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


Empirical Studies of Asset Building over the Life Course

AuthorData SourceSample/Study PopulationMethodOutcomes AnalyzedKey Explanatory VariablesFindingsAuthors 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.