no 

theoretical basis for suggesting that one treatment would be preferred to the other three, we 

selected the CV-Sell option because it is more relevant to policy discussions. For example, 

offering retirees an opportunity to sell their annuities for lump sums is a transaction observed in

recent years (e.g., GM offered retirees lump sums in lieu of their life annuities). The Sell 

measure is also less likely than the Buy measure to be bounded by people’s access to liquidity.

Accordingly, all respondents were asked the CV-Sell question in one of the two waves, whereas 

the other three versions (CV-Buy, EV-Sell, and EV-Buy) were asked in a randomized order in 

the other wave. The placement of the CV-Sell question in the first or second wave was 

randomized across respondents. The two surveys were administered approximately two weeks 
13

apart. Below, we test whether responses to CV-Sell differ depending on whether this question 

was in the first or second wave.

Two other studies have employed a branching approach to elicit annuity valuations, 

although they were much more limited in focus.11 Cappelletti, Guazzarotti, and Tommasino

(2013) used a national survey of Italian households in 2008 to ask whether people would give up 

half their monthly pension income (assumed to be €1000) in exchange for a lump sum of 

€60,000, paid immediately. Depending on their responses, individuals were branched to higher 

or lower lump-sum amounts. Liebman and Luttmer (2011) conducted a 2008 survey on the 

perceived labor supply incentives in Social Security, and they included in their survey a question 

asking people for the equivalent variation of a $100/month increase in their Social Security 

benefits (this is equivalent to our EV-Sell question.) Because each of those papers used only a 

single elicitation method, neither addresses the hypotheses tested in this paper. 

D. Other Sources of Experimental Variation

We also randomized along a number of other dimensions for two reasons. First, we 

randomized the order of the options within a question, to test whether respondents were taking 

the survey seriously (as opposed to, say, always choosing option A). Second, we tested for 

anchoring effects in our benchmark question (CV-Sell) as well as whether responses varied with 

the magnitude of the change in the benefit, to provide a further assessment of the role of 

cognitive limitations. Furthermore, we asked a version of the questions designed to control for 

political risk, to ensure that our results were not driven by concern over the system’s pending 

insolvency. Each of these factors is discussed in detail after we present our main results. 

III. Evaluating Heterogeneity in Annuity Valuations

Figure 1 reports the cumulative distribution function (CDF) of the responses to the CVSell and CV-Buy questions, while Figure 2 provides a similar plot for EV-Sell and EV-Buy. 

																																																												 11 In addition to the two studies discussed in the text, a subset of the present authors has previously sought

to elicit subjective annuity valuations in two HRS experimental modules. Errors in questions or response 

coding unfortunately interfered with analysis. One module in the 2004 HRS asked respondents their 

willingness to trade $500 of a hypothetical $1000 monthly Social Security benefit for a lump sum. 

Although the lump-sum amount offered to unmarried individuals was approximately actuarially fair, the 

amount offered to married couples was too low. A second module was fielded in the 2008 HRS, but 

coding instructions provided to field interviewers led to an inability to distinguish answers at the two 

extremes, i.e., those who placed a zero value on an annuity versus those who placed a very high value on 

annuities. 
14

Given our bracketing of responses, the two Figures plot both the upper and lower bounds for 

each respondent’s annuity valuation.

12 In Figure 1, the midpoint of the upper and lower bounds

for the CV-Sell question indicates a valuation of $13,750 for a $100-per-month change in Social 

Security benefits. The CV-Buy question midpoint valuation is only $3,000. In Figure 2, the 

comparable valuations are $12,500 for EV-Sell and $3,000 for EV-Buy. By comparison, the 

median actuarial value of this annuity for respondents in our sample is $16,855 (computed using 

Social Security’s intermediate assumptions of a three percent interest rate and intermediate 

mortality).

Figures 1 and 2 here

Four patterns are evident in these two figures. First, median valuations are all 

substantially below the actuarial value. Second, there is substantial dispersion of valuations in all 

four valuation approaches. Third, the distributions of EV and CV valuations appear similar, 

holding fixed whether it is a Buy or Sell valuation, although we will see below that the 

correlation is far from perfect. Fourth, there is a very large difference between the Sell and Buy 

valuations, regardless of whether it is elicited in a CV or an EV setting. After briefly discussing

each of these issues, we will then delve deeper into analyzing differences in valuations within 

and across individuals.

A.Median Valuations

If one simply pools the responses to our four valuation questions – CV-Sell, CV-Buy, 

EV-Sell and EV-Buy – 70% of the responses have an upper bound below the actuarially fair 

level and 64% of the responses have an upper bound that is at least $5,000 below the actuarially 

fair level.13 This finding is interesting given the ongoing discussion in the literature about the 

“annuity puzzle,” i.e., the fact that life-cycle models typically place a high utility value on 

annuitization, whereas most consumers appear to avoid purchasing them. Yet there are many 

reasons – some rational (e.g., bequest motives, a desire for liquidity), some behavioral (e.g., 

present-biased preferences) – for why people might value an annuity below its actuarially fair 

																																																												 12 The CV-Sell figure plots valuations only for individuals who saw the $100 increment first (the other 

three annuity valuation questions are only asked for $100 increments). Other respondents saw higher 

annuity amounts first and, as we discuss below, this anchoring effect led to a higher valuation in their 

response to CV-Sell. 13As in the figure, we limit the sample for the CV-Sell response to individuals who saw the $100 

increment first to avoid anchoring effects. If we double the weight on the remaining half of the CV-Sell 

responses (to compensate for the fact we dropped CV-Sell responses affected by anchoring), the 

percentages become 68% and 61%, respectively.
15

level and it is not the purpose of this paper to explain the annuity puzzle. Rather, our goal is to

test whether valuations are consistent with optimizing behavior. We do this not by comparing 

elicited values with theoretical values, but rather by examining the stability and consistency of 

stated preferences. The remainder of our results should be viewed in light of this important 

distinction.

B. Dispersion of Valuations

The CDFs in Figures 1 and 2 reveal substantial heterogeneity in respondent valuations. 

For example, five percent of the sample reports upper-bound CV-Buy valuations of $1,500 or 

less, levels so low that they are difficult to explain using any “rational” economic model, since 

the $100 monthly annuity payments would provide more than this amount in only 16 months. 

The exception would be if some individual were virtually certain that he would die in that period, 

but we find that these outliers persist even when we control for respondents’ self-reported health 

status and survival probabilities. At the other extreme, 16 percent of the respondents reported

lower-bound CV-Sell annuity values of $60,000 or higher – nearly four times the actuarial value

of the annuity. Moreover, over six percent of the respondents in the CV-Sell approach said they 

would not accept a lump sum of less than $200,000. This is unexpected, since even if someone 

earned only a 60 basis-point (0.60%) annual return on the $200,000 lump sum, he could replace 

the $100 per month he was giving up with this return and still keep the lump sum of $200,000.

As discussed in more detail below, these findings are not explained by subjective life 

expectancy, concerns about political risk, or other plausible “rational” explanations.

14 In other 

words, many respondents appear to be having difficulty providing economically meaningful 

																																																												 14 We control for political risk in two ways in this study. First, we asked individuals about their 

confidence that the Social Security system will be able to provide them with the level of future benefits 

they are supposed to get under current law. Including responses to this question as a control variable in 

various analyses does not substantially affect our findings. Second, we asked a version of our CV-Sell 

annuity valuation question in which we explicitly instructed individuals not to consider political risk by 

stating: “From now on, please assume that you are absolutely certain that Social Security will make 

payments as promised, and that there is no chance at all of any benefit changes in the future other than the 

trade-offs discussed in the question below.” Using the most unbiased comparison available (i.e., 

comparing the response to the no-political-risk question to the baseline CV-Sell question for those for 

whom the two questions were asked in different waves of the survey), we find that the response to the nopolitical-risk question is a statistically insignificant 10 percent lower than the response to the baseline 

CV-Sell question. Taken literally, this implies a negative risk premium. We believe, however, the more 

likely explanation is that our question may have had the unintended effect of making political risk more 

salient, rather than less. Overall, our analysis suggests that the incorporation of political risk does not alter 

our main findings. 
16

values for the Social Security annuity, at least in the tails of the CDF.

C. Comparing CV and EV

As noted above, we obtain the EV-Sell questions by simply adding $100 to both of the 

options in the CV-Sell questions. Given the small magnitude of the shift (relative to mean 

estimated monthly benefits of $1,395), we expect that a fully rational decision maker would 

provide quite similar assessments across these two ways of eliciting value. Although the 

distributions of CV-Sell and EV-Sell look similar in Figures 1 and 2 (as do the distributions of 

CV-Buy and EV-Buy), individual responses are not as correlated as one might infer. Table 2

reports the correlations across the four different measures.15 In column 1, we see that although 

CV-Sell and EV-Sell are significantly positively correlated, the correlation of +0.31 is far from 

perfect. It is notable that we asked the CV-Sell and the EV-Sell questions in different survey 

waves; thus each individual answered the two questions at least two weeks apart. Given this lag, 

it is unlikely that the correlation is driven by anchoring or memory effects that could arise if the 

questions had been asked within the same questionnaire. At +0.72, the correlation between CVBuy and EV-Buy is substantially higher than the correlation between CV-Sell and EV-Sell. 

However, given that CV-Buy and EV-Buy were asked in the same wave, we cannot rule out that 

anchoring effects may have contributed to this higher correlation.

Table 2 here

D.Sell Prices Exceed Buy Prices

The most striking pattern that emerges from Figures 1 and 2 is that the distributions of 

annuity valuations from the Buy solicitations are substantially below those of the Sell 

solicitations. Recall that the Sell question asked how much a person would have to be 

compensated to give up part of his Social Security annuity, whereas the Buy question asked how 

much he would be willing to pay to increase his Social Security annuity. In Figure 1, the median 

midpoint response drops from $13,750 for CV-Sell to only $3,000 for CV-Buy, and responses at 

other points on the distribution drop as well.

If we observed this result only in the CV case, one might argue that this could result from 

status quo bias (Samuelson and Zeckhauser 1988) or an endowment effect (Kahneman, Knetsch, 

																																																												 15 To control for correlations induced by common experimental manipulations, we regress the log 

midpoint value of the response on controls for the relevant manipulations and then correlate the residuals, 

which are reported in Table 2. Uncorrected correlations are similar and shown in Online Appendix Table 

A.2.
17

and Thaler 1991). Yet Figure 2 shows that an almost identical shift occurs when we use the EVSell and EV-Buy responses, where the status quo is not an option because both the annuity and 

the lump sum are represented as deviations away from the initial endowment. 

To rule out the possibility that answers might be driven by liquidity constraints, we also 

asked respondents about their ability to come up with the money needed for the lump sum. The 

vast majority (91 percent) indicated that their choice was not due to liquidity constraints,

16 and 

the clear divergence in valuations persists even when we focus on the non-liquidity constrained 

sub-sample. 

Rather than status quo bias, endowment effects, or liquidity constraints, we conjecture 

that this wedge is the outcome of valuation difficulties on the part of respondents. This

conjecture has two testable implications. First, individuals who have difficulty valuing annuities 

may seek to protect themselves by agreeing to an annuity transaction only if the annuity is priced 

very attractively, which would lead them to demand a high price to sell, but offer a low price to 

buy. We refer to this as a “reluctance to exchange,” which would imply that buy valuations will 

be negatively correlated with sell valuations. Second, it implies that the size of the wedge 

between buy and sell valuations will be decreasing with cognitive abilities. 

E. Negative Buy-Sell Correlations

Although Figures 1 and 2 reveal large differences in the distributions of responses 

between Sell and Buy valuations, they do not reveal whether within-person responses to these 

alternative valuation measures are correlated. Hence we cannot yet conclude that the entire 

distribution is shifting to the left, or whether the same individuals are also changing their 

positions in the distribution depending on whether they see a Sell or Buy measure. This is 

addressed in Table 2, where we find a striking negative correlation between Buy and Sell 

valuations. Specifically, the correlation between CV-Sell and CV-Buy is -0.11, whereas the 

																																																												 16 Specifically, we asked whether each respondent could come up with $5,000 “if you had to” and, 

separately, whether he could come up with the lump sum needed to purchase the higher annuity. The time 

frame for accessing the money was the same time frame as in the annuity valuation question, namely one 

year from now or the respondent’s expected claim date, whichever was later. About two-thirds of the 

respondents answered that they were certain they could come up with $5,000, and over 90 percent 

responded that they could come up with the amount probably or certainly. About 82 percent of 

respondents indicated that they could come up with the lowest lump-sum amount that they declined to 

pay. Of the 18 percent that indicated they could not come up with this amount, half said that even if they 

had the money, they would decline to pay the lump sum. Thus, for 91 percent of the respondents, liquidity 

constraints were not the reason for the low reported annuity valuation in the CV-Buy trade-off question. 
18

correlation between EV-Sell and EV-Buy is -0.15; both are highly statistically significant. These 

negative correlations suggest that individuals who place above-average values on a $100 

monthly annuity when asked to sell it tend to be willing to pay less than average to purchase a

100/month annuity flow. The negative correlation also suggests substantial movement within the 

distributions, rather than just a downward shift for everyone when we move from a Sell to a Buy

elicitation method. This pattern is consistent with individuals who have difficulty valuing 

annuities being reluctant to exchange annuities because they wish to ensure that they are not 

losers in a transaction they have difficulty evaluating.

F. The Role of Cognition and the Sell-Buy Spread

If the Sell-Buy differential is the result of differences in decision-making abilities, then 

the size of the spread should be correlated with measures of cognition. To explore this, we 

construct a measure of the “Spread” that is equal to the absolute value of the difference between 

the log CV-Sell and the log CV-Buy valuation of a $100 change in monthly Social Security 

benefits. The spread is defined as an absolute value because a discrepancy between Sell and Buy 

valuations in either direction is indicative of a lack of optimizing behavior. In practice, the 

spread is dominated by the 80 percent of individuals who place a higher value on CV-Sell than 

on CV-Buy. Because the spread is measured as the difference in log points, this variable reflects 

the relative value of Sell and Buy solicitations. The mean of the Spread variable is 2.58 and its 

median is 2.30, indicating that the median individual reports Sell and Buy valuations that differ 

by a factor of 10. In the large majority that places a higher value on the Sell than the Buy 

valuation, the mean and median of the Spread variable are respectively 2.63 and 2.35, which 

indicates that the median person in this sample demands a price to sell a $100 per month annuity 

that is 10.5 times higher than the price that he is willing to pay to purchase this same annuity.

In Figure 3, we show the bivariate relation between the CV Sell-Buy Spread and various 

measures of cognition. Panel A groups respondents according to an index of financial literacy, 

measured as the sum of correct answers to the three questions devised for the Health and 

Retirement Study and used in the ALP to rate respondents’ financial literacy levels.

17 Consistent 

with our hypothesis that the discrepancy between Sell and Buy is driven by differences in 

decision-making ability, we find that that the wedge between the responses falls sharply with 

																																																												 17 The three questions test for an understanding of inflation, compound interest, and risk diversification

(for a complete listing of the questions see Lusardi and Mitchell 2014).
19

measured increases in financial literacy. In Panel B, we make use of the scores on a number 

scoring test, where respondents are shown incomplete sequences of numbers and are asked to 

complete the sequence (e.g.: 7, 8, ..., 10). Based on six such sequences and a RASCH scoring 

algorithm (Linacre 1999), each respondent is assigned a score. We find a sharp and monotonic 

decline in the Sell-Buy Spread across the quintiles of this numeracy measure. In Panel C, we 

split the Spread measure by level of education and once again we find a sharply declining 

pattern. Panel D uses an overall cognition index, which is the first principal component of the 

three measures of cognition.18 Not surprisingly, given the patterns in the earlier panels and the 

fact that the weight on each of the three factors is roughly equal, we find a strong, monotonic, 

negative relation between the Sell-Buy Spread and our cognition index.19

Figure 3 here

We repeat this analysis in a regression framework in Table 3, along with controls for

other factors. In column 1, we regress the Spread against age dummies and controls for 

experimental variations (to be discussed in the next section). We note that the coefficient on age 

65+ is positive and significant; these older individuals have a 44 percentage point larger absolute 

difference between their Sell and Buy valuations than the reference age category (age 34 or 

younger) and the difference remains large (39 percentage points) and significant if we compare 

them to the age 50-64 category. This finding is important for two reasons. First, it addresses 

concerns that younger individuals may be less attuned to our decision context because they are 

further removed from making the comparable real-world decision and also because they may be 

less likely to be familiar with how Social Security benefits are paid as monthly income for life. 

Yet we find the opposite: younger individuals have more consistent valuations than do older 

individuals. Second, this evidence fits with our hypothesis regarding cognitive functioning. 

Agarwal, Driscoll, Gabaix, and Laibson (2009) show that cognitive functioning declines at older 

ages and may contribute to a decline in financial decision making. As we will see below, adding 

																																																												 18 Although we use principal components analysis to construct the weights in the cognition index, the 

contributions of each of the three components turn out to be very close to equal. Thus, we obtain very 

similar results if we construct an index in which we simply give each of the three components equal 

weight.

19 Online Appendix Figure A.1 shows that findings of Figure 3 (monotonically declining spreads in each 

of the three measures of cognition as well as in the index) also hold when the Sell-Buy spread is based on 

EV valuations rather than CV valuations. Online Appendix Figure A.2 shows that we obtain declining 

spreads (but not always monotonically so) when we use the spread between CV and EV valuations (both 

using the Sell condition).
20

direct controls for cognition strengthens, rather than mitigates, this relation.

Table 3 here

In column 2, we add in our three direct measures of cognition. All three of them –

financial literacy, education, and numeracy – are significantly negatively correlated with the 

Sell-Buy Spread. Each additional correct answer on the financial literacy questions reduces the 

spread by 0.32 log points. Moving up one education category reduces the spread by 0.24 log 

points. A one standard deviation increase in the score on the number series questions reduces the 

spread by 0.31 log points. In column 3, we combine these measures into our cognition index, and 

we find a strongly significant relation. A one standard deviation increase in cognition 

corresponds to a 0.59 log point reduction in the Sell-Buy Spread. In column 4, we add additional 

controls for demographics and preferences, including sex, marital status, race and ethnicity, 

family income, home ownership, self-reported health, children, risk aversion, return 

expectations, and political risk, among others.20 Even with this rich set of additional controls, the 

coefficient on the cognition index remains highly significant, with a coefficient of -0.42. The 

results of Table 3 are similar if we use the Sell-Buy Spread based on EV valuations rather than 

on CV valuations (see Online Appendix Table A.4). If we take the spread between CV-Sell and 

EV-Sell, which have similar values on average because they are both Sell measures, we continue 

to find significant negative effects of the cognition index on the spread, though the magnitude of 

the coefficient drops by half (see Online Appendix Table A.5). This indicates that individuals 

with higher values of the cognition index give more internally consistent answers even in settings 

where the “reluctance-to-exchange” motive should not be prominent.

21

Thus far, we have shown that an important fraction of the population has implied annuity 

values that are difficult to reconcile with optimizing behavior under any plausible set of 

parameters. We have also documented a large divergence between the price at which individuals 

are willing to buy an annuity and the price at which they are willing to sell an annuity, and we 

																																																												 20 Definitions and summary statistics of control variables are given in Online Appendix Table A.1. The

coefficients on the control variables can be found in Column 1 of Online Appendix Table A.3. 21 In Online Appendix Figure A.3 and Table A.6, we show that the negative correlation between Sell and 

Buy valuations decreases in absolute value for those with higher levels of cognition. In addition, the 

positive correlation between CV and EV valuations tends to increase with cognition. Hence, cognition 

also has effects in the expected direction on our correlations. However, because correlations cannot be 

measured at the individual level (but only for subsamples), these results have less statistical precision. 

We therefore placed them in the Online Appendix and present the effect of cognition on the Sell-Buy 

Spread given that the Sell-Buy Spread is measured at the individual level.
21

showed that this cannot be explained by liquidity constraints or endowment effects. Moreover, 

buy and sell valuations are negatively correlated, and the size of the sell-buy disparity is highly 

correlated with cognitive ability, as measured by education, financial literacy, and numeracy. 

The next section extends our analysis in several additional directions. 

IV.Robustness and Further Results 

A. Are the Responses Meaningful?

In view of the implausible values in the tails of the distributions, the negative correlation 

across Sell and Buy valuations, and the large Sell-Buy Spread, some might surmise that a subset 

of respondents might not have taken the survey seriously (or perhaps did not understand it).22

Nevertheless, we have already shown that there is information contained in the elicited 

valuations: respondents provide consistent responses to similarly constructed offers (e.g., CVSell and EV-Sell) despite being asked in different waves two weeks apart. Additionally, as part 

of our experimental design, we included two additional sources of variation solely designed to 

test whether responses were meaningful. First, we randomized the order of the scenarios to 

which people were exposed.

23 Second, we also randomized the order of the options within a 

question (i.e., whether the lump-sum increment was the first or the second response). If the order 

of the questions or the order of the options within the questions mattered, this would suggest that 

individuals had difficulty with the survey itself. Therefore we test for sensitivity to “asked in 

wave 1” and “lump-sum option shown first” at the same time we test for sensitivity to anchoring 

and starting values in the next sub-section. As we shall see, the evidence is consistent with 

respondents reading and understanding the questions. 

B. Sensitivity to Anchoring and Starting Values

We also incorporated two sources of experimental variation designed to further test for 

the consistency of valuations with optimizing behavior. First, we varied the starting values of the 

size of the lump sum, randomizing across $10,000, $20,000 and $30,000; we refer to this as “log 

																																																												 22 In the mechanism design literature on contingent valuation, concerns of this type are often referred to as 

being about whether the choices are consequential. The concern is that if respondents do not believe their 

survey responses are consequential, they may not dedicate effort to the survey. 

23 We first randomized at the individual level whether CV-Sell was asked in the first or second wave of 

our survey; CV-Buy, EV-Sell, and EV-Buy were asked in the other wave of the survey. Within the wave 

in which CV-Buy, EV-Sell, and EV-Buy were asked, we randomized their order over each of the six 

possible orderings.
22

of starting value.” Second, in the CV-Sell case, we varied the order of size of the increment of 

the monthly benefit. Specifically, we presented the CV-Sell version multiple times to each 

respondent for X=$100, X=$500, X=$SSB (i.e., the entire amount of the respondent’s Social 

Security benefits), and for a random X that was a multiple of $100 (less than min($SSB-100, 

2000), and not equal to 100 or 500). We also randomized whether we asked the CV-Sell with the 

Xs arranged in increasing order or with the Xs arranged in decreasing order. We control for this 

randomization in the regressions (i.e., whether people were shown values from small-to-large or 

large-to-small). We refer to this in our tables as “asked after larger version.”

All four of these randomizations (two used to test for meaningfulness of responses and 

two to test for consistency with optimizing behavior) were conducted independently. A simple 

correlation analysis (not detailed here) confirmed that this randomization was indeed done 

correctly, such that variation along each dimension was orthogonal to the variation along the 

other dimensions. 

C. Results of these Extensions

If individuals have difficulty valuing annuities, then we would expect to find that people 

would be sensitive to irrelevant cues such as starting values and the whether asked after a larger 

version. Conversely, we do not necessarily expect that the order of the scenarios or the options 

would matter for valuation decisions, as long as the respondent tried to answer the questions. Our 

results appear in the first column of Table 4, where we regress the log midpoint of our baseline 

CV-Sell variable (using a $100 variation in Social Security benefits) against the four variables 

capturing all sources of randomization.24

Table 4 here

Several findings stand out. First, there is no evidence that individuals simply elected the 

first option shown (i.e., there is no effect of “Lump sum shown last”), giving some comfort that 

the respondents did take care in answering the survey questions. Relatedly, it does not matter 

whether the question was asked in the first or second wave (i.e., “Asked in wave 1” has a small 

and insignificant coefficient estimate). Second, there is bias with respect to both of the other 

measures, as would be expected if individuals had difficulty valuing annuities. Specifically, the

starting value had a statistically significant coefficient of +0.37. Because both the annuity 

																																																												 24 We do this analysis on the CV-Sell version because only the CV-Sell version asks for different 

increment sizes of the Social Security amount. This means that we can randomize the order in which the 

increment sizes are shown only for the CV-Sell version.
23

valuation and the starting value are measured in logs, this means that increasing the first lumpsum amount shown by 10% raised respondents’ valuations by an average of around 3.7%. 

Furthermore, when the CV-Sell question was shown after a CV-Sell question with a larger 

change in Social Security benefits (so the order was large-to-small), respondents reported a 0.7

log-point higher average valuation of the annuity than if the baseline CV-Sell question was seen 

first.

We next re-run this regression on two respondent sub-samples: those in the top (col. 2) 

and those in the bottom quintile (col. 3) of the cognition index. We find the effect of the log of 

the starting value is insignificant for individuals with higher cognitive abilities, whereas it is 

substantially more important (the coefficient is 0.92) for those in the bottom quintile of 

cognition. This suggests that less cognitively capable people are more sensitive to anchoring 

effects. Interestingly, however, the effect of “asked after larger version” is significant and similar 

in magnitude across the cognition quintiles. Moreover, the “asked after larger version” effect is 

extremely persistent across any cut of the data by measures of cognition. Whether the lump-sum 

option was shown as the first or second option continues to be insignificant for all quintiles, 

although we do now find that whether the question was asked in wave 1 or wave 2 is significant 

in the lowest cognition quintile. That is, respondents in the bottom quintile appear to provide a 

significantly higher valuation in the second wave, though why this is so is not evident.

In column 4, we return to the full sample and include interactions between the cognition 

index and each of the four survey manipulations. Results tell a similar story: people are sensitive 

to starting values and even more so if they have lower cognitive abilities. “Asked after larger 

version” is highly significant, but is not mitigated by cognition. “Asked in wave 1” and “lumpsum option shown last” continue to be statistically insignificant.

D. Explaining Annuity Valuations

A key reason that life annuities play such an important role in life-cycle economic models 

is that they provide a cost-effective way to smooth consumption by insuring against longevity 

risk. Although numerous authors have calculated the welfare gains associated with annuitization, 

there is conflicting evidence on the extent to which real-world individuals actually value the 

insurance. Brown (2001) showed that a utility-based measure of annuity valuation was correlated 

with a binary measure of intended annuitization of asset balances. Bütler and Teppa (2007)

documented similar findings in the Swiss system. Nevertheless, such measures explain only a 
24

very small fraction of the variation in the annuitization decision. Brown, Kling, Mullainathan, and 

Wrobel (2008) suggested that the ubiquitous framing of retirement planning in terms of wealth 

accumulation has conditioned individuals to ignore the insurance aspects of annuities and view 

them through an investment lens, consistent with individuals resorting to simplified decisionmaking heuristics in the face of complexity (Benartzi, Previtero, and Thaler 2011).

We hypothesize that when individuals confront trade-offs of the type presented in our 

survey, they find it difficult to sort through the lifetime utility implications and instead resort to 

thinking in simpler financial terms. To test this hypothesis, we regress annuity valuations against 

various determinants of annuity demand in our data. Column 1 of Table 5 regresses the average 

of CV-Sell and CV-Buy valuations against the actuarial value of the annuity offer presented

(which varies by cohort, age at annuitization, and sex; it also assumes a real interest rate of three 

percent).

25 The actuarial value term has a coefficient of 1.02, suggesting that there is 

approximately a one-for-one correspondence between the annuity’s actuarial value and 

individuals’ subjective valuations of the annuity. Column 2 replaces the actuarial value with a 

utility-based measure. Following Brown (2001), this value is derived from a parameterized lifecycle model with variation coming from age at annuitization, mortality differences by cohort and 

sex, marital status (which determines whether it is a single or joint life optimization), risk 

aversion, current levels of non-annuitized wealth, current annuitized wealth, and interactions of 

these variables through the utility-maximizing model. We find that the coefficient on this 

theoretical, utility-based annuity value in column 2 is not significantly different from zero, 

though it is highly significantly different from one.

26 In columns 3 and 4, we repeat this analysis 

using even more control variables and we obtain very similar results.27

Table 5 here

Overall, we view these results as being consistent with individuals using simpler financial 

																																																												 25 We use the CV versions because, unlike the EV versions, they were asked in different waves of the 

survey. We take the average of CV-Sell and CV-Buy because there is no a priori reason to consider one 

more credible than the other.

26 In results not detailed here, when we include both the actuarial value and the utility-based measure, we 

continue to find that the coefficient on the actuarial value is approximately one and that the utility-based 

measure is insignificant.

27 In Online Appendix Table A.7, we repeat the regressions in columns 1 and 3 of Table 5, but now we 

use each of our four separate valuation measures (CV-Sell, CV-Buy, EV-Sell and EV-Buy). In seven of 

the eight additional specifications, we cannot reject the hypothesis that the coefficient on the actuarial 

value is equal to one, but we can reject that it is equal to zero. In unreported results, we also find that the 

utility-based measure is not significant using these alternative dependent variables. 
25

decision rules (e.g., “How long will it take me to break even?”) rather than taking into account

the more complex consumption-smoothing and insurance considerations. We also note that the 

R-squared values are very low: around 0.05 to 0.06 depending on the specification. These 

findings are consistent with prior studies (e.g., Brown 2008) which also found it difficult to 

account for the observed variation in annuitization decisions.

Table 6 reports coefficient estimates on the annuity valuations for respondents based on

our cognition index. Here we provide both estimated coefficients and the root mean squared 

error (MSE) for each specification. Although the coefficients differ non-monotonically across the 

cognition quintiles, in all cases they are not significantly different from 1.28 We do, however, 

find that the root MSE is monotonically declining with the level of cognition. Recalling that our 

dependent variable is in logs, these differences are economically meaningful. For example, the 

root mean squared distance from the regression line declines by 0.24 log-points when one moves 

from the bottom to the second cognition quintile and a decline of a full 0.60 log-points when one 

moves from the bottom to the top cognition quintile. In other words, decisions made by less 

cognitively capable individuals are substantially noisier than those made by the more cognitively 

able.

Table 6 here

E. Robustness to Outliers and to Age of Sample

To verify that possible outliers have no major effects on the results, we replicated Figure 

3 and Tables 2 through 6 using only annuity valuation measures that have been winsorized at the 

10th and 90th percentiles. The results for these winsorized variables are very similar (see Online 

Appendix Figure A.4 and Tables A.8 through A.12). We also examine the possibility that our 

results might be driven by a lack of interest in our questions by younger respondents, who may 

not have given retirement planning much thought. We therefore replicated Figure 3 and Tables 2 

through 6 using only respondents age 50 and above. The results for this sample are very similar 

(see Online Appendix Figure A.5 and Tables A.13 through A.17).

																																																												 28 We also run a single regression in which the cognition index is interacted with the actuarial value

(including the same controls as in Table 6, the direct effect of the cognition index and the direct effect of 

actuarial value). We find that the interaction term has a coefficient estimate of 0.001 (s.e.: 0.146), which 

confirms that the effect of the actuarial value on the annuity valuation does not differ significantly by 

cognition.
26

V. Discussion and Conclusions

This paper provides support for the hypothesis that there is important cross-sectional 

variation in individuals’ abilities to value a stream of life annuity payments. Furthermore, this 

variation is highly correlated with an index of cognition we built using measures of education, 

financial literacy, and numeracy. On average, we find that consumers tend to value annuities less

when given the opportunity to buy more, but they value them more when given the opportunity 

to sell annuities in exchange for a lump sum. Because this finding holds even when no status quo

option is available, this finding is not driven by standard status quo or endowment effects. 

Instead, our results are consistent with consumers having difficulty ascertaining the value of an 

annuity. As a result, many people are only willing to buy or sell an annuity when it appears to be 

an exceptionally good deal and this tendency is strongest among the least cognitively able. We 

also show that people are sensitive to framing and starting values, which provides further 

evidence against optimizing behavior. Finally, we show that the cross-sectional variation in 

subjective annuity valuations is correlated with the relatively simple actuarial value, but not with 

amore cognitively-challenging utility-based value, and that decisions made by less cognitively 

adept individuals are substantially noisier than those of the more cognitively able.

Our findings raise questions about whether consumers are able to make utilitymaximizing choices when confronted with a decision about whether to buy longevity protection. 

Although our results do not speak directly to why average annuity values are so low (and thus do 

not speak directly to what the literature calls the “annuity puzzle”), our findings do imply that 

one should not necessarily infer from the observed lack of annuitization that optimizing agents 

prefer lump sums over annuities. For example, the fact that so few people annuitize their defined

contribution pension balances when given the opportunity to do so should not be interpreted as 

conclusive revealed preference evidence that they do not value longevity protection. 

In addition to advancing our intellectual understanding, our results also have considerable 

policy relevance. For example, in early 2014, the UK eliminated its mandatory annuitization 

requirement, thus leaving the decision of whether or not to annuitize retirement account balances 

in the hands of individuals (Donaldson and Hutton 2014). By contrast, in Singapore, the 

government now requires retirees to purchase life annuities sufficient to cover subsistence needs

(Fong, Mitchell, and Koh 2011). In the U.S., policymakers are increasingly expressing interest in 

ways to further encourage annuitization in 401(k) and other defined contribution plans (Gale,
27

Iwry, John, and Walker 2008). There is also an emerging debate in the U.S. about whether to 

encourage or discourage a particular form of “de-risking” of corporate defined benefit pension 

plans, by offering retirees a lump sum in lieu of lifelong pension benefits. In these and other 

instances, our results suggest that many individuals lack the cognitive ability to make appropriate 

judgments about how to manage their money in retirement. Accordingly, policymakers would do 

well to recognize this substantial heterogeneity in people’s optimization capabilities when 

individuals make crucially important financial decisions. 
28

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0

0.10.20.30.40.50.60.70.80.9

1

$0 $20,000 $40,000 $60,000 $80,000 

Fraction Below

Lump Sum Compensating for a $100 Change in Monthly Social 

Valuation when BuyingValuation when BuyingValuation when Selling, Valuation when Selling, 

Median when Selling: $13,750

Median when Buying: $3,000

Figure 1: Cumulative Distribution Functions of CV-Sell and CV-Buy Annuity Valuations 
0

0.10.20.30.40.50.60.70.80.9

1

$0 $20,000 $40,000 $60,000 $80,000 

Fraction below

Lump Sum Equivalent to a $100 Change in Monthly Social 

Valuation when BuyinValuation when BuyinValuation when Selling

Median Valuation when Selling

when Selling: $12,500

Median when Buying: $3,000

Figure 2: Cumulative Distribution Functions of EV-Sell and EV-Buy Annuity Valuations 
0

1

2

3

4

1 2 3 4 5

Mean Sell-Buy Spread

Number Series Score (quintiles)

Panel B

0

1

2

3

4

5

HS dropout High school Some 

college

Bachelor's 

degree

Professional 

Degree

Mean Sell-Buy Spread

Education

Panel C

0

1

2

3

4

1 2 3 4 5

Mean Sell-Buy Spread

Cognition Index (quintiles)

Panel D

Figure 3: Sell-Buy Spread by Measures of Decision-Making Ability

Note: The whiskers represent 95% confidence intervals. The Sell-Buy Spread is measured as the absolute value of the difference between the log

CV-Sell valuation and the log CV-Buy valuation of a $100 change in monthly Social Security benefits. For the Financial Literacy measure, we 

grouped those with no correct answers with those with one correct answer because there are very few observations (4%) with no correct answers.

0

1

2

3

4

0 or 1 2 3

Mean Sell-Buy Spread

Financial Literacy (number of correct answers)

Panel A
Table 1: Characteristics of the ALP Sample

(1) (2) (3)

ALP Sample

Mean CPS Mean Difference ALP-CPS

Age 51.4 46.2 5.19***

 Age: 18-34 0.16 0.31 -0.15***

 Age: 35-49 0.25 0.27 -0.02

 Age: 50-64 0.41 0.25 0.16***

 Age: 65+ 0.18 0.17 0.01

Female 0.58 0.51 0.07***

Married 0.60 0.54 0.07***

Race/Ethnicity

 Non-Hispanic White 0.80 0.68 0.12***

 Non-Hispanic Black 0.08 0.12 -0.04***

 Hispanic 0.09 0.14 -0.05***

 Other Race/Ethnicity 0.03 0.07 -0.03***

Education

 High School Dropout 0.02 0.13 -0.11***

 High School 0.16 0.30 -0.14***

 Some College 0.37 0.28 0.09***

 Bachelor's Degree 0.25 0.18 0.07***

 Professional Degree 0.19 0.10 0.10***

Ln Family Income 10.89 10.77 0.13***

 Family Income: Below 25k 0.18 0.24 -0.07***

 Family Income: 25k-50k 0.27 0.24 0.04***

 Family Income: 50k-75k 0.21 0.18 0.03***

 Family Income: 75k-100k 0.13 0.12 0.01**

 Family Income: Above 100k 0.21 0.23 -0.02*

Household size 2.68 3.00 -0.33***

 Household size of one 0.22 0.14 0.08***

 Household size of two 0.36 0.33 0.03***

 Household size of three 0.15 0.19 -0.04***

 Household size of four + 0.27 0.33 -0.06***

Region

 Northeast 0.17 0.18 -0.02*

 Midwest 0.24 0.22 0.02**

 South 0.35 0.37 -0.01

 West 0.24 0.23 0.01

Observations 2,112 146,785

Notes: * significant at 10%, ** significant at 5%, *** significant at 1%. In both the ALP and the 

CPS the sample is restricted to those aged 18 and older. The ALP sample was collected between 

June and August of 2011. The CPS data are from March 2011 and use CPS person weights; the 

ALP data are unweighted. 
Pairwise correlations

CV-Sell (in logs)

EV-Sell (in logs)

CV-Buy (in logs)

EV-Buy (in logs)

Table 2: Correlations between Annuity Valuation Measures

CV-Sell

(in logs)

EV-Sell

(in logs)

CV-Buy

(in logs)

EV-Buy

(in logs)

1

0.31*** 1

-0.11*** -0.17*** 1

Notes: * significant at 10%, ** significant at 5%, *** significant at 1%. Each entry gives the pairwise 

correlation between the variable listed in the column and in the row. All four variables are a measures of 

the valuation that an individual places on $100 change in monthly Social Security benefits. See the text 

for exact definitions. All measures are expressed in logs of the midpoint between the upper and lower 

bounds. To correct for correlations induced by common experimental manipulations (such as the starting 

value) across the four variables, we regress each variable on the relevant experimental manipulations and 

take the residual. The correlations between the resulting residuals are shown in the Table; uncorrected 

pairwise correlations are very similar and provided in Online Appendix Table A.2.

-0.11*** -0.15*** 0.72*** 1
Explanatory Variables (1) (2) (3) (4)

Age 35 to 49 -0.10 0.08 0.09 0.22

(0.13) (0.13) (0.13) (0.13)

Age 50 to 64 0.05 0.33*** 0.34*** 0.42***

(0.12) (0.12) (0.12) (0.13)

Age 65 and older 0.44*** 0.66*** 0.68*** 0.66***

(0.14) (0.14) (0.14) (0.16)

Cognition index, standardized -0.59*** -0.42***

(0.04) (0.07)

Financial literacy index, 0-3 scale -0.32***

(0.06)

Education index, 1-5 scale -0.24***

(0.04)

Number series score, standardized -0.31***

(0.05)

Controls for demographics and preferences No No No Yes

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0279 0.1230 0.1233 0.1677

Number of observations 2065 2065 2065 2065

Mean of dependent variable 2.58 2.58 2.58 2.58

Standard deviation of dependent variable 1.85 1.85 1.85 1.85

Table 3: Explaining the Sell-Buy Spread

Dependent Variable: Absolute Value of Difference 

between Log CV-Sell and Log CV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Each 

column contains an OLS regression of the Sell-Buy Spread (absolute value of the difference between log CV-Sell and log CVBuy) on the explanatory variables listed in the rows. CV-Sell is the lump-sum amount given to the individual that would 

exactly compensate the individual for a $100 decrease in monthly Social Security benefits. CV-Buy is the lump-sum amount 

the individual is just willing to pay for a $100 increase in monthly Social Security benefits. All regressions also include 

controls for missing values of explanatory variables and controls for experimental manipulations, namely: log of starting 

value, asked after larger version, asked in wave 1, lump-sum option shown last. The financial literacy index is equal to the 

number of correct answers to three financial literacy questions (mean: 2.12 s.d.: 0.80). The education index equals 1 for high 

school dropouts, 2 for high school graduates, 3 for some college, 4 for bachelor's degree, and 5 for professional degree (mean: 

3.42 s.d.: 1.05). The number series score is a standardized measure of performance on a number of questions that involve 

completing number series. The cognition index is the first principal component of the financial literacy index, the education 

index, and the number series score. The coefficients on the demographic and preference variables of the regression in column 

4 are shown in Online Appendix Table A.3 column 1. 
Explanatory Variables

Log of starting value

Asked after larger version

Asked in wave 1

Lump-sum option shown last 

Log of starting value

 × Cognition index

Asked after larger version

 × Cognition index

Asked in wave 1

 × Cognition index

Lump-sum option shown last 

 × Cognition index

Cognition index

Adjusted R2

N

Mean of dependent variable

Standard deviation of dependent variable

Table 4: Effects of Randomizations and Interactions with the Cognition Index

(1) (2) (3) (4)

Dependent Variable: log CV-Sell

Entire sample Top quintile of 

cognition index

Bottom quintile 

of cognition 

index

Entire sample

 0.37*** 0.17 0.92*** 0.39***

(0.07) (0.13) (0.21) (0.07)

 0.70*** 0.70*** 0.77*** 0.69***

(0.07) (0.12) (0.19) (0.07)

0.04 0.01 0.38** 0.05

(0.07) (0.12) (0.19) (0.07)

0.09 0.01 -0.03 0.08

(0.07) (0.12) (0.19) (0.07)

 -0.20**

(0.08)

-0.09

(0.07)

-0.03

(0.07)

0.03

(0.07)

 -0.17***

(0.04)

0.0600 0.0832 0.0827 0.0737

2,090 385 412 2,090

10.02 9.82 10.27 10.02

1.56 1.19 1.96 1.56

Notes: Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Each column 

contains an OLS regression of the baseline CV-Sell measure on the explanatory variables listed in the rows. The baseline CV-Sell 

measure is the lump-sum amount given to the individual that would exactly compensate the individual for a $100 decrease in 

monthly Social Security benefits. CV-Sell is expressed in logs of the midpoint between the upper and lower bounds. The starting 

value for the annuity valuation was randomized at $10,000, $20,000, or $30,000. "Asked after larger version" equals one if the 

baseline CV-Sell measure was asked after a CV-Sell question in which Social Security benefits were varied by more than $100. 

Whether this occured was randomized. "Asked in wave 1" is a dummy variable that equals one if the CV-Sell question was asked 

in the first wave, and "Lump sum option shown last" is a dummy variable that equals one if the option involving the lump-sum 

amount was shown after the alternative option. Both dummy variables were randomized. The cognition index is the first principal 

component of the financial literacy index, the education index, and the number series score. All variables interacted with the 

cognition index are demeaned so that the coefficient on the cognition index can be interpreted as the effect of the cognition 

literacy index when the interaction variables are equal to their sample means.
Explanatory Variables (1) (2) (3) (4)

Log actuarial value 1.02*** 0.84***

(0.25) (0.26)

Log theoretical utility-based annuity value 0.04 0.18

(0.04) (0.13)

Age -0.05*** -0.02 -0.04** -0.01

(0.01) (0.01) (0.02) (0.01)

Age squared/100 0.06*** 0.02 0.04*** 0.01

(0.02) (0.01) (0.02) (0.01)

Female -0.08 0.01 -0.03 0.01

(0.06) (0.05) (0.06) (0.06)

Married 0.05 0.04 0.08 0.13*

(0.06) (0.06) (0.06) (0.07)

Black 0.05 0.08 0.03 0.04

(0.12) (0.12) (0.12) (0.12)

Hispanic 0.34*** 0.36*** 0.32*** 0.32***

(0.11) (0.11) (0.12) (0.12)

Other -0.08 -0.08 -0.08 -0.09

(0.13) (0.13) (0.12) (0.13)

Education index, 1-5 scale -0.03 -0.03

(0.03) (0.03)

Log family income 0.03 0.02

(0.04) (0.04)

Owns an annuity -0.07 -0.04

(0.06) (0.06)

Owns home -0.16* -0.16**

(0.08) (0.08)

Log financial wealth 0.01 -0.01

(0.02) (0.03)

Self-reported health index, 1-5 scale -0.03 -0.03

(0.03) (0.03)

Ever had kids -0.03 -0.04

(0.06) (0.06)

Risk aversion (standardized) 0.02 0.02

(0.03) (0.03)

Precaution (standardized) -0.07** -0.07**

(0.03) (0.03)

Expects returns greater than 3% p.a. 0.10* 0.10*

(0.06) (0.06)

Confident SS will pay promised benefits, 1-4 scale 0.12*** 0.14***

(0.03) (0.03)

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2 0.0543 0.0473 0.0649 0.0607

Number of observations 2065 2065 2065 2065

Mean of dependent variable 9.18 9.18 9.18 9.18

Standard deviation of dependent variable 1.22 1.22 1.22 1.22

Table 5: Explaining Annuity Valuations

Dependent Variable: Mean of log CV-Sell and log CV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Each column contains an OLS regression 

of annuity valuation (mean of log CV-Sell and log CV-Buy) on the explanatory variables listed in the rows. CV-Sell is the lump-sum amount given to the 

individual that would exactly compensate the individual for a $100 decrease in monthly Social Security benefits. CV-Buy is the lump-sum amount the 

individual is just willing to pay for a $100 increase in monthly Social Security benefits. All regressions also include controls for missing values of explanatory 

variables and controls for experimental variation, namely: log of starting value, asked after larger version, asked in wave 1, lump-sum option shown last. To 

calculate the theoretical utility-based annuity value, we solve the lifecycle dynamic programming problem for a household that matches the respondent on age, 

gender, marital status, spousal age (if married), start date of the annuity, financial wealth, existing annuity wealth, and coefficient of risk aversion, assuming a 

real discount rate of three percent per year. We solve this lifecycle dynamic programming problem twice: once for the CV-Sell equivalent wealth and once for 

the CV-Buy equivalent wealth. We take the log of both amounts and average them. The education index equals 1 for high school dropouts, 2 for high school 

graduates, 3 for some college, 4 for bachelor's degree, and 5 for professional degree. Summary statistics and sources for the explanatory variables are provided 

in Online Appendix Table A.1.
(1) (2) (3) (4) (5)

Coefficient on log 

actuarial value

p-value on 

coefficient=1 

Root 

MSE

Adjusted

 R2 N

1. Bottom quintile 0.46 0.483 1.488 0.0922 404

(0.77)

2. Second quintile 0.76 0.686 1.246 0.0259 451

(0.59)

3. Third quintile 1.24** 0.618 1.163 0.0204 392

(0.49)

4. Fourth quintile 0.77 0.650 1.034 0.0498 433

(0.50)

5. Fifth quintile 1.49*** 0.340 0.889 0.0677 385

(0.51)

Sample split by quintiles of the cognition index

Dependent Variable: 

Mean of log CV-Sell and log CV-Buy

Notes: Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Here we 

estimate specification 1 of Table 5 by subsample. Each row contains an OLS regression of the log annuity valuation 

(mean of CV-Sell and CV-Buy) on the log actuarial value and additional controls. Additional controls are those in 

specification 1 of Table 5. The cognition index is the first principal component of the financial literacy index, the 

education index, and the number series score.

Table 6: Predictive Power of Actuarial Value by Quintile of the Cognition Index
Online Appendices for:

Cognitive Constraints on Valuing Annuities

Jeffrey R. Brown, Arie Kapteyn, Erzo F.P. Luttmer, and Olivia S. Mitchell

Online Appendix Tables and Figures ............................................. page A-2

Online Appendix A: The Rand American Life Panel ......................... page A-24

Online Appendix B: Survey Instrument ........................................ page A-28
0 

1 

2 

3 

4 

1 2 3 4 5 

EV Sell-Buy Spread 

Cognition Index (quintiles) 

Panel D 

0 

1 

2 

3 

4 

1 2 3 4 5 

EV Sell-Buy Spread 

Number Series Score (quintiles) 

Panel B 

0 

1 

2 

3 

4 

0 or 1 2 3 

EV Sell-Buy Spread 

Financial Literacy (number of correct answers) 

Panel A 

0 

1 

2 

3 

4 

5 

HS dropout High school Some 

college 

Bachelor's 

degree 

Professional 

Degree 

EV Sell-Buy Spread 

Education 

Panel C 

Figure A.1: EV Sell-Buy Spread by Measures of Decision-Making Ability

 

Note: The whiskers represent 95% confidence intervals. This figure is identical to Figure 3 except that the Sell-Buy Spread is based on EV 

valuations rather than CV valuations. The EV Sell-Buy Spread is measured as the absolute value of the difference between the log EV-Sell 

valuation and the log EV-Buy valuation of a $100 change in monthly Social Security benefits.

A-2
0 

1 

2 

1 2 3 4 5 

CV-EV Sell Spread 

Number Series Score (quintiles) 

Panel B 

0 

1 

2 

HS dropout High school Some 

college 

Bachelor's 

degree 

Professional 

Degree 

CV-EV Sell Spread 

Education 

Panel C 

0 

1 

2 

1 2 3 4 5 

CV-EV Sell Spread 

Cognition Index (quintiles) 

Panel D 

0 

1 

2 

0 or 1 2 3 

CV-EV Sell Spread 

Financial Literacy (number of correct answers) 

Panel A 

Figure A.2: EV-CV Sell Spread by Measures of Decision-Making Ability 

 

Note: The whiskers represent 95% confidence intervals. This figure is identical to Figure 3 except that the graphs plot the EV-CV Sell Spread 

rather than the CV Sell-Buy Spread. The EV-CV Sell Spread is measured as the absolute value of the difference between the log EV-Sell valuation 

and the log CV-Sell valuation of a $100 change in monthly Social Security benefits.

A-3
0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

1 2 3 4 5 

Correlation between CV Valuations 

and EV Valuations 

Cognition Index (quintiles) 

Panel D: CV vs. EV 

-0.4 

-0.3 

-0.2 

-0.1 

0 

0.1 

0.2 

1 2 3 4 5 

Correlation between Sell Valuations 

and Buy Valuations 

Cognition Index (quintiles) 

Panel B: Sell vs. Buy 

-0.4 

-0.3 

-0.2 

-0.1 

0 

0.1 

0.2 

1 2 3 4 5 

Correlation between CV-Sell 

and CV-Buy 

Cognition Index (quintiles) 

Panel A: CV-Sell vs. CV-Buy 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

1 2 3 4 5 

Correlation between CV-Sell 

and EV-Sell 

Cognition Index (quintiles) 

Panel C: CV-Sell vs. EV-Sell 

Figure A.3: Correlations of Log Annuity Valuations by the Cognition Index 

Note: The whiskers represent 95% confidence intervals. Confidence intervals are based on exact percentiles in 10,000 bootstrap replications. All annuity 

valuations are expressed in logs. The Sell Valuation is the average of log CV-Sell and log EV-Sell. The Buy Valuation is the average of log CV-Buy and log 

EV-Buy. The CV Valuation is the average of log CV-Sell and log CV-Buy. The EV Valuation is the average of log EV-Sell and log EV-Buy. To correct for 

correlations induced by common experimental manipulations (such as the starting value) across the four valuation measures, we regress each valuation measure 

on the relevant experimental manipulations and take the residual. These regressions are run separately for each quintile of the cognition index. The correlations 

between the resulting residuals are shown in the figure.

A-4
0 

1 

2 

3 

4 

1 2 3 4 5 

Mean Sell-Buy Spread 

Number Series Score (quintiles) 

Panel B 

0 

1 

2 

3 

4 

5 

HS dropout High school Some 

college 

Bachelor's 

degree 

Professional 

Degree 

Mean Sell-Buy Spread 

Education 

Panel C 

0 

1 

2 

3 

4 

1 2 3 4 5 

Mean Sell-Buy Spread 

Cognition Index (quintiles) 

Panel D 

0 

1 

2 

3 

4 

0 or 1 2 3 

Mean Sell-Buy Spread 

Financial Literacy (number of correct answers) 

Panel A 

Figure A.4: Winsorized Sell-Buy Spread by Measures of Decision-Making Ability 

 

Note: The whiskers represent 95% confidence intervals. This figure is identical to Figure 3 except that the Sell-Buy Spread was calculated based 

on annuity valuation measures that were winsorized at the 10th and 90th percentiles. The Sell-Buy Spread is measured as the absolute value of the 

difference between the winsorized log CV-Sell valuation and the winsorized log CV-Buy valuation of a $100 change in monthly Social Security 

benefits.

A-5
0 

1 

2 

3 

4 

1 2 3 4 5 

Mean Sell-Buy Spread 

Number Series Score (quintiles) 

Panel B 

0 

1 

2 

3 

4 

5 

HS dropout High school Some 

college 

Bachelor's 

degree 

Professional 

Degree 

Mean Sell-Buy Spread 

Education 

Panel C 

0 

1 

2 

3 

4 

1 2 3 4 5 

Mean Sell-Buy Spread 

Cognition Index (quintiles) 

Panel D 

0 

1 

2 

3 

4 

0 or 1 2 3 

Mean Sell-Buy SPread 

Financial Literacy (number of correct answers) 

Panel A 

Figure A.5: Sell-Buy Spread by Measures of Decision-Making Ability for the Age 50+ Sample 

 

Note: The whiskers represent 95% confidence intervals. This figure is identical to Figure 3 except that the sample is restricted to respondents age 

50 and above. The Sell-Buy Spread is measured as the absolute value of the difference between the log EV-Sell valuation and the log EV-Buy 

valuation of a $100 change in monthly Social Security benefits.

A-6
Table A.1: Further Summary Statistics

(1) (2) (3) (4)

Mean Std. Dev. N Source

Annuity Valuation Measures

CV-Sell (log of category midpoint) 10.02 1.56 2090 Q.2.3

CV-Sell if $100 increment is shown first 9.67 1.51 1046 Q.2.3

EV-Sell (log of category midpoint) 9.48 1.71 2089 Q.6.3

CV-Buy (log of category midpoint) 8.34 2.06 2086 Q.6.3

EV-Buy (log of category midpoint) 8.51 2.17 2082 Q.6.3

Average of CV-Sell and CV-Buy (in logs) 9.18 1.22 2065

CV Sell-Buy Spread (in logs) 2.58 1.85 2065

Log actuarial value 9.68 0.20 2112 See note

Log theoretical utility-based annuity value 9.38 0.79 2112 See note

Randomization Variables

Log of starting value 9.80 0.45 2112

Asked after larger version 0.50 0.50 2112

Asked in wave 1 0.47 0.50 2112

Lump-sum option shown last 0.52 0.50 2112

Control Variables Not Already Listed in Table 1

Financial literacy index 2.12 0.80 2112 Q.3.2.1 - Q.3.2.3

 Financial literacy index = 0 0.04 0.20 2112

 Financial literacy index = 1 0.14 0.34 2112

 Financial literacy index = 2 0.47 0.50 2112

 Financial literacy index = 3 0.35 0.48 2112

Education index, 1-5 scale 3.42 1.05 2112 Preloaded from ALP

Number series score (standardized) 0.00 1.00 2112 Preloaded from ALP

Log family income (annual) 10.89 0.93 2104 Preloaded from ALP

Owns an annuity 0.50 0.50 2110 Q.3.5.1, Q.3.5.3

Owns home 0.75 0.43 1885 Preloaded from ALP

Log financial wealth (if financial wealth ≥ $1000) 11.71 1.51 1468 Preloaded from ALP

Self-reported health index, 1-5 scale 3.56 0.89 2109 Q.3.1

Ever had kids 0.73 0.45 2090 Preloaded from ALP

Risk aversion (standardized) 0.00 1.00 2098 Q.3.3.1 - Q3.3.6

Precaution (standardized) 0.00 1.00 2104 Q.3.3.7 - Q3.3.8

Expects returns greater than 3% p.a. 0.41 0.49 2103 Q.3.8.3

Confident SS will pay promised benefits, 1-4 scale 2.34 0.84 2109 Q.3.7

Notes: The upperbound of the top category is assumed to be $1 million. Log actuarial value is calculated by us based on cohort mortality 

tables, age at annuitization, and sex, assuming a real interest rate of three percent per year. To calculate the theoretical utility-based 

annuity value, we solve the lifecycle dynamic programming problem for a household that matches the respondent on age, gender, marital 

status, spousal age (if married), start date of the annuity, financial wealth, existing annuity wealth, and coefficient of risk aversion 

assuming a real discount rate of three percent per year. We solve this lifecycle dynamic programming problem twice: once for the CVSell equivalent wealth and once for the CV-Buy equivalent wealth. We take the log of both amounts and average them. The education 

index corresponds to the education categories in Table 1, with higher values corresponding to higher levels of eduction. The number 

series score is based on six questions where a respondent was shown an incomplete sequence of numbers and asked to complete the 

sequence. Missing values (14% of observations) are set equal to the mean and variable is standized. "Owns an annuity" equals one for 

anyone who currently receives or in the future expects to receive annuity income other than from Social Security. Higher values of the 

self-reported health index correspond to better health. Risk aversion is the standardized sum of Q.3.3.1 to Q.3.3.6 (with Q.3.3.3, Q.3.3.5, 

and Q3.3.6 reverse coded). Precaution is the standardized sum of Q.3.3.7. and Q.3.3.8. Higher values of the variable "confidence that 

Social Security will pay promised benefits" correspond to greater levels of confidence. 

A-7
Table A.2: Uncorrected Correlations between Annuity Valuation Measures

Pairwise correlations

CV-Sell (in logs)

EV-Sell (in logs)

CV-Buy (in logs)

EV-Buy (in logs)

CV-Sell

(in logs)

EV-Sell

(in logs)

CV-Buy

(in logs)

EV-Buy

(in logs)

1

0.32*** 1

-0.10*** -0.16*** 1

Notes: * significant at 10%, ** significant at 5%, *** significant at 1%. Each entry gives the pairwise 

correlation between the variable listed in the column and in the row. This table shows that the results in 

Table 2 are not sensitive to the corrections for common experimental manipulations that were applied to 

the correlations in Table 2. 

-0.10*** -0.14*** 0.72*** 1

A-8
Dependent Variable:

Absolute Value of 

Difference between 

Log CV-Sell 

and Log CV-Buy

Dependent Variable:

Absolute Value of 

Difference between 

Log EV-Sell 

and Log EV-Buy

Dependent Variable:

Winsorized 

CV Sell-Buy Spread

Explanatory Variables (1) (2) (3)

Age 35 to 49 0.22 0.29* 0.22*

(0.13) (0.15) (0.12)

Age 50 to 64 0.42*** 0.48*** 0.41***

(0.13) (0.15) (0.12)

Age 65 and older 0.66*** 0.56*** 0.65***

(0.16) (0.19) (0.14)

Cognition index, standardized -0.42*** -0.65*** -0.40***

(0.07) (0.08) (0.06)

Female 0.26*** 0.30*** 0.20***

(0.08) (0.09) (0.07)

Married 0.08 -0.02 -0.01

(0.13) (0.10) (0.08)

Black 0.48*** 0.35* 0.32**

(0.18) (0.19) (0.15)

Hispanic 0.27* 0.53*** 0.21

(0.15) (0.18) (0.13)

Other 0.68*** 0.28 0.45**

(0.23) (0.28) (0.19)

Log family income -0.05 -0.03 -0.04

(0.06) (0.06) (0.05)

Owns an annuity -0.03 -0.07 -0.02

(0.08) (0.10) (0.07)

Owns home -0.06 -0.28** -0.07

(0.11) (0.13) (0.10)

Self-reported health index, 1-5 scale -0.10** 0.07 -0.07*

(0.05) (0.05) (0.04)

Ever had kids -0.14 -0.10 -0.08

(0.09) (0.11) (0.08)

Risk aversion (standardized) -0.21*** -0.13*** -0.20***

(0.04) (0.05) (0.04)

Precaution (standardized) 0.01 0.00 0.01

(0.04) (0.05) (0.04)

Expects returns greater than 3% p.a. -0.15* -0.18** -0.15**

(0.08) (0.09) (0.07)

Confident SS will pay promised benefits, 0.17*** 0.17*** 0.14***

1-4 scale (0.05) (0.06) (0.04)

Controls for experimental variation Yes Yes Yes

Adjusted R2 0.1677 0.1577 0.1727

Number of observations 2065 2067 2065

Mean of dependent variable 2.58 2.27 2.49

Standard deviation of dependent variable 1.85 2.10 1.63

Table A.3: Full Regressions Explaining the Sell-Buy Spread

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Each column contains an 

OLS regression of the Sell-Buy Spread listed in the column heading on the explanatory variables listed in the rows. Column 1 shows all the 

coefficients of the regression in Table 3 column 4. Column 2 shows all the coefficients of the regression in Online Appendix Table A.4 column 

4. Column 3 shows all the coefficients of the regression in Online Appendix Table A.9 column 3.

A-9
Explanatory Variables (1) (2) (3) (4)

Age 35 to 49 -0.20 0.03 0.05 0.29*

(0.15) (0.14) (0.14) (0.15)

Age 50 to 64 -0.14 0.22* 0.25* 0.48**

(0.14) (0.13) (0.13) (0.15)

Age 65 and older 0.03 0.32** 0.35** 0.56**

(0.16) (0.15) (0.15) (0.19)

Cognition index, standardized -0.76*** -0.65***

(0.05) (0.08)

Financial literacy index, 0-3 scale -0.38***

(0.06)

Education index, 1-5 scale -0.30***

(0.05)

Number series score, standardized -0.43***

(0.06)

Controls for demographics and preferences No No No Yes

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0039 0.1327 0.1318 0.1577

Number of observations 2067 2067 2067 2067

Mean of dependent variable 2.27 2.27 2.27 2.27

Standard deviation of dependent variable 2.10 2.10 2.10 2.10

Table A.4: Explaining the Sell-Buy Spread within EV Valuations

Dependent Variable: Absolute Value of Difference 

between Log EV-Sell and Log EV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table 

is identical to Table 3, except that this table is based on EV valuations whereas Table 3 was based on CV valuations. Each 

column contains an OLS regression of the Sell-Buy Spread (absolute value of the difference between log EV-Sell and log EVBuy) on the explanatory variables listed in the rows. EV-Sell is the lump-sum amount equivalent to a $100 increase in monthly 

Social Security benefits. EV-Buy is the lump-sum amount the individual is just willing to pay in lieu of a $100 decrease in 

monthly Social Security benefits. For further details, see the note to Table 3. The coefficients on the demographic and 

preference variables of the regression in column 4 are shown in Online Appendix Table A.3 column 2. 

A-10
Explanatory Variables (1) (2) (3) (4)

Age 35 to 49 -0.02 0.06 0.07 0.13

(0.10) (0.10) (0.10) (0.11)

Age 50 to 64 0.06 0.19** 0.19** 0.28**

(0.09) (0.09) (0.09) (0.11)

Age 65 and older 0.14 0.24** 0.25** 0.33**

(0.11) (0.11) (0.10) (0.13)

Cognition index, standardized -0.27*** -0.22***

(0.03) (0.05)

Financial literacy index, 0-3 scale -0.14***

(0.05)

Education index, 1-5 scale -0.13***

(0.03)

Number series score, standardized -0.13***

(0.03)

Controls for demographics and preferences No No No Yes

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0189 0.0537 0.0546 0.0588

Number of observations 2068 2068 2068 2068

Mean of dependent variable 1.42 2.49 2.49 2.49

Standard deviation of dependent variable 1.39 1.63 1.63 1.63

Table A.5: Explaining the Spread Between CV-Sell and EV-Sell

Dependent Variable: Absolute Value of Difference 

between Log CV-Sell and Log EV-Sell

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table 

is identical to Table 3, except that this table examines the spread between CV and EV sell valuations whereas Table 3 examined 

the CV Sell-Buy Spread. Each column contains an OLS regression of the absolute value of the difference between log CV-Sell 

and log EV-Sell on the explanatory variables listed in the rows. CV-Sell is the lump-sum amount given to the individual that 

would exactly compensate the individual for a $100 decrease in monthly Social Security benefits. EV-Sell is the lump-sum 

amount equivalent to a $100 increase in monthly Social Security benefits. For further details, see the note to Table 3.

A-11
Dependent Variable: 

CV-Buy

Dependent Variable: 

Average of CV-Buy 

and EV-Buy

Dependent Variable: 

EV-Sell

Dependent Variable: 

Average of EV-Sell 

and EV-Buy

Explanatory Variables (1) (2) (3) (4)

CV-Sell -0.13*** 0.35***

(0.03) (0.03)

CV-Sell × Cognition Index, standardized 0.06 0.02

(0.03) (0.03)

Average of CV-Sell and EV-Sell -0.23***

(0.03)

Average of CV-Sell and EV-Sell 0.18***

 × Cognition Index, standardized (0.03)

Average of CV-Sell and CV-Buy 0.55***

(0.02)

Average of CV-Sell and CV-Buy 0.10***

 × Cognition Index, standardized (0.02)

Cognition index, standardized -0.06 -0.14*** -0.10** -0.10***

(0.05) (0.04) (0.04) (0.03)

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0320 0.0731 0.1109 0.2800

Number of observations 2065 2105 2068 2028

Mean of dependent variable 8.33 8.43 9.47 8.98

Standard deviation of dependent variable 2.05 1.97 1.71 1.28

Table A.6: Cognition Index and Correlations Between Valuations

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. Each column contains an OLS 

regression of the variable listed in the column heading on the explanatory variables listed in the rows. All annuity valuation measures are in logs. All 

regressions include controls for experimental variation, namely: log of starting value, asked after larger version, asked in wave 1, lump-sum option shown 

last. All variables interacted with the cognition index are demeaned so that the coefficient on the cognition index can be interpreted as the effect of the 

cognition literacy index when the interaction variables are equal to their sample means. Columns 1 and 2 show that the negative correlation between buy 

and sell valuations decreases in size for higher values of the Cognition Index, though only significantly so in column 2. Columns 3 and 4 show that the 

positive correlation between CV and EV valuations increases in size for higher values of the Cognition Index, though only significantly so in column 4. 

A-12
(1) (2) (3) (4) (5) (6) (7)

Dependent 

Variable

Coefficient on log 

actuarial value

p-value on 

coefficient =1 Controls

Root 

MSE

Adjusted

 R2 N

1. 1.02*** 0.940 Basic 1.187 0.0543 2065

(0.25)

2. CV-Sell 1.05*** 0.883 Basic 1.496 0.0816 2090

(0.34)

3. CV-Buy 0.98** 0.955 Basic 2.026 0.0315 2086

(0.44)

4. EV-Sell 0.74** 0.492 Basic 1.692 0.0190 2089

(0.37)

5. EV-Buy 0.84* 0.734 Basic 2.140 0.0269 2082

(0.48)

6. 0.84*** 0.536 Extensive 1.180 0.0649 2065

(0.26)

7. CV-Sell 0.63* 0.281 Extensive 1.478 0.1034 2090

(0.34)

8. CV-Buy 1.03** 0.945 Extensive 2.012 0.0455 2086

(0.45)

9. EV-Sell 0.36 0.095 Extensive 1.680 0.0330 2089

(0.38)

10. EV-Buy 0.96* 0.930 Extensive 2.129 0.0370 2082

(0.49)

Table A.7: Robustness of Predictive Power of Actuarial Value

Mean of CV-Sell 

and CV-Buy

Mean of CV-Sell 

and CV-Buy

Notes: Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. 

Each row contains an OLS regression of the log annuity valuation measure listed in column 1 on the log actuarial 

value and additional controls. The annuity valuation measures CV-Sell, CV-Buy, EV-Sell, and EV-Buy are 

defined in the text. All valuations are expressed in logs of the midpoint between the upper and lower bounds. 

Additional controls in rows 1-5 are those in specification 1 of Table 5, whereas the additional controls in rows 6-

10 are those in specification 3 of Table 5. Rows 1 and 6 replicate columns 1 and 3 of Table 5, respectively.

A-13
Table A.8: Correlations Between Winsorized Annuity Valuation Measures

Pairwise correlations

CV-Sell (in logs)

EV-Sell (in logs)

CV-Buy (in logs)

EV-Buy (in logs)

CV-Sell

(in logs)

EV-Sell

(in logs)

CV-Buy

(in logs)

EV-Buy

(in logs)

1

0.32*** 1

-0.10*** -0.14*** 1

Notes: * significant at 10%, ** significant at 5%, *** significant at 1%. This table is identical to Table 2 

except that all annuity valuation measures are winsorized at the 10th and 90th percentiles. For further 

details, see the note to Table 2

-0.09*** -0.12*** 0.72*** 1

A-14
Explanatory Variables (1) (2) (3) (4)

Age 35 to 49 -0.05 0.11 0.11 0.22**

(0.12) (0.11) (0.11) (0.12)

Age 50 to 64 0.09 0.34*** 0.35*** 0.41***

(0.11) (0.10) (0.10) (0.12)

Age 65 and older 0.48*** 0.68*** 0.69*** 0.65***

(0.12) (0.12) (0.12) (0.14)

Cognition index, standardized -0.53*** -0.40***

(0.04) (0.06)

Financial literacy index, 0-3 scale -0.29***

(0.05)

Education index, 1-5 scale -0.22***

(0.03)

Number series score, standardized -0.28***

(0.04)

Controls for demographics and preferences No No No Yes

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0354 0.1349 0.1351 0.1727

Number of observations 2065 2065 2065 2065

Mean of dependent variable 2.49 2.49 2.49 2.49

Standard deviation of dependent variable 1.63 1.63 1.63 1.63

Table A.9: Explaining the Winsorized Sell-Buy Spread

Dependent Variable: Absolute Value of Difference 

between Log CV-Sell (winsorized) and Log CV-Buy 

(winsorized)

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table is 

identical to Table 3 except that the Sell-Buy spead was calculated based on annuity valuation measures that were winsorized at the 

10th and 90th percentiles. For further details, see the note to Table 3. The coefficients on the demographic and preference 

variables of the regression in column 4 are shown in Online Appendix Table A.3 column 3.

A-15
Explanatory Variables

Log of starting value 0.30*** 0.19* 0.70*** 0.31***

(0.06) (0.11) (0.14) (0.05)

Asked after larger version 0.62*** 0.68*** 0.62*** 0.61***

(0.05) (0.10) (0.13) (0.05)

Asked in wave 1 0.05 0.04 0.24* 0.05

(0.05) (0.10) (0.13) (0.05)

Lump-sum option shown last 0.07 0.04 0.01 0.06

(0.05) (0.10) (0.13) (0.05)

Log of starting value -0.11**

 × Cognition index (0.06)

Asked after larger version -0.03

 × Cognition index (0.05)

Asked in wave 1 0.00

 × Cognition index (0.05)

Lump-sum option shown last 0.04

 × Cognition index (0.05)

Cognition index -0.15***

(0.03)

Adjusted R2

0.0783 0.1089 0.0991 0.0933

N 2090 385 412 2090

Mean of dependent variable 10.01 9.82 10.27 10.01

Standard deviation of dependent variable 1.20 1.19 1.96 1.20

Table A.10: Effects of Randomizations for Winsorized CV-Sell Annuity Valuation

(1) (2) (3) (4)

Dependent Variable: Winsorized Log CV-Sell

Entire sample

Top quintile of 

cognition 

index

Bottom quintile 

of cognition 

index

Entire sample

Notes: Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table is 

identical to Table 4 except that the dependent variable is winsorized at the 10th and 90th percentiles. For further details, see the 

note to Table 4. 

A-16
Explanatory Variables (1) (2) (3) (4)

Log actuarial value 0.93*** 0.79***

(0.23) (0.24)

Log theoretical utility-based annuity value 0.04 0.17

(0.03) (0.12)

Age -0.05*** -0.02* -0.04*** -0.01

(0.01) (0.01) (0.01) (0.01)

Age squared/100 0.06*** 0.02 0.04*** 0.01

(0.01) (0.01) (0.02) (0.01)

Female -0.08 -0.01 -0.04 0.00

(0.05) (0.05) (0.06) (0.06)

Married 0.06 0.05 0.08 0.13*

(0.05) (0.05) (0.06) (0.07)

Black 0.09 0.12 0.09 0.09

(0.11) (0.11) (0.11) (0.11)

Hispanic 0.32*** 0.35*** 0.33*** 0.33***

(0.10) (0.11) (0.11) (0.11)

Other -0.01 -0.02 -0.02 -0.03

(0.12) (0.12) (0.11) (0.12)

Education index, 1-5 scale -0.02 -0.02

(0.03) (0.03)

Log family income 0.02 0.02

(0.04) (0.04)

Owns an annuity -0.06 -0.03

(0.06) (0.06)

Owns home -0.15** -0.16**

(0.08) (0.08)

Log financial wealth 0.01 -0.01

(0.02) (0.03)

Self-reported health index, 1-5 scale -0.02 -0.03

(0.03) (0.03)

Ever had kids -0.03 -0.04

(0.06) (0.06)

Risk aversion (standardized) 0.02 0.02

(0.03) (0.03)

Precaution (standardized) -0.07** -0.07**

(0.03) (0.03)

Expects returns greater than 3% p.a. 0.12** 0.12**

(0.05) (0.05)

Confident SS will pay promised benefits, 1-4 scale 0.11*** 0.12***

(0.03) (0.03)

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0529 0.0463 0.0633 0.0590

Number of observations 2065 2065 2065 2065

Mean of dependent variable 9.17 9.17 9.17 9.17

Standard deviation of dependent variable 1.13 1.13 1.13 1.13

Table A.11: Explaining Winsorized Annuity Valuations

Dependent Variable: 

Mean of Windsorized Log CV-Sell and Winsorized Log CV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. 

This table is identical to Table 5 except that the dependent variable was calculated based on annuity valuation measures 

that were winsorized at the 10th and 90th percentiles. For further details, see the note to Table 5.

A-17
(1) (2) (3) (4) (5)

Coefficient on log 

actuarial value

p-value on 

coefficient =1 Root MSE

Adjusted

 R2 N

1. Bottom quintile 0.44 0.431 1.373 0.0847 404

(0.71)

2. Second quintile 0.45 0.309 1.135 0.0186 451

(0.54)

3. Third quintile 1.27*** 0.559 1.085 0.0194 392

(0.45)

4. Fourth quintile 0.83* 0.720 0.973 0.0520 433

(0.46)

5. Fifth quintile 1.32*** 0.492 0.847 0.0703 385

(0.47)

Table A.12: Predictive Power of Actuarial Value by the Cognition Index for Winsorized Outcomes

Dependent Variable: 

Mean of log CV-Sell and log CV-Buy

Sample split by quintiles of the cognition index

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table is identical to 

Table 6 except that the dependent variable was calculated based on annuity valuation measures that were winsorized at the 10th and 90th 

percentiles. For further details, see the note to Table 6.

A-18
Table A.13: Correlations between Annuity Valuation Measures for the Age 50+ Sample

Pairwise correlations

CV-Sell (in logs)

EV-Sell (in logs)

CV-Buy (in logs)

EV-Buy (in logs)

CV-Sell

(in logs)

EV-Sell

(in logs)

CV-Buy

(in logs)

EV-Buy

(in logs)

1

0.29*** 1

-0.11*** -0.17*** 1

Notes: * significant at 10%, ** significant at 5%, *** significant at 1%. This table is identical to Table 2 except that the 

sample is restricted to respondents age 50 and above. For further details, see the note to Table 2

-0.11*** -0.17*** 0.72*** 1

A-19
Explanatory Variables (1) (2) (3) (4)

Age 65 and older 0.39*** 0.33*** 0.34*** 0.20*

(0.11) (0.11) (0.11) (0.12)

Cognition index, standardized -0.58*** -0.46***

(0.05) (0.08)

Financial literacy index, 0-3 scale -0.25***

(0.08)

Education index, 1-5 scale -0.23***

(0.05)

Number series score, standardized -0.36***

(0.06)

Controls for demographics and preferences No No No Yes

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0468 0.1374 0.1358 0.1807

Number of observations 1223 1223 1223 1223

Mean of dependent variable 2.68 2.68 2.68 2.68

Standard deviation of dependent variable 1.82 1.82 1.82 1.82

Table A.14: Explaining the Sell-Buy Spread for the Age 50+ Sample

Dependent Variable: Absolute Value of Difference 

between Log CV-Sell and Log CV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table 

is identical to Table 3 except that the sample is restricted to respondents age 50 and above. For further details, see the note to 

Table 3. 

A-20
Explanatory Variables

Log of starting value 0.36*** 0.20 0.92*** 0.40***

(0.10) (0.15) (0.29) (0.10)

Asked after larger version 0.78*** 0.92*** 0.88*** 0.75***

(0.08) (0.14) (0.27) (0.09)

Asked in wave 1 0.02 0.04 0.44* 0.03

(0.08) (0.14) (0.26) (0.09)

Lump-sum option shown last 0.00 0.00 -0.27 -0.03

(0.08) (0.14) (0.27) (0.09)

Log of starting value -0.16

 × Cognition index (0.10)

Asked after larger version -0.04

 × Cognition index (0.09)

Asked in wave 1 -0.08

 × Cognition index (0.09)

Lump-sum option shown last 0.13

 × Cognition index (0.09)

Cognition index -0.20***

(0.05)

Adjusted R2

0.0729 0.1384 0.0881 0.0900

N 1236 262 200 1236

Mean of dependent variable 10.12 9.89 10.55 10.12

Standard deviation of dependent variable 1.53 1.20 1.94 1.53

Table A.15: Effects of Randomizations for the Age 50+ Sample

(1) (2) (3) (4)

Dependent Variable: log CV-Sell

Entire sample Top quintile of 

cognition index

Bottom quintile 

of cognition 

index

Entire sample

Notes: Robust standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This 

table is identical to Table 4 except that the sample is restricted to respondents age 50 and above. For further details, see 

the note to Table 4.

A-21
Explanatory Variables (1) (2) (3) (4)

Log actuarial value 1.61*** 1.61***

(0.49) (0.50)

Log theoretical utility-based annuity value 0.10** 0.33**

(0.05) (0.16)

Age -0.17* 0.07 -0.18** 0.04

(0.09) (0.05) (0.09) (0.05)

Age squared/100 0.16** -0.05 0.17** -0.02

(0.08) (0.04) (0.08) (0.04)

Female -0.05 0.08 -0.03 0.05

(0.08) (0.07) (0.08) (0.07)

Married 0.05 0.06 0.06 0.17*

(0.07) (0.07) (0.08) (0.10)

Black 0.09 0.15 0.10 0.11

(0.18) (0.18) (0.17) (0.17)

Hispanic 0.13 0.21 0.10 0.13

(0.20) (0.20) (0.21) (0.21)

Other -0.30 -0.26 -0.28 -0.26

(0.22) (0.21) (0.22) (0.22)

Education index, 1-5 scale 0.01 0.01

(0.04) (0.04)

Log family income 0.05 0.06

(0.05) (0.05)

Owns an annuity -0.03 0.00

(0.08) (0.08)

Owns home -0.21* -0.19*

(0.11) (0.11)

Log financial wealth 0.02 -0.03

(0.03) (0.04)

Self-reported health index, 1-5 scale -0.01 -0.02

(0.04) (0.04)

Ever had kids -0.09 -0.09

(0.08) (0.08)

Risk aversion (standardized) -0.02 -0.03

(0.04) (0.04)

Precaution (standardized) -0.07* -0.07*

(0.04) (0.04)

Expects returns greater than 3% p.a. 0.10 0.09

(0.07) (0.07)

Confident SS will pay promised benefits, 1-4 scale 0.12*** 0.13***

(0.04) (0.04)

Controls for experimental variation Yes Yes Yes Yes

Adjusted R2

0.0584 0.0534 0.0710 0.0661

Number of observations 1223 1223 1223 1223

Mean of dependent variable 9.17 9.17 9.17 9.17

Standard deviation of dependent variable 1.19 1.19 1.19 1.19

Table A.16: Explaining Annuity Valuations for the Age 50+ Sample

Dependent Variable: 

Mean of Log CV-Sell and Log CV-Buy

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table 

is identical to Table 5 except that the sample is restricted to respondents age 50 and above. For further details, see the note to 

Table 5.

A-22
(1) (2) (3) (4) (5)

Coefficient on log 

actuarial value

p-value on 

coefficient =1 Root MSE

Adjusted

 R2 N

1. Bottom quintile 2.52* 0.297 1.429 0.1055 196

(1.45)

2. Second quintile 3.55*** 0.035 1.196 0.0530 256

(1.20)

3. Third quintile 1.16 0.889 1.198 0.0211 239

(1.17)

4. Fourth quintile 0.74 0.757 1.017 0.0999 270

(0.84)

5. Fifth quintile 0.22 0.364 0.899 0.0531 262

(0.86)

Table A.17: Predictive Power of Actuarial Value by the Cognition Index for the Age 50+ Sample

Dependent Variable: 

Mean of log CV-Sell and log CV-Buy

Sample split by quintiles of the cognition index

Notes: Robust standard errors between parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. This table is identical 

to Table 6 except that the sample is restricted to respondents age 50 and above. For further details, see the note to Table 6.

A-23
Online Appendix A: The Rand American Life Panel 

Sample Construction 

Our survey was conducted in the RAND American Life Panel (ALP). The ALP consists 

of a panel of U.S. households that regularly takes surveys over the Internet. An advantage over 

most other Internet panels is that the respondents to the ALP need not have Internet when they 

get recruited (as described in more detail below) and thus can be based on a probability sample 

of the U.S. population.1

 This is in contrast with so-called convenience Internet samples, where 

respondents are volunteers who already have Internet and, for example, respond to banners 

placed on frequently visited web-sites, in which they are invited to complete surveys and earn 

money by doing so. The problem with convenience Internet samples is that their statistical 

properties are unknown. There is fairly extensive literature comparing probability Internet 

samples like the ALP to convenience Internet samples, as well as literature seeking to establish if 

convenience samples can somehow be made population-representative by reweighting. 

For instance, Chang and Krosnick (2009) simultaneously administered the same 

questionnaire (on politics) to an RDD (random digit dialing) telephone sample, an Internet 

probability sample, and a non-probability sample of volunteers who do Internet surveys for 

money. They found that the telephone sample had the most random measurement error, while the 

non-probability sample had the least. At the same time, the latter sample exhibited the most bias 

(also after reweighting), producing the most accurate self-reports from the most biased sample. 

The probability Internet sample exhibited more random measurement error than the nonprobability sample (but less than the telephone sample) and less bias than the non-probability 

Internet sample. On balance, the probability Internet sample produced the most accurate results. 

Yeager et al. (2009) conducted a follow-up study comparing one probability Internet sample, one 

RDD telephone sample, and seven non-probability Internet samples and a wider array of 

outcomes. Their conclusions were the same: both the telephone sample and the probability 

Internet sample showed the least bias; reweighting the non-probability samples did not help (for 

some outcomes, the bias got worse; for others, better). They also found that response rates do not 

appear critical for bias. Even with relatively low response rates, the probability samples yielded

																																																												

1 Other probability Internet surveys include the Knowledge Networks panel in the U.S. 

(http://www.knowledgenetworks.com/knpanel/index.html), and the CentERpanel and LISS panel in the Netherlands 

(http://www.centerdata.nl/en/centerpanel and http://www.centerdata.nl/en/MESS ). Of these panels, the 

CentERpanel is the oldest (founded in 1991).

A-24
unbiased estimates. It is not clear a priori why non-probability samples do so much worse. As 

the authors note, it appears that there are some fundamental differences between Internet users 

and non-Internet users that cannot be redressed by reweighting. Indeed, Couper et al. (2007) and 

Schonlau et al. (2009) show that weighting and matching do not eliminate differences between 

estimates based on samples of respondents with and without Internet access. Several other 

studies point at equally mixed results, including Vehovar et al. (1999); Duffy et al. (2005); 

Malhotra and Krosnick (2007); Taylor (2000); and Loosveldt and Sonck (2008). 

ALP respondents have been recruited in one of four ways. Most were recruited from 

respondents ages 18+ to the Monthly Survey (MS) of the University of Michigan’s Survey 

Research Center (SRC). The MS is the leading consumer sentiment survey that incorporates the 

long-standing Survey of Consumer Attitudes and produces, among others, the widely used Index 

of Consumer Expectations. Each month, the MS interviews approximately 500 households, 300 

of which are a random-digit-dial (RDD) sample and 200 of which are re-interviewed from the 

RDD sample surveyed six months previously. Until August 2008, SRC screened MS respondents 

by asking them if they would be willing to participate in a long-term research project (with 

approximate response categories “no, certainly not,” “probably not,” “maybe,” “probably,” “yes, 

definitely”). If the response category is not “no, certainly not,” respondents were told that the 

University of Michigan is undertaking a joint project with RAND. They were asked if they 

would object to SRC sharing information about them with RAND so that they could be contacted 

later and asked if they would be willing to actually participate in an Internet survey. Respondents 

who did not have Internet were told that RAND would provide them with free Internet. Many 

MS respondents were interviewed twice. At the end of the second interview, an attempt was 

made to convert respondents who refused in the first round. This attempt included mentioning 

the fact that participation in follow-up research carries a reward of $20 for each half-hour 

interview. Respondents from the Michigan monthly survey without Internet were provided with 

so-called WebTVs (http://www.webtv.com/pc/), which allowed them to access the Internet using 

their television and a telephone line. The technology enabled respondents who lacked Internet 

access to participate in the panel and, further, use the WebTVs for browsing the Internet or 

email. The ALP has also recruited respondents through a snowball sample (respondents 

suggesting friends or acquaintances who might also want to participate), but we do not use any 

respondents recruited through the snowball sample in our paper. A new group of respondents 

A-25
(approximately 500) has been recruited after participating in the National Survey Project created 

at Stanford University with SRBI. This sample was recruited in person, and at the end of their 

one-year participation, respondents were asked whether they were interested in joining the 

RAND American Life Panel. Most of these respondents were given a laptop and broadband 

Internet access. Recently, the American Life Panel has begun recruiting based on a random mail 

and telephone sample using the Dillman et al. method (2008), with a goal of achieving 5,000 

active panel members, including a 1,000-person Spanish language subsample. If these new 

participants do not yet have Internet access, they are also provided with a laptop and broadband 

Internet access. 

Calculation of Social Security Benefits 

For most ALP respondents, we have previously estimated monthly Social Security 

benefits (described in Brown et al., 2013). To do so, we took respondents through a fairly 

detailed set of questions asking about years in which they had labor earnings and an 

approximation of earnings in those years. We then fed these earnings through a benefit calculator 

provided by SSA to calculate individuals’ “Primary Insurance Amount” (PIA), which is 

equivalent to the benefit the individual would receive if he were to retire at his normal retirement 

age. Next, we applied SSA’s actuarial adjustment for earlier or later claiming. We also asked 

respondents if the estimated benefit amount seemed reasonable to them, and we gave them an 

opportunity to change this estimate if they believed it was not a good approximation. All 

subsequent lump-sum and annuity questions then pivot off this estimated monthly Social 

Security benefit amount. 

For the few respondents who indicated they did not expect to receive benefits (nor expect 

one from a living or deceased spouse), we imputed “standard monthly benefit amounts” based on 

age, sex, and educational level. We then asked these respondents to assume, for the purposes of 

the questions to follow, that they would receive this benefit, as follows: 

Even though we understand that you are not eligible to receive Social Security benefits, 

we would like to ask you to complete this survey assuming you would be eligible. In 

other words, please answer in this survey what you would have done or chosen if you 

would be eligible for Social Security benefits. 

A-26
Online Appendix References 

Brown, Jeff R., Arie Kapteyn, and Olivia S. Mitchell. 2013. “Framing and Claiming: How InformationFraming Affects Expected Social Security Claiming Behavior.” Journal of Risk and Insurance. 

Forthcoming.

Chang, Linchiat and Jon A. Krosnick. 2009. “National Surveys via RDD Telephone Interviewing versus 

the Internet: Comparing Sample Representativeness and Response Quality.” Public Opinion 

Quarterly. 73: 641-648.

Couper, Mick P., Arie Kapteyn, Matthias Schonlau, and Joachim Winter. 2007. “Noncoverage and 

Nonresponse in an Internet Survey.” Social Science Research. 36(1): 131-148.

Dillman, Don A., Jolene D. Smyth, and Leah Melani Christian. 2008. Internet, Mail, and Mixed-Mode 

Surveys: The Tailored Design Method, 3rd edition. Hoboken, NJ: Wiley. 

Duffy, Bobby, Kate Smith, George Terhanian, and John Bremer. 2005. “Comparing Data from Online 

and Face-to-face Surveys.” International Journal of Market Research. 47: 615–639

Loosveldt, Geert and Nathalie Sonck. 2008. “An Evaluation of the Weighting procedures for an Online 

Access Panel Survey.” Survey Research Methods. 2: 93–105.

Malhotra, Neil and Jon A. Krosnick. 2007. “The Effect of Survey Mode and Sampling on Inferences 

about Political Attitudes and Behavior: Comparing the 2000 and 2004 ANES to Internet Surveys 

with Non-probability Samples.” Political Analysis. 15: 286–323.

Schonlau, Matthias, Arthur van Soest, Arie Kapteyn, and Mick Couper. 2009. “Selection Bias in Web 

Surveys and the Use of Propensity Scores.” Sociological Methods and Research. 37: 291-318.

Taylor, Humphrey. 2000. “Does Internet Research Work? Comparing Online Survey Results with 

Telephone Surveys.” International Journal of Market Research. 42(1): 51–63.

Vehovar, Vasja, Zenel Batagelj, and Katja Lozar Manfreda. 1999. “Web surveys: Can the weighting solve 

the problem?” Proceedings of the Survey Research Methods Section. American Statistical 

Association: 962–967.

Yeager, David S., Jon A. Krosnick, LinChiat Chang, Harold S. Javitz, Matthew S. Levindusky, Alberto 

Simpser, and Rui Wang. 2009. “Comparing the Accuracy of RDD Telephone Surveys and Internet 

Surveys Conducted with Probability and Non-Probability Samples.” Working paper. Stanford 

University.

A-27
Online	Appendix	B	–	Survey Instrument	

Introduction	for	users	of	this	survey	instrument

• The	survey	instrument	was	fielded	as	well-being	modules	179	and	180	on	the	RAND	American	Life	Panel	

(ALP)

• Items	which	are	bolded	are	instructions	to	programmer or	comments	to	the	reader.	

• Items	which	are	non-bolded	are	asked	of	respondents.	

• Items	shown	on	the	screen	in	bold	are	marked	by	<b>	for	the	start	of	the	bolded	text	and	by	</b>	at	the	

end	of	the	bolded	text.

• We	changed	the	names	of	the	four	elicitation	methods	in	the	written	up	version	of	the	paper	compared	

to	the	names	used	in	this	survey	instrument.		CV-minus	corresponds	to	CV-Buy,	EV-minus	to	EV-Buy,	

CV-plus	to	CV-Sell,	and	EV-plus	to	EV-Sell.

A.	Randomizations

We	independently	randomize	the	following	variables:

1. VERSION_A	[0,	1]:	whether	we	ask	CV-plus	in	wave	1	or	wave	2		

1	if	we	ask	CV-Plus	in	wave	1	of	the	survey	(survey	version	A)

0	otherwise

2. VAR_ORDER	[1,	6]:	Order	of	CV-minus

1. Order:	CV-minus,	EV-plus,	EV-minus

2. Order:	CV-minus,	EV-minus,	EV-plus

3. Order:	EV-plus,	CV-minus,	EV-minus

4. Order:	EV-plus,	EV-minus,	CV-minus

5. Order:	EV-minus,	CV-minus,	EV-plus

6. Order:	EV-minus,	EV-plus,	CV-minus

3. LS_FIRST	[0,	1]:	whether	we	ask	the	option	that	mentions	the	lumpsum	amount	first	

1	if	we	ask	the	option	with	the	lumpsum	amount	first

0	otherwise

4. SMALLTOLARGE	[0,	1]:	the	order	in	which	we	present	the	changes	in	SS

1	if	we	show	the	ΔSS	from	smallest	to	largest	

0	otherwise

5. LS_STARTVALUE		[1,	3]:	Size	of	the	first	lumpsum	amount	shown

1. low	starting	value	($10,000)	

2. medium	starting	value	($20,000)	

3. high	starting	value	($30,000)	

6. ORDER_STOCK	[1,2]:	Order	of	choices	in	Q.3.2.3

1. List	“single	company	stock”	first

2. List	“stock	mutual	fund”	first

If	VAR_ORDER=1	OR	VAR_ORDER=2,	we	set	CVM_ORDER	=	1

If	VAR_ORDER=3	OR	VAR_ORDER=5,	we	set	CVM_ORDER	=	2

If	VAR_ORDER=4	OR	VAR_ORDER=6,	we	set	CVM_ORDER	=	3

If	VAR_ORDER=3	OR	VAR_ORDER=4,	we	set	EVP_ORDER	=	1

If	VAR_ORDER=1	OR	VAR_ORDER=6,	we	set	EVP_ORDER	=	2

If	VAR_ORDER=2	OR	VAR_ORDER=5,	we	set	EVP_ORDER	=	3

If	VAR_ORDER=5	OR	VAR_ORDER=6,	we	set	EVM_ORDER	=	1

If	VAR_ORDER=2	OR	VAR_ORDER=4,	we	set	EVM_ORDER =	2

If	VAR_ORDER=1	OR	VAR_ORDER=3,	we	set	EVM_ORDER	=	3

A-28
B.	Survey	waves	and	versions	A	and	B

We	fielded the	survey	in	two	waves.	We	have	two	versions,	version	A	and	version	B.		The	only	difference

between these	versions	is	that	in	version	B	the	order	of	sections	2	and	6	is	switched	(compared	to	version	A).		

The	survey	instrument	below	is	for	version	A.			The	following	table	specifies	the	order	for	the	sections	for	

versions	A	and	B.

Version	A Version	B

Wave	1 Section	1	(“Intro	wave	1”)	 Section	1	(“Intro	wave	1”)	

Section	2	(“CVPlus”)	 Section	6	(“Other	tradeoffs”)	

Section	3	(“Background”)	 Section	3	(“Background”)	

Section	4	(“Close	wave	1”)	 Section	4	(“Close	wave	1”)	

Wave	2 Section	5	(“Intro	wave	2”)	 Section	5	(“Intro	wave	2”)	

Section	6	(“Other	tradeoffs”)	 Section	2	(“CVPlus”)	

Section	7	(“No	Political	Risk”)	 Section	7	(“No	Political	Risk”)	

Section	8	(“Close	wave	2”)	 Section	8	(“Close	wave	2”)	

Respondents	with	the	VERSION_A=1	are given	version	A	and	respondents	with	VERSION_A=0	are given	version	

B.	

C.	Syntax

Note:

• The	number	between	parentheses	before	a	choice	box	was not	displayed	on	the	screen.		It	only	

indicates	how	that	choice	should	be	coded.

• Comments	between	square	brackets	are	programming	notes.

• Variable	names	between	square	brackets	were replaced	by	the	contents	of	the	variable.

A-29
D. Lookup	tables	(matrices)	for	Lumpsum	amounts	and	BEN_DEFAULT

The	following	tables	show	lumpsum (one-time	Social	Security	payment)	amounts	for	three	different	starting	

values:	low,	medium	and	high,	which	are	randomized	as	mentioned	above.	

10,000 4,000 2,000 1,000 500 Row 1

1,500 Row 2

3,000 2,500 Row 3

3,500 Row 4

7,000 5,500 4,750 Row 5

6,250 Row 6

8,500 7,750 Row 7

9,250 Row 8

30,000 20,000 15,000 12,500 Row 9

17,500 Row 10

25,000 22,500 Row 11

27,500 Row 12

60,000 40,000 35,000 Row 13

50,000 Row 14

100,000 80,000 Row 15

200,000 Row 16

Col. 1 Col. 2 Col. 3 Col. 4 Col. 5

Table1: Lumpsum amounts for LOW starting value

We put	the	values	of	the	table	1	in	the	16x5	matrix	LS_LOW.	The	ith row	and	jth column	of	this matrix is	denoted	

by	LS_LOW[i,j]	

A-30
20,000 4,000 2,000 1,000 500 Row 1

1,500 Row 2

3,000 2,500 Row 3

3,500 Row 4

10,000 7,000 5,500 Row 5

8,500 Row 6

15,000 12,500 Row 7

17,500 Row 8

60,000 30,000 25,000 22,500 Row 9

27,500 Row 10

40,000 35,000 Row 11

50,000 Row 12

100,000 80,000 70,000 Row 13

90,000 Row 14

200,000 150,000 Row 15

500,000 Row 16

Col. 1 Col. 2 Col. 3 Col. 4 Col. 5

Table2: Lumpsum amounts for MEDIUM starting value

We put	the	values	of	the	table	2	in	the	16x5	matrix	LS_MED.	The	ith row	and	jth column	of	this matrix is	denoted	

by	LS_MED[i,j]	

A-31
30,000 10,000 4,000 2,000 1,000 Row 1

3,000 Row 2

7,000 5,500 Row 3

8,500 Row 4

20,000 15,000 12,500 Row 5

17,500 Row 6

25,000 22,500 Row 7

27,500 Row 8

60,000 40,000 35,000 32,500 Row 9

37,500 Row 10

50,000 45,000 Row 11

55,000 Row 12

100,000 80,000 70,000 Row 13

90,000 Row 14

200,000 150,000 Row 15

500,000 Row 16

Col. 1 Col. 2 Col. 3 Col. 4 Col. 5

Table3: Lumpsum amounts for HIGH starting value

We put	the	values	of	the	table	3	in	the	16x5	matrix	LS_HIGH.	The	ith row	and	jth column	of	this matrix is	denoted	

by	LS_HIGH[i,j]	

A-32
The	following	code	was	used	for the	BEN_DEFAULT	value	table:	

EDUCATION 1	=	less	than	high	school	(<	12	years)

	 	 2=	high	school	degree	(12	years),

	 	 3=some	college	(>12	and	<	16	years)

	 	 4	=	college	degree	(=	16	years)

	 	 5	=	more	than	college	(>16	years)

The	variable	BEN_DEFAULT	was	created	and	set	to the	following	values:

Education Gender Marital	status BEN_DEFAULT

1	(<12	years) Female Married	 660

2	(12	years) Female Married	 790

3	(>	12	years	and	<16	years) Female Married	 850

4	(16	years) Female Married	 830

5	(>16	years) Female Married	 850

1	(<12	years) Male Married	 1070

2	(12	years) Male Married	 1280

3	(>	12	years	and	<16	years) Male Married	 1270

4	(16	years) Male Married	 1330

5	(>16	years) Male Married	 1360

1	(<12	years) Female Not	Married 920

2	(12	years) Female Not	Married 1080

3	(>	12	years	and	<16	years) Female Not	Married 1120

4	(16	years) Female Not	Married 1230

5	(>16	years) Female Not	Married 1160

1	(<12	years) Male Not	Married 1040

2	(12	years) Male Not	Married 1190

3	(>	12	years	and	<16	years) Male Not	Married 1130

4	(16	years) Male Not	Married 1350

5	(>16	years) Male Not	Married 1380

A-33
INITIALIZATIONS:	BEGINNING	OF	SURVEY	

_________________________________________________________________________________________

[NOTE:	THIS	IS	THE	BEGINNING	OF	THE	SURVEY.]	

Preloaded	variables	from	previous	waves	that	are	used	in	skip	patterns	are:

GENDER				 	 	1	=		male;	2	=	female

AGE	 	 		 	The	respondent’s	current	age	in	years

HIGHESTEDUCATION	 	1	Less	than	1st	grade

2	1st, 2nd, 3rd, or	4th	grade

3	5th	or	6th	grade

4	7th	or	8th	grade

5	9th	grade

6	10th	grade

7	11th	grade

8	12th	grade	NO	DIPLOMA

9	HIGH	SCHOOL	GRADUATE	high	school	DIPLOMA		or	the	equivalent	(For	example:	GED)

10	Some	college	but	no	degree

11	Associate	degree	in	college	Occupational/vocational	program

12	Associate degree	in	college	Academic	program

13	Bachelor's	degree	(For	example:	BA,AB,BS)

14	Master's	degree	(For	example:	MA,MS,MEng,MEd,MSW,MBA)

15	Professional	School	Degree	(For	example:	MD,DDS,DVM,LLB,JD)

16	Doctorate	degree	(For	example:	PhD,EdD)

		

For	missing	values/	answers	for	variables	mentioned	above,	the	questions	were	asked	again	in	the	beginning	of	wave	1.	

The	variable	“MARRIED”	was	collected	regardless	of	whether	data	was	available.	The	following	question	was	asked	to	all	

respondents	in	the	beginning	of	wave	1.	

[MARRIED]	Married	

Are	you	currently	married?	

(0)		Not	Currently	Married

(1)			Currently	married	

Other	preloaded	questions include:

PIA_EST	 	 Estimated	PIA	(Social	Security	Primary	Insurance	Amount)	based	on	previous	questions	asked	about	

earnings	history.		So	this	is	the	Social	Security	benefit	one	would	get	if	one	claimed	at	full	retirement	age.

WORK_FOR_PAY 1	YES;		2	NO.		This	is	a	standard	variable	that	records	whether	the	respondent	has	ever worked	for	pay.

If	this	variable	was missing	for	any	respondent	then	they	were	asked	the	following	question:

[WORK_FOR_PAY]	Work	For	Pay	

Did	you	work	for	pay	more	than	10	years?

(1)	 Yes	

(2)			No	

A	new	data-only	variable	SPOUSE was	created:	

Set	SPOUSE	=	“husband”	if	GENDER==“female”

Set	SPOUSE	=	“wife”	if	GENDER==“male”

A	new	data	only	variable	FLAGHYPO	was	created	and	set	FLAGHYPO=0.

A	new	data	only	variable	USE_DEFAULT	was	created	and	set	USE_DEFAULT=0.

A-34
SECTION	1:	INTRODUCTION	TO	WAVE	1	

[NOTE:	THIS	SECTION	FINDS THE	SOCIAL	SECURITY	STATUS,	ELIGIBILITY,	AND	CLAIM	AGE	OF	RESPONDENTS.	IT	ESTIMATES	

THE	SOCIAL	SECURITY	BENEFIT	AMOUNT	OF	RESPONDENTS	BASED	ON	THEIR	AGE,	EARNINGS	HISTORY,	AND	YEARS	IN	THE	

WORK	FORCE	AND	PROVIDES	RESPONDENTS	AN	OPPORTUNITY	TO	CORRECT	THE	ESTIMATE. THIS	SECTION	WAS	ASKED	OF	

ALL	RESPONDENTS.]		

[WAIT_WARNING]		Wait	Warning

The	page	after	this	may	take	several	seconds to	load.	Please	click	the	next	button	now,	and	do	not	click	your	browser's	back	

button	while	the	page	is	loading.	Thank	you.	

Q.1.1:	[INTRODUCTION	TO	SECTION	1]

We	are	interested	in	understanding	how	and	when	people	would	like	to	receive	their	Social	Security	benefits.	

In	this	survey,	we	sometimes	ask	questions	that	are	difficult	to	answer	exactly.	Please	take	time	to	consider	the	questions	and	

give	us	your	best	guess even	if	you	do	not	know	the	exact	answer.		Having	your	best	guess	will	be	very	helpful	to	us.

Thank	you	very	much	for	your	participation!

		

Q.1.2:	[SS_STATUS]	Social	Security	Status

In	this	survey,	we	mean	by	“Social	Security	benefits”	any	benefits	that	you	yourself	receive	or	will	receive	from	the	Social	

Security	program,	including	retiree,	disability,	spouse,	or	survivor	benefits.

Which	of	the	following	statements	best	describes	you?

(1)	 I	receive	Social	Security	benefits	now.

(2)	 I	don’t	receive	Social	Security	benefits	now	but,	under	current	law,	I	will	be	eligible	to	receive	them	in	the	

future.		

(3)	 I	will	never	be	eligible	under	current	law	to	receive	Social	Security	benefits.	

[Create	new	variable	SS_STATUS_ORIG,	and	set	SS_STATUS_ORIG=SS_STATUS]

[ASK	IF	SS_STATUS=3]

Q.1.3:	[SS_ELIG]	Social	Security	Eligibility

Why	do	you	think	you	will	never be	eligible	to receive Social	Security	benefits?

(1)	 My	main	job	was/is	not	covered	by	Social	Security.

(2)	 I	don’t	have	or	will	not	have	a	sufficient	work	history	to	become	eligible	for	Social	Security	benefits.	

(3)	 I	do	not	think	Social	Security	will	be	around	by	the	time	I	would	start	claiming	benefits

(4)	 Other:	________________	[Give	open-ended	text	box]

[SHOW	IF	SS_ELIG=3]

Q.1.4:	[SS_AROUND]	Assume	Social	Security	will	still	be	around

Please	answer	the	questions	in	this	survey	assuming	that	current	Social	Security	rules	still	apply	when	you	first	claim	

Social	Security	benefits.		Thank	you.

[IF	SS_ELIG=3,	SET	SS_STATUS=2]

[ASK	IF	SS_STATUS=3]

Q.1.5:	[SSEL_SPOUSE] Eligible	For	Social	Security	Based	on	Spouse

People who are	not	eligible	to	receive Social	Security	based	on	their	own	work	history	may receive	Social	Security	benefits	

based	on	the	earnings	history	of	their	spouse,	late	spouse,	or	ex-spouse.

Do	you	think	you	may	be	eligible	to	receive benefits	based	on	the	past	or	future	earnings	of	your	[SPOUSE],	late	

[SPOUSE],	ex-[SPOUSE],	or	perhaps	a	future	[SPOUSE]?	

A-35
(1)	 Yes

(2)	 No

[IF	SSEL_SPOUSE==1 AND	SS_STATUS==3: SET	SS_STATUS=2]

[IF	(SSEL_SPOUSE==2 OR	SSEL_SPOUSE=MISSING) AND	SS_STATUS==3: SET	FLAGHYPO=1]

[ASK	IF	FLAGHYPO=1]		

[If	FLAGHYPO=1	AND	AGE>=62,	SET	SS_STATUS=1]	/*	if	eligible,	they	would	be	claiming	*/

[If	FLAGHYPO=1	AND	AGE<62,	SET	SS_STATUS=2]		/*	if	eligible,	they	would	not	yet	have	claimed	*/

[If	FLAGHYPO=1,	SET	CLAIM_AGE=62]

[If	FLAGHYPO=1,	SET	BEN_EST=BEN_DEFAULT]

Q.1.6:	[HYPOELIGIBLE] Hypothetically	Eligible

Even	though	we	understand	that	you	are	not	eligible	to	receive	Social	Security	benefits,	we	would	like	to	ask	you	to	complete	

this	survey	assuming	you	would	be	eligible.		In	other	words,	please	answer	in	this	survey	what	you	would	have	done	or	chosen	

if	you	would	be	eligible	for	Social	Security	benefits.	

The	typical	Social	Security	benefit	for	[If	MARRIED=1,	insert “a	married”;	if	MARRIED=0,	insert “an	unmarried”] [If	

GENDER=“male”	insert “man”;	if	GENDER=“female”	insert “woman”] with	[insert	the	respondent’s	educational	attainment	

based	on	the	categories	in	EDUCATION] is	$[BEN_DEFAULT]	per	month.

For	the	purpose	of	this	survey,	let's	assume	that	you	[If	SS_STATUS==2,	insert	“are	supposed	to”] get	a	Social	Security	benefit	

of	$[BEN_DEFAULT]	per	month,	and	that	you	[If	SS_STATUS==1,	insert	“started	receiving”;	If	SS_STATUS==2,	insert	“would	

start	receiving”] Social	Security	benefits	at	age	62.

[If	FLAGHYPO=1,	GO	TO	SECTION	2]

Q.1.7.1:	[CLAIM_AGE]	Social	Security	benefits	claim	age

[ASK	IF	SS_STATUS==1 (RECEIVING	BENEFITS):]

At	what	age	did	you	start	receiving	Social	Security	benefits?

At	age:	_________

Q.1.7.2: [CHECKCLAIM_AGE1	] CHECK	CLAIM	AGE	

[ASK	IF	SS_STATUS==1 AND	CLAIMING	AGE	> CALCULATED AGE]	

		

You	told	us	earlier	that	you	are	already	receiving	Social	Security	benefits.	Therefore,	the	age	at	which	you started	receiving	

Social	Security	benefits	cannot	be higher	than	your	current	age.	Please	go	back	and	revise	your	answer.

Q.1.7.3:	[CLAIM_AGE]	Social	Security	benefits	claim	age

[ASK	IF	SS_STATUS==2 (NOT	RECEIVING	BENEFITS	YET)]:

At	what	age	do	you	plan	to	start	receiving	Social	Security	benefits?

At	age:	__________

Q.1.7.4:	[CHECKCLAIM_AGE1 ]	Check	Claim	Age

[ASK	IF	SS_STATUS==2 (NOT	RECEIVING	BENEFITS	YET) AND	CLAIMING AGE < CALCULATED AGE]:

You	told	us	earlier	that	you	are	not	currently	receiving	Social	Security	benefits.	Therefore,	the age	at	which	you	plan	to	start	

receiving	Social Security	benefits	cannot	be	lower	than	your	current	age.	Please	go	back and	revise	your	answer.

[ALWAYS: (THE	CODE	BELOW	ENSURES	CLAIM_AGE WILL	NEVER	BE	MISSING)]

[SET	CLAIM_AGE_ORIG=CLAIM_AGE]

[SET	CLAIM_AGE=MAX(AGE+1, 62) IF	CLAIM_AGE=MISSING	AND SS_STATUS==2]

[SET	CLAIM_AGE=MIN(AGE, 62) IF	CLAIM_AGE=MISSING	AND SS_STATUS==1]

[NOTE: THE SERIES OF QUESTIONS 1.8.1-1.8.6 BELOW WERE ONLY ASKED IF THE VARIABLE PIA_EST WAS MISSING (AND 

THEREFORE COULD NOT BE PRELOADED). THE VARIABLE PIA_EST CONTAINS THE ESTIMATED SOCIAL SECURITY MONTHLY 

BENEFITS IF THE PERSON CLAIMS BENEFITS AT THE FULL RETIREMENT AGE, AND IS BASED ON A SERIES OF QUESTIONS ASKED 

A-36
PREVIOUSLY IN THE AMERICAN LIFE PANEL ABOUT THE PERSON’S WORK HISTORY. THE SERIES OF QUESTIONS AND THE SKIP 

LOGIC BELOW IS IDENTICAL TO THE EARLIER SERIES OF QUESTIONS ASKED TO ESTIMATE PIA_EST.] 

[IF	WORK_FOR_PAY	≠ “YES”	OR	PIA_EST	≠ missing,	Skip	Q1.8.1	through	Q.1.8.6]

Q.1.8.1:	[YEAR_START_WORK]	Year	Start	Work	For	Pay	

																	In	what	year	did	you	first	start	to	work	for	pay?	

[Note	to	programmer:	Insert	“a	drop	down	box	from	1900	to	2011”]

[ASK	IF	YEAR_	START_WORK	< YEAR_OF_BIRTH	+	14]

Q.1.8.2:	[WORKED_TO_EARLY]	Start	Working	Before	14

																	You	said	you	started	working	before	you	were	14	years	old.	 You	indicated	that	you	started	working	before	you	were	

14	years	old.	For	what	follows,	we	will	just	assume	that	pay	from	age	14	on	counts.		If	you	believe	you	made	an	error	

in	entering	the	date	you	began	working,	you	may	go	back	and	change	it.

Q.1.8.3:	[INCOME_INTRO]	Income	Introduction	

																		We	would	now	like	to	ask	you	some	more	information	about	the	period	when	you	started	working	for	pay	(in																																											

[YEAR_START_WORK])	up	to	now.

[Note	to	Programmer:	LOOP	FROM	1	TO	[number	of	questions	INCOME_CAT]	DO:			/*	number	of	periods	loop	*/

Q.1.8.4:	[INCOME_NOT_WORK]	Income	Not	Work

														 [Insert “[income	cat	start	year	fill]	-	[income	cat	end	year	fill]”] Was	there	ever	a	time	when	you	did	not	work	

in	the “[income	cat	start	year	fill]	-	[income	cat	end	year	fill]” period?

																	(1)	 Yes

																	(2)	 No		

[ASK	IF	INCOME_NOT_WORK=YES]

		Q.1.8.5:	[INCOME_NOT_WORK_HOW_LONG]	Income	Not	Work	Months

																	[Insert “[income	cat	start	year	fill]	-	[income	cat	end	year	fill]”] How	many	months	in	total do	you																												

estimate	not	working	for	pay	in	the	“[income	cat	start	year	fill]	-	[income	cat	end	year	fill]”	period?

																	(1)	 ________________	[Give	open-ended	text	box	allowing	only	integers]		

[ASK IF INCOME_CAT fill amount 1 = and INCOME_CAT fill amount 2 = and INCOME_CAT fill amount 3]

Q.1.8.6:	[INCOME_CAT]	Income	categories

																		[Insert “[income	cat	start	year	fill]	-	[income	cat	end	year	fill]”] Could	you	please	give	us	an	estimate	of	how	

much	you	were making	on	average	per	year	in	the	“[income	cat	start	year	fill]	-	[income	cat	end	year	fill]”

period “[fill	for	income	cat	question]?	

																		(1)  More	than	$[INCOME_CAT	fill	amount	1]

																		(2)	 Between	$[INCOME_CAT	fill	amount	2]	and	$[INCOME_CAT	fill	amount	1]

																		(3)	 Between	$[INCOME_CAT	fill	amount	3]	and	$[INCOME_CAT	fill	amount	2]

																		(4)	 Less	than	$[INCOME_CAT	fill	amount	3]

End	Do		/*	End	of	number	of	periods	loop	*/

[Note	 to	 programmers:	 based	 on	 the	 responses	 to	 Q1.8.1	 through	 Q1.8.6,	 calculate	 PIA_EST using	 the	 same	

algorithm	as	used	in	the	earlier	ALP	module]	

[NOTE TO PROGRAMMERS: PLEASE USE THE VARIABLE PIA_EST (PRELOADED OR CALCULATED FROM Q.1.8.1-Q.1.8.6) AND 

THE VARIABLE CLAIM_AGE ASKED ABOVE TO COMPUTE THE ESTIMATED MONTHLY SOCIAL SECURITY BENEFITS FOR EACH 

RESPONDENT IF THEY START CLAIMING AT CLAIM_AGE. PLEASE USE THE SOCIAL SECURITY AGE ADJUSTMENT RULES 

A-37
APPROPRIATE FOR THE BIRTH COHORT THAT THE RESPONDENT BELONGS TO IN THIS CALCULATION. PLEASE NAME THE 

RESULTING VARIABLE BEN_EST_ORIG1.] 

IF BEN_EST_ORIG1 ≥ 200, THEN SET BEN_EST_ORIG=BEN_EST_ORIG1.

IF BEN_EST_ORIG1 < 200, THEN SET BEN_EST_ORIG=200

IF BEN_EST_ORIG1 = MISSING, THEN SET BEN_EST_ORIG=MISSING. 

[SHOW IF SS_STATUS==1 (CURRENTLY RECEIVING BENEFITS) AND (AGE ≥ 60 OR BEN_EST_ORIG=MISSING)]

Q.1.9:	[BEN_LEVEL]	Social	Security	Benefits	Level

Approximately	how	much	are	your	monthly	Social	Security	benefits?

Even	if	you	do	not	know	exactly,	please	give	your	best	guess.	

(As	before,	please	report	any	Social	Security	benefits	paid	to	you	yourself	(by	check	or	direct	deposit),	not	benefits	paid	to	any	

other	member	in	your	household).

$________	[NUMBER	BOX	WITH	RANGE	0-3500]	per	month.

[Note	to	programmers:	please	record	the	first	answer	to	this	question	in	BEN_LEVEL1.	

[Display If	BEN_LEVEL1	(social	security	benefits	level)	>	3500]

[checkBEN_LEVEL3500]	Social	Security	Benefits	Level	Check	>	3500

Due	to	how	Social	Security	calculates	your	benefits,	it	is	very	unlikely	that	your	monthly	benefit	will	be	this	high.	

Please	go	back	and	change	your	answer	to something	between	$0	and	$3500	per	month.

[Display If	BEN_LEVEL1	(social	security	benefits	level)	<	0]	

[checkBEN_LEVEL0] Social	Security	Benefits	Level	Check	<0

Monthly	Social	Security	benefits	cannot	be	negative.	Please	go	back	and	change	your	answer	to	something	between	

$0	and	$3500	per	month.	

The	final	response	for	all	respondents	should	be	recorded	in	the	variable	BEN_LEVEL.]

[SET BEN_EST=BEN_LEVEL]

[GO	TO	Q.1.13,	“USE_DEFAULT”]

[Ask	if	BEN_EST_ORIG	≠ missing:]

Q.1.10:	[BEN_OVERRIDE,	BEN_NEW]	ESTIMATED	SOCIAL	SECURITY	BENEFITS	

Based	on	the	information	you	have	provided	to	us about	your	own	earnings	history,	we	estimate	that,	under	current	Social	

Security	Law,	you	are	supposed	to	get a	Social	Security	retirement	benefit	of	approximately	$[BEN_EST_ORIG]	per	month	if	you	

claim	benefits	at	age	[CLAIM_AGE].	

Think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	Social	Security	will	

adjust	future	dollar	amounts	for	inflation).

Our	estimate	does	not	take	into	account	Social	Security	benefits	you	may	receive	based	on	the	earnings	of	a	past or current	

spouse.

Do	you	think	our	estimate	is	about	right	for	benefits	you	yourself are	supposed	to	get	from	the	Social	Security	program	whether	

these	benefits	are	retiree,	disability,	spouse,	or	survivor	benefits?	

(1)	 Yes,	I	believe	the	Social	Security	benefits	I	am	supposed	to	get	are	roughly	$[BEN_EST_ORIG]	per	month.	

(2)	 No,	I	believe	the	Social	Security	benefits	I	am	supposed	to	get	are	roughly	$________	[NUMBER	BOX	WITH	

RANGE	0-6000,	variable	name:	BEN_NEW] per	month.

A-38
The	instrument	made sure	that	those	who	check	option	2	fill	in	the	number	box;	and	they	were reminded once	if	they	left it	

blank. The	first	answer	to	their	question	was	recorded in	BEN_NEW1.		

[DISPLAY	IF	BEN_NEW1	(benefits	new)	>	6000]

[checkBEN_NEW6000]		Benefits	New	Check	>	6000

Due	to	how	Social	Security	calculates	your	benefits,	it	is	very	unlikely	that	your	monthly	benefit	will	be	this	high.	

Please	go	back	and	change	your	answer	to something	between	$0	and	$6000	per	month.	

[DISPLAY	If	BEN_NEW1	(benefits	new)	<	0	]	

[checkBEN_NEW0]		Benefits	New	Check	<	0

Monthly	Social	Security	benefits	cannot	be	negative.	Please	go	back	and	change	your	answer	to	something	between	

$0	and	$6000	per	month.

[DISPLAY		IF	BEN_OVERRIDE==2 and	BEN_NEW	==	missing]

[checkBEN_NEWempty]		Benefits	New	Check	Value	Empty

You	selected the	second	option	but	did	not	fill in	a	value.	Your	answers	are	important	to	us.	Please	go	back	and	fill	in	a	

value.

The	final	response	for	the	number	box	for	each	respondent	was	recorded	in	the	variable	BEN_NEW.]

IF	BEN_OVERRIDE==1,	SET	BEN_EST=BEN_EST_ORIG

IF	BEN_OVERRIDE==2,	SET	BEN_EST=BEN_NEW

[ASK	IF	BEN_OVERRIDE==2]

Q.1.11	[OVERRIDE_WHY]	Reason	why	R	changed	our	estimate

Thank	you	for	correcting	our	estimate	of	your	Social	Security	benefits.		We	are	interested	in	knowing	what	this	correction	was	

based	on.		Please	check	all	boxes	that	apply.

(1)	 I	know	the	amount	of	Social	Security	that	I	am	supposed	to	get	from	my	annual	Social	Security	mailing

(2)	 I	included	Social	Security	survivor	benefits that	you	did	not	include	in	your	estimate

(3)	 I	included	Social	Security	spousal	benefits	that	you	did	not	include	in	your	estimate

(4)	 I	included	Social	Security	disability	benefits	that	you	did	not	include	in	your	estimate

(5)	 Your	estimate	simply didn’t	appear	right	to	me

(6)	 Other	_______________________	[Give	open-ended	text	box]

[Note	to	programmer:	Respondent	is	allowed to	select	multiple	options]

[Ask	if	BEN_EST_ORIG=missing:]

Q.1.12:	[BEN_EST_OWN]	

In	this	question,	we	would	like	get	your	estimate	of	the	Social	Security	benefits	you	are	supposed	to	get	under	current	law	if	

you	claim	benefits	at	age	[CLAIM_AGE].

As	a	point	of	reference,	the	typical	Social	Security	benefit	for	[If	MARRIED=1,	insert “a	married”;	if	MARRIED=0,	insert “an	

unmarried”] [If	GENDER=“male”	insert “man”;	if	GENDER=“female”	insert “woman”] with	[insert	the	respondent’s	

educational	attainment	based	on	the	categories	in	EDUCATION] is	$[BEN_DEFAULT]	per	month.	

Please	give	your	answer	in	terms	of	what	a	dollar	buys	you	today	(because	Social	Security	will	adjust	future	dollar	amounts	for	

inflation).	Please	report	benefits	you yourself are	supposed	to	get	from	the	Social	Security	program	whether	these	benefits	are	

retiree,	disability,	spouse,	or	survivor	benefits?	

A-39
Even	if	you	do	not	know	exactly,	please	give	your	best	guess.	

I	believe	the	Social	Security	benefits	I	am	supposed	to	get	are	roughly	$________	[NUMBER	BOX	WITH	RANGE	0-6000,	variable	

name:	BEN_EST_OWN] per	month	if	I	claim	benefits	at	age	[CLAIM_AGE].

[DISPLAY	IF BEN_EST_OWN1	(benefits	estimate	own)	>	6000]

[checkBEN_EST_OWN6000]		Benefits	Estimate	Own	Check	>	6000

Due	to	how	Social	Security	calculates	your	benefits,	it	is	very	unlikely	that	your	monthly	benefit	will	be	this	high.	Please	go	back	

and	change	your	answer	to	something	between	$0	and	$6000	per	month.

[DISPLAY IF BEN_EST_OWN1	(benefits	estimate	own)	<0]

[checkBEN_EST_OWN0		benefits	estimate	own	check	<	0]	

Monthly	Social	Security	benefits	cannot	be	negative.	Please	go	back	and	change	your answer	to	something	between	$0	and	

$6000 per	month.

The	final	response	for	the	number	box	for	all	respondents	were recorded	in	the	variable	BEN_EST_OWN.]

[Set	BEN_EST=BEN_EST_OWN]

[Ask	if	BEN_EST=missing]		

Q.1.13	[USE_DEFAULT,	BEN_EST_RAW]:	Tell	respondent	that	we	are	using	a	default	amount	for	his/her	SS	benefits

The	typical	Social	Security	benefit	for	[If	MARRIED=1,	insert “a	married”;	if	MARRIED=0,	insert “an	unmarried”] [If	

GENDER=“male”	insert “man”;	if	GENDER=“female”	insert “woman”] with	[insert	the	respondent’s	educational	attainment	

based	on	the	categories	in	EDUCATION] is	$[BEN_DEFAULT]	per	month.	For	the	rest	of	the	survey,	let's	assume	that	you	[If	

SS_STATUS==2,	insert	“are	supposed	to”] get	a	Social	Security	benefit	of	$[BEN_DEFAULT]	per	month.

[Ask	if	BEN_EST<200]		

Q.1.14 [USE_DEFAULT,	BEN_EST_RAW]:	Tell	respondent	that	we	are	using	a	default	amount	for	his/her	SS	benefits

Thank	you	for	providing	us	with	your	estimated	Social	Security	benefits	of	$[BEN_DEFAULT]	per	month.	Some	of	the	questions	

that	follow	only	apply	for	Social	Security	benefits	of	at	least	$200	per	month.	For	purposes	of	answering	the	remaining	

questions	in	this	survey,	we	would	therefore	like	to	ask	you	to	please	assume	that	your	Social	Security	benefits	would	be	$200	

per	month.

[Set	BEN_EST_RAW=BEN_EST]

[Set	BEN_EST=BEN_DEFAULT]

[Set	USE_DEFAULT=1]

A-40
SECTION	2:		CV-PLUS	ANNUITY	TRADE-OFF	QUESTIONS	

_______________________________________________________________________________________________________

[NOTE:		A	COMPENSATING	VARIATION	(CV-PLUS)	MEASURE	IS	ELICITED	IN	THIS	SECTION.	HERE	RESPONDENTS	ARE	ASKED	TO	

MAKE	A	TRADE-OFF	CHOICE	BETWEEN	REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY	BENEFIT	LEVEL	AND THEIR	

EXISTING	SOCIAL	SECURITY	MONTLY	BENENIT	AMOUNT	MINUS	A	VARYING	AMOUNT	(SS_VARAMT)	AND	AN	ADDITION	OF	A	

ONE-TIME	PAYMENT	/LUMPSUM	AMOUNT	(LS_AMT).]

Q.2.1.	[TRADEOFF	–	INTRO].		Introduction	to	annuity	tradeoff	questions

In	the	next	few	questions,	we	are	going	to	ask	you	to	make	a	number	of	choices about	Social	Security	benefits.		

Please	assume	that	all	amounts	shown	are	after	tax	(i.e.,	you	don’t	owe	any	tax	on	any	of	the	amounts	we	will	show	

you).

Think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	Social	Security	

will	adjust	future	dollar	amounts	for	inflation).

[WAIT_WARNING]		Wait	Warning

The	page	after	this	may	take	several	seconds	to	load.	Please	click	the	next button	now,	and	do	not	click	your	browser's	back	

button	while	the	page	is	loading.	Thank	you.

[Notes	to	programmers:

There	are	two	nested	loops	in	this	section.

1. The	outer	loop	has	2,	3	or	4	iterations	and	loops	over	the	change	in	the	Social	Security	amount:	SS_VARAMT.		The	

outerloop	is	indexed	by	the	variable	i.				(The	number	of	iterations	depends	on	the	respondent’s	estimated	Social	

Security	benefits.		For	most	respondents	there	are	four	iterations,	but	the	number	is	lower	for	a	few	respondents	

with	very	low	benefit	amounts).	

2. The	inner	loop	has	4	or	5	iterations	and	loops	over	the	amount	of	the	one-time	payment	offered: LS.		The	inner	

loop	is	indexed	by	the	variable	j.		(The	number	if	iterations	is	generally	4.	It	is	only	5	when	we	set	SS_VARAMT=100)

Let	the	variable	SS_VARAMT	be	a	vector	of	length	N_VARAMT.		Let	SS_VARAMT[i]	denote	the	ith element	of	SS_VARAMT.

Initialization	of	SS_VARAMT

If	BEN_EST	<	300

Set	N_VARAMT=2

SS_VARAMT={100,	BEN_EST}

Endif

If	BEN_EST	≥	300	AND	BEN_EST<600

Set	N_VARAMT=3

Create	the	vector	AMT={200,	300,	400}

Delete	elements	of	AMT	that	are	greater	than	(BEN_EST-100)

Pick	at	random	one	element	of	AMT,	call	this	element	AMTRND

Set	SS_VARAMT={100,	BEN_EST,	AMTRND}

Endif

If	BEN_EST	≥	600

Set	N_VARAMT=4

A-41
Create	the	vector	AMT={200,	300,	400,	600,	700,	800,	900,	1000,	1100,	1200,	1300,	1400,	1500,	1600,	1700,	1800,	

1900,	2000}				/*	Note	that	the	amount	500	is	missing	from	this	list	on	purpose	*/

Delete	elements	of	AMT	that	are	greater	than	(BEN_EST-100)

Pick	at	random	one	element	of	AMT,	call	this	element	AMTRND

Set SS_VARAMT={100,	500,	BEN_EST,	AMTRND}

Endif

If	SMALLTOLARGE==1

Sort	the	elements	if	SS_VARAMT	from	smallest	to	largest	(i.e.	such	that	SS_VARAMT[i]	<	SS_VARAMT[i+1]	for	all	i)

Else

Sort	the	elements	if	SS_VARAMT	from	largest	to	smallest	(i.e.	such	that	SS_VARAMT[i]	>	SS_VARAMT[i+1]	for	all	i)

Endif

Initialization	of	LS_AMT

If	LS_STARTVALUE ==1

Set	the	16x5	matrix	LS_AMT=LS_LOW

Elseif	LS_STARTVALUE ==3

Set	the	16x5	matrix	LS_AMT=LS_HIGH

Else

Set	the	16x5	matrix	LS_AMT=LS_MED

Endif

Start	of	the	nested	loops	that	ask	CV-PLUS

For	i=1	to	N_VARAMT																										/*	START	OF	THE	OUTER	LOOP	FOR	CVPLUS	*/

If	SS_VARAMT=100

Set	N_LS=5

Else

Set	N_LS=4

Endif

[Show	if	i>1]

Q.2.2:[CV-Plus	Roadmap]	Roadmap	to	new	series	of	CV-plus	tradeoff	question.

Now	we	would	like	to	show	a	similar	series	of	questions	about	choices	between	two	money	amounts,	but	the	amount	of	

the	Social	Security	benefits	in	one	of	the	options	is	different	from before.

As	before,	please	assume	that	all	amounts	shown	are	after	tax	(i.e.,	you	don’t	owe	any	tax	on	any	of	the	amounts	we	will	

show	you).

As	before,	please	think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	

Social	Security	will	adjust	future	dollar	amounts	for	inflation).

A-42
Q.2.3:	[CVPLUS]:		CV-PLUS	TRADE-OFF	QUESTION

Set	ROW=1

For	j=1	to	N_LS																								/*	START	OF	THE	INNER	LOOP	FOR	CVPLUS*/

[If	j	=	1,	Display:	]

In	this	question,	we	are	going	to	ask	you	to	make	a	choice	between	two	money	amounts.	

[Else,	Display:]

Now	we	ask	you	the	same	question	but	with	a	different	amount	for	the	one-time	payment.

[Endif]

Please	click	on	the	option	that	you	would	prefer.	

[SHOW	IF	MARRIED==1:] Benefits	paid	to	your	[SPOUSE]	will	stay	the	same	for	either	choice.

Suppose	Social	Security	gave	you	a	choice	between:

[If	LS_FIRST=0,	Display:	]

(1)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”]	Social	Security	

benefit	of	$[BEN_EST]	per	month.	

or	

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits	but receiving	a	one-time payment	of	$[LS_AMT[ROW,j]] [If	

CLAIM_AGE>AGE+1,	insert	“at	age	[CLAIM_AGE]”;	else	insert	“one	year	from	now”].		

[Else	Display:]

(2)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT[i]] per	month	and receiving	a	onetime payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	[CLAIM_AGE]”;	else	

insert	“one	year	from	now”].	

[Endif]

[Else	Display:]

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits	but receiving	a	one-time payment	of	$[LS_AMT[ROW,j]] [If	

CLAIM_AGE>AGE+1,	insert	“at	age	[CLAIM_AGE]”;	else	insert	“one	year	from	now”].	

[Else	Display:]

(2)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT[i]] per	month	and receiving	a	onetime payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	[CLAIM_AGE]”;	else	

insert	“one	year	from	now”].			

[Endif]

or

(1)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	Security	

benefit	of	$[BEN_EST]	per	month.	

[Endif]

[If	Respondent	selects	option	1	(so	NOT	the	one-time	payment)]

[Set	ROW=ROW+2^(4-j)]			/*	Note:	this	will	increase	the	offered	one-time	payment	in	the	next	iteration	*/

[Endif]

[If	the	respondent	does	not	check	either	option,	prompt	once	using	the	standard	ALP	language	in	situations	like	

these	(something	along	the	lines	of	“Are	just	sure	you	do	not	want	to	choose	between	these	two	options.”).		If	the	

respondent	still	does	not	answer, set	j=5	so	that	we	skip	out	of	the	inner	loop	to	the	next	iteration	of	the	outer	

loop]

j=j+1																										/*	END	OF	THE	INNERLOOP	FOR	CV	PLUS	*/		

EndFor	

i=i+1

EndFor																													/*	END	OF	THE	OUTERLOOP	FOR	CV	PLUS	*/

A-43
SECTION	3:	BACKGROUND	QUESTIONS	

[NOTE:	THIS	SECTION	ASKS	QUESITONS	IN	ORDER	TO	COLLECT	CONTROL	VARIABLES	SUCH	AS	SUBJECTIVE	HEALTH,	

FINANCIAL	LITERACY,	RISK	AVERSION,	ANNUITY	HOLDINGS,	MORTALITY	EXPECTATIONS,	PERCEPTION	OF	POLITICAL	RISK,	

AND	LIQUIDITY	CONSTRAINTS.	]

________________________________________________________________________________________________________

Q.3.1:	[HEALTH]	Subjective	Health			[ms1_C901]

Would	you	say	your	health	is	.	.	.?		

(1)	Excellent

(2)	Very	good

(3)	Good

(4)	Fair

(5)	Poor

Financial	Literacy

Q	3.2.1 [INT_RATE_LITERACY]	Interest	rate	literacy [ms5_L001]						

Suppose	you	had	$100	in	a	savings	account	and	the	interest	rate	was	2%	per	year.	After	5	years,	how	much	do	you	think	you	

would	have	in	the	account	if	you	left	the	money	to	grow?

(1) More	than	$102	

(2) Exactly	$102	

(3) Less	than	$102	

(4) Don’t	Know

Q	3.2.2	[INFLATION]	Inflation	Literacy	(ms5_L003)						

Imagine	that	the	interest	rate	on	your	savings	account	was	1%	per	year	and	inflation	was	2%	per	year.	After	1	year,	would	you

be	able	to	buy	more	than,	exactly	the	same	as,	or	less	than	today	with	the	money	in	this	account?	

(1) More	than	today	

(2) Exactly	the	same	as	today	

(3) Less	than	today	

(4) Don’t	Know

Q	3.2.3	[SAFER] Stock	literacy	[ms5_P002]		

Please	tell	us	whether	this	statement	is	true	or	false?	Buying	a	[If	ORDER_STOCK=1,	insert	“single	company	stock”;	else	insert	

“stock	mutual	fund”]	usually	provides	a	safer	return	than	a	[If	ORDER_STOCK=1,	insert	“stock	mutual	fund”;	else	insert	“single	

company	stock”].	

(1) True	

(2) False	

(3) Don't	know

Q	3.2.4	[FINKNOWL] Self-rated	Financial	Knowledge [ms33_FINKNOWL]				

How	would	you	rate	your	knowledge	about	financial	matters?	

(1) Very	High	

(2) High

(3) Moderate	

(4) Low

(5) Very	Low

Q	3.2.5	[HS_FIN_EDUC_PROGRAM]	High	school	financial	education	programs [ms5_B003]			

Did	your	high	school	offer	financial	education	programs?

(1) Yes

(2) No

A-44
Q	3.2.6		[EMP_FIN_EDUC_PROGRAM]	Employer’s	financial	education	program [ms5_Z001]				

Did	any	of	the	employers	you	work	for	or	worked	for	offer	financial	education	programs	(for	example	retirement	seminars)?

(1) Yes

(2) No

(3) Don’t	know	

Q	3.2.7	[RET_SAV_PLAN]	Retirement	savings	plan	[JV357	in	HRS]

Have	you	developed	a	plan	for	retirement	saving?

(1) Yes		

(2) More	or	less

(3) No	

[IF	RET_SAV_PLAN	=	1		skip to	Q3.2.8,	IF	RET_SAV_PLAN	=	2		skip	to	Q3.2.8,	IF	RET_SAV_PLAN	=	3		skip	to	Q3.3.1]

Q	3.2.8 [RET_SAV_PLAN_1]	Able	to	stick	to	retirement	saving	plan				[JV358	in	HRS]			

How	often	have	you	stuck	to	this	plan:	would	you	say	always,	mostly,	rarely,	or	never?

(1) Always

(2) Mostly

(3) Rarely

(4) Never

Questions	on	Risk	Aversion	and	Precautionary	Savings	Motives

[NOTE:	Options from	1	to	7	are	included	for	the	following	questions	where	1	indicates	“completely	agree”	and	7	indicates	

“completely	disagree”.]

[RISKAVERSION_INTRO]: The	following	statements	concern	saving	and	taking	risks.	Please	indicate	for	each	statement	to	what	

extent	you	agree	or	disagree,	on	a	scale	from	1	to	7.	Here	1	indicates	you	“completely	agree”	and	7	indicates	you	“completely	

disagree.”	

		

Q	3.3.1	[SPAAR1] Safe	investments	1

I	think	it	is	more	important	to	have	safe	investments	and	guaranteed	returns,	than	to	take	a	risk	to	have	a	chance	to	get	the

highest	possible	returns.	

Q	3.3.2	[SPAAR2]	Safe	investments	2

I	would	never	consider	investing	in	the	stock	market	because	I	find	it	too	risky.	

Q	3.3.3	[SPAAR3]	Safe	investments	3

If	I	think	an	investment	will	be	profitable,	I	am	prepared	to	borrow	money	to	make	this	investment.	

Q	3.3.4	[SPAAR4]	Safe	investments	4

I	want	to	be	certain	that	my	investments	are	safe.	

Q	3.3.5	[SPAAR5]	Safe	investments	5

I	think	I	should	take	greater	financial	risks	to	improve	my	financial	position.	

Q	3.3.6	[RISKREWARD]	Risk Reward

I	am	prepared	to	take	the	risk	to	lose	money,	when	there	is	also	a	chance	to	gain	money.

Q	3.3.7	[PRE_MOTIVES1]	Precautionary	motives	1	

I	save	to	have	some	money	to	cover	unforeseen	expenses

Q	3.3.8	[PRE_MOTIVES2]	Precautionary	motives	2		

I	save	to	have	enough	money	in	my	bank	account	to	be	sure	I	will	be	able	to	meet	my	financial	liabilities	

A-45
Planning	Horizon		

Q	3.4	[FIN_PLAN]	Financial	planning	methods												[MS16_FD004]				

In	deciding	how	much	of	their	income	to	spend	or	save,	people	are	likely	to	think	about	different	financial	planning	periods. In	

planning	your	household	saving	and	spending,	which	time	period	is most	important	to	you?	

(1) Next few	months

(2) Next	year

(3) Next	few	years

(4) Next	5-10	years

(5) Longer	than	10	years

(6) I	don’t	plan

Annuity	Holdings

Q	3.5.1 [ANNUITY_NOW]	Any	receipt	of pension	or	annuity	benefits	now

Not	including	Social	Security	benefits,	do	you	[If	MARRIED=1,	insert	“	and	your	[SPOUSE]”] currently	receive	any	monthly	

pension	or	benefits	that	will	continue	for	as	long	as	you	live?

(7) Yes

(8) No

(9) Other	_____________

[IF	ANNUITY_NOW	=	1,	skip	to	3.5.2,	IF	ANNUITY_NOW	=	2,	skip	to	3.5.3,	IF	ANNUITY_NOW	=	3,	skip to	3.5.3]	

Q	3.5.2	[ANNUITY_NOW1]	Current	monthly	pension	or	annuity	benefits

If	you	add	it	all	up,	about	how	much	is	the	total	amount	per	month?	$	________________	

Q	3.5.3	[ANNUITY_LATER]	Monthly	pension	or	annuity	benefits	later	

Not	including	Social	Security	benefits,	do	you	[If	MARRIED=1,	insert	“	and	your	[SPOUSE]”] anticipate	receiving	in	the	future	

any	monthly	pension	or	payments	that	will	continue	for	as	long	as	you	live?	

(1) Yes

(2) No

(3) Other	_____________

[IF	ANNUITY_LATER	=	1	skip	to	3.5.4,	IF	ANNUITY_LATER	=	2	skip to	3.6.1,	IF	ANNUITY_LATER	=	3	skip	to	3.6.1]

Q	3.5.4	[ANNUITY_LATER1]	Monthly	pension	or	annuity	benefits	1	

Adding	it	all	up,	about	how	much	will	the	total	amount	be	per	month?	$	________________

Mortality	Expectations

[Note	to	Programmer:	ASK	Q3.6.1	IF	AGE	<	65,	ELSE	GO	TO	Q3.6.2]

Q	3.6.1	[MORT_EXP1]	Mortality	expectations	1		[HRS	LP028	]

What is	the	percent	chance	that	you	will	live	to	be	75	or	more?		Please	move	the	slider	below	to	indicate	what	you	think	

will	be	the	chance	that	you	will	live	to	be	75	or	more where	0	means	“absolutely	no	chance”	and	100	means	“absolutely	

certain”.

[Note	to	Programmer:	Slider	is	inserted as	shown	below:]

									|------------------------------------------------------------------|	

								00---10---20---30---40---50---60---70---80---90---100

Absolutely																																																																Absolutely	

No	chance																																																																		Certain	

								

A-46
[ASK	Q3.6.2	IF	AGE	<	90,	ELSE	GO	TO	Q3.7]

Q	3.6.2	[MORT_EXP2]	Mortality	expectations	2					[HRS	LP029]

[Note	to	Programmer:

Fill	values	assigned	as	follows	

									85	(IF	AGE	IS	LESS	THAN	65)			/*	note:	this	non-monotonic	pattern	follows	the	HRS	version	of	this	question	*/

									80	(IF	AGE	IS	65-69)

									85	(IF	AGE	IS	70-74)

									90	(IF	AGE	IS	75-79)

									95	(IF	AGE	IS	80-84)

									100	(IF	AGE	IS	85+)]	

What	is	the	percent	chance	that	you	will	live	to	be	[FILLVALUE]	or	more?	Please	move	the	slider	below	to	indicate	what	

you	think	will	be	the	chance	that	you	will	live	to	be	[FILLVALUE] or	more,	where	0	means	“absolutely	no	chance”	and	100	

means	“absolutely	certain”.		

[Note	to	Programmer:	Slider	inserted as	shown	below:]

									|------------------------------------------------------------------|	

								00---10---20---30---40---50---60---70---80---90---100

Absolutely																																																																Absolutely	

No	chance																																																																		Certain	

								

Q.3.7:		[PLCTCL_TRST]	Perception	of	political	risk		[Greenwald	language]

How	 confident	 are	 you	 that	 the	 Social	 Security	 system	 will	 be	 able	 to	 provide	 you	 with	 the	 level	 of	 future	 benefits	 you	 are	

supposed	to	get	under	current	law?

(1) Very	confident

(2) Somewhat	confident

(3) Not	too	confident

(4) Not	at	all	confident

Expected	Returns

[The	following	questions	are	displayed	as	a	table]

Q	3.8.1	[SAV_PLAN_1]	Saving	Plan	1

Suppose	you	received	an	additional	$10,000	this	year.		Thinking	about	your	household	financial	situation,	how	do	you	think	you	

would	use	it?		

Please	type	how	much	of	the	$10,000	you	would	use	for	each	action	in	the	boxes	below.		You	can	put	the	money	in	as	many	or	

as	few	categories	as	you	wish.

About	how	much	of	it	do	you	think	you	would	use	to:

(1) spend			 	 	 $_____	

(2) pay	off	credit	cards		 	 $_____	

(3) pay	off	other	debt		 	 $_____

(4) save	 	 	 $_____

(5) other	 	 	 $_____	

Respondents	are	prompted to	make	sure	the	amounts	add	to $10,000

[	DISPLAY	IF	total	<>	10000	]

[checkSAV_PLAN_1total]		check	total

Your	total	adds	up	to	$[].	Please	go	back	and	change	the	numbers	in	the	table	so	they	add	up	to	$10,000	or	choose	next	to	

continue.

[The	following	questions	are	displayed	as	a	table]

Q	3.8.2	[SAV_PLAN_2]	Saving	Plan	2

Now	assume	that you	cannot	spend	the	$10,000,	but	have to	save	it for	the	future.	How would	you	do	that?		

A-47
Please	type	how	much	of	the	$10,000	you	would	use	for	each	action	in	the	boxes	below.		You	can	put	the	money	in	as	many	or	

as	few	categories	as	you	wish. Please	do	not	use	commas,	dollar	signs	or	decimal	points.	

(1) Put	some	in	the	bank		 	 $_____

(2) Invest	some	in	bonds	 		 $_____

(3) Invest	some	in	CDs		 	 $_____

(4) Invest	some	in	stocks	 	 $_____

(5) Buy	property/real	estate $_____

(6) Other		 	 	 $_____

[NOTE:	Respondents	are	prompted to	make	sure	the	amounts	add	to	$10,000.]

[	DISPLAY	IF	total	<>	10000	]

[checkSAV_PLAN_2total]		check	total

Your	total	adds	up	to	$[]	Please	go	back	and	change	the	numbers	in	the	table	so	they	add	up	to	$10,000	or	choose	next	to	

continue.]

		

Q	3.8.3	[SAV_PLAN_3]	Saving	Plan	3	

Of	the	$10,000	that	you	had	to	save,	about	how	much	do	you	think	you	might	earn	on	it	per	year,	on	average?	

(1) Less	than	or	equal	to	0%,	

(2) 1-3%,	

(3) 4-6%,	

(4) 7-9%

(5) 10-12%

(6) 13%	or	more

	 	 	 		

Liquidity	Constraints	

Q	3.9.1	[ComeUp5000]	Come	up	with	$5000	

If	you	had	to,	could	you	come	up	with	$5,000	[If	CLAIM_AGE>AGE+1,	insert	“by	age	CLAIM_AGE”;	else	insert	“within	one	year	

from	now”]?		

(1) Yes,	I	am	certain	I	could

(2) I	probably	could	

(3) I	probably	could	not

(4) No,	I	definitely	could	not

[Ask	if	ComeUp5000=1	or	ComeUp5000=2]

Q	3.9.2	[FromWhere5000_MC,	FromWhere_Box]	How	would	you	get	the	$5000

How	would	you	come	up	with	this	$5000?

Choose	all	that	apply:

(1) I	would	use	my	current	savings	or	investments

(2) I	would	save	the	money	out	of	my	income	between	now	and	[If	CLAIM_AGE>AGE+1,	insert	“age	CLAIM_AGE”;	

else	insert	“one	year	from	now”]?		

(3) I	would	borrow	it	from	family	or	friends

(4) I	would	use	one	or	more	credit	cards

(5) I	would	use	a	home	equity	loan	or	home	mortgage

(6) I	would	take	out	a	payday	loan	or	use	a	pawnshop

(7) I	would	sell	something	that	I	own,	not	including	my	home

(8) I	would	work	additional	hours,	now	or	in	the	future

(9) Other:	______________		[provide	open-ended	text	box]

________________________________________________________________________________________________________

A-48
SECTION	4:		CLOSING	OF	WAVE	1	

[NOTE:	THIS	IS	ASKED	OF	ALL	RESPONDENTS	AT	THE	END	OF	WAVE	1.]

Q	4.1.1:	[CS_001]	HOW	PLEASANT	INTERVIEW

Could	you	tell	us	how	interesting	or	uninteresting	you	found	the	questions	in	this	interview?

(1) Very	interesting

(2) Interesting

(3) Neither	interesting	nor	uninteresting

(4) Uninteresting

(5) Very	uninteresting

Q	4.1.2:	[CS_003]		Comments

Do	you	have	any	other	comments	on	the	interview?	Please	type	these	in	the	box below.

[Insert	Open	box]

______________________________________________________________________________	

SECTION	5:		INTRODUCTION	TO	WAVE	2	

_______________________________________________________________________________________________________

[NOTE:	THIS	SECTION	ASKS	THE	EITHER	THE	CV-PLUS	VERSION	(VERSION	B)	OR	THE	OTHER	VARIATIONS	WHICH	CONSISTS	OF	

CV-MINUS,	EV-PLUS	AND	EV-MINUS	(VERSION	A)	DEPENDING	ON	THE	RANDOMIZATION.	IT	ALSO	ASKS	THE	“NO	POLITICAL	

RISK	VERSION”	OF	CV-PLUS	TO	ALL	RESPONDENTS.]

_______________________________________________________________________________________________________

[WAIT_WARNING]		Wait	Warning

The	page	after	this	may	take	several	seconds to	load.	Please	click	the	next	button	now,	and	do	not	click	your	browser's	back	

button	while	the	page	is	loading.	Thank	you.

Q.5.1.	[WAVE2_INTRO]		Introduction	to	wave	2

Recently,	we	asked	you	a	number	of	questions	about	when	and	how	you	would	like	to	receive	your	Social	Security	benefits.		

We	very	much	appreciate	your	help.	Today,	we	would	like	to	ask	you	some	more	questions	about	this.	

Once	again,	please	take	time	to	consider	the	questions	and	give	us	your	best	guess even	if	you	do	not	know	the	exact	answer.		

Having	your	best	guess	will	be	very	helpful	to	us.

Thank	you	very	much	for	your	participation!

[If	SS_STATUS==2	and		FLAGHYPO=0	(not	currently	receiving	SS	and	not	hypothetical	benefits),	show:]

Q5.2.	[BENEFIT_REMINDER]	Reminding	the	respondent	about	his/her	benefits

We	would	like	to	remind	you	that	in	the	prior	survey	we	used	$[BEN_EST]	as	the	monthly	Social	Security	benefits	that	your	are

supposed	to	get	under	current	law	if	you	claim	benefits	at	age	[CLAIM_AGE].

A-49
As	before,	we	mean	by	“Social	Security	benefits”	any	benefits	that	you	yourself	receive	or	will	receive	from	the	Social	Security	

program,	including	retiree,	disability,	spouse,	or	survivor	benefits.

[IF	SS_ELIG=3,	DISPLAY:]	/*	So	to	those	who	said	in	wave	1	that	they	thought	SS	would	no	longer	be	around	*/

Please	answer	the	questions	in	this	survey	assuming	that	current	Social	Security	rules	still	apply	when	you	first	claim	

Social	Security	benefits.		Thank	you.

[ENDIF]

[If	FLAGHYPO=1,	Show]

Q.5.3:	[ReminderHypoEligible]	Reminder	for	those	ineligble,	but	who	answer	questions	hypothetically

Even	though	you	told	us	in	the	prior	survey	that	you	are	not	eligible	to	receive	Social	Security	benefits,	we	would	like	to	again	

ask	you	to	complete	our	survey	assuming	you	would	be	eligible.	In	other	words,	please	answer	in	this	survey	what	you	would	

have	done	or	chosen	if	you	would	be	eligible	for	Social	Security	benefits.	

The	typical	Social	Security	benefit	for	[If	MARRIED=1,	insert “a	married”;	if	MARRIED=0,	insert “an	unmarried”] [If	

GENDER=“male”	insert “man”;	if	GENDER=“female”	insert “woman”] with	[insert	the	respondent’s	educational	attainment	

based	on	the	categories	in	EDUCATION] is	$[BEN_DEFAULT]	per	month.

For	the	purpose	of	this	survey,	let's	assume	that	you	[If	SS_STATUS==2,	insert	“are	supposed	to”] get	a Social	Security	benefit	

of	$[BEN_DEFAULT]	per	month,	and	that	you	[If	SS_STATUS==1,	insert	“started	receiving”;	If	SS_STATUS==2,	insert	“would	

start	receiving”] Social	Security	benefits	at	age	62.

SECTION	6:		CV-MINUS	AND	EV	ANNUITY	TRADE-OFF	QUESTIONS	

[NOTE:	COMPENSATING	VARIATION	(CV-MINUS	)	AND	EQUILVALENT	VARIATION	(EV-PLUS/EV-MINUS)	MEASURES	ARE	

ELICITED	IN	THIS	SECTION.	SPECIFICALLY,	RESPONDENTS	ARE	ASKED	TO	MAKE	A	TRADE-OFF	CHOICE	BETWEEN:

1)	CV-MINUS:		REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY	BENEFIT	LEVEL	AND THEIR	EXISTING	SOCIAL	SECURITY	

MONTLY	BENENIT	AMOUNT	WITH	AN	INCREMENT	OF	A	VARYING	AMOUNT	(SS_VARAMT)	MINUS	 A	ONE-TIME	PAYMENT	

/LUMPSUM	AMOUNT	(LS_AMT).	

2)	EV-PLUS:		REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY		BENEFIT	LEVEL	WITH	A	ONE-TIME	PAYMENT/LUMPSUM	

AMOUNT	(LS_AMT)	AND REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY		BENEFIT	LEVEL	WITH	AN	INCREMENT	OF	A	

VARYING	AMOUNT	(SS_VARAMT)	

3)	EV-MINUS:	REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY	BENEFIT	LEVEL	MINUS A	ONE-TIME	

PAYMENT/LUMPSUM	AMOUNT	(LS_AMT)		AND REPONDENTS’	EXISITING	SOCIAL	SECURITY	MONTHLY		BENEFIT	LEVEL	WITH	

A	DECREMENT	OF	A	VARYING	AMOUNT	(SS_VARAMT).	]

Q.6.1.	[TRADEOFF	–	INTRO].		Introduction	to	annuity	tradeoff	questions

In	the	next	few	questions,	we	are	going	to	ask	you	to	make	a	number	of	choices	about	Social	Security	benefits.	

Please	assume	that	all	amounts	shown	are	after	tax	(i.e.,	you	don’t	owe	any	tax	on	any	of	the	amounts	we	will	show	

you).

Think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	Social	Security	

will	adjust	future	dollar	amounts	for	inflation).

[WAIT_WARNING]		Wait	Warning

A-50
																		The	page	after	this	may	take	several	seconds	to	load.	Please	click	the	next	button	now,	and	do	not	click	your	

browser's	back	button	while	the	page	is	loading.	Thank	you.

[Note	to	programmer:	

There	are	two	nested	loops	in	this	section.

1. The	outer	loop	has	3	iterations	and	loops	over	the	version	of	the	tradeoff	question	that	is	asked	(either	CV-Minus,	

EV-Plus,	or	EV-minus).		The	outerloop	is	indexed	by	the	variable	i.

2. The	inner	loop	has	4	iterations	and	loops	over	the	amount	of	the	one-time	payment	offered: LS.		The	inner	loop	is	

indexed	by	the	variable	j.

Initialization	of	SS_VARAMT

Set	SS_VARAMT=100

Initialization	of	LS_AMT

If	LS_STARTVALUE	==1

Set	the	16x5	matrix	LS_AMT=LS_LOW

Elseif	LS_STARTVALUE	==3

Set	the	16x5	matrix	LS_AMT=LS_HIGH

Else

Set	the	16x5	matrix	LS_AMT=LS_MED

Endif

Start	of	the	nested	loops	that	ask	other	versions	of	the	tradeoff	questions

For	i=1	to	3																										/*	START	OF	THE	OUTER	LOOP	FOR	OTHER	TRADEOFF	QUESTIONS	*/

[Show	if	i>1]

Q.6.2:	[Roadmap	other	versions]	Roadmap	to	other	tradeoff	questions.

Now	we	would	like	to	show	a	different	series	of	questions	about	choices	about	Social	Security	benefits.

As	before,	please	assume	that	all	amounts	shown	are	after	tax	(i.e.,	you	don’t	owe	any	tax	on	any	of	the	amounts	we	will	

show	you).

As	before,	think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	Social	

Security	will	adjust	future	dollar	amounts	for	inflation).

Q.6.3:	[OtherTradeoff]:		OTHER	VERSIONS	OF	TRADE-OFF	QUESTION

Set	ROW=1

For	j=1	to	4																								/*	START	OF	THE	INNER	LOOP	FOR	OTHER	TRADEOFF	QUESTIONS	*/

[If	j	=	1,	Display:	]

In	this	question,	we	are	going	to	ask	you	to	make	a	choice	between	two	money	amounts.	

[Else,	Display:]

Now	we	ask	you	the	same	question	but	with	a	different	amount	for	the	one-time	payment.

[Endif]

Please	click	on	the	option	that	you	would	prefer.	

A-51
[SHOW	IF	MARRIED==1:] Benefits	paid	to	your	[SPOUSE]	will	stay	the	same	for	either	choice.

Suppose	Social	Security	gave	you	a	choice	between:

[If	CVM_ORDER==i]					/*	THIS	GIVES	THE	CV-MINUS	VERSION	*/

[Note	to	programmer:	The way	the	ROW	variable	is	updated	depends	on	how	each	option	is	numbered]

[If	LS_FIRST=0,	Display:	]

(2)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month.	

or	

(1)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	+	SS_VARAMT]	per	month	and making	a	one-time

payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	

year from	now”] to	Social	Security.	

[Else	Display:]

(1)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	+	SS_VARAMT]	per	month	and making	a	one-time

payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	

year	from	now”] to	Social	Security.	

or

(2)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month.	

[Endif]

[Endif]																						/*	End	of	CV-Minus	version	*/

[If	EVP_ORDER==i]					/*	THIS	GIVES	THE	EV-PLUS	VERSION	*/

[Note	to	programmer:	The way	the	ROW	variable	is	updated	depends	on	how	each	option	is	numbered]

[If	LS_FIRST=0,	Display:]

(1)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	+	SS_VARAMT]	per	month.

or

(2)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month	and receiving	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”].	

[Else	Display:]

(2)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month	and receiving	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”].	

or	

(1)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	+	SS_VARAMT]	per	month.

[Endif]

[Endif]																				/*	End	of	EV-Plus	Version	*/

[If	EVM_ORDER==i]					/*	THIS	GIVES	THE	EV-MINUS	VERSION	*/

A-52
[Note	to	programmers:	The way	the	ROW	variable	is	updated	depends	on	how	each	option	is	numbered]

[If	LS_FIRST=0,	Display:	]

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits.	

[Else	Display:]

(2)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT]	per	month.

[Endif]

or

(1)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month	and making	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”] to	Social	Security.	

[Else	Display:]

(1)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	

Security	benefit	of	$[BEN_EST]	per	month	and making	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”] to	Social	Security.		

or	

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits.	

[Else	Display:]

(2)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT]	per	month.

[Endif]

[Endif]

[Endif]																	/*	End	of	EV-Minus	version	*/

[If	Respondent	selects	option	1]													/*so	making	the	one-time	payment	(for	CV-minus	and	EV-minus)

																																																																																								or		NOT	receiving	the	one-time	payment	(for	EV-plus)	*/

[Set	ROW=ROW+2^(4-j)]								/*	Note:	this	will	increase	the	size	of	one-time	payment	in	the	next	iteration,	

																																																																																						so	it	makes	option	1	less	attractive	*/

[Endif]

[If	the	respondent	does	not	check	either	option,	prompt	once	using	the	standard	ALP	language	in	situations	like	

these	(something	along	the	lines	of	“Are	you sure	you	do	not	want	to	choose	between	these	two	options.”).		If	the	

respondent	still	does	not	answer,	set	j=5	so	that	we	skip	out	of	the	inner	loop	to	the	next	iteration	of	the	outer	

loop]

j=j+1																										/*	END	OF	THE	INNERLOOP	FOR	OTHER	TRADEOFF	QUESTIONS	*/		

EndFor	

i=i+1

EndFor																													/*	END	OF	THE	OUTERLOOP	FOR	OTHER	TRADEOFF	QUESTIONS	*/

A-53
SECTION	7:		NO-POLITICAL-RISK	TRADE-OFF	QUESTION	

[NOTE:	THIS	SECTION	REPLICATES	ONE	OF	THE	COMPENSATION	VARIATION	MEASURES	(CV-PLUS)	BUT	ASSUMING	THAT	

THERE	IS	NO	POLITICAL	RISK.	IT	IS	ASKED	OF	ALL	RESPONDENTS	AT	THE	END	OF	WAVE	2.]		

Q.7.1.	[NOPOLRISK	–	INTRO].		Introduction	to	annuity	tradeoff	question	without	political	risk

The	next	few	questions	are	similar	to	the	questions	we	have	asked	before	about	choices	about	Social	Security	benefits	

but	with	one	difference:	

<b> From	now	on,	please	assume	that	you	are	absolutely	certain to	receive	all	income	promised	as	future	Social	

Security	benefits	or	as	a	future	one-time	payment.		In	other	words,	please	assume	that	it	is	absolutely	certain that	

Social	Security	will	make	payments	as	promised,	and	that	there	is	no	chance	at	all	of	any	benefit	changes	in	the	future	

other	than	the	trade-offs	discussed	in	the	question	below. </b>

As	before,	please	assume	that	all	amounts	shown	are	after	tax	(i.e.,	you	don’t	owe	any	tax	on	any	of	the	amounts	we	

will	show	you).

As	before,	think	of	any	dollar	amount	mentioned	in	this	survey	in	terms	of	what	a	dollar	buys	you	today	(because	

Social	Security	will	adjust	future	dollar	amounts	for	inflation).

[NOTE	TO	PROGRAMMER:	

Initialization	of	SS_VARAMT

Set	SS_VARAMT=100

Initialization	of	LS_AMT

If	LS_STARTVALUE	==1

Set	the	16x5	matrix	LS_AMT=LS_LOW

Elseif	LS_STARTVALUE	==3

Set	the	16x5	matrix	LS_AMT=LS_HIGH

Else

Set	the	16x5	matrix	LS_AMT=LS_MED

Endif	]	

Q.7.2:	[NOPOLRISK]:		NO-POLITICAL-RISK	VERSION	OF	CV-PLUS	TRADE-OFF	QUESTION

Set	ROW=1

For	j=1 to	4																								/*	START	OF	THE	NOPOLRISK	LOOP	*/

[If	j	=	1,	Display:	]

In	this	question,	we	are	going	to	ask	you	to	compare	and	make	a	choice	between	two	money	amounts.	

[Else,	Display:]

Now	we	ask	you	the	same	question	but	with	a	different	amount	for	the	one-time	payment.

[Endif]

Reminder:	<b> Please	assume	that	you	are	absolutely	certain to	receive	all	income	promised	as	future	Social	Security	

benefits	or	as	a	future	one-time	payment.		In	other	words,	please	assume	that	it	is	absolutely	certain that	Social	Security	

will	make	payments	as	promised,	and	that	there	is	no	chance	at	all	of	any	benefit	changes	in	the	future	other	than	the	

trade-offs	discussed	in	the	question	below. <end	b>

Please	click	on	the	option	that	you	would	prefer.	

[SHOW	IF	MARRIED==1:] Benefits	paid	to	your	[SPOUSE]	will	stay	the	same	for	either	choice.

A-54
Suppose	Social	Security	gave	you	a	choice	between:

[If	LS_FIRST=0,	Display:	]

(1)	 Receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	Security	benefit	

of	$[BEN_EST]	per	month.	

or	

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits	but	receiving	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”].	

[Else	Display:]

(2)	 Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT] per	month	and receiving	a	one-time

payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	

year	from	now”].

[Endif]

[Else	Display:]

[If	BEN_EST	≤ SS_VARAMT,	Display:	]

(2)	 Receiving	no	Social	Security	benefits	but	receiving	a	one-time payment	of	$[LS_AMT[ROW,j]]	[If	

CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”].

[Else	Display:]

(2)		Receiving	a	Social	Security	benefit	of	$[BEN_EST	-	SS_VARAMT] per	month	and receiving	a	one-time

payment	of	$[LS_AMT[ROW,j]]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	

year	from	now”].	

[Endif]

or

(1)	 Receiving	your	[If	SS_STATUS==1, insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	Security	benefit	

of	$[BEN_EST]	per	month.	

[Endif]

[If	Respondent	selects	option	1	(so	NOT	the	one-time	payment)]

[Set	ROW=ROW+2^(4-j)]				/*	Note:	this	will	increase	the	offered	one-time	payment	in	the	next	iteration	*/

[Endif]

[If	the	respondent	does	not	check	either	option,	prompt	once	using	the	standard	ALP	language	in	situations	like	these	

(something	along	the	lines	of	“Are	just	sure	you	do	not	want	to	choose	between	these	two	options.”).		If	the	respondent	

still	does	not	answer,	set	j=5	so	that	we	skip	out	of	the	inner	loop	to	the	next	iteration	of	the	outer	loop]

j=j+1																										/*	END	OF	THE	NOPOLRISK	LOOP	*/		

EndFor	

__________________________________________________________________________	

Liquidity	Constraints	

[ASK	Q	7.3.1	–	Q	7.3.4	IF	lowest	value	of	lumpsum	amount	in	CV	MINUS	respondent	was	unwilling	to	pay	<100000]	

Q	7.3.1	[CanPayLS]	Could	respondent	pay	the	lumpsum	in	CV-Minus	

[Generate	a	variable	CVM_MIN	that	is	equal	to	the	lowest	value	of	the	lumpsum	amount	in	the	CV-MINUS	question	that	the	

respondent	was	unwilling	to	pay.		So,	CVM_MIN	is	the	minimum	of	the	sequence	of	[LS_AMT[ROW,j]]	displayed	in	CVMINUS	

restricted	to	those	j	where	the	respondent	chose	option	(2).]

Previously,	you	told	us	you	would	rather	receive	your	[If	SS_STATUS==1,	insert	“current”; if	SS_STATUS==2,	insert	“expected”]

Social	Security	benefit	of	$[BEN_EST] per	month	than	make	a	one-time payment	of	$[CVM_MIN] [If	CLAIM_AGE>AGE+1,	insert	

“at	age	CLAIM_AGE”;	else	insert	“one	year	from	now”] to	receive	a	Social	Security	benefit	of	$[BEN_EST	+	SS_VARAMT]	per	

month.

Please	indicate	your	reason	for	this	choice:

(1) I	cannot	come	up	with	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“by	age	CLAIM_AGE”;	else	insert	“within	one	

year	from	now”].

(2) I	could	come	up	with	$[CVM_MIN]	but	I	do	not	want	to	use	the	money	this	way.

A-55
[ASK	IF	Could	respondent	pay	the	lumpsum	in	CV-Minus	=	I	cannot	come	up	with	$^FLCVM_MIN	^FLCanPayLS3]

[Ask	if	CanPayLS=1	:]

Q	7.3.2 [PayIfHadMoney]	Would	R	pay	the	LS	if	R	had	the	money?

Suppose	you	had	an	additional		$[CVM_MIN]	in	savings	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	insert	“one	

year	from	now”],	do	you	think	you	would	make	a	one-time payment	of	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	

CLAIM_AGE”;	else	insert	“one	year	from	now”] to	receive	a	Social	Security	benefit	of		$[BEN_EST	+	SS_VARAMT]	per	month	

(instead	of	receiving	your	[If	SS_STATUS==1,	insert	“current”;	if	SS_STATUS==2,	insert	“expected”] Social	Security	benefit	of	

$[BEN_EST]	per	month)?

(1) Yes

(2) No

[ASK IF	Would	R	pay	the LS	if	R	had	the	money	=	No	]

[Ask	if	PayIfHadMoney	=2	:]

[The	following	questions	are	displayed	as	a	table]

Q.7.3.3:	[ WhyNotHypo]		Why	not	pay	LS	even	if	R	had	the	money

Why	do	you	choose	not	to	make	a	one-time payment	of	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	

insert	“one	year	from	now”] to	receive	a	Social	Security	benefit	of		$[BEN_EST	+	SS_VARAMT]	per	month	even	if	you	had	at	

least	$[CVM_MIN]	in	savings?

Why do you choose not to make	a	one-time payment	of	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	

insert	“one	year	from	now”] to	receive	a	Social	Security	benefit	of		$[BEN_EST	+	SS_VARAMT]	per	month	even	if	you	had	an	

additional	$[CVM_MIN]	in	savings?

Please	choose	the	main	reason:

(1) I don’t expect to live long enough for this to be a good deal

(2) I believe I can invest the money better on my own

(3) I would prefer to spend more money now rather than having more to spend later

(4) I would prefer to use the money to help pay for an upcoming large expense

(5) I would prefer to save the money for an emergency

(6) I would prefer to leave the money to my family

(7) I don’t trust that the government will make good on the deal

(8) Other:_______________ [provide open-ended text box]

Else	

[The	following	questions	are	displayed	as a	table]

[Ask	if	CanPayLS=2	:]

Q	7.3.4 [WhyNotActual]	Why	not	pay	LS	even	if	R	had	the	money

Why	do	you	choose	not	to	make	a	one-time payment	of	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	

insert	“one	year	from	now”]	to	receive	a	Social	Security	benefit	of		$[BEN_EST	+	SS_VARAMT]	per	month	even	though	you	can	

come	up	with	$[CVM_MIN]?

Why	do	you	choose	not	to	make	a	one-time payment	of	$[CVM_MIN]	[If	CLAIM_AGE>AGE+1,	insert	“at	age	CLAIM_AGE”;	else	

insert	“one	year	from	now”] to	receive	a	Social	Security	benefit	of		$[BEN_EST	+	SS_VARAMT]	per	month	even	though	you can	

come	up	with	$[CVM_MIN]?

Please	choose	the	main	reason:

A-56
(1) I	don’t	expect	to	live	long	enough	for	this	to	be	a	good	deal

(2) I	believe	I	can	invest	the	money	better	on	my	own

(3) I	would	prefer	to	spend	more	money	now	rather	than	having	more	to	spend	later

(4) I	would	prefer	to	use	the	money	to	help	pay	for	an	upcoming	large	expense

(5) I	would	prefer	to	save	the	money	for	an	emergency

(6) I	would	prefer	to	leave	the	money	to	my	family

(7) I	don’t	trust	that	the	government	will	make	good	on	the	deal

(8) Other:_______________			[provide	open-ended	text	box]

[ASK	IF	CALCULATED	AGE	<	65]

Q.7.4.1:	[P028]		Die	Before	75

What	is	the	percent	chance	that	you	will	die	before	age	75?	Please	move	the	slider	below	to	indicate	what	you	think	will	be	the	

chance	that	you	will	die	before	age	75,	where	0	means	"absolutely	no	chance"	and	100	means	"absolutely	certain".	

[ASK	IF	CALCULATED	AGE	<	90]

Q.7.4.2:	[ P029]		Die	Before	[85,	90,	95,	100]

What	is	the	percent	chance	that	you	will	die	before	age	[85/90/95/100]?	Please	move	the	slider	below	to	indicate	what	you	

think	will	be	the	chance	that	you	will	die	before	age	[85/90/95/100],	where	0	means	"absolutely	no	chance"	and	100	means	

"absolutely	certain".		

_____________________________________________________________________________	

SECTION	8.		CLOSING	OF	WAVE	2	

[NOTE:	THIS	IS	ASKED	OF	ALL	RESPONDENTS	AT	THE	END	OF	WAVE	2.]

Q	8.1:	[CS_001]		HOW	PLEASANT	INTERVIEW

Could	you	tell	us	how	interesting	or	uninteresting	you	found	the	questions	in	this	interview?

(1) Very	interesting

(2) Interesting

(3) Neither	interesting	nor	uninteresting

(4) Uninteresting

(5) Very	uninteresting

Q	8.2:	[CS_003]		Comments

Do	you	have	any	other	comments	on	the	interview?	Please	type	these	in	the	box	below.

[Insert	Open	box]

A-57