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Count Me In? Identifying Factors That Predict Centers' Application to Boston's Mixed-Delivery Universal Pre-K Program

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Title: Count Me In? Identifying Factors That Predict Centers' Application to Boston's Mixed-Delivery Universal Pre-K Program
Language: English
Authors: Paola Andrea Guerrero-Rosada, Christina Weiland, Catherine Snow, Meghan McCormick, Society for Research on Educational Effectiveness (SREE)
Source: Society for Research on Educational Effectiveness. 2024.
Availability: Society for Research on Educational Effectiveness. 2040 Sheridan Road, Evanston, IL 60208. Tel: 202-495-0920; e-mail: contact@sree.org; Web site: https://www.sree.org/
Peer Reviewed: Y
Publication Date: 2024
Document Type: Reports - Research
Education Level: Early Childhood Education
Preschool Education
Elementary Education
Kindergarten
Primary Education
Descriptors: Predictor Variables, Preschool Education, Kindergarten, Access to Education, Delivery Systems, Educational Quality, Demography, Child Care Centers, Neighborhoods, Accountability
Geographic Terms: Massachusetts (Boston)
Abstract: Background: Universal Prekindergarten (UPK) programs often expand by offering Pre-K in both public schools and in community-based providers (CBOs). This approach gives families more options and allow UPK programs to increase access more quickly. However, little is known about which CBOs select into these systems. This is an important gap in the literature because CBOs typically serve proportionately more children from families marginalized in terms of race/ethnicity and income than school-based programs (Crosnoe et al., 2016; Sandstrom & Chaudry, 2012; Schumacher et al., 2007; Weiland et al., 2023), and are more likely to serve areas of concentrated disadvantage. Equitable implementation of UPK programs requires understanding how UPK applier centers differ from non-appliers, to identify participation barriers and improve the selection of potential providers. Purpose: Using data from Boston's expansion to a UPK system, we examine differences in centers' Pre-K capacity, structural quality, and demographic characteristics of the communities and children served by UPK applier and non-applier centers. Findings can inform the scale-up of UPK programs as they balance the need to expand access while maintaining/improving quality. This work also illustrates a geo-spatial approach to using population level data for equitable recruitment of UPK providers. We address the following research questions: 1. Do community-based centers applying to Boston UPK differ from non-appliers in their capacity, structural quality, and the demographic characteristics of the communities where they are located? 2. Do proxies of structural quality from Boston applier and non-applier community-based centers vary across census block groups and neighborhoods? We hypothesized capacity and quality differences between UPK appliers and non-appliers. Setting: This research took place in the greater Boston area. Population: Our sample included the total population of Boston licensed early care and education centers in the 2018-2019 school year (N = 223). Of these centers, 32 applied for Boston UPK supports across the 2019-2020 (UPK year 1; N = 28 centers) and 2020-2021 (UPK year 2; N = 4 additional centers) school years (see Table 1). Program: Historically, public PreK in Boston was limited to programs in public schools and was not universally available to all age-eligible children. In 2019, the city began moving to a mixed-delivery model by offering additional slots at partner community-based PreK programs, and Boston Public Schools Department of Early Childhood disseminated a call for licensed centers to participate in its UPK program. The call was open to all state licensed programs with capacity to serve children in a four-year-old-only classroom. Appliers submitted organizational information which the UPK program used to assess programs' potential to align with the Boston UPK requirements. We examined applications during the first two years of the program to identify whether a) Boston UPK attracted a pool of high-quality providers as intended, and b) publicly available administrative and geospatial tools could be used to enhance equity in the recruitment process. Research Design: Using descriptive and geo-spatial methods, we estimated unconditional differences in the structural quality, capacity, and demographics at the center's location. We then used multi-level linear probability models to estimate quality differences accounting for center and community characteristics. Finally, we used optimized hotspot analysis to assess these differences in a spatial framework, examining statistically significant clusters of low- or high-quality across the city. Data Collection and Analysis: We used administrative data from the Boston UPK program (2019 and 2020), the Licensing Education Analytic Database (LEAD) data for the 2018-2019 school year, item-level data from licensing visits provided by the Massachusetts Department of Early Education and Care, and a list of all centers that were NAEYC accredited in Boston between 2014 and 2020. We also used data from the 2019 American Community Survey 5-year estimates at the census block group level and data at the child-level from the Child Care Financial Assistance (CCFA) system for centers receiving subsidies. Findings: Before accounting for the demographic composition at the center census block group, UPK appliers had a larger capacity than non-appliers (0.81 SD), were more likely to receive subsidies (35 pp, p < 0.000), to participate in QRIS (36 pp, p < 0.000), and to have NAEYC accreditation (39 pp, p < 0.000). There were no differences in centers' compliance with licensing standards, our proxy for structural quality at the population level. When models accounted for the demand for early education services (proxied by the population count of children younger than five years) and the demographic characteristics of the communities served by the center, our linear probability models showed that UPK applier and non-applier centers were statistically identical in their capacity and quality (see Table 2). The change in magnitude and statistical significance of the coefficient representing centers' probability of receiving subsidies and participating in QRIS after accounting for demographic composition at the census block group suggests that centers serving communities with more subsidy-eligible children are more likely to apply to UPK. A spatial analysis revealed hotspots with higher QRIS participation in two neighborhoods, a hotspot with lower QRIS participation across four neighborhoods, and a hotspot with a higher number of NAEYC accredited centers located in a centralized neighborhood. Our population-level quality proxy was able to accurately identify centers with low compliance in the full population and among UPK centers. We identified a neighborhood with lower average compliance than the rest of the city, suggesting that this area requires targeted supports to expand high-quality services (see Figure 1). Conclusions: UPK appliers are more likely to participate in QRISs and receive CCFA subsidies. Research needs to explore how UPK programs can attract centers serving non-subsidized families to effectively decrease socio-economic disparities in access. Among centers receiving subsidies, UPK applier and non-appliers serve children with similar demographic characteristics. Geo-spatial analysis shows quality variation across neighborhoods, with hotspots of high participation in accountability systems (QRIS), high-quality (NAEYC), and low quality (compliance) across the city. Findings provide a model method for UPK programs facing the challenge of expanding access without inadvertently increasing disparities in quality.
Abstractor: As Provided
Entry Date: 2024
Access URL: https://www.sree.org/2024-conference
Accession Number: ED663049
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  Data: Background: Universal Prekindergarten (UPK) programs often expand by offering Pre-K in both public schools and in community-based providers (CBOs). This approach gives families more options and allow UPK programs to increase access more quickly. However, little is known about which CBOs select into these systems. This is an important gap in the literature because CBOs typically serve proportionately more children from families marginalized in terms of race/ethnicity and income than school-based programs (Crosnoe et al., 2016; Sandstrom &amp; Chaudry, 2012; Schumacher et al., 2007; Weiland et al., 2023), and are more likely to serve areas of concentrated disadvantage. Equitable implementation of UPK programs requires understanding how UPK applier centers differ from non-appliers, to identify participation barriers and improve the selection of potential providers. Purpose: Using data from Boston&#39;s expansion to a UPK system, we examine differences in centers&#39; Pre-K capacity, structural quality, and demographic characteristics of the communities and children served by UPK applier and non-applier centers. Findings can inform the scale-up of UPK programs as they balance the need to expand access while maintaining/improving quality. This work also illustrates a geo-spatial approach to using population level data for equitable recruitment of UPK providers. We address the following research questions: 1. Do community-based centers applying to Boston UPK differ from non-appliers in their capacity, structural quality, and the demographic characteristics of the communities where they are located? 2. Do proxies of structural quality from Boston applier and non-applier community-based centers vary across census block groups and neighborhoods? We hypothesized capacity and quality differences between UPK appliers and non-appliers. Setting: This research took place in the greater Boston area. Population: Our sample included the total population of Boston licensed early care and education centers in the 2018-2019 school year (N = 223). Of these centers, 32 applied for Boston UPK supports across the 2019-2020 (UPK year 1; N = 28 centers) and 2020-2021 (UPK year 2; N = 4 additional centers) school years (see Table 1). Program: Historically, public PreK in Boston was limited to programs in public schools and was not universally available to all age-eligible children. In 2019, the city began moving to a mixed-delivery model by offering additional slots at partner community-based PreK programs, and Boston Public Schools Department of Early Childhood disseminated a call for licensed centers to participate in its UPK program. The call was open to all state licensed programs with capacity to serve children in a four-year-old-only classroom. Appliers submitted organizational information which the UPK program used to assess programs&#39; potential to align with the Boston UPK requirements. We examined applications during the first two years of the program to identify whether a) Boston UPK attracted a pool of high-quality providers as intended, and b) publicly available administrative and geospatial tools could be used to enhance equity in the recruitment process. Research Design: Using descriptive and geo-spatial methods, we estimated unconditional differences in the structural quality, capacity, and demographics at the center&#39;s location. We then used multi-level linear probability models to estimate quality differences accounting for center and community characteristics. Finally, we used optimized hotspot analysis to assess these differences in a spatial framework, examining statistically significant clusters of low- or high-quality across the city. Data Collection and Analysis: We used administrative data from the Boston UPK program (2019 and 2020), the Licensing Education Analytic Database (LEAD) data for the 2018-2019 school year, item-level data from licensing visits provided by the Massachusetts Department of Early Education and Care, and a list of all centers that were NAEYC accredited in Boston between 2014 and 2020. We also used data from the 2019 American Community Survey 5-year estimates at the census block group level and data at the child-level from the Child Care Financial Assistance (CCFA) system for centers receiving subsidies. Findings: Before accounting for the demographic composition at the center census block group, UPK appliers had a larger capacity than non-appliers (0.81 SD), were more likely to receive subsidies (35 pp, p &lt; 0.000), to participate in QRIS (36 pp, p &lt; 0.000), and to have NAEYC accreditation (39 pp, p &lt; 0.000). There were no differences in centers&#39; compliance with licensing standards, our proxy for structural quality at the population level. When models accounted for the demand for early education services (proxied by the population count of children younger than five years) and the demographic characteristics of the communities served by the center, our linear probability models showed that UPK applier and non-applier centers were statistically identical in their capacity and quality (see Table 2). The change in magnitude and statistical significance of the coefficient representing centers&#39; probability of receiving subsidies and participating in QRIS after accounting for demographic composition at the census block group suggests that centers serving communities with more subsidy-eligible children are more likely to apply to UPK. A spatial analysis revealed hotspots with higher QRIS participation in two neighborhoods, a hotspot with lower QRIS participation across four neighborhoods, and a hotspot with a higher number of NAEYC accredited centers located in a centralized neighborhood. Our population-level quality proxy was able to accurately identify centers with low compliance in the full population and among UPK centers. We identified a neighborhood with lower average compliance than the rest of the city, suggesting that this area requires targeted supports to expand high-quality services (see Figure 1). Conclusions: UPK appliers are more likely to participate in QRISs and receive CCFA subsidies. Research needs to explore how UPK programs can attract centers serving non-subsidized families to effectively decrease socio-economic disparities in access. Among centers receiving subsidies, UPK applier and non-appliers serve children with similar demographic characteristics. Geo-spatial analysis shows quality variation across neighborhoods, with hotspots of high participation in accountability systems (QRIS), high-quality (NAEYC), and low quality (compliance) across the city. Findings provide a model method for UPK programs facing the challenge of expanding access without inadvertently increasing disparities in quality.
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    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Preschool Education
        Type: general
      – SubjectFull: Kindergarten
        Type: general
      – SubjectFull: Access to Education
        Type: general
      – SubjectFull: Delivery Systems
        Type: general
      – SubjectFull: Educational Quality
        Type: general
      – SubjectFull: Demography
        Type: general
      – SubjectFull: Child Care Centers
        Type: general
      – SubjectFull: Neighborhoods
        Type: general
      – SubjectFull: Accountability
        Type: general
      – SubjectFull: Massachusetts (Boston)
        Type: general
    Titles:
      – TitleFull: Count Me In? Identifying Factors That Predict Centers' Application to Boston's Mixed-Delivery Universal Pre-K Program
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