Background Measures of Economic Mobility Intergenerational economic mobility describes how people’s incomes in adulthood compare with their parents’ incomes in the past or at similar ages. Several measures are used to assess the degree of economic mobility, but fundamentally, a society exhibits more economic mobility when incomes are less related to parents’ income. By contrast, where economic mobility is lacking, individuals are more likely to remain at the economic position of their upbringing. Economists traditionally measure economic mobility in three ways: Absolute economic mobility - whether people make more money (in inflation-adjusted dollars) than their parents did at a similar age (see fig. 1). For example, in 1970, 92 percent of 30-year-olds made more money in inflation-adjusted terms than their parents did at similar ages, implying an absolute economic mobility rate of 92 percent. Relative economic mobility - whether people are at a higher income percentile compared to their parents’ income percentile in the past. For example, according to one estimate, there was an 8 percent chance that a person born in the United States from 1980-1982 to parents in the bottom 20 percent of the income distribution would move to the top 20 percent of the income distribution for their birth cohort by the time he or she was approximately 30 years old. Intergenerational income elasticity (IGE) - the strength of the relationship between a person’s income and their parents’ income. The higher the number, between zero and one, the greater the relationship between parental income and children’s adult income (see fig. 2). For example, if IGE is zero, there is complete mobility between generations; parents’ income does not influence their children’s future income at all. If IGE is 1, there is no mobility between generations, as everyone stays at the same income level in which they were born. IGE measures the “persistence of advantage” from one generation to the next at all points along the economic ladder and therefore captures how much inequality is passed down through generations. A single standard measure of intergenerational economic mobility does not exist, and some researchers use more than one. Each of the three measures provides some insight into the level of opportunity available for people to better their economic circumstances relative to the circumstances of their birth. Many factors may be related to the level of economic opportunity available to an individual, including but not limited to overall macroeconomic conditions (e.g., economic growth), education, race, gender, geography (the region, commuting zone, county, or neighborhood in which a person lives), health care, and neighborhood characteristics. Characteristics of Millennials Millennials have a number of unique characteristics that distinguish them from previous generations. According to data from SCF, Millennials are a more diverse group than previous generations—40 percent of Millennial households are headed by someone who belongs to a racial or ethnic minority group. Millennials are also the most educated generation to date in terms of college degree attainment (see fig. 3). An estimated 62 percent of Millennial households had someone with at least an associate’s degree in 2016. Not only did Millennial households have more college degrees overall, a greater percentage of Millennial households in 2016 had advanced degrees, including master’s, doctorate, and professional degrees, compared to previous generations at similar ages. Meanwhile, only 44 percent of Millennials 25-34 years old were married or living with a partner and had children in 2016, while 54 percent of Baby Boomers were partnered and had children by age 34. Economic Mobility is Linked to Parental Income, and Varies by Race and Geography The 20 studies that we reviewed indicate that economic mobility has remained flat or declined in the United States over the last 40 years; none of the studies we reviewed found that economic mobility has increased (see text box). Additionally, estimates of intergenerational income elasticity (IGE) suggest that economic status persists across generations, particularly for the lowest and highest income groups. Studies identified parental income, race, and geography as key determinants of one’s economic mobility. These findings could have future implications for Millennials. Parental Income is a Key Predictor of Economic Mobility, Especially among the Lowest and Highest Earners money than their parents at the same age declined between 1970 and 2010 (see fig. 4). One study attributes this decline to an unequal distribution of economic growth, noting it has primarily benefited the highest earners. It remains to be seen if this downward trend will continue for the Millennial generation. The research we reviewed indicates that economic mobility varies by race. The findings on economic mobility and race suggest that not all groups of Millennials may experience the same levels of economic opportunity. Blacks experience less upward intergenerational mobility than whites. In particular, black men are less likely to be upwardly mobile and more likely to be downwardly mobile than white men, even with similar levels of education. Meanwhile, children of low-income white families have had higher rates of upward mobility over time than black children with similar socioeconomic characteristics. Some minority groups have higher economic mobility than others. One study that examined additional racial groups found high earnings among children of low-income Asian households, and found that Asians are likely to remain at income levels comparable to or above-white Americans, though these findings are largely driven by first-generation immigrants. Additionally, Hispanic Americans are moving up the income distribution across generations, although their overall economic mobility is somewhat lower than whites. Meanwhile, American Indians are more likely than whites to be downwardly mobile, even those in the wealthiest 1 percent. Childhood Location Affects Economic Mobility in Adulthood, but Outcomes Differ by Subgroups The research we reviewed indicates that the region, state, commuting zone, county, and most especially, the neighborhood in which one grows up affects economic mobility and future earnings, but these effects vary by demographic and income groups. Economic mobility varies by location. One study found that areas within the United States offer disparate opportunities, with some localities supporting higher rates of economic mobility than others (see fig. 5). In particular, counties in the southeastern United States were found to have lower levels of economic mobility than counties in the rural Midwest. Another study found that a child’s neighborhood has a statistically significant effect on life chances, and that growing up in a low-income, metropolitan neighborhood has a strong negative effect on future earnings. Conversely, growing up in an affluent neighborhood can have almost as large an impact on future earnings as completing a bachelor’s degree. Specific neighborhood characteristics drive differing rates of economic mobility. Several researchers linked economic mobility to certain area and neighborhood characteristics, including rates of poverty, racial segregation, economic inequality, the proportion of single-parent households, and school quality. Researchers identified racial segregation as a neighborhood characteristic broadly associated with lower mobility. One study found that economic segregation is also negatively associated with economic mobility. One study identified three neighborhood characteristics that are correlated with a weaker relationship between race and mobility: low poverty rates, a high percentage of low-income black fathers present, and low levels of racial bias among whites. According to this study, neighborhoods with these characteristics had higher mobility for black boys and a relatively small black-white mobility gap. The effects of geography on future earnings vary by race, socioeconomic status, and gender. The effects of race and neighborhood characteristics on economic mobility are related and hard to disentangle. For example, one study found that black boys have lower incomes in adulthood than white boys who grow up in the same neighborhood in 99 percent of Census tracts, even when accounting for income. This highlights the effect of race on economic mobility when children face the same neighborhood conditions. Conversely, the same study also found that 4.2 percent of black children grow up in neighborhoods with the characteristics associated with higher levels of mobility, compared to 62.5 percent of white children. This is in line with another study that found that neighborhoods can amplify racial inequality across generations. Another study notes that Hispanic and black children tend to live in neighborhoods with low mobility for those of their racial group, whereas white children tend to live in neighborhoods with higher mobility rates for whites. Neighborhood effects can also vary by socioeconomic status and gender. Regarding socioeconomic status, one study found that place may matter less for children from higher-income families, as they may be better able to insulate themselves from the effects of local conditions (e.g., by switching to private schools if public schools are weak.) Regarding gender, the same study finds that neighborhood matters more for boys than girls. Across studies, common themes emerged that suggest Millennials might not have the same level of economic mobility enjoyed by their parents’ generation. While the studies in our review varied in their estimates of key measures of economic mobility and its determinants, the studies were consistent in their findings that absolute economic mobility is declining, relative mobility is flat or declining, and economic status is somewhat rigid from one generation to the next. Moreover, the studies that examined drivers of mobility found that a child’s race and neighborhood have a significant effect on their economic mobility as adults. This is particularly relevant for Millennials because of their racial and ethnic diversity. It is not clear whether Millennials’ diversity and higher levels of education will lead to a reversal of these trends, or whether these trends will continue into the future. Millennials Have Similar Average Incomes and Lower Average Net Worth Compared to Previous Generations Despite Being More Educated If economic mobility is flat or falling, knowing how a cohort is doing at the beginning of its members’ working lives sheds light on the potential challenges that lie ahead as the cohort ages and moves toward retirement. We analyzed data from the Survey of Consumer Finances (SCF) to provide a snapshot of how Millennials are faring economically as young adults. We compared the financial circumstances of Millennial households in 2016 to Generation X households in 2001 and Baby Boomer households in 1989; in each year, we estimated measures of financial well-being for households in which the head of household, or any spouse or partner, was 25-34 years old. We found that incomes across the three generations have remained relatively flat, which is consistent with our review of economic mobility studies. We also found that Millennials have lower net worth, which we define as assets minus debt. With respect to assets, we found that Millennials are saving for retirement, but the accumulation of wealth through homeownership has decreased as fewer Millennials are buying homes. In terms of debt, Millennials hold large amounts of student debt compared to previous generations, but are also more likely to be college educated. Millennial Households Had Similar Average Incomes as Previous Generations Despite Higher Educational Attainment Rates Millennial households in 2016 had similar average real incomes compared to previous generations at similar ages, according to our analysis of SCF data (see fig. 6). Our analysis showed that median incomes were also similar across young adult households in the Millennial and Baby Boomer generations and that Millennial households had slightly lower median incomes than Generation X households (see fig. 7). We also examined average and median incomes among households with college degrees and found similar results. These findings suggest that, on average, real income levels have been stagnant for young adult households across these three generations. As described in figure 3, Millennial households are more likely to be college-educated compared to previous generations. While college graduates generally have higher incomes than non-college graduates, the income of degree holders has remained flat over time. A recent study from the Federal Reserve Board of St. Louis found that the college income premium, the increase in earnings for college graduates compared to non-college graduates, does exist. According to this study, in the first quarter of 2018, college graduates received weekly wages that were 80 percent higher than high school graduates. However, college graduates in recent years have not made higher incomes than college graduates in the past, as they have had relatively flat inflation-adjusted wages since 2001. Millennials Had Lower Levels of Net Worth Than Previous Generations, With Lower Homeownership Rates and Higher Student Debt Overall, Millennial households in 2016 had significantly lower average and median net worth, defined as assets minus debt, than Generation X households at similar ages in 2001, according to our analysis of SCF data (see figs. 8 and 9). This may be explained by lower homeownership rates than previous generations, as well as larger amounts of student debt. Median net worth was much lower for Millennial households in the bottom 50 percent of the net worth distribution compared to previous generations. While median net worth for the lowest net worth quartile of Baby Boomers and Generation X was around zero, it was substantially negative for Millennials in the lowest quartile, indicating that debt was greater than assets (see fig. 10). The median net worth of those Millennial households in the highest 25 percent was also significantly lower than the median net worth of those at the top in previous generations. We analyzed both average and median net worth to examine how net worth was concentrated among young households. Our analysis showed that estimates of median net worth were much lower than estimates of average net worth across all three generations, suggesting that net worth was unevenly distributed among these households and that a relatively small number of households held a substantial percentage of total net worth. As a part of our analysis of net worth, we examined specific types of assets and debt, including homeownership, retirement resources, and student loans, and found the following: Millennials had lower rates of homeownership compared to previous generations. Our analysis of SCF data showed that a significantly lower percentage of Millennial households in 2016 were homeowners compared to previous generations in 2001 and 1989 (see fig. 11). We estimated that about 43 percent of Millennial households owned homes, compared to 51 percent of Generation X households and 49 percent of Baby Boomers. As a result of lower rates of homeownership, Millennial households had less mortgage debt, but also less home equity, compared to households in other generations at similar ages. Home equity has historically been an important source of retirement security as people age. It is unclear whether Millennial households will reach similar rates of homeownership as previous generations, but it is possible they may be more likely to buy homes at older ages compared to previous generations. Millennials were as likely to have retirement resources as previous generations. A similar percentage of Millennials had retirement resources in 2016 (either defined benefit pensions or retirement accounts, such as an IRA, 401(k), or other account-type pension), compared to Baby Boomers in 1989 and Generation X in 2001 (see fig. 12). Millennials have a similar average value of retirement accounts as Generation X (see fig. 13). This may be due, in part, to auto- enrollment policies, which create default retirement savings accounts for workers, and are relatively new. Millennials have a higher average value of defined contribution retirement accounts compared to Baby Boomers, likely because of the shift over time in the retirement system from defined benefit pensions to account-type pensions, such as 401(k)s. Student loans were the key source of debt that distinguished Millennials from previous generations. We found that Millennial households were significantly more likely to have student loans than previous generations at similar ages (see fig. 14). We measured the potential burden of student loan debt by estimating student loan-to-income ratios and found that this measure was significantly higher for Millennial households in 2016 compared to previous generations when they were young. On average, Millennial households in 2016 had a student loan-to-income ratio that exceeded 100 percent compared to ratios of under 50 percent in previous generations (see fig. 15). While the student loan-to-income ratio has increased over time for households of all incomes, it has most greatly affected lower-income households. For example, while we estimated that the average student loan-to-income ratio was about 100 percent for young households in the bottom income quartile in 2001, we estimated it was significantly higher for young households in the bottom income quartile in 2016 (see fig. 16). These findings suggest that, on average, it could take Millennials several more years’ worth of total income to pay back total household student loan debt (without interest). Although Millennial households have more student debt than previous generations, they may also benefit from federal student loan repayment plans and forgiveness programs. Households that qualify for these programs may not have to repay their student debt in full, though to date about half of student loans are still under standard repayment plans and few potentially qualified borrowers have been granted forgiveness (see textbox). Income-Driven Repayment (IDR) plans, available through the Department of Education for federal student loans, generally base student loan payment amounts on a borrower’s income and extend repayment periods from the standard 10 years to up to 25 years with any remaining balance forgiven at the end of the period. Some borrowers may qualify for very low payments and these payments count toward loan forgiveness at the end of the repayment period. As of September 2018, almost half ($414 billion) of the $859 billion in outstanding Direct Loans were being repaid by student loan borrowers using IDRs. The long-term effects of higher educational attainment, along with higher education loans, on Millennial households is unclear. It is possible that those with advanced degrees may be better situated over time to repay their student loans. However, while an estimated 18 percent of Millennial households in 2016 had advanced degrees (master’s degree or above), an estimated 45 percent had student loans, indicating that many Millennial households with student loans did not have an advanced degree. In addition, while the college income premium is real, high levels of student debt may affect the ability to accumulate wealth, which may be why average net worth levels have decreased for college graduates. (PSLF) program forgives federal student loan balances for eligible borrowers who have made 10 years of qualifying payments while in certain public service jobs. As of March 2019, the Department of Education reported that 1,089,846 borrowers had an approved Employment Certification Form, the first step in potentially qualifying for PSLF. However, 99 percent of applicants were denied PSLF, highlighting the confusion with respect to applying and ultimately getting debt relief from these programs. The Millennial generation is different from previous generations on several measures of financial well-being, so there is uncertainty about how they will do financially as they age. On one hand, they have higher levels of educational attainment, and college graduates earn substantially more than non-college graduates. On the other hand, despite Millennials completing college degrees at higher rates than previous generations, average and median income are not higher for Millennials overall, which is consistent with flat intergenerational economic mobility and persistence of economic status across generations. Millennials also have less home equity than past generations because they are buying homes at lower rates. Given relatively stagnant average income across generations, it is not clear whether Millennials will begin earning more and buying homes later in life or whether lower homeownership rates will persist over time. Millennials are saving for retirement at rates comparable to Generation X, and saving early in life should benefit Millennials in the long run. Yet, they have significantly higher levels of student loan debt than past generations. Some Millennials may ultimately qualify for programs that help them lower their federal student loan debt, but it remains to be seen how these factors will affect Millennials’ financial circumstances in the long run, including in retirement. Agency Comments We provided a draft of this report for review and comment to the Departments of Labor (DOL) and the Treasury and to the Social Security Administration (SSA). We received technical comments from DOL, which we incorporated as appropriate. Treasury and SSA provided no comments. As agreed with your office, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days from the report date. At that time, we will send copies to the appropriate congressional committees, the Secretaries of Labor and the Treasury as well as the Administrator of the Social Security Administration. 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 key contributions to this report are listed in appendix II. Appendix I: Objectives, Scope, and Methodology Our objectives were to examine (1) what is known about intergenerational income mobility, and (2) how the financial circumstances of Millennials compare to previous generations. In order to determine what is known about intergenerational income mobility (which we use interchangeably with “economic mobility”) in the United States, we conducted a literature review of relevant, recent economic studies. We identified the majority of the studies we reviewed through systematic searches of databases such as ProQuest, Scopus, and EBSCO using search terms such as “economic mobility,” “income mobility,” “intergenerational income mobility,” or “intergenerational income elasticity.” We searched for scholarly and peer-reviewed publications, working papers, government reports, and think tank reports. We also reviewed studies recommended during expert interviews as well as some included in the bibliographies of key studies on the topic of economic mobility. We used four criteria to target our literature search. In order to be included, studies had to: (1) include original estimates of at least one of three measures of intergenerational economic mobility: absolute economic mobility, relative economic mobility, and intergenerational income elasticity; (2) focus on the United States; (3) be published in the past 5 years (2014-2019), or 2 years if a working paper (2017-2019); and (4) be published in a U.S.-based publication. We then reviewed over 280 abstracts and further evaluated approximately 90 potentially appropriate studies, eliminating ones that did not meet our four criteria. A technical review of each study by at least two GAO economists included an assessment of key findings about economic mobility, methodology, data, assumptions, and limitations. Twenty studies met our four criteria and, based on our technical review, had sufficient methodological rigor for the purpose of providing information on economic mobility. Researchers attempting to estimate the degree of economic mobility in the United States face challenges in acquiring datasets with precise income measurements and that track incomes across generations with sufficient sample sizes. Potential reasons why researchers produce different estimates of economic mobility measures include: Differences in Datasets and Their Respective Limitations. Different datasets may not equally represent every segment of the population. For example, studies making use of the Panel Study of Income Dynamics (PSID) are not generalizable to populations not included in large numbers when the PSID began, such as recent immigrants and institutionalized populations. In addition, some studies rely on data that are not fully representative of the entire income distribution, either because they do not include a sufficient sample of households with very high income or, conversely, households with very low or zero earnings. Some datasets do not capture individuals who are not working or not filing taxes during the period of analysis. For instance, in one study making use of tax data, the authors noted that if parents never file a tax return, they cannot be linked to their child. In that study, parents of approximately 5 percent of children were not identified. In some cases, the data capture a limited age range, which leaves open the possibility of somewhat different results among different age ranges. In addition to different sampling strategies, datasets also capture different variables for each individual or household observed. Even the most comprehensive datasets currently available may lack the data to completely account for factors that may influence mobility, such as changes in family structure over time or detailed individual demographic characteristics for both parent and child households. Differences in Treatment or Construction of Variables. Estimates of intergenerational income mobility can be affected by choices the researcher makes, such as selecting a price deflator to inflation-adjust parents’ incomes; selecting the ages at which children and parents will be compared, accounting for changing trends in household size and composition; determining the value of non-cash benefits (e.g., employer-sponsored health insurance); and determining work-related costs associated with dual-earner households (e.g., child care). Some studies impute earnings for non-tax filers, and different methods of imputation may lead to slightly different results; in other studies, those with no reported income or observations with other missing variables (e.g., demographic characteristics) may simply be dropped from the dataset. How “parent” and “child” are defined may also differ across datasets (e.g., a parent could be the first adult to claim a child on their tax return, or could be an adult male living with a minor child in a household). Additionally, some studies required the researchers to construct datasets that matched parents and children at different points in time. Each researcher makes choices about how to handle the data, which can lead to different estimates. While we did not perform checks on these constructed data, the studies in our review generally included descriptions of the data and methodologies used as well as the difficulties and limitations associated with dataset construction, which we evaluated in our technical review. Differences in Choice of Economic Mobility Measure and Model Specification. Each measure of economic mobility provides a slightly different lens on mobility and has different interpretations. Absolute economic mobility, which compares the inflation-adjusted income of parents and children at similar ages, tends to reflect trends in overall economic growth and distribution of that growth. For instance, 92 percent of 30-year-olds in 1970 made more in inflation adjusted terms than their parents did at that age, while about half of children born in the 1980’s grew up to make more money than their parents by age 30. The difference may largely have been due to higher economic growth and a more equitable distribution of that growth along the income distribution from 1940-1970, whereas growth was slower and distributed differently between 1970 and the present. IGE offers a different metric with different limitations. Studies that estimate IGE regress log child income on log parent income. This conveniently yields a coefficient that can be interpreted as “the percent change in child income given a 1 percent change in parent income.” However, such estimates tend to be unstable because the relationship is non- linear and sensitive to the treatment of children with zero or very small incomes (because the log of zero is mathematically undefined). IGE is very sensitive to assumptions about the income of those with missing income data and typically does not include households with zero earnings, and so excludes some households with no income. Additionally, elasticities are sensitive to changes in cross-sectional income distributions (like during recessions). If children’s income distribution becomes more unequal, then the elasticity will become larger, all else equal. Despite these limitations, based on our technical review, all of the studies summarized in the report are of sufficient methodological rigor for the purpose of providing information on economic mobility. The authors of the studies we reviewed were generally aware of and transparent regarding the limitations of the datasets they worked with, and carried out analyses to test their results for robustness to different assumptions. Although there were differences in study datasets and methodologies, common themes emerge from the body of literature we reviewed. For example: None of the studies we reviewed found economic mobility to be increasing—all found it to be either flat or declining. While there was variation among studies regarding the exact degree to which parental income influences individuals’ income as adults, all studies we reviewed that examined parental income found it to be an important determinant of economic mobility. None of the studies that examined race found blacks to have higher mobility than whites. The studies we reviewed that examine geography agree that different locations have different economic mobility and that part of this variation is connected to the characteristics of a given place (such as school quality or level of segregation), not just to the characteristics of people who choose to live there. In other words, while the studies varied in their point estimates of various measures of economic mobility and its determinants, there was broad consensus among the studies regarding the sign (positive versus negative) and interpretation of the estimates. Additionally, these studies represent an advance in the data and analysis capabilities relative to past studies that examined economic mobility, largely because improved computing power has enabled more complex analyses of large datasets comprised of millions of records. See table 1 for the list of studies included in our review. Analysis of Millennials’ Financial Circumstances After considering possible datasets, we chose the Survey of Consumer Finances (SCF) for this analysis because the data are appropriate for estimating measures of income and wealth across generations, including asset and debt categories of interest like homeownership and student debt. The SCF is a triennial survey of U.S. households sponsored by the Board of Governors of the Federal Reserve System in cooperation with the Department of the Treasury. Every 3 years, the SCF interviews a different sample of households and aims to be representative of households across economic strata, including the top of the wealth distribution. The SCF provides information on household balance sheets, including detailed information on assets and debts, as well as pensions, labor force participation, and demographic characteristics at the time of interview. We compared the financial circumstances of young households across 3 years of the SCF, as each year was representative of a generation (or birth cohort) when someone in the household (either the head of household or a spouse or partner) was 25-34 years old, following similar previous GAO work. Data Limitations Our analysis of SCF data allowed us to make intergenerational comparisons, but not to follow the same individuals over time, so we were not be able to compare children to their parents using these data. While our analysis allowed us to make comparisons, it did not allow us to make statements as to why Millennials are different or similar to other generations. Moreover, our data analysis focused on relatively older Millennials whose experiences may be different than those born later in the generation, especially due to the timing of the Great Recession. The SCF dataset is based on self-reported data and as a result, the data are subject to nonsampling error, including the ability to get information about all sample cases; difficulties of definition; differences in the interpretation of questions; and errors made in collecting, recording, coding, and processing data. Also, demographic analyses using these data may be limited based on the sample size needed to produce reliable estimates. Lastly, we cannot make predictions about the future financial circumstances of Millennials based on this snapshot in time. There are also limitations with the SCF with respect to making comparisons by gender. In a household headed by a single person, the head is taken to be the single core individual. However, in households headed by a central couple who is of mixed sex, the head is taken to be the male in the household. This assumption makes it difficult to make reliable comparisons by gender. Finally, the SCF generally asks questions of household heads and their spouses (and not others living in the household), so it likely underemphasizes young adults who were still living with their parents, which is more prevalent for the Millennial generation. Thus, there may be some selection bias in the SCF with respect to relatively more financially well-off Millennials. For the data used in our analysis, we reviewed documentation and tested the data for anomalies. We determined that these data were sufficiently reliable for the purposes of this report. Analysis of SCF We defined young households in each generation as those in which the household head or any spouse or partner was 25-34 years old. We compared Millennial households in 2016 to Generation X households in 2001 and Baby Boomer households in 1989. Baby Boomers were born from 1946 to 1964 and were 25-43 years old in 1989, so we used the 1989 SCF for Baby Boomer households when they were young adults. Generation X individuals were born from 1965 to 1981 and were 20- 36 years old in 2001, so we used the 2001 SCF for Generation X households when they were young adults. Millennials were born from 1982 to 2000 and were 16-34 years old in 2016, so we used the 2016 SCF for Millennial households when they were young adults. We used the SCF’s measures of income, net worth, assets, and debt from the summary extract data as measures of financial circumstances. We defined household income as the sum of income across all sources. Income includes a family’s cash income, before taxes, for the full calendar year preceding the survey. The components of income are wages, self-employment and business income, taxable and tax- exempt interest, dividends, realized capital gains, benefits from social safety net programs, pensions and withdrawals from retirement accounts, Social Security, alimony and other support payments, and miscellaneous sources of income for all members of the primary economic unit in the household. We defined household net worth as assets minus debt. Assets include financial assets, including liquid assets in bank accounts, certificates of deposit, money market accounts, stocks and bonds, cash value of life insurance, retirement accounts, and other financial assets. Assets also include nonfinancial assets, such as the value of vehicles, primary residences, other residential property, businesses, and other nonfinancial assets. Debt includes mortgages, home equity loans, credit card balances, education loans, vehicle loans, other installment loans, and other debt, including loans against pensions or life insurance. Households could have financial resources outside of net worth, including future income from defined benefit plans or Social Security; however, we did not attempt to estimate the actuarial present value of these financial resources in our net worth calculation given the long time horizon to retirement and the amount of uncertainty associated with such a measurement. In addition, in our professional judgment, the inclusion of these financial resources would not have altered our finding that Millennials have lower net worth compared to previous generations; the inclusion of these financial resources would likely have widened the gap further between Millennials and previous generations because previous generations had greater access to DB plans than the Millennial generation. We estimated means and medians for variables of interest, both overall and by quartile. We estimated the standard errors and constructed the confidence intervals taking into account the dual-frame sample design in order to estimate the sampling variance for these estimates. One part of the design is a standard, multistage area-probability design, while the second part is a special over-sample of relatively wealthy households. This is done in order to accurately capture financial information about the population at large as well as characteristics specific to the relatively wealthy. The two parts of the sample are adjusted for sample nonresponse and combined using weights to make estimates from the survey data nationally representative of households overall. Unless otherwise indicated, estimates in this report are statistically significant at the p<.05 level, and the error bars in the figures represent the 95 percent confidence intervals for the estimates. We conducted this performance audit from November 2018 to December 2019 in accordance with generally accepted government auditing standards. Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives. We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives. Appendix II: GAO Contact and Staff Acknowledgments GAO Contact Staff Acknowledgments In addition to the contact named above, Michael J. Collins (Assistant Director), Jessica K. Rider (Analyst-In-Charge), Jessica Mausner, Kathleen McQueeney, and Layla Y. Moughari made key contributions to this report. Also contributing to this report were James Bennett, Alicia Cackley, Pin-En Annie Chou, Justin Dunleavy, Sarah C. Gilliland, Gina M. Hoover, Susan J. Irving, Dan Luo, Sheila R. McCoy, John W. Mingus Jr., Corinna Nicolaou, Oliver M. Richard, Vernette G. Shaw, Joseph Silvestri, Almeta Spencer, Frank Todisco, and Adam Wendel.


Background Administration and Oversight of the School Meals Programs The Food and Nutrition Service at USDA is responsible for overseeing the school meals programs at the federal level, which includes issuing regulations and guidance. The school meals programs are administered at the state level by a designated state agency—generally an education or agriculture agency—that issues guidance to school districts providing the meals. School districts are responsible for certifying students as eligible for free or reduced-price meals, providing children with nutritionally balanced meals each school day, and counting and claiming eligible meals for federal reimbursement, among other things. USDA and state agencies also conduct oversight of the school meals programs. Figure 1 summarizes the responsibilities of the different entities within the school meals programs. All students in schools operating the school meals programs may participate in the programs, and eligible students may be certified to receive free or reduced-price meals. Individual students can be certified into the school meals programs either through household application or direct certification. Household application. A household can submit an application that lists all sources of household income and the names of all household members, among other information. School districts compare this information to income-eligibility guidelines to determine whether the student is eligible for free or reduced-price meals. Alternatively, the household can indicate on the application that it participates in certain public-assistance programs—such as the Supplemental Nutrition Assistance Program (SNAP)—or that the student meets an approved designation, which confers categorical (automatic) eligibility for free meals. No documentation to support income listings—such as tax returns or pay stubs—is required at the time of application. Direct certification. Under the direct certification method, data matching is used to identify and certify students who are categorically eligible for free meals. State agencies are required by statute to match student enrollment records against SNAP records and may also match against records for other public-assistance programs or approved designations. Students who are directly certified into the school meals programs are eligible for free meals without a household application. Alternatively, schools can use certain program provisions to serve meals at no charge to all students (i.e., eligibility is not determined for each student individually on an annual basis). Community eligibility provision. Schools and school districts may apply for community eligibility if their percentage of students identified as eligible for free meals without an application—known as the identified student percentage—is at or above 40 percent. Meals served at schools using the community eligibility provision are reimbursed using a formula based on the identified student percentage. Other special provisions. Under these special provisions, schools generally use standard procedures to certify free and reduced-price eligible students and count meals by eligibility category to establish a base year. Following the base year, schools serve free meals to all students and are reimbursed based on the information collected in the base year. Meal Counting and Claiming USDA reimburses state agencies, which in turn reimburse school districts, for qualifying meals through the process of meal counting and claiming. A meal is reimbursable if it meets federal nutrition requirements and is not reimbursable if it is missing a required food component or fails to meet the meal pattern requirements. Meals are recorded at the point of sale in a school. Generally, individual meals are recorded as either free, reduced price, or paid based on the student’s certification status, unless the school is operating under community eligibility or a special provision. Reimbursable meals are tallied at each school. School districts aggregate meal tallies from each school and then report to the respective state agencies on a monthly basis. State agencies then aggregate the reports from each district and submit tallies of free, reduced-price, and paid meals to USDA. USDA then reimburses states for the amount reported. Meals in each category (free, reduced-price, and paid) are reimbursed at different rates. Oversight Given that the school meals programs are administered on a daily basis at schools across the country, USDA officials stated that the agency relies on two key oversight practices—management evaluations and administrative reviews—to monitor these programs. Management evaluations. USDA conducts management evaluations of state agencies’ administration of the school meals programs. USDA uses risk-based criteria, such as the level of turnover in state agency staff, to select the state agencies to review each year. According to USDA officials, starting in fiscal year 2019, USDA will automatically select a state agency for review if it has not been reviewed in the past 3 years. According to USDA guidance, examples of operations it reviews during management evaluations include (1) state oversight of certification and verification of students into the school meals programs, (2) administrative reviews of school districts conducted by state agencies, (3) claims for reimbursement, and (4) state oversight of compliance with federal meal pattern requirements, among other areas. According to USDA officials, if USDA considers state agencies reviewed in one year as high risk for program noncompliance, those agencies may receive an additional management evaluation in the following year focused on technical assistance. Administrative reviews. USDA develops guidance for administrative reviews in which state agencies review school districts’ administration of the school meals programs. State agencies are required to conduct administrative reviews of each of their school districts at least once in a 3-year review cycle. USDA guidance states that the objectives of administrative reviews include identifying noncompliance, providing technical assistance, and assessing fiscal actions. Among other things, state agency staff are to review a school district’s certification records and its meal counting and claiming data for the most recent month for which a claim for reimbursement was submitted. State agency staff are also to review school meals served while the staff are on-site to determine whether the meals contain the required food components. State agency staff are to record any identified noncompliance and also provide technical assistance to school district staff. Estimation of Improper Payments in the School Meals Programs The Improper Payments Information Act of 2002 (IPIA), as amended, requires agencies to identify, estimate, and report their improper payment amounts and to develop and implement improper payment reduction plans, among other things. USDA estimates improper payments for the school meals programs through a model based on its APEC study, which is conducted by contractors. The most recent APEC study (APEC II) was released in May 2015 and covered activity during the 2012–2013 school year. USDA conducts an APEC study about every 5 years, with APEC III expected to be released in 2020. Conducting the APEC study involves multiple sampling and data analysis efforts, including the following examples from APEC II. In-person surveys. Contractors conducted in-person surveys of over 3,000 sampled households to collect information on each household’s circumstances at the time of application, including income, household size, and receipt of other public-assistance benefits. Using this information, contractors determined a sampled student’s eligibility status and compared it to the school district’s master benefit list and application or direct certification documentation. Data matching. Contractors assessed the accuracy of the identified student percentage—the figure used to determine reimbursement for schools using the community eligibility provision—for over 100 sampled schools. To do so, contactors used an iterative process to match sampled students to SNAP and Temporary Assistance for Needy Families records, as well as additional data sources if necessary. Observation of meal service. Contractors observed approximately 25,000 lunch transactions and 23,000 breakfast transactions at over 400 sampled schools to identify the food items in each meal at the point of sale, whether the meal was served to a student or nonstudent, and whether the meal was recorded as reimbursable. USDA determined that conducting the APEC study annually would not be feasible. Instead, the APEC study includes a model that allows USDA to use program participation data to report estimated improper payment error rates on an annual basis. Changes in program participation data result in small changes to the estimated improper payment error rates USDA reports during years between APEC studies. Fraud Risk Management Standards and Guidance According to federal standards and guidance, executive-branch agency managers are responsible for managing fraud risks and implementing practices for combating those risks. Federal internal control standards call for agency management officials to assess the internal and external risks their entities face as they seek to achieve their objectives. The standards state that as part of this overall assessment, management should consider the potential for fraud when identifying, analyzing, and responding to risks. In July 2015, GAO issued the Fraud Risk Framework, which provides a comprehensive set of key components and leading practices that serve as a guide for agency managers to use when developing efforts to combat fraud in a strategic, risk-based way. The Fraud Risk Framework describes leading practices in four components, as shown in figure 2. The Fraud Reduction and Data Analytics Act of 2015, enacted in June 2016, requires the Office of Management and Budget (OMB) to establish guidelines for federal agencies to create controls to identify and assess fraud risks and design and implement antifraud control activities. The act further requires OMB to incorporate the leading practices from the Fraud Risk Framework in the guidelines. In July 2016, OMB published guidance about enterprise risk management and internal controls in federal executive departments and agencies. Among other things, this guidance affirms that managers should adhere to the leading practices identified in the Fraud Risk Framework. Further, the act requires federal agencies to submit to Congress a progress report each year for 3 consecutive years on the implementation of controls established under OMB guidance, among other things. It is important to note that fraud and “fraud risk” are distinct concepts. Fraud is challenging to detect because of its deceptive nature. Additionally, once suspected fraud is identified, alleged fraud cases may be prosecuted. If the court determines that fraud took place, then fraudulent spending may be recovered. Fraud risk exists when individuals have an opportunity to engage in fraudulent activity, have an incentive or are under pressure to commit fraud, or are able to rationalize committing fraud. When fraud risks can be identified and mitigated, fraud may be less likely to occur. Although the occurrence of fraud indicates there is a fraud risk, a fraud risk can exist even if fraud has not yet been identified or occurred. For example, suspicious billing patterns or complexities in program design may indicate a risk of fraud. Information to help identify potential fraud risks may come from various sources, including whistleblowers, agency officials, contractors, law-enforcement agencies, or beneficiaries. USDA Has Reported Taking Various Steps to Reduce Improper Payment Error Rates, but Redefined What It Considers to Be an Improper Payment in Fiscal Year 2018 USDA Reported Taking Steps to Reduce School Meals Improper Payment Error Rates USDA has reported various actions aimed at lowering the school meals improper payment error rates in its agency financial reports. These actions—including onetime actions and longer-term efforts—cover multiple aspects of the programs. The actions USDA reported included the creation of a new household application prototype intended to reduce applicant errors and the development of training for food service workers to address administrative errors. USDA also reported on mechanisms to collect information on program errors to support agency analysis and monitoring efforts. Examples of the reported actions are illustrated in figure 3 below. Because the study used to develop improper payment error rates in school meals programs—APEC—is conducted about once every 5 years, the effect of these actions is currently unknown. Estimated improper payment error rates reported in years between APEC studies will generally not reflect the effect of most actions until the next study is released. Currently, the next APEC study is expected to be released in 2020. Reported Error Rates for Fiscal Year 2018 Are Not Comparable to Prior Years Because of a Change in Definition USDA changed what it considers to be an improper payment in the school meals programs for fiscal year 2018 reporting, resulting in improper payment error rates that are not comparable to those of prior years. Specifically, USDA determined that meal claiming errors do not meet the definition of an improper payment. According to USDA, meal claiming errors occur when meals are incorrectly categorized as reimbursable or nonreimbursable at the point of sale. For example, a meal claiming error occurs when a meal that is missing a required meal component (e.g., the required quantity of a vegetable) is counted as reimbursable. USDA officials reported that the rationale for the change in what constitutes an improper payment is that meal claiming error does not result in the payment of federal funds for services that were not provided or that were provided to ineligible recipients. Agency officials also stated that the remedy for meal claiming error is to add the missing food component to the meal, so correcting the error would not reduce program payments. Although the errors will not be considered in determining the reported estimated improper payment error rates, USDA officials stated that the agency is committed to reducing meal claiming error and will continue to measure it as part of its periodic APEC studies. Prior to fiscal year 2018 reporting, meal claiming errors were considered improper payments. As a result, this change contributed to a significant decrease in the estimated improper payment error rates for the school meals programs reported for fiscal year 2018, as shown in figure 4. Accordingly, the results shown for 2015 through 2018 in figure 4 are not comparable. Limited Risk Assessment Hinders USDA’s Ability to Better Ensure Oversight Practices Address Fraud Risks Although USDA considers certain program integrity risks through specific processes, it has not assessed fraud risks in the school meals programs. As a result, USDA cannot determine whether its key oversight processes—extensive efforts designed for broad monitoring purposes— address areas at risk for fraud. The assess component of the Fraud Risk Framework calls for federal managers to plan regular fraud risk assessments and to assess risks to determine a fraud risk profile. Furthermore, federal internal control standards state that management should consider the potential for fraud when identifying, analyzing, and responding to risks. According to USDA officials, fraud in the school meals programs would look the same as nonfraudulent errors. For example, income listed on an application may be misreported intentionally or unintentionally. Consequently, agency officials stated that they have not established a process to plan or conduct a specific fraud risk assessment for these programs. Instead, fraud risks are considered through the agency’s efforts to assess overall program integrity risk in the programs. We have previously reported that integrating fraud risk management into a larger program integrity approach could limit the amount of resources and attention focused specifically on fraud prevention, detection, and response. The deceptive nature of fraud makes it harder to detect than nonfraudulent errors, potentially requiring control activities that are specifically designed to prevent and detect criminal intent. According to agency officials, USDA’s efforts to assess overall program integrity risks in the school meals programs include researching, monitoring, and reporting activities designed to identify areas of program operations susceptible to improper payments and program error. Specifically, agency officials stated that these efforts include research projects—the APEC study used to estimate improper payment error rates and other smaller-scale, informal projects—and a consideration of specific risks when annually determining which states USDA will review through management evaluations. These efforts to assess overall program integrity risk serve specific purposes and are not designed to identify or address fraud risks in the school meals programs. For example, the purpose of one research project mentioned by USDA officials was to identify challenges related to alternative service models for the School Breakfast Program, which include serving breakfast in locations other than a cafeteria and at a later time in the morning. USDA has not developed a process to consider these disparate efforts to comprehensively assess fraud risks. As a result, USDA’s efforts do not align with the overarching concepts of planning and conducting fraud risk assessments in the Fraud Risk Framework. The Fraud Risk Framework identifies leading practices for planning fraud risk assessments. Specifically, the leading practices include tailoring the fraud risk assessment to the program and planning to conduct the assessment at regular intervals and when there are changes to the program or operating environment. The leading practices also include identifying the tools, methods, and sources for gathering information about fraud risks and involving relevant stakeholders in the assessment process. Information to help identify potential fraud risks may come from various sources, including whistleblowers, agency officials, contractors, law-enforcement agencies, or beneficiaries. Existing oversight efforts— such as USDA’s management evaluations and administrative reviews— may also be a useful source, as information on errors and noncompliance may highlight areas at risk for fraud. The Fraud Risk Framework also identifies leading practices for conducting fraud risk assessments, as illustrated in figure 5. Without a process to plan and conduct regular assessments, USDA cannot identify and assess fraud risks facing the school meals programs. Such information is necessary to appropriately design and implement an antifraud strategy—including specific controls like USDA’s key oversight processes—and evaluate and adapt its strategy and controls to improve fraud risk management in these programs. Conclusions Historically, the school meals programs have reported high estimated improper payment error rates. USDA has reported various steps to reduce the error rates, though a change in what USDA considers an improper payment in the school meals programs resulted in error rates for fiscal year 2018 that are not comparable to those of prior years. Although the two concepts are different, high improper payment error rates may suggest that the school meals programs may also be inherently vulnerable to fraud. However, USDA has not established a process to plan and conduct regular fraud risk assessments for the school meals programs, and existing efforts to assess specific risks in the school meals programs do not comprehensively consider fraud risks. According to leading practices, such an assessment is a pivotal step in managing fraud risks, helping to ensure that USDA’s key oversight efforts are targeted at areas at greatest risk for fraud in these programs, and helping safeguard the government’s substantial investment in them. Recommendation for Executive Action The Administrator of the Food and Nutrition Service should establish a process to plan and conduct regular fraud risk assessments for the school meals programs that align with the leading practices in the Fraud Risk Framework. (Recommendation 1) Agency Comments and Our Evaluation We provided a draft of this report to USDA for review and comment. On April 29, 2019, the Director of the Office of Program Integrity for Child Nutrition provided us with the agency’s oral comments on the draft report. FNS officials generally agreed with the recommendation in the draft report. FNS officials noted that the agency does not currently conduct a formal fraud risk assessment, but they explained that the agency considers fraud risks through multiple existing efforts. These efforts include APEC and other studies, as well as key oversight processes. For example, FNS noted that the APEC study aims to identify the factors that contribute to errors in the school meals programs. Officials explained that this study includes an interview of sampled households, in part to determine whether these households underreported income on their applications and whether such underreporting suggests anything about the applicants’ intent. As noted in our report, we agree that these efforts may be a useful source of information on areas at risk for fraud. However, we continue to believe that additional action is necessary to comprehensively assess fraud risks in the school meals programs, consistent with the Fraud Risk Framework. USDA also provided technical comments, which we incorporated into the report, as appropriate. As agreed with your offices, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days from the report date. At that time, we will send copies to the appropriate congressional committees, the Secretary of Agriculture, the FNS Administrator, 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) 512-6722 or bagdoyans@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 key contributions to this report are listed in appendix I. Appendix I: GAO Contact and Staff Acknowledgments GAO Contact Staff Acknowledgments In addition to the contact named above, the following staff members made key contributions to this report: Gabrielle M. Fagan, Assistant Director; James M. Healy, Analyst in Charge; and Matthew L. McKnight. Also contributing to this report were Rachel Frisk, Maria McMullen, Jean McSween, and Sabrina Streagle.