Would People Behave Differently If They Better Understood Social Security? Evidence From a Field Experiment*
Jeffrey B. Liebman Erzo F.P. Luttmer September 28, 2010

Abstract This paper presents the results of a field experiment in which a random subsample of older workers was given information about key Social Security provisions, while a control group was not. The experiment was designed to examine whether it is possible to affect individual behavior using a relatively inexpensive informational intervention about the provisions of a public program and to explore what mechanisms underlie the behavior change. We find that our relatively mild intervention (sending an informational brochure and an invitation to a webtutorial) significantly increased labor force participation one year later and that this effect is driven by female subjects. The information intervention increased the perceived returns to working longer, especially among female respondents, which suggests that the behavioral response can be attributed at least in part to updated information about Social Security.

Key words: Social Security Incentives; Field Experiment; Labor Force Participation; Knowledge; Expectations; Retirement; Benefit Claim Age, Earnings Test.

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Liebman: Harvard Kennedy School and NBER. Luttmer: Economics Department at Dartmouth College, and NBER. Corresponding author: Erzo Luttmer, Erzo.FP.Luttmer@Dartmouth.Edu. We thank John Geanakoplos, David Laibson, Annamaria Lusardi, Brigitte Madrian, Susann Rohwedder, and Stephen Zeldes for helpful comments. We thank Kate Mikels, Abdul Tariq, and Victoria Levin for superb research assistance. This research was supported by the U.S. Social Security Administration through grant #10-M-98363-1-01 to the National Bureau of Economic Research as part of the SSA Retirement Research Consortium. The findings and conclusions expressed are solely those of the author(s) and do not represent the views of SSA, any agency of the Federal Government, or the NBER. All errors are our own.

1. Introduction The provisions of government tax, social insurance, and means-tested transfer programs create complex sets of incentives for individuals making labor supply, retirement, and savings decisions. If individuals do not understand or otherwise come to correctly perceive the incentives, they may make economic decisions that are privately suboptimal and may also fail to participate effectively as political actors.1 An important policy question is whether there exist relatively inexpensive approaches to providing information that improve decision making and ultimately increase individual wellbeing. For example, to what extent could a simple brochure mailed by the Social Security Administration correct the widespread misperceptions about the Social Security earnings test? The answer to this question will depend in part on why incentives are currently misperceived. In some cases, the necessary information may be straightforward to understand, but expensive (in either monetary or psychic terms) to acquire. In other cases, the information about program rules may be readily available, but the calculation necessary to determine an individual’s own incentives may be very complicated. In still other cases, cognitive biases may cause people to misperceive even relatively simple incentive schedules. Finally, powerful social cues may point people toward a suboptimal decision, even when the correct information is also readily available. While these factors that can produce poor decision making are well documented, there is little evidence on how easy they are to overcome. This paper presents the results of a field experiment in which a random subsample of older workers was given information about key Social Security provisions, while a control group was not given the information. One year after the information was provided, we administered a follow-up survey and measured the impact of the information provision on labor supply and Social Security benefit claiming behavior. We find that our relatively mild intervention (a mailed brochure combined with a 15-minute online tutorial) raised the fraction of the sample remaining in the labor force by 4 percentage points. These impacts, while statistically significant for the entire sample, appear to be driven by female sample members. We do not find statistically significant effects of the treatment on the probability that respondents have started claiming Social Security benefits.

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In some cases these privately suboptimal decisions can be socially optimal. See Liebman and Zeckhauser (2008).

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To explore the mechanism by which the intervention affected behavior, we ask respondents a series of questions about their understanding of the incentives that the Social Security program provides for labor supply and benefit claiming. We find that the information intervention increased the perceived return to working longer (especially among women), which suggests that at least part of labor supply response can be traced to knowledge updating, though we cannot rule out that the gestalt of our message also contributed to the change in behavior. 2. Background It is becoming increasingly clear that responses of economic actors to the incentives created by government tax- and spending programs are affected not only by the size of the incentives, but also by contextual factors that affect how the incentives are perceived by the individuals. Duflo et al. (2006) show that customers of a tax preparation firm who were offered a match of contributions to retirement savings accounts were much more responsive to the simple and transparent match offer than are U.S. taxpayers who face a similar match via a provision in the U.S. tax code. Chetty, Looney, and Kroft (2009) find that changes in excise taxes yield larger behavioral responses than economically equivalent changes in sales taxes, most likely because the sales taxes are added at the tax register and are therefore less salient. Kling et al. (2009) posit that many Medicare beneficiaries are poorly informed about the prices offered by different prescription drug plans and show that providing information can cause beneficiaries to switch to lower-priced plans. Chetty and Saez (2009) demonstrate that EITC recipients alter their labor supply when the incentives provided by the program are explained by a tax preparer.2 The context-specific nature of behavioral responses to incentives increases the dimensionality of the challenge for researchers seeking to reach a consensus about the magnitude of behavioral responses to policy provisions because there will not be a single parameter that can be averaged across multiple studies if the studies measure behavior in disparate contexts. However, the sensitivity of behavioral responses to how incentives are perceived by individual decision makers also provides policy makers with an additional tool; relatively inexpensive interventions that provide information or alter the framing of decisions have the potential to significantly improve economic well-being.
2

Not all changes in contextual factors produce changes in behavior. Mastrobuoni (2010) shows that while the mailing of Social Security statements by the SSA increases knowledge about benefit levels, it does not significantly alter retirement behavior.

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Social Security policy is likely to be a particularly fruitful area in which to apply these insights. Decisions about when to retire and when to claim benefits can have large implications for well-being over many subsequent years. Such decisions also have elements of irreversibility that make it hard to undo poor decisions. And retirement-related decisions are very challenging to get right.3 The Social Security tax and benefit schedules themselves involve several features that make it hard to perceive incentives correctly – complex nonlinearities and interactions with other sources of retirement income that make calculations difficult, a remote connection between choices and payoffs because benefits are often not received for many years after the point at which labor supply decisions are made, and a dependence of one’s own incentives on individual specific factors such as martial history and life expectancy that make it hard to learn one’s own incentives from that of a peer. In addition to schedule complexity, these choices involve uncertainty and tradeoffs over time – the two contexts in which difficulty in decision-making have been the most widely documented.4 Moreover, how people perceive the incentives of Social Security factors critically in several policy debates. For example, there is no consensus on the extent to which people perceive the marginal future Social Security benefits they receive when they work an additional hour.5 Knowing the answer to this question is important for understanding the amount of deadweight loss caused by the OASDI payroll tax and also for assessing the potential welfare gains from switching to a system – either based on notional defined contribution accounts or funded personal retirement accounts – with more transparent linkage between initial collections and later benefits. Similarly, there remains no consensus about why so many people retire and claim benefits at age 62. Knowing the answer to this question is important for understanding the welfare implications for adjusting the earliest eligibility age and the full-benefit age. In an earlier paper (Liebman and Luttmer, 2009), we administered a survey about Social Security benefit rules to a representative sample of Americans aged 50-70. We found that the majority of respondents believe that their Social Security benefits increase with labor supply, i.e.,
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There is a large literature on the accuracy of people’s perceptions about future levels of Social Security and pension benefits and the implications of lack of knowledge on retirement and saving behavior. See Bernheim (1988), Mitchell (1988), Gustman and Steinmeier (2005), Rohwedder and Kleinjans (2006), Dominitz, Hung, and van Soest (2007), and Chan and Huff Stevens (2008). 4 See Liebman and Zeckhauser (2004) for a discussion of decision making under complex schedules. See Liebman and Zeckhauser (2008) for a review of the literature on contexts in which individuals have difficulties making wise decisions. 5 See Liebman, Luttmer, and Seif (2009) for evidence on this subject.

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that the Social Security benefit rules provide a positive work incentive. The magnitude of this perceived incentive varies across respondents, but people generally cite an incentive that is somewhat greater than the actual figure. We also surveyed people about their understanding of various provisions in the Social Security benefit rules, and found that some of these provisions (e.g., effects of delayed benefit claiming, and rules on widow benefits) are relatively well understood while others (rules on spousal benefits, provisions on which years of earnings are taken into account) are less well understood. In order to achieve the potential welfare gains from improving people’s understanding of the tradeoffs they face, researchers need to make progress in two areas. The first is in understanding the reasons for current misperceptions. The second is in testing interventions to discover which approaches are most effective in correcting misperceptions. Our current study makes three contributions in these areas. First, it provides further confirmation that interventions affecting people’s perceptions of incentives can affect individual behavior in important policy contexts. Second, by measuring both the change in behavior and the change in people’s perceptions it helps show how the intervention worked to change behavior. In some of the studies cited above, it is unclear whether behavioral changes were obtained because people better understood their choices or because they simply accepted advice of a trusted intermediary to “save more” or “work more.” As we discuss later in the paper, the normative and policy implications of these sorts of interventions hinge critically on the mechanisms through which they have their impacts. Third, the study demonstrates an approach to altering perceived incentives – a combination of a mailing and an online tutorial – that is both relatively inexpensive and which allows full researcher control over the information that is provided. In particular, it does not rely on a caseworker-style approach in which one-on-one counseling is used to influence people. For many applications, the approach we demonstrate is likely to be scalable to the population level once an effective intervention is identified. It also removes the ambiguity over what information or message was provided to the sample member that, along with cost, is a drawback of the caseworker approach. 3. Survey Design and Experimental Manipulations 3.1 Population for the Information Intervention

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We contracted with Knowledge Networks to administer our experimental intervention and a follow-up survey to a sample of its panel of respondents. These panelists, originally recruited through random-digit dialing, agree to take a 15-20 minute survey once a week via the Internet using a PC or WebTV in exchange for free Internet and WebTV access. In addition, the panelists often receive incentive payments and rewards through a loyalty program. Knowledge Networks collects basic demographic characteristics for all its panelists, and its panelists are roughly representative of the adult U.S. population according to these characteristics. The population eligible for our randomized experiment consists of all working individuals between the ages of 55 and 70 who were invited to participate in our 2008 survey of knowledge about Social Security (see Liebman and Luttmer, 2009, for details). The 2008 survey oversampled working individuals between the ages of 60 and 65 because this group is likely to retire in the near future and, therefore, would be most likely to display behavioral effects of the information intervention within the time frame of our experiment. We exclude from the experimental sample those individuals who were disqualified from the 2008 survey (e.g., because they worked in a sector not covered by Social Security). The resulting experimental sample consists of 2483 individuals, who were randomized into a treatment and a control group prior to the 2008 survey. All individuals in the experimental sample were working in 2008 according to the employment status variable that is part of Knowledge Networks’ standard demographic profile variables. The vast majority (90%) of the sample was aged between 60 and 65 in November 2008. 3.2 Information Intervention The members of the treatment group received an informational intervention that consisted of two parts. In February of 2009, Knowledge Networks sent them by regular mail an informational brochure that we had created for this purpose. In March of 2009, members of the treatment group were invited to participate in a web-tutorial about Social Security. The completion rate of web-tutorial was 76.8 percent for the treatment group as a whole and 91.6 percent for treatment group members who also took part in the follow-up survey. Members of the control group did not receive any materials from us (either in the mail or online). In both the brochure and the web-tutorial, we provided information on three topics. First, we provided information about longevity (fractions surviving until age 90) to emphasize the need

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for an adequate planning horizon for retirement finances. Second, we explained the relationship between retirement age and the standard of living during retirement. In particular, we explain how Social Security benefits rise with the age at which benefits are claimed and how Social Security benefits depend on a person’s work history. Third, we provide information about the Social Security earnings test and explain that cuts in current benefits due to the earnings test are offset by higher benefits in the future. In designing the information intervention, we took into account that many respondents may have trouble remembering benefit formulae and information presented with figures and statistics. We therefore complemented such information with vignettes about actual retirees whom we had interviewed and from whom we had received permission to incorporate their information in the brochure or web-tutorial. These vignettes helped to underline the broader message of the intervention and contribute to the “gestalt” of the message. Before giving information about Social Security in the brochure, we explained that the brochure was sent to them by Knowledge Networks on behalf of us (“researchers at Harvard University”) as a follow-up to a recent survey (our 2008 baseline survey) that the individual had been invited to take. Moreover, we emphasized that the brochure was not a comprehensive source of Social Security facts, and we provided phone numbers and links to additional sources of information about Social Security. We printed the brochure on glossy paper, used a relatively large font, and provided it with a professional layout in color in order to entice respondents to read it. The complete brochure is included in Appendix A. In addition, members of the treatment group were invited take a Knowledge Networks survey that provided them with information about Social Security. This web-tutorial covered the same three basic topics as the brochure, but was tailored to each respondent’s situation – something we were able to do because it was administered online. For example, we gave information about the typical longevity of people of the same sex as the respondent; the characters in examples had the same sex as the respondent, and returns to delaying claiming were calculated exactly based on the respondent’s birth cohort. The web-tutorial also contained a number of questions about the information we presented in order to induce the respondents to pay attention to the information. In the web-tutorial, as in the mail survey, we emphasized the gestalt of the information intervention by including vignettes of actual retirees. At the end of the

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web-tutorial, we provided phone numbers and internet links to further resources for information about Social Security. The complete web-tutorial is included in Appendix B. 3.3 Follow-up Survey In April 2010, thirteen months after the information intervention, members from both the treatment group and the control group were invited to participate in our follow-up survey. The follow-up survey was designed to measure the effects of the information intervention on labor supply and Social Security benefit claiming behavior. In order to explore what mechanisms underlie the behavior change, it also included questions designed to measure understanding of the incentives that the Social Security program provides for labor supply and benefit claiming. In addition, because one year is a relatively short period in which to observe changes in retirement behavior, the survey also contained questions about planned future behavior. The follow-up survey contains 67 questions and the median time to complete it was 18 minutes. We paid respondents a $5 incentive for completing the survey. The follow-up survey was fielded between April 8 and June 9. While the vast majority (90%) of respondents completed the survey in April, we kept the survey open until June to maximize the response rate. The full survey instrument is provided in Appendix C. Of the 2483 members of the experimental sample, 1596 completed the follow-up survey for an overall response rate of 64.3 percent. Not all members of the experimental sample, however, were invited to take the follow-up survey because 11.4 percent of the sample had left the Knowledge Networks panel or had said they were temporarily unavailable for surveys. Thus, the completion rate among individuals invited to the take the follow-up survey was 72.5 percent. The particular way in which invitations to take the follow-up survey were extended minimized the opportunity for differential non-response between the treatment and control groups. As is typical of Knowledge Network survey invitations, the invitation simply invited sample members to take a survey – without specifying the subject of the survey – and provided a link to click on if the sample member wanted to participate. Conditional on clicking on the link, only 3.8 percent of treatment group members and 4.0 percent of control group members failed to

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complete the survey.6 Conditional on completing the survey, the item-response rates were very high, generally well above 95%. Despite the blinded nature of the survey invitation, the response rate to the follow-up survey was 4.4 percent higher in the treatment group than the control group, and this difference is significant at the 5-percent level. This difference can be explained by the fact that members of the treatment group were more likely to still be active Knowledge Networks panelists as of April 1st, 2010, i.e., before we invited panelists to take the follow-up survey. Among panelists active on April 1st, the response rates for the treatment group and for the control group were not significantly different from each other. Thus, the differential response rate is unrelated to the topic or content of the follow-up survey, greatly alleviating concerns about it biasing the results.7 In forming our analysis sample, we drop one observation of a sample member for which the age according the Knowledge Network profile variable increased by three years between November 2008 and April 2010, which is logically impossible. This yields a final sample of 1595 observations for our main analyses. 4. Results 4.1 Sample Characteristics Table 1 shows the demographic composition of the sample that completed the follow-up survey. We split the table by the gender of the respondent because labor market responses often differ by gender. About three quarters of the sample performed paid work in the calendar month previous to the follow-up survey, and this fraction is only slightly lower for women. The labor force participation rate may seem high for this age group, but recall that only individuals who were working in 2008 were eligible to participate in the experiment. About forty percent of sample members are receiving Social Security benefits in 2010, which implies that a nonnegligible fraction is combining work and benefit receipt. The fraction receiving benefits in 2010 is slightly higher for women than for men.
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An additional 1 percent of respondents in each group was skipped out of the survey early in administration once screening questions revealed that they were not eligible for Social Security. 7 In theory, the information intervention could have affected panelist decisions about whether to remain active with Knowledge Networks. Given that panelists take weekly surveys, it is highly unlikely that our survey (out of the one hundred that a typical panelist would have taken over the past two years) would have had a quantitatively significant impact on the decision to remain active.

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We present the demographic characteristics of sample members at the time of the baseline survey (2008) because the regressions control for demographics as measured in 2008. We include the 2008 controls rather than the 2010 controls in the regressions because the treatment could conceivably affect some of the control variables in 2010. The specific controls that we present and use in regressions are maintained by Knowledge Networks and are therefore available for all sample members, even those who did not participate in the 2008 survey. Knowledge Networks recruits its panelists (e.g., using random digit dialing) such that the demographic characteristics of its panelists are broadly representative of those in the U.S. adult population. However, because we conditioned our sample on the respondent being in a narrow age range (90% of our panel is between 60 and 65 in November 2008) and on the respondent working in 2008, the demographic characteristics of our sample are not representative of the general population but instead are representative of working individuals approaching the age at which most people are making retirement decisions. The mean age in our sample in 2010 is 63. About 89 percent of the sample is white, and about 85 percent has at least some college or more. These percentages would be high for the general population in our age group, but recall that our sample is limited to working individuals. About 55 percent of the female respondents are married as are about 78 percent of the men. This difference is mostly accounted for by the fact that working women in this age range are much more likely to be divorced than are working men. The sample is geographically dispersed, with all regions of the country well represented. About 80 percent of respondents live in one- or twoperson households. Finally, respondents come from households from throughout the income distribution, though higher-income households are relatively well represented because of the higher-than-average labor market participation in our sample. We tested whether the demographic characteristics of the treatment and control groups are jointly statistically different by regressing treatment status on the full set of demographics listed in Table 1. The full set of demographics is jointly insignificant (p-value 0.765), consistent with successful random assignment. We control for baseline demographics in our regressions in order to increase the statistical precision of treatment effect estimates and to adjust for any random differences between the treatment and control groups in observable characteristics. 4.2 Manipulation Checks

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To verify that our experimental manipulation was effective and was implemented correctly, we asked two questions about the information intervention to all respondents at the end of the follow-up survey. We first asked them whether they recalled receiving “about a year ago … the following informational brochure” about Social Security rules, showing them a picture of the front page of our informational brochure on the screen. We then asked them whether they recalled participating “about a year ago … in an online module that provided additional information about Social Security rules, and which was tailored to each person’s individual information.” We further told the respondents “To help you remember this survey from the many other surveys you have taken, the online module contained stories about the experiences of two retirees, 91-year-old Leon and 66-year-old Elena,” and showed them the same pictures of these two retirees as they had seen in the online module. The first row of Table 2 presents the results for the recall rate of the brochure. The first column shows that 4.8 percent of sample members in the control group report receiving the brochure, even though they should not have received, and to the best of our knowledge did not receive, the brochure. We believe this 4.8 percent may have confused our brochure with other mailings they have received about Social Security. The second column shows that the recall rate is 28.6 percentage points higher for respondents in the treatment group than for those in the control group, and that this effect is highly significant.8 For consistency with the other regressions in the paper, the treatment effect is estimated in an OLS regression of the outcome variable (recall of the brochure) on an indicator of belonging to the treatment group and a set of demographic controls, but the estimate is extremely similar if the demographic controls are omitted. The significant treatment effect on the brochure recall rate is reassuring in that it shows that our treatment had an impact on the respondents and appears to have been implemented correctly. Yet, even in the treatment group, the recall rate is only 33.4%. In other words, a majority of respondents in the treatment group do not recall receiving the brochure, even though they should all have received it. Because it is possible to be influenced by information without recalling where or when one received the information, we do not believe the treatment effects on other outcome variables should be scaled up by the reciprocal of the treatment effect on recall.

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Our finding that a aignificant fraction of sample members recall a mailing about Social Security is consistent with the Mastrobuoni (2010) finding that the annual mailing of Social Security benefit statements affects expectations of future benefit levels.

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We therefore do not treat the recall rate as a first-stage regression for estimating a treatment-onthe-treated effect. Columns 3 and 4 show the control group recall rate and the treatment effect for female respondents while columns 5 and 6 show these estimates for male respondents. While the treatment effect is slightly larger for female respondents than for male respondents, this difference is not statistically significant, as column 7 indicates. The second row of Table 2 shows the results for the recall rate of participating in the web-tutorial on Social Security. While only 1.0 percent incorrectly recalls taking part in this tutorial, the treatment effect is only 8.4 percentage points. Though this estimate is highly statistically significant, it implies that merely 9.4 percent of respondents in the treatment group recall participating in the web-tutorial. We know that 76 percent of individuals in the treatment group in fact participated in the web-tutorial, and even among the participants the recall rate is only 10.2 percent. We surmise that the low treatment effect on recall of the web-tutorial is related to the fact that the respondents take online surveys from Knowledge Networks quite frequently (typically a couple per month), and it is hard for them to recall with confidence based on the relatively limited information we provided them whether they took our web-tutorial. The remaining columns of row 2 show that the treatment effect on recall of the web-tutorial is very similar for women and men. 4.3 Impacts of the Intervention on Labor Supply and Benefit Claiming Behavior The experiment was designed to investigate the effect of better knowledge about the Social Security benefit rules on (i) labor supply and (ii) Social Security claiming behavior. The first three rows of Table 3 present the effects on labor supply; the final row contains the results for claiming behavior. Our simplest measure of labor supply is the answer to the question whether the respondent performed any paid work in the previous calendar month (generally March 2010). In the control group, 74.4 percent of respondents worked in the previous month. This percentage may seem surprisingly high at first, but recall again that only individuals who were working in 2008 were included in the experiment. The information intervention increased this percentage by 4.2 percentage points, and this effect is (just) significant at the 5-percent level. The treatment effect is due almost entirely to the female subsample. Female respondents are 7.2 percentage

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points more likely to work if they received the information intervention, while male respondents in the treatment group are a statistically insignificant 0.3 percentage points more likely to work. We also measured labor supply by hours worked in the previous calendar month and own earnings in the previous calendar month. Consistent with our finding that labor force participation increased, we find positive point estimates for the effect of the information intervention on hours worked and earnings, but only the estimate on earnings for female respondents is significant at the 5-percent level. The dispersion across respondents in hours worked and earnings is relatively high, which increases standard errors relative to the mean value of the variable, and which makes it more difficult to detect a statistically significant effect. For example, the treatment effect for the participation variable already becomes significant when it reaches 5.6% of the control group mean. For the hours and earnings variables, however, it needs to reach at least 7.7% and 15.8% of the control group mean, respectively, to become statistically significant. It is not the case that the information intervention primarily induced labor force participation at only very minimal hours or earnings. If we redefine labor force participation to include just individuals who work at least 20 hours per month, we continue to find significant effects of the information treatment on labor force participation in the entire sample and in the subsample of female respondents. If we redefine labor force participation to only include individuals with at least $500 in monthly earnings, we continue to find significant effects of the information treatment on female labor force participation, though the effect for the entire sample falls just short of marginal significance (p-value 0.110). Overall, the first three rows of Table 3 provide clear evidence that the information treatment increased labor supply among female respondents. The final row of Table 3 shows results regarding the claiming of Social Security benefits. The sample is smaller than that for the labor supply results because we measured benefit claiming only among those who were 60 and older but not claiming benefits at the time of our baseline survey (November 2008). The table shows that just under 30 percent of those who were 60 or older but not claiming benefits at the time of our baseline survey have started claiming Social Security benefits by the time of the follow-up survey (April/May 2010). The information treatment did not have a significant effect on this percentage.

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Table 4 examines the robustness of the estimates in Table 3 to two alternative specifications: (i) the omission of control variables and (ii) a probit regression for binary outcome variables and a median regression for continuous outcome variables. Table 4 shows that the key finding of Table 3 – a positive treatment effect on female labor supply – is robust. The treatment effect on female labor force participation is statistically significant and similar in magnitude for all specifications. Similar to the findings in Table 3, the estimated effects on hours and earnings are positive and consistent in magnitude with the increase in labor force participation, but only occasionally statistically significant. Also in line with the findings of Table 3, we never detect a significant effect of our treatment on Social Security claiming behavior. 5. Understanding the Results This section contains additional analysis and discussion of the results, with a focus on three questions: (i) what aspects of the intervention led to the behavioral responses; (ii) why were the experimental impacts limited to female sample members; and (iii) whether we would expect to see additional behavioral responses if we were able to measure outcomes after more time has passed. 5.1 Which Aspects of the Intervention Led to the Behavioral Response? In designing this project, we and others we consulted with had significant doubts about whether the relatively mild intervention that was feasible given our resources could affect understanding about Social Security and alter behavior.9 We were particularly concerned that if we tested too weak an intervention and found no impact, we would have learned little – since it would always be possible that a slightly stronger intervention would have had an impact. We therefore decided to combine several different approaches to providing information in order to maximize the strength of the intervention, but did this subject to the constraint that the intervention had to be easily scalable, both in the cost per sample member and in the information delivery mechanism. Specifically, we offered each treatment group sample member both an informational mailing and an online tutorial. Within each, we combined specific information
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Lusardi and Mitchell (2007) similarly caution that one should not expect meaningful impacts from one-time financial literacy interventions – not because the financial education is ineffective per se but because the “cure” is likely to be inadequate.

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about Social Security rules, more general information about the importance of taking steps to ensure adequate income during retirement, and vignettes in which actual retirees discussed their own experiences in ways that reinforced the more specific information. The multi-faceted treatment raises the question of which aspects of the intervention led to the behavioral responses. Knowing the answer to this question is relevant if one were thinking scaling up this intervention or of applying this finding to another policy domain. Would a mailing alone be sufficient? A web-tutorial alone? Or was the reinforcement through delivering the information through two different mechanisms critical? Similarly, was the specific knowledge about the program essential, or are vignettes the best way to communicate information in a salient manner? Are there interaction effects from presenting information in multiple ways? Knowing the answers to these questions is important not only for learning how to scale up interventions at the least cost, but also for drawing normative policy recommendations from the findings. In particular, if the behavioral impact comes about because people now understand the program better and therefore make better decisions, then the normative implications are clear – we have made sample members better off by providing them with information. In contrast, if the intervention had its effect because it communicated an overall message of “continuing to work until older ages has benefits,” then the normative implications are more complex. Such an intervention could persuade people to continue to work for whom it is not optimal. The policy dilemma in this latter case is analogous to that in the literature in behavioral economics about setting defaults. On the one hand, it seems innocuous to change or set a default, as long as people have an opportunity to opt out. On the other hand, if defaults are shown to affect outcomes, then there will likely be some people who are affected by the defaults in a way that makes them worse off. Indeed, the analogy to defaults is even more direct since Social Security already provides a significant amount of information about retirement incentives to the U.S. population. To the extent that the content of the information and the way in which it is presented affects people’s choices, a proposal to alter what information is provided and how it is delivered is exactly analogous to altering a default. Because our information delivery mechanism, unlike the caseworker approach, allows for complete researcher control over what information is provided, it would in theory be straightforward to do follow-up experiments to determine which components of the intervention

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are necessary to produce the behavioral response we observed. One would simply randomize people into different treatments each containing different subsets of the experimental intervention. In practice, one would have to be selective since sample size considerations would limit the number of different permutations that could be tested. In the current experiment, we followed a different path to learn about the mechanisms through which the intervention had its effects. We asked questions to measure whether the intervention changed the amount of knowledge sample members had about different aspects of Social Security. Table 5 presents the effects on knowledge about the three main topics on which the information intervention focused: the likelihood that retirees live into their 90s, the effects of working longer and claiming later on Social Security benefits, and the earnings test. To assess knowledge about longevity, we asked respondents about (i) the likelihood that a typical 65 year-old individual of the same gender as the respondent lives to age 90 or beyond and (ii) the likelihood that at least one member of a typical 65 year-old couple lives to age 90 or beyond. Based on the 2005 life tables published by the National Center for Health Statistics, the correct answers to the first question is 21 percent for males and 33 percent for females, and the correct answer to the second question is 47 percent. Panel A of Table 5 shows that the respondents in the control group are too optimistic about survival probabilities. Male respondents in the control group on average think there is a 50 percent chance that a typical 65 year-old male will live to 90 or beyond. Female respondents in the control group on average think there is a 65 percent chance that a typical 65 year-old female will live to 90 or beyond. Respondents in the control group on average believe that the probability that at least one member of a 65 year-old couple lives to 90 or beyond is 65 percent. Our information intervention had no economically or statistically significant effects on the perceived survival probabilities. All point estimates are positive, consistent with respondents remembering the gestalt of our message (“You may live longer than you think”) rather than the longevity figures we provided, but the size of the treatment effect is 2 percentage points or less in all cases. Panels B and C present the effects of our information treatment on incentives to work longer and on incentives to delay claiming Social Security benefits. The responses to the four questions related to work incentives from Social Security benefits are presented in Panel B. The first of these four questions is a multiple choice question that asks whether the respondent’s Social Security benefits will be the same, higher, or lower if the respondent works fewer years.

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This is the exactly the same question as the one we analyzed in Liebman and Luttmer (2009), and, consistent with our previous findings, about two thirds of respondents in the control group believe that benefits increase with number of years worked.10 Row 3 shows that our information treatment had a very small positive but statistically insignificant effect on this response, which is perhaps not surprising given that the response was already correct on average in the control group. Row 4 analyzes the quantitative version of the same question, in which we ask respondents about the percentage change in their Social Security benefits per additional year worked. The mean response in the control group (5.2 percent increase per additional year worked) is similar to our earlier findings and, as we explain in Liebman and Luttmer (2009), overestimates the true return by about a factor of two for a typical worker. The information treatment has no economically meaningful or statistically significant effect on the quantitative answer to the perceived return.11 Because we were aware that our information intervention might have an effect on perceptions of incentives even when respondents’ ability to quantify incentives precisely is limited, we also asked a question in a more intuitive format. We asked whether you get a “better deal” or “worse deal” from Social Security if you work fewer years, where we explain that a “better deal” means that “money saved by paying into Social Security for fewer years would be greater than the cut to [your] Social Security benefits.” We pose this question to those respondents who believe that Social Security benefits increase with the number of years worked (recall that the size of this group was not affected by the information treatment). Row 5 shows that 53.7 percent of control group respondents believe they get a better deal by working more years, and that the information treatment increases this fraction by a statistically significant 6.5 percentage points. This effect is driven by female respondents, for whom the information treatment increases the fraction perceiving a better deal by 12 percentage points from a base of 49.7 percent. There is virtually no effect for male respondents, and we can reject the hypothesis that the effect for female and male respondents is the same at the 10-percent level. Thus, when
10

Social Security rules dictate that extra years of work will either increase or not change a person’s benefits, depending upon (i) whether or not the person claims solely on his or her own record and (ii) whether the additional year will be part of the 35 highest years that enter the AIME calculation. In Liebman and Luttmer (2009), we explain that it is plausible that the Social Security benefits to approximately one third of respondents would not be affected by a small change in number of years worked. 11 We also examined the effect of the information treatment on the variances of responses of variables that are measured on a continuous scale. Since these effects are quite sensitive to the treatment of outliers, we do not report them here.

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the question about incentives to work for more years is asked in a more intuitive format, our information treatment had an economically sizeable and statistically significant positive effect on the perception of this incentive for females – the population subgroup that exhibited a behavioral labor supply response. We also ask respondents a multiple choice question about which years of earnings determine one’s Social Security benefits. In line with results in our earlier paper, just under 40 percent of individuals in the control group are aware that the benefits are based on their X highest years of earnings (the other options for this multiple choice question were “most recent years of earnings,” “earnings at particular ages,” and “the number of years worked, but not the earnings in those years”). We ask them about the value of X, but in coding the answer as correct, we do not require them to correctly answer that X is 35. Row 6 shows that the information intervention marginally significantly raises the fraction of respondents that answers that benefits are based on years with highest earnings by 5.8 percentage points. Again, the effect appears to be driven by female respondents, for whom the effect is 10.2 percentage points and statistically significant, although we cannot reject the hypothesis that the effect for men and women is the same. Panel C examines the effect of the information treatment on perceptions of the effect of the age at which someone starts claiming Social Security benefits on benefit levels. We first ask respondents a multiple-choice question about the effect on their own benefits of claiming a year later than they were planning. Row 7 shows that about two thirds of the control group thinks their benefits would increase by claiming a year later, and that this fraction is not statistically significantly affected by our information intervention, though for the subgroup of female respondents the effect is positive and marginally statistically significant. To elicit the schedule of incentives at different claim ages (rather than just the respondent’s own planned claim age), we then ask respondents to fill out a table that shows how benefits vary by claim age for a typical worker that would receive $1000 in monthly benefits if he claimed at age 62. In this table, we elicit benefits for initial claim ages of 66, 70, and 74. Based on the entries in this table, we can calculate for each of the three age ranges (62-66, 66-70, and 70-74) whether (a) the respondent thinks benefits increase by delaying claiming in that age range and (b) the increase in benefits per year of delay in claiming as a percentage of the benefit level at the full-benefit retirement age (66 for our sample).

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Rows 8 through 13 shows the results for these six variables. Clear majorities (91% and 80%) in the control group correctly respond that benefits increase with claim age in the 62-66 and the 66-70 age ranges, but only a minority (37%) correctly responds that benefits don’t depend on claim age in the 70-74 age range. The mean perceived annual percentage increase in benefits per year of delayed claiming is quite accurate for the 62-66 age range (perception of 7.1% vs. actual value of 6.25%), is too low for the 66-70 age range (perception of 3.1% vs. actual value of 8.0%), and too high for the 70-74 age range (perception of 3.4% vs. actual value of 0%). The information treatment had no significant effects on these responses for the sample as a whole, though it increased the fraction of male respondents that perceive a positive return for delaying claiming between the ages of 66 and 70 by a statistically significant 5.6 percentage points. Consistent with this finding, the information treatment also has a positive and statistically significant effect on male respondents’ perceptions of the percentage increase in benefits per year of delayed claiming. The final area on which the information intervention focused was knowledge about the earnings test. A solid majority (62%) of the respondents in the control group is aware that current Social Security benefits will be reduced for someone aged 64 who already claims benefits but has earnings that exceed a certain amount. The information intervention did not significantly raise this percentage. A minority (39%) of the control group respondents who are aware of the earnings test also knows that any current reduction in benefits due to the earnings test is offset by an increase in benefits in the future. The information treatment increased this minority by a marginally statistically significant 5.9 percentage points, thus raising the perception in the treatment group of the returns to working after having started claiming benefits. Overall, we take away three points from Table 5. First, the information intervention generally moved perceptions towards the benefits of working longer and claiming later, even if many of the treatment effects are insignificant. This result suggests that at least part of the behavioral labor supply response is likely to have occurred through a higher perceived return to working more years. Second, the treatment had a strong and significant effect on the perceived incentive for women to work more years, which closely matches our earlier finding that the labor supply response is driven primarily by female respondents. Third, the information intervention has a significant effect on men’s perceptions of the return to delaying claiming between the ages of 66 and 70, which, as we will see later in Table 6, is consistent with the increase in planned

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Social Security claim ages among male respondents. Thus, while we cannot rule out that the “gestalt” of the information intervention may be partly responsible for the observed responses in realized and planned behavior, Table 5 strongly suggests that at least one pathway though which the intervention had an effect on behavior was by changing perceptions of incentives. 5.2 Why Are Behavioral Impacts Limited to Females? The labor supply impacts in Table 3 are accounted for almost entirely by female sample members, raising the question of why this is so. Male and female sample members were equally likely to recall the intervention, ruling out different exposure to the treatment as an explanation. In addition, for most questions about knowledge of Social Security, the control group means are similar for males and females, ruling out different baseline knowledge as an explanation. The one exception to this pattern is the “better deal” question which asks whether the incremental benefits one gets for working more years are enough to outweigh the incremental payroll tax contributions one makes. Females in the control group are a statistically significant 9.4 percentage points less likely than males to think that the incremental benefits are enough to outweigh the incremental contributions, and the treatment effect essentially brings females to the same level as males on this variable. We speculate that some women may believe that as secondary earners, they get little or no marginal Social Security benefits from additional work. This would have been true for most women twenty years ago, but the majority of women retiring today receive retirement benefits based on their own earnings record rather than based on 50 percent of their husband’s earnings. It is possible that our intervention affected women by counteracting the notion that working women get no benefit on the margin from Social Security. 5.3 Treatment Effects on Planned or Expected Behavior Because only just over one year elapsed between the administration of the information intervention and the collection of the follow-up data, the time span during which respondents could possibly change their behavior in response to the new information was limited.12 In an attempt to capture effects of the information intervention on future behavior, we also asked the respondents a number of questions about their planned future behavior. We think of these

12

Delaying the administration of the follow-up survey beyond one year has the drawback that it increases attrition from the panel.

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responses as more speculative than our responses on current behavior because the planned behavior measures are likely less reliable (cheap talk) and more noisy (respondents might not have firm plans yet or have thought future decisions through) than realized behavior. Keeping these caveats in mind, we nevertheless present the planned outcome variables in Table 6. The first variable about planned future behavior is the respondent’s retirement status defined using the same two questions as in our baseline survey. According to this definition, the respondent is retired if she does not “currently work for pay (with at least $2500 in annual earnings)” and does “not expect that in the future [she] will work for pay (with at least $2500 in annual earnings).” Apart from the expected future labor supply component, this question differs from our main labor supply variable in that it specifies a minimal level of annual earnings and that it is less precise about the timing (“currently” here versus “in [the previous calendar month] of 2010” for our main labor supply question”). The first row of Table 6 shows that the information treatment had no significant effect on the retirement measure, though, consistent with our labor supply findings, the point estimates are negative for the sample as a whole and particularly for the female subsample. We measure the respondent’s “point estimate” of her expected or realized retirement age by a question in which we ask “at what age did you last work for pay (with at least $2500 in annual earnings)” for those who reported being retired and “at what age do you plan to stop working for pay or to reduce your earnings to a minimal amount” for those reporting not being retired. We ask for the age in years and months. The second row reports that we find no significant effects on the expected or realized retirement age though, consistent with the first row, the point estimates are positive for the sample as a whole and the positive effects are concentrated in the subsample of female respondents. In an attempt to capture uncertainty about future retirement plans and in the hope to measure future retirement plans more precisely, we elicited the probability density function (pdf) of future retirement dates for non-retired respondents using the “bins and balls” question format from Delavande and Rohwedder (2008). We first explain the format of the question using a hypothetical example of uncertainty regarding “temperatures in Boston tomorrow.” We then present respondents with an interactive screen in which they are asked to allocate 20 balls over nine age bins and tell them the “more likely you think that you will retire at a given age, the more balls you should put in that age bin.” The age bins each have a one-year age range except

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for the bottom bin, which is labeled “[minimum age] or earlier,” and the top bin, which is labeled “[maximum age] or later.” The bins are centered on the respondent’s point estimate of her retirement age, but with the restriction that the minimum age is at least as high as her current age. Row 3 of Table 6 reports the effect of the information treatment on the mean of the pdf of expected retirement dates from the bins-and-balls question for those not yet retired and the realized retirement age for those already retired. The results are very similar to those for the point estimate of the retirement age reported in the previous row, and none of the estimates are significant. The information intervention could have helped respondents to decide when to retire without, on average, moving retirement dates forwards or backwards. We test for such effects by testing whether the information intervention reduced the standard deviation of the pdf of future retirement dates. We assign a standard deviation of zero to respondents who are already retired since their uncertainty about when to retire has been resolved. Row 4 of Table 6 shows that the information intervention had economically small and statistically insignificant effects on respondents’ uncertainty about their retirement date. In row 5 of Table 6, we report the effect of the information intervention on the respondents’ subjective expectation of “working for pay at least part-time” after the respondent has started claiming Social Security benefits. This question is asked on a 4-point likert scale and, for ease of presentation, we code respondents answering “very likely” or “likely” as a one. Close to 70 percent reports being likely or very likely to work after claiming benefits, and the information treatment had no statistically significant impact on this percentage. We ask respondents about their realized or planned age of starting to claim Social Security benefits, and, for those not claiming benefits, we also ask about their pdf of benefit claim ages using the bins-and-balls format. The nine bins run from “61 or earlier” to “69 or later.” We use these questions to create three variables: the point estimate of the claim age, the mean of the pdf of the claim age, and the standard deviation of the claim age. We use the realized claim age for those already claiming benefits and assign them a zero standard deviation. We limit the sample to those not claiming benefits at the time of the baseline survey in 2008 because only those not claiming benefits in 2008 could possibly be affected by the information treatment.

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Rows 6 and 7 show that the information treatment delayed the expected claim age by about a third of year for male respondents. This effect is statistically significant at the 10-percent level when we elicit claim age as a point estimate and significant at the 5-percent level when we measure it as the mean of the pdf. This finding is consistent with our earlier finding that the information intervention increased the perception among male respondents of the return to delaying claiming between the ages of 66 and 70 (rows 9 and 12 of Table 5). Indeed, we find that the delay in expected claiming among male respondents takes the form of more respondents planning to claim beyond the age of 66; in response to information treatment, male respondents put 8 percent more probability mass in the bins corresponding to ages 67 and higher, and this effect is significant at the 5-percent level. The information treatment, however, did not statistically significantly affect the expected claim ages among female respondents or in the entire sample, and we can reject the hypothesis that male and female respondents adjusted their claim age in the same way in response to the information treatment. As Row 8 shows, we do not find a significant effect of the information treatment on respondents’ uncertainty about their expected claim age. Overall, Table 6 shows that the information intervention generally had no significant effects on planned or expected outcomes. We suspect that this lack of statistically significant findings is partly due to the fact that expectations are not measured as precisely as realized outcomes. The one exception to the lack of statistically significant findings is that the information intervention increased the expected claim age among male respondents. This change in expected behavior closely matches our earlier finding that the information intervention increased the perceived return to delaying claiming among men. We take this close correspondence between changes in perceived incentives and changes in expected behavior as a further indication that the intervention worked at least partly through changing perceptions of incentives. 5. Conclusion The field experiment described in this paper demonstrates that a relatively mild informational intervention can have important impacts on people’s retirement behavior. Moreover, we show that the intervention affected people’s perceptions of the returns to remaining in the labor force and that both the labor supply impacts and the impacts on

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perceptions were concentrated in the same population subgroups – suggesting that the behavioral response can be attributed at least in part to updated information about Social Security. We do not, however, have a conclusive explanation for why the labor supply impacts were concentrated among female sample members, though we speculate that the intervention may have countered perceptions that, as secondary earners, women receive no additional Social Security benefits from incremental labor effort. This study was designed to answer the threshold question of whether an easily scalable information intervention could alter behavior. The intervention therefore delivered information in several different mutually reinforcing ways. Before drawing policy implications from this intervention and other similar interventions in which information provision alters behavior, it will be important to learn more about the mechanisms through which such interventions produce their effects. To the extent that informational interventions affect behavior by educating sample members and allowing them to make better choices, such interventions unambiguously raise sample member welfare. In contrast, if interventions have their impact by delivering a general message such as “working to older ages is better,” then the normative implications are more complicated since the message may or may not be accurate for a particular sample member. Because our experimental mechanism allows for complete researcher control over the message delivered to sample members, it would be straightforward to conduct a follow-up study that delivered only subsets of the intervention so as to determine the relative importance of each form of information as well as the existence of interaction effects from reinforcing the message by providing information in multiple ways.

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References Bernheim, B. Douglas. 1988. “Social Security Benefits: An Empirical Study of Expectations and Realizations,” in Rita Ricardo-Campbell and Edward P. Lazear (eds.), Issues in Contemporary Retirement, Stanford: Hoover Institution Press, pp. 312-345. Chan, Sewin, and Ann Huff Stevens. 2008. “What You Don’t Know Can’t Help You: Pension Knowledge and Retirement Decision-Making,” Review of Economics and Statistics, 90(2), pp. 253-266. Chetty, Raj, Adam Looney, and Kory Kroft. 2009. “Salience and Taxation: Theory and Evidence”, American Economic Review, 99(4), pp. 1145-1177. Chetty, Raj, and Emmanuel Saez. 2009. “Teaching the Tax Code: Earnings Responses to an Experiment with EITC Recipients,” NBER Working Paper No. 14836. Delavande, Adeline, and Susann Rohwedder. 2008. “Eliciting Subjective Expectations in Internet Surveys,” Rand Working Paper 589. Dominitz, Jeff, Angela Hung, and Arthur van Soest. 2007. “Future Beneficiary Expectations of the Returns to Delayed Social Security Benefit Claiming and Choice Behavior,” Michigan Retirement Research Center Working Paper 2007-164. Duflo, Esther, William Gale, Jeffrey Liebman, Peter Orszag, and Emmanuel Saez. 2006. “Savings Incentives for Low- and Middle-Income Families: Evidence from a Field Experiment with H&R Block,” Quarterly Journal of Economics, 121(4), pp. 1311-1346. Gustman, Alan L., and Thomas L. Steinmeier. 2005. “Imperfect Knowledge of Social Security and Pensions,” Industrial Relations, 44(2), pp. 373-97. Kling, Jeffrey R., Sendhil Mullainathan, Eldar Shafir, Lee Vermeulen, and Marian V. Wrobel. 2009. “Misperception in Choosing Medicare Drug Plans.” Unpublished manuscript. Harvard University. Liebman, Jeffrey B., Erzo F.P. Luttmer, and David G. Seif. 2009. “Labor Supply Responses to Marginal Social Security Benefits: Evidence from Discontinuities,” Journal of Public Economics, 93(11-12), pp. 1119-1284. Liebman, Jeffrey B., and Erzo F.P. Luttmer. 2009. “The Perception of Social Security Incentives for Labor Supply and Retirement: The Median Voter Knows More Than You’d Think.” Unpublished manuscript, Harvard University. Liebman, Jeffrey B. and Richard Zeckhauser. 2004. “Schmeduling.” Unpublished manuscript. Harvard University. Liebman, Jeffrey B., and Richard J. Zeckhauser. 2008. “Simple Humans, Complex Insurance, Subtle Subsidies,” in Using Taxes to Reform Health Insurance: Pitfalls and Promises, Henry J. Aaron and Leonard E. Burman (eds.), Washington D.C.: Brookings Institution Press, 230-252. Lusardi, Annamaria, and Olivia Mitchell. 2007. “Financial Literacy and Retirement Preparedness. Evidence and Implications for Financial Education,” Business Economics, 42(1), pp. 35-44. Mastrobuoni, Giovanni. 2010. “The Role of Information for Retirement Behavior: Evidence based on the Stepwise Introduction of the Social Security Statement,” Unpublished Manuscript, Collegio Carlo Alberto. Mitchell, Olivia S. 1988. “Worker Knowledge of Pension Provisions,” Journal of Labor Economics, 6(1), pp. 21-39. Rohwedder, Susann, and Kristin J. Kleinjans. 2006. “Dynamics of Individual Information about Social Security.” Unpublished Manuscript, Rand. 25

APPENDIX A – BROCHURE MAILED TO TREATMENT GROUP

Why was I sent this brochure?
We are researchers at Harvard University who are interested in understanding the amount of Social Security benefits that people receive or expect to receive. Recently, we invited you to take an online survey by Knowledge Networks. Several respondents told us that they would like additional information about Social Security, so we prepared this informational brochure for you. For confidentiality, Knowledge Networks did not share your personal information with us but agreed to send you this brochure on our behalf. This is not meant to be a comprehensive source of Social Security facts; so if you would like more information about Social Security, see the references listed at the end of this brochure. In this brochure, we highlight three things you should consider as you approach retirement: 1. You may live longer than you think. 2. Postponing your retirement typically means more money for a more comfortable retirement. 3. It pays to work, even while you’re receiving Social Security benefits. We hope that you find the information useful as you plan for your future.

2

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1 cover? • • 90.

You may live longer than you think.

“I worry about not meeting basic needs.”
Consider the following story about Mr. Reichson, who regrets retiring when he did . Mr. Reichson worked as a software engineer and retired at age 57. He didn’t save for retirement and is feeling constrained by his current financial situation. In his own words, “I worry about not meeting basic needs— clothing, grooming accessories…It’s too much money to go on outings or things like that…I can’t afford it.” If he could do it all over again, he would retire “as late as possible…To keep getting an income by working—it’s a much larger income than Social Security benefits.” Mr. Reichson is right. If he had worked longer, he would have had more money to enjoy his retirement. But he may not have realized that a longer work history usually also translates into higher Social Security benefits, as we’ll see in the next section.

You might have already started saving money for retirement, but have you thought about how many years you’ll need to

According to the National Center for Health Statistics, out of all 65-year-olds in America today, about one in four will live to age 90. For the average 65-year-old couple living in America today, there is a 47% chance that at least one spouse will live to age

With many people living into their 90s, savings often need to last for a long time after retirement. As you’re approaching retirement, you’ll need to make some important decisions. Proper planning can mean the difference between living comfortably and just covering your basic needs. There are a few simple things you can do to make sure you’re prepared for your retirement, and we’ll talk about some of them in this brochure.

2

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2
• •

Postponing your retirement typically means more money for a more comfortable retirement.

“People should think it over and plan ahead... stick it out a few more years until you’re 67.”
The following is a story about Jean, who wishes she had prepared more for retirement. Jean worked as a housekeeper for 12 years, but she didn’t plan for her retirement. According to Jean, “I never watched my finances that well…I should have done it differently, worked.” She urges people nearing retirement to wait until their full retirement age before retiring: “People should think it over and plan ahead…stick it out a few more years until you’re 67.”

Many people decide to retire early without carefully considering the pros and cons of their retirement age. Here are three reasons why it can benefit you to wait a few extra years before retiring: Working longer will help you put away more money for retirement. If you work a few more years, you’ll not only add to your existing savings, but you’ll avoid spending the money you’ve already saved in the meantime. Social Security benefits generally go up the longer you wait to claim them, up until age 70. For each year you postpone claiming, your retired worker benefits increase by about 9.5% of the amount you’d get at age 62. For instance, a worker who would get $1000 in monthly retired worker benefits if he claimed at age 62 could expect about $1330 in benefits claiming at age 66, while postponing claiming until age 70 would translate into about $1760 in monthly benefits (see graph on next page). • In addition to the increase from delayed claiming, working a few more years generally raises your Social Security benefits because most people’s benefits depend on their work history.

How Delayed Claiming Increases Monthly Social Security Benefits
$1,800 $1,600 $1,400 $1,200 $1,000 $800 $600 $400 $200 $0

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* This graph illustrates the example on the previous page for a person expecting $1000/month in retired worker benefits at age 62

63 64 65 66 67 68 Age of First Claiming Benefits

69

70

4

5

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• •

It pays to work, even while you’re receiving Social Security benefits.

“I’m not going to retire now... I want to retire at about 80.”
Giulia, 72, understands the advantage of working while receiving Social Security. Even though she first claimed benefits at age 62, she still works nights in a restaurant kitchen for the added financial security. Giulia worries about making rent payments and uses the extra income to support her grandchildren. She often talks to other seniors about the benefits of working. “That’s why old people started to work again… in the senior center… 85-year-olds and they are still working.” Giulia does not regret her decision to work and plans to continue for a few more years: “I’m not going to retire now… I want to stay for another 6-7 years; I want to retire at about 80.” For many seniors like Giulia who are already receiving Social Security benefits, the extra income from working can create a better financial situation. A few hours of work each week can help make ends meet, especially for those with little or no savings.

Some people who claim benefits at age 62 stop working between ages 62 and 66 because they fear they will lose their benefits. In truth, if your benefits are reduced from working, the money is not actually lost. Instead, the reduction is returned to you later in the form of increased benefits. This complicated provision is called the Earnings Test. Here’s how it works: Working longer will help you put away more money for retirement. If you work a few more years, you’ll not only add to your existing savings, you will also avoid spending the money you’ve already saved in the meantime. If you do claim before full retirement age, your monthly benefits will be reduced if you earn more than certain threshold per year; in 2009, that amount is $14,160. • • Once you reach your full retirement age, your benefits are never reduced, no matter how much you earn. If your benefits were ever lowered from working, they are recalculated to a higher amount when you reach full retirement age—meaning that the total benefits you can expect to receive over your lifetime are not really cut, even if they might be temporarily reduced.

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This study is conducted by professors Liebman and Luttmer at Harvard’s Kennedy School of Government. If you have any questions about this study, feel free to contact professor Luttmer at the address listed below: *

We have also listed a number of resources below in case you have further questions about Social Security.

Prof. Erzo Luttmer Associate Professor of Public Policy John F. Kennedy School of Government Harvard University 79 JFK Street, Mailbox 25 Cambridge, MA 02138 erzo_luttmer@harvard.edu
* Contacting the researchers directly would likely expose your identity to those conducting the study. For any questions related to your selection and participation in this panel, please call the Knowledge Networks panel relations group at 1-800-782-6899.

The U.S. Social Security Administration http://www.ssa.gov/ 1-800-772-1213

The American Association of Retired Persons (AARP) http://www.aarp.org/money/social_security/ 1-888-OUR-AARP (1-888-687-2277)

USA.gov: Senior Citizens’ Resources http://www.usa.gov/Topics/Seniors.shtml 1-800-FED-INFO (1-800-333-4636)

Knowledge Networks Ashley Business Park, Bldg G 570 South Avenue East Cranford, NJ 07016

APPENDIX B –WEB-TUTORIAL ON SOCIAL SECURITY A note about the “syntax” of this questionnaire Example questions: Q1.2: [BROCH_REC] Whether or Not Respondent Received Brochure Did you receive the informational brochure in the mail? Yes………………............................................................................................ 1 No…………... .................................................................................................. 2 Q1.9: [Longevity Introduction] Background Information about Living Longer When planning your retirement, it’s important to budget for more years than you may realize because Americans are living longer than ever before. [SHOW IF XGENDER==1 (MALE RESPONDENT)] Out of all the 65-year-old men in America today, about one in five will live to the age of 90 or beyond, and about one in ten will live to the age of 95 or beyond. [SHOW IF XGENDER==2 (FEMALE RESPONDENT)] Out of all the 65-year-old women in America today, about one in three will live to the age of 90 or beyond, and about one in six will live to the age of 95 or beyond. [SHOW IF XMARIT ==1 (i.e., married)] You should also think about your [SPOUSE]’s financial situation when planning for retirement. For the average 65-year-old couple living in America today, there is a 47% chance that at least one spouse will live to age 90 or beyond. Explanation: • Q1.2: [BROCH_REC] Whether or Not Respondent Received Brochure denotes (i) the question number, (ii) the variable name, and (iii) a description of the question. None of this appears on the screen for the respondents. • Remarks between square brackets are just for the programmer. • Variables are denoted in all capitals. E.g. XGENDER is a variable. • Any programming remarks before the question name apply to the whole question. Variable names that should be replaced by the string they hold are indicated in all capitals between square brackets. E.g. [SPOUSE] is replaced by the string “husband” or “wife”, which is the content of the variable SPOUSE. • The solid lines indicate that a new screen should be shown. • The numbers in parentheses in front of the selections boxes do not appear on the screen; they only indicate the value the variable will take if the relevant selection box is checked. The following data-only variables are created for all respondents: XGENDER denotes gender, 1: Male, 2: Female CREATE A NEW DATA ONLY VARIABLE SPOUSE: SET SPOUSE = “husband” IF XGENDER==2 SET SPOUSE = “wife” IF XGENDER==1 CREATE A NEW DATA ONLY VARIABLE ILL_HISHER: SET ILL_HISHER = “his” IF XGENDER==1 SET ILL_HISHER = “her” IF XGENDER==2 CREATE A NEW DATA ONLY VARIABLE ILL_HESHE: SET ILL_HESHE = “he” IF XGENDER==1 SET ILL_HESHE = “she” IF XGENDER==2 CREATE A NEW DATA ONLY VARIABLE ILL_HIMHER: SET ILL_HIMHER = “him” IF XGENDER==1 SET ILL_HIMHER = “her” IF XGENDER==2 CREATE A NEW DATA ONLY VARIABLE TOM_ANN:

B- 1

SET TOM_ANN = “Tom” IF XGENDER==1 SET TOM_ANN = “Ann” IF XGENDER==2

Section 1: Longevity Q1.1: [General Introduction] Introduction to the Online Module, No Specific SS Rules Yet We are researchers at Harvard University who are interested in understanding the amount of Social Security benefits that people receive or expect to receive. Recently, we invited you to take an online survey. Since several respondents told us that they would like additional information about Social Security rules, we sent an informational brochure to you. This online module is designed to complement the brochure because it provides additional information and is tailored to your situation. Participation in this online module will help create clearer and easier-to-understand materials about the Social Security program.

Q1.2: [BROCH_REC] Whether or Not Respondent Received Brochure Did you receive the informational brochure in the mail? Yes………………............................................................................................ 1 No…………... .................................................................................................. 2 [Ask if BROCH_REC==1 AND XMARIT≠1 (i.e. not married)] Q1.3: [BROCH_READ1] Whether or Not Respondent Read Brochure Did you read the brochure? Yes, I read the brochure……………… .......................................................... 1 No, I did not read the brochure………………............................................... 2

[Ask if BROCH_REC==1 AND XMARIT ==1 (i.e., married)] Q1.4: [BROCH_READ2] Whether or Not Respondent or Spouse Read Brochure Did you or your [SPOUSE] read the brochure? Yes, I read the brochure……………… .......................................................... 1 Yes, my [SPOUSE] read the brochure…………... ....................................... 2 Yes, both my [SPOUSE] and I read the brochure……………… ................ 3 No, neither of us read the brochure…………... ............................................. 4 Q1.5: Confirming Age of the Respondent In order for us to provide you with the most accurate information about Social Security, we need to know your date of birth. Knowledge Networks has your date of birth listed as [XBMONTH] [XBDAY], [XBYEAR]. Is this correct? Yes………………............................................................................................ 1 No…………... .................................................................................................. 2

[Ask if Q1.5==2 (i.e., listed birth date is incorrect)] Q1.6: [XBMONTH] [XBDAY], [XBYEAR] Age Correction Please enter your date of birth. Here is an example of how to enter in a date: if you were born on August 23, 1954, you would enter the number 8 for month, 23 for day, and 1954 for year.

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Type in the numbers to answer MM _____ DD ____ YYYY _________

[Create new variable BIRTHYEAR=XBYEAR. If XBDAY==1 AND XBMONTH==1, SET BIRTHYEAR = (XBYEAR – 1).] [SHOW IF XBMONTH==1 AND XBDAY ==1] Q1.7: [JAN_BIRTH] Follow-Up for Respondents Born January 1 When calculating benefits, Social Security treats people born on January 1 as though they were born during the previous year. Since you were born on January 1st in [XBYEAR], your benefits are calculated as if you were born in [BIRTHYEAR]. Therefore, for the remainder of this survey, we will give you examples for a typical person born in [BIRTHYEAR]. [For all respondents: Drop the following variables: XBYEAR, XBMONTH, XBDAY] Q.1.8: [WHOSE_RECORD] Type of Social Security benefits the person expects to claim We also need to know the type of Social Security benefits you claim or expect to claim. On whose earnings record do you think your benefits are based? They are based … ... only on my own earnings record.................................................................................................................................... 1 ... only on my current [SPOUSE]’s earnings record......................................................................................................... 2 ... only on my previous [SPOUSE]’s earnings record ...................................................................................................... 3 ... only on my late [SPOUSE]’s earnings record .............................................................................................................. 4 ... on both my own and my current [SPOUSE]’s earnings records ................................................................................. 5 ... on both my own and my previous [SPOUSE]’s earnings records............................................................................... 6 ... on both my own and my late [SPOUSE]’s earnings records ....................................................................................... 7 Q1.9: [Longevity Introduction] Background Information about Living Longer When planning your retirement, it’s important to budget for more years than you may realize because Americans are living longer than ever before. [SHOW IF XGENDER==1 (MALE RESPONDENT)] Out of all the 65-year-old men in America today, about one in five will live to the age of 90 or beyond, and about one in ten will live to the age of 95 or beyond. [SHOW IF XGENDER==2 (FEMALE RESPONDENT)] Out of all the 65-year-old women in America today, about one in three will live to the age of 90 or beyond, and about one in six will live to the age of 95 or beyond. [SHOW IF XMARIT ==1 (i.e., married)] You should also think about your [SPOUSE]’s financial situation when planning for retirement. For the average 65-year-old couple living in America today, there is a 47% chance that at least one spouse will live to age 90 or beyond. Q1.10: [Longevity Story] Story about Living Longer Among those nearing retirement age, it’s common to worry about future finances, and for good reason: retirees must grapple with the reality of paying for living expenses for years to come. However, with a bit of planning, retirees can live comfortably well beyond retirement age. The following is a story about 91-year-old Leon, who says, “I’m the other side of 91.”

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Leon worked as a chemistry teacher for 26 years before retiring at age 70. Each year, he put aside the maximum amount out of his paycheck toward his pension. He also knew that waiting until age 70 to claim Social Security would increase his monthly benefits. Leon says that his current financial situation is “better than I’ve ever had. Between my pension and my Social Security…I don’t worry about anything.” According to Leon, people approaching retirement should continue to work as long as they’re healthy. Q1.11: [LONG_PLAN] “Plan B,” Being Stuck without Savings How would you cover your expenses if you live longer than anticipated, and your savings run low? Check all that apply. [Show check boxes for each; allow multiple picks] [Q.1.11_A] I’m not worried about running out of money because I have adequate savings [Q.1.11_B] My pension benefits would be enough to cover living expenses [Q.1.11_C] My Social Security benefits would suffice [Q.1.11_D] I would eliminate my future travel plans [Q.1.11_E] I would cut back on extra expenses, like movies and eating out [Q.1.11_F] I would reduce my spending on food and clothing [Q.1.11_G] I would move in with children/relatives [Q.1.11_H] I would rely on public housing or government assistance Q1.12: [LIV_EXP] How People Generally Cover Living Expenses when They Retire (“Plan A”) Would you consider any of the following to make sure that you have enough money to cover many years after your retirement? Check all that apply. [Show check boxes for each; allow multiple buttons to be selected] [Q1.12_A] Claiming Social Security benefits later so that my monthly benefits are higher [Q1.12_B] Working more years to build my savings to a more comfortable level [Q1.12_C] Delaying retirement so that my monthly Social Security benefits are higher [LET Q1.12_A take on a value of 1 if not selected and a value of 2 if selected. Similarly for the other two radio buttons] Q1.13: [Living Expenses Follow-Up] Follow-Up to Living Expenses Question (“Plan A”) [SHOW IF Q1.12_A==2 AND Q1.12_B==2 AND Q1.12_C==2 (Respondent would consider all possibilities)] You just answered that you’d consider all three ways to ensure you have enough money for retirement: • • • Claiming Social Security benefits later so that my monthly benefits are higher Working more years to build my savings to a more comfortable level Delaying retirement so that my monthly Social Security benefits are higher

We’re going to take a look at each of these choices in more detail throughout this survey. During the process, we’ll be giving you a lot of information, and you may learn something new that you didn’t already know about Social Security. [SHOW IF Q1.12_A==1 OR Q1.12_B==1 OR Q1.12_C==1 (Respondent would not consider all possibilities)] You just answered that you would not consider all three ways to ensure you have enough money for retirement: • Claiming Social Security benefits later so that my monthly benefits are higher

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• •

Working more years to build my savings to a more comfortable level Delaying retirement so that my monthly Social Security benefits are higher

We’re going to take a look at each of these choices in more detail throughout this survey. During the process, we’ll be giving you a lot of information, and you may learn something new that you didn’t already know about Social Security. Section 2: Higher Benefits from Claiming Later, Working Longer, and Saving More [IF Q1.8==4 OR Q1.8==7, SKIP TO SECTION 3 (Respondent expects to claim widow/widower benefits; Only a couple percent are widows, so going through the trouble of explaining widow benefits, which are rather complicated, is not worth it)] Q.2.1: [Claim Age Introduction] Benefits to waiting until full retirement age IF BIRTHYEAR ≤ 1937, THEN YEARS=65, MONTHS=0, MONTHTOT=36 IF BIRTHYEAR == 1938, THEN YEARS=65, MONTHS=2, MONTHTOT=38 IF BIRTHYEAR == 1939, THEN YEARS=65, MONTHS=4, MONTHTOT=40 IF BIRTHYEAR == 1940, THEN YEARS=65, MONTHS=6, MONTHTOT=42 IF BIRTHYEAR == 1941, THEN YEARS=65, MONTHS=8, MONTHTOT=44 IF BIRTHYEAR == 1942, THEN YEARS=65, MONTHS=10, MONTHTOT=46 IF BIRTHYEAR ≥ 1943 AND BIRTHYEAR ≤ 1954, THEN YEARS=66, MONTHS=0, MONTHTOT=48 IF BIRTHYEAR == 1955, THEN YEARS=66, MONTHS=2, MONTHTOT=50 IF BIRTHYEAR == 1956, THEN YEARS=66, MONTHS=4, MONTHTOT=52 IF BIRTHYEAR == 1957, THEN YEARS=66, MONTHS=6, MONTHTOT=54 IF BIRTHYEAR == 1958, THEN YEARS=66, MONTHS=8, MONTHTOT=56 IF BIRTHYEAR == 1959, THEN YEARS=66, MONTHS=10, MONTHTOT=58 IF BIRTHYEAR ≥ 1960, THEN YEARS=67, MONTHS=0, MONTHTOT=60 IF BIRTHYEAR ≤ 1934, THEN CRED=11 IF BIRTHYEAR == 1935 OR BIRTHYEAR == 1936, THEN CRED=12 IF BIRTHYEAR == 1937 OR BIRTHYEAR == 1938, THEN CRED=13 IF BIRTHYEAR == 1939 OR BIRTHYEAR == 1940, THEN CRED=14 IF BIRTHYEAR == 1941 OR BIRTHYEAR == 1942, THEN CRED=15 IF BIRTHYEAR ≥ 1943, THEN CRED=16 [RETIREMENT Benefits at age XX as a percentage of benefits at full-benefit age] BEN62 = 100 - 36*(5/9) - (MONTHTOT-36)*(5/12) BEN64 = 100 - (MONTHTOT-24)*(5/9) IF MONTHTOT ≥ 48, THEN BEN66=100 - (MONTHTOT-48)*(5/9) IF MONTHTOT< 48, THEN BEN66=100 + (48-MONTHTOT)*CRED/24 BEN68 = 100 + (72-MONTHTOT)*CRED/24 BEN70 = 100 + (96-MONTHTOT)*CRED/24 [Percentage increase relative to benefit at age 62 for own RETIREMENT benefit] PCT_FULL = 100 * ( 100 / BEN62 - 1) PCT64 = 100 * (BEN64 / BEN62 - 1) PCT66 = 100 * (BEN66 / BEN62 - 1) PCT68 = 100 * (BEN68 / BEN62 - 1) PCT70 = 100 * (BEN70 / BEN62 - 1) [SPOUSE Benefits at age XX as a percentage of benefits at full-benefit age] SP_BEN62 = 100 - 36*(25/36) - (MONTHTOT-36)*(5/12) SP_BEN64 = 100 - (MONTHTOT-24)*(25/36) IF MONTHTOT ≥ 48, THEN SP_BEN66=100 - (MONTHTOT-48)*(25/36) IF MONTHTOT < 48, THEN SP_BEN66=100 [Percentage increase relative to benefit at age 62 for own SPOUSE benefit] SP_PCT_FULL = 100 * ( 100 / SP_BEN62 - 1) SP_PCT64 = 100 * (SP_BEN64 / SP_BEN62 - 1) SP_PCT66 = 100 * (SP_BEN66 / SP_BEN62 - 1)

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[SHOW IF XAGE < 62 AND (Q1.8==1 OR Q1.8==Refused) (Respondent is younger than the earliest claim age, plans to claim on own earnings record)] If you’re like many people nearing retirement age, you’ve probably already thought about when you’re going to start collecting Social Security benefits. You can start collecting retirement benefits as early as age 62 or as late as age 70. Your Social Security benefits go up the longer you wait to claim them, up until age 70. For a person born in [BIRTHYEAR], full retirement age is [YEARS] years, [MONTHS] months old. If you start claiming retirement benefits at your full retirement age, your benefits will be [PCT_FULL]% higher than what you would receive if you started claiming at 62. Waiting until age 70 to claim retirement benefits would give you [PCT_70]% higher benefits than what you would receive at age 62. [SHOW IF XAGE < 62 AND (Q1.8==2 OR Q1.8==3 OR Q1.8==5 OR Q1.8==6) (Respondent is younger than the earliest claim age, plans to claim on spouse’s earnings record)] If you’re like many people nearing retirement age, you’ve probably already thought about when you’re going to start collecting Social Security benefits. You answered before that you plan to claim at least part of your benefits based on the earnings history of your current or previous [SPOUSE]. You can start collecting Social Security spouse benefits as early as age 62. Your Social Security spouse benefits go up the longer you wait to claim them, up until full retirement age. For a person born in [BIRTHYEAR], full retirement age is [YEARS] years, [MONTHS] months old. If you start claiming your spouse benefits at your full retirement age, your benefits will be [SP_PCT_FULL]% higher than what you would receive if you started claiming at 62. [SHOW IF 623500, SET BEN_LEVEL=3500] Q.2.5: [BEN_MAX] Benefits Level Follow-up D [SHOW IF SS_STATUS==1 (RECEIVING BENEFITS)] Please assume for the remainder of the survey that your Social Security benefits are $3500 per month. [SHOW IF SS_STATUS==2 AND RETIRED==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] Please assume for the remainder of the survey that if you start collecting Social Security at age [CLAIM_AGE], your Social Security benefits will be $3500 per month. [SHOW IF SS_STATUS==2 AND RETIRED==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] Please assume for the remainder of the survey that if you stop working for pay at age [RET_AGE] and start collecting Social Security at age [CLAIM_AGE], your Social Security benefits will be $3500 per month. [SHOW IF MARRIED==1 AND SS_STATUS_S≠3] Q.2.6: [BEN_LEVEL_S] Expected Spouse’s Social Security Benefits Level [SHOW IF SS_STATUS_S==1 (RECEIVING BENEFITS)] Approximately how much are your [SPOUSE]’s monthly Social Security benefits? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household). $________ per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] How much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess.

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(Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] How much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] stops working for pay at age [RET_AGE_S] and starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [FOR ALL RESPONDENTS: CREATE A NEW VARIABLE BEN_ORIG_S = BEN_LEVEL_S] [ASK IF (BEN_LEVEL_S=MISSING OR BEN_LEVEL_S==0) AND MARRIED==1 AND SS_STATUS_S≠3] Q.2.7: [BEN_LEVEL2_S] Spouse Benefits Level Follow-up A We understand this is a hard question to answer. We would really like to have your best guess, even if this guess is not exactly right. [SHOW IF SS_STATUS_S==1 (RECEIVING BENEFITS)] As your best guess, approximately how much are your [SPOUSE]’s monthly Social Security benefits? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household). $________ per month. [IF BEN_LEVEL2_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL2_S] [SHOW IF SS_STATUS_S==2 AND RETIRED_S==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] As your best guess, how much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [IF BEN_LEVEL2_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL2_S] [SHOW IF SS_STATUS_S==2 AND RETIRED_S==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] As your best guess, how much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] stops working for pay at age [RET_AGE_S] and starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [IF BEN_LEVEL2_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL2_S] [ASK IF BEN_LEVEL_S>3500 AND MARRIED==1 AND SS_STATUS_S≠3] Q.2.8: [BEN_LEVEL3_S] Spouse Benefits Level Follow-up B Social Security benefits are never higher than $3500 per month. [SHOW IF SS_STATUS_S==1 (RECEIVING BENEFITS)] Given that monthly benefits are less than $3500, how much are your [SPOUSE]’s monthly Social Security benefits? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household). $________ per month. [IF BEN_LEVEL3_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL3_S]

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[SHOW IF SS_STATUS_S==2 AND RETIRED_S==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] Given that monthly benefits are less than $3500, how much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [IF BEN_LEVEL3_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL3_S] [SHOW IF SS_STATUS_S==2 AND RETIRED_S==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] Given that monthly benefits are less than $3500, how much do you expect your [SPOUSE]’s monthly Social Security benefits to be if your [SPOUSE] stops working for pay at age [RET_AGE_S] and starts collecting benefits at age [CLAIM_AGE_S]? Even if you do not know exactly, please give your best guess. (Please report any Social Security benefits paid to your [SPOUSE], not benefits paid to you or other members in your household. Also, please give your answer in today’s dollars, and ignore any inflation that may occur between today and when your [SPOUSE] collects benefits). $________ per month. [IF BEN_LEVEL3_S≠ MISSING, SET BEN_LEVEL_S=BEN_LEVEL3_S] [SHOW IF (BEN_LEVEL_S=MISSING OR BEN_LEVEL_S==0) AND MARRIED==1 AND SS_STATUS_S≠3] [IF BEN_LEVEL_S=MISSING, SET BEN_LEVEL_S=1000] Q.2.9: [BEN_DEFAULT_S] Spouse Benefits Level Follow-up C [SHOW IF SS_STATUS_S==1 (RECEIVING BENEFITS)] Please assume for the remainder of the survey that your [SPOUSE]’s Social Security benefits are $1000 per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] Please assume for the remainder of the survey that if your [SPOUSE] starts collecting Social Security at age [CLAIM_AGE_S], [SP_HISHER] Social Security benefits will be $1000 per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] Please assume for the remainder of the survey that if your [SPOUSE] stops working for pay at age [RET_AGE_S] and starts collecting Social Security at age [CLAIM_AGE_S], [SP_HISHER] Social Security benefits will be $1000 per month. [SHOW IF BEN_LEVEL_S>3500 AND MARRIED==1 AND SS_STATUS_S≠3] [IF BEN_LEVEL_S>3500, SET BEN_LEVEL_S=3500] Q.2.10: [BEN_MAX_S] Spouse Benefits Level Follow-up D [SHOW IF SS_STATUS_S==1 (RECEIVING BENEFITS)] Please assume for the remainder of the survey that your [SPOUSE]’s Social Security benefits are $3500 per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] Please assume for the remainder of the survey that if your [SPOUSE] starts collecting Social Security at age [CLAIM_AGE_S], [SP_HISHER] Social Security benefits will be $3500 per month. [SHOW IF SS_STATUS_S==2 AND RETIRED_S==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] Please assume for the remainder of the survey that if your [SPOUSE] stops working for pay at age [RET_AGE_S] and starts collecting Social Security at age [CLAIM_AGE_S], [SP_HISHER] Social Security benefits will be $3500 per month.

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Section 3: Extensive-Margin Incentives [IF RET_AGE == 0, SKIP TO NEXT SECTION (SINCE RET_AGE=0 MEANS THE PERSON NEVER WORKED)] [IF SKIPDIS==1, SKIP TO NEXT SECTION (SINCE SKIPDIS==1 MEANS THE PERSON RECEIVES DISABILITY BENEFITS)] [ASK IF RET_AGE ≠ MISSING AND CLAIM_AGE ≠ MISSING AND SKIPDIS ≠ 1]] Q.3.1: [EXT_INCENT_MC] Extensive-margin incentives, multiple choice [SHOW IF SS_STATUS==1 AND RETIRED==0 (RECEIVING BENEFITS, NOT RETIRED)] You answered before that you have not yet stopped working for pay, that your Social Security benefits are $[BEN_LEVEL] per month, and that you started collecting benefits at age [CLAIM_AGE]. Suppose you had stopped working for pay at age [CLAIM_AGE -xRET_CHG], and started collecting benefits as you did, at age [CLAIM_AGE]. As your best guess, what would happen to your current Social Security benefits? [SHOW IF SS_STATUS==1 AND RETIRED==1 (RECEIVING BENEFITS, ALREADY RETIRED)] You answered before that you stopped working for pay at age [RET_AGE], that your Social Security benefits are $[BEN_LEVEL] per month, and that you started collecting benefits at age [CLAIM_AGE]. Suppose you had stopped working for pay [IF xRET_CHG==1, INSERT: one year; IF xRET_CHG==2, INSERT: two years; IF xRET_CHG==5, INSERT: five years] earlier, at age [RET_AGE -xRET_CHG], and started collecting benefits as you did, at age [CLAIM_AGE]. As your best guess, what would happen to your current Social Security benefits? [SHOW IF SS_STATUS==2 AND RETIRED==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] You answered before that you expect your Social Security benefits to be $[BEN_LEVEL] per month if you stop working for pay at age [RET_AGE] and start collecting benefits at age [CLAIM_AGE]. Suppose you stop working for pay [IF xRET_CHG==1, INSERT: one year; IF xRET_CHG==2, INSERT: two years; IF xRET_CHG==5, INSERT: five years] earlier, at age [RET_AGE -xRET_CHG], and start collecting benefits as planned, at age [CLAIM_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==2 AND RETIRED==1 (RETIRED, BUT NOT RECEIVING BENEFITS)] You answered before that you stopped working for pay at age [RET_AGE] and that you expect your Social Security benefits to be $[BEN_LEVEL] per month if you start collecting benefits at age [CLAIM_AGE]. Suppose you had stopped working for pay [IF xRET_CHG==1, INSERT: one year; IF xRET_CHG==2, INSERT: two years; IF xRET_CHG==5, INSERT: five years] earlier, at age [RET_AGE -xRET_CHG], and start collecting benefits as planned, at age [CLAIM_AGE]. As your best guess, what would happen to your Social Security benefits? [ASK FOR ALL RESPONDENTS TO Q3.1:] My Social Security benefits would still be $[BEN_LEVEL] per month..........................................................................................1 My Social Security benefits would be higher than $[BEN_LEVEL] per month....................................................................................2 My Social Security benefits would be lower than $[BEN_LEVEL] per month....................................................................................3 [ASK IF (EXT_INCENT_MC==2 OR EXT_INCENT_MC==3) AND RET_AGE ≠ MISSING AND CLAIM_AGE ≠ MISSING] Q.3.2: [EXT_INCENT_AMT] Extensive-margin incentive, new amount of benefit What would be the new amount of your Social Security benefits? My best guess is that the new amount of my Social Security benefits would be $_________ per month. [ASK IF EXT_INCENT_AMT=100 AND EXT_INCENT_MC==3 AND RET_AGE ≠ MISSING AND CLAIM_AGE ≠ MISSING] Q.3.2B: [EXT_INCENT3_AMT] Follow-up, Extensive-margin incentive, new amount of benefit You just answered that the new amount of your Social Security benefits would be [EXT_INCENT_AMT] per month. Did you mean that the new benefit level would be [EXT_INCENT_AMT] lower, so that the new benefit level would be $[BEN_LEVEL] - $[EXT_INCENT_AMT] = $[BEN_LEVEL - EXT_INCENT_AMT]? Yes, that is what I meant. The new benefit level would be $[BEN_LEVEL EXT_INCENT_AMT] per month............................................1 No, I meant that the new benefit level would be $[EXT_INCENT_AMT] per month .........................................................................................2

[Ask if EXT_INCENT_MC==3] Q.3.3: [INCENT_WK_LNG] Incentives To Work Longer [ASK IF SS_STATUS==1 AND RETIRED==0 (RECEIVING BENEFITS, NOT RETIRED)] You answered before that you think that your benefits would be lower if you had stopped working for pay at age [CLAIM_AGE -xRET_CHG]. If you had stopped working earlier, you would also have paid into Social Security for fewer years. [ASK IF SS_STATUS==1 AND RETIRED==1 (RECEIVING BENEFITS, RETIRED)] [SHOW IF XRET_CHG==1] You answered before that you thought that your benefits would be lower if you had stopped working for pay one year earlier. If you had stopped working earlier, you would have also paid into Social Security for one fewer year. [SHOW IF XRET_CHG==2] You answered before that you thought that your benefits would be lower if you had stopped working for pay two years earlier. If you had stopped working earlier, you would also have paid into Social Security for two fewer years. [SHOW IF XRET_CHG==5] You answered before that you thought that your benefits would be lower if you had stopped working for pay five years earlier. If you had stopped working earlier, you would also have paid into Social Security for five fewer years.

[ASK IF SS_STATUS==2 AND RETIRED==0 (NOT RECEIVING BENEFITS, NOT RETIRED)] [SHOW IF XRET_CHG==1] You answered before that you think that your benefits will be lower if you stop working for pay one year earlier than you have planned. If you stop working earlier, you will also pay into Social Security for one fewer year. [SHOW IF XRET_CHG==2] You answered before that you think that your benefits will be lower if you stop working for pay two years earlier than you have planned. If you stop working earlier, you will also pay into Social Security for two fewer years. [SHOW IF XRET_CHG==5] You answered before that you think that your benefits will be lower if you stop working for pay five years earlier than you have planned. If you stop working earlier, you will also pay into Social Security for five fewer years. [ASK IF SS_STATUS==2 AND RETIRED==1 (NOT RECEIVING BENEFITS, BUT RETIRED)] [SHOW IF XRET_CHG==1] You answered before that you think that your benefits would be lower if you had stopped working for pay one year earlier. If you had stopped working earlier, you would also have paid into Social Security for one fewer year. [SHOW IF XRET_CHG==2] You answered before that you think that your benefits would be lower if you had stopped working for pay two years earlier. If you had stopped working earlier, you would also have paid into Social Security for two fewer years. [SHOW IF XRET_CHG==5] You answered before that you think that your benefits would be lower if you had stopped working for pay five years earlier. If you had stopped working earlier, you would also have paid into Social Security for five fewer years.

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[SHOW FOR ALL RESPONDENTS TO Q.3.3 WITH SS_STATUS==1 AND RETIRED==0)] Consider all the Social Security benefits you receive over your lifetime. Which of the following is true. If I had stopped working at age [CLAIM_AGE -xRET_CHG], I would get… a worse deal from Social Security; i.e., money saved by paying into Social Security for fewer years would be less than the cut to my Social Security benefits.... ...............1 roughly the same deal from Social Security; i.e., money saved by paying into Social Security for fewer years would roughly equal the cut to my Social Security benefits...… .................................................................................................................2 a better deal from Social Security; i.e., money saved by paying into Social Security for fewer years would be greater than the cut to my Social Security benefits...… ......3 [SHOW FOR ALL RESPONDENTS TO Q.3.3 WITH RETIRED==1] Consider all the Social Security benefits you receive over your lifetime. Which of the following is true. If I had stopped working [xRET_CHG] year(s) earlier, I would get… a worse deal from Social Security; i.e., money saved by paying into Social Security for fewer years would be less than the cut to my Social Security benefits.... ...............1 roughly the same deal from Social Security; i.e., money saved by paying into Social Security for fewer years would roughly equal the cut to my Social Security benefits...… .................................................................................................................2 a better deal from Social Security; i.e., money saved by paying into Social Security for fewer years would be greater than the cut to my Social Security benefits...… ......3 [SHOW FOR ALL RESPONDENTS TO Q.3.3 WITH SS_STATUS==2 AND RETIRED==0] Consider all the Social Security benefits you receive over your lifetime. Which of the following is true. If I stop working [xRET_CHG] year(s) earlier, I will get… a worse deal from Social Security; i.e., money saved by paying into Social Security for fewer years will be less than the cut to my Social Security benefits.......................1 roughly the same deal from Social Security; i.e., money saved by paying into Social Security for fewer years will roughly equal the cut to my Social Security benefits...… ......................................................................................................................................2 a better deal from Social Security; i.e., money saved by paying into Social Security for fewer years will be greater than the cut to my Social Security benefits...… ..........3

Section 4: Knowledge about Early-Retirement Penalty/Delayed Retirement Credit and about Earnings Test [IF SKIPDIS==1, SKIP TO NEXT SECTION] [ASK IF CLAIM_AGE ≠ MISSING ] [IF XCLM_CHG==1 , SET ALT_AGE=CLAIM_AGE+1] [IF XCLM_CHG==1 , SET ALT_WHEN=“ONE YEAR LATER”] [IF XCLM_CHG==2 , SET ALT_AGE=CLAIM_AGE-1] [IF XCLM_CHG==2 , SET ALT_WHEN=“ONE YEAR EARLIER”] [IF CLAIM_AGE ≤ 62 , SET ALT_AGE=CLAIM_AGE+1] [IF CLAIM_AGE ≤ 62 , SET ALT_WHEN=“ONE YEAR LATER”] [IF CLAIM_AGE > 69 , SET ALT_AGE=CLAIM_AGE-1] [IF CLAIM_AGE > 69 , SET ALT_WHEN=“ONE YEAR EARLIER”] Q.4.1: [AGEDEP_NY] Benefits sensitive to age of claiming? [SHOW IF SS_STATUS==1 AND RETIRED==0 (RECEIVING BENEFITS, NOT RETIRED)]

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You answered before that you have not yet stopped working for pay, that your Social Security benefits are $[BEN_LEVEL] per month, and that you started collecting benefits at age [CLAIM_AGE]. Suppose you had started collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE], but stop working for pay as planned, at age [RET_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==1 AND RETIRED==1 AND RET_AGE ≠ 0 (RECEIVING BENEFITS, RETIRED)] You answered before that you stopped working for pay at age [RET_AGE], that your Social Security benefits are $[BEN_LEVEL] per month, and that you started collecting benefits at age [CLAIM_AGE]. Suppose you had started collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE], but stopped working for pay as you did, at age [RET_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==1 AND RETIRED==1 AND RET_AGE = 0 (RECEIVING BENEFITS, NEVER WORKED)] You answered before that your Social Security benefits are $[BEN_LEVEL] per month, and that you started collecting benefits at age [CLAIM_AGE]. Suppose you had started collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==2 AND RETIRED==0 (NOT YET RECEIVING BENEFITS, NOT RETIRED)] You answered before that you expect your Social Security benefits to be $[BEN_LEVEL] per month if you stop working for pay at age [RET_AGE] and start collecting benefits at age [CLAIM_AGE]. Suppose you start collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE], but stop working for pay as planned, at age [RET_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==2 AND RETIRED==1 AND RET_AGE ≠ 0 (NOT YET RECEIVING BENEFITS, BUT RETIRED)] You answered before that you stopped working for pay at age [RET_AGE] and that you expect your Social Security benefits to be $[BEN_LEVEL] per month if you start collecting benefits at age [CLAIM_AGE]. Suppose you start collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE], but stopped working for pay as you did, at age [RET_AGE]. As your best guess, what would happen to your Social Security benefits? [SHOW IF SS_STATUS==2 AND RETIRED==1 AND RET_AGE == 0 (NOT YET RECEIVING BENEFITS, NEVER WORKED)] You answered before that you expect your Social Security benefits to be $[BEN_LEVEL] per month if you start collecting benefits at age [CLAIM_AGE]. Suppose you start collecting Social Security benefits [ALT_WHEN], at age [ALT_AGE]. As your best guess, what would happen to your Social Security benefits? [ASK FOR ALL RESPONDENTS TO Q4.1:] My Social Security benefits would still be $[BEN_LEVEL] per month..........................................................................................1 My Social Security benefits would be higher than $[BEN_LEVEL] per month....................................................................................2 My Social Security benefits would be lower than $[BEN_LEVEL] per month....................................................................................3 Q.4.2: [AGEDEP_RATE] Early retirement penalty / Delayed retirement credit [ASK IF XDEP_V==1] Consider a person who stops working for pay at age 62. Suppose this person would receive Social Security benefits of $1000 per month if he or she started collecting benefits at age 62. We are interested to know what you think would happen to his/her Social Security benefits if he/she still stopped working for pay at age 62 but started collecting Social Security benefits at a different age. (Please ignore the effects of inflation.) As my best guess, if the person …. …. started collecting benefits at age 62, the benefits would be $ 1000 per month. …. started collecting benefits at age 66, the benefits would be $ ____ per month. …. started collecting benefits at age 70, the benefits would be $ ____ per month. …. started collecting benefits at age 74, the benefits would be $ ____ per month. [ASK IF XDEP_V==2] Consider a person who stops working for pay at age 62. Suppose this person would receive Social Security benefits of $1000 per month if he or she started collecting benefits at age 66. We are interested to know what you think would happen to his/her Social Security benefits if he/she still stopped working for pay at age 62 but started collecting Social Security benefits at a different age. (Please ignore the effects of inflation.)

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As my best guess, if the person …. …. started collecting benefits at age 62, the benefits would be $ ____ per month. …. started collecting benefits at age 66, the benefits would be $ 1000 per month. …. started collecting benefits at age 70, the benefits would be $ ____ per month. …. started collecting benefits at age 74, the benefits would be $ ____ per month. Q.4.3: [EARNTEST_EXIST] Knowledge of existence of the earnings test [SHOW IF PPAGE < 62 AND RETIRED==0] Suppose that you stopped working at age 62 and also claimed Social Security benefits that year. [SHOW IF PPAGE < 62 AND RETIRED==1] Suppose that you claimed Social Security benefits at age 62. [SHOW IF PPAGE ≥ 62 AND (RET_AGE > 62 OR RETIRED==0)] Suppose that you had stopped working at age 62 and also claimed Social Security benefits that year. [SHOW IF PPAGE ≥ 62 AND (RET_AGE ≤ 62 AND RETIRED==1)] Suppose that you had claimed Social Security benefits at age 62. [SHOW IF PPAGE ≤ XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] What would have happened to your Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] if, after retiring, you return to work for one year at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] earning $20,000 that year. My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would stay exactly the same whether or not I work that year. ...............................................1 My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would increase if I work that year. ............................................................................................2 My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would decrease if I work that year. ............................................................................................3 [SHOW IF PPAGE > XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] What would have happened to your Social Security at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] if, after retiring, you return to work for one year at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] earning $20,000 that year. My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would have stayed exactly the same whether or not I had worked that year. ...........................1 My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would have increased if I had worked that year.........................................................................2 My Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would have decreased if I had worked that year.........................................................................3 [SHOW IF EARNTEST_EXIST == 1] Q.4.4a: [EARNTEST_THRESA] Earnings threshold of the earnings test, A [SHOW IF PPAGE ≤ XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] You just indicated that you thought that your Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would stay the same if you earn $20,000 in that year. Is there any amount of earnings at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] that would cause a reduction in your benefits? [SHOW IF PPAGE > XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] You just indicated that you thought that your Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would have stayed the same if you had earned $20,000 in that year. Is there any amount of earnings at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] that would have caused a reduction in your benefits? [SHOW FOR ALL RESPONDENTS TO Q4.4A:] Yes, once a person earns more than roughly _________ dollars per year at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68], benefits start being reduced. ..............................1

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No, benefits are never reduced no matter how much a person earns at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68]. ..............................................................................2 [IF R CHECKS BOX (2), SET EARNTEST_THRESA=999999] [SHOW IF EARNTEST_EXIST == 3] Q.4.4b: [EARNTEST_THRESB] Earnings Threshold of the earnings test, B [SHOW IF AGE ≤ XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] You just indicated that you thought that your Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would be reduced if you earn $20,000 in that year. Is there any amount that you could earn at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] without having your benefits reduced? [SHOW IF AGE > XEARNTST (IF XEARNTST=1, 64; IF XEARNTST=2, 68)] You just indicated that you thought that your Social Security benefits at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] would have been reduced if you had earned $20,000 in that year. Is there any amount that you could have earned at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] without having your benefits reduced? [SHOW FOR ALL RESPONDENTS TO Q4.4B] Yes, a person can earn roughly _________ dollars per year at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68] before benefits start being reduced. ..........................................1 No, benefits are always reduced if you have any earnings at age [IF XEARNTST=1, INSERT 64; IF XEARNTST=2, INSERT 68]. .....................................................................................................2 [IF R CHECKS BOX (2), SET EARNTEST_THRESB=0] [ASK IF EARNTEST_EXIST==3 OR (EARNTEST_EXIST==1 AND EARNTEST_THRESA