Effects of a State Pre-kindergarten Program on the Kindergarten Readiness and Attendance of At-Risk Four-Year-Olds

Abstract

The effect of public pre-kindergarten (pre-K) on the short-term outcomes of children from disadvantaged backgrounds is well established; however, the mechanisms for this effect are not well understood. Of the many factors that influence how pre-K participants progress during and after kindergarten, one understudied factor is the effect of pre-K participation on kindergarten attendance. The effects of absenteeism are cumulative, and habits established early in the school years are likely to affect later school outcomes. Thus, if pre-K improves kindergarten attendance, participants may be poised for later school success. To begin to test this hypothesis, we conducted a quasi-experimental study to examine the kindergarten readiness of 19,490 children and attendance records of 39,113 children who either were enrolled in Michigan’s Great Start Readiness Program (GSRP) or were placed on waitlists because their GSRP sites were full. Using variants of multilevel modeling, we found, as expected, that GSRP children performed better than waitlisted children on the Kindergarten Readiness Assessment. Examination of kindergarten attendance records found that waitlisted children were more likely to be absent than their counterparts who participated in GSRP, with particularly strong effects for children who were Black, economically disadvantaged, or English Language Learners.

Publicly funded pre-kindergarten (pre-K) programs aim to improve the academic and social-emotional outcomes of young children, particularly those from disadvantaged backgrounds. The ongoing question in the literature and in the public square is whether and to what extent they do so. That center-based (as opposed to home-based) preschools help make children ready for kindergarten is well established (see meta-analyses and research compilations by Duncan & Magnuson, 2013; Fischer et al., 2020; Murano et al., 2020; Yoshikawa et al., 2016), though exceptions do exist (e.g., Allee et al., 2024). Effects on readiness skills have sometimes been found to be more pronounced for children from economically disadvantaged backgrounds and particularly for English Language Learners (ELLs, e.g., Bassok et al., 2019; Duncan & Magnuson, 2013; Fischer et al., 2020; Phillips et al., 2017; Watts et al., 2023).

Less clear is how preschool participation affects medium- and long-term outcomes. The few randomized control trials of publicly funded pre-K programs (Lipsey et al., 2018; Puma et al., 2010, 2012; Weiland et al., 2020) and many quasi-experimental program evaluations (Duncan & Magnuson, 2013; Yoshikawa et al., 2016) have found that gains in children’s cognitive outcomes at the end of pre-K faded by third grade. The most recent results of the randomized control trial of Tennessee’s state-funded pre-K program show that program participants were significantly behind non-participants on several measures by grade 6 (Durkin et al., 2022); however, a long-term study of North Carolina’s program found lingering positive effects through grade 5 (Watts et al., 2023). Meanwhile, longer-term studies have often found positive effects of preschool participation on academic, social, and even economic outcomes in adolescence and young adulthood (e.g., Amadon et al., 2022; Duncan & Magnuson, 2013; McCoy et al., 2017).

The mechanisms by which preschool participation may affect later academic and social outcomes are not fully understood (Bassok et al., 2019; Heckman et al., 2013; Phillips et al., 2017). Certainly, the mechanisms are not limited to the effect of instruction on narrowly defined cognitive skills (Hustedt et al., 2018; Reynolds & Ou, 2016). For example, one study found that play-based preschool experiences improved kindergarten readiness by teaching children to learn, explore, communicate, and empathize (Fyffe et al., 2014). In addition to program content and approach, a wide variety of factors influence preschool and subsequent outcomes (Watts et al., 2023), including the quality of the preschool (e.g., Allee et al., 2024; Sylva et al., 2011; Yoshikawa et al., 2016), the quality of later education (e.g., Bailey et al., 2020; Phillips et al., 2017; Yoshikawa et al., 2016), and family and societal influences (e.g., Bivens et al., 2016; Burger, 2010). The influences on children’s development are many and complex.

One of the challenges in understanding whether and how pre-K participation affects students’ subsequent academic and social-emotional development is a relative dearth of information about what happens to children between kindergarten entry, when the effects of preschool on school readiness are assessed, and grade 3, when the first standardized tests of academic achievement are typically administered. To help fill in this gap, we studied how participation in a state-funded pre-K program affected both kindergarten readiness and kindergarten attendance. Our hypothesis was that pre-K participation would improve not only children’s kindergarten readiness but also their kindergarten attendance. Research suggests that pre-K impacts on school attendance may have lasting and cumulative effects on children’s subsequent academic and social-emotional development.

Literature Review

Kindergarten Readiness

Numerous studies have documented the positive effect of children’s academic readiness at kindergarten entry on later academic and socioeconomic performance (e.g., Davoudzadeh et al., 2015; Fitzpatrick et al., 2020; Quirk et al., 2017). Examinations of social-emotional readiness at kindergarten entry have found positive outcomes as much as 30 years later (Vergunst et al., 2019).

Definitions of kindergarten readiness are evolving, as kindergarten itself has evolved since the passage of the No Child Left Behind Act of (2001), to emphasize academic skills, acquired through paper-and-pencil tasks, over social and behavioral skills, acquired through play and child-directed activities (Bassok et al., 2016; Brown et al., 2023). Many believe, with Bassok et al. (2016), that kindergarten has become “the new first grade.” Meanwhile, qualitative studies have found that many preschool and some kindergarten teachers still tend to emphasize play, child-directed activities, and the development of social-emotional skills (Akaba et al., 2020; Brown et al., 2023; Hustedt et al., 2018). Such discrepancies between preschool and school programs and environments make the transition difficult for many children, perhaps particularly for children from low-income backgrounds (Cook & Coley, 2021; Pears & Peterson, 2018). Most researchers and educators (including those cited here and the Michigan Department of Education) include both academic and social-emotional milestones in their definition of kindergarten readiness.

Families’ involvement in young children’s learning and development is an important factor in kindergarten readiness and in child development generally. Home learning activities have been associated with the kindergarten readiness, defined in both academic and social-emotional terms, of low-income children (Barnett et al., 2020; Puccioni, 2018; Sheridan et al., 2020; Welsh et al., 2014). The quantity of learning activities parents provide at home can be influenced by preschool centers’ family engagement efforts (Barnett et al., 2020). Head Start parent-focused interventions have shown effects on kindergarten readiness that last at least into early elementary school (e.g., Bierman et al., 2018, 2019). One family engagement tactic shown to influence academic learning and socioemotional development is home visits (Bierman et al., 2018; Loughlin-Presnal & Bierman, 2017; Nix et al., 2018).

Kindergarten Absenteeism

The prevalence of absenteeism in kindergarten is well established. Every year, about 10% of kindergarten children are chronically absent, defined as missing 18 or more days per year (Chang et al., 2015). The definition is equivalent to about 10% of the school year, and reflects the most common definition of chronic absenteeism selected by states under the Every Student Succeeds Act of 2015 (Jordan & Miller, 2017). Elementary school absence rates tend to be highest in kindergarten, decreasing year by year until at least grade 5 (Balfanz & Byrnes, 2012; Chang et al., 2015).

Attendance and Academic Outcomes

The link between attendance and academic outcomes is well established, though the phenomenon is less thoroughly studied among children in early elementary school than those in later grades. Children with better attendance rates in elementary grades have better academic outcomes in elementary school (Aucejo & Romano, 2016; Gottfried, 2009, 2011, 2019; Morrissey et al., 2014). Ansari and colleagues have found benefits of good elementary attendance that last into adolescence (Ansari & Pianta, 2019) and even young adulthood (Ansari et al., 2020). One study found that chronic absenteeism among students in grades 3 and 4 was associated with lower test scores not only for those students but among their classmates as well (Gottfried, 2019).

Though the impact of absenteeism specifically on test scores is greater in later grades (Gershenson et al., 2017), attendance in kindergarten matters in subtle ways. For one thing, some studies have shown that better attendance in kindergarten leads to better academic outcomes in kindergarten (Gershenson et al., 2017; Morrissey et al., 2014; Ready, 2010). Perhaps more importantly, children with better kindergarten attendance tend to have better attendance in first grade and beyond (Ansari & Pianta, 2019; Chang et al., 2015; Connolly & Olson, 2012). Conversely, chronic absenteeism in kindergarten has been linked to poor academic outcomes in first grade and beyond (Chang et al., 2015; Gottfried, 2014). These findings are particularly important in light of research suggesting that the effects of absence in early elementary school, starting in kindergarten, are cumulative (Ansari & Gottfried, 2021; Ansari et al., 2020): the more absences year over year, the greater the effect on academic and other outcomes.

Studies that focus, as ours does, on the effects of pre-K program participation on kindergarten attendance are fewer in number. A consensus for a positive effect is emerging, but with notable exceptions, which may result from methodological choices. The Tennessee randomized control trial, for example, found that the treatment group of children who attended the state pre-K program had a high average attendance rate of 95%, identical to that of the control group (Lipsey et al., 2018). However, focusing on aggregate attendance rates, as the Tennessee study does, can easily obscure the impact of absenteeism for individual students and at-risk groups (Chang et al., 2015). Furthermore, Balfanz and Byrnes (2012) point out that a district can have a 90% average daily attendance even though up to 40% of students miss 10% or more of school days.

Many analyses therefore examine the effects on individual children of chronic absenteeism. Gottfried (2015) found that enrollment in center-based preschool had a significant effect in reducing chronic absenteeism in kindergarten, regardless of family socioeconomic status (SES). He speculates that the mechanism by which preschool enrollment improves academic and socioemotional outcomes may be precisely that participation reduces chronic absenteeism in the early grades. Analysis of city-funded pre-K and Head Start programs in Baltimore (Connolly & Olson, 2012) showed that chronic absenteeism in pre-K was often but not consistently repeated in K–3; children who began to attend more consistently in kindergarten were likely to continue to do so in grades 1–3. Ehrlich et al. (2018) found that chronic absenteeism in pre-K predicted chronic absenteeism in K–2, with corresponding lack of progress in academic skills. Amadon et al. (2022) found that Tulsa participants in Oklahoma’s universal pre-K program had better attendance records and less chronic absenteeism than matched nonparticipants during kindergarten, elementary school, and high school (but not middle school).

Reasons for Absenteeism

Illness is the most common reason for school absence (Balfanz & Byrnes, 2012; Chang et al., 2015). However, childhood illnesses rarely add up to chronic absenteeism unless some other factor is at play, such as a chronic illness that is poorly managed due to lack of access to affordable health care (Balfanz & Byrnes, 2012). Health, thus, is one of many factors in absenteeism that is profoundly influenced by socioeconomic status, discussed in the next section. Also, every parent and teacher is familiar with the phenomenon of children feigning illnesses to avoid school. Absentee rates spike in years when children attend a new building, including kindergarten (Balfanz & Byrnes, 2012; Gottfried, 2015), indicating that the stress of a new experience is part of the phenomenon (Balfanz & Byrnes, 2012). Another factor is that some caregivers do not understand that attendance is as important in the early grades as in later years (Balfanz & Byrnes, 2012; Chang et al., 2015).

Attendance researchers, most notably Gottfried (2015; Gottfried & Kirksey, 2021), explain that family routines, such as regular bedtimes and morning routines, support attendance. This observation may help to explain why pre-K participation can improve kindergarten attendance: Pre-K families already have routines in place before kindergarten.

The Role of Race/Ethnicity and Socioeconomic Status

Race/ethnicity and SES have profound associations with academic achievement. Gaps between the test scores of White children on the one hand and Black and Hispanic children on the other have been thoroughly documented (as described in, for example, Bond & Lang, 2018; Reardon et al., 2015), though some have challenged the practice of presenting White children’s aggregate achievement as a norm against which children of other races are measured (e.g., Howard, 2010). Much recent research has explored the intersections of race/ethnicity with SES and other environmental factors (e.g., Bond & Lang, 2018; Rothstein & Wozny, 2013). Researchers have increasingly taken nuanced approaches focusing on the intersections among race/ethnicity, SES measures including not only income but also parental education and family structure, and the consequences of poverty, including poor health, frequent mobility, and lack of educational resources in the home, all of which have documented effects on academic achievement and school readiness (e.g., Bradbury et al., 2015; Henry et al., 2020; Reardon, 2011; Reardon & Portilla, 2016; Rhoades Cooper & Lanza, 2014). One widely accepted proposition is that racial/ethnic and SES-related academic gaps begin before kindergarten (e.g., Duncan & Magnuson, 2011; Reardon et al., 2015). Many studies have found that pre-K programs have stronger effects on the academic readiness of children placed at risk by such factors as SES, racial/ethnic background, or home language other than English (e.g., Burger, 2010; Phillips et al., 2017; Rhoades Cooper & Lanza, 2014; Yoshikawa et al., 2016).

Similarly, race/ethnicity and SES are related to kindergarten attendance patterns. Black and Hispanic children tend to have higher kindergarten absenteeism than White and Asian children (Chang et al., 2015). Children from homes with low-SES are more likely to be absent from kindergarten than higher-income children (Balfanz & Byrnes, 2012; Chang et al., 2015; Gershenson et al., 2017). Reasons cited in the research include homelessness and housing instability, poor health on the part of children or caregivers, parents’ work hours, unstable family structures, issues with transportation, and others (Chang et al., 2015; Gottfried, 2015). The comparatively greater prevalence of absenteeism among low-SES children is particularly concerning in light of evidence suggesting that schooling makes more difference in the academic growth of low-SES children than of high-SES children (Aucejo & Romano, 2016; Balfanz & Byrnes, 2012; Gershenson et al., 2017).

The Current Study

Our study adds to the evidence on how publicly funded pre-K education affects kindergarten readiness and attendance. As part of an ongoing longitudinal study, we examined data from Michigan’s Great Start Readiness Program (GSRP), which targets children from disadvantaged backgrounds. Taking advantage of the natural experiment arising from the fact that some GSRP sites do not have the capacity to serve all interested and eligible families, we compared a control group of children placed on waitlists, because the local GSRP sites did not have space, with the GSRP treatment group. Though we understand the interactions among risk factors and potentially multifaceted experiences associated with economic disadvantage, racial discrimination, and social determinants of health, we were constrained by our data source to simple definitions of independent variables related to gender, race/ethnicity, disability, economic status, and ELL status. We compared the scores of the GSRP treatment group and the Waitlisted Control group on the Kindergarten Readiness Assessment (WestEd, 2014). Next, we compared the kindergarten attendance of the GSRP and Waitlisted Control groups.

Research Questions

Our examination of short-term outcomes for GSRP participants and nonparticipants was guided by the following research questions.

1. :

The pre-K effects on kindergarten readiness:

• a. To what extent does GSRP participation improve children’s kindergarten readiness, as measured by the assessment chosen by the Michigan Department of Education, in comparison to nonparticipants?

• b. To what extent does GSRP participation have differential effects on kindergarten readiness for children of different subgroups, categorized by gender, race/ethnicity, disability status, ELL status, and economic disadvantage?

2. :

The pre-K effects on kindergarten attendance:

• a. To what extent does GSRP participation improve children’s kindergarten attendance, in comparison to nonparticipants?

• b. To what extent does GSRP participation have differential effects on kindergarten attendance for children of different subgroups, categorized by gender, race/ethnicity, disability status, ELL status, and economic disadvantage?

Method

This study employed a quasi-experimental model to compare the kindergarten readiness and subsequent attendance of eligible children who attended GSRP with the readiness and attendance of eligible children who applied but were placed on a waitlist.

Program Context

GSRP is Michigan’s state-funded pre-K program for four-year-old children, currently administered by the Michigan Department of Lifelong Education, Advancement, and Potential (MiLEAP; formerly administered by Michigan Department of Education in 1985–2023) to improve developmental outcomes for children who are at risk of educational failure.

Eligibility for GSRP Enrollment

Children are eligible for GSRP if their families have incomes below 250% of the federal poverty level (FPL) or if they have any of several risk factors such as experiencing developmental delay or disability, living in a single-parent family, or suffering from abuse or neglect (Wu et al., 2020). Family income is the most important factor: The lowest-income families get priority for enrollment. If two families have the same percentage of FPL, the one with more eligibility factors is admitted first.

Crucially for this study, enrollment is based not only on children’s eligibility but also on the availability of open slots at GSRP sites. Children whose parents apply for enrollment but whose sites are full are placed on a waitlist. This fact enabled us to choose a design that compensates for the ethical and logistical difficulties of random assignment: We assigned children who enrolled to the treatment condition and children placed on the waitlist to the control condition. The two groups thus are generally comparable in terms of the families’ interest and commitment to enroll their children in pre-K and their proximity to a GSRP site. However, the GSRP participants in the treatment group are at higher risk than the waitlisted children in the control group. Children with lower incomes and more risk factors were admitted first, so waitlisted children at a given site had higher incomes and fewer risk factors than those who were admitted. We used statistical methods to control for and examine the effects of differences in income and risk factors.

GSRP Curriculum

The GSRP Implementation Manual (MiLEAP, 2023) specifies that curricula must be child-focused and play-based; classrooms are to be set up as interest-based play areas with minimal space for large-group or desk-based activities. Adult–child interactions and relationships are acknowledged as key to children’s growth (MiLEAP, 2023).

GSRP Family Engagement

GSRP has a strong family engagement component. The GSRP Implementation Manual (MiLEAP, 2023) requires children’s lead or associate teachers to conduct two home visits and two conferences per year with each family. Teachers are also required to complete monthly logs in which they track the parent involvement strategies they used with the families of each child. Program guidance stresses ways in which teachers and administrators can encourage family involvement in program activities and mitigate barriers including transportation, scheduling, and language differences. Administrators are required to complete an inventory of involvement strategies used with families based on Epstein’s (2002) framework for school, family, and community partnership (MiLEAP, 2023).

Participants

The sample for this analysis consists of 40,713 kindergarten children who were either enrolled in Michigan’s GSRP or placed on the GSRP waitlist in the 2018–2019 school year. This is the latest year for which data were available in their present form; in 2019, the state education department stopped the requirement of administering a kindergarten readiness assessment. At the time of testing, the children were between 57 and 71 months old (mean = 67; SD = 3.6), or from 4 years, 9 months to 5 years, 11 months. Approximately 48.2% of the children in the sample were female, 8.7% were ELLs, 82.2% were economically disadvantaged, 25.6% were Black, 10.5% were Hispanic or Latino, 55.6% were White, and 8.3% were other races. Table 1 outlines the demographic characteristics of the treatment and control groups.

Table 1 Descriptive summary of study variables

Measures

Kindergarten Readiness Assessment

The level of academic development of the children in the study sample as they transitioned into kindergarten was measured using the Kindergarten Readiness Assessment (KRA; WestEd, 2014), selected by the Michigan Department of Education. KRA, which is administered mostly through teacher observation, has been proven to be a reliable and valid assessment of children’s readiness for school (Maryland State Department of Education, 2017; WestEd, 2014). Kindergarten teachers’ observations of children’s skills have been found to predict academic success in elementary school (Gullo & Impellizeri, 2022). KRA yields one overall score and four domain scores. The four domains are Social foundations, Physical development and wellbeing, Language and literacy, and Mathematics. This article reports on the overall score rather than domain scores.

Kindergarten Absence Rate

Children’s levels of kindergarten attendance were determined from attendance records obtained from Michigan’s Center for Educational Performance and Information. In alignment with the federal Every Student Succeeds Act (ESSA) of 2015, the Michigan Department of Education defines chronic absenteeism as missing 10% or more of total possible school days in a school year (MI School Data, 2020). However, we used a more nuanced definition: We defined the absence rate for each child as the ratio of school days absent to the total number of school days in the school year. Thus, we used the proportion of days absent from school along a continuum rather than separating children into two groups, chronically absent and not chronically absent.

Independent Variables

To control for the effects of demographic characteristics and to examine whether and to what extent these characteristics moderated the relationship between GSRP participation and the outcome variables, we included the following characteristics as independent variables: gender, disability status, ELL status, economic disadvantage, and race/ethnicity. The race/ethnicity categories are White, Black, Hispanic/Latino, and “other.” This last category includes Native Hawaiian or Pacific Islander, American Indian or Alaskan, Asian, and multiracial; we collapsed these categories into “other” to avoid data sparsity. The Black category was treated as the reference group for race/ethnicity. We considered use of school locality, that is, urban, suburban, or rural, as another variable. However, the small size of the Waitlisted Control group for both the readiness and attendance analyses (see Table 1) restricted the number of variables whose interactions could be meaningfully analyzed.

Procedure

KRA is administered to only a fraction of all kindergarten-aged children in Michigan. Of the 40,713 children in our original sample, only 19,495 children took part in KRA for the 2019–2020 school year; hence, our analysis of kindergarten readiness used this subsample. Of the 19,495 children, five (0.02%) were missing entries for their gender. Following Enders (2010), we excluded cases with missing data from the analysis. Thus, we were left with 19,490 records as the analytic sample for kindergarten readiness.

The analysis for absenteeism considered all 40,713 children. However, 234 children were missing attendance records for the 2019–2020 school year, and another 1,366 were missing values for their gender or race. Thus, a total of 1600 (3.9%) of records were missing data. After excluding these records, we had 39,113 records for the kindergarten absenteeism analysis (Fig. 1).

Fig. 1

Flow diagram of samples for analysis

To assess the impact of educational interventions, researchers must account for the fact that children are nested in classrooms and schools. We therefore adopted a multilevel modeling approach, with children as the first level of data and schools as the second level. This approach accounts for dependency in outcomes within schools by partitioning and modeling variation at both levels of the data hierarchy (Dunn et al., 2015; Raudenbush & Bryk, 2002; Snijders & Bosker, 2011). Although classrooms might have been an even more salient cluster level, we did not have sufficient data to identify classrooms for many children in our sample; nearly 50% of the 40,713 children had no entries for their classroom IDs. We therefore nested children within schools only. Students who attended the same school presumably had similar experiences that could affect both KRA administration and school attendance. We chose schools rather than GSRP sites because the Waitlist Control group could not be clustered into GSRP sites and because kindergarten sites influence kindergarten behaviors such as attendance. Furthermore, 73% of GSRP children stayed in the school district in which their GSRP site was located, so GSRP sites and school sites had high overlap.

Assessing the Immediate Impact of GSRP on Kindergarten Readiness

Using the multilevel modeling approach, we assessed the immediate effect of GSRP participation by examining its effect on the KRA score, comparing the average performance of the treatment and control groups. We also examined the scores of the four KRA domains. The model is presented in Appendix A. Model estimations were conducted in MLwiN, version 3.05 (Charlton et al., 2020).

Assessing the Short-Term Impact of GSRP on Kindergarten Attendance

In the absence of data on children’s academic progress in kindergarten, which is maintained in classrooms and schools but not statewide, kindergarten attendance is a potent indicator of the short-term behavioral impact of GSRP. Whereas the KRA is administered near the beginning of the kindergarten year, children’s attendance records cover the full kindergarten school year, ending about 15 months after students finish their GSRP year.

For the attendance records, children’s schools serve as well-defined clusters with meaningful environmental impacts on children’s attendance. However, some children in our sample changed schools during their kindergarten year. Of the 40,479 children in the sample with attendance records, 38,414 attended only one school, 1,940 attended two, 110 attended three, 13 attended four, and two attended five schools during their kindergarten year. Thus, children are not perfectly nested within schools. To properly account for the impact of every school a child attended, we employed the multiple membership multilevel model (MMMM; Leckie & Owen, 2013), with a binomial link function, to examine the effect of GSRP participation on proportion of days absent from school. The model is outlined in Appendix A. MMMMs use membership weights to define the effect of the cluster variable (in this case, school) on the outcome as a function of all clusters to which a subject belongs (Beretvas, 2011). For our study, we weighted the influence of each school by the proportion of total attendance in that school, regardless of enrollment status. For example, say a child was enrolled in school A for 90 days and school B for 90 days. She attended school A for 20 days and school B for 80 days, for a total of 100 days. In that case, we weighted school A at 0.2 and school B at 0.8. The school the child attended the most is assumed to have the most influence on the child’s attendance pattern.

Results

All models used the following child demographic variables as covariates: gender, race/ethnicity, disability status, ELL status, and economic disadvantage. We used these covariates as moderators to determine whether the effects of GSRP participation on kindergarten readiness or attendance differed with respect to any of these variables. Due to sample size limitations, particularly with the Waitlisted Control group, we kept all slope parameters fixed at the school level and restricted the examination of interaction effects to two-way interactions only. Table 1 provides a descriptive summary for the kindergarten readiness and attendance subsamples in this study and compares GSRP and Waitlisted Control group children.

Sample Balance and Standard Deviations

KRA overall test scores ranged from 202 to 298 for both groups. The score showed a significant difference between the Waitlisted Control and GSRP groups. The Waitlisted Control group had slightly higher standard deviation (13.34) than the GSRP group (12.57), suggesting more variation in scores for the control group than for the treatment group. The demographic variables reveal significant imbalances between the two groups in the distribution of disability status, economically disadvantaged status, and race. Because state policy prioritizes children from low-income families, the Waitlisted Control group has higher proportions than the GSRP group of children who are White, who are not economically disadvantaged, and who have no disability.

The sample for the absenteeism analysis shows similar values for the average proportion of days absent in both groups, and the standard deviations were also equal across groups. However, as in the sample for kindergarten readiness, the GSRP and Waitlisted Control groups were balanced with respect to gender and ELL status, but significantly unbalanced with respect to disability, economic disadvantage, and race/ethnicity due to the state enrollment priority policy.

The Impact of GSRP Participation on Kindergarten Readiness

To assess the impact of GSRP participation on children’s kindergarten readiness, we examined the results of multilevel linear regression of each child’s overall KRA score based on GSRP participation, using waitlisted children as the comparison group and controlling for child demographic characteristics. This analysis was based on the sample of 19,490 children who participated in the KRA assessment and had complete demographic data. Table 2 shows that, as expected, economic disadvantage and disability are negatively associated with kindergarten readiness. Black children had significantly lower KRA scores than did children from other racial/ethnic groups, and male children had lower scores than female children.

Table 2 Estimates of multilevel model for kindergarten readiness

The average kindergarten readiness of children in the Waitlisted Control group was lower than that of children in the GSRP group. However, the differences are significant only in relation to economic disadvantage and ELL status. Holding all other factors constant, economically disadvantaged GSRP participants outperformed their disadvantaged waitlisted peers on the KRA by 6 points, on average. The 95% credible interval ranges are shown in Fig. 2. Similarly, holding all other factors constant, GSRP participants whose native language was not English outperformed their ELL waitlisted peers by 10 points on average (see Fig. 3). None of the other demographic variables had any significant moderating effect on the relationship between GSRP participation and kindergarten readiness.

Fig. 2

Predicted average KRA scores and confidence intervals by economically disadvantaged status and GSRP participation

Fig. 3

Predicted Average KRA scores and confidence intervals by ELL status and GSRP participation

We also analyzed children’s scores on the four KRA domains, controlling for the demographic variables. The results did not significantly add to our understanding of the effect of GSRP participation; domain scores basically tracked to the overall KRA scores. For all four domains, GSRP participation had a significant effect on the scores of children from economically disadvantaged backgrounds. Other significant effects emerged in the Mathematics domain for race, disability, and ELL status and in the Physical development domain for race and disability. Implications for research or practice are difficult to discern, so we chose not to publish the results of each domain. Contact the lead author for a full description of our methods and findings.

The Impact of GSRP Participation on Kindergarten Attendance

Table 3 shows the results for the effect of GSRP participation on children’s attendance, controlling for demographic variables and the random effect of schools. The outcome variable is the likelihood of being absent from kindergarten. Analysis was conducted with data from 39,113 children who had attendance records for the 2019–2020 school year and had complete demographic data. As in the analysis of kindergarten readiness, we included all demographic variables as covariates and moderators of the effect of GSRP participation on absenteeism.

Table 3 Estimates of multiple membership multilevel binary logistic model for kindergarten absence (n = 39,113)

As Table 3 shows, the odds of being absent from school were generally higher for the Waitlisted Control group than for the GSRP group. However, the gaps between the two groups differ significantly by ELL status, economic disadvantage, and race. ELL Waitlisted Control children had 29% higher odds of being absent than ELL GSRP students, while the difference among native English speakers is not statistically significant (Fig. 4). Among economically disadvantaged children, those on the waitlist had 14% higher odds of being absent than their GSRP peers, holding all other factors constant. Again, the differences for less economically disadvantaged children are not significant (Fig. 5). While the results in Table 3 show a significant interaction effect for gender and waitlist, the wide confidence interval suggests a low precision for this estimate.

Fig. 4

Predicted likelihood and confidence intervals for the probability of kindergarten absence by ELL status and GSRP participation

Fig. 5

Predicted likelihood and confidence intervals for the probability of kindergarten absence by economically disadvantaged status and GSRP participation

Among both treatment and control groups, Black students were significantly more likely to be absent than members of other racial/ethnic groups. However, the difference between Waitlisted Control and GSRP groups is much higher among Black children than any other racial/ethnic group, suggesting that Black children benefitted significantly more from GSRP participation than their peers. Figure 6 shows the confidence intervals and the average likelihood of absence by racial/ethnic group.

Fig. 6

Predicted likelihood and confidence intervals for the probability of kindergarten absence by race and GSRP participation

Discussion

This study employed a quasi-experimental model to compare the kindergarten readiness and attendance records of program-eligible children who attended the Great Start Readiness Program (GSRP) with those of students who were eligible for the program but were waitlisted. We employed a multilevel model to examine kindergarten readiness and a multiple membership multilevel model to examine kindergarten attendance.

Effects on Kindergarten Readiness and Attendance

In keeping with decades of research, our study found that GSRP participation had significant effects on participants’ kindergarten readiness. Our finding that GSRP had a positive and significant effect on kindergarten attendance also aligns with previous findings, though the literature in this area is less extensive.

The findings that the effects of GSRP participation on both kindergarten readiness and attendance were significant for economically disadvantaged children suggests that the program is functioning as intended to improve outcomes for the neediest children. Because both treatment and control families in our quasi-experimental design sought to enroll their children in GSRP, the differences in outcomes are not likely to be due to differences in families’ views on the importance of pre-K education or their motivation levels. The fact that waitlisted children had fewer risk factors than the children who were prioritized for admission to oversubscribed GSRP sites makes the improved outcomes for the treatment group even more remarkable. Furthermore, we could not control for the possibility that children in the Waitlisted Control group attended a center-based preschool or licensed child care program other than GSRP or Head Start. This possibility makes it likely that our study underestimates the effect of GSRP on readiness and attendance.

Differential attendance results for ELLs and Black children suggest that GSRP may be improving the prospects of children in these groups for better school performance. This finding adds to the body of evidence suggesting that state-funded pre-K programs improve short-term outcomes for children in groups that otherwise tend to start school at a disadvantage (Duncan & Magnuson, 2013; Yoshikawa et al., 2016).

The significance of kindergarten attendance lies in its effect on later attendance and then, in turn, on elementary school academic outcomes. Although these effects have been described by previous researchers, the literature on the effects of attendance is smaller than that on academic readiness and subsequent performance.

Limitations

One limitation of this study is the choice of the cluster unit. Although children’s learning experience occurred within classrooms, information about the classrooms, such as teacher demographics and years of experience, was not available. As a result, the cluster unit was set at the school level; differences across classrooms within a school were not accounted for. Also, we clustered children by kindergarten school rather than by GSRP site. The choice was motivated by clear links between the outcomes of interest and the school site, including the fact that children’s kindergarten attendance is likely to be influenced by factors related to the kindergarten site, and by the fact that waitlisted children could not be clustered into GSRP sites.

Another limitation of this study is the exclusion of some pre-K factors that can affect child outcomes. Factors such as pre-K attendance and pre-K program quality have been shown to affect child outcomes (e.g., Allee et al., 2024; Yoshikawa et al., 2016). We did not have access to data on children’s GSRP attendance and did not include program quality in our analysis. Average daily attendance levels were likely to have been high because consistent attendance was a requirement imposed by the state. However, we could not obtain GSRP attendance data for individual participants. On the Michigan Program Quality Assessment, virtually all GSRP classrooms in 2018–2019 had a rating of at least 3 out of 5; most were rated at 4 or above (Wu et al., 2020). This lack of variation would have limited our ability to examine the effects of program quality statistically. Moreover, because we clustered children at the school rather than the program level, none of these program-level variables could be included in the model.

An alternative to assessing program-level variables would have been to include some school-level contextual variables like urban, rural, or suburban locality. However, the relatively small number of waitlisted children in our sample limited the value of this analysis. The cross-tabulation of our model variables with whether school was rural, suburban, or urban revealed several sparse cells. For instance, there was only one waitlisted child in an urban school who met the condition of having a disability and belonging to the “other” race group. We therefore decided to exclude the locality variable to avoid generating unreliable model estimates.

We also had no information about school attendance initiatives during the GSRP participants’ kindergarten year. Though Michigan began reporting attendance and absenteeism under the Every Student Succeeds Act in 2015–2016 (MI School Data, 2020), we find no evidence of any statewide attendance initiative. Because of the ESSA reporting, regional grantees and local districts are likely paying more attention to attendance and chronic absenteeism. However, any district attendance initiative would affect treatment and control children equally, so it is not likely to be a mechanism for the effect of GSRP on attendance.

Finally, some children in the Waitlisted Control group may have attended private preschools or participated in other educational alternatives. Our data show only that these children did not attend any publicly funded pre-K program. If waitlisted children participated in some other preschool education program, our models would have underestimated the effect of GSRP.

Implications

Implications for Public Policy

One reason for continuing to examine the effects of publicly funded pre-K programs is to justify the enormous public expenditure already invested. Meanwhile, many states and the federal government are considering expanding access to pre-K, whether to serve all four-year-olds in universal programs or to provide enough seats to serve all low-income children in targeted programs. In 2018–2019, the year of this study’s cohort of GSRP children, Michigan invested $244.6 million in GSRP. Annual spending has increased steadily since then. Michigan’s governor has called to expand GSRP to serve children from all backgrounds regardless of income (MiLEAP, 2024). Considering this investment, the people and policymakers of Michigan deserve to know whether and to what extent the program is working.

Meanwhile, policymakers and voters both in and outside of Michigan have a stake in the results of our study, which joins a vast body of literature on the effects of pre-K on later child outcomes. The fact that these studies do not add up to a single obvious conclusion is understandable and even expected in light of the multitude of factors that affect child development. Some of these factors pertain to the pre-K environment, such as the populations served, program content and format, and program and teacher quality. Far more factors are outside the purview of the pre-K program because they have to do with families, communities, public health, schools, and the other myriad influences on children’s development.

One reason to continue to examine how participation in a publicly funded pre-K program affects short-term academic readiness, beyond the enormous public investment, is that policies, practices, and programming change over time. So do environmental factors such as families’ perception of pre-K education and the accessibility of other center-based programs. All of these factors can affect child outcomes.

Our study provides one glimpse into what a consortium of education researchers called the “black box” question (Phillips et al., 2017, p. 2); that is, the mechanisms by which pre-K education affects both short-term and long-term outcomes. Effects on kindergarten attendance could be one of those mechanisms (Gottfried, 2015). Children who attend kindergarten consistently are likely to continue to have good attendance in first grade and beyond (Ansari & Pianta, 2019; Chang et al., 2015; Connolly & Olson, 2012). Children with good attendance in elementary school have better academic outcomes in elementary school (Aucejo & Romano, 2016; Gottfried, 2009, 2011, 2019; Morrissey et al., 2014) and into high school (Ansari & Pianta, 2019; Ansari et al., 2020). One policy implication of this research is that government funders should invest in pre-K and kindergarten attendance. Policies can encourage pre-K providers to track not just average daily attendance but also individual students’ absence rates. Funders can then provide resources to help pre-K administrators and teachers intervene when children have high absence rates.

Implications for Practice

One mechanism for the attendance effect may be GSRP’s family engagement component, required of all GSRP grantees. GSRP staff conduct home visits and family conferences, show families how to engage in learning activities at home, and involve caregivers in children’s activities in the classroom (MiLEAP, 2023). Researchers have noted that pre-K participation can build family habits that facilitate their children’s pre-K attendance (Chang et al., 2015; Gottfried, 2015). In light of this research, pre-K programs could, with family input, investigate ways to support families in getting their children to class every day.

Future Research Steps

The next steps for our own research are to track academic, behavioral, and attendance outcomes in third grade for this 2018–2019 cohort of GSRP and waitlisted children. Studies will examine the extent to which kindergarten readiness differences continue as academic and behavioral advantage in grade 3 and whether GSRP impacts on kindergarten attendance yields later positive effects on elementary attendance and academic or behavioral outcomes.

Though we would like to be able to correlate GSRP attendance with school attendance rates, we have access to school attendance but not GSRP attendance data. GSRP implemented an attendance policy in which children with excessive absences, determined by site administrators, lose their slots. Thus, the GSRP participants in the study were assumed to have good pre-K attendance. Researchers who have access to data on individual students’ pre-K attendance can add to the scant, but policy-relevant, literature on the correlation between pre-K and school attendance. If research confirms that pre-K individual attendance predicts school attendance, this finding would have significant implications for policy and practice.

Access to data on pre-K attendance at the individual level would also facilitate studies on how pre-K programs with low rates of chronic absenteeism and high kindergarten attendance achieve these results. Qualitative data from administrators, teachers, and families could further open the “black box” (Phillips et al., 2017, p. 2) to show what programs can do to help families understand the importance of attendance in pre-K and support them in acting on that understanding. Pre-K programs like GSRP are already working with families to overcome barriers such as health, work schedule, and transportation issues. The opinions of families and front-line staff on possible additional efforts could be particularly useful in policy decisions and program planning.

Thus, future research should continue to investigate which publicly funded pre-K efforts are having effects on kindergarten readiness and pre-K and kindergarten attendance. No doubt, children and families can benefit from practices based on the findings of the current study showing that GSRP enrollment and attendance had significant positive effects on kindergarten attendance, with differential effects by race/ethnicity, SES, and ELL status. The nation’s youngest learners, particularly those most at risk, deserve researchers’ best efforts to discover which policies and practices are most likely to enable them to thrive.

Appendix A

Model Estimation Formulae

Multilevel Model for KRA Scores

Level 1:

$${Y}_{ij}={\beta }_{0j}+{\beta }_{1j}Wait+{\beta }_{2j}Female+{\beta }_{3j}Disability+{\beta }_{4j}ELL+{\beta }_{5j}Black+{\beta }_{6j}HispanicLatino+{\beta }_{7j}Others+{\beta }_{8j}EconDisadv+{\beta }_{9j}Wait*Female+{\beta }_{10j}Wait*Disability+{\beta }_{11j}Wait*ELL+{\beta }_{12j}Wait*Black +{\beta }_{13j}Wait*HispLatino+{\beta }_{14j}Wait*Others+{\beta }_{15j}Wait*EconDisadv+{\varepsilon }_{ij}$$

Level 2:

$${\beta }_{0j}={\gamma }_{00}+{u}_{0j;}$$

$${\beta }_{1j}={\gamma }_{10};$$

$${\beta }_{2j}={\gamma }_{20}$$

$${\beta }_{3j}={\gamma }_{30;}$$

$${\beta }_{4j}={\gamma }_{40;}$$

$${\beta }_{5j}={\gamma }_{50;}$$

$${\beta }_{6j}={\gamma }_{60;}$$

$${\beta }_{7j}={\gamma }_{70};$$

$${\beta }_{8j}={\gamma }_{80};$$

$${\beta }_{9j}={\gamma }_{90};$$

$${\beta }_{10j}={\gamma }_{100};$$

$${\beta }_{11j}={\gamma }_{110};$$

$${\beta }_{12j}={\gamma }_{120};$$

$${\beta }_{13j}={\gamma }_{130;}$$

$${\beta }_{14j}={\gamma }_{140};$$

$${\beta }_{15j}={\gamma }_{150}$$

Combined model:

$${Y}_{ij}={\gamma }_{00}+{\gamma }_{10}Wait+{\gamma }_{20}Female+{\gamma }_{30}Disability+{\gamma }_{40}ELL+{\gamma }_{50}Black+{\gamma }_{60}HispanicLatino+{\gamma }_{70}Others+{\gamma }_{80}EconDisadv+{\gamma }_{90}Wait*Female+{\gamma }_{100}Wait*Disability+{\gamma }_{110}Wait*ELL+{\gamma }_{120}Wait*Black +{\gamma }_{130}Wait*HispanicLatino+{\gamma }_{140}Wait*Others+{\gamma }_{150}Wait*EconEconDisadv+{u}_{0j}+{\varepsilon }_{ij}$$

where ${Y}_{ij}$ is the overall KRA score for student i, who attended school j, ${\varepsilon }_{ij}$ is the residual for student i in school j and ${u}_{0j}$ is the residual for school j.

Multiple Membership Multilevel Binary Logistic Model for Absenteeism

$$logit\left({Y}_{i\{j\}}\right)={\beta }_{0j}+{\beta }_{1}Wait+{\beta }_{2}Female+{\beta }_{3}Disability+{\beta }_{4}ELL+{\beta }_{5}Black+{\beta }_{6}HispanicLatino+{\beta }_{7}Others+{\beta }_{8}EconDisadv+{\beta }_{9}Wait*Female+{\beta }_{10}Wait*Disability+{\beta }_{11}Wait*ELL+{\beta }_{12}Wait*Black +{\beta }_{13}Wait*HispanicLatino+{\beta }_{14}Wait*Others+{\beta }_{15}Wait*EconDisadv+\sum_{h\epsilon \{j\}}{w}_{ih}{u}_{0h}$$

where ${Y}_{i\{j\}}$ is the probability that student i, who attended set $\{j\}$ of schools will be absent from school, ${w}_{ih}$ is the weight (in this case, proportion) assigned to student i’s association with school h of the j schools for that student; and ${u}_{0h}$ is the random effect of school h.