Since the early 1900s, female life expectancy has exceeded male life expectancy, resulting in women outnumbering men in the older age groups. Although gender differences in life expectancy have been decreasing, women age 65 and over continue to outnumber men age 65 and over. This trend is projected to continue over the next 4 decades. Further, the population age 65 and over is expected to more than double from 2010 to 2050. The population of women among the “oldest-old”— those 85 and over—is also projected to grow. Today, of those age 65 and over, one-sixth of women and one-tenth of men are among the oldest-old and this is projected to grow to almost one-quarter of women and one-fifth of all men by 2050. Women’s workforce participation surged over the last half of the 20th century. Among women ages 25 to 54, the rate of labor force participation jumped from 42 percent by the end of the 1950s to about 74 percent by the late 1980s. The rate continued to grow in the 1990s but at a slower pace. Over the last decade, the rate declined slightly from its peak of 76.8 percent in 1999, and was 74.7 percent in 2011. Labor force participation rates have varied by generation, with women born in the baby boom generation much more likely to be in the workforce than preceding generations. have increased significantly for women ages 55 to 64 (see fig. 1). The baby boom generation consists of individuals born from 1946 to 1964. Although the composition of retirement income—the proportion of income coming from different sources—varies greatly for individual households, Social Security benefits, pension income, and earnings make up the bulk of income for the U.S. population age 65 and over. Social Security provides retirement benefits to eligible workers, based on their work and earnings history. Social Security also provides benefits to eligible workers who become disabled before reaching retirement age, as well as spouses, widow(er)s, and children of eligible workers. Although all Social Security benefits are based upon a common formula, they are calculated in different ways for each beneficiary type. The level of the monthly benefit is adjusted for inflation and varies depending on the age at which the beneficiary chooses to begin receiving benefits. Generally, beneficiaries may begin receiving retirement benefits at age 62; however, the payments will be higher if they wait to receive benefits at their full retirement age, which varies from 65 to 67, depending on the beneficiary’s birth year. The monthly retirement benefit continues to rise for workers who delay benefits beyond their full retirement age, up to age 70. Employees and employers pay payroll taxes that finance Social Security benefits. However, Social Security faces a long-term financing shortfall resulting largely from lower birth rates and longer life spans. According to the Social Security Trustees, the Social Security Trust Funds could be exhausted by 2033 and unable to pay full benefits. Pension income from employer-provided retirement plans falls into two broad categories: DB and DC pension plans. DB plans typically provide retirement benefits to each retiree in the form of an annuity that provides a monthly payment for life, the value of which is typically determined by a formula based on particular factors specified by the plan, such as salary or years of service. Under DC plans, workers and employers may make contributions into individual accounts. Workers can also save for retirement through an individual retirement account (IRA). IRAs allow workers to receive favorable tax treatment for making contributions to an individual account. At retirement, participants’ distribution options vary depending on the type of pension plan. Private sector DB plans must offer participants a benefit in the form of a lifetime annuity (either immediately or deferred). An annuity can help to protect a retiree against risks, including the risk of outliving one’s assets (longevity risk) and, when an inflation-adjusted annuity is provided, the risk of inflation diminishing one’s purchasing power. Some DB plans also give participants a choice to take a lump sum cash settlement (distribution) or roll over funds to an IRA, instead of taking a lifetime annuity. In contrast, DC plan sponsors are not required to offer a lifetime annuity and more often provide participants with a lump sum distribution as the only option. Other options for DC participants may include leaving money in the plan, taking a partial distribution, rolling their plan savings into an IRA, or purchasing an annuity, which are typically only available outside of the plan. In addition, whether a pension plan is a DB or DC has implications for whether a spouse is entitled to the pension’s benefits. The Employee Retirement Income Security Act of 1974 (ERISA) requires that DB plans include a survivor’s benefit, called a qualified joint and survivor annuity. Thus, after a worker with a DB plan dies, the surviving spouse continues to receive an annuity, but typically at a reduced level. A qualified joint and survivor annuity may only be waived through a written spousal consent. Under most DC plans, the plan is written so that the employee may, during his or her lifetime, make withdrawals from the account or roll over the balance into an IRA without spousal consent, provided that the employee’s vested account balance is payable in full on death to the surviving spouse. Over the past quarter-century, the percentage of private sector workers participating in employer-sponsored pension plans has held steady at about 50 percent. Although some workers choose not to participate in an employer-sponsored pension plan, the large majority of nonparticipating workers do not have access to one. In addition, over the last 3 decades, the U.S. retirement system has undergone a major transition from one based primarily on DB plans to one based on DC plans, increasing workers’ exposure to economic volatility and usually shifting the burden of saving to the individual worker, which makes them more reliant on their own decision making. As we have previously reported, from 1990 to 2008, the number of active participants in private sector DB plans fell by 28 percent, from about 26 million to about 19 million. Over the same period, the number of active participants in DC plans increased by 90 percent, from about 35 million to about 67 million. DC plans generally do not offer annuities, so retirees are left with increasingly important decisions about managing their retirement savings to ensure they have income throughout retirement. These decisions may be more difficult to make in times of economic volatility. For example, two recent recessions—one beginning in March 2001 and ending in November 2001 and the other beginning in December 2007 and ending in June 2009— resulted in major stock indices falling dramatically. The long-term effects of financial market fluctuations on retirement income security are uncertain, but the effects may vary based on factors such as age, type of pension plan, and employment status. Employment status, in particular, can pose serious challenges for retirement security. As we recently reported, long-term unemployment can reduce an older worker’s future monthly retirement income in numerous ways such as by reducing the number of years the worker can accumulate DB plan retirement benefits or DC plan savings, by motivating workers to claim Social Security at an earlier age, and by leading workers to draw down retirement savings to pay for expenses during unemployment. From 1998 to 2009, working women surpassed men in their likelihood of having an employer that offered a pension plan, but were slightly less likely to be eligible for and to participate in those plans. However, this gap, narrowed over time. In fact, by 2009, the same proportion of working women and men ultimately participated in some type of plan (either a DB or a DC) as shown in figure 2. Nonetheless, women’s contribution rates to DC plans remained lower than those of men. While working men and women were just as likely to have employers that offered pension plans in 1998, by 2009, these women were more likely than men to work for employers that offered pension plans (see fig. 3). This may be due to the sectors and industries in which women worked. For example, a greater proportion of women than men worked in the public and nonprofit sectors—sectors that have higher proportions of workers with access to plans offered by employers—than the for-profit sector. Women were also more likely to work in the education and health industries—industries that have higher proportions of workers with access to plans offered by employers. In contrast, men had higher rates of self- employment over this period, and self-employed individuals were much less likely to have retirement plans. In addition, from 1998 to 2009, the proportion of working women and men with employers that offered pension plans declined after 2003, possibly reflecting the decline in the number of employers offering DB plans.women working for employers offering DC plans increased, rising from 41 to 49 percent (see fig. 3). With the exception of 1998, women were more likely to work for employers that offered DC plans than were men. The composition of women’s and men’s retirement income did not vary greatly over the last decade despite changes in the economy and pension system, largely because their main income sources—Social Security and DB plans—were shielded from fluctuations in the financial market. However, women, especially widows and those 80 years and over, depended on Social Security benefits for a larger percentage of their income than men. In contrast, women received a lower share of their income from earnings than men. Women age 65 and over also had less retirement income on average and higher rates of poverty than men in that age group. Specifically, for the population age 65 and over, women’s median income was approximately 25 percent lower than men in the same age group for all years. Moreover, women in this age group were nearly twice as likely to be living in poverty than men. The composition of household income for women and men age 65 and over fluctuated only slightly from 1998 to 2010, despite changes in the economy and the pension system (see fig. 8). The composition of household income did not fluctuate drastically largely because Social Security and DB benefits comprised nearly three-quarters of household income for women and slightly less (around 70 percent) for men, providing them with guaranteed monthly income for life. Women tended to receive a higher proportion of household income from Social Security. In fact, in 2010, 16 percent of women age 65 and over depended solely on Social Security for income compared to 12 percent of men. At the same time, the share of income from earnings increased slightly for men and women, but was consistently lower for women than for men. Furthermore, the share of income from DC plans was very low (1 to 2 percent) across the entire period for both men and women. This is due to the fact that the lion’s share of people age 65 and over did not report receiving any income from regular distributions from DC plans. As shown in figures 9 to 11, in 2010, the composition of household income for individuals age 65 and over also varied by demographic group. Among marital-status categories, widowed women depended on Social Security benefits for a larger percentage of their income (58 percent) than other women (see fig. 9). In fact, about 21 percent of all widowed women depended on Social Security as their sole source of income. Separated women and men received higher shares of income from earnings, and married women and men received relatively higher shares of their income from DB plans. As shown in figure 10, among different age groups, women age 80 and over received the highest share of their income from Social Security (61 percent). In fact, about 20 percent of them depended on Social Security for their sole source of income. Men in the youngest age category (65 to 69) received a higher share of their income from earnings (31 percent) relative to other groups, while individuals in the oldest age categories received the smallest share of income from earnings, likely reflecting the declining ability to work at older ages. Finally, among racial and ethnic groups, White and Black women and men age 65 and over received the highest share of income from Social Security (see fig. 11). In contrast, Asians and Hispanics tended to receive a lower share of their incomes from Social Security. Asian men and women received a disproportionately higher share of income from earnings relative to other racial and ethnic categories. White and Black women and men received higher shares of income from DB plans, compared to Hispanics and Asians. Women age 65 and over had consistently lower median incomes than men across age and most race groups over time. Over the last decade, the median incomes of women age 65 and over were approximately 25 percent lower than their male counterparts. Median incomes, did, however, vary by demographic category (see fig. 12). Demographic groups with the lowest median incomes included women who were either unmarried—especially those who had been separated or never married— over the age-of 80, or Black or Hispanic. In addition, a greater proportion of women age 65 and over lived in households with incomes below the poverty line than men in the same age group. Consistent with their relatively lower median incomes, the demographic groups with the highest poverty rates were women who were not married, over the age of 80, or non-White (see fig. 13).contrast, married people and White men had the lowest poverty rates. When women nearing or in retirement—women over age 50—became divorced, widowed or unemployed, the effects on their households’ total assets and income were detrimental, according to our analysis (see table 1). Further, divorce and widowhood had more pronounced effects for women than for men. These effects may be contributing to elderly women’s higher poverty rates and lower levels of income compared to men’s. We also found, not surprisingly, that a decline in health after age 50 had a negative effect on household assets and income. Lastly, we also examined the effect of caring for elderly parents on income and assets, but we did not find statistically significant negative relationships. All of these effects may not be generalizable to younger cohorts as women’s labor force participation and, correspondingly, their assets and income, have changed over the last several decades. As shown in figure 14, the effects of divorce or separation after age 50 had substantial, negative effects on women’s total household assets and income. For both women and men, assets fell by about 40 percent with a divorce or separation. The effects were less substantial for those living in households where at least one member was age 65 or over, but these women and men still lost about one-third of their total assets. The effects for income were more pronounced for women than for men. Women’s income fell by 41 percent, nearly twice that of men’s (23 percent). The effects were largest for women living in households where all members were age 64 or younger; for these women, income fell by 44 percent. However, while divorce had very detrimental effects, we found that, for women ages 51 and over, divorce or separation was less prevalent than widowhood. Specifically, for those age 85 and over in our sample, 4 percent of women and 2 percent of men had been divorced or separated. Not only did women’s total household assets and income decline substantially with widowhood, but the effects were more pronounced for women than for men (see fig. 15). For example, while men’s income fell 22 percent after widowerhood, women’s income fell by an even greater amount—37 percent. The effects were larger for women living in younger households than women living in older households. Specifically, women in households where all members were age 64 or younger experienced a 31 percent decrease in assets and a 47 percent decrease in income.Adding to these effects, widowhood was a much more common experience for women than men in our sample. In fact, women were at least twice as likely as men to become widowed between any two survey periods. Consequently, 70 percent of women age 85 and over were widowed compared to only 24 percent of men age 85 and over. Similar to becoming widowed, unemployment had negative effects on total household assets and income, although the effects were similar for women and men (see fig. 16).income decline by about 7 to 9 percent. The effects on income were most acute for households where at least one member of the household was age 65 or over. For these households, men’s assets fell by 14 percent Women and men saw their assets and and their income fell by 12 percent. For women, there was not a significant decline in assets but their income fell by 13 percent. In addition, older workers may have difficulty finding another job. However, unemployment was not very prevalent in the HRS sample, in part because many survey respondents were retired. On average, only 1 percent of men and women reported being out of work and actively looking for a job. For men and women ages 51 to 64, this percentage rose slightly to 2 percent. As shown in figure 17, a decline in self-reported health status also had negative effects on total household income and assets, although to a lesser degree than widowhood, divorce, and unemployment. For all households in our sample, income fell by 4 percent for women and 3 percent for men when self-reported health status changed from excellent, very good or good to fair or poor. The effects of a decline in health on assets varied by household type. The differences between women and men were the largest for younger households, where all members were age 64 or younger. For example, the loss of assets was greater for men (13 percent) compared to women (5 percent). Although the effects of a decline in health were smaller than the effects of some of the other life events in our analysis, more individuals experienced this event than any other. Almost 30 percent of individuals ages 65 to 84 reported being in poor health (see table 2). For individuals ages 85 and over, 40 percent reported being in poor health. Interestingly, as shown in table 2, women and men suffered from poor health at similar rates across age categories. Further, we found that, between any two HRS surveys, about 2 percent of both women and men reported entering a period of poor health. Lastly, we found that providing elderly parents with financial assistance or helping parents with basic activities of daily living (i.e., bathing, dressing, and eating) had a slightly positive effect on household assets and income. However, often these effects were not significantly different from zero, possibly because of limitations in our data and methods. In addition, we found that only a small percentage of the sample provided these types of assistance to their parents. Also, women and men age 51 through 64 were much more likely to provide assistance than women and men age 65 and over. But, as the baby boomers age, more children may be called upon to help their parents financially or with basic activities. Through our interviews with experts and our literature review, we found that a range of existing policy options could help improve retirement income security for women. Our analysis focuses on how women would be affected by these policy options. While each of these options would be available for both women and men, they could help address some of the specific challenges women face in ensuring a secure retirement. For example, some options would expand the use of existing tax incentives, encouraging women to save more. Another set of options would expand access to and strengthen spousal protections for retirement savings. These options could increase women’s retirement savings and preserve their retirement income if they become divorced or widowed. Other sets of options could motivate women nearing retirement to work longer and save more, ensure lifetime retirement income, or enhance benefit adequacy. These options could help shield women from the effects of divorce, widowhood, and unemployment and decrease their risk of living in poverty. All of the options have cost implications that would need to be considered prior to implementation. Moreover, as with federal spending programs, any option that results in reduced or deferred federal tax revenue may require an offset, such as raising revenue elsewhere or cutting spending. While the federal government could bear some of these costs, workers and plan sponsors could be responsible for others. Also, although some of the options could have positive effects on women on their own, there could be an offsetting effect. If the plan sponsor, for example, is responsible for the increased cost of sponsorship and makes changes to the plan to offset those increased costs, women may not ultimately benefit from the policy option. Lastly, some of these changes may require legislative changes. Some of the policy options we identified could expand the use of existing tax incentives for individuals to save for retirement during their working years (see table 3). These options could help lower- and moderate- income workers, as well as workers who take time out of the workforce to care for family members. Since women have lower earnings than men, on average, and are more likely to take time out of the workforce to care for family members, women may especially benefit from these options. However, pension experts are concerned that women may not be as financially literate as men, hindering them from taking full advantage of options for saving for retirement. Experts also identified a set of policy options that would offer new opportunities to accumulate earnings credits for Social Security (see table 4). These options could enhance the retirement security of workers who experience a period of unemployment or who take time out of the workforce to care for family members. For example, counting unemployment insurance payments as creditable earnings under Social Security may be particularly helpful for women who become unemployed later in life and experience a notable decrease in their assets and income. However, because they would extend eligibility or increase benefits, these options would increase costs for Social Security and decrease solvency. Other policy options could either expand access to retirement savings in DC plans and IRAs or strengthen spousal protections for retirement savings (see table 5). These options could address a variety of challenges women face, including their lower levels of income in retirement. In addition, they could preserve retirement income after a divorce or after becoming widowed. For example, requiring that a wife provides consent whenever a husband takes a distribution from his DC savings would protect the wife’s access to household income in retirement. However, these options could increase costs for plan sponsors. For example, requiring notarized spousal consent whenever a husband takes a distribution could increase the administrative costs that must be paid by plan sponsors. Experts identified three policy options that could motivate women nearing retirement to remain in the workforce and delay claiming Social Security benefits, thereby giving them more time to save for retirement and increasing their Social Security benefits (see table 6). Because women tend to have less income in retirement than men, and because elderly women face higher poverty rates than elderly men, these options for boosting retirement savings and benefits may improve women’s overall retirement income security. For example, the full retirement age for Social Security could be increased, thus providing workers who are able to work with an incentive to keep doing so—potentially saving more for retirement in the process. However, each of these options has disadvantages. In the case of increasing the full retirement age, this option may not prove to be effective because women may not be able to work longer or may choose to exit the workforce before the full retirement age. They would, in turn, suffer reductions in Social Security income. Experts also identified several policies that would ensure lifetime retirement income for women (see table 7). Women may especially benefit from these options, given that they (1) have lower levels of retirement income than men, (2) are more likely to live longer, and (3) are also more likely to become widowed. For example, Treasury recently proposed modifying the required minimum distribution rules so that individuals could use part of their retirement savings to purchase a longevity annuity. This option would provide older women with guaranteed additional income, which may be helpful if they live long lives or outlive a spouse. These options, however, often have cost implications for either federal tax revenue or plan sponsors. For example, if individuals purchased longevity annuities using tax-qualified retirement savings, the tax revenue generated from withdrawing these savings would be deferred until the annuity started paying out. There are also a number of policy options that could enhance Social Security benefits for vulnerable groups at risk of not having sufficient income or assets in retirement, including widows, divorced women, low- income women and women age 85 and over (see table 8).increasing the Social Security Survivor’s benefit to 75 percent of the deceased worker’s benefit would provide widows with more monthly income, helping to keep some women out of poverty. However, all of these options would increase existing costs or introduce new costs and, in turn, would decrease the solvency of the system. To retirement security experts, our findings paint a familiar if disconcerting picture. Although increases in women’s labor force and retirement plan participation have led to a marginal improvement in women’s prospects for achieving a more secure retirement, our report also highlights the substantial risks women continue to face in accumulating adequate retirement income. Yet, despite the differential risks women face, retirement security in America continues to be a national dilemma that transcends gender differences. It is important to note that much of the relative improvement in women’s retirement security has been a consequence of deterioration in men’s retirement security. Recent economic volatility, coupled with the continued shift toward defined contribution plans, exposes all workers to more financial risk than previous generations. Further, older workers’ financial security is increasingly dependent on individual choices regarding how much to save, how to invest those savings, at what age to retire, and how to make those savings last throughout retirement. Much of the total workforce continues to approach retirement age with no traditional pension. Unchecked, this problem will only grow in severity. Nevertheless, women face a unique set of circumstances, which warrant special attention. In particular, our findings show that the disruptions that occur as a result of later-in-life events, such as divorce and widowhood, can be financially devastating for women. In addition, women’s greater likelihood of being single, higher life expectancy, and lower average earnings continue to make saving for retirement and avoiding late-life poverty a challenge. The challenges facing women’s retirement income security do not lack for potential resolutions. In fact, our discussions with experts identified a number of policy options that would improve retirement income security for women. These options range from changes to Social Security to altering the private pension system. While these options involve tradeoffs and difficult choices, they have the potential to improve the retirement income security of men as well. Ultimately, such efforts provide opportunities to improve the retirement security of many Americans. We provided a draft of this report to the Department of Labor, the Department of the Treasury, and the Social Security Administration for review and comment. While none of the agencies provided official comments, each provided technical comments, which we incorporated as appropriate. As agreed with your office, unless you publicly announce its contents earlier, we plan no further distribution until 30 days after the date of this letter. At that time, we will send copies of this report to the Secretary of Labor, the Secretary of the Treasury, the Commissioner of Social Security, and other interested parties. In addition, the report will be available at no charge on the GAO website at http://www.gao.gov. If you or your staff have any questions about this report, please contact me at (202) 512-7215 or jeszeckc@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made contributions to this report are listed in appendix II. To analyze factors that affect women’s retirement security, we examined (1) how women’s access to and participation in employer-sponsored retirement plans compare to men’s and how they have changed over time; (2) how women’s retirement income compares to men’s and how the composition of their income has changed with economic conditions and trends in pension design; (3) how events occurring later in life affect women’s retirement income; and (4) what policy options are available to help increase women’s retirement income security. This appendix provides a detailed account of the information and methods we used to answer these questions. Section 1 describes the key information sources we used. Sections 2 through 4 describe the empirical methods we used to answer questions 1 through 3 respectively and the results of supplementary analyses. To answer our questions, we obtained information from a variety of sources including two nationally representative surveys—the Survey of Income and Program Participation (SIPP) and the Health and Retirement Study (HRS)—the academic literature on retirement security, and a range of experts in the area of women’s retirement security. Table 9 summarizes the data sources used to answer each question. This section provides a description of our data sources and the steps we took to ensure their reliability. To answer Questions 1 and 2, we analyzed data collected through the SIPP, a nationally representative survey conducted by the U.S. Census Bureau that collects detailed information on income sources and pension plan coverage, among many other areas. The survey is conducted in a series of national panels, with sample sizes ranging from approximately 14,000 to 36,700 interviewed households. The duration of each panel ranges from 2 ½ years to 4 years. Within each panel, the data are collected in a series of “waves” which take place in 4-month cycles. Within each wave, Census administers a core survey consisting of questions that are asked at every interview, and several modules relating to a particular topic. We used data from the core survey and the topical module on retirement and pension coverage from the last four SIPP panels, which began in 1996, 2001, 2004, and 2008 respectively. For all but the 2008 panel, the topical module on retirement and pension coverage was administered in Wave 7. For objective 1, we matched core data from Wave 3 of the 2008 panel with the topical module data, which was also administered in Wave 3. This ensured that the demographic data used in the analysis for that objective would match the time frame of the topical module data. However, to obtain the most up to date income data for objective 2, we used core data from Wave 7, which was the most recently available data as of October 2011. Table 10 shows the waves and questionnaires we used to answer each objective. It also shows the years that the data were collected during each panel and wave listed. The bolded years correspond to the years of data that are presented in the figures in objectives 1 and 2. In comparison to other nationally representative surveys, the SIPP had several main advantages. First, the SIPP collects separate information on defined benefit (DB) and defined contribution (DC) plans. Other surveys, such as the Current Population Survey, do not distinguish between income from and participation in DB and DC plans. Second, the SIPP sample is larger than comparable surveys, such as the Survey of Consumer Finances (SCF). Consequently, it is possible to produce point estimates for demographic subcategories with a higher degree of reliability. Further, in comparison to the SCF, which oversamples wealthy households, the SIPP oversamples lower-income households—arguably an important component of an analysis of income security. Despite its advantages, the SIPP has two limitations for our analysis. First, as with most survey data, SIPP data are self-reported. This can be problematic for the reporting of data on income sources and pension plan participation. For example, respondents might incorrectly report that they participate in a pension plan when they do not participate in one. Second, despite the fact that SIPP differentiates between participation in a DB or DC plan, it does not contain full information on whether an individual’s employer offers a DB plan. To answer question 3—on the effects of events occurring later in life on women’s retirement income security—we analyzed data collected through the HRS, a nationally representative survey primarily sponsored by the National Institute of Aging and conducted by the Institute for Social Research at the University of Michigan. This longitudinal survey collects data on individuals over age 50 and contains detailed information on health, marital status, assets, income, and care for elders. Respondents were first surveyed in 1992, when they were age 51 to 61 and continued to be surveyed every 2 years. Additional cohorts were added in later years to maintain the representation of the older population. Table 11 presents the cohorts that are included in the HRS sample. Respondents are resurveyed every 2 years. The data in our analysis span from the initial 1992 survey through the early release data for 2010, the most current data available. Our analysis follows over 30,000 individuals from the HRS sample. One of the main advantages of the HRS is that the same households are interviewed at different points of time, allowing us to examine the correlation of changes in life events to changes in household assets and income. Further, RAND, a research organization, cleans and processes the HRS data to create a user-friendly longitudinal dataset that has consistent and intuitive naming conventions, model-based imputations for missing wealth and income data, and spousal counterparts of most individual-level variables. We used these data for our analysis. However, there are three limitations for our analysis. First, the women currently in the HRS survey may have very different retirement experiences from women in the workforce today due to changes in demographic trends and workforce participation. Second, as with the SIPP, data from the HRS are self-reported. Third, total household assets cannot be broken out at the individual level. For each of the datasets described above, we conducted a data reliability assessment of selected variables by conducting electronic data tests for completeness and accuracy, reviewing documentation on the dataset, or interviewing knowledgeable officials about how the data are collected and maintained and their appropriate uses. When we learned that particular fields were not sufficiently reliable, we did not use them in our analysis. For example, we chose not to use data from the SIPP Topical Module on Annual Income and Retirement Accounts because many of the fields in that survey are not edited by the Census Bureau. For the purposes of our analysis, we found the variables that we ultimately reported on to be sufficiently reliable. To gain an understanding of the challenges women face in attaining a secure retirement and policy options that could enhance women’s retirement security, we conducted an extensive literature review and interviewed a range of experts. To identify existing studies, we conducted searches of various databases, such as EconLit, Electronic Collections Online, ProQuest, Academic OneFile, WorldCat, and Policy File. From these sources, we identified 128 articles that appeared in journals since 2007 and were relevant to our research objective on policy options that could enhance women’s retirement security. From the articles identified in the preliminary search, we reviewed article abstracts, when available, to determine which articles contained information germane to our report and reviewed those articles. In addition, we reviewed articles that were collected during the previous GAO study on women’s retirement security that contained information relevant to our empirical analyses, described below, and reviewed articles that were suggested to us by the experts we interviewed. We performed these searches and identified articles from May 2011 to October 2011. To supplement the literature review, we conducted interviews with experts. To ensure that we obtained a balanced perspective, we interviewed experts with a range of perspectives and from different types of organizations including government, academia, advocacy groups, and the private sector. We also consulted several experts in government and academia on technical issues related to our analysis. Specifically, we interviewed agency officials at the departments of the Treasury and Labor, the Social Security Administration, and the Bureau of the Census; academic experts at the Employee Benefits Research Institute, Heritage Foundation, University of Pennsylvania, Stanford University, Urban Institute, and Wellesley College; and industry experts and advocates from the American Council on Life Insurers, Anna Rappaport Consulting, Financial Engines, the Institute for Women’s Policy Research, the National Women’s Law Center, AARP, the Pension Rights Center, the National Academy of Social Insurance, Social Security Works, and the Women’s Institute for a Secure Retirement. To determine the proportion of men and women that (1) work for an employer that offers a plan, (2) are eligible for a plan, and (3) participate in a plan, we used data from the SIPP topical module on retirement and pension plan coverage. Specifically, we constructed five dummy variables using a combination of various questions in the SIPP. Table 12 shows the information we used to construct each variable. For each of these variables, we used SIPP individual-level weights to compute point estimates and, in conjunction with other factors, calculate the standard errors of those estimates so that we could accurately account for the complex survey design. We consulted statisticians from the U.S. Bureau of the Census on the appropriate use of these weights. To better understand the factors that might explain gender differences in each of these variables, we developed a series of empirical models. Following the literature, we controlled for the following factors in our models: (1) demographic characteristics including gender, age, marital status, children present in the household, single parenthood, race and ethnicity, citizenship, immigrant status, and education level; and (2) occupational characteristics including part-time employment status, self- employment status, years of tenure, work experience, occupation, industry, sector, union status, and size of employing firm.these models, we used logistic regression—an appropriate technique when the dependent variable is binary, or has two categories such as participating in a plan or not participating in a plan. Logistic regression also allows for the coefficients to be converted into odds ratios, which are described below. We conducted the modeling analyses in a series of steps whereby with each step, the sample of men and women that was included in the analysis was conditional on the previous step. Specifically, the first analysis involved analyzing the probability of working for an employer that offered a pension plan for all workers in the sample. The second analysis involved analyzing the probability of being eligible for a plan for those men and women that worked for an employer offering a plan. The third analysis involved analyzing the probability of participating in a plan for those that were eligible for their employer-sponsored plan. In conjunction with understanding the factors associated with each dependent variable in our models, it is essential to also understand how women and men differ in those factors. Taken together, the information from the model and information from a comparison of men’s and women’s characteristics enables us to understand what factors make women more or less likely to be employed by an employer that offers a plan, be eligible for the plan, and participate in the plan. For example, if we know that women are disproportionately more likely to work part-time and that part- time status is an important factor associated with plan participation, we can infer that women’s higher rates of part-time status might contribute to their lower rates of plan participation. Table 13 compares the characteristics of men and women for each of the factors that we control for, across each year of the study period. Generally, the characteristics of men and women in the working population did not change dramatically over the study period. Correspondingly, when we compare men and women in each year, several relationships between them were consistent across all of the study years. In terms of demographic characteristics, women were more likely than men to be widowed and divorced. Women were also more likely to have children present in the household, be single parents, and work part time. A higher proportion of men than women were Hispanic, and this proportion increased over the study period. In terms of occupational characteristics, several gender differences persisted across the study years. Women consistently had higher levels of education and were more likely to work in the public or nonprofit sectors. Men were more likely to work in the private sector, be self- employed, have longer tenure at their current position, have more work experience, and to be in a union. Although the occupational and industry categories in the SIPP data changed midway through the study periods, the distributions of men and women across occupations and industry were generally consistent for the last 2 study years. Specifically, the top three occupations for women were office and administrative support; sales and related services; and education, training, and library services, with 20, 10, and 10 percent of women working in these occupations respectively in 2009. Men tended not to be as concentrated in just a few occupations. In 2009, the highest proportions of men were employed in management (9 percent), sales and related occupations (8 percent), construction and extraction (8 percent), and transportation and material moving (8 percent). Similarly, in 2009, the top three industries for women were health care and social assistance (21 percent), educational services (14 percent), and retail trade (10 percent). For men in this year, the top three industries in which men were employed were manufacturing (13 percent), construction (9 percent), and retail trade (9 percent). Table 14 shows the results of two models that analyze factors associated with the probability of working for an employer that offers (1) any type of pension plan (DB or DC) or (2) a DC plan. The first column presents the variables that were included in each model. The third and fifth columns present odds ratios that are estimated for each variable in the model. The interpretation of the odds ratio for a particular variable depends on whether the variable has only two or more than two categories.For dichotomous (or dummy) variables, odds ratios that are statistically significant and greater than 1.00 indicate that individuals with that characteristic are more likely to work for an employer that offers a plan. For example, an odds ratio of 1.25 for women would mean that women are 1.25 times more likely to work for an employer that offers a plan. Odds ratios that are significantly lower than 1.00 indicate that individuals with that characteristic are less likely to work for an employer that offers a plan. For categorical variables with more than two categories, a statistically significant odds ratio that is greater/less than 1.00 indicates that individuals in that category are more/less likely to work for an employer that offers a plan than individuals in the category that is chosen as the referent or comparison category. As shown in the body of the report, before controlling for differences between men and women in demographic and occupational characteristics, a greater proportion of women worked for employers that offered plans in 2009. Interestingly, table 14 shows that after accounting for demographic and occupational characteristics, women have slightly lower odds of working for an employer that offers a DC plan than men. In fact, the positive gender effect for women is eliminated when we control for occupational characteristics using a statistical model (results not shown below). In other words, women’s higher likelihood of working for an employer that offers a plan is largely due to the types of occupations and industries in which women work. (The odds ratios for the specific occupations and industries, which are too numerous to discuss here, are listed in the table.) Odds (O) are mathematically related to but not the same as probabilities (P), that is O=P/. While dummy and categorical variables are both discrete variables, a dummy variable takes on a value of 0 or 1. A categorical variable takes a value that is one of several possible categories and there is no intrinsic ordering to the categories. We found that several other factors are associated with the likelihood of working for an employer that offers a plan. While the details are shown in the table, the factors that were positively associated with working for an employer that offers either a DB or DC plan (and that were statistically significant at the 95 percent confidence level) included age; being divorced (relative to married); education level; U.S. citizenship; working in the government or nonprofit sector (in comparison to the private sector); having 5 to 9 years of work experience (in comparison to having less than 5 years); union membership; job tenure; and firm size. Factors that were negatively associated with working for an employer that offers a plan included being never married (in comparison to being married); being a single parent; being Black, Hispanic, or Asian (in comparison to White, non-Hispanics); being a naturalized immigrant; working part time; and being self-employed. While the results across both models were generally consistent, some results were significant in one model but not the other. Table 15 shows the results of a model we estimated to analyze factors associated with whether an individual is eligible for their employer’s plan. It is presented in the same format as table 14. As shown in the body of the report, women had lower rates of plan eligibility across all 4 study years. The results of the model show that, even after controlling for demographic and occupational differences between men and women, women had significantly lower rates of eligibility in 2009. Perhaps most interesting is the odds ratio for part-time status, which indicates that part- time workers are approximately one-third as likely to be eligible for their employer’s plan as full-time workers. In addition, work experience and tenure are also significantly and positively related with eligibility. Union status is also positively associated with plan eligibility. Table 16 shows the results of two models we estimated to analyze factors associated with the probability of participating in (1) any type of pension plan (DB or DC) or (2) a DC plan. Again, it is presented in the same format as tables 14 and 15. As shown in the body of the report, before controlling for differences between men and women in demographic and occupational characteristics, a smaller proportion of women participated in an employer-sponsored pension plan. Our analysis shows that the gender differences in plan participation are largely accounted for by differences between men and women in demographic and occupational characteristics. Similar to our other models, we identify a number of factors that are related to plan participation. The factors that were positively related to participating in either a DB or a DC (and that are statistically significant at the 95 percent level) include age; education-level; being Asian (relative to whites); U.S. citizenship; working in the nonprofit or government sector (relative to the private sector); work-experience; union membership; and tenure. Factors that were negatively related to participating in a plan included being a single parent; working part-time; and being Black or Hispanic. A number of industries and occupations, too numerous to list, were statistically significant as shown in the table below. To compute median incomes and income composition for men and women in different demographic groups, we used information from the core questionnaire of the SIPP data (as described above). We used the last month of the 4-month reporting period (within each “wave”) with the assumption that individuals will more accurately recollect income from the most recent month than income from 4 months ago. To obtain an annual income estimate, we multiplied the monthly reported income by 12. The poverty rate was computed using a SIPP variable that indicates the poverty threshold for an individual’s household. The Census Bureau uses a set of money-income thresholds that vary by family size and composition to determine who is in poverty. If a family’s total income is less than the family’s threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). All of our income composition, median, and poverty level estimates were computed at the individual level, using household-level information. In other words, median incomes were computed by applying all household income to each individual in the household and taking the median across all individuals within a certain category (e.g., gender, or gender and race). For married individuals, this means that spousal income was included in these estimates. Correspondingly, we used SIPP individual-level weights to compute our point estimates and, in conjunction with other factors, calculate the standard errors of those estimates so that we could accurately account for the complex survey design. This method might result in overstated estimates from earnings if workers do not work all 12 months of the year. These patterns held across all the years we analyzed. demographic patterns of life-expectancy and the ages of marital partners.Since women typically marry older men, and women typically have longer life-expectancies than men, it is not surprising that a sample of older individuals will include fewer married women than married men, as the spouses of older women are more likely to have died than the spouses of older men. For this reason, the sample of married older women could differ from the sample of married older men, so their household characteristics—including income—may not be the same. Further, the difference between the ages of the spouses of married men and married women could also result in different estimates of median income and income composition. For example, if women tended to be married to older men, the income composition of the household might be skewed away from earnings and towards Social Security. Conversely, if men tended to be married to younger women, a higher share of income might come from earnings. We estimated the relationship between events that occur later in life and income and assets using fixed-effects panel regressions. The main advantage of fixed-effects models is that they are designed to isolate the effect of the event from all other permanent characteristics of the individual. We estimated our models using data from the HRS, which follows households over time. Our analysis focuses on life events that occur after age 50, as the HRS follows individuals age 51 and over. It is also possible that the survey response rate was higher for married men than for married women. of observation (e.g., proportion that became divorced between period 1 and period 2). Table 19 uses the first method and presents some descriptive statistics on the women and men in our sample. Specifically, it shows the average values of characteristics for different ages for women and men. Real assets and real income. At ages 51 to 64 women and men have similar levels of assets. However, after age 65, men’s average level of household assets becomes larger than the average level for women. Men’s average levels of household income are higher than women’s at every age level. Marital status. The rates of marriage and widowhood are relatively comparable between women and men before age 65. For example, 6 percent of women and 1 percent of men younger than age 65 were widowed. However, at older ages, more women were estimated to be widowed than men. Poor health. Individuals were classified as being in poor health based on a survey question of self-reported health, which asked the individual to rate his or her health on a scale from 1 to 5, where 1 is excellent and 5 is poor. An answer of “fair” or “poor” was classified as being in poor health. As table 17 shows, rates of poor health were comparable between women and men at all age groups. Unemployment. This variable captures the percentage of individuals that responded to a labor force question as being “unemployed”. It is important to note that this is not equivalent to an unemployment rate—as individuals classified as not in the labor force were included in the denominator. Women and men were equally likely to report being unemployed. Helping parents financially or with daily activities. These variables capture the percentage of households that provided financial help or assistance with basic daily activities to either the parents of the respondent or spouse. Again, it appears that these rates were comparable for women and men. Table 18 uses the second method to show the proportion of women and men that had a life event status change during the period of analysis. As table 18 shows: Divorce/separation. During the period in which both members of the household are less than 65, less than 1 percent of men experienced divorce or separation between any of the two waves. For women, the proportion was negative – indicating that more women went from divorced or separated to married than from married to divorced or separated. Widowhood. During the earlier period, about 1 percent of women became widowed between any of the two waves. This proportion increased to more than 2 percent as the household aged and was twice the rate for men. Decline into poor health. The rate of health decline was similar for women and men. On average, approximately 2 percent of women and men reported a decline in health from one period to another. Unemployment. Very few women and men reported a change to and from unemployment in our data. Helping parents financially or with daily activities. The proportion of women’s and men’s households providing personal or financial assistance fell as the household aged. This may be because older households were less likely to have living parents requiring assistance. Percent change in real assets. In the earlier period, assets for women and men increased at a rate of about 6 percent per 2-year period. Alternatively, the rate of asset growth became negative as the household aged. Percent change in real income. In both younger and older households, incomes fell at a rate of approximately 5 percent per 2- year period, on average. In order to examine whether the effects of certain events occurring later in life differ by gender, we used fixed-effects regression models. For example, we estimated how changes in health lead to changes in household assets and income. Researchers use the fixed-effects method because much of the differences in income and wealth between households are consistent over time (as poorer households tend to stay poor and richer households tend to stay rich). The fixed-effects method sweeps away these “time invariant” differences, thus better isolating the effect of health or other life events from other aspects of households that could explain differences. In addition to the fixed-effects analysis, we also developed “cross-section” regression models. In these models, we attempted to control for a set of demographic and other variables, such as education and age that could be correlated with life events, household assets, and household income. A challenge to this approach is that many factors that affect assets and income are unobserved, and lead to mistaken conclusions. For example, if an individual earns a low wage, that may be connected with poor health and the accumulation of assets. So, while the researcher is attempting to estimate the effect of health on income, what is actually measured is the effect of income on health. In general, in our cross-section models, we found that effects were larger in magnitude than in the fixed-effects models, but these models were not as good a fit to the data as the fixed- effects models. Specifically, we estimated variations of the following equation, separately by gender: (1) Log (Household Assets or Income) = α + α + β*(poor health) + χ *(marital status) + δ*(other control variables) Where, α and α indicate fixed effects for the individual and wave. β is the effect of poor health and δ and χ are the effect of other control variables and marital status.By including a dummy variable for each wave, we attempted to control for all national-level changes that could have affected assets and income, and also have been associated with the life events. Therefore, β can be interpreted as the effect of poor health, measured as the percent difference in average assets between periods where an individual reports poor versus not-poor health. Due to the additional controls, this average percent difference is measured relative to the changes over time, and also relative to the other time-variant measures captured, such as changes in marital status. However, while some of the life-events are likely associated with the passage of time, the regression does not assume that relationship. For example, if an individual switches from poor health to good health, the fixed-effects regression will also use those transitions to estimate the size of the effect. Similarly, the fixed-effects regression will also use transitions from married to widowed, as well as widowed to married, to estimate the effect of widowhood. Other control variables that we included were age (measured as date of wave minus birth year), race and education (categorical), cohort of HRS survey, Census region, region of birth (12 categories, including non-U.S.). In general, in the cross-section models, we found that education was positively related to assets and income, while minority status was negatively related. With some slight variation, we based our choice of control variables on Coile and Milligan. (See Courtney Coile and Kevin Milligan, “How Household Portfolios Evolve After Retirement: The Effect of Aging and Health Shocks,” The Review of Income and Wealth, vol. 55 no. 2 (Malden, MA: June 2009)). In order to estimate effects in terms of percents, we estimated the effects on the log of assets or income. In addition, we transformed the coefficients to more closely approximate percent changes by taking the exponent of the estimated coefficient and subtracting 1. Regression variables were weighted by household weights. the possibility of endogenous relationships. For example, if an individual’s health declined because income fell, and not the other way around, that bias could affect our findings. Some of the life events we examined were likely correlated with changes in household structure, such as changes in marital status. However, if the income of a household falls when an individual leaves, the remaining individuals may not be worse off when it comes to resources because the household now requires fewer resources to meet its needs. To address this, we adjusted the estimated effects by household size; the household’s income and assets were scaled by the square root of the individuals in the household. The rationale for using the square root is because the effect of reducing members is diminishing (changing from 1 to 2 has a larger effect than going from 9 to 10). In addition, this analysis estimated the effect of an individual’s life event on household assets or income. We did not attempt to determine to what extent a spouse’s life event (for married individuals) may have affected household assets or income). Table 19 contains the effects of the first event we analyzed: divorce. We analyzed the effect of divorce on household assets and income, both with and without controlling for the number of people in the household. Across almost all the groups and specifications, the effect of divorce is to reduce assets and income, with larger effects for women than for men. Adjusting for household size tended to reduce the magnitude of the effects. Effect on assets. Divorce tended to reduce assets for more women than men, with comparable sizes of effects for women and men. For example, among all households, the decline in assets associated with divorce was 41 percent for women and 39 percent for men. When the size of the household was adjusted for, the size of the effect declined, but was still statistically significant. Effect on income. Divorce reduced income for both women and men, with larger effects for women than men. For example, among all households, the decline in income associated with divorce was 41 percent for women and 23 percent for men. When household size was adjusted for, the size of the effects were much smaller in magnitude. Table 20 contains the results for widowhood. As with divorce, we analyzed the effect of widowhood on household assets and income, both with and without controlling for the number of people in the household. Across almost all the groups and specifications, the effect of widowhood is to reduce assets and income, with larger effects for women than for men. Adjusting for household size tended to reduce the magnitude of the effects. Effect on assets. Widowhood reduced assets for both women and men, with larger effects for women than men. For example, among all households, the decline in assets associated with widowhood was 32 percent for women and 27 percent for men. However, part of this effect seems to be associated with the size of the household. Among the households in which at least one member was 65 and over, the decline in assets was not significant when household size was adjusted for. Effect on income. Widowhood reduced income for both women and men, with larger effects for women than men. For example, among all households, the decline in income associated with widowhood was 37 percent for women and 22 percent for men. Again, part of this effect seems to be associated with the size of the household. When household size was adjusted for, the size of the effects were much smaller in magnitude. As shown in table 21, unemployment tended to reduce assets and income, with comparable effects for women and men. The effects did not seem to dissipate when household size was adjusted for. Effect on assets. Unemployment reduced assets for both women and men, with comparable effects for women and men. For example, among all households, the decline in assets associated with unemployment was 7 percent for women and 7 percent for men. An exception to this difference was in cases in which at least one member was 65 or over. For those individuals, the decline in household assets was only 2 percent for women and 15 percent for men. Effect on income. Unemployment reduced income for both women and men, with comparable effects for women and men. For example, among all households, the decline in income associated with unemployment was 6 percent for women and 8 percent for men. In general, across the specifications, the effect of a decline into poor health tended to reduce assets and income, with comparable effects for women and men (see table 22). One notable difference however, were the larger estimated effects of men’s poor health on assets, but only in the case where both members of the household were less than 65 years of age. Specifically, we found that for individuals living in these households, poor health in men was associated with a drop in household assets of 13 percent, but 5 percent for women. In general, the magnitude of the effect on assets was in the 10 percent range for both women and men, and is statistically significant. The effects on income are about half that magnitude, but follow the same direction as the effects on assets. There is little difference in the effects when the level of assets and income are estimated with a correction for the size of the household. As shown in table 23, the results for either helping parents financially or with basic daily activities—eating, dressing, and bathing—were not as consistently significantly negative as the other life events. In the fixed- effects regression, the effect of personal assistance did not appear to be statistically significant, while the effect of financial assistance tended to be significantly positive. It may be that when households have more assets or income they are more likely to provide assistance—which could explain these findings. There is little difference in the effects when the level of assets and income are estimated with a correction for the size of the household. To further understand these relationships, we explored the characteristics of those helping their parents with the basic daily activities of bathing, dressing, and eating. We found that only 2 percent of the sample provided both financial help and help with basic daily activities. Further, those in the labor force (i.e., working or unemployed and looking for work) were more likely to help their parents with basic daily activities than those retired or not in the labor force. Michael Collins, Assistant Director; Erin M. Godtland, Senior Economist, and Jennifer Gregory, Senior Analyst, led the engagement. In addition, James Bennett, Benjamin Bolitzer, David Chrisinger, Cynthia Grant, Jean Lee, Grant Mallie, Ashley McCall, Michael Morris, Rhiannon Patterson, Mark Ramage, James Rebbe, Douglas Sloane, Jeff Tessin, Shana Wallace, and Erin White made valuable contributions.

Summary: Elderly women, who comprise a growing portion of the U.S. population, have historically been at greater risk of living in poverty than elderly men. Several factors contribute to the higher rate of poverty among elderly women including their tendency to have lower lifetime earnings, take time out of the workforce to care for family members, and outlive their spouses. Other factors could affect older womens financial insecurity. These include the economic downturn and changing trends in pension plan offerings. In light of these circumstances, GAO was asked to examine (1) how womens access to and participation in employer-sponsored retirement plans compare to mens and how they have changed over time, (2) how womens retirement income compares to mens and how the composition of their incomethe proportion of income coming from different sourceschanged with economic conditions and trends in pension design, (3) how later-in-life events affect womens retirement income security, and (4) what policy options are available to help increase womens retirement income security. To answer these questions, GAO analyzed data from two nationally representative surveys, conducted a broad literature review, and interviewed a range of experts in the area of retirement security. GAO is making no recommendations. GAO received technical comments on a draft of this report from the Department of Labor, the Department of the Treasury and the Social Security Administration, and incorporated them, as appropriate. Over the last decade, working womens access to and participation in employer-sponsored retirement plans have improved relative to men. Indeed, from 1998 to 2009, women surpassed men in their likelihood of working for an employer that offered a pension plan, largely because the proportion of men covered by a plan declined. Furthermore, as employers have continued to terminate their defined benefit (DB) plans and have switched to defined contribution (DC) plans, the proportion of women who worked for employers that offered a DC plan increased. Correspondingly, womens participation rates in DC plans increased slightly over this same period while mens participation fell, thereby narrowing the participation difference between men and women to 1 percentage point. At the same time, however, women contributed to their DC plans at lower levels than men. Although the composition of income for women age 65 and over did not vary greatly over the perioddespite changes in the economy and pension system women continued to have less retirement income on average and live in higher rates of poverty than men in that age group. The composition of womens income varied only slightly, in part, because their main income sourcesSocial Security and DB benefitswere shielded from fluctuations in the market. Women, especially widows and those age 80 and over, depended on Social Security benefits for a larger percentage of their income than men. For example, in 2010, 16 percent of women age 65 and over depended solely on Social Security for income compared to 12 percent of men. At the same time, the share of household income women received from earnings increased over the period, but was consistently lower than for men. Moreover, womens median income was approximately 25 percent lower than mens over the last decade, and the poverty rate for women in this age group was nearly two times higher than mens in 2010. For women approaching or in retirement, becoming divorced, widowed or unemployed had detrimental effects on their income security. Moreover, divorce and widowhood had more pronounced effects for women than for men. For example, womens household income, on average, fell by 41 percent with divorce, almost twice the size of the decline that men experienced. For widowhood, womens household income fell by 37 percentwhile mens declined by only 22 percent. Unemployment also had a detrimental effect on income security, though the effects were similar for women and men; household assets and income fell by 7 to 9 percent. A range of existing policy options could address some of the income security challenges women face in retirement. For example, some would expand existing tax incentives to save for retirement while others would improve access to annuities. All of these options have advantages and disadvantages that would need to be evaluated prior to implementation. For example, increasing Social Security benefits for widows could provide additional income for women who have few options to increase their retirement savings. However, increasing benefits would also increase costs to the Social Security program and have implications for its long-term solvency.