Millennial Generation: Information on the Economic Status of Millennial Households Compared to Previous Generations

The idea that individuals should have the opportunity to economically advance beyond the circumstances of their birth is a familiar element of the American Dream. In an economically mobile society, it is possible for individuals to improve their economic circumstances through effort, education, investment, and talent. In addition to opportunities through the private, public, and nonprofit sectors, the federal government also promotes economic mobility through many efforts, including supporting education, job training, business incentives and development, and child health and well-being programs.

However, a recent survey indicates that over approximately the last two decades fewer people report being satisfied with the opportunity to get ahead by working hard. According to recent studies, the Millennial generation, who comprise the largest portion of the American workforce, report feeling overwhelmed by their financial situation and concerned about their future financial security.

GAO was asked to review trends in economic mobility and Millennials' economic situation compared to previous generations. This report examines (1) what is known about intergenerational income mobility, and (2) how the financial circumstances of Millennials compare to previous generations. To perform this work GAO conducted an extensive literature review and analyzed data from the nationally representative Survey of Consumer Finances.

Recent research indicates that, across three key measures, economic mobility in the United States is limited. Specifically, the Millennial generation (those born between 1982 and 2000) might not have the same opportunity as previous generations had to fare better economically than their parents. According to studies GAO reviewed, the share of people making more money than their parents at the same age (absolute mobility) has declined over the last 40 years, and the chances of moving up the income distribution (relative mobility) have been flat over time. Using a third measure of economic mobility (intergenerational income elasticity), researchers have found that income in adulthood is linked to how much a person's parents made, and that between one-third and two-thirds of economic status is passed down from parents to children. This is especially true of the lowest and highest income groups. Researchers also identified race and geography as key determinants of an individual's economic mobility.

Millennials have different financial circumstances than Generation X (born 1965-1981) and Baby Boomers (born 1946-1964), and in light of flat or declining economic mobility, there is uncertainty about how they will fare financially as they age. A snapshot of data that allowed GAO to compare Millennials aged 25-34 to the previous two generations at similar ages showed that Millennial households were more likely than other generations to be college educated; however, incomes have remained flat across the three generations, implying that Millennials have not yet benefited from the potential additional lifetime income earned by college graduates. Millennial households had significantly lower median and average net worth than Generation X households at similar ages (see figure), especially among those with low net worth. Median net worth for the lowest quartile of Baby Boomers and Generation X was around zero, but it was substantially negative for Millennials, indicating that debt was greater than assets for the median low net worth Millennial household. Regarding assets, a significantly lower percentage of Millennials owned homes compared to previous generations at similar ages, but had retirement resources at rates comparable to Generation X and Baby Boomers. Finally, Millennials were more likely to have student loan debt that exceeded their annual income. It remains to be seen how these factors will affect Millennials' financial circumstances in the long run, including retirement.

The idea that individuals should have the opportunity to advance  economically beyond the circumstances of their birth is a familiar element  of the American Dream. In an economically mobile society, it is possible  for individuals to improve their economic circumstances through effort,  education, investment, and talent. In addition to opportunities through the  private, public, and nonprofit sectors, the federal government also  promotes economic mobility through many efforts, including supporting  education, job training, homeownership, business incentives and  development, and child health and well-being. Intergenerational income  mobility, or how members of a generation compare to their parents in  terms of income or rank in the income distribution, is often used to  capture the degree to which a society is economically mobile. However,  recent research indicates a falling share of people are earning more (in  inflation adjusted dollars) than their parents earned at similar ages.

In 2018, an estimated 63 percent of Americans were satisfied with “the  opportunity for a person to get ahead in this nation by working hard,”  down from an estimated 76 percent in 2001. Some of this decrease may  be attributable to attitudes of those in the Millennial generation (born from  1982 to 2000). According to recent studies, Millennials, who now make up  the largest portion of the American workforce, report feeling overwhelmed  by their financial situation and concerned about their economic futures.

In light of these issues, you asked us to review trends in economic  mobility and Millennials’ economic situation, including how Millennials are  faring financially compared to previous generations. This report examines  (1) what is known about intergenerational income mobility, and (2) how  the financial circumstances of Millennials compare to previous  generations.

To report on what is known about intergenerational income mobility  (which we use interchangeably with “economic mobility”) we conducted a  literature review of relevant, recent economic studies. To be included,  studies had to (1) produce original estimates of economic measures of  intergenerational income mobility; (2) focus on the United States (U.S.);  (3) be published in the last 5 years (2014-2019), or 2 years if a working  paper (2017-2019); and (4) be published in a U.S.-based publication. We  identified the majority of the studies through systematic searches of  databases. We also identified several studies through other research and  expert interviews. We examined 20 studies that met our selection criteria  and that we determined in our technical review were reliable for the  purpose of providing information on economic mobility. The bibliography  in appendix I lists all of the studies included in the literature review.

To compare the financial circumstances of Millennials to the previous two  generations, Generation X and Baby Boomers, we used the Survey of  Consumer Finances (SCF), typically a triennial survey of U.S. households  sponsored by the Board of Governors of the Federal Reserve System in  cooperation with the Department of Treasury. Every 3 years, SCF staff  interview a different group of households with the goal of creating a  sample that is representative of households across economic strata,  including the top of the wealth distribution. Millennials are defined as  people born from 1982 to 2000 (i.e., those who were 18-37 years old in  2019). We defined young households in each generation as those in  which either the head, and/or spouse or partner, was 25-34 years old. We  compared young Millennial households in 2016 to young Generation X  (born 1965-1981) and Baby Boomer (born 1946-1964) households in  2001 and 1989, respectively.

We analyzed SCF data to estimate income, net worth, assets, and debt  from the three generations at points in time when each was at a similar  young age. We defined household income as the sum of income across  all sources, such as wages and salaries, including interest on financial  assets or benefits from social safety net programs. We defined household  net worth as assets minus debt. Assets include savings accounts, stocks,  bonds, and retirement accounts, such as 401(k)s or individual retirement  accounts. Assets could also be nonfinancial, including the value of  houses or vehicles. Households could have financial resources outside of  net worth, including future income from defined benefit (DB) retirement  plans or Social Security; however, we did not attempt to estimate the  future value of these financial resources in our net worth calculation given  the long time horizon to retirement for young Millennials. All financial  estimates presented in this report are in 2016 dollars.

We reviewed documentation about the SCF, tested the data for  anomalies, and reviewed related controls. We determined that the SCF  data were sufficiently reliable for the purposes of this report. See  appendix I for more detailed information about our scope and  methodology.

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.

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.

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.

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.

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.

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.

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.

Improving economic status through hard work, education, and investment has been the “American Dream” for generations. But is it out of reach for Millennials? We looked into it and found:

Over the last 40 years, fewer Americans are making more than their parents did at the same age

Predetermined factors like parental income, race, and geography play key roles in determining future earnings

Millennials have significantly more student debt, lower levels of homeownership, and less net worth than previous generations

Millennials are highly educated, but it’s not clear whether this will boost their income in the long run