RACIAL INEQUALITY IN THE UNITED STATES: ANALYZING THE
WEALTH GAP
A Thesis
Submitted to the Faculty of the
Graduate School of Arts and Sciences
of Georgetown University
in partial fulfillment of the requirements for the
degree of
Master of Public Policy
By
Shehnaz Jagpal, B.A.
Washington, DC
April 10, 2007
TABLE OF CONTENTS
Introduction……………………………………………………………………………….1
Literature Review……………………………………………………………………...….3
Data analysis and research design………………………………………………..………12
Hypothesis, Data sources and methodology…………………………………..…………13
Limitations………………………………………………………………….……………16
Determinants of racial inequality……………………………………….………………..19
Income and age…………………………………………………………………..21
Educational achievement and net worth………………………...……………….22
Life opportunities: the example of the U.S criminal justice system……………..23
The concentration of wealth and intergenerational disadvantages…...………….25
Wealth, gender and marital status……………………..…………………………27
Education and income……………………………………………………………29
Occupational categories and race…………………………...……………………31
Analysis…………………………………………………………..………………………35
Policy Implications………………………………………………………………………43
Conclusion……………………………………………………………………………….47
Appendix A: Determining the Proper Model and Specification...………………...…….49
Appendix B: Regression Diagnostic Graphs.……………………………………………50
Appendix C: Multicollinearity and Correlation Coefficients……………………………60
References………………………………………………………………………………..63
ii
RACIAL INEQUALITY IN THE UNITED STATES: ANALYZING THE
WEALTH GAP
Shehnaz Jagpal, B.A.
Thesis Advisor: Robert W. Bednarzik, Ph.D.
Abstract
Recent analyses of economic well-being by race have shifted their focus from
income inequality to the wealth gap. Dr. Melvin L. Oliver and Dr. Thomas M. Shapiro’s
pioneering work in Black Wealth/White Wealth: New Perspective on Racial Inequality
(1995) suggests that public policies designed to achieve racial equality by closing the
income gap are insufficient to address the depth of inequality revealed by the wealth gap.
This thesis builds on their work, using 2001 data from the Survey of Income and Program
Participation (SIPP) and an adapted model to gauge changes in wealth holdings during
the 1990’s. Key findings include the negative association between race and gender in
acquiring wealth; African-Americans, especially single-parent females, are
disproportionately marginalized in wealth accumulation. Policy recommendations
contribute to emerging deliberations on asset-based welfare policy and revising
affirmative action criteria to account for class status. Specific instruments to advance
low-income homeownership and provide opportunities to increase asset ownership for
poor women are suggested as means to achieve racial equality.
iii
Introduction
Concentrated poverty in urban areas is one of the most often cited causes of the
persistence of an African-American ‘underclass’ (Massey & Denton, 1993). Income
inequality rose in the United States between 1970 and 2001. This is evidenced by the
Gini coefficient, which measures income concentration, and it rose from 0.40 in 1970 to
0.47 in 2001 (Census Bureau, 2001). Yet, income inequalities alone do not account for
the persistence of the racial divide in the United States. Keister and Moller (2000)
demonstrate the limitations of analyzing disparities in income by highlighting starkly
varying U.S. Gini coefficients for wealth (0.84) as opposed to income (0.52) in 1989.
They and others (Conley, 1999a; Hao, 2004; Oliver & Shapiro, 1995) argue that analyses
of wealth are thus fundamental to developing appropriate policy options to confront racial
inequality.
Numerous advantages are correlated with wealth acquisition, which cannot be
realized by gains in income alone; for example, the ability of better-educated parents with
significant liquid assets to provide funds for a college education or a down payment for a
house. Acquiring wealth offers the opportunity to break the cycle of poverty. And,
because sizeable wealth is directly transferable from one generation to the next, scholars
conclude that substantial wealth assures that life opportunities and social position remain
within a family over long periods (Conley, 1999a; Oliver & Shapiro, 1995, 2006).
1
Although income is a necessary condition for generating wealth, it is not a
sufficient one. Indeed, the correlation between income and wealth holdings is
surprisingly weak. Lerman and Mikesell (1988) found that when asset-income, i.e.,
income generated from wealth, was excluded, the correlation between income and wealth
was only 0.26. This implies that income-based explanations are deficient in accounting
for inequality of opportunity across races (Keister & Moller, 2000). It also implies that
policies that seek to address racial inequalities by narrowing income disparities are
insufficient.
Oliver and Shapiro (1995) broke new ground in discussions of racial inequality in
the United States by analyzing wealth inequality. Using data from the 1987 Survey of
Income and Program Participation (SIPP), they conducted multiple regression analyses
with income, net worth (NW) and net financial assets (NFA) as the dependent variables.
They found that NFA is the best indicator of control over future financial resources while
NW more accurately indicates the intergenerational transmission of wealth. They also
conclude that blacks’ claim to middle-class status relies on income and not assets. For
example, they found that the NW of African-Americans was far less than the NW of
whites; and especially that middle-class blacks owned only 15 cents for each dollar of
NW owned by whites in the same income class.
This thesis builds on Oliver and Shapiro’s work using an adapted model and more
recent SIPP data with the goals of: 1) seeing if patterns they identified continue to apply
2
and 2) recommending policy options to close the racial wealth divide. Given their
findings about the black and white middle classes, this thesis will assess NW and NFA
changes for these groups. If wealth disparities persist, it is important to develop policies
that enhance current economic, social and human capital among disadvantaged groups.
Literature Review
This literature review demonstrates the rationale for studying wealth in addition to
income with an eye toward generating policy solutions that address racial inequality more
comprehensively. Home equity represents the majority of NW for the American middleclass; this is especially so for middle-class blacks (Oliver & Shapiro, 1995). The review
thus looks first at studies of homeownership and the evolution of the African-American
middle-class. Because of the inability of income analysis to explain differences in blackwhite life opportunities, it looks next at the cross-generation disadvantages associated
with minority wealth accumulation. For example, Spriggs (2004) suggests that wealth
inequalities reflect the cumulative income differences from one generation to the next.
Finally, it reviews assessments of recent Federal housing policies which aim to decrease
racial inequality and considers their effects on the black-white wealth gap.
3
Progress of wealth analyses: why study wealth versus income
Wealth data to evaluate the economic conditions of African-Americans were
unavailable until the 1967 Survey of Economic Opportunity (SEO). The first
comparative analysis of wealth accumulation by race was done in 1971 by Henry Terrell.
His findings indicated that ongoing discussions of income inequalities understated the
level of economic inequality for blacks significantly. Regardless of income levels, black
households bore the burden of historically lower average incomes compared with whites
of similar backgrounds. Terrell’s work brought attention to the fact that the history of
disparities in opportunity has major consequences for addressing income inequalities
(Terrell, 1971).
Oliver and Shapiro (1990) echoed this view, showing that at least one-third of
U.S. households are asset poor. They suggested that social welfare and redistributive
policies founded on income analyses radically underestimated racial inequalities. In
addition, Charles and Durst’s (2002) analysis of wealth inequalities by race, as evidenced
by the transition to homeownership, suggests disadvantages faced by blacks because of
the lack of access to intergenerational transmission of wealth. Similarly, Conley (1999b)
argues that resulting class differences from historically-rooted family wealth differences
are difficult to address because they are the outcomes of past economic trends. Keister
and Moller (2000) clarify some of the intergenerational processes and their impact on
wealth accumulation. They identify cultural capital, transferred through education and
4
other life experiences, as an important asset in wealth accumulation. Based on
limitations of each type of transfer in the black community, they agree with Oliver and
Shapiro’s conclusion that wealth inequalities will persist for future generations.
Blau and Graham (1990) established the parameters for wealth analysis theory.
They analyzed wealth data from the late 1970’s of younger households in order to
determine the composition of wealth for different groups. Controlling for racial
differences in income and a range of demographic factors, their analysis found that over
three-quarters of the wealth gap between blacks and whites remained unexplained. They
hypothesized that policies developed to address income differences would not suffice to
narrow or close the racial divide where wealth inequalities are larger than income
differences.
Oliver and Shapiro (1995) expand this theory by adding sociological factors. The
authors contend that social context creates radically different investment opportunities for
blacks and whites. They use a three-staged approach to answer the question of why
wealth accumulation varies radically by race. Stage one assesses the explanatory power
of human capital, labor market, and sociological factors; the objective is to analyze how
education and income, for example, work together to explain racial wealth differences.
Stage two probes the role of policy and institutional discrimination in wealth disparities.
Following Cose (1993), they note how residential segregation limits minorities’ access to
high-quality schools and jobs. The third stage examines the intergenerational
5
transmission of inequality, demonstrating how historic racial inequality is reproduced
across generations.
Wolff (1998) expands on the theory of wealth analysis, suggesting that wealth
analysis provides a more comprehensive understanding of economic disparities because:
(1) liquid assets which can be converted into money are a source of consumption funds
independent of current income; (2) homeownership provides more fiscal options to the
owner; (3) times of economic crisis can be managed more easily by households with
access to liquid assets, and; (4) there is a relationship between the distribution of power
and wealth.
In contrast, Conley (1999a) acknowledges the persistence of racial inequality but
suggests that “the consolidation of race and class is the fundamental problem” (p. 23).
Conley’s analysis of net worth begins with a full model that includes race and class as
explanatory variables. He finds that parental wealth is the strongest predictor of his
“wealth-inclusive class model”1 and importantly, that the impact of race disappears when
parental class is controlled for. In short, Conley argues that public policies to address the
wealth gap should change their focus from race to class.
1
Conley’s net worth model begins with simple comparisons of young black and white families. He
controls for income, age, gender, and education and finds dramatic differences by race. Next, he controls
for parental class status, and the impact of race becomes insignificant. Controlling for parental net worth in
his full model, neither race nor class are significant, suggesting the importance of inheritances in the
transmission of wealth within a household (Conley, 1999a: 47).
6
Homeownership and the evolution of the African-American middle class
Massey and Denton (1993) made a significant contribution to our understanding
of racialized economic disparities and persistent residential segregation by showing that
the evolution of the ghetto is the result of deliberate public policies. However, later
analyses of private wealth delve deeper. Oliver and Shapiro (1995) argue that differential
mortgage rate approvals by race offer evidence of institutionalized racism affecting home
ownership. Charles and Hurst (2002) offer supporting evidence in a recent analysis of
the transition to homeownership. Following a sample of black and white renters, the
authors studied racial differences in the likelihood of being approved for a mortgage.
They found significant evidence signaling that African-Americans are twice as likely as
comparable white households to have mortgage applications rejected. This conclusion
controls for credit histories and other proxy indicators of wealth, suggesting racialized
home-lending practices. In a similar vein, Jackman and Jackman (1980) used national
survey data to study racial inequality in homeownership. Controlling for socioeconomic
characteristics and family composition, their analysis indicated that the probability of
homeownership is significantly lower among African-Americans and whites of
comparable backgrounds. They argued that the impediments blacks confront in the
housing market severely restrict the possibility of improving their living standards.
Kenneth Jackson (1985) argued that the most salient outcome of the ghetto is the
impact on the black middle class, which did not enter the housing market on the same
7
terms as middle class whites. Jackson attributes this difference partly to policies of the
Federal Home Owner’s Loan Corporation (HOLC). Established in 1933, the HOLC
granted long-term mortgage loans to prevent foreclosure of urban homes until 1936.
Jackson contends that the system of standardized property appraisals used by the HOLC
institutionalized racist lending practices. Individual and community loans were provided
on the basis of qualitative assessments of properties which included the racial
composition of neighborhoods. With the infamous practice of redlining, mixed race and
predominantly black neighborhoods were characterized as undesirable and not fit for
investment. Thus, blacks’ access to Federal mortgage monies for homes in the suburbs
was severely limited.
More recently, although Alba et al. (2002) demonstrated that while suburban
African-Americans are more integrated than central city blacks, a comparison of middleclass blacks and whites found no point at which the two groups were on par in terms of
location. Middle-class African-Americans tend to live in more ethnically diverse
neighborhoods but their white neighbors are often poorer than they are. These findings
are consistent with Massey and Denton’s (1993) research which concluded that
residential segregation systematically undermines the socioeconomic health of AfricanAmericans.
8
Recent Federal housing policy: impact on the wealth gap
Let us turn to recent changes in Federal housing policy to see whether policies
that foster mixed-income communities are a viable option for addressing racial
imbalances in the United States. Since 1992, HUD has awarded billions of Federal
dollars through Housing Opportunities for People Everywhere (HOPE VI) grants. The
objective of HOPE VI grants is to alleviate concentrated poverty by supporting mixedincome communities in formerly blighted neighborhoods. Evaluations of the program
remain limited. Despite some successes, several policy-relevant issues remain
unanswered. These include appropriate targeting of limited resources and limited
opportunities and choices faced by public housing residents (Popkin et al., 2004).
A related measure, the Quality Housing and Work Responsibility Act of 1998,
includes provisions to reduce concentrated poverty, promote economic self-sufficiency,
improve public housing management via deregulation, rehabilitate or replace existing
public housing facilities and streamline Section 8 voucher programs. Yet, the impact of
this Act is also questionable. For example, although data suggest that concentrated
poverty is being alleviated because of the Act’s income-based targeting, it is unclear
whether self-sufficiency can be improved by this policy. Solomon (2005) found that
while the number of working residents in public housing neighborhoods increased,
incomes did not increase significantly. As such, improvements in self-sufficiency may
owe more to the improving economy than to changes resulting from the Act.
9
Other Federal initiatives to promote mixed-income communities include tax
credits for low income housing and community development block grants. However, the
feasibility of these policies is also unclear because research on preconditions of these
programs, including the economic background of residents, remains limited (Schwartz &
Tajbakhsh, 1997).
In general, effects of current Federal housing policies are unclear. Nonetheless,
Conley (1999b) suggests that public policy shift its current focus on affirmative action in
education and employment to promoting asset equality. He also recommends including
net worth in determining class status for class-based quotas.
Recent developments in wealth analysis and gaps in the literature
Oliver and Shapiro recently issued a tenth anniversary edition of their original
work, but they did not update their regression analyses. The 2006 edition includes two
additional chapters. The first gauges changes in wealth since 1995 using data from
various sources, including the Urban Institute, the Black Investor Survey and the Joint
Center for Housing Studies. These recent studies indicate that although blacks made
notable gains in wealth accumulation during the 1990’s, the recession and collapse of the
stock market in the early 2000’s effectively erased most of the progress. The data also
suggest improvements in the homeownership gap: black homeownership increased by
17.3 percent from 1995 to 2004, reducing the homeownership gap from 28.5 percent to
10
26.2 percent (Oliver & Shapiro, 2006: 213). However, gains in homeownership and
increased access to credit were mitigated by sharp increases in debt. Moreover, because
blacks and Hispanics continued to face subprime lending, Oliver and Shapiro suggest that
minorities appear to be converting current debt into mortgage debt (Oliver & Shapiro,
2006:216). In short, they argue that macroeconomic shocks negatively impacted gains in
asset accumulation during the 1990’s, returning minority net worth to its status in the mid
1980’s. 2 Indeed, a larger portion of African-American families had zero or negative net
worth in the early 2000’s compared to the late 1980’s. Median black net worth peaked at
$8,774 in 1999 but declined to $5,988 by 2002, effectively reverting to the levels
observed in 1987 (Oliver & Shapiro, 2006: 215).
Given the persistence of racial wealth inequalities, it is timely to revisit and revise
the original model developed by Oliver and Shapiro and use updated SIPP data from
2001 to conduct a similar analysis. The adapted model used here analyzes income, NFA
and NW as the dependent variables with race, gender, income and age as the main
explanatory variables. Multiple regression analysis suggests that the adapted model has
higher explanatory power than Oliver and Shapiro’s model, especially for NFA. As such,
this model could serve as a springboard for future analyses of the racial wealth gap.
2
The second chapter expands on the idea of asset-based policy, first articulated by Michael Sherraden
(1991) and briefly discussed in the first edition of Oliver and Shapiro’s book.
11
Data analysis and research design
As discussed in the literature review, the model developed by Oliver and Shapiro
(1995) serves as the basis for the relationships examined in this thesis. Using 2001 data
from the Survey of Income and Program Participation (SIPP), the present study adapts
their model with the goal of assessing changes in the wealth gap during the 1990’s.
Preliminary analysis of the 2001 SIPP data suggests the continuing importance of age and
income in analyzing wealth. Race, gender and marital status are important factors in
determining income and consumption, and thus wealth. These relationships are analyzed
in the following section, based on the 2001 SIPP data. The relationships among
education, occupation and wealth are also analyzed. The model expands on Oliver and
Shapiro’s (1995) model by including the type of household as an explanatory variable.
Policy recommendations build on emerging asset-based policy and suggest ways to
accelerate implementation. The following section provides details about the data and
models used to analyze wealth during the 1990’s using the third wave of the 2001 SIPP.3
3
The SIPP was developed in the late 1970’s as a necessary complement to the Current Population Survey
(CPS). The CPS is a cross-sectional database, and respondents were asked to recall their income and
participation in Federal programs each March. In response to data collection issues with the CPS, the
Department of Health, Education and Welfare (HEW) began the Income Survey Development Program
(ISDP), a longitudinal survey, in which respondents were contacted every three months to track labor force
participation and also to capture assets and liabilities data. The ISDP evolved into the SIPP, which was
revised significantly in 1996. SIPP respondents are contacted every three months, and each household
member older than 15 years is interviewed. Each household is contacted four times for lengthy interviews,
making the three-month reference period cumbersome. As such, as of this writing, the Census Bureau is
considering revising or replacing the SIPP.
12
Hypothesis, Data Sources and Methodology
This section presents the hypothesis plus the data and model used to test them.
Three measures of wealth will be examined:
1. Income
2. Net worth (NW)
3. Net financial assets (NFA)
An analysis of income offers a baseline to gauge net worth and net financial assets
(Oliver & Shapiro, 1995). They hypothesize that NFA is the most accurate predictor of
control over future financial resources and that NW is a better tool to measure the
intergeneration transfer of wealth, i.e., inheritances. Using their framework, the
following hypotheses about wealth in the 1990’s will be tested:
H1 (a): Race is positively correlated with wealth inequalities in the United States
H1 (b): Age is negatively correlated with NW and NFA, and positively correlated
with income.
H1(c): Female-headed, single-parent households are less likely to acquire private
wealth
Ordinary least squares (OLS) regression analysis will be used to test these
hypotheses. The regression analysis is based on the model developed by Oliver and
Shapiro (1995) and pursuant work about wealth inequalities and their impact on public
policy (Conley, 1999a; Keister & Moller, 2000; Darity & Frank, 2003). By using data
from the 2001 SIPP, this analysis will allow for comparisons to Oliver and Shapiro’s
findings. Besides using more current data, this paper builds on Oliver and Shapiro’s
work by adding a dummy variable for household type. A comparison of white and black
13
female-headed households will also add to the previous work by providing a fuller
picture of the impact of gender and marital status on wealth accumulation and life
opportunities. 4
Exhibit 1: Variable Definitions and Sources
VARIABLE
THTOTINC
Net worth
NFA
Race
Region
Education
Age
Age sq
THTOTINC
Gender
RNFKIDS
EMS
RHTYPE
TJBOCC1
DEFINITION
Total household income5
Net Worth (Total household assets minus total household
debt)
Net Financial Assets (Total household wealth, minus home
and vehicle equity)
White or black
FIPS State Code. SIPP state codes are broken down into
the four standard U.S. regions used by the Census Bureau:
NE, MW, South, West
Less than HS, High School, College, College plus
Age as of last birthday
Age*Age
Total monthly household income
Male or female
Total number of children under 18 in family
Marital Status
Household head
Occupation
SOURCE
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
SIPP
Based on the literature review and theory, the hypothesized relationships between
the dependent and independent variables are summarized in Exhibit 2:
4
Oliver and Shapiro’s model includes a variable which measures the marginal impact of being widowed on
NW and NFA. The model in this thesis will look more specifically at the differences in asset accumulation
by marital status of the head of household, i.e., being married or a single man or woman householder.
While their work analyzed these differences in separate regressions, household type was not among their
explanatory variables for measuring wealth.
5
This variable takes on negative values in some cases because it includes a measure of total household
property income, which can be negative. The other two variables captured in total household income are
earned income and total ‘other’ income, which include only positive values.
14
Exhibit 2: Variable Description, Predicted Relationship, Rationale
VARIABLE
DESCRIPTION
PREDICTED
RELATIONSHIP
N/A
Y1
THTOTINC
Continuous
Y2
Y3
X1
X2
Net worth
NFA
Race
Region
Continuous
Continuous
Dummy
Dummy
X3
X4
X5
X6
X7
X8
X9
X10
Education
Age
Agesq
THTOTINC
Male
RNFKIDS
EMS
RHTYPE
Dummy
Continuous
Continuous
Continuous
Dummy
Continuous
Dummy
Dummy
N/A
N/A
Negative
Negative (for
South)
Positive
Negative6
Positive7
Positive
Negative
Negative
Negative
Negative
X11
TJBOCC1
Categorical
Positive
RATIONALE/
PREVIOUS STUDIES
Income regression provides
baseline to gauge trends in
wealth, Oliver & Shapiro,
1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Oliver & Shapiro, 1995
Theory (Conley, 1999a;
Hao, 2004)
Oliver & Shapiro, 1995
The data for this model will come from the third wave of the 2001 SIPP. Wave 3
includes information about assets and liabilities and thus provides relevant information
for wealth analysis. The two main dependent variables are defined as follows:
1. Net worth (NW) is the value of total assets, including home and vehicle equity,
minus all debt
2. Net financial assets (NFA) are the total available household financial resources,
i.e., total household wealth, excluding home and vehicle equity
The main difference between NW and NFA is that NFA excludes vehicle and
home equity. The independent variables are drawn from previous studies and theory
(Blau & Graham, 1990; Oliver & Shapiro, 1995; Keister & Moller, 2000). Race is
6
7
Coefficient on Age is positive only for THTOTINC regression
Coefficient on Agesq is negative only for THTOTINC regression
15
predicted to be negatively correlated with wealth. Income, age, gender and race are
expected to be the most significant variables in determining wealth holdings. Because of
the prevalence of female-headed households in the black community, data on black
women’s wealth will be included in the analysis. Oliver and Shapiro (1995) compared
single households by race and found that single white households had a median NW of
$20,083 while single black households had NW of only $800. They also compared
median NW by gender and marital status for whites and blacks. White female-headed
households had a median NW of $23,530 compared to $500 for black female-headed
households. Divorced white households had a median NW of $14,342 while divorced
black households had only $1,373. These dramatic differences provide a useful
benchmark to assess changes in the racial wealth gap.
Limitations
Although the model described was adapted from Oliver and Shapiro’s (1995)
work, it has limitations. As several analysts of wealth note, it is difficult to quantify the
historical role of the intergeneration transmission of poverty or wealth (Conley, 1999a;
Hao, 2004; Oliver & Shapiro, 1995). However, because wealth data became more
readily available with the initiation of the SIPP, wealth analysis helps to gauge the
historical consequences of inequality more accurately than income data. The SIPP tends
to over-sample the poor, which allows for analyses of wealth beyond the limited sample
of the Survey of Consumer Finances (SCF) but this feature of the data is also
16
problematic. Many households in the sample have either zero or negative values for net
worth and net financial assets, presenting a challenge for appropriate model specification.
Determining the appropriate functional form to minimize loss of data is a challenge, but
one that is addressable by following other analyses of wealth using SIPP data (Hao,
2004).
This analysis seeks to gauge the impact of inheritances on wealth to better
understand their role in the intergenerational transmission of wealth. However, data on
inheritances are not readily available in the SIPP (Hao, 2004), so it is not feasible to
account for the marginal impact of this important factor. Conley’s (1999a) critique of
Oliver and Shapiro’s (1995) methodology applies to this variable and their model,
including the adapted one tested here, more generally. He highlights the limitations of
using cross-sectional data from the SIPP versus longitudinal data from the Panel Study of
Income Dynamics (PSID). Since 1984, the PSID tracks families that were in the original
1968 sample, allowing assets and liabilities to be reviewed every five years. Conley
(1999a) argues that this feature of the PSID allows researchers to study changes in wealth
over time. “…by putting parental wealth into a model as a contributing factor, we can
determine a ‘truer’ net effect of race in determining net worth or other socioeconomic
outcomes” (Conley, 1999a:46). In short, the argument is that controlling for parental
class factors effectively eliminates any significant impact of race on wealth.
17
Yet, if the model proposed here supports the hypotheses based on human capital
theory, this analysis can contribute to policy solutions that seek to increase education and
life opportunities for minority children. Moreover, because the SCF analyses discussed
in the next section include inheritance data, they provide a good complement to the SIPP
analysis.
18
Determinants of racial inequality: discussion of key variables
•
Life opportunities: human and cultural capital
Good public infrastructure, including quality public schools and healthcare options,
facilitates access to wealth. Because most public schools in ghettos lack the
infrastructure needed to build the human capital of young African-Americans,
residential and income-based segregation result. Cultural capital, transferred through
better education, health status, and life experiences, is correlated with acquiring
wealth. The inability to invest in children’s human capital continues to place blacks
at a disadvantage. Conley (1999b) highlights this disparity in his analysis of the
racial wealth gap. He argues that when ‘class-equalized’ blacks and whites are
compared, blacks are as likely to complete college as whites. Better infrastructure
and equal access to life opportunities ensure that wealth is maintained across
generations within a household.
•
Historical economic trends
Public policies built to enhance equal opportunity are insufficient if they fail to
account for the burden of historically lower wages and asset accumulation
opportunities among African-Americans. As Conley (1999b) argues, when parental
assets are equalized to compare the wealth holdings of young adults, the racial wealth
gap vanishes. This implies that racial inequality could disappear if public policies are
explicitly designed to close the racial wealth gap. The complexity lies in developing
practicable policies that can achieve this. Moreover, quantifying the historical impact
19
of generations of disadvantages is challenging (Conley, 1999a; Oliver & Shapiro,
1995).
•
Liquidity constraints
Spriggs (in Smith, 2000) argues that African-Americans’ financial behavior and asset
preferences must be situated in the context of liquidity constraints that frame their
economic conditions. African-Americans remain disadvantaged in access to credit
and loans. Liquidity constraints impact both short and long-term consumption as
seen in asset allocation behavior. Parents with access to liquid assets can provide
funds for a down payment on a house or to help with higher educational costs
(Conley, 1999b; Oliver & Shapiro, 1995; Keister & Moller, 2000).
•
Public policy
The historical impact of racially-biased Federal home-lending policy, coupled with
lack of investment in the ghetto, continues to impact wealth portfolios of AfricanAmericans today (Oliver & Shapiro, 1995). Current Federal housing policy, with a
focus on mixed-income communities, may be the first of many steps needed to assure
‘equality of achievement’ versus ‘equality of opportunity’ (Oliver & Shapiro, 1995).
Glover-Blackwell (2006) suggests increasing affordable housing in opportunity-rich
neighborhoods as a tool to confront persistent residential segregation and
corresponding limited life opportunities.
20
Graph 1: Median monthly household income in the SIPP sample by age and race
in the United States, 2001
5000
4500
White
Black
4000
Income
3500
3000
2500
2000
1500
1000
500
0
Adult (25- Middle Age
40)
(40-55)
Old Age
(55-65)
Elderly (6585)
Age Group
Source: Survey of Income and Program Participation, 2001
Income and Age
Graph 1 shows that white households had substantially higher median incomes
than black households in 2001 in the United States. A partial explanation lies in the fact
that African-American workers are overrepresented in lower-paying sectors of the
economy, such as services. Moreover, the gap initially widens with age as white incomes
increase more during middle age than black incomes before narrowing in old age.
However, income declines for both whites and blacks after their peak earning years,
which is why researchers use age, as well as income, to explain wealth disparities (Hao,
2004; Oliver & Shapiro, 1995).
21
Educational achievement and net worth
Net worth is typically defined as the sum of all assets, including home and vehicle
equity, minus all debts at a given point. Net worth is an important predictor of wealth
transfers across generations (Oliver and Shapiro, 1995). While wealth data were not
readily available until the 1970’s, the advent of the SIPP now allows analysts to measure
trends in wealth in the United States. SIPP data show net worth increasing with age (See
graph 2). Moreover, the positive relationship between net worth and age is magnified by
education. Graph 2 shows that higher education and higher net worth go hand-in-hand,
regardless of age. For example, the median net worth of a household head aged 55-59
years for a high school dropout was $47,000 and climbed to over $300,000 for a college
graduate in the same age cohort. In short, age and education appear positively correlated
with wealth.
22
Graph 2: Age net-worth profiles of households with 2 or more people by household
head education, 2001
Source: Urban Institute tabulations from wave 3 of the 2001 SIPP panel
Life opportunities: the example of the U.S. criminal justice system
The prevalence of female-headed households in the black community suggests
limited opportunities to invest in the human capital of young African-Americans.
Concentrated poverty, poor pubic infrastructure, and insufficient policy support for lowincome women trying to make socio-economic gains all affect the life opportunities of
future generations. The impact of broken families on early childhood development
serves to perpetuate racial inequality. This inequality is perhaps clearest in the U.S.
criminal justice system. African-American males represented 13 percent of all monthly
23
drug users yet accounted for 35 percent of drug possession arrests, 55 percent of drug
convictions and 74 percent of prison sentences (The Sentencing Project, 2001). One in
every three black men can expect to be imprisoned during their lifetime. Black women
are six times more likely than white women to be incarcerated during their lifetime and
one of every 14 African-American children has at least one parent in prison (Mauer &
King, 2004). Graph 3 shows the tremendous increase in the absolute numbers of AfricanAmericans in prison from 1954 to 2002. These figures are also alarming in a relative
sense because the U.S. black population had only increased from about 10 percent to 12
percent from 1950 to 2000 (McKinnon, 2001).
Graph 3: Number of African-Americans in prison or jail, selected years – 1954-2002
Source: Mauer & King, 2004: The Sentencing Project
24
The concentration of wealth and intergenerational disadvantages
Recent analyses of the Survey of Consumer Finances (SCF) provide contrasting
views about the role of race in asset accumulation. Wolff’s (2000) study of trends in
wealth ownership found that white households invested 23 percent of their total assets in
stocks compared to only 11 percent among black households. In contrast, Straight’s 2002
study of the 1998 SCF data suggested that asset allocation was more equally distributed
across races. Comparing data from the 1995 and 1998 SCF, Straight argued that the
proportions of specific types of asset holdings display differences by race, but that they
were not very extreme. He concluded that asset allocation was more balanced when
families with similar social and demographic characteristics were compared.
In 1994, African-Americans families had an average net worth equivalent to less
than one-fifth that of white families (Gittleman & Wolff, 2000). Over 24 percent of
white households received an inheritance with the average transfer amount equaling 115
thousand dollars. By comparison, only 11 percent of black households reported receiving
inheritance monies and the mean value of the inheritance was 32 thousand dollars (Wolff,
2000). This analysis highlights the importance of inheritances and the disadvantaged
position of blacks in the intergenerational transmission of wealth. The current situation
must be contextualized in the history leading up to the current wealth gap. The ratio of
mean black to white wealth by family remains consistently low, at approximately 0.19
(Wolff, 2000). That is, whites are roughly five times wealthier than blacks.
25
Wolff’s findings on inheritances in the SCF were consistent with Hao’s (2004)
analysis of immigration and wealth using SIPP data. Acknowledging the difficulties
associated with collecting early-age inheritance data, Hao (2004) conducted a separate
analysis for the youngest age cohort in the SIPP as a way to distinguish youth
inheritances from future wealth accumulation. Her analysis identified the largest earlyage inheritance deficit among African-Americans at the lower tails of wealth distribution.
These findings support the importance of intergenerational transmission of wealth in
later-stage wealth acquisition. As many wealth analysts note, simply stated, wealth
begets wealth.
Graph 4: Monthly median household income by household head and race, 2001
Married Couple HH
Male HH
Race
Female HH
Black
White
0
1000
2000
3000
Income
Source: SIPP, 2001
26
4000
5000
Wealth, Gender and Marital Status
Gender, race and marital status of the head of household all appear to be
correlated with wealth holdings. Households headed by women had lower median
incomes, and the proportion of female-only householders was much higher for black
households. Thirty-two percent of black households were headed by women compared to
only ten percent for white households. Coupled with lower income among black than
white households, this suggests that gender, race and marital status of the householder
could play a role in wealth differences. Oliver and Shapiro (1995) noted the significance
of gender in their analysis. In trying to explain it, they propose a ‘sociology of race and
wealth,’ which combines economic human capital theory with different investment
opportunities blacks and whites face. This thesis identifies related elements of human
capital theory and sociological trends which contribute to the disproportionately higher
share of female householders in black households. Elements of human capital theory
relevant to wealth analysis include education, work experience and skills. Basically,
blacks and whites face differential opportunities to build human capital, and this is
important to take into consideration when analyzing wealth (Hao, 2004; Oliver &
Shapiro, 1995).
Data from the 2001 SIPP show that households headed by married couples had
higher median incomes than households headed alone by either a male or female.
Among single-parent household heads, men had higher median incomes than women,
with a monthly median income of $3,240 compared to $2,149. As Table 1 and Graph 4
27
illustrate, the intra-gender differences are more pronounced when race is factored in. For
example, households headed by single-parent white women had a median income of
$2,381 compared to $1,679 for black female headed households. Other wealth measures
indicate an even more dramatic difference by race. Households headed by single black
women had net financial assets equal to zero compared to $900 for similar white
households. This is consistent with Hao’s (2004) analyses of wealth data using the SIPP,
which suggested that non-married households remained at the low-end of wealth
distribution compared to married couple headed households with children. The latter
show greater variation in wealth accumulation, depending on which stage of the life cycle
the household is experiencing, i.e., married couple households’ wealth is highly variable
relative to that of non-married households.
Table 1: Total household wealth by race, gender and household type, 2001
Household
type
Married Couple
households
Male-headed
households
Female-headed
households
• White
Female
• Black
Female
Number of
observations
Median
monthly
income
Net worth
Net financial
assets
17,961
13,410
$4,360
$96,303
$26,800
1,042
$3,240
$26,272
$2,000
3,509
$2,149
$5,354
$280
2,357
$2,381
$9,948
$900
1,152
$1,679
$1,500
$0
Source: SIPP, 2001
NOTE: households are the unit of observation
28
Education and Income
Higher levels of education are correlated with higher incomes. However, returns
to education suggest racial bias. Monthly median earned income for African-Americans
with educational levels beyond a Bachelor’s degree was lower than that of similarly
educated whites ($3,275 versus $4,584 in 2001). These findings are especially relevant
for an analysis of wealth among the U.S. middle class. Middle class status is defined
using Oliver and Shapiro’s (1995) parameters. These include: college-level education,
total household income, and occupational category. Yet, even when comparing returns to
education for college-educated blacks and whites, the difference was significant: blacks
earned about $1,300 a month less than equally educated whites.8
Table 2 shows the overall trends in educational achievement by race and gender
in the United States in 2000. By gender, the percentage of men with a college or
advanced degree was higher than for women. By race, whites had higher educational
attainment than blacks in all education categories. While these data from the Census
Bureau provide a good benchmark to compare the SIPP findings to, it is important to note
that these educational categories are not exclusive. For example, the figure of 80.1
percent of men with a high school diploma includes those with advanced degrees, as they
would have completed high school before entering higher education. By contrast, SIPP
household heads report their highest level of education in exclusive categories. For
example, of the 22,533 white household heads, 2.7 percent held either a professional or
8
Data from SIPP, 2001
29
doctoral degree in 2001. Similarly, of the 3,606 black households, just under one percent
held either a professional or doctoral degree.
Table 2: Educational achievement by race and gender, 2000
Characteristic Population
25 and over
Gender
High
school +
Some
College +
College +
Advanced
Degree
Male
87,077,686
80.1%
52.5%
26.1%
10%
Female
95,133,953
80.7%
51.1%
22.8%
7.8%
White
143,085,659
83.6%
54.1%
26.1%
9.5%
Black
19,858,095
72.3%
42.5%
14.3%
4.8%
Race
Source: Bauman, K.J. & Graf, N.L, Educational Attainment: 2000, Census 2000 Brief
30
Table 3: Wealth by Race and Different Conceptions of the Middle Class,
2001
Monthly
Net
Net Financial
Income
Worth
Assets
White
College degree
$5,217
$161,548
$59,000
White collar
$5,471
$93,454
$30,645
College degree
$4,369
$55,280
$4,000
White collar
$4,546
$21,214
$0
Black
Source: SIPP, 2001
NOTE: Table 3 is an adaptation of Oliver and Shapiro’s definition of the middle class. All
figures are median values. White collar is defined as earned monthly income between $3,248 and
$6,496 in the Executive, Administrative, Managerial or Professional Specialty occupational
category.
Occupation categories and race
Occupational categories provide an important tool to measure middle-class status
which could offer wealth gains that are transferable from the current generation to the
next. A larger percentage of African-Americans were in service sector occupations in
2001 (14.2 percent versus 6.9 percent for whites). Conversely, a much smaller
percentage of blacks were in the executive managerial and professional sectors. These
disparities imply that despite gains in employment opportunities, other factors play a role
in keeping racial divisions in the workplace. Service sector jobs are associated with
lower education, training and wages compared to managerial and professional jobs. As
31
such, blacks working in this sector are less able to provide enhanced life opportunities to
their children. Lack of access to high-quality education and training in residentially
segregated neighborhoods leaves blacks marginalized in the two occupational categories
most closely associated with middle-class status, managerial and professional specialty.
These differences result in a lack of intergenerational transfer of wealth. This perpetuates
the black-white wealth gap as reflected in limited life opportunities, which lead to
disparate life achievements. Lastly, an analysis of income and wealth by occupation
supports the hypothesis that wealth measures reveal deeper racial inequality than income
analyses. As demonstrated in Table 3, middle-class African-Americans earned $4,546
compared to $5,471 earned by middle class whites. The differences in net worth and net
financial assets (NFA) were much more dramatic: middle-class blacks owned $21,214 in
net worth compared to middle-class whites who owned $93,454. The NFA figures were
even more striking, with middle-class blacks having $0 compared to $30,645 for whites.
An interesting trend emerges when comparing Asian households to either white or
black households; Asian households appear to outperform all other races in terms of
income. An analysis of median incomes in the professional sector provides evidence
supporting this trend: whites had a median monthly income of $5,585, blacks had $4,421
and Asians had $6,485 in 2001.9 Yet, wealth measures demonstrated marginalization by
race: median wealth for Asian households in the 2001 SIPP was $58,285, substantially
9
These figures do not account for full or part time work status, which would attenuate the differences here.
For example, women tend to work more part-time jobs than men, and African-American women are more
likely to work part-time or be unemployed. Moreover, single African-American mothers were more likely
than single white mothers to be in the labor force, suggesting sharper economic burdens on single black
women (Bureau of Labor Statistics, 2006)
32
lower than white households. The figure for median NFA among middle-class Asian
households was also significantly lower at $18,200. These findings are consistent with
Hao’s (2004) comparison of wealth concentration between white and Asian households.
She concluded that although Asian households have higher wealth holdings than other
minorities, wealth is distributed more unequally for this group than white households.
33
Table 4 Regression of Income, Net Worth and Net Financial Assets
Dependent variable
Independent variable
lnIncome
lnNW
lnNFA
Household Income
Black
South
Some College
Age
Age Squared
Male
Children
Widow
Lone parent
Singles
KBOccs
Intercept
R-squared
N
F-stat
-0.271
(-9.59 )***
-0.154
(-8.88)***
0.378
(20.76)***
0.051
(14.56)***
-0.0005
(-15.48)***
0.157
(8.98)***
-0.038
(-3.90)***
0.262
(8.78)***
-0.529
(-20.58)***
-0.976
(-39.58)***
0.611
(33.47)***
0.0002
(21.49)***
-2.082
(-15.59)***
-0.053
(-0.63)
1.04
(11.81)***
0.331
(21.38)***
-0.002
(-13.85)***
0.499
(5.75)***
-0.015
(-0.32)
0.801
(5.52)***
-1.675
(-12.12)***
-1.473
(-12.89)**
0.29
(2.51)**
0.0002
(22.34)***
-1.972
(-24.80)***
-0.739
(-13.26)***
2.011
(33.77)***
0.101
(10.76)***
-0.0005
(-4.95)***
0.3044
(5.33)***
-0.3541
(-11.94)***
0.295
(2.73)***
-1.715
(-20.35)***
-1.588
(-20.22)***
0.955
(14.11)***
6.734
(71.52)***
-3.965
(-9.36)***
2.808
(11.87)***
0.19
26,139
720.4
0.18
26,139
503.07
0.27
26,139
907.42
Heteroskedasticity robust t-statistics in parentheses
* significant at 10% level; ** significant at 5% level; *** significant at 1%
level
34
Analysis
Table 4 presents the results of the regression analyses for the three different
dependent variables: income, net worth (NW) and net financial assets (NFA).10 The
model builds on the work of Oliver and Shapiro (1995). However, because of the noted
collinearity between income and total number of household workers, the model here used
different independent variables including categorical variables for the head of
household.11 The income regression provides a baseline to measure the NW and NFA
findings against. Ordinary least squares (OLS) regression was used to tests three
hypotheses:
1. Race is negatively associated with acquiring wealth for African-Americans
2. Age is positively correlated with income and negatively associated with NW
and NFA
3. Female-headed, single-parent households are disadvantaged in acquiring
wealth.
These three hypotheses are a subset of the overarching hypothesis that wealth
analysis reveals more deeply entrenched racial inequality than does income analysis.
10
The original model included the total number of household workers and work experience as independent
variables. These were not included in the models here for several reasons including the potential
collinearity between income and the number of workers, and the statistical insignificance of the work
experience variable in all three models; dropping these variables did not significantly affect the results.
11
The independent variables used were tested for multicollinearity by running correlations between the
different explanatory variables. The results suggest no collinearity in the explanatory variables. See
Appendix C for correlation coefficient matrices for each regression.
35
Because the SIPP tends to over-sample the poor, many observations in the dataset had
zero or negative values for NW and NFA. Indeed, it is precisely this feature of the SIPP
that makes it such a powerful database for analyzing economic well-being among lower
and middle-income U.S. households. Moreover, as Hao (2004) notes, mean and median
values of NW demonstrate the positive skew of wealth measures. In the 2001 SIPP,
mean NW was $166,365 while median NW was $53,085. Oliver and Shapiro (1995)
substituted values of 1 for households with 0 values for NW, arguing that this allowed
them to address the highly negative skew of wealth and include households with 0 wealth
in their regressions. It is unclear how they dealt with negative values of NW. They then
ran two versions of their regressions: the first used logged values of NW and NFA while
the second did not. Because their findings were basically identical, they did not use logs
in their final models, citing ease of interpretation and the ability to present unit changes in
dollars (Oliver & Shapiro, 1995: 220).
The study here uses logs to provide an equation that better fits the data. The
initial OLS regressions on NW and NFA without logs had weak predictive power.
However, logging the NW and NFA dependent variables truncated the data significantly,
resulting in the loss of 4,500 to over 9,000 observations, depending on which variable
was logged.
36
A better solution was found in Lingxin Hao’s (2004) work, which used SIPP data
to analyze the impact of immigration on the distribution of U.S. wealth. To include zero
and negative values of NW, Hao created transformed logs of NW as follows:
1. If NW if positive, the log of NW is simply the log of the value
2. If NW is zero, the log of NW is set equal to zero
3. If NW is negative, the absolute value of NW is logged and assigned a negative
sign.12
Following Hao, NW and NFA were transformed into logs as described. For
consistency, household income was also logged for the baseline regression. The Rsquared and F-values of these models suggests much better specification: for NW, the Rsquared is 0.18, close to the 0.20 value reported by Oliver and Shapiro and for NFA, it is
0.27, significantly higher than the 0.10 value reported in Oliver and Shapiro’s model.13
Yet, because NFA are considered a stronger predictor of control over future
financial resources, i.e., inheritances, the model seems promising for future analyses of
wealth using SIPP data and the adapted NFA log-transformed data. This is especially
12
Hao, 2004. Immigration and wealth inequality in the U.S.
These findings suggest that the adapted model, explains more of the variation in wealth inequality than
the Oliver and Shapiro’s model. This is likely the result of using the transformed log variables for NW and
NFA, and retention of more of the observations for analysis. The regression diagnostics included in
Appendix B show that the models appear properly specified and free of multicollinearity; however, there is
the possibility of heteroskedasticity in the NW and NFA models. Despite the potential of
heteroskedasticity, the models are still robust with the Huber-White transformed standard errors and tstatistics shown in parentheses in Table 4. This correction assumes heteroskedasticity is present and makes
changes to the standard errors as described in the notes for Table 4. See Appendix B for the original
regression results from Black Wealth/White Wealth.
13
37
relevant because of the policy implications for addressing the racial wealth gap. All else
equal, blacks accumulate about 2 percent less wealth measured by either NW or NFA,
relative to white households. This important finding supports the hypothesis that wealth
analysis shows deeper inequality than does income analysis. That is, income differences
attenuate the depth of racial inequality.
Race has a negative impact in all three models, with the largest impact seen in the
NW model. That is, African-Americans have lower measures of wealth relative to
whites. The findings on race parallel those of previous studies on the wealth gap. Oliver
and Shapiro (1995) found a highly negative and statistically significant impact of race on
income, NW and NFA. As Hao’s (2004) quantile regression analyses suggest, the
disproportionate representation of African-Americans in the lower distribution of wealth
reflects important trends: discrimination in the housing market, an inheritance deficit, and
low returns to lower levels of human capital acquired during childhood. Similarly,
Conley (1999a) finds that distribution of wealth in the black community is more uneven
than in the white community. Conversely, Conley (1999a) found that class impacts NW
more than race, though he notes potential reverse causality between lower class status and
race.
Regressions were run using three different measure of wealth as the dependent
variable – income, net worth (NW) and net financial assets (NFA). The results of these
regressions follow. Except for the dummy variables for southern region residence and
38
children, all the other independent variables were statistically significant across all three
models. The region and children variables were insignificant in the NW specification,
which uses the sum of all assets minus liabilities, as the dependent variable. Oliver and
Shapiro (1995) found a negative and statistically significant relationship between
southern-region residence in their income and NW. The models here demonstrate that
regional location was significant and negative for the income and NFA models but not for
the NW model. This discrepancy may be the result of using different years of data, plus
controlling for the type of household where NW can vary widely. Children were only
statistically significant and negative in Oliver and Shapiro’s (1995) NFA model. This is
only partially consistent with the findings here. The negative impact children can have
on NFA was confirmed, but they also appeared to have a negative effect on income. In
contrast to Oliver and Shapiro (1995), the impact of children on NW was highly variable.
This could stem from the inclusion of the household type variable, as different
households make different decisions about how much to invest in children, depending on
which stage of the life-cycle households are in. For example, older households may
experience a higher negative effect from having children as parents transfer lifetime
savings to their children (Hao, 2004).
The significance of income and age in determining NW and NFA provided
empirical evidence supporting human capital theory. Age plays a positive and significant
role in determining household income, NW and NFA. This is contrary to the hypothesis
based on Oliver and Shapiro’s (1995) model, in which age was positive for the income
39
regression only, while negative for NW and NFA. The results in Table 4 demonstrate
age having a positive impact in all three regressions. The relationship was strongest for
NW: a one-year increase in age increases predicted NW by 39.23 percent, income by
5.23 percent and NFA by 10.63 percent.14 Similarly, the high t-statistics on income in the
NW and NFA models suggest a robust positive relationship between income and these
variables. In short, households acquire more wealth as they age, but at a diminishing
rate, and higher incomes are associated with increases in wealth holdings.
The sign on the age-squared variable was negative across all three models. As
regression diagnostics [graphs (f) and (i)] in Appendix B of predicted values for NW and
NFA against this variable indicate, diminishing returns seem to set in around the age of
64, consistent with Hao’s (2004) findings. In contrast, Oliver and Shapiro (1995) found a
positive relationship between this variable and the two wealth measures. However, the
finding here is consistent with trends depicted in Graph 1 and makes intuitive sense to the
extent that older households transfer money for future generations. Hao’s (2004) work
on immigration and wealth also suggests that households experience diminishing returns
to age after the age of 64 as parents invest in the human capital of their children or
provide other forms of intergenerational wealth transfers.
14
Applying simple algebraic properties of the logarithmic and exponential functions provides the exact
percentage change in the predicted dependent variables: %∆y = 100 [exp(β)-1]. This is not an unbiased
estimator because exp (.) is a nonlinear function. But it is a consistent estimator of 100[exp(β)-1] because
the probability limit passes through the continuous functions while the expected value operator does not
(Wooldridge, J.M., 2006: 198)
40
Another important difference from Oliver and Shapiro’s (1995) findings is the
sign on the dummy variable for gender in all three models. They found a statistically
significant positive relationship between males and income and negative relationships for
men and NW and NFA.15 The regression results from the 2001 SIPP here demonstrate
that being male appears to have a significant impact on acquiring income, NW and NFA.
Coupled with the strong negative impact of lone-parent head of household status, the
models here support the hypothesis that female-headed, single-parent households are less
likely to acquire income and wealth. In the NW model, for example, a black femaleheaded household was predicted to have a decrease in NW based on race, household type,
and being female. In other words, black female-headed households face a cumulative
deficit in NW compared to white, male and married couple-headed households. These
findings are consistent with previous studies on household type and wealth acquisition;
both Conley (1999a) and Hao (2004) found that female-headed black households were
disadvantaged in accumulating wealth. Conley’s (1999a) analysis of wealth among
young households used the Panel Study on Income Dynamics and found that femaleheaded households had lower NW even after accounting for their lower incomes.
The findings for the head of household independent variable were statistically
significant and in the hypothesized direction. Compared to married couple-headed
households, single-parent and never-married individual headed households were
15
Oliver and Shapiro (1995) note that the negative sign on the male variable in their NW and NFA
specifications is a “statistical anomaly . . . created by the interaction between maleness and the number of
workers in a household. In fact, the zero-order correlation between wealth and male householders is almost
zero.”
41
marginalized in acquiring income and wealth. These findings are rooted in human capital
theory and are further supported by the positive and statistically significant signs on the
knowledge-based occupations variable in all three models. That is, knowledge-based
occupations generate more income, NW and NFA than non-knowledge-based jobs such
as services.
The positive and significant coefficients for income and education in all three
specifications suggest the need to complement current public policy based on human
capital theory with additional policy measures to accelerate closing the racial wealth gap.
For example, all else equal, having some college-level education results in a 37.8 percent
increase in income, a 104 percent increase in NW and an over 200 percent increase in
NFA. Moreover, income is positively and significantly related to both measures of
wealth: each additional dollar of income generates an increase of 0.02 percent in NW and
NFA or wealth more generally. The magnitude of the coefficient on income is supported
in Lerner and Mikesell’s (1988) findings of the weak correlation between income and
wealth, suggesting that while income is a necessary condition for wealth acquisition, it is
not a sufficient one.
Importantly, Hao (2004) argues that investments in human capital may be
ineffective in closing the wealth gap if inheritances are the primary determinant of
positive wealth during childhood. Following the classic life-cycle theory, Hao (2004)
suggests that early-age inheritance is the most important factor in determining future
42
asset accumulation. Data on youth inheritances are difficult to obtain because they are
hard to collect (Hao, 2004); future wealth research would benefit from enhanced data
collection tools for younger age cohorts.16
Policy Implications
Since the Civil Rights Act, many contend that African-Americans are offered
equal legal protections and thus, equal opportunities. Yet, the prevalence of concentrated
poverty and blacks’ disproportionate representation in lower-income jobs and
impoverished neighborhoods imply that no policies to date have effectively bridged the
racial divide. Blacks are disproportionately represented in service-sector jobs and earned
less income for professional and managerial jobs compared to whites in 2001. As wealth
analysts argue, policymakers’ exclusive focus on income as a measure of inequality
obscures more complex, historically-based racial inequality.
Cumulative disadvantages across generations contribute to the persistence of a
black underclass, despite gains in education and access to better jobs. Nowhere is the
role of intergenerational transmission of poverty more evident than in the black middle
class and specifically, in regard to home equity. Although blacks have made significant
strides in education, employment and politics, they continue to bear the brunt of the
16
Moreover, the 2001 SIPP has evolved since the time that the original models were done and
macroeconomic shocks in the early 2000’s were shown to have erased blacks’ wealth gains during the
1990’s (Oliver & Shapiro, 2006).
43
cumulative disadvantages in acquiring wealth. Yet, the data analysis suggests that having
some college-level education is positively associated with acquiring wealth, with the
highest increase seen in NFA. Thus, expanding opportunities for higher education based
on wealth criteria is crucial to closing the racial wealth gap.
As the models tested here suggest, race is significantly and negatively correlated
with wealth for African-Americans. Moreover, black women are disproportionately
disadvantaged in wealth acquisition supporting the work of Conley (1999a); Hao (2004);
and Oliver and Shapiro (1995 & 2006). It follows that policies designed to increase
income equality are deficient in addressing racial inequality.
A new set of policies to combat this situation began to develop in the early
1990’s. They included: (1) implementation of Individual Development Accounts (IDAs)
which demonstrate the capabilities of lower-income people to save; (2) the 1998 passage
of the Assets for Independence Act, which provides Federal funds for IDAs; and (3)
revisions to the Temporary Assistance to Needy Families (TANF) eligibility criteria,
allowing recipients to accumulate assets (Oliver & Shapiro, 2006: 245).
Michael Sherraden (1991) proposed individual development accounts (IDAs) as a
mechanism for asset-based welfare policy. IDAs are universal, matched savings
accounts. Implementation of these individual-level accounts is in its nascent stages, but
recent data suggest that IDAs are having a positive impact on homeownership (Mills et
44
al., 2004). In addition, preliminary impact evaluations of these programs demonstrate the
positive “multiplier” effects of IDAs: grant recipients purchase homes, accumulate assets
and display enhanced capacity to manage household finances (Mills et al., 2004).
Similarly, Sherraden and Zhan (2003) find a positive association between asset
accumulation and children’s educational achievement among single mothers. This is
especially important given the findings that being African-American, female, and
especially being a single female head of household are negatively and significantly
correlated with accumulating wealth. More than 34 state-level TANF plans reference
IDAs and 13 states are using their TANF funds for IDA programs (Parrish et al., 2006).
Affirmative action programs need to be reevaluated. They are facing increased
judicial resistance, suggesting new approaches should be explored. The findings here
indicate that African-Americans are underrepresented in middle and upper-class jobs, and
marginalized in higher education. Conley (1999a), for example, recommends a shift
toward class-based affirmative action policies. Individual net worth would be accounted
for in determining eligibility, thus providing African-Americans with most of the
benefits, while simultaneously extracting race from the affirmative action debate.
Moreover, current affirmative action policies do not do nearly enough within their
existing framework to confront gender discrimination. More vigorous enforcement is
desirable. Maximizing the net benefit from gender-based affirmative action under the
current rules could address the disproportionate marginalization of low-income women in
45
wealth accumulation seen in the SIPP 2001 analysis. This would be especially important
for single black parents, as suggested by the cumulative negative impact of race, gender
and marital status of the household head described in the data analysis.
Holzer and Neumark (2006) conclude that persistent education and employment
gaps for American minorities warrant policies that complement affirmative action. The
positive impact of knowledge-based occupations on wealth suggests the importance of
education and training in determining economic well-being. Thus, expanding affirmative
action in employment should be coupled with job training to ensure that individuals have
the needed skills when considered for a job.
International development policy may suggest a useful tool in addressing the
wealth gap. The Grameen Bank’s system of microfinance has made remarkable
achievements in reducing poverty in Bangladesh.17 For example, the Grameen Bank
provides cell phones to women who live in rural areas with limited communications
infrastructure. The recipient becomes the communications point-person for her village,
and in turn, she begins to accumulate income and assets from her work (Stiglitz, 2006).
The Grameen Bank’s micro-credit strategies could serve as a blueprint for empowering
low-income American women, which is in keeping with the cumulative disadvantages
faced by lone-parent female-headed households in asset accumulation shown here.
17
Indeed, Bangladesh appears to be the only developing country that can realistically reach the UN’s
Millennium Development Goal of reducing poverty by half (M. Yunus, interview with Charlie Rose, 2006).
46
This policy tool is justifiable on two grounds: first, documented international
success in poverty reduction by using this instrument; and, second, the ongoing statelevel encouragement of microenterprise work. Moreover, because the 1996 welfare
revisions require TANF recipients to work, microenterprise offers the dual advantage of
allowing recipients to continue receiving benefits and providing innovative opportunities
to break the cycle of poverty. The latter is particularly important because of the
demonstrated weakness of income alone to break families out of poverty. Finally, these
recommendations are in line with the regional equity and asset building strategies
proposed by scholars working in the new framework of asset-based policy (Oliver &
Shapiro, 2006; powell, 2006; Rank, 2005; Shapiro, 2004). Building net worth and net
financial assets are the sufficient conditions to complement the necessity of first
increasing income.
Conclusion
Policymakers’ exclusive focus on income as a measure of inequality obscures
more complex and historically-based asset inequality. Oliver and Shapiro’s (1995)
analysis demonstrated that comparing blacks and whites of similar backgrounds still left
about three-fourths of the wealth gap unexplained. The analysis of 2001 SIPP data
supports the hypothesis that wealth differences suggest more racial inequality than do
income differences. It follows that policies designed to increase income equality are
deficient in addressing racial inequality. Cumulative disadvantages across generations
47
contribute to racial division in the United States. Based on the 2001 SIPP data, this thesis
offers Civil Rights and low-income advocacy organizations a range of actionable policies
to consider in closing the racial wealth gap.
48
APPENDIX A: Determining the Proper Model and Specification
A. Addressing potential heteroskedasticity
The ‘hettest’ function in STATA performs three versions of the Breusch-Pagan and
Cook-Weisberg test for heteroskedasticity. All three versions of this test present
evidence of heteroskedasticity.18 The steps for the Breusch-Pagan test are as follows:
1. Estimate OLS model and obtain the squared OLS residuals for each observation
2. Using them as the dependent variable, run the regression of u2=σ0+σ1x1+σ2x2 + . . . . .
+σkxk +error. Save the R2 value.
3. Form the F-statistic using the R2 value from the preceding equation using the Fk, n-k-1
distribution; if the p-value is smaller than the chosen significance level, reject the null
hypothesis of homoskedasticity.
Because heteroskedasticity is observed in all three regressions, the STATA ‘_robust’
command is used to produce standard errors. This procedure computes a robust variance
estimator based on a list of independent variables of equation-level scores and a
covariance matrix. The heteroskedasticity robust t-statistics are generated using the
White test, which adds the squares and cross products of all independent variables.
Heteroskedasticity robust Huber-White t-statistics are presented in parentheses in Table
4.19 For large samples, robust standard errors are valid whether heteroskedasticity exists
or not.
18
Adapted from STATA help menu
Adapted from Introductory Econometrics: A Modern Approach (2006) by Jeffrey L. Wooldridge (3rd
Edition).
19
49
APPENDIX B: REGRESSION DIAGNOSTIC GRAPHS
To test for heteroskedasticity, residuals were plotted against the dependent variable. The
initial results suggested that assumptions of OLS were violated, and that the error was
correlated with the dependent outcome. Yet, because the ‘_robust’ command in STATA
assumes violation of the homoskedasticity assumption, heteroskedasticity is accounted
for as described in Appendix A. Graph a shows the pattern of the residuals and the
dependent variable, the log of income; graph b shows predicted values of the log of
income based on the heteroskedasticity robust model run with STATA, and it illustrates a
higher degree of randomness.
-20
-15
Residuals
-10
-5
0
5
a) Heteroskedasticity test: residuals and lnHHINC
-10
-5
0
lhhinc
50
5
10
-20
-15
Residuals
-10
-5
0
5
b) Residuals and predicted values for lnHHINC (model specification scatterplot)
6
7
Fitted values
8
9
To assess non-linear returns to age, the predicted values for the log of income were
plotted against the Age-squared variable. As discussed in the analysis section,
diminishing returns to age appear to set in around 64 for all three measures of wealth.
Graph c shows the fitted values of the log of income plotted against age-squared.
51
6.5
7
Fitted values
7.5
8
c) Fitted values of HHINC and Age squared variable:
0
2000
4000
Agesq
6000
8000
To visually assess the presence of heteroskedasticity, residuals of the log of net worth
were plotted against the dependent variable, the log of NW. Graph d shows that the
pattern of the residuals is non-random, i.e., there is the potential of heteroskedasticity.
Still, using the Huber-White transformed t-statistics provides robust standard errors, and
the coefficients themselves remain unbiased.
52
-30
-20
Residuals
-10
0
10
20
d) Heteroskedasticity test: residuals and lnTNW:
-20
-10
0
lNW
10
20
To test whether the model for the net worth regression was correctly specified, residuals
were plotted against the dependent variable, the log of NW. Although the patterns of the
residuals in Graph e are non-random, the ‘_robust’ command in STATA assumes
violation of the homoskedasticity assumption and generates heteroskedasticity robust
Huber-White standard errors; thus, model specification is not a problem.
53
-30
-20
Residuals
-10
0
10
20
e) Residuals and predicted values for lnTNW:
0
10
20
Fitted values
30
40
To test the returns to age, the age-squared variable was plotted against the predicted
values for the log of NW. As Graph f indicates, the data suggest diminishing returns to
age set in around the age of 64, consistent with other analyses of wealth and age.
54
2
4
Fitted values
6
8
10
12
f) Fitted values of log Net Worth and Age squared variable:
0
2000
4000
Agesq
6000
8000
A plot of residuals against observed net financial assets (NFA) was used to test for the
presence of heteroskedasticity. As Graph g indicates, the errors are not normally and
identically distributed, so the STATA ‘_robust’ command was used to allow for violation
of the homoskedasticity assumption to make the model robust despite this problem.
55
-30
-20
Residuals
-10
0
10
20
g) Heteroskedasticity test: residuals and NFA
-20
-10
0
lNFA
10
20
As with the income and net worth regressions, residuals were plotted against the
predicted values of NFA to check for model specification. Because the
heteroskedasticity is accounted for by the Huber-White correction, the model is robust
despite the non-random pattern of residuals seen in Graph h, which shows a larger degree
of randomness.
56
-30
-20
Residuals
-10
0
10
20
h) Residuals and predicted NFA (model specification scatterplot):
-10
0
10
Fitted values
20
30
Plotting the predicted NFA values based on the model used in this analysis against the
age-squared variable indicates similar results to those seen in the income and NW
models, i.e., that diminishing returns to age set in around the age of 64 as seen in Graph i.
57
5
6
Fitted values
7
8
9
i) Fitted log NFA values and Age squared variable:
0
2000
4000
Agesq
58
6000
8000
Table B-1: Regression results from Black Wealth/White Wealth, Oliver & Shapiro
(1995: 130)
Regression of Income, Net Worth, and Net Financial Assets
Intercept
Race
South
Highest Grade Completed
Age
Age Squared
Work Experience
Upper-White
Collar
No. of Workers
Household
Income
Male
Children
Widow
2
R
N
*p<0.05
Income
Net Worth
Net Financial
Assets
-33,035.00***
-5,176.76***
-3,072.80***
831.37***
1,390.49***
-12.91***
-40.42
32,243.00
-27,075.00***
-9,352.00***
666.18**
-3,620.57***
72.03***
-104.33
44,888.00***
-14,354.00***
-1,958.15
396.29
-3,442.74***
54.31***
-6.29
3,705.02***
11,401.00***
28,635.00***
-10,715.00***
23,841.00***
-9,259.61***
28,11.54***
300.23
-6,173.47***
0.386
7,625
2.28***
-9,389.11
-778.70
-6,898.71
0.203
7,625
1.31***
-6,703.24***
-2,783.85**
-8,581.18
0.107
7,625
**p<0.01
***p<0.001
59
60
lnHHINC
Black
South
SomeColl
Age
Agesq
Male
Children
Widow
Lonepar
Singles
KBOccs
lnHHINC
1
-0.1268
-0.0699
0.2112
-0.0487
-0.0791
0.1316
0.07
-0.1437
-0.0965
-0.2633
0.2319
1
0.1562
-0.0841
-0.0316
-0.0331
-0.116
0.0697
0.0195
0.2098
-0.0008
-0.0581
black
1
-0.0529
0.0089
0.0096
-0.0182
-0.0031
0.0172
0.0212
-0.021
-0.0304
South
1
-0.1488
-0.1643
0.044
-0.0096
-0.1601
-0.0982
-0.003
0.3288
SomeColl
1
0.9857
-0.0264
-0.3815
0.4851
-0.1394
0.1575
-0.1858
Age
1
-0.042
-0.386
0.5237
-0.133
0.1859
-0.204
Agesq
1
-0.033
-0.244
-0.265
-0.095
0.0047
male
1
-0.1749
0.2121
-0.4227
0.0295
Children
1
0.0742
0.3393
-0.14
Widow
1
-0.3098
-0.0595
Lonepar
1
0.0174
Singles
1
KBOccs
To test for multicollinearity, correlations were run between the explanatory variables for each of the three
models. The only high correlation (over 0.7) is seen between the Age and Age-squared variables; these figures are
highlighted in the matrices below. This is to be expected given that one is a transformation of the other.
Appendix C: Multicollinearity and Correlation Coefficients
61
lnTNW
HHINC
Black
South
SomeColl
Age
Agesq
Male
Children
Widow
Lonepar
Singles
KBOccs
lnTNW
1
0.195
-0.1565
-0.0311
0.0918
0.2965
0.2694
0.0938
-0.0943
0.0737
-0.1589
-0.06
0.0407
1
-0.1121
-0.0668
0.2532
-0.1113
-0.1454
0.1345
0.0953
-0.1796
-0.117
-0.2363
0.2705
HHINC
1
0.1562
-0.0841
-0.0316
-0.0331
-0.116
0.0697
0.0195
0.2098
-0.0008
-0.0581
black
1
-0.0529
0.0089
0.0096
-0.0182
-0.0031
0.0172
0.0212
-0.021
-0.0304
South
1
-0.1488
-0.1643
0.044
-0.0096
-0.1601
-0.0982
-0.003
0.3288
SomeColl
1
0.9857
-0.0264
-0.3815
0.4851
-0.1394
0.1575
-0.1858
Age
CORRELATION COEFFICIENT MATRIX: NET WORTH REGRESSION
1
-0.042
-0.386
0.5237
-0.133
0.1859
-0.204
Agesq
1
-0.033
-0.244
-0.265
-0.095
0.0047
male
1
-0.1749
0.2121
-0.4227
0.0295
Children
1
0.0742
0.3393
-0.14
Widow
1
-0.3098
-0.0595
Lonepar
1
-0.0174
Singles
1
KB
Occs
62
lnNFA
HHINC
Black
South
SomeColl
Age
Agesq
Male
Children
Widow
Lonepar
Singles
KBOccs
1
0.3359
-0.2361
-0.1194
0.2853
0.1543
0.1307
0.1309
-0.1056
-0.0382
-0.2155
-0.0983
0.1856
1
-0.1121
-0.0668
0.2532
-0.1113
-0.1454
0.1345
0.0953
-0.1796
-0.117
-0.2363
0.2705
1
0.1562
-0.0841
-0.0316
-0.0331
-0.116
0.0697
0.0195
0.2098
-0.0008
-0.0581
1
-0.0529
0.0089
0.0096
-0.0182
-0.0031
0.0172
0.0212
-0.021
-0.0304
1
-0.1488
-0.1643
0.044
-0.0096
-0.1601
-0.0982
-0.003
0.3288
1
0.9857
-0.0264
-0.3815
0.4851
-0.1394
0.1575
-0.1858
1
-0.042
-0.386
0.5237
-0.133
0.1859
-0.204
1
-0.033
-0.244
-0.265
-0.095
0.0047
CORRELATION COEFFICIENT MATRIX: NET FINANCIAL ASSETS REGRESSION
lnNFA
HHINC
black
South
SomeColl Age
Agesq male
1
-0.1749
0.2121
-0.4227
0.0295
Children
1
0.0742
0.3393
-0.14
Singles
1
-0.3098
1
-0.0595 -0.0174
Widow Lonepar
1
KB
Occs
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