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OCR for page 177
7
The Effect of Welfare on Child Outcomes
Janet Currie
There is broad support for the idea that welfare should benefit poor children.
Yet most research on welfare programs, as well as much of the debate about
welfare reform, has focused on the way that parents respond to incentives created
by welfare, rather than on its effects on children. Less work has been devoted to
the fundamental question of whether any of the web of programs supporting poor
families benefit children.
If it can be shown that they do, then there are many other questions to be
addressed: First, are the benefits short or long term? Second, which types of
programs or combinations of programs are most effective; for example, do cash
or in-kind programs produce bigger benefits for children? Third, do welfare
programs have different effects on different groups, and if so why? Fourth, how
exactly do successful programs work? And finally, can efficacious programs
pass the more stringent test of cost-effectiveness?
This review focuses on the eight large federal programs shown in Table 7-1:
Aid to Families with Dependent Children (AFDC), which has been replaced with
the new Temporary Aid for Needy Families program (TANF); the Earned In-
come Tax Credit (EITC); housing assistance; Food Stamps; the Supplemental
Feeding Program for Women, Infants, and Children (WIC); school nutrition pro-
grams; Medicaid; and Head Start. The programs are evaluated with respect to
their effects on the health and educational achievement of children. Where
possible, documented effects on long-term outcomes are noted. The first section
of this chapter is a brief discussion of how we know what we know about these
programs. The evidence regarding the effects of cash programs and in-kind
programs, respectively, is then reviewed in the next two sections.
177
OCR for page 178
178
THE EFFECT OF WELFARE ON CHILD OUTCOMES
TABLE 7-1 Trends in Program Expenditures (billion 1995 $)
1975 1980 1990
1995
Cash Transfers
AFDC
Total 23.8 21.8 21.822.0
Federal only 13.1 11.8 11.912.0
Earned Income Tax Credit
Total 3.4 3.7 8.122.2
Refunded portion of credit 2.5 2.6 6.219.0
In-Kind Transfersa
Housing assistance 7.0 10.0 18.223.7
Food Stamps 13.5 17.4 19.425.7
WIC 0.7 1.4 2.53.5
School nutrition
School lunch 5.4 5.8 4.35.3
School breakfast 0.4 0.5 0.71.2
Medicaid
Totalb 35.1 46.3 76.3111.2
Federal only 20.1 27.0 47.886.6
To dependent children 6.3 6.0 10.717.8
To adults in families with
dependent children 5.9 6.4 10.114.0
Head Start 1.1 1.3 1.93.5
aAll but the Food Stamps figure for 1975 are actually from 1972.
bThe Medicaid figures for 1980 are actually from 1981.
SOURCE: U.S. House of Representatives (1993, 1994, 1996).
The evidence indicates that contrary to much current publicity, the system is
not entirely "broken" when judged using the metric of child well-being: there are
specific programs that produce important benefits for children. Nevertheless, not
all programs are equally effective, and benefits are not equally distributed across
children. Hence, a review of what we know about these programs can provide a
useful starting point for welfare reform, as well as highlighting gaps in what we
need to know in order to carry out intelligent reform. The last section of the paper
discusses fruitful directions for future research and the importance of enhanced
data collection efforts.
HOW WE KNOW WHAT WE KNOW
A comprehensive review of the program evaluation literature is far beyond
the scope of this chapter. However, since several different methods are used in
the studies discussed here, some comment on methodology is in order. A some-
what fuller, nontechnical discussion can be found in Currie (1995a) or Heckman
(1990).
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JANET CURRIE
179
The fundamental problem facing researchers and policy makers is that the
children of welfare recipients may have bad outcomes for reasons that have
nothing to do with the receipt of assistance per se. It is possible that a program
could have substantial benefits for poor children and still leave many children
disadvantaged relative to better-off peers.
Evidently, parents of children on welfare are worse off than other parents in
observable ways: they are poorer, likely to have less education, and may also
have health problems. Many datasets available to researchers contain at least
crude measures of these observable variables so that observed differences be-
tween parents on welfare and other parents can be accounted for using standard
regression models.
To take a simple example, suppose that children of high school dropouts
have lower scores on standardized tests than children of college graduates. Then
if mothers on welfare are more likely to be high school dropouts than college
graduates, a simple comparison of the two group' s average scores might tell you
more about the effects of maternal education than about the effects of welfare. A
simple way to "control" for the effects of education in order to focus on the
effects of welfare might involve drawing a sample of high school dropouts and
comparing children of welfare mothers to other children within this group. Any
differences between the welfare children and the others could then be attributed
to welfare use and not to maternal education. Multiple regression techniques
simply allow one to control for the effects of several observable variables at the
same time.
The problem becomes much more difficult however if parents on welfare
also differ from other parents in ways that are not observed. For example, they
may lack motivation or be discouraged by previous misfortune. Failure to prop-
erly control for these differences could lead one to incorrectly infer that it was
being on welfare that was associated with negative child outcomes, rather than
these underlying conditions. Some underlying problem, such as maternal depres-
sion, might cause both welfare dependence and negative child outcomes.
There are basically two approaches to this issue of unobserved characteris-
tics. First, one may design a social experiment, randomly assigning eligibles to a
"treatment" group and a "control" group. Random assignment ensures that, on
average, the two groups will have the same observed and unobserved characteris-
tics. In principle, one can then assess the effect of the treatment simply by
comparing mean outcomes for the two groups, just as one would do in a drug
trial. The key advantage of an experimental evaluation is its transparency.
One disadvantage of social experiments is that they may be very expensive.
But there are several disadvantages in addition to high cost (Heckman, 1990~.
These include differential attrition between treatments and controls (which causes
the treatment group to become less and less like the comparison group over time);
the fact that subjects assigned to the control group may not accept their fate
passively (for example, subjects denied training in a government program might
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180
THE EFFECT OF WELFARE ON CHILD OUTCOMES
sign up for an alternative program); and the fact that it may be difficult to use the
experiment to examine differential effects of the treatment on different groups.
Nonexperimental evaluations attempt to control statistically for unobserved
variables associated both with participation in the program and with the outcome
of interest. One method of doing this is to find a third set of variables, called
"instruments," that are associated with participation in the program but not with
the important unobserved variables. For example, a researcher interested in the
effects of participation in Medicaid on child health might argue that the generos-
ity of state AFDC benefits is associated with participation in Medicaid because of
the link between AFDC recipiency and Medicaid eligibility, but that the level of
AFDC benefits does not have any effect on child health other than through its
effect on participation in Medicaid. If this assumption were true, then the level of
AFDC benefits would qualify as an "instrumental variable."
This instrument would be used (along with other observable characteristics
of the mother) to predict Medicaid participation, and predicted participation would
be substituted for actual participation in the model explaining child health. The
idea is that predicted participation will depend only on observable characteristics
and differences in state AFDC benefit levels, and not on the unobserved charac-
teristics of the mother. The procedure is analogous to an experiment in which
AFDC benefit levels are varied across states, Medicaid participation responds,
and only this source of variation in participation rates is used to identify the
effects of Medicaid on health.
The difficulty with instrumental variables techniques is that the key assump-
tions may not be satisfied. Suppose that states with more generous AFDC ben-
efits also have higher-income populations and that higher incomes are associated
with better child health. Then unless one takes account of this relationship, one
will tend to find a spurious positive relationship between participation in Medic-
aid and child health. Alternatively, suppose that states raise AFDC benefit levels
in response to poor child health. Then one might observe a spurious negative
relationship between predicted Medicaid participation and child health.
An alternative approach involves assuming that the relevant omitted charac-
teristics are fixed within a family or for the same child over time. Suppose for
example that the relevant unobserved variable is maternal attitudes towards edu-
cation and that this remains fixed over some period of time. Suppose further that
one sibling participated in Head Start and one did not. Then comparing the
sibling who participated to the one that did not provides a measure of the effect of
Head Start that is not affected by the fact that, on average, mothers of Head Start
children may have more positive (or negative?) views of education than other
similarly situated mothers. Of course, the problem with this approach is that the
relevant variable may not be fixed within households or over time.
The studies discussed below all rely on one of these methodological ap-
proaches. Their conclusions are only as valid as the assumptions underlying the
chosen approach. It is in cases where the same result has been obtained using
OCR for page 181
JANET CURRIE
181
different assumptions and data sources that we can be most confident of the
conclusions.
WHAT WE KNOW ABOUT CASH PROGRAMS
Aid to Families with Dependent Children
The term "welfare" has usually been identified with the Aid to Families with
Dependent Children program. This oldest and largest of the federal welfare
programs provided cash transfers to (predominantly female-headed) families with
children. This is the program that recent welfare reforms (the Personal Respon-
sibility and Work Opportunity Reconciliation Act of 1996 [PRWORA]) effec-
tively ended, replacing it with the new Temporary Aid for Needy Families pro-
gram. TANF differs from AFDC because it ends the "entitlement" of all needy
families to welfare benefits, because it introduces time limits on welfare benefits
and because it provides states with much more latitude in developing their own
welfare programs. Nevertheless, since most of what we know about cash welfare
programs comes from studies of AFDC, and because many states will respond to
TANF by only gradually altering their AFDC programs, it is of interest to sum-
marize this literature here.
Like TANF, AFDC was administered at the state level within federal guide-
lines. As a result, program characteristics varied widely from state to state. For
example, as of January 1993, the maximum monthly AFDC grant for a one-
parent family of four persons varied from $164 in Alabama to $923 in Alaska
(U.S. House of Representatives, 1993~. On average the federal government pays
54 percent of benefit costs, as shown in Table 7- 1. The continuous erosion of real
AFDC benefit levels over the past 15 years provides compelling evidence of the
unpopularity of this program: the average monthly AFDC benefit declined from
$483 (1993 dollars) in 1980 to $373 in 1993, even though the average family size
remained constant at three persons (U.S. House of Representatives, 1994~.
One of the problems involved in evaluating the effects of AFDC on children
is that the benefits of a cash transfer program can be expected to be diffuse.
Small increases in household expenditures on a wide range of items may produce
overall benefits for children without affecting any one indicator a great deal. A
second problem is that although income is often used as a shorthand summary of
a household's socioeconomic status, it is in practice extremely difficult to sepa-
rate the effects of income from the effects of other family background character-
istics including neighborhoods (Mayer, 1996~.
Most research about the effects of AFDC on children focuses on the fact that
daughters of women who participate in AFDC are themselves more likely to
participate (cf. Gottschalk, 1990; Murray, 1984~. What is less clear is whether
the relationship is causal or whether it merely reflects the fact that the children of
the poor are more likely to be poor older studies tended to conclude that the
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182
THE EFFECT OF WELFARE ON CHILD OUTCOMES
relationship was not causal, but studies using more recent data have questioned
this conclusion. See Moffitt (1992) for a fuller discussion of this issue.
There has been comparatively little research linking maternal AFDC partici-
pation to other child outcomes, but the empirical issues are the same. First, it is
necessary to control for some measure of income as well as for AFDC status
since otherwise the estimated effects of participation are likely to reflect the
relative poverty of AFDC mothers. Second, within the group of poor women,
one would like to control for the fact that women choose whether or not to go
onto AFDC. Blank and Ruggles (1996) show that only 60 percent of eligible
women actually take up welfare benefits. Those who do are likely to differ from
those who do not in many unobservable respects.
Hill and O'Neill (1994) find that, when instrumental variables methods are
used to take account of unobserved variables that might be correlated with AFDC
status, AFDC participation has no effect on children's scores on a standardized
test of vocabulary, given income. Currie (1995a) confirms that their results hold
up even when sibling comparisons are used to account for unobserved maternal
background characteristics. Currie and Cole (1993) use data from the 1979 to
1988 waves of the National Longitudinal Survey of Youth (NLSY) to examine
the effect of AFDC participation during pregnancy on the utilization of prenatal
care and birthweight. They use both sibling comparisons and instrumental vari-
ables methods to take account of unobserved variables that might be correlated
with both participation in the AFDC program and outcomes,] and find that AFDC
participation has no additional significant effect on birthweight given income.
Together, these studies suggest that income from AFDC has much the same
effect on children as family income from any other source.
The Earned Income Tax Credit:
A Comparison to the Negative Income Tax
The slack in the growth of AFDC payments over time has been taken up by
the growth in expenditures on the Earned Income Tax Credit, which doubled
between 1975 and 1990. The EITC was introduced in 1975 as a means of
granting tax relief to low-income tax payers. Because it is administered through
the tax system, the EITC is not always viewed as a welfare program. However,
unlike most tax credits, the EITC is "refundable," that is, if the amount of the
credit exceeds the taxpayer's federal income tax liability, then the difference is
refunded. Table 7-1 shows that, in fact, most EITC expenditures are outlays of
this kind rather than forgone tax dollars. The EITC differs from traditional cash
welfare programs primarily because the majority of recipients work and benefits
are available to all kinds of families. Thus, it creates fewer perverse incentives
than AFDC.
1They instrument AFDC participation using state-level variation in program characteristics.
OCR for page 183
JANET CURRIE
183
If it is difficult to identify the effects of cash transfers under AFDC, the
problems involved in identifying the effects of the EITC are even more formi-
dable. The fundamental problem is that the amount of the credit depends on the
parents' earnings, and earnings are likely to reflect many unobserved factors
relevant to child well-being. However, the EITC is in many respects similar to
the negative income tax (NIT), an income guarantee program that was subjected
to exhaustive scrutiny through four large-scale social experiments, although it
was never implemented.2 The four experiments were conducted in New Jersey
and Pennsylvania; Seattle and Denver; Gary, Indiana; and rural areas of North
Carolina and Iowa. It is important to note that the North Carolina and Gary
samples were much poorer than the others.
The income guarantees paid out under the NIT program were large relative
to cash transfers that have been made under the EITC. The average payments in
the Seattle-Denver experiment, for example, ranged from $919 to $2,031 (1972
dollars), depending on the treatment group. By way of comparison, the poverty
line for a family of three persons was $3,099 in 1972. In 1992, the maximum
EITC was $1,384 and the poverty line $11,280. Since NIT participants were
randomly assigned to "treatment" and "control" groups, the NIT experiments
provide a unique opportunity to assess the effects of income transfers per se on
the well-being of children in poor families.
Despite the large transfers, findings about the effects of the NIT are inconsis-
tent across studies and experimental populations. In addition, econometric esti-
mates are sometimes at odds with those derived from simple comparisons of
treatments and controls. For example, Kehrer and Wolin (1979) find that the
mean birthweight of infants born to the treatment group in the Gary experiment
was actually lower than the birthweight of the controls. Yet estimates from their
structural model suggest that the infants of treatments had higher birthweights in
9 out of 12 maternal age groups.
O'Conner et al. (1976) examine the effect of the NIT on child nutrition using
data from the rural experiment. Among subjects in North Carolina, they found
positive and significant treatment effects on nutrient intakes. However, the treat-
ment did not appear to have any significant effect in Iowa, a finding that the
authors attribute to the relative poverty of the North Carolina sample.
2Under a NIT, a family that earns no income is guaranteed a minimum income G. Families with
earnings Y receive a payment D, where D = G - to Y. The quantity B = G/t~ is referred to as the break-
even level of income since workers who earn more than B receive no payments. If income is equal to
the wage multiplied by hours worked, and workers face a tax rate t, then workers on the NIT earn w
(1 - t- to) for every hour of work, whereas workers with incomes above B earn w(1 - I). That is,
workers on the NIT face a higher tax rate. The EITC differs from the NIT in that the EITC has no
income guarantee. Also, since at first the size of the credit increases with earnings, the EITC lowers
effective marginal tax rates for the poorest rather than raising them. After a certain level of income,
the credit begins to be phased out, creating a higher implicit tax rate.
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184
THE EFFECT OF WELFARE ON CHILD OUTCOMES
Maynard and Crawford (1976) found that elementary school children from
NIT families in North Carolina showed statistically significant improvements in
attendance, standardized tests, and grades. However, there were no effects for
elementary school children in Iowa. Once again, this pattern of results is attrib-
uted to the fact that the children in North Carolina were more disadvantaged than
those in Iowa. Maynard and Murnane (1979) found that in the Gary experiment
the NIT treatment had positive effects on reading scores of young children but
that these effects were statistically significant only among children whose fami-
lies had been in the program for 3 or more years.
Finally, in an analysis of data from the New Jersey experiment, Mallar
(1977) found that teenagers whose parents were enrolled in NIT were 20 percent
to 90 percent more likely to complete high school depending on the NIT plan.
However, Venti (1984) found only an 11 percent increase in the probability of
completing high school for youth in the Seattle-Denver experiment. This lower
estimate seems more probable in view of the relatively short duration of the
experiments and the many long-term factors (such as achievement in early grades)
that have been linked to educational attainment. These results may also be
related to the fact that, in all four experiments, youths in treatment households
were less likely to be employed than controls (Robins, 1985~.
These studies suggest that the relatively large income transfers made to
families under the NIT had a positive effect on the nutritional status and educa-
tional attainment of children in the poorest families. However, the magnitudes
vary greatly from study to study. Perhaps unsurprisingly, studies of the effects of
the NIT on consumption also show that families spent much of the subsidy on
goods that may not have been directly related to the well-being of their children.
For example, the NIT appears to have had a negative effect on the labor supply of
married women,3 and positive effects on housing expenditures and purchases of
consumer durables (Robins, 1985; Michael, 1978~.4
WHAT WE KNOW ABOUT IN-KIND PROGRAMS
A parallel "in-kind" welfare system has grown up alongside the cash system.
This system aims to directly provide for a child's "basic needs": decent housing,
food, medical care, and quality early education. Table 7-1 shows that expendi-
tures on virtually all of these programs have shown steady growth over time (the
exception being the School Lunch Program). Table 7-2 indicates that in contrast
to stagnant AFDC caseloads, caseloads for most in-kind programs have been
. .
Increasing.
3No convincing evidence of a link between maternal employment and children's well-being has
been found. See Blau and Grossberg (1990) and Desai et al. (1989).
4The NIT may also have increased the probability of marital dissolution, although this finding
remains controversial (cf. Cain and Wissoker, 1990; Hannan and Tuma, 1990).
OCR for page 185
JANET CURRIE
TABLE 7-2 Trends in Caseloads (millions)
185
1975 1980 1990 1995
Cash Transfers
AFDC
Total recipients11.1 10.6 11.5 13.6
Child recipients7.8 7.2 7.8 9.3
Earned Income Tax Credit
No. of families6.2 7.0 12.6 17.4
In-Kind Transfers
Housing Assistance
No. of households3.2a 4.0 5.4 5.8
Food stamps
Total recipients16.3 19.2 20.0 26.6
WIC
No. of womeno.2a 0.4 1.0 1.6
No. of infantso.2a 0.5 1.4 1.8
No. of children0.5a 1.0 2.1 3.5
School nutrition
School lunch
No. any meals26.3a 26.6 24.1 25.6
No. free meals10.5a 10.0 10.3 12.4
School Breakfast2.5a 3.6 4.0 6.3
No. free meals2.oa 2.8 3.3 5.1
Medicaid
Total recipients22.0 21.6 25.3 35.1
Child recipients9.6 9.3 11.2 17.2
Head start0.3 0.4 0.5 0-7
aThese figures are for 1977.
SOURCE: u.s. House of Representatives (1993,1994,1996).
Initial evaluation of these in-kind programs is more straightforward than the
evaluation of cash transfer programs because we can ask whether the program
has an impact on the specific child outcome it was designed to affect. For
example, we can ask whether receipt of housing assistance is associated with
improvements in housing or whether household participation in the Food Stamps
program improves a child' s diet.
We might then wish to ask whether the program has additional effects on
related child outcomes. For example, better nutrition could influence a child's
cognitive abilities. Also, subsidies to food and housing may influence child
outcomes more generally by relaxing the family's budget constraint (see Moffitt,
1989, and Citro and Michael, 1995, for discussions of the valuation of in-kind
benefits).5 However, since the effects of income transfers are discussed above,
5The National Research Council (vitro and Michael, 1995) concludes that for simplicity~s sake,
``near-cash,, benefits such as Food stamps and housing assistance should be counted at their dollar
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186
THE EFFECT OF WELFARE ON CHILD OUTCOMES
the focus in this section is on any effects of participation in in-kind programs on
the specific outcomes that the programs were designed to affect. In practice, this
restriction eliminates very few studies from consideration.6
Housing Assistance
In contrast to AFDC and Food Stamps, housing assistance is not an entitle-
ment: when funds allocated to the program run out, people who are eligible must
be wait-listed. It is estimated that about half of federal expenditures on housing
assistance directly benefit children, while the elderly are the other large group of
beneficiaries.
Most expenditures are on rental assistance programs rather than on low-rent
public housing (which is what many people think of as "public housings. And
since 1982, most new authorizations for rental housing assistance have been for
Section 8 programs (Pedone, 1988~. The Section 8 existing housing program
provides rent subsidies to families who find an apartment of their own choosing,
as long as the rent is below the "Fair Market Rent" established by the Depart-
ment of Housing and Urban Development (HUD) and the unit meets minimum
quality standards. Rental assistance typically reduces a family's rental pay-
ments to 30 percent of its income, after deductions for certain expenses are taken
into account.
Deficient housing is hazardous to children. For example, lead poisoning is
three times more common among poor children than among nonpoor children
and is directly related to housing conditions. The risk of accidental death is also
three times higher for poor children, and some of this increased risk may be due
to hazards in the home (Starfield, 1985~. In 1989,18 percent of poor households
(2.2 million households) lived in housing with severe or moderate physical prob-
lems compared to 7 percent of nonpoor households.7
It is not known whether, in general, housing assistance enables families in
deficient housing to move to adequate housing. A 1988 HUD study found that
more than half of public housing households lived in projects that needed moder-
ate to substantial rehabilitation just to meet HUD's own standards. The estimated
cost of bringing these units up to standard would have exceeded $20 billion 1986
dollars (Lazere et al., 1991~.
value when comparing the resources available to different households, and various procedures for
valuing housing benefits are discussed. However, the panel also recommends that health insurance
be excluded from these comparisons because it is too hard to come up with a meaningful estimate of
its value to households in different circumstances.
6An exception that deserves mention is Meyers et al. (1993) who found that in a sample of poor
children in Boston, those who received housing assistance were less likely to be anemic. The study
did not control for selection into public housing.
7Problems that HUD classifies as severe include lack of basic plumbing facilities, serious heating
breakdowns, and rat infestations. An example of a moderate deficiency is the use of unvented gas,
oil, or kerosene heaters as primary heating equipment.
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JANET CURRIE
187
Section 8 programs require families to locate a landlord willing to participate
and to arrange with the landlord for inspections and repairs within a fixed period
of time. One case study of 56 single mothers in eastern Massachusetts in 1985
and 1986 found that after waiting an average of 2 years to receive a certificate, 24
women returned them unused because they were unable to find housing that met
program requirements within the allotted time (Mulroy, 1988~. On the other
hand, there is some evidence that recipients of vouchers pay higher rent (Kennedy
and Finkel, 1987; Apgar, 1990) and move to better neighborhoods (Johnson,
1986~. The often dismal social conditions in many public housing projects must
be weighed against any improvements in the physical housing stock. However, it
is very difficult to identify the effects of neighborhoods and schools because any
relationship we observe between neighborhood characteristics and individual
outcomes could reflect the characteristics of the individual or of his or her family
that placed them in these neighborhoods in the first place.
The Gautreaux program sheds light on this issue. Under the program, resi-
dents in public housing projects can apply for Section 8 housing certificates and
move to private apartments. Some apartments are in predominantly white sub-
urbs, while others are in the inner city. Although the persons admitted to the
program are not a random sample of public housing residents,8 Rosenbaum
(Rosenbaum et al., 1986; Rosenbaum, 1992) asserts that the program assigns
apartments in an approximately random manner, since people get whatever is
available when they reach the top of the waiting list. He finds that 7 years after
their move, children who had moved to the suburbs were 15 percent less likely to
have dropped out of school, 16 percent more likely to be in a college-track
program, and 34 percent more likely to be employed than those who had moved
within the inner city. All of these differences are statistically significant at the 90
percent level of confidence.
These findings suggest that voucher programs can have a positive effect on
the life chances of children if they enable families to find housing in better
neighborhoods. On the other hand, they suggest that the disamenities associated
with large public housing projects may have significant negative effects. How-
ever, the study is marred by high rates of attrition from the sample. HUD is
currently conducting an experimental evaluation of a program similar to Gau-
treaux in four cities.9 An experimental evaluation that took care to minimize
attrition could shed great light on the possible beneficial effects of housing vouch-
ers, and on the issue of the effects of neighborhoods more generally.
Despite their bad reputations, housing projects may be better than much of
8Applicants are screened to make sure that they have paid their rent regularly and that they have
adequate housekeeping abilities. The program does not serve families with more than four children
because few large housing units are available in the suburbs. In addition, the act of applying for an
apartment in an unknown location may indicate that a person is strongly motivated to improve his or
her circumstances.
9Personal communication, Lawrence Katz, Department of Economics, Harvard University, 1997.
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94
THE EFFECT OF WELFARE ON CHILD OUTCOMES
is inappropriate, given that Head Start is intended to affect a range of outcomes
(see McKey et al., 1985~. Evidence from the Perry Preschool Project, which
found that program children were less likely to drop out of high school, engage in
crime, or become pregnant as teenagers, is often cited. However, since the
project included only 58 treatments and 65 controls, was funded at about twice
the rate of a typical Head Start program, and did not involve a national sample, it
is not clear that the findings generalize.
Currie and Thomas (1995b) examine sibling comparisons from a national
sample and find that children who were in Head Start have higher test scores at
the end of the program than either stay-at-home siblings or siblings who went to
other preschools. The effects are of the same magnitude for both black and white
children and indicate that Head Start closes one-third of the gap between these
children and others. But consistent with the experimental studies, they find that
the effects on black children fade out rapidly. These results suggest that the
positive effects of Head Start may be undermined by subsequent deprivation
among these children.
In contrast, the effects on the test scores of white children do not fade out.
Moreover, white children 10 and over are significantly less likely to have re-
peated a grade if they attended Head Start and are thus less likely to have experi-
enced the age/grade delay that often leads to high school noncompletion. Both
black and white children who attended Head Start were more likely to be immu-
nized than stay-at-home siblings, although there was no effect on height-for-age,
a measure of long-term nutritional status.
In related work, Currie and Thomas (1996a) find that Head Start has large
and lasting effects on the test scores of Latino students. A closer inspection of the
data reveals that these positive effects are largest for Mexican-origin children and
smallest for Puerto Rican children. However, due to sample size limitations it is
not possible to sort out the effects of ethnicity and the effects of region. It is
possible, for example, that the ethnic differences reflect differences in the pro-
grams available in New York, where Puerto Rican children tend to be located,
and California and Texas, where Mexican-origin children are concentrated, rather
than any independent effect of ethnicity per se.
Currie and Thomas (1996b) ask whether differences in school quality can
explain differences in the pattern of "fadeout" in test scores between whites and
blacks. Specifically, the initial positive effects of the Head Start program may be
undermined if Head Start children were subsequently exposed to inferior schools.
And since we see fadeout for blacks but not for whites, it would have to be the
case that black Head Start children are attending worse schools than other black
children but that the same was not true among whites.
Currie and Thomas test this hypothesis using a sample of eighth graders
from the National Educational Longitudinal Study of 1988 (NELS). Their work
builds on earlier research by Lee and Loeb (1995) who showed, using these data,
that the schools attended by Head Start children are of worse quality in some
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JANET CURRIE
195
observable dimensions than the schools attended by other children. Even if
family income and parent's education are controlled for, children who attended
Head Start have lower test scores than other children. This result is to be ex-
pected if Head Start does not entirely compensate for early disadvantages.
However, among black children, the gap between Head Start children and
other children is virtually eliminated when we compare children within the same
school. That is, within schools, black Head Start children do no worse than other
black children. But since they perform more poorly than other children on
average, they must be attending schools in which all black children do badly. If
a "quality" school is defined as one in which children do well, then these results
suggests that black children who attend Head Start go on to attend schools of
significantly worse quality than other black children. In contrast, among non-
Hispanic white children there appears to be little difference in the schools at-
tended by Head Start and other children.
WHAT WE NEED TO KNOW
The preceding discussion is summarized in Table 7-3. The table presents a
matrix of programs and effects. Differences in the effects of programs across
groups have been suppressed, although one theme that has emerged from the
discussion so far is that they are important. The most striking feature of Table 7-
3 is that there are many empty cells we clearly need to learn a great deal more
about the effects of welfare before we can make informed public policy. In some
cases, research has been limited by lack of appropriate data. In others, existing
information has not yet been fully exploited. This section highlights some unan-
swered research questions and discusses the extent to which better data collection
efforts could help.
Effects of Welfare on Long-Run Outcomes
Ultimately, what many people care about is whether investments in children
today will produce productive, well-socialized adults tomorrow. However, Table
7-3 highlights the fact that little is known about the effects of welfare on long-
term outcomes. Lack of data places major limitations on this type of research.
Many important outcomes can only be examined 10 to 15 years after childhood
participation in welfare programs. There are few existing datasets that combine
information about childhood participation in welfare, other family background
characteristics, and the outcomes of interest.
One exception is the National Longitudinal Survey's Child-Mother file
(NLSCM). The NLSCM contains information about the children of a sample of
approximately 6,300 women who were between the ages of 14 and 21 in 1978.
Information about childhood participation in AFDC, the Food Stamp Program,
Medicaid, Head Start, and WIC is available. By the time the 1994 wave is
OCR for page 196
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released, there will be more than 800 children over 16. Of course, since these
children will have been born to young mothers, they will not be a nationally
representative sample of 16 year olds. Still, this sample is a valuable resource.
If future waves of the survey continue to be funded, it will grow in size and in
representativeness and allow us to address many questions about the relation-
ship between welfare and long-term outcomes such as schooling attainment,
teen parenthood, and crime.
A second exception is a special supplement to the Panel Study of Income
Dynamic (PSID) that was fielded in 1995. This module contains retrospective
information about early childhood education and criminal activity that can be
linked to data about welfare participation from the original PSID file. The PSID
is currently undertaking an even more ambitious data collection effort, the 1997
Child Development Supplement. The survey of 3,500 0 to 12-year-old children
will have assessments of cognitive, behavioral, and health status. Data are being
collected from the mother, a second caregiver, the absent parent (if relevant),
teachers, school administrators, and the children themselves. The survey will
also include time diaries for caregivers, children, and teachers, to examine inputs
into child development. Finally, other inputs such as resources in the home and
neighborhood will also be measured. Once again, this information can be linked
to data about welfare participation from the main files, and follow-up on these
children may help to identify long-term effects of participation. Fielding this
type of supplement to existing data sources promises to be a cost-effective method
of providing information on the link between the current outcomes of young
adults and their participation in various programs as children.
An additional issue that can be addressed is whether there are links between
the short-term outcomes that have been examined in previous research and longer-
term outcomes. If it is found that particular short-term outcomes are reliable
"markers" for longer-term outcomes, then future evaluations of welfare programs
may not require as much costly long-term follow-up of the participants.
Why Do Effects Appear to Vary with Race, Ethnicity, and Natality?
The PSID and NLSCM datasets will both support analyses stratified by race,
ethnicity, and nasality. However, in many cases the sample sizes are very small.
In order to properly document differences in outcomes, or even in utilization, it
will be necessary to add questions to existing large-scale datasets. For example,
the Census asks questions only about the use of cash welfare, even though expen-
ditures on in-kind programs constitute the largest and fastest-growing share of the
welfare bill.
A second problem is that large-scale, individual-level datasets typically lack
information about neighborhoods and administrative procedures that could be
used to test specific hypotheses about group differences. For example, one might
believe that black children on Medicaid receive fewer visits for illness than white
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198
THE EFFECT OF WELFARE ON CHILD OUTCOMES
children because the providers that serve them are overcrowded and it is more
difficult to get additional appointments. It would be very useful to know the
extent to which group differences are associated with the administration of wel-
fare programs, rather than with differences in parental tastes or circumstances.
It is unlikely that many detailed questions of this type will be added to large-
scale surveys, but it would be possible to match data from other sources to the
surveys if finer geographical information were made available to researchers.
While issues of confidentiality are important, the amount of information that
could be gained if it were routinely possible to match survey data to, say, zip-
code-level data from other sources can hardly be underestimated.
This type of matching is also greatly facilitated by the existence of a central
agency that collects program information (and is willing to give it to researchers).
There is a real danger that further Revolution of responsibility for welfare to the
states will result in a loss of information about the administration of programs,
making it more difficult to identify program effects using state-level variation in
the programs.
How Do Programs Interact?
One glaring omission from this survey is that there has been no discussion of
multiple program participation. Many children are covered by more than one
program. For example, AFDC participants are covered by Medicaid and are
automatically eligible for Food Stamps. As of 1990, half of AFDC children
received free school lunches, 35 percent lived in public or subsidized rental
housing, and 19 percent participated in WIC. Conversely, half of all Food Stamp
recipients, 42 percent of Medicaid recipients, 38 percent of WIC recipients, and
24 percent of those in public housing also received AFDC. Moffitt (1992) esti-
mates that in 1984, 26.4 percent of nonelderly single-parent families received
AFDC, Medicaid, and Food Stamps, and 11 percent received at least one benefit
in addition to AFDC.
It is impossible to say how multiple program participation affects the child
outcomes discussed above since there has been little research on this topic.
Some programs may be duplicative, while others may interact to produce more
positive outcomes. For example, Currie and Thomas (1995b) found that chil-
dren in Head Start were more likely to be immunized than other children, even
though many Head Start children would have been eligible for free vaccinations
under the Medicaid program in any case. Head Start may help families to enroll
in Medicaid, may help them locate a Medicaid provider, or may bypass Medic-
aid altogether by arranging for children to be immunized at the Head Start
center.
An analysis of multiple program participation would assist us in answering
the question of whether the current patchwork system of programs is an efficient
way to provide welfare. The proliferation of programs increases possibilities for
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JANET CURRIE
199
fraud, waste, and mismanagement. On the other hand, the evidence surveyed
here suggests that targeting specific benefits directly to individual children has
advantages in terms of ensuring that specific benefits are received. We need to
know more about the balance between these benefits and costs.
How Do Successful Programs Work?
Data limitations place severe restrictions on our ability to look inside the
"black box" of welfare programs. For example, we can show that expansions in
Medicaid eligibility have been related to reductions in child mortality rates at the
state level, but we do not know why. It could be due either to increased use of
preventive care or to more intensive palliative care for sick children. The two
possibilities have quite different implications for child well-being as well as for
efficiency and program costs. Better information about what goes on during doctor
visits and about objective measures of child health status (short of mortality statis-
tics) could help us to address this question. It might be possible, for example, to add
questions about anemia, lead poisoning, and anthropometrics (e.g., height-for-age,
weight-for-height) to the next National Health Interview Survey.
Still, the most likely scenario is one in which we chip away at these questions
using an interactive, multidisciplinary approach: analysis of large-scale surveys
can be used to develop broad hypotheses, which can then be tested using case
studies. The case studies can then be used to develop more precise hypotheses
about the survey data and to suggest supplemental survey questions.
Cost-Effectiveness
Evidently, if a program has no effect at all on a desired outcome, then it
cannot be considered cost-effective. Many of the programs discussed above
have passed this initial test they can be shown to have positive effects. The
question remains however, of whether they are cost-effective, that is, whether
the benefits outweigh the costs. The figures discussed above for WIC are quite
impressive in this regard. Cost-effectiveness studies exist for other small-scale
early intervention programs (not reviewed here) but have not generally been
conducted for large-scale federal programs. Although it is unlikely that there
will be agreement on all of the costs and benefits that should be included in such
an analysis, some rough calculations under varying assumptions would no doubt
be useful to policy makers.
CONCLUSIONS
This survey chapter discusses eight large federal welfare programs that af-
fect children. The available evidence is incomplete but suggests a consistent
story: programs that target services directly to children have the largest measured
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200
THE EFFECT OF WELFARE ON CHILD OUTCOMES
effects, while unrestricted cash transfer programs have the smallest, perhaps
because their benefits are more diffuse or because the amounts of money in-
volved are typically quite small.
There are also sinking and largely unexplained differences in the effects of
some programs by race, ethnicity, andlor nasality. These differences could reflect
nonlineanties in the effects of programs that is, one might expect larger effects
for poorer than for richer children, and children from some groups are more likely
to be poor. Alternatively they may reflect differences in the programs available
to children of different origins or unobserved differences between participants
from different groups that have not been adequately accounted for.
This survey concludes with five questions for future research: (1) Do wel-
fare programs have long-term effects on children? (2) Why do programs have
differential effects by race, ethnicity, and nasality? (3) How do programs interact?
(4) How exactly do successful programs work? (5) Are programs cost-effective?
These questions indicate that though we know much more than we did even 5
years ago about the effects of welfare on children, there is still much work to be
done if we are to make informed decisions about public policy.
ACKNOWLEDGMENTS
The author is grateful to Lindsay Chase-Lansdale, Greg Duncan, Bentley
MacLeod, and Robert Moffitt for helpful comments. Support from the Alfred P.
Sloan Foundation, the National Science Foundation under grant #SBR-9512670,
and the National Institute of Child Health and Human Development under grant
#ROlHD31722-OlA2 is gratefully acknowledged. The author is solely respon-
sible for the opinions expressed.
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Representative terms from entire chapter:
child outcomes
