Asset Building over the Life Course

11/30/2008

 

Poor Finances:  Assets and Low-Income Households

Asset Building over the Life Course

Final Report

By: Mark R. Rank Center for Social Development Washington University in Saint Louis

November 2008

This report was prepared for and funded by the U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (HHS/ASPE) under Order Number GS23F8198H / HHSP233200400131U to the Urban Institute and its collaborators at the Center for Social Development (CSD) at Washington University in St. Louis, and the New America Foundation.  This report was prepared between September 2004 and June 2006.  John Tambornino, Jeremías Alvarez, and Linda Mellgren at DHHS were project officers, Signe-Mary McKernan of the Urban Institute was overall project director, and Michael Sherraden directed the work at CSD.  Views expressed are those of the authors and do not represent official positions of the Department of Health and Human Services, the Urban Institute, its trustees, or its sponsors.

Project Home Page:Poor Finances: Assets and Low-Income Households

This report is available on the Internet at:http://aspe.hhs.gov/hsp/07/PoorFinances/LifeCourse/

Printer Friendly Version in PDF format (35 pages)

Contents

Executive Summary

Acknowledgments

Introduction to the Series

Chapters

  1. Introduction:  Background of the Life Course Perspective
  2. Applying a Life Course Perspective to Asset Building
  3. The Lack of Assets Among Low-Income Households
  4. Factors Affecting Asset Building Across the Life Course
    1. Intergenerational Transmission of Assets
    2. Race and Ethnicity
    3. Income
    4. Family Structure
    5. Life Stages and the Timing of Life Events
  5. The Utility of the Life Course Framework
  6. Conclusion

References

Appendix Exhibit.  Empirical Studies of Asset Building over the Life Course

Endnotes

Acknowledgments

This report has benefited from comments of the project teams at the Urban Institute, the New American Foundation, and the Center for Social Development, as well as helpful comments and suggestions from Jeremías Alvarez, Gretchen Lehman, Ann McCormick, and Linda Mellgren of ASPE and Leonard Sternbach and John Tambornino at the Administration for Children and Families (ACF).  Eunhee Han, Jung Hee Han, and Jennifer Kim at the Center for Social Development provided research assistance.

The author of this report is affiliated with the Center for Social Development (CSD) at Washington University in St. Louis.  CSDs research on savings among low-income populations has been funded by many organizations.  DHHS funded this report.  For supporting CSDs applied research contributing to this report, the author is grateful to the Ford Foundation, the Charles Stewart Mott Foundation, the Annie E. Casey Foundation, the F.B. Heron Foundation, the Ewing Marion Kauffman Foundation, and the MetLife Foundation.

This report is part of a series entitled Poor Finances:  Assets and Low-Income Households, produced in a partnership among the Urban Institute, the New America Foundation, and CSD.  The partnership has been cordial and productive, and the author thanks the team members at the Urban Institute and the New America Foundation for their support.

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Executive Summary

During life, the average individual passes through several stages which tend to correspond with particular social and economic events, beginning with primary education in ones youth, moving through marriage, parenthood, and career advancement, and ending with retirement.  This concept, known as the life course, has had a long and distinguished history in the social and applied sciences, and it provides a very useful framework for thinking about how individual lives unfold and how particular events and transitions affect these trajectories. 

The area of asset building lends itself quite naturally to a life course framework.  By its very nature, asset accumulation unfolds over an individuals lifetime, and the effects of such accumulation can best be understood within the context of the entire life course.  Whether the asset is a college degree, a home, or retirement savings, the process of asset building is more readily understood within a life course framework.

Research indicates that many Americans, particularly those in the bottom half of the income distribution, are vulnerable to periods of economic deprivation at points along the life course.  The presence of assets can partially alleviate the shocks of such deprivation, and assets are particularly important to lower-income households to help temper some of the negative effects of poverty, as well as provide protection against future economic shocks.  Yet for many lower-income households, assets are in short supply, particularly financial assets such as savings or stocks.  In fact, between one quarter and one third of all Americans have failed to accumulate any financial assets whatsoever.  The major asset that is held by lower income households is their home.  However, for low-income households, their home value and the amount of equity accrued over the course of their lives are substantially less than their middle- and upper-income counterparts. 

This report examines five factors of importance to understanding the lack of assets among low-income households within a life course framework:

  1. Intergenerational Transmission of Assets:  Analyses of generational economic mobility in American society have shown that, while some mobility occurs, socioeconomic status as a whole tends to perpetuate itself. Individuals with lower-income parents are likely to remain lower income themselves.  Similarly, individuals whose parents are affluent are likely to remain affluent.  The primary reason for this is that parents who have more resources and opportunities can transfer more resources and opportunities to their children.  These differences in turn affect childrens future life chances and outcomes, including their accumulation of assets.
  2. Race and Ethnicity:  A large body of research indicates that race and ethnicity play an important role in constraining the ability of individuals to accumulate significant assets during their lifetimes.  According to this research, the black/white wealth gap is significantly larger than the income gap.  One study found that the typical black household earns roughly 60 cents for every dollar earned by their white counterpart, while they hold only 10 cents of wealth for every dollar of wealth held by a white household.
  3. Income:  An important factor in the building of assets across adulthood is having an adequate and stable source of income.  Research has shown that the accumulation of assets over the life course largely depends on having an income surplus, along with the belief that ones income will remain relatively stable from one month to the next.
  4. Family Structure:  Research shows that family structure and changes in family structure strongly affect the accumulation of assets.  In particular, single-mother families are at a disadvantage compared to married-couple families.  An additional family structure factor of importance across the life course is size of family of origin.  One study found that the number of siblings a child has impacts his or her net worth as an adult, possibly as a result of the dilution of parental resources.
  5. Life Stages and the Timing of Life Events:  Life cycle stages are often defined in terms of family compositional changes combined with a rough estimate of the chronological age of an individual.  Conventional wisdom holds that there are particular stages in the life cycle itself that correspond with scarcity and prosperity.  Additionally, the timing of events (e.g. pregnancy, unemployment, divorce, etc.) at particular points in the life course can have profound effects on later patterns of asset accumulation.

The process of accumulating assets takes place over an individuals lifetime, and the life course concept provides a valuable framework for understanding this process.  A life course framework is particularly helpful in understanding the difficulties lower-income households face in accumulating assets.  Empirical research has demonstrated that lower-income households lack assets, and five factors are identified as being particularly important to understanding this lack of assets across the life course.  These factors are best understood within the context of time, aging, development, and sequencing, all of which represent the essence of the life course perspective.

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Introduction: Background of the Life Course Perspective

Over the course of the average individuals life he or she will pass through several stages: childhood, adolescence, young adulthood, middle age, and the senior years.  These stages tend to correspond with particular social and economic events beginning with primary education in ones youth, moving through marriage, parenthood, and career advancement, and ending with retirement.  This concept, known as the life course, has had a long and distinguished history in the social and applied sciences (Dewilde 2003; Elder 1994; Moen, Elder, and Luscher 1995; Riley 1999; Settersten and Mayer 1997).  It has provided a very useful framework for thinking about how individual lives unfold, and how particular events and transitions affect these trajectories (Elder 1995; Voyer 2004).  Life course has been defined as the social processes extending over the individual life span or over significant portions of it, especially [with regard to] the family cycle, educational and training histories, and employment and occupational careers (Mayer and Tuma 1990, 3).  In addition, as Settersten and Mayer (1997) have argued, While these dimensions describe the primary activities across life, a more complete picture of the life course must also include more marginal periods and events  such as brief periods of training, second or part-time jobs, periods of unemployment or sickness (252).

Several of the earliest social scientific studies examining these more marginal periods incorporated a life course perspective. Rowntrees (1902) description of 11,560 working-class families in the English city of York was pioneering in developing this approach.  Rowntree estimated the likelihood of falling into poverty at various stages of the life course (based upon household economic conditions in 1899).  His research indicated that working-class families were more likely to experience poverty at certain stages in the family life cycle when they were economically vulnerable (e.g., the period of starting a new family or during retirement).  Similarly, Hunter (1904) in his book, Poverty, attempted to place impoverishment within the context of the life course.  Like Rowntree, Hunter viewed poverty as a life event tending to occur for working-class families at several points during their life course.

During the 1950s and 1960s, the concept of the family life cycle became a central organizing concept.  Families were viewed as progressing through distinct stages, which included getting married, having children, the empty nest, and so on (Duvall 1957; Glick 1947; Hill 1964).  To understand family dynamics, it was felt important to understand these stages as well as the transitions from one stage of the family life cycle to another.

However, by the early 1970s the idea of the family life cycle came under growing criticism.  As Dewilde (2003) notes, the family life cycle approach was attacked on both theoretical and methodological grounds.  It assumed a normative nature in terms of the stages that families were viewed as progressing through.  This has become less salient over time, with the recognition that there are and always have been many types and variations of family progression.  Second, the emphasis on the family life cycle may underestimate the significance of many other events and trajectories that individuals pass through during their lives.  Third, there is considerable methodological difficulty in delineating the different stages of the family life cycle.  As a result of these and other concerns, researchers have increasingly emphasized the broader concept of the life course in understanding various aspects of individual development and aging.  As Dewilde points out,

it should be noted that, as a concept, the life course is more flexible and more complex than either the life cycle or the family cycle.  Moreover, differentiation and heterogeneity are usually regarded as given in the life-course perspective. Indeed, the study of events, transitions and trajectories is inherent in an approach based on a multidimensional life-course concept (2003, 115).

The life course approach has emphasized the importance of several key concepts for understanding individual development, including historical time, cohorts, transitions, trajectories, life events, and turning points (Hutchison 2005).  Key themes that have characterized life course research over the past 30 years have included the interplay of human lives and historical time; the timing and sequencing of lives; the linkages of human lives with each other; the importance of individual decision-making; the diversity in life course trajectories; and the factors that lead to developmental risk or protection across the life course (Elder 1994; Hutchison 2005).

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Applying a Life Course Perspective to Asset Building

The area of asset building lends itself quite naturally to a life course framework.  By its very nature, asset accumulation unfolds over an individuals lifetime, and the effects of such accumulation can best be understood within the context of the entire life course.  Whether the asset is a college degree, a home, or retirement savings, the process of asset building is readily understood within the wider framework of the life course (see Voyer 2004 for an extended example of this process).

In addition, understanding the dynamics of asset building is important to further our understanding of the life course.  Assets may provide individuals and households a greater means to fully reach their potential during their lives.  As Sherraden (1991) argues, When people are accumulating assets, they behave differently and the world responds to them differently as well (295).  This includes a variety of possible positive effects, including greater labor force attachment, political and civic interest, marital stability, health benefits, and so on (Bynner 2001).[1]

In short, asset building allows individuals and families to more fully develop their human capacity and potential.  This is particularly important for lower-income households and individuals.  Many social scientists have concluded that one of the defining characteristics of poverty and economic deprivation is the undermining of human potential (Rank 2004; Sen 1992).  The accumulation of assets may be critical in allowing lower-income households to avoid some of the more detrimental effects of poverty.

This leads to a second important function of asset building within the context of the life course.  Assets allow individuals and households to accrue some amount of security to be used during times of economic downturn.  Economists refer to this as the ability of assets to protect consumption against unexpected shocks (Cagetti 2003).  Recent research suggests that this function may be increasingly important in todays society. For example, the work of Rank and Hirschl (1999a) estimates that an individuals lifetime risk of experiencing poverty at some point during adulthood is very high.  Between the ages of 20 and 75, 58 percent of Americans will experience at least one year below the official poverty line, while 75 percent will encounter a year below 150 percent of the poverty line.  The life-course risk of poverty is particularly high during early adulthood (Rank and Hirschl 2001).  Furthermore, two thirds of Americans will rely on a means-tested safety net program between the ages of 20 and 65 (Rank and Hirschl 2002), and 40 percent of Americans will use such a program in five or more separate years. Additional work (Sandoval, Hirschl, and Rank 2004) indicates that this risk of experiencing poverty has been on the increase during the past 30 years, particularly during the 1990s, mirroring an increase in job and work insecurity (Fligstein and Shin 2004).

Similar findings have been observed outside of the United States as well.  For example, Leisering and Leibfried (1999) write with regard to their life course analysis of poverty in Germany:

Poverty is no longer (if ever it was) a fixed condition or a personal or group characteristic, but rather it is an experience or stage in the life course.  It is not necessarily associated with a marginal position in society but reaches well into the middle class.  Poverty is specifically located in time and individual biographies, and, by implication, has come to transcend traditional social boundaries of class (239).

Hackers (2004; 2005) work has also documented the increasing prevalence of income volatility, particularly downward mobility. Using the Panel Study of Income Dynamics (PSID), Hacker (2004) found that income instability in the mid-1990s was nearly five times higher than in the early 1970s.  He notes that such patterns of rising income instability and insecurity mirror an overall trend in the United States: As both employment-based social benefits and government programs have eroded, social risks have shifted from collective intermediaries  government, employers, large insurance pools  onto individuals and families (252).

All of this work indicates that many Americans, particularly those in the bottom half of the income distribution, are vulnerable to periods of economic deprivation at points along the life course. The presence of assets can partially alleviate the shocks of such deprivation, along with the earlier mentioned capacity-building function.  Yet how widespread are such assets for lower-income households?

Before turning to this question, it is important to note that longitudinal work on the patterns and processes of asset building across the life course is still at a very early stage in its development.  One of the reasons for this is that long-running longitudinal asset data have not been available until recently.  For example, the Panel Study of Income Dynamics (PSID)  the longest ongoing economic and demographic longitudinal data set in the United States  has been gathering information on the same households since 1968, yet as its name implies, the primary focus of data collection has been on income rather than asset dynamics.  It was not until 1984 that the PSID included a set of questions asking about household assets (since then this module of questions has been included in the PSID waves of 1989, 1994, 1999, 2001, 2003, and 2005).  The result is that there is much more life course information on the dynamics of income than the dynamics of asset building, and that much of the household asset information currently available continues to be based on cross-sectional rather than longitudinal research designs.[2]

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The Lack of Assets Among Low-Income Households

Empirical research indicates that a significant percentage of the population are lacking in assets, particularly financial assets such as savings or stocks.[3] Oliver and Shapiro (1990) find that one-third of American households have no financial assets at all.  Wolff (1998) shows that families in the middle income quintile have financial assets that would maintain their standard of living without income for 1.2 months, while those in the bottom quintile would not be able to replace their income for any period of time.  Carney and Gale (2001) report that 20 percent of all households have no basic transaction accounts (i.e., a savings or checking account) and that more than half of all households have less than $5,000 in financial assets.  Those in the bottom 25 percent of the income distribution have virtually no financial assets whatsoever.

In analyzing the level of financial assets for workers experiencing a spell of unemployment, Gruber (2001) finds that for the median worker, financial asset holdings are sufficient to replace 5.4 weeks of earnings.  This represented approximately three quarters of their lost income from a spell of unemployment.  However, for nearly one third of workers, not even 10 percent of lost income could be replaced through their financial asset holdings.

The term asset poverty is used by many to capture this concept of lacking adequate assets, as poverty is officially defined by income.  Although the concept of asset poverty had been suggested by Ruggles and Williams (1989) and Oliver and Shapiro (1995), Haveman and Wolff (2000) were the first to provide an operational definition, classifying a household or person as being asset poor if the access that they have to wealth-type resources is insufficient to enable them to meet their basic needs for some limited period of time.  They then constructed several different measures of asset poverty based upon this overall definition.  For example, wealth-type resources might be defined in terms of a households overall net worth, basic needs could be defined as being above the official poverty line, while limited period of time might consist of three months.  Consequently, in this example using Haveman and Wolffs definition, a household that does not have sufficient net worth to sustain themselves above the poverty line for three months would be considered asset poor.

Using these and similar measures, Haveman and Wolff (2000) were able to estimate the cross-sectional rates of asset poverty for the years 1983, 1989, 1992, 1995, and 1998 using the Survey of Consumer Finances (SCF).  Their findings revealed that the incidence of asset poverty was quite high, typically between 25 and 45 percent of all U.S. households.

More recently, Caner and Wolff (2004) analyzed PSID data for 1984, 1989, 1994, and 1999.  Consistent with Haveman and Wolffs (2000) research, they find that overall rates of asset poverty during these years varied between 26 and 42 percent.  Measures of asset poverty that relied on net worth were on the lower side of this range, while measures using only liquid wealth were higher.  They also find that asset poverty was greatest during young adulthood, and then decreased as individuals reached their 40s, 50s, and 60s.  For example, in 1999, asset poverty (as measured through net worth) was 80 percent for those under age 25, 44 percent for those age 25 to 34, 23 percent for those age 35 to 49, 9 percent for those age 50 to 61, 11 percent for those age 62 to 69, and 11 percent for those age 70 and over.  Race, education, and owning a home were important factors affecting the likelihood of asset poverty, as well as changes in family structure.

The major asset owned by Americans (including low-income households) is owner-occupied housing  44 percent of all U.S. wealth is based in home equity (U.S. Census Bureau 2001).  Across the life course, most Americans will purchase homes and subsequently build some amount of equity in their homes.  This is quite consistent with the strong emphasis in American society on the importance of homeownership as a vital component of the American Dream (Cullen 2003).  A preliminary analysis of the PSID data from 1968 to 2003 by Hirschl and Rank (2006) shows that by the age of 35, 74 percent of Americans have purchased homes, and by age 50, 88 percent.  Even for individuals with less education, the percentages are high (e.g., 63 percent of those with less than 12 years of education have purchased homes by age 35, and 78 percent have done so by age 50).

However, for low-income households, their home value and the amount of equity accrued over the course of their lives are substantially less than their middle- and upper-income counterparts.  In an analysis of 5,000 PSID respondents who were initially renting, Reid (2004) finds that the financial returns to homeownership are small for low-income minorities, low-income whites, and middle-income minorities  even when homes are owned for 10 or more years.  For example, she estimates that the average value of housing for low-income minority homeowners increased from $50,000 to $65,000 over a 10-year period. 

Additionally, Reid (2004) found that there was movement in and out of the homeowner category for these groups:

My analysis shows that homeownership is an incredibly fluid category, with many families moving in and out of homeownership several times over the course of their lives Four years after buying a house, less than half of low-income minority households in the sample remain homeowners.  Low-income white households fare better, but still only 60 percent remain homeowners after four years (20).

In sum, previous empirical work indicates that a lack of assets across the life course is typical for low income households.  We now turn to several reasons that partially explain this shortage of assets.

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Factors Affecting Asset Building Across the Life Course

In seeking to understand the life course patterns of asset building, and in particular why lower-income households lack assets, research has found several factors to be important.  In this section five such factors will be discussed: the intergenerational transfer of assets, race/ethnicity, income, family structure, and the timing of life events.

A.  Intergenerational Transmission of Assets

Analyses of generational economic mobility in American society have shown that, while some amount of mobility occurs, socioeconomic status as a whole tends to perpetuate itself (Beeghley 2005; Fischer et al. 1996).  Individuals with lower-income parents are likely to remain lower income themselves. Similarly, individuals whose parents are affluent are likely to remain affluent.  The primary reason for this is that parents who have more resources and opportunities can transfer more resources and opportunities to their children.  These differences in turn affect childrens future life chances and outcomes, including their accumulation of assets.

Research over the past fifteen years has revealed a sizable correlation between fathers and sons incomes, averaging around 0.4 (Aughinbaugh 2000; Corcoran et al. 1992; Mulligan 1997; Solon 1992; Zimmerman 1992).  This finding means that 42 percent of the sons of fathers with income that fall in the bottom 5 percent of the income distribution will be in the bottom quintile of the income distribution when they grow up, while only 5 percent will reach the top quintile.  On the other hand, if a father has income in the top 5 percent of the income distribution, 42 percent of his sons will earn incomes in the top quintile of the income distribution, while only 5 percent will fall into the bottom quintile (Solon 1992).  Recent studies find even higher correlations.  For example, using Social Security records for fathers and sons earnings, Mazumder (2001) reports an intergenerational correlation of .6 (in addition, see Bowles, Gintis, and Groves 2005).

A similar pattern of intergenerational stability emerges for wealth.  Gale and Scholz (1994) estimate that intended family transfers and bequests account for 51 percent of current U.S. wealth, while an additional 12 percent of wealth is acquired through the payment of college expenses by parents.  Consequently, nearly two-thirds of the net worth that individuals acquire comes through family transfers.  An even higher estimate comes from Kotlikoff and Summers (1981), who argue that, as of 1974, more than 80 percent of the net worth in this country was the result of intergenerational transfers. Parents with considerable wealth are therefore able to successfully pass on these assets and advantages to their children.  As a result, it is estimated that children of the very rich have roughly 40 times better odds of being very rich than do the children of the poor (Gokhale and Kotlikoff 2002, 268).

One important mechanism through which wealthier families are able to utilize their assets intergenerationally is through the educational process.  Wealthy families are able to acquire high-quality primary and secondary educations for their children.  This is accomplished either by purchasing a home in an affluent school district or by sending their children to private schools.  Shapiros (2004) in-depth interviews conducted with parents in Boston, St. Louis, and Los Angeles support this point.  As Shapiro and Johnson note (2000), By accessing quality school systems parents ensure specific kinds of schooling for their children and in this way help to pass their own social position along to the next generation (2).  This process has been shown to be robust with quantitative data as well.  Hochschild and Scovronick summarize this body of research with the following, Inequalities in family wealth are a major cause of inequalities in schooling, and inequalities of schooling do much to reinforce inequalities of wealth among families in the next generation (2003, 23). 

This process continues with higher education.  As McMurrer and Sawhill (1998) observe:

Family background has a significant and increasing effect on who goes to college, where, and for how long.  With the rewards for going to college greater than ever, and family background now a stronger influence over who reaps those rewards, the United States is at risk of becoming more class stratified in coming decades (69).

Lower-income parents who are lacking in assets are largely unable to maximize the educational opportunities for their children, which in turn hinders their childrens ability to build assets during their own adulthood. 

Other mechanisms for transferring wealth include inter vivos transfers and inheritances, each of which serves to reinforce existing disparities in asset accumulation across the life course. As Keister (2000) observes, The transfer of wealth from one generation to the next may be the single most important determinant of who owns what, how they got it, and what effects it has on both individual- and system-level outcomes (252).

B.  Race and Ethnicity

A large body of work (Conley 1999; Feagin 2000; Oliver and Shapiro 1995; Shapiro 2004) indicates that race, and particularly being African American, plays an important role in constraining the ability of individuals to accumulate significant assets during their lifetimes.  According to this research, the black/white wealth gap is significantly larger than the income gap.  Shapiro (2004) found that the typical black household earns roughly 60 cents for every dollar earned by their white counterpart, while they hold only 10 cents of wealth for every dollar of wealth held by a white household.

Part of this racial effect is related to the first factor discussed  the intergenerational transmission of wealth.  Black families have much less wealth to transfer from one generation to the next, resulting in continued patterns of inequality.  As Shapiro (2004) writes, The enormous racial wealth gap perpetuates race inequality in the United States.  Racial inequality appears intransigent because the way families use wealth transmits advantages from generation to generation (183).  Shapiro (2004) finds that the most important factors explaining differences in net worth between white and black families are differences in inheritance, family income, and homeownership.  Likewise, Conley (1999) also demonstrates the importance of intergenerational differences in the transmission of wealth to explain the current black/white gap in asset holding.

Additionally, patterns of residential segregation mean that black children are more likely than white children from similar social class backgrounds to attend schools that are severely segregated and lacking in resources (Massey and Denton 1993; Orfield and Yun 1999).  These patterns also apply to Latino children, albeit to a lesser extent (Orfield and Lee 2004).  As a result, minority children are less prepared to compete in the labor market, which in turn affects their ability to build assets. 

Furthermore, racial and ethnic minorities continue to be discriminated against in the housing market.  Research has indicated that black and Hispanic renters are more likely to be excluded from housing made available to white renters; black and Hispanic home buyers learn about fewer available homes than white home buyers; and blacks and Hispanics are more likely to be turned down for home loans than their white counterparts (Yinger 1995; 2001).  For example, one study found that blacks and Hispanics applying for mortgage loans in Boston were 82 percent more likely to be turned down than whites, even after controlling for credit qualifications and type of loan (Munnell et al. 1996).  A reanalysis by Ross and Yinger (2002) resulted in similar patterns.  The result of such housing market discrimination is higher rent burdens, poorer quality housing, and increased residential segregation for African Americans and Hispanic Americans.  This, in turn, reduces the ability of racial minorities to build significant wealth.   

C.  Income

A third important factor in the building of assets across adulthood is having an adequate and stable source of income.  As Edin (2001) and others have demonstrated, the accumulation of assets over the life course largely depends on having an income surplus, along with the belief and faith that ones income will remain relatively stable from one month to the next.  As Warren and Britton (2003) note, It is likely that people with low, insecure incomes  resulting from unemployment and/or intermittent or low-paid employment  are less able to accumulate various types of economic capital over the course of their lives.  Conversely, people with secure employment and higher incomes have more opportunities to acquire different kinds of assets (103).

The role of income in building assets and wealth across time has been empirically demonstrated in a number of studies (Keister 2000; Ziliak 2003).  Using a simulation model, Keister (2000) finds a strong positive association between income levels and wealth mobility (as measured by increase in decile of net worth) during the 1980s and early 1990s.  According to Keister:

For those making more than $100,000, the increase in the odds of upward mobility was a remarkable 7.535 times greater than for those in the omitted income category (those earning less than $10,000).  These increases in odds are even more incredible given that they are estimated with many other demographic influences on wealth ownership and mobility controlled (226-227).

Having a strong and reliable source of income is clearly fundamental to an individuals and a familys ability to build assets over time.  Although it is true that even those in poverty have the ability to save (Schreiner and Sherraden 2007; Schreiner, Clancy, and Sherraden 2002), a critical factor in the building of assets is nevertheless the level and stability of income over time.

It should be noted that a substantial body of research has demonstrated over the past 40 years that income is highly dependent on human capital, including education, work experience, and skills. Consequently, these human capital factors can be seen as playing an indirect role in asset building over the life course through their direct effects on income.  However, there is evidence to suggest that education also exerts an independent effect on asset building above and beyond its effects through increased income.  For example, Keister (2000) shows a sizeable effect of education on upward wealth mobility, controlling for income and other demographic factors.  This effect may be the result of several different mechanisms, including a greater propensity to save and defer consumption among those with higher levels of education.

D.  Family Structure

A fourth factor particularly important in the life course patterns of asset building is family structure.  A vast body of research shows that family structure and changes in family structure strongly affect the accumulation of wealth.  In particular, single-mother families are at a disadvantage compared to married-couple families.  In the Caner and Wolff (2004) study mentioned earlier, marriage is found to be an important avenue for escaping from asset poverty, while single parenthood is a route into asset poverty.  This same study also noted that for the elderly, decreases in the asset poverty rates were associated with marriage and increases in the asset poverty rate were associated with being unmarried.  Additionally, Reid (2004) finds that experiencing a divorce is one of the most important factors in the transition from owning to renting, regardless of race or income.  For low- and middle-income households, a divorce increases the likelihood of leaving homeownership by 9.8 and 10.6 times respectively (21).  Lupton and Smith (1999), using both the Health and Retirement Survey and the PSID, find a large and significant effect of marriage on the accumulation of financial assets and net worth across the life course.  And finally, in an analysis of data from the National Longitudinal Survey of Youth 1979 cohort (NLSY-79), Zagorsky (2005) reports that married respondents experienced a net worth increase of 77 percent over single respondents during the time of the study.  Those who experienced a divorce suffered a significant drop in their overall net worth.

An additional family structure factor of importance across the life course is size of family of origin.  Keister (2003) utilizes the NLSY-79 to show that number of siblings has a large negative effect on childrens overall levels of net worth as adults.  Keister argues that this is the result of a dilution of resources available to each child in the family of origin.  She asserts that children in large families tend to receive lower quality educational experiences and less education.  According to Keister, a large number of children reduces:

parental savings, inter vivos transfers, and the wealth that is available to bequeath at the end of the parents lives.  Decreased educational attainment and intergenerational resource transfers, in turn, alter financial behavior and saving trajectories.  As a result, those from larger families accumulate smaller portfolios throughout their lives (539).

E.  Life Stages and the Timing of Life Events

The final factor of importance in understanding asset building from a life course perspective is the timing of particular life events in relation to the stages of the life cycle.  As noted earlier, life cycle stages are often defined in terms of family compositional changes combined with a rough estimate of the chronological age of an individual (e.g., childhood, young adulthood, starting a family, empty nesthood, retirement).

As mentioned at the beginning of this report, this risk of economic deprivation vis-à-vis the family life cycle was noted in some of the earliest pioneering work on poverty.  Illustrative of this, Rowntree (1902) described how certain stages of the life cycle were associated with a greater risk of economic hardship:

the life of a labourer is marked by five alternating periods of want and comparative plenty. During early childhood, unless his father is a skilled worker, he probably will be in poverty; this will last until he, or some of his brothers or sisters, begin to earn money Then follows the period during which he is earning money and living under his parents roof This period of comparative prosperity may continue after marriage until he has two or three children, when poverty will again overtake him While the children are earning, and before they leave the home to marry, the man enjoys another period of prosperity  possibly, however, only to sink back again into poverty when his children have married and left him, and he himself is too old to work, for his income has never permitted his saving enough for him and his wife to live upon from for more than a very short period (169-172).

While circumstances and labor patterns have changed, there is still a general sense that there are particular stages in the life cycle itself that correspond with hardship and prosperity.  For example, individuals in young adulthood are often low-income because they are students or working in entry-level jobs, but the conventional wisdom is that over their lives they will earn more, save more, and become more financially stable. 

Economists have also turned to the life cycle in some of their early work  for example, Modigliani and Brumbergs (1954) utilization of the life cycle to understand savings and wealth behavior.  Recent work has continued to show the importance of the life cycle in understanding patterns of income and wealth accumulation (Gourinchas and Parker 2002; Keister 2000; Kennickell and Starr-McCluer 1997; Rigg and Sefton 2004).  Individuals at earlier stages of the adult life cycle tend to have relatively few assets; those in their prime earning years of the 40s and 50s tend to see their assets grow; and, in the retirement years, asset holdings generally decrease.

In addition, particular events at certain stages of the life cycle can have large effects on the ability of individuals to accumulate assets in later adulthood.  For example, a teenager who has a child out-of-wedlock will likely experience a cascading negative effect on her ability to build assets later in life.  She may have to drop out of school, thus decreasing her ability to find high-wage employment and in turn significantly hindering her ability to save a portion of her income.  This not only impacts her life trajectory, it affects her children as parental age has been shown to be a factor in the accumulation of assets available to children.  Using the National Education Longitudinal Study of 1988, Powell, Steelman, and Carini (2006) find, The older the mother, the more likely and the earlier parents started to save for college, the more they actually saved for college, the more likely the child attended a private high school, and the more likely the child used a computer in the home for educational purposes (1374).  Consequently, this study provides empirical support for the negative effects of childbirth at early ages on the human capital development and subsequent assets of the next generation.

Likewise, the timing of other unanticipated events (unemployment, health problems, divorce) at particular points in the life course can have profound effects on later patterns of asset accumulation (e.g., see Voyer 2004).  Conversely, the presence of assets may reduce the likelihood and/or the severity of such events, resulting in a virtuous cycle that then leads to greater asset accumulation as individuals age.

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The Utility of the Life Course Framework

The life course framework appears to be an extremely helpful tool in understanding the process of asset building in general, and asset building among lower-income households in particular.  As mentioned earlier, the process of accumulating assets is one that takes place over the course of an individuals lifetime, and examining asset building from a life course framework would appear to be a natural fit.  The life course introduces the factors of time, aging, development, and sequencing, all of which are important in asset accumulation.

Building upon these findings, one can begin to illustrate why many lower-income households have little or no assets.  Although there are a multitude of life course patterns and trajectories, a common pattern begins with a child born to low-income parents who lack the resources to maximize her early developmental and educational experiences.  If there are several children in the household, parental resources (both time and money) are stretched even further.  Even if the parents own their home, it may be located in a resource-poor and low achieving school district.  As a result, the child may not be able to acquire all of the necessary skills, abilities, and credentials to compete in the high-wage labor market.

This scenario is further complicated because factors such as lower family income, neighborhood instability, and/or race may also contribute to a greater risk of an early detrimental life event for the child, such as an out-of-wedlock birth, illness, or incarceration.  These events subsequently impact the childs ability to invest in her own human capital, as attending a community or technical college is difficult and earning a four-year college degree may become virtually impossible. 

Even without adverse life events, the lack of familial resources and other factors make completing higher education difficult. As a result, the adult child becomes locked into the low-wage labor market for much of her adulthood.  Her income is low, her job stability is precarious, and the jobs she finds typically lack key benefits such as health insurance.  Under these circumstances, it is much more difficult for her to save and build assets in her early and middle adulthood, generally ones prime earning years.  Her parents lack the financial resources to help her through crises or with major purchases, such as a down payment on a home.  Not having any assets to draw upon during periods of unemployment and economic hardship makes such periods even more tumultuous, and she is frequently in debt. 

Given these circumstances, she is simply unable to accrue much in the way of assets and wealth over the course of her life.  Again, if there is a disadvantageous life event, such as divorce, along the way, asset accumulation becomes even more difficult.  As she reaches her late 50s and early 60s, she approaches retirement with only a small amount accrued in the Social Security system, and perhaps an equally small amount of home equity. With little in the way of assets or retirement funds, and with only a modest Social Security check, she is likely to be on the verge of poverty throughout her elderly years (see Rank and Hirschl, 1999b).  She may be forced to continue working in retirement, unable to rely on her children, who are likely caught in the same cycle.

This example illustrates from a life course perspective why lower-income households lack assets.  It is a process that unfolds across time and is largely dependent upon prior events and processes.  While the life course framework can be used to explain why individuals lack assets, it can also be used to identify points in time where policies and programs could change the anticipated life course for the better.  For example, if children from lower-income families do not have access to the resources necessary to believe that they can go to college, programs could be developed that try to correct that perceived lack of opportunity.  One such program is the Gaining Awareness and Readiness for Undergraduate Program (GEAR UP), a federally funded grant program administered by the Department of Education. GEAR UP grantees provide services to low-income middle and high school students to increase their preparation for postsecondary education and may also provide scholarships.  The privately funded Saving for Education, Entrepreneurship, and Downpayment (SEED) initiative includes experimental matched savings account programs for children.  It is hoped that such accounts may make going to college a more realistic option for young adults like those in our example, and hence may alter their labor force trajectories and subsequent ability to accumulate assets.  A life course perspective is thus both fluid and dynamic.

Additionally, research on asset accumulation can greatly benefit from employing a life course framework because it can help in the identification of periods where individuals and families are particularly vulnerable or, alternatively, open to growth.  Yet in order to do so, there is a pressing need for longitudinal data.  While, data sources are discussed at length in another report in this series,[4] it is important to point out here that in order to analyze the life course dynamics of asset building, long-running longitudinal data sets that track assets are essential.  Several such data sets currently exist, including the Panel Study of Income Dynamics and the National Longitudinal Survey of Youth 1979 and now, also, the NLSY 1997.  In order to model the process of asset building across the life course, researchers need to take full advantage of the longitudinal nature of these data sets.

Conclusion

The life course provides a valuable framework for understanding the process of asset accumulation as it unfolds across a lifetime, and is particularly helpful in understanding the difficulties low-income households face in this area.  Empirical research has demonstrated that low-income households lack assets.  In understanding this lack of assets across the life course, five factors are particularly important.  These include a shortage of parental resources resulting in little intergenerational transmission of assets, being nonwhite and its cumulative effects over time, earning low levels of income throughout ones prime earning years, experiencing single parenthood or other family disruptions, and experiencing inopportune life events.  These factors are best understood within the context of time, aging, development, and sequencing, all of which represent the essence of the life course perspective.

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Appendix Exhibit. Empirical Studies of Asset Building over the Life Course

Empirical Studies of Asset Building over the Life Course
Author Data Source Sample/Study Population Method Outcomes Analyzed Key Explanatory Variables Findings Authors Principal Conclusions
Bynner (2001) The National Child Development Study (British Birth Cohort Studies). 11,400 individuals born in 1958 in Britain. OLS. Labor market experiences (years with full-time employment), marital breakdown, health (general health, depression, smoking), citizenship and values (voted in last election, interests in politics), and parenting (only for 10% sample surveyed at age 37). Total value of financial assets at 23 and total value of saving and investment at 23. After controlling for a wide range of possible alternative explanatory variables, both saving and investment have strong effects on positive labor market experience. Individuals with saving at 23 are less likely to have marital breakdown in later life, rate themselves as more healthy, and reveal greater commitment to work. The possession of investment at 23 is positively related to individuals political interest. The study finds a really low threshold value of assets (between 300-600 £) above which no obvious additional asset effects are observed. This study shows that assets have strong effects on various outcome variables. The presence of the asset at a low level matters, rather than its monetary value. Research should further explore why assets have these effects.
Caner and Wolff (2004) 1984, 1989, 1994, 1999 PSID. Full sample, except for those with missing value on house and family weights, and except for those with extreame and large value on wealth data. (1) Descriptive. (2) Probit. Possiblity of becoming asset poor, possibility of escaping from asset poverty. Demographic variables, life time events such as change of job status, retirement, ending or getting marriage, having children, starting or closing business, home ownership, becoming disabled, and inheritance. (1) The overall rates of asset poverty during 1984-1999 varied between 26 and 42 percent. (2) Marriage is positively associated with the probability of escaping poverty, while single parenthood is positively associated with the probability of becoming asset poor. The lifetime events are correlated with transitions into and move out of asset poverty.
Carney and Gale (2001) 1984, 1985, 1986, 1990, 1991, 1992 panels SIPP. Households with heads aged between 25-64. (1) Descriptive. (2) Standard Heckman two-stage regressions. Net worth, financial assets, housing equity, and having transaction accounts. Age, race, public assistance participation, education, income, marital status, employment, and family type. (1) 20 percent of all households have no basic transaction accounts(i.e., a savings or checking account) and that more than half of all households have less than $5,000 in financial assets. Those in the bottom 25 percent of the income distribution have virtually no financial assets whatsoever. (2) Income, age, education, and marital status are significantly associated with the level of net worth and financial assets. (3) The ownership of transanctions account is associated with large increases in the likelihood of owning other forms of wealth. NA.
Edin (2001) Qualitative data collected by the author. Low-income single mothers in Chicago, Charleston, and South Carolina (N=198), non-custorial low income fathers in Philadelphia (N=180). (1) Qualitative. (2) In-depth interview. Types of assets held by single parents and the effects of these assets. NA. The accumulation of assets over the life course is largely dependent upon having an income surpuls, along with the belief and faith that ones income will remain relatively stable from one month to next. NA.
Gale and Scholz (1994) 1983, 1986 SCF. Full sample (2,822 households including 359 in the high-income sample). Descriptive. Net worth. Inter vivos transfers, inheritances. Intended family transfers and bequests are estimated to account for 51% of current U.S. wealth. Of 51%, intended family transfers account for 20% and bequests account for 31%. Additional 12% was acquired through the payment of college expenses by parents. Consequently, approximately two-thirds of the net worth that individuals acquire comes through family transfers. Intended transfers are an important source of wealth.
Goktale and Kotlikoff (2002) 1995 SCF combined with wage trajectory estimated with CORSIM, a dynamic microsimulation model of the U.S. economy. Household heads aged 60-69. Simulation. Net worth at age 66. Net worth of parents. The children of the very rich have roughly 40 times better odds of being very rich than do the children of the poor. NA.
Gruber (2001) 1984-1992 panels SIPP. All unemployment spells during the observation. (1) Descriptive. (2) Selection-corrected regressions. Change in log real wealth. Employment status (unemployed, labor force leavers, and employed), gender, marital status, race, duration of spell, the generosity of unemployment insurance, and education. (1) On Average, about 50%-60% of the sample have wealth lower than their expected income loss from unemployment. The typical worker has gross financial assets that can replace 73% of realized income loss. Almost one-third cannot even replace 10% of loss. (2) Among the unemployed, older men, whites, and those on temporary layoff have much more adequate saving. The adequacy of wealth holdings drops very rapidly with duration of unemployment. (3) Individuals draw their wealth down less rapidly as Unemployment Insurance benefits are more generous. The financial assets holdings of the unemployed are really low and heterogenous.
Haveman and Wolff (2000) 1983, 1989, 1992, 1995, 1998 SCF. Full sample (both core and high-income supplement). Descriptive. Asset poverty measure with Marketable Wealth(MW), asset poverty measure with Marketable Wealth less Home Equity (MW-HE), asset poverty measure with Liquid Wealth (LIQ). NA. (1) Except the MW-HE measure, the 1998 level of asset poverty exceeded its 1983 level. (2) The asset poverty rates fall monotonically by age and education. (3) Whites and homeowners are much less likely to be in asset poverty. (4) Female-headed families with children have the highest asset poverty rate. NA.
Keister (2000) 1983-1995 SCF. Full sample. (1) Descriptive. (2) Logistic regression. (3) Simulation. The odds of having family net worth greater than its income, the odds of moving into upper decile in net worth distribution, family debt holdings, the odds of moving into top 10% of the wealth distribution, the odds of movement out of bottom 20%, and the odds of movement into bottom 20%. Househeads demographic characteristics (age , race, marital status, income, and education). (1) Median net worth distribution by age group shows that it is lowest among youngest group (younger than 35), highest among mid-age group (45-64 years), and midean net worth among retirement age group (65 or older) smaller than middle-age group. (2) Being married, being white, having high income, and having high education are positive association with the odds of upward mobiltiy. (1) Wealth accumulation increases throughout the working years and declines after retirement, but the dissaving is less extreme than the life cycle theory predicts. (2) Marital status, race, income, and education affect wealth mobility.
Keister (2003) 1985-2000 NLSY79. Full sample excluding those with missing values on wealth data. (1) Estimated generalized least-squares (EGLS) regression. (2) Logistic regression. The dollar value of net worth, the probability of receiving a trust, the probability of receiving an inheritance, the probability of owning a home, and the probability of owning stocks. Total number of siblings, parents income and education in 1978, respondents education, age, race, marital status, income, and family religion and family structure during childhood. Number of siblings has a significantly negative association with net worth, the probability of receiving trust account, the probability of receiving inheritance, the probability of owing a home, and the probability of owing stocks. Number of siblings affect wealth, at least in part by reducing the resources available to each child. Siblings reduce direct financial transfers from parents to children. Sibship size affects investment behavior.
Kotlikoff and Summers (1981) Aggregated data from various sources, such as National Income accounts, IRS Statistics of Income. NA. Descriptive. Aggregated wealth (net worth). NA. The vast majority, more than 80%, of aggregate U.S. capital fomation is result of intergenerational transfers. The view of U.S. capital formation as arising, in the main, from essentially homogeneous individuals or married spouses saving when young for their retirement is factually incorrect.
Lupton and Smith (1999) HRS, 1984, 1989, 1994 PSID. Full sample excluding top and bottom 1% of net worth distribution. (1) OLS. (2) Median regression. (3) Quantile regression. Household wealth changes and household saving behavior between wave difference in net worth. Marital status, marital status change between waves, marriage duration. (1) Controlling for rae and age, on average married couples saved about $11,000 to $14,000 more over a five year observation period than non-married household saved. (2) Households whose head was married in 1984 and 1989 but then unmarried by 1994 decreased saving by almost $21,000 after controlling for demorgraphic characteristics. (3) Households whose head was not married in 1984 and 1989 but then married by 1994 increased saving by $16,537. Married people apparently save significantly more than other households, but, comparing duration effects on saving of married households to all unmarried households, the gap in saving between these two marital states decreases with time.
Munnell, Browne, McEneaney, and Tootell (1996) Federal Reserve Bank of Boston survey (Boston Fed Study). Loan applications for conventional mortgages in the Boston area in 1990, including all applications made by blacks and Hispanics and a random sample made by whites. (1) OLS. (2) Bionominal logit. Probability of mortgage loan application denial. Risk of default (housing expense/income, total debt payment/income, net worth, consumer credit history, mortgage credit history, public record history, unemployment region, self-employed, loan/appraised value), cost of default (denied private mortgage insurance), loan characteristics (two-to four-family home), personal characteristics (race). (1) Black and Hispanic applicants in the Boston area, on average, have less wealth, weaker credit histories, and higher loan-to-value ratios than white applicants. (2) Taking account these information on applicant and property characteristics reduces the difference between minority and white in denial rate from originally reported a relative rejection ratio of 2.8 to 1 to roughly 1.8 to 1. (3) White applicants with the same personal and property characteristics as black and Hispanic applicants would have experienced a rejection rate of 20 percent while black and Hispanics rate of 28 percent. Black and Hispanic mortgage applicants in the Boston area were over 80 percent more likely to be rejected than white applicants with similar personal and property characteristics.
Powell, Steelman, and Carini (2006) 1988 National Education Longitudinal Study. Nationally representative eighth graders whose biological or adoptive mothers were interviewed for the survey. (1) OLS. (2) Logistic regression. (3) Tobit model. Economic resources for childs education: saved for college, when started saving, amount saved, willing to incur debt, private school, educational objects, computer in home. Maternal age. (1) In the bivariate analysis, maternal age is significantly positively correlated with all 7 variables of economic resources, except the willingness to incur debt (a negative relatioionship with maternal age). (2) After controling for family income, race, education, childs gender, number of siblings, birth order of the child, and marital status of the mother, maternal age has a significant coefficient. Maternal age has significantly positive effects on economic resources for childs education.
Reid (2004) PSID. 5,300 renters who had not owned a home in past five years. (1) Descriptive. (2) Multivariate. Home ownership status, value of home, home equity. Income, race. (1) Among low-income renters, whites, married couples, professionals, and those with at least HS degree were more likely to buy homes. (2) Many homeowners, especially low-income and minority, return to renting. (3) Financial returns to home ownership were very small for low-income minorities, low-income whites, and middle-income minorities. Still, housing wealth is essentially the only asset for many low-income minority home owners and some do experience appreciation. (4) Experiencing a divorce is one of the most important factors in the transition from owing to renting, regardless of race or income. (1) Homeownership disproportionately benefits white and middle- and upper-income households. (2) Increasing homeownership among blacks will not substantially reduce the racial wealth gap. (3) Homeownership is an incredibly fluid category, with many families moving in and out of homeownership several times over the course of their lives.
Ross and Yinger (2002) Public version of Boston Fed Studys data set. About 3,000 loan applications for conventional mortgages in the Boston area in 1990, including all applications made by blacks and Hispanics and a random sample made by whites. Probit. Mortgage loan approval. Expense-to-income, debt-to-income, net worth, predicted unemployment, self-employed, loan-to-value ratio, denied PMI, multifamily unit, fixed-rate mortgage, special loan, mortage term in year, receiving downpaymant as gift, cosigner, minority status, age, gender, marrital status, owner-occupied home, House in a poor Census tract, House in a minority Census tract, bankruptcy, mortgage credit, consumer credit, having unverified information in application, application met lender guideline or not. (1) Even after controlling for explanatory variables not included in most previous studies (e.g. whether an application meet lender guideline), the estimated impacts of minority status on loan approval remains statistically significant. (2) Even after dropping all cases that appear to involve negotiations, the effect of minority status remains significant. (3) Minority households are less likely to be approved than white equally qualified in all different types of model specification. The white-minority disparity in loan approval found by the Boston Fed Study cannot be explained by omitted variables, data errors, misclassification, endogeneity of loan terms, or underwriting standard variation. The Boston Fed Study provide strong evidence of racial discrimination in mortgage loan approval.
Ruggles and Williams (1989) 1984 panel SIPP. Full sample. Descriptive. Simulated poverty entries and spell durations based on monthly data. Financial assets. Asset holdings are sufficient to eliminate nearly 40% of short-term poverty entries. Three-fifths of poverty entries (based on monthly data) have too few assets to eliminate their poverty gap over the duration of the poverty spell. Including financial assets in family resources to calculate poverty entry and spell has different effects on children and the elderly. Even when asset holdings are taken into account in family resources, subannual spells of poverty are extremely common.
Schreiner and Sherraden (2007) Administrative data from ADD. Over 2,000 participants in 14 IDA programs. (1) Descriptive. (2) Multivariate. IDA saving. Match rate, match cap. Participants who were eligible for higher match rates were more likely to be savers but had lower monthly net savings. When both of these effects are considered, higher match rates increased average saving. Higher match caps were associated with greater saving. Net IDA deposits increased substantially during tax season. Higher match rates increase inclusion. Many IDA participants were saving for fixed goals.
Shapiro (2004) Qualitative data from in-depth interviews, SIPP, PSID. In-depth interview sample of 200 poor to middle-class families with school-age children in Boston, LA, and St. Louis. Descriptive. Receipt of transfer or financial assistance, effects of transfer/financial assistance. Race. (1) Sizable inheritances and inter vivos gifts can give young families a head start(ex: Allows home purchase in neighborhood with good schools). (2) Whites are more likely than blacks to receive sizable transfers. (3) Families with assets are able to acquire high-quality education for their children, and their education can transfer their economic advantages to their children. Transfer of transformative assets perpetuates inequality.
Warren and Britton (2003) The 1995-96 Family Resources Survey (Britain). A representative sample of 26,000 households in Britain. (1) Descriptive. (2) Regression. Net worth (pension, home equity, financial assets). Ethnicity. There are extreme differences of asset distributions in terms of ethnic diversity. The White, Chinese, and Indian working-age families have the highest levels of assets. Other ethnic groups (Bangladeshi, Black-Caribbean, Black-African, and Pakistani) are significantly associated with having lower levels of assets. 30% of Chinese and White families are in the income-rich/asset-rich group. However, for some other ethnic groups (Pakistani, Black-Other, Black-African, and Bangladeshi), more than 50% of families are in the income-poor/asset-poor group. Taking into account wealth and assets is helpful to show a more comprehensive picture of ethnic economic diversity. The low levels of asset accumulation for some ethnic groups show life-course ecnomic disadvantages.
Zagorsky (2005) 1985-2000 NLSY79. 9000 young baby boomers who participated inmore than half (>6) of the NLSY79 surveys. Regression. Ln (Net worth). Marital statusin 2000, number of years in each martial status during the observation period, age, gender, race, education, income, self-employed. (1) Married respondents experienced a net worth increase of 77 percent over single respondents. (2) Net worth of divorced respodents started falling four years before divorce and their average net worth is lower by 77 percent than that of single respondents. Marriage and divorce do have effect on wealth.
Ziliak (2003) 1980-1991 PSID. 1,210 male and female household heads between the ages of 25 - 52 in 1980 who did not change marital status over the sample period (14,520 person-year). (1) Generalized method-of-moments (GMM). (2) Decomposition. Ln(liquid-wealth-to-premanent-income ratio), Ln(net-wealth-to-permanent-income ratio). Permanent asset-tested transfer income (12 year average over observation period), permanent non-asset tested transfer income. (1) Permanent asset-tested transfer income and permanent non-asset-tested transfor income have significantly negative associations with liquid-asset-to-income-ratio. The former has much larger effect on liquid asset accumulation. (2) Both asset-tested and non-asset tested transfer income have negative but not statistically significant effect on net-wealrth-to-income ratio. (3) Decomposition results indicate that virtually all rich-poor liquid asset gap is attributable to differences in average characteristics, not differences in coefficients. NA.

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Endnotes

[1]  A forthcoming report in the Poor Finances series, Effects of Asset Holding by Robert Lerman and Signe-Mary McKernan, examines in detail the theory and empirical evidence related to the effects of asset holding.

[2]  For the details of data limitations see the Poor Finances report Assessing Asset Data on Low-Income Households:  Current Availability and Options for Improvement, Ratcliffe et al., 2007, available online at:  http://aspe.hhs.gov/hsp/07/PoorFinances/data/

[3]  Another report in this Poor Finances series provides a detailed portrait of the assets of low-income households.  See The Balance Sheets of Low-Income Households:  What We Know about Their Assets and Liabilities, Carasso and McKernan, 2007, available online at:  http://aspe.hhs.gov/hsp/07/PoorFinances/balance/

[4]  Assessing Asset Data on Low-Income Households:  Current Availability and Options for Improvement, Ratcliffe et al., 2007, available at:  http://aspe.hhs.gov/hsp/07/PoorFinances/data/


Questions about the Project or any of the Papers?

Please contact: Gretchen Lehman Social Science Analyst Office of Human Services Policy Office of the Assistant Secretary for Planning and Evaluation U.S. Dept. of Health and Human Services 200 Independence Ave SW, Room 426F.6 Washington, DC 20201 202-401-6614 (phone) 202-690-6562 (fax)Gretchen.Lehman@hhs.gov

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