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ISSN 1063-5297
ISBN 1-931493-29

Preface - Dimitri B. Papadimitriou

Asset Poverty in the United States

About the Authors

Introduction

The U.S. poverty measure is an important indicator that influences public awareness of well-being, as well as public policies and programs. Income has been the main focus of poverty measurement, and income maintenance has been the primary goal of public policies designed to alleviate poverty. However, using income as the basis to measure and alleviate poverty ignores the importance of wealth.

Wealth is central to a household’s economic security. Assets provide liquidity in times of economic hardship and can be used to pay for further education, to buy a house, or to maintain a decent standard of living after retirement. Owner-occupied housing, moreover, is an important part of household wealth, as it provides services and frees up resources that would otherwise be spent on rent. People without assets are forced to live from one paycheck to the next, require assistance when their income flow is interrupted, and are discouraged from actively seeking a better life (e.g., moving to a better neighborhood, looking for a more desirable job).

In this brief we study the characteristics of households that lack enough savings to sustain them during a period of economic hardship.1 We define an asset poverty measure whereby a household is considered to be asset poor if it does not have enough wealth to meet its basic needs for a limited period of time. The size and severity of asset poverty in the United States is estimated using data from the Panel Study of Income Dynamics (PSID). Our approach is novel, since it is the first thorough analysis of the level and determinants of and trends in asset poverty. We find that, contrary to a sharp decline in the official measure of poverty, which is based on income, the asset poverty rate barely changed over the 1984–99 period and the severity of poverty increased, despite economic growth and a booming stock market.

This brief begins with a literature review followed by a definition and estimates of asset poverty. We then analyze the effects of compositional changes on the overall asset poverty rate. Comparisons are subsequently made between the asset poverty rates we derived from the PSID and the Survey of Consumer Finances (SCF), and between these rates and the official rates.We study the differences between household and individual asset poverty rates, the characteristics of poor households, the trends and persistence of asset poverty, and the role of major lifetime events affecting transitions in and out of asset poverty.

Background

Wealth is a source of consumption, since it can be converted into cash in times of economic stress and provides consumption services, such as owner-occupied housing. Many economists and other social scientists have cited the importance of wealth as an indicator of well-being and status in society. Their studies have shown that wealth is more unevenly distributed than income and that wealth inequality rose during the 1990s, as the upper deciles of the population experienced the largest gains in wealth (Oliver and Shapiro 1990,Wolff 2001).Wolff deduced that, “it is not surprising that the fraying of the private safety net . . . has led to a growing sense of economic insecurity in the country.” These findings are striking, since economic growth and a rising stock market gave the false impression that everyone was accumulating wealth.

Another area of research is the racial wealth gap (Conley 1999, Gittleman and Wolff 2000). Conley found that the racial disparities in education, welfare receipts, and out-of-wedlock childbirth that persisted even after controlling for income could be explained when parental wealth and socioeconomic status were taken into account. Gittleman and Wolff found that raising African American family incomes and saving rates to levels associated with whites would only slightly narrow the racial wealth gap, so they are dubious about the effectiveness of corresponding policy proposals.

Sherraden (1991, 2001) proposed the idea of “welfare based on assets,” which emphasized the role of institutions in saving. The mechanisms of asset accumulation in the United States (e.g., home mortgage interest deductions, 401(k)s, individual retirement accounts, and education savings accounts) benefit the rich more, simply because the poor do not employ them. Moreover, policies operating via tax benefits do not help the poor. Sherraden sees a need, therefore, for new asset-based programs designed with the poor in mind, such as Individual Development Accounts (IDAs), which are savings deposits that are matched by private or public sources.

Some researchers have suggested adding wealth to income when measuring poverty (e.g.,Weisbrod and Hansen 1968, Moon 1977, Crystal and Shea 1990, and Rendall and Speare 1993). Using an income–net worth measure rather than an income measure, they found differences in the incidence and characteristics of poverty, including a lower incidence of poverty, a younger distribution of poor households, and more minority households that are poor. Ruggles and Williams (1989) and Ruggles (1990) found that, after accounting for asset holdings, over 60 percent of households remained in poverty, half of the elderly were eliminated from poverty, and there was an increase in the average duration of poverty.

Wealthandwant comments: One might argue that the period that must be considered for elderly people should be longer than for others, since one hopes that their regular income -- Social Security and pension -- should be consistent enough that it will not be interrupted. Thus, in retirement, we are looking at whether the third leg of the stool is sufficient to provide for those needs which the first two do not. Some of those needs are ongoing; others occur suddenly.

In this brief we follow the approach used by Haveman and Wolff (2001) to define asset poverty, since we are interested in estimating the population that would be unable to sustain consumption at or above the poverty level due, mainly, to a loss of income. We extend the Haveman and Wolff approach using data from the PSID, which is a better data source for the low-income population than the SCF (used in the referenced study).2 We also perform regression analyses and study the persistence and transitions of asset poverty.

The Definition of Asset Poverty

We adopt the definition of asset poverty in Haveman and Wolff (2001): A household or person is “asset-poor” if the access they have to “wealth-type resources” is insufficient for them to meet their “basic needs” for a limited “period of time.”We specify basic needs, period of time, and wealth-type resources in the spirit of the Haveman and Wolff study.

We use three alternative wealth measures to specify basic needs:

(1) Net Worth (NW), which includes the current value of all marketable assets less the current value of all debts;

(2) Net Worth minus Home Equity (NW-HE), which includes all items in NW, except for home equity; and

(3) Liquid Wealth (LIQ), which measures the value of cash and other kinds of easily monetized assets (see Appendix for a description of the wealth data in the PSID).

The reference family asset poverty threshold in current dollars was $2,589 in 1984, $3,089 in 1989, $3,693 in 1994, and $4,151 in 1999. Asset poverty was estimated using a headcount index and poverty gap ratio that were introduced by Foster, Greer, and Thorbecke (1984). The headcount index gives an estimate of the share of households that would be unable to survive for three months if forced to liquidate all wealth and consume the proceeds. The poverty gap ratio measures the per capita wealth that would have to be transferred to asset-poor households (as a percentage of the poverty line) in order to bring the asset-poor households to the asset poverty line.

Wealthandwant comments: So the underlying assumption is that one can meet the basic needs of a family of four people if one has available the amount designated by the Federal Poverty Guideline. For 2006, for a family of four, that figure is $20,000. Unfortunately, as the Self-Sufficiency Standard Studies have demonstrated, there are very few places where one can meet the most basic needs of a family on $20,000 per year — $5,000 per quarter — $1,667 per month, particularly if that family includes one or more children below all-day-school age. And this definition of Asset Poverty also assumes that one can reduce monthly housing costs down to well below the Fair Market Rent level instantly upon loss of family income. This is not realistic. In summary, this definition of Asset Poverty only begins to touch on the real depth of the problem.

It would be useful to run this same analysis using 6 months at poverty level, and 9 months, which would be proxies for living for 3 months at 200% of poverty and 3 months at 300% of poverty.

Remember when we used to talk about having 6 months' living expenses put aside for a rainy day? For most of us, that is considerably more than we have available. And, as the authors of "The Two Income Trap" point out, most married couples no longer have the safety net represented by having a second spouse who could step into the job market; the earnings of both partners are required to meet their fixed costs. "Fully 75 percent of family income is earmarked for recurrent monthly expenses. Even if they are able to trim around the edges, families are faced with a sobering truth: every one of those expensive items — mortgage, car payments, insurance, childcare — is a fixed cost. Families must pay them each and every month, through good times and bad; there is no way to cut back from one month to the next, as can be done with spending on clothing or food. Short of moving out of the house, withdrawing their children from preschool, or canceling the insurance policy altogether, they are stuck." -- Elizabeth Warren, Harvard Magazine, Jan-Feb 2006.

Asset Poverty in the United States, 1984 to 1999

The Evolution of Wealth

Tables 1A and 1B describe the mean and selected percentiles of the NW, NW-HE, and LIQ measures during the 1984–99 period (in 1999 dollars). The mean value of household wealth increased steadily, although at different growth rates for the various measures. The median net worth (50th percentile) increased from $43,000 to $56,500, or 31.5 percent. The 25th percentile increased slightly (from $1,600 to $2,000), but the 95th percentile increased from $483,100 to $799,000, or 61.2 percent. The lower tail of the net worth distribution did not increase as fast as the upper tail, so there was a skewed progression in favor of the upper percentiles. In contrast, the poorest 10 percent of the American population was in debt in 1984, and their debt continued to increase between 1984 and 1999.

Table 1B Wealth Measures by Percentile, 1984–99
		(thousands of 1999 dollars)				% Change
	Percentile	1984	1989	1994	1999	1984–99
NW	Mean	127.9	162.6	168.7	217.1	27.1%
	10	-0.4	-1.1	-1.7	-1.8	-%
	25	1.6	1.3	2.0	2.0	25.0%
	50	43.0	41.7	50.7	56.5	31.5%
	75	132.3	152.5	167.7	195.0	47.4%
	95	483.1	585.0	664.2	779.0	61.2%
Median as percent of mean		33.6%	25.6%	30.1%	26.0%
Notice how similar the mean and the 75th percentile are: aboout 75% of us are below average! See Wobegon
NW-HE	Mean	81.9	107.5	116.0	158.7	31.3%
	10	-1.6	-3.2	-5.1	-5.0	-%
	25	0.0	0.0	0.0	0.0	0.0%
	50	7.2	8.5	11.3	12.0	66.2%
	75	57.7	67.2	84.4	100.0	73.3%
	95	352.8	399.0	495.3	621.0	76.0%
Median as percent of mean		8.8%	7.9%	9.7%	7.6%
The mean is well above the 75th percentile. More than 75% of us are below average.
LIQ	Mean	36.3	49.3	68.8	72.5	35.7%
	10	0.0	0.0	0.0	0.0	0.0%
	25	0.5	0.5	0.6	0.5	4.2%
	50	5.6	6.7	9.0	6.0	7.0%
	75	28.9	39.0	56.3	40.5	40.3%
	95	163.6	201.5	298.3	289.0	76.7%
Median as percent of mean		15.4%	13.6%	13.1%	8.3%
The mean is increasing compared to the 75th percentile.
Note: Data based on four weighted, cross-sectional snapshots of households in each year. Source: Authors’ calculations from PSID surveys.

The rise in liquid assets was also highly skewed in favor of the upper tail of the wealth distribution. The median increased from $5,600 in 1984 to $9,000 in 1994, before declining to $6,000 in 1999 (a 7.0 percent increase over the period). In contrast, the 95th percentile increased 76.7 percent.

Changes in Asset Poverty

Table 2A shows our estimates of the headcount index of asset poverty for U.S. households. According to the NW measure, almost 26 percent of households were asset poor in 1999, while 40 percent and 42 percent were asset poor according to the NW-HE and LIQ measures, respectively. According to our calculations, more than 46 percent of households had less than $5,000 worth of liquid assets to cushion adverse shocks. We note that there seems to be almost no change in the overall asset poverty rates during the 1984–99 period.

Table 2A Overall Household Asset Poverty Rates (Headcount Index)
	1984	1989	1994	1999
NW	26.4%	27.1%	26.1%	25.9%
NW-HE	41.7%	41.3%	40.5%	40.1%
LIQ	41.8%	38.9%	37.8%	41.7%
Source: Authors’ calculations from PSID surveys.
Wealthandwant.com comments: The headcount index gives an estimate of the percentage of households that would be unable to survive for three months at the Federal Poverty Guideline level if forced to liquidate all NETWORTH, or NETWORTH other than HOME EQUITY, or LIQUID ASSETS and consume the proceeds.

The NW measure yields the lowest estimate of asset poverty, as it is the most inclusive measure of wealth. The poverty rate increases by almost 15 percentage points when home equity is excluded. This is consistent with the fact that home equity is the most widely held asset and, therefore, an important part of household wealth in the United States. It is interesting that the NW-HE and LIQ estimates are close. This occurs because only a small percentage of households own illiquid assets (e.g., real estate, business assets) apart from their primary residence. We focus, therefore, on the NW and NW-HE poverty measures in subsequent sections of this brief.

The stability of the headcount index gives the false impression that the recession of the early 1990s had no adverse effect on asset-poor households. The large increase in the poverty gap ratio between 1989 and 1994, as shown in Table 2B, suggests, however, that the recession was harsh on almost a quarter of the population, since the average asset-poor household seems to have lost assets.Moreover, contrary to popular belief, asset poverty rates did not go down during the economic expansion of the late 1990s. NW and NW-HE poverty rates stayed the same, while the LIQ poverty rate increased from 37.8 percent in 1994 to 41.7 percent in 1999. In contrast to the asset poverty rates, the NW and NW-HE poverty gap ratios fell, although the NW-HE gap ratio stayed above 100 percent in 1999. In terms of volatility, the LIQ poverty gap ratio was quite stable during the 1984–99 period (ranging from 31 to 33 percent), while the NW and NW-HE ratios were quite volatile (ranging from 62 to 113 percent).

Table 2B Overall Household Poverty Gap Ratios (P1 indices)
	1984	1989	1994	1999
NW	61.5	75.7	89.4	82.3
NW-HE	85.0	93.7	112.8	108.7
LIQ	33.3	30.7	30.8	32.3
Source: Authors’ calculations from PSID surveys.
Wealthandwant.com comments: The poverty gap ratio measures the per household wealth that would have to be transferred to asset-poor households (as a percentage of the poverty line) in order to bring the asset-poor households to the asset poverty line.
So for a family of four, and assuming things have remained constant since 1999, looking at the Federal Poverty Guideline for 2006 of $20,000 ($3,400 for each additional person), the average family of four whose Net Worth falls below $5,000 (3 months at $20,000 per year) would need 82% of that, or $4,100 to bring their Net Worth up to $5,000 — and that is 25.9% of all families. (Table 2A, line 01) 40.1% (Table 2A, line 02) of families have Net Worth other than Home Equity of less than $5,000 (four-person HH equivalent), and on average would need 108.7% of $5,000 to bring them up to $5,000 in Net Worth other than Home Equity! 41.7% (Table 2A, line 03) of families have Liquid Assets less than $5,000, and on average would need 32.3% of $5,000 to bring them up to that level.

Wealthandwant.com comments: In 2006, the Federal Poverty Guideline for a family of four is $20,000. Using the methodology of this study, that means a criterion of assets of $5,000 for 3 months for a family of four. What do tables 2A and 2B tell us?
		1984	1989	1994	1999
1. They tell us that about one quarter of us have total net worth less than enough to support ourselves at the Federal Poverty Guideline level for 3 months, and that on average within that quarter, the number of weeks of FPG living that net worth would provide has declined from 5 weeks to a bit over 2 weeks. Total Net Worth!! One quarter of us!!
NW	pct of population (Table 2A)	26.4%	27.1%	26.1%	25.9%
	poverty gap % (Table 2B)	61.5	75.7	89.4	82.3
	extent of gap in 2006 dollars for family of four	$3,075 out of $5,000	$3,785 out of $5,000	$4,470 out of $5,000	$4,115 out of $5,000
	Net Worth equivalent for a family of four	$1,925	$1,215	$530	$885
	weeks of poverty-level living	5.0	3.2	1.4	2.3
2. They tell us that when we put aside home equity, 4 out of 10 of us have other net worth insufficient to sustain us for 3 months at the poverty level, and that on average, those 40% have negative net worth! In other words, other than their home equity (for those who have any, and many don't) 40% of us don't have enough to get by from one paycheck to the next. Any bump in the road is disastrous.
NW-HE	pct of population (Table 2A)	41.7%	41.3%	40.5%	40.1%
	poverty gap % (Table 2B)	85.0	93.7	112.8	108.7
	extent of gap in 2004 dollars for family of four	$4,250 out of $5,000	$4,685 out of $5,000	$5,640 versus $5,000	$5,435
	Net Worth excluding Home Equity equivalent for a family of four	$750	$315	-$640	-$435
	weeks of poverty-level living	2.0	.8	0	0
3. They tell us that we're a bit better off in terms of liquid assets. 40% of us don't have enough in liquid assets to support ourselves at the FPG for 3 months, but on average, that 40% has on hand about 9 weeks' worth of liquid assets. This wouldn't be terrible news, except in light of what we know about net worth other than home equity: that an awful lot of us have assets below $0! But 9 weeks is not much of a cushion, and many have a good deal less than that.
LIQ	pct of population (Table 2A)	41.8%	38.9%	37.8%	41.7%
	poverty gap % (Table 2B)	33.3	30.7	30.8	32.3
	extent of gap in 2006 dollars for family of four	$1,665 out of $5,000	$1,535 out of $5,000	$1,540 out of $5,000	$1,615 out of $5,000
	Liquid Assets equivalent for a family of four	$3,335	$3,465	$3,460	$3,385
	weeks of poverty-level living	8.7	9.0	9.0	8.8
Now consider what you've read about payday loans, and tax anticipation loans. Does it make sense that a lot of people must rely on such high-interest borrowing vehicles?

Our estimates of asset ownership rates and asset holdings of poor households imply that there was a noticeable increase in indebtedness from the 1980s to the 1990s. Mortgage and nonmortgage debt jumped substantially and exceeded asset holdings. According to the NW-HE wealth measure, the poor had negative wealth in 1999 —

• nonmortgage debt of $6,999, combined with
• business ($177),
• real estate ($82), and
• checking and saving accounts ($1,099).

The Structure of Asset Poverty, by Group

Tables 3 and 4 present asset poverty rates and the poverty gap ratios for various demographic groups. Households are classified according to the race, age, and education level of the head of household, as well as by housing tenure and family type.

We find striking differences in the asset poverty rate by racial group, regardless of the wealth measure. Nonwhites are more than twice as likely as whites to be asset poor, and their poverty gap ratio is much higher. By the NW measure, whites experienced a small decline in the asset poverty rate (21 to 19 percent), while the nonwhite rate declined from 52 percent in 1984 to 48 percent in 1994, before increasing to 50 percent in 1999. A similar pattern describes the NW-HE measure: the poverty gap ratio is more severe among nonwhites and greatly exceeds 100 percent through the period.

Table 3 Asset Poverty Rates, by Group
Wealthandwant.com comments: The headcount index gives an estimate of the share of households that would be unable to survive for three months if forced to liquidate all wealth and consume the proceeds.
	Percent				Percentage Point Change
	1984	1989	1994	1999	1984–89	1989–94	1994–99	1984–99
A. Total
NW	26.4	27.1	26.1	25.9	0.7	-1.0	-0.2	-0.5
NW-HE	41.7	41.3	40.5	40.1	-0.3	-0.8	-0.4	-1.5
B. Race/Ethnicity
White
NW	21.4	22.1	21.9	19.0	0.8	-0.2	-2.9	-2.4
NW-HE	35.5	35.3	35.4	31.8	-0.2	0.1	-3.6	-3.7
Nonwhite
NW	52.3	48.8	47.7	50.0	-3.5	-1.1	2.3	-2.4
NW-HE	73.7	67.7	66.8	69.3	-6.0	-0.9	2.5	-4.4
C. Age Groups
Ages <25
NW	72.2	77.2	70.9	79.6	4.9	-6.3	8.7	7.3
NW-HE	79.0	84.9	86.7	87.7	5.9	1.8	0.9	8.7
Ages 25–34
NW	43.1	42.5	38.7	44.0	-0.6	-3.9	5.3	0.9
NW-HE	59.4	59.7	54.3	65.1	0.2	-5.4	10.7	5.6
Ages 35–49
NW	16.9	16.6	17.1	22.6	-0.3	0.4	5.6	5.7
NW-HE	36.7	37.7	35.2	40.2	1.1	-2.5	5.0	3.5
Ages 50–61
NW	11.7	8.7	10.2	9.5	-3.1	1.5	-0.7	-2.3
NW-HE	27.4	23.8	23.8	24.9	-3.6	0.0	1.1	-2.5
Ages 62–69
NW	11.4	9.3	9.1	11.1	-2.1	-0.2	2.0	-0.3
NW-HE	21.9	22.3	22.5	23.2	0.4	0.2	0.7	1.3
Ages 70+
NW	11.9	12.5	16.6	11.2	0.6	4.1	-5.4	-0.7
NW-HE	25.4	25.0	31.8	22.7	-0.4	6.9	-9.1	-2.7
D. Education
<High School
NW	33.6	30.0	30.8	34.3	-3.6	0.8	3.5	0.7
NW-HE	54.7	50.5	55.0	58.1	-4.2	4.5	3.1	3.4
High School
NW	27.1	22.4	23.9	18.2	-4.7	1.5	-5.6	-8.8
NW-HE	42.8	39.1	42.5	35.4	-3.7	3.4	-7.2	-7.4
Some College
NW	24.6	16.6	18.6	18.8	-7.9	1.9	0.3	-5.8
NW-HE	37.7	32.0	31.0	31.3	-5.7	-1.0	0.3	-6.4
College Graduate
NW	15.2	8.9	9.2	8.8	-6.3	0.4	-0.5	-6.4
NW-HE	22.5	19.2	17.5	16.6	-3.3	-1.8	-0.8	-5.8
E.Housing Tenure
Homeowner
NW	2.4	3.5	5.6	5.9	1.2	2.1	0.3	3.5
NW-HE	27.8	26.4	26.9	26.1	-1.5	0.5	-0.8	-1.8
Renter
NW	62.4	64.0	66.0	66.3	1.6	2.0	0.3	3.9
NW-HE	62.4	64.0	66.0	66.3	1.6	2.0	0.3	3.9
F. Family Type
<65 yrs, Married, Children
NW	19.6	20.2	21.3	19.9	0.6	1.1	-1.4	0.3
NW-HE	44.7	42.1	40.0	40.7	-2.6	-2.1	0.6	-4.0
<65 yrs, Married, No Children
NW	10.7	10.5	13.1	14.7	-0.3	2.6	1.6	4.0
NW-HE	23.1	23.4	26.8	27.4	0.2	3.4	0.6	4.3
<65 yrs, Female Head, Children
NW	67.4	62.7	60.9	58.5	-4.7	-1.8	-2.3	-8.9
NW-HE	82.8	79.1	77.0	73.7	-3.7	-2.0	-3.3	-9.0
65+ yrs, Married
NW	6.4	4.6	4.7	3.1	-1.8	0.1	-1.6	-3.3
NW-HE	18.6	17.1	17.6	13.2	-1.6	0.5	-4.4	-5.5
65+ yrs, Female Head
NW	15.9	17.7	23.9	18.3	1.8	6.2	-5.5	2.4
NW-HE	29.3	32.0	40.8	32.9	2.7	8.8	-7.9	3.6
65+ yrs, Male Head
NW	15.8	16.7	20.6	21.6	1.0	3.9	0.9	5.8
NW-HE	23.4	22.5	33.8	28.9	-0.9	11.2	-4.9	5.5
Source: Authors’ calculations from PSID surveys.

We find that, although there is no apparent common trend before 1994, the 1994–99 period shows an increase in asset poverty rates for most age groups (except those older than 62).With the exception of the oldest group, the poverty gap rose continuously during the 1984–99 period, rising at the steepest rate for the under-25 group and remaining above 100 percent for the 34-and-under age groups. The asset poverty indices generally decrease with age. The poverty gap ratios for household heads who are 34 and under are much greater than 100 percent, since they have, on average, negative wealth.

Asset poverty rates decrease with higher education levels, and there is a striking difference between high school dropouts and high school graduates, as well as between college dropouts and college graduates. All groups experienced declining rates during the 1984–89 period, with mixed results thereafter. With the exception of household heads with less than a high school diploma, there was a reduction in asset poverty throughout the 1984–99 period. Among college graduates, asset poverty rates were reduced by almost half, according to the NW measure, and poverty gap ratios declined according to both measures, but stayed above 100 percent throughout the period, according to the NW-HE measure. In contrast, the poverty gap ratio doubled for the least educated group.

Table 4 Poverty Gap Ratio, by Group
Wealthandwant.com comments: The poverty gap ratio measures the per capita wealth that would have to be transferred to asset-poor households (as a percentage of the poverty line) in order to bring the asset-poor households to the asset poverty line. Numbers over 100.0 indicate that on average, those (counted by the headcount figure, above) who lack sufficient assets to meet their needs for 3 months, actually have, on average, negative Net Worth or negative Net Worth other than Home Equity.
	NW				NW–HE
	1984	1989	1994	1999	1984	1989	1994	1999
Total	61.5	75.7	89.4	82.3	85.0	93.7	112.8	108.7
Race/Ethnicity
White	54.8	72.8	85.8	80.2	76.5	87.4	108.8	104.3
Nonwhite	96.5	88.3	107.6	89.7	129.0	121.2	133.6	124.3
Age Groups
Ages <25	136.0	175.0	139.4	375.8	142.9	185.1	162.0	387.1
Ages 25–34	106.0	137.8	150.1	175.3	129.8	148.7	181.2	207.5
Ages 35–49	64.2	74.5	89.2	78.9	103.0	102.4	120.2	107.3
Ages 50–61	18.0	24.3	49.0	51.9	43.7	56.5	81.0	86.2
Ages 62–69	16.6	16.5	20.7	28.9	28.2	31.5	41.6	51.3
Ages 70 +	11.9	25.4	63.3	14.6	23.0	26.1	40.9	26.1
Education
<High School	42.9	62.0	68.4	87.5	68.8	88.4	100.8	114.5
High School	52.8	55.9	75.9	68.2	72.2	74.9	101.9	89.6
Some College	72.7	106.9	104.4	108.0	101.8	121.7	135.9	132.3
College Graduate	97.5	93.3	111.7	74.3	119.6	102.4	117.6	107.5
Housing Tenure
Homeowner	17.8	25.9	37.0	26.5	56.9	55.5	72.8	65.7
Renter	127.2	153.1	189.0	197.4	127.2	153.1	189.0	197.4
Family Type
<65 yrs, Married, Children	60.0	78.5	74.0	58.8	100.6	103.9	103.9	91.0
<65 yrs, Married, No Children	40.2	38.2	76.7	63.2	64.0	66.2	114.8	90.0
<65 yrs, Female Head, Children	98.1	104.6	109.7	145.4	120.2	129.0	133.6	176.5
65+ yrs, Married	8.8	27.4	67.8	6.7	22.0	19.5	29.2	19.9
65+ yrs, Female Head	14.1	19.2	27.8	27.7	27.5	34.6	48.4	42.7
65+ yrs, Male Head	22.0	15.4	35.2	52.4	22.1	20.2	52.3	65.0
Source: Authors’ calculations from PSID surveys.

Wealthandwant.com comments: Table 4.5 Here are the preceding two tables, combined into one. The Headcount Index answers the question "how wide" -- percent of people affected -- and the Poverty Gap Ratio speaks to "how deep" -- how far below 13 weeks of living expenses at FPG the average person is. A measure of 50 means that on average they have about 6.5 weeks' worth of assets; a measure of 75 means that one average they have assets sufficient to sustain them for just over 3 weeks at the Federal Poverty Level; a measure of 25 means that on average they have enough assets to sustain them for about 10 weeks. A measure over 100 means that on average their net worth is below $0 A measure of 200 means that in order to have enough to get by for 3 months at poverty level, they would need to pay off debts representing 3 months poverty-income for their family size and then have an equal amount added to their assets. Notice that debt for young people (those under 35) has soared, both in its incidence (headcount index) and depth (poverty gap ratio). Notice, too, that single elderly people are increasingly net worth poor. This indicates how very dependent they are on Social Security and pension income; the third leg of the stool is totally missing for nearly a third of them.
		Net Worth					Net Worth other than Home Equity
		1984	1989	1994	1999		1984	1989	1994	1999
Total	Headcount Index	26.4	27.1	26.1	25.9		41.7	41.3	40.5	40.1
	Poverty gap ratio	61.5	75.7	89.4	82.3		85.0	93.7	112.8	108.7
Race/Ethnicity
White	Headcount Index	21.4	22.1	21.9	19.0		35.5	35.3	35.4	31.8
	Poverty gap ratio	54.8	72.8	85.8	80.2		76.5	87.4	108.8	104.3
Nonwhite	Headcount Index	52.3	48.8	47.7	50.0		73.7	67.7	66.8	69.3
	Poverty gap ratio	96.5	88.3	107.6	89.7		129.0	121.2	133.6	124.3
Age Groups
Ages <25	Headcount Index	72.2	77.2	70.9	79.6		79.0	84.9	86.7	87.7
	Poverty gap ratio	136.0	175.0	139.4	375.8		142.9	185.1	162.0	387.1
Ages 25–34	Headcount Index	43.1	42.5	38.7	44.0		59.4	59.7	54.3	65.1
	Poverty gap ratio	106.0	137.8	150.1	175.3		129.8	148.7	181.2	207.5
Ages 35–49	Headcount Index	16.9	16.6	17.1	22.6		36.7	37.7	35.2	40.2
	Poverty gap ratio	64.2	74.5	89.2	78.9		103.0	102.4	120.2	107.3
Ages 50–61	Headcount Index	11.7	8.7	10.2	9.5		27.4	23.8	23.8	24.9
	Poverty gap ratio	18.0	24.3	49.0	51.9		43.7	56.5	81.0	86.2
Ages 62–69	Headcount Index	11.4	9.3	9.1	11.1		21.9	22.3	22.5	23.2
	Poverty gap ratio	16.6	16.5	20.7	28.9		28.2	31.5	41.6	51.3
Ages 70 +	Headcount Index	11.9	12.5	16.6	11.2		25.4	25.0	31.8	22.7
	Poverty gap ratio	11.9	25.4	63.3	14.6		23.0	26.1	40.9	26.1
Education
<High School	Headcount Index	33.6	30.0	30.8	34.3		54.7	50.5	55.0	58.1
	Poverty gap ratio	42.9	62.0	68.4	87.5		68.8	88.4	100.8	114.5
High School	Headcount Index	27.1	22.4	23.9	18.2		42.8	39.1	42.5	35.4
	Poverty gap ratio	52.8	55.9	75.9	68.2		72.2	74.9	101.9	89.6
Some College	Headcount Index	24.6	16.6	18.6	18.8		37.7	32.0	31.0	31.3
	Poverty gap ratio	72.7	106.9	104.4	108.0		101.8	121.7	135.9	132.3
College Graduate	Headcount Index	15.2	8.9	9.2	8.8		22.5	19.2	17.5	16.6
	Poverty gap ratio	97.5	93.3	111.7	74.3		119.6	102.4	117.6	107.5
Housing Tenure
Homeowner	Headcount Index	2.4	3.5	5.6	5.9		27.8	26.4	26.9	26.1
	Poverty gap ratio	17.8	25.9	37.0	26.5		56.9	55.5	72.8	65.7
Renter	Headcount Index	62.4	64.0	66.0	66.3		62.4	64.0	66.0	66.3
	Poverty gap ratio	127.2	153.1	189.0	197.4		127.2	153.1	189.0	197.4
Family Type
<65 yrs,Married, Children	Headcount Index	19.6	20.2	21.3	19.9		44.7	42.1	40.0	40.7
	Poverty gap ratio	60.0	78.5	74.0	58.8		100.6	103.9	103.9	91.0
<65 yrs,Married, No Children	Headcount Index	10.7	10.5	13.1	14.7		23.1	23.4	26.8	27.4
	Poverty gap ratio	40.2	38.2	76.7	63.2		64.0	66.2	114.8	90.0
<65 yrs, Female Head, Children	Headcount Index	67.4	62.7	60.9	58.5		82.8	79.1	77.0	73.7
	Poverty gap ratio	98.1	104.6	109.7	145.4		120.2	129.0	133.6	176.5
65+ yrs,Married	Headcount Index	6.4	4.6	4.7	3.1		18.6	17.1	17.6	13.2
	Poverty gap ratio	8.8	27.4	67.8	6.7		22.0	19.5	29.2	19.9
65+ yrs, Female Head	Headcount Index	15.9	17.7	23.9	18.3		29.3	32.0	40.8	32.9
	Poverty gap ratio	14.1	19.2	27.8	27.7		27.5	34.6	48.4	42.7
65+ yrs, Male Head	Headcount Index	15.8	16.7	20.6	21.6		23.4	22.5	33.8	28.9
	Poverty gap ratio	22.0	15.4	35.2	52.4		22.1	20.2	52.3	65.0
Source: Authors’ calculations from PSID surveys.

The most striking observation in terms of housing tenure is the huge and persistent gap in asset poverty rates between homeowners and renters, although there is no distinct difference between the two groups in terms of changes in asset poverty rates. Homeowners are much wealthier than renters, even after excluding home equity, since asset poverty rates among renters are more than twice as high as homeowners (66 percent versus 26 percent). Furthermore, the severity of asset poverty among renters is much worse than homeowners, as asset-poor renters have negative wealth, on average. This observation mirrors the difference in poverty gap ratios. According to the NW and NW-HE measures, the poverty gap ratios among homeowners were approximately 25 percent and 60 percent, respectively (with the exception of 1994), while the ratio for renters greatly exceeded 100 percent at all times and was close to 200 percent in 1999.

The most significant result related to asset poverty rates by family type in Table 3 is that nonelderly female-headed families with children have the highest rate of asset poverty, although the rates declined over time. Table 4, however, shows another side of the story—the poverty gap increased. This result is expected, considering the high unemployment rate among single mothers and their dependency on government assistance, and the high living expenses for families with children. In 1984 this group held almost no wealth, according to the NW measure, and after 1989, its wealth turned negative. The poverty gap ratio was almost always greater than 100 percent and it increased to 177 percent by 1999, according to the NW-HE measure.

The second-highest rate of asset poverty is for families with children. However, this group is about half as likely to be asset poor when both parents are present than when the father is absent. The lowest asset poverty rate by family type is associated with elderly married couples.

Changes in asset poverty rates show that the elderly group is not homogenous.Marriage, apparently, is an important factor that determines the level and trend in asset poverty. Between 1984 and 1999, asset poverty rates decreased among the married elderly and increased among the unmarried elderly. A similar picture emerges with regard to the poverty gap ratios.

The Effects of Changes in Population Composition on Asset Poverty Rates

The U.S. population experienced some striking compositional changes during the 1984–99 period, due to such factors as immigration and aging. We now analyze how the changes in population shares and in asset poverty rates within groups interacted to keep the asset poverty rates the same.

We decompose the change in the NW poverty measure using a shiftshare analysis for five categories: race/ethnicity, age, education, housing tenure, and family type. To estimate hypothetical asset poverty rates, we keep the group poverty rates constant at their 1984 levels and make adjustments for changes in composition. This technique disaggregates the total change into changes in NW poverty rates of various groups and their changing share of the total population. Researchers using this technique usually find that compositional factors have only a modest impact (e.g., Danziger and Gottschalk 1995, Freeman 2001).

Our estimates of hypothetical poverty rates compared to the actual NW poverty rates (Table 5) suggest that changes in race/ethnicity and family type had a negligible effect on the overall poverty rate. Changes in age, education, and housing tenure, however, had some effect. The aging U.S. population, combined with decreasing poverty rates among older groups, would have pulled the NW poverty rate down to 20.3 percent in 1999, but increasing poverty in the younger groups kept the overall poverty rate at 25.9 percent. Similarly, the increase in homeownership would have reduced the overall poverty rate to 22.0 percent in 1999, but it was counterbalanced by an increase in poverty rates for renters and homeowners alike after 1984. The effect of higher education is smaller, since it would have lowered the overall poverty rate to 24.9 percent, which is close to the actual NW poverty rate.

Table 5 Effect of Changes in Population Composition on Asset Poverty Rates: Hypothetical and Actual NW Poverty Rates
Hypothetical NW poverty rates
Categories	1984	1989	1994	1999
Race/Ethnicity of the Head	26.4	27.0	26.2	26.3
Age of the Head	26.4	24.7	23.2	20.3
Education of the Head	26.4	25.8	24.9	24.9
Housing Tenure	26.4	25.9	23.0	22.0
Family Type	26.4	26.9	25.0	25.5
Actual NW poverty rates	26.4	27.1	26.1	25.9
Source: Authors’ calculations from PSID surveys.

A Comparison of PSID and SCF Asset Poverty Rates

Haveman and Wolff (2001) computed comparable asset poverty rates using the SCF. The SCF is different from the PSID in two main aspects:

(1) it oversamples the rich and, therefore, potentially wealthy households; and

(2) it provides a more detailed picture of assets and debts. By including information on the current value of pension plans, the SCF yields a more inclusive measure of wealth than the PSID.

Since the SCF oversamples high-income households and collects information on pension wealth, its asset poverty rates are expected to be lower than those using the PSID. As shown in Table 6, this is generally true. We note that in 1989, an overlapping year in the time series of the two databases, the poverty rates by demographic group are generally lower using the SCF database.

According to the SCF data, the overall NW poverty rate rose by 2.3 percentage points between 1983 and 1989, and by 0.8 percentage points between 1989 and 1998 — a total increase of 3.1 percentage points over 15 years. In contrast, using PSID data, the poverty rate increased by 0.7 percentage points between 1984 and 1989, and subsequently declined 1.2 percentage points for a net decline of 0.5 percentage points. The NW-HE poverty rate shows virtually no change during the period, according to the SCF data, whereas the poverty rate fell by 1.5 percentage points, according to the PSID data.

The results also differ by demographic characteristics. The SCF data set indicates a significant increase in the NW poverty rate among whites (3.4 percentage points between 1983 and 1998), whereas the PSID data show a decline (2.4 percentage points between 1984 and 1999). In contrast, the PSID results show virtually no change in the NW poverty rate among nonwhites (a 1984–89 decline matched by a 1989–99 increase), whereas the SCF results indicate a 2.1 percentage-point decline among blacks and Hispanics (a sharp increase in poverty of 6.2 percent from 1983 to 1989, followed by a steeper decline of 8.3 percent from 1989 to 1998). While the SCF shows increases in NW poverty rates for all age groups (among the two youngest groups, in particular), the PSID shows increases among the three youngest groups, but either no change or a decline among the three oldest groups. Both surveys suggest a substantial rise in NW poverty rates over time among homeowners and renters, but the results by family type vary.

It is difficult to determine whether the two sets of results are inconsistent or whether one set is more accurate. As noted earlier, the SCF provides better estimates of household wealth, since its survey asks more detailed questions about assets and debts. On the other hand, the SCF survey is weighted toward high-income households, whereas the PSID tends to oversample the poor and, therefore, may give more accurate assessments of wealth for low-income households.

A Comparison of Official and Asset Poverty Rates

Table 7 compares our asset poverty rates with the official poverty rates based on income. Our unit of analysis is the household, while the official units are the family and the individual. Since our definition of household is not equivalent to the official definition of family, we base our comparison on the individual.5

Table 7 Comparison of Official and Asset Poverty Rates by Age, Race, and Gender
			1984	1989	1994	1999
All Individuals	Official		14.4	12.8	14.5	11.8
	Asset-based	NW	24.4	25.4	24.8	27.9
		NW-HE	43.8	42.9	41.3	42.5
White (Non-Hispanic)	Official		10.0	8.3	9.4	7.7
	Asset-based	NW	19.3	20.2	20.2	19.7
		NW-HE	37.3	36.5	35.9	32.4
Black	Official		33.8	30.7	30.6	23.6
	Asset-based	NW	52.2	51.1	51.4	57.6
		NW-HE	78.4	75.2	74.0	75.6
Hispanic	Official		28.4	26.2	30.7	22.8
	Asset-based	NW	37.7	35.4	30.5	52.3
		NW-HE	62.4	53.7	44.3	77.2
Ages < 18	Official		21.5	19.6	21.8	16.9
	Asset-based	NW	31.4	33.6	30.8	36.1
		NW-HE	56.2	54.6	49.5	52.9
Ages 18-64	Official		11.7	10.2	11.9	10.0
	Asset-based	NW	23.8	24.8	24.3	28.1
		NW-HE	41.8	41.7	40.2	42.2
Ages 65 +	Official		12.4	11.4	11.7	9.7
	Asset-based	NW	10.2	10.0	12.2	9.7
		NW-HE	23.2	22.5	26.2	21.4
Male	Official		12.8	11.2	12.8	10.3
	Asset-based	NW	23.6	24.6	24.5	27.8
		NW-HE	42.9	42.1	41.1	42.3
Female	Official		15.9	14.4	16.3	13.2
	Asset-based	NW	25.2	26.2	25.2	28.1
		NW-HE	44.7	43.7	41.6	42.6
Sources: Official poverty rates: U.S. Bureau of the Census, Current Population Survey, Historical Poverty Tables by People. Asset poverty rates: Authors’ calculations from PSID surveys and the experimental poverty thresholds.

We follow the Census Bureau’s convention when grouping individuals by race/ethnicity, age, and gender. The individual asset poverty rate is defined as the ratio of the number of individuals in asset-poor households to the total population. The race of household members is determined by the race of the household head.

Our asset-based poverty rates are, on average, two to four times higher than the official poverty rates for almost all groups. We observe the same ranking among racial groups (whites have lower rates than nonwhites). Among age groups, however, the official poverty rate is slightly higher than the NW measure for the elderly in the first two survey years. We also note that asset and income poverty rates for females are greater than those for males, and that the disparity in the official poverty rates appears to be greater than that for the asset poverty rates.

As expected, the official poverty rate follows the U.S. business cycle — decreasing during economic booms, as incomes go up, and increasing during recession.However, the trend for asset poverty rates seems to move countercyclically — rising in the expansionary periods (1984–89 and 1994–99) and declining during recession (the beginning of the 1990s). This suggests, perhaps, that saving rates decline during economic booms and the decline is large enough to offset the appreciation of assets.

According to the NW-HE measure, overall asset poverty fell during the period from 1984 to 1999, which is consistent with the official measure. According to the NW measure, however, asset poverty rose during the period.

A Comparison of Household and Individual Asset Poverty Rates

Table 8 outlines NW poverty rates by size of household. It is apparent that the increase in NW poverty for individuals (see Table 7), despite the stagnation in poverty rates for all households, reflects changes in the NW poverty rates for households of different size.

Table 8 NW Poverty Rates by Household Size
Size	2006 equivalent asset threshold*	1984	1989	1994	1999
1	$2,450	36.8	36.1	36.3	33.4
2	$3,300	18.3	20.1	18.9	19.6
3	$4,150	26.5	27.4	26.7	25.7
4	$5,000	21.4	21.4	22.8	21.7
5+		26.1	28.9	27.5	31.5
All		26.4	27.1	26.1	25.9
Source: Authors’ calculations from PSID surveys.
* 25% of the 2006 Federal Poverty Guideline.

In the period from 1984 to 1999, one-person households had the highest NW poverty rate, while two-person households had the lowest rate. In 1999 the one-person household poverty rate declined to 33.4 percent (from approximately 37 percent), the rate for households with two to four individuals remained approximately the same, and the rate for households with five or more individuals increased to 31.5 percent (from approximately 26 percent). Since our sample’s average household size essentially stayed the same, the increase in NW poverty among large households and the decrease among one-person households is the reason that the household NW poverty rate stayed the same, while the individual poverty rate went up.

Characteristics of the Asset-Poor

We trace the independent effect of each factor on NW and NW-HE asset poverty measures by estimating a probit model for each survey year. All independent variables in the model are dummy variables that represent household characteristics. To prevent multicollinearity, we exclude the dummy variables for whites, the 50–61 age group,6 the lowest education group, and the unmarried nonelderly group. The dependent variable is a binary variable that takes the value of one, if the household is asset poor, and zero, if not.

We find that, relative to the excluded 50-61 age group, households whose heads are older than 61 are less likely to belong to the asset-poor group than households whose heads are younger than 50 (e.g., according to our calculations, the 25–34 age group in 1984 was 14 percent more likely and the oldest age group 10 percent less likely to be NW poor than the 50–61 year old group).7 Our estimates also confirm that more schooling reduces the chances of being asset poor. For example, in 1984, household heads who had graduated from high school were 9 percent less likely than high school dropouts to be NW-HE poor. Some college experience reduced the probability by another 2 percent, and a college degree reduced the probability a further 3 percent.8

Race is another important factor that determines asset poverty. Keeping other factors constant, households whose heads are white are 8–10 percent less likely to be NW poor than nonwhites. The effects of education and race are even greater in terms of the NW-HE poverty measure: being white lowers the probability by 19–26 percent, while a college degree lowers the probability by 11–20 percent.

Comparing different family types, we observe that nonelderly couples with children and female-headed households with children are more likely to be asset poor relative to the excluded group (unmarried nonelderly). Childless couples and the married elderly are less likely to be asset poor, while the results are mixed for the unmarried elderly.

Homeownership is a very important factor, since homeowners are 42 percent and 20 percent less likely than renters to be NW poor and NW-HE poor, respectively.

Trends in Asset Poverty

To identify trends in the likelihood of becoming asset poor for the various demographic groups,we test the hypothesis that the beta coefficient remains the same from one survey year to the next.9 We find that, over the 1984–99 period, households whose heads have a high school diploma or some college experience showed an upward trend, while those with a college degree showed a downward trend. The incremental effect of a college degree on reducing asset poverty increased during the period.

Surprisingly, we find that the contribution to asset poverty of being white, relative to nonwhite, went up, although the level of asset poverty among whites remained low. The 35–49 age group’s contribution to asset poverty experienced an upward trend relative to the excluded 50–61 age group, and, using the NW-HE definition of wealth, the 62–69 age group was also up. All other age groups experienced a downward trend.

We observe some unexpected trends for some family types. Being married with children became less important as a determinant of asset poverty, while being a childless married couple became more important. Surprisingly, the contribution to asset poverty from nonelderly female household heads with children went down. Married elderly households exhibited a downward trend in NW poverty, but unmarried elderly households exhibited an upward trend.10

Not working (e.g., being unemployed, retired, or a student) contributed less to asset poverty in 1999 than in 1984. Another asset poverty trend is that the propensity to be asset poor went up among homeowners, according to the NW measure.

To summarize, in the period from 1984 to 1999, households with one or more of the following characteristics became worse off in terms of asset poverty: employed, 35–49 years old, married without children, white, low education, single, or unmarried elderly. The contribution of a college degree to reducing asset poverty increased over time. To our surprise, the importance of being nonwhite, married with children, or a female household head with children diminished over time as a determinant of asset poverty.

The Persistence of Poverty

Table 9 shows the probability of being asset poor, which is conditional on being asset poor in the previous survey year.11 Our estimates are based on a longitudinal sample that is restricted to households whose heads remain in the sample over a five-year period. Our previous estimate showed that approximately 26 percent of households are NW poor in any given year, while Table 9 shows that about 60 percent of those households remain poor five years later. The persistence of poverty is higher (about 70 percent), according to the NW-HE measure, because of the importance of home equity. The data also show that it was more difficult to move out of NW poverty during the 1989–94 period and out of NW-HE poverty during the 1994–99 period.

Table 9 Persistence in Asset and Income Poverty
	1984–89		1989–94		1994–99		1984–89
	NW	NW-HE	NW	NW-HE	NW	NW-HE	Income
Total	62.0	68.7	62.6	68.6	59.7	72.1	41.6
Race/Ethnicity
White	54.9	63.4	59.6	64.9	52.0	67.1	32.6
Nonwhite	75.6	81.9	68.6	77.7	77.3	86.0	54.8
Age Groups
Ages <25	61.8	70.8	64.5	78.9	70.6	79.4	34.9
Ages 25-34	60.9	66.6	57.4	65.6	56.6	73.0	37.0
Ages 35-49	56.9	67.1	62.1	63.9	61.7	73.4	38.9
Ages 50-61	66.0	69.6	62.3	68.2	48.6	64.6	42.7
Ages 62-69	75.4	68.8	87.6	82.7	62.1	62.1	44.5
Ages 70 +	71.1	79.0	82.2	77.5	61.6	71.3	53.6
Education
<High School	73.4	79.4	74.9	82.8	75.0	84.8	54.2
High School	67.0	72.2	64.0	68.4	55.4	69.6	27.7
Some College	50.5	57.0	47.8	57.6	58.3	68.5	16.3
College Graduate	31.7	41.8	51.4	50.7	47.5	62.0	7.9
Housing Tenure
Homeowner	26.3	60.8	30.7	58.5	24.0	63.4	35.9
Renter	63.7	73.6	65.4	74.9	65.9	78.7	44.3
Family Type
<65 yrs, Married, Children	53.0	65.1	54.1	63.6	53.6	72.6	29.2
<65 yrs, Married, No Children	43.1	54.4	46.3	56.8	42.8	61.8	25.2
<65 yrs, Female Head, Children	84.8	90.7	82.2	86.9	80.5	86.8	60.5
65+ yrs, Married	64.3	73.1	98.5	82.4	47.2	55.7	30.4
65+ yrs, Female Head	77.5	80.4	84.9	75.5	64.3	76.1	57.6
65+ yrs, Male Head	73.4	91.4	93.2	100.0	70.6	67.9	37.9
Note: Groupings are based on the characteristics of the household head, and survey samples from the first year are weighted. Source: Authors’ calculations from PSID surveys. Income poverty data from 1985 and 1990 surveys.

Whites have lower conditional poverty rates than nonwhites. The persistence of asset poverty among nonwhites increased between 1984 and 1999. The picture for the various age groups is different from our earlier analysis (that poverty decreases with age): the conditional poverty rates are lowest for households whose heads are between 35 and 61 years, and there is a smaller degree of wealth mobility for the youngest and oldest groups.

Education seems to be an important determinant of the probability of staying in poverty, since college graduates have the lowest conditional probabilities. Homeowners are half as likely as renters to stay in NW poverty, but these groups are not very different in terms of the NW-HE measure. Families headed by the elderly or by females with children have the highest chance of staying in asset poverty (an approximately 85 percent probability for households headed by a female with children).

We find that our income poverty estimates of the probability of remaining poor are much smaller than the conditional asset poverty rates (an income-poor household in 1984 had a 41.6 percent probability of being poor in 1989). Our other findings are that younger households have more income mobility than older ones, and, as expected, that nonwhites, single mothers, and the elderly are more likely to stay in income poverty. The lowest conditional probability is estimated for college graduates.

We investigated the correlation between movements in and out of asset poverty with major lifetime events, since changes in family composition, the job market, or health may impact a family’s wealth.12 We are unaware of any previous research about the impact of lifetime events on asset poverty transitions.

Our analysis is based on probit model estimations that explain the movements in and out of NW poverty. For the three longitudinal samples (1984–89, 1989–94, and 1994–99),we ran two separate probit regressions on the probability that a household would change its NW poverty status: one for the NW poor and one for the nonpoor. For each sample, the first regression explained the movement out of NW poverty, while the second regression explained the movement into poverty.We controlled for race, age, and education of the household head, and for being a female head with children.

Controlling for all other factors, we find that marriage has been a way out of NW poverty and that its effect has increased over time. Terminating a marriage, on the other hand, increases the chances of becoming asset poor. In the 1994–99 sample, surprisingly, getting married increased the chances of falling into poverty, although the effect is not statistically significant.

Job market experiences of the household head appear to affect a household’s wealth, but some of our results were unexpected. Finding a job had a strong positive effect for the poor in the 1984–89 sample, but a weak negative effect thereafter. Moreover, for the nonpoor, finding a job makes it more likely that the household head will fall below the NW poverty threshold. We can only speculate that these nonpoor household heads were previously unemployed and surviving on nonlabor income or assets. Perhaps the household head had liquidated most of his or her assets and was desperate to take any job.

Retiring or becoming disabled have mixed effects on the probability of moving in or out of NW poverty, while homeowners who become renters have a higher chance of transition into asset poverty. Although purchasing a home appears to help a household escape asset poverty, its effect diminishes over time.

Inheritances significantly affect the probability of transition, since they usually involve considerable amounts of money. They increase the likelihood of escaping poverty for the poor and decrease the likelihood of falling into poverty for the nonpoor (with the exception of the 1994–99 sample).

The coefficient estimates for starting a business are positive and statistically significant—business owners are more likely to escape asset poverty. The direction and degree of correlation between a change in the number of children in a household and the transition probabilities are uncertain, however.

To summarize, lifetime events, such as changes in job status, marital status, homeownership, and business ownership status, are correlated with the transition probabilities of moving in or out of asset poverty.

Conclusion

Household wealth is an important factor in understanding the distribution of well-being. Wealth provides economic protection during hard times and enables people to invest in their future. During the last two decades, wealth inequality has increased.While mean net worth increased substantially, the share of the population that is vulnerable to economic shocks, due to a lack of sufficient assets, remained the same. It is clear that economic and financial developments in the United States benefited only a small part of the population in the 1984–99 period. Asset poverty rates did not go down, even in the long expansionary period in the late 1990s. Given the high persistence of asset poverty, there is good reason to suspect that a high number of asset-poor households stayed in asset poverty throughout the 15-year period.

Poverty reduction policy in the United States has focused exclusively on income maintenance.While such government programs have benefited many families, they are not adept at making the poor self-sufficient. The programs’ short-term focus and, especially, their asset limits, make some families dependent on government assistance. These programs, therefore, should be supplemented with new ones that provide incentives for the poor to accumulate assets.

Acknowledgments

We thank Giorgio Topa, Dalton Conley,Vincenzo Quadrini,Amy Schwartz, Mark Lindeman, and participants at the Bard quantitative seminar and the Eastern Economic Association 2003 Conference for helpful suggestions and comments.We gratefully acknowledge financial support provided by The Ford Foundation.

Notes

1. By economic hardship we mean hardship caused mainly by income loss, although for some population groups, such as the elderly, income loss may not be a concern, since their incomes are mostly secure. Other causes of economic hardship may be the loss of health, which most often affects the elderly, or the breakdown of the family.

2. The PSID consists of a cross-sectional national sample and a national sample of low-income families.

3. The choice of three months as the time period is reasonable. A key source of economic hardship is job loss, and the expected duration of unemployment ranged from 10 to 19 weeks (or 2.2 to 4.2 months) during the 1967–2002 period (Federal Reserve Bank of San Francisco 2002). To check the sensitivity of our poverty rates to the choice of time period, we estimated rates for two and four months, which varied from the reported rates by 1 to 2 percentage points.

4. Specifically, this scale fixes the ratio of the scale for two adults and one adult to 1.41. For single parents the scale is (A+0.8+0.5*(C-1))0.7, where A is the number of adults and C is the number of children. All other families use the formula (A+0.5*C)0.7. See Short (2001) or Citro and Michael (1995) for more information.

5. The U.S. Census Bureau defines a family as a group of two people or more (one of whom is the householder) who are related by birth, marriage, or adoption, and reside together. The PSID definition of a family unit (FU) is a group of people living together who are usually related by blood, marriage, or adoption. Unrelated persons can be part of an FU, if they are permanently living together and share incomes and expenses. Obviously, the two definitions are not equivalent. The Census Bureau definition excludes one-person units and the PSID definition includes all persons living together (if they share incomes and expenses), although they may not be related.

6. This middle-age preretirement group was selected as the reference group due to its relatively stable asset poverty rate and population share.

7. These marginal effects are the product of the coefficient estimate and the adjustment factor (Caner and Wolff 2002).

8. The education dummies take the value of one, if the household head has at least the specified degree, and zero, if not. For example, the “high school” dummy is equal to one, if the head has 12 or more years of formal education, and zero otherwise. For a college graduate, all three of the education dummies are equal to one. Thus, the estimate of the coefficient on an education dummy is an estimate of the additional value of obtaining the degree relative to the lower degree.

9. Marginal effect vectors are functions of sample means, which normally change over time. In order to keep our results free of the influence of changing, we choose to identify trends by looking at differences in beta coefficients and not in the marginal effects. The changes in beta coefficients indicate the changes in the contribution of each independent variable to the index. Due to the nonlinearity of the probit model, it is impossible to interpret these changes as changes in the contribution to the probability of being asset poor.

10. For instance, for a male head who is 71 years or older, beta coefficients for age and family type in the NW poverty regression sum to –0.641 in 1984; the sum declines to –0.394 in 1999.

11. For example, the conditional probability of being LIQ-poor in the second survey year (t2) can be expressed as: P(LIQpoort2 | LIQpoort1) = P(LIQpoort2 n LIQpoort1)/P(LIQpoort1)

12. The analysis of changes in family composition is somewhat limited in this brief, since the longitudinal samples are restricted to households where the head remains the same. The only change allowed is the movement of family members, such as the marriage of the head or the birth of a child.

Appendix

Data source

The following components of household wealth are available in the PSID data:

(1) Main Home: The net value equals the house value minus the remaining mortgage principal.

(2) Other Real Estate: The net value of any real estate other than the main home, such as a second home, land, rental real estate, or money owed on a land contract.

(3) Farm and Business: The net value of farm or business assets.

(4) Stocks: Value of shares of stock of publicly held corporations; mutual funds; or investment trusts, including stocks in IRAs (a separate item in 1999).

(5) Checking and Saving Accounts: Value of checking or saving accounts; money market funds and investment trusts; savings bonds; and Treasury Bills, including IRAs (a separate item in 1999).

(6) Other Savings: Any other savings or assets, such as bond funds, cash values of life insurance policies, a valuable collection for investment purposes, or rights in a trust or estate.

(7) Other Debts: Any other debt besides mortgage, such as credit card debt, student loans, medical and legal bills, and loans from relatives.

Definition of wealth

The three measures of wealth are defined as follows:

“Net Worth” (NW), or marketable wealth, is the sum of the items (1) to (6), minus (7).

“Net Worth minus Home Equity”(NW-HE) is the sum of items (2) to (6), minus (7).

“Liquid wealth” (LIQ) is the sum of (4), (5), and (6).

References

Caner, Asena, and Edward N.Wolff. 2002. “Asset Poverty in the United States, 1984–1999: Evidence from the Panel Study of Income Dynamics.”Working Paper No. 356. Annandale-on-Hudson, N.Y.: The Levy Economics Institute.

Citro, Constance F. and Robert T.Michael, eds. 1995.Measuring Poverty: A New Approach.Washington, D.C.: National Academy Press.

Conley, Dalton. 1999. Being Black, Living in the Red: Race,Wealth and Social Policy in America. Berkeley: University of California Press.

Crystal, Stephen, and Dennis Shea. 1990. “The Economic Well-Being of the Elderly.” The Review of Income and Wealth. Series 36, No. 3, September: 227–47.

Danziger, Sheldon, and Peter Gottschalk. 1995. America Unequal. Cambridge,Massachusetts: Harvard University Press.

Federal Reserve Bank of San Francisco. 2002. “Recent Trends in Unemployment Duration.” FRBSF Economic Letter, No. 2002-35, November.

Foster, James E., Joel Greer, and Erik Thorbecke. 1984. “A Class of Decomposable Poverty Measures.” Econometrica. Vol. 52, Issue 3: 761–66.

Freeman, Richard B. 2001. “The Rising Tide Lifts . . . ?”National Bureau of Economic Research.Working Paper No. 8155.

Gittleman, Maury, and Edward N.Wolff. 2000. “Racial Wealth Disparities: Is the Gap Closing?”Working Paper No. 311. Annandale-on-Hudson, N.Y.: The Levy Economics Institute.

Haveman, Robert, and Edward N.Wolff. 2001. “Who Are the Asset Poor? Levels, Trends, and Composition, 1983–1998.” Mimeo.

Moon, Marilyn. 1977. The Measurement of Economic Welfare: Its Applications to the Aged Poor. New York: Academic Press.

Oliver,Melvin L., and Thomas M. Shapiro. 1990. “Wealth of a Nation: At Least One-Third of Households are Asset-Poor.” The American Journal of Economics and Sociology.Vol. 49, No. 2, April: 129–50.

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Ruggles, Patricia. 1990. Drawing the Line: Alternative Poverty Measures and Their Implications for Public Policy.Washington, D.C.: Urban Institute Press.

Ruggles, Patricia, and Roberton Williams. 1989. “Longitudinal Measures of Poverty: Accounting for Income and Assets over Time.” The Review of Income and Wealth. Series 35, No. 3, September: 225–44.

Sherraden,Michael. 1991. Assets and the Poor: A New American Welfare Policy. New York:M. E. Sharpe.

——— 2001.“Asset Building Policy and Programs for the Poor,” in Thomas M. Shapiro and Edward N.Wolff, eds., Assets for the Poor:Benefits of Spreading Asset Ownership. New York: Russell Sage Foundation.

Short, Kathleen. 2001. “Experimental Poverty Measures: 1999.”U.S. Census Bureau, Current Population Reports: 60–216.Washington, D.C.: U.S. Government Printing Office.

Weisbrod, Burton A., and W. Lee Hansen. 1968. “An Income–Net Worth Approach to Measuring Economic Welfare.” American Economic Review. Vol. 58, No. 5, December: 1315–29.

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About the Authors

Asena Caner is a Research Scholar at The Levy Economics Institute. She is a member of a research team that developed the Levy Institute Measure of Economic Well-Being (LIMEW), a model providing policy options and guidance toward improving the distribution of economic well-being in the United States. A recent LIMEW publication outlines the model’s concept, measurement, and findings for the United States in 1989 and 2000. Her research interests include poverty, income and wealth distribution, entrepreneurship and household wealth, and financial liberalization. Caner received a Ph.D. in economics from New York University in 2003.

Edward N. Wolff is a Senior Scholar at The Levy Economics Institute and a professor of economics at New York University, where he has taught since 1974. He is currently a Visiting Scholar at the Russell Sage Foundation in New York (2003–2004), managing editor of The Review of Income and Wealth, and a Research Associate at the National Bureau of Economic Research. Wolff is a past president of the Eastern Economic Association and serves as a council member of the International Input Output Association and the International Association for Research in Income and Wealth. He has acted as a consultant to the Economic Policy Institute, World Bank, United Nations, World Institute for Development Economics Research (WIDER Institute), and Mathematica Policy Research.

Wolff leads a Levy Institute research team that developed the LIMEW model.His principal research areas are productivity growth and the distribution of income and wealth. He is the author (or coauthor) of Top Heavy: A Study of Increasing Inequality of Wealth in America (2002); Retirement Insecurity: The Income Shortfalls Awaiting the Soon-to-Retire (2002); and Downsizing in America: Reality, Causes, and Consequences (2003). Wolff received a Ph.D. from Yale University in 1974.

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