Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 5
2
Defining and Describing the Workforce
U
nderstanding the characteristics and roles of the early childhood
care and education (ECCE) workforce is not a straightforward
task. Even counting the number of workers engaged in the care
and education of young children raises questions—about whom to include
and how to categorize the nature of their work, for example.
The committee was charged to plan a workshop that would yield a
description of the early childhood workforce and outline the parameters
that define it. However, the field currently lacks a clear conceptual defini-
tion and comprehensive data on the ECCE workforce on which presenta -
tions of existing data could be based. Thus, Richard Brandon, principal at
RNB Consulting, in collaboration with several colleagues,1 developed an
initial conceptual definition as a starting point for discussion of key issues
in defining and describing the ECCE workforce (see Appendix B). In
addition, the planning committee commissioned Michelle Maroto at the
University of Washington to work with Brandon to review existing fed -
eral data sources and published research studies and to compile currently
available descriptive data on the ECCE workforce. Brandon presented
both the conceptual definition and descriptive data on the ECCE work-
force at the workshop. Additional presentations focused on the nature of
federal workforce data systems, an innovative state model for improving
ECCE workforce data systems, and lessons learned from K–12 federal
data systems.
1 David Blau, Sharon Lynn Kagan, Dixie Sommers, and Marcy Whitebook.
5
OCR for page 6
6 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
DEFINING THE WORKFORCE—A FRESH LOOK
The ECCE workforce is exceptionally varied, a fact that reflects the
varying purposes for which care and education are provided and the
varying expectations of those who fund and oversee it. The workforce is
composed of individuals with little or no training who provide mainly
custodial care without attention to educational goals at one end of the
spectrum, to individuals with specialized postgraduate degrees providing
carefully planned educational experiences at the other, with many others
in between. At its most basic level, caregiving can involve caring or pro -
viding for a child’s safety, meeting basic needs around feeding, diapering,
or toileting, and assisting with dressing, bathing, and sleep routines. At
its most complex, teaching can involve carefully implementing research-
based curriculums, individualizing care and instruction, and addressing
the full range of developmental domains (e.g., cognitive, language, social–
emotional, fine and gross motor, executive functioning) in groups and
one-on-one activities.
Terminology can be problematic with such a wide spectrum of work
represented, even among the members of the workforce themselves. For
example, those working in settings whose primary purpose is educa -
tional are often referred to as teachers. The terms “caregiver,” “child care
worker,” or “child care provider” are more often associated with settings
whose primary purpose is enabling parents to work. Those who provide
child care, particularly through informal friend, family, or neighbor rela-
tionships, may not see themselves as having any particular job title as
either caregivers or teachers. This makes selecting an appropriate term
for the full range of members of the ECCE workforce challenging. This
report uses the terms, “ECCE workforce” or “caregivers and teachers”
as a means for being inclusive. The terms “workforce” or “workers”
refer to anyone who works for pay. Using these definitions, the planning
committee focused the workshop primarily on those who are paid for
providing early care and education.
The full range of care and education can be offered in a variety of
settings (e.g., private homes, centers, elementary schools, workplaces,
or houses of worship), funded by numerous sources (e.g., parent tuition,
child care subsidies, state funds, and federal dollars), and licensed or
unlicensed. Many children are cared for by family, friends, and neighbors,
both paid and unpaid. Despite these differences in location, funding, or
licensed status, the primary purpose of the setting (i.e., providing educa-
tion or enabling a parent to work) may be the most important dimension
on which settings differ, along with how well that purpose is implemented
(Pianta et al., 2009). Settings established for these differing purposes may
be governed by differing sets of rules and expectations, which can affect
variation in workforce characteristics. The teachers and caregivers within
OCR for page 7
7
DEFINING AND DESCRIBING THE WORKFORCE
these settings are nearly all women receiving low pay for their work, but
they are also diverse in many respects (e.g., demographic characteristics
qualifications, knowledge, beliefs, and attitudes). Whatever their charac -
teristics and qualifications, these teachers and caregivers have a profound
impact on the lives of children and families (see Chapter 4). Therefore,
clearly defining, describing, and monitoring this workforce over time is
important.
Brandon presented selection criteria that could be used to develop
shared definitions of terms and categories, which might then support
efforts to achieve greater consistency and improve research and policy
making. The conceptual definition Brandon presented focused on chil -
dren from birth to age 5, and used criteria consistent with the distinctions
applied by the Bureau of Labor Statistics (BLS) between occupations (roles
and functions) and sectors of the economy or industries (the organizations
or establishments that provided particular services). (These are discussed
further below.) These distinctions are important, he explained, because
consistent definitions will allow researchers to build on the considerable
investment that has already been made in federal data systems, rather
than developing new, separate definitions for the early childhood sector.
Consistency will also support comparisons of the ECCE workforce to
workers in other economic sectors, as well as analyses of their key fea-
tures. Moreover, Brandon pointed out, distinguishing and cross-tabulating
occupations by sectors provides opportunities for analyses that can lead
to improvements in recruitment, training, and professional development.
Despite the potential benefits of consistency, Brandon enumerated
a number of concerns with the existing federal definitions of the early
childhood workforce. For example, the existing federal data system
groups together those who work with young children and those who
work with school-aged children (with some exceptions described later
in this chapter). This is a problem because “the duties are very different
for dealing with young children and school-aged children,” Brandon
observed. Thus, he hoped to provide points to be considered in future
revisions to the federal definitions that would make them more accu-
rate and useful. Brandon and his colleagues developed a definition that
includes three primary components: occupation, sector, and enterprise.
Figure 2-1 provides a visual representation of these components.
A first step was to define the occupation clearly. All federal agencies,
including the BLS and the Census Bureau, use the Standard Occupa-
tional Classification (SOC) to identify distinct occupations based on the
nature of the work performed. According to the Classification Principles
and Coding Guidelines of the 2010 Standard Occupational Classification
Manual (SOC Manual), “the SOC covers all occupations in which work is
performed for pay or profit” (OMB, 2010, p. 1). Thus, in the case of early
OCR for page 8
8 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
ECCE ECCE
ECCE Sector
Occupation Enterprise
FIGURE 2-1 Components of the early childhood care and education (ECCE)
workforce.
SOURCE: Brandon, 2011.
Figure 2-1
childhood workers, Brandon and his colleagues identified two criteria
for inclusion: (1) the work involves direct care for or education of infants
and children from birth through age 5,2 and (2) the worker is paid for
this work. These criteria were designed to distinguish early childhood
workers from others who work with children, such as social workers,
family counselors, or nurses and pediatricians. Teachers and child care
workers would be included, as would proprietors or directors of child
care centers, specialists, and family support workers or home visitors, as
long as they work directly with children, rather than only with adults.
This definition includes family members, friends, and neighbors who
provide care, as long as they are paid. Brandon expressed the view that
this definition is “radically broader than anything in [the current] federal
data systems.” Individuals who provide unpaid care or instruction would
not be included. Brandon acknowledged that these unpaid caregivers
are a sizeable and important group and pose a challenge for the field to
consider. He suggested that this group should be the subject of future
analyses. He noted that the Census Bureau defines a worker who is paid
for at least 1 hour of work during the week in which data were collected
as a member of an occupation, and that “we have to think both about
what makes sense within the field and what makes it possible to compare
early care and education workers to other workers.”
To define the ECCE sector, Brandon and his colleagues considered the
employers of individuals in the occupation. The North American Industry
Classification System (NAICS) maintained by the Office of Management
and Budget (OMB) classifies businesses by the type of product produced
or service provided. They applied this approach to the ECCE workforce.
Thus, the ECCE sector includes those people in the occupation whose paid
work involves direct instruction or care of young children, as well as
others who work for establishments that provide such care, such as non-
2
During discussion, Brandon clarified that he selected “care or education to be maximally
inclusive of all paid ECCE workers.” A number of participants indicated that they preferred
the term “care and education.”
OCR for page 9
9
DEFINING AND DESCRIBING THE WORKFORCE
teaching directors or supervisors, trainers, or those involved in adminis -
trative, transportation, food service, or janitorial services.
Brandon also described a broader category, the enterprise, which
includes all individuals involved in the ECCE sector as well as others
whose paid work has a direct effect on caregiving or educational prac-
tice, such as professional development providers, mentors, and coaches
employed by entities outside of the sector; those employed by states
or local jurisdictions to provide referrals or resources or run licensure
programs; and university faculty who teach prospective teachers. The
enterprise does not have a defined counterpart within the federal classi -
fication systems, but Brandon and his colleagues considered it important
to represent the contributions of this broader group. This category would
also include family support workers or home visitors who work primarily
with parents and not with children because their work may influence
children or child care workers, Brandon noted. It would not include
advocates or policy makers who may nevertheless have an influence on
the nature of early childhood programs.
Workshop participants had many comments and questions about
these conceptual definitions. Some cautioned that the definition of the
occupation might be too exclusive, for example, because it excludes home
visitors or special educators, whose primary functions may be working
with parents or other teachers as a means of affecting child development.
One noted that professional societies have “worked hard to make sure
that … everybody in the building … understands that they contribute to
the growth and development of children.” Participants also reinforced the
point that lack of data on the contribution of unpaid workers is a signifi-
cant challenge to policy makers.
Another cautioned against being too inclusive, however, arguing that
overly broad classification criteria will make it difficult to evaluate the
impact of the workforce on the quality of care and education, as well as
on child development. Although it is important to recognize the range
of individual contributions, one participant suggested that more rigor is
necessary to achieve consistency in measurement. Another participant
agreed, noting that including speech–language pathologists or others
who belong to another well-defined profession may add an unnecessary
complication. More than one participant raised the challenges of crafting
a definition that fit the roles of those working with children with special
needs. For example, teachers, caregivers, and specialists may share similar
responsibilities for working directly with these children. This blurring of
roles can make distinguishing one occupation from another difficult.
Several participants raised concerns regarding the terminology used
in the definitions, noting, for example, that it is important to view the work
as a combination of care and instruction, rather than care or instruction.
OCR for page 10
10 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
All providers should be expected to offer “direct support for development
and learning,” one observed. Yet others questioned the word “instruc-
tion,” suggesting that “education” or “intentionality” may be more appro-
priate terms for work with very young children and infants. Participants’
comments indicated that the conceptual definition of the ECCE workforce
has practical implications for data collecting and reporting, as well as
policy implications for how the field itself and others view it. Several
participants noted that a desire for consistency with federal data systems
could be difficult to reconcile with the need to capture unique aspects of
ECCE, including the diverse work settings, team-based approaches, and
progressive roles. Committee members encouraged participants to con-
tinue sharing their ideas for further honing a workable conceptual defini -
tion of the ECCE workforce that would serve both data and policy goals.
THE FEDERAL STATISTICAL SYSTEM
Data collected by the federal government is the source of most of the
current national information about the ECCE workforce. Understanding
these federal data systems is needed as ECCE considers how to define its
occupational borders in ways that promote accuracy and comparability
with other occupations. Dixie Sommers, assistant commissioner, BLS, U.S.
Department of Labor, provided an overview of the type of information
that is collected and how the system is structured.
The three primary sources of federal data on the ECCE workforce
are: (1) the BLS, (2) the Census Bureau (within the U.S. Department of
Commerce), and (3) the National Center for Education Statistics (NCES)
(within the U.S. Department of Education). The federal statistical system
is decentralized, Sommers explained, with individual federal statistical
agencies responsible for data collection that pertains to their missions.
The OMB oversees data collection standards through its Office of Infor-
mation and Regulatory Affairs. This office sets policies—for example,
to protect the privacy of respondents or standardize classifications of
information—and also coordinates data collection to prevent redundancy
across agencies.
The purpose of standardizing classifications is to ensure that data col-
lected across agencies can be compared, Sommers explained. Interagency
committees made up of federal experts in statistics and in the relevant
fields or occupational areas make recommendations about data collec-
tion based on research and public comment, and the OMB establishes
and revises the standards in response to these recommendations. The
classification systems cover four broad areas: industries, occupations,
metropolitan areas, and race/ethnicity categories. As noted above, NAICS
classifies businesses by type of product produced or service provided,
OCR for page 11
11
DEFINING AND DESCRIBING THE WORKFORCE
and includes child care or instructional providers. However, it does not
distinguish providers by the ages of the children served or identify those
who are part of an establishment that has another primary purpose (e.g.,
a child care center in an elementary school because the child care center is
not a stand-alone business). Table 2-1 illustrates how the NAICS defines
the three industries that are most relevant to ECCE: elementary and sec-
ondary schools, child day-care services, and private households (in which
paid child care is provided).
The SOC classifies jobs and workers according to the nature of the
tasks and functions performed, and sometimes the qualifications asso-
ciated with them. Classification principles and coding guidelines are
used to guide decisions to add new occupations or change existing ones,
Sommers explained. For example, principles guide the classification of
managers and supervisors and how to determine whether a particular
type of work constitutes a new occupation. Box 2-1 presents the defini -
tions of two ECCE occupations from the SOC Manual (OMB, 2010). The
2010 edition of the SOC Manual included a few changes in the early child-
hood context, such as distinguishing special education preschool teachers
from special education kindergarten teachers.
Sommers noted a few problems with the existing federal definitions
in comparison with the conceptual definition of the ECCE workforce pre -
TABLE 2-1 Definitions Used in the North American Industry
Classification System
Code and Title Definition
611110 Elementary and Establishments primarily engaged in furnishing academic
Secondary Schools courses and associated course work that comprise a basic
preparatory education. A basic preparatory education
ordinarily constitutes kindergarten through 12th grade. This
industry includes school boards and school districts.
624410 Child Day Care Establishments primarily engaged in providing day care of
Services infants or children. These establishments generally care for
preschool children, but may care for older children when
they are not in school and may also offer prekindergarten
educational programs.
814110 Private Private households primarily engaged in employing
Households workers on or about the premises in activities primarily
concerned with the operation of the household. These
private households may employ individuals, such as cooks,
maids, nannies, butlers, and outside workers, such as
gardeners, caretakers, and other maintenance workers.
SOURCE: Sommers, 2011. Based on OMB, 2007.
OCR for page 12
12 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
BOX 2-1
Definitions from the
2010 Standard Occupational Classification Manual (OMB, 2010)
Preschool Teachers, Except Special Education (code 25-2011)
Instruct preschool children in activities designed to promote social, physical, and
intellectual growth needed for primary school in preschool, day care center, or
other child development facility. Substitute teachers are included in “Teachers and
Instructors, All Other” (25-3099). May be required to hold state certification. Ex-
cludes “Childcare Workers” (39-9011) and “Special Education Teachers” (25-2050).
Childcare Workers (code 39-9011)
Attend to children at schools, businesses, private households, and child care
institutions. Perform a variety of tasks, such as dressing, feeding, bathing, and
overseeing play. Excludes “Preschool Teachers, Except Special Education” (25-
2011) and “Teacher Assistants” (25-9041).
SOURCE: OMB, 2010.
sented earlier. They do not consistently distinguish workers who care for
or educate children from birth through age 5 from those who work with
school-aged children. Furthermore, researchers in the field indicate that
the definitions of child care workers and preschool teachers do not reflect
the reality of their work, especially the overlap in their respective roles.
Sommers provided a list of federal data sources that provide informa -
tion relevant to the early childhood sector. Table 2-2 includes only the data
sources produced by the BLS and the Census Bureau. She pointed out
different methods of collecting information, each of which has strengths
and weaknesses, in terms of the type of information collected, the level of
detail possible, and other factors. For example, household surveys such
as the Current Population Survey and the American Community Survey
provide a broad look at the entire paid labor force, including those who
are self-employed and those who are “unpaid family workers,” but they
may not provide the desired level of detail about particular occupations
or industries. The BLS’ business establishment survey, the Occupational
Employment Statistics (OES) survey, yields more detail about occupations
than the household surveys do. In addition, this survey’s large sample
size yields considerable detail by industry and geographic region. These
establishments are sampled from a business list that is based on admin -
istrative data (mainly unemployment insurance tax reports). The survey
does not collect demographic information, however.
Further information on these federal data systems may be found in a
OCR for page 13
TABLE 2-2 Standard Federal Data Sources
Employment by Employment by Includes Self- Demographic
Source Industry Occupation Employed Geographic Detail Characteristics
Census Bureau, Household Surveys
Current Population Less than full Less than full Yes State, MSA Yes
Survey (CPS) detail detail
American Community Less than full Less than full Yes Small area detail Yes
Survey (ACS) detail detail
Bureau of Labor Statistics, Establishment Surveys
Current Employment Significant detail No No State, MSA No
Statistics (CES)
Occupational 4-digit NAICS Nearly full detail No State, MSA, non- No
Employment Statistics metro areas
(OES)
Bureau of Labor Statistics, Administrative Data
Quarterly Census Full NAICS detail No No State, county No
of Employment and
Wages (QCEW)
NOTE: MSA: Metropolitan Statistical Area; NAICS: North American Industry Classification System.
SOURCE: Sommers, 2011.
13
OCR for page 14
14 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
paper Sommers prepared for this workshop that is included in Appendix
B. This detailed report: (1) describes the SOC and the NAICS in detail; (2)
discusses issues specific to ECCE related to these systems; and (3) profiles
relevant BLS and Census data sources, including key meta-data and the
advantages and limitations of each source for understanding the ECCE
workforce.
DESCRIBING THE WORKFORCE
In planning the workshop, the planning committee noted both that the
federal data sources do not correspond completely to the reality of the jobs
early childhood workers do, and that these data systems are not designed
to capture all of the types of information that would be useful to have
about this workforce. Therefore, they commissioned Michelle Maroto,
who collaborated with Richard Brandon to review available descriptive
data and to compile a portrait of the ECCE workforce (see Appendix B
for the complete review). Also included in Appendix B with this descrip-
tive summary of findings is a complete list and bulleted description of
each study reviewed, including the study title, researching organization,
purpose, design, sample, methods, limitations, and associated references
for each study reviewed. In addition, spreadsheets of the data gathered to
compile the description of the workforce were prepared and will be made
available on the Institute of Medicine (IOM) project website.
The authors selected 50 studies for review, assigning greater weight to
those that were nationally representative and included the most different
types of child care settings. The studies they included covered all types
of teachers and caregivers who work with children from birth to age 5
(except those who are unpaid friends, family members, or neighbors). In
many cases, preschool teacher data also included data about kindergarten
teachers because data from the Census Bureau do not distinguish between
these two types of teachers.3 Brandon summarized their findings at the
workshop.
To complement the review of federal data sources and existing
research studies, Brandon reported on his recent work to identify the
number of ECCE workers in the workforce (Brandon et al., 2011). This
estimate of the size of the workforce was based on data from the National
Household Education Survey Early Childhood Supplement, 2005, which
includes parent reports about where their children spend time and the
ratio of children to adults in child care settings. From these data and some
3 Tabulations of previously unpublished data from Census and BLS sources regarding
worker characteristics and benefits are provided in the accompanying background paper,
Appendix B.
OCR for page 15
15
DEFINING AND DESCRIBING THE WORKFORCE
statistical adjustments, the authors calculated the number of individuals
required to provide the care. This method made it possible to break the
numbers down by the ages of the children: approximately one-fourth
of the paid workers are caring for infants, one-third for toddlers, and
nearly half for children ages 3 to 5 (see Appendix B for further details; the
full analysis is published in Brandon et al., 2011). Using these methods,
Brandon reported an estimate of approximately 2.2 million individuals
who are paid members of the ECCE workforce. These workers make up
a significant proportion (approximately 30 percent) of the overall instruc-
tional workforce in the United States, which includes those engaged in
teaching at the early education, elementary, secondary, and postsecondary
levels (Brandon et al., 2011). An additional 3.2 million individuals pro-
vide non-parental care without being paid. Figure 2-2 shows how these
workers are distributed among different types of settings.
Using the review that he and Maroto completed, Brandon described
the demographic characteristics of the paid ECCE workforce. He noted
that the Census Bureau provides the most current nationally representa-
tive data on the workforce, but does not distinguish among those who
work in different types of settings (child care, preschool, or elementary
school settings; licensed versus unlicensed), or between preschool from
kindergarten teachers. One of the primary concerns about these data, he
noted, is that they include public school kindergarten teachers. Teachers
in K–12 schools often have to meet minimum qualifications that most
workers in other early childhood settings are not required to meet. There -
FFN: Paid Non-relatives
(11%)
Center-based
FFN: Paid Relatives (51%)
(27%)
Family Child Care
(12%)
FIGURE 2-2 The paid early childhood care and education (ECCE) workforce by
type of worker, 2005.
NOTE: FFN = Friends, Family, and Neighbors.
SOURCE: Brandon et al., 2011.
OCR for page 16
16 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
fore, Brandon focused on characteristics “where there is no particular
reason to assume that characteristics of people caring for young children
are substantially different from those who include school-aged children”
for his presentation.
These data indicate that the median age for these workers is 39 to
47, that 90 to 98 percent of them are female, and that 75 to 80 percent of
teachers and 81 to 85 percent of directors are non-Hispanic whites. Of
the total population, 48 percent are married, 33 percent never married,
and 18 percent were formerly married. Sixty-eight percent have children
living at home.
Brandon described a serious wage penalty for those who work in the
early childhood sector: women working in early child care (other than
preschool) earn 31 percent less than women with similar qualifications
working in other occupations4 (Brandon et al., 2011). These workers’
annual earnings range from approximately $31,000 for preschool and
kindergarten teachers, $21,000 for assistant teachers, to approximately
$18,000 for other child care workers. Paid family child care workers earn
an average of about $14,000 per year. Four to 5 percent of teachers have a
second job, and 3 percent of family child care workers do. Annual median
household incomes (in 2010 dollars) are about $40,000 for child care
workers (based on data from the Cost, Quality and Child Outcomes Study;
Helburn, 1995) and about $68,000 for prekindergarten teachers (National
Prekindergarten Study; Gilliam and Marchesseault, 2005), as compared
with the current approximate $60,000 median for all households.5
Brandon described the qualifications of this workforce, noting that,
“we see across the different categories of workers a very great range of
educational background.” He reported that among child care workers,
7 to 12 percent have an associate’s degree (A.A.), 13 to 21 percent have
a bachelor’s degree (B.A.), and 2 to 4 percent have a master’s degree
(M.A.) or professional degree. Twenty-four to 25 percent of preschool
teachers have gone beyond the B.A., and just 2 to 4 percent of family child
care workers have done so. Estimates of how many preschool teachers
with a Child Development Associate (CDA) credential range from 23 to
76 percent. Twenty-nine to 57 percent of preschool teachers have state
certification, and 34 to 39 percent have a teaching certificate or license.
Estimates of average years of experience in the field for all child care
workers vary by study. One estimate is 4 to 5 years (based on data from
4 This comparison is based on a regression model of wage prediction, consistent with the
economics literature, in which education was the dominant factor followed by child care
versus other occupations.
5 This comparison does not include adjustments for the number of working people per
household or the average age, education, and work experience of the workers.
OCR for page 17
17
DEFINING AND DESCRIBING THE WORKFORCE
the National Institute of Child Health and Human Development [NICHD]
Study of Early Child Care and Youth Development); while another is 7
to 12 years (based on data from the National Center for Early Develop -
ment and Learning Survey and the Head Start Family and Child Experi-
ences Survey). (Brandon emphasized that these figures are for the years
of experience gained up to the point at which the survey was given, so
the average number of years workers stay in the field would be longer.)
Fifty-three to 62 percent of Head Start teachers belong to a professional
association (ACF, 2006; FACES, 1997, 2000). Citing data from the 2010 Cur-
rent Population Survey and 2009 American Community Survey, Brandon
reported that approximately 4 to 6 percent of child care workers, and less
than 1 percent of family child care workers, belong to a union. Approxi -
mately 21 percent of preschool and kindergarten teachers belong to a
union.
IMPROVING DATA COLLECTION
The challenge of describing the ECCE workforce makes clear that
existing national-level data are inadequate to thoroughly and accurately
describe the ECCE workforce, Brandon explained. In particular, no com -
prehensive and reliable data correspond to the conceptual definition of
the workforce he presented. The workshop focused next on what would
be required to improve the availability of data, and Brandon described
some relevant factors.
A logic model that Brandon (2010) developed for the National Survey
of Early Care and Education (NSECE) informed the development of a list
of ECCE workforce data elements. The model includes both distal and
proximal influences on the quality of early childhood care and education,
and their complex interrelationships. For example, distal characteristics,
such as demographic characteristics, may affect the professional develop-
ment a teacher or caregiver attains, which can in turn affect the attitudes
and beliefs that shape the quality of caregiving and instruction. Those
elements interact with compensation levels. Brandon noted that most of
the nationally representative data relate to demographic characteristics
of workers and general characteristics of the labor force, factors that
may have only distant and indirect relationships to important outcomes.
Brandon observed that the factors with the greatest influence on outcomes
for children include the more proximal teacher and caregiver characteris -
tics, such as their attitudes, engagement, and skills, as well as the stability
of the staff. He added that state and federal policies can affect these char-
acteristics in various ways.
Informed by the logic model, as well as the process of attempting to
use existing data to describe the ECCE workforce, Brandon and his col-
OCR for page 18
18 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
leagues developed a list of potential types and sources of information that
would facilitate future efforts by researchers and policy makers. Brandon
presented this list of seven broad categories of information:
• Numbers of individuals in the ECCE workforce;
• Distribution of these workers across different child care and edu-
cational settings (e.g., center- or home-based, private or public,
etc.), ages of children served, and occupational roles (e.g., direc-
tors, lead teachers, teaching assistants, aides, specialists);
• Characteristics of the teachers and caregivers (e.g., demographics,
qualifications, conditions of employment, compensation and ben-
efits, tenure on the job and in the field);
• Attitudes, attributes, and activities of the teachers and caregivers
(e.g., attitudes toward children and parents, job stress and satis-
faction, nature of caregiving activities);
• Characteristics of the workplaces (e.g., distribution of staff, sup-
ports and professional development offered, turnover, finances,
working conditions);
• Distribution of the ECCE workforce and characteristics (e.g.,
state/county, rural/urban/suburban location, demographic char-
acteristics of children served, prices); and
• Quality of early instruction and caregiving.
A broad range of data sources is needed to be able to answer impor-
tant policy questions about the ECCE workforce and the ways that it
affects child outcomes, Brandon observed. Some information can be
found in existing data or planned data collection, such as the NSECE. 6
However, he stressed the need to use multiple sources.
Brandon explained how a federal–state partnership for collecting
various types of data might evolve. He described the frequency of data
collection, the jurisdictions that might most easily collect each type of
data, and the appropriate collection instrument that would be best for
each type. “We need to be able to get down to a small geographic level,”
he observed. “We need to have geographic units that are relevant to
policy.” One “uber-survey,” he explained, could not collect all the kinds
of information needed. The Early Childhood Longitudinal Study and the
NICHD studies provide useful information, he noted, but more ongoing
data collection at the state and community levels is needed as well. 7
6 The NSECE is a national data collection effort that will update a 1990 study. For more
information see http://www.researchconnections.org/childcare/resources/19778.
7 For more information on these studies, see http://nces.ed.gov/ecls/ and http://www.
nichd.nih.gov/, respectively.
OCR for page 19
19
DEFINING AND DESCRIBING THE WORKFORCE
With these issues as a context, discussion turned to lessons to be
learned from existing data collection efforts.
Learning from National Education Data Systems
The data available about people employed in elementary and sec-
ondary education are more complete than what is currently available
regarding the ECCE workforce, so K–12 education provides a useful
example. As Jerry West, senior fellow at Mathematica Policy Research
and former director of the Early Childhood and Household Studies Pro-
gram at the U.S. Department of Education’s NCES, explained, most of the
K–12 data are collected by NCES or by states in collaboration with NCES.
These studies include federal–state and public–private sector collabora -
tive efforts, which collectively provide a fairly comprehensive picture of
education in the United States, as well as information about education in
other countries.
The data collection systems cover preschool through postsecondary
education and include methods that are designed for different purposes.
One broad category is universe surveys, in which data are collected about
all of the units within a particular population. The Common Core of Data
(CCD), which collects information about all K–12 public schools in the
United States, is perhaps the best known of these. The Private School
Universe Survey (PSS) is the CCD’s counterpart for private schools. 8
These surveys provide basic information about the characteristics of the
schools, such as descriptive and demographic information about students
and staff, and fiscal data. They also provide the sampling frames (i.e., the
enumeration of the populations from which samples should be drawn for
analysis of more in-depth questions) for other studies that collect more
detailed information about schools, staff, and students.
The sampling studies include cross-sectional surveys (which examine
a particular characteristic at a particular time), longitudinal studies (which
track changes in a population over time), and hybrids. For example, one
component of the Early Childhood Longitudinal Study-Kindergarten
Class of 1998–1999 (ECLS-K) focuses on the class of children who were
in kindergarten during the 1998–1999 school year.9 The study collected a
variety of information about this population’s kindergarten experience
and has also followed the children’s progress in subsequent years. As a
baseline, the ECLS-K sample provides one-time estimates of the charac-
teristics of kindergarten programs and kindergarten teachers nationally.
8 See http://nces.ed.gov/ccd/ for details about the CCD. For details on the PSS, see
http://nces.ed.gov/pss/.
9 See http://nces.ed.gov/ecls/kindergarten.asp for details about the ECLS-K.
OCR for page 20
20 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
TABLE 2-3 National Center for Education Statistics Studies by
Reporting Level and Topic
Reporting Level
Topic School School District State National
Student enrollment CCD CCD CCD CCD, PSS,
and characteristics PSS ECLS-K, NHES,
CPS, FRSS
Teacher/staff CCD CCD CCD, PSS, CCD, PSS, SASS,
PSS SASS ECLS-K,
FRSS
Public school CCD CCD CCD CCD, SASS,
characteristics SASS ECLS-K, FRSS
Private school PSS — PSS PSS, SASS,
characteristics ECLS-K, FRSS
Student outcomes — — NAEP NAEP, ECLS-K,
TIMSS
NOTE: CCD: Common Core of Data; CPS: Current Population Survey; ECLS-K: Early Child-
hood Longitudinal Study, Kindergarten Class of 1998–1999; FRSS: Fast Response Survey
System; NAEP: National Assessment of Educational Progress; NHES: National Household
Education Surveys Program; PSS: Private School Universe Survey; SASS: Schools and Staff -
ing Survey; TIMSS: Trends in International Mathematics and Science Study.
SOURCE: West, 2011. Adapted from U.S. Department of Education, National Center for
Education Statistics. (2005). Programs and Plans of the National Center for Education Statistics,
2005 Edition (NCES 2005113). Washington, DC: National Center for Education Statistics.
Another way to classify the NCES studies is by whether they pro-
duce data at the school, district, state, or national level. Table 2-3 arranges
key NCES studies by level of reporting and topic. West noted that while
all NCES data collection efforts provide some national-level data, only
the CCD and its private school counterpart provide school-level data.
However, even those studies provide only limited information about
teachers, such as the number in each school. The Schools and Staffing
Survey (SASS) is a hybrid that uses questionnaires to examine selected
topics related to school personnel, and provides more detailed informa-
tion about teachers.10
In thinking about changes or additions to the current system, West
noted, one might begin with the type of information that is needed, such
as basic information about the size and composition of the ECCE work-
10 See http://nces.ed.gov/surveys/sass/ for details about the SASS.
OCR for page 21
21
DEFINING AND DESCRIBING THE WORKFORCE
force, or details about recent trends and changes. Universe surveys can
provide such information as the number of teachers or caregivers, the
number or percentage of teachers with a B.A., or the number of full- or
part-time teachers. “But they don’t do a really good job of providing
information that allows you to look at the interaction of those charac -
teristics,” he added. “If you have a question about what percentage of
full-time teachers with a B.A. are teaching or caring for 3-year-olds or
the percentage of first-time teachers who are engaged in professional
development activities—those types of characteristics are not really well
collected through the universe surveys which typically report aggregated
data.” Answers to these more complex questions often require individual-
level data.
The universe surveys are also not very useful for studying emerging
issues, so NCES has used the Fast Response Survey System to collect
information about issues that cannot be incorporated quickly into ongoing
data collection efforts. Fast Response efforts have looked at questions such
as how many public schools have a prekindergarten program and the
nature of kindergarten teachers’ beliefs about children’s skills and school
readiness. Many questions—such as whether levels of teacher attrition or
mobility are changing—require longitudinal data not usually collected in
universe surveys.
West noted that the existing system is a collaborative and cooperative
one, which is essential because the participant responses are voluntary.
Showing how respondents may benefit from the availability of the infor-
mation is important, he noted. Collaboration is also important because of
the need to address discrepancies that may exist between state-level data
reporting requirements and those at the federal level. The CCD identifies
a data coordinator in each state to coordinate data issues, and a Federal
Forum is convened annually that includes representatives from each state
to review issues of comparability, changes in data collected, and other
data topics. According to West, the PSS requires “buy-in” from “private
school organizations to help everyone reach a consensus that there [is]
actually value in private school organization as being a part of a federal
education data system.” NCES nurtures that relationship through an
annual meeting with the private school community to address data issues.
Because no one study can answer every important question, West
stressed the importance of having a coordinated system. He proposed a
system for collecting data on the ECCE workforce, modeled on the NCES
approach, as shown in Table 2-4. This table presents a method for selecting
an appropriate NCES model on which to base an ECCE data system. This
chart shows: (1) type of data collection and purpose for ECCE; (2) NCES
model for that type of data; (3) type of data source; (4) sampling unit; and
(5) level of data desired (individual versus aggregate). As explained by
OCR for page 22
22
TABLE 2-4 A Potential Approach to Data Collection
Data System
Workforce Workforce
Public Program Private Program Survey—Center- Survey—Home- One-Time Surveys—New
Universe Universe Based Programs Based Care or Emerging Topics
NCES model Common Core of Private School Schools and NHES, CPS Fast Response Survey
Data Universe Survey Staffing Survey System
Data source Administrative Survey Survey Survey Survey
records
Sampling unit All programs All programs Program and Household sample Varies by topic/issue
teacher samples
Level of data Aggregate Aggregate Individual and Individual and Varies by topic/issue
aggregate aggregate
NOTE: CPS: Current Population Survey; NCES: National Center for Education Statistics.
SOURCE: West, 2011.
OCR for page 23
23
DEFINING AND DESCRIBING THE WORKFORCE
West, “for example … if you’re interested in describing a publicly funded
program and you want to have a universe of publicly funded programs
… the model would be the Common Core of Data,” which use data from
administrative records. West recommended a Schools and Staffing model
for center-based ECCE because it provides data both at the program and
individual levels. However, he suggested a household survey approach,
such as that used in the Current Population Survey, to reach home-based
programs. He acknowledged that family child care centers are challenging
to categorize in that they could potentially fit either center- or home-based
approaches.
This model builds on the work of NCES by including both universe
studies that require significant investment and ongoing support as well
as sampling studies or fast track or other models for answering different
questions. These efforts would require ongoing coordination and col-
laboration with participants who will take on the burden of responding
to regular data collection. He recommended taking the time to learn from
existing educational data systems, noting that their history provides many
useful lessons and resources.
Learning from a State Example
States vary in how they collect their own data, but Pennsylvania has
been at the forefront in the development of a state data system for early
childhood care and education. Harriet Dichter, national director of the
First Five Years Fund and former secretary of the Pennsylvania Depart-
ment of Public Welfare, described Pennsylvania’s Early Childhood Data
System, which the state created to coordinate all of the early childhood
programs through the Pennsylvania Office of Child Development and
Early Learning (OCDEL). A key element of this task was the develop-
ment of an integrated data system. This office, the mission of which is
to “ensure access to high-quality child and family services,” is jointly
governed by two state agencies, the Departments of Education and Public
Welfare. The four primary responsibilities of OCDEL are:
1. Certification—licensing and inspection of child care facilities;
2. Early intervention services—including technical assistance and
early interventions with infants, toddlers, and preschool students;
3. Subsidy services—including parent counseling and referral and
Child Care Works (a subsidized child care program); and
4. Early learning services—including public prekindergarten for at-
risk preschoolers, full-day kindergarten, Head Start, family sup-
port programs, and other programs.
OCR for page 24
24 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
OCDEL staff recognized the need to create an integrated data system
that would allow them to monitor their progress and improve quality and
outcomes for children. Pennsylvania’s Enterprise to Link Information for
Children Across Networks (PELICAN) is the information management
system developed to meet this goal. It links data from all agencies and
programs that serve young children. The Early Learning Network (ELN),
one element of PELICAN, is a web-based network for the collection of
information about children and the programs that serve them. The data
sources are drawn from the many programs serving children and families
that are part of PELICAN.
Dichter explained that assessment and accountability were already
a part of Pennsylvania’s practice: tools used include regular program
monitoring and site visits, environmental rating scales and independent
third-party review, and performance measures and targets. ELN, a com-
prehensive data system designed to integrate data about finances, pro-
grams, teachers, families, and children, brought standardization across all
OCDEL programs. Specifically, the information includes child outcomes,
child and family demographics, teacher qualifications and experience, pro-
gram quality (environmental rating scores), and program demographics,
including salaries and benefits for staff. ELN also allows for connections
with other data systems, such as those of the Pennsylvania departments
of education, child welfare, health, and juvenile justice. Children and
teachers are assigned unique secure identification numbers so information
can be exchanged without compromising their privacy. The system also
integrates professional development data, which helps improve technical
assistance to programs and teachers.
ELN has brought a number of other benefits, Dichter explained. It
provides feedback to parents about their children’s progress, without
subjecting the children to multiple assessments. It provides a pool of data
that can be used to identify which programs best meet particular kinds of
needs, and also provides a basis for evaluating the effectiveness of Penn -
sylvania’s services to young children.
Dichter noted that both PELICAN and ELN were developed in phases
over several years, which required creativity, a sustained commitment,
and political will from all involved. The developers relied on support
from foundations and federal grants, as well as the input of an advisory
committee that solicited ideas from all stakeholders. She noted that the
existing models and sources of support for the development of these pro-
grams were limited. Furthermore, national efforts are sorely needed to
develop funding for state-level efforts that can promote consistent defini -
tions and other constructive kinds of standardization.
Looking forward, she observed that the integrated management and
data collection system is the foundation for further research on important
OCR for page 25
25
DEFINING AND DESCRIBING THE WORKFORCE
questions. A comprehensive data system provides a base for addressing
critical policy and efficacy questions. For example, data can answer ques -
tions regarding:
• Interactions among the learning environment, teacher character-
istics, and child development and outcomes;
• The ways in which children’s peers and other aspects of the class-
room context affect their development;
• The effects of children’s and teachers’ mobility on the continuity
of service and on outcomes for children;
• The effects of receiving multiple services from multiple providers
over time;
• The effects of multiple risks for children; and
• Variations in access to high-quality child care by region.
SUMMARY
Brandon summarized key messages from these presentations and
discussions, observing that a large, complex, and well-coordinated federal
structure provides comparable data across all occupations and indus -
tries in the United States. The ECCE field needs to decide the extent to
which the benefits of participating in this structure outweigh the difficul -
ties associated with fitting complex and overlapping occupational roles
within it. The possibility of comparing data across multiple contexts is an
important consideration, he stressed. At the same time, the coordination
of federal education and state data at the K–12 level provides a useful
model for the ECCE field to examine. Data collection efforts in education
have demonstrated the importance of cooperation among federal and
state agencies as well as public and private enterprises. The Pennsylvania
example illustrated the extremely valuable role that integrated data col-
lection can play in supporting sound policy and improvements in quality
in ECCE. All three multicomponent systems demonstrate several key
points, he noted:
• No one specific data collection effort can meet all needs;
• All require collaboration; and
• All must develop over time.
However, he added, much work is needed both to capitalize on existing
systems and to develop new ones at the state and federal levels that can
provide the comprehensive data the ECCE field needs. At present, a par-
ticipant noted, the registries and workforce data systems developed by
states as part of their professional development and quality improvement
OCR for page 26
26 THE EARLY CHILDHOOD CARE AND EDUCATION WORKFORCE
efforts are the main sources of workforce data for program developers
and policy makers. As participants also noted, although capitalizing on
existing systems is important, so is capturing the reality of ECCE as it is
practiced. Most existing data systems focus on describing and measuring
the activities of individuals, while the field often emphasizes serving the
needs of children through team-based and collaborative efforts. The best
way to reconcile these two goals will require further discussion of the
merits of different data models and measurement approaches.
