The Savings and Credit Management of Low-Income, Low-Wealth Black and White Families

William D. BradfordEndowed Professor of Business and Economic Development

Professor of FinanceSchool of Business Administration

University of WashingtonBox 353200

Seattle, WA 98195-3200

August 2002

Email: bradford@u.washington.eduPhone: 206 543 4559Fax: 206 221 6856

AUTHOR’S NOTE: I am grateful to Timothy Bates and Constance Dunham for their helpful comments on previous versions of this article.

The Savings and Credit Management of Low-Income, Low-Wealth Black and White Families

Abstract

First, the greater reluctance of low-income, low-wealth Black families to use checking

and savings accounts than low-income, low-wealth White families is only partially explained by

regression models that consider a wide array of demographic variables. The greater reluctance

impedes Black families from getting the benefits of participating in financial markets. Second,

the success of low-income, low-wealth Black families in managing credit is not found to differ

from that of low-income, low-wealth White families. But the Black families owe less credit card

and other noncollateralized debt than the White families, implying either that Black families have

a lower demand for such debt or that lenders are biased against them. Third, Black families are

less likely to achieve wealth increases than White families because of differences in labor income

and, to a lesser degree, gifts and inheritances. This has implications for the continued wealth

disparity between Black Americans and White Americans.

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The Savings and Credit Management of Low-Income, Low-Wealth Black and White Families

This study examines the savings and credit management of U.S. families that are both

low income and low wealth. I seek to answer three questions. First, how and why do low-

income, low-wealth Black and White families differ in participation in financial markets? I look

specifically at the families’ use of transaction accounts (checking and savings accounts) and

noncollateralized debt (primarily credit cards and personal loans). Second, how do these

families differ in credit management success, that is, paying bills on time and maintaining good

relationships with creditors? Third, how and why do these families differ in success at increasing

family wealth over time?

These questions are significant for three reasons. First, public and private providers of

financial services can be more effective in working with low-income, low-wealth consumers if

they better understand these consumers. Second, Black applicants are known to be rejected more

often for mortgage loans and commercial loans than White applicants.1 After controlling for

relevant variables, if low-income, low-wealth Black families display less success at credit

management than comparable White families, then higher rejection rates are not surprising,2 and

the use of public resources to provide training on managing credit relationships may be as

valuable as finding and punishing acts of prejudicial lending discrimination. Third, with regard

to wealth management, the mean and median wealth of Black families are much lower than those

of White families in the United States (Altonji, Doraszelski, & Segal, 2000, Blau & Graham,

1990, Oliver & Shapiro, 1990, Wolff, 1998), and movement toward wealth equality is slow at

best (Bradford, 2000a). This lack of parity has been described as an endemic problem in the

1 On mortgage lending see the articles cited by Yinger (1998) and Ladd (1998). On commercial lending, see Ando (1988), Bates (1997), Bates & Bradford (1992), Blanchflower, Levine, & Zimmerman (1998) and Cavalluzzo & Cavalluzzo (1998). 2 This relationship leads to statistical discrimination (Phelps, 1972). Firms use race to infer likelihood of default on the loan. Here, discrimination does not reflect prejudicial discrimination (as discussed by Becker, 1971, for example) but rather an attempt to minimize costs of gathering more information.

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United States associated with free-enterprise capitalism and racism (Cotton, 1998). However, if

Black families build less wealth than economically comparable White families, then the financial

decisions of Black families are at least partially responsible for sustaining the wealth gap. 3 This

would imply that both the financial decisions of Black families and the socioeconomic system

should be changed if more evenly distributed wealth is the goal.

In this article, I use longitudinal data from the nationally representative Panel Study of

Income Dynamics (PSID), which allows me to follow and observe the income, wealth, asset

portfolios, and demographic information of the families over time. The data extend from 1984 to

1999, although not all of the analyses cover this entire period. The findings of this study can be

summarized in reference to the three questions as follows.

1. The use of transaction accounts and noncollateralized debt of low-income, low-

wealth families. Using PSID data for 1984, 1989, 1994, and 1999, I find that a lower percentage

of Black families hold transaction accounts than do White families in each income quartile and

each wealth quartile. Logistic and ordinary least squares (OLS) regressions were conducted on

Black and White families that were below median income and wealth in the PSID data. The

regressions show that, after controlling for the impacts of demographic traits, family income, and

family wealth, Black families are less likely to hold transaction accounts than White families; and

for those families that do have transaction accounts, Black families hold lower dollar amounts

than White families. With regard to debt, Black families, compared to White families, owe more

noncollateralized debt in the top two wealth and income quartiles but less in the bottom two

quartiles. The regressions show that, controlling for the effect of the independent variables, low-

income, low-wealth Black families are less likely to owe noncollateralized debt than comparable

3 The focus here is on changes in wealth instead of accumulated wealth. The latter reflects the activities of prior periods, not necessarily the wealth management over the current period. Most researchers have developed models that observe wealth accumulation instead of wealth changes. (See Altonji et al, 2000; Blau & Graham, 1990; Hurst, Luoh & Stafford, 1998; and Menchik & Jianakoplos, 1997.)

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White families; and for families that owe this form of debt, Black families owe less than White

families.

2. The credit management of low- income, low-wealth families. This study also

examines the credit management and wealth management of low-income, low-wealth Black and

White families from 1989 to 1994. Credit management includes the family’s difficulty in paying

bills and its experience with debt consolidation loans, creditors demanding payment, wage

attachments, liens against and repossessions of property, and bankruptcy. In univariate analyses,

the Black families and White families on balance achieve the same success rates at credit

management. However, logistic regressions show that the credit management success of low-

income, low-wealth Black families is equal to or greater than that achieved by the low-income,

low-wealth White families after controlling for demographic and other relevant variables.

3. The wealth management of low- income, low-wealth families. In univariate

comparisons, low-income, low-wealth Black families achieve lower levels of wealth management

success in terms of whether wealth increased, and the size of the increase, than comparable White

families. In the logistic regressions predicting whether wealth increases, I find that the wealth of

Black families is less likely to increase, controlling for the impact of the other independent

variables. However, when labor income, gifts and inheritances are subtracted from the change in

wealth, race becomes statistically insignificant. Thus, differences between Black families and

White families in labor income and, to a lesser extent, gifts and inheritances can explain

differences in wealth management as defined by whether their wealth increases. A second

measure of wealth management is the amount of change in wealth. In univariate comparisons,

the mean change in the wealth of White families is found to exceed that of Black families.

However, in the OLS regressions predicting the amount of change in wealth, race does not matter.

Characteristics such as age, education, number of children, receipt of a monetary gift or

inheritance, and amount of labor income dominate race in predicting the amount of change in

wealth.

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Some implications of these findings are as follows. First, the greater reluctance of low-

income, low-wealth Black families to use checking and savings accounts than low-income, low-

wealth White families remains largely unexplained. The reluctance may be an impediment to the

benefits of participating in financial markets. For example, holding a transaction account is

positively associated with holding noncollateralized debt. Second, the result that Black families

perform as well as or better than White families in credit management, along with the lower

likelihood of Black families owing noncollateralized debt, implies either that Black families have

a lower demand for such debt or that lenders impose prejudicial lending bias against these

families. Third, the finding that low-income, low-wealth Black families are less likely to achieve

wealth increases than low-income, low-wealth White families because of differences in labor

income and, to a lesser degree, gifts and inheritances has implications for wealth disparity

between Black Americans and White Americans. White families--even low-wealth White

families--have a greater propensity to inherit wealth, and studies have found that labor markets

discriminate against Black workers (Holzer & Neumark, 2000). To the extent that gifts,

inheritances and labor income are distributed in the favor of White families, Black families will

generate lower levels of wealth increases than will White families in the United States. Thus the

wealth gap between Black and White families may expand, but this does not result from inferior

wealth management by low-income, low-wealth Black families compared to low-income, low-

wealth White families.

In the remainder of this article, I cover background and data for the study. Next, I discuss

my hypotheses and methodology and provide descriptive statistics. Then, I discuss the results of

the tests, and finally present my conclusions.

Background and Data

Data

The PSID is an annual longitudinal survey of a national sample of U.S. households

conducted by the Survey Research Center of the University of Michigan. The total sample is

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representative of the U.S. population when sample weights provided by PSID are used. Black

and low-income families were initially oversampled in the PSID, 4 so I have used weights

throughout this article to adjust for both the differential initial sampling probabilities and the

differential nonresponse that has arisen since the beginning of the study in 1968.5 Starting in

1984, at 5-year intervals, PSID has gathered wealth data on its panel families.6 This study uses

the amount and components of the families’ wealth for the years 1984, 1989, 1994, and 1999. I

also follow the changes in wealth of families with the same head of household from 1989 to 94.7

In addition to family wealth, PSID provides information on the financial experiences of the

family as reported by the head of household or spouse. In 1996, PSID included a series of

questions on the financial difficulties of the family between 1991 and 1996. This supplement

includes information about whether families have difficulty in paying bills when they were due,

and their experiences with debt consolidation loans, creditors demanding payment, wage

attachments, and liens against and repossessions of property.

Measures of Participation in Financial Markets

Families participate in financial markets through the nature of the assets they hold and the

ways in which they finance their assets and expenditures. Previous studies have examined the

4 These data contain essentially only Black and White families. Other ethnic groups (Latinos, Asian, and Native Americans) are not represented in the sample. 5 Attrition averages about 3% from 1 year to the next. About 60% of the original sample was still being interviewed in 1998. Researchers who have compared PSID with other data have concluded that these weighting procedures make the study representative of the nonimmigrant U.S. population (Hill, 1992).6 Wealth includes real estate (main home, second home, rental real estate, land contract holdings), cars, trucks, motor homes, boats, farm or business, stocks, bonds, mutual funds, savings and checking accounts, money market funds, certificates of deposit, government savings bonds, Treasury bills, IRAs, bond funds, cash value of life insurance policies, valuable collections for investment purposes, and rights in trust or estate, less mortgage, credit card, and other debt on such assets. This measure does not include wealth in the form of private pensions or expected Social Security retirement benefits. I add two observations about the PSID wealth data here. First, PSID does not capture wealth information on households at the very top of the wealth distribution. The majority of the measurement problems in PSID occur beyond the 98th percentile of the wealth distribution, possibly even beyond 99.5%. Juster, Smith. and Stafford (1999) found that the PSID wealth data for 1989 lined up closely with those from the 1989 Survey of Consumer Finances through the 99.5 percentile. Of course, a major concern in this study is the experience of African Americans, who are oversampled by PSID and who are considered to be underrepresented in the top 99.5 percentile of wealth holders in the United States. (Hurst, Luoh, & Stafford, 1998)7Wealth data on families are available for 1999, but the information needed to connect the 1994 families with their 1999 status is incomplete.

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participation of families in purchasing homes. Here, I observe (a) the holding of transaction

accounts at financial institutions--checking and savings accounts- and (b) the use of

noncollateralized debt. Noncollateralized debt, which is reported in the PSID wealth data,

includes credit cards, student loans, medical or legal bills, and personal loans; it does not include

mortgages and automobile debt.

Measures of Credit Management

The measures of credit management follow from special supplements of the 1996 PSID.

The questions in the supplements are paraphrased below:

Between 199 and 1994 did you:a. Find yourself unable to pay your bills when they were due?b. Obtain a loan to consolidate or pay off your debts?c. Have a creditor call or come to see you to demand payment?d. Have your wages attached or garnisheed by a creditor?e. Have a lien filed against your property because you could not pay a bill?f. Have your home, car, or other property repossessed?

Between 1989 and 1994 did you: g. File for bankruptcy?

The concept of credit management success is paying bills on time and maintaining good

relationships with creditors. These outcomes quantify credit management success along a

discrete continuum, based upon the seven questions posed. “Successful” is denoted as one and

“unsuccessful” as zero in the measures of credit management. The first measure of credit

management is the most conservative:

CRM1 = 1 if No to all of a through g = 0 if Yes to at least one of a through g

When CRM1 = 1, the family had no problems with paying bills when due, and no problems with

creditors. Families for which CRM2 = 1 consists of families for which CRM1 = 1 plus families

that answered yes to a or b, but no to c through g.

CRM2 = 1 if No to all of c through g = 0 if Yes to at least one of c through g

Success at CRM2 means no creditor called to press for payment and no legal proceedings

associated with creditors. The family may have had trouble at some point paying bills when they

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were due or may have obtained a loan to consolidate or pay off debts. Presumably, the family was

able to manage through such times without difficulty with creditors. Families for which CRM3 =

1 consists of families for which CRM2 = 1 plus those who answered yes to c and no to d through

g:

CRM3 = 1 if No to all of d through g = 0 if Yes to at least one of d through g

For CRM3, a family is unsuccessful in credit management if creditors pursued such legal

measures as garnishments, liens and property repossession or if bankruptcy was declared.8

Measures of Wealth Management

Two categories of wealth management will be analyzed. The first category is based on

whether the family’s wealth increased between 1989 and 1994. Assume for the moment that

flows in and out of the family unit occur only at the start and end of the period. The wealth at the

end of the period is equal to the wealth at the start, plus the net change in the value of the

wealth claims held over the period, , plus income earned, Y, plus gifts and inheritances

received, G, less expenditures, E.

W94 = Wealth in 1994

W89 = Wealth in 1989

G = Gifts and inheritances from others from 1989 to 94.

= Net change in the value of assets and debts held in 1989.

Y = Income between 1989 and 1994 (except for )

E = Expenditures for consumption, contributions, gifts to others, medical expenses, etc

from 1989 to 94.8 Instead of considering bankruptcy alone, I added the other measures of severe financial distress to CRM3. This helps to avoid problems caused by the relationship between the bankruptcy decision and the bankruptcy laws of the state in which the family resides. It is likely that the other items under CRM3 happen when the severe financial distress associated with bankruptcy occurs, even when bankruptcy is not declared.

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The first category of wealth management contains four measures that make different adjustments

to changes in wealth. The first measure reflects whether or not family wealth changed between

1989 and 1994. Thus WLM1 is defined as

WLM1 = 1 if

= 0 if

The second measure subtracts out gifts and inheritances from the change in wealth to adjust for

wealth changes due to gifts and inheritances.

WLM2 = 1 if

= 0 if

The third measure adjusts by subtracting out labor income from WLM1. If labor income is

systematically lower, for example, for Black families (due to current or past racial biases),

subtracting out labor income can provide a clearer picture of the ability to build wealth without

the differences that occur because of different experiences in the labor markets.9

WLM3 = 1 if

= 0 if

where Y0 denotes other (nonlabor) income. Finally, the fourth measure takes out both gift or

inheritance inflows and labor income:

WLM4 = 1 if

= 0 if

Two comments on this category of wealth management are relevant here. First, it is

likely that funds will move in and out of the categories in W89 within the 5-year period instead of

just at the endpoints. One can define to include such intermediate flows without a loss of

relevance for the measures here. Second, all of the measures in wealth management are in current

dollars. Current dollar measures are the most clear to the family unit as it evaluates its financial

position. The family will translate the changes in current dollar wealth to changes in constant

9 The degree to which Black workers receive lower wages because of racial discrimination is a topic of ongoing controversy. See Holzer and Neumark (2000).

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dollar wealth depending on the change in the cost of the goods and services it expects to

consume. This can differ for each family, and data on these differences are not available.

The second category of wealth management is the dollar change in wealth between 1989

and 1994. As I will discuss below, regression models will specify to what extent Black and

White families differ in the changes in wealth between 1989 and 1994, after controlling for

various traits that also affect wealth accumulation. In particular, the elimination of gifts,

inheritances, and labor income may exclude information that is helpful in understanding their role

in wealth accumulation. These two variables are included in the regression models that predict

the amount of change in family wealth.

Methodology, Hypotheses and Descriptive Statistics

Methodology

Multivariate regression models are used to examine the relationship between transaction

accounts or noncollateralized debt and race while considering the impact of the other family

traits. For example, the logistic regression model estimates PT, the probability that a family

holds transaction accounts, as a function of the independent variables:

PT = Probability (family holds transaction accounts) = F[Race; Other Independent Variables]

In addition to the head of the family’s race, the independent variables are the head of the

family’s age, education, health, marital status, gender, employment status, the number of children

in the family and other dependents; and whether the head or spouse receives public assistance

(ADC/AFDC, SSI or other welfare payments). The descriptive variables relate to those reported

as of 1989. The gifts or inheritances received refers to the 1989-94 period. Appendix A contains

descriptions of the independent variables. Finally, I include real total family income and family

wealth, both in 1999 dollars. The same model holds for PD, the probability that the family owes

credit cards and other noncollateralized debt. For credit management, the logistic regression

model estimates PC, the probability that a family achieved success in credit management (e.g.,

CRM1 = 1) as a function of the dependent variables:

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PC = Probability (CRMi = 1) = F[Race; Other Independent Variables]

Here, I follow the experience of each of the families from 1989 to 1994. In addition to

the independent variables mentioned above, I also included whether the head or spouse (for

married couples) received a gift or inheritance during the period studied, and the education level

and health status of the spouse. The model and independent variables used for wealth

management are similar to those of credit management. The probability, PW, associated with the

discrete measures WLM1-WLM4 is a function of the independent variables in the logistic

models. Thus

PW = Probability (WLMi = 1) = F[Race; Other Independent Variables]

The treatment of gifts and inheritances in WLM1–WLM4 is different from that in the credit

management tests. The receipt of gifts and inheritances is omitted from the independent variables

in the regressions for WLM1–WLM4, because the dollar amount of gifts and inheritances is a

component of WLM1–WLM4. In the analyses of the magnitude of the changes in wealth, the

following relationships are assumed:

= F[Race; Other Independent Variables]

where W represents family wealth. OLS regressions are used to determine the impact of race and

other variables on (a) the dollar amount of transaction accounts held by the family, (b) the dollar

amount of credit card and other noncollateralized debt, and (c) the change in the wealth of the

sample families. With regard to the change in wealth, the total labor income of the head of

family and spouse (if any) from 1989 to 1994 and the starting wealth (wealth in 1989) are

included as independent variables. The regressions for both categories of wealth management

include CRM1 as an independent variable. This provides an analysis of the association between

credit management and wealth management. Finally, I include a dummy variable indicating

whether the family holds a transaction account (Yes = 1, No = 0) in the analyses of

noncollateralized debt, credit management and wealth management. This allows me to pursue the

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issue of how holding a transaction account is associated with these areas of family financial

management.

Hypotheses

It is expected that, controlling for demographic variables, the participation of low-

income, low-wealth Black and White families in financial markets are not different. As noted

earlier, the analyses focus on transaction accounts and noncollateralized debt. Thus, the specific

hypothesis is that, controlling for demographic variables, Black and White families do not differ

in the holding of transaction accounts and the use of noncollateralized debt.

With regard to credit management, I hypothesize that credit management is positively

related to age and education. Age and education should increase accumulated knowledge about

effective short-term money management. Married couples are expected to achieve more success

in credit management than families headed by single people. The former potentially have two

people discussing and participating in money matters. Several of the independent variables

describe possible positive or negative shocks to the family’s budget. Poor health is expected to

have a negative impact on credit management.10 The receipt of a gift or inheritance and the receipt

of public assistance are expected to have positive impacts on credit management. The number of

children and the number of dependents outside of the family are expected to have negative

impacts on credit management.

Based on previous studies, I hypothesize that homeownership has a negative impact on

credit management (Hurst & Stafford, 1998). Mortgage payments reduce budget flexibility, and

some families overcommit funds in purchasing a home. With regard to work status, I expect that

being unemployed relative to self-employment, earning a wage or salary both have a negative

impact on credit management. Self-employment has been found to increase wealth at a faster rate

over time than wage or salary status (Bradford, 2000b; Quadrini, 1999), and thus funds to manage

10 Smith (1995) found that healthier households are wealthier households. The direction of causality is tricky. The relationship between health and credit management is examined here.

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the family’s finances should be more plentiful. Retirement status is expected to have positive

effect, compared to self-employment. Retirees presumably have more time to manage finances,

and typically their budgets are simpler and (except for health) more predictable.

The relationship between a family’s credit management and accumulated wealth is

analyzed by including wealth at the start—family wealth in 1989. I hypothesize that higher

wealth has a positive impact on credit management. At lower wealth levels, the family has less to

draw on to make payments when unforeseen outflows occur. No difference is expected between

single female- and single male-headed families in credit management. A major focus of this

study is the financial management of Black families compared to White families. I hypothesize

that race has no statistically significant impact on credit management after considering the impact

of the other variables.

Prior studies have focused on predicting wealth accumulation instead of changes in

wealth. This is because most sources of wealth data do not follow the same families over an

extended time period. The relationships between wealth management and the independent

variables are hypothesized to be the same as those for credit management except for two items.

First, unlike the negative relationship hypothesized between credit management and

homeownership, a positive relationship is expected between wealth management and

homeownership. The family will have a greater motivation to own a home as its wealth

increases, for tax and investment purposes as well as the mental satisfaction of homeownership.

Second, it is expected that the coefficient on race is negative for WLM1 but declines in influence

going from WLM1 to WLM4. WLM1 reflects whether the wealth of the family increased. The

differences between Black families and White families in the probability of wealth increases are

expected to be associated with differences in gifts, inheritances, and labor income. WLM2

subtracts out gifts and inheritances, WLM3 subtracts out labor income, and WLM4 subtracts out

both. To the extent that the probabilities of increases in wealth are similar for Black families and

White families except for the differences in gifts, inheritances, and labor income, then the race

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variable will become less influential as one moves from WLM1 to WLM4. Finally, the dollar

amount of gifts and inheritances and labor income are included as independent variables in the

regressions that predict the dollar amount of changes in wealth. It is expected that both of these

variables have a positive impact on changes in wealth over the studied-period.

Descriptive Statistics

Appendix B shows the median income and wealth of the PSID families in 1984, 1989,

1994, and 1999 and the percentages of families below these medians by race and family type

(married couple, single male, and single female). Table 1 contains various statistics on the

families that were below the median income and median wealth in 1984, 1989, 1994, and 1999.

Each year is independent: A given family may be included in only 1 year of the data if it was

below median income and wealth in only 1 of the 4 years. Table 1 shows that the heads of the

Black families and White families were about the same age (40.6 years vs. 40.2 years) and the

heads of Black families had less education. For example, 40% of the heads of Black families had

less than a high school education, compared to 29% for the heads of White families. Six percent

of the heads of Black families had a college degree, compared to 15% of the heads of White

families. A lower percentage of Black families owned their own homes (24% vs. 30%) and a

higher proportion reported fair or poor health (28% vs. 21%). Black families had more children

(1.0 vs. 0.7) than did the White families. The income and wealth data show that the mean

incomes and wealth of the Black families were less than those of the White families. This same

relationship holds for each family category: White married couples had higher incomes and

wealth than Black married couples, etc. These figures combine the real incomes and wealth over

all of the 4 years. The same relationships for income and wealth hold at each of the 4 years.

( Table 1 goes here)

Results of the Tests

Transaction Accounts

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For many families, deposit accounts at financial institutions are the means through which

most of their large transactions occur and, correspondingly, the tools through which the family

interfaces with financial markets. Table 2 compares the patterns of ownership of transaction

accounts for all households and separately for Black families and White families. These figures

combine the results for 1984, 1989, 1994, and 1999. The table partitions transaction accounts and

noncollateralized debt by both wealth quartile and income quartile. Table 2 shows that for both

wealth and income, White families use transaction accounts more than Black families in the same

quartile. In addition, the lower the economic status, the greater the difference between Black

families and White families in the proportion of families holding transaction accounts. For

example, the difference in the number of families that hold transaction accounts is less than 10%

for the top wealth quartile, but the difference for the bottom wealth quartile is 38%. Overall, 48%

of Black families hold transaction accounts, and 86% of White families do.

(Table 2 goes here)

These univariate comparisons of transaction accounts do not control for age, education,

income, etc. I want to disentangle the relationships that the independent variables have with

holding transaction accounts, so that a clearer picture of the differences between Black families

and White families may emerge. Because this study focuses on Black and White families in the

lower economic status, in each of the four years examined, I selected those families with below

median income and below median wealth. Table 3 shows the logistic regressions and OLS

regressions on the odds of holding transaction accounts and the dollar amount of transaction

accounts, respectively, for families both below median income and below median wealth.

The logistic regression of combined Black and White families shows that, after

controlling for all of the other variables, Black families are less likely to hold transaction

accounts than are White families. The separate logistic regressions show that, for both Black and

White families, age has a nonlinear relationship with holding transaction accounts, more

education increases the odds of holding a transaction account, married couples are more likely to

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hold transaction accounts than single males (the reference group), families with public assistance

are less likely to hold transaction accounts, and greater income and greater wealth increase the

odds of holding a transaction account. The unemployed are less likely to hold a transaction

account than those self-employed. Differences emerge in that, for White families, owning a

home and good health are positively associated with holding a checking or savings account,

whereas these relationships are negative for Black families. The White self-employed are more

likely to hold a transaction account than White workers and retirees, but the differences between

the Black self-employed and Black workers and retirees are not statistically significant.

Table 3 also includes OLS regressions of the dollars held in transaction accounts for

those families that have such accounts. The combined regression results show that Black families

hold $451 less than White families after controlling for the impact of the other variables. A

major difference between the Black and White families in the OLS regression coefficients is that

for White families the amount held in transaction accounts increases with education, but

education does not positively affect the size of the transaction accounts of Black families. Black

married couples and single female heads hold less than Black single males; category of the

household head has no statistical significance among White families.

(Table 3 goes here)

Credit Management

As discussed earlier, PSID information on the families’ financial difficulties between

1991 and 94 is utilized for this study. Data were also gathered in a separate part of the 1996

PSID on the two most recent bankruptcy filings, with details about the reasons and effects of the

proceedings. Information on any bankruptcy that occurred between the 1989 and 94 is used in

this study. Based upon the required information, included in the study are data on 1,951 families

below the sample 1989 medians in income and wealth that had the same head from 1989 to 9411 11 The requirement that the same person heads the family is a way of identifying the same family over time and linking single years of data. The requirement also connects the credit management outcomes to the same decision makers. Of course, the results then refer to families with the same head of household.

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and for which data on credit management and wealth management are available. There were 831

families with a White head of household and 1,120 families with a Black head of household.

Table 4 contains the summary statistics on the 1,951 families. The first two columns

describe all of the White families and all of the Black families, respectively. The next six

columns compare White and Black families by category: married couples, single males and single

females. The credit performance of the Black families overall is comparable to that of the White

families. Although White families report a 79% success rate for CRM1 compared to 77% for

Black families, Black families report higher success rates in CRM2 (89% vs. 90%) and CRM3

(96% vs. 97%). The comparability of performance for Black and White families also holds by

family category. In terms of wealth management, for WLM1 and WLM2, the success rates of

Black families are lower than those of White families. For WLM3 and WLM4, the success rates

of the Black families are slightly higher than those of the White families. The same relationships

hold in the comparison between Black married couples and White married couples. But on

balance, Black single males perform more favorably in comparison to White single males, and

Black single females perform less favorably then White single females. Similar to the

relationships in Table 1, Table 4 also shows that the mean starting wealth and labor income of the

Black families are less than those of the White families, and this also holds for each category of

Black family in comparison to its corresponding White family. In addition, the mean change in

wealth of the Black families is less than that of the White families, and this relationship also

holds for each family category, with the largest disparity between the advantage of single White

females over single Black females. A smaller fraction of Black families report gift and

inheritance income than do White families, and this relationship holds for each category of

family. Not shown in the table is that, of those families reporting gifts and inheritances, the mean

for White families is $33,652, and the mean for Black families is $23,917.

(Table 4 goes here)

Table 5 contains the results of the logistic regressions for credit management.

17

The dependent variables in the credit management regressions are CRM1, CRM2, and CRM3.

The credit management regressions show that, after considering the impact of the other variables,

race is not statistically significant in predicting credit management success if credit management

is defined as CRM1. However, Black is positive and statistically significant for CRM2 and

CRM3, indicating that Black families experience more success in these measures after

considering the impacts of the other variables.

In these regressions, the single male and single female categories are negatively related to

credit management success in CRM1 and CRM2, and are positively related for CRM3. The

married couple category is the reference category, implying that the credit management success

of married couples compared to single heads of household differs across the measures of success.

Credit management success is positively related to age, but the negative and statistically

significant coefficient for age squared in CRM1 and CRM2 indicates a nonlinear relationship

between credit management and age. The reference category for education is less than high

school completion. Education also has an inconsistent impact on credit management. Although

education is positively related to credit management success for CRM1, the relationship is less

consistent for CRM2 and CRM3. Owning a home has a negative impact on credit management,

indicating that the size and inflexibility of mortgage payments and real estate taxes increase

financial pressures on families. Better health for the head and better health for the spouse are

positively related to credit management success. As hypothesized, more children and other

dependents negatively impact credit management. Receiving public assistance is positively

related to credit management for CRM1 and CRM2 but negatively related for CRM3.

Self-employment is the reference category in the variables that express work status. For

CRM1 and CRM2, self-employment is positively related to credit management success,

compared to retirement and unemployment, and has no differential impact compared to the

worker category. For CRM3, however, the negative coefficient for the three categories implies

that the self-employed have more severe financial problems such as liens and bankruptcy than

18

workers, retirees, and the unemployed. The impact of gifts and inheritances differs among the

measures of credit management. For CRM1, the impact is negative, but for CRM2 and CRM3,

the impact is positive. The coefficient for CRM3 is not statistically significant. The negative

impact for CRM1 may reflect family heads who had difficulty in paying bills (thus unsuccessful

in CRM1) and then requested and received funds from relatives. The receipt of those funds

enabled the families to achieve relatively better outcomes in CRM2 and CRM3. The relationship

between the spouse’s education and credit management is inconsistent for CRM1 and CRM3, but

for CRM2 credit management success is positively associated with the spouse’s education. The

association between income and credit management success is not statistically significant for

CRM1 but is positive and statistically significant for CRM2 and CRM3. More income enables

the family to avoid serious financial difficulty. With regard to the association between credit

management and accumulated wealth, as the level of family wealth increases, the odds of credit

management success improve. Credit management success increases with the family’s wealth.

Finally, holding a transaction account has a positive impact on credit management for CRM1 but

is not statistically significant for CRM2 and CRM3.

(Table 5 goes here)

Noncollateralized Debt

This study has observed the credit management experience of Black and White families

between 1989 and 1994, and has found that race does not matter in predicting credit management

success. How do these findings match with the actual amount of noncollateralized debt owed by

Black and White families? In other words, is the amount of noncollateralized debt provided to

Black and White families also race neutral when controlling for independent variables? The

bottom section of Table 2 compares noncollateralized debt of Black and White families by

income quartile and wealth quartile. The results for consumer debt differ from those of

transaction accounts. First, for both wealth and income, in the top 2 quartiles, a higher proportion

of Black families owe this form of debt than do White families. In the bottom 2 quartiles, a lower

19

proportion of Black families borrow than do White families, and the gap is widest at the lowest

wealth quartile. At the lowest wealth quartile, 63% of the White families owe noncollateralized

debt, versus 40% for Black families. This difference reflects that low income and low wealth

families do not fully overlap and that wealth and income may have a different correlation with

borrowing debt.

Table 6 reports the logistic and OLS regressions that predict whether the family owes

noncollateralized debt and the dollar amount of this debt, respectively. The logistic regression

shows that Black families are less likely to hold noncollateralized debt than White families after

controlling for the other variables. The results also show that holding a transaction account is

positively related to holding noncollateralized debt. In addition, noncollateralized debt has a

nonlinear relationship with age because the coefficients for age and age squared are statistically

significant. The likelihood of owing noncollateralized debt increases with education. Married

couples and single females are more likely to owe this form of debt than single males. The use of

noncollateralized debt is negatively related to public assistance status and the number of children.

Homeownership is positively associated with having noncollateralized debt. Real income is

positively associated with owing noncollateralized debt, but wealth is negatively associated. The

individual race logistic regressions show that families of the Black self-employed owe less

noncollateralized debt than Black families in the other categories; the reverse is true for White

self-employed workers.

Table 6 also reports the results of regressions predicting the dollar amount of

noncollateralized debt families owed. The OLS regressions show that among families owing

noncollateralized debt, Black families hold $1,291 less than do White families when the impact

of the independent variables are controlled for. The results also show that the amount of

noncollateralized debt is positively associated with holding a transaction account. Age and

education are positively related to the amount of noncollateralized debt for both Black and White

families. Marriage and homeownership are positively related to families’ amount of

20

noncollateralized debt. Public assistance is negatively associated with noncollateralized debt for

White families, but the relationship is not statistically significant for Black families. Real income

is positively related to debt, but wealth is negatively related to the amount of debt. Although the

self-employed borrow more than the other work categories for White families, this relationship

does not hold for Black families.

(Table 6 goes here)

Wealth Management

Table 7 contains the results of the logistic regressions utilizing the discrete measures of

wealth management: WLM1–WLM4. As hypothesized, the coefficient reflecting race is

negative and statistically significant for WLM1, remains negative and statistically significant but

is smaller in absolute size for WLM2, and is statistically insignificant for WLM3 and WLM4.

Thus, the differences between Black families and White families in the probability of increasing

wealth dissipate when gifts, inheritances, and labor income are removed. The key equalizer

between Black families and White families is eliminating of the differences produced by labor

income and its effect on wealth changes. This is obvious when comparing WLM1 to WLM3.

Holding a transaction account has a positive relationship with WLM1 and WLM2, but it

is negatively associated with WLM3 and WLM4. The latter two adjust for labor income,

demonstrating that a transaction account is not positively related to wealth management when the

impact of labor income is controlled for. Public assistance status is positively associated with

each of the measures of wealth management. The coefficients for single male and single female

are negative and statistically significant for WLM1 and WLM2, but they are positive and

statistically significant for WLM3 and WLM4. Relative to married couples, single males and

females experience lower increases in wealth before and after adjusting for gifts and inheritances.

The positive coefficients for WLM3 and WLM4 indicate that single males and females, in

comparison to married couples, achieve higher levels of nonlabor income plus gains on wealth

holdings less expenditures.

21

(Table 7 goes here)

Table 7 also shows that age has a small negative impact on wealth management for

WLM1 and WLM2 and a small positive impact on wealth management for WLM3 and WLM4.

Age squared and age have the same sign throughout the models. The implication is that age has a

small impact on wealth management but interpretation of the impact is not clear. Wealth

management success does not consistently increase with education and is negative for WLM4.

As expected, owning a home has a positive relationship with wealth management, indicating that

the blend of investment gain, tax benefit, and mental satisfaction of homeownership is positively

related to increases in wealth. Better health is negatively related to wealth management success

for the head of household, but good health for the spouse is positively related to wealth

management success. This may indicate that better health may reduce the precautionary motive

for accumulating wealth for the household’s head. More children is inconsistent with wealth

management success, but other dependents have a positive association with all measures of

wealth management success. The regression coefficient for the bottom wealth quartile is positive

and statistically significant. This indicates that those in the lowest wealth quartile have a greater

probability of increasing wealth. Self-employment is the reference for the work category. For

WLM1 and WLM2, the employee status has an advantage over self-employment in terms of the

probability of increasing wealth. Of course, if labor income is removed, the advantage of

employee status over self-employment becomes a disadvantage, as indicated in WLM3 and

WLM4. As expected, retirement and unemployment have a negative impact on wealth

management compared to self-employment; the two have a positive impact on wealth

management when labor income is removed.

Nevertheless, excluding differences between families in terms of gifts, inheritances and

labor income may omit information that is helpful in understanding their role in wealth

accumulation. Thus, Table 8 reports the results of four OLS regression models using change in

wealth as the dependent variable for measuring wealth management. All of the models include

22

gifts and inheritances as an independent variable. Model 1 excludes both total labor income and

starting wealth from the independent variables. Model 2 adds the total labor income of the family

head (and spouse, if any) from 1989 to 1994 as an independent variable. Model 3 excludes labor

income but includes family wealth in 1989 as an independent variable. Model 4 includes both

labor income and 1989 wealth as independent variables. The F-statistics show that each of the

regressions in Table 4 is statistically significant at the 0.01 level, and the adjusted coefficients of

determination range from 0.08 to 0.10.

None of the models in Table 8 indicates that race matters in predicting changes in wealth.

All of the regression coefficients for Black are positive but not statistically significant. Low-

income, low-wealth Black families build wealth at the same pace as low-income, low-wealth

White families after controlling for the other variables. Holding a transaction account is not

statistically significant when labor income is included in the model. Education is positively

related to changes in wealth, but the coefficients for age are typically not statistically significant.

The number of children has a negative effect on the change in wealth. Owning a home has a

positive relationship with changes in wealth. Being in better health for the head has a

consistently negative impact on the change in wealth, although better health for the spouse is not

statistically significant. The number of dependents outside the family unit is positively associated

with change in wealth. Public assistance is not statistically significant in predicting the dollar

amount of the change in wealth. The work status dummies are not statistically significant,

meaning that the change in wealth of the low-income, low-wealth self-employed is not different

from the other employment categories when the impacts of other variables are controlled for. As

expected, labor income is positively associated with wealth changes, although starting wealth is

not statistically significant. The low starting wealth of these families has less impact on wealth

changes during the period studied than does their labor income.

(Table 8 goes here)

Conclusions and Discussion

23

This study uses PSID data covering 1984 through 1999 to examine various aspects of the

financial management of low-income, low-wealth families. First, the greater reluctance of low-

income, low-wealth Black families to use checking and savings accounts than low-income, low-

wealth White families is only partially explained by regression models that consider a wide array

of demographic variables. For many families, transaction accounts are the tools through which

they begin to successfully interface with financial markets. Thus, related topics for future

research include: What are the impediments for low-income, low-wealth Black families to hold

transaction accounts at financial institutions, and to what extent are there impediments to other

minorities of lower economic status? Second, the credit management success of low-income,

low-wealth Black families is equal to or greater than that of low-income, low-wealth White

families, based on three measures. Black families, however, owe less credit card and other

noncollateralized debt than White families, implying either that Black families have a lower

demand for such debt or that lenders are biased against them. The extent of bias against Black

borrowers is an ongoing area of research, and the results here show that the issue is relevant when

comparing the credit card and other noncollateralized debt of low-income, low-wealth Black and

White families. Third, wealth increases faster among poor White families faster than among poor

Black families because White families have more income from labor, gifts, and inheritances than

do Black families. This points to related topics for future research: To what extent does this

relationship hold for Black and White families across the income and wealth spectrum, and what

are the implications for the concentration of wealth? These results show that the lower wealth

gains of poor Black families are not due to inferior wealth management. If these relationships

hold generally for Black families compared to White families, the disproportionate concentration

of wealth among White families in the United States will continue for the foreseeable future.

24

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Ando, F. (1988). Capital issues and minority-owned businesses. Review of Black Political Economy, 16, 77-109.

Bates, T. (1997). Unequal access: financial institution lending to black- and white-owned small business start-ups. Journal of Urban Affairs, 19, 487-495.

Bates, T. & Bradford, W. (1992). Factors affecting new firm success and their use in venture capital financing. Journal of Small Business Finance 2, 23-38.

Becker, G. (1971). The Economics of Discrimination. Chicago: University of Chicago Press.

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Blau, F. & Graham, J. (1990). Black-White differences in wealth and asset composition. Quarterly Journal of Economics, 105, 331-339.

Bradford, W. (2000a). Black family wealth in 2000. In State of the Black Economy, 2000 (pp.103-145). New York: National Urban League.

Bradford, W. (2000b). The wealth dynamics of self-employment for Black and White families, Unpublished Working Paper, University of Washington.

Cavalluzzo, K. & Cavalluzzo, L. (1998). Market structure and discrimination: the case of small businesses. Journal of Money, Credit and Banking. 30, 771-792.

Cotton, J. (1998). On the permanence or impermanence of Black-White economic inequality. Review of Black Political Economy. 26, 47-56.

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Holzer, H. & Neumark, D. (2000). Assessing affirmative action. Journal of Economic Literature. 38, 483-568.

Hurst, E., Luoh, M. & Stafford, F. (1998). The Wealth Dynamics of American Families, 1984-1994 Brookings Papers on Economic Activities 1998:1, 267-329.

Hurst, Erick & Stafford, F. (1998). Grasshoppers and ants: mortgage refinancing and bankruptcy. Unpublished Working Paper, University of Michigan.

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Juster, F., J. Smith, J. & Stafford, F. (1999). The measurement and structure of household wealth. Labor Economics. 6, 253-275.

Ladd, H. (1998). Evidence on discrimination in mortgage lending. Journal of Economic Perspectives. 12, 41-62.

Menchik, P. & Jianakoplos, N. (1997). Black-White wealth inequality: is inheritance the reason? Economic Inquiry. 35, 428-442.

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Phelps, E. (1972). The statistical theory of racism and sexism. American Economic Review. 62, 659-61.

Quadrini, V. (1999). The importance of entrepreneurship for wealth concentration and mobility .Review of Income and Wealth. 45, 1-19.

Smith, J. (1995). Racial and ethnic differences in wealth in the health and retirement study Journal of Human Resources. 30, s158-s183.

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26

Appendix A: Description of Variables

Race: Black head of household = 1; White head of household = 0

Age: Years

Education of household head (spouse): Less than High School High School Only High School Plus College (No Degree) College Degree

Own home in 1989: yes = 1; no = 0

Health of head (spouse): excellent, very good or good = 1; fair or poor = 0

Type of Household:

Male single, divorced, or separated Female single, divorced, or separatedMarried couple not separated

Inheritances or gifts received, 1989-94: dollar amount

Children younger than 18 years old in residence: number

Number of dependents outside of family: number

Employment category: worker, retired, self-employed or unemployed

Public Assistance: If the household head or spouse received ADC/AFDC, SSI, or other welfare. yes = 1, no = 0

27

Appendix B: Various Statistics on PSID Family Income and Wealth

Median Income ($) Median Wealth ($) 1984 1989 1994 1999 1984 1989 1994 1999

22,000 27,590 32,899 41,300 31,350 38,000 49,200 58,185 % of Families Below Median:

Black FamiliesMarried Couples 44.8 44.1 41.8 44.0 65.9 62.4 67.4 71.9Single Males 79.3 81.1 78.9 77.4 91.8 90.1 87.9 89.9Single Females 91.1 88.6 90.5 88.9 87.3 87.5 83.5 87.9Group 73.5 74.6 75.5 73.5 80.7 80.9 78.5 83.8

White FamiliesMarried Couples 28.3 28.1 27.3 27.1 32.4 30.0 32.3 33.7Single Males 60.8 57.5 59.5 62.4 67.9 67.6 65.8 66.9Single Females 77.6 74.1 75.3 75.3 61.7 60.8 58.7 58.4Group 46.5 46.0 45.9 46.3 45.5 45.0 45.3 46.3

Dollars in Current Dollars. Sample weights are used in calculations.SOURCE: PSID Core and Supplemental Wealth Files

28

TABLE 1

Descriptive Statistics for Low-Income, Low-Wealth Families

Combined Data for 1984, 1989, 1994 and 1999

White White White Black Black Black

All White All Black Married Single Single Married Single Single

Families Families Couples Males Females Couples Males Females

Number of Families 4,361 5,858 1,787 1,034 1,540 1,396 1,283 3,179

Age (Years) 40.2 40.6 40.7 34.6 43.3 44.3 36.8 41.1

Standard deviation 76.9 36.4 64.1 64.6 91.3 31.7 29.9 39.7

Age, Head (%)

Less than 35 years 48.2 42.7 45.8 62.6 41.0 33.7 52.2 41.5

35 through 54 years 30.1 37.7 31.9 28.2 30.0 37.0 38.6 37.6

More than 54 years 31.7 19.6 22.3 9.2 29.0 29.4 9.2 20.9

Education, Head (%):

Less than high schol 29.1 40.4 34.5 21.9 29.8 49.9 33.2 40.5

High school only 36.6 34.6 40.3 33.8 35.7 37.3 33.3 34.4

College, no degree 19.7 19.2 15.7 25.5 18.9 9.5 26.2 19.2

College degree 14.6 5.8 9.5 18.9 15.6 3.3 7.3 6.0

Work Status, Head (%):

Retiree 15.2 16.6 18.0 10.0 16.4 26.2 16.1 14.4

Worker 65.3 57.5 65.4 72.2 61.0 60.8 62.5 54.8

Self-Employed 6.4 1.9 9.0 7.3 4.1 2.1 3.4 1.3

Unemployed 13.1 24.0 7.7 10.5 18.5 10.9 18.0 29.5

Number of Children 0.62 0.97 1.19 0.15 0.52 1.29 0.25 1.14

29

Standard deviation 5.06 3.25 5.07 2.87 5.10 2.85 1.81 3.59

Number of Dependents Outside 0.17 0.18 0.17 0.27 0.13 0.12 0.49 0.10

Standard deviation 3.52 1.52 3.26 3.96 3.56 0.95 2.43 1.12

Health Fair or Poor (%) 20.9 28.0 21.2 16.8 23.2 35.9 24.5 27.9

Own Home (%) 29.5 24.1 50.7 14.7 24.0 44.5 15.9 21.8

Means (1999 dollars):

Family Income 21,103 16,651 25,003 20,582 18,712 22,503 15,855 15,548

Family Wealth 9,889 7,047 12,668 7,163 9,654 13,072 7,168 5,482

Source: Author's calculations based on data from the PSID. Proportions may not add to 1.0 because of rounding.

30

TABLE 2

Percentage of Familiesa Holding Transaction Accounts and Noncollateralized Debt

by Wealth Quartile and Income Quartile

1. Transaction Accounts Top Bottom Quartile 1 Quartile 2 Quartile 3 Quartile 4 All Families

Black White Black White Black White Black White Black WhiteBy Family Wealth

% of Families in this Group 3.2 28.5 14.9 26.5 29.8 24.1 52.1 20.9 100.0 100.0

% of Families in this Group

with Transaction Accounts 87.6 97.1 72.9 92.5 61.3 86.0 30.1 63.9 47.6 86.3

By Family Income

% of Families in this Group 7.7 28.7 16.4 27.0 27.8 24.3 48.0 20.0

% of Families in this Group

with Transaction Accounts 88.5 96.3 76.4 91.9 58.3 85.4 25.0 65.4

2. Noncollateralized Debt

Quartile 1 Quartile 2 Quartile 3 Quartile 4 All FamiliesBlack White Black White Black White Black White Black White

By Family Wealth

% of Families in this Group 3.2 28.5 14.9 26.5 29.8 24.1 52.1 20.9 100.0 100.0

% of Families in this Group

with Noncollateralized Debt 51.9 39.1 55.9 52.1 49.0 58.6 39.8 63.3 45.3 52.3

By Family Income

% of Families in this Group 7.7 28.7 16.4 27.0 27.8 24.3 48.0 20.0

% of Families in this Group

with Noncollateralized Debt 68.2 56.1 61.2 58.2 49.4 51.9 33.8 39.5

31

N = 10,219

Note. The entire sample was separated into quartile rankings by either wealth or income and by Black family or

White family. The sample includes observations of individual family wealth or income in 1984, 1989, 1994, and

1999. SOURCE: PSID core and supplemental files, and the author's calculations. an = 10,219.

32

TABLE 3

Regression Models Explaining the Holding of Transaction Accounts at Financial Institutions by

Low-Income, Low-Wealth Families

1. Logistic Regressions of Whether Family Holds a Transaction Account

Combined Families Black Families White Families

Independent Variables Coefficient Chi-Square Coefficient Chi-Square Coefficient Chi-Square

Intercept -0.2965 45.6 *** -1.4969 187.4 *** -0.3437 47.5 ***

Black -1.1635 6,568.3 ***

Age 0.0016 7.0 *** -0.0077 32.4 *** 0.0027 15.5 ***

Age Squared -0.2368 278.7 *** -0.1798 43.9 *** -0.2574 235.0 ***

Education= Less than high school -0.5949 1,449.5 *** -0.3405 111.5 *** -0.6591 1,343.5 ***

Education= College, no degree 0.4891 772.9 *** 0.3281 99.0 *** 0.5618 696.6 ***

Education= College degree 1.2740 2,530.8 *** 1.2413 513.4 *** 1.2890 2,009.6 ***

Married 0.2399 147.0 *** 0.0562 1.7 0.2588 133.2 ***

Single Female 0.4647 785.5 *** 0.4233 166.3 *** 0.4815 618.2 ***

Number of Children -0.1786 692.9 *** -0.2380 378.0 *** -0.1494 321.9 ***

Public Assistance -0.6961 1,132.7 *** -0.4537 136.7 *** -0.7741 981.5 ***

Health Excellent Or Good 0.2505 216.6 *** 0.0042 0.1 0.3563 310.8 ***

Own Home 0.2239 180.5 *** -0.2122 34.9 *** 0.3391 316.4 ***

Real Income/$10,000 0.4938 5,028.2 *** 0.6703 2,426.5 *** 0.4299 2,768.0 ***

Real Wealth/$10,000 0.0580 261.9 *** 0.1947 359.5 *** 0.0401 150.4 ***

Retired 0.1028 8.5 *** 0.3716 14.5 *** 0.1594 16.7 ***

Worker -0.2380 63.5 *** 0.1410 2.5 -0.2665 68.9 ***

Unemployed -0.6756 417.1 *** -0.6424 45.5 *** -0.5915 267.1 ***

Year=1984 0.1831 116.3 *** 0.0964 8.3 *** 0.2236 126.9 ***

33

Year=1994 -0.5133 924.4 *** -0.6410 363.9 *** -0.4811 596.2 ***

Year=1999 0.0769 15.9 *** 0.1647 20.9 *** 0.0531 5.4 **

Minus 2 Log L 149,989*** 38,301*** 110,072***

N 10,042 5,560 4,482

2. OLS Regressions of the Dollar Amount of Transaction Accounts

Independent Variables Coefficient t-value Coefficient t-value Coefficient t-value

Intercept -311.7 -0.7 1,515.4 1.7 * -926.5 -1.5

Black -451.0 -2.4 **

Age 67.5 10.2 *** 0.7 0.1 76.4 9.3 ***

Age Squared -358.6 -2.3 ** -86.2 -0.4 -391.2 -2.0 *

Education= Less than high school -403.6 -2.1 ** 393.0 1.4 -504.7 -2.1 **

Education= College, no degree 477.0 2.7 *** -441.0 -1.7 * 668.2 3.0 ***

Education= College degree 959.8 5.0 *** 85.6 0.3 1,163.4 4.9 ***

Married -199.3 -1.0 -1,158.7 -3.4 *** -11.7 0.0

Single Female -145.9 -0.9 -690.8 -2.7 *** 31.4 0.1

Number of Children -171.7 -2.2 ** -1.3 0.0 -206.9 -2.0 **

Public Assistance -1,318.3 -4.5 *** -1,186.9 -3.2 *** -1,323.7 -3.4 ***

Health Excellent Or Good 149.3 0.7 23.4 0.1 242.4 0.9

Own Home -1,397.6 -8.5 *** -2,415.8 -9.3 *** -1,326.3 -6.5 ***

Real Income/$10,000 394.8 5.2 *** 269.8 2.4 ** 422.8 4.4 ***

Real Wealth/$10,000 388.3 16.4 *** 1,235.3 18.1 *** 350.2 12.5 ***

Retired -292.0 -0.8 63.1 0.1 -287.7 -0.6

Worker -677.2 -2.3 ** -577.6 -0.8 -613.3 -1.7 *

Unemployed -77.0 -0.2 -484.9 -0.6 23.3 0.1

Year=1984 -440.1 -2.5 ** -356.9 -1.3 -451.1 -2.0 **

34

Year=1994 290.4 1.5 1,152.6 4.2 *** 130.1 0.5

Year=1999 -49.9 -0.3 1,032.7 3.8 *** -219.1 -0.9

F-value 24.3 *** 18.9 *** 16.4 ***

Adjusted R2 0.13 0.23 0.13

N 4,671 1,745 2,935

*p < .10. **p < .05. ***p < .01.

SOURCE: PSID data and the author's calculations.

35

TABLE 4

Statistics on Credit Management, Wealth Management and Labor Income, 1989-94, for

Families That Were Low-Income, Low-Wealth in 1989

White Whit

e White Black Black Black

All White All Black Married Single Single Married Single Single

Families Families Couples Males Females Couples Males Females

Number of families 831 1,120 359 205 267 245 273 602

% Success Rates:

CRM1 78.7 76.7 73.8 76.7 83.7 75.1 78.6 76.3

CRM2 89.4 90.4 86.4 88.9 92.1 89.9 93.2 89.5

CRM3 95.7 96.8 94.7 95.5 96.7 94.4 99.1 96.6

WLM1 62.4 54.0 68.7 64.8 56.2 63.8 65.2 48.0

WLM2 61.3 53.8 65.8 64.2 56.0 63.4 64.9 47.8

WLM3 21.1 22.7 18.2 15.3 27.5 21.9 32.4 19.6

WLM4 20.6 22.6 17.7 15.3 26.6 21.5 32.4 19.5

% Received Gift/Inheritance, 1989-94 3.7 0.3 4.2 3.3 3.5 0.4 0.4 0.2

Dollars:

Mean Starting Wealth (1989) $9,630 $6,429 $12,345 $6,186 $10,045 $13,840 $6,473 $4,791

Mean Change in Wealth 1989-94 $27,068 $15,313 $25,066 $25,784 $29,483 $15,379 $20,459 $13,520

Mean Labor Income 1989-94 $60,164 $41,575 $76,759 $76,369 $36,845 $62,648 $50,295 $34,195

SOURCE: PSID core and supplemental wealth files and the author's calculations.

36

TABLE 5

Results of the Logistic Regressions

Credit Management, 1989-1994

Familiesa in the Low-Income, Low-Wealth Category in 1989

Dependent Variable: CRM1 CRM2 CRM3

Coefficient Chi-Square Coefficient Chi-Square Coefficient Chi-Square

Independent Variables:

Intercept -0.8001 33.2 *** 0.6348 11.2 *** 4.0387 64.5 ***

Black -0.0410 1.3 0.2608 26.2 *** 0.6358 62.3 ***

Married Couple -0.2836 20.5 *** -0.4691 32.6 *** 0.3097 6.3 **

Single Female -0.1542 13.5 *** -0.2278 16.1 *** 0.1657 3.6 *

Age 0.0291 313.2 *** 0.0371 241.7 *** 0.0147 19.3 ***

Age Squared -0.4228 161.1 *** -0.3118 50.5 *** 0.0619 0.7

Education of Head:

High school only 0.2964 55.8 *** 0.1084 4.1 ** -0.2192 7.6 ***

College, no degree 0.2048 18.9 *** -0.0448 0.5 -0.3542 14.0 ***

College degree 0.4512 50.7 *** -0.3589 20.4 *** 1.4600 51.6 ***

Own Home -0.6804 231.8 *** -0.6668 127.9 *** -1.0929 161.3 ***

Health Excellent or Good 0.1669 14.4 *** 0.0538 0.8 0.5416 40.9 ***

Number of Children -0.0588 14.1 *** -0.2391 150.5 *** -0.3065 108.9 ***

Number of Dependents Outside -0.2090 68.7 *** -0.2834 83.9 *** -0.0553 1.0

Worker 0.0055 0.0 -0.3271 9.8 *** -3.0388 45.1 ***

Retiree 0.7185 53.2 *** 0.1742 1.6 -1.8841 16.0 ***

37

Unemployed 0.5192 38.1 *** 0.4757 16.1 *** -1.1295 5.8 **

Received Gift/Inheritance,1989-94 -0.5568 38.6 *** 0.4846 9.0 *** 0.0789 0.1

Public Assistance 0.3510 40.6 *** 0.3521 21.4 *** -0.2973 7.5 ***

Education of Wife:

High school only 0.1855 9.1 *** 0.3937 24.3 *** 0.2008 3.0 *

College, no degree 0.3322 13.3 *** 0.3782 10.7 *** -0.2101 1.4

College degree -0.0839 0.4 0.6049 10.3 *** -1.1742 21.2 ***

Health of Wife Excellent Or Good 0.6822 73.6 *** 0.3340 9.5 *** 0.6869 22.9 ***

Transaction Account 0.0785 4.6 ** 0.0433 0.8 0.0727 1.0

Labor Income/$10,000 0.0014 0.1 0.0460 54.3 *** 0.0345 12.8 ***

(1989 Wealth)/$10,000 0.2808 324.1 *** 0.2281 122.0 *** 0.3025 88.3 ***

Minus 2 Log L 27,767*** 17,536*** 8,833***

aN = 1,882

*p < .10. **p < .05. ***p < .01.

SOURCE: PSID Data and the Author's calculations.

38

TABLE 6

Regression Models Explaining Noncollateralized Debt of Low-Income, Low-Wealth Families

1. Logistic Regressions of Whether the Family Owes Noncollateralized Debt

Combined Families Black Families White Families

Independent Variables Coefficient Chi-Square Coefficient Chi-Square Coefficient Chi-Square

Intercept -0.0510 1.5 -1.1371 109.5 *** 0.0372 0.6

Black -0.2423 294.8 ***

Age -0.0174 917.1 *** -0.0109 79.3 *** -0.0183 764.4 ***

Age Squared 0.1010 58.3 *** -0.0005 0.0 0.1489 89.1 ***

Education= Less than high school -0.2683 318.9 *** -0.1893 41.9 *** -0.3362 355.2 ***

Education= College, no degree 0.6066 1,388.3 *** 0.7594 592.8 *** 0.5520 815.4 ***

Education= College degree 0.8936 1,930.1 *** 1.4823 739.4 *** 0.7587 1,166.2 ***

Married 0.3529 363.5 *** 0.4371 115.0 *** 0.3319 246.3 ***

Single Female 0.2401 246.8 *** 0.3970 165.8 *** 0.2037 131.3 ***

Number of Children -0.0285 20.6 *** -0.0273 6.6 *** -0.0259 10.6 ***

Public Assistance -0.3549 314.7 *** -0.5182 250.9 *** -0.2816 120.2 ***

Health Excellent Or Good -0.1530 87.3 *** -0.2303 63.5 *** -0.1157 33.3 ***

Own Home 0.8741 2,647.8 *** 0.7941 532.8 *** 0.8532 1,863.5 ***

Transaction Account 0.7102 2,765.2 *** 0.7424 799.6 *** 0.7160 2,016.7 ***

Real Income/$10,000 0.2531 1,520.2 *** 0.1596 151.1 *** 0.2714 1,273.9 ***

Real Wealth/$10,000 -0.3808 6,302.2 *** -0.3308 952.8 *** -0.3889 5,178.6 ***

Retired -0.0358 1.2 1.0769 118.4 *** -0.3034 66.9 ***

Worker -0.0046 0.0 0.6470 48.5 *** -0.0682 5.4 **

Unemployed -0.2772 81.5 *** 0.3049 10.2 *** -0.2738 64.3 ***

39

Year=1984 -0.1824 134.4 *** -0.0471 2.3 -0.2284 153.9 ***

Year=1994 -0.1725 116.9 *** -0.3615 141.4 *** -0.1128 35.5 ***

Year=1999 -0.1728 95.1 *** -0.3898 129.1 *** -0.1116 28.3 ***

Minus 2 Log L 171,182*** 45,663*** 124,238***

N 10,042 5,560 4,482

2. OLS Regressions of the Dollar Amount of Noncollateralized Debt

Independent Variables Coefficient t-value Coefficient t-value Coefficient t-value

Intercept 3,536 3.0 *** -474 -0.3 4,038 2.5 **

Black -1,291 -3.2 ***

Age 95 5.6 *** 80 5.0 *** 82 3.5 ***

Age Squared 370 1.0 -16 -0.1 868 1.7 *

Education= Less than high school -731 -1.6 -174 -0.4 -852 -1.3

Education= College, no degree 799 1.9 * 833 2.2 ** 572 1.0

Education= College degree 1,314 2.8 *** 2,346 4.7 *** 1,114 1.8 *

Married 2,226 4.6 *** 1,239 2.4 ** 2,527 3.9 ***

Single Female 171 0.4 -789 -2.0 ** 603 1.1

Number of Children -598 -3.5 *** 226 1.7 * -919 -3.7 ***

Public Assistance -1,255 -2.0 ** -439 -1.0 -1,877 -2.0 **

Health Excellent Or Good 125 0.3 -75 -0.2 -122 -0.2

Own Home 10,358 26.2 *** 3,441 8.8 *** 11,003 20.2 ***

Transaction Account 2,060 5.3 *** 1,094 3.4 *** 1,889 3.4 ***

Real Income/$10,000 1,430 8.2 *** 850 5.2 *** 1,489 6.2 ***

Real Wealth/$10,000 -6,247 -120.3 *** -2,352 -24.2 *** -6,454 -98.4 ***

Retired -5,333 -5.6 *** -1,217 -0.8 -5,570 -4.4 ***

Worker -6,157 -8.4 *** -997 -0.7 -6,371 -6.8 ***

40

Unemployed -4,678 -5.5 *** 226 0.2 -4,813 -4.3 ***

Year=1984 -1,264 -2.9 *** -1,703 -4.5 *** -1,038 -1.7 ***

Year=1994 1,887 4.4 *** 1,312 3.4 *** 2,180 3.6 ***

Year=1999 2,806 6.1 *** 1,384 3.3 *** 3,231 5.1 ***

F-value 732.8 *** 39.3 *** 512.4 ***

Adjusted R2 0.78 0.30 0.80

N 4,368 1,893 2,475

*p < .10. **p < .05. ***p < .01.

SOURCE: PSID Core and Supplemental Wealth Files and the author's calculations.

41

TABLE 7

Results of Logistic Regressions

Wealth Management, 1989-1994

Families in the Low-Income, Low-Wealth Category in 1989

Dependent Variable: WLM1 WLM2 WLM3 WLM4

Chi- Chi- Chi- Chi-Coeff. Square Coeff. Square Coeff. Square Coeff. Square

Independent Variables:

Intercept -0.3563 9.5 *** -0.3072 7.1 *** -3.7988 575.9 *** -4.1266 654.0 ***

Black -0.2099 49.7 *** -0.2002 45.4 *** -0.0026 0.0 0.0205 0.3

CRM1 0.0523 2.9 * 0.0333 1.2 0.1379 9.8 *** 0.1779 15.6 ***

Single Male -0.3175 34.6 *** -0.1587 8.9 *** 0.5291 59.6 *** 0.5255 57.5 ***

Single Female -0.6570 165.2 *** -0.4718 87.7 *** 0.1091 2.9 * 0.0262 0.2

Age -0.0051 19.7 *** -0.0067 34.6 *** 0.0239 279.2 *** 0.0254 303.7 ***

Age Squared -0.0654 4.6 ** -0.0617 4.2 ** 0.1206 7.8 *** 0.1953 19.9 ***

Education of Head:

High school only 0.0801 6.5 ** 0.0539 2.9 * -0.3500 72.7 *** -0.4293 105.0 ***

College, no degree 0.3792 89.5 *** 0.3438 74.3 *** -0.0297 0.3 -0.0029 0.0

College degree 0.4964 97.6 *** 0.4063 67.1 *** -0.0364 0.3 -0.0150 0.0

Own Home 0.8540 557.1 *** 0.8521 561.0 *** 0.5156 127.9 *** 0.6210 178.4 ***

Health Excellent or Good -0.0692 4.2 ** -0.0971 8.4 *** -0.2369 37.1 *** -0.1840 21.8 ***

Number of Children -0.0965 47.3 *** -0.0863 38.4 *** 0.0846 21.1 *** 0.0839 20.1 ***

Number of Dependents Outside 0.2422 88.4 *** 0.2491 94.8 *** 0.2881 108.0 *** 0.2677 90.3 ***

Worker 0.4703 49.9 *** 0.4897 55.3 *** 0.0685 0.4 0.0211 0.0

Retiree 0.0479 0.4 0.1028 1.9 1.4254 173.5 *** 1.4625 180.8 ***

42

Unemployed 0.1312 3.4 * 0.1225 3.0 * 1.1032 110.3 *** 1.1074 110.1 ***

Education of Wife:

High school only 0.0024 0.0 0.0593 1.2 0.1990 8.3 *** 0.2127 9.3 ***

College, no degree -0.4498 33.8 *** -0.4937 42.1 *** -0.0036 0.0 -0.4480 10.6 ***

College degree 0.3408 6.8 *** 0.2744 4.9 ** -0.6688 10.0 *** -0.6910 10.6 ***

Wife's Health Excellent Or Good 0.2511 14.6 *** 0.1199 3.4 * 0.1918 5.8 *** 0.3182 15.3 ***

Transaction Account 0.2096 49.4 *** 0.2170 53.4 *** -0.1344 12.1 *** -0.1556 15.8 ***

Public Assistance 0.3397 68.3 *** 0.3368 67.6 *** 0.2806 35.9 *** 0.2994 40.1 ***

Bottom Wealth Quartile, 1989 0.6341 361.7 *** 0.6268 357.0 *** 0.2669 34.8 *** 0.3655 62.2 ***

Minus 2 Log L 37,858*** 38,196*** 24,987*** 24,328***

N 1,951

*p < .10. **p < .05. ***p < .01.

SOURCE: PSID core and supplemental wealth files and the author's calculations.

43

TABLE 8

OLS Regressions of Wealth Management,1989-94

Families in the Low-Income, Low-Wealth Category in 1989

Dependent Variable: Change in Wealth 1989-1994 (Dollars)

Model 1 Model 2 Model 3 Model 4t- t- t- t-

Coeff. value Coeff. value Coeff. value Coeff. valueIndependent Variables:

Intercept 66 0.0 -8,063 -1.2 -429 -0.1 -8,414 -1.2

Black 2,285 1.2 2,557 1.4 2,250 1.2 2,526 1.4

CRM1 1,901 1.0 1,966 1.1 2,185 1.2 2,200 1.2

Married Couple -739 -0.2 *** -1,178 -0.4 -698 -0.2 -1,141 -0.4

Single Female -8,296 -4.0 *** -6,405 -3.1 *** -8,457 -4.1 *** -6,552 -3.1 ***

Age 39 0.6 127 1.8 * 58 0.8 142 2.0 **

Age Squared 2,764 1.5 2,454 1.4 2,715 1.5 2,416 1.3

Education of Head:

High school only 3,755 1.9 * 2,359 1.2 3,971 2.0 ** 2,548 1.3

College, no degree 7,445 3.1 *** 6,314 2.6 *** 7,599 3.1 *** 6,449 2.7 ***

College degree 16,090 5.4 *** 10,856 3.6 *** 16,186 5.4 *** 10,972 3.6 ***

Own Home 4,925 2.7 *** 3,733 2.1 *** 6,719 3.1 *** 5,222 2.4 **

Health Excellent or Good -4,303 -2.1 ** -5,293 -2.6 ** -4,093 -2.0 ** -5,112 -2.5 **

Number of Children -1,686 -2.0 *** -1,361 -1.6 -1,705 -2.0 ** -1,379 -1.6

Number of Dependents Outside 6,225 4.4 *** 6,134 4.4 *** 6,400 4.5 *** 6,279 4.5 ***

Worker 3,903 1.0 563 0.1 3,600 0.9 336 0.1

Retiree -945 -0.2 2,804 0.6 -1,387 -0.3 2,412 0.5

Unemployed 4,565 1.1 6,286 1.5 4,215 1.0 5,985 1.4

44

Gift or Inheritance 0.56 5.5 *** 0.51 5.0 *** 0.56 5.5 *** 0.51 5.0 ***

Labor Income 0.16 6.8 *** 0.16 6.8 ***

Starting Wealth -0.11 -1.5 -0.09 -1.3

Education of Wife:

High school only 1,078 0.3 -670 -0.2 807 0.3 -881 -0.3

College, no degree -4,956 -1.0 -9,144 -1.9 * -5,056 -1.1 -9,197 -2.0 *

College degree 1,545 0.2 -5,661 -0.8 1,452 0.2 -5,686 -0.8

Wife's Health Excellent Or Good 5,258 1.3 4,944 1.2 5,115 1.3 4,827 1.2

Transaction Account 3,079 1.7 * 704 0.4 3,442 1.9 * 1,022 0.6

Public Assistance 524 0.2 1,146 0.5 410 0.2 1,048 0.4

Adjusted R2 0.08 0.10 0.08 0.10

F-Statistic 7.9 *** 9.7 *** 7.6 *** 9.4 ***

N 1,896

*p < .10. **p < .05. ***p < .01.

Note. Cases are limited to those whose initial wealth and change in wealth is between -$200,000 and

$1,200,000. SOURCE: PSID core and supplemental wealth files and the author's calculations.

45

46