SUMMER 1998
VOLUME 32, NUMBER 1
145
PETER R. MUESER AND ROBERT 0. WEAGLEY
Influence of Client-Based Subsidies
on the Market for Child Care
Federal support for child care subsidies targeted to poor households
has grown dramatically in recent years. The analysis presented here
examines the impact of such subsidies on child care fees charged to aU
clients using Missouri data on provider fees and subsidy payments. It
is found that patterns for fees and subsidies across providers imply
that child care markets are largely competitive. Growth in subsidies
observed over the period 1991-1993 increased fees and, by inference,
improved quality for subsidized clients. Subsidies also induced an
increase in fees for clients not covered by subsidies, an increase most
likely due to the cost of expanding the child care market.
Implementation of federal legislation has resulted in a dramatic
increase in child care subsidies available to low income families.'
Moreover, recent welfare reforms have focused on work requirements and lifetime limits on public assistance which increasingly
necessitate the provision of child care to low-income families. New
federal legislation substitutes block grants for many existing programs and repeals requirements for states to fund early childhood
development programs. This means decision making regarding levels
'Most important are the Family Support Act (1988) and the Child Care and Development
Block Grant (1990).
Peter R. Mueser is Professor, Department of Economics, University of Missouri-Columbia.
Robert 0. Weagley is Professor, Department of Consumer and Family Economics, University
of Missouri-Columbia.
The research reported here was funded in part by the Division of Family Services, Missouri
Department of Social Services. Gregory Vadner, Dons Hallford, and George Lauer, at the
Division of Family Services, provided assistance in obtaining and interpreting data. Francis
Cheung, Rachel Connelly, Michelle Mathews, Michael White, Neil Raymon, and Douglas
Wissoker provided useful comments o n earlier drafts of the paper. Sheng-Shyr Cheng provided
research assistance. The paper's content and any errors in analysis or interpretationare entirely
the responsibility of the authors. An earlier version of this paper was presented at the 1995
meetings of the Population Association of America.
The Journal of Consumer Affairs, Vol. 32, No. 1, 1998
0022-0078/O002-145
1.50/0
Copyright 1998 by The American Council on Consumer Interests
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THE JOURNAL OF CONSUMER AFFAIRS
of assistance, in particular child-care, is shifting to the states. States
are no longer required to conduct biennial surveys of child care
market rates, nor mandated to base their rates of assistance on
market rates. On the other hand, federal welfare reform does restrict
direct payments to households with children, instituting work
requirements and imposing a five-year-lifetime limit on welfare
receipt for any household. As single parents receiving welfare face
pressure to obtain employment, the need for child care subsidies will
grow. Decision makers must think critically about child-care needs of
the welfare population, families at risk of entering welfare, and the
working poor.
There is substantial literature that examines the effects of child
care costs and subsidies on the choices made by families (Blau 1991,
Culkin, et al. 1991, Kimmel 1992), some research focusing on the
market for child care workers (Blau 1992, 1993), and recent research
on the long-term benefits to children receiving quality child care
(Campbell and Ramey 1993, Kontos 1991). Little work, however, has
examined the impact of subsidies on the market for child care and, in
particular, no research has focused on the influence of subsidies on
the price of child care.
The analysis here uses data on fees charged in January 1991 and
January 1993 reported by child care providers in Missouri. Over the
period 1991-1993, annual subsidies to support child care in Missouri
more than doubled to $40 million. We will examine changes in the
child care market during that time to infer likely effects of the subsidy program on fees. Until recent changes in the law, subsidy programs in all states had a common basic structure dictated by federal
legislation. Now, with states required to develop programs according
to their own specifications, information about the incentive effects of
various programs is of particular value to state policy makers. The
focus will be on subsidies for paid child care provided by nonrelatives
outside the child’s home.
PREVIOUS STUDIES
An extensive amount of literature has examined the impact of child
care availability and fees on family employment and child care
choices. The decision of whether a woman works may depend on the
availability and price of child care, and relative prices for various
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147
kinds of care strongly influence which will be ch0sen.l Additionally,
studies have addressed indicators of child care quality.3
Studies that have examined the impact of government subsidies are
more limited. Liebowitz et al. (1992) allowed for variations in the
child-care tax credit across states and over time for 1978-1986 and
found that high levels of child care tax credits induced women to
return to work. Michalopoulos et al. (1992) used variations in tax
credits across states as part of a structural model estimating demand
for child care services. Their simulations suggested that subsidy
increases would induce substantial growth in the use of child care services and would increase expected hours of work for parents. Walker
(1992) found that fees for family child care providers receiving government subsidies were higher than for providers who did not receive
subsidies.
Two studies by Blau focused on the market for child care workers.
An analysis across states during the period 1976-1986 (Blau 1992)
found that wages for child care workers were not influenced by the
levels of child care subsidy. Noting that wages remained almost flat
in the face of substantial market growth over this period, Blau suggested that the supply of child care workers was highly elastic. In
contrast, a structural model (Blau 1993) estimated modest elasticities
of supply in the range from 1.2 to 1.9.
None of these studies examined the impact of client-based subsidies, which grew in importance with federal legislative mandates that
required subsidies at the 75th percentile of fees found in biennial
surveys of child care providers. Although the studies by Blau (1992,
1993) and Walker (1992) included crude measures of subsidy, their
analyses did not indicate how growth in these subsidy programs
influenced provider fees and quality of care p r ~ v i d e d . ~
The structure of client-based subsidies differs from tax credits.
First, only a small portion of households are eligible for client-based
subsidies, and they tend to be the poorest households. In contrast,
tax credits are available to all families with children in paid care.
See Blau (1991); Hofferth and Wissoker (1992);Hotz and Blau (1992);and Kimmel(1992).
3See Arnett (1989); Cost, Quality & Child Outcomes Study Team (1995); Feine (1992);
Phillips and Howes (1987); and Whitebook, Howes, and Phillips (1989).
4Blau’sanalyses employed a measure indicating the maximum subsidized fee provided by
the state’s child care program but did not include any measure of the number of eligible
children nor the size of the state’s budget for subsidy programs. Walker’s analysis merely
employed a dummy indicating whether the provider received any government subsidy.
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Second, the structure of client-based subsidies is such that they
increase dollar for dollar with child care prices to a state-specified
maximum. The impact of such client subsidies on provider prices
and, as a result, on non-subsidized consumers may, therefore, differ
from that expected for tax-based subsidies.
STRUCTURE OF CHILD CARE SUBSIDIES
Over the period of our study, Missouri provided direct client subsidies for child care under a variety of programs with a basic structure
dictated by federal legislation, which provided a substantial share of
the funding. Although eligibility criteria varied across programs, all
programs were designed to provide assistance to households with low
incomes. No effort was made to provide subsidies to all who met the
eligibility criteria. Only those in contact with the Missouri Division of
Family Services were evaluated for eligibility, and budget limitations
required many eligible households be placed on waiting lists.
The state distinguished 27 classes of child care service, based on
age of child, hours of care per day, and size of the child care provider. For each of seven geographic areas in the state, a maximum
subsidized fee for one day’s care for each class of service was specified. If a provider’s fee was less than this amount the state paid the
fee, while if a fee exceeded this maximum, the state paid the maximum and the client was responsible for the difference. Eligible
households had their choice of child-care providers. Although the
subsidy payment was often made directly to a provider, the subsidy
was on behalf of a particular client.
Many clients were required to provide a copayment, which
depended on family income and number of ~ h i l d r e n .This
~ copayment was a fixed dollar amount per day; it did not depend on the fee
charged by the child care provider.
While federal guidelines would appear to require that the maximum subsidized fee be at the 75th percentile, prior to November
1991, maximums in Missouri were substantially below average
market rates. Following a 1991 survey of child care providers, fee
maximums were raised in November 1991 by an average of over 50
’The term “copayment” is reserved to refer to these payments, excluding payments made
by clients to providers charging fees above the maximum subsidized fee.
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percent to accord with market conditions and federal guidelines.
These maximum subsidized fees remained in effect through fiscal
year 1993. The number of families receiving subsidies also grew over
this period, increasing by more than 50 percent between the 1991 and
1993 fiscal years. As a result, child care subsidy payments increased
from $19 million in fiscal year 1991 to $40 million in fiscal year 1993.
MODELING THE CHILD CARE MARKE’F
This section investigates the expected impact of government subsidies on child care fees. The child care market displays characteristics that must be incorporated into the formal model. Child care providers are heterogeneous, and fees differ quite dramatically even
within a limited geographic area. To allow for such differences, an
unmeasured quality is posited for each provider and will be taken to
identify characteristics of the service that clients value. Higher prices,
therefore, correspond to higher quality, allowing for the possibility
that child care providers with different prices may coexist in a single
competitive market.
While this definition of quality, capturing unmeasured services
valued by customers, is appropriate in our formal model, it is also
reasonable to assume that this measure is correlated with professional
definitions of quality. Higher quality centers are found t o have better
educated and trained teachers with lower rates of employee turnover
(Clarke-Stewart , 1992), adequate adult-child ratios, and licensing
(Fiene, 1992). This is confirmed by a recent report which defined
quality child care as care provided by trained day-care professionals
implementing need-based plans for individual children to enhance
development of children’s independence. The report concluded that
the “cost to providing care is modestly and positively related to the
level of quality of services. The additional cost to produce goodquality services compared t o mediocre-quality care was about 10%”
(Cost, Quality, and Child Outcomes Study Team, 1995, 7).
In the following sections, we spell out the implications of two
models. The first assumes that the child care market is perfectly competitive, and the second that providers have monopoly power. The
empirical analysis that follows will attempt to determine which model
6A more detailed treatment of the models presented in this section is available from the
authors.
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best explains pricing patterns observed in child care markets. Distinguishing between these models is important for determining the
effects of child care subsidies. The sections that follow use this
knowledge of the child care market to interpret data regarding the
impacts of child care subsidies.
For both models, it is useful to specify a common notation to
describe the subsidy structure. The state-specified maximum subsidized fee will be denoted p*. If the provider’s fee, p, is less than p*,
then the state pays p. As the client, i, must make a copayrrrent of mi,
which varies by income and family size, the effective subsidy for this
client is si = p - mi; the effective cost to the client is mi. If p equals
or exceeds p*, the state pays the provider p*, so the effective subsidy
is p* - mi; the cost to the subsidized client is then p - p* + mi.
Competition
Perfect competition is consistent with the observation that entry
into the child care industry is easy, and customers frequently have a
choice between multiple providers. In the model, perfect competition
does not require that providers offer the same level of quality, only
that customers identify quality that prompts them to patronize a particular supplier.
Under perfect competition, clients can choose among child care
providers in a given market according to the desirability of the pricequality mix offered. Competition implies price is equal to marginal
cost, and there is a market quality-price locus, p(q). Each consumer
chooses a level of quality that maximizes a utility which incorporates
both quantity and quality of care. With variation across households
in both the importance of quality and characteristics that are perceived as quality, there will be a dispersion of prices reflecting the
cost of producing and demand for different levels of quality.
Consider a subsidy-eligible household that would, in the absence
of the subsidy, choose a level of quality (q) such that p(q) is less than
p* (Figure 1). The choice is given by the point on the curve p(q)
which maximizes utility. Noting that preferred combinations involve
lower prices and higher quality, the point of tangency between the
indifference curve and p(q) identifies the choice (p,,,qo). The subsidy
program replaces the price function with the single copayment mb
which is independent of quality up to the quality level q*. The effective opportunity locus is, therefore, a horizontal line to q*, at which
SUMMER 1998
VOLUME 32, NUMBER 1
151
FIGURE 1
Consumer Decisions Under Perfect Competition
P'
Po
Price
Quality
Yo
4*
point it slopes up parallel to p(q). The optimum on this locus will be
at a higher quality level, q*, such that p(q*) = p*, identified by utility
level Uz.Hence, while the subsidy allows the household to purchase
services at price mi, the provider receives price p* for any subsidized
household that would otherwise choose a lower price (and lower
quality) service.
For a subsidized household originally choosing quality such that
p(q) >p*, Figure 1 makes clear that although quality may shift on the
new budget line, the impact is more modest, depending on substitution and income effects.
For any client receiving the subsidy, there will also be an increase
in the quantity consumed due to the decline in the per unit price.
Monopoly
Some observers have questioned the competitiveness of the childcare market (Walker 1991). Quality may not be fully observable and
information about providers may be difficult to obtain. Of parents
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patronizing child care providers, approximately three of four report
learning about the provider through informal channels, such as personal referrals or direct acquaintance (Walker 1991). If search costs
are high enough or customers are able to obtain information about a
limited number of providers, each provider may exercise some level
of local monopoly power. The second model examines the market in
the case of a simple monopoly. In the monopoly model, the provider
chooses both price and quality.
Consider a standard monopoly model where the provider chooses
price, p, and the quality level, q, producing quantity level, y, to maximize profits:
We take y = Zyi, where y i = y i p - sbq), household i’s demand is
assumed to be decreasing in p- si (price net of the subsidy) and increasing in q. C(y,q) is a conventional cost function c y> 0, c m > 0,
and cq >O. The provider is assumed not to discriminate among clients
in fees or quality. The average per unit subsidy is defined as S =
c&yi/cyi.
Price decisions of the monopolist can be illustrated in a simplified
structure provided in Figure 2, which ignores quality. Curve AB indicates a demand curve in the absence of any subsidy, and the curve
CDB illustrates a possible curve where some of the monopolist’s
clients are eligible for the subsidy. The subsidy causes the curve to
shift up and to the right, and to have a kink at the maximum subsidized fee, p*. The per unit subsidy is constant when the price is above
that level but varies directly with the price at lower prices. The
marginal revenue curve MR is the curve associated with this subsidized demand, so the profit maximizing quantity corresponds to the
point where MR crosses the firm’s marginal cost curve.
The firm’s response to a change in the subsidy depends on the price
the firm is charging. The portion of the subsidized curve indicating
demand below p* becomes steeper and moves to the right as the proportion of subsidized clients increases. Using this result, if the firm
chooses a price below p* (as it does when it faces the marginal cost
curve MC $, the firm’s response to an increase in the proportion eligible for subsidies is always to increase prices (dp/dS >O). Of course, a
change in the p* has no effect on such a firm, because this has no
effect on the relevant portion of curve DB.
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153
FIGURE 2
Monopolist’s Decision
Price
P*
Quantity
\
B
If the price is set at p*, as it is when the marginal cost curve is M C ,
it is clear that, over some range, an increase in p* will cause a lockstep increase in optimal p (dp/dp* = 1). In contrast, an increase in the
proportion subsidized (p*) may have no effect on the price choice,
but there are conditions under which such a firm will respond to such
an increase by raising p to a level above p*. It is easy to show that
subsidy growth cannot cause the firm to reduce its price.
Finally, consider the case where the firm chooses a price above p*.
The firm’s response to an increase in the subsidy level, due either to
an increase in p* or an increase in the number of eligible households,
is indeterminate. An increase in p* or the proportion subsidized have
similar effects on CD, the part of the demand curve above p*. The
slope of this portion of the curve depends on the particular demand
characteristics of those who are subsidized. As only low income
households are eligible for child care subsidies, their demand elasticity may be great. Subsidies to this group may cause the slope of CD
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to decline in absolute value, which can cause a decline in the price
chosen by the monopolist.
The provider also chooses quality. When p is less than p*, the subsidy will create incentives for subsidized clients to choose higher
levels of quality. As a result, the monopolist will increase quality as
the proportion of clients subsidized increases. Effects on quality are
ambiguous where the provider charges over p*.
Although these results are similar to those in the competitive
model, they differ in several important respects. Both models predict
increases in quality for customers originally paying fees below p*.
However, in the competitive model all fee growth corresponds to an
increase in quality, while part of fee growth for the monopolist
reflects growth in the provider’s profit margin as customers become
less price sensitive. For customers in a competitive market who would
pay less than fee p*, the impact of subsidy eligibility is to raise the fee
to p*, a much larger impact than predicted by the monopoly model.
DATA AND ANALYSIS
The data consist of information on child care fees in January 1991
and January 1993 based on responses by child care providers in
Missouri to two mail surveys administered by the Division of Family
Services. Respondents specified their fees on a daily basis for each
category of child care provided, with categories defined by age of
child and hours of care per day.’ In addition to fee information,
respondents in the 1993 survey indicated the number of children
served in each age range.
Questionnaires for the first survey were mailed in December 1990
to all 3,272 licensed child care providers in the state and to 659 unlicensed providers. completed responses were obtained from 1,909
(58.3 percent) licensed providers and 183 (27.8 percent) unlicensed
providers. Questionnaires for the second survey were mailed in
December 1992 to 3,629 licensed providers and 621 unlicensed providers. Completed responses were obtained from 2,45 1 (67.5 percent)
licensed providers and 213 (34.3 percent) unlicensed providers.
‘Reported fees include all payments made for a specified service, gross of any subsidy. State
regulations require that fees for subsidized and unsubsidized clients be the same.
sSome unlicensed providers chose t o register with the state for various reasons. As
unlicensed providers are eligible for subsidies, these providers were included in the survey.
SUMMER 1998
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155
Providers serving fewer than five children or those with religious
affiliations are not required to be licensed in Missouri. Other data
suggest that fewer than half of the state's child care providers are
actually licensed, so the sample is not representative of the population of providers. Furthermore, it is possible that providers systematically misrepresent their fees. Previous work suggests many
clients are given substantial discounts (Nelson 1990), and these may
not be reflected in reported fees.
To investigate these issues, a random telephone survey of households in Missouri was conducted in the fall of 1993, asking respondents who had used child care to indicate fees they paid the previous
January. These responses were then compared with average fees that
child care providers reported charging in January, where the latter
were averaged for seven geographic areas within the state, and distinguished by class of service. Average fees reported by households
differed from fees reported by providers by less than five percent.
This difference was not statistically significant. It is, therefore,
reasonable to assume provider reports give a reasonable measure of
client fees actually paid. l o
In addition to provider fee reports, state records were obtained for
the number of subsidized clients for whom providers received direct
payments during the periods July 1990 to June 1991 and July 1992
through June 1993. Payments made directly to clients could not be
traced. Approximately five percent of total payments were made
directly to clients in the first period, while in the second period it was
20 percent.
Plan of Analysb
Table 1 displays average reported fees for each category of care for
both January 1991 and 1993, as well as the percentage increase in
fees. While the cost of full-day care for preschoolers increased 6.6
percent over the two-year period, part-time day care for preschool
children increased over 33 percent. Costs of the other categories of
'Households with at least one child age eleven or under responded regarding child care
arrangements for a total of 284 children. Of these, 119 had paid child care fees, and 71 had
usable dollar estimates of child care rates.
'"Details of the Missouri household survey are found in Mueser and Weagley (1993).
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TABLE 1
Average Reported Daily Feesain Dollars for 1991, 1993,
and Rate of Growth
All Respondents
Measure
Mean
Dollars
St.
Dev.
11.67
12.44
6.6
4.99
7.41
8.17
10.3
Selected Sample
Mean
Dollars
St.
Dev.
Nb
1436
1920
11.69
12.70
8.6
5.12
8.08
743
756
4.13
4.15
978
1175
7.46
8.18
9.7
4.37
3.99
496
456
4.35
5.70
31.0
2.97
3.29
775
973
4.43
5.79
30.7
3.09
3.74
3 88
357
10.47
11.13
6.3
3.84
3.83
1848
2418
10.48
11.21
7.0
3.72
3.74
95 5
689
6.79
7.61
12.2
3.40
3.29
1355
1646
6.88
7.58
10.2
3.74
3.21
689
639
4.01
5.36
33.7
2.30
2.80
1014
1275
4.06
5.35
31.8
2.38
3.09
518
473
9.30
10.34
11.2
3.48
3.52
1558
1969
9.40
10.28
9.4
3.33
3.20
754
760
6.41
7.19
12.2
2.98
3.05
1309
1623
6.42
7.11
10.7
3.16
2.58
625
619
3.79
5.05
33.2
2.07
3.38
1145
1499
3.77
4.83
28.1
2.04
2.42
547
560
Nb
Infanr
Full day care
1991
1993
Rate of growth (Yo)
Half day care
1991
1993
Rate of growth (Yo)
Part day care
1991
1993
Rate of growth (Yo)
Preschool
Full day care
1991
1993
Rate of growth (Yo)
Half day care
1991
1993
Rate of growth (Yo)
Part day care
1991
1993
Rate of growth (Yo)
School Age
Full day care
1991
1993
Rate of growth (Yo)
Half day care
1991
1993
Rate of growth (Yo)
Part day care
1991
1993
Rate of growth (Yo)
5.13
(continued on next page)
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VOLUME 32, NUMBER 1
TABLE 1 (continued)
Selected Sample
All Respondents
Mean
Dollars
Measure
St.
Dev.
Nb
NA
NA
Mean
Dollars
St.
Dev.
Nb
Composite Fee
1991
1993
Rate of growth
NA
(070)
9.87
10.68
8.2
4.18
4.14
1007
1007
a“Full day” is 5 or more hours, “half day” 3 to 5 hours, and “part day” less than 3 hours.
kifferences in the Ns indicate where a category of care was not offered in both periods.
care increased at rates somewhere between these two extremes.
During this period the Consumer Price Index increased 7.3 percent.
In examining what role the growth in subsidies may have played in
fee growth, two paths of influence are of concern. First, subsidies
may increase overall demand for child care services. If the supply of
inputs is not perfectly elastic so that the aggregate supply curve of
child care services is upward sloping, prices in the market will
increase. Even where firms have monopoly power, such aggregate
effects will occur if they do not control the market for the input. In
the analysis, measures of subsidy at the level of the county are available. Any market level effect of the subsidy should influence all providers in the relevant market due to a shift in the demand curve,
without regard to differences in the number of subsidized clients
served .
There will also be differences across providers in fees charged
depending on the number of subsidized clients served. The expected
relationship between subsidy and fee differs according to the structure of the child care market. If the child care market is competitive,
the theory focuses on individual consumer decisions regarding quality (and thus price). Those eligible for subsidies would have no incentive to patronize a provider charging less than p*, the maximum subsidized fee. Those who would choose lower-priced providers, in the
absence of the subsidy, patronize a provider charging exactly p*.
Looking across providers, this selection suggests a complex relationship between the proportion of subsidized clients served by a provider and fees charged. Figure 3 illustrates the expected distribution
of providers according to the proportion of clients receiving
subsidies.
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THE JOURNAL OF CONSUMER AFFAIRS
FIGURE 3
Expected Distribution of Providers by Proportion of Clients Subsidized
X
X
X
X
X
x
x
X
X
I1
x
X
X
X
X
X
X
X
X
X
X
X
I
X
X
X
x
P*
x
I11 x
x x x x X X X X x x
x
x
X
x
X
X
Provider
Fee
x
X
X
X
X
X
X
X
0
Proportion Subsidized ->
1
Among providers who have no subsidized clients, some will charge
fees below p*, and some above p*, whereas those with any subsidized
clients never charge fees below p*. As a result, the average fee
charged by providers with no subsidized clients is expected to be
below that of providers with some subsidized clients. However,
among providers serving some subsidized clients, greater subsidy will
be associated with lower fees. The reason is that there will be a large
number of subsidized clients who wish to choose a provider charging
exactly p*. To accommodate them, providers with larger numbers of
subsidized clients will be very likely to charge p*. On the other hand,
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providers with smaller numbers of subsidized clients may well charge
higher fees, reflecting quality levels which are attractive to relatively
few subsidized clients.
In contrast, for a monopolistic provider, the number of subsidized
clients in its market is exogenous. If the monopolist's fee is below the
maximum subsidized fee, p*, an increase in the number of subsidized
clients will increase the fee it charges. If the fee is initially above p*,
the impact is indeterminate.
To test these theoretical relationships, a model that predicts the fee
charged by each provider as a function of the proportion of clients
who are subsidized, the proportion of the total county day-care population who received a subsidy, and other provider and county characteristics was fit. Each of the variables will be discussed.
To measure the relationship between subsidies and fees at the provider level, the total number of clients the provider received direct
reimbursement for was divided by the total number of child days of
care provided. As noted above, the hypothesized relationship
between this variable and fees depends on whether the market is competitive or monopolistic. l 1
In contrast to the subsidy at the provider level, the measure of
county subsidy is exogenous to the provider's fee. Hence, the effect
of this measure may reflect a causal relationship between subsidy
payments and child care pricing. The variable to measure this market
level effect was the number of individuals in a county who received
direct reimbursement divided by published estimates of the total
number of children needing care as of 1990. The greater the proportion of population receiving direct subsidy payments, the greater the
expected price of day-care, as clients purchase greater quantities of
day-care due to receipt of the subsidy.
Other provider and county level data were also included in the estimated equations as control variables. The total number of children
reported to actually be in the care of the provider was included to
control for economies of scale in the production of services. Therefore, its effect is expected to be negative. To control for type of care
"It should be stressed that the relationship between number of subsidized clients and fee
charged by a provider does not identify a causal relationship, as, in the competitive model, it is
a result of selection of providers by clients. However, the estimates of this model are used to
examine the extent to which subsidized clients pay fees that differ from unsubsidized clients,
controlling for market and provider characteristics.
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being provided, the number of infants and the number of preschool
children were included as regressors. Given that the omitted category
is the number of school aged children, ceteris paribus, it is expected
that larger numbers of children in each of the younger aged categories will positively affect the estimated composite fee.
To control for the effect of the formal classification of the daycare provider on the maximum subsidized fee, dummy-variables for
each provider’s classification were included. The omitted category
was “family home,” while “group home” and “day-care center’’
were the included categories. As each of these latter classifications
represent categories of care with increasing maximum subsidized fee,
with number of children actually receiving care being held constant,
the expected sign on each of these dummy variables is positive.
Two variables were included to control for possible cost function
factors. In Missouri, day-care providers must be licensed if they serve
at least five children and are not sponsored by a religious organization. It is expected that many non-religious, smaller-size care providers may involve less formal arrangements. l2 Variables to control
for possible cost factors were a dummy variable for licensed day care,
with an expected positive effect to reflect greater per unit cost of
care, and a dummy variable for minority owned, to reflect possible
interactions among ownership and customer base.
Median county household income in 1989 was used in the empirical
model as a proxy for household income. Quality day care is posited
to be a norqal good and the expected sign on median income is positive. The number of AFDC recipients as a percentage of those under
age 18 in each county was entered as a control for a variety of social
and economic factors influencing the local environment. The variable is highly correlated with the amount of child-care subsidy payments to residents in a county. Models with the variable present will
have to be interpreted carefully.
Other county variables were included to control for the effect of
recent economic change and demographic structure in the larger
market. The following variables are expected to positively affect daycare fees: percentage change in county employment over 1980-90,
12A survey of Missouri parents found that 36 percent of all care is being provided by relatives, 22 percent by babysitters, 14 percent by child-care centers, and 29 percent by other
arrangements. The percentage of children cared for in centers was found to be directly proportional to parental income (Klein et al., 1993).
SUMMER 1998
VOLUME 32, NUMBER 1
161
percentage of the population in 1990 with 12 or more years of education, percentage of workers who commuted outside of the county of
residence in 1990, county labor force participation rates of mothers
with children under the age of six, estimated total number of children
under the age of six divided by the total population, and the logarithm of the county population in 1990.
Comparison between the two periods will be critical to infer the
impact of the subsidy. Models using a sample of providers who
responded to both surveys were estimated. Each survey asked providers to identify fees for nine classes of service; however, many
respondents omitted some classes of service, presumably because
they did not offer them. To prevent differential response in the two
periods from influencing results, consideration was limited to those
classes of service for which fees were reported in both periods.
Data do not allow one to determine the class of service received by
a subsidized client. As a result, a single composite measure of each
provider’s fees was constructed. Using classes of service for which
fees were reported in both periods, a composite fee defined as a
weighted average of the fees charged for the various classes of service
was computed. Fees in each age category were weighted by the proportion of children the provider reported in that category, and fees
for different numbers of hours were weighted to approximate the
proportion of children receiving each type of care, as indicated by a
survey of Missouri households (see notes to Table 2).
As each provider’s fee composite is based on a different set of
reported fees, differences among providers due to differences in
classes of service reported are controlled by a set of eight variables
(class of service controls) to capture differences in fees for the nine
classes of service. Each variable indicates the proportional weighting
used to construct each provider’s composite fee measure. As such, its
coefficient can be interpreted as the (log) difference between the predicted fee for that service and the omitted fee class (preschool fulltime care).
Effect of Nonresponse
To be in the analysis, a provider had t o report fees in both surveys
for at least one class of service and valid data on other variables. Fees
were eliminted which exhibited implausibly large changes between
0.061
0.089
1991
1993
1993
1993
1993
0.118
0.276
Proportion of subsidized served by provider: Number of clients for which
the provider received reimbursement divided by the number of children
reported in care in 1993. Where this value exceeded one, it was
truncated to one.
Proportion subsidized in county: Number of individuals receiving direct
subsidies from all providers in the county in fiscal year divided by an
estimate of the total number of children needing care in 1990 (see
below).
Proportion of composite fee attributed to service type:
Infant-Full day
Half day
Part day
Preschool-Full day
Half day
Part day
School age-Full day
Half day
Part day
Total number of children reported in provider’s care
Total number of infants reported in provider’s care
Total number of preschool children reported in provider’s care
1 if provider classified as “day care center”
1 if provider classified as “group home”c
1 if provider minority owned
1991
1993
1991
1993
Logarithm of the composite fee charged by the providera
33.958
6.395
20.096
0.449
0.162
0.364
2,513
3,494
3,494
3,494
3,494
3,494
~
35.601
8.530
22.861
0.447
0.162
0.356
0.043
0.053
0.372
0.356
0.359
0.348
~
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
N
(continued on next page)
28.060
2.574
11.709
0.280
0.027
0.157
0.062
0.090
0.006
0.003
0.54.4
0.025
0.014
0.125
0.063
0.053
30.997
3.929
18.0%
0.275
0.027
0.159
3,479
3,479
2,513
2,513
0.166
0.044
0.055
0.25 1
0.355
2.217
2.304
0.255
0.275
Mean
NA
NA
N
St.
Dev.
Mean
St.
Dev.
Year
Selected Sample
All Respondents
Variable
TABLE 2
Variable Definitions and Descriptive Statistics
1990
1990
1990
1989
Year
0.198
0.235
0.121
0.041
0.059
0.015
3,494
3,494
3,494
0.184
0.218
0.126
0.035
0.050
0.016
0.221
0.052
0.215
0.048
1.492
0.427
3,494
3,494
0.392
0.190
11.543
0.239
6.737
0.266
9.588
6,873
26.791
9.029
16.556
0.007
0.402
3,492
3,494
1.533
0.440
11.567
0.262
67.309
0.924
11.894
26,163
25.247
73.870
27.540
0.052
0.203
3,479
2,427
3,476
3,476
3,476
3,476
3,476
3,479
6.633
0.265
9.491
7,053
27.416
8.888
16.203
0.007
66.879
0.924
11.939
26,242
25.921
73.780
28.185
0.052
Mean
~
N
~
_
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
1,007
St.
Dev.
N
St.
Dev.
Mean
Selected Sample
All Respondents
aProviders were asked to report fees for nine classes of service, based on age of child (infant, preschool, school age) and number of hours per day (5 or
more, 3 to 5 , less than 3). For each age group, a weighted average based on the proportion of children in each category of hours in the Missouri household survey was constructed (infants, .90, .05, .05; preschool, .90, .05, .05; school age, .45, .30, .25). These age-specific fees were then weighted by the
number of children that the provider reported serving in each age category to produce the composite fee. The procedure was modified to omit any fee
category that the provider failed to report in either period, with weighting adjusted to reflect such omissions. Hence, a particular provider’s composite
fees for both periods are based on the same classes of service, but across providers the composite may be based on different classes of service.
”sum of the number of infants, preschool children, and school age children reported by provider.
Troviders are classified as “center,” “group home,” or “family home,” with the categorization corresponding approximately to number of children
served ( >20, 10-20, < 10). The classification influences the maximum subsidized fee.
dData are based on 1990 U.S. census reports as compiled in Office of Socioeconomic Data Analysis (1992).
Qmitted category comprises all counties outside metropolitan areas (98 counties).
1 if provider licensed
AFDC recipients as a percentage of those under age of 18 in the countyC
Median county household income (dollars)
Percentage change in county employment, 1980-90
Percentage of the county population with 12 or more years of education
Percentage of county workers commuting
Estimated total number of children under the age of 6 needing care in the
county in 1990, based on number of children with working mothers,
divided by total population
County labor force participation rate of mothers with children under the
age of 6
Logarithm of county total population in 1990
1 if county is in St. Louis metropolitan area (Jefferson, Franklin, St.
Charles, and St. Louis counties and St. Louis city)e
1 if county is in Kansas City metropolitan area (Jackson, Cass, Clay,
Lafayette, Platte, and Ray counties)
1 if county is in Springfield metropolitan area (Greene and Christian
counties)
1 if county is in Joplin metropolitan area (Jasper and Newton counties)e
1 if county is in Columbia metropolitan area (Boone county)
1 if county is in St. Joseph metropolitan area (Buchanan county)
Variable
TABLE 2 (continued)
_
;j;
w
e
5
N
c
P
c
00
W
5
P
164
THE JOURNAL OF CONSUMER AFFAIRS
the surveys. l3 The “Selected Sample’’ in Tables 1 and 2 identifies the
subsample of providers employed in the analysis.
Average fees charged per day of care, reported in Table 1, differ by
relatively little for the full sample and the selected sample. In contrast, average values for several independent variables, reported in
Table 2, differ substantially. The most important difference is the
proportion of subsidized clients in the 1991 fiscal year. In the full
sample, for the average provider, 11.8 percent of clients received a
subsidy, while in the selected sample, the figure was over 25 percent.
This difference suggests that providers with more subsidized clients
in 1991 were much more likely to respond to the survey in 1991.
Table 2 suggests there is virtually no difference in the proportion of
subsidized clients in 1993 between the full and selected sample. However, this is an artifact of variable construction. Number of children
served by a provider is available only for providers who responded to
the 1993 survey, so proportion of subsidized clients in 1993 is available for those responding to the 1993 survey. Thus, even in the “full
sample” the reported mean is based on those who responded to the
1993 survey.
Other differences between the selected and the full sample statistics
are much smaller. Providers in the selected sample serve an average
of 31 children, rather than 28 in the full sample. The distribution of
the selected sample across geographic areas is very similar to that of
providers in the full sample, so too are measures reflecting county
characteristics.
Relationship Between Subsidies and Fees
Table 3 presents simple regression equations predicting the logarithm of composite fees for 1991 and 1993. Two variables are used
to capture the relationship between the number of subsidized clients
served by a provider and fees charged: a dummy variable which identifies providers serving no subsidized clients and a continuous
measure of the proportion of clients who received subsidies. In both
periods, estimated coefficients indicate a relationship consistent with
the basic structure suggested by the competitive model. The negative
“Initial inspection of fee reports revealed cases where it appeared that fees were reported on
an hourly rather than a daily basis. Fees which changed by more than $20, or by more than a
factor of two were omitted. The basic results were not substantially changed by this selection.
SUMMER 1998
VOLUME 32, NUMBER 1
165
TABLE 3
Model 1: Basic Equation Predicting Child Care Fees
Dependent Variables
~
Independent Variables
Log
1991 Fees
t
Log
1993 Fees
~
Intercept
Proportion subsidized served
by provider (1991, 1993)
1 if no subsidized clients served by
provider (1991, 1993)
Proportion of composite fee attributed to
class of service:
Full day infant
Half day infant
Part day infant
Half day preschool
Part day preschool
Full day school age
Half day school age
Part day school age
Proportion subsidized in county
(1991, 1993)
Adjusted R 2
t
~
2.4%9
-0.258 1
-6.3
2.4550
-0.1166
-3.0
-0.1823
-6.6
-0.1209
-4.7
0.2205
-0.1102
-0.3312
-0.3230
-0.8943
-0.2799
-0.6681
-1.1271
-0.03 73
3.9
-0.3
-0.5
-2.5
-5.4
-4.0
-8.0
-14.6
-0.2
0.1960
-0.2375
-0.221 7
-0.2066
-0.7758
-0.2621
-0.6758
-1.0804
-0.55440
3.6
-0.7
-0.3
-1.7
-4.9
-3.9
-8.4
-14.5
2.9
0.3077
0.3053
coefficient estimated for no subsidized clients indicates such providers have lower fees than providers with small numbers of subsidized clients. Returning to Figure 3 , this is due to the fact that providers charging less than the maximum subsidized fee are predicted to
serve no subsidized clients, which reduces the average fee of providers with no subsidies (labeled as I, Figure 3) to below that for subsidized providers (11). The negative coefficient on the proportion of
clients who are subsidized implies that as providers increase the proportion of subsidized clients, fees decline (e.g., from I1 to 111). The
competitive model predicts this pattern, as providers charging fees
very close to the maximum subsidized fee are expected to have larger
proportions of subsidized clients.
The above pattern is not consistent with predictions of the monopoly model. Although the monopoly model implies that fees increase
with the proportion of subsidized clients, it does not predict a discontinuity at zero. The square of the proportion of subsidized clients
was not statistically significant when added to any of the reported
models, allowing us to reject the conjecture that the impact of the
dummy variable is merely picking up a continuous nonlinearity in the
relationship involving the proportion of subsidized clients. The sig-
166
THE JOURNAL OF CONSUMER AFFAIRS
TABLE 4
Model 2: Equation Predicting Child Care Fees
Controlling Provider and County Characteristics
Dependent Variables
Log
Log
Independent Variables
1991 Fees
t
1993 Fees
t
Intercept
Proportion subsidized served by provider
1.0782
-0.0761
-2.5
1.1762
-0.0040
-0.2
(1991,1993)
1 if no subsidized clients (1991,1993)
Proportion subsidized in county
-0.0639
0.2948
-3.1
-0.0224
0.0110
-1.3
1.0
-0.0007
0.0087
0.0027
0.0870
0.0694
-0.1064
0.0193
0.0065
-0.9
7.2
2.8
3.8
1.6
-4.8
0.6
3.0
-0.0003
0.0079
0.0021
0.1092
0.0786
-0.0260
0.0735
0.0074
-0.5
7.3
2.4
5.4
2.1
-1.3
2.6
3.6
3.0
0.6
3.9
-0.0002
0.0081
3.0
-0.3
4.7
-0.0006
-0.0029
-0.6
-1.5
-0.0013
-0.0035
-1.6
-2.1
0.5032
0.0346
0.3
2.0
2.4231
0.0158
1.6
1.0
0.1
(1991,1993)
Total number of children in provider’s care
Total number of infants in care
Total number of preschool children in care
1 if “day care center”
1 if “group home”
1 if minority owned
1 if licensed
AFDC recipients as percent of under 18
years
County median household income
Percentage change in employment, 1980-90
Percentage of county adults with 12+ years
education
Percentage of county workers commuting
County labor force participation rate of
mothers with child <6
Estimated under 6 population needing care
Logarithm of county population
Class of service
Dummy variables for region
Adjusted R2
o.ooo012
0.0003
0.0071
o.oooo11
Controlled
Controlled
Controlled
Controlled
0.6879
0.7295
nificance of not serving subsidized clients, consistent with the competitive theory, suggests that there is a true discontinuity in the
relationship.
The proportion subsidized in the county was found to have a nonsignificant coefficient in the first period and a positive, significant
coefficient in the second period. The positive coefficient implies that
providers in counties with larger proportions of subsidized clients
have higher fees.
Estimates in Table 3 control none of the characteristics of the child
care provider or the local market. Table 4 presents estimates for an
equation that controls for seven additional characteristics of the provider, seven socioeconomic characteristics of the county, as well as
six dummy variables for differences between seven regions in the
SUMMER 1998
167
VOLUME 32,NUMBER 1
state. With these other variables controlled, effects of the proportion
of subsidized clients served by the provider are much the same,
although effects are generally smaller, and they are not statistically
significant in the second period.
With these additional controls, the effect of the proportion receiving subsidy in the county is not statistically significant in either
period. A single variable is responsible for this nonsignificance, the
density of AFDC recipients in the county. Table 5 reports results
where the AFDC measure is omitted. The coefficient of the county
subsidy measure is statistically significant in predicting fees in each
period. The interpretation of this result is discussed below.
Table 6 reports three estimates of lag adjustment formulations,
which parallel the specifications for 1993 but include the 1991 fee as
an independent variable. The coefficient on the lagged variable in the
TABLE 5
Model 3: Equation Predicting Child Care Fees
Controlling Provider and County Characteristics but Omitting AFDC
Dependent Variables
Log
Log
Independent Variables
1991 Fees
t
1993 Fees
Intercept
Proportion subsidized served by provider
1.1235
-0.0725
-2.4
1.2970
-0.0002
-0.0
-0.0618
0.7650
-3.0
3.2
-0.0207
0.4861
-1.2
2.8
-0.OOO6
0.0088
0.0026
0.0913
0.0636
-0.0949
0.0211
0.000007
-0.0001
0.0053
-0.9
7.2
2.7
4.0
1.5
-4.3
0.7
2.0
-0.2
3.0
-0.0003
0.0079
0.0020
0.1131
0.0662
-0.0153
0.0745
O.ooo007
-0.0007
0.0053
-0.4
7.3
2.3
5.5
1.7
-0.8
2.6
2.0
-1.6
3.4
-0.0001
-0.0040
0.2
-2.2
-0.0007
-0 .W48
-0.9
-2.9
1.6959
0.0555
1 .o
3.5
3.4662
0.0379
2.3
2.7
t
-
(1991,1993)
1 if no subsidized clients (1991,1993)
Proportion subsidized in county
(1991,1993)
Total number of children in provider’s care
Total number of infants in care
Total number of preschool in care
1 if “day care center”
1 if “group home”
1 if minority owned
1 if licensed
County median household income
Percentage change in employment, 1980-90
Percentage of county with 12+ years
education
Percentage of county workers commuting
County labor force participation rate of
mothers with child <6
Estimated under 6 population needing care
Logarithm of county population
Class of service
Dummy variables for region
Adjusted R’
Controlled
Controlled
Controlled
Controlled
0.6853
0.7262
168
THE JOURNAL OF CONSUMER AFFAIRS
TABLE 6
Equations Predicting the Logarithm of the 1993 Composite Child Care Fees
with Lagged Fee
Independent Variable
Intercept
Proportion subsidized served
1 if no subsidized clients 1993
Proportion subsidized in
county, 1993
Logarithm of 1991 composite
fee
AFDC recipients as percent
of under 18 years
Class of service
Dummy variables for region
Adjusted R’
Coefficient
t
Coefficient
t
Coefficient
t
0.4189
0.04156
-0.0053
0.2393
2.3
-0.4
2.7
0.4998
0.0186
-0.0023
-0.0078
1.0
-0.2
-0.1
0.5344
0.0199
-0.0016
0.1576
1.1
-0.1
1.4
0.8414
60.6
0.6612
34.8
0.6645
35.1
0.0024
1.7
0.8520
Controlled
Controlled
Controlled
Controlled
0.8792
0.8790
second and third equations suggests that a third of the adjustment
toward equilibrium occurs in the two years between periods. Several
coefficients differ substantially from those reported in the previous
equations. The coefficient of proportion subsidized served by the
provider is positive and for providers with no subsidized clients in
1993 is essentially zero. Hence, in the two-year period, providers with
large proportions of subsidized clients exhibited faster fee growth,
although the coefficient is not statistically significant. The coefficient
of the proportion receiving subsidies in the county shows a positive
relationship, although it disappears when AFDC participation is
included (column 2).
Relative Growth of Fees for Subsidized Clients
While care must be exercised in identifying causal relations, it is
useful to examine how fees paid by subsidized and unsubsidized
clients changed between the two periods. A simple measure of change
in relative fees may be obtained by weighting fees charged by each
provider by the number of subsidized and unsubsidized children
served and summing across providers. Because the composite is
based on a different class of service for each provider, it is necessary
to correct reported composite fees for variations in class of service.
Results are reported in panel A of Table 7, which report differences
in the logarithms of fees. Line 1 of panel A shows that in 1991, fees
for subsidized clients were about 12 percent lower than fees for com-
SUMMER 1998
VOLUME 32, NUMBER 1
169
TABLE 7
Logarithm of Ratio of Subsidized to Unsubsidized Child Care Fees
Panel A a
Controlling for class of service only
(1)
(2)
(3)
(4)
1991 fees,
1991 fees,
1993 fees,
1993 fees,
1991 subsidy counts (observed)
1993 subsidy counts
1991 subsidy counts
1993 subsidy counts (observed)
-0.1238
-0.0686
-0.0760
-0.0272
0.0552
0.0478
0.0966
Panel B
Controlling for class of service, provider characteristics,
and county characteristics
(1) 1991 fees, 1991 subsidy counts (observed)
(2) 1991 fees, 1993 subsidy counts
(3) 1993 fees, 1991 subsidy counts
(4) 1993 fees, 1993 subsidy counts (observed)
-0.0229
-0.0183
-0.0075
-0.0002
0.01 16
0.0224
0.0297
~
Key
Tomposite fees for 1991 and 1993 for each provider i are corrected usingcoefficients of variables for the proportion of the composite fee attributed to each service type: full day, half day,
and part day-infant; full day, half day, and part day-preschool; and full day, half day, and
part day-school aged; as estimated in an equation similar to that in model 1, omitting the
variable measuring the proportion of the county day care population receiving a subsidy
payment.
komposite fees 1991 and 1993 are corrected using all coefficients in model 2.
Row 1 column a: EiN9l:ln(P9li) - CiN91iUln(P91i)
Row 2 column a: EiN93:ln(P91i) - CiN93Yln(P9li)
Row 3 column a: EIN91;ln(P93i) - CiN9IiUln(P93i)
Row 4 column a: EiN93:ln(P93i) - CiN93i"ln(P93i)
N91iS,N93:, number of subsidized clients served by provider i in 1991, 1993.
N91;, N93?, number of unsubsidized clients served by provider i in 1991, 1993.
P9Ii, P93i corrected composite fee charge by provider i in 1991, 1993.
Column b: Difference from row 1.
parable service for unsubsidized clients. Line 4, column a, indicates
that two years later this gap had declined to less than three percent.
This implies that the relative fees of subsidized clients increased by
nearly ten percent (column b). This shift is due both to an increase in
the number of subsidized clients served by higher fee providers and
by an increase in fees charged by providers already serving subsidized
clients.
Lines 2 and 3 provide an indication of the relative importance of
these two effects. Line 2 indicates that, if fees had remained unchanged, the shift in subsidized clients across providers would have
caused the gap between unsubsidized and subsidized clients to decline
from 0.1238 to 0.0686 or by 0.0552. In contrast, if changes in fees
between 1991 and 1993 had occurred with no shift in the numbers of
170
THE JOURNAL OF CONSUMER AFFAIRS
subsidized clients served by each provider, the gap would have
declined to 0.0760 or by 0.0478. It can be concluded that, over this
two-year period, fees increased faster for providers serving subsidized clients, but that this effect was less important than increases
in numbers of subsidized clients served by higher fee providers.
The above clearly supports the view that the increased subsidies
allowed clients to choose higher priced and very likely higher quality
providers. A provider’s quality shifts slowly, as higher staff-to-child
ratios, increased staff education, and administrators’ experience
(Cost, Quality & Child Outcomes Study Team, 1995) take time to
achieve. This implies increases in the quality of care demanded would
occur largely in the form of movements toward providers already
providing quality care.
Panel B performs a similar exercise but controls for provider and
county characteristics available in the data. Given that quality differences are controlled by these variables, differences observed in
panel A will be reduced. Row 1 indicates that such controls reduce
the gap between unsubsidized and subsidized clients to two percent in
1991, and that there is no observed gap in 1993. These results help
confirm that observed differences between subsidized and unsubsidized clients reflect quality differentials captured by the provider and
county control variables.
Even with controls, changes in the allocation of subsidized clients
across providers account for about a third of the shift over two years,
while changes in fees across providers account for two-thirds. This
provides limited support for the view that growth in subsidies has
allowed subsidized providers to increase fees faster than others. Of
course, the two percent growth attributed t o such fee increases may
also reflect unmeasured quality improvements for providers serving
subsidized clients.
Finally, results in Table 7 allow us t o reject claims that subsidized
clients pay appreciably more than others. While subsidized clients may
well pay fees above those they would pay in the absence of the subsidy, such effects merely put them on a par with unsubsidized clients.
As this permits subsidized clients t o obtain quality similar to other
clients-a view the analyses support-there appears to be appreciable
social benefits t o the subsidy program. Improvements in the cognitive development of children who receive subsidies, associated with
higher fees, may be particularly valuable.
The monopoly model suggests that subsidies would cause pro-
SUMMER 1998
VOLUME 32, NUMBER 1
171
viders with large and growing numbers of subsidized clients to
increase fees. The evidence implies that observed growth in subsidized fees is not, for the most part, due to responses by monopolistic providers. The small predicted growth in subsidized fees, once
provider and market characteristics are controlled, argues against
empirical importance of a monopoly in this market.
Growth of Fees in Subsidized Markets
If supply curves for factors used in producing child care services
are upward sloping, providers in factor markets with greater child
care demand should charge higher fees. The proportion of children in
the county who receive subsidies should cause a demand shift. If this
causal path is important, subsidies could raise child care fees even for
those providers who serve no subsidized clients. In the model 1 specification (Table 3), in which provider and county characteristics are
not controlled, the coefficient of the county subsidy measure is not
statistically significant in the first period but is positive and significant in the second. In model 2 (Table 4), which controls for provider
and county characteristics, the coefficient is not statistically significant in either period.
Model 3 estimates (Table 5) show that, if the single variable indicating the level of AFDC participation in the county is omitted, the
coefficient for the proportion of the children receiving subsidies in
the county is statistically significant and positive in both periods.
Subsidy programs were in large part designed to aid families who had
been on AFDC and those who were engaged in training programs
while on AFDC. Hence, a strong relationship between the levels of
AFDC and child care subsidies in the county exists, and correlations
of 0.75 and 0.67 are observed in the two periods between these two
variables. As expected, a comparison of Models 2 and 3 indicates
that inclusion of both variables inflates standard errors.
In Table 4,where both measures are included, the AFDC measure
is statistically significant. It is not surprising that the coefficient of
AFDC is more robust than the subsidy measure. The measure of subsidy receipt suffers from several sources of error. It omits subsidized
clients who received direct payment from the state. Approximately
five percent of funds were paid directly to clients in 1991, while 20
percent were paid this way in 1993. In effect, the AFDC variable may
better capture the subsidy level than the variable intended to measure
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subsidies. The difference in the subsidy accuracy between the two
periods may account for the smaller coefficient obtained in the equation predicting fees in the second period (model 3).
Results in the lagged equations (Table 6) are generally consistent
with those reported above. As it essentially predicts changes over a
two-year period, the coefficient on the proportion receiving subsidy
in the county is generally smaller.
What do these estimates suggest about the impact of subsidies on
child care fees across counties? Consider the coefficients on the
county subsidy measure reported in Table 5 . As the difference
between the estimates for each period is not statistically significant,
assume the mean value, 0.63, applies to both periods. The mean
value of the proportion receiving subsidy across the sample of providers is 0.06 in the first period, implying that fees are approximately
3.8 percent higher in that period than they would be in the absence of
any subsidy. The mean proportion receiving subsidy across the
sample of providers in the second period is 0.09, implying fees are
approximately 5.7 percent higher because of the subsidy. Taken
together, the implication is that between the two periods, fees grew
by 1.9 percent as a result of increases in subsidies.
The lag adjustment formulation (Table 6) allows for the possibility
that fees may not respond immediately to available subsidies. Consider the third specification, which controls for provider and county
characteristics but omits AFDC. The coefficient for the proportion
of the county receiving some subsidy across the sample of providers
for 1993 implies that fees grew by 1.4 percent over that period due to
subsidies to consumers (0.158 x 0.09). Two-thirds of the growth was
'due to a lagged effect of subsidies already in place in 1991, while a
third was due to increased subsidies. The lag fee coefficient is 0.664,
implying that only about a third of the adjustment occurs over the
two-year period. Hence, in the long run, if subsidy levels remained at
the current level, fees would be expected to be 4.2 percent higher than
in the absence of subsidies (0.09 x 0.158/(1- 0.664)).
To summarize, the coefficients reported in Tables 5 and 6 suggest
that subsidies observed in 1993 have shifted the demand for child services in each county so that average fees for all child-care consumers
are, or will be, four to six percent higher than they would be in the
absence of the subsidies. How do estimates of the impact of the subsidy at the county level compare with those predicted by theory?
The average subsidy in 1993 for children in the relevant market
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173
probably amounts to about seven percent of total fees paid.I4 Blau
(1993) estimated elasticity of supply of labor to child care in the range
from 1.2 to 1.9, which may be used as an approximate elasticity of
supply for child care services. Studies suggest an elasticity of demand
for child care in the range from -0.2 to -0.7 (Blau and Robins
1988; Hotz and Kilburn 1991; Ribar 1993), although a few are much
larger. These ranges imply a total impact between 0.6 percent and 2.6
percent. l5 If the elasticity of demand were as great as - 1.9 (Ribar
1992), and the elasticity of supply were 1.2, the total predicted impact
would be 4.4 percent. Hence, the conclusion that subsidies increased
fees by four to six percent implies a somewhat larger effect than
recent estimated elasticities of supply and demand suggest but are not
outside the possible range.
CONCLUSION AND DISCUSSION
Fees paid by subsidized clients were about 12 percent below fees
paid by others in 1991, but the gap had declined to three percent by
1993. The increase occurred largely as a result of subsidized clients
choosing more expensive providers in the second period. Observed
correlations between child-care cost and quality indicate an increase
in the level of quality for day care as a result of the subsidy program.
The subsidy may have succeeded in increasing the quality of care purchased by subsidized clients, one of the goals of government subsidy
for child care.
With diminishing governmental budgets, if child-care-subsidy programs are to continue to be effective in providing quality care to lowincome children, state policies must develop linkages to other service
providers (e.g., schools and Head Start) to meet the needs of the low141n the second period, the average subsidy paid per child is a little over $1,000. The mean
value for the proportion of the county day-care consumers that receives a subsidy payment suggests that in the average county, nine percent of children in need of care receive a subsidy.
However, this probably underestimates the impact of the subsidy. The subsidy measure
includes only payments made directly t o providers which are either registered or licensed by the
state. As a survey of clients suggests that half use less formal child care arrangements, the
impact of subsidies on the more formal portion of the market may be as high as 18 percent.
This would imply an average annual subsidy of $180 per child. Because the average fee is
approximately $10 per day, or $2,500 per year, the average subsidy for all children in the relevant market, as a percentage of the fee, would then be 7.2.
'This estimate is based on a simple supply and demand analysis which implies that
dlnP/d(S/P) = - E/(N - E), where P i s the fee, S/P is the subsidy as a proportion of the price,
E is the elasticity of demand, and N is the elasticity of supply.
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THE JOURNAL OF CONSUMER AFFAIRS
income. States need to be mindful of quality differentials and set
payment rates to encourage quality. By providing greater rates to
accredited programs and rates sufficient to provide competitive
wages to reflect the skills and qualifications of child care professionals, state programs will encourage quality.
Our county level analyses suggest that child care providers in counties with greater subsidy levels charge higher fees, whether or not they
serve subsidized clients. The coefficients suggest that between 1.4 and
1.9 percent of the growth in fees between 1991 and 1993 could be
attributed to subsidies and would indicate all consumers face higher
prices as a result of the subsidy. In the long run, market fees may be
four to six percent higher because of the subsidies. The model implies
these increases are due to the real cost of producing more child care
services, including higher quality child care services. Hence, if the
program succeeds in improving the access of poor families to quality
child care-as the analysis implies-such costs would be modest if
the end result is a move to high quality education for all American
children.
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