National Poverty Center Gerald R. Ford School of Public Policy, University of Michigan www.npc.umich.edu
“Consumption, Income, and the Well‐Being of Families and Children” This paper was delivered at a National Poverty Center conference. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Poverty Center or any sponsoring agency. Consumption, Income, and Well-Being
Among the Mature Population
Kerwin Kofi Charles*
Sheldon Danziger **
Laurie Pounder**
Robert F. Schoeni**
April 2006
Abstract: Using nationally representative survey data, from a mature
population that includes measures of income, consumption, and a broad
set of indicators of well-being, we study whether income or consumption
better identifies individuals' material well-being. We find that, for the
mature population, income appears superior to consumption. We provide
some speculative evidence about why this relationship is different than
that found for other populations in the recent literature.
* University of Chicago. ** University of Michigan. Financial support was received from a grant
to the National Poverty Center at the University of Michigan from the Assistant Secretary for
Planning and Evaluation, U. S. Department of Health and Human Services. Address
correspondence to Robert Schoeni, University of Michigan, Institute for Social Research, Ann
Arbor, MI 48109; bschoeni@umich.edu. Any views expressed in this paper are those of the
authors and not necessarily those of the National Poverty Center.
CONSUMPTION, INCOME, AND WELL-BEING
AMONG THE MATURE POPULATION
I. INTRODUCTION
What is the best way to summarize an individual’s well-being – especially whether or not
they fall below the poverty threshold? In both policy applications and academic research,
conventional practice is to classify individuals as poor on the basis of their income. Incomebased poverty measures have much to recommend them. First, because income is readily
available on many household surveys and from various business and tax records, estimating
income poverty is straightforward. Second, because income data have been collected for many
years and in most countries, trends in poverty within a country over time and comparisons in
poverty across countries can be readily studied. Finally, an income-based measure corresponds
to the size of a households’ budget set – the amount of goods and services it could command.
Income-based poverty measures are agnostic about differences in preferences which
might cause households to allocate resources differently out of their disposable income.
Measuring poverty using income classifies households based on what they are able to do given
the resources they command, rather than based on what they choose to do with those resources –
a distinction that is appealing because it denotes poverty as an economic constraint.
Despite these advantages, many authors have argued that measuring poverty on the basis
of consumption might be superior to the conventional income-based approach (Jencks, 1984;
Mayer and Jencks, 1989; Slesnick, 2001). Because income fluctuations from one year to the
next tend to be greater than consumption fluctuations, income measured in any given period is, at
best, an imperfect indicator of a household’s true underlying level of hardship. By contrast,
theory suggests that households smooth consumption over time, with the relatively constant level
consumed over the lifecycle reflecting people’s true underlying level of command over
resources, or their latent “permanent income.”
That an individual’s income at any point in time is only weakly related to her underlying
level of economic hardship is documented by Jencks and Mayer (1989; 1993) who show that
income-based poverty explains only about one quarter of the variation in explicit questions of
2
material hardship.1 Similarly, Meyer and Sullivan (2003; 2004) show that hardship among
single-mother families is better predicted by low consumption than by low reported income.
A second reason for measuring poverty based on consumption relates to measurement
error. Meyer and Sullivan (2003) show that reported consumption at the lower end of the
income distribution often exceeds reported income. Of course, this fact does not reveal whether
it is income or consumption that is mis-reported. However, they argue it is likely that most
measurement error is attached to income, especially for those at the lower end of the distribution
due to differences in the complexity of reporting income and consumption. For persons of low
income, flows of income into the household come from a range of earnings and transfers, some
of which may be in kind (Edin and Lein, 1997). Moreover, because eligibility for certain
government programs requires that a household’s income remain below some level, low income
respondents may be inclined to underreport income to survey interviewers. The situation is
different for consumption; households gain nothing by claiming to have consumed less or more
than is true, and, it is argued, consumption for these households can be more succinctly
summarized.
In this paper, we use the Health and Retirement Study to evaluate income poverty,
consumption poverty and their associations with a range of hardship indicators among mature
Americans (those over 52 years of age). The connection between income, consumption and
well-being differs for older persons compared to the rest of the population. For example, theory
suggests that mature persons are more likely to have consumption which exceeds income for
reasons of life-cycle consumption so that an income/consumption differential does not
necessarily suggest the presence of measurement error in income. Mature persons also confront
circumstances that make their poverty experiences qualitatively different from that of younger
persons. One factor is labor force withdrawal, which lowers earnings and raises dependence on
previously-acquired savings to maintain consumption levels and the receipt of government
transfers like Social Security. Finally, elderly persons typically experience reductions in health
and physical functioning that contribute to significant expenditures on health and personal care.
We begin by measuring the incidence of poverty using both income and consumption
measures. We find that fewer mature persons are consumption poor than are income poor.
1
Indeed, these authors find that controls for permanent income do not appreciably increase the explanatory power of
income-based measures, further raising concerns about how accurately any measure of income measures the level of
hardship a family confronts.
3
These results are similar to those of Meyer and Sullivan (2003) who study income and
consumption poverty among welfare mothers, and Slesnick (1993) and Cutler and Katz (1991)
who find that the prevalence of consumption poverty is generally lower than its income-based
counterpart, especially for the elderly. We document that the consumption poor are not merely a
subset of the income poor because many consumption poor households are not classified as poor
under an income-based measure.
Having estimated two measures of poverty among mature persons, we consider which
measure is more highly correlated with a range of indicators of economic and other hardships.
Previous work, with the possible exception of Cutler and Katz (1991), suggests that the
consumption based measures should be preferred.2 For example, although he does not directly
measure hardship, Poterba (1990) argues that consumption-based measures are superior to
income measures as indicators of economic well-being for the elderly.3 Similarly, Slesnick
argues that consumption-based rather than income-based measures best identify persons who
most need assistance. Finally, Meyer and Sullivan (2003; 2004), in a several recent papers
focused on single mother families, find that their consumption poverty tends to be more strongly
correlated with alternative measures of material hardship than is true for income poverty.
Following a similar procedure to that of Meyer and Sullivan, we relate households’
income poverty and consumption poverty to a range of indicators of economic hardship,
including subjective wellbeing, home quality, and food security. We find that, across many
measures, consumption poverty is not more closely associated with hardship than is income
poverty. Thus, the superiority of consumption-based poverty measures over income-based
measures cannot be presumed for all subpopulations. For mature persons, a complete picture of
poverty seems to require knowing about both the degree to which both household income and
consumption do not rise to particular levels. We discuss in the end of the paper why this might
be more likely for the mature population than for other groups of poor persons.
The remainder of the paper is as follows. In the next section, we describe the data we
analyze from the Health and Retirement Study (HRS). In Section III, we classify mature
2
Cutler and Katz (1991) contend that income-based poverty measures are not appreciably worse than consumption
measures. They show that consumption and income moved very closely in the 1980s and that groups with large
declines in income tended to have similar declines in consumption. They conclude that, “ Standard income-based
measures do not seem to be a misleading guide to change in the distribution of permanent income.”
3
Poterba implicitly argues that the divergence between income and consumption is a measure of the degree of mismeasurement in the former. He shows that the divergence between one’s position in the income and expenditure
distributions is larger for mature persons than for other groups, hence he recommends the use of consumption.
4
individuals by whether they are poor as defined by income on the one hand and consumption on
the other, and assess the degree to which these classifications overlap. In Sections IV and V, we
relate individuals’ positions in the income and consumption distributions to their levels of
material hardship. Section IV looks at the relationship between well-being and being
specifically in the two categories of persons who are income and consumption poor; Section V
studies how well-being is related to consumption and income throughout the entire distribution
of these two variables. In Section VI, we discuss alternative explanations for our finding that
consumption seems to a worse – and certainly does not do a better – job of identifying hardship
for mature persons than do income based measures.
II.
DATA
The 2001 Consumption and Activities Mail Survey (CAMS) of the Health and
Retirement Study (HRS) panel was mailed to a random sample of 5,000 HRS households, who
were interviewed by telephone in 2000. With a response rate of seventy-seven percent, the 2001
CAMS data has 3,866 household observations. Designed to be nationally representative of the
population over age 50 as of 1998, by 2001 the HRS, and therefore the CAMS, includes
households with respondents aged 53 and over.
The CAMS asked respondents about individual activities and household consumption
across twenty-six separate categories. Consumption expenditures were reported in weekly,
monthly, or annual amounts. Respondents were also asked for purchase prices paid in the past
twelve months on vehicles and on specific household durable goods.4
Comparing the consumption categories included in the CAMS with the more-detailed
Consumer Expenditure Survey (CE) shows that the CAMS accounts for over 90 percent of total
CE spending. Despite a less-comprehensive list of categories, after annualizing amounts
4
Household durables in the CAMS include refrigerators, dishwashers, washing machines, TVs, and computers.
5
reported as weekly or monthly in the CAMS, total average annual spending in the CAMS is
somewhat higher than in the CE for this population (Hurd and Rohwedder, 2006).5
The expenditure information in the CAMS is not equivalent to the economic concept of
consumption. This is particularly true for the elderly – many of whom consume housing with
almost no expenditures because they own their homes outright. Thus, total consumption differs
from CAMS total spending in two ways. First, the purchase price for vehicles and household
durables is not included in current consumption. (Any difference between purchase price and
actual expenditure that year, most relevant for vehicles, is not available in CAMS). Instead, a
value for vehicle consumption is imputed based on the relationship between household
characteristics and net outlays on new and used cars and trucks in the 2001 CE. Second, for
homeowners, spending on mortgage, property tax, and homeowners’ insurance is replaced by an
imputed rental equivalence value for their home. Rental equivalence values were estimated
using the relationship between housing characteristics and reported rental equivalence for owned
homes in the 2001 CE. These imputations are described more fully in the appendix.
Income data are drawn from the 2002 HRS core interview which administered a detailed
set of questions about all income sources. The core HRS interview is also the source of
numerous measures of well-being.
III. THE JOINT DISTRIBUTION OF INCOME AND CONSUMPTION POVERTY
Table 1 shows the joint distribution of families across the two poverty measures. The
columns divide the sample into three income-based poverty categories; the rows do the same for
consumption poverty. A household’s income is divided by the official poverty line defined for
its household size and whether or not the family head is aged 65 or older. Poor families are those
with a ratio of less than 100 percent of their poverty threshold; near poor families are those with
incomes between 101 and 150 percent of the relevant threshold; and non-poor are those with
5
Following a procedure suggested by Hurd and Rohwedder (2006), the CAMS data were modified to address
design and data entry issues. To identify egregious cases of decimal placement error or respondent error where
monthly or annual dollar figures were reported as weekly or monthly spending, for certain spending categories, each
household’s reported annualized spending in the 2001 CAMS was compared to its response in the 2003 CAMS.
Where spending within a category varied between these years by more than a factor of 6, reasonable ranges were
used to determine if one observation was distorted by a factor of 12 (monthly for annual), 52/12 (weekly for
monthly), or 100 (misplaced decimal). In addition, twelve outliers in the categories of home maintenance and yard
supplies were adjusted for decimal placement and time period errors based on visual inspection of the handwritten
questionnaire.
6
family incomes greater than 150 percent of the relevant threshold. This classification scheme is
similar to those used by other authors who have studied income and consumption poverty.
The last row in the first column shows that in 2001, 13.9 percent of individuals ages 53
and older were income poor in the HRS/CAMS. The second and third columns of the last row
show that 11.2 and 74.9 percent of HRS/CAMS respondents were, respectively, near-poor and
nonpoor.
The consumption poverty rate of 6.9 percent shown in the first row and last column of the
table is half the income poverty rate. This consumption poverty rate is very similar to
consumption poverty rates found for the 1980s by authors like Cutler and Katz (1991), and
Slesnick (1989).6 Another 8.8 percent of respondents are near consumption poor and 84.3
percent are not consumption poor.
The table allows for an easy examination of the extent to which households’ poverty
status overlap as defined by the two measures. We find that only thirty percent (4.2/13.9) of the
income poor are also consumption poor. About two-thirds of the income poor are not
consumption poor: twenty-two percent (3.1/13.9) are consumption near-poor and 47 percent are
not consumption poor. Thus, given that a household is income poor, it is relatively difficult to
predict the category into which it falls on the dimension of consumption poverty.
Is it similar difficult to predict income poverty, conditional on knowing a household’s
consumption poverty? The first row of Table1 shows that fully forty percent of the consumption
poor [(1.2 + 1.5)/6.9] are not income poor; these consumption poor families are either income
near poor or non-poor as measured by income. The joint distribution presented in Table 1
shows that the consumption poor are not simply a subset of the income poor; to a significant
degree the populations corresponding to the two different poverty measures are not overlapping.
Table 2 displays the relationship between income and consumption for the full
distribution of income (columns) and consumption (rows), divided into deciles. Both income
and consumption are adjusted for family size differences using the official poverty line
equivalence scales. The table shows that at both the upper and lower ends of the distribution,
individuals’ relative positions in the income and consumption distributions are quite similar. For
example, among individuals in the lowest income decile, 39 percent are in the lowest
6
Cutler and Katz estimate elderly consumption poverty ranging from 4 to 6 percent over the 1980s. Slesnick has
rates of 5 to 11 percent for those ages 55 to 64 and 2 to 4 percent for those over age 65, averaging about 6 percent
across the years and the two age groups.
7
consumption decile, and 67 percent are within the bottom three deciles. Only 10 percent of the
lowest income decile rise to the top three deciles in the consumption distribution. The patterns
are broadly similar reading across the rows. Eighty percent of those in the lowest consumption
decile are in the bottom three income deciles, while only 1 percent of those in either of the top
two income deciles are in the lowest consumption decile.
In the middle of both the income and consumption distributions, individuals tend to be
more evenly distributed across deciles as defined by the alternative measures. For example
individuals in the 5th consumption decile are equally likely to be in any income decile from the
3rd to the 8th. Similarly, individuals in the 5th income decile are distributed relatively evenly
across the 3rd to 8th deciles of consumption. Overall, the simple correlation between an
individual’s income-to-needs rank and their consumption-to-needs rank is 0.54. Looking across
the entries in the table, it is clear that this relatively small number reflects the fluidity in the
middle of the two distributions. However, for persons in either the lowest income or
consumption deciles, being in one of these very low-resource groups is rarely associated with
being in a substantially higher category according to the other measure.
IV.
INCOME AND CONSUMPTION POVERTY AND WELL-BEING
The HRS/CAMS includes many measures of well-being across several domains,
including physical health, mental health, housing, food security, and wealth. Intuitively, it
would seem that a measure which more accurately represented true underlying poverty would be
much more closely associated with these alternative objective indicators of hardship. Our
results indicate that, consumption poverty appears much more weakly associated with direct
measures of hardship than income poverty.
The top panel of Table 3 shows results for twelve indicators of physical health. Only for
one—arthritis--is there a statistically greater prevalence among the consumption poor than
among the income poor, where “poor” is here defined as being in the lowest decile. For the other
categories of physical health, mean prevalence does not differ significantly for those in the
bottom decile of income and consumption.
Within the second decile, five physical health indicators--hypertension, heart disease,
arthritis, activities of daily living (ADL), and instrumental activities of daily living (IADL)--are
statistically significant and worse for the income poor relative to the consumption poor. In no
8
case is the prevalence of any physical health problem higher for the second consumption decile
than for the second income decile.
A similar pattern is evident for the five mental health problems shown in the second
panel of Table 3. In the two cases where there are statistically significant differences between
the bottom income decile and the bottom consumption decile--not enjoying life in the past week,
and not being happy in the past week--the income poor have lower well-being.
The nine housing variables shown in the third panel include subjective measures, such as
quality of housing and neighborhoods, and objective measures, such as the number of rooms in
the home. However, some objective measures, specifically the number of rooms and the type of
housing (single family home, mobile home, etc), are included in the imputation of rental
equivalence and therefore directly affect the consumption measure. Therefore, it is not
surprising that the bottom income group has more rooms in their homes than the bottom
consumption group (5.1 vs. 4.6 rooms). Since these measures are mechanically related to
consumption, they cannot be interpreted as independent indicators of well-being from the
perspective of comparing income and consumption measures.
For the housing and neighborhood quality measures that are not included in imputed
consumption, the estimated correlations with hardship do not, in general, suggest that the
consumption poor are worse off than the income poor. For example, individuals in the lowest
income decile report living in worse neighborhoods than those in the lowest consumption decile:
24.7 vs. 16.6 percent, respectively, say their neighborhood is fair or poor quality.
The rental equivalence consumption attributed to homeowners also means that the bottom
consumption decile contains relatively few homeowners, generating low mean housing wealth
for the consumption poor. This translates into somewhat higher mean total asset levels for the
income poor relative to the consumption poor: $88,400 vs. $52,800. Average values for wealth
are, however, highly susceptible to outliers. Because of this concern, we trim 1 percent from the
top and bottom of the wealth distributions. The mean asset values for income and consumption
poor with this trimmed sample fall to $59,400 and $45,500 respectively, which are not
significantly different (not shown in tables). We also find no significant difference in nonhousing wealth. (The trimmed estimates show slightly higher values for the consumption poor).
Finally, total assets including housing wealth is indistinguishable at the median of the
distribution.
9
Socio-demographic factors are highly correlated with well-being, and the distribution of
these factors for each of the two groups is displayed at the bottom of Table 3. The two groups
are fairly similar in terms of education and racial composition. The age distributions, however,
differ. The age distribution of the bottom two consumption deciles looks not unlike the age
distribution for the whole sample. However, for income, the mean age jumps from 64.9 for the
lowest decile to 71.7 for the second decile. Compared to the second decile, the lowest decile is
less populated by the retired and widows and more populated by disabled, unemployed, maritally
separated, and other households with recent income losses, all of which tend to be younger than
the retirees and widows that make up more of the second decile.
In Table 4, we report differences in well-being between the income poor and the
consumption poor for each measure shown in Table 3. The first and fifth columns, labeled
“All,” replicate the differences from columns three and six of Table 3, respectively. The other
columns show these differences when the sample is restricted to retirees or to widows, or when
medical out of pocket expenditures (“No MOOP”) are excluded from consumption. The deciles
evaluated in each column pertain to the bottom and second lowest decile within that subsample.
Much interest in old-age poverty focuses on widows, who account for 35 percent of poor
people 65 and older. Among widows, there is no evidence that the consumption poor are worse
off than the income poor. For example, six measures of well-being are found to be statistically
significantly different between the income and consumption poor, but four show that well-being
is worse for the income poor than the consumption poor. And the only two comparisons that
favor consumption variables are both mechanically related to imputed consumption (single
family home and number of rooms in the house). In the second decile, well-being is worse for
the consumption poor for two of the three significantly different indicators that are not used to
impute consumption.
Table 4 also shows analyses restricted to individuals who report themselves as having
retired from the labor force. There are even fewer statistical differences for the retired: only two
indicators not used to impute consumption had significant differences between those in the
bottom deciles of income and consumption. One was worse for the consumption poor (arthritis)
and one was worse for the income poor (home condition).
Thus far, we have included medical out-of-pocket spending (MOOP) in total
consumption. Some MOOP spending, such as nursing-home services or home health care,
10
represents direct consumption of services and should be included in consumption expenditures.
But some MOOP spending, such as on prescription drugs, may be treated as analogous to
durable goods purchases, with flows of good health resulting from the purchase. Returning to
the entire sample, columns 4 and 8 in Table 4 compare well-being between the income and
consumption poor if MOOP is excluded from consumption. The results are quite similar to
those in columns 1 and 5 respectively for all spending. On the whole, we find that across
multiple measures, and for particular sub-groups of the elderly, consumption poverty is either
less closely or equally (in a statistical sense) related to hardship than is income poverty.
V. INCOME, CONSUMPTION AND WELLBEING THROUGHOUT THE
DISTRIBUTION IS WELL-BEING
The preceding section looks at the relationship between poverty and well-being according
to income and consumption. In this section, we study how well-being is related to income and
consumption through the full distributions for these two variables. Formally, for each of the 23
indicators of well-being, we estimate a regression controlling for age and gender, with the key
covariate being the individual’s percentile in the distribution of income-to-needs. Identical
models are estimated using the percentile of the consumption-to-needs distribution as the key
covariate. Again, the logic here is that the estimated relationship between well-being and
percentile rank would be larger for whichever outcome (income or consumption) better reflects
underlying well-being.
For all 23 measures, we expect that higher income and consumption will be associated
with higher well-being, keeping in mind that for some measures (i.e., “own a second home” and
“number of rooms”) a higher value means a better outcome. The results in Table 5 show that
this expectation is confirmed for 22 of the 23 measures, with the exception being cancer, which
shows a small positive association with both income and consumption. All of the regression
coefficients in Table 5 are significant. Logit models are estimated for all dichotomous dependent
variables and OLS for the remaining dependent variables.
The association with income and consumption is substantial for many outcomes. An
increase in income-to-needs percentile by 10 points lowers the probability of being in poor or
fair health by 4.5 percentage points (column 2). A similar increase in the consumption-to-needs
percentile is associated with a reduction in the probability of being in fair or poor health of 2.9
11
percentage points (column 4). BTthe consumption effect is statistically significantly smaller
than the income effect (column 5). Taking all of the results together, for only 2 of the 23
indicators (cancer and number of rooms) is consumption more strongly associated with the
indicator than is income, and the difference between the measures for these two indicators is not
significant. Clearly the evidence does not support the use of consumption over income.
The regression specifications in Table 5 implicitly presume an underlying linear
relationship for the patterns of interest. We loosened this assumption by calculating nonparametric estimates of the relationships of interest. Figures 1-8 display non-parametric kernel
density plots for eight different well-being measures assuming a band width of 0.4 (0.2 leads to
the same substantive conclusions), the top panel for income and the bottom panel for
consumption. These plots reveal the same basic pattern as those shown in Table 5. That is, there
is no evidence indicating that consumption is more strongly associated with well-being.
The models in Table 5 use the percentile of the income-to-needs ratio or the
consumption-to-needs ratio as the key covariate. An alternative would be to focus on the
marginal effect on well-being of a needs-adjusted dollar increase in income or consumption.
These results (Table 6) are qualitatively quite similar to those shown earlier, in that they again
show a generally weaker estimated relationship between consumption and well-being than that
between income and well-being.
VI. WHY IS CONSUMPTION NOT A BETTER MEASURE OF WELL-BEING?
Why does consumption appear to be an inferior measure of well-being and actual
hardship among mature individuals as compared to income?
Earlier, we noted that several
considerations ought conceptually to affect the relative desirability of income versus
consumption as indicators of underlying well-being. In the particular case of the mature
population, three of these factors, lower the relative desirability of consumption as a measure of
well-being. One consideration is measurement error. Unlike the young single mothers or other
at-risk groups, income flows for the elderly may well be measured with considerably less error.
Social Security income is a large part of the transfers the elderly receive, and the value of these
flows can be readily determined. Simply because of the relatively small errors that confound it,
income for the elderly may more accurately reflect underlying well-being.
12
Another consideration has to do with the fact that some aspects of measured consumption
do not reflect expenditures on outlays for things that are unambiguously “good”. A prime
example of this type of consumption is that spent on health care. Individuals oftentimes devote
resources to health care because in fact their well-being has deteriorated and they need medical
treatment. For no other group do health expenditures constitute as large a part of overall
measured consumption as it does for the elderly. For this reason alone, we would expect
consumption – especially measured in the form of expenditures as is done in the paper and in
most of the literature – to be an imperfect indicator of true well-being for the elderly.
A third consideration about consumption-based measures is that such measures
necessarily involve an element of choice. Thus, someone measured in the data as consumption
poor might be able to afford more than he or she chooses to expend because of factors related to
preferences. Many factors related to preferences probably do not differ between the elderly and
other groups, but this is not true about discount rates, time preference, bequest motives, or
changing marginal utility for important consumption items. Is there evidence that some portion
of the consumption poor are actually consuming less than they could because of preferences?
In much of the analysis above, we have compared low income and low consumption
households decile by decile and examined the association of income and consumption with wellbeing throughout the distributions. However, both for policy purposes and conceptual clarity, it
is also important to describe the population who is income poor and the population who is
consumption poor using standard poverty thresholds. In this section, where we briefly review
the evidence about the role of preference, we therefore focus on groups characterized by whether
they are poor according to the two measures.
Table 7 presents the HRS well-being measures for four groups: individuals who are both
income and consumption poor, income poor but consumption non-poor, income non-poor but
consumption poor, and neither income nor consumption poor.7 The results in the first column
demonstrate that the group which is poor by both measures is noticeably, and sometimes
dramatically, worse off than any of the other groups. Most measures within each domain-physical health, mental health, housing, food, wealth, and work and income--show very low
7
Any concern about comparing two groups of different sizes (there are more income poor households than
consumption poor households) could be addressed by adding the consumption near-poor to the consumption poor
group (but no change with the income groups). This would effectively equalize the number of households in the
second and third columns of Table 7. Such an exercise shows no substantive change to our analysis or conclusions.
13
levels of well-being for this group. The mean wealth for this truly poor group is about one-tenth
that of groups who are poor by only measure, shown in the second and third column. About
sixty percent of those who are both income and consumption poor have less than $1000 of net
wealth, even including housing assets. The median value for their non-housing assets was just
$60 in 2000. The very low outcomes for this group for most dimensions of well-being suggest
that their poverty status by either measure reflects the presence of genuine resource constraints.
At the other extreme are persons who are not poor by either measure. The mean values
for this group are shown in the fourth column. Two findings are noteworthy. One is that, by
every measure, these non-poor persons fare better than any other group. The other result is that
even though they do not appear to be resource-constrained, some outcomes, especially health, are
quite negative.8 While this group is far less likely than others to rate its health as “poor” or
“fair”, more than eighty percent have some major health condition. Similarly, one-fifth
describes their activities over the past week as having been an “effort”. Thus, as discussed
above, the relationship between well-being and control over material resources (or consumption)
is complicated for elderly households.
Of course, the most interesting numbers in Table 7 are for those persons who are poor by
one definition, but not poor by the other. Are income-poor/consumption non-poor households
worse off than consumption-poor/income non-poor households? And, what does the difference in
their objective indicators of well-being suggest about the degree to which low consumption
among the elderly reflects an aspect of choice rather than resource constraint?
The table indicates that income poor but consumption non-poor persons (group 2) are, by
virtually every measure, either similar to or worse off than consumption poor/ income non-poor
persons (group 3).. Among the well-being measures not included in the imputation of
consumption, cancer prevalence is the only one that shows substantially lower well-being for the
latter group.
Group 3 appears to be composed primarily of older persons, who on average are still
accumulating wealth, and have relatively low levels of food insecurity and depression, and
higher levels of life enjoyment and neighborhood quality. In contrast, the income poor persons
in group 2 are, on average, spending down wealth, and have markedly worse food insecurity,
8
That this group is probably not resource constrained is evidenced by the fact that thirteen percent of this group
owns a second home. Also, the mean asset values for group overall is more than $350,000.
14
health problems, depression, home condition, and neighborhood quality relative to group3. This
income poor group spends five times more than the consumption poor/income non-poor on
health expenditures. In addition, the median non-housing wealth for group 2 decreased by 20
percent during this period in contrast to an increase for group 3 of 26 percent.
The only measure(s) for which group 3 could be considered worse off than group 2 are
the objective housing measures. Among group 3, fifty-five percent are renters and only thirtytwo percent are homeowners. They are more likely to live in mobile homes, less likely to live in
single family homes, and have on average fewer rooms in their homes. However, despite
homeownership and size of home, subjective measures of home and neighborhood quality
suggest that housing conditions for group 2 may actually be worse than that of group 3. In fact,
22 percent of group 2 said their home condition was only fair or poor compared to 13 percent of
group 3; 18 percent of group 2 said their neighborhood quality and safety was only fair or poor
versus just 5 percent for group 3. The latter is the same as the rate among individuals who are not
poor measured either by income or consumption.
Overall, the elderly consumption poor who are not income poor do not seem to be
particularly needy. They are happy and relatively healthy for their age. The markedly lower
health expenditures they make as a result partially accounts for their much lower overall
consumption. In contrast, the elderly income-poor/ consumption non-poor demonstrate some of
the hardship of those who are poor by both measures. They are likely to have consumption that
is exaggerated by larger than average health expenditures and imputed housing consumption that
does not align well with their subjective assessments of housing and neighborhood satisfaction.
Separate analyses for the populations under and over 65 years old lead to similar
characterizations of groups 2 and 3 (not shown in tables; available on request). For the 53-64
year olds, individuals in the income poor group (group 2) are much more likely to be disabled
and unemployed (Table 7). The median household in this group lost over half their income
between these two periods while the median for the consumption poor/income non-poor (column
3) experienced gains of 6 percent.
To summarize, the income poor who are not consumption poor seem to face some
economic constraints and hardships and have exaggerated measured consumption relative to
their standard of living. The consumption poor/income non-poor are older but healthier, have
15
high subjective measures of satisfaction with their lives and housing, and a large portion of them
are still accumulating wealth.
VII. DISCUSSION
Our results in this paper suggest that income-based measures appear to better distinguish
the truly poor than do consumption-based measures. Using a large number of indicators from a
broad set of domains including physical health, mental health, housing, food security, and
wealth, when there are differences they are in favor of income as a proxy for well-being. The
income poor appear to have lower well-being than the consumption poor, and income is more
strongly predictive of well-being.
Measurement error is an important consideration when comparisons are made between
income and consumption. For persons with low income, flows of resources into the household
come from a range of sources, including earnings, government transfers, and family transfers, as
well as assistance in kind (Edin and Lein, 1997). This complexity of sources of support may
lead to under-reporting in typical income surveys. While this may be the case for younger poor
families, it may not be as significant for older low-income people. Social Security is the major
income source for most elderly households, especially the poor. Social Security is a regular
payment made each month with no stigma attached, making it less susceptible to mis-reporting
than other government transfers.
The typical approach to estimating consumption is to use survey data on expenditures
combined with characteristics of durable goods ownership to impute a flow of services from the
durables. While collecting accurate income data is difficult, calculating consumption has its own
challenges. Some expenditures, such as those for medical care, are quite challenging to measure.
Moreover, the imputation of the flows of services from cars, homes, and other durables are
derived from imperfect methods that most likely have substantial error. The consumption data
used in our analysis, although comparable to the Consumer Expenditure Survey in the aggregate,
uses broad categories of spending recalled over a combination of weekly, monthly, and annual
time periods, thus making measurement error a potential concern.
Finally, consumption and income measures are distinct economic phenomenon and
represent different components of financial well-being. Researchers and policy analysts should
continue to assess and evaluate both domains and strive to improve their measurement. While a
16
single summary indicator is useful for some policy driven purposes, both measures should be
examined by the scientific community.
17
APPENDIX: IMPUTATON OF TOTAL CONSUMPTION
Similar to Cutler and Katz (1991), the household characteristics used to impute vehicle
consumption include income, family size, education, age, and gender of head, total household
expenditures (less vehicle expenditures), and total expenditures squared. The CE measure of
vehicle consumption, not outlays, is regressed on these characteristics, all of which are available
in the HRS. The coefficients from this imputation regression are applied to households that
either report owning a vehicle in the 2000 HRS or report paying vehicle insurance in the CAMS.
The imputation regression is reported in Table A1.
To impute the flow of consumption from housing, housing characteristics including
property value, census district, urban/rural, number of rooms, and type of housing (such as single
family, apartment, or trailer) were regressed on reported rental equivalence in the 2001 CE for
homes owned by households where the head was over age 52. The coefficients were then
applied to each household’s housing characteristics as reported in the 2002 HRS. This
regression, whose estimates are reported in Table A2, has an R-square of .40, very similar to that
for the hedonic regression in Johnson, Shipp, and Garner (1997) that regresses actual rent paid
by renters on factors such as location, rooms, and housing type.
18
REFERENCES
Cutler, David and Lawrence Katz (1991) “Macroeconomic Performance and the Disadvantaged”
Brookings Papers on Economic Activity Vol. 1991 No.2.
Edin, Kathryn and Laura Lein. 1997. Making Ends Meet: How Single Mothers Survive Welfare
and Low-Wage Work. New York: Russell Sage Foundation.
Jencks, Christopher. 1984. “The Hidden Prosperity of the 1970s.” Public Interest. 77 (Fall): 3761.
Johnson, David, Stephanie Shipp, and Thesia Garner (1997) “Developing Poverty Thresholds
Using Expenditure Data” in Proceedings of the Government and Social Statistics Section,
American Statistical Association, August 1997.
Mayer, Susan E. and Christopher Jencks. 1989. “Poverty and the Distribution of Material
Resources.” Journal of Human Resources, 24:88-114.
Mayer, Susan E. and Christopher Jencks. 1993. “Recent Trends in Economic Inequality in the
United States: Income vs. Expenditure vs. Well-Being,” in Poverty and Prosperity in America at
the Close of the Twentieth Century, eds: Edward Wolff and Demitri Popademitrious. New York:
St. Martin’s Press.
Meyer, Bruce D. and James X. Sullivan. 2004. “The Effect of Welfare and Tax Reform: The
Material Well-Being of Single Mothers in the 1980s and 1990s,” Journal of Public Economics,
88, July, 1387-1420.
__________. 2003. “Measuring the Well-Being of the Poor Using Income and Consumption”
Journal of Human Resources, 38:S, 1180-1220.
Poterba, James M. 1990. “Is the Gasoline Tax Regressive?” NBER Working Paper #3578,
January.
Slesnick, Daniel T. 1993. “Gaining Ground: Poverty in the Postwar United States.” Journal of
Political Economy 1901(1): 1-38.
Slesnick, Daniel T. 2001. Consumption and Social Welfare: Living Standard and Their
Distribution in the United States, Cambridge University Press, Cambridge.
19
0
.2
Fair or Poor Health
.4
.6
.8
1
Fair or Poor Health Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
.2
Fair or Poor Health
.4
.6
.8
1
Fair or Poor Health Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
bandwidth = .4
Figure 1. Proportion in Fair or Poor Health
20
80
100
0
.2
Food Security
.4
.6
.8
1
Food Security Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
.2
Food Security
.4
.6
.8
1
Food Security Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
bandwidth = .4
Figure 2. Proportion Food Secure
21
80
100
0
.2
Happy Last Week
.4
.6
.8
1
Happiness Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
.2
Happy Last Week
.4
.6
.8
1
Happiness Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
bandwidth = .4
Figure 3. Proportion Not Unhappy in the Past Week
22
100
0
Felt Depressed Last Week
.2
.4
.6
.8
1
Depression Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
Felt Depressed Last Week
.2
.4
.6
.8
1
Depression Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
bandwidth = .4
Figure 4. Proportion Who Felt Depressed in the Last Week
23
100
0
Fair or Poor Home Condition
.2
.4
.6
.8
1
Fair or Poor Home Condition Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
Fair or Poor Home Condition
.2
.4
.6
.8
1
Fair or Poor Home Condition Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
100
bandwidth = .4
Figure 5. Proportion Who Rated The Condition of Their Home Fair or Poor
24
0
.2
Enjoyed Life Last Week
.4
.6
.8
1
Life Enjoyment Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
.2
Enjoyed Life Last Week
.4
.6
.8
1
Life Enjoyment Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
bandwidth = .4
Figure 6. Proportion Who Were Enjoying Life in the Past Week
25
100
0
Number of ADLs Difficult
2
4
6
ADL Difficulty Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
Number of ADLs Difficult
2
4
6
ADL Difficulty Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
bandwidth = .4
Figure 7. Number of ADLs Have Difficulty With
26
100
0
Number of IADLs Difficult
1
2
3
4
5
IADL Difficulty Across the Income Distribution
0
20
40
60
Percentiles of Income
80
100
bandwidth = .4
0
Number of IADLs Difficult
1
2
3
4
5
IADL Difficulty Across the Consumption Distribution
0
20
40
60
Percentiles of Consumption
80
bandwidth = .4
Figure 8. Number of IADLs Have Difficulty With
27
100
Table 1. Overlap Between Income and Consumption Poverty
Income Poverty
Consumption Poverty
Poor
Near Poor*
Non-Poor
Total
Poor
Near Poor*
4.2%
3.1%
6.6%
13.9%
1.2%
2.2%
7.8%
11.2%
Non-Poor
1.5%
3.5%
70.0%
74.9%
*Near Poor= 101-150% of poverty; Non-Poor=>150% of poverty.
Notes:
1. For homeowners, actual expenditure on mortgage, property tax, and home insurance
is replaced with imputed rental equivalence.
2. Consumption includes imputed flow value for owned vehicles.
3. Consumption does not include either actual expenditure or flow value for small durables
such as washing machines, refridgerators, or computers.
4. Sample is restricted to households that report at least $500 in total spending and
that remain in the HRS sample for the 2002 core survey.
28
Total
6.9%
8.8%
84.3%
100.0%
Table 2. Conditional Distribution of Income Poverty and Consumption Poverty by Decile
Consumption
Decile
1 (lowest)
2
3
4
5
6
7
8
9
10
1 (lowest)
39
18
10
9
4
7
5
3
2
5
2
3
27
23
15
8
8
7
4
4
2
2
14
17
18
10
14
7
8
4
6
3
Income Decile
4
5
6
6
18
11
14
13
13
10
6
6
5
29
5
8
14
16
11
11
12
11
7
6
2
5
11
14
13
9
11
12
11
12
7
8
9
10
3
4
11
12
13
13
14
11
11
9
3
4
7
12
11
8
13
18
13
10
1
1
3
7
8
19
14
15
19
13
1
1
1
1
5
8
9
16
24
34
Table 3. Measures of Well-Being & Demographics, by Income & Consumption
Bottom Decile
Income Consumption Difference
Physical health
Percent Reporting:
Fair or poor health
46.8%
47.8% -1.0%
Any major health condition
89.5%
91.9% -2.4%
Hypertension
57.4%
60.5% -3.1%
Diabetes
27.9%
27.3%
0.6%
Cancer
12.2%
13.8% -1.6%
Lung disease
16.2%
18.1% -1.9%
Heart disease
27.5%
32.2% -4.7%
Arthritis
65.4%
72.3% -6.9%
Stroke
9.2%
8.6%
0.6%
Mean Values of:
ADL difficulty (max 6)
0.67
0.68
-0.01
ADL get help (max 6)
0.23
0.22
0.01
IADL difficulty (max 5)
0.53
0.52
0.02
Mental health
Percent Reporting:
Not enjoying life in past week
13.6%
8.3%
5.3%
Not happy in past week
24.8%
18.7%
6.1%
Depressed in past week
30.9%
28.9%
2.0%
Activities an effort past week
45.5%
41.1%
4.4%
Psych. health condition
30.6%
28.2%
2.4%
Housing & neighborhood
Percent Reporting:
Own home
50.6%
25.1% 25.5%
Home condition fair/poor
23.3%
19.8%
3.5%
Neighborhood fair/poor
24.7%
16.6%
8.1%
Single family home
60.2%
44.5% 15.7%
Mobile home
7.6%
12.2% -4.6%
Own 2nd home
4.1%
2.1%
2.0%
Own vehicle
56.6%
55.2%
1.4%
Mean Values of:
Rooms in house
5.1
4.6
0.50
Home condition (range=1-5, 1=excellent)
2.7
2.6
0.10
Food
Food Insecure
18.1%
14.9%
3.2%
Wealth
Mean wealth: all assets
$88,400
$52,800 $35,600
Mean wealth: non-housing
$44,300
$36,900 $7,400
Median Wealth: all assets
$10,000
$4,000 $6,000
Median Wealth: non-housing
$800
$1,500 -$700
Demographics
Black
24.5%
21.2%
3.3%
Hispanic
22.1%
20.0%
2.1%
Married
18.7%
24.8% -6.1%
Widowed
32.2%
34.8% -2.6%
High school or less education
80.3%
80.8% -0.5%
More than high school education
18.9%
19.2% -0.3%
Age of head
64.9
68.2
-3.3
Household size
2.41
2.42
-0.01
* (**) indicates statistically significant difference at the 0.10 (0.05) level.
30
Second Lowest Decile
Income Consumption Difference
42.2%
91.7%
59.7%
24.3%
14.0%
17.0%
34.6%
71.0%
8.0%
38.8%
88.7%
52.6%
22.3%
15.7%
15.0%
26.1%
65.6%
8.4%
3.4%
3.0%
7.1% **
2.0%
-1.7%
2.0%
8.5% **
5.4% *
-0.4%
0.73
0.26
0.52
0.55
0.20
0.32
0.19 **
0.06
0.20 **
**
*
7.0%
16.2%
24.1%
37.1%
24.2%
9.4%
19.0%
20.1%
34.6%
24.6%
-2.4%
-2.8%
4.0%
2.5%
-0.4%
**
55.1%
17.1%
9.8%
57.3%
9.0%
1.9%
67.0%
58.8%
15.2%
12.4%
57.9%
13.5%
4.0%
74.4%
-3.7%
1.9%
-2.6%
-0.6%
-4.5% *
-2.1% *
-7.4% *
4.9
2.6
5.0
2.6
-0.1
0.0
10.3%
8.4%
1.9%
$91,000
$42,700
$28,400
$4,700
$94,700
$58,800
$36,600
$7,400
-$3,700
-$16,100
-$8,200
-$2,700
14.6%
10.2%
19.0%
49.9%
78.8%
21.2%
71.7
1.95
16.9%
8.6%
33.9%
35.9%
77.4%
22.6%
68.2
2.23
-2.3%
1.6%
-14.9%
14.0%
1.4%
-1.4%
3.5
-0.28
**
**
**
**
**
**
**
**
**
**
**
Table 4. Well-Being & Demographics, by Income and Consumption: Subgroubs
(Difference: Income minus Consumption)
All
Bottom Decile
Retired
Widows
Physical health
Percent Reporting:
Fair or poor health
-1.0%
-0.9%
2.9%
Any major health condition
-2.4%
-4.0%
-2.8%
Hypertension
-3.1%
-3.4%
7.7%
Diabetes
0.6%
4.3%
4.4%
Cancer
-1.6%
-0.7%
-2.9%
Lung disease
-1.9%
0.1%
3.8%
Heart disease
-4.7%
-2.3%
-3.4%
Arthritis
-6.9% ** -8.2% **
2.7%
Stroke
0.6%
0.3%
3.8%
Mean Values of:
ADL difficulty (max 6)
-0.01
0.09
0.04
ADL get help (max 6)
0.01
0.09
0.08
IADL difficulty (max 5)
0.02
0.11
-0.16
Mental health
Percent Reporting:
Not enjoying life in past week
5.3% **
2.5%
5.3%
Not happy in past week
6.1% *
6.4%
-2.5%
Depressed in past week
2.0%
2.3%
7.5%
Activities an effort past week
4.4%
6.8%
11.0%
Psych. health condition
2.4%
1.6%
4.3%
Housing
Percent Reporting:
Home condition fair/poor
3.5%
5.6%
10.0%
Neighborhood fair/poor
8.1% **
5.2%
6.7%
Single family home
15.7% ** 18.7% **
27.2%
Mobile home
-4.6% ** -0.1%
-0.4%
Own 2nd home
2.0%
0.6%
1.6%
Mean Values of:
Rooms in house
0.50 **
0.47 **
0.71
Home condition (range=1-5, 1=excellent)
0.10
0.21 *
0.49
Food
Food insecure
3.2%
5.2%
5.0%
Wealth
Mean wealth: all assets
$35,600 ** $22,900
$1,000
Mean wealth: non-housing
$7,400
-$800
-$17,400
Median Wealth: all assets
$6,000
Median Wealth: non-housing
-$700
Demographics
Black
3.3%
6.8%
2.3%
Hispanic
2.1%
0.7%
3.0%
Married
-6.1%
-9.3%
0.0%
Widowed
-2.6%
-0.5%
0.0%
High school or less education
-0.5%
0.6%
-1.9%
More than high school education
-0.3%
-1.1%
1.9%
Age of head
-3.3 **
-4.6 **
-4.8
Household size
-0.01
0.27
0.38
* (**) indicates statistically significant difference at the 0.10 (0.05) level.
31
No MOOP
-2.5%
-3.3%
-0.7%
-0.9%
-2.7%
-1.1%
-4.1%
-8.2% **
-0.2%
0.03
0.06
0.04
*
*
**
4.1%
6.5% **
2.5%
5.5%
0.3%
**
5.4% *
7.2% **
20.1% **
-5.3%
1.1%
**
**
0.61 **
0.15 *
4.0%
**
Second Lowest Decile
Retired
Widows
All
3.4%
3.0%
7.1% **
2.0%
-1.7%
2.0%
8.5% **
5.4% *
-0.4%
0.19 **
0.06
0.20 **
-5.0%
-2.4%
-3.1%
-0.7%
1.3%
7.5%
-5.0%
-2.1%
-8.9% ** -10.7% **
-1.5%
-5.7%
-4.3%
-8.7%
0.7%
2.9%
-1.8%
-1.9%
-0.12
-0.07
0.02
-0.07
0.04
0.16
No MOOP
3.2%
0.9%
1.7%
4.2%
-2.1%
0.2%
3.7%
4.9%
-0.9%
0.09
0.00
0.11
-2.4%
-2.8%
4.0%
2.5%
-0.4%
-0.8%
-6.7% *
-1.3%
-1.3%
-1.2%
-3.5%
-8.8% **
-4.6%
6.7%
-4.7%
-1.9%
-2.4%
3.0%
1.2%
2.2%
1.9%
-2.6%
-0.6%
-4.5% *
-2.1% *
2.6%
3.8%
7.4%
-7.4%
-0.9%
7.4% *
4.5%
9.4% *
-8.6% **
0.7%
1.4%
0.0%
-1.0%
-5.0% **
-1.6%
0.31 *
0.15
0.0
0.0
-0.1
0.0
0.32 *
0.04
1.9%
-0.9%
2.3%
0.4%
$35,600 **
$3,400
-$3,700
-$16,100
-$8,200
-$2,700
$1,700
-$18,300
$20,700
$3,900
-$7,200
-$18,300
5.2%
6.6%
-1.5%
-7.4%
1.4%
-2.2%
-4.7 **
0.1
-2.3%
1.6%
-14.9%
14.0%
1.4%
-1.4%
3.5
-0.28
**
**
**
**
1.3%
-0.7%
-10.9% **
7.0%
-1.6%
-1.1%
0.9
-0.16
-3.1%
1.2%
0.0%
0.0%
4.4%
-4.4%
0.9
0.01
-3.0%
-2.4%
-20.0% **
16.8% **
-0.6%
0.8%
3.1
-0.3 **
Table 5. Logit and OLS Coefficients for Predicting Well-Being Measures:
Coefficient on Percentile of Income-to-Needs and Consumption-to-Needs
Well-being indicator/dependent variable
Physical health
Fair or poor health
Any major health condition
Hypertension
Diabetes
Cancer
Lung
Heart disease
Arthritis
Stroke
ADL difficulty+ (max 6)
Income-to-needs percentile
Marginal effect
Coefficient of decile change++
[1]
[2]
-0.242
-0.082
-0.071
-0.168
0.037
-0.132
-0.098
-0.065
-0.177
Consumption-to-needs percentile
Difference
Marginal effect
Coefficient of decile change++ in effect: [2]-[4]
[3]
[4]
[5]
**
**
**
**
**
**
**
**
**
-0.045
-0.009
-0.018
-0.023
0.005
-0.013
-0.017
-0.015
-0.008
-0.155
-0.040
-0.038
-0.108
0.031
-0.102
-0.051
-0.050
-0.100
**
**
**
**
**
**
**
**
**
-0.029
-0.006
-0.010
-0.015
0.004
-0.010
-0.009
-0.012
-0.005
-0.016
-0.003
-0.008
-0.008
0.001
-0.003
-0.008
-0.004
-0.003
**
-0.061 **
-0.610
-0.034 **
-0.340
-0.270 **
*
**
**
*
ADL help+ (max 6)
-0.021 **
-0.210
-0.013 **
-0.130
-0.080 *
IADL difficulty+ (max 5)
Mental health
Not enjoying life last week
Not happy last week
Feel depressed last week
Activities an effort last week
Psychological disorder
Housing
Home fair or poor
Neighborhood fair or poor
Own 2nd home
-0.042 **
-0.420
-0.024 **
-0.240
-0.180 **
-0.119
-0.142
-0.190
-0.224
-0.166
**
**
**
**
**
-0.007
-0.015
-0.023
-0.040
-0.020
-0.051
-0.077
-0.112
-0.129
-0.124
**
**
**
**
**
-0.003
-0.008
-0.014
-0.023
-0.015
-0.004
-0.007
-0.009
-0.016
-0.005
-0.299 **
-0.324 **
0.248 **
-0.020
-0.018
0.021
-0.201 **
-0.177 **
0.218 **
-0.015
-0.011
0.019
-0.006 **
-0.007 **
0.002
0.014 **
-0.011 **
0.142
-0.110
0.015 **
-0.009 **
0.151
-0.088
-0.009
-0.022 **
-0.306 **
-0.011
-0.023 **
-0.009
-0.002 **
Number of rooms+
Home condition (range=1-5, 1=excellent)+
Food
Food insecure
+
OLS regressions. ++Derivative of logits evaluated at sample means.
*Significant at the 90% level **Significant at the 95% level
Note: All regressions include age & gender as covariates.
32
*
**
**
**
*
Table 6. Logit and OLS Coefficients for Predicting Well-Being Measures
Coefficient on Income-to-Needs and Consumption-to-Needs
Income-to-needs
Coefficient
[1]
Well-being indicator/dependent variable
Physical health
Fair or poor health
Any major health condition
Hypertension
Diabetes
Cancer
Lung
Heart disease
Arthritis
Stroke
-0.175
-0.023
-0.030
-0.084
0.011
-0.101
-0.035
-0.027
-0.171
Derivative
[2]
Coefficient
[3]
**
**
**
**
-0.032
-0.003
-0.007
-0.012
0.001
-0.009
-0.006
-0.006
-0.007
-0.060
-0.012
-0.025
-0.053
0.004
-0.050
-0.005
-0.028
-0.079
ADL difficulty+ (max 6)
-0.017 **
ADL help+ (max 6)
-0.006 **
IADL difficulty+ (max 5)
Mental health
Not enjoying life last week
Not happy last week
Feel depressed last week
Activities an effort last week
Psychological disorder
Housing
Home fair or poor
Neighborhood fair or poor
Own 2nd home
Number of rooms+
Home condition (range=1-5, 1=excellent)
Food
Food insecure
+
**
**
**
**
Consumption-to-needs
++
Difference
in effect: [2]-[4]
[5]
**
**
-0.012
-0.002
-0.006
-0.007
0.000
-0.005
-0.001
-0.007
-0.004
-0.020 **
-0.001
-0.001
-0.004
0.001
-0.005
-0.005 *
0.000
-0.004 *
-0.170
-0.013 **
-0.130
-0.040
-0.060
-0.007 **
-0.070
0.010
-0.011 **
-0.110
-0.013 **
-0.130
0.020
-0.098
-0.090
-0.150
-0.165
-0.092
**
**
**
**
-0.006
-0.009
-0.018
-0.029
-0.011
-0.098
-0.082
-0.104
-0.106
-0.105
**
**
**
**
**
-0.006
-0.009
-0.013
-0.019
-0.013
0.000
-0.001
-0.005
-0.009 **
0.002
-0.302 **
-0.413 **
0.063 **
-0.018
-0.017
0.006
-0.140 **
-0.162 **
0.107 **
-0.011
-0.011
0.010
-0.007 **
-0.007 **
-0.004 **
0.056 **
0.560
0.094 **
0.940
-0.380 **
-0.036 **
-0.360
-0.044 **
-0.440
0.080
-0.129 **
-0.006
-0.324 **
-0.013
0.007 **
+
OLS regressions. ++Derivative of logits evaluated at sample means.
*Significant at the 90% level **Significant at the 95% level
Note. All regressions include age & gender as covariates
33
**
Derivative
[4]
++
**
**
**
Table 7. Well-Being, Demographics, & MOOP, by Groups Defined by Poverty Status
Income &
Consumption
Poor (N=173)
Group 1
Income Poor, Consumption
Non-Poor (N=346)
Group 2
Income Non-Poor
Consumption Poor (N=123)
Group 3
Physical health
Percent Reporting:
Fair or poor health
56.9%
41.5%
Any major health condition
93.2%
88.1%
Hypertension
59.7%
56.6%
Diabetes
25.2%
29.1%
Cancer
11.1%
11.9%
Lung disease
19.5%
15.3%
Heart disease
36.6%
24.9%
Arthritis
76.9%
64.1%
Stroke
9.2%
8.4%
Mean Values of:
ADL difficulty (max 6)
0.643
0.704
ADL get help (max 6)
0.137
0.282
IADL difficulty (max 5)
0.623
0.495
Mental health
Percent Reporting:
Not enjoying life in past week
13.6%
11.4%
Not happy in past week
21.1%
23.4%
Depressed in past week
37.8%
26.6%
Activities an effort past week
48.5%
41.0%
Psych. health condition
37.7%
26.4%
Housing
Percent Reporting:
Own Home
11.4%
66.9%
Home condition fair/poor
24.5%
21.9%
Neighborhood fair/poor
27.3%
18.1%
Single family home
39.0%
67.1%
Mobile home
7.9%
8.0%
Own 2nd home
0.8%
5.2%
Own Vehicle
38.5%
64.0%
Mean Values of:
Rooms in house
4.3
5.4
Home condition (1=excellent)
2.6
2.7
Food
Food Insecure
20.2%
14.2%
Wealth
2000 mean wealth: all assets
$10,500
$134,400
2000 mean wealth: non-housing
$4,800
$83,400
2000 median wealth: all assets
$300
$35,500
2000 median wealth: non-housing
$60
$2,500
2002 mean wealth: all assets
$12,500
$109,100
2002 mean wealth: non-housing
$9,700
$49,300
2002 median wealth: all assets
$200
$35,000
2002 median wealth: non-housing
$60
$2,000
Change in median wealth: non-housing
0%
-20%
Work and income
Disabled^
57%
42%
Unemployed (2000-2001)^
1%
15%
Median income 2001^
$6,600
$6,600
Change in median income^
-8%
-56%
MOOP & Demographics
MOOP
$700
$4,300
Share of consumption on MOOP
6.9%
12.2%
Black
24%
22%
Hispanic
25%
16%
Married
11%
23%
Widowed
38%
34%
Divorced
26%
21%
High School or Less Educ
86%
78%
More Than HS Educ
14%
22%
Age Head
68.0
66.1
Household Size
2.59
2.21
^Analysis restricted to sample ages 53-64.
*=significant at the 90% level and **=significant at the 95% level for the test between groups 2 and 3.
34
36.5%
88.8%
51.6%
23.3%
19.7%
16.1%
24.6%
63.9%
9.2%
Income & Consumption
Non-Poor (N=3059)
Group 4
21.3%
82.8%
48.4%
14.1%
14.0%
9.7%
21.2%
58.0%
4.5%
0.441
0.121 *
0.345
0.28
0.084
0.159
2.1% **
9.6% **
14.9% **
32.9%
18.8%
7.0%
12.9%
13.5%
20.7%
13.9%
36.1%
12.7%
5.2%
51.8%
14.0%
5.5%
73.0%
**
**
**
**
*
83.5%
6.6%
5.3%
75.4%
6.2%
13.7%
92.5%
4.8 **
2.3 **
5.8
2.1
7.4% *
$138,400
$112,400
$30,400
$9,500
$117,000
$87,200
$29,300
$12,000
26%
3.4%
$386,300
$286,100
$175,500
$77,800
$382,400
$262,600
$191,000
$79,100
2%
22% **
7% *
$26,200
6%
9%
5%
$56,300
-5%
$800 **
10.0%
16%
11%
32%
37%
16%
77%
23%
71.5 **
2.17
$4,400
10.3%
7%
5%
57%
24%
13%
52%
48%
66.9
1.96
Table A1. Regression to Impute Vehicle Consumption Using Consumer Expenditure Survey
Dependent Variable: Vehicle Consumption
Total vehicle expenditures
Total vehicle expenditures squared
Pretax income
Age of reference person
Family size
Male
Education
Less than high school
High school
Some college
College (omitted group)
Intercept
R-Squared
*Significant at the 90% level
Coefficient
0.052 **
-1.76xE-9
0.005
-43 **
386 **
-11
Std. Error
0.0086
5.18xE-9
0.0039
14
126
267
249
956 **
332
416
342
400
2287
1106
0.215
**Significant at the 95% level
35
Table A2. Regression to Impute Housing Consumption Using Consumer Expenditure Survey
Dependent Variable: Rental Equivalence
Coefficient
0.0037 **
-1.7xE-9 **
Property value
Property value squared
Census divisions (New England omitted)
Mid-Atlantic
South Atlantic
East North Central
West North Central
East South Central
West South Central
Mountain
Pacific
Urban
Urban*Mid-Atlantic
Urban*West North Central
Urban*Mountain
Number of rooms in house
Housing type
Duplex
Apartment
Mobile home
Other housing
Single family home (omitted)
Intercept
R-Squared
*Significant at the 90% level
-408
-247
-505
-122
-459
-267
-467
-22
16
300
261
259
28
**
**
**
**
**
**
**
**
**
**
**
60
29
47
29
35
32
59
31
31
57
47
58
3
-144 **
114 **
-31
53
46
39
30
81
542 **
46
0.397
**Significant at the 95% level
36
Std. Error
0.0001
9.4xE-11