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IGSL
UCB

 
   

PACE WORKING PAPER SERIES 03—1

Targeting Investments for Universal PPESChOOI

Which Families to Serve First?
Who Will Respond?

INSTITUTE OF GOVERNMENTAL
STUDIES LIBRARY

FEB 03 ZUI7
UNIVERSITY OF CALIFORNIA

Policy Analysis for California Education
University of California and Stanford University

2003

 

 

WORKING PAPER SERIES 03-1

Targeting Investments for Universal Preschool

Which Families to Serve First?
Who Will Respond?

Bruce Fuller
Danny Shih—Cheng Huang

University of California, Berkeley and Davis
Stanford University

2003

Policy Analysis for

California Education

 

PACE

 

 

Table Of Contents

Summary—How to Best Focus Investments? ..................................................................................... 1
Institutional Backdrop—Family Access to Uneven Child-Care Options .......................... 2
Little Growth in Preschools and Centers ...................................................................................... 2
Enter the Idea of Universal Preschool ............................................................................................ 5
The Merits and Risks of Alternative Strategies ............................................................................... 4
Three Targeting Strategies to Expand Preschool Access ........................................................ 6
Focus on Communities with Low School Achievement ...................................................... 7
Focus on Communities with Low Supply ...................................................................................... 7
Focus on Communities with High Pent-up Demand ........................................................... 12
Comparing Who Benefits Under Alternative Strategies ................................................ ”l8
Poflcynnpficafions ................................................................................................................................................ 18

Endnotes ....................................................................................................................................................................... 2O

 

 

 

Summary—How to Best FOCUS
Investments?

Several states are extending access to preschool for a widening
range of families. Georgia has made dramatic progress toward
providing preschool slots for all three and four—year old
children. Illinois, New Jersey, and New York have taken steps to
broaden access, slowed by the economic recession and shifting
government priorities.

California may be joining these states. Two blue—ribbon panels
have urged the state to create a plan for moving toward univer—
sal preschool (UPS), focusing first on low—income families. The
proposed master plan for education, recently translated into
legislation, calls for incremental implementation of UPS. Los
Angeles and San Mateo counties have allocated funds for
concerted planning efforts.

It remains doubtful whether Sacramento or local counties can
move forward until the state budget picture improves, not to
mention concern over the federal budget picture.

Recent history offers a more upbeat picture, suggesting that
political will behind preschool access will continue to grow.
California steadily pumped new dollars into child care and
preschool programs until the recent downturn, rising from
$800 million in 1996 to $3.1 billion in the 2002—03 fiscal year.
Yet much of this spending growth went for license—exempt
child care, allowing preschools and center—based programs to
barely keep pace with the state’s growth in child population.l

As state and local advocates advance the idea of UPS, their
efforts are unfolding under tenuous economic conditions and
long—term constraints on public financing. Initial capital for

preschool expansion is coming from First 5 Children and

Family commissions. But any sustainable move toward a more
accessible, higher quality network of preschool programs would
require program realignment and investment at the state level.

This paper, then, asks—How might public funds be effectively
targeted to yield strong enrollment demand by parents and
discernible effects on young children’s early development and
school readiness?

We aim to illuminate how state and local planners might weigh
the plusses and minuses of differing targeting priorities. We do
not to advocate a particular policy. For instance, Los Angeles
County—which has allocated $100 million for UPS—could
focus new investments on

>3 Communities with the lowest performing students on stan-
dardized tests, scores which are tightly correlated with
neighborhood wealth or poverty.

Communities with the lowest supply of preschool slots for
young children, neighborhoods that may be poor or work—
ing class in composition.

Communities where the pent-up demand for preschool is
highest, that is, neighborhoods where family demand
outstrips current supply.

This paper analyzes how these alternative targeting mecha—
nisms—if applied to Los Angeles County—would yield differ—
ing allocations to various communities.

The risks associated with not entertaining alternative targeting
methods are great. Scarce public dollars could be inefficiently
spent, for example, directed to communities where an excess
supply of preschool programs already exists (given current
levels of family demand). In addition, early efforts to broaden

 

preschool access will hopefully yield a strong demand response
from parents who are eager to enroll their children. Yet funding
could be allocated to communities where few families respond,
due to low maternal employment rates or tepid responses by
certain groups of parents.

Current patterns of family demand, in part, are conditioned by
supply conditions. That is, unless families have the option of
quality center—based programs, they cannot express demand for
it. Yet if additional preschool slots were created, the demand
response would be unequal across communities, given that
maternal employment, parent education, and family structures
vary—factors that have been shown to contribute to demand for
preschool programs.2

While the present paper utilizes local data from Los Angeles
County, we illustrate how policy makers and planners might
think through allocation options—estimating how particular
communities and families benefit more, or less, under different
targeting strategies. We discuss the advantages of experimenting
with alternative expansion strategies, rather then investing
exclusively in one method.

This paper is organized in the following manner: We briefly
describe the current institutional arrangements and financing
streams that support the state’s current network of preschool
organizations, family child-care homes (FCCHs), and individual
caregivers, increasingly subsidized via child-care vouchers.

For each of the three targeting strategies, we describe which Los
Angeles communities (zip—code areas) would benefit most,
compared to those that would benefit least. We show that
alternative allocation strategies can yield quite different effects
among low- and middle—income communities. We also consider
the a priori criteria that might be used in judging the wisdom of
targeting options.

 

Institutional Backdrop—Family
Access to Uneven Child-Care Options

In California, great strides have been made in widening family
access to child care. Spending from Sacramento on preschool
and child—care programs has almost quadrupled since 1996,
rising from $800 million to $3.] billion annually in the current
fiscal year. This excludes federal Head Start funding that climbed
steadily until recently.

Little Growth in Preschools and centers

Surprisingly little is known about what families and taxpayers
are getting in return from this rising investment. We do know
that many more families now benefit from public support that
pays for center—based care and individual providers, often kith
or kin members who care for young or school—age children. The
number of low—income families in California who benefit has
risen by at least a quarter million since the 1996 reforms in
family policy, advanced by Washington and Sacramento.3

Much of this new funding reimburses individual caregivers who
serve low—income parents, many who are moving off the
welfare rolls and into jobs. Center—based programs—includ—
ing state preschools, so—called contracted centers, and programs
in middle—class communities—have benefited comparatively
little from this rising public investment. The number of pre—
school slots per 100 children, age 0—5 years—old, rose from 13 to
14 between 1996 and 2000.4 This includes both subsidized
programs and preschools that rely on parental fees for operat—
ing revenues. In short, the preschool sector was able to stay
just a bit ahead of child population growth during a period
of unprecedented growth in child—care spending—while
remarkable growth occurred in unlicensed, individual care.

 

Two pieces of good news include the fact that California contin-
ues to focus its public investment on low—income and working—
class families—where effects on children’s early development
and school readiness appear to be the greatestf’ lnequities in
basic availability remain stark: preschool enrollment capacity is
up to four times higher in affluent Los Angeles communities, for
example, compared to poor neighborhoods, as detailed below.
But progress has been made in reducing these gaps. We will even
see how excess capacity may be outstripping current levels of
parental demand in some poor areas of Los Angeles.

The other encouraging finding is that Sacramento’s quality
standards and monitoring practices are resulting in relatively
high quality among preschools situated in some of the state’s
poorest communities. Unlike the public schools, quality is not
always correlated with community wealth or poverty when in
comes to preschool programs. Two recent studies of quality,
conducted in Los Angeles, Santa Clara, and San Francisco
counties reveal that class sizes, staffing ratios, and teacher
qualifications are often stronger in preschools that are subsi—
dized and regulated by the state education department, com—
pared to independent preschools in middle—class areas that must
live off of parent fees.6 And the quality of these center-based
programs yields stronger effects on children’s early cognitive
growth and language development, compared to children
spending their days exclusively with home—based caregivers.7

Enter the Idea Of Universal Preschool

California’s early architects of universal preschool (UPS) are
beginning to seriously ask, Which families should first benefit from
improved access to quality programs? Resources will likely remain
tight for years to come. Universal financing of preschools—
extending full-day kindergarten, for example, down to three
and four year—olds—would be regressive, granting public support

to affluent families who currently pay their own way. And some
evidence shows that preschool supply is sometimes lower in
working and lower—middle class communities, not in the poorest
neighborhoods.8 So, the issue of how best to target constrained
funding on types of communities or families becomes pivotal.

The policy issue of who should initially benefit is embedded in the
institutional question of how preschool supply should be grown
and quality improved. Much ofthe growth in the broad child—
care “system” has occurred through demand-side financing. This
includes the rapid rise in child—care vouchers—encouraged by
federal block—grant funding since 1990—and support of efforts to
improve market information made available to parents. In
California, vouchers are supported through the Alternative
Payments Program, created in the 19705, long before the parental—
choice mandate from Washington, and child—care support that
flows through the state welfare program (CalWORKs).

In contrast, efforts to directly expand the supply of state pre—
schools or centers operated by school districts and community—
based organizations have been relatively modest. Within the
state education department’s programs, the voucher side has
grown seven—fold since 1996, while center-based programs have
expanded by about 80 percent? Governors Wilson and Davis
have advocated for significant expansion of half—day preschool
programs, but the lion’s share of funding increases have moved
through child-care vouchers.

Some UPS advocates talk of creating high quality pre—
kindergarten programs run by school districts, available for
three and four year—olds. This would require determined
building of supply of new preschool organizations.

Another strategy is to craft a UPS umbrella—perhaps replete
with quality standards and stronger funding within a county——
that would incrementally incorporate existing center—based

 

programs and a subset of family child-care homes (FCCHs). This
strategy holds the advantage of building from the state’s $3.1
billion investment in child care. And this approach could nurture
a shared mission for a variety of licensed child-care organiza—
tions. This assumes that a consensus could be built around
quality standards and forms of monitoring.

These how questions—the institutional parameters within
which targeting issues are examined—are thus interwoven with
the who benefits questions. For example, if new preschools were
to be built, embedded in the public schools, then a certain array
of targeting options would emerge—options that would differ
from an incorporation strategy that provided supplemental
grants or incentivized vouchers for centers or FCCHs that were
part of the Los Angeles UPS program.

Our analysis, for the moment, sets aside these institutional
questions, focusing on the families or communities on which
UPS dollars might be targeted. Yet in weighing allocation op—
tions, the organizing structure that oversees expansion and
quality—improvement efforts must be considered.

 

The Merits and Risks of
Alternative Strategies

How might we weigh the plusses and minuses of different
allocation strategies? A few criteria could be employed as we
consider the three targeting strategies examined in this paper:

Which set of children would benefit most from preschool
access, beyond those who already can enroll? The empirical
literature is fairly clear that experience within quality preschool
programs contributes most strongly to the cognitive and

language development of children from low-income back-
grounds. We know much less about whether preschooling
advances these proficiencies for middle—class youngsters.
And the effects on social development have been more
difficult to detect for children of any social class.10

If California is to sustain gains in student test scores—espe-
cially at the elementary school level—improvements in
children’s readiness to enter kindergarten could play a strong
role. A related argument for targeting resources on poor
communities is that recent family policy reforms have strongly
encouraged mothers to leave home and find a job. As Govern-
ment has intensified work pressures—making a variety of
family supports contingent upon maternal employment—this
has boosted demand for child care. Yet these same families have
little discretionary income—even if they do land a low-wage
job—to pay for child care in the open market.

Which new families will most vigorously respond to increased
preschool supply? Some UPS advocates have suggested that new
dollars might be targeted on those communities where
children’s school achievement levels are lowest. This generally
means focusing on the poorest communities. This flows, in
part, from the first criterion specified above.

But this approach has two shortcomings, prompting the
analysis described below. First, California has progressively
targeted preschool and child—care spending on low—income
communities over the past three decades. We will see that this
policy strategy has paid-off for families residing in many poor
zip—code areas. Further expansion in these same communities may
yield a tepid demand response, since parents who are interested
in center—based programs already participate in many cases.

This distribution of existing preschool supply was recognized
in the Los Angeles child care needs analysis conducted in 2000.

 

This report from the local child care planning council includes
a detailed breakdown of the shortfall between each zip code’s
current availability of licensed programs (preschools, centers,
and FCCHs) and likely levels of parental demand, based on a
zip code’s maternal labor force participation and count of
welfare—to—work clients.”

A related way to connect current supply and likely family
demand is to ask which low—income communities possess
significant pent-up demand. For example, if preschool supply
is relatively strong and maternal employment rates are low, the
numbers of parents on waiting lists or seeking slots in pre-
schools may be quite low. The factors that contribute to
expressed demand for preschools are difficult to move in the
short run, including maternal employment, household income,
marital rates, and social networks that may provide informal
child care and suppress demand for formal preschooling. Below
we map how pent-up demand is distributed across Los Angeles
County, based on local directors’ reports of waiting lists.

Note that we are not normatively arguing that only children of
working mothers should attend preschool. No one makes that
argument for attendance in kindergarten or public schools.
We are simply recognizing that expressed demand for centers
and nonparental child care is related to maternal employ—
ment. This relationship will not change dramatically in the
medium term.

Which targeting strategy will best serve the collateral policy
goal of raising the quality of care that children receive? UPS
engineers must face the trade-off between expanding access
and improving quality. Parents and taxpayers will likely hold
Government accountable on serving more children. This is
relatively easy to track. Gains in quality are more difficult to
measure and vividly portray. It may be that improving quality

in low-income communities will yield strong child—level effects.
But expanding preschool slots in working-class communities,
where pent—up demand is sometimes strongest, could yield
tangible returns in terms of enrollment growth. Ideally, target—
ing strategies can yield gains in access and quality, but this
requires differentiated targeting strategies.

t Which strategy allows us to learn more about family demand
and how child-care organizations respond to incentives for
expansion and quality improvements? California will move out
into the UPS terrain largely in empirical darkness. The
experiences of Georgia or New York will be useful. But
California—especially Los Angeles County—includes diverse
families and large gaps in local preschool supply. Targeting
strategies could systematically experiment to determine where
the family demand response is stronger or weaker. At the same
time, gains in child development and school readiness also
must be factored—in. That is, expanding in ways that yield few
learning benefits for children is unwise. At the same time,
building high—quality preschools where few parents show—up
would be senseless.

One way to proceed is to target new funding on three or four
distinctly different communities—from very poor to lower
middle—class—and see how parents and existing child care
providers respond. We can estimate what the demand—response
would be, given existing levels of preschool supply and the
strength of demand factors. But allocating funds to varying
communities would allow for pragmatic experimentation in
the field.

 

1 Ala F l. Alternative Indicators of Need for Quality Preschool Programs

 

Service Planning Percentage of Percentage of Mean API Preschool Families on
Area [SPA] children In students score enrollment waiting lists as
poverty fluent . capacity per % of enrollment

in English 100 children capacity

1. Antelope Valley 22% 90% 654 35 5%

2. San Fernando 26% 65% 692 54 20%

3. San Gabriel 28% 71% 659 35 25%

4. Metro 48% 45% 608 29 40%

5. West 1 9% 80% 749 69 38%

6. South 51% 51 % 528 24 27%

7. East 33% 62% 601 18 54%

8. Harbor/ South Bay 30% 70% 700 43 26%

 

 

 

 

 

 

 

 

Notes Poverty and English proficiency data are for 1998. API scores are for 2001. Enrollment capacity and waiting list estimates are for 2000 and 1999, as
reported by preschool and center directors.

.- 1... p: , .. .

across the county. Table 1 provides a quick grasp of how school
Three Targeting Strategies to achievement levels (for children in elementary schools),
Expand Preschool Access preschool supply, and pent—tip family demand differ across

SPAs. Maps and more detailed data for each of these indicators

Next we compare which communities would benefit under of preschool “need are provided below.
different W‘lVS ofalloc'itin new UPS dollars. We will describe c. c . . c. .
" ‘ ‘ g y ' . ‘ . First, you can see that the SPA regions differ markedly on basic
each method, display maps of which communities (Zip—code . g « g g g . . c .‘
~ ‘ .- ‘ ‘ demographic features, such as child poverty rates and English
areas) would benefit. then contrast the differing effects among 7 a g. g . ‘ ‘ g \ t
L * lanOua ye iroficiencv amon V itiblic school students. In SPA 4
tl tl t t ' b ‘ ’ ‘
ie iree s m eOies. . . . . .
‘ P tmetro—downtown L./\.)y for example, 48% of all children live

Let‘s first look at 'l‘able 1 to provide an overview of how difffi‘tz below the poverty line, and just 45% of all students are fluent
ent regions vary remarkably across Los Angeles County. The in EllgliSh. In contrast, SPA 5 (WC‘St LA.) disiilal's 'd relatively
county has created Service Planning Areas (SPAs) that are low poverty rate, 19% of all children, and high English

helpful in mapping demographic and economic differences PTOPUUK)» 80% 0t (‘11 511M611”

 

Importantly, SPA 7 (East L.A., out to Whittier, and south to
Lakewood) displays the lowest level of preschool enrollment
capacity, just 18 slots for every 100 children, age 3—5 years—old.
Preschool directors reported the longest waiting lists, equal to
54% of the SPA’s enrollment capacity. In sharp contrast, West
LA. has 69 slots for 3—5 year—olds for every 100, and moderately
long waiting lists, equal to 38% of enrollment capacity.12

Certain indicators do cluster together: SPA’s with high child

poverty rates also show the lowest API scores, for instance. But
preschool enrollment capacity, as well as waiting list counts,
are not consistently associated with community poverty levels.

FOCUS Oh Communities With the Lowest SChOOI
Achievement Levels

Table I reported on children’s mean achievement levels across
the Los Angeles SPA regions. In Figure l we map these same
achievement levels for elementary schools located across LA.
zip codes. These averages are school-level academic perfor-
mance index (API) scores for 200 l . The API scores are assigned
to each school, based on the performance of children on the
annual SAT-9 exams given to children attending grades 2
through 6.” We calculated the mean API scores for all elemen—
tary schools situated within each LA. zip code, reporting only
on those zips with at least three schools.

We see that school performance corresponds with community
characteristics. One way to clarify these patterns is to correlate
API averages to the median income of families residing in
each zip code. In Figure 2, we display average API scores
after sorting the zip codes into four groups, ranging from the
poorest one—fourth of all zips to the most affluent one-fourth.

The poorest quarter of all zip codes are those with median
household incomes below $32,644, according to 2000 census

data. For this set, the mean API score for elementary schools
equaled 565 in spring 2001. In contrast, the most affluent
quarter of all L.A. zips—those with household incomes averag—
ing over $53,596—displayed much higher API scores, with a
mean score of 775 in the same year. The overall correlation (r)
between zip—code wealth and API scores is a wopping 0.80. This
means that almost two—thirds of all the variation in API scores
among zips can be explained by median household income.
API scores are highly correlated with maternal employment
rates (r2052), as well, and the percentage of the zip code’s
population that is Latino (rt—0.77).

In short, if new UPS dollars were focused on zip codes with
the lowest level of school achievement, these funds would
go to the poorest L.A. communities. In many cases, this
would include neighborhoods with high concentrations of
Latino families.

But note that Latino communities tend to have lower maternal
employment rates. The correlation between maternal employ—
ment and percentage of population, Latino equals —0.62. So, by
allocating new funding to heavily Latino zip—code areas, it
would go to communities with much lower maternal employ—
ment rates, on average, leading to lower family demand for
nonparental child care.

FOCUS Oh Communities With the Lowest Supply
Of Preschool organizations

A second option is to take into account the existing stock of
preschool organizations and their enrollment capacity. It may
make sense to expand access in poor communities——unless
supply is already sufficient. But then we need to understand
how current preschool supply relates to the demographic
features of communities across the county?

 

Los Ange/es County

 

Figure 1: Mean API Scores for Elemen’rary Schools, 2001 AC
P E

     
  

\"ENTUQA

SAN
BERNARDINO

 

 

Mean API Scores
by Zip Code
‘ 457 - 590 Highways

591—690
- 691-911
g ,1 \ SN

Insufficiem data

‘63

 

Data from me Cavornia Depanmen? o‘ Educat on

Map Created by G'eealnfo Network \mvwgreenrnfaorg
" Dam tables ava‘iab‘e from PACE.

- _.. __M_ 7

 

 

 

 

 

FIGURE 2. Average school API scores by household income of zip codes

[zip codes sorted into quartiles]

800

 

 

scores. More affluent West LA. and other
suburban areas display higher levels of
enrollment capacity, compared to South-
Central and East LA. Indeed the overall
correlation between the median house-
hold income of zip codes and their
enrollment capacity equals 0.53. That is,
we can account for about one—quarter of
all the variation in preschool supply
simply by knowing the median household
income of zip codes.

Yet look at Figure 5—where we report
enrollment capacity for each of the four
groups of zip codes, from the poorest t0
the richest quartile of zips. Note that the
number ofenrollment slots for children,
age 3-5, is much higher in the richest

 

 

300

Poorest zip Quartile 2 Quartile 3
codes [n=42] [n=44] [n=43]

Figure 3 displays the average enrollment capacity (supply) of
preschool or center-based programs at the SPA level which
serve children, age 3—5. This capacity is expressed as the num—
ber of enrollment slots operating in 2000 for every 100 children
in this age bracket. This is commonly dubbed, preschool or
center enrollment capacity per 100 children. These data are
collected by local resource and referral agencies in LA. and
then compiled by the California Child Care Resource and
Referral Network in San Francisco.”

Figure 4 displays the same supply indicator at the zip—code
level. This map is similar to the earlier map of schools’ API

quartile of zip codes, equaling 56 slots for

Most every 100 children in this age cohort. This
affluent zip
codes [n=44]

compares to just 23 slots per 100 in the
poorest quartile of zips.

But also you see that enrollment capacities are quite similar
between the first and second quartiles, that is, across the zip
codes falling below the median household income level for LA.
($41,808 in 2000).'5 Within these lower—income quartiles, 23
and 29 enrollment slots were operating for every 100 children,
age 3-5, respectively. For the third quartile—the quarter of LA.
zips resting just above the median household income—
enrollment capacity inches upward to 33 slots per 100 children.

Put another way, the correlation between household income
and preschool supply (enrollment capacity) is almost zero
when we look at the 44 zip codes in the poorest quartile

1O

 

Los Angeles County

Figure 3: Enrollmen’r Capacity of Preschools
per 100 Children, Age 3—5 Years by SPA, 2000

 

PACE

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I I 7— , I ,i' i , l, Enrollment Capacity
l I X ,j ,, I I by SPA Region
’ " l >~ «< '7‘, V ' f , l .
' "i r l" i - \_ i, I I—* 0 _ 25 Highways
. i l l’ l ‘ fl' ‘\ II I ,, " i
- 8 K W. A _ H k A,“ l I l 25 — 35
\ ‘1’”“i’ ' “ W}; _ a ,, ‘. o 2 4 o 8 Miles
:I i. if 1- .- r‘ = i - 35 and above
V)" ,5" (It , N , _ 1“, ‘ : i
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I! I I Data provlded by the California Child Care Resource and Referral Network.
J .83 4 Map created by Greenlnfo Network, www.greenlnf0.0rg
I Dala lables available from PACE.

 

4“

 

 

Los Ange/es County

Figure 4: Enrollment Capacity of Preschools
per 100 Children, Age 3—5 Years, by Zip Code, 2000 PACE

 

     

VENTURA

SAN
BERNARID'VO

 

‘RIVFRSioE

   

ORANGE

 

“~- 7 Enrollment COPOCHY
z) by Zip Code

, 0 _ 20 Highways
3 PM m-%

i i 35 and above

\ ’2‘“ Insufficient data 0 2 4 b 8 was

 

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IR '0 Data prowded by the California Child Care Resource and Referral Network.

 

Mao created by Greenlnfo Network, wwwgreeninfoorg

 

 

 

1

Data fables available from PACE.

11

12

 

(1:004). For the second quartile, the coefficient rises slightly
but remains statistically insignificant (120.17). The moderately
high association between household income and preschool
supply is being driven largely by the richest quartile of zip
codes, where enrollment capacity jumps to 57 slots for every
100 children in the 35 age bracket.

In sum, if planners were to focus only on community poverty
levels (which remember is tightly correlated with AP] scores)
they would imprecisely target UPS support. Among zip
codes in the poorest quartile, preschool enrollment capacity
ranges from 5 to 46 child slots per 100 children. But ifonly

r KURE 5. Enrollment capacity of preschools in zip codes per 100

children, age 3-5
60

50

40

3O

20

 

 

 

 

community poverty or API levels were used to determine
allocations, considerable dollars would be inefficiently awarded
to communities that already display strong supply, while
ignoring communities that have very low supply.

The supply of family child—care homes (FCCHS), and their
enrollment capacity, is more consistently related to household
income. Within the poorest quartile of zip codes, FCCHs
provide 2 slots for every 100 children, age 0-] l years—old. This
wider denominator is used since FCCHs often provide slots for
school—age children. FCCH enrollment capacity in the richest
quartile of zips equals 5 slots per 100.

Isolating on the lowest quartile, house—
hold income remains moderately corre—
lated with PCCH enrollment capacity
among this bottom fourth of the zips
(720.36). This is useful to know: if UPS
planners want to expand capacity
through FCCH expansion, targeting
dollars based on a community’s poverty
level or school APl scores is a good proxy
for knowing about FCCH supply, unlike
the imprecision of doing this for pre-
schools and centers. On the other hand,
we report below how vacancy levels are
moderate to high in many FCCHs across
the county.

FOCUS ON Communities With

 

 

10 the Highest Levels of Pent-up
Demand for Preschool
0 So, should the state or counties allocate
Poorest zip Quartile 2 Ouartlle 3 Most UPS dollars to those communities with
codes [n=44] [n=44] [n=42] affluent zip

codes [n=44]

 

the lowest supply of preschool capacity? This would be an
improvement on simply allocating to the poorest neighbor—
hoods, or to those with the lowest API scores.

Yet ideally an allocation strategy would take into account the
balance between current supply and family demand. For ex—
ample, let’s take two hypothetical zip codes with the same
number of preschool enrollment slots. Then let us assume that
in zip code A we observe no vacant enrollment slots and long
waiting lists at existing preschools, as well as a comparatively
high maternal employment rate. In contrast, within zip code B
we observe vacant enrollment slots, no families on waiting lists,
and low maternal employment. The likely demand response
would be much greater in zip A compared to zip B.

Thus a third targeting strategy would be to concentrate new
resources on communities where pent-up demand for
preschooling is greatest—where current supply is especially scarce
relative to the level of family demand. This pent—up demand
approach, as with the first two strategies, must be balanced
against other criteria, such as ensuring that UPS dollars go into
communities where parents can not afford preschool fees, or
where child—level gains are likely to be the strongest.

This targeting method does require thicker data from preschool
organizations. The estimates that follow stem from PACE’s
1999 survey ofjust under one thousand preschool or center
directors and one thousand FCCH providers, when we asked
about vacancy levels and waiting lists. These phone interviews
were conducted for the needs assessment, conducted by the
LA. child care planning council.

One limitation with these existing data is that they reflect
complete interviews with directors from just over one-third of
all preschool organizations that were operating countywide in
1998, and about one—sixth of all FCCHs. In addition, the

sample was drawn to provide reliable estimates at the SPA, not
the zip—code level. We only report data for zip codes in which
we completed at least three director interviews.l6 But this does
not guarantee that zip—level estimates of enrollment vacancies
or waiting lists are reliable. A survey of all preschool directors
would be required to estimate true levels of pent—up demand.
Even then, other demand factors—including maternal educa—
tion and employment levels, family income, ethnicity and
language—should complement waiting list data.'7

Table 2 describes average vacancy levels as reported by pre-
school directors, averaged across zip codes within each SPA.
The differences are modest. SPAs 4 and 5, running from central
Los Angeles, including South—Central, west to Santa Monica
and Culver City, show the lowest vacancy levels at 9 and 7
vacant slots, respectively. Vacancy levels range up to 14 slots (in
the San Fernando Valley, suburban SPA 2), representing about
one—fifth of their total enrollment capacity.

Table 3 displays corresponding data on waiting lists that may,
or may not, be maintained by preschool directors. In general,
fewer directors reported waiting lists (with one or more
families signed—up), relative to the greater number who re—
ported vacancies. Still, these survey data led to an estimated
100,000 families who appeared on a waiting list for a preschool
program.18 We do not know ifsome families appear on mul—
tiple waiting lists, signing—up at different preschools.

Table 3 does show some variation across SPAs in the average
number of families appearing on waiting lists, ranging from 8.3
families in the far north, SPA l, which includes Lancaster and
Palmdale, to 30 families in central Los Angeles and in East LA.
and suburban communities to the east, including South Gate,
Vernon, and Whittier (SPAS 4 and 7).

13

14

 

When we make estimates of vacancy levels at the zip-code level,
clear patterns are more difficult to discern. This may be due to
measurement error, since our sampling strategy was designed
to yield reliable SPA, not zip—level, estimates.

With this in mind, Figure 6 displays the number of vacant slots
as a percentage of enrollment capacity. We only include zip
codes for which data were reported by at least three preschool
directors. Vacancy levels are somewhat higher in zip codes with
stronger maternal employment rates and higher household
income. This can be seen on the map—with red areas on the
west side of the Valley and the far eastern suburbs. But we also
see relatively high vacancy levels reported in parts of South—
Central and East LA. This suggests that preschool supply in
some poor communities has kept pace with family demand. On
the other hand, other zip codes in these poorer regions show
low vacancy rates.

The waiting list estimates are fairly consistent, showing the flip—
side of vacancy patterns. Figure 7 shows generally longer
waiting lists, as a share ofenrollment capacity, in zip codes that
have lower household income and weaker maternal employ-
ment. These correlations are weak, however. You can see that
preschool directors in many South—Central zip codes reported
relatively short waiting lists, again suggesting that supply has
kept pace with expressed demand. On the other hand, two zip
codes right along the Harbor Freeway, south from USC, and
parts of East LA. display longer waiting lists. Preschool direc—
tors in the North Hollywood and Northridge areas also re-
ported a good deal of excess demand, with long waiting lists.

Our statistical analysis did show that predominately Latino zip
codes often have longer waiting lists, suggesting that in a subset
of these communities, demand is outpacing available supply.

More complete survey data should be collected to see if this

' at L Preschool enrollment vacancy levels by SPA for Los Angeles County, 1999

serving children,

 

Service Planning Count of all Count of
Area [SPA] center-based preschools

programs

surveyed 3-5 years
1. Antelope Valley 36 25
2. San Fernando 1 92 1 68
3. San Gabriel 144 1 31
4. Metro 77 71
5. West 58 55
6. South 92 86
7. East 71 61
8. Harbor/ South Bay 1 25 108

 

 

 

Count of Mean number Vacant slots as
preschools of vacant percentage of
reporting one slots enrollment
or more capacity
vacant slots

8 1 1 1 8%

94 14 20%

88 1 2 20%

42 9 1 6%

25 7 1 3%

48 1O 20%

41 1 3 20%

54 1 0 1 7%

 

 

 

 

 

 

TARLE Preschool waiting lists by SPA for Los Angeles County, 1999

 

Service Planning Count of surveyed Count of pre- Mean number Children on waiting

Area [SPA] preschools serving schools with of children on list as percentage of
children, age 3-5 years a waiting list waiting list* enrollment capacity

1. Antelope Valley 25 7 8 16%

2. San Fernando 1 68 75 33 41%

3. San Gabriel 1 31 6O 23 42%

4. Metro 71 37 30 56%

5. West 55 37 26 49%

6. South 86 46 29 54%

7. East 61 28 31 43%

8. Harbor/South Bay 108 61 3O 55%

 

 

 

 

 

 

 

* Includes only preschools that maintain a waiting list.

relationship is robust across a greater number of preschools.
This analysis of pent-up demand could further pinpoint those
Latino communities where supply is relatively low and demand
factors are strong.

A thorough analysis of FCCH capacity is beyond the scope of
the present paper. We did f1 nd, however, that vacancy rates were
fairly high, especially compared to generally few vacancies in
preschools. ln SPA 3, for example, which includes the San
Gabriel suburbs ofArcadia and Pomona, FCCH providers
reported that vacancies equaled 37% of their licensed capacity.
Even when we asked about their preferred number of children,
vacancy levels ranged from 24% of enrollment capacity in SPA
5 (West LA.) to 29% in SPA 4 (central Los Angeles).

In short, if quality FCCH programs were to be included in the
UPS initiative, many more children could be accommodated by

the existing stock of FCCH providers. And these providers
express strong interest in enrolling additional children for
whom they already have licensed capacity. On the other
hand, vacancy levels drop lower in some poorer commu—
nities, where the sector is less robust economically, with
fewer stable FCCHs surviving over time, compared to
middle—class communities where both supply and vacancy
levels range higher.

Other methods can be used to estimate pent—up demand, or
what some would call unmet need, for preschool programs.
The county’s needs assessment, for instance, estimated
potential family demand from the number of preschool—age
children in each zip, the maternal employment rate, and
stronger demand linked to parents’ welfare—to—work
participation. An assumption was made about the share

15

16

 

Los Angeles Counfy

Figure 6: Vocon’r Child Slols in Preschools os Percen’roge "
of Enrollmen’r Copoci’ry by Zip Code, 1999 PACE

 

 

Percentage of Voconl Slots

by Zip Code
0 _ 10% Highways
ll — l9°/o
- 20% and above >
Insufficient dole 0 2 4 c s Miies

Doro crowded by the Los Angeles County Child Core Planning Cowcrl & DPSS
Mop created by Green nfo Network, wwwvgreeninforcrg

Estimates bosed on data from a limited number or center directors

Data tables available from PACE

 

 

 

Los Angeles County

Figure 7: Families on Waiting Lists for Preschool Slots as a
Percentage of Enrollment Capacity by Zip Code, 1999

r 3 I
Wit-rt” \‘ A-

‘ J?

70 Niles

 

 

 

 

Percentage of Families on Waiting Lists
by Zip Code
0 - 20% Highways
M 20 - 29%
- 30% and above

Insufficient data 9 ? A o 5 Miles

Data provrded by the Los Angeles County Child Care Planning Councrl & DPSS.

Map created by Greenlnfo Network, wwwgreeninfdorg

Estimates based on data from a limited number of center direcTorsr
Data tables available from PACE

 

 

I

PACE

17

18

 

who would prefer to use licensed care, including preschools
or FCCHs. Then, this potential level of family demand was
compared to current supply.19

Comparing Who Benefits Under Alternative
Targeting Strategies

Figure 8 illustrates how these targeting strategies would result
in the allocation of new dollars to differing communities. This
map displays the 25 zip codes that come in lowest on each of
two indicators examined above: those zips with the lowest
mean API scores and those with the lowest enrollment capacity.

First, let’s look at the 25 zip codes with the lowest API scores for
elementary schools, marked in green. These are concentrated in
south—central LA. and heavily Latino communities in the
Valley, including the city of San Fernando. Some of the orange
areas, particularly in East LA. and the San Gabriel area, are
among the lowest APl zips. The orange zips are those that
would qualify under both targeting criteria.

Second, the blue zip codes have the lowest enrollment capacity
supply per 100 children, age 3-5. These are largely outside
South-Central, appearing in Long Beach, parts of East L.A., and
the North Hollywood area. Again, the orange zips also include
some of the lowest 25 zips in terms of preschool supply.

When we include the third criteria of long waiting lists, one zip
code—90262—falls into all three targeting criteria. This is the
Lynwood community which falls among the lowest 25 zips in
terms of API scores, enrollment capacity, and among those
with the longest waiting lists.

This map illustrates how relying on a single targeting criteria,
such as low API scores, would lead to allocations that could
ignore neighborhoods with the greatest scarcity of preschool

supply. The good news is that this likely stems from three
decades of progressively targeting child care and preschool aid
on the poorest communities. But this means that other com-
munities—ranging from low—income to lower middle—class in
family composition—display lower enrollment capacity and
higher pent-up demand.

 

POIiCV Implications

California has dramatically boosted investments in public child
care since the mid—1990s. The state’s current budget shortfall
and shifting federal priorities have slowed progress, limiting the
number of low—income and blue—collar families who can find
affordable, quality care. Even during the boom years, little
growth occurred in quality preschools. lnstead, billions of new
dollars have been allocated into unregulated child care pro-
vided by individuals.

Thus within these historical constraints and institutional
arrangements, funding aimed at broadening access to pre—
school for young children must be allocated prudently. And
with the state now spending $3.1 billion in child care and
preschool programs annually, meaningful steps toward UPS
will necessarily involve recasting current funding streams, now
flowing from 23 different programs run from Sacramento.

So, how should we weigh alternative ways of targeting scarce
resources on new preschool efforts? This report suggests three
possible strategies, driven by two policy aims: Expand
access for children who will benefit most and where families
will respond in a robust fashion, expressing strong demand
for preschooling.

 

Los Angeles County

Figure 8: Alternative Targeting Criteria: Low APl Scores &

it: 2!; We:

Low Preschool/Center Supply, 1999—2001

PACE

 

 

Zip codes with one targeting criterion
’ Bottom 25 - lowest mean APl scores

Bottom 25 — fewest preschool/center slots

Zip codes belonging to two groups
Zips not among bottom 25 0 2 " ’3 ”“95

Map created by Greenlnta Network, wwwgreenlnioprg

Estimates based on le5 With at least three school or preschool organizations.
Data tables available from PACE.

 

 

 

19

2O

 

State and local governments could allocate taxpayer dollars to
the communities where school children perform at very low
levels. In the case of Los Angeles, this would move dollars into
the poorest communities. But some of these neighborhoods
already enjoy relatively strong supply of center-based programs.
Improving the quality of these centers and preschools may be
advisable. But it’s not clear that expansion of enrollment slots
would spark increased demand from parents, especially where
maternal employment levels remain low.

In this light, considering school performance (API scores) and
relative supply of center—based programs would yield more
effective investments. We need to learn more about how
different groups of parents will respond to the availability of
new preschool slots. Ideally, dollars could be invested in
communities where pent—up demand is greatest, that is, where
families’ interest in preschools far outstrips available supply.

Overall, we have much to learn about how families will respond
to various preschool options. The fact that we have so much to
learn argues for alternative allocation strategies, aimed at
different kinds of neighborhoods. This would allow for system-
atic learning about how parents respond to new options — and
how preschool organizations themselves respond to new
incentives and challenges.

 

Acknowledgements

Much of the data informing this paper stem from the Los Angeles
County needs assessment, conducted in 1999-2000 with the
Local Child Care Planning Council and county Department of
Public Social Services. Special thanks to Kathy Malaske—Samu.
PACE’s work on this earlier project was codirected by Elizabeth
Burr and Diane Hirshberg. The supply data are collected by
local child-care agencies and compiled by the Child Care
Resource and Referral Network. Patricia Siegel and Shelly
Waters Boots at the Network have been generous collaborators
over the years. The mapping work contained in the paper was
done by Lynn Frederico at Greenlnfo Inc, San Francisco.

 

Endnotes

1

We use the terms “center—based program” and “preschool”
synonymously, given that the boundary between high—quality
preschools and custodial care in centers has blurred. State funding
categories do distinguish between half—day state preschools and
contracted centers that provide full—day care in most cases. Spending
data are from state Department of Finance budget worksheets.

[v

For a review of this empirical literature, see Fuller, B., Kagan, S.L.,
Caspary, G., & Gauthier, C. (2002). Welfare reform and child care
for low-income families. Future of Children, 12:97—119.

w

Hirshberg, D. (2002) Child care supply and demand under
CalWORKs: Early impacts of welfare reform for California’s children.
Berkeley: University of California (PACE Working Paper 02—3).

A

Fuller, B., Waters Boots, 8., Castilla, E., & Hirshberg, D. (2002). A
stark plateau: California families see little growth in child care
centers. Berkeley: University of California (PACE Policy Brief 02—2).

 

5 Phillips, D.,Voran, M., Kisker, E., & Whitebook, M. (1994) Child
care for children in poverty: Opportunity or inequity? Child
Development, 65:472-492. Burchinal, M. (1999) Child care
experiences and developmental outcomes. In S. Helburn (Ed.) The
silent crisis in U.S. child care. Annals ofthe American Academy of
Political and Social Science, vol. 563:73—97.

A

‘ Fuller, B., Holloway, S., Bozzi, L., Burr, E., Cohen, N., 8( Suzuki, 8. (in
press) Preschool and child-care quality in California neighborhoods:
Policy success, remaining gaps. Early Education and Development.

7 Fuller, B., Kagan, S.L., & Loeb, S. (2002) New lives for poor families?
Mothers and young children move through welfare reform. Berkeley
and New York: University of California and Teachers College,
Columbia University.

3 Phillips, D.,Voran, M., Kisker, E., & Whitebook, M. (1994) Child
care for children in poverty: Opportunity or inequity? Child
Development, 65:472—492. Fuller, B., & Strath, A. (2001) The child—
care and preschool workforce: Demographics, earnings, and
unequal distribution. Education Evaluation and Policy Analysis, 23.

° Data provided by the Child Development Division, California
Department of Education, and summarized in Fuller et al., A stark
plateau: California families see little growth in child care centers.

'0 For empirical reviews, see Barnett, W.S. (1995) Long—term effects of
early childhood programs on cognitive and school outcomes. Future
ofChildren, 5:25-50. Burchinal, M. (1999) Child care experiences
and developmental outcomes. In S. Helburn (Ed.) The silent crisis
in US. child care. Annals ofthe American Academy ofPolitical and
Social Science, vol. 563:73—97.

” Malaske-Samu, K. with Muranaka, A. (2000) Child care counts: An
analysis of the supply and demand for early care and education
services in Los Angeles County. Los Angeles: County Chief
Administrative Office and the Child Care Planning Committee.

llThis ratio is the average count of families on waiting lists, divided
by the enrollment capacity in those preschools or centers reporting
that a waiting list is maintained. This ratio is estimated only in those
zip codes where at least three preschool directors participated in the
1999 LPC/PACE survey.

l3API data were extracted from the state Department of Education
website: http://api.cde.ca.gov/datafiles.html.

MMore detailed supply data appear in the Network’s annual Child
Care Portfolio. New 2002 data will shortly be available to update
this analysis.

15The overall median household income level for Los Angeles County
in 2000 equaled $42,189, according to the census bureau. The
median state income was $47,493.

1“ We obtained basic data from 237 zip codes for Los Angeles County.
This count of zips falls to 174 when we select only those zips with at
least three organizations: three elementary schools for API scores,
enrollment capacity data for at least three preschools or center—
based programs, and waiting list reports from at least three
directors. This increases the reliability of our estimates.

‘7 Preschool directors undoubtedly vary in how they maintain parental
waiting lists. Parents also may sign-up at a variety of centers,
creating duplications on waiting lists. These kinds of ambiguities
create measurement error in any estimate of pent-up demand.

l8This weighted estimated, based on the sample survey data, is
detailed in Malaske—Samu 8( Muranaka (2002).

‘9 Malaske—Samu & Muranaka (2002). See the appendix tables.

21

 

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Cll‘le?

Policy Analysis for California Education PACE
University of California, Berkeley and Davis
Stanford University

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Berkeley, CA 94720—1670

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